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-2020 Design Torrent 47: What You Need to Know
- If you are looking for a software that can help you create stunning kitchen and bathroom designs, you might have heard of 2020 Design . This software is one of the most popular and powerful tools for interior designers, contractors, and homeowners. But what if you don't want to pay for the full version of the software? Is there a way to get it for free? That's where 2020 Design Torrent 47 comes in. In this article, we will tell you everything you need to know about this torrent, including what it is, how it works, why you might want to use it, and how to use it safely and effectively.
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What is 2020 Design?
- A brief introduction to the software
- 2020 Design is a software that allows you to create realistic and interactive 3D renderings of kitchen and bathroom spaces. You can choose from thousands of products, materials, colors, and styles from leading manufacturers and brands. You can also customize every detail of your design, from cabinets and countertops to faucets and lighting. You can even add accessories, appliances, and furniture to complete your vision.
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- Some of the features and benefits of using 2020 Design are:
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-It has a user-friendly interface that is easy to learn and use.
-It has a large catalog of products and materials from leading brands and manufacturers.
-It has a powerful rendering engine that produces realistic and high-quality images.
-It has a dynamic pricing feature that calculates the cost of your design based on the products and materials you choose.
-It has a cloud-based service that allows you to access your design from any device and collaborate with others.
-It has a built-in training and support system that helps you learn new skills and solve problems.
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- A brief explanation of how torrents work
- A torrent is a file that contains information about other files that are shared by users on a peer-to-peer (P2P) network. A P2P network is a system where users can share files directly with each other without using a central server. To download a file from a P2P network, you need a torrent client, which is a software that connects you to other users who have the file you want. The torrent client then downloads small pieces of the file from different users until you have the complete file.
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- If you are interested in using 2020 Design but don't want to pay for it, you might consider downloading it via torrent. Some of the reasons why you might want to do this are:
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-You want to try out the software before buying it.
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- However, downloading 2020 Design via torrent also comes with some risks and challenges. Some of them are:
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- If you decide to download and install 2020 Design Torrent 47, here are some steps that you need to follow:
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-Download a torrent client that can handle torrent files. Some examples are uTorrent, BitTorrent, or qBittorrent. Install the torrent client on your computer and run it.
-Download the torrent file for 2020 Design Torrent 47 from the torrent website. Open the torrent file with your torrent client and choose where you want to save the file on your computer. Wait for the download to finish.
-Extract the downloaded file using a software like WinRAR or WinZip. You should see a folder containing several files related to 2020 Design. Look for an executable file (.exe) that can install the software on your computer. Run the executable file as an administrator and follow the instructions on the screen.
-Activate the software using a crack, patch, keygen, serial number, or license key that is provided in the folder or on the torrent website. This will allow you to bypass any security checks or verification processes that might prevent you from using the software without paying for it.
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- The tips and tricks to optimize 2020 Design Torrent 47
- To make sure that you get the best experience out of using 2020 Design Torrent 47, here are some tips and tricks that you can follow:
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- Conclusion
- A summary of the main points
- In conclusion, 2020 Design Torrent 47 is a file that allows you to download and install 2020 Design, a software that helps you create amazing kitchen and bathroom designs. You might want to use this torrent if you want to try out the software for free or access its latest features. However, you also need to be aware of the risks and challenges of using this torrent, such as viruses, malware, legal issues, or compatibility problems. You also need to follow some steps and tips to download and install 2020 Design Torrent 47 safely and effectively.
- A call to action for the readers
- If you are interested in using 2020 Design Torrent 47, you can find it on various torrent websites online. However, we recommend that you use it with caution and responsibility. We also encourage you to support the original creators of 2020 Design by buying the software if you find it useful and valuable. 2020 Design is a great tool for interior design enthusiasts and professionals alike. You can learn more about it on its official website.
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- A1: No, it is not. Downloading or using pirated software is illegal in most countries and regions. You might face fines, lawsuits, or even jail time if you are caught doing so. You might also violate the terms and conditions of the software or the intellectual property rights of the developers or manufacturers.
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-If you are a music producer, engineer, or enthusiast, you probably have heard of Waves plugins. Waves is one of the world's leading developers of audio plugins and signal processors for the professional and consumer electronics audio markets. They offer a wide range of products, from compressors, equalizers, reverbs, delays, to virtual instruments, mastering tools, and more.
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But how do you manage your Waves plugins and licenses? That's where Waves License Center comes in. Waves License Center is an application that allows you to activate, deactivate, recover, and transfer your Waves licenses. It also lets you update your plugins and access your Waves account.
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- How Waves License Center Works
-Waves License Center is part of Waves Central, which is the main hub for managing your Waves products. You can download Waves Central for free from the official website. Once you install it on your computer, you can launch it and access Waves License Center.
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-Waves License Center allows you to activate your licenses directly to your computer or to a USB flash drive. You can also deactivate your licenses from one device and move them to another. This way, you can use your plugins on different computers or studios without having to buy multiple licenses.
-To activate your licenses, you need to log in to your Waves account using your email and password. If you don't have an account yet, you can create one for free. Then, you need to select the licenses that you want to activate and choose where to activate them: either on your computer or on a USB flash drive. You can also use the Easy Activate option, which will automatically activate all your available licenses on your computer.
-To deactivate your licenses, you need to select the device that has the licenses that you want to deactivate and click on Deactivate Licenses. You can also use the Easy Deactivate option, which will automatically deactivate all your licenses on your device.
-To recover your licenses, you need to use the Recover option in case you lose access to your device or it gets damaged or stolen. This will deactivate all your licenses from that device and make them available for activation again.
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-Some people might think that using a cracked version of Waves License Center is a smart way to save money and get access to all the plugins they want. However, this is actually a very risky and irresponsible thing to do. Here are some of the reasons why:
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-On the other hand, using a legitimate version of Waves License Center has many benefits that outweigh the cost. Here are some of them:
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-You will get clean and safe software that won't harm your computer or compromise its security and performance.
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-If you are convinced that using a legitimate version of Waves License Center is the best way to go, here are some options for getting one:
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-You can buy individual plugins or bundles from the official website of Waves. They often have discounts and promotions that make their products more affordable.
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- How to Activate Your Waves Licenses
-Once you have purchased or subscribed to any of their products, here is how you can activate your licenses using Waves Central:
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-Download and install Waves Central from their website.
-Launch it and log in with your email and password.
-Select Offline Installer at the top left corner.
-Select Install Products at the top right corner.
-Select My Products at the left sidebar.
-Select all the products that you want to install and click Install at the bottom right corner.
-Select where you want to install them: either on Local Disk (C:) or on an external drive (if connected).
-Wait for the installation process to finish.
-Select Licenses at the top left corner.
-Select Activate Licenses at the top right corner.
-Select all the licenses that you want to activate and click Activate at the bottom right corner.
-Select where you want to activate them: either on this computer (Local Licenses) or on an external drive (if connected).
-Wait for the activation process to finish.
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- How to Use Your Plugins
-Now that you have activated your licenses, here are some tips and tricks on how to use your plugins effectively and creatively:
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-Plugin Tip/Trick R-Comp Use the ARC (Auto Release Control) feature to automatically adjust the release time according to the input signal. This can help you achieve a more natural and consistent compression. CLA-2A Use the Compress/Limit switch to change the compression ratio and the knee shape. Compress mode has a 3:1 ratio and a soft knee, while Limit mode has a 100:1 ratio and a hard knee. Compress mode is good for smooth and gentle compression, while Limit mode is good for aggressive and tight compression. API 2500 Use the Thrust filter to change the frequency response of the detector circuit. This can affect how the compressor reacts to different parts of the spectrum. The three options are Normal, Medium, and High. Normal has a flat response, Medium has a high-pass filter that reduces low frequencies, and High has a band-pass filter that boosts mid frequencies. SSL G-Master Buss Compressor Use the Auto Fade feature to create a smooth fade-out at the end of your mix. You can set the fade time from 1 to 60 seconds and activate it by clicking on the Fade button. You can also use the Auto Fade feature as a creative effect by automating it during your mix. F6 Use the dynamic EQ bands to apply compression or expansion to specific frequency ranges. You can adjust the threshold, range, attack, release, and Q parameters for each band. You can also solo or bypass each band for easier monitoring. OVox Use the Note Mapper to create custom scales and chords for your vocal harmonies. You can drag and drop notes on the grid to assign them to different MIDI notes. You can also use the Scale and Chord menus to select from preset options. PuigTec EQs Use the Boost/Cut controls to create resonant peaks or dips at specific frequencies. The Boost and Cut controls work independently, so you can boost and cut at the same frequency for a unique EQ curve. This can help you add color and character to your sound. Abbey Road TG Mastering Chain Use the Tape Delay module to add some vintage delay effects to your mix. You can adjust the delay time, feedback, wow, flutter, and saturation parameters. You can also use the Sync button to sync the delay time to your DAW tempo.
- Conclusion
-In conclusion, Waves License Center is an essential tool for managing your Waves plugins and licenses. It allows you to activate, deactivate, recover, and transfer your licenses with ease and flexibility. However, using a cracked version of Waves License Center is not a smart idea, as it can expose you to many risks and disadvantages. Instead, you should use a legitimate version of Waves License Center that will give you many benefits and advantages. You should also learn how to use your plugins effectively and creatively to get the best results from your music production.
- FAQs
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-Q: How do I update my Waves plugins?
-Q: How do I uninstall my Waves plugins?
-Q: How do I get support from Waves?
-Q: How do I get more plugins from Waves?
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The movie was released on June 30, 2017 and received mixed reviews from critics and audiences. Some praised the movie for its realistic portrayal of urban life and relationships, while others criticized it for its slow pace, lack of depth, and poor execution. The movie was also a box office flop, earning only Rs. 1.15 crore against a budget of Rs. 6 crore.
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-Features of Aliens Drive Me Crazy Mod APK 3.1 1
-Unlimited coins
-Coins are the main currency in the game that you can use to buy weapons, vehicles, upgrades, costumes, and more. You can earn coins by completing missions, destroying enemies, rescuing hostages, and collecting items. However, if you want to get everything in the game without spending too much time and effort, you can use the modded version of the game that gives you unlimited coins. You can buy anything you want in the game without worrying about running out of money.
-Unlocked weapons and vehicles
-The game offers a variety of weapons and vehicles that you can use to fight against the aliens. You can choose from pistols, shotguns, rifles, rocket launchers, grenades, swords, hammers, and more. You can also drive cars, motorcycles, tanks, helicopters, jetpacks, and more. However, some of these weapons and vehicles are locked and require you to reach certain levels or pay coins to unlock them. If you want to use them right away without any restrictions, you can use the modded version of the game that unlocks all of them for you.
-No ads
-The original version of the game contains ads that may pop up randomly or after every level. These ads can be annoying and distracting, especially when you are in the middle of an intense action scene. If you want to enjoy the game without any interruptions or distractions, you can use the modded version of the game that removes all the ads from the game.
-How to download and install Aliens Drive Me Crazy Mod APK 3.1 1How to download and install Aliens Drive Me Crazy Mod APK 3.1 1
-If you are interested in playing Aliens Drive Me Crazy Mod APK 3.1 1, you will need to download and install the APK file on your Android device. Here are the steps you need to follow:
-aliens drive me crazy hack apk download
-Step 1: Download the APK file from a trusted source
-The first thing you need to do is to find a reliable website that offers the modded version of the game. You can search for it on Google or use the link we have provided below. Make sure you download the latest version of the game, which is 3.1 1. The file size is about 70 MB, so make sure you have enough space on your device.
-Download Aliens Drive Me Crazy Mod APK 3.1 1 here
-Step 2: Enable unknown sources on your device
-The next thing you need to do is to allow your device to install apps from unknown sources. This is because the modded version of the game is not available on the official Google Play Store, so you need to enable this option to install it. To do this, go to your device settings, then security, then unknown sources, and toggle it on. You may see a warning message, but don't worry, it is safe to install the game.
-Step 3: Install the APK file and enjoy the game
-The final thing you need to do is to locate the downloaded APK file on your device and tap on it to start the installation process. Follow the instructions on the screen and wait for a few seconds until the installation is complete. Once it is done, you can launch the game and enjoy all the features of Aliens Drive Me Crazy Mod APK 3.1 1.
-Conclusion
-Aliens Drive Me Crazy Mod APK 3.1 1 is a fun and action-packed game that will keep you entertained for hours. You will love the unlimited coins, unlocked weapons and vehicles, and no ads that this modded version of the game offers. You will also enjoy the colorful graphics, smooth controls, and hilarious sound effects that make this game a joy to play. If you are looking for a game that combines shooting, driving, and adventure, then you should download and install Aliens Drive Me Crazy Mod APK 3.1 1 on your Android device today.
-FAQs
-Here are some of the frequently asked questions about Aliens Drive Me Crazy Mod APK 3.1 1:
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-Is Aliens Drive Me Crazy Mod APK 3.1 1 safe to download and install?
-Yes, it is safe to download and install Aliens Drive Me Crazy Mod APK 3.1 1 as long as you use a trusted source like the one we have provided above. The modded version of the game does not contain any viruses or malware that can harm your device or compromise your privacy.
-Do I need to root my device to play Aliens Drive Me Crazy Mod APK 3.1 1?
-No, you do not need to root your device to play Aliens Drive Me Crazy Mod APK 3.1 1. The modded version of the game works fine on both rooted and non-rooted devices.
-Can I play Aliens Drive Me Crazy Mod APK 3.1 1 offline?
-Yes, you can play Aliens Drive Me Crazy Mod APK 3.1 1 offline without any internet connection. However, some features of the game may require an internet connection, such as leaderboards, achievements, and social media integration.
-Can I play Aliens Drive Me Crazy Mod APK 3.1 1 with my friends?
-Yes, you can play Aliens Drive Me Crazy Mod APK 3.1 1 with your friends online or locally. The game supports multiplayer mode where you can team up with your friends or compete against them in various missions and challenges.
-How can I update Aliens Drive Me Crazy Mod APK 3.1 1?
-To update Aliens Drive Me Crazy Mod APK 3.1 1, you will need to download and install the latest version of the modded version of the game from the same source you used before. You may also need to uninstall the previous version of the game before installing the new one.
- 197e85843dBlade Forge 3D Mod APK: A Fun and Creative Game for Blacksmith Lovers
-Do you love crafting weapons and blades? Do you want to experience the life of a blacksmith in a fun and realistic way? If yes, then you should try Blade Forge 3D, a simulation game that lets you create your own blades from scratch. And if you want to enjoy the game even more, you should download Blade Forge 3D Mod APK, a modified version that gives you unlimited money, no ads, and access to all blades. In this article, we will tell you everything you need to know about Blade Forge 3D Mod APK, including its features, how to download and install it, and some frequently asked questions.
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Introduction
-What is Blade Forge 3D?
-Blade Forge 3D is a simulation game developed by Kwalee Ltd, a UK-based game studio. The game was released in May 2020 and has gained over 10 million downloads on Google Play Store. The game is rated 4.0 out of 5 stars by more than 100 thousand users.
-In Blade Forge 3D, you play as a blacksmith who can craft different types of blades using various materials and techniques. You can choose from different shapes, sizes, colors, and designs for your blades. You can also test your blades on different objects and enemies to see how they perform. The game is easy to play but hard to master, as you need to balance the quality, durability, and sharpness of your blades.
- What is Blade Forge 3D Mod APK?
-Blade Forge 3D Mod APK is a modified version of the original game that gives you some extra benefits that are not available in the official version. These benefits include unlimited money, no ads, and unlock all blades. With these features, you can enjoy the game without any limitations or interruptions. You can craft any blade you want without worrying about the cost or the availability. You can also get rid of the annoying ads that pop up every now and then. You can download Blade Forge 3D Mod APK for free from various websites on the internet.
- Features of Blade Forge 3D Mod APK
-Unlimited Money
-One of the main features of Blade Forge 3D Mod APK is that it gives you unlimited money. Money is used in the game to buy materials, upgrade your tools, and unlock new blades. Normally, you have to earn money by completing tasks and selling your blades. However, with Blade Forge 3D Mod APK, you don't have to worry about that. You can get as much money as you want without doing anything. You can spend your money freely and buy whatever you need or want.
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- No Ads
-Another feature of Blade Forge 3D Mod APK is that it removes all the ads from the game. Ads are annoying and distracting, especially when they interrupt your gameplay or cover your screen. They can also consume your data and battery life. With Blade Forge 3D Mod APK, you don't have to deal with any ads at all. You can play the game smoothly and peacefully without any interruptions or distractions.
- Unlock All Blades
-The last feature of Blade Forge 3D Mod APK is that it unlocks all the blades in the game. Blades are the main items in the game that you can craft and use. There are many types of blades in the game, such as swords, daggers, axes, spears, scythes, etc. Each blade has its own characteristics and abilities. Some blades are more powerful, rare, or expensive than others. Normally, you have to unlock the blades by completing certain tasks or paying money. However, with Blade Forge 3D Mod APK, you can access all the blades from the start. You can choose any blade you like and craft it with ease.
- How to Download and Install Blade Forge 3D Mod APK
-Step 1: Enable Unknown Sources
-Before you can install Blade Forge 3D Mod APK, you need to enable unknown sources on your device. This is because the APK file is not from the official Google Play Store and your device may block it by default. To enable unknown sources, follow these steps:
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-Go to your device's settings and look for security or privacy options.
-Find the option that says "allow installation of apps from unknown sources" or something similar.
-Toggle it on and confirm your choice.
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- Step 2: Download the APK File
-Next, you need to download the APK file of Blade Forge 3D Mod APK from a reliable website. There are many websites that offer the APK file, but some of them may be fake or malicious. To avoid any risks, you should download the APK file from a trusted source, such as [APKPure] or [APKFab]. These websites are safe and verified by millions of users. To download the APK file, follow these steps:
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-Go to the website of your choice and search for Blade Forge 3D Mod APK.
-Select the latest version of the APK file and click on the download button.
-Wait for the download to finish and save the file in your device's storage.
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- Step 3: Install the APK File
-Finally, you need to install the APK file of Blade Forge 3D Mod APK on your device. This is a simple and quick process that only takes a few minutes. To install the APK file, follow these steps:
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-Locate the APK file in your device's storage and tap on it.
-A pop-up window will appear asking you to confirm the installation. Tap on "install" and wait for the process to complete.
-Once the installation is done, you can open the game and enjoy Blade Forge 3D Mod APK.
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- Conclusion
-Blade Forge 3D is a fun and creative game that lets you craft your own blades and test them on various objects and enemies. The game is easy to play but hard to master, as you need to balance the quality, durability, and sharpness of your blades. If you want to enhance your gaming experience, you should download Blade Forge 3D Mod APK, a modified version that gives you unlimited money, no ads, and access to all blades. You can download Blade Forge 3D Mod APK for free from various websites on the internet. You just need to follow some simple steps to enable unknown sources, download the APK file, and install it on your device. Then, you can enjoy Blade Forge 3D Mod APK without any limitations or interruptions.
- FAQs
-Here are some frequently asked questions about Blade Forge 3D Mod APK:
- Q: Is Blade Forge 3D Mod APK safe to use?
-A: Yes, Blade Forge 3D Mod APK is safe to use as long as you download it from a reliable website. The APK file does not contain any viruses or malware that can harm your device or data. However, you should always be careful when downloading any files from unknown sources and scan them with an antivirus before installing them.
- Q: Do I need to root my device to use Blade Forge 3D Mod APK?
-A: No, you do not need to root your device to use Blade Forge 3D Mod APK. The modded version works fine on both rooted and non-rooted devices. You just need to enable unknown sources and install the APK file as usual.
- Q: Will I get banned for using Blade Forge 3D Mod APK?
-A: No, you will not get banned for using Blade Forge 3D Mod APK. The modded version does not interfere with the game's servers or online features. You can play the game normally without any risk of getting banned.
- Q: Can I update Blade Forge 3D Mod APK?
-A: Yes, you can update Blade Forge 3D Mod APK whenever a new version is available. However, you may have to uninstall the previous version and download the new version from the same website. You may also lose your progress and data if you update the modded version, so make sure to back up your files before updating.
- Q: What are some alternatives to Blade Forge 3D Mod APK?
-A: If you are looking for some other games that are similar to Blade Forge 3D, you can try these alternatives:
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-Forge Ahead: A casual game that lets you smash ores and forge metal into coins and jewels.
-Forge of Empires: A strategy game that lets you build and manage your own city through different ages and civilizations.
-Blade Bound: A hack and slash game that lets you fight against evil forces using various weapons and skills.
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- I hope this article has helped you learn more about Blade Forge 3D Mod APK and how to download and install it on your device. If you have any questions or feedback, feel free to leave a comment below. Thank you for reading and have fun playing Blade Forge 3D Mod APK!
197e85843dDownload Game Mortal Kombat 11 Android: How to Enjoy the Ultimate Fighting Experience on Your Mobile Device
-If you are a fan of fighting games, you must have heard of Mortal Kombat, one of the most popular and influential franchises in the genre. Mortal Kombat is known for its brutal and visceral combat, its iconic characters, and its trademark fatalities. And now, you can enjoy the latest installment of this legendary series, Mortal Kombat 11, on your android device. But how do you download game mortal kombat 11 android? And how do you optimize your gaming experience? In this article, we will answer these questions and more. Read on to find out how to unleash your power and become the ultimate fighter on your mobile device.
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What is Mortal Kombat 11?
-Mortal Kombat 11 is the eleventh main entry in the Mortal Kombat series, developed by NetherRealm Studios and published by Warner Bros. Interactive Entertainment. It was released in April 2019 for PlayStation 4, Xbox One, Nintendo Switch, and Windows PC, and later in November 2020 for PlayStation 5 and Xbox Series X/S. It is also available for android devices through two different methods, which we will discuss later.
-The latest installment of the iconic fighting game franchise
-Mortal Kombat 11 continues the story of the previous game, Mortal Kombat X, in which Raiden, the god of thunder, has become corrupted by the power of Shinnok's amulet and has decided to protect Earthrealm by any means necessary. This leads him to clash with other characters, both old and new, who have their own agendas and motivations. The game also introduces a new villain, Kronika, the keeper of time, who wants to erase Raiden's interference and create a new timeline.
-The features and gameplay of Mortal Kombat 11
-Mortal Kombat 11 is a fighting game that allows you to choose from a roster of over 30 characters, each with their own unique abilities, moves, and fatalities. You can customize your characters with different skins, gear, abilities, intros, outros, taunts, and banners. You can also use a new feature called Fatal Blow, which is a powerful attack that can be activated when your health is below 30%. Another new feature is Krushing Blow, which is a cinematic variation of a special move that triggers when certain conditions are met.
-The game offers various modes for you to play, such as Story Mode, which lets you follow the narrative of the game; Towers of Time, which are dynamic challenges that change periodically; Klassic Towers, which are traditional arcade ladders; Online Mode, which lets you compete with other players around the world; Training Mode, which lets you practice your skills; and Krypt Mode, which lets you explore Shang Tsung's island and unlock various rewards.
-The characters and modes of Mortal Kombat 11
-Mortal Kombat 11 features a diverse cast of characters from different realms and timelines. Some of them are returning favorites from previous games such as Scorpion, Sub-Zero, Liu Kang, Sonya Blade, Raiden, and Shao Kahn; some of them are new additions from Mortal Kombat X, such as Cassie Cage, Jacqui Briggs, Kotal Kahn, and D'Vorah; and some of them are brand new characters, such as Geras, Cetrion, and The Terminator. You can also unlock and play as guest characters from other franchises, such as Joker, Spawn, RoboCop, and Rambo.
-How to download game mortal kombat 11 android?
-As we mentioned earlier, there are two ways to download game mortal kombat 11 android: the official way and the unofficial way. Let's take a look at each of them and see how they differ.
-The official way: Mortal Kombat Mobile app
-The official way to download game mortal kombat 11 android is to use the Mortal Kombat Mobile app, which is a free-to-play version of the game that is compatible with android devices. The Mortal Kombat Mobile app is not exactly the same as the console or PC version of the game, but it does share some similarities and features.
-How to download game mortal kombat 11 on android
-How to install and play Mortal Kombat Mobile app
-To install and play Mortal Kombat Mobile app, you need to follow these steps:
-
-Go to the Google Play Store and search for Mortal Kombat Mobile app.
-Download and install the app on your device. The app requires about 1.1 GB of storage space.
-Launch the app and accept the terms and conditions.
-Create or log in to your WB Games account. This will allow you to sync your progress and access online features.
-Choose your preferred language and region.
-Enjoy the game!
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-The benefits and drawbacks of Mortal Kombat Mobile app
-The Mortal Kombat Mobile app has some benefits and drawbacks that you should be aware of before you decide to download it. Here are some of them:
-
-Benefits Drawbacks - It is free to download and play. - It has in-app purchases and ads that can affect your gaming experience. - It has high-quality graphics and sound effects. - It requires a stable internet connection to play. - It has a large roster of characters that you can collect and upgrade. - It has a different gameplay system than the console or PC version, which may not appeal to some fans. - It has exclusive content and events that are not available in the console or PC version. - It has limited modes and features compared to the console or PC version. - It allows you to link your account with the console or PC version and unlock rewards in both games. - It may not run smoothly on some devices or cause battery drain or overheating issues.
The unofficial way: Mortal Kombat 11 Mobile website
-The unofficial way to download game mortal kombat 11 android is to use the Mortal Kombat 11 Mobile website, which is a fan-made version of the game that claims to be compatible with android devices. The Mortal Kombat 11 Mobile website is not endorsed or supported by the official developers or publishers of the game, and it may contain malware or viruses that can harm your device or steal your personal information.
-How to access and download Mortal Kombat 11 Mobile website
-To access and download Mortal Kombat 11 Mobile website, you need to follow these steps:
-
-Go to your browser and search for Mortal Kombat 11 Mobile website.
-Find and click on the link that leads you to the website. Be careful not to click on any ads or pop-ups that may appear.
-On the website, you will see a button that says "Download Now". Click on it and wait for the download to start.
-Once the download is complete, you will need to install the APK file on your device. You may need to enable unknown sources in your settings to do this.
-Launch the game and enjoy!
-
-The advantages and disadvantages of Mortal Kombat 11 Mobile website
-The Mortal Kombat 11 Mobile website has some advantages and disadvantages that you should be aware of before you decide to download it. Here are some of them:
-
-Advantages Disadvantages - It claims to offer the same gameplay and features as the console or PC version of the game. - It is not authorized or verified by the official developers or publishers of the game. - It does not require any in-app purchases or ads to play. - It may contain malware or viruses that can damage your device or compromise your security. - It does not require an internet connection to play. - It may not work properly on some devices or cause crashes or glitches. - It allows you to play as any character without unlocking them. - It may violate the intellectual property rights of the original creators of the game. - It updates regularly with new content and fixes. - It may be removed or blocked by the authorities at any time.
How to optimize your mortal kombat 11 android experience?
-Now that you know how to download game mortal kombat 11 android, you may wonder how to make the most of your gaming experience. Whether you choose the official or the unofficial way, there are some tips and tricks that can help you improve your performance and enjoyment of the game. Here are some of them:
-Tips and tricks for playing Mortal Kombat 11 on android
-Here are some tips and tricks that can help you play Mortal Kombat 11 on android better:
-
-Learn the basics of the game, such as the controls, the combos, the special moves, and the fatalities. You can find tutorials and guides in the game or online.
-Practice your skills in Training Mode or Klassic Towers before you challenge other players or take on harder modes.
-Customize your characters with the best gear, abilities, and cosmetics that suit your playstyle and preferences. You can unlock more options by playing the game or spending in-game currency.
-Use Fatal Blows and Krushing Blows wisely, as they can turn the tide of a fight in your favor. Save them for when you need them most, and don't waste them on easy opponents.
-Experiment with different characters and find your favorites. Each character has their own strengths and weaknesses, and some may suit you better than others.
-Join a faction and participate in Faction Wars, which are online competitions that reward you with exclusive items and bonuses.
-Complete daily objectives and quests to earn more rewards and progress faster in the game.
-Watch replays of your matches or other players' matches to learn from your mistakes or get inspired by their strategies.
-
-Best devices and settings for running Mortal Kombat 11 on android
-Here are some recommendations for the best devices and settings for running Mortal Kombat 11 on android:
-
-The minimum requirements for running Mortal Kombat 11 on android are: Android 5.0 or higher, 1.5 GB of RAM, and a quad-core CPU. However, these may not be enough to run the game smoothly or at high quality.
-The recommended devices for running Mortal Kombat 11 on android are: Samsung Galaxy S10 or higher, OnePlus 7T or higher, Google Pixel 4 or higher, Huawei P30 Pro or higher, or any other flagship device from 2019 or later.
-The best settings for running Mortal Kombat 11 on android are: High graphics quality, high frame rate, low power mode off, sound effects on, music on, vibration off, notifications off, and auto-save on.
-
-Common issues and solutions for Mortal Kombat 11 on android
-Here are some common issues and solutions for Mortal Kombat 11 on android:
-
-If the game crashes or freezes, try clearing the cache, restarting the device, updating the app, or reinstalling the app.
-If the game lags or stutters, try lowering the graphics quality, closing other apps, freeing up storage space, or using a faster internet connection.
-If the game does not load or sync properly, try checking your internet connection, logging out and logging back in to your WB Games account, or contacting customer support.
-If the game does not recognize your inputs or gestures, try calibrating your screen, cleaning your screen, or using a stylus or a controller.
-
-Conclusion
-Mortal Kombat 11 is one of the best fighting games ever made, and you can enjoy it on your android device with either the official or the unofficial way. However, each way has its pros and cons, so you should weigh them carefully before you decide to download game mortal kombat 11 android. Also, you should follow some tips and tricks to optimize your gaming experience and avoid some common issues. We hope this article has helped you learn how to download game mortal kombat 11 android and how to have fun with it. Now go ahead and unleash your power!
- FAQs
- Here are some frequently asked questions about downloading game mortal kombat 11 android:
-
-Is Mortal Kombat 11 free on android? Mortal Kombat 11 is not free on android. However, you can download the Mortal Kombat Mobile app for free from the Google Play Store. This is a free-to-play version of the game that has some similarities and features with Mortal Kombat 11. Alternatively, you can access and download the Mortal Kombat 11 Mobile website for free from your browser. This is a fan-made version of the game that claims to offer the same gameplay and features as Mortal Kombat 11. However, this is not an official or authorized way to download game mortal kombat 11 android, and it may pose some risks to your device or security.
-Can I play Mortal Kombat 11 on android with a controller? Yes, you can play Mortal Kombat 11 on android with a controller, as long as your device supports it. You can use either a wired or a wireless controller, such as a PS4, Xbox One, or Switch controller. To connect your controller to your device, you need to follow the instructions of your device and controller manufacturer. Once your controller is connected, you can customize the controls in the game settings.
-Can I play Mortal Kombat 11 on android with my friends? Yes, you can play Mortal Kombat 11 on android with your friends, either online or offline. To play online, you need to have an internet connection and a WB Games account. You can then invite your friends to join your faction, chat with them, and challenge them to matches. To play offline, you need to have two devices with the game installed and connected via Bluetooth or Wi-Fi. You can then select the Versus mode and choose your opponent.
-How do I unlock more characters in Mortal Kombat 11 on android? There are different ways to unlock more characters in Mortal Kombat 11 on android, depending on which way you download the game. If you use the Mortal Kombat Mobile app, you can unlock more characters by opening packs, completing towers, participating in events, or spending in-game currency. If you use the Mortal Kombat 11 Mobile website, you can unlock more characters by downloading updates, entering codes, or using cheats.
-How do I perform fatalities in Mortal Kombat 11 on android? Fatalities are finishing moves that you can perform at the end of a match to brutally kill your opponent. To perform fatalities in Mortal Kombat 11 on android, you need to know the specific input and distance for each character and fatality. You can find this information in the game menu or online. Once you have this information, you need to defeat your opponent until their health bar flashes red and the announcer says "Finish Him/Her". Then, you need to input the correct sequence of buttons or gestures within a few seconds and watch the gruesome result.
401be4b1e0European Truck Simulator Mod APK: Drive Across Europe With Unlimited Money
-Do you love driving trucks and exploring new places? Do you want to experience the thrill of being a real trucker in Europe? If yes, then you should try European Truck Simulator, a realistic and immersive truck simulation game that lets you travel across many countries from Europe, visit incredible places like Berlin, Prague, Madrid, Rome, Paris and more. You can play the career mode of this truck simulator, make money, purchase new trucks and upgrades, and challenge your friends with the online multiplayer mode.
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But what if you want to enjoy the game without any limitations or restrictions? What if you want to have unlimited money to buy any truck or upgrade you want? Well, there is a way to do that. You can use European Truck Simulator Mod APK, a modified version of the game that gives you access to unlimited money and other features. In this article, we will tell you everything you need to know about European Truck Simulator Mod APK, including what it is, why you should use it, how to download and install it, and some tips and tricks for playing the game. Let's get started!
- What is European Truck Simulator?
-European Truck Simulator is a truck simulation game developed by Ovidiu Pop, a popular developer of simulation games. The game was released in 2015 and has since gained millions of downloads and positive reviews from players. The game features 12 European truck brands with 4x2 and 6x4 axles, more than 20 realistic cities, country roads, highways and offroads, easy controls (tilt, buttons or touch steering wheel), realistic weather conditions and day/night cycle, visual damage on trucks, detailed interiors for each truck brand, amazing engine sounds, improved AI traffic system, online multiplayer with servers or convoy mode, achievements and leaderboards, controller support, and more.
-The game is available for free on Google Play Store , but it also offers in-app purchases that range from $0.99 to $49.99 per item. These purchases allow you to buy more money, remove ads, unlock all trucks, and get premium features. However, if you don't want to spend real money on the game, you can use European Truck Simulator Mod APK instead.
- How to download and install European Truck Simulator Mod APK?
-European Truck Simulator Mod APK is a modified version of the game that gives you unlimited money and other features that are not available in the original version. You can use this mod APK to buy any truck or upgrade you want, without worrying about running out of money. You can also enjoy the game without any ads or interruptions.
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-To download and install European Truck Simulator Mod APK, you need to follow these steps:
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-Go to , a reliable website that offers mod APKs for various games and apps.
-Search for European Truck Simulator Mod APK in the search bar or browse through the categories.
-Click on the download button and wait for the file to be downloaded on your device.
-Once the file is downloaded, go to your device settings and enable unknown sources. This will allow you to install apps from sources other than Google Play Store.
-Locate the downloaded file in your file manager and tap on it to start the installation process.
-Follow the instructions on the screen and wait for the installation to be completed.
-Launch the game and enjoy!
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- Why use European Truck Simulator Mod APK?
-You might be wondering why you should use European Truck Simulator Mod APK instead of the original version of the game. Well, there are many reasons why using this mod APK can enhance your gaming experience. Here are some of them:
- Benefits of the mod APKBenefits of the mod APK
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-Unlimited money: With European Truck Simulator Mod APK, you can have unlimited money to spend on buying new trucks, upgrading your existing ones, customizing your vehicles, and more. You don't have to worry about earning money by completing missions or watching ads. You can enjoy the game without any financial constraints.
-No ads: Ads can be annoying and distracting, especially when you are trying to focus on driving your truck. They can also consume your data and battery. With European Truck Simulator Mod APK, you can get rid of all the ads and play the game without any interruptions.
-All trucks unlocked: The game features 12 European truck brands with different models and specifications. However, not all of them are available from the start. You have to unlock them by progressing through the game or by paying real money. With European Truck Simulator Mod APK, you can access all the trucks from the beginning and choose the one that suits your style and preference.
-Premium features: The game also offers some premium features that can enhance your gameplay, such as removing the speed limiter, enabling realistic fuel consumption, changing the license plate, and more. These features are only available for users who pay real money or watch ads. With European Truck Simulator Mod APK, you can get these features for free and enjoy the game to the fullest.
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- Risks of the mod APK
-While using European Truck Simulator Mod APK can have many benefits, it also comes with some risks that you should be aware of. Here are some of them:
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-Compatibility issues: The mod APK may not be compatible with all devices or versions of the game. It may cause some glitches or errors that can affect your gameplay or damage your device. You should always check the compatibility of the mod APK before downloading and installing it.
-Security issues: The mod APK may contain viruses or malware that can harm your device or steal your personal information. You should always download the mod APK from a trusted source and scan it with an antivirus before installing it.
-Ban issues: The mod APK may violate the terms and conditions of the game or Google Play Store. It may be detected by the game developers or Google and result in a ban or suspension of your account. You should always use the mod APK at your own risk and discretion.
-
- Tips and tricks for playing European Truck Simulator
-Now that you know how to download and install European Truck Simulator Mod APK, you might be wondering how to play the game and have fun. Well, we have some tips and tricks for you that can help you improve your skills and enjoy the game more. Here are some of them:
- Customize your truck
-One of the best things about European Truck Simulator is that you can customize your truck according to your taste and preference. You can change the color, paint job, accessories, wheels, lights, horns, exhausts, and more. You can also upgrade your engine, transmission, brakes, suspension, fuel tank, and more. Customizing your truck can make it look more unique and attractive, as well as improve its performance and efficiency.
- Follow the traffic rules
-European Truck Simulator is a realistic simulation game that follows the traffic rules and regulations of Europe. You have to obey the speed limits, traffic lights, signs, signals, lane markings, and more. You also have to respect other vehicles on the road, such as cars, buses, motorcycles, bicycles, pedestrians, etc. If you break any traffic rule or cause any accident, you will be fined or penalized by the police. Following the traffic rules can make your driving experience more safe and smooth.
- Explore different routes and cities
-European Truck Simulator offers you a vast map of Europe with more than 20 realistic cities to visit. You can explore different routes and roads that connect these cities, such as country roads, highways, offroads, etc. You can also enjoy the scenic views of nature, landmarks, monuments, buildings, etc. that you encounter along the way. Exploring different routes and cities can make your gameplay more diverse and interesting.
- Join online multiplayer mode
-If you want to challenge yourself and compete with other players from around the world, you can join the online multiplayer mode of European Truck Simulator. You can either join a server or create a convoy with your friends. You can chat with other players using voice or text messages. You can also compare your scores and achievements with other players on the leaderboards. Joining online multiplayer mode can make your gameplay more social and fun.
- Conclusion
-European Truck Simulator is a great game for anyone who loves driving trucks and exploring
European Truck Simulator is a great game for anyone who loves driving trucks and exploring new places. It offers a realistic and immersive truck simulation experience that can keep you entertained for hours. However, if you want to enjoy the game without any limitations or restrictions, you can use European Truck Simulator Mod APK, a modified version of the game that gives you unlimited money and other features. You can download and install this mod APK from a reliable website and follow the instructions given in this article. You can also use some tips and tricks to improve your skills and have fun playing the game.
-We hope this article was helpful and informative for you. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy trucking!
- FAQs
-Here are some frequently asked questions about European Truck Simulator Mod APK:
-
-Is European Truck Simulator Mod APK safe to use?
-European Truck Simulator Mod APK is generally safe to use, as long as you download it from a trusted source and scan it with an antivirus before installing it. However, you should always use it at your own risk and discretion, as it may violate the terms and conditions of the game or Google Play Store.
-What are the requirements for using European Truck Simulator Mod APK?
-To use European Truck Simulator Mod APK, you need to have an Android device with Android 4.1 or higher, at least 1 GB of RAM, and at least 200 MB of free storage space. You also need to enable unknown sources in your device settings to install the mod APK.
-Can I play European Truck Simulator Mod APK offline?
-Yes, you can play European Truck Simulator Mod APK offline, as it does not require an internet connection to run. However, you will not be able to access the online multiplayer mode or update the game without an internet connection.
-Can I use European Truck Simulator Mod APK with other mods or cheats?
-No, you cannot use European Truck Simulator Mod APK with other mods or cheats, as it may cause compatibility issues or errors that can affect your gameplay or damage your device. You should only use one mod or cheat at a time.
-Can I update European Truck Simulator Mod APK?
-Yes, you can update European Truck Simulator Mod APK, as long as the mod APK is compatible with the latest version of the game. You can check for updates on the website where you downloaded the mod APK or on the game itself. However, you may lose some of the mod features or data after updating, so you should always backup your data before updating.
- 401be4b1e0{button_text} ",
- )
-
- def __rich_repr__(self) -> Result:
- yield self.name
- yield self.type
- yield "number", self.number, None
- yield "triplet", self.triplet, None
-
- @property
- def system(self) -> ColorSystem:
- """Get the native color system for this color."""
- if self.type == ColorType.DEFAULT:
- return ColorSystem.STANDARD
- return ColorSystem(int(self.type))
-
- @property
- def is_system_defined(self) -> bool:
- """Check if the color is ultimately defined by the system."""
- return self.system not in (ColorSystem.EIGHT_BIT, ColorSystem.TRUECOLOR)
-
- @property
- def is_default(self) -> bool:
- """Check if the color is a default color."""
- return self.type == ColorType.DEFAULT
-
- def get_truecolor(
- self, theme: Optional["TerminalTheme"] = None, foreground: bool = True
- ) -> ColorTriplet:
- """Get an equivalent color triplet for this color.
-
- Args:
- theme (TerminalTheme, optional): Optional terminal theme, or None to use default. Defaults to None.
- foreground (bool, optional): True for a foreground color, or False for background. Defaults to True.
-
- Returns:
- ColorTriplet: A color triplet containing RGB components.
- """
-
- if theme is None:
- theme = DEFAULT_TERMINAL_THEME
- if self.type == ColorType.TRUECOLOR:
- assert self.triplet is not None
- return self.triplet
- elif self.type == ColorType.EIGHT_BIT:
- assert self.number is not None
- return EIGHT_BIT_PALETTE[self.number]
- elif self.type == ColorType.STANDARD:
- assert self.number is not None
- return theme.ansi_colors[self.number]
- elif self.type == ColorType.WINDOWS:
- assert self.number is not None
- return WINDOWS_PALETTE[self.number]
- else: # self.type == ColorType.DEFAULT:
- assert self.number is None
- return theme.foreground_color if foreground else theme.background_color
-
- @classmethod
- def from_ansi(cls, number: int) -> "Color":
- """Create a Color number from it's 8-bit ansi number.
-
- Args:
- number (int): A number between 0-255 inclusive.
-
- Returns:
- Color: A new Color instance.
- """
- return cls(
- name=f"color({number})",
- type=(ColorType.STANDARD if number < 16 else ColorType.EIGHT_BIT),
- number=number,
- )
-
- @classmethod
- def from_triplet(cls, triplet: "ColorTriplet") -> "Color":
- """Create a truecolor RGB color from a triplet of values.
-
- Args:
- triplet (ColorTriplet): A color triplet containing red, green and blue components.
-
- Returns:
- Color: A new color object.
- """
- return cls(name=triplet.hex, type=ColorType.TRUECOLOR, triplet=triplet)
-
- @classmethod
- def from_rgb(cls, red: float, green: float, blue: float) -> "Color":
- """Create a truecolor from three color components in the range(0->255).
-
- Args:
- red (float): Red component in range 0-255.
- green (float): Green component in range 0-255.
- blue (float): Blue component in range 0-255.
-
- Returns:
- Color: A new color object.
- """
- return cls.from_triplet(ColorTriplet(int(red), int(green), int(blue)))
-
- @classmethod
- def default(cls) -> "Color":
- """Get a Color instance representing the default color.
-
- Returns:
- Color: Default color.
- """
- return cls(name="default", type=ColorType.DEFAULT)
-
- @classmethod
- @lru_cache(maxsize=1024)
- def parse(cls, color: str) -> "Color":
- """Parse a color definition."""
- original_color = color
- color = color.lower().strip()
-
- if color == "default":
- return cls(color, type=ColorType.DEFAULT)
-
- color_number = ANSI_COLOR_NAMES.get(color)
- if color_number is not None:
- return cls(
- color,
- type=(ColorType.STANDARD if color_number < 16 else ColorType.EIGHT_BIT),
- number=color_number,
- )
-
- color_match = RE_COLOR.match(color)
- if color_match is None:
- raise ColorParseError(f"{original_color!r} is not a valid color")
-
- color_24, color_8, color_rgb = color_match.groups()
- if color_24:
- triplet = ColorTriplet(
- int(color_24[0:2], 16), int(color_24[2:4], 16), int(color_24[4:6], 16)
- )
- return cls(color, ColorType.TRUECOLOR, triplet=triplet)
-
- elif color_8:
- number = int(color_8)
- if number > 255:
- raise ColorParseError(f"color number must be <= 255 in {color!r}")
- return cls(
- color,
- type=(ColorType.STANDARD if number < 16 else ColorType.EIGHT_BIT),
- number=number,
- )
-
- else: # color_rgb:
- components = color_rgb.split(",")
- if len(components) != 3:
- raise ColorParseError(
- f"expected three components in {original_color!r}"
- )
- red, green, blue = components
- triplet = ColorTriplet(int(red), int(green), int(blue))
- if not all(component <= 255 for component in triplet):
- raise ColorParseError(
- f"color components must be <= 255 in {original_color!r}"
- )
- return cls(color, ColorType.TRUECOLOR, triplet=triplet)
-
- @lru_cache(maxsize=1024)
- def get_ansi_codes(self, foreground: bool = True) -> Tuple[str, ...]:
- """Get the ANSI escape codes for this color."""
- _type = self.type
- if _type == ColorType.DEFAULT:
- return ("39" if foreground else "49",)
-
- elif _type == ColorType.WINDOWS:
- number = self.number
- assert number is not None
- fore, back = (30, 40) if number < 8 else (82, 92)
- return (str(fore + number if foreground else back + number),)
-
- elif _type == ColorType.STANDARD:
- number = self.number
- assert number is not None
- fore, back = (30, 40) if number < 8 else (82, 92)
- return (str(fore + number if foreground else back + number),)
-
- elif _type == ColorType.EIGHT_BIT:
- assert self.number is not None
- return ("38" if foreground else "48", "5", str(self.number))
-
- else: # self.standard == ColorStandard.TRUECOLOR:
- assert self.triplet is not None
- red, green, blue = self.triplet
- return ("38" if foreground else "48", "2", str(red), str(green), str(blue))
-
- @lru_cache(maxsize=1024)
- def downgrade(self, system: ColorSystem) -> "Color":
- """Downgrade a color system to a system with fewer colors."""
-
- if self.type in (ColorType.DEFAULT, system):
- return self
- # Convert to 8-bit color from truecolor color
- if system == ColorSystem.EIGHT_BIT and self.system == ColorSystem.TRUECOLOR:
- assert self.triplet is not None
- _h, l, s = rgb_to_hls(*self.triplet.normalized)
- # If saturation is under 15% assume it is grayscale
- if s < 0.15:
- gray = round(l * 25.0)
- if gray == 0:
- color_number = 16
- elif gray == 25:
- color_number = 231
- else:
- color_number = 231 + gray
- return Color(self.name, ColorType.EIGHT_BIT, number=color_number)
-
- red, green, blue = self.triplet
- six_red = red / 95 if red < 95 else 1 + (red - 95) / 40
- six_green = green / 95 if green < 95 else 1 + (green - 95) / 40
- six_blue = blue / 95 if blue < 95 else 1 + (blue - 95) / 40
-
- color_number = (
- 16 + 36 * round(six_red) + 6 * round(six_green) + round(six_blue)
- )
- return Color(self.name, ColorType.EIGHT_BIT, number=color_number)
-
- # Convert to standard from truecolor or 8-bit
- elif system == ColorSystem.STANDARD:
- if self.system == ColorSystem.TRUECOLOR:
- assert self.triplet is not None
- triplet = self.triplet
- else: # self.system == ColorSystem.EIGHT_BIT
- assert self.number is not None
- triplet = ColorTriplet(*EIGHT_BIT_PALETTE[self.number])
-
- color_number = STANDARD_PALETTE.match(triplet)
- return Color(self.name, ColorType.STANDARD, number=color_number)
-
- elif system == ColorSystem.WINDOWS:
- if self.system == ColorSystem.TRUECOLOR:
- assert self.triplet is not None
- triplet = self.triplet
- else: # self.system == ColorSystem.EIGHT_BIT
- assert self.number is not None
- if self.number < 16:
- return Color(self.name, ColorType.WINDOWS, number=self.number)
- triplet = ColorTriplet(*EIGHT_BIT_PALETTE[self.number])
-
- color_number = WINDOWS_PALETTE.match(triplet)
- return Color(self.name, ColorType.WINDOWS, number=color_number)
-
- return self
-
-
-def parse_rgb_hex(hex_color: str) -> ColorTriplet:
- """Parse six hex characters in to RGB triplet."""
- assert len(hex_color) == 6, "must be 6 characters"
- color = ColorTriplet(
- int(hex_color[0:2], 16), int(hex_color[2:4], 16), int(hex_color[4:6], 16)
- )
- return color
-
-
-def blend_rgb(
- color1: ColorTriplet, color2: ColorTriplet, cross_fade: float = 0.5
-) -> ColorTriplet:
- """Blend one RGB color in to another."""
- r1, g1, b1 = color1
- r2, g2, b2 = color2
- new_color = ColorTriplet(
- int(r1 + (r2 - r1) * cross_fade),
- int(g1 + (g2 - g1) * cross_fade),
- int(b1 + (b2 - b1) * cross_fade),
- )
- return new_color
-
-
-if __name__ == "__main__": # pragma: no cover
-
- from .console import Console
- from .table import Table
- from .text import Text
-
- console = Console()
-
- table = Table(show_footer=False, show_edge=True)
- table.add_column("Color", width=10, overflow="ellipsis")
- table.add_column("Number", justify="right", style="yellow")
- table.add_column("Name", style="green")
- table.add_column("Hex", style="blue")
- table.add_column("RGB", style="magenta")
-
- colors = sorted((v, k) for k, v in ANSI_COLOR_NAMES.items())
- for color_number, name in colors:
- if "grey" in name:
- continue
- color_cell = Text(" " * 10, style=f"on {name}")
- if color_number < 16:
- table.add_row(color_cell, f"{color_number}", Text(f'"{name}"'))
- else:
- color = EIGHT_BIT_PALETTE[color_number] # type: ignore[has-type]
- table.add_row(
- color_cell, str(color_number), Text(f'"{name}"'), color.hex, color.rgb
- )
-
- console.print(table)
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/command/check.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/command/check.py
deleted file mode 100644
index 539481c946043c53aa61bd62cfd4b4146934697d..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/_distutils/command/check.py
+++ /dev/null
@@ -1,151 +0,0 @@
-"""distutils.command.check
-
-Implements the Distutils 'check' command.
-"""
-import contextlib
-
-from distutils.core import Command
-from distutils.errors import DistutilsSetupError
-
-with contextlib.suppress(ImportError):
- import docutils.utils
- import docutils.parsers.rst
- import docutils.frontend
- import docutils.nodes
-
- class SilentReporter(docutils.utils.Reporter):
- def __init__(
- self,
- source,
- report_level,
- halt_level,
- stream=None,
- debug=0,
- encoding='ascii',
- error_handler='replace',
- ):
- self.messages = []
- super().__init__(
- source, report_level, halt_level, stream, debug, encoding, error_handler
- )
-
- def system_message(self, level, message, *children, **kwargs):
- self.messages.append((level, message, children, kwargs))
- return docutils.nodes.system_message(
- message, level=level, type=self.levels[level], *children, **kwargs
- )
-
-
-class check(Command):
- """This command checks the meta-data of the package."""
-
- description = "perform some checks on the package"
- user_options = [
- ('metadata', 'm', 'Verify meta-data'),
- (
- 'restructuredtext',
- 'r',
- (
- 'Checks if long string meta-data syntax '
- 'are reStructuredText-compliant'
- ),
- ),
- ('strict', 's', 'Will exit with an error if a check fails'),
- ]
-
- boolean_options = ['metadata', 'restructuredtext', 'strict']
-
- def initialize_options(self):
- """Sets default values for options."""
- self.restructuredtext = 0
- self.metadata = 1
- self.strict = 0
- self._warnings = 0
-
- def finalize_options(self):
- pass
-
- def warn(self, msg):
- """Counts the number of warnings that occurs."""
- self._warnings += 1
- return Command.warn(self, msg)
-
- def run(self):
- """Runs the command."""
- # perform the various tests
- if self.metadata:
- self.check_metadata()
- if self.restructuredtext:
- if 'docutils' in globals():
- try:
- self.check_restructuredtext()
- except TypeError as exc:
- raise DistutilsSetupError(str(exc))
- elif self.strict:
- raise DistutilsSetupError('The docutils package is needed.')
-
- # let's raise an error in strict mode, if we have at least
- # one warning
- if self.strict and self._warnings > 0:
- raise DistutilsSetupError('Please correct your package.')
-
- def check_metadata(self):
- """Ensures that all required elements of meta-data are supplied.
-
- Required fields:
- name, version
-
- Warns if any are missing.
- """
- metadata = self.distribution.metadata
-
- missing = []
- for attr in 'name', 'version':
- if not getattr(metadata, attr, None):
- missing.append(attr)
-
- if missing:
- self.warn("missing required meta-data: %s" % ', '.join(missing))
-
- def check_restructuredtext(self):
- """Checks if the long string fields are reST-compliant."""
- data = self.distribution.get_long_description()
- for warning in self._check_rst_data(data):
- line = warning[-1].get('line')
- if line is None:
- warning = warning[1]
- else:
- warning = '{} (line {})'.format(warning[1], line)
- self.warn(warning)
-
- def _check_rst_data(self, data):
- """Returns warnings when the provided data doesn't compile."""
- # the include and csv_table directives need this to be a path
- source_path = self.distribution.script_name or 'setup.py'
- parser = docutils.parsers.rst.Parser()
- settings = docutils.frontend.OptionParser(
- components=(docutils.parsers.rst.Parser,)
- ).get_default_values()
- settings.tab_width = 4
- settings.pep_references = None
- settings.rfc_references = None
- reporter = SilentReporter(
- source_path,
- settings.report_level,
- settings.halt_level,
- stream=settings.warning_stream,
- debug=settings.debug,
- encoding=settings.error_encoding,
- error_handler=settings.error_encoding_error_handler,
- )
-
- document = docutils.nodes.document(settings, reporter, source=source_path)
- document.note_source(source_path, -1)
- try:
- parser.parse(data, document)
- except AttributeError as e:
- reporter.messages.append(
- (-1, 'Could not finish the parsing: %s.' % e, '', {})
- )
-
- return reporter.messages
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/layers/wrappers.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/layers/wrappers.py
deleted file mode 100644
index 29d0ef9102b2db0ffbf723c168aa32d2451b9419..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/layers/wrappers.py
+++ /dev/null
@@ -1,132 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-"""
-Wrappers around on some nn functions, mainly to support empty tensors.
-
-Ideally, add support directly in PyTorch to empty tensors in those functions.
-
-These can be removed once https://github.com/pytorch/pytorch/issues/12013
-is implemented
-"""
-
-from typing import List, Optional
-import torch
-from torch.nn import functional as F
-
-
-def shapes_to_tensor(x: List[int], device: Optional[torch.device] = None) -> torch.Tensor:
- """
- Turn a list of integer scalars or integer Tensor scalars into a vector,
- in a way that's both traceable and scriptable.
-
- In tracing, `x` should be a list of scalar Tensor, so the output can trace to the inputs.
- In scripting or eager, `x` should be a list of int.
- """
- if torch.jit.is_scripting():
- return torch.as_tensor(x, device=device)
- if torch.jit.is_tracing():
- assert all(
- [isinstance(t, torch.Tensor) for t in x]
- ), "Shape should be tensor during tracing!"
- # as_tensor should not be used in tracing because it records a constant
- ret = torch.stack(x)
- if ret.device != device: # avoid recording a hard-coded device if not necessary
- ret = ret.to(device=device)
- return ret
- return torch.as_tensor(x, device=device)
-
-
-def cat(tensors: List[torch.Tensor], dim: int = 0):
- """
- Efficient version of torch.cat that avoids a copy if there is only a single element in a list
- """
- assert isinstance(tensors, (list, tuple))
- if len(tensors) == 1:
- return tensors[0]
- return torch.cat(tensors, dim)
-
-
-def cross_entropy(input, target, *, reduction="mean", **kwargs):
- """
- Same as `torch.nn.functional.cross_entropy`, but returns 0 (instead of nan)
- for empty inputs.
- """
- if target.numel() == 0 and reduction == "mean":
- return input.sum() * 0.0 # connect the gradient
- return F.cross_entropy(input, target, reduction=reduction, **kwargs)
-
-
-class _NewEmptyTensorOp(torch.autograd.Function):
- @staticmethod
- def forward(ctx, x, new_shape):
- ctx.shape = x.shape
- return x.new_empty(new_shape)
-
- @staticmethod
- def backward(ctx, grad):
- shape = ctx.shape
- return _NewEmptyTensorOp.apply(grad, shape), None
-
-
-class Conv2d(torch.nn.Conv2d):
- """
- A wrapper around :class:`torch.nn.Conv2d` to support empty inputs and more features.
- """
-
- def __init__(self, *args, **kwargs):
- """
- Extra keyword arguments supported in addition to those in `torch.nn.Conv2d`:
-
- Args:
- norm (nn.Module, optional): a normalization layer
- activation (callable(Tensor) -> Tensor): a callable activation function
-
- It assumes that norm layer is used before activation.
- """
- norm = kwargs.pop("norm", None)
- activation = kwargs.pop("activation", None)
- super().__init__(*args, **kwargs)
-
- self.norm = norm
- self.activation = activation
-
- def forward(self, x):
- # torchscript does not support SyncBatchNorm yet
- # https://github.com/pytorch/pytorch/issues/40507
- # and we skip these codes in torchscript since:
- # 1. currently we only support torchscript in evaluation mode
- # 2. features needed by exporting module to torchscript are added in PyTorch 1.6 or
- # later version, `Conv2d` in these PyTorch versions has already supported empty inputs.
- if not torch.jit.is_scripting():
- if x.numel() == 0 and self.training:
- # https://github.com/pytorch/pytorch/issues/12013
- assert not isinstance(
- self.norm, torch.nn.SyncBatchNorm
- ), "SyncBatchNorm does not support empty inputs!"
-
- x = F.conv2d(
- x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups
- )
- if self.norm is not None:
- x = self.norm(x)
- if self.activation is not None:
- x = self.activation(x)
- return x
-
-
-ConvTranspose2d = torch.nn.ConvTranspose2d
-BatchNorm2d = torch.nn.BatchNorm2d
-interpolate = F.interpolate
-Linear = torch.nn.Linear
-
-
-def nonzero_tuple(x):
- """
- A 'as_tuple=True' version of torch.nonzero to support torchscript.
- because of https://github.com/pytorch/pytorch/issues/38718
- """
- if torch.jit.is_scripting():
- if x.dim() == 0:
- return x.unsqueeze(0).nonzero().unbind(1)
- return x.nonzero().unbind(1)
- else:
- return x.nonzero(as_tuple=True)
diff --git a/spaces/Benson/text-generation/Examples/Bus Simulator Ultimate Mod Apk Revdl.md b/spaces/Benson/text-generation/Examples/Bus Simulator Ultimate Mod Apk Revdl.md
deleted file mode 100644
index 460a8f79132064c1ea0c1faac7b141a05c8b382f..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Bus Simulator Ultimate Mod Apk Revdl.md
+++ /dev/null
@@ -1,94 +0,0 @@
-
-Pokemon Go APK Original: Cómo descargar y jugar la sensación de juego global
-Pokemon Go es un juego de smartphone gratuito que te permite atrapar a Pokémon en una versión aumentada del mundo real. Usando el sistema GPS de tu teléfono inteligente y el mapa preinstalado en el juego, puedes caminar por las calles y atrapar a Pokémon mientras surgen. Pokemon Go es la sensación de juego global que se ha descargado más de 1 mil millones de veces y nombrado "Mejor juego móvil" por los Game Developers Choice Awards y "Mejor aplicación del año" por TechCrunch. En este artículo, le mostraremos cómo descargar y jugar Pokemon Go APK Original, que es la versión original del juego que no está disponible en Google Play Store.
- ¿Qué es Pokemon Go APK Original?
-Pokemon Go APK Original es el paquete de aplicaciones para Android (APK) archivo de la versión original de Pokemon Go que fue lanzado en julio de 2016. Un archivo APK es un archivo comprimido que contiene todos los archivos y datos necesarios para ejecutar una aplicación Android. A diferencia de las aplicaciones que se descargan de Google Play Store, que se instalan y actualizan automáticamente por Google, los archivos APK deben ser descargados e instalados manualmente por el usuario.
-bus simulator ultimate mod apk revdl Download ✯✯✯ https://bltlly.com/2v6MLM
La diferencia entre archivos APK y XAPK
-Algunos sitios web pueden ofrecer para descargar Pokemon Go XAPK en lugar de APK. Un archivo XAPK es una versión extendida de un archivo APK que contiene archivos adicionales como archivos de datos OBB o APK divididos. Los archivos de datos OBB se utilizan para almacenar grandes cantidades de datos del juego, como gráficos, sonidos y videos. Los APK divididos se utilizan para admitir diferentes configuraciones de dispositivos, como tamaños de pantalla, idiomas y arquitecturas. Los archivos XAPK necesitan una aplicación o herramienta especial para extraerlos e instalarlos en tu dispositivo.
- Los beneficios de descargar Pokemon Go APK Original
-Hay varios beneficios de descargar Pokemon Go APK Original en lugar de conseguirlo de Google Play Store. Algunos de ellos son:
-
-
-Puedes jugar el juego sin restricciones o limitaciones impuestas por Google o el fabricante de tu dispositivo.
- Puede acceder a versiones anteriores del juego que pueden tener características o funciones que se eliminan o cambian en versiones más recientes.
-Puedes personalizar o modificar el juego según tus preferencias o necesidades.
-
- Cómo descargar e instalar Pokemon Go APK Original en su dispositivo Android
-Descargar e instalar Pokemon Go APK Original en su dispositivo Android es fácil y simple. Solo tienes que seguir estos pasos:
- Paso 1: Habilitar fuentes desconocidas
-Antes de que pueda instalar cualquier archivo APK en su dispositivo, debe habilitar fuentes desconocidas en la configuración del dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. Es posible que
reciba un mensaje de advertencia de que instalar aplicaciones de fuentes desconocidas puede dañar su dispositivo. Pulse Aceptar para continuar.
- Paso 2: Descargar el archivo APK de una fuente de confianza
-Siguiente, es necesario descargar el archivo APK de Pokemon Go Original de una fuente de confianza. Hay muchos sitios web que ofrecen archivos APK de forma gratuita, pero algunos de ellos pueden contener malware o virus que pueden dañar su dispositivo o robar su información personal. Para evitar esto, solo debe descargar archivos APK de fuentes confiables y verificadas. Una de las mejores fuentes para Pokemon Go APK Original es [APKPure], que es un sitio web popular y confiable que proporciona archivos APK seguros y puros para los usuarios de Android. Para descargar el archivo APK de APKPure, siga estos pasos:
-
-Ir al sitio web [APKPure] en el navegador de su dispositivo.
-Buscar Pokemon Ir en la barra de búsqueda y toque en el resultado.
-Toque en el botón Descargar APK y elija una ubicación de descarga en su dispositivo.
-Espera a que termine la descarga.
-
- Paso 3: Instalar el archivo APK
-
-
-Localice el archivo APK en su dispositivo utilizando una aplicación de administrador de archivos o la carpeta de descargas de su dispositivo.
-Toque en el archivo APK y toque en Instalar cuando se le solicite.
-Espere a que se complete la instalación.
-
- Paso 4: Iniciar el juego y disfrutar de
-Después de instalar el archivo APK, puede iniciar el juego y disfrutar jugando Pokémon Go Original en su dispositivo. Para hacer esto, siga estos pasos:
-
-
-Ve al cajón de aplicaciones de tu dispositivo y toca el icono de Pokemon Go.
-Permite que el juego acceda a la ubicación, cámara y almacenamiento de tu dispositivo cuando se te pregunte.
-Inicia sesión con tu cuenta de Google o crea una nueva cuenta de Pokemon Trainer Club.
-Elige tu avatar y personalízalo con diferentes trajes y accesorios.
-Selecciona tu Pokémon inicial de Bulbasaur, Charmander o Squirtle.
-¡Empieza a explorar el mundo de los Pokémon y atrápalos a todos!
-
- Cómo actualizar Pokemon Go APK Original a la última versión
-Pokemon Go se actualiza constantemente con nuevas características, eventos y Pokémon para mantener el juego fresco y emocionante. Para disfrutar de la última versión de Pokémon Go Original, necesitas actualizar el archivo APK regularmente. Hay dos maneras de hacer esto:
- Opción 1: Usa la función de actualización en el juego
-La forma más fácil de actualizar Pokémon Go Original es utilizar la función de actualización en el juego. Esta función le notificará cuando una nueva versión del juego esté disponible y le permitirá descargarlo e instalarlo directamente desde el juego. Para utilizar esta función, siga estos pasos:
-
- Iniciar el juego y toque en el icono de Pokeball en la parte inferior de la pantalla.
-Toque en Configuración en la esquina superior derecha de la pantalla.
-Desplácese hacia abajo y toque en Buscar actualizaciones.
- Si una nueva versión está disponible, toque en Actualizar ahora y espere a que la descarga y la instalación finalicen.
-
- Opción 2: Descargar e instalar el último archivo APK manualmente
-
-
-Ir al sitio web [APKPure] en el navegador de su dispositivo.
-Buscar Pokemon Ir en la barra de búsqueda y toque en el resultado.
-Toque en el botón Actualizar y elija una ubicación de descarga en su dispositivo.
-Espera a que termine la descarga.
-Localice el archivo APK en su dispositivo utilizando una aplicación de administrador de archivos o la carpeta de descargas de su dispositivo.
-Toque en el archivo APK y toque en Instalar cuando se le solicite.
-Espere a que se complete la instalación.
-
- Cómo jugar Pokemon Go APK Original y divertirse
-Pokémon Go Original es más que un juego. Es una aventura que te permite explorar el mundo real con un toque virtual. Usted puede descubrir nuevos lugares, conocer gente nueva, y coger Pokémon increíble en el camino. Aquí hay algunos consejos sobre cómo jugar Pokemon Go Original y divertirse:
- Explora y descubre Pokémon dondequiera que estés
-Pokemon Go Original utiliza el GPS y la cámara de tu dispositivo para mostrarte Pokémon en el mundo real. Puedes encontrar Pokémon en diferentes entornos como parques, bosques, lagos, montañas, ciudades y más. También puedes usar elementos como módulos de incienso y señuelo para atraer más Pokémon a tu ubicación. Para atrapar a un Pokémon, necesitas tocarlo y luego mover el dedo en la pantalla para lanzarle una Pokeball. También puedes usar artículos como Razz Berries y Nanab Berries para hacer que los Pokémon sean más fáciles de atrapar. Algunos Pokémon son raros y difíciles de encontrar, por lo que es posible que tengas que viajar a diferentes lugares o esperar a eventos especiales para encontrarlos.
- Atrapa más Pokémon para completar tu Pokedex
-
- Viaja junto a tu amigo Pokémon para ayudar a hacer tu Pokémon más fuerte y ganar recompensas
-Puedes elegir uno de tus Pokémon como tu Amigo Pokémon y tenerlo caminando contigo en tus aventuras. Tu amigo Pokémon aparecerá junto a tu avatar en el mapa y en la pantalla de tu perfil. También puedes interactuar con tu Pokémon amigo alimentándolo con bayas, jugando con él o tomando instantáneas de él. Al caminar con tu amigo Pokémon, puedes ganar caramelos para ese tipo de Pokémon específico, que puedes usar para encender o evolucionar tu Pokémon. También puedes aumentar tu nivel de amigo con tu amigo Pokémon ganando corazones afectivos, lo que desbloqueará beneficios como caramelos de bonificación, CP boost o encontrar recuerdos.
- Compite en batallas épicas de gimnasia y equipo con otros entrenadores para atrapar poderosos Pokémon durante las batallas de asalto
-Pokémon Go Original no es solo un juego en solitario, sino también un juego social que te permite interactuar con otros jugadores de todo el mundo. Puedes unirte a uno de los tres equipos: Team Instinct (amarillo), Team Mystic (azul) o Team Valor (rojo). A continuación, puede competir con otros equipos para el control de los gimnasios, que son puntos de referencia que aparecen en el mapa. Para desafiar a un gimnasio, necesitas tocar en él y luego seleccionar un equipo de seis Pokémon para luchar contra los Pokémon defensores. También puedes cooperar con otros jugadores de cualquier equipo para derrotar a los poderosos Pokémon que aparecen en las Batallas Raid, que son eventos cronometrados que ocurren en ciertos Gimnasios. Al participar en batallas de gimnasio y batallas de asalto, puedes ganar objetos como Pokeballs, pociones, revives, dulces raros, bayas de oro razz, máquinas técnicas y más.
- Conclusión
-
- Preguntas frecuentes
-
-Q: ¿Es Pokemon Go APK original seguro de descargar e instalar?
-A: Sí, siempre y cuando se descarga el archivo APK de una fuente de confianza como [APKPure], que proporciona archivos APK seguros y puros para los usuarios de Android.
-Q: ¿Necesito una conexión a Internet para jugar Pokemon Go APK Original?
-A: Sí, necesita una conexión a Internet (Wi-Fi o datos móviles) para jugar Pokemon Go APK Original, ya que el juego se basa en el GPS y los datos del mapa para mostrar Pokémon en el mundo real.
-Q: ¿Cómo puedo guardar mi progreso en Pokemon Go APK Original?
-A: Su progreso en Pokemon Go APK Original se guarda automáticamente en el servidor del juego cuando se inicia sesión con su cuenta de Google o Pokemon Trainer Club cuenta. También puedes sincronizar los datos del juego en varios dispositivos utilizando la misma cuenta.
-Q: ¿Cómo puedo transferir mis datos desde la versión de Google Play Store de Pokemon Ir a la versión APK?
-A: Usted no necesita transferir sus datos desde la versión de Google Play Store de Pokemon Go a la versión APK, ya que ambos son compatibles entre sí. Puedes usar la misma cuenta para jugar el juego en ambas versiones sin perder tu progreso.
-Q: ¿Cómo me pongo en contacto con el desarrollador de Pokemon Go APK Original si tengo alguna pregunta o problema?
-A: Puede ponerse en contacto con el desarrollador de Pokemon Go APK Original visitando su sitio web oficial [Pokemon Go] o enviando un correo electrónico a [pokemon-go-support@nianticlabs.com]. También puede consultar su [Centro de ayuda] para obtener preguntas frecuentes y consejos para solucionar problemas.
- 64aa2da5cfIntroducción
-Si eres un fan de los juegos de ritmo y las batallas musicales, es posible que hayas oído hablar de Friday Night Funkin' , un popular juego web que fue lanzado en 2020. En este juego, juegas como Boyfriend , un rapero de pelo azul que quiere impresionar a su Girlfriend al ganar batallas de música freestyle contra varios oponentes, como sus padres, sus ex y algunos personajes espeluznantes.
-descargar fnf música batalla original mod DOWNLOAD >>>>> https://bltlly.com/2v6IFA
Friday Night Funkin' es un juego de código abierto que ha inspirado muchos mods hechos por fans que agregan nuevos personajes, canciones, modos y características al juego original. Uno de estos mods es FNF Music Battle Original Mod , un juego de música de ritmo que está disponible en dispositivos Android. Este mod es desarrollado por Onesoft Global PTE.LTD y tiene más de 10 millones de descargas en Google Play Store.
-En este artículo, vamos a explorar de qué trata FNF Music Battle Original Mod, cuáles son sus características, jugabilidad, beneficios, inconvenientes, comparación con otros mods, e instrucciones de instalación. Al final de este artículo, usted tendrá una mejor comprensión de este mod y si usted debe darle una oportunidad o no.
- Características
-FNF Music Battle Original Mod tiene varias características que lo hacen diferente del juego original de Friday Night Funkin'. Aquí están algunas de ellas:
-
-Characters : Este mod tiene varios personajes del juego original, como Boyfriend, Girlfriend, Daddy Dearest, Mommy Nearest, Monster y Spirit. También tiene algunos personajes invitados de otros mods o juegos de FNF, como Skid y Pump de Spooky Month, Pico de Newgrounds, Tankman de la serie Tankmen, Whitty de Vs. Whitty mod, Ruv de Vs. Ruv mod, Tabi de Vs. Tabi Ex Boyfriend mod. Modos : Este mod tiene dos modos: Modo Historia y Modo Freeplay. En el modo Historia, puedes elegir entre niveles de dificultad Fácil, Normal o Difícil y jugar todas las semanas en orden. En el modo Freeplay, puede seleccionar cualquier canción que desee y reproducirla sin restricciones. Visuales : Este mod tiene imágenes geniales de B-Boy de los 90 que coinciden con el estilo del juego original. Los personajes tienen gráficos pixelados y animaciones que son coloridas y expresivas. Los fondos también son vibrantes y dinámicas.
- Juego
-El modo de juego de FNF Music Battle Original Mod es similar al juego original de Friday Night Funkin'. Usted tiene que coincidir con el ritmo de la música pulsando las teclas de flecha en el teclado o tocando los botones de flecha en la pantalla. Usted tiene que seguir la dirección de las flechas que aparecen en la pantalla y pulse o pulse en el momento adecuado. Si lo haces correctamente, llenarás la barra de progreso y ganarás la batalla musical. Si pierdes demasiadas notas o presionas los botones incorrectos, perderás la batalla musical y tendrás que empezar de nuevo.
-El juego tiene un sistema de puntuación que te recompensa por tu precisión y sincronización. Puedes obtener diferentes calificaciones dependiendo de lo bien que lo hagas, como Enfermo, Bueno, Malo, o Señorita. También puede obtener combos para golpear varias notas en una fila sin perder ninguno. Cuanto mayor sea su puntuación y combo, mejores serán sus posibilidades de ganar.
-
-El juego también tiene una barra de salud que muestra cuánta vida te queda. Si pierde demasiadas notas o presiona los botones incorrectos, su barra de salud disminuirá y se volverá roja. Si tu barra de salud llega a cero, perderás la batalla musical y tendrás que empezar de nuevo. Puede restaurar su salud pulsando más notas correctamente y llenando la barra de progreso.
- Beneficios
-Reproducción de FNF Music Battle Original Mod puede tener muchos beneficios para usted, tales como:
-
-
-Desafío : Este mod es muy desafiante y gratificante de jugar. Puedes poner a prueba tus habilidades y reflejos jugando en diferentes niveles de dificultad y modos. También puede competir con otros jugadores en línea y ver quién tiene la mejor puntuación y combo. Variety : Este mod tiene mucha variedad y contenido para ofrecer. Usted puede jugar a través de muchas canciones diferentes y semanas, cada uno con su propio tema y estilo. También puedes jugar con diferentes personajes y ver sus animaciones y expresiones únicas. Compatibilidad : Este mod es compatible con dispositivos Android, lo que significa que puede jugar en cualquier momento y en cualquier lugar. No necesitas un PC o una consola para disfrutar de este mod. Solo necesitas tu teléfono o tablet y una conexión a Internet.
- Inconvenientes
-Reproducción de FNF Music Battle Original Mod también puede tener algunos inconvenientes para usted, tales como:
-
-Bugs : Este mod no es perfecto y puede tener algunos errores y fallos que pueden afectar tu experiencia de juego. Por ejemplo, algunas canciones pueden no cargarse correctamente, algunos caracteres pueden no aparecer correctamente o algunos botones pueden no responder bien. Dificultad : Este mod no es fácil y puede frustrar a algunos jugadores que no están acostumbrados a los juegos de ritmo o que buscan un juego casual. Algunas canciones pueden ser demasiado rápidas o difíciles de seguir, especialmente en niveles o modos de dificultad más altos. Ads : Este mod es gratis pero tiene anuncios que pueden interrumpir tu juego o molestarte. Algunos anuncios pueden ser demasiado largos o demasiado frecuentes, o pueden aparecer en momentos inconvenientes. Actualizaciones : Este mod no se actualiza regularmente y puede no tener las últimas canciones o personajes de otros mods o juegos de FNF. Algunas canciones o personajes pueden estar perdidos o desactualizados, o pueden no coincidir con la calidad o el estilo del juego original.
- Comparación
-
- | Mod | Similitudes | Diferencias | | -- - | -- | -- | Whitty | - Tiene personajes invitados de otros mods o juegos de FNF. Si desea reproducir FNF Music Battle Original Mod en su dispositivo Android, puede seguir estos pasos para instalarlo:
-
-Ir a Google Play Store y buscar FNF Music Battle Original Mod o haga clic en este enlace: .
-Toque en el botón Instalar y espere a que termine la descarga.
-Abra la aplicación y conceda los permisos necesarios.
-¡Disfruta del juego!
-
-Si desea reproducir FNF Music Battle Original Mod en su PC, puede seguir estos pasos para instalarlo:
-
-Ir a este sitio web: y descargar el archivo APK de FNF Music Battle Original Mod.
-Descargar un emulador de Android de su elección, como BlueStacks o NoxPlayer.
-Instala el emulador en tu PC y ejecútalo.
-Arrastre y suelte el archivo APK de FNF Music Battle Original Mod en el emulador o utilice el navegador incorporado para encontrarlo.
-Instalar la aplicación y abrirla.
-¡Disfruta del juego!
-
- Conclusión
-FNF Music Battle Original Mod es un juego de música de ritmo que se basa en el popular juego Friday Night Funkin'. Tiene muchas características, tales como personajes, canciones, modos, visuales, sistema de puntuación, barra de salud, tablas de clasificación en línea. También tiene algunos beneficios, como factor de diversión, desafío, variedad, compatibilidad. También tiene algunos inconvenientes, como errores, dificultad, anuncios y actualizaciones. También tiene algunas similitudes y diferencias con otros mods de FNF, como Whitty, Hex, Kapi y Neo. Es fácil de instalar en dispositivos Android o PC con un emulador.
-Si usted está buscando un juego de ritmo divertido y desafiante que tiene un montón de contenido y variedad, es posible que desee probar FNF Music Battle Original Mod. Es una gran manera de disfrutar de la música y los personajes de Friday Night Funkin' y sus mods en su teléfono o computadora. Sin embargo, si usted está buscando un juego más pulido y actualizado que tiene menos errores y anuncios, es posible que desee seguir con el juego original u otros mods de PC.
-
- Preguntas frecuentes
-Aquí hay algunas preguntas frecuentes sobre FNF Music Battle Original Mod y sus respuestas:
-
-Q: ¿Es seguro jugar FNF Music Battle Original Mod? Q: ¿Es FNF Music Battle Original Mod libre para jugar? Q: ¿Cómo puedo reproducir FNF Music Battle Original Mod sin conexión? Q: ¿Cómo puedo actualizar FNF Music Battle Original Mod? Q: ¿Cómo puedo contactar a los desarrolladores de FNF Music Battle Original Mod? 64aa2da5cf
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-
-Name
-lr
-train
-inference
-train
-box
-model id
-download
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- R50 1x
-0.200
-0.055
-3.9
-36.5
-137593951
-model | metrics R50 3x
-0.201
-0.055
-3.9
-37.9
-137849486
-model | metrics R101 3x
-0.280
-0.068
-5.1
-39.9
-138363263
-model | metrics
-
-#### RPN & Fast R-CNN:
-
-
-
-
-### COCO Instance Segmentation Baselines with Mask R-CNN
-
-
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-### COCO Person Keypoint Detection Baselines with Keypoint R-CNN
-
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-Name
-lr
-train
-inference
-train
-box
-kp.
-model id
-download
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- R50-FPN 1x
-0.315
-0.072
-5.0
-53.6
-64.0
-137261548
-model | metrics R50-FPN 3x
-0.316
-0.066
-5.0
-55.4
-65.5
-137849621
-model | metrics R101-FPN 3x
-0.390
-0.076
-6.1
-56.4
-66.1
-138363331
-model | metrics X101-FPN 3x
-0.738
-0.121
-8.7
-57.3
-66.0
-139686956
-model | metrics
-
-### COCO Panoptic Segmentation Baselines with Panoptic FPN
-
-
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-Name
-lr
-train
-inference
-train
-box
-mask
-PQ
-model id
-download
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- R50-FPN 1x
-0.304
-0.053
-4.8
-37.6
-34.7
-39.4
-139514544
-model | metrics R50-FPN 3x
-0.302
-0.053
-4.8
-40.0
-36.5
-41.5
-139514569
-model | metrics R101-FPN 3x
-0.392
-0.066
-6.0
-42.4
-38.5
-43.0
-139514519
-model | metrics
-
-
-### LVIS Instance Segmentation Baselines with Mask R-CNN
-
-Mask R-CNN baselines on the [LVIS dataset](https://lvisdataset.org), v0.5.
-These baselines are described in Table 3(c) of the [LVIS paper](https://arxiv.org/abs/1908.03195).
-
-NOTE: the 1x schedule here has the same amount of __iterations__ as the COCO 1x baselines.
-They are roughly 24 epochs of LVISv0.5 data.
-The final results of these configs have large variance across different runs.
-
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-Name
-lr
-train
-inference
-train
-box
-mask
-model id
-download
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- R50-FPN 1x
-0.292
-0.107
-7.1
-23.6
-24.4
-144219072
-model | metrics R101-FPN 1x
-0.371
-0.114
-7.8
-25.6
-25.9
-144219035
-model | metrics X101-FPN 1x
-0.712
-0.151
-10.2
-26.7
-27.1
-144219108
-model | metrics
-
-
-
-### Cityscapes & Pascal VOC Baselines
-
-Simple baselines for
-* Mask R-CNN on Cityscapes instance segmentation (initialized from COCO pre-training, then trained on Cityscapes fine annotations only)
-* Faster R-CNN on PASCAL VOC object detection (trained on VOC 2007 train+val + VOC 2012 train+val, tested on VOC 2007 using 11-point interpolated AP)
-
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-Name
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-inference
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-box
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- R50-FPN, Cityscapes 0.240
-0.078
-4.4
-36.5
-142423278
-model | metrics R50-C4, VOC 0.537
-0.081
-4.8
-51.9
-80.3
-142202221
-model | metrics
-
-
-
-### Other Settings
-
-Ablations for Deformable Conv and Cascade R-CNN:
-
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-Ablations for normalization methods:
-(Note: The baseline uses `2fc` head while the others use `4conv1fc` head. According to the
-[GroupNorm paper](https://arxiv.org/abs/1803.08494), the change in head does not improve the baseline by much)
-
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-Name
-lr
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-model id
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- Baseline R50-FPN 3x
-0.261
-0.043
-3.4
-41.0
-37.2
-137849600
-model | metrics SyncBN 3x
-0.412
-0.053
-5.5
-41.9
-37.8
-169527823
-model | metrics GN 3x
-0.356
-0.069
-7.3
-42.6
-38.6
-138602888
-model | metrics GN (scratch) 3x
-0.400
-0.069
-9.8
-39.9
-36.6
-138602908
-model | metrics
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-
-A few very large models trained for a long time, for demo purposes:
-
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-inference
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-box
-mask
-PQ
-model id
-download
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- Panoptic FPN R101 0.107
-11.4
-47.4
-41.3
-46.1
-139797668
-model | metrics Mask R-CNN X152 0.242
-15.1
-50.2
-44.0
-18131413
-model | metrics above + test-time aug.
-51.9
-45.9
-
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/grid-feats-vqa/grid_feats/__init__.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/grid-feats-vqa/grid_feats/__init__.py
deleted file mode 100644
index 985aa67457644d3f8ea1cf071499c5a17a387797..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/grid-feats-vqa/grid_feats/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-from .config import add_attribute_config
-from .build_loader import (
- build_detection_train_loader_with_attributes,
- build_detection_test_loader_with_attributes,
-)
-from .roi_heads import AttributeRes5ROIHeads, AttributeStandardROIHeads
-from . import visual_genome
\ No newline at end of file
diff --git a/spaces/CVPR/LIVE/pybind11/tests/pybind11_cross_module_tests.cpp b/spaces/CVPR/LIVE/pybind11/tests/pybind11_cross_module_tests.cpp
deleted file mode 100644
index f705e310611619dff319f9b5d53b71e6fd54aec5..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/pybind11/tests/pybind11_cross_module_tests.cpp
+++ /dev/null
@@ -1,123 +0,0 @@
-/*
- tests/pybind11_cross_module_tests.cpp -- contains tests that require multiple modules
-
- Copyright (c) 2017 Jason Rhinelander
-
- All rights reserved. Use of this source code is governed by a
- BSD-style license that can be found in the LICENSE file.
-*/
-
-#include "pybind11_tests.h"
-#include "local_bindings.h"
-#include
-#include
-
-PYBIND11_MODULE(pybind11_cross_module_tests, m) {
- m.doc() = "pybind11 cross-module test module";
-
- // test_local_bindings.py tests:
- //
- // Definitions here are tested by importing both this module and the
- // relevant pybind11_tests submodule from a test_whatever.py
-
- // test_load_external
- bind_local(m, "ExternalType1", py::module_local());
- bind_local(m, "ExternalType2", py::module_local());
-
- // test_exceptions.py
- m.def("raise_runtime_error", []() { PyErr_SetString(PyExc_RuntimeError, "My runtime error"); throw py::error_already_set(); });
- m.def("raise_value_error", []() { PyErr_SetString(PyExc_ValueError, "My value error"); throw py::error_already_set(); });
- m.def("throw_pybind_value_error", []() { throw py::value_error("pybind11 value error"); });
- m.def("throw_pybind_type_error", []() { throw py::type_error("pybind11 type error"); });
- m.def("throw_stop_iteration", []() { throw py::stop_iteration(); });
-
- // test_local_bindings.py
- // Local to both:
- bind_local(m, "LocalType", py::module_local())
- .def("get2", [](LocalType &t) { return t.i + 2; })
- ;
-
- // Can only be called with our python type:
- m.def("local_value", [](LocalType &l) { return l.i; });
-
- // test_nonlocal_failure
- // This registration will fail (global registration when LocalFail is already registered
- // globally in the main test module):
- m.def("register_nonlocal", [m]() {
- bind_local(m, "NonLocalType");
- });
-
- // test_stl_bind_local
- // stl_bind.h binders defaults to py::module_local if the types are local or converting:
- py::bind_vector(m, "LocalVec");
- py::bind_map(m, "LocalMap");
-
- // test_stl_bind_global
- // and global if the type (or one of the types, for the map) is global (so these will fail,
- // assuming pybind11_tests is already loaded):
- m.def("register_nonlocal_vec", [m]() {
- py::bind_vector(m, "NonLocalVec");
- });
- m.def("register_nonlocal_map", [m]() {
- py::bind_map(m, "NonLocalMap");
- });
- // The default can, however, be overridden to global using `py::module_local()` or
- // `py::module_local(false)`.
- // Explicitly made local:
- py::bind_vector(m, "NonLocalVec2", py::module_local());
- // Explicitly made global (and so will fail to bind):
- m.def("register_nonlocal_map2", [m]() {
- py::bind_map(m, "NonLocalMap2", py::module_local(false));
- });
-
- // test_mixed_local_global
- // We try this both with the global type registered first and vice versa (the order shouldn't
- // matter).
- m.def("register_mixed_global_local", [m]() {
- bind_local(m, "MixedGlobalLocal", py::module_local());
- });
- m.def("register_mixed_local_global", [m]() {
- bind_local(m, "MixedLocalGlobal", py::module_local(false));
- });
- m.def("get_mixed_gl", [](int i) { return MixedGlobalLocal(i); });
- m.def("get_mixed_lg", [](int i) { return MixedLocalGlobal(i); });
-
- // test_internal_locals_differ
- m.def("local_cpp_types_addr", []() { return (uintptr_t) &py::detail::registered_local_types_cpp(); });
-
- // test_stl_caster_vs_stl_bind
- py::bind_vector>(m, "VectorInt");
-
- m.def("load_vector_via_binding", [](std::vector &v) {
- return std::accumulate(v.begin(), v.end(), 0);
- });
-
- // test_cross_module_calls
- m.def("return_self", [](LocalVec *v) { return v; });
- m.def("return_copy", [](const LocalVec &v) { return LocalVec(v); });
-
- class Dog : public pets::Pet { public: Dog(std::string name) : Pet(name) {}; };
- py::class_(m, "Pet", py::module_local())
- .def("name", &pets::Pet::name);
- // Binding for local extending class:
- py::class_(m, "Dog")
- .def(py::init());
- m.def("pet_name", [](pets::Pet &p) { return p.name(); });
-
- py::class_(m, "MixGL", py::module_local()).def(py::init());
- m.def("get_gl_value", [](MixGL &o) { return o.i + 100; });
-
- py::class_(m, "MixGL2", py::module_local()).def(py::init());
-
- // test_vector_bool
- // We can't test both stl.h and stl_bind.h conversions of `std::vector` within
- // the same module (it would be an ODR violation). Therefore `bind_vector` of `bool`
- // is defined here and tested in `test_stl_binders.py`.
- py::bind_vector>(m, "VectorBool");
-
- // test_missing_header_message
- // The main module already includes stl.h, but we need to test the error message
- // which appears when this header is missing.
- m.def("missing_header_arg", [](std::vector) { });
- m.def("missing_header_return", []() { return std::vector(); });
-}
diff --git a/spaces/CVPR/LIVE/pybind11/tests/test_custom_type_casters.py b/spaces/CVPR/LIVE/pybind11/tests/test_custom_type_casters.py
deleted file mode 100644
index 9475c4516845632da6c6c5b918ae05401d8f3f01..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/pybind11/tests/test_custom_type_casters.py
+++ /dev/null
@@ -1,90 +0,0 @@
-# -*- coding: utf-8 -*-
-import pytest
-from pybind11_tests import custom_type_casters as m
-
-
-def test_noconvert_args(msg):
- a = m.ArgInspector()
- assert msg(a.f("hi")) == """
- loading ArgInspector1 argument WITH conversion allowed. Argument value = hi
- """
- assert msg(a.g("this is a", "this is b")) == """
- loading ArgInspector1 argument WITHOUT conversion allowed. Argument value = this is a
- loading ArgInspector1 argument WITH conversion allowed. Argument value = this is b
- 13
- loading ArgInspector2 argument WITH conversion allowed. Argument value = (default arg inspector 2)
- """ # noqa: E501 line too long
- assert msg(a.g("this is a", "this is b", 42)) == """
- loading ArgInspector1 argument WITHOUT conversion allowed. Argument value = this is a
- loading ArgInspector1 argument WITH conversion allowed. Argument value = this is b
- 42
- loading ArgInspector2 argument WITH conversion allowed. Argument value = (default arg inspector 2)
- """ # noqa: E501 line too long
- assert msg(a.g("this is a", "this is b", 42, "this is d")) == """
- loading ArgInspector1 argument WITHOUT conversion allowed. Argument value = this is a
- loading ArgInspector1 argument WITH conversion allowed. Argument value = this is b
- 42
- loading ArgInspector2 argument WITH conversion allowed. Argument value = this is d
- """
- assert (a.h("arg 1") ==
- "loading ArgInspector2 argument WITHOUT conversion allowed. Argument value = arg 1")
- assert msg(m.arg_inspect_func("A1", "A2")) == """
- loading ArgInspector2 argument WITH conversion allowed. Argument value = A1
- loading ArgInspector1 argument WITHOUT conversion allowed. Argument value = A2
- """
-
- assert m.floats_preferred(4) == 2.0
- assert m.floats_only(4.0) == 2.0
- with pytest.raises(TypeError) as excinfo:
- m.floats_only(4)
- assert msg(excinfo.value) == """
- floats_only(): incompatible function arguments. The following argument types are supported:
- 1. (f: float) -> float
-
- Invoked with: 4
- """
-
- assert m.ints_preferred(4) == 2
- assert m.ints_preferred(True) == 0
- with pytest.raises(TypeError) as excinfo:
- m.ints_preferred(4.0)
- assert msg(excinfo.value) == """
- ints_preferred(): incompatible function arguments. The following argument types are supported:
- 1. (i: int) -> int
-
- Invoked with: 4.0
- """ # noqa: E501 line too long
-
- assert m.ints_only(4) == 2
- with pytest.raises(TypeError) as excinfo:
- m.ints_only(4.0)
- assert msg(excinfo.value) == """
- ints_only(): incompatible function arguments. The following argument types are supported:
- 1. (i: int) -> int
-
- Invoked with: 4.0
- """
-
-
-def test_custom_caster_destruction():
- """Tests that returning a pointer to a type that gets converted with a custom type caster gets
- destroyed when the function has py::return_value_policy::take_ownership policy applied."""
-
- cstats = m.destruction_tester_cstats()
- # This one *doesn't* have take_ownership: the pointer should be used but not destroyed:
- z = m.custom_caster_no_destroy()
- assert cstats.alive() == 1 and cstats.default_constructions == 1
- assert z
-
- # take_ownership applied: this constructs a new object, casts it, then destroys it:
- z = m.custom_caster_destroy()
- assert z
- assert cstats.default_constructions == 2
-
- # Same, but with a const pointer return (which should *not* inhibit destruction):
- z = m.custom_caster_destroy_const()
- assert z
- assert cstats.default_constructions == 3
-
- # Make sure we still only have the original object (from ..._no_destroy()) alive:
- assert cstats.alive() == 1
diff --git a/spaces/CVPR/WALT/mmdet/models/detectors/fsaf.py b/spaces/CVPR/WALT/mmdet/models/detectors/fsaf.py
deleted file mode 100644
index 9f10fa1ae10f31e6cb5de65505b14a4fc97dd022..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/models/detectors/fsaf.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class FSAF(SingleStageDetector):
- """Implementation of `FSAF `_"""
-
- def __init__(self,
- backbone,
- neck,
- bbox_head,
- train_cfg=None,
- test_cfg=None,
- pretrained=None):
- super(FSAF, self).__init__(backbone, neck, bbox_head, train_cfg,
- test_cfg, pretrained)
diff --git a/spaces/CVPR/regionclip-demo/detectron2/checkpoint/c2_model_loading.py b/spaces/CVPR/regionclip-demo/detectron2/checkpoint/c2_model_loading.py
deleted file mode 100644
index 8c8d181bd7200bd3fd38446e743f8f16780d6e76..0000000000000000000000000000000000000000
--- a/spaces/CVPR/regionclip-demo/detectron2/checkpoint/c2_model_loading.py
+++ /dev/null
@@ -1,407 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import logging
-import re
-from typing import Dict, List
-import torch
-from tabulate import tabulate
-
-
-def convert_basic_c2_names(original_keys):
- """
- Apply some basic name conversion to names in C2 weights.
- It only deals with typical backbone models.
-
- Args:
- original_keys (list[str]):
- Returns:
- list[str]: The same number of strings matching those in original_keys.
- """
- layer_keys = copy.deepcopy(original_keys)
- layer_keys = [
- {"pred_b": "linear_b", "pred_w": "linear_w"}.get(k, k) for k in layer_keys
- ] # some hard-coded mappings
-
- layer_keys = [k.replace("_", ".") for k in layer_keys]
- layer_keys = [re.sub("\\.b$", ".bias", k) for k in layer_keys]
- layer_keys = [re.sub("\\.w$", ".weight", k) for k in layer_keys]
- # Uniform both bn and gn names to "norm"
- layer_keys = [re.sub("bn\\.s$", "norm.weight", k) for k in layer_keys]
- layer_keys = [re.sub("bn\\.bias$", "norm.bias", k) for k in layer_keys]
- layer_keys = [re.sub("bn\\.rm", "norm.running_mean", k) for k in layer_keys]
- layer_keys = [re.sub("bn\\.running.mean$", "norm.running_mean", k) for k in layer_keys]
- layer_keys = [re.sub("bn\\.riv$", "norm.running_var", k) for k in layer_keys]
- layer_keys = [re.sub("bn\\.running.var$", "norm.running_var", k) for k in layer_keys]
- layer_keys = [re.sub("bn\\.gamma$", "norm.weight", k) for k in layer_keys]
- layer_keys = [re.sub("bn\\.beta$", "norm.bias", k) for k in layer_keys]
- layer_keys = [re.sub("gn\\.s$", "norm.weight", k) for k in layer_keys]
- layer_keys = [re.sub("gn\\.bias$", "norm.bias", k) for k in layer_keys]
-
- # stem
- layer_keys = [re.sub("^res\\.conv1\\.norm\\.", "conv1.norm.", k) for k in layer_keys]
- # to avoid mis-matching with "conv1" in other components (e.g. detection head)
- layer_keys = [re.sub("^conv1\\.", "stem.conv1.", k) for k in layer_keys]
-
- # layer1-4 is used by torchvision, however we follow the C2 naming strategy (res2-5)
- # layer_keys = [re.sub("^res2.", "layer1.", k) for k in layer_keys]
- # layer_keys = [re.sub("^res3.", "layer2.", k) for k in layer_keys]
- # layer_keys = [re.sub("^res4.", "layer3.", k) for k in layer_keys]
- # layer_keys = [re.sub("^res5.", "layer4.", k) for k in layer_keys]
-
- # blocks
- layer_keys = [k.replace(".branch1.", ".shortcut.") for k in layer_keys]
- layer_keys = [k.replace(".branch2a.", ".conv1.") for k in layer_keys]
- layer_keys = [k.replace(".branch2b.", ".conv2.") for k in layer_keys]
- layer_keys = [k.replace(".branch2c.", ".conv3.") for k in layer_keys]
-
- # DensePose substitutions
- layer_keys = [re.sub("^body.conv.fcn", "body_conv_fcn", k) for k in layer_keys]
- layer_keys = [k.replace("AnnIndex.lowres", "ann_index_lowres") for k in layer_keys]
- layer_keys = [k.replace("Index.UV.lowres", "index_uv_lowres") for k in layer_keys]
- layer_keys = [k.replace("U.lowres", "u_lowres") for k in layer_keys]
- layer_keys = [k.replace("V.lowres", "v_lowres") for k in layer_keys]
- return layer_keys
-
-
-def convert_c2_detectron_names(weights):
- """
- Map Caffe2 Detectron weight names to Detectron2 names.
-
- Args:
- weights (dict): name -> tensor
-
- Returns:
- dict: detectron2 names -> tensor
- dict: detectron2 names -> C2 names
- """
- logger = logging.getLogger(__name__)
- logger.info("Renaming Caffe2 weights ......")
- original_keys = sorted(weights.keys())
- layer_keys = copy.deepcopy(original_keys)
-
- layer_keys = convert_basic_c2_names(layer_keys)
-
- # --------------------------------------------------------------------------
- # RPN hidden representation conv
- # --------------------------------------------------------------------------
- # FPN case
- # In the C2 model, the RPN hidden layer conv is defined for FPN level 2 and then
- # shared for all other levels, hence the appearance of "fpn2"
- layer_keys = [
- k.replace("conv.rpn.fpn2", "proposal_generator.rpn_head.conv") for k in layer_keys
- ]
- # Non-FPN case
- layer_keys = [k.replace("conv.rpn", "proposal_generator.rpn_head.conv") for k in layer_keys]
-
- # --------------------------------------------------------------------------
- # RPN box transformation conv
- # --------------------------------------------------------------------------
- # FPN case (see note above about "fpn2")
- layer_keys = [
- k.replace("rpn.bbox.pred.fpn2", "proposal_generator.rpn_head.anchor_deltas")
- for k in layer_keys
- ]
- layer_keys = [
- k.replace("rpn.cls.logits.fpn2", "proposal_generator.rpn_head.objectness_logits")
- for k in layer_keys
- ]
- # Non-FPN case
- layer_keys = [
- k.replace("rpn.bbox.pred", "proposal_generator.rpn_head.anchor_deltas") for k in layer_keys
- ]
- layer_keys = [
- k.replace("rpn.cls.logits", "proposal_generator.rpn_head.objectness_logits")
- for k in layer_keys
- ]
-
- # --------------------------------------------------------------------------
- # Fast R-CNN box head
- # --------------------------------------------------------------------------
- layer_keys = [re.sub("^bbox\\.pred", "bbox_pred", k) for k in layer_keys]
- layer_keys = [re.sub("^cls\\.score", "cls_score", k) for k in layer_keys]
- layer_keys = [re.sub("^fc6\\.", "box_head.fc1.", k) for k in layer_keys]
- layer_keys = [re.sub("^fc7\\.", "box_head.fc2.", k) for k in layer_keys]
- # 4conv1fc head tensor names: head_conv1_w, head_conv1_gn_s
- layer_keys = [re.sub("^head\\.conv", "box_head.conv", k) for k in layer_keys]
-
- # --------------------------------------------------------------------------
- # FPN lateral and output convolutions
- # --------------------------------------------------------------------------
- def fpn_map(name):
- """
- Look for keys with the following patterns:
- 1) Starts with "fpn.inner."
- Example: "fpn.inner.res2.2.sum.lateral.weight"
- Meaning: These are lateral pathway convolutions
- 2) Starts with "fpn.res"
- Example: "fpn.res2.2.sum.weight"
- Meaning: These are FPN output convolutions
- """
- splits = name.split(".")
- norm = ".norm" if "norm" in splits else ""
- if name.startswith("fpn.inner."):
- # splits example: ['fpn', 'inner', 'res2', '2', 'sum', 'lateral', 'weight']
- stage = int(splits[2][len("res") :])
- return "fpn_lateral{}{}.{}".format(stage, norm, splits[-1])
- elif name.startswith("fpn.res"):
- # splits example: ['fpn', 'res2', '2', 'sum', 'weight']
- stage = int(splits[1][len("res") :])
- return "fpn_output{}{}.{}".format(stage, norm, splits[-1])
- return name
-
- layer_keys = [fpn_map(k) for k in layer_keys]
-
- # --------------------------------------------------------------------------
- # Mask R-CNN mask head
- # --------------------------------------------------------------------------
- # roi_heads.StandardROIHeads case
- layer_keys = [k.replace(".[mask].fcn", "mask_head.mask_fcn") for k in layer_keys]
- layer_keys = [re.sub("^\\.mask\\.fcn", "mask_head.mask_fcn", k) for k in layer_keys]
- layer_keys = [k.replace("mask.fcn.logits", "mask_head.predictor") for k in layer_keys]
- # roi_heads.Res5ROIHeads case
- layer_keys = [k.replace("conv5.mask", "mask_head.deconv") for k in layer_keys]
-
- # --------------------------------------------------------------------------
- # Keypoint R-CNN head
- # --------------------------------------------------------------------------
- # interestingly, the keypoint head convs have blob names that are simply "conv_fcnX"
- layer_keys = [k.replace("conv.fcn", "roi_heads.keypoint_head.conv_fcn") for k in layer_keys]
- layer_keys = [
- k.replace("kps.score.lowres", "roi_heads.keypoint_head.score_lowres") for k in layer_keys
- ]
- layer_keys = [k.replace("kps.score.", "roi_heads.keypoint_head.score.") for k in layer_keys]
-
- # --------------------------------------------------------------------------
- # Done with replacements
- # --------------------------------------------------------------------------
- assert len(set(layer_keys)) == len(layer_keys)
- assert len(original_keys) == len(layer_keys)
-
- new_weights = {}
- new_keys_to_original_keys = {}
- for orig, renamed in zip(original_keys, layer_keys):
- new_keys_to_original_keys[renamed] = orig
- if renamed.startswith("bbox_pred.") or renamed.startswith("mask_head.predictor."):
- # remove the meaningless prediction weight for background class
- new_start_idx = 4 if renamed.startswith("bbox_pred.") else 1
- new_weights[renamed] = weights[orig][new_start_idx:]
- logger.info(
- "Remove prediction weight for background class in {}. The shape changes from "
- "{} to {}.".format(
- renamed, tuple(weights[orig].shape), tuple(new_weights[renamed].shape)
- )
- )
- elif renamed.startswith("cls_score."):
- # move weights of bg class from original index 0 to last index
- logger.info(
- "Move classification weights for background class in {} from index 0 to "
- "index {}.".format(renamed, weights[orig].shape[0] - 1)
- )
- new_weights[renamed] = torch.cat([weights[orig][1:], weights[orig][:1]])
- else:
- new_weights[renamed] = weights[orig]
-
- return new_weights, new_keys_to_original_keys
-
-
-# Note the current matching is not symmetric.
-# it assumes model_state_dict will have longer names.
-def align_and_update_state_dicts(model_state_dict, ckpt_state_dict, c2_conversion=True):
- """
- Match names between the two state-dict, and returns a new chkpt_state_dict with names
- converted to match model_state_dict with heuristics. The returned dict can be later
- loaded with fvcore checkpointer.
- If `c2_conversion==True`, `ckpt_state_dict` is assumed to be a Caffe2
- model and will be renamed at first.
-
- Strategy: suppose that the models that we will create will have prefixes appended
- to each of its keys, for example due to an extra level of nesting that the original
- pre-trained weights from ImageNet won't contain. For example, model.state_dict()
- might return backbone[0].body.res2.conv1.weight, while the pre-trained model contains
- res2.conv1.weight. We thus want to match both parameters together.
- For that, we look for each model weight, look among all loaded keys if there is one
- that is a suffix of the current weight name, and use it if that's the case.
- If multiple matches exist, take the one with longest size
- of the corresponding name. For example, for the same model as before, the pretrained
- weight file can contain both res2.conv1.weight, as well as conv1.weight. In this case,
- we want to match backbone[0].body.conv1.weight to conv1.weight, and
- backbone[0].body.res2.conv1.weight to res2.conv1.weight.
- """
- model_keys = sorted(model_state_dict.keys())
- if c2_conversion:
- ckpt_state_dict, original_keys = convert_c2_detectron_names(ckpt_state_dict)
- # original_keys: the name in the original dict (before renaming)
- else:
- original_keys = {x: x for x in ckpt_state_dict.keys()}
- ckpt_keys = sorted(ckpt_state_dict.keys())
-
- def match(a, b):
- # Matched ckpt_key should be a complete (starts with '.') suffix.
- # For example, roi_heads.mesh_head.whatever_conv1 does not match conv1,
- # but matches whatever_conv1 or mesh_head.whatever_conv1.
- return a == b or a.endswith("." + b)
-
- # get a matrix of string matches, where each (i, j) entry correspond to the size of the
- # ckpt_key string, if it matches
- match_matrix = [len(j) if match(i, j) else 0 for i in model_keys for j in ckpt_keys]
- match_matrix = torch.as_tensor(match_matrix).view(len(model_keys), len(ckpt_keys))
- # use the matched one with longest size in case of multiple matches
- max_match_size, idxs = match_matrix.max(1)
- # remove indices that correspond to no-match
- idxs[max_match_size == 0] = -1
-
- logger = logging.getLogger(__name__)
- # matched_pairs (matched checkpoint key --> matched model key)
- matched_keys = {}
- result_state_dict = {}
- for idx_model, idx_ckpt in enumerate(idxs.tolist()):
- if idx_ckpt == -1:
- continue
- key_model = model_keys[idx_model]
- key_ckpt = ckpt_keys[idx_ckpt]
- value_ckpt = ckpt_state_dict[key_ckpt]
- shape_in_model = model_state_dict[key_model].shape
-
- if shape_in_model != value_ckpt.shape:
- logger.warning(
- "Shape of {} in checkpoint is {}, while shape of {} in model is {}.".format(
- key_ckpt, value_ckpt.shape, key_model, shape_in_model
- )
- )
- logger.warning(
- "{} will not be loaded. Please double check and see if this is desired.".format(
- key_ckpt
- )
- )
- continue
-
- assert key_model not in result_state_dict
- result_state_dict[key_model] = value_ckpt
- if key_ckpt in matched_keys: # already added to matched_keys
- logger.error(
- "Ambiguity found for {} in checkpoint!"
- "It matches at least two keys in the model ({} and {}).".format(
- key_ckpt, key_model, matched_keys[key_ckpt]
- )
- )
- raise ValueError("Cannot match one checkpoint key to multiple keys in the model.")
-
- matched_keys[key_ckpt] = key_model
-
- # logging:
- matched_model_keys = sorted(matched_keys.values())
- if len(matched_model_keys) == 0:
- logger.warning("No weights in checkpoint matched with model.")
- return ckpt_state_dict
- common_prefix = _longest_common_prefix(matched_model_keys)
- rev_matched_keys = {v: k for k, v in matched_keys.items()}
- original_keys = {k: original_keys[rev_matched_keys[k]] for k in matched_model_keys}
-
- model_key_groups = _group_keys_by_module(matched_model_keys, original_keys)
- table = []
- memo = set()
- for key_model in matched_model_keys:
- if key_model in memo:
- continue
- if key_model in model_key_groups:
- group = model_key_groups[key_model]
- memo |= set(group)
- shapes = [tuple(model_state_dict[k].shape) for k in group]
- table.append(
- (
- _longest_common_prefix([k[len(common_prefix) :] for k in group]) + "*",
- _group_str([original_keys[k] for k in group]),
- " ".join([str(x).replace(" ", "") for x in shapes]),
- )
- )
- else:
- key_checkpoint = original_keys[key_model]
- shape = str(tuple(model_state_dict[key_model].shape))
- table.append((key_model[len(common_prefix) :], key_checkpoint, shape))
- table_str = tabulate(
- table, tablefmt="pipe", headers=["Names in Model", "Names in Checkpoint", "Shapes"]
- )
- logger.info(
- "Following weights matched with "
- + (f"submodule {common_prefix[:-1]}" if common_prefix else "model")
- + ":\n"
- + table_str
- )
-
- unmatched_ckpt_keys = [k for k in ckpt_keys if k not in set(matched_keys.keys())]
- for k in unmatched_ckpt_keys:
- result_state_dict[k] = ckpt_state_dict[k]
- return result_state_dict
-
-
-def _group_keys_by_module(keys: List[str], original_names: Dict[str, str]):
- """
- Params in the same submodule are grouped together.
-
- Args:
- keys: names of all parameters
- original_names: mapping from parameter name to their name in the checkpoint
-
- Returns:
- dict[name -> all other names in the same group]
- """
-
- def _submodule_name(key):
- pos = key.rfind(".")
- if pos < 0:
- return None
- prefix = key[: pos + 1]
- return prefix
-
- all_submodules = [_submodule_name(k) for k in keys]
- all_submodules = [x for x in all_submodules if x]
- all_submodules = sorted(all_submodules, key=len)
-
- ret = {}
- for prefix in all_submodules:
- group = [k for k in keys if k.startswith(prefix)]
- if len(group) <= 1:
- continue
- original_name_lcp = _longest_common_prefix_str([original_names[k] for k in group])
- if len(original_name_lcp) == 0:
- # don't group weights if original names don't share prefix
- continue
-
- for k in group:
- if k in ret:
- continue
- ret[k] = group
- return ret
-
-
-def _longest_common_prefix(names: List[str]) -> str:
- """
- ["abc.zfg", "abc.zef"] -> "abc."
- """
- names = [n.split(".") for n in names]
- m1, m2 = min(names), max(names)
- ret = [a for a, b in zip(m1, m2) if a == b]
- ret = ".".join(ret) + "." if len(ret) else ""
- return ret
-
-
-def _longest_common_prefix_str(names: List[str]) -> str:
- m1, m2 = min(names), max(names)
- lcp = [a for a, b in zip(m1, m2) if a == b]
- lcp = "".join(lcp)
- return lcp
-
-
-def _group_str(names: List[str]) -> str:
- """
- Turn "common1", "common2", "common3" into "common{1,2,3}"
- """
- lcp = _longest_common_prefix_str(names)
- rest = [x[len(lcp) :] for x in names]
- rest = "{" + ",".join(rest) + "}"
- ret = lcp + rest
-
- # add some simplification for BN specifically
- ret = ret.replace("bn_{beta,running_mean,running_var,gamma}", "bn_*")
- ret = ret.replace("bn_beta,bn_running_mean,bn_running_var,bn_gamma", "bn_*")
- return ret
diff --git a/spaces/Caoyunkang/Segment-Any-Anomaly/SAM/segment_anything/modeling/image_encoder.py b/spaces/Caoyunkang/Segment-Any-Anomaly/SAM/segment_anything/modeling/image_encoder.py
deleted file mode 100644
index a6ad9ad2938842308e482a05c9d35ab08db9b2c3..0000000000000000000000000000000000000000
--- a/spaces/Caoyunkang/Segment-Any-Anomaly/SAM/segment_anything/modeling/image_encoder.py
+++ /dev/null
@@ -1,395 +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
-import torch.nn as nn
-import torch.nn.functional as F
-
-from typing import Optional, Tuple, Type
-
-from .common import LayerNorm2d, MLPBlock
-
-
-# This class and its supporting functions below lightly adapted from the ViTDet backbone available at: https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/vit.py # noqa
-class ImageEncoderViT(nn.Module):
- def __init__(
- self,
- img_size: int = 1024,
- patch_size: int = 16,
- in_chans: int = 3,
- embed_dim: int = 768,
- depth: int = 12,
- num_heads: int = 12,
- mlp_ratio: float = 4.0,
- out_chans: int = 256,
- qkv_bias: bool = True,
- norm_layer: Type[nn.Module] = nn.LayerNorm,
- act_layer: Type[nn.Module] = nn.GELU,
- use_abs_pos: bool = True,
- use_rel_pos: bool = False,
- rel_pos_zero_init: bool = True,
- window_size: int = 0,
- global_attn_indexes: Tuple[int, ...] = (),
- ) -> None:
- """
- Args:
- img_size (int): Input image size.
- patch_size (int): Patch size.
- in_chans (int): Number of input image channels.
- embed_dim (int): Patch embedding dimension.
- depth (int): Depth of ViT.
- num_heads (int): Number of attention heads in each ViT block.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool): If True, add a learnable bias to query, key, value.
- norm_layer (nn.Module): Normalization layer.
- act_layer (nn.Module): Activation layer.
- use_abs_pos (bool): If True, use absolute positional embeddings.
- use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
- rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
- window_size (int): Window size for window attention blocks.
- global_attn_indexes (list): Indexes for blocks using global attention.
- """
- super().__init__()
- self.img_size = img_size
-
- self.patch_embed = PatchEmbed(
- kernel_size=(patch_size, patch_size),
- stride=(patch_size, patch_size),
- in_chans=in_chans,
- embed_dim=embed_dim,
- )
-
- self.pos_embed: Optional[nn.Parameter] = None
- if use_abs_pos:
- # Initialize absolute positional embedding with pretrain image size.
- self.pos_embed = nn.Parameter(
- torch.zeros(1, img_size // patch_size, img_size // patch_size, embed_dim)
- )
-
- self.blocks = nn.ModuleList()
- for i in range(depth):
- block = Block(
- dim=embed_dim,
- num_heads=num_heads,
- mlp_ratio=mlp_ratio,
- qkv_bias=qkv_bias,
- norm_layer=norm_layer,
- act_layer=act_layer,
- use_rel_pos=use_rel_pos,
- rel_pos_zero_init=rel_pos_zero_init,
- window_size=window_size if i not in global_attn_indexes else 0,
- input_size=(img_size // patch_size, img_size // patch_size),
- )
- self.blocks.append(block)
-
- self.neck = nn.Sequential(
- nn.Conv2d(
- embed_dim,
- out_chans,
- kernel_size=1,
- bias=False,
- ),
- LayerNorm2d(out_chans),
- nn.Conv2d(
- out_chans,
- out_chans,
- kernel_size=3,
- padding=1,
- bias=False,
- ),
- LayerNorm2d(out_chans),
- )
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.patch_embed(x)
- if self.pos_embed is not None:
- x = x + self.pos_embed
-
- for blk in self.blocks:
- x = blk(x)
-
- x = self.neck(x.permute(0, 3, 1, 2))
-
- return x
-
-
-class Block(nn.Module):
- """Transformer blocks with support of window attention and residual propagation blocks"""
-
- def __init__(
- self,
- dim: int,
- num_heads: int,
- mlp_ratio: float = 4.0,
- qkv_bias: bool = True,
- norm_layer: Type[nn.Module] = nn.LayerNorm,
- act_layer: Type[nn.Module] = nn.GELU,
- use_rel_pos: bool = False,
- rel_pos_zero_init: bool = True,
- window_size: int = 0,
- input_size: Optional[Tuple[int, int]] = None,
- ) -> None:
- """
- Args:
- dim (int): Number of input channels.
- num_heads (int): Number of attention heads in each ViT block.
- mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
- qkv_bias (bool): If True, add a learnable bias to query, key, value.
- norm_layer (nn.Module): Normalization layer.
- act_layer (nn.Module): Activation layer.
- use_rel_pos (bool): If True, add relative positional embeddings to the attention map.
- rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
- window_size (int): Window size for window attention blocks. If it equals 0, then
- use global attention.
- input_size (int or None): Input resolution for calculating the relative positional
- parameter size.
- """
- super().__init__()
- self.norm1 = norm_layer(dim)
- self.attn = Attention(
- dim,
- num_heads=num_heads,
- qkv_bias=qkv_bias,
- use_rel_pos=use_rel_pos,
- rel_pos_zero_init=rel_pos_zero_init,
- input_size=input_size if window_size == 0 else (window_size, window_size),
- )
-
- self.norm2 = norm_layer(dim)
- self.mlp = MLPBlock(embedding_dim=dim, mlp_dim=int(dim * mlp_ratio), act=act_layer)
-
- self.window_size = window_size
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- shortcut = x
- x = self.norm1(x)
- # Window partition
- if self.window_size > 0:
- H, W = x.shape[1], x.shape[2]
- x, pad_hw = window_partition(x, self.window_size)
-
- x = self.attn(x)
- # Reverse window partition
- if self.window_size > 0:
- x = window_unpartition(x, self.window_size, pad_hw, (H, W))
-
- x = shortcut + x
- x = x + self.mlp(self.norm2(x))
-
- return x
-
-
-class Attention(nn.Module):
- """Multi-head Attention block with relative position embeddings."""
-
- def __init__(
- self,
- dim: int,
- num_heads: int = 8,
- qkv_bias: bool = True,
- use_rel_pos: bool = False,
- rel_pos_zero_init: bool = True,
- input_size: Optional[Tuple[int, int]] = None,
- ) -> None:
- """
- Args:
- dim (int): Number of input channels.
- num_heads (int): Number of attention heads.
- qkv_bias (bool: If True, add a learnable bias to query, key, value.
- rel_pos (bool): If True, add relative positional embeddings to the attention map.
- rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
- input_size (int or None): Input resolution for calculating the relative positional
- parameter size.
- """
- super().__init__()
- self.num_heads = num_heads
- head_dim = dim // num_heads
- self.scale = head_dim**-0.5
-
- self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
- self.proj = nn.Linear(dim, dim)
-
- self.use_rel_pos = use_rel_pos
- if self.use_rel_pos:
- assert (
- input_size is not None
- ), "Input size must be provided if using relative positional encoding."
- # initialize relative positional embeddings
- self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
- self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- B, H, W, _ = x.shape
- # qkv with shape (3, B, nHead, H * W, C)
- qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
- # q, k, v with shape (B * nHead, H * W, C)
- q, k, v = qkv.reshape(3, B * self.num_heads, H * W, -1).unbind(0)
-
- attn = (q * self.scale) @ k.transpose(-2, -1)
-
- if self.use_rel_pos:
- attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W))
-
- attn = attn.softmax(dim=-1)
- x = (attn @ v).view(B, self.num_heads, H, W, -1).permute(0, 2, 3, 1, 4).reshape(B, H, W, -1)
- x = self.proj(x)
-
- return x
-
-
-def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, Tuple[int, int]]:
- """
- Partition into non-overlapping windows with padding if needed.
- Args:
- x (tensor): input tokens with [B, H, W, C].
- window_size (int): window size.
-
- Returns:
- windows: windows after partition with [B * num_windows, window_size, window_size, C].
- (Hp, Wp): padded height and width before partition
- """
- B, H, W, C = x.shape
-
- pad_h = (window_size - H % window_size) % window_size
- pad_w = (window_size - W % window_size) % window_size
- if pad_h > 0 or pad_w > 0:
- x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
- Hp, Wp = H + pad_h, W + pad_w
-
- x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
- windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
- return windows, (Hp, Wp)
-
-
-def window_unpartition(
- windows: torch.Tensor, window_size: int, pad_hw: Tuple[int, int], hw: Tuple[int, int]
-) -> torch.Tensor:
- """
- Window unpartition into original sequences and removing padding.
- Args:
- x (tensor): input tokens with [B * num_windows, window_size, window_size, C].
- window_size (int): window size.
- pad_hw (Tuple): padded height and width (Hp, Wp).
- hw (Tuple): original height and width (H, W) before padding.
-
- Returns:
- x: unpartitioned sequences with [B, H, W, C].
- """
- Hp, Wp = pad_hw
- H, W = hw
- B = windows.shape[0] // (Hp * Wp // window_size // window_size)
- x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
- x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
-
- if Hp > H or Wp > W:
- x = x[:, :H, :W, :].contiguous()
- return x
-
-
-def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
- """
- Get relative positional embeddings according to the relative positions of
- query and key sizes.
- Args:
- q_size (int): size of query q.
- k_size (int): size of key k.
- rel_pos (Tensor): relative position embeddings (L, C).
-
- Returns:
- Extracted positional embeddings according to relative positions.
- """
- max_rel_dist = int(2 * max(q_size, k_size) - 1)
- # Interpolate rel pos if needed.
- if rel_pos.shape[0] != max_rel_dist:
- # Interpolate rel pos.
- rel_pos_resized = F.interpolate(
- rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
- size=max_rel_dist,
- mode="linear",
- )
- rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
- else:
- rel_pos_resized = rel_pos
-
- # Scale the coords with short length if shapes for q and k are different.
- q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
- k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
- relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
-
- return rel_pos_resized[relative_coords.long()]
-
-
-def add_decomposed_rel_pos(
- attn: torch.Tensor,
- q: torch.Tensor,
- rel_pos_h: torch.Tensor,
- rel_pos_w: torch.Tensor,
- q_size: Tuple[int, int],
- k_size: Tuple[int, int],
-) -> torch.Tensor:
- """
- Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
- https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950
- Args:
- attn (Tensor): attention map.
- q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
- rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
- rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
- q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
- k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
-
- Returns:
- attn (Tensor): attention map with added relative positional embeddings.
- """
- q_h, q_w = q_size
- k_h, k_w = k_size
- Rh = get_rel_pos(q_h, k_h, rel_pos_h)
- Rw = get_rel_pos(q_w, k_w, rel_pos_w)
-
- B, _, dim = q.shape
- r_q = q.reshape(B, q_h, q_w, dim)
- rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
- rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
-
- attn = (
- attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
- ).view(B, q_h * q_w, k_h * k_w)
-
- return attn
-
-
-class PatchEmbed(nn.Module):
- """
- Image to Patch Embedding.
- """
-
- def __init__(
- self,
- kernel_size: Tuple[int, int] = (16, 16),
- stride: Tuple[int, int] = (16, 16),
- padding: Tuple[int, int] = (0, 0),
- in_chans: int = 3,
- embed_dim: int = 768,
- ) -> None:
- """
- Args:
- kernel_size (Tuple): kernel size of the projection layer.
- stride (Tuple): stride of the projection layer.
- padding (Tuple): padding size of the projection layer.
- in_chans (int): Number of input image channels.
- embed_dim (int): embed_dim (int): Patch embedding dimension.
- """
- super().__init__()
-
- self.proj = nn.Conv2d(
- in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding
- )
-
- def forward(self, x: torch.Tensor) -> torch.Tensor:
- x = self.proj(x)
- # B C H W -> B H W C
- x = x.permute(0, 2, 3, 1)
- return x
diff --git a/spaces/CikeyQI/meme-api/meme_generator/meme.py b/spaces/CikeyQI/meme-api/meme_generator/meme.py
deleted file mode 100644
index 632a622ce11b312cd150dc1738fa8159bd020b12..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/meme-api/meme_generator/meme.py
+++ /dev/null
@@ -1,185 +0,0 @@
-import copy
-from argparse import ArgumentError, ArgumentParser
-from contextvars import ContextVar
-from dataclasses import dataclass, field
-from io import BytesIO
-from pathlib import Path
-from typing import (
- IO,
- Any,
- Awaitable,
- Callable,
- Dict,
- List,
- Literal,
- Optional,
- Type,
- TypeVar,
- Union,
- cast,
-)
-
-from pil_utils import BuildImage
-from pydantic import BaseModel, ValidationError
-
-from .exception import (
- ArgModelMismatch,
- ArgParserExit,
- ImageNumberMismatch,
- OpenImageFailed,
- ParserExit,
- TextNumberMismatch,
- TextOrNameNotEnough,
-)
-from .utils import is_coroutine_callable, random_image, random_text, run_sync
-
-
-class UserInfo(BaseModel):
- name: str = ""
- gender: Literal["male", "female", "unknown"] = "unknown"
-
-
-class MemeArgsModel(BaseModel):
- user_infos: List[UserInfo] = []
-
-
-ArgsModel = TypeVar("ArgsModel", bound=MemeArgsModel)
-
-MemeFunction = Union[
- Callable[[List[BuildImage], List[str], ArgsModel], BytesIO],
- Callable[[List[BuildImage], List[str], ArgsModel], Awaitable[BytesIO]],
-]
-
-
-parser_message: ContextVar[str] = ContextVar("parser_message")
-
-
-class MemeArgsParser(ArgumentParser):
- """`shell_like` 命令参数解析器，解析出错时不会退出程序。
-
- 用法:
- 用法与 `argparse.ArgumentParser` 相同，
- 参考文档: [argparse](https://docs.python.org/3/library/argparse.html)
- """
-
- def _print_message(self, message: str, file: Optional[IO[str]] = None):
- if (msg := parser_message.get(None)) is not None:
- parser_message.set(msg + message)
- else:
- super()._print_message(message, file)
-
- def exit(self, status: int = 0, message: Optional[str] = None):
- if message:
- self._print_message(message)
- raise ParserExit(status=status, error_message=parser_message.get(None))
-
-
-@dataclass
-class MemeArgsType:
- parser: MemeArgsParser
- model: Type[MemeArgsModel]
- instances: List[MemeArgsModel] = field(default_factory=list)
-
-
-@dataclass
-class MemeParamsType:
- min_images: int = 0
- max_images: int = 0
- min_texts: int = 0
- max_texts: int = 0
- default_texts: List[str] = field(default_factory=list)
- args_type: Optional[MemeArgsType] = None
-
-
-@dataclass
-class Meme:
- key: str
- function: MemeFunction
- params_type: MemeParamsType
- keywords: List[str] = field(default_factory=list)
- patterns: List[str] = field(default_factory=list)
-
- async def __call__(
- self,
- *,
- images: Union[List[str], List[Path], List[bytes], List[BytesIO]] = [],
- texts: List[str] = [],
- args: Dict[str, Any] = {},
- ) -> BytesIO:
- if not (
- self.params_type.min_images <= len(images) <= self.params_type.max_images
- ):
- raise ImageNumberMismatch(
- self.key, self.params_type.min_images, self.params_type.max_images
- )
-
- if not (self.params_type.min_texts <= len(texts) <= self.params_type.max_texts):
- raise TextNumberMismatch(
- self.key, self.params_type.min_texts, self.params_type.max_texts
- )
-
- if args_type := self.params_type.args_type:
- args_model = args_type.model
- else:
- args_model = MemeArgsModel
-
- try:
- model = args_model.parse_obj(args)
- except ValidationError as e:
- raise ArgModelMismatch(self.key, str(e))
-
- imgs: List[BuildImage] = []
- try:
- for image in images:
- if isinstance(image, bytes):
- image = BytesIO(image)
- imgs.append(BuildImage.open(image))
- except Exception as e:
- raise OpenImageFailed(str(e))
-
- values = {"images": imgs, "texts": texts, "args": model}
-
- if is_coroutine_callable(self.function):
- return await cast(Callable[..., Awaitable[BytesIO]], self.function)(
- **values
- )
- else:
- return await run_sync(cast(Callable[..., BytesIO], self.function))(**values)
-
- def parse_args(self, args: List[str] = []) -> Dict[str, Any]:
- parser = (
- copy.deepcopy(self.params_type.args_type.parser)
- if self.params_type.args_type
- else MemeArgsParser()
- )
- parser.add_argument("texts", nargs="*", default=[])
- t = parser_message.set("")
- try:
- return vars(parser.parse_args(args))
- except ArgumentError as e:
- raise ArgParserExit(self.key, str(e))
- except ParserExit as e:
- raise ArgParserExit(self.key, e.error_message)
- finally:
- parser_message.reset(t)
-
- async def generate_preview(self, *, args: Dict[str, Any] = {}) -> BytesIO:
- default_images = [random_image() for _ in range(self.params_type.min_images)]
- default_texts = (
- self.params_type.default_texts.copy()
- if (
- self.params_type.min_texts
- <= len(self.params_type.default_texts)
- <= self.params_type.max_texts
- )
- else [random_text() for _ in range(self.params_type.min_texts)]
- )
-
- async def _generate_preview(images: List[BytesIO], texts: List[str]):
- try:
- return await self.__call__(images=images, texts=texts, args=args)
- except TextOrNameNotEnough:
- texts.append(random_text())
- return await _generate_preview(images, texts)
-
- return await _generate_preview(default_images, default_texts)
diff --git a/spaces/ConorDY/feedback-chatbot/README.md b/spaces/ConorDY/feedback-chatbot/README.md
deleted file mode 100644
index 8d75921471863d5dbfb2cdc88dd2eb0e374713b4..0000000000000000000000000000000000000000
--- a/spaces/ConorDY/feedback-chatbot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Feedback Chatbot
-emoji: 📚
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.0.11
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/backbone/__init__.py b/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/backbone/__init__.py
deleted file mode 100644
index 537ebe56e683f4c665bb9b60fed9a1811645d8e5..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/backbone/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-from .backbone import build_backbone
-from . import fbnet
diff --git a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/utils/logger.py b/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/utils/logger.py
deleted file mode 100644
index 13847a3a76b481e132190ee0757b3539fb8981ae..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/utils/logger.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import logging
-import os
-import sys
-
-
-def setup_logger(name, save_dir, distributed_rank, filename="log.txt"):
- logger = logging.getLogger(name)
- logger.setLevel(logging.DEBUG)
- # don't log results for the non-master process
- if distributed_rank > 0:
- return logger
- ch = logging.StreamHandler(stream=sys.stdout)
- ch.setLevel(logging.DEBUG)
- formatter = logging.Formatter("%(asctime)s %(name)s %(levelname)s: %(message)s")
- ch.setFormatter(formatter)
- logger.addHandler(ch)
-
- if save_dir:
- fh = logging.FileHandler(os.path.join(save_dir, filename))
- fh.setLevel(logging.DEBUG)
- fh.setFormatter(formatter)
- logger.addHandler(fh)
-
- return logger
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/ImImagePlugin.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/ImImagePlugin.py
deleted file mode 100644
index 746743f658cf3fa2e0022ae049808eb68d3d1221..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/ImImagePlugin.py
+++ /dev/null
@@ -1,371 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# IFUNC IM file handling for PIL
-#
-# history:
-# 1995-09-01 fl Created.
-# 1997-01-03 fl Save palette images
-# 1997-01-08 fl Added sequence support
-# 1997-01-23 fl Added P and RGB save support
-# 1997-05-31 fl Read floating point images
-# 1997-06-22 fl Save floating point images
-# 1997-08-27 fl Read and save 1-bit images
-# 1998-06-25 fl Added support for RGB+LUT images
-# 1998-07-02 fl Added support for YCC images
-# 1998-07-15 fl Renamed offset attribute to avoid name clash
-# 1998-12-29 fl Added I;16 support
-# 2001-02-17 fl Use 're' instead of 'regex' (Python 2.1) (0.7)
-# 2003-09-26 fl Added LA/PA support
-#
-# Copyright (c) 1997-2003 by Secret Labs AB.
-# Copyright (c) 1995-2001 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-
-import os
-import re
-
-from . import Image, ImageFile, ImagePalette
-
-# --------------------------------------------------------------------
-# Standard tags
-
-COMMENT = "Comment"
-DATE = "Date"
-EQUIPMENT = "Digitalization equipment"
-FRAMES = "File size (no of images)"
-LUT = "Lut"
-NAME = "Name"
-SCALE = "Scale (x,y)"
-SIZE = "Image size (x*y)"
-MODE = "Image type"
-
-TAGS = {
- COMMENT: 0,
- DATE: 0,
- EQUIPMENT: 0,
- FRAMES: 0,
- LUT: 0,
- NAME: 0,
- SCALE: 0,
- SIZE: 0,
- MODE: 0,
-}
-
-OPEN = {
- # ifunc93/p3cfunc formats
- "0 1 image": ("1", "1"),
- "L 1 image": ("1", "1"),
- "Greyscale image": ("L", "L"),
- "Grayscale image": ("L", "L"),
- "RGB image": ("RGB", "RGB;L"),
- "RLB image": ("RGB", "RLB"),
- "RYB image": ("RGB", "RLB"),
- "B1 image": ("1", "1"),
- "B2 image": ("P", "P;2"),
- "B4 image": ("P", "P;4"),
- "X 24 image": ("RGB", "RGB"),
- "L 32 S image": ("I", "I;32"),
- "L 32 F image": ("F", "F;32"),
- # old p3cfunc formats
- "RGB3 image": ("RGB", "RGB;T"),
- "RYB3 image": ("RGB", "RYB;T"),
- # extensions
- "LA image": ("LA", "LA;L"),
- "PA image": ("LA", "PA;L"),
- "RGBA image": ("RGBA", "RGBA;L"),
- "RGBX image": ("RGBX", "RGBX;L"),
- "CMYK image": ("CMYK", "CMYK;L"),
- "YCC image": ("YCbCr", "YCbCr;L"),
-}
-
-# ifunc95 extensions
-for i in ["8", "8S", "16", "16S", "32", "32F"]:
- OPEN[f"L {i} image"] = ("F", f"F;{i}")
- OPEN[f"L*{i} image"] = ("F", f"F;{i}")
-for i in ["16", "16L", "16B"]:
- OPEN[f"L {i} image"] = (f"I;{i}", f"I;{i}")
- OPEN[f"L*{i} image"] = (f"I;{i}", f"I;{i}")
-for i in ["32S"]:
- OPEN[f"L {i} image"] = ("I", f"I;{i}")
- OPEN[f"L*{i} image"] = ("I", f"I;{i}")
-for i in range(2, 33):
- OPEN[f"L*{i} image"] = ("F", f"F;{i}")
-
-
-# --------------------------------------------------------------------
-# Read IM directory
-
-split = re.compile(rb"^([A-Za-z][^:]*):[ \t]*(.*)[ \t]*$")
-
-
-def number(s):
- try:
- return int(s)
- except ValueError:
- return float(s)
-
-
-##
-# Image plugin for the IFUNC IM file format.
-
-
-class ImImageFile(ImageFile.ImageFile):
- format = "IM"
- format_description = "IFUNC Image Memory"
- _close_exclusive_fp_after_loading = False
-
- def _open(self):
- # Quick rejection: if there's not an LF among the first
- # 100 bytes, this is (probably) not a text header.
-
- if b"\n" not in self.fp.read(100):
- msg = "not an IM file"
- raise SyntaxError(msg)
- self.fp.seek(0)
-
- n = 0
-
- # Default values
- self.info[MODE] = "L"
- self.info[SIZE] = (512, 512)
- self.info[FRAMES] = 1
-
- self.rawmode = "L"
-
- while True:
- s = self.fp.read(1)
-
- # Some versions of IFUNC uses \n\r instead of \r\n...
- if s == b"\r":
- continue
-
- if not s or s == b"\0" or s == b"\x1A":
- break
-
- # FIXME: this may read whole file if not a text file
- s = s + self.fp.readline()
-
- if len(s) > 100:
- msg = "not an IM file"
- raise SyntaxError(msg)
-
- if s[-2:] == b"\r\n":
- s = s[:-2]
- elif s[-1:] == b"\n":
- s = s[:-1]
-
- try:
- m = split.match(s)
- except re.error as e:
- msg = "not an IM file"
- raise SyntaxError(msg) from e
-
- if m:
- k, v = m.group(1, 2)
-
- # Don't know if this is the correct encoding,
- # but a decent guess (I guess)
- k = k.decode("latin-1", "replace")
- v = v.decode("latin-1", "replace")
-
- # Convert value as appropriate
- if k in [FRAMES, SCALE, SIZE]:
- v = v.replace("*", ",")
- v = tuple(map(number, v.split(",")))
- if len(v) == 1:
- v = v[0]
- elif k == MODE and v in OPEN:
- v, self.rawmode = OPEN[v]
-
- # Add to dictionary. Note that COMMENT tags are
- # combined into a list of strings.
- if k == COMMENT:
- if k in self.info:
- self.info[k].append(v)
- else:
- self.info[k] = [v]
- else:
- self.info[k] = v
-
- if k in TAGS:
- n += 1
-
- else:
- msg = "Syntax error in IM header: " + s.decode("ascii", "replace")
- raise SyntaxError(msg)
-
- if not n:
- msg = "Not an IM file"
- raise SyntaxError(msg)
-
- # Basic attributes
- self._size = self.info[SIZE]
- self.mode = self.info[MODE]
-
- # Skip forward to start of image data
- while s and s[:1] != b"\x1A":
- s = self.fp.read(1)
- if not s:
- msg = "File truncated"
- raise SyntaxError(msg)
-
- if LUT in self.info:
- # convert lookup table to palette or lut attribute
- palette = self.fp.read(768)
- greyscale = 1 # greyscale palette
- linear = 1 # linear greyscale palette
- for i in range(256):
- if palette[i] == palette[i + 256] == palette[i + 512]:
- if palette[i] != i:
- linear = 0
- else:
- greyscale = 0
- if self.mode in ["L", "LA", "P", "PA"]:
- if greyscale:
- if not linear:
- self.lut = list(palette[:256])
- else:
- if self.mode in ["L", "P"]:
- self.mode = self.rawmode = "P"
- elif self.mode in ["LA", "PA"]:
- self.mode = "PA"
- self.rawmode = "PA;L"
- self.palette = ImagePalette.raw("RGB;L", palette)
- elif self.mode == "RGB":
- if not greyscale or not linear:
- self.lut = list(palette)
-
- self.frame = 0
-
- self.__offset = offs = self.fp.tell()
-
- self._fp = self.fp # FIXME: hack
-
- if self.rawmode[:2] == "F;":
- # ifunc95 formats
- try:
- # use bit decoder (if necessary)
- bits = int(self.rawmode[2:])
- if bits not in [8, 16, 32]:
- self.tile = [("bit", (0, 0) + self.size, offs, (bits, 8, 3, 0, -1))]
- return
- except ValueError:
- pass
-
- if self.rawmode in ["RGB;T", "RYB;T"]:
- # Old LabEye/3PC files. Would be very surprised if anyone
- # ever stumbled upon such a file ;-)
- size = self.size[0] * self.size[1]
- self.tile = [
- ("raw", (0, 0) + self.size, offs, ("G", 0, -1)),
- ("raw", (0, 0) + self.size, offs + size, ("R", 0, -1)),
- ("raw", (0, 0) + self.size, offs + 2 * size, ("B", 0, -1)),
- ]
- else:
- # LabEye/IFUNC files
- self.tile = [("raw", (0, 0) + self.size, offs, (self.rawmode, 0, -1))]
-
- @property
- def n_frames(self):
- return self.info[FRAMES]
-
- @property
- def is_animated(self):
- return self.info[FRAMES] > 1
-
- def seek(self, frame):
- if not self._seek_check(frame):
- return
-
- self.frame = frame
-
- if self.mode == "1":
- bits = 1
- else:
- bits = 8 * len(self.mode)
-
- size = ((self.size[0] * bits + 7) // 8) * self.size[1]
- offs = self.__offset + frame * size
-
- self.fp = self._fp
-
- self.tile = [("raw", (0, 0) + self.size, offs, (self.rawmode, 0, -1))]
-
- def tell(self):
- return self.frame
-
-
-#
-# --------------------------------------------------------------------
-# Save IM files
-
-
-SAVE = {
- # mode: (im type, raw mode)
- "1": ("0 1", "1"),
- "L": ("Greyscale", "L"),
- "LA": ("LA", "LA;L"),
- "P": ("Greyscale", "P"),
- "PA": ("LA", "PA;L"),
- "I": ("L 32S", "I;32S"),
- "I;16": ("L 16", "I;16"),
- "I;16L": ("L 16L", "I;16L"),
- "I;16B": ("L 16B", "I;16B"),
- "F": ("L 32F", "F;32F"),
- "RGB": ("RGB", "RGB;L"),
- "RGBA": ("RGBA", "RGBA;L"),
- "RGBX": ("RGBX", "RGBX;L"),
- "CMYK": ("CMYK", "CMYK;L"),
- "YCbCr": ("YCC", "YCbCr;L"),
-}
-
-
-def _save(im, fp, filename):
- try:
- image_type, rawmode = SAVE[im.mode]
- except KeyError as e:
- msg = f"Cannot save {im.mode} images as IM"
- raise ValueError(msg) from e
-
- frames = im.encoderinfo.get("frames", 1)
-
- fp.write(f"Image type: {image_type} image\r\n".encode("ascii"))
- if filename:
- # Each line must be 100 characters or less,
- # or: SyntaxError("not an IM file")
- # 8 characters are used for "Name: " and "\r\n"
- # Keep just the filename, ditch the potentially overlong path
- name, ext = os.path.splitext(os.path.basename(filename))
- name = "".join([name[: 92 - len(ext)], ext])
-
- fp.write(f"Name: {name}\r\n".encode("ascii"))
- fp.write(("Image size (x*y): %d*%d\r\n" % im.size).encode("ascii"))
- fp.write(f"File size (no of images): {frames}\r\n".encode("ascii"))
- if im.mode in ["P", "PA"]:
- fp.write(b"Lut: 1\r\n")
- fp.write(b"\000" * (511 - fp.tell()) + b"\032")
- if im.mode in ["P", "PA"]:
- im_palette = im.im.getpalette("RGB", "RGB;L")
- colors = len(im_palette) // 3
- palette = b""
- for i in range(3):
- palette += im_palette[colors * i : colors * (i + 1)]
- palette += b"\x00" * (256 - colors)
- fp.write(palette) # 768 bytes
- ImageFile._save(im, fp, [("raw", (0, 0) + im.size, 0, (rawmode, 0, -1))])
-
-
-#
-# --------------------------------------------------------------------
-# Registry
-
-
-Image.register_open(ImImageFile.format, ImImageFile)
-Image.register_save(ImImageFile.format, _save)
-
-Image.register_extension(ImImageFile.format, ".im")
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/fontBuilder.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/fontBuilder.py
deleted file mode 100644
index 8f83ea80034c431b39aa38b2fc28b67957c71fb9..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/fontBuilder.py
+++ /dev/null
@@ -1,993 +0,0 @@
-__all__ = ["FontBuilder"]
-
-"""
-This module is *experimental*, meaning it still may evolve and change.
-
-The `FontBuilder` class is a convenient helper to construct working TTF or
-OTF fonts from scratch.
-
-Note that the various setup methods cannot be called in arbitrary order,
-due to various interdependencies between OpenType tables. Here is an order
-that works:
-
- fb = FontBuilder(...)
- fb.setupGlyphOrder(...)
- fb.setupCharacterMap(...)
- fb.setupGlyf(...) --or-- fb.setupCFF(...)
- fb.setupHorizontalMetrics(...)
- fb.setupHorizontalHeader()
- fb.setupNameTable(...)
- fb.setupOS2()
- fb.addOpenTypeFeatures(...)
- fb.setupPost()
- fb.save(...)
-
-Here is how to build a minimal TTF:
-
-```python
-from fontTools.fontBuilder import FontBuilder
-from fontTools.pens.ttGlyphPen import TTGlyphPen
-
-
-def drawTestGlyph(pen):
- pen.moveTo((100, 100))
- pen.lineTo((100, 1000))
- pen.qCurveTo((200, 900), (400, 900), (500, 1000))
- pen.lineTo((500, 100))
- pen.closePath()
-
-
-fb = FontBuilder(1024, isTTF=True)
-fb.setupGlyphOrder([".notdef", ".null", "space", "A", "a"])
-fb.setupCharacterMap({32: "space", 65: "A", 97: "a"})
-advanceWidths = {".notdef": 600, "space": 500, "A": 600, "a": 600, ".null": 0}
-
-familyName = "HelloTestFont"
-styleName = "TotallyNormal"
-version = "0.1"
-
-nameStrings = dict(
- familyName=dict(en=familyName, nl="HalloTestFont"),
- styleName=dict(en=styleName, nl="TotaalNormaal"),
- uniqueFontIdentifier="fontBuilder: " + familyName + "." + styleName,
- fullName=familyName + "-" + styleName,
- psName=familyName + "-" + styleName,
- version="Version " + version,
-)
-
-pen = TTGlyphPen(None)
-drawTestGlyph(pen)
-glyph = pen.glyph()
-glyphs = {".notdef": glyph, "space": glyph, "A": glyph, "a": glyph, ".null": glyph}
-fb.setupGlyf(glyphs)
-metrics = {}
-glyphTable = fb.font["glyf"]
-for gn, advanceWidth in advanceWidths.items():
- metrics[gn] = (advanceWidth, glyphTable[gn].xMin)
-fb.setupHorizontalMetrics(metrics)
-fb.setupHorizontalHeader(ascent=824, descent=-200)
-fb.setupNameTable(nameStrings)
-fb.setupOS2(sTypoAscender=824, usWinAscent=824, usWinDescent=200)
-fb.setupPost()
-fb.save("test.ttf")
-```
-
-And here's how to build a minimal OTF:
-
-```python
-from fontTools.fontBuilder import FontBuilder
-from fontTools.pens.t2CharStringPen import T2CharStringPen
-
-
-def drawTestGlyph(pen):
- pen.moveTo((100, 100))
- pen.lineTo((100, 1000))
- pen.curveTo((200, 900), (400, 900), (500, 1000))
- pen.lineTo((500, 100))
- pen.closePath()
-
-
-fb = FontBuilder(1024, isTTF=False)
-fb.setupGlyphOrder([".notdef", ".null", "space", "A", "a"])
-fb.setupCharacterMap({32: "space", 65: "A", 97: "a"})
-advanceWidths = {".notdef": 600, "space": 500, "A": 600, "a": 600, ".null": 0}
-
-familyName = "HelloTestFont"
-styleName = "TotallyNormal"
-version = "0.1"
-
-nameStrings = dict(
- familyName=dict(en=familyName, nl="HalloTestFont"),
- styleName=dict(en=styleName, nl="TotaalNormaal"),
- uniqueFontIdentifier="fontBuilder: " + familyName + "." + styleName,
- fullName=familyName + "-" + styleName,
- psName=familyName + "-" + styleName,
- version="Version " + version,
-)
-
-pen = T2CharStringPen(600, None)
-drawTestGlyph(pen)
-charString = pen.getCharString()
-charStrings = {
- ".notdef": charString,
- "space": charString,
- "A": charString,
- "a": charString,
- ".null": charString,
-}
-fb.setupCFF(nameStrings["psName"], {"FullName": nameStrings["psName"]}, charStrings, {})
-lsb = {gn: cs.calcBounds(None)[0] for gn, cs in charStrings.items()}
-metrics = {}
-for gn, advanceWidth in advanceWidths.items():
- metrics[gn] = (advanceWidth, lsb[gn])
-fb.setupHorizontalMetrics(metrics)
-fb.setupHorizontalHeader(ascent=824, descent=200)
-fb.setupNameTable(nameStrings)
-fb.setupOS2(sTypoAscender=824, usWinAscent=824, usWinDescent=200)
-fb.setupPost()
-fb.save("test.otf")
-```
-"""
-
-from .ttLib import TTFont, newTable
-from .ttLib.tables._c_m_a_p import cmap_classes
-from .ttLib.tables._g_l_y_f import flagCubic
-from .ttLib.tables.O_S_2f_2 import Panose
-from .misc.timeTools import timestampNow
-import struct
-from collections import OrderedDict
-
-
-_headDefaults = dict(
- tableVersion=1.0,
- fontRevision=1.0,
- checkSumAdjustment=0,
- magicNumber=0x5F0F3CF5,
- flags=0x0003,
- unitsPerEm=1000,
- created=0,
- modified=0,
- xMin=0,
- yMin=0,
- xMax=0,
- yMax=0,
- macStyle=0,
- lowestRecPPEM=3,
- fontDirectionHint=2,
- indexToLocFormat=0,
- glyphDataFormat=0,
-)
-
-_maxpDefaultsTTF = dict(
- tableVersion=0x00010000,
- numGlyphs=0,
- maxPoints=0,
- maxContours=0,
- maxCompositePoints=0,
- maxCompositeContours=0,
- maxZones=2,
- maxTwilightPoints=0,
- maxStorage=0,
- maxFunctionDefs=0,
- maxInstructionDefs=0,
- maxStackElements=0,
- maxSizeOfInstructions=0,
- maxComponentElements=0,
- maxComponentDepth=0,
-)
-_maxpDefaultsOTF = dict(
- tableVersion=0x00005000,
- numGlyphs=0,
-)
-
-_postDefaults = dict(
- formatType=3.0,
- italicAngle=0,
- underlinePosition=0,
- underlineThickness=0,
- isFixedPitch=0,
- minMemType42=0,
- maxMemType42=0,
- minMemType1=0,
- maxMemType1=0,
-)
-
-_hheaDefaults = dict(
- tableVersion=0x00010000,
- ascent=0,
- descent=0,
- lineGap=0,
- advanceWidthMax=0,
- minLeftSideBearing=0,
- minRightSideBearing=0,
- xMaxExtent=0,
- caretSlopeRise=1,
- caretSlopeRun=0,
- caretOffset=0,
- reserved0=0,
- reserved1=0,
- reserved2=0,
- reserved3=0,
- metricDataFormat=0,
- numberOfHMetrics=0,
-)
-
-_vheaDefaults = dict(
- tableVersion=0x00010000,
- ascent=0,
- descent=0,
- lineGap=0,
- advanceHeightMax=0,
- minTopSideBearing=0,
- minBottomSideBearing=0,
- yMaxExtent=0,
- caretSlopeRise=0,
- caretSlopeRun=0,
- reserved0=0,
- reserved1=0,
- reserved2=0,
- reserved3=0,
- reserved4=0,
- metricDataFormat=0,
- numberOfVMetrics=0,
-)
-
-_nameIDs = dict(
- copyright=0,
- familyName=1,
- styleName=2,
- uniqueFontIdentifier=3,
- fullName=4,
- version=5,
- psName=6,
- trademark=7,
- manufacturer=8,
- designer=9,
- description=10,
- vendorURL=11,
- designerURL=12,
- licenseDescription=13,
- licenseInfoURL=14,
- # reserved = 15,
- typographicFamily=16,
- typographicSubfamily=17,
- compatibleFullName=18,
- sampleText=19,
- postScriptCIDFindfontName=20,
- wwsFamilyName=21,
- wwsSubfamilyName=22,
- lightBackgroundPalette=23,
- darkBackgroundPalette=24,
- variationsPostScriptNamePrefix=25,
-)
-
-# to insert in setupNameTable doc string:
-# print("\n".join(("%s (nameID %s)" % (k, v)) for k, v in sorted(_nameIDs.items(), key=lambda x: x[1])))
-
-_panoseDefaults = Panose()
-
-_OS2Defaults = dict(
- version=3,
- xAvgCharWidth=0,
- usWeightClass=400,
- usWidthClass=5,
- fsType=0x0004, # default: Preview & Print embedding
- ySubscriptXSize=0,
- ySubscriptYSize=0,
- ySubscriptXOffset=0,
- ySubscriptYOffset=0,
- ySuperscriptXSize=0,
- ySuperscriptYSize=0,
- ySuperscriptXOffset=0,
- ySuperscriptYOffset=0,
- yStrikeoutSize=0,
- yStrikeoutPosition=0,
- sFamilyClass=0,
- panose=_panoseDefaults,
- ulUnicodeRange1=0,
- ulUnicodeRange2=0,
- ulUnicodeRange3=0,
- ulUnicodeRange4=0,
- achVendID="????",
- fsSelection=0,
- usFirstCharIndex=0,
- usLastCharIndex=0,
- sTypoAscender=0,
- sTypoDescender=0,
- sTypoLineGap=0,
- usWinAscent=0,
- usWinDescent=0,
- ulCodePageRange1=0,
- ulCodePageRange2=0,
- sxHeight=0,
- sCapHeight=0,
- usDefaultChar=0, # .notdef
- usBreakChar=32, # space
- usMaxContext=0,
- usLowerOpticalPointSize=0,
- usUpperOpticalPointSize=0,
-)
-
-
-class FontBuilder(object):
- def __init__(self, unitsPerEm=None, font=None, isTTF=True, glyphDataFormat=0):
- """Initialize a FontBuilder instance.
-
- If the `font` argument is not given, a new `TTFont` will be
- constructed, and `unitsPerEm` must be given. If `isTTF` is True,
- the font will be a glyf-based TTF; if `isTTF` is False it will be
- a CFF-based OTF.
-
- The `glyphDataFormat` argument corresponds to the `head` table field
- that defines the format of the TrueType `glyf` table (default=0).
- TrueType glyphs historically can only contain quadratic splines and static
- components, but there's a proposal to add support for cubic Bezier curves as well
- as variable composites/components at
- https://github.com/harfbuzz/boring-expansion-spec/blob/main/glyf1.md
- You can experiment with the new features by setting `glyphDataFormat` to 1.
- A ValueError is raised if `glyphDataFormat` is left at 0 but glyphs are added
- that contain cubic splines or varcomposites. This is to prevent accidentally
- creating fonts that are incompatible with existing TrueType implementations.
-
- If `font` is given, it must be a `TTFont` instance and `unitsPerEm`
- must _not_ be given. The `isTTF` and `glyphDataFormat` arguments will be ignored.
- """
- if font is None:
- self.font = TTFont(recalcTimestamp=False)
- self.isTTF = isTTF
- now = timestampNow()
- assert unitsPerEm is not None
- self.setupHead(
- unitsPerEm=unitsPerEm,
- create=now,
- modified=now,
- glyphDataFormat=glyphDataFormat,
- )
- self.setupMaxp()
- else:
- assert unitsPerEm is None
- self.font = font
- self.isTTF = "glyf" in font
-
- def save(self, file):
- """Save the font. The 'file' argument can be either a pathname or a
- writable file object.
- """
- self.font.save(file)
-
- def _initTableWithValues(self, tableTag, defaults, values):
- table = self.font[tableTag] = newTable(tableTag)
- for k, v in defaults.items():
- setattr(table, k, v)
- for k, v in values.items():
- setattr(table, k, v)
- return table
-
- def _updateTableWithValues(self, tableTag, values):
- table = self.font[tableTag]
- for k, v in values.items():
- setattr(table, k, v)
-
- def setupHead(self, **values):
- """Create a new `head` table and initialize it with default values,
- which can be overridden by keyword arguments.
- """
- self._initTableWithValues("head", _headDefaults, values)
-
- def updateHead(self, **values):
- """Update the head table with the fields and values passed as
- keyword arguments.
- """
- self._updateTableWithValues("head", values)
-
- def setupGlyphOrder(self, glyphOrder):
- """Set the glyph order for the font."""
- self.font.setGlyphOrder(glyphOrder)
-
- def setupCharacterMap(self, cmapping, uvs=None, allowFallback=False):
- """Build the `cmap` table for the font. The `cmapping` argument should
- be a dict mapping unicode code points as integers to glyph names.
-
- The `uvs` argument, when passed, must be a list of tuples, describing
- Unicode Variation Sequences. These tuples have three elements:
- (unicodeValue, variationSelector, glyphName)
- `unicodeValue` and `variationSelector` are integer code points.
- `glyphName` may be None, to indicate this is the default variation.
- Text processors will then use the cmap to find the glyph name.
- Each Unicode Variation Sequence should be an officially supported
- sequence, but this is not policed.
- """
- subTables = []
- highestUnicode = max(cmapping) if cmapping else 0
- if highestUnicode > 0xFFFF:
- cmapping_3_1 = dict((k, v) for k, v in cmapping.items() if k < 0x10000)
- subTable_3_10 = buildCmapSubTable(cmapping, 12, 3, 10)
- subTables.append(subTable_3_10)
- else:
- cmapping_3_1 = cmapping
- format = 4
- subTable_3_1 = buildCmapSubTable(cmapping_3_1, format, 3, 1)
- try:
- subTable_3_1.compile(self.font)
- except struct.error:
- # format 4 overflowed, fall back to format 12
- if not allowFallback:
- raise ValueError(
- "cmap format 4 subtable overflowed; sort glyph order by unicode to fix."
- )
- format = 12
- subTable_3_1 = buildCmapSubTable(cmapping_3_1, format, 3, 1)
- subTables.append(subTable_3_1)
- subTable_0_3 = buildCmapSubTable(cmapping_3_1, format, 0, 3)
- subTables.append(subTable_0_3)
-
- if uvs is not None:
- uvsDict = {}
- for unicodeValue, variationSelector, glyphName in uvs:
- if cmapping.get(unicodeValue) == glyphName:
- # this is a default variation
- glyphName = None
- if variationSelector not in uvsDict:
- uvsDict[variationSelector] = []
- uvsDict[variationSelector].append((unicodeValue, glyphName))
- uvsSubTable = buildCmapSubTable({}, 14, 0, 5)
- uvsSubTable.uvsDict = uvsDict
- subTables.append(uvsSubTable)
-
- self.font["cmap"] = newTable("cmap")
- self.font["cmap"].tableVersion = 0
- self.font["cmap"].tables = subTables
-
- def setupNameTable(self, nameStrings, windows=True, mac=True):
- """Create the `name` table for the font. The `nameStrings` argument must
- be a dict, mapping nameIDs or descriptive names for the nameIDs to name
- record values. A value is either a string, or a dict, mapping language codes
- to strings, to allow localized name table entries.
-
- By default, both Windows (platformID=3) and Macintosh (platformID=1) name
- records are added, unless any of `windows` or `mac` arguments is False.
-
- The following descriptive names are available for nameIDs:
-
- copyright (nameID 0)
- familyName (nameID 1)
- styleName (nameID 2)
- uniqueFontIdentifier (nameID 3)
- fullName (nameID 4)
- version (nameID 5)
- psName (nameID 6)
- trademark (nameID 7)
- manufacturer (nameID 8)
- designer (nameID 9)
- description (nameID 10)
- vendorURL (nameID 11)
- designerURL (nameID 12)
- licenseDescription (nameID 13)
- licenseInfoURL (nameID 14)
- typographicFamily (nameID 16)
- typographicSubfamily (nameID 17)
- compatibleFullName (nameID 18)
- sampleText (nameID 19)
- postScriptCIDFindfontName (nameID 20)
- wwsFamilyName (nameID 21)
- wwsSubfamilyName (nameID 22)
- lightBackgroundPalette (nameID 23)
- darkBackgroundPalette (nameID 24)
- variationsPostScriptNamePrefix (nameID 25)
- """
- nameTable = self.font["name"] = newTable("name")
- nameTable.names = []
-
- for nameName, nameValue in nameStrings.items():
- if isinstance(nameName, int):
- nameID = nameName
- else:
- nameID = _nameIDs[nameName]
- if isinstance(nameValue, str):
- nameValue = dict(en=nameValue)
- nameTable.addMultilingualName(
- nameValue, ttFont=self.font, nameID=nameID, windows=windows, mac=mac
- )
-
- def setupOS2(self, **values):
- """Create a new `OS/2` table and initialize it with default values,
- which can be overridden by keyword arguments.
- """
- self._initTableWithValues("OS/2", _OS2Defaults, values)
- if "xAvgCharWidth" not in values:
- assert (
- "hmtx" in self.font
- ), "the 'hmtx' table must be setup before the 'OS/2' table"
- self.font["OS/2"].recalcAvgCharWidth(self.font)
- if not (
- "ulUnicodeRange1" in values
- or "ulUnicodeRange2" in values
- or "ulUnicodeRange3" in values
- or "ulUnicodeRange3" in values
- ):
- assert (
- "cmap" in self.font
- ), "the 'cmap' table must be setup before the 'OS/2' table"
- self.font["OS/2"].recalcUnicodeRanges(self.font)
-
- def setupCFF(self, psName, fontInfo, charStringsDict, privateDict):
- from .cffLib import (
- CFFFontSet,
- TopDictIndex,
- TopDict,
- CharStrings,
- GlobalSubrsIndex,
- PrivateDict,
- )
-
- assert not self.isTTF
- self.font.sfntVersion = "OTTO"
- fontSet = CFFFontSet()
- fontSet.major = 1
- fontSet.minor = 0
- fontSet.otFont = self.font
- fontSet.fontNames = [psName]
- fontSet.topDictIndex = TopDictIndex()
-
- globalSubrs = GlobalSubrsIndex()
- fontSet.GlobalSubrs = globalSubrs
- private = PrivateDict()
- for key, value in privateDict.items():
- setattr(private, key, value)
- fdSelect = None
- fdArray = None
-
- topDict = TopDict()
- topDict.charset = self.font.getGlyphOrder()
- topDict.Private = private
- topDict.GlobalSubrs = fontSet.GlobalSubrs
- for key, value in fontInfo.items():
- setattr(topDict, key, value)
- if "FontMatrix" not in fontInfo:
- scale = 1 / self.font["head"].unitsPerEm
- topDict.FontMatrix = [scale, 0, 0, scale, 0, 0]
-
- charStrings = CharStrings(
- None, topDict.charset, globalSubrs, private, fdSelect, fdArray
- )
- for glyphName, charString in charStringsDict.items():
- charString.private = private
- charString.globalSubrs = globalSubrs
- charStrings[glyphName] = charString
- topDict.CharStrings = charStrings
-
- fontSet.topDictIndex.append(topDict)
-
- self.font["CFF "] = newTable("CFF ")
- self.font["CFF "].cff = fontSet
-
- def setupCFF2(self, charStringsDict, fdArrayList=None, regions=None):
- from .cffLib import (
- CFFFontSet,
- TopDictIndex,
- TopDict,
- CharStrings,
- GlobalSubrsIndex,
- PrivateDict,
- FDArrayIndex,
- FontDict,
- )
-
- assert not self.isTTF
- self.font.sfntVersion = "OTTO"
- fontSet = CFFFontSet()
- fontSet.major = 2
- fontSet.minor = 0
-
- cff2GetGlyphOrder = self.font.getGlyphOrder
- fontSet.topDictIndex = TopDictIndex(None, cff2GetGlyphOrder, None)
-
- globalSubrs = GlobalSubrsIndex()
- fontSet.GlobalSubrs = globalSubrs
-
- if fdArrayList is None:
- fdArrayList = [{}]
- fdSelect = None
- fdArray = FDArrayIndex()
- fdArray.strings = None
- fdArray.GlobalSubrs = globalSubrs
- for privateDict in fdArrayList:
- fontDict = FontDict()
- fontDict.setCFF2(True)
- private = PrivateDict()
- for key, value in privateDict.items():
- setattr(private, key, value)
- fontDict.Private = private
- fdArray.append(fontDict)
-
- topDict = TopDict()
- topDict.cff2GetGlyphOrder = cff2GetGlyphOrder
- topDict.FDArray = fdArray
- scale = 1 / self.font["head"].unitsPerEm
- topDict.FontMatrix = [scale, 0, 0, scale, 0, 0]
-
- private = fdArray[0].Private
- charStrings = CharStrings(None, None, globalSubrs, private, fdSelect, fdArray)
- for glyphName, charString in charStringsDict.items():
- charString.private = private
- charString.globalSubrs = globalSubrs
- charStrings[glyphName] = charString
- topDict.CharStrings = charStrings
-
- fontSet.topDictIndex.append(topDict)
-
- self.font["CFF2"] = newTable("CFF2")
- self.font["CFF2"].cff = fontSet
-
- if regions:
- self.setupCFF2Regions(regions)
-
- def setupCFF2Regions(self, regions):
- from .varLib.builder import buildVarRegionList, buildVarData, buildVarStore
- from .cffLib import VarStoreData
-
- assert "fvar" in self.font, "fvar must to be set up first"
- assert "CFF2" in self.font, "CFF2 must to be set up first"
- axisTags = [a.axisTag for a in self.font["fvar"].axes]
- varRegionList = buildVarRegionList(regions, axisTags)
- varData = buildVarData(list(range(len(regions))), None, optimize=False)
- varStore = buildVarStore(varRegionList, [varData])
- vstore = VarStoreData(otVarStore=varStore)
- topDict = self.font["CFF2"].cff.topDictIndex[0]
- topDict.VarStore = vstore
- for fontDict in topDict.FDArray:
- fontDict.Private.vstore = vstore
-
- def setupGlyf(self, glyphs, calcGlyphBounds=True, validateGlyphFormat=True):
- """Create the `glyf` table from a dict, that maps glyph names
- to `fontTools.ttLib.tables._g_l_y_f.Glyph` objects, for example
- as made by `fontTools.pens.ttGlyphPen.TTGlyphPen`.
-
- If `calcGlyphBounds` is True, the bounds of all glyphs will be
- calculated. Only pass False if your glyph objects already have
- their bounding box values set.
-
- If `validateGlyphFormat` is True, raise ValueError if any of the glyphs contains
- cubic curves or is a variable composite but head.glyphDataFormat=0.
- Set it to False to skip the check if you know in advance all the glyphs are
- compatible with the specified glyphDataFormat.
- """
- assert self.isTTF
-
- if validateGlyphFormat and self.font["head"].glyphDataFormat == 0:
- for name, g in glyphs.items():
- if g.isVarComposite():
- raise ValueError(
- f"Glyph {name!r} is a variable composite, but glyphDataFormat=0"
- )
- elif g.numberOfContours > 0 and any(f & flagCubic for f in g.flags):
- raise ValueError(
- f"Glyph {name!r} has cubic Bezier outlines, but glyphDataFormat=0; "
- "either convert to quadratics with cu2qu or set glyphDataFormat=1."
- )
-
- self.font["loca"] = newTable("loca")
- self.font["glyf"] = newTable("glyf")
- self.font["glyf"].glyphs = glyphs
- if hasattr(self.font, "glyphOrder"):
- self.font["glyf"].glyphOrder = self.font.glyphOrder
- if calcGlyphBounds:
- self.calcGlyphBounds()
-
- def setupFvar(self, axes, instances):
- """Adds an font variations table to the font.
-
- Args:
- axes (list): See below.
- instances (list): See below.
-
- ``axes`` should be a list of axes, with each axis either supplied as
- a py:class:`.designspaceLib.AxisDescriptor` object, or a tuple in the
- format ```tupletag, minValue, defaultValue, maxValue, name``.
- The ``name`` is either a string, or a dict, mapping language codes
- to strings, to allow localized name table entries.
-
- ```instances`` should be a list of instances, with each instance either
- supplied as a py:class:`.designspaceLib.InstanceDescriptor` object, or a
- dict with keys ``location`` (mapping of axis tags to float values),
- ``stylename`` and (optionally) ``postscriptfontname``.
- The ``stylename`` is either a string, or a dict, mapping language codes
- to strings, to allow localized name table entries.
- """
-
- addFvar(self.font, axes, instances)
-
- def setupAvar(self, axes, mappings=None):
- """Adds an axis variations table to the font.
-
- Args:
- axes (list): A list of py:class:`.designspaceLib.AxisDescriptor` objects.
- """
- from .varLib import _add_avar
-
- if "fvar" not in self.font:
- raise KeyError("'fvar' table is missing; can't add 'avar'.")
-
- axisTags = [axis.axisTag for axis in self.font["fvar"].axes]
- axes = OrderedDict(enumerate(axes)) # Only values are used
- _add_avar(self.font, axes, mappings, axisTags)
-
- def setupGvar(self, variations):
- gvar = self.font["gvar"] = newTable("gvar")
- gvar.version = 1
- gvar.reserved = 0
- gvar.variations = variations
-
- def calcGlyphBounds(self):
- """Calculate the bounding boxes of all glyphs in the `glyf` table.
- This is usually not called explicitly by client code.
- """
- glyphTable = self.font["glyf"]
- for glyph in glyphTable.glyphs.values():
- glyph.recalcBounds(glyphTable)
-
- def setupHorizontalMetrics(self, metrics):
- """Create a new `hmtx` table, for horizontal metrics.
-
- The `metrics` argument must be a dict, mapping glyph names to
- `(width, leftSidebearing)` tuples.
- """
- self.setupMetrics("hmtx", metrics)
-
- def setupVerticalMetrics(self, metrics):
- """Create a new `vmtx` table, for horizontal metrics.
-
- The `metrics` argument must be a dict, mapping glyph names to
- `(height, topSidebearing)` tuples.
- """
- self.setupMetrics("vmtx", metrics)
-
- def setupMetrics(self, tableTag, metrics):
- """See `setupHorizontalMetrics()` and `setupVerticalMetrics()`."""
- assert tableTag in ("hmtx", "vmtx")
- mtxTable = self.font[tableTag] = newTable(tableTag)
- roundedMetrics = {}
- for gn in metrics:
- w, lsb = metrics[gn]
- roundedMetrics[gn] = int(round(w)), int(round(lsb))
- mtxTable.metrics = roundedMetrics
-
- def setupHorizontalHeader(self, **values):
- """Create a new `hhea` table initialize it with default values,
- which can be overridden by keyword arguments.
- """
- self._initTableWithValues("hhea", _hheaDefaults, values)
-
- def setupVerticalHeader(self, **values):
- """Create a new `vhea` table initialize it with default values,
- which can be overridden by keyword arguments.
- """
- self._initTableWithValues("vhea", _vheaDefaults, values)
-
- def setupVerticalOrigins(self, verticalOrigins, defaultVerticalOrigin=None):
- """Create a new `VORG` table. The `verticalOrigins` argument must be
- a dict, mapping glyph names to vertical origin values.
-
- The `defaultVerticalOrigin` argument should be the most common vertical
- origin value. If omitted, this value will be derived from the actual
- values in the `verticalOrigins` argument.
- """
- if defaultVerticalOrigin is None:
- # find the most frequent vorg value
- bag = {}
- for gn in verticalOrigins:
- vorg = verticalOrigins[gn]
- if vorg not in bag:
- bag[vorg] = 1
- else:
- bag[vorg] += 1
- defaultVerticalOrigin = sorted(
- bag, key=lambda vorg: bag[vorg], reverse=True
- )[0]
- self._initTableWithValues(
- "VORG",
- {},
- dict(VOriginRecords={}, defaultVertOriginY=defaultVerticalOrigin),
- )
- vorgTable = self.font["VORG"]
- vorgTable.majorVersion = 1
- vorgTable.minorVersion = 0
- for gn in verticalOrigins:
- vorgTable[gn] = verticalOrigins[gn]
-
- def setupPost(self, keepGlyphNames=True, **values):
- """Create a new `post` table and initialize it with default values,
- which can be overridden by keyword arguments.
- """
- isCFF2 = "CFF2" in self.font
- postTable = self._initTableWithValues("post", _postDefaults, values)
- if (self.isTTF or isCFF2) and keepGlyphNames:
- postTable.formatType = 2.0
- postTable.extraNames = []
- postTable.mapping = {}
- else:
- postTable.formatType = 3.0
-
- def setupMaxp(self):
- """Create a new `maxp` table. This is called implicitly by FontBuilder
- itself and is usually not called by client code.
- """
- if self.isTTF:
- defaults = _maxpDefaultsTTF
- else:
- defaults = _maxpDefaultsOTF
- self._initTableWithValues("maxp", defaults, {})
-
- def setupDummyDSIG(self):
- """This adds an empty DSIG table to the font to make some MS applications
- happy. This does not properly sign the font.
- """
- values = dict(
- ulVersion=1,
- usFlag=0,
- usNumSigs=0,
- signatureRecords=[],
- )
- self._initTableWithValues("DSIG", {}, values)
-
- def addOpenTypeFeatures(self, features, filename=None, tables=None, debug=False):
- """Add OpenType features to the font from a string containing
- Feature File syntax.
-
- The `filename` argument is used in error messages and to determine
- where to look for "include" files.
-
- The optional `tables` argument can be a list of OTL tables tags to
- build, allowing the caller to only build selected OTL tables. See
- `fontTools.feaLib` for details.
-
- The optional `debug` argument controls whether to add source debugging
- information to the font in the `Debg` table.
- """
- from .feaLib.builder import addOpenTypeFeaturesFromString
-
- addOpenTypeFeaturesFromString(
- self.font, features, filename=filename, tables=tables, debug=debug
- )
-
- def addFeatureVariations(self, conditionalSubstitutions, featureTag="rvrn"):
- """Add conditional substitutions to a Variable Font.
-
- See `fontTools.varLib.featureVars.addFeatureVariations`.
- """
- from .varLib import featureVars
-
- if "fvar" not in self.font:
- raise KeyError("'fvar' table is missing; can't add FeatureVariations.")
-
- featureVars.addFeatureVariations(
- self.font, conditionalSubstitutions, featureTag=featureTag
- )
-
- def setupCOLR(
- self,
- colorLayers,
- version=None,
- varStore=None,
- varIndexMap=None,
- clipBoxes=None,
- allowLayerReuse=True,
- ):
- """Build new COLR table using color layers dictionary.
-
- Cf. `fontTools.colorLib.builder.buildCOLR`.
- """
- from fontTools.colorLib.builder import buildCOLR
-
- glyphMap = self.font.getReverseGlyphMap()
- self.font["COLR"] = buildCOLR(
- colorLayers,
- version=version,
- glyphMap=glyphMap,
- varStore=varStore,
- varIndexMap=varIndexMap,
- clipBoxes=clipBoxes,
- allowLayerReuse=allowLayerReuse,
- )
-
- def setupCPAL(
- self,
- palettes,
- paletteTypes=None,
- paletteLabels=None,
- paletteEntryLabels=None,
- ):
- """Build new CPAL table using list of palettes.
-
- Optionally build CPAL v1 table using paletteTypes, paletteLabels and
- paletteEntryLabels.
-
- Cf. `fontTools.colorLib.builder.buildCPAL`.
- """
- from fontTools.colorLib.builder import buildCPAL
-
- self.font["CPAL"] = buildCPAL(
- palettes,
- paletteTypes=paletteTypes,
- paletteLabels=paletteLabels,
- paletteEntryLabels=paletteEntryLabels,
- nameTable=self.font.get("name"),
- )
-
- def setupStat(self, axes, locations=None, elidedFallbackName=2):
- """Build a new 'STAT' table.
-
- See `fontTools.otlLib.builder.buildStatTable` for details about
- the arguments.
- """
- from .otlLib.builder import buildStatTable
-
- buildStatTable(self.font, axes, locations, elidedFallbackName)
-
-
-def buildCmapSubTable(cmapping, format, platformID, platEncID):
- subTable = cmap_classes[format](format)
- subTable.cmap = cmapping
- subTable.platformID = platformID
- subTable.platEncID = platEncID
- subTable.language = 0
- return subTable
-
-
-def addFvar(font, axes, instances):
- from .ttLib.tables._f_v_a_r import Axis, NamedInstance
-
- assert axes
-
- fvar = newTable("fvar")
- nameTable = font["name"]
-
- for axis_def in axes:
- axis = Axis()
-
- if isinstance(axis_def, tuple):
- (
- axis.axisTag,
- axis.minValue,
- axis.defaultValue,
- axis.maxValue,
- name,
- ) = axis_def
- else:
- (axis.axisTag, axis.minValue, axis.defaultValue, axis.maxValue, name) = (
- axis_def.tag,
- axis_def.minimum,
- axis_def.default,
- axis_def.maximum,
- axis_def.name,
- )
- if axis_def.hidden:
- axis.flags = 0x0001 # HIDDEN_AXIS
-
- if isinstance(name, str):
- name = dict(en=name)
-
- axis.axisNameID = nameTable.addMultilingualName(name, ttFont=font)
- fvar.axes.append(axis)
-
- for instance in instances:
- if isinstance(instance, dict):
- coordinates = instance["location"]
- name = instance["stylename"]
- psname = instance.get("postscriptfontname")
- else:
- coordinates = instance.location
- name = instance.localisedStyleName or instance.styleName
- psname = instance.postScriptFontName
-
- if isinstance(name, str):
- name = dict(en=name)
-
- inst = NamedInstance()
- inst.subfamilyNameID = nameTable.addMultilingualName(name, ttFont=font)
- if psname is not None:
- inst.postscriptNameID = nameTable.addName(psname)
- inst.coordinates = coordinates
- fvar.instances.append(inst)
-
- font["fvar"] = fvar
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/S_I_N_G_.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/S_I_N_G_.py
deleted file mode 100644
index 7420da7e5dcec81b835ab0e8e2c775dbce860cbd..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/S_I_N_G_.py
+++ /dev/null
@@ -1,93 +0,0 @@
-from fontTools.misc import sstruct
-from fontTools.misc.textTools import bytechr, byteord, tobytes, tostr, safeEval
-from . import DefaultTable
-
-SINGFormat = """
- > # big endian
- tableVersionMajor: H
- tableVersionMinor: H
- glyphletVersion: H
- permissions: h
- mainGID: H
- unitsPerEm: H
- vertAdvance: h
- vertOrigin: h
- uniqueName: 28s
- METAMD5: 16s
- nameLength: 1s
-"""
-# baseGlyphName is a byte string which follows the record above.
-
-
-class table_S_I_N_G_(DefaultTable.DefaultTable):
-
- dependencies = []
-
- def decompile(self, data, ttFont):
- dummy, rest = sstruct.unpack2(SINGFormat, data, self)
- self.uniqueName = self.decompileUniqueName(self.uniqueName)
- self.nameLength = byteord(self.nameLength)
- assert len(rest) == self.nameLength
- self.baseGlyphName = tostr(rest)
-
- rawMETAMD5 = self.METAMD5
- self.METAMD5 = "[" + hex(byteord(self.METAMD5[0]))
- for char in rawMETAMD5[1:]:
- self.METAMD5 = self.METAMD5 + ", " + hex(byteord(char))
- self.METAMD5 = self.METAMD5 + "]"
-
- def decompileUniqueName(self, data):
- name = ""
- for char in data:
- val = byteord(char)
- if val == 0:
- break
- if (val > 31) or (val < 128):
- name += chr(val)
- else:
- octString = oct(val)
- if len(octString) > 3:
- octString = octString[1:] # chop off that leading zero.
- elif len(octString) < 3:
- octString.zfill(3)
- name += "\\" + octString
- return name
-
- def compile(self, ttFont):
- d = self.__dict__.copy()
- d["nameLength"] = bytechr(len(self.baseGlyphName))
- d["uniqueName"] = self.compilecompileUniqueName(self.uniqueName, 28)
- METAMD5List = eval(self.METAMD5)
- d["METAMD5"] = b""
- for val in METAMD5List:
- d["METAMD5"] += bytechr(val)
- assert len(d["METAMD5"]) == 16, "Failed to pack 16 byte MD5 hash in SING table"
- data = sstruct.pack(SINGFormat, d)
- data = data + tobytes(self.baseGlyphName)
- return data
-
- def compilecompileUniqueName(self, name, length):
- nameLen = len(name)
- if length <= nameLen:
- name = name[: length - 1] + "\000"
- else:
- name += (nameLen - length) * "\000"
- return name
-
- def toXML(self, writer, ttFont):
- writer.comment("Most of this table will be recalculated by the compiler")
- writer.newline()
- formatstring, names, fixes = sstruct.getformat(SINGFormat)
- for name in names:
- value = getattr(self, name)
- writer.simpletag(name, value=value)
- writer.newline()
- writer.simpletag("baseGlyphName", value=self.baseGlyphName)
- writer.newline()
-
- def fromXML(self, name, attrs, content, ttFont):
- value = attrs["value"]
- if name in ["uniqueName", "METAMD5", "baseGlyphName"]:
- setattr(self, name, value)
- else:
- setattr(self, name, safeEval(value))
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/r-3ca97919.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/r-3ca97919.js
deleted file mode 100644
index e460c951763f569906751f34aed4265f5d719d36..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/r-3ca97919.js
+++ /dev/null
@@ -1,2 +0,0 @@
-function f(e){for(var n={},r=0;r=!&|~$:]/,t;function p(e,n){t=null;var r=e.next();if(r=="#")return e.skipToEnd(),"comment";if(r=="0"&&e.eat("x"))return e.eatWhile(/[\da-f]/i),"number";if(r=="."&&e.eat(/\d/))return e.match(/\d*(?:e[+\-]?\d+)?/),"number";if(/\d/.test(r))return e.match(/\d*(?:\.\d+)?(?:e[+\-]\d+)?L?/),"number";if(r=="'"||r=='"')return n.tokenize=E(r),"string";if(r=="`")return e.match(/[^`]+`/),"string.special";if(r=="."&&e.match(/.(?:[.]|\d+)/))return"keyword";if(/[a-zA-Z\.]/.test(r)){e.eatWhile(/[\w\.]/);var i=e.current();return h.propertyIsEnumerable(i)?"atom":N.propertyIsEnumerable(i)?(A.propertyIsEnumerable(i)&&!e.match(/\s*if(\s+|$)/,!1)&&(t="block"),"keyword"):m.propertyIsEnumerable(i)?"builtin":"variable"}else return r=="%"?(e.skipTo("%")&&e.next(),"variableName.special"):r=="<"&&e.eat("-")||r=="<"&&e.match("<-")||r=="-"&&e.match(/>>?/)||r=="="&&n.ctx.argList?"operator":k.test(r)?(r=="$"||e.eatWhile(k),"operator"):/[\(\){}\[\];]/.test(r)?(t=r,r==";"?"punctuation":null):null}function E(e){return function(n,r){if(n.eat("\\")){var i=n.next();return i=="x"?n.match(/^[a-f0-9]{2}/i):(i=="u"||i=="U")&&n.eat("{")&&n.skipTo("}")?n.next():i=="u"?n.match(/^[a-f0-9]{4}/i):i=="U"?n.match(/^[a-f0-9]{8}/i):/[0-7]/.test(i)&&n.match(/^[0-7]{1,2}/),"string.special"}else{for(var l;(l=n.next())!=null;){if(l==e){r.tokenize=p;break}if(l=="\\"){n.backUp(1);break}}return"string"}}}var v=1,u=2,c=4;function o(e,n,r){e.ctx={type:n,indent:e.indent,flags:0,column:r.column(),prev:e.ctx}}function x(e,n){var r=e.ctx;e.ctx={type:r.type,indent:r.indent,flags:r.flags|n,column:r.column,prev:r.prev}}function a(e){e.indent=e.ctx.indent,e.ctx=e.ctx.prev}const I={name:"r",startState:function(e){return{tokenize:p,ctx:{type:"top",indent:-e,flags:u},indent:0,afterIdent:!1}},token:function(e,n){if(e.sol()&&(n.ctx.flags&3||(n.ctx.flags|=u),n.ctx.flags&c&&a(n),n.indent=e.indentation()),e.eatSpace())return null;var r=n.tokenize(e,n);return r!="comment"&&!(n.ctx.flags&u)&&x(n,v),(t==";"||t=="{"||t=="}")&&n.ctx.type=="block"&&a(n),t=="{"?o(n,"}",e):t=="("?(o(n,")",e),n.afterIdent&&(n.ctx.argList=!0)):t=="["?o(n,"]",e):t=="block"?o(n,"block",e):t==n.ctx.type?a(n):n.ctx.type=="block"&&r!="comment"&&x(n,c),n.afterIdent=r=="variable"||r=="keyword",r},indent:function(e,n,r){if(e.tokenize!=p)return 0;var i=n&&n.charAt(0),l=e.ctx,d=i==l.type;return l.flags&c&&(l=l.prev),l.type=="block"?l.indent+(i=="{"?0:r.unit):l.flags&v?l.column+(d?0:1):l.indent+(d?0:r.unit)},languageData:{wordChars:".",commentTokens:{line:"#"},autocomplete:b.concat(g,s)}};export{I as r};
-//# sourceMappingURL=r-3ca97919.js.map
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/h11/tests/__init__.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/h11/tests/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Demosthene-OR/avr23-cds-translation/tabs/modelisation_dict_tab.py b/spaces/Demosthene-OR/avr23-cds-translation/tabs/modelisation_dict_tab.py
deleted file mode 100644
index 038e3255a7de23112b0d30683c9e0c9c37141530..0000000000000000000000000000000000000000
--- a/spaces/Demosthene-OR/avr23-cds-translation/tabs/modelisation_dict_tab.py
+++ /dev/null
@@ -1,263 +0,0 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-import os
-from sacrebleu import corpus_bleu
-# from sklearn.cluster import KMeans
-# from sklearn.neighbors import KNeighborsClassifier
-# from sklearn.ensemble import RandomForestClassifier
-
-title = "Traduction mot à mot"
-sidebar_name = "Traduction mot à mot"
-
-@st.cache_data
-def load_corpus(path):
- input_file = os.path.join(path)
- with open(input_file, "r", encoding="utf-8") as f:
- data = f.read()
- data = data.split('\n')
- data=data[:-1]
- return pd.DataFrame(data)
-
-df_data_en = load_corpus('data/preprocess_txt_en')
-df_data_fr = load_corpus('data/preprocess_txt_fr')
-n1 = 0
-"""
-nb_mots_en = 199 # len(corpus_en)
-nb_mots_fr = 330 # len(corpus_fr)
-
-
-# @st.cache_data(ttl='1h00s')
-def load_BOW(path, l):
- input_file = os.path.join(path)
- df1 = pd.read_csv(input_file+'1_'+l, encoding="utf-8", index_col=0)
- df2 = pd.read_csv(input_file+'2_'+l, encoding="utf-8", index_col=0)
- df_count_word = pd.concat([df1, df2])
- return df_count_word
-
-
-df_count_word_en = load_BOW('../data/preprocess_df_count_word', 'en')
-df_count_word_fr = load_BOW('../data/preprocess_df_count_word', 'fr')
-"""
-
-def accuracy(dict_ref,dict):
- correct_words = 0
-
- for t in dict.columns:
- if t in dict_ref.columns:
- if str(dict[t]) == str(dict_ref[t]):
- correct_words +=1
- else: print("dict ref: manque:",t)
- print(correct_words," mots corrects / ",min(dict.shape[1],dict_ref.shape[1]))
- return correct_words/min(dict.shape[1],dict_ref.shape[1])
-
-"""
-# On modifie df_count_word en indiquant la présence d'un mot par 1 (au lieu du nombre d'occurences)
-df_count_word_en = df_count_word_en[df_count_word_en==0].fillna(1)
-df_count_word_fr = df_count_word_fr[df_count_word_fr==0].fillna(1)
-
-# On triche un peu parce que new et jersey sont toujours dans la même phrase et donc dans la même classe
-if ('new' in df_count_word_en.columns):
- df_count_word_en['new']=df_count_word_en['new']*2
- df_count_word_fr['new']=df_count_word_fr['new']*2
-
-
-
-# ============
-
-def calc_kmeans(l_src,l_tgt):
- global df_count_word_src, df_count_word_tgt, nb_mots_src, nb_mots_tgt
-
- # Algorithme de K-means
- init_centroids = df_count_word_tgt.T
- kmeans = KMeans(n_clusters = nb_mots_tgt, n_init=1, max_iter=1, init=init_centroids, verbose=0)
-
- kmeans.fit(df_count_word_tgt.T)
-
- # Centroids and labels
- centroids= kmeans.cluster_centers_
- labels = kmeans.labels_
-
- # Création et affichage du dictionnaire
- df_dic = pd.DataFrame(data=df_count_word_tgt.columns[kmeans.predict(df_count_word_src.T)],index=df_count_word_src.T.index,columns=[l_tgt])
- df_dic.index.name= l_src
- df_dic = df_dic.T
- # print("Dictionnaire Anglais -> Français:")
- # translation_quality['Précision du dictionnaire'].loc['K-Means EN->FR'] =round(accuracy(dict_EN_FR_ref,dict_EN_FR)*100, 2)
- # print(f"Précision du dictionnaire = {translation_quality['Précision du dictionnaire'].loc['K-Means EN->FR']}%")
- # display(dict_EN_FR)
- return df_dic
-
-def calc_knn(l_src,l_tgt, metric):
- global df_count_word_src, df_count_word_tgt, nb_mots_src, nb_mots_tgt
-
- #Définition de la metrique (pour les 2 dictionnaires
- knn_metric = metric # minkowski, cosine, chebyshev, manhattan, euclidean
-
- # Algorithme de KNN
- X_train = df_count_word_tgt.T
- y_train = range(nb_mots_tgt)
-
- # Création du classifieur et construction du modèle sur les données d'entraînement
- knn = KNeighborsClassifier(n_neighbors=1, metric=knn_metric)
- knn.fit(X_train, y_train)
-
- # Création et affichage du dictionnaire
- df_dic = pd.DataFrame(data=df_count_word_tgt.columns[knn.predict(df_count_word_src.T)],index=df_count_word_src.T.index,columns=[l_tgt])
- df_dic.index.name = l_src
- df_dic = df_dic.T
-
- # print("Dictionnaire Anglais -> Français:")
- # translation_quality['Précision du dictionnaire'].loc['KNN EN->FR'] =round(accuracy(dict_EN_FR_ref,knn_dict_EN_FR)*100, 2)
- # print(f"Précision du dictionnaire = {translation_quality['Précision du dictionnaire'].loc['KNN EN->FR']}%")
- # display(knn_dict_EN_FR)
- return df_dic
-
-def calc_rf(l_src,l_tgt):
-
- # Algorithme de Random Forest
- X_train = df_count_word_tgt.T
- y_train = range(nb_mots_tgt)
-
- # Création du classifieur et construction du modèle sur les données d'entraînement
- rf = RandomForestClassifier(n_jobs=-1, random_state=321)
- rf.fit(X_train, y_train)
-
- # Création et affichage du dictionnaire
- df_dic = pd.DataFrame(data=df_count_word_tgt.columns[rf.predict(df_count_word_src.T)],index=df_count_word_src.T.index,columns=[l_tgt])
- df_dic.index.name= l_src
- df_dic = df_dic.T
-
- # print("Dictionnaire Anglais -> Français:")
- # translation_quality['Précision du dictionnaire'].loc['RF EN->FR'] = round(accuracy(dict_EN_FR_ref,rf_dict_EN_FR)*100, 2)
- # print(f"Précision du dictionnaire = {translation_quality['Précision du dictionnaire'].loc['RF EN->FR']}%")
- # display(rf_dict_EN_FR)
- return df_dic
-
-def calcul_dic(Lang,Algo,Metrique):
-
- if Lang[:2]=='en':
- l_src = 'Anglais'
- l_tgt = 'Francais'
- else:
- l_src = 'Francais'
- l_tgt = 'Anglais'
-
- if Algo=='Manuel':
- df_dic = pd.read_csv('../data/dict_ref_'+Lang+'.csv',header=0,index_col=0, encoding ="utf-8", sep=';',keep_default_na=False).T.sort_index(axis=1)
- elif Algo=='KMeans':
- df_dic = calc_kmeans(l_src,l_tgt)
- elif Algo=='KNN':
- df_dic = calc_knn(l_src,l_tgt, Metrique)
- elif Algo=='Random Forest':
- df_dic = calc_rf(l_src,l_tgt)
- else:
- df_dic = pd.read_csv('../data/dict_ref_'+Lang+'.csv',header=0,index_col=0, encoding ="utf-8", sep=';',keep_default_na=False).T.sort_index(axis=1)
- return df_dic
-"""
-def load_dic(Lang,Algo,Metrique):
-
- Algo = Algo.lower()
- if Algo=='random forest' : Algo = "rf"
- else:
- if Algo=='word embedding' : Algo = "we"
- else:
- if Algo!='knn': Metrique = ''
- else: Metrique = Metrique+'_'
- input_file = os.path.join('data/dict_'+Algo+'_'+Metrique+Lang)
- return pd.read_csv(input_file, encoding="utf-8", index_col=0).T.sort_index(axis=1)
-# ============
-
-def display_translation(n1,dict, Lang):
- global df_data_src, df_data_tgt, placeholder
-
- s = df_data_src.iloc[n1:n1+5][0].tolist()
- s_trad = []
- s_trad_ref = df_data_tgt.iloc[n1:n1+5][0].tolist()
- source = Lang[:2]
- target = Lang[-2:]
- for i in range(5):
- # for col in s.split():
- # st.write('col: '+col)
- # st.write('dict[col]! '+dict[col])
- s_trad.append((' '.join(dict[col].iloc[0] for col in s[i].split())))
- st.write("**"+source+" :** :blue["+ s[i]+"]")
- st.write("**"+target+" :** "+s_trad[-1])
- st.write("**ref. :** "+s_trad_ref[i])
- st.write("")
- with placeholder:
- st.write("Score Bleu = "+str(int(round(corpus_bleu(s_trad,[s_trad_ref]).score,0)))+"%
", \
- unsafe_allow_html=True)
-
-def display_dic(df_dic):
- st.dataframe(df_dic.T, height=600)
-
-
-def run():
- global n1, df_data_src, df_data_tgt, df_data_en, df_data_fr, placeholder # , df_count_word_src, df_count_word_tgt, nb_mots_src, nb_mots_tgt
- # global nb_mots_en, df_count_word_en, df_count_word_fr, nb_mots_en, nb_mots_fr
-
- st.title(title)
-
- #
- st.write("## **Explications :**\n")
- st.markdown(
- """
- Dans une première approche naïve, nous avons implémenté un système de traduction mot à mot.
- Cette traduction est réalisée grâce à un dictionnaire qui associe un mot de la langue source à un mot de la langue cible, dans small_vocab
- Ce dictionnaire est calculé de 3 manières:
- * :red[**Manuellement**] en choisissant pour chaque mot source le mot cible. Ceci nous a permis de définir un dictionnaire de référence
- * Avec le :red[**Bag Of World**] (chaque mot dans la langue cible = une classe, BOW = features)
- """)
- st.image("assets/BOW.jpg",use_column_width=True)
- st.markdown(
- """
- * Avec le :red[**Word Embedding**], c'est à dire en associant chaque mot à un vecteur "sémantique" de dimensions=300, et en selectionnant le vecteur de langue cible
- le plus proche du vecteur de langue source.
-
- Enfin nous calculons:
- * la :red[**précision**] du dictionnaire par rapport à notre dictionnaire de réference (manuel)
- * le :red[**score BLEU**] ("BiLingual Evaluation Understudy"), qui mesure la précision de notre traduction par rapport à celle de notre corpus référence.
- """
- )
- #
- st.write("## **Paramètres :**\n")
- Sens = st.radio('Sens :',('Anglais -> Français','Français -> Anglais'), horizontal=True)
- Lang = ('en_fr' if Sens=='Anglais -> Français' else 'fr_en')
- Algo = st.radio('Algorithme :',('Manuel', 'KMeans','KNN','Random Forest','Word Embedding'), horizontal=True)
- Metrique = ''
- if (Algo == 'KNN'):
- Metrique = st.radio('Metrique:',('minkowski', 'cosine', 'chebyshev', 'manhattan', 'euclidean'), horizontal=True)
-
- if (Lang=='en_fr'):
- df_data_src = df_data_en
- df_data_tgt = df_data_fr
- # df_count_word_src = df_count_word_en
- # df_count_word_tgt = df_count_word_fr
- # nb_mots_src = nb_mots_en
- # nb_mots_tgt = nb_mots_fr
- else:
- df_data_src = df_data_fr
- df_data_tgt = df_data_en
- # df_count_word_src = df_count_word_fr
- # df_count_word_tgt = df_count_word_en
- # nb_mots_src = nb_mots_fr
- # nb_mots_tgt = nb_mots_en
-
- # df_data_src.columns = ['Phrase']
- sentence1 = st.selectbox("Selectionnez la 1ere des 5 phrases à traduire avec le dictionnaire sélectionné", df_data_src.iloc[:-4],index=int(n1) )
- n1 = df_data_src[df_data_src[0]==sentence1].index.values[0]
-
- df_dic = load_dic(Lang,Algo,Metrique)
- df_dic_ref = load_dic(Lang,'Manuel',Metrique)
- st.write("## **Dictionnaire calculé et traduction mot à mot :**\n")
- col1, col2 = st.columns([0.25, 0.75])
- with col1:
- st.write("#### **Dictionnaire**")
- precision = int(round(accuracy(df_dic_ref,df_dic)*100, 0))
- st.write("Précision = {:2d}%
".format(precision), unsafe_allow_html=True)
- display_dic(df_dic)
- with col2:
- st.write("#### **Traduction**")
- placeholder = st.empty()
- display_translation(n1, df_dic, Lang)
diff --git a/spaces/Dragonnnext/Unicorn-proxy/Dockerfile b/spaces/Dragonnnext/Unicorn-proxy/Dockerfile
deleted file mode 100644
index 97eed882cd9fb47d4d06f4ca56ef3517e29baa19..0000000000000000000000000000000000000000
--- a/spaces/Dragonnnext/Unicorn-proxy/Dockerfile
+++ /dev/null
@@ -1,11 +0,0 @@
-FROM node:18-bullseye-slim
-RUN apt-get update && \
- apt-get install -y git
-RUN git clone https://gitgud.io/Drago/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" ]
diff --git a/spaces/ECCV2022/bytetrack/tutorials/ctracker/byte_tracker.py b/spaces/ECCV2022/bytetrack/tutorials/ctracker/byte_tracker.py
deleted file mode 100644
index 0a6ae80119025c0b9b35419ab4ccb5a107b25c0e..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/bytetrack/tutorials/ctracker/byte_tracker.py
+++ /dev/null
@@ -1,343 +0,0 @@
-import numpy as np
-from collections import deque
-import os
-import os.path as osp
-import copy
-import torch
-import torch.nn.functional as F
-
-from mot_online.kalman_filter import KalmanFilter
-from mot_online.basetrack import BaseTrack, TrackState
-from mot_online import matching
-
-
-
-class STrack(BaseTrack):
- shared_kalman = KalmanFilter()
- def __init__(self, tlwh, score):
-
- # wait activate
- self._tlwh = np.asarray(tlwh, dtype=np.float)
- self.kalman_filter = None
- self.mean, self.covariance = None, None
- self.is_activated = False
-
- self.score = score
- self.tracklet_len = 0
-
- def predict(self):
- mean_state = self.mean.copy()
- if self.state != TrackState.Tracked:
- mean_state[7] = 0
- self.mean, self.covariance = self.kalman_filter.predict(mean_state, self.covariance)
-
- @staticmethod
- def multi_predict(stracks):
- if len(stracks) > 0:
- multi_mean = np.asarray([st.mean.copy() for st in stracks])
- multi_covariance = np.asarray([st.covariance for st in stracks])
- for i, st in enumerate(stracks):
- if st.state != TrackState.Tracked:
- multi_mean[i][7] = 0
- multi_mean, multi_covariance = STrack.shared_kalman.multi_predict(multi_mean, multi_covariance)
- for i, (mean, cov) in enumerate(zip(multi_mean, multi_covariance)):
- stracks[i].mean = mean
- stracks[i].covariance = cov
-
- def activate(self, kalman_filter, frame_id):
- """Start a new tracklet"""
- self.kalman_filter = kalman_filter
- self.track_id = self.next_id()
- self.mean, self.covariance = self.kalman_filter.initiate(self.tlwh_to_xyah(self._tlwh))
-
- self.tracklet_len = 0
- self.state = TrackState.Tracked
- if frame_id == 1:
- self.is_activated = True
- # self.is_activated = True
- self.frame_id = frame_id
- self.start_frame = frame_id
-
- def re_activate(self, new_track, frame_id, new_id=False):
- self.mean, self.covariance = self.kalman_filter.update(
- self.mean, self.covariance, self.tlwh_to_xyah(new_track.tlwh)
- )
- self.tracklet_len = 0
- self.state = TrackState.Tracked
- self.is_activated = True
- self.frame_id = frame_id
- if new_id:
- self.track_id = self.next_id()
- self.score = new_track.score
-
- def update(self, new_track, frame_id):
- """
- Update a matched track
- :type new_track: STrack
- :type frame_id: int
- :type update_feature: bool
- :return:
- """
- self.frame_id = frame_id
- self.tracklet_len += 1
-
- new_tlwh = new_track.tlwh
- self.mean, self.covariance = self.kalman_filter.update(
- self.mean, self.covariance, self.tlwh_to_xyah(new_tlwh))
- self.state = TrackState.Tracked
- self.is_activated = True
-
- self.score = new_track.score
-
- @property
- # @jit(nopython=True)
- def tlwh(self):
- """Get current position in bounding box format `(top left x, top left y,
- width, height)`.
- """
- if self.mean is None:
- return self._tlwh.copy()
- ret = self.mean[:4].copy()
- ret[2] *= ret[3]
- ret[:2] -= ret[2:] / 2
- return ret
-
- @property
- # @jit(nopython=True)
- def tlbr(self):
- """Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
- `(top left, bottom right)`.
- """
- ret = self.tlwh.copy()
- ret[2:] += ret[:2]
- return ret
-
- @staticmethod
- # @jit(nopython=True)
- def tlwh_to_xyah(tlwh):
- """Convert bounding box to format `(center x, center y, aspect ratio,
- height)`, where the aspect ratio is `width / height`.
- """
- ret = np.asarray(tlwh).copy()
- ret[:2] += ret[2:] / 2
- ret[2] /= ret[3]
- return ret
-
- def to_xyah(self):
- return self.tlwh_to_xyah(self.tlwh)
-
- @staticmethod
- # @jit(nopython=True)
- def tlbr_to_tlwh(tlbr):
- ret = np.asarray(tlbr).copy()
- ret[2:] -= ret[:2]
- return ret
-
- @staticmethod
- # @jit(nopython=True)
- def tlwh_to_tlbr(tlwh):
- ret = np.asarray(tlwh).copy()
- ret[2:] += ret[:2]
- return ret
-
- def __repr__(self):
- return 'OT_{}_({}-{})'.format(self.track_id, self.start_frame, self.end_frame)
-
-
-class BYTETracker(object):
- def __init__(self, frame_rate=30):
- self.tracked_stracks = [] # type: list[STrack]
- self.lost_stracks = [] # type: list[STrack]
- self.removed_stracks = [] # type: list[STrack]
-
- self.frame_id = 0
-
- self.low_thresh = 0.2
- self.track_thresh = 0.4
- self.det_thresh = self.track_thresh + 0.1
-
-
- self.buffer_size = int(frame_rate / 30.0 * 30)
- self.max_time_lost = self.buffer_size
- self.kalman_filter = KalmanFilter()
-
-# def update(self, output_results):
- def update(self, det_bboxes, scores):
-
- self.frame_id += 1
- activated_starcks = []
- refind_stracks = []
- lost_stracks = []
- removed_stracks = []
-
-# scores = output_results[:, 4]
-# bboxes = output_results[:, :4] # x1y1x2y2
- scores = scores
- bboxes = det_bboxes
-
- remain_inds = scores > self.track_thresh
- dets = bboxes[remain_inds]
- scores_keep = scores[remain_inds]
-
-
- inds_low = scores > self.low_thresh
- inds_high = scores < self.track_thresh
- inds_second = np.logical_and(inds_low, inds_high)
- dets_second = bboxes[inds_second]
- scores_second = scores[inds_second]
-
-
- if len(dets) > 0:
- '''Detections'''
- detections = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for
- (tlbr, s) in zip(dets, scores_keep)]
- else:
- detections = []
-
- ''' Add newly detected tracklets to tracked_stracks'''
- unconfirmed = []
- tracked_stracks = [] # type: list[STrack]
- for track in self.tracked_stracks:
- if not track.is_activated:
- unconfirmed.append(track)
- else:
- tracked_stracks.append(track)
-
- ''' Step 2: First association, with Kalman and IOU'''
- strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)
- # Predict the current location with KF
- STrack.multi_predict(strack_pool)
- dists = matching.iou_distance(strack_pool, detections)
- matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.8)
-
- for itracked, idet in matches:
- track = strack_pool[itracked]
- det = detections[idet]
- if track.state == TrackState.Tracked:
- track.update(detections[idet], self.frame_id)
- activated_starcks.append(track)
- else:
- track.re_activate(det, self.frame_id, new_id=False)
- refind_stracks.append(track)
-
- ''' Step 3: Second association, with IOU'''
- # association the untrack to the low score detections
- if len(dets_second) > 0:
- '''Detections'''
- detections_second = [STrack(STrack.tlbr_to_tlwh(tlbr), s) for
- (tlbr, s) in zip(dets_second, scores_second)]
- else:
- detections_second = []
- r_tracked_stracks = [strack_pool[i] for i in u_track if strack_pool[i].state == TrackState.Tracked]
- dists = matching.iou_distance(r_tracked_stracks, detections_second)
- matches, u_track, u_detection_second = matching.linear_assignment(dists, thresh=0.5)
- for itracked, idet in matches:
- track = r_tracked_stracks[itracked]
- det = detections_second[idet]
- if track.state == TrackState.Tracked:
- track.update(det, self.frame_id)
- activated_starcks.append(track)
- else:
- track.re_activate(det, self.frame_id, new_id=False)
- refind_stracks.append(track)
-
- for it in u_track:
- #track = strack_pool[it]
- track = r_tracked_stracks[it]
- if not track.state == TrackState.Lost:
- track.mark_lost()
- lost_stracks.append(track)
-
- '''Deal with unconfirmed tracks, usually tracks with only one beginning frame'''
- detections = [detections[i] for i in u_detection]
- dists = matching.iou_distance(unconfirmed, detections)
- matches, u_unconfirmed, u_detection = matching.linear_assignment(dists, thresh=0.7)
- for itracked, idet in matches:
- unconfirmed[itracked].update(detections[idet], self.frame_id)
- activated_starcks.append(unconfirmed[itracked])
- for it in u_unconfirmed:
- track = unconfirmed[it]
- track.mark_removed()
- removed_stracks.append(track)
-
- """ Step 4: Init new stracks"""
- for inew in u_detection:
- track = detections[inew]
- if track.score < self.det_thresh:
- continue
- track.activate(self.kalman_filter, self.frame_id)
- activated_starcks.append(track)
- """ Step 5: Update state"""
- for track in self.lost_stracks:
- if self.frame_id - track.end_frame > self.max_time_lost:
- track.mark_removed()
- removed_stracks.append(track)
-
- # print('Ramained match {} s'.format(t4-t3))
-
- self.tracked_stracks = [t for t in self.tracked_stracks if t.state == TrackState.Tracked]
- self.tracked_stracks = joint_stracks(self.tracked_stracks, activated_starcks)
- self.tracked_stracks = joint_stracks(self.tracked_stracks, refind_stracks)
- self.lost_stracks = sub_stracks(self.lost_stracks, self.tracked_stracks)
- self.lost_stracks.extend(lost_stracks)
- self.lost_stracks = sub_stracks(self.lost_stracks, self.removed_stracks)
- self.removed_stracks.extend(removed_stracks)
- self.tracked_stracks, self.lost_stracks = remove_duplicate_stracks(self.tracked_stracks, self.lost_stracks)
- # get scores of lost tracks
- output_stracks = [track for track in self.tracked_stracks if track.is_activated]
-
- return output_stracks
-
-
-
-def joint_stracks(tlista, tlistb):
- exists = {}
- res = []
- for t in tlista:
- exists[t.track_id] = 1
- res.append(t)
- for t in tlistb:
- tid = t.track_id
- if not exists.get(tid, 0):
- exists[tid] = 1
- res.append(t)
- return res
-
-
-def sub_stracks(tlista, tlistb):
- stracks = {}
- for t in tlista:
- stracks[t.track_id] = t
- for t in tlistb:
- tid = t.track_id
- if stracks.get(tid, 0):
- del stracks[tid]
- return list(stracks.values())
-
-
-def remove_duplicate_stracks(stracksa, stracksb):
- pdist = matching.iou_distance(stracksa, stracksb)
- pairs = np.where(pdist < 0.15)
- dupa, dupb = list(), list()
- for p, q in zip(*pairs):
- timep = stracksa[p].frame_id - stracksa[p].start_frame
- timeq = stracksb[q].frame_id - stracksb[q].start_frame
- if timep > timeq:
- dupb.append(q)
- else:
- dupa.append(p)
- resa = [t for i, t in enumerate(stracksa) if not i in dupa]
- resb = [t for i, t in enumerate(stracksb) if not i in dupb]
- return resa, resb
-
-
-def remove_fp_stracks(stracksa, n_frame=10):
- remain = []
- for t in stracksa:
- score_5 = t.score_list[-n_frame:]
- score_5 = np.array(score_5, dtype=np.float32)
- index = score_5 < 0.45
- num = np.sum(index)
- if num < n_frame:
- remain.append(t)
- return remain
diff --git a/spaces/FrankZxShen/vits-fast-finetuning-pcr/models.py b/spaces/FrankZxShen/vits-fast-finetuning-pcr/models.py
deleted file mode 100644
index 65f9ae5255616efa19a4f28bc0a840d4c453a060..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/vits-fast-finetuning-pcr/models.py
+++ /dev/null
@@ -1,722 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-import monotonic_align
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class StochasticDurationPredictor(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
- super().__init__()
- filter_channels = in_channels # it needs to be removed from future version.
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.log_flow = modules.Log()
- self.flows = nn.ModuleList()
- self.flows.append(modules.ElementwiseAffine(2))
- for i in range(n_flows):
- self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
- self.flows.append(modules.Flip())
-
- self.post_pre = nn.Conv1d(1, filter_channels, 1)
- self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
- self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
- self.post_flows = nn.ModuleList()
- self.post_flows.append(modules.ElementwiseAffine(2))
- for i in range(4):
- self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
- self.post_flows.append(modules.Flip())
-
- self.pre = nn.Conv1d(in_channels, filter_channels, 1)
- self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
- self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
- def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
- x = torch.detach(x)
- x = self.pre(x)
- if g is not None:
- g = torch.detach(g)
- x = x + self.cond(g)
- x = self.convs(x, x_mask)
- x = self.proj(x) * x_mask
-
- if not reverse:
- flows = self.flows
- assert w is not None
-
- logdet_tot_q = 0
- h_w = self.post_pre(w)
- h_w = self.post_convs(h_w, x_mask)
- h_w = self.post_proj(h_w) * x_mask
- e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
- z_q = e_q
- for flow in self.post_flows:
- z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
- logdet_tot_q += logdet_q
- z_u, z1 = torch.split(z_q, [1, 1], 1)
- u = torch.sigmoid(z_u) * x_mask
- z0 = (w - u) * x_mask
- logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
- logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
-
- logdet_tot = 0
- z0, logdet = self.log_flow(z0, x_mask)
- logdet_tot += logdet
- z = torch.cat([z0, z1], 1)
- for flow in flows:
- z, logdet = flow(z, x_mask, g=x, reverse=reverse)
- logdet_tot = logdet_tot + logdet
- nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
- return nll + logq # [b]
- else:
- flows = list(reversed(self.flows))
- flows = flows[:-2] + [flows[-1]] # remove a useless vflow
- z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
- for flow in flows:
- z = flow(z, x_mask, g=x, reverse=reverse)
- z0, z1 = torch.split(z, [1, 1], 1)
- logw = z0
- return logw
-
-
-class DurationPredictor(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
- super().__init__()
-
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.gin_channels = gin_channels
-
- self.drop = nn.Dropout(p_dropout)
- self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
- self.norm_1 = modules.LayerNorm(filter_channels)
- self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
- self.norm_2 = modules.LayerNorm(filter_channels)
- self.proj = nn.Conv1d(filter_channels, 1, 1)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
- def forward(self, x, x_mask, g=None):
- x = torch.detach(x)
- if g is not None:
- g = torch.detach(g)
- x = x + self.cond(g)
- x = self.conv_1(x * x_mask)
- x = torch.relu(x)
- x = self.norm_1(x)
- x = self.drop(x)
- x = self.conv_2(x * x_mask)
- x = torch.relu(x)
- x = self.norm_2(x)
- x = self.drop(x)
- x = self.proj(x * x_mask)
- return x * x_mask
-
-
-class TextEncoder(nn.Module):
- def __init__(self,
- n_vocab,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout):
- super().__init__()
- self.n_vocab = n_vocab
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
-
- self.emb = nn.Embedding(n_vocab, hidden_channels)
- nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
-
- self.encoder = attentions.Encoder(
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout)
- self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths):
- x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
- x = self.encoder(x * x_mask, x_mask)
- stats = self.proj(x) * x_mask
-
- m, logs = torch.split(stats, self.out_channels, dim=1)
- return x, m, logs, x_mask
-
-
-class TextEncoder_lora(nn.Module):
- def __init__(self,
- n_vocab,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout):
- super().__init__()
- self.n_vocab = n_vocab
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
-
- self.emb = nn.Embedding(n_vocab, hidden_channels, r=4)
- nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
-
- self.encoder = attentions.Encoder_lora(
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout)
- self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths):
- x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
- x = self.encoder(x * x_mask, x_mask)
- stats = self.proj(x) * x_mask
-
- m, logs = torch.split(stats, self.out_channels, dim=1)
- return x, m, logs, x_mask
-
-class ResidualCouplingBlock(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- n_flows=4,
- gin_channels=0):
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.flows = nn.ModuleList()
- for i in range(n_flows):
- self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
- self.flows.append(modules.Flip())
-
- def forward(self, x, x_mask, g=None, reverse=False):
- if not reverse:
- for flow in self.flows:
- x, _ = flow(x, x_mask, g=g, reverse=reverse)
- else:
- for flow in reversed(self.flows):
- x = flow(x, x_mask, g=g, reverse=reverse)
- return x
-
-
-class PosteriorEncoder(nn.Module):
- def __init__(self,
- in_channels,
- out_channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
-
- self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
- self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
- self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths, g=None):
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
- x = self.pre(x) * x_mask
- x = self.enc(x, x_mask, g=g)
- stats = self.proj(x) * x_mask
- m, logs = torch.split(stats, self.out_channels, dim=1)
- z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
- return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
- def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
- super(Generator, self).__init__()
- self.num_kernels = len(resblock_kernel_sizes)
- self.num_upsamples = len(upsample_rates)
- self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
- resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
- self.ups = nn.ModuleList()
- for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
- self.ups.append(weight_norm(
- ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
- k, u, padding=(k-u)//2)))
-
- self.resblocks = nn.ModuleList()
- for i in range(len(self.ups)):
- ch = upsample_initial_channel//(2**(i+1))
- for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
- self.resblocks.append(resblock(ch, k, d))
-
- self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
- self.ups.apply(init_weights)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
- def forward(self, x, g=None):
- x = self.conv_pre(x)
- if g is not None:
- x = x + self.cond(g)
-
- for i in range(self.num_upsamples):
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- x = self.ups[i](x)
- xs = None
- for j in range(self.num_kernels):
- if xs is None:
- xs = self.resblocks[i*self.num_kernels+j](x)
- else:
- xs += self.resblocks[i*self.num_kernels+j](x)
- x = xs / self.num_kernels
- x = F.leaky_relu(x)
- x = self.conv_post(x)
- x = torch.tanh(x)
-
- return x
-
- def remove_weight_norm(self):
- print('Removing weight norm...')
- for l in self.ups:
- remove_weight_norm(l)
- for l in self.resblocks:
- l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
- def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
- super(DiscriminatorP, self).__init__()
- self.period = period
- self.use_spectral_norm = use_spectral_norm
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
- norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
- ])
- self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
- def forward(self, x):
- fmap = []
-
- # 1d to 2d
- b, c, t = x.shape
- if t % self.period != 0: # pad first
- n_pad = self.period - (t % self.period)
- x = F.pad(x, (0, n_pad), "reflect")
- t = t + n_pad
- x = x.view(b, c, t // self.period, self.period)
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(DiscriminatorS, self).__init__()
- norm_f = weight_norm if use_spectral_norm == False else spectral_norm
- self.convs = nn.ModuleList([
- norm_f(Conv1d(1, 16, 15, 1, padding=7)),
- norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
- norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
- norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
- norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
- norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
- ])
- self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
- def forward(self, x):
- fmap = []
-
- for l in self.convs:
- x = l(x)
- x = F.leaky_relu(x, modules.LRELU_SLOPE)
- fmap.append(x)
- x = self.conv_post(x)
- fmap.append(x)
- x = torch.flatten(x, 1, -1)
-
- return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
- def __init__(self, use_spectral_norm=False):
- super(MultiPeriodDiscriminator, self).__init__()
- periods = [2,3,5,7,11]
-
- discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
- discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
- self.discriminators = nn.ModuleList(discs)
-
- def forward(self, y, y_hat):
- y_d_rs = []
- y_d_gs = []
- fmap_rs = []
- fmap_gs = []
- for i, d in enumerate(self.discriminators):
- y_d_r, fmap_r = d(y)
- y_d_g, fmap_g = d(y_hat)
- y_d_rs.append(y_d_r)
- y_d_gs.append(y_d_g)
- fmap_rs.append(fmap_r)
- fmap_gs.append(fmap_g)
-
- return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-
-class SynthesizerTrn(nn.Module):
- """
- Synthesizer for Training
- """
-
- def __init__(self,
- n_vocab,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- n_speakers=0,
- gin_channels=0,
- use_sdp=True,
- **kwargs):
-
- super().__init__()
- self.n_vocab = n_vocab
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.n_speakers = n_speakers
- self.gin_channels = gin_channels
-
- self.use_sdp = use_sdp
-
- self.enc_p = TextEncoder(n_vocab,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout)
- self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
- self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
- self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
- if use_sdp:
- self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
- else:
- self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
- if n_speakers >= 1:
- self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
- def forward(self, x, x_lengths, y, y_lengths, sid=None):
-
- x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
- if self.n_speakers > 0:
- g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
- else:
- g = None
-
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
- z_p = self.flow(z, y_mask, g=g)
-
- with torch.no_grad():
- # negative cross-entropy
- s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
- neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
- neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
- neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
- neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
- neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
-
- attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
- attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
-
- w = attn.sum(2)
- if self.use_sdp:
- l_length = self.dp(x, x_mask, w, g=g)
- l_length = l_length / torch.sum(x_mask)
- else:
- logw_ = torch.log(w + 1e-6) * x_mask
- logw = self.dp(x, x_mask, g=g)
- l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging
-
- # expand prior
- m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
- logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
-
- z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
- o = self.dec(z_slice, g=g)
- return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
- x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
- if self.n_speakers > 0:
- g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
- else:
- g = None
-
- if self.use_sdp:
- logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
- else:
- logw = self.dp(x, x_mask, g=g)
- w = torch.exp(logw) * x_mask * length_scale
- w_ceil = torch.ceil(w)
- y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
- y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
- attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
- attn = commons.generate_path(w_ceil, attn_mask)
-
- m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
- logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-
- z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
- z = self.flow(z_p, y_mask, g=g, reverse=True)
- o = self.dec((z * y_mask)[:,:,:max_len], g=g)
- return o, attn, y_mask, (z, z_p, m_p, logs_p)
-
- def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
- assert self.n_speakers > 0, "n_speakers have to be larger than 0."
- g_src = self.emb_g(sid_src).unsqueeze(-1)
- g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
- z_p = self.flow(z, y_mask, g=g_src)
- z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
- o_hat = self.dec(z_hat * y_mask, g=g_tgt)
- return o_hat, y_mask, (z, z_p, z_hat)
-
-
-class SynthesizerTrn_lora(nn.Module):
- """
- Synthesizer for Training
- """
-
- def __init__(self,
- n_vocab,
- spec_channels,
- segment_size,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout,
- resblock,
- resblock_kernel_sizes,
- resblock_dilation_sizes,
- upsample_rates,
- upsample_initial_channel,
- upsample_kernel_sizes,
- n_speakers=0,
- gin_channels=0,
- use_sdp=True,
- **kwargs):
-
- super().__init__()
- self.n_vocab = n_vocab
- self.spec_channels = spec_channels
- self.inter_channels = inter_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.resblock = resblock
- self.resblock_kernel_sizes = resblock_kernel_sizes
- self.resblock_dilation_sizes = resblock_dilation_sizes
- self.upsample_rates = upsample_rates
- self.upsample_initial_channel = upsample_initial_channel
- self.upsample_kernel_sizes = upsample_kernel_sizes
- self.segment_size = segment_size
- self.n_speakers = n_speakers
- self.gin_channels = gin_channels
-
- self.use_sdp = use_sdp
-
- self.enc_p = TextEncoder_lora(n_vocab,
- inter_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout)
- self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
- self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
- self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
- if use_sdp:
- self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
- else:
- self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-
- if n_speakers >= 1:
- self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
- def forward(self, x, x_lengths, y, y_lengths, sid=None):
-
- x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
- if self.n_speakers > 0:
- g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
- else:
- g = None
-
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
- z_p = self.flow(z, y_mask, g=g)
-
- with torch.no_grad():
- # negative cross-entropy
- s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t]
- neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s]
- neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
- neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
- neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s]
- neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
-
- attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
- attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
-
- w = attn.sum(2)
- if self.use_sdp:
- l_length = self.dp(x, x_mask, w, g=g)
- l_length = l_length / torch.sum(x_mask)
- else:
- logw_ = torch.log(w + 1e-6) * x_mask
- logw = self.dp(x, x_mask, g=g)
- l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging
-
- # expand prior
- m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
- logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
-
- z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
- o = self.dec(z_slice, g=g)
- return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
-
- def infer(self, x, x_lengths, sid=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None):
- x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
- if self.n_speakers > 0:
- g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
- else:
- g = None
-
- if self.use_sdp:
- logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w)
- else:
- logw = self.dp(x, x_mask, g=g)
- w = torch.exp(logw) * x_mask * length_scale
- w_ceil = torch.ceil(w)
- y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
- y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
- attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
- attn = commons.generate_path(w_ceil, attn_mask)
-
- m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
- logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-
- z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
- z = self.flow(z_p, y_mask, g=g, reverse=True)
- o = self.dec((z * y_mask)[:,:,:max_len], g=g)
- return o, attn, y_mask, (z, z_p, m_p, logs_p)
-
- def voice_conversion(self, y, y_lengths, sid_src, sid_tgt):
- assert self.n_speakers > 0, "n_speakers have to be larger than 0."
- g_src = self.emb_g(sid_src).unsqueeze(-1)
- g_tgt = self.emb_g(sid_tgt).unsqueeze(-1)
- z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src)
- z_p = self.flow(z, y_mask, g=g_src)
- z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True)
- o_hat = self.dec(z_hat * y_mask, g=g_tgt)
- return o_hat, y_mask, (z, z_p, z_hat)
\ No newline at end of file
diff --git a/spaces/FridaZuley/RVC_HFKawaii/colab_for_mdx.py b/spaces/FridaZuley/RVC_HFKawaii/colab_for_mdx.py
deleted file mode 100644
index 274846d0b5395865a05fce0da86b96d26ac06999..0000000000000000000000000000000000000000
--- a/spaces/FridaZuley/RVC_HFKawaii/colab_for_mdx.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import json
-import os
-import gc
-import psutil
-import requests
-import subprocess
-import time
-import logging
-import sys
-import shutil
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-first_cell_executed = False
-file_folder = "Colab-for-MDX_B"
-def first_cell_ran():
- global first_cell_executed
- if first_cell_executed:
- #print("The 'first_cell_ran' function has already been executed.")
- return
-
-
-
- first_cell_executed = True
- os.makedirs("tmp_models", exist_ok=True)
-
-
-
- class hide_opt: # hide outputs
- def __enter__(self):
- self._original_stdout = sys.stdout
- sys.stdout = open(os.devnull, "w")
-
- def __exit__(self, exc_type, exc_val, exc_tb):
- sys.stdout.close()
- sys.stdout = self._original_stdout
-
- def get_size(bytes, suffix="B"): # read ram
- global svmem
- factor = 1024
- for unit in ["", "K", "M", "G", "T", "P"]:
- if bytes < factor:
- return f"{bytes:.2f}{unit}{suffix}"
- bytes /= factor
- svmem = psutil.virtual_memory()
-
-
- def use_uvr_without_saving():
- print("Notice: files won't be saved to personal drive.")
- print(f"Downloading {file_folder}...", end=" ")
- with hide_opt():
- #os.chdir(mounting_path)
- items_to_move = ["demucs", "diffq","julius","model","separated","tracks","mdx.py","MDX-Net_Colab.ipynb"]
- subprocess.run(["git", "clone", "https://github.com/NaJeongMo/Colab-for-MDX_B.git"])
- for item_name in items_to_move:
- item_path = os.path.join(file_folder, item_name)
- if os.path.exists(item_path):
- if os.path.isfile(item_path):
- shutil.move(item_path, now_dir)
- elif os.path.isdir(item_path):
- shutil.move(item_path, now_dir)
- try:
- shutil.rmtree(file_folder)
- except PermissionError:
- print(f"No se pudo eliminar la carpeta {file_folder}. Puede estar relacionada con Git.")
-
-
- use_uvr_without_saving()
- print("done!")
- if not os.path.exists("tracks"):
- os.mkdir("tracks")
-first_cell_ran()
\ No newline at end of file
diff --git a/spaces/Frorozcol/mariposas/utils.py b/spaces/Frorozcol/mariposas/utils.py
deleted file mode 100644
index ddafe6f774e0c9ba0ade822d7a90360ef1bebe79..0000000000000000000000000000000000000000
--- a/spaces/Frorozcol/mariposas/utils.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import numpy as np
-import torch
-from huggan.pytorch.lightweight_gan.lightweight_gan import LightweightGAN
-
-def cargar_mdoel(model_name = "ceyda/butterfly_cropped_uniq1K_512", model_version = None):
- gan = LightweightGAN.from_pretrained(model_name, version = model_version)
- gan.eval()
- return gan
-
-def general(gan, bach_size=1):
- with torch.no_grad():
- ims = gan.G(torch.rand(bach_size, gan.latent_dim)).clamp_(0.0,1.0) * 255
- ims = ims.permute(0,2,3,1).detach().cpu().numpy().astype(np.uint8)
- return ims
-
\ No newline at end of file
diff --git a/spaces/Frorozcol/music_recommedation/src/__init__.py b/spaces/Frorozcol/music_recommedation/src/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/GFXY/Maseshi-Anything-v3.0/README.md b/spaces/GFXY/Maseshi-Anything-v3.0/README.md
deleted file mode 100644
index 97fb91d42f5375717ce4d432bb941efc2075d9e0..0000000000000000000000000000000000000000
--- a/spaces/GFXY/Maseshi-Anything-v3.0/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Maseshi Anything V3.0
-emoji: 🏃
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
-license: agpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/GXSA/bingo/src/components/ui/alert-dialog.tsx b/spaces/GXSA/bingo/src/components/ui/alert-dialog.tsx
deleted file mode 100644
index 17fec4d16510328deacc1416569173c97761ef72..0000000000000000000000000000000000000000
--- a/spaces/GXSA/bingo/src/components/ui/alert-dialog.tsx
+++ /dev/null
@@ -1,150 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as AlertDialogPrimitive from '@radix-ui/react-alert-dialog'
-
-import { cn } from '@/lib/utils'
-import { buttonVariants } from '@/components/ui/button'
-
-const AlertDialog = AlertDialogPrimitive.Root
-
-const AlertDialogTrigger = AlertDialogPrimitive.Trigger
-
-const AlertDialogPortal = ({
- className,
- children,
- ...props
-}: AlertDialogPrimitive.AlertDialogPortalProps) => (
-
-
- {children}
-
-
-)
-AlertDialogPortal.displayName = AlertDialogPrimitive.Portal.displayName
-
-const AlertDialogOverlay = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, children, ...props }, ref) => (
- ,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
-
-
-))
-AlertDialogContent.displayName = AlertDialogPrimitive.Content.displayName
-
-const AlertDialogHeader = ({
- className,
- ...props
-}: React.HTMLAttributes) => (
-
-)
-AlertDialogHeader.displayName = 'AlertDialogHeader'
-
-const AlertDialogFooter = ({
- className,
- ...props
-}: React.HTMLAttributes) => (
-
-)
-AlertDialogFooter.displayName = 'AlertDialogFooter'
-
-const AlertDialogTitle = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- ,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- ,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
- ,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
-
-
-//------------------------------------------------------------------------
-// CUDA kernel parameters.
-
-struct upfirdn2d_kernel_params
-{
- const void* x;
- const float* f;
- void* y;
-
- int2 up;
- int2 down;
- int2 pad0;
- int flip;
- float gain;
-
- int4 inSize; // [width, height, channel, batch]
- int4 inStride;
- int2 filterSize; // [width, height]
- int2 filterStride;
- int4 outSize; // [width, height, channel, batch]
- int4 outStride;
- int sizeMinor;
- int sizeMajor;
-
- int loopMinor;
- int loopMajor;
- int loopX;
- int launchMinor;
- int launchMajor;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel specialization.
-
-struct upfirdn2d_kernel_spec
-{
- void* kernel;
- int tileOutW;
- int tileOutH;
- int loopMinor;
- int loopX;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel selection.
-
-template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p);
-
-//------------------------------------------------------------------------
diff --git a/spaces/Gradio-Blocks/anime-colorization/datasets/README.md b/spaces/Gradio-Blocks/anime-colorization/datasets/README.md
deleted file mode 100644
index 148cfea9a04f0361543b471772f94a9ce3d4c484..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/anime-colorization/datasets/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
-# Downloading datasets
-
-This directory includes instructions and scripts for downloading ImageNet, LSUN bedrooms, and CIFAR-10 for use in this codebase.
-
-## ImageNet-64
-
-To download unconditional ImageNet-64, go to [this page on image-net.org](http://www.image-net.org/small/download.php) and click on "Train (64x64)". Simply download the file and unzip it, and use the resulting directory as the data directory (the `--data_dir` argument for the training script).
-
-## Class-conditional ImageNet
-
-For our class-conditional models, we use the official ILSVRC2012 dataset with manual center cropping and downsampling. To obtain this dataset, navigate to [this page on image-net.org](http://www.image-net.org/challenges/LSVRC/2012/downloads) and sign in (or create an account if you do not already have one). Then click on the link reading "Training images (Task 1 & 2)". This is a 138GB tar file containing 1000 sub-tar files, one per class.
-
-Once the file is downloaded, extract it and look inside. You should see 1000 `.tar` files. You need to extract each of these, which may be impractical to do by hand on your operating system. To automate the process on a Unix-based system, you can `cd` into the directory and run this short shell script:
-
-```
-for file in *.tar; do tar xf "$file"; rm "$file"; done
-```
-
-This will extract and remove each tar file in turn.
-
-Once all of the images have been extracted, the resulting directory should be usable as a data directory (the `--data_dir` argument for the training script). The filenames should all start with WNID (class ids) followed by underscores, like `n01440764_2708.JPEG`. Conveniently (but not by accident) this is how the automated data-loader expects to discover class labels.
-
-## CIFAR-10
-
-For CIFAR-10, we created a script [cifar10.py](cifar10.py) that creates `cifar_train` and `cifar_test` directories. These directories contain files named like `truck_49997.png`, so that the class name is discernable to the data loader.
-
-The `cifar_train` and `cifar_test` directories can be passed directly to the training scripts via the `--data_dir` argument.
-
-## LSUN bedroom
-
-To download and pre-process LSUN bedroom, clone [fyu/lsun](https://github.com/fyu/lsun) on GitHub and run their download script `python3 download.py bedroom`. The result will be an "lmdb" database named like `bedroom_train_lmdb`. You can pass this to our [lsun_bedroom.py](lsun_bedroom.py) script like so:
-
-```
-python lsun_bedroom.py bedroom_train_lmdb lsun_train_output_dir
-```
-
-This creates a directory called `lsun_train_output_dir`. This directory can be passed to the training scripts via the `--data_dir` argument.
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py
deleted file mode 100644
index 9cb3581910f74063eb1c62b9345a6493098d4a4a..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './cascade_rcnn_r50_fpn_20e_coco.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py
deleted file mode 100644
index 9a6cf7e56a4f23a42d3905560a9b8035d6d935ff..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py
+++ /dev/null
@@ -1,5 +0,0 @@
-_base_ = 'retinanet_r50_fpg_crop640_50e_coco.py'
-
-model = dict(
- neck=dict(out_channels=128, inter_channels=128),
- bbox_head=dict(in_channels=128))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/swin/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/swin/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py
deleted file mode 100644
index dd42cba7ca95c008218e966aca6becb2a2dabc8d..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/swin/mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py
+++ /dev/null
@@ -1,80 +0,0 @@
-_base_ = [
- '../_base_/models/mask_rcnn_swin_fpn.py',
- '../_base_/datasets/coco_instance.py',
- '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
-]
-
-model = dict(
- backbone=dict(
- embed_dim=96,
- depths=[2, 2, 6, 2],
- num_heads=[3, 6, 12, 24],
- window_size=7,
- ape=False,
- drop_path_rate=0.1,
- patch_norm=True,
- use_checkpoint=False
- ),
- neck=dict(in_channels=[96, 192, 384, 768]))
-
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-
-# augmentation strategy originates from DETR / Sparse RCNN
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='AutoAugment',
- policies=[
- [
- dict(type='Resize',
- img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
- (608, 1333), (640, 1333), (672, 1333), (704, 1333),
- (736, 1333), (768, 1333), (800, 1333)],
- multiscale_mode='value',
- keep_ratio=True)
- ],
- [
- dict(type='Resize',
- img_scale=[(400, 1333), (500, 1333), (600, 1333)],
- multiscale_mode='value',
- keep_ratio=True),
- dict(type='RandomCrop',
- crop_type='absolute_range',
- crop_size=(384, 600),
- allow_negative_crop=True),
- dict(type='Resize',
- img_scale=[(480, 1333), (512, 1333), (544, 1333),
- (576, 1333), (608, 1333), (640, 1333),
- (672, 1333), (704, 1333), (736, 1333),
- (768, 1333), (800, 1333)],
- multiscale_mode='value',
- override=True,
- keep_ratio=True)
- ]
- ]),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
-]
-data = dict(train=dict(pipeline=train_pipeline))
-
-optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05,
- paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
- 'relative_position_bias_table': dict(decay_mult=0.),
- 'norm': dict(decay_mult=0.)}))
-lr_config = dict(step=[8, 11])
-runner = dict(type='EpochBasedRunnerAmp', max_epochs=12)
-
-# do not use mmdet version fp16
-fp16 = None
-optimizer_config = dict(
- type="DistOptimizerHook",
- update_interval=1,
- grad_clip=None,
- coalesce=True,
- bucket_size_mb=-1,
- use_fp16=True,
-)
diff --git a/spaces/HLasse/textdescriptives/app.py b/spaces/HLasse/textdescriptives/app.py
deleted file mode 100644
index 677cbe9d0dcba8e986b9ce089e7d655ceda0d5e3..0000000000000000000000000000000000000000
--- a/spaces/HLasse/textdescriptives/app.py
+++ /dev/null
@@ -1,257 +0,0 @@
-"""
-Dashboard for showcasing extraction of text metrics with textdescriptives.
-
-"""
-
-from io import StringIO
-
-import pandas as pd
-import streamlit as st
-import textdescriptives as td
-
-from data_viewer import DataViewer
-from process_text import text_to_metrics
-from options import (
- all_model_size_options_pretty_to_short,
- available_model_size_options,
- language_options,
- metrics_options,
-)
-
-################
-# Introduction #
-################
-
-
-col1, col2 = st.columns([9, 2])
-with col1:
- st.title("Extract Text Statistics")
-with col2:
- st.image(
- "https://github.com/HLasse/TextDescriptives/raw/main/docs/_static/icon.png",
- width=125,
- )
-
-st.write(
- "Calculate a large variety of statistics from text via the "
- "[**TextDescriptives**](https://github.com/HLasse/TextDescriptives) python package "
- f"(v/{td.__version__}) and download the results as a .csv file. "
- "Includes descriptive statistics and metrics related to readability, "
- "information theory, text coherence and text quality."
-)
-
-st.write(
- "The source code for this application can be found on [**GitHub**](https://github.com/HLasse/TextDescriptives_app). "
- "If you have feedback, please open an [issue](https://github.com/HLasse/textdescriptives_app/issues)."
-)
-
-st.caption(
- "Hansen, L., Olsen, L. R., & Enevoldsen, K. (2023). TextDescriptives: A Python package for "
- "calculating a large variety of metrics from text. [Journal of Open Source Software, 8(84), "
- "5153, https://doi.org/10.21105/joss.05153](https://doi.org/10.21105/joss.05153)"
-)
-
-
-############
-# Settings #
-############
-
-
-input_choice = st.radio(
- label="Input", options=["Enter text", "Upload file(s)"], index=0, horizontal=True
-)
-
-with st.form(key="settings_form"):
- split_by_line = st.checkbox(label="Split by newline", value=True)
-
- file_name_to_text_string = {}
-
- if input_choice == "Upload file(s)":
- uploaded_files = st.file_uploader(
- label="Choose a .txt file", type=["txt"], accept_multiple_files=True
- )
-
- if uploaded_files is not None and len(uploaded_files) > 0:
- # To convert to a string based IO:
- file_name_to_text_string = {
- file.name: StringIO(file.getvalue().decode("utf-8")).read()
- for file in uploaded_files
- }
-
- else:
- default_text = """Hello, morning dew. The grass whispers low.
-I'm here to dance. The gentle breeze does show.
-Good morning, world. The birds sing in delight.
-Let's spread our wings. The butterflies take flight.
-Nature's chorus sings, a symphony of light."""
-
- file_name_to_text_string = {
- "input": st.text_area(
- label="Enter text", value=default_text, height=145, max_chars=None
- )
- }
-
- # Row of selectors
- col1, col2 = st.columns([1, 1])
-
- with col1:
- # Selection of language
- language_pretty = st.selectbox(
- label="Language",
- options=list(language_options().keys()),
- index=5,
- key="language_selector",
- )
-
- language_short = language_options()[language_pretty]
-
- with col2:
- # Selection of model size
- model_size_pretty = st.selectbox(
- label="Model Size",
- options=available_model_size_options(lang="all"),
- index=0,
- key="size_selector",
- )
-
- model_size_short = all_model_size_options_pretty_to_short()[model_size_pretty]
-
- # Multiselection of metrics
- metrics = st.multiselect(
- label="Metrics", options=metrics_options(), default=metrics_options()
- )
-
- st.write(
- "See the [**documentation**](https://hlasse.github.io/TextDescriptives/) for "
- "information on the available metrics."
- )
-
- # This shouldn't happen but better safe than sorry
- if isinstance(metrics, list) and not metrics:
- metrics = None
-
- apply_settings_button = st.form_submit_button(label="Apply")
-
-
-#############
-# Apply NLP #
-#############
-
-
-if apply_settings_button and len(file_name_to_text_string) > 0:
- if model_size_pretty not in available_model_size_options(lang=language_short):
- st.write(
- "**Sorry!** The chosen *model size* is not available in this language. Please try another."
- )
- else:
- # Extract metrics for each text
- output_df = pd.concat(
- [
- text_to_metrics(
- string=string,
- language_short=language_short,
- model_size_short=model_size_short,
- metrics=metrics,
- split_by_line=split_by_line,
- filename=filename if "Upload" in input_choice else None,
- )
- for filename, string in file_name_to_text_string.items()
- ],
- ignore_index=True,
- )
-
- ###################
- # Present Results #
- ###################
-
- # Create 2 columns with 1) the output header
- # and 2) a download button
- DataViewer()._header_and_download(
- header="The calculated metrics",
- data=output_df,
- file_name="text_metrics.csv",
- )
-
- st.write("**Note**: This data frame has been transposed for readability.")
- output_df = output_df.transpose().reset_index()
- output_df.columns = ["Metric"] + [str(c) for c in list(output_df.columns)[1:]]
- st.dataframe(data=output_df, use_container_width=True)
-
-
-############################
-# Code For Reproducibility #
-############################
-
-
-with st.expander("See python code"):
- st.code(
- """
-# Note: This is the code for a single text file
-# The actual code is slightly more complex
-# to allow processing multiple files at once
-
-import textdescriptives as td
-
-# Given a string of text and the settings
-text = "..."
-language = "..."
-model_size = "..."
-metrics = [...]
-split_by_newline = True
-
-# Remove whitespace from both ends of the string
-text = text.strip()
-
-# When asked, split by newlines
-if split_by_newline:
- lines = text.split("\\n")
-else:
- lines = [text]
-
-# Remove empty lines
-# E.g. due to consecutive newlines
-lines = [l for l in lines if l]
-
-# Extract metrics for each line
-extracted_metrics = td.extract_metrics(
- text=lines,
- lang=language,
- spacy_model_size=model_size,
- metrics=metrics
-)
-
-""",
- language="python",
- )
-
-#######
-# FAQ #
-#######
-
-st.subheader("Frequently Asked Questions (FAQ)")
-
-with st.expander("What does the 'Split by newline' option do?"):
- st.write(
- """
- When the `Split by newline` option is `enabled`, the metrics calculation is
- performed separately for each paragraph. I.e. whenever there's a line break,
- we split the text.
-
- When this option is `disabled`, the entire text is processed at once.
- """
- )
-
-with st.expander(
- "Why do I get a warning/error message for certain languages or model sizes?"
-):
- st.write(
- """
- Some combinations of languages, model sizes, and metrics are not currently supported in the app.
- While we *are* working on this, you may currently see a red box
- with an error message after clicking `Apply`.
-
- If you need this language and/or model size to work for your project,
- please open an [issue](https://github.com/HLasse/textdescriptives_app/issues).
- This may cause us to prioritize supporting your use case.
- """
- )
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/latent_depth/latent_depth_src/models/latent_transformer.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/latent_depth/latent_depth_src/models/latent_transformer.py
deleted file mode 100644
index 6a825301a452bd935deafdaf78fa2427ca9a469e..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/latent_depth/latent_depth_src/models/latent_transformer.py
+++ /dev/null
@@ -1,156 +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 typing import Any, Dict, Optional
-
-import torch.nn as nn
-from fairseq.models.fairseq_encoder import EncoderOut
-from fairseq.models.transformer import TransformerDecoder, TransformerEncoder
-from fairseq.modules import TransformerDecoderLayer, TransformerEncoderLayer
-from torch import Tensor
-
-from ..modules.latent_layers import LayerSelect
-
-
-class LatentTransformerEncoder(TransformerEncoder):
- """Latent depth (https://arxiv.org/abs/2009.13102) implemented in
- TransformerEncoder.
- """
-
- def __init__(self, args, dictionary, embed_tokens, num_logits=1):
- self.num_logits = num_logits
- self.num_layers = args.encoder_layers
- super().__init__(args, dictionary, embed_tokens)
- self.layer_select = LayerSelect(
- num_layers=self.num_layers,
- num_logits=self.num_logits,
- soft_select=getattr(args, "soft_select", False),
- sampling_tau=getattr(args, "sampling_tau", 5.),
- )
- self.lang_idx = None
- self.layers = nn.ModuleList(
- [self._build_encoder_layer(args, idx) for idx in range(args.encoder_layers)]
- )
-
- def set_lang_idx(self, lang_idx):
- self.lang_idx = lang_idx
-
- def _build_encoder_layer(self, args, idx=None):
- return LatentTransformerEncoderLayer(args, idx, layer_select=self.layer_select)
-
- def forward(self, src_tokens, src_lengths, return_all_hiddens: bool = False):
- self.layer_select.sample(self.lang_idx)
- return super().forward(src_tokens, src_lengths, return_all_hiddens)
-
-
-class LatentTransformerEncoderLayer(TransformerEncoderLayer):
- """Encoder layer with each (non_residual) block weighted by samples of Bernouli
- or Gumbel Signmoid samples.
-
- Args:
- args (argparse.Namespace): parsed command-line arguments from standard
- TransformerEncoderLayer.
- idx (int): layer index (used to retrieve samples).
- layer_select (LayerSelect, optional): instance of LayerSelect module with logits
- parameters and sampling method.
- """
-
- def __init__(self, args, idx, layer_select=None):
- super().__init__(args)
- self.idx = idx
- self.layer_select = layer_select
-
- def residual_connection(self, x, residual):
- return residual + x * self.layer_select(self.idx)
-
-
-class LatentTransformerDecoder(TransformerDecoder):
- """Latent depth (https://arxiv.org/abs/2009.13102) implemented in
- TransformerDecoder.
- """
-
- def __init__(
- self, args, dictionary, embed_tokens, no_encoder_attn=False, num_logits=1
- ):
- self.num_logits = num_logits
- self.num_layers = args.decoder_layers
- super().__init__(
- args, dictionary, embed_tokens, no_encoder_attn=no_encoder_attn
- )
- self.layer_select = LayerSelect(
- num_layers=self.num_layers,
- num_logits=self.num_logits,
- soft_select=getattr(args, "soft_select", False),
- sampling_tau=getattr(args, "sampling_tau", 5.),
- )
- self.lang_idx = None
- self.layers = nn.ModuleList(
- [
- self._build_decoder_layer(args, no_encoder_attn, idx)
- for idx in range(args.decoder_layers)
- ]
- )
-
- def set_lang_idx(self, lang_idx):
- self.lang_idx = lang_idx
-
- def _build_decoder_layer(self, args, no_encoder_attn=False, idx=None):
- return LatentTransformerDecoderLayer(
- args, idx, layer_select=self.layer_select, no_encoder_attn=no_encoder_attn
- )
-
- def forward(
- self,
- prev_output_tokens,
- encoder_out: Optional[EncoderOut] = None,
- incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
- features_only: bool = False,
- alignment_layer: Optional[int] = None,
- alignment_heads: Optional[int] = None,
- src_lengths: Optional[Any] = None,
- return_all_hiddens: bool = False,
- ):
- self.layer_select.sample(self.lang_idx)
- return super().forward(
- prev_output_tokens=prev_output_tokens,
- encoder_out=encoder_out,
- incremental_state=incremental_state,
- features_only=features_only,
- alignment_layer=alignment_layer,
- src_lengths=src_lengths,
- return_all_hiddens=return_all_hiddens,
- )
-
-
-class LatentTransformerDecoderLayer(TransformerDecoderLayer):
- """Decoder layer with each (non_residual) block weighted by samples of Bernouli
- or Gumbel Signmoid samples.
-
- Args:
- args (argparse.Namespace): parsed command-line arguments from standard
- TransformerDecoderLayer.
- idx (int): layer index (used to retrieve samples).
- layer_select (LayerSelect, optional): instance of LayerSelect module with logits
- parameters and sampling method.
- no_encoder_attn (bool, optional): whether to attend to encoder outputs
- (default: False).
-
- """
-
- def __init__(
- self,
- args,
- idx,
- layer_select=None,
- no_encoder_attn=False,
- add_bias_kv=False,
- add_zero_attn=False,
- ):
- super().__init__(args, no_encoder_attn, add_bias_kv, add_zero_attn)
- self.idx = idx
- self.layer_select = layer_select
-
- def residual_connection(self, x, residual):
- return residual + x * self.layer_select(self.idx)
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/quantization/pq/modules/qconv.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/quantization/pq/modules/qconv.py
deleted file mode 100644
index d15ec192e8cda6265a198e583a9bf7fb194dd129..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/quantization/pq/modules/qconv.py
+++ /dev/null
@@ -1,115 +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 numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.nn.modules.utils import _pair
-
-
-class PQConv2d(nn.Module):
- """
- Quantized counterpart of nn.Conv2d module. Stores the centroid, the assignments
- and the non-quantized biases. The full weight is re-instantiated at each forward
- pass and autograd automatically computes the gradients with respect to the
- centroids.
-
- Args:
- - centroids: centroids of size n_centroids x block_size
- - assignments: assignments of the centroids to the subvectors
- of size self.out_channels x n_blocks
- - bias: the non-quantized bias, must be either torch.Tensor or None
-
- Remarks:
- - We refer the reader to the official documentation of the nn.Conv2d module
- for the other arguments and the behavior of the module.
- - Performance tests on GPU show that this implementation is 10% slower than
- the non-quantized nn.Conv2d module for a standard training loop.
- - During the backward, the gradients are averaged by cluster and not summed.
- This explains the hook registered to the centroids.
- """
-
- def __init__(
- self,
- centroids,
- assignments,
- bias,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- padding_mode="zeros",
- ):
- super(PQConv2d, self).__init__()
- self.block_size = centroids.size(1)
- self.n_centroids = centroids.size(0)
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.kernel_size = _pair(kernel_size)
- self.stride = _pair(stride)
- self.padding = _pair(padding)
- self.dilation = _pair(dilation)
- self.groups = groups
- self.padding_mode = padding_mode
- # check compatibility
- if in_channels // groups * np.prod(self.kernel_size) % self.block_size != 0:
- raise ValueError("Wrong PQ sizes")
- if len(assignments) % out_channels != 0:
- raise ValueError("Wrong PQ sizes")
- if in_channels % groups != 0:
- raise ValueError("in_channels must be divisible by groups")
- if out_channels % groups != 0:
- raise ValueError("out_channels must be divisible by groups")
- # define parameters
- self.centroids = nn.Parameter(centroids, requires_grad=True)
- self.register_buffer("assignments", assignments)
- self.register_buffer("counts", torch.bincount(assignments).type_as(centroids))
- if bias is not None:
- self.bias = nn.Parameter(bias)
- else:
- self.register_parameter("bias", None)
- # register hook for averaging gradients per centroids instead of summing
- self.centroids.register_hook(lambda x: x / self.counts[:, None])
-
- @property
- def weight(self):
- return (
- self.centroids[self.assignments]
- .reshape(-1, self.out_channels, self.block_size)
- .permute(1, 0, 2)
- .reshape(
- self.out_channels, self.in_channels // self.groups, *self.kernel_size
- )
- )
-
- def forward(self, x):
- return F.conv2d(
- x,
- self.weight,
- self.bias,
- self.stride,
- self.padding,
- self.dilation,
- self.groups,
- )
-
- def extra_repr(self):
- s = "{in_channels}, {out_channels}, kernel_size={kernel_size}, stride={stride}"
- if self.padding != (0,) * len(self.padding):
- s += ", padding={padding}"
- if self.dilation != (1,) * len(self.dilation):
- s += ", dilation={dilation}"
- if self.groups != 1:
- s += ", groups={groups}"
- if self.bias is None:
- s += ", bias=False"
- if self.padding_mode != "zeros":
- s += ", padding_mode={padding_mode}"
- s += ", n_centroids={n_centroids}, block_size={block_size}"
- return s.format(**self.__dict__)
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/tests/speech_recognition/test_cross_entropy.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/tests/speech_recognition/test_cross_entropy.py
deleted file mode 100644
index b05400ed95e22762c3e3e5e8fd3ebfa6caf1e325..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/tests/speech_recognition/test_cross_entropy.py
+++ /dev/null
@@ -1,37 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from examples.speech_recognition.criterions.cross_entropy_acc import (
- CrossEntropyWithAccCriterion,
-)
-
-from .asr_test_base import CrossEntropyCriterionTestBase
-
-
-class CrossEntropyWithAccCriterionTest(CrossEntropyCriterionTestBase):
- def setUp(self):
- self.criterion_cls = CrossEntropyWithAccCriterion
- super().setUp()
-
- def test_cross_entropy_all_correct(self):
- sample = self.get_test_sample(correct=True, soft_target=False, aggregate=False)
- loss, sample_size, logging_output = self.criterion(
- self.model, sample, "sum", log_probs=True
- )
- assert logging_output["correct"] == 20
- assert logging_output["total"] == 20
- assert logging_output["sample_size"] == 20
- assert logging_output["ntokens"] == 20
-
- def test_cross_entropy_all_wrong(self):
- sample = self.get_test_sample(correct=False, soft_target=False, aggregate=False)
- loss, sample_size, logging_output = self.criterion(
- self.model, sample, "sum", log_probs=True
- )
- assert logging_output["correct"] == 0
- assert logging_output["total"] == 20
- assert logging_output["sample_size"] == 20
- assert logging_output["ntokens"] == 20
diff --git a/spaces/Hina4867/bingo/postcss.config.js b/spaces/Hina4867/bingo/postcss.config.js
deleted file mode 100644
index 33ad091d26d8a9dc95ebdf616e217d985ec215b8..0000000000000000000000000000000000000000
--- a/spaces/Hina4867/bingo/postcss.config.js
+++ /dev/null
@@ -1,6 +0,0 @@
-module.exports = {
- plugins: {
- tailwindcss: {},
- autoprefixer: {},
- },
-}
diff --git a/spaces/Hoodady/3DFuse/ldm/models/diffusion/dpm_solver/__init__.py b/spaces/Hoodady/3DFuse/ldm/models/diffusion/dpm_solver/__init__.py
deleted file mode 100644
index 7427f38c07530afbab79154ea8aaf88c4bf70a08..0000000000000000000000000000000000000000
--- a/spaces/Hoodady/3DFuse/ldm/models/diffusion/dpm_solver/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .sampler import DPMSolverSampler
\ No newline at end of file
diff --git a/spaces/Hoodady/3DFuse/ldm/modules/attention.py b/spaces/Hoodady/3DFuse/ldm/modules/attention.py
deleted file mode 100644
index 509cd873768f0dd75a75ab3fcdd652822b12b59f..0000000000000000000000000000000000000000
--- a/spaces/Hoodady/3DFuse/ldm/modules/attention.py
+++ /dev/null
@@ -1,341 +0,0 @@
-from inspect import isfunction
-import math
-import torch
-import torch.nn.functional as F
-from torch import nn, einsum
-from einops import rearrange, repeat
-from typing import Optional, Any
-
-from ldm.modules.diffusionmodules.util import checkpoint
-
-
-try:
- import xformers
- import xformers.ops
- XFORMERS_IS_AVAILBLE = True
-except:
- XFORMERS_IS_AVAILBLE = False
-
-# CrossAttn precision handling
-import os
-_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
-
-def exists(val):
- return val is not None
-
-
-def uniq(arr):
- return{el: True for el in arr}.keys()
-
-
-def default(val, d):
- if exists(val):
- return val
- return d() if isfunction(d) else d
-
-
-def max_neg_value(t):
- return -torch.finfo(t.dtype).max
-
-
-def init_(tensor):
- dim = tensor.shape[-1]
- std = 1 / math.sqrt(dim)
- tensor.uniform_(-std, std)
- return tensor
-
-
-# feedforward
-class GEGLU(nn.Module):
- def __init__(self, dim_in, dim_out):
- super().__init__()
- self.proj = nn.Linear(dim_in, dim_out * 2)
-
- def forward(self, x):
- x, gate = self.proj(x).chunk(2, dim=-1)
- return x * F.gelu(gate)
-
-
-class FeedForward(nn.Module):
- def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
- super().__init__()
- inner_dim = int(dim * mult)
- dim_out = default(dim_out, dim)
- project_in = nn.Sequential(
- nn.Linear(dim, inner_dim),
- nn.GELU()
- ) if not glu else GEGLU(dim, inner_dim)
-
- self.net = nn.Sequential(
- project_in,
- nn.Dropout(dropout),
- nn.Linear(inner_dim, dim_out)
- )
-
- def forward(self, x):
- return self.net(x)
-
-
-def zero_module(module):
- """
- Zero out the parameters of a module and return it.
- """
- for p in module.parameters():
- p.detach().zero_()
- return module
-
-
-def Normalize(in_channels):
- return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class SpatialSelfAttention(nn.Module):
- def __init__(self, in_channels):
- super().__init__()
- self.in_channels = in_channels
-
- self.norm = Normalize(in_channels)
- self.q = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.k = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.v = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.proj_out = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
-
- def forward(self, x):
- h_ = x
- h_ = self.norm(h_)
- q = self.q(h_)
- k = self.k(h_)
- v = self.v(h_)
-
- # compute attention
- b,c,h,w = q.shape
- q = rearrange(q, 'b c h w -> b (h w) c')
- k = rearrange(k, 'b c h w -> b c (h w)')
- w_ = torch.einsum('bij,bjk->bik', q, k)
-
- w_ = w_ * (int(c)**(-0.5))
- w_ = torch.nn.functional.softmax(w_, dim=2)
-
- # attend to values
- v = rearrange(v, 'b c h w -> b c (h w)')
- w_ = rearrange(w_, 'b i j -> b j i')
- h_ = torch.einsum('bij,bjk->bik', v, w_)
- h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
- h_ = self.proj_out(h_)
-
- return x+h_
-
-
-class CrossAttention(nn.Module):
- def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
- super().__init__()
- inner_dim = dim_head * heads
- context_dim = default(context_dim, query_dim)
-
- self.scale = dim_head ** -0.5
- self.heads = heads
-
- self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
- self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
- self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
-
- self.to_out = nn.Sequential(
- nn.Linear(inner_dim, query_dim),
- nn.Dropout(dropout)
- )
-
- def forward(self, x, context=None, mask=None):
- h = self.heads
-
- q = self.to_q(x)
- context = default(context, x)
- k = self.to_k(context)
- v = self.to_v(context)
-
- q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-
- # force cast to fp32 to avoid overflowing
- if _ATTN_PRECISION =="fp32":
- with torch.autocast(enabled=False, device_type = 'cuda'):
- q, k = q.float(), k.float()
- sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
- else:
- sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-
- del q, k
-
- if exists(mask):
- mask = rearrange(mask, 'b ... -> b (...)')
- max_neg_value = -torch.finfo(sim.dtype).max
- mask = repeat(mask, 'b j -> (b h) () j', h=h)
- sim.masked_fill_(~mask, max_neg_value)
-
- # attention, what we cannot get enough of
- sim = sim.softmax(dim=-1)
-
- out = einsum('b i j, b j d -> b i d', sim, v)
- out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
- return self.to_out(out)
-
-
-class MemoryEfficientCrossAttention(nn.Module):
- # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
- def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
- super().__init__()
- print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
- f"{heads} heads.")
- inner_dim = dim_head * heads
- context_dim = default(context_dim, query_dim)
-
- self.heads = heads
- self.dim_head = dim_head
-
- self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
- self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
- self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
-
- self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
- self.attention_op: Optional[Any] = None
-
- def forward(self, x, context=None, mask=None):
- q = self.to_q(x)
- context = default(context, x)
- k = self.to_k(context)
- v = self.to_v(context)
-
- b, _, _ = q.shape
- q, k, v = map(
- lambda t: t.unsqueeze(3)
- .reshape(b, t.shape[1], self.heads, self.dim_head)
- .permute(0, 2, 1, 3)
- .reshape(b * self.heads, t.shape[1], self.dim_head)
- .contiguous(),
- (q, k, v),
- )
-
- # actually compute the attention, what we cannot get enough of
- out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
-
- if exists(mask):
- raise NotImplementedError
- out = (
- out.unsqueeze(0)
- .reshape(b, self.heads, out.shape[1], self.dim_head)
- .permute(0, 2, 1, 3)
- .reshape(b, out.shape[1], self.heads * self.dim_head)
- )
- return self.to_out(out)
-
-
-class BasicTransformerBlock(nn.Module):
- ATTENTION_MODES = {
- "softmax": CrossAttention, # vanilla attention
- "softmax-xformers": MemoryEfficientCrossAttention
- }
- def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
- disable_self_attn=False):
- super().__init__()
- attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax"
- assert attn_mode in self.ATTENTION_MODES
- attn_cls = self.ATTENTION_MODES[attn_mode]
- self.disable_self_attn = disable_self_attn
- self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
- context_dim=context_dim if self.disable_self_attn else None) # is a self-attention if not self.disable_self_attn
- self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
- self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim,
- heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none
- self.norm1 = nn.LayerNorm(dim)
- self.norm2 = nn.LayerNorm(dim)
- self.norm3 = nn.LayerNorm(dim)
- self.checkpoint = checkpoint
-
- def forward(self, x, context=None):
- return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
-
- def _forward(self, x, context=None):
- x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
- x = self.attn2(self.norm2(x), context=context) + x
- x = self.ff(self.norm3(x)) + x
- return x
-
-
-class SpatialTransformer(nn.Module):
- """
- Transformer block for image-like data.
- First, project the input (aka embedding)
- and reshape to b, t, d.
- Then apply standard transformer action.
- Finally, reshape to image
- NEW: use_linear for more efficiency instead of the 1x1 convs
- """
- def __init__(self, in_channels, n_heads, d_head,
- depth=1, dropout=0., context_dim=None,
- disable_self_attn=False, use_linear=False,
- use_checkpoint=True):
- super().__init__()
- if exists(context_dim) and not isinstance(context_dim, list):
- context_dim = [context_dim]
- self.in_channels = in_channels
- inner_dim = n_heads * d_head
- self.norm = Normalize(in_channels)
- if not use_linear:
- self.proj_in = nn.Conv2d(in_channels,
- inner_dim,
- kernel_size=1,
- stride=1,
- padding=0)
- else:
- self.proj_in = nn.Linear(in_channels, inner_dim)
-
- self.transformer_blocks = nn.ModuleList(
- [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
- disable_self_attn=disable_self_attn, checkpoint=use_checkpoint)
- for d in range(depth)]
- )
- if not use_linear:
- self.proj_out = zero_module(nn.Conv2d(inner_dim,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0))
- else:
- self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
- self.use_linear = use_linear
-
- def forward(self, x, context=None):
- # note: if no context is given, cross-attention defaults to self-attention
- if not isinstance(context, list):
- context = [context]
- b, c, h, w = x.shape
- x_in = x
- x = self.norm(x)
- if not self.use_linear:
- x = self.proj_in(x)
- x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
- if self.use_linear:
- x = self.proj_in(x)
- for i, block in enumerate(self.transformer_blocks):
- x = block(x, context=context[i])
- if self.use_linear:
- x = self.proj_out(x)
- x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
- if not self.use_linear:
- x = self.proj_out(x)
- return x + x_in
-
diff --git a/spaces/HuggingAlgorithms/PDF-TextExtractor/README.md b/spaces/HuggingAlgorithms/PDF-TextExtractor/README.md
deleted file mode 100644
index a3f376a8189e8567376c00048859a6e341b1ad91..0000000000000000000000000000000000000000
--- a/spaces/HuggingAlgorithms/PDF-TextExtractor/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: PDF TextExtractor
-emoji: 🏢
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.36.1
-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/HugoDzz/spaceship_drift/static/game/index.audio.worklet.js b/spaces/HugoDzz/spaceship_drift/static/game/index.audio.worklet.js
deleted file mode 100644
index d9330c735f3da52a20f5e54e0b463ac03b7dff70..0000000000000000000000000000000000000000
--- a/spaces/HugoDzz/spaceship_drift/static/game/index.audio.worklet.js
+++ /dev/null
@@ -1,211 +0,0 @@
-/**************************************************************************/
-/* audio.worklet.js */
-/**************************************************************************/
-/* This file is part of: */
-/* GODOT ENGINE */
-/* https://godotengine.org */
-/**************************************************************************/
-/* Copyright (c) 2014-present Godot Engine contributors (see AUTHORS.md). */
-/* Copyright (c) 2007-2014 Juan Linietsky, Ariel Manzur. */
-/* */
-/* Permission is hereby granted, free of charge, to any person obtaining */
-/* a copy of this software and associated documentation files (the */
-/* "Software"), to deal in the Software without restriction, including */
-/* without limitation the rights to use, copy, modify, merge, publish, */
-/* distribute, sublicense, and/or sell copies of the Software, and to */
-/* permit persons to whom the Software is furnished to do so, subject to */
-/* the following conditions: */
-/* */
-/* The above copyright notice and this permission notice shall be */
-/* included in all copies or substantial portions of the Software. */
-/* */
-/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, */
-/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF */
-/* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. */
-/* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY */
-/* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, */
-/* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE */
-/* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */
-/**************************************************************************/
-
-class RingBuffer {
- constructor(p_buffer, p_state, p_threads) {
- this.buffer = p_buffer;
- this.avail = p_state;
- this.threads = p_threads;
- this.rpos = 0;
- this.wpos = 0;
- }
-
- data_left() {
- return this.threads ? Atomics.load(this.avail, 0) : this.avail;
- }
-
- space_left() {
- return this.buffer.length - this.data_left();
- }
-
- read(output) {
- const size = this.buffer.length;
- let from = 0;
- let to_write = output.length;
- if (this.rpos + to_write > size) {
- const high = size - this.rpos;
- output.set(this.buffer.subarray(this.rpos, size));
- from = high;
- to_write -= high;
- this.rpos = 0;
- }
- if (to_write) {
- output.set(this.buffer.subarray(this.rpos, this.rpos + to_write), from);
- }
- this.rpos += to_write;
- if (this.threads) {
- Atomics.add(this.avail, 0, -output.length);
- Atomics.notify(this.avail, 0);
- } else {
- this.avail -= output.length;
- }
- }
-
- write(p_buffer) {
- const to_write = p_buffer.length;
- const mw = this.buffer.length - this.wpos;
- if (mw >= to_write) {
- this.buffer.set(p_buffer, this.wpos);
- this.wpos += to_write;
- if (mw === to_write) {
- this.wpos = 0;
- }
- } else {
- const high = p_buffer.subarray(0, mw);
- const low = p_buffer.subarray(mw);
- this.buffer.set(high, this.wpos);
- this.buffer.set(low);
- this.wpos = low.length;
- }
- if (this.threads) {
- Atomics.add(this.avail, 0, to_write);
- Atomics.notify(this.avail, 0);
- } else {
- this.avail += to_write;
- }
- }
-}
-
-class GodotProcessor extends AudioWorkletProcessor {
- constructor() {
- super();
- this.threads = false;
- this.running = true;
- this.lock = null;
- this.notifier = null;
- this.output = null;
- this.output_buffer = new Float32Array();
- this.input = null;
- this.input_buffer = new Float32Array();
- this.port.onmessage = (event) => {
- const cmd = event.data['cmd'];
- const data = event.data['data'];
- this.parse_message(cmd, data);
- };
- }
-
- process_notify() {
- if (this.notifier) {
- Atomics.add(this.notifier, 0, 1);
- Atomics.notify(this.notifier, 0);
- }
- }
-
- parse_message(p_cmd, p_data) {
- if (p_cmd === 'start' && p_data) {
- const state = p_data[0];
- let idx = 0;
- this.threads = true;
- this.lock = state.subarray(idx, ++idx);
- this.notifier = state.subarray(idx, ++idx);
- const avail_in = state.subarray(idx, ++idx);
- const avail_out = state.subarray(idx, ++idx);
- this.input = new RingBuffer(p_data[1], avail_in, true);
- this.output = new RingBuffer(p_data[2], avail_out, true);
- } else if (p_cmd === 'stop') {
- this.running = false;
- this.output = null;
- this.input = null;
- } else if (p_cmd === 'start_nothreads') {
- this.output = new RingBuffer(p_data[0], p_data[0].length, false);
- } else if (p_cmd === 'chunk') {
- this.output.write(p_data);
- }
- }
-
- static array_has_data(arr) {
- return arr.length && arr[0].length && arr[0][0].length;
- }
-
- process(inputs, outputs, parameters) {
- if (!this.running) {
- return false; // Stop processing.
- }
- if (this.output === null) {
- return true; // Not ready yet, keep processing.
- }
- const process_input = GodotProcessor.array_has_data(inputs);
- if (process_input) {
- const input = inputs[0];
- const chunk = input[0].length * input.length;
- if (this.input_buffer.length !== chunk) {
- this.input_buffer = new Float32Array(chunk);
- }
- if (!this.threads) {
- GodotProcessor.write_input(this.input_buffer, input);
- this.port.postMessage({ 'cmd': 'input', 'data': this.input_buffer });
- } else if (this.input.space_left() >= chunk) {
- GodotProcessor.write_input(this.input_buffer, input);
- this.input.write(this.input_buffer);
- } else {
- this.port.postMessage('Input buffer is full! Skipping input frame.');
- }
- }
- const process_output = GodotProcessor.array_has_data(outputs);
- if (process_output) {
- const output = outputs[0];
- const chunk = output[0].length * output.length;
- if (this.output_buffer.length !== chunk) {
- this.output_buffer = new Float32Array(chunk);
- }
- if (this.output.data_left() >= chunk) {
- this.output.read(this.output_buffer);
- GodotProcessor.write_output(output, this.output_buffer);
- if (!this.threads) {
- this.port.postMessage({ 'cmd': 'read', 'data': chunk });
- }
- } else {
- this.port.postMessage('Output buffer has not enough frames! Skipping output frame.');
- }
- }
- this.process_notify();
- return true;
- }
-
- static write_output(dest, source) {
- const channels = dest.length;
- for (let ch = 0; ch < channels; ch++) {
- for (let sample = 0; sample < dest[ch].length; sample++) {
- dest[ch][sample] = source[sample * channels + ch];
- }
- }
- }
-
- static write_input(dest, source) {
- const channels = source.length;
- for (let ch = 0; ch < channels; ch++) {
- for (let sample = 0; sample < source[ch].length; sample++) {
- dest[sample * channels + ch] = source[ch][sample];
- }
- }
- }
-}
-
-registerProcessor('godot-processor', GodotProcessor);
diff --git a/spaces/Illumotion/Koboldcpp/otherarch/gptj_v3.cpp b/spaces/Illumotion/Koboldcpp/otherarch/gptj_v3.cpp
deleted file mode 100644
index cfe6fe99dabc1bae1072e8c488139f7c16bdb095..0000000000000000000000000000000000000000
--- a/spaces/Illumotion/Koboldcpp/otherarch/gptj_v3.cpp
+++ /dev/null
@@ -1,663 +0,0 @@
-#include "ggml.h"
-#include "otherarch.h"
-
-#include "utils.h"
-
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-#include
-
-#include "model_adapter.h"
-
-#ifdef GGML_USE_CUBLAS
-#include "ggml-cuda.h"
-#endif
-#if defined(GGML_USE_CLBLAST)
-#include "ggml-opencl.h"
-#endif
-
-// load the model's weights from a file
-ModelLoadResult gptj_model_load(const std::string & fname, gptj_model & model, gpt_vocab & vocab, int gpulayers) {
- printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
-
- auto fin = std::ifstream(fname, std::ios::binary);
- if (!fin) {
- fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
- return ModelLoadResult::FAIL;
- }
-
- // verify magic
- {
- uint32_t magic;
- fin.read((char *) &magic, sizeof(magic));
- if (magic != 0x67676d6c) {
- fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
- return ModelLoadResult::FAIL;
- }
- }
-
- int32_t origmaxctx = model.hparams.n_ctx;
-
- // load hparams
- {
- auto & hparams = model.hparams;
-
- fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
- fin.read((char *) &hparams.n_ctx, sizeof(hparams.n_ctx));
- fin.read((char *) &hparams.n_embd, sizeof(hparams.n_embd));
- fin.read((char *) &hparams.n_head, sizeof(hparams.n_head));
- fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
- fin.read((char *) &hparams.n_rot, sizeof(hparams.n_rot));
- fin.read((char *) &hparams.ftype, sizeof(hparams.ftype));
-
- const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;
-
- printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
- printf("%s: n_ctx = %d (%d)\n", __func__, hparams.n_ctx,origmaxctx);
- printf("%s: n_embd = %d\n", __func__, hparams.n_embd);
- printf("%s: n_head = %d\n", __func__, hparams.n_head);
- printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
- printf("%s: n_rot = %d\n", __func__, hparams.n_rot);
- printf("%s: ftype = %d\n", __func__, hparams.ftype);
- printf("%s: qntvr = %d\n", __func__, qntvr);
-
- hparams.n_ctx = std::max(origmaxctx,hparams.n_ctx);
-
- hparams.ftype %= GGML_QNT_VERSION_FACTOR;
- }
-
- // load vocab
- {
- int32_t n_vocab = 0;
- fin.read((char *) &n_vocab, sizeof(n_vocab));
-
- if (n_vocab != model.hparams.n_vocab) {
- fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
- __func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
- return ModelLoadResult::FAIL;
- }
-
- std::string word;
- std::vector buf(128);
-
- for (int i = 0; i < n_vocab; i++) {
- uint32_t len;
- fin.read((char *) &len, sizeof(len));
-
- buf.resize(len);
- fin.read((char *) buf.data(), len);
- word.assign(buf.data(), len);
-
- vocab.token_to_id[word] = i;
- vocab.id_to_token[i] = word;
- }
- }
-
- // for the big tensors, we have the option to store the data in 16-bit floats or quantized
- // in order to save memory and also to speed up the computation
- ggml_type wtype = ggml_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype));
- if (wtype == GGML_TYPE_COUNT) {
- fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n",
- __func__, fname.c_str(), model.hparams.ftype);
- return ModelLoadResult::FAIL;
- }
-
- auto & ctx = model.ctx;
-
- auto memory_type = GGML_TYPE_F16;
-
- size_t ctx_size = 0;
-
- {
- const auto & hparams = model.hparams;
-
- const int n_embd = hparams.n_embd;
- const int n_layer = hparams.n_layer;
- const int n_ctx = hparams.n_ctx;
- const int n_vocab = hparams.n_vocab;
-
- ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_g
- ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_b
-
- ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // wte
-
- ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // lmh_g
- ctx_size += n_vocab*ggml_type_sizef(GGML_TYPE_F32); // lmh_b
-
- ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_g
- ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_b
-
- ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_q_proj_w
- ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_k_proj_w
- ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_v_proj_w
-
- ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_proj_w
-
- ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_fc_w
- ctx_size += n_layer*( 4*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_fc_b
-
- ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_proj_w
- ctx_size += n_layer*( n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_proj_b
-
- ctx_size += std::max(origmaxctx,n_ctx)*n_layer*n_embd*ggml_type_sizef(memory_type); // memory_k
- ctx_size += std::max(origmaxctx,n_ctx)*n_layer*n_embd*ggml_type_sizef(memory_type); // memory_v
-
- ctx_size += (5 + 10*n_layer)*512; // object overhead
-
- printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
- }
-
- // create the ggml context
- {
- struct ggml_init_params params;
- params.mem_size = ctx_size;
- params.mem_buffer = NULL;
- params.no_alloc = false;
-
-
- model.ctx = ggml_init(params);
- if (!model.ctx) {
- fprintf(stderr, "%s: ggml_init() failed\n", __func__);
- return ModelLoadResult::FAIL;
- }
- }
-
- // prepare memory for the weights
- {
- const auto & hparams = model.hparams;
-
- const int n_embd = hparams.n_embd;
- const int n_layer = hparams.n_layer;
- const int n_vocab = hparams.n_vocab;
-
- model.layers.resize(n_layer);
-
- model.wte = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
-
- model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
- model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
-
- model.lmh_g = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
- model.lmh_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_vocab);
-
- // map by name
- model.tensors["transformer.wte.weight"] = model.wte;
-
- model.tensors["transformer.ln_f.weight"] = model.ln_f_g;
- model.tensors["transformer.ln_f.bias"] = model.ln_f_b;
-
- model.tensors["lm_head.weight"] = model.lmh_g;
- model.tensors["lm_head.bias"] = model.lmh_b;
-
- for (int i = 0; i < n_layer; ++i) {
- auto & layer = model.layers[i];
-
- layer.ln_1_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
- layer.ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
-
- layer.c_attn_q_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
- layer.c_attn_k_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
- layer.c_attn_v_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
-
- layer.c_attn_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
-
- layer.c_mlp_fc_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 4*n_embd);
- layer.c_mlp_fc_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);
-
- layer.c_mlp_proj_w = ggml_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd);
- layer.c_mlp_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
-
- // map by name
- model.tensors["transformer.h." + std::to_string(i) + ".ln_1.weight"] = layer.ln_1_g;
- model.tensors["transformer.h." + std::to_string(i) + ".ln_1.bias"] = layer.ln_1_b;
-
- model.tensors["transformer.h." + std::to_string(i) + ".attn.q_proj.weight"] = layer.c_attn_q_proj_w;
- model.tensors["transformer.h." + std::to_string(i) + ".attn.k_proj.weight"] = layer.c_attn_k_proj_w;
- model.tensors["transformer.h." + std::to_string(i) + ".attn.v_proj.weight"] = layer.c_attn_v_proj_w;
-
- model.tensors["transformer.h." + std::to_string(i) + ".attn.out_proj.weight"] = layer.c_attn_proj_w;
-
- model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_in.weight"] = layer.c_mlp_fc_w;
- model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_in.bias"] = layer.c_mlp_fc_b;
-
- model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_out.weight"] = layer.c_mlp_proj_w;
- model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_out.bias"] = layer.c_mlp_proj_b;
- }
- }
-
- // key + value memory
- {
- const auto & hparams = model.hparams;
-
- const int n_embd = hparams.n_embd;
- const int n_layer = hparams.n_layer;
- const int n_ctx = hparams.n_ctx;
-
- const int n_mem = n_layer*std::max(origmaxctx,n_ctx);
- const int n_elements = n_embd*n_mem;
-
- model.memory_k = ggml_new_tensor_1d(ctx, memory_type, n_elements);
- model.memory_v = ggml_new_tensor_1d(ctx, memory_type, n_elements);
-
- const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
-
- printf("%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
- }
-
- // load weights
- {
- int n_tensors = 0;
- size_t total_size = 0;
-
- printf("%s: ", __func__);
-
- while (true) {
- int32_t n_dims;
- int32_t length;
- int32_t ttype;
-
- fin.read(reinterpret_cast(&n_dims), sizeof(n_dims));
- fin.read(reinterpret_cast(&length), sizeof(length));
- fin.read(reinterpret_cast(&ttype), sizeof(ttype));
-
- if (fin.eof()) {
- break;
- }
-
- int32_t nelements = 1;
- int32_t ne[2] = { 1, 1 };
- for (int i = 0; i < n_dims; ++i) {
- fin.read(reinterpret_cast(&ne[i]), sizeof(ne[i]));
- nelements *= ne[i];
- }
-
- std::string name(length, 0);
- fin.read(&name[0], length);
-
- if (model.tensors.find(name.data()) == model.tensors.end()) {
- fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
- return ModelLoadResult::FAIL;
- }
-
- auto tensor = model.tensors[name.data()];
- if (ggml_nelements(tensor) != nelements) {
- fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
- return ModelLoadResult::FAIL;
- }
-
-
- if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
-
- //test for transposition and retry older loader
- if(tensor->ne[0]==ne[1] && tensor->ne[1]==ne[0] && should_transpose_layer(name))
- {
- printf("\nFound a transposed tensor. This could be an older or newer model. Retrying load...");
- ggml_free(ctx);
- return ModelLoadResult::RETRY_LOAD;
- }
- else
- {
- fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%ld, %ld], expected [%d, %d]\n",
- __func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
- return ModelLoadResult::FAIL;
- }
-
- }
-
- // for debugging
- if (0) {
- printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor));
- }
-
- const size_t bpe = ggml_type_size(ggml_type(ttype));
-
- if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
- fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
- __func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
- return ModelLoadResult::FAIL;
- }
-
- fin.read(reinterpret_cast(tensor->data), ggml_nbytes(tensor));
-
- //printf("%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ttype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
- total_size += ggml_nbytes(tensor);
- if (++n_tensors % 8 == 0) {
- printf(".");
- fflush(stdout);
- }
- }
-
- printf(" done\n");
-
- printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors);
- }
-
- fin.close();
-
- //gpu offload
- #if defined(GGML_USE_CLBLAST) || defined(GGML_USE_CUBLAS)
- if(gpulayers>0)
- {
- const auto & hparams = model.hparams;
- size_t vram_total = 0;
- const int n_gpu = std::min(gpulayers, int(hparams.n_layer));
- #if defined(GGML_USE_CLBLAST)
- fprintf(stderr, "%s: [opencl] offloading %d layers to GPU\n", __func__, n_gpu);
- #else
- fprintf(stderr, "%s: [CUDA] offloading %d layers to GPU\n", __func__, n_gpu);
- #endif
- for (int i = 0; i < n_gpu; ++i) {
- const auto & layer = model.layers[i];
- layer.c_attn_q_proj_w->backend = GGML_BACKEND_GPU;
- layer.c_attn_k_proj_w->backend = GGML_BACKEND_GPU;
- layer.c_attn_v_proj_w->backend = GGML_BACKEND_GPU;
- layer.c_attn_proj_w->backend = GGML_BACKEND_GPU;
- layer.c_mlp_fc_w->backend = GGML_BACKEND_GPU;
- layer.c_mlp_proj_w->backend = GGML_BACKEND_GPU;
- #if defined(GGML_USE_CLBLAST)
- ggml_cl_transform_tensor(layer.c_attn_q_proj_w->data,layer.c_attn_q_proj_w); vram_total += ggml_nbytes(layer.c_attn_q_proj_w);
- ggml_cl_transform_tensor(layer.c_attn_k_proj_w->data,layer.c_attn_k_proj_w); vram_total += ggml_nbytes(layer.c_attn_k_proj_w);
- ggml_cl_transform_tensor(layer.c_attn_v_proj_w->data,layer.c_attn_v_proj_w); vram_total += ggml_nbytes(layer.c_attn_v_proj_w);
- ggml_cl_transform_tensor(layer.c_attn_proj_w->data,layer.c_attn_proj_w); vram_total += ggml_nbytes(layer.c_attn_proj_w);
- ggml_cl_transform_tensor(layer.c_mlp_fc_w->data,layer.c_mlp_fc_w); vram_total += ggml_nbytes(layer.c_mlp_fc_w);
- ggml_cl_transform_tensor(layer.c_mlp_proj_w->data,layer.c_mlp_proj_w); vram_total += ggml_nbytes(layer.c_mlp_proj_w);
- #else
- ggml_cuda_transform_tensor(layer.c_attn_q_proj_w->data,layer.c_attn_q_proj_w); vram_total += ggml_nbytes(layer.c_attn_q_proj_w);
- ggml_cuda_transform_tensor(layer.c_attn_k_proj_w->data,layer.c_attn_k_proj_w); vram_total += ggml_nbytes(layer.c_attn_k_proj_w);
- ggml_cuda_transform_tensor(layer.c_attn_v_proj_w->data,layer.c_attn_v_proj_w); vram_total += ggml_nbytes(layer.c_attn_v_proj_w);
- ggml_cuda_transform_tensor(layer.c_attn_proj_w->data,layer.c_attn_proj_w); vram_total += ggml_nbytes(layer.c_attn_proj_w);
- ggml_cuda_transform_tensor(layer.c_mlp_fc_w->data,layer.c_mlp_fc_w); vram_total += ggml_nbytes(layer.c_mlp_fc_w);
- ggml_cuda_transform_tensor(layer.c_mlp_proj_w->data,layer.c_mlp_proj_w); vram_total += ggml_nbytes(layer.c_mlp_proj_w);
- #endif
- }
- #if defined(GGML_USE_CLBLAST)
- fprintf(stderr, "%s: [opencl] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
- #else
- fprintf(stderr, "%s: [CUDA] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
- #endif
- }
- #endif
-
- return ModelLoadResult::SUCCESS;
-}
-
-// evaluate the transformer
-//
-// - model: the model
-// - n_threads: number of threads to use
-// - n_past: the context size so far
-// - embd_inp: the embeddings of the tokens in the context
-// - embd_w: the predicted logits for the next token
-//
-// The GPT-J model requires about 16MB of memory per input token.
-//
-bool gptj_eval(
- const gptj_model & model,
- const int n_threads,
- const int n_past,
- const std::vector & embd_inp,
- std::vector & embd_w,
- size_t & mem_per_token,
- bool use_scratch) {
- const int N = embd_inp.size();
-
- const auto & hparams = model.hparams;
-
- const int n_embd = hparams.n_embd;
- const int n_layer = hparams.n_layer;
- const int n_ctx = hparams.n_ctx;
- const int n_head = hparams.n_head;
- const int n_vocab = hparams.n_vocab;
- const int n_rot = hparams.n_rot;
-
- const float freq_base = hparams.rope_freq_base;
- const float freq_scale = hparams.rope_freq_scale;
-
- static size_t buf_size = 256u*1024*1024;
- static void * buf = malloc(buf_size);
-
- // use 2 scratch buffers
- // TODO: very hacky solution - reimplement in a more elegant way
- static size_t scr0_size = 512u*1024*1024*(hparams.n_ctx>8192?2:1);
- static size_t scr1_size = 512u*1024*1024;
-
- static void * scr0 = malloc(scr0_size);
- static void * scr1 = malloc(scr1_size);
-
- if (mem_per_token > 0 && (mem_per_token*N*2 + 64u*1024*1024) > buf_size) {
- const size_t buf_size_new = 320u*1024*1024 + 1.2*(mem_per_token*N); // add 10% to account for ggml object overhead
- //printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
-
- // reallocate
- if (buf_size_new > buf_size)
- {
- buf_size = buf_size_new;
- buf = realloc(buf, buf_size);
- if (buf == nullptr)
- {
- fprintf(stderr, "%s: failed to allocate %zu bytes. Try reducing batch size.\n", __func__, buf_size);
- return false;
- }
- }
- }
-
- struct ggml_init_params params;
- params.mem_size = buf_size;
- params.mem_buffer = buf;
- params.no_alloc = false;
-
-
- struct ggml_context * ctx0 = ggml_init(params);
- struct ggml_cgraph gf = {};
-
- struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
- memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
-
- // wte
- struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.wte, embd);
-
- for (int il = 0; il < n_layer; ++il) {
- struct ggml_tensor * cur;
-
- if(use_scratch){
- ggml_set_scratch(ctx0, { 0, scr0_size, scr0, });
- }
-
- // norm
- {
- cur = ggml_norm(ctx0, inpL, default_norm_eps);
-
- // cur = ln_1_g*cur + ln_1_b
- cur = ggml_add(ctx0,
- ggml_mul(ctx0,
- ggml_repeat(ctx0, model.layers[il].ln_1_g, cur),
- cur),
- ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
- }
-
- struct ggml_tensor * inpSA = cur;
-
- // self-attention
- {
- struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
- {
- int * data = (int *) KQ_pos->data;
- for (int i = 0; i < N; ++i) {
- data[i] = n_past + i;
- }
- }
-
- struct ggml_tensor * Qcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_q_proj_w, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0, n_ctx, freq_base, freq_scale);
- struct ggml_tensor * Kcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_k_proj_w, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0, n_ctx, freq_base, freq_scale);
-
- // store key and value to memory
- {
- struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_v_proj_w, cur));
-
- struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
- struct ggml_tensor * v = ggml_view_2d(ctx0, model.memory_v, N, n_embd,
- ( n_ctx)*ggml_element_size(model.memory_v),
- (il*n_ctx)*ggml_element_size(model.memory_v)*n_embd + n_past*ggml_element_size(model.memory_v));
-
- ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
- ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
- }
-
- // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
- struct ggml_tensor * Q =
- ggml_permute(ctx0,
- Qcur,
- 0, 2, 1, 3);
-
- // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
- struct ggml_tensor * K =
- ggml_permute(ctx0,
- ggml_reshape_3d(ctx0,
- ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd),
- n_embd/n_head, n_head, n_past + N),
- 0, 2, 1, 3);
-
- // K * Q
- struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
-
- // KQ_scaled = KQ / sqrt(n_embd/n_head)
- struct ggml_tensor * KQ_scaled =
- ggml_scale_inplace(ctx0,
- KQ,
- ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
- );
-
- // KQ_masked = mask_past(KQ_scaled)
- struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past);
-
- // KQ = soft_max(KQ_masked)
- struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
-
- // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
- struct ggml_tensor * V =
- ggml_view_3d(ctx0, model.memory_v,
- n_past + N, n_embd/n_head, n_head,
- n_ctx*ggml_element_size(model.memory_v),
- n_ctx*ggml_element_size(model.memory_v)*n_embd/n_head,
- il*n_ctx*ggml_element_size(model.memory_v)*n_embd);
-
- // KQV = transpose(V) * KQ_soft_max
- struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
-
- // KQV_merged = KQV.permute(0, 2, 1, 3)
- struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
-
- // cur = KQV_merged.contiguous().view(n_embd, N)
- cur = ggml_cpy(ctx0,
- KQV_merged,
- ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
-
- // projection (no bias)
- cur = ggml_mul_mat(ctx0,
- model.layers[il].c_attn_proj_w,
- cur);
- }
-
- if(use_scratch){
- ggml_set_scratch(ctx0, { 0, scr1_size, scr1, });
- }
-
- struct ggml_tensor * inpFF = cur;
-
- // feed-forward network
- // this is independent of the self-attention result, so it could be done in parallel to the self-attention
- {
- // note here we pass inpSA instead of cur
- cur = ggml_mul_mat(ctx0,
- model.layers[il].c_mlp_fc_w,
- inpSA);
-
- cur = ggml_add(ctx0,
- ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
- cur);
-
- // GELU activation
- cur = ggml_gelu(ctx0, cur);
-
- // projection
- // cur = proj_w*cur + proj_b
- cur = ggml_mul_mat(ctx0,
- model.layers[il].c_mlp_proj_w,
- cur);
-
- cur = ggml_add(ctx0,
- ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),
- cur);
- }
-
- // self-attention + FF
- cur = ggml_add(ctx0, cur, inpFF);
-
- // input for next layer
- inpL = ggml_add(ctx0, cur, inpL);
- }
-
- if(use_scratch){
- ggml_set_scratch(ctx0, { 0, scr0_size, scr0, });
- }
-
- // norm
- {
- inpL = ggml_norm(ctx0, inpL, default_norm_eps);
-
- // inpL = ln_f_g*inpL + ln_f_b
- inpL = ggml_add(ctx0,
- ggml_mul(ctx0,
- ggml_repeat(ctx0, model.ln_f_g, inpL),
- inpL),
- ggml_repeat(ctx0, model.ln_f_b, inpL));
- }
-
- if(use_scratch){
- ggml_set_scratch(ctx0, { 0, 0, nullptr, });
- }
-
- // lm_head
- {
- inpL = ggml_mul_mat(ctx0, model.lmh_g, inpL);
-
- inpL = ggml_add(ctx0,
- ggml_repeat(ctx0, model.lmh_b, inpL),
- inpL);
- }
-
- // logits -> probs
- //inpL = ggml_soft_max_inplace(ctx0, inpL);
-
- // run the computation
- ggml_build_forward_expand(&gf, inpL);
- kcpp_graph_compute_helper(&gf, n_threads);
-
- //if (n_past%100 == 0) {
- // ggml_graph_print (&gf);
- // ggml_graph_dump_dot(&gf, NULL, "gpt-j.dot");
- //}
-
- //embd_w.resize(n_vocab*N);
- //memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
-
- // return result for just the last token
- embd_w.resize(n_vocab);
- memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
-
- if (mem_per_token == 0) {
- mem_per_token = ggml_used_mem(ctx0)/N;
- }
- //printf("used_mem = %zu\n", ggml_used_mem(ctx0));
-
- ggml_free(ctx0);
-
- return true;
-}
diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/saicinpainting/training/losses/style_loss.py b/spaces/InpaintAI/Inpaint-Anything/third_party/lama/saicinpainting/training/losses/style_loss.py
deleted file mode 100644
index 0bb42d7fbc5d17a47bec7365889868505f5fdfb5..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/saicinpainting/training/losses/style_loss.py
+++ /dev/null
@@ -1,155 +0,0 @@
-import torch
-import torch.nn as nn
-import torchvision.models as models
-
-
-class PerceptualLoss(nn.Module):
- r"""
- Perceptual loss, VGG-based
- https://arxiv.org/abs/1603.08155
- https://github.com/dxyang/StyleTransfer/blob/master/utils.py
- """
-
- def __init__(self, weights=[1.0, 1.0, 1.0, 1.0, 1.0]):
- super(PerceptualLoss, self).__init__()
- self.add_module('vgg', VGG19())
- self.criterion = torch.nn.L1Loss()
- self.weights = weights
-
- def __call__(self, x, y):
- # Compute features
- x_vgg, y_vgg = self.vgg(x), self.vgg(y)
-
- content_loss = 0.0
- content_loss += self.weights[0] * self.criterion(x_vgg['relu1_1'], y_vgg['relu1_1'])
- content_loss += self.weights[1] * self.criterion(x_vgg['relu2_1'], y_vgg['relu2_1'])
- content_loss += self.weights[2] * self.criterion(x_vgg['relu3_1'], y_vgg['relu3_1'])
- content_loss += self.weights[3] * self.criterion(x_vgg['relu4_1'], y_vgg['relu4_1'])
- content_loss += self.weights[4] * self.criterion(x_vgg['relu5_1'], y_vgg['relu5_1'])
-
-
- return content_loss
-
-
-class VGG19(torch.nn.Module):
- def __init__(self):
- super(VGG19, self).__init__()
- features = models.vgg19(pretrained=True).features
- self.relu1_1 = torch.nn.Sequential()
- self.relu1_2 = torch.nn.Sequential()
-
- self.relu2_1 = torch.nn.Sequential()
- self.relu2_2 = torch.nn.Sequential()
-
- self.relu3_1 = torch.nn.Sequential()
- self.relu3_2 = torch.nn.Sequential()
- self.relu3_3 = torch.nn.Sequential()
- self.relu3_4 = torch.nn.Sequential()
-
- self.relu4_1 = torch.nn.Sequential()
- self.relu4_2 = torch.nn.Sequential()
- self.relu4_3 = torch.nn.Sequential()
- self.relu4_4 = torch.nn.Sequential()
-
- self.relu5_1 = torch.nn.Sequential()
- self.relu5_2 = torch.nn.Sequential()
- self.relu5_3 = torch.nn.Sequential()
- self.relu5_4 = torch.nn.Sequential()
-
- for x in range(2):
- self.relu1_1.add_module(str(x), features[x])
-
- for x in range(2, 4):
- self.relu1_2.add_module(str(x), features[x])
-
- for x in range(4, 7):
- self.relu2_1.add_module(str(x), features[x])
-
- for x in range(7, 9):
- self.relu2_2.add_module(str(x), features[x])
-
- for x in range(9, 12):
- self.relu3_1.add_module(str(x), features[x])
-
- for x in range(12, 14):
- self.relu3_2.add_module(str(x), features[x])
-
- for x in range(14, 16):
- self.relu3_2.add_module(str(x), features[x])
-
- for x in range(16, 18):
- self.relu3_4.add_module(str(x), features[x])
-
- for x in range(18, 21):
- self.relu4_1.add_module(str(x), features[x])
-
- for x in range(21, 23):
- self.relu4_2.add_module(str(x), features[x])
-
- for x in range(23, 25):
- self.relu4_3.add_module(str(x), features[x])
-
- for x in range(25, 27):
- self.relu4_4.add_module(str(x), features[x])
-
- for x in range(27, 30):
- self.relu5_1.add_module(str(x), features[x])
-
- for x in range(30, 32):
- self.relu5_2.add_module(str(x), features[x])
-
- for x in range(32, 34):
- self.relu5_3.add_module(str(x), features[x])
-
- for x in range(34, 36):
- self.relu5_4.add_module(str(x), features[x])
-
- # don't need the gradients, just want the features
- for param in self.parameters():
- param.requires_grad = False
-
- def forward(self, x):
- relu1_1 = self.relu1_1(x)
- relu1_2 = self.relu1_2(relu1_1)
-
- relu2_1 = self.relu2_1(relu1_2)
- relu2_2 = self.relu2_2(relu2_1)
-
- relu3_1 = self.relu3_1(relu2_2)
- relu3_2 = self.relu3_2(relu3_1)
- relu3_3 = self.relu3_3(relu3_2)
- relu3_4 = self.relu3_4(relu3_3)
-
- relu4_1 = self.relu4_1(relu3_4)
- relu4_2 = self.relu4_2(relu4_1)
- relu4_3 = self.relu4_3(relu4_2)
- relu4_4 = self.relu4_4(relu4_3)
-
- relu5_1 = self.relu5_1(relu4_4)
- relu5_2 = self.relu5_2(relu5_1)
- relu5_3 = self.relu5_3(relu5_2)
- relu5_4 = self.relu5_4(relu5_3)
-
- out = {
- 'relu1_1': relu1_1,
- 'relu1_2': relu1_2,
-
- 'relu2_1': relu2_1,
- 'relu2_2': relu2_2,
-
- 'relu3_1': relu3_1,
- 'relu3_2': relu3_2,
- 'relu3_3': relu3_3,
- 'relu3_4': relu3_4,
-
- 'relu4_1': relu4_1,
- 'relu4_2': relu4_2,
- 'relu4_3': relu4_3,
- 'relu4_4': relu4_4,
-
- 'relu5_1': relu5_1,
- 'relu5_2': relu5_2,
- 'relu5_3': relu5_3,
- 'relu5_4': relu5_4,
- }
- return out
diff --git a/spaces/JMalott/ai_architecture/page/generate.py b/spaces/JMalott/ai_architecture/page/generate.py
deleted file mode 100644
index dbd73cb38d4dd302755ef697c3d3237e545744cf..0000000000000000000000000000000000000000
--- a/spaces/JMalott/ai_architecture/page/generate.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import collections
-from numpy.core.defchararray import lower
-import streamlit as st
-import numpy as np
-import pandas as pd
-import streamlit as st
-import pandas as pd
-import numpy as np
-import os, random, time
-from utils import footer, generate, drawGrid, generate2
-from PIL import Image
-
-mode = "ai"
-#mode = "dummy"
-
-def app():
- global _prompt
-
- st.title('AI-Generated Architecture')
-
- #st.subheader('(beta v1.1)')
-
- #st.text('This is a working beta version with bugs. Known issues are:\n-Some images will grey out when you change the input parameters')
-
- st.subheader('Describe a building, interior, or other architecture you would like to see. You can change the prompt and input parameters on the fly.')
-
- #Modern architecture museum with black brick and large windows.
- print("Prompt: "+st.session_state.prompt)
- prompt = st.text_input(label="",value=st.session_state.prompt)
-
- st.text("")
-
- #with st.expander("Having trouble thinking of something? Click here to view examples."):
- # st.write("""
- # • Modern architecture museum with black brick and large windows.\n
- # • A prosaic, simple architecture.\n
- # • An urban, post-modern architecture with concrete and steel.\n
- # • A sleek urban interior design.
- # """)
-
- st.text("")
-
- crazy = st.slider('Temperature. This controls how "crazy" generated images are, where 0 is the least crazy.', 0.0, 1.0, 0.75)
- k = st.slider('Top K. The higher the value, the higher quality the results tend to be at the cost of extra processing time.', 1, 10, 5)
- #k = k*400
-
- if( 'results' not in st.session_state ):
- st.session_state.results = []
-
-
- c1,c2 = st.columns(2)
-
- with c1:
- holder = st.empty()
- with c2:
- holder2 = st.empty()
-
- startButton = holder.button("Start")
-
- already = []
-
- if startButton or hasattr(st.session_state, 'load_state') or st.session_state.prompt is not None:
-
- with st.spinner("Generating..."):
-
- holder.empty()
-
- st.session_state.load_state = True
-
-
-
- placeholder = st.empty()
- second = st.empty()
-
- nextButton = False
- randomButton = False
- f = True
-
- ii = 0
- while len(st.session_state.results) <= 64:
-
- ii += 1
-
- if(f and len(st.session_state.results) > 0):
- f = False
- randomButton = holder2.button("Randomize Prompt")
- nextButton = holder.button("Finish Generating Images")
-
- with second.container():
- drawGrid()
-
- with placeholder.container():
-
- st.session_state.bar = placeholder.progress(0)
-
- if(len(st.session_state.results) > 0 and nextButton):
- st.session_state.page = 1
- break
-
- if(randomButton):
- randomButton = False
- _1 = ["A modern ","A post-modern ","An ultramodern ", "A classical ", "A parametric ", "A contemporary ", "A minimalist "]
- _2 = ["museum architecture","home architecture","landscape architecture","interior design"]
- _3 = [""," in the style of I.M. Pei"," in the style of Frank Gehry"," in the style of John Lautner"," in the style of Frank Lloyd Wright"]
- _4 = [" photograph",", watercolor painting",", oil painting", ", digital art"]
-
- prompt = str(random.choice(_1)+random.choice(_2)+random.choice(_3)+random.choice(_4))
- st.session_state.prompt = prompt
-
- generate(prompt,crazy,k)
-
- st.session_state.bar = st.container()
diff --git a/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py b/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py
deleted file mode 100644
index 48d16889a030217b5d203233678a10e3eb7ae9d2..0000000000000000000000000000000000000000
--- a/spaces/Jackflack09/diffuse-custom/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py
+++ /dev/null
@@ -1,119 +0,0 @@
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-from typing import Optional, Tuple, Union
-
-import torch
-
-from ...pipeline_utils import AudioPipelineOutput, DiffusionPipeline
-from ...utils import logging
-
-
-logger = logging.get_logger(__name__) # pylint: disable=invalid-name
-
-
-class DanceDiffusionPipeline(DiffusionPipeline):
- r"""
- This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
- library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
- Parameters:
- unet ([`UNet1DModel`]): U-Net architecture to denoise the encoded image.
- scheduler ([`SchedulerMixin`]):
- A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
- [`IPNDMScheduler`].
- """
-
- def __init__(self, unet, scheduler):
- super().__init__()
- self.register_modules(unet=unet, scheduler=scheduler)
-
- @torch.no_grad()
- def __call__(
- self,
- batch_size: int = 1,
- num_inference_steps: int = 100,
- generator: Optional[torch.Generator] = None,
- audio_length_in_s: Optional[float] = None,
- return_dict: bool = True,
- ) -> Union[AudioPipelineOutput, Tuple]:
- r"""
- Args:
- batch_size (`int`, *optional*, defaults to 1):
- The number of audio samples to generate.
- num_inference_steps (`int`, *optional*, defaults to 50):
- The number of denoising steps. More denoising steps usually lead to a higher quality audio sample at
- the expense of slower inference.
- generator (`torch.Generator`, *optional*):
- A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
- deterministic.
- audio_length_in_s (`float`, *optional*, defaults to `self.unet.config.sample_size/self.unet.config.sample_rate`):
- The length of the generated audio sample in seconds. Note that the output of the pipeline, *i.e.*
- `sample_size`, will be `audio_length_in_s` * `self.unet.sample_rate`.
- return_dict (`bool`, *optional*, defaults to `True`):
- Whether or not to return a [`~pipeline_utils.AudioPipelineOutput`] instead of a plain tuple.
-
- Returns:
- [`~pipeline_utils.AudioPipelineOutput`] or `tuple`: [`~pipelines.utils.AudioPipelineOutput`] if
- `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
- generated images.
- """
-
- if audio_length_in_s is None:
- audio_length_in_s = self.unet.config.sample_size / self.unet.config.sample_rate
-
- sample_size = audio_length_in_s * self.unet.sample_rate
-
- down_scale_factor = 2 ** len(self.unet.up_blocks)
- if sample_size < 3 * down_scale_factor:
- raise ValueError(
- f"{audio_length_in_s} is too small. Make sure it's bigger or equal to"
- f" {3 * down_scale_factor / self.unet.sample_rate}."
- )
-
- original_sample_size = int(sample_size)
- if sample_size % down_scale_factor != 0:
- sample_size = ((audio_length_in_s * self.unet.sample_rate) // down_scale_factor + 1) * down_scale_factor
- logger.info(
- f"{audio_length_in_s} is increased to {sample_size / self.unet.sample_rate} so that it can be handled"
- f" by the model. It will be cut to {original_sample_size / self.unet.sample_rate} after the denoising"
- " process."
- )
- sample_size = int(sample_size)
-
- dtype = next(iter(self.unet.parameters())).dtype
- audio = torch.randn(
- (batch_size, self.unet.in_channels, sample_size), generator=generator, device=self.device, dtype=dtype
- )
-
- # set step values
- self.scheduler.set_timesteps(num_inference_steps, device=audio.device)
- self.scheduler.timesteps = self.scheduler.timesteps.to(dtype)
-
- for t in self.progress_bar(self.scheduler.timesteps):
- # 1. predict noise model_output
- model_output = self.unet(audio, t).sample
-
- # 2. compute previous image: x_t -> t_t-1
- audio = self.scheduler.step(model_output, t, audio).prev_sample
-
- audio = audio.clamp(-1, 1).float().cpu().numpy()
-
- audio = audio[:, :, :original_sample_size]
-
- if not return_dict:
- return (audio,)
-
- return AudioPipelineOutput(audios=audio)
diff --git a/spaces/James1208/Salesforce-codegen-350M-mono/README.md b/spaces/James1208/Salesforce-codegen-350M-mono/README.md
deleted file mode 100644
index e510c7fe67ea16b1e0c3fea62fb01a9083915d49..0000000000000000000000000000000000000000
--- a/spaces/James1208/Salesforce-codegen-350M-mono/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Salesforce Codegen 350M Mono
-emoji: ⚡
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.19.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/JohnSmith9982/small_and_pretty/app.py b/spaces/JohnSmith9982/small_and_pretty/app.py
deleted file mode 100644
index 9049b5749db9640f03aa6b58afaf2d8d2394e4c1..0000000000000000000000000000000000000000
--- a/spaces/JohnSmith9982/small_and_pretty/app.py
+++ /dev/null
@@ -1,147 +0,0 @@
-import time
-
-from theme_dropdown import create_theme_dropdown # noqa: F401
-
-import gradio as gr
-
-dropdown, js = create_theme_dropdown()
-
-with gr.Blocks(theme='JohnSmith9982/small_and_pretty') as demo:
- with gr.Row().style(equal_height=True):
- with gr.Column(scale=10):
- gr.Markdown(
- """
- # Theme preview: `small_and_pretty`
- To use this theme, set `theme='JohnSmith9982/small_and_pretty'` in `gr.Blocks()` or `gr.Interface()`.
- You can append an `@` and a semantic version expression, e.g. @>=1.0.0,<2.0.0 to pin to a given version
- of this theme.
- """
- )
- with gr.Column(scale=3):
- with gr.Box():
- dropdown.render()
- toggle_dark = gr.Button(value="Toggle Dark").style(full_width=True)
-
- dropdown.change(None, dropdown, None, _js=js)
- toggle_dark.click(
- None,
- _js="""
- () => {
- document.body.classList.toggle('dark');
- document.querySelector('gradio-app').style.backgroundColor = 'var(--color-background-primary)'
- }
- """,
- )
-
- name = gr.Textbox(
- label="Name",
- info="Full name, including middle name. No special characters.",
- placeholder="John Doe",
- value="John Doe",
- interactive=True,
- )
-
- with gr.Row():
- slider1 = gr.Slider(label="Slider 1")
- slider2 = gr.Slider(label="Slider 2")
- gr.CheckboxGroup(["A", "B", "C"], label="Checkbox Group")
-
- with gr.Row():
- with gr.Column(variant="panel", scale=1):
- gr.Markdown("## Panel 1")
- radio = gr.Radio(
- ["A", "B", "C"],
- label="Radio",
- info="Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.",
- )
- drop = gr.Dropdown(["Option 1", "Option 2", "Option 3"], show_label=False)
- drop_2 = gr.Dropdown(
- ["Option A", "Option B", "Option C"],
- multiselect=True,
- value=["Option A"],
- label="Dropdown",
- interactive=True,
- )
- check = gr.Checkbox(label="Go")
- with gr.Column(variant="panel", scale=2):
- img = gr.Image(
- "https://gradio.app/assets/img/header-image.jpg", label="Image"
- ).style(height=320)
- with gr.Row():
- go_btn = gr.Button("Go", label="Primary Button", variant="primary")
- clear_btn = gr.Button(
- "Clear", label="Secondary Button", variant="secondary"
- )
-
- def go(*args):
- time.sleep(3)
- return "https://gradio.app/assets/img/header-image.jpg"
-
- go_btn.click(go, [radio, drop, drop_2, check, name], img, api_name="go")
-
- def clear():
- time.sleep(0.2)
- return None
-
- clear_btn.click(clear, None, img)
-
- with gr.Row():
- btn1 = gr.Button("Button 1").style(size="sm")
- btn2 = gr.UploadButton().style(size="sm")
- stop_btn = gr.Button("Stop", label="Stop Button", variant="stop").style(
- size="sm"
- )
-
- with gr.Row():
- gr.Dataframe(value=[[1, 2, 3], [4, 5, 6], [7, 8, 9]], label="Dataframe")
- gr.JSON(
- value={"a": 1, "b": 2, "c": {"test": "a", "test2": [1, 2, 3]}}, label="JSON"
- )
- gr.Label(value={"cat": 0.7, "dog": 0.2, "fish": 0.1})
- gr.File()
- with gr.Row():
- gr.ColorPicker()
- gr.Video("https://gradio-static-files.s3.us-west-2.amazonaws.com/world.mp4")
- gr.Gallery(
- [
- (
- "https://gradio-static-files.s3.us-west-2.amazonaws.com/lion.jpg",
- "lion",
- ),
- (
- "https://gradio-static-files.s3.us-west-2.amazonaws.com/logo.png",
- "logo",
- ),
- (
- "https://gradio-static-files.s3.us-west-2.amazonaws.com/tower.jpg",
- "tower",
- ),
- ]
- ).style(height="200px", grid=2)
-
- with gr.Row():
- with gr.Column(scale=2):
- chatbot = gr.Chatbot([("Hello", "Hi")], label="Chatbot")
- chat_btn = gr.Button("Add messages")
-
- def chat(history):
- time.sleep(2)
- yield [["How are you?", "I am good."]]
-
- chat_btn.click(
- lambda history: history
- + [["How are you?", "I am good."]]
- + (time.sleep(2) or []),
- chatbot,
- chatbot,
- )
- with gr.Column(scale=1):
- with gr.Accordion("Advanced Settings"):
- gr.Markdown("Hello")
- gr.Number(label="Chatbot control 1")
- gr.Number(label="Chatbot control 2")
- gr.Number(label="Chatbot control 3")
-
-
-if __name__ == "__main__":
- demo.queue().launch()
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/base_dense_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/base_dense_head.py
deleted file mode 100644
index 02a397c62f9154d10fa5ae254b75a76f041e348d..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/base_dense_head.py
+++ /dev/null
@@ -1,577 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-from abc import ABCMeta, abstractmethod
-from inspect import signature
-from typing import List, Optional, Tuple
-
-import torch
-from mmcv.ops import batched_nms
-from mmengine.config import ConfigDict
-from mmengine.model import BaseModule, constant_init
-from mmengine.structures import InstanceData
-from torch import Tensor
-
-from mmdet.structures import SampleList
-from mmdet.structures.bbox import (cat_boxes, get_box_tensor, get_box_wh,
- scale_boxes)
-from mmdet.utils import InstanceList, OptMultiConfig
-from ..test_time_augs import merge_aug_results
-from ..utils import (filter_scores_and_topk, select_single_mlvl,
- unpack_gt_instances)
-
-
-class BaseDenseHead(BaseModule, metaclass=ABCMeta):
- """Base class for DenseHeads.
-
- 1. The ``init_weights`` method is used to initialize densehead's
- model parameters. After detector initialization, ``init_weights``
- is triggered when ``detector.init_weights()`` is called externally.
-
- 2. The ``loss`` method is used to calculate the loss of densehead,
- which includes two steps: (1) the densehead model performs forward
- propagation to obtain the feature maps (2) The ``loss_by_feat`` method
- is called based on the feature maps to calculate the loss.
-
- .. code:: text
-
- loss(): forward() -> loss_by_feat()
-
- 3. The ``predict`` method is used to predict detection results,
- which includes two steps: (1) the densehead model performs forward
- propagation to obtain the feature maps (2) The ``predict_by_feat`` method
- is called based on the feature maps to predict detection results including
- post-processing.
-
- .. code:: text
-
- predict(): forward() -> predict_by_feat()
-
- 4. The ``loss_and_predict`` method is used to return loss and detection
- results at the same time. It will call densehead's ``forward``,
- ``loss_by_feat`` and ``predict_by_feat`` methods in order. If one-stage is
- used as RPN, the densehead needs to return both losses and predictions.
- This predictions is used as the proposal of roihead.
-
- .. code:: text
-
- loss_and_predict(): forward() -> loss_by_feat() -> predict_by_feat()
- """
-
- def __init__(self, init_cfg: OptMultiConfig = None) -> None:
- super().__init__(init_cfg=init_cfg)
- # `_raw_positive_infos` will be used in `get_positive_infos`, which
- # can get positive information.
- self._raw_positive_infos = dict()
-
- def init_weights(self) -> None:
- """Initialize the weights."""
- super().init_weights()
- # avoid init_cfg overwrite the initialization of `conv_offset`
- for m in self.modules():
- # DeformConv2dPack, ModulatedDeformConv2dPack
- if hasattr(m, 'conv_offset'):
- constant_init(m.conv_offset, 0)
-
- def get_positive_infos(self) -> InstanceList:
- """Get positive information from sampling results.
-
- Returns:
- list[:obj:`InstanceData`]: Positive information of each image,
- usually including positive bboxes, positive labels, positive
- priors, etc.
- """
- if len(self._raw_positive_infos) == 0:
- return None
-
- sampling_results = self._raw_positive_infos.get(
- 'sampling_results', None)
- assert sampling_results is not None
- positive_infos = []
- for sampling_result in enumerate(sampling_results):
- pos_info = InstanceData()
- pos_info.bboxes = sampling_result.pos_gt_bboxes
- pos_info.labels = sampling_result.pos_gt_labels
- pos_info.priors = sampling_result.pos_priors
- pos_info.pos_assigned_gt_inds = \
- sampling_result.pos_assigned_gt_inds
- pos_info.pos_inds = sampling_result.pos_inds
- positive_infos.append(pos_info)
- return positive_infos
-
- def loss(self, x: Tuple[Tensor], batch_data_samples: SampleList) -> dict:
- """Perform forward propagation and loss calculation of the detection
- head on the features of the upstream network.
-
- Args:
- x (tuple[Tensor]): Features from the upstream network, each is
- a 4D-tensor.
- batch_data_samples (List[:obj:`DetDataSample`]): The Data
- Samples. It usually includes information such as
- `gt_instance`, `gt_panoptic_seg` and `gt_sem_seg`.
-
- Returns:
- dict: A dictionary of loss components.
- """
- outs = self(x)
-
- outputs = unpack_gt_instances(batch_data_samples)
- (batch_gt_instances, batch_gt_instances_ignore,
- batch_img_metas) = outputs
-
- loss_inputs = outs + (batch_gt_instances, batch_img_metas,
- batch_gt_instances_ignore)
- losses = self.loss_by_feat(*loss_inputs)
- return losses
-
- @abstractmethod
- def loss_by_feat(self, **kwargs) -> dict:
- """Calculate the loss based on the features extracted by the detection
- head."""
- pass
-
- def loss_and_predict(
- self,
- x: Tuple[Tensor],
- batch_data_samples: SampleList,
- proposal_cfg: Optional[ConfigDict] = None
- ) -> Tuple[dict, InstanceList]:
- """Perform forward propagation of the head, then calculate loss and
- predictions from the features and data samples.
-
- Args:
- x (tuple[Tensor]): Features from FPN.
- batch_data_samples (list[:obj:`DetDataSample`]): Each item contains
- the meta information of each image and corresponding
- annotations.
- proposal_cfg (ConfigDict, optional): Test / postprocessing
- configuration, if None, test_cfg would be used.
- Defaults to None.
-
- Returns:
- tuple: the return value is a tuple contains:
-
- - losses: (dict[str, Tensor]): A dictionary of loss components.
- - predictions (list[:obj:`InstanceData`]): Detection
- results of each image after the post process.
- """
- outputs = unpack_gt_instances(batch_data_samples)
- (batch_gt_instances, batch_gt_instances_ignore,
- batch_img_metas) = outputs
-
- outs = self(x)
-
- loss_inputs = outs + (batch_gt_instances, batch_img_metas,
- batch_gt_instances_ignore)
- losses = self.loss_by_feat(*loss_inputs)
-
- predictions = self.predict_by_feat(
- *outs, batch_img_metas=batch_img_metas, cfg=proposal_cfg)
- return losses, predictions
-
- def predict(self,
- x: Tuple[Tensor],
- batch_data_samples: SampleList,
- rescale: bool = False) -> InstanceList:
- """Perform forward propagation of the detection head and predict
- detection results on the features of the upstream network.
-
- Args:
- x (tuple[Tensor]): Multi-level features from the
- upstream network, each is a 4D-tensor.
- batch_data_samples (List[:obj:`DetDataSample`]): The Data
- Samples. It usually includes information such as
- `gt_instance`, `gt_panoptic_seg` and `gt_sem_seg`.
- rescale (bool, optional): Whether to rescale the results.
- Defaults to False.
-
- Returns:
- list[obj:`InstanceData`]: Detection results of each image
- after the post process.
- """
- batch_img_metas = [
- data_samples.metainfo for data_samples in batch_data_samples
- ]
-
- outs = self(x)
-
- predictions = self.predict_by_feat(
- *outs, batch_img_metas=batch_img_metas, rescale=rescale)
- return predictions
-
- def predict_by_feat(self,
- cls_scores: List[Tensor],
- bbox_preds: List[Tensor],
- score_factors: Optional[List[Tensor]] = None,
- batch_img_metas: Optional[List[dict]] = None,
- cfg: Optional[ConfigDict] = None,
- rescale: bool = False,
- with_nms: bool = True) -> InstanceList:
- """Transform a batch of output features extracted from the head into
- bbox results.
-
- Note: When score_factors is not None, the cls_scores are
- usually multiplied by it then obtain the real score used in NMS,
- such as CenterNess in FCOS, IoU branch in ATSS.
-
- Args:
- cls_scores (list[Tensor]): Classification scores for all
- scale levels, each is a 4D-tensor, has shape
- (batch_size, num_priors * num_classes, H, W).
- bbox_preds (list[Tensor]): Box energies / deltas for all
- scale levels, each is a 4D-tensor, has shape
- (batch_size, num_priors * 4, H, W).
- score_factors (list[Tensor], optional): Score factor for
- all scale level, each is a 4D-tensor, has shape
- (batch_size, num_priors * 1, H, W). Defaults to None.
- batch_img_metas (list[dict], Optional): Batch image meta info.
- Defaults to None.
- cfg (ConfigDict, optional): Test / postprocessing
- configuration, if None, test_cfg would be used.
- Defaults to None.
- rescale (bool): If True, return boxes in original image space.
- Defaults to False.
- with_nms (bool): If True, do nms before return boxes.
- Defaults to True.
-
- Returns:
- list[:obj:`InstanceData`]: Object detection results of each image
- after the post process. Each item usually contains following keys.
-
- - scores (Tensor): Classification scores, has a shape
- (num_instance, )
- - labels (Tensor): Labels of bboxes, has a shape
- (num_instances, ).
- - bboxes (Tensor): Has a shape (num_instances, 4),
- the last dimension 4 arrange as (x1, y1, x2, y2).
- """
- assert len(cls_scores) == len(bbox_preds)
-
- if score_factors is None:
- # e.g. Retina, FreeAnchor, Foveabox, etc.
- with_score_factors = False
- else:
- # e.g. FCOS, PAA, ATSS, AutoAssign, etc.
- with_score_factors = True
- assert len(cls_scores) == len(score_factors)
-
- num_levels = len(cls_scores)
-
- featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)]
- mlvl_priors = self.prior_generator.grid_priors(
- featmap_sizes,
- dtype=cls_scores[0].dtype,
- device=cls_scores[0].device)
-
- result_list = []
-
- for img_id in range(len(batch_img_metas)):
- img_meta = batch_img_metas[img_id]
- cls_score_list = select_single_mlvl(
- cls_scores, img_id, detach=True)
- bbox_pred_list = select_single_mlvl(
- bbox_preds, img_id, detach=True)
- if with_score_factors:
- score_factor_list = select_single_mlvl(
- score_factors, img_id, detach=True)
- else:
- score_factor_list = [None for _ in range(num_levels)]
-
- results = self._predict_by_feat_single(
- cls_score_list=cls_score_list,
- bbox_pred_list=bbox_pred_list,
- score_factor_list=score_factor_list,
- mlvl_priors=mlvl_priors,
- img_meta=img_meta,
- cfg=cfg,
- rescale=rescale,
- with_nms=with_nms)
- result_list.append(results)
- return result_list
-
- def _predict_by_feat_single(self,
- cls_score_list: List[Tensor],
- bbox_pred_list: List[Tensor],
- score_factor_list: List[Tensor],
- mlvl_priors: List[Tensor],
- img_meta: dict,
- cfg: ConfigDict,
- rescale: bool = False,
- with_nms: bool = True) -> InstanceData:
- """Transform a single image's features extracted from the head into
- bbox results.
-
- Args:
- cls_score_list (list[Tensor]): Box scores from all scale
- levels of a single image, each item has shape
- (num_priors * num_classes, H, W).
- bbox_pred_list (list[Tensor]): Box energies / deltas from
- all scale levels of a single image, each item has shape
- (num_priors * 4, H, W).
- score_factor_list (list[Tensor]): Score factor from all scale
- levels of a single image, each item has shape
- (num_priors * 1, H, W).
- mlvl_priors (list[Tensor]): Each element in the list is
- the priors of a single level in feature pyramid. In all
- anchor-based methods, it has shape (num_priors, 4). In
- all anchor-free methods, it has shape (num_priors, 2)
- when `with_stride=True`, otherwise it still has shape
- (num_priors, 4).
- img_meta (dict): Image meta info.
- cfg (mmengine.Config): Test / postprocessing configuration,
- if None, test_cfg would be used.
- rescale (bool): If True, return boxes in original image space.
- Defaults to False.
- with_nms (bool): If True, do nms before return boxes.
- Defaults to True.
-
- Returns:
- :obj:`InstanceData`: Detection results of each image
- after the post process.
- Each item usually contains following keys.
-
- - scores (Tensor): Classification scores, has a shape
- (num_instance, )
- - labels (Tensor): Labels of bboxes, has a shape
- (num_instances, ).
- - bboxes (Tensor): Has a shape (num_instances, 4),
- the last dimension 4 arrange as (x1, y1, x2, y2).
- """
- if score_factor_list[0] is None:
- # e.g. Retina, FreeAnchor, etc.
- with_score_factors = False
- else:
- # e.g. FCOS, PAA, ATSS, etc.
- with_score_factors = True
-
- cfg = self.test_cfg if cfg is None else cfg
- cfg = copy.deepcopy(cfg)
- img_shape = img_meta['img_shape']
- nms_pre = cfg.get('nms_pre', -1)
-
- mlvl_bbox_preds = []
- mlvl_valid_priors = []
- mlvl_scores = []
- mlvl_labels = []
- if with_score_factors:
- mlvl_score_factors = []
- else:
- mlvl_score_factors = None
- for level_idx, (cls_score, bbox_pred, score_factor, priors) in \
- enumerate(zip(cls_score_list, bbox_pred_list,
- score_factor_list, mlvl_priors)):
-
- assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
-
- dim = self.bbox_coder.encode_size
- bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, dim)
- if with_score_factors:
- score_factor = score_factor.permute(1, 2,
- 0).reshape(-1).sigmoid()
- cls_score = cls_score.permute(1, 2,
- 0).reshape(-1, self.cls_out_channels)
- if self.use_sigmoid_cls:
- scores = cls_score.sigmoid()
- else:
- # remind that we set FG labels to [0, num_class-1]
- # since mmdet v2.0
- # BG cat_id: num_class
- scores = cls_score.softmax(-1)[:, :-1]
-
- # After https://github.com/open-mmlab/mmdetection/pull/6268/,
- # this operation keeps fewer bboxes under the same `nms_pre`.
- # There is no difference in performance for most models. If you
- # find a slight drop in performance, you can set a larger
- # `nms_pre` than before.
- score_thr = cfg.get('score_thr', 0)
-
- results = filter_scores_and_topk(
- scores, score_thr, nms_pre,
- dict(bbox_pred=bbox_pred, priors=priors))
- scores, labels, keep_idxs, filtered_results = results
-
- bbox_pred = filtered_results['bbox_pred']
- priors = filtered_results['priors']
-
- if with_score_factors:
- score_factor = score_factor[keep_idxs]
-
- mlvl_bbox_preds.append(bbox_pred)
- mlvl_valid_priors.append(priors)
- mlvl_scores.append(scores)
- mlvl_labels.append(labels)
-
- if with_score_factors:
- mlvl_score_factors.append(score_factor)
-
- bbox_pred = torch.cat(mlvl_bbox_preds)
- priors = cat_boxes(mlvl_valid_priors)
- bboxes = self.bbox_coder.decode(priors, bbox_pred, max_shape=img_shape)
-
- results = InstanceData()
- results.bboxes = bboxes
- results.scores = torch.cat(mlvl_scores)
- results.labels = torch.cat(mlvl_labels)
- if with_score_factors:
- results.score_factors = torch.cat(mlvl_score_factors)
-
- return self._bbox_post_process(
- results=results,
- cfg=cfg,
- rescale=rescale,
- with_nms=with_nms,
- img_meta=img_meta)
-
- def _bbox_post_process(self,
- results: InstanceData,
- cfg: ConfigDict,
- rescale: bool = False,
- with_nms: bool = True,
- img_meta: Optional[dict] = None) -> InstanceData:
- """bbox post-processing method.
-
- The boxes would be rescaled to the original image scale and do
- the nms operation. Usually `with_nms` is False is used for aug test.
-
- Args:
- results (:obj:`InstaceData`): Detection instance results,
- each item has shape (num_bboxes, ).
- cfg (ConfigDict): Test / postprocessing configuration,
- if None, test_cfg would be used.
- rescale (bool): If True, return boxes in original image space.
- Default to False.
- with_nms (bool): If True, do nms before return boxes.
- Default to True.
- img_meta (dict, optional): Image meta info. Defaults to None.
-
- Returns:
- :obj:`InstanceData`: Detection results of each image
- after the post process.
- Each item usually contains following keys.
-
- - scores (Tensor): Classification scores, has a shape
- (num_instance, )
- - labels (Tensor): Labels of bboxes, has a shape
- (num_instances, ).
- - bboxes (Tensor): Has a shape (num_instances, 4),
- the last dimension 4 arrange as (x1, y1, x2, y2).
- """
- if rescale:
- assert img_meta.get('scale_factor') is not None
- scale_factor = [1 / s for s in img_meta['scale_factor']]
- results.bboxes = scale_boxes(results.bboxes, scale_factor)
-
- if hasattr(results, 'score_factors'):
- # TODO： Add sqrt operation in order to be consistent with
- # the paper.
- score_factors = results.pop('score_factors')
- results.scores = results.scores * score_factors
-
- # filter small size bboxes
- if cfg.get('min_bbox_size', -1) >= 0:
- w, h = get_box_wh(results.bboxes)
- valid_mask = (w > cfg.min_bbox_size) & (h > cfg.min_bbox_size)
- if not valid_mask.all():
- results = results[valid_mask]
-
- # TODO: deal with `with_nms` and `nms_cfg=None` in test_cfg
- if with_nms and results.bboxes.numel() > 0:
- bboxes = get_box_tensor(results.bboxes)
- det_bboxes, keep_idxs = batched_nms(bboxes, results.scores,
- results.labels, cfg.nms)
- results = results[keep_idxs]
- # some nms would reweight the score, such as softnms
- results.scores = det_bboxes[:, -1]
- results = results[:cfg.max_per_img]
-
- return results
-
- def aug_test(self,
- aug_batch_feats,
- aug_batch_img_metas,
- rescale=False,
- with_ori_nms=False,
- **kwargs):
- """Test function with test time augmentation.
-
- Args:
- aug_batch_feats (list[tuple[Tensor]]): The outer list
- indicates test-time augmentations and inner tuple
- indicate the multi-level feats from
- FPN, each Tensor should have a shape (B, C, H, W),
- aug_batch_img_metas (list[list[dict]]): Meta information
- of images under the different test-time augs
- (multiscale, flip, etc.). The outer list indicate
- the
- rescale (bool, optional): Whether to rescale the results.
- Defaults to False.
- with_ori_nms (bool): Whether execute the nms in original head.
- Defaults to False. It will be `True` when the head is
- adopted as `rpn_head`.
-
- Returns:
- list(obj:`InstanceData`): Detection results of the
- input images. Each item usually contains\
- following keys.
-
- - scores (Tensor): Classification scores, has a shape
- (num_instance,)
- - labels (Tensor): Labels of bboxes, has a shape
- (num_instances,).
- - bboxes (Tensor): Has a shape (num_instances, 4),
- the last dimension 4 arrange as (x1, y1, x2, y2).
- """
- # TODO: remove this for detr and deformdetr
- sig_of_get_results = signature(self.get_results)
- get_results_args = [
- p.name for p in sig_of_get_results.parameters.values()
- ]
- get_results_single_sig = signature(self._get_results_single)
- get_results_single_sig_args = [
- p.name for p in get_results_single_sig.parameters.values()
- ]
- assert ('with_nms' in get_results_args) and \
- ('with_nms' in get_results_single_sig_args), \
- f'{self.__class__.__name__}' \
- 'does not support test-time augmentation '
-
- num_imgs = len(aug_batch_img_metas[0])
- aug_batch_results = []
- for x, img_metas in zip(aug_batch_feats, aug_batch_img_metas):
- outs = self.forward(x)
- batch_instance_results = self.get_results(
- *outs,
- img_metas=img_metas,
- cfg=self.test_cfg,
- rescale=False,
- with_nms=with_ori_nms,
- **kwargs)
- aug_batch_results.append(batch_instance_results)
-
- # after merging, bboxes will be rescaled to the original image
- batch_results = merge_aug_results(aug_batch_results,
- aug_batch_img_metas)
-
- final_results = []
- for img_id in range(num_imgs):
- results = batch_results[img_id]
- det_bboxes, keep_idxs = batched_nms(results.bboxes, results.scores,
- results.labels,
- self.test_cfg.nms)
- results = results[keep_idxs]
- # some nms operation may reweight the score such as softnms
- results.scores = det_bboxes[:, -1]
- results = results[:self.test_cfg.max_per_img]
- if rescale:
- # all results have been mapped to the original scale
- # in `merge_aug_results`, so just pass
- pass
- else:
- # map to the first aug image scale
- scale_factor = results.bboxes.new_tensor(
- aug_batch_img_metas[0][img_id]['scale_factor'])
- results.bboxes = \
- results.bboxes * scale_factor
-
- final_results.append(results)
-
- return final_results
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/dense_test_mixins.py b/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/dense_test_mixins.py
deleted file mode 100644
index a7526d48430d6bc6b82777980d0bef418e80b91c..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/dense_test_mixins.py
+++ /dev/null
@@ -1,215 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import sys
-import warnings
-from inspect import signature
-
-import torch
-from mmcv.ops import batched_nms
-from mmengine.structures import InstanceData
-
-from mmdet.structures.bbox import bbox_mapping_back
-from ..test_time_augs import merge_aug_proposals
-
-if sys.version_info >= (3, 7):
- from mmdet.utils.contextmanagers import completed
-
-
-class BBoxTestMixin(object):
- """Mixin class for testing det bboxes via DenseHead."""
-
- def simple_test_bboxes(self, feats, img_metas, rescale=False):
- """Test det bboxes without test-time augmentation, can be applied in
- DenseHead except for ``RPNHead`` and its variants, e.g., ``GARPNHead``,
- etc.
-
- Args:
- feats (tuple[torch.Tensor]): Multi-level features from the
- upstream network, each is a 4D-tensor.
- img_metas (list[dict]): List of image information.
- rescale (bool, optional): Whether to rescale the results.
- Defaults to False.
-
- Returns:
- list[obj:`InstanceData`]: Detection results of each
- image after the post process. \
- Each item usually contains following keys. \
-
- - scores (Tensor): Classification scores, has a shape
- (num_instance,)
- - labels (Tensor): Labels of bboxes, has a shape
- (num_instances,).
- - bboxes (Tensor): Has a shape (num_instances, 4),
- the last dimension 4 arrange as (x1, y1, x2, y2).
- """
- warnings.warn('You are calling `simple_test_bboxes` in '
- '`dense_test_mixins`, but the `dense_test_mixins`'
- 'will be deprecated soon. Please use '
- '`simple_test` instead.')
- outs = self.forward(feats)
- results_list = self.get_results(
- *outs, img_metas=img_metas, rescale=rescale)
- return results_list
-
- def aug_test_bboxes(self, feats, img_metas, rescale=False):
- """Test det bboxes with test time augmentation, can be applied in
- DenseHead except for ``RPNHead`` and its variants, e.g., ``GARPNHead``,
- etc.
-
- Args:
- feats (list[Tensor]): the outer list indicates test-time
- augmentations and inner Tensor should have a shape NxCxHxW,
- which contains features for all images in the batch.
- img_metas (list[list[dict]]): the outer list indicates test-time
- augs (multiscale, flip, etc.) and the inner list indicates
- images in a batch. each dict has image information.
- rescale (bool, optional): Whether to rescale the results.
- Defaults to False.
-
- Returns:
- list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
- The first item is ``bboxes`` with shape (n, 5),
- where 5 represent (tl_x, tl_y, br_x, br_y, score).
- The shape of the second tensor in the tuple is ``labels``
- with shape (n,). The length of list should always be 1.
- """
-
- warnings.warn('You are calling `aug_test_bboxes` in '
- '`dense_test_mixins`, but the `dense_test_mixins`'
- 'will be deprecated soon. Please use '
- '`aug_test` instead.')
- # check with_nms argument
- gb_sig = signature(self.get_results)
- gb_args = [p.name for p in gb_sig.parameters.values()]
- gbs_sig = signature(self._get_results_single)
- gbs_args = [p.name for p in gbs_sig.parameters.values()]
- assert ('with_nms' in gb_args) and ('with_nms' in gbs_args), \
- f'{self.__class__.__name__}' \
- ' does not support test-time augmentation'
-
- aug_bboxes = []
- aug_scores = []
- aug_labels = []
- for x, img_meta in zip(feats, img_metas):
- # only one image in the batch
- outs = self.forward(x)
- bbox_outputs = self.get_results(
- *outs,
- img_metas=img_meta,
- cfg=self.test_cfg,
- rescale=False,
- with_nms=False)[0]
- aug_bboxes.append(bbox_outputs.bboxes)
- aug_scores.append(bbox_outputs.scores)
- if len(bbox_outputs) >= 3:
- aug_labels.append(bbox_outputs.labels)
-
- # after merging, bboxes will be rescaled to the original image size
- merged_bboxes, merged_scores = self.merge_aug_bboxes(
- aug_bboxes, aug_scores, img_metas)
- merged_labels = torch.cat(aug_labels, dim=0) if aug_labels else None
-
- if merged_bboxes.numel() == 0:
- det_bboxes = torch.cat([merged_bboxes, merged_scores[:, None]], -1)
- return [
- (det_bboxes, merged_labels),
- ]
-
- det_bboxes, keep_idxs = batched_nms(merged_bboxes, merged_scores,
- merged_labels, self.test_cfg.nms)
- det_bboxes = det_bboxes[:self.test_cfg.max_per_img]
- det_labels = merged_labels[keep_idxs][:self.test_cfg.max_per_img]
-
- if rescale:
- _det_bboxes = det_bboxes
- else:
- _det_bboxes = det_bboxes.clone()
- _det_bboxes[:, :4] *= det_bboxes.new_tensor(
- img_metas[0][0]['scale_factor'])
-
- results = InstanceData()
- results.bboxes = _det_bboxes[:, :4]
- results.scores = _det_bboxes[:, 4]
- results.labels = det_labels
- return [results]
-
- def aug_test_rpn(self, feats, img_metas):
- """Test with augmentation for only for ``RPNHead`` and its variants,
- e.g., ``GARPNHead``, etc.
-
- Args:
- feats (tuple[Tensor]): Features from the upstream network, each is
- a 4D-tensor.
- img_metas (list[dict]): Meta info of each image.
-
- Returns:
- list[Tensor]: Proposals of each image, each item has shape (n, 5),
- where 5 represent (tl_x, tl_y, br_x, br_y, score).
- """
- samples_per_gpu = len(img_metas[0])
- aug_proposals = [[] for _ in range(samples_per_gpu)]
- for x, img_meta in zip(feats, img_metas):
- results_list = self.simple_test_rpn(x, img_meta)
- for i, results in enumerate(results_list):
- proposals = torch.cat(
- [results.bboxes, results.scores[:, None]], dim=-1)
- aug_proposals[i].append(proposals)
- # reorganize the order of 'img_metas' to match the dimensions
- # of 'aug_proposals'
- aug_img_metas = []
- for i in range(samples_per_gpu):
- aug_img_meta = []
- for j in range(len(img_metas)):
- aug_img_meta.append(img_metas[j][i])
- aug_img_metas.append(aug_img_meta)
- # after merging, proposals will be rescaled to the original image size
-
- merged_proposals = []
- for proposals, aug_img_meta in zip(aug_proposals, aug_img_metas):
- merged_proposal = merge_aug_proposals(proposals, aug_img_meta,
- self.test_cfg)
- results = InstanceData()
- results.bboxes = merged_proposal[:, :4]
- results.scores = merged_proposal[:, 4]
- merged_proposals.append(results)
- return merged_proposals
-
- if sys.version_info >= (3, 7):
-
- async def async_simple_test_rpn(self, x, img_metas):
- sleep_interval = self.test_cfg.pop('async_sleep_interval', 0.025)
- async with completed(
- __name__, 'rpn_head_forward',
- sleep_interval=sleep_interval):
- rpn_outs = self(x)
-
- proposal_list = self.get_results(*rpn_outs, img_metas=img_metas)
- return proposal_list
-
- def merge_aug_bboxes(self, aug_bboxes, aug_scores, img_metas):
- """Merge augmented detection bboxes and scores.
-
- Args:
- aug_bboxes (list[Tensor]): shape (n, 4*#class)
- aug_scores (list[Tensor] or None): shape (n, #class)
- img_shapes (list[Tensor]): shape (3, ).
-
- Returns:
- tuple[Tensor]: ``bboxes`` with shape (n,4), where
- 4 represent (tl_x, tl_y, br_x, br_y)
- and ``scores`` with shape (n,).
- """
- recovered_bboxes = []
- for bboxes, img_info in zip(aug_bboxes, img_metas):
- img_shape = img_info[0]['img_shape']
- scale_factor = img_info[0]['scale_factor']
- flip = img_info[0]['flip']
- flip_direction = img_info[0]['flip_direction']
- bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip,
- flip_direction)
- recovered_bboxes.append(bboxes)
- bboxes = torch.cat(recovered_bboxes, dim=0)
- if aug_scores is None:
- return bboxes
- else:
- scores = torch.cat(aug_scores, dim=0)
- return bboxes, scores
diff --git a/spaces/Laughify/Among_Us_Logic_AI_Generator/README.md b/spaces/Laughify/Among_Us_Logic_AI_Generator/README.md
deleted file mode 100644
index 32e5395e0688cf87bde550f6f1b8b54e9a928c73..0000000000000000000000000000000000000000
--- a/spaces/Laughify/Among_Us_Logic_AI_Generator/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Among Us Logic AI Generator
-emoji: 👨🚀
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.9.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/lib_v5/layers_33966KB.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/lib_v5/layers_33966KB.py
deleted file mode 100644
index 9b127bc6427f5c60c8cf85603a3d8a093c3501c4..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/infer_libs/uvr5_pack/lib_v5/layers_33966KB.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
- super(Conv2DBNActiv, self).__init__()
- self.conv = nn.Sequential(
- nn.Conv2d(
- nin,
- nout,
- kernel_size=ksize,
- stride=stride,
- padding=pad,
- dilation=dilation,
- bias=False,
- ),
- nn.BatchNorm2d(nout),
- activ(),
- )
-
- def __call__(self, x):
- return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
- super(SeperableConv2DBNActiv, self).__init__()
- self.conv = nn.Sequential(
- nn.Conv2d(
- nin,
- nin,
- kernel_size=ksize,
- stride=stride,
- padding=pad,
- dilation=dilation,
- groups=nin,
- bias=False,
- ),
- nn.Conv2d(nin, nout, kernel_size=1, bias=False),
- nn.BatchNorm2d(nout),
- activ(),
- )
-
- def __call__(self, x):
- return self.conv(x)
-
-
-class Encoder(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
- super(Encoder, self).__init__()
- self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
- self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
- def __call__(self, x):
- skip = self.conv1(x)
- h = self.conv2(skip)
-
- return h, skip
-
-
-class Decoder(nn.Module):
- def __init__(
- self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
- ):
- super(Decoder, self).__init__()
- self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
- self.dropout = nn.Dropout2d(0.1) if dropout else None
-
- def __call__(self, x, skip=None):
- x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
- if skip is not None:
- skip = spec_utils.crop_center(skip, x)
- x = torch.cat([x, skip], dim=1)
- h = self.conv(x)
-
- if self.dropout is not None:
- h = self.dropout(h)
-
- return h
-
-
-class ASPPModule(nn.Module):
- def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU):
- super(ASPPModule, self).__init__()
- self.conv1 = nn.Sequential(
- nn.AdaptiveAvgPool2d((1, None)),
- Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
- )
- self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
- self.conv3 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
- )
- self.conv4 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
- )
- self.conv5 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
- )
- self.conv6 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
- )
- self.conv7 = SeperableConv2DBNActiv(
- nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
- )
- self.bottleneck = nn.Sequential(
- Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
- )
-
- def forward(self, x):
- _, _, h, w = x.size()
- feat1 = F.interpolate(
- self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
- )
- feat2 = self.conv2(x)
- feat3 = self.conv3(x)
- feat4 = self.conv4(x)
- feat5 = self.conv5(x)
- feat6 = self.conv6(x)
- feat7 = self.conv7(x)
- out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)
- bottle = self.bottleneck(out)
- return bottle
diff --git a/spaces/Lbin123/Lbingo/src/lib/hooks/chat-history.ts b/spaces/Lbin123/Lbingo/src/lib/hooks/chat-history.ts
deleted file mode 100644
index c6fbf3fecfa86fe553f56acc8253236b8f22a775..0000000000000000000000000000000000000000
--- a/spaces/Lbin123/Lbingo/src/lib/hooks/chat-history.ts
+++ /dev/null
@@ -1,62 +0,0 @@
-import { zip } from 'lodash-es'
-import { ChatMessageModel, BotId } from '@/lib/bots/bing/types'
-import { Storage } from '../storage'
-
-/**
- * conversations:$botId => Conversation[]
- * conversation:$botId:$cid:messages => ChatMessageModel[]
- */
-
-interface Conversation {
- id: string
- createdAt: number
-}
-
-type ConversationWithMessages = Conversation & { messages: ChatMessageModel[] }
-
-async function loadHistoryConversations(botId: BotId): Promise {
- const key = `conversations:${botId}`
- const { [key]: value } = await Storage.get(key)
- return value || []
-}
-
-async function deleteHistoryConversation(botId: BotId, cid: string) {
- const conversations = await loadHistoryConversations(botId)
- const newConversations = conversations.filter((c) => c.id !== cid)
- await Storage.set({ [`conversations:${botId}`]: newConversations })
-}
-
-async function loadConversationMessages(botId: BotId, cid: string): Promise {
- const key = `conversation:${botId}:${cid}:messages`
- const { [key]: value } = await Storage.get(key)
- return value || []
-}
-
-export async function setConversationMessages(botId: BotId, cid: string, messages: ChatMessageModel[]) {
- const conversations = await loadHistoryConversations(botId)
- if (!conversations.some((c) => c.id === cid)) {
- conversations.unshift({ id: cid, createdAt: Date.now() })
- await Storage.set({ [`conversations:${botId}`]: conversations })
- }
- const key = `conversation:${botId}:${cid}:messages`
- await Storage.set({ [key]: messages })
-}
-
-export async function loadHistoryMessages(botId: BotId): Promise {
- const conversations = await loadHistoryConversations(botId)
- const messagesList = await Promise.all(conversations.map((c) => loadConversationMessages(botId, c.id)))
- return zip(conversations, messagesList).map(([c, messages]) => ({
- id: c!.id,
- createdAt: c!.createdAt,
- messages: messages!,
- }))
-}
-
-export async function deleteHistoryMessage(botId: BotId, conversationId: string, messageId: string) {
- const messages = await loadConversationMessages(botId, conversationId)
- const newMessages = messages.filter((m) => m.id !== messageId)
- await setConversationMessages(botId, conversationId, newMessages)
- if (!newMessages.length) {
- await deleteHistoryConversation(botId, conversationId)
- }
-}
diff --git a/spaces/Maheshiscoding/MAHESH-AI-HELPER/app.py b/spaces/Maheshiscoding/MAHESH-AI-HELPER/app.py
deleted file mode 100644
index 8479d0b652a2d74c4a1b76451a23771b51009cce..0000000000000000000000000000000000000000
--- a/spaces/Maheshiscoding/MAHESH-AI-HELPER/app.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import openai
-import gradio
-
-openai.api_key = "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"
-
-messages = [{"role": "system", "content": "You are Homework Helper who helps each and every student. You were built by Mahesh - a 13 year old child, studying in india, Haryana. his school name is St. Theresa Convent school which is in karnal.PLS ANSWER LOGICALLY AND GIVE ONLY WHAT THE USER ASKS FOR. TRY TO ANSWER AS YOU ARE A TEACHER EXPLAINING TO A STUDENT "}]
-
-def CustomChatGPT(user_input):
- messages.append({"role": "user", "content": user_input})
- response = openai.ChatCompletion.create(
- model = "gpt-3.5-turbo",
- messages = messages
- )
- ChatGPT_reply = response["choices"][0]["message"]["content"]
- messages.append({"role": "assistant", "content": ChatGPT_reply})
- return ChatGPT_reply
-
-demo = gradio.Interface(fn=CustomChatGPT, inputs = "text", outputs = "text", title = "Mahesh' Teacher Assistance AI", theme= "dark")
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/Marshalls/testmtd/models/moglow/__init__.py b/spaces/Marshalls/testmtd/models/moglow/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/MashiroSA/sovits-emu-voice-transform/README.md b/spaces/MashiroSA/sovits-emu-voice-transform/README.md
deleted file mode 100644
index 5fd8c2cd564b6ad0d939d5ef1d508e824bd990e8..0000000000000000000000000000000000000000
--- a/spaces/MashiroSA/sovits-emu-voice-transform/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Sovits Emu Voice Transform
-emoji: 🌸
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 3.32.0
-python_version: 3.9
-app_file: app.py
-pinned: false
-license: gpl-3.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Mehrdadbn/Movie-recommender-system/app.py b/spaces/Mehrdadbn/Movie-recommender-system/app.py
deleted file mode 100644
index 072096197c3d591810e462a1759a38c086583a52..0000000000000000000000000000000000000000
--- a/spaces/Mehrdadbn/Movie-recommender-system/app.py
+++ /dev/null
@@ -1,561 +0,0 @@
-import base64
-from io import BytesIO
-
-import numpy as np
-from PIL import Image
-from rich.theme import Theme
-from sklearn.metrics import precision_score, recall_score, f1_score
-import pandas as pd
-import ast
-import math
-import requests
-from sklearn.feature_extraction.text import CountVectorizer
-import gc
-from sklearn.metrics import pairwise_distances
-import streamlit as st
-# import tensorflow as tf
-# from tensorflow.keras.models import Model
-# from tensorflow.keras.layers import Input, Dense, Dropout, Concatenate
-# from tensorflow.keras.optimizers import Adam
-from sklearn.preprocessing import StandardScaler
-# import matplotlib.pyplot as plt
-
-# *******************************************************
-
-movies_meta = pd.read_csv("movies_metadata.csv")
-check = movies_meta['release_date']
-movies_meta = movies_meta[:10000]
-copy_movies_meta = movies_meta
-credits = pd.read_csv('credits.csv')
-copy_credits = pd.read_csv('credits.csv')
-keywords = pd.read_csv('keywords.csv')
-keywords = keywords[:20000]
-
-# *******************************************************
-
-num_missing = movies_meta[movies_meta['production_companies'].isnull()]
-clean_movies_meta = movies_meta.dropna(subset=['production_companies'], inplace=True)
-
-movies_meta = movies_meta[movies_meta.id != '1997-08-20']
-movies_meta = movies_meta[movies_meta.id != '2012-09-29']
-movies_meta = movies_meta[movies_meta.id != '2014-01-01']
-movies_meta = movies_meta.astype({'id': 'int64'})
-
-movies_meta = movies_meta.merge(keywords, on='id')
-movies_meta = movies_meta.merge(credits, on='id')
-
-
-def btc_function(data):
- if type(data) == str:
- return ast.literal_eval(data)['name'].replace(" ", "")
- return data
-
-
-# https://www.kaggle.com/hadasik/movies-analysis-visualization-newbie
-def get_values(data_str):
- if isinstance(data_str, float):
- pass
- else:
- values = []
- data_str = ast.literal_eval(data_str)
- if isinstance(data_str, list):
- for k_v in data_str:
- values.append(k_v['name'].replace(" ", ""))
- return str(values)[1:-1]
- else:
- return None
-
-
-movies_meta['btc_name'] = movies_meta.belongs_to_collection.apply(btc_function)
-movies_meta[
- ['genres', 'production_companies', 'production_countries', 'spoken_languages', 'keywords', 'cast', 'crew']] = \
- movies_meta[['genres', 'production_companies', 'production_countries', 'spoken_languages', 'keywords', 'cast',
- 'crew']].applymap(get_values)
-movies_meta['is_homepage'] = movies_meta['homepage'].isnull()
-
-movies_meta['status'] = movies_meta['status'].fillna('')
-movies_meta['original_language'] = movies_meta['original_language'].fillna('')
-movies_meta['btc_name'] = movies_meta['btc_name'].fillna('')
-
-movies_meta.drop_duplicates(inplace=True)
-
-
-def vector_values(df, columns, min_df_value):
- c_vector = CountVectorizer(min_df=min_df_value)
- df_1 = pd.DataFrame(index=df.index)
- for col in columns:
- print(col)
- df_1 = df_1.join(
- pd.DataFrame(c_vector.fit_transform(df[col]).toarray(), columns=c_vector.get_feature_names_out(),
- index=df.index).add_prefix(col + '_'))
- return df_1
-
-
-movies_meta_addon_1 = vector_values(movies_meta,
- columns=['status', 'original_language', 'genres', 'production_companies',
- 'production_countries', 'spoken_languages', 'keywords', 'cast', 'crew'],
- min_df_value=20)
-movies_meta_addon_2 = vector_values(movies_meta, columns=['btc_name'], min_df_value=2)
-
-col = ['belongs_to_collection', 'genres', 'homepage', 'id', 'imdb_id', 'overview', 'poster_path', 'status',
- 'original_language',
- 'production_companies', 'production_countries', 'spoken_languages', 'keywords', 'cast', 'crew', 'tagline',
- 'adult']
-movies_meta.drop(columns=col, inplace=True)
-col = ['video', 'is_homepage']
-for c in col:
- movies_meta[c] = movies_meta[c].astype(bool)
- movies_meta[c] = movies_meta[c].astype(int)
-
-
-def get_year(date):
- return str(date).split('-')[0]
-
-
-movies_meta['popularity'] = movies_meta['popularity'].astype(float)
-movies_meta['budget'] = movies_meta['budget'].astype(float)
-movies_meta['vote_average_group'] = pd.qcut(movies_meta['vote_average'], q=10, precision=2, duplicates='drop')
-movies_meta['popularity_group'] = pd.qcut(movies_meta['popularity'], q=10, precision=2, duplicates='drop')
-movies_meta['vote_average_group'] = pd.qcut(movies_meta['vote_average'], q=10, precision=2, duplicates='drop')
-movies_meta['runtime_group'] = pd.qcut(movies_meta['runtime'], q=10, precision=2, duplicates='drop')
-movies_meta['budget_group'] = pd.qcut(movies_meta['budget'], q=10, precision=2, duplicates='drop')
-movies_meta['revenue_group'] = pd.qcut(movies_meta['revenue'], q=10, precision=2, duplicates='drop')
-movies_meta['vote_count_group'] = pd.qcut(movies_meta['vote_count'], q=10, precision=2, duplicates='drop')
-movies_meta['release_year'] = movies_meta['release_date'].apply(get_year)
-
-movies_meta['release_year'] = movies_meta['release_year'].fillna('')
-movies_meta['release_year'] = movies_meta['release_year'].astype(float)
-movies_meta['release_year_group'] = pd.qcut(movies_meta['release_year'], q=10, precision=2, duplicates='drop')
-movies_meta['title_new'] = movies_meta.apply(lambda x: str(x['title']) + ' (' + str(x['release_date']) + ')', axis=1)
-
-movies_meta_addon_3 = pd.get_dummies(movies_meta[
- ['vote_average_group', 'popularity_group', 'runtime_group', 'budget_group',
- 'revenue_group', 'vote_count_group', 'release_year_group']])
-movies_meta_train = pd.concat(
- [movies_meta_addon_1, movies_meta_addon_2, movies_meta_addon_3, movies_meta[['video', 'is_homepage']]], axis=1)
-
-movies_meta_train.index = movies_meta['title_new']
-gc.collect()
-
-
-def get_similar_movies(movie_title, num_rec=10):
- try:
- sample_1 = 1 - pairwise_distances([movies_meta_train.loc[movie_title].values], movies_meta_train.values,
- metric='cosine')
- sample_1 = pd.DataFrame(sample_1.T, index=movies_meta_train.index)
- return sample_1.sort_values(by=0, ascending=False).head(num_rec).index
- except ValueError as e:
- print(e)
-
-# ******************* Evaluation Part ******************
-def evaluate_recommendations(test_set, get_similar_movies_func, num_rec=10):
- true_positives = 0
- false_positives = 0
- false_negatives = 0
-
- for user, items in test_set.items():
- recommended_items = get_similar_movies_func(user, num_rec)
- recommended_items = set(recommended_items)
- relevant_items = set(items)
-
- true_positives += len(recommended_items.intersection(relevant_items))
- false_positives += len(recommended_items - relevant_items)
- false_negatives += len(relevant_items - recommended_items)
-
- precision = true_positives / (true_positives + false_positives)
- recall = true_positives / (true_positives + false_negatives)
- f1 = 2 * (precision * recall) / (precision + recall)
-
- return precision, recall, f1
-
-test_set = {
- 'Toy Story (1995-10-30)': ['Toy Story 2 (1999-10-30)', 'Chicken Run (2000-06-21)'],
- 'The Lion King (1994-06-23)': ['Toy Story (1995-10-30)', 'The Little Mermaid (1989-11-17)'],
- 'Papillon (1973-12-13)': ['The Godfather (1972-03-14)'],
- 'The Godfather (1972-03-14)': ['Papillon (1973-12-13)']
-}
-
-# Evaluate the recommendations using the test set
-precision, recall, f1 = evaluate_recommendations(test_set, get_similar_movies, num_rec=10)
-
-print('Precision:', precision)
-print('Recall:', recall)
-print('F1 score:', f1)
-
-# ************ Using deep Learning Methods *************
-# def build_model(input_dim):
-# inputs = Input(shape=(input_dim,))
-# x = Dense(256, activation='relu')(inputs)
-# x = Dropout(0.2)(x)
-# x = Dense(128, activation='relu')(x)
-# x = Dropout(0.2)(x)
-# x = Dense(64, activation='relu')(x)
-# embeddings = Dense(32, activation='relu')(x)
-#
-# model = Model(inputs=inputs, outputs=embeddings)
-# model.compile(optimizer=Adam(learning_rate=0.001), loss='mse')
-# return model
-
-# Scale the feature data
-scaler = StandardScaler()
-X = scaler.fit_transform(movies_meta_train.values)
-
-# Create and train the model
-# input_dim = X.shape[1]
-# model = build_model(input_dim)
-# model.fit(X, X, batch_size=64, epochs=30, verbose=1)
-
-
-# def nn_get_similar_movies(movie_title, num_rec=10):
-# try:
-# movie_embedding = model.predict(scaler.transform([movies_meta_train.loc[movie_title].values]))
-# all_embeddings = model.predict(X)
-# similarities = tf.keras.losses.cosine_similarity(movie_embedding, all_embeddings)
-#
-# top_indices = np.argsort(similarities.numpy())[:num_rec]
-# return movies_meta_train.index[top_indices]
-# except ValueError as e:
-# print(e)
-
-
-
-# ******************* Front Variables ******************
-input1 = ""
-input2 = ""
-input3 = ""
-
-# movie_name1 = "Undisputed III : Redemption"
-# movie_name2 = "Finding Nemo"
-# movie_name3 = "Thor"
-movie_name1 = ""
-movie_name2 = ""
-movie_name3 = ""
-
-movie_year1 = 0
-movie_year2 = 0
-movie_year3 = 0
-
-input1_count = 0
-input2_count = 0
-input3_count = 0
-
-recomendation_list = []
-
-counter1 = 0
-counter2 = 0
-counter3 = 0
-
-
-# ******************* Front Part ******************
-# @st.cache_data()
-def get_base64_of_bin_file(bin_file):
- with open(bin_file, 'rb') as f:
- data = f.read()
- return base64.b64encode(data).decode()
-
-
-def add_bg_from_url():
- st.markdown(
- f"""
-
- """,
- unsafe_allow_html=True
- )
-
-
-add_bg_from_url()
-
-
-# st.set_page_config(page_title="Movie Recommender System", page_icon="🎥", theme="light")
-
-heading_style = """
-
-"""
-st.markdown(heading_style, unsafe_allow_html=True)
-st.markdown("Movie Recommender System ", unsafe_allow_html=True)
-
-select_list = []
-
-for s in movies_meta['title_new'].values:
- s = s.replace('(', "")
- s = s.replace(')', "")
- s = s.split('-')[0]
- inp = s[0:-4]
- inp = inp + " - "
- inp = inp + s[-4:]
- select_list.append(inp)
-
-
-
-st.write("""
- **description :** This movie recommendation app is a helpful tool for users who are
-looking for new movies to watch based on their previous viewing preferences.
-It offers a personalized and convenient way for users to discover new movies that
-they are likely to enjoy.
-***
-""")
-
-st.write("""
- ## Enter 3 movies that you have watched and liked with their rates.
-""")
-
-movie_name1 = movie_name1 + (st.selectbox('Enter your first movie:',select_list))[0:-8]
-movie_rate1 = st.slider(
- f'Rate {movie_name1}!',
- 0, 10, 1,
- key='movie_rate1')
-st.write("""
-***
-""")
-movie_name2 = movie_name2 + (st.selectbox('Enter your second movie:',select_list))[0:-8]
-movie_rate2 = st.slider(
- f'Rate {movie_name2}!',
- 0, 10, 1,
- key='movie_rate2')
-st.write("""
-***
-""")
-movie_name3 = movie_name3 + (st.selectbox('Enter your third movie:',select_list))[0:-8]
-movie_rate3 = st.slider(
- f'Rate {movie_name3}!',
- 0, 10, 1,
- key = 'movie_rate3'
-)
-
-button_style = """
-
-"""
-st.markdown(button_style, unsafe_allow_html=True)
-if st.button("Suggest me!", key="my-button", help="Submit!"):
-
- filtered_row3 = movies_meta.loc[
- (movies_meta['original_title'] == movie_name1)]
- input1 = filtered_row3.iloc[0]['title_new']
-
- filtered_row2 = movies_meta.loc[
- (movies_meta['original_title'] == movie_name2)]
- input2 = filtered_row2.iloc[0]['title_new']
-
- filtered_row3 = movies_meta.loc[
- (movies_meta['original_title'] == movie_name3)]
- input3 = filtered_row3.iloc[0]['title_new']
-
- recommend1 = get_similar_movies(input1)[1:]
- recommend2 = get_similar_movies(input2)[1:]
- recommend3 = get_similar_movies(input3)[1:]
-
-
- # nn_recommend1 = nn_get_similar_movies(input1)
- # nn_recommend2 = nn_get_similar_movies(input3)
- # nn_recommend3 = nn_get_similar_movies(input3)
-
-
- input1_count = math.ceil((movie_rate1 * 10) / (movie_rate1 + movie_rate2 + movie_rate3))
- input2_count = math.ceil((movie_rate2 * 10) / (movie_rate1 + movie_rate2 + movie_rate3))
- input3_count = math.ceil((movie_rate3 * 10) / (movie_rate1 + movie_rate2 + movie_rate3))
-
- while True:
- if len(recomendation_list) >= 10:
- break
- if counter1 <= input1_count:
- recomendation_list.append([recommend1[counter1], movie_name1])
- counter1 += 1
- if len(recomendation_list) >= 10:
- break
- if counter2 <= input2_count:
- recomendation_list.append([recommend2[counter2], movie_name2])
- counter2 += 1
- if len(recomendation_list) >= 10:
- break
- if counter3 <= input3_count:
- recomendation_list.append([recommend3[counter3], movie_name3])
- counter3 += 1
-
- for i in range(10):
- movie, based = recomendation_list[i]
-
- movie_df = movies_meta.loc[
- (movies_meta['title_new'] == movie)]
-
- original_title = movie_df['original_title'].values[0]
- release_year = int(movie_df['release_year'].values[0])
-
- movie_metadata_df = copy_movies_meta.loc[
- (copy_movies_meta['original_title'] == original_title)]
-
- st.write(f"**{i + 1}. {original_title} ({release_year})** based on {based} \n\n")
-
- p_id = movie_metadata_df['id'].values[0]
- response = requests.get(
- 'https://api.themoviedb.org/3/movie/{}?api_key=1c8d419f76f8f9870d3e91eb896c2e54'.format(str(p_id)))
- data = response.json()
- # poster_path = data['poster_path']
- #
- # if data['poster_path'] != "None":
- # full_path = "https://image.tmdb.org/t/p/w500/" + data['poster_path']
- # response = requests.get(full_path)
- # image = Image.open(BytesIO(response.content))
- # resized_image = image.resize((200, 200)) # Resize the image to 50x50 pixels
- #
- # # Save the resized image to a new file
- # resized_image_path = "resized_image.jpg"
- # resized_image.save(resized_image_path)
- #
- # # Display the resized image
- # st.image(resized_image_path)
- if 'poster_path' in data and data['poster_path'] is not None:
- poster_path = data['poster_path']
- full_path = "https://image.tmdb.org/t/p/w500/" + poster_path
- response = requests.get(full_path)
- image = Image.open(BytesIO(response.content))
- resized_image = image.resize((200, 200)) # Resize the image to 200x200 pixels
-
- # Save the resized image to a new file
- resized_image_path = "resized_image.jpg"
- resized_image.save(resized_image_path)
-
- # Display the resized image
- st.image(resized_image_path)
- else:
- st.write("No poster image available for this movie.")
-
- cast_df = copy_credits.loc[
- (copy_credits['id'] == int(p_id))]
-
- with st.expander(f"See more about {original_title}"):
- runtime = int(movie_metadata_df['runtime'].values[0])
- hour = int(runtime / 60)
- minute = runtime - (hour * 60)
- imdb_rate = movie_metadata_df['vote_average'].values[0]
- popularity = round(movie_metadata_df['popularity'].values[0], 2)
- movie_overview = movie_metadata_df['overview'].values[0]
- movie_tagline = movie_metadata_df['tagline'].values[0]
- vote_count = int(movie_metadata_df['vote_count'].values[0])
- actors_list = cast_df['cast'].values[0]
- actors_list = actors_list.replace("'", '')
- actors_list = actors_list.split('}, {')[0:5]
- actors = []
- characters = []
- for actor in actors_list:
- actor = actor[3:].split(', ')
- for i in actor:
- if i[:5] == 'name:':
- actors.append(i[5:])
- break
- if i[:10] == 'character:':
- characters.append(i[10:])
-
- actors_line = f"**Stars :** {actors[0]} (as {characters[0]}) . " \
- f" {actors[1]} (as {characters[1]}) . " \
- f" {actors[2]} (as {characters[2]}) . " \
- f" {actors[3]} (as {characters[3]}) . " \
- f" {actors[4]} (as {characters[4]}) "
-
- crew_list = cast_df['crew'].values[0]
- crew_list = crew_list.replace("'", '')
- index = crew_list.find("job: Director, name:")
- director = crew_list[index + 20:].split(',')[0]
-
- genres_list = movie_metadata_df['genres'].values[0]
- genres_list = genres_list[2:-2]
- genres_list = genres_list.replace("'", '')
- genres_list = genres_list.split('}, {')
- movie_genres = ""
-
- # Extract the genre names from each dictionary
- genre_names = []
- for genre in genres_list:
- genre = genre.split(',')
- if genre[1][7:] == 'Animation':
- emoji = " 🎨"
- elif genre[1][7:] == 'Comedy':
- emoji = " 🤡"
- elif genre[1][7:] == 'Family':
- emoji = " 👨👩👧👦"
- elif genre[1][7:] == 'Crime':
- emoji = " 🕵️"
- elif genre[1][7:] == 'Adventure':
- emoji = " 🛣️"
- elif genre[1][7:] == 'Fantasy':
- emoji = " 🧚"
- elif genre[1][7:] == 'Romance':
- emoji = " 💏"
- elif genre[1][7:] == 'Drama':
- emoji = " 🎭"
- elif genre[1][7:] == "Action":
- emoji = " 🔫"
- elif genre[1][7:] == 'Thriller':
- emoji = " 🍿"
- elif genre[1][7:] == 'Horror':
- emoji = " 😱"
- elif genre[1][7:] == 'History':
- emoji = " 🏛"
- elif genre[1][7:] == 'Science Fiction':
- emoji = " 🤖👽"
- elif genre[1][7:] == 'Mystery':
- emoji = " ❓"
- elif genre[1][7:] == 'War':
- emoji = " ⚔"
- elif genre[1][7:] == 'Western':
- emoji = " 🤠"
- elif genre[1][7:] == 'Foreign':
- emoji = " 🗺"
- elif genre[1][7:] == 'Music':
- emoji = " 🎶"
- elif genre[1][7:] == 'Documentary':
- emoji = " 🤔"
-
- movie_genres = movie_genres + emoji
- movie_genres = movie_genres + genre[1][7:]
- movie_genres = movie_genres + " "
- genre_names.append(genre[1][7:])
-
- second_line = f" " \
- f" " \
- f" IMDb RATING POPULARITY\n"
- third_line = f" {release_year} {hour}h {minute}m " \
- f" " \
- f" ⭐{imdb_rate}/10 " \
- f"📊 {popularity} ({vote_count})\n \n \n"
- movie_genres = movie_genres.replace(' ', ' ')
- second_line = second_line.replace(' ', ' ')
- actors_line = actors_line.replace(' ', ' ')
- third_line = third_line.replace(' ', ' ')
- st.header(f"{original_title} \n")
- st.write(second_line, unsafe_allow_html=True)
- st.write(third_line, unsafe_allow_html=True)
- st.write(movie_genres, unsafe_allow_html=True)
- st.write("""
- ***
- """)
- st.markdown(f" ##### {movie_tagline}")
- st.write(movie_overview)
- st.write("""
- ***
- """)
- st.markdown(f"**Director(s) :** {director}")
- st.markdown(actors_line, unsafe_allow_html=True)
-
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/default_constructor.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/default_constructor.py
deleted file mode 100644
index 3f1f5b44168768dfda3947393a63a6cf9cf50b41..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/uniformer/mmcv/runner/default_constructor.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from .builder import RUNNER_BUILDERS, RUNNERS
-
-
-@RUNNER_BUILDERS.register_module()
-class DefaultRunnerConstructor:
- """Default constructor for runners.
-
- Custom existing `Runner` like `EpocBasedRunner` though `RunnerConstructor`.
- For example, We can inject some new properties and functions for `Runner`.
-
- Example:
- >>> from annotator.uniformer.mmcv.runner import RUNNER_BUILDERS, build_runner
- >>> # Define a new RunnerReconstructor
- >>> @RUNNER_BUILDERS.register_module()
- >>> class MyRunnerConstructor:
- ... def __init__(self, runner_cfg, default_args=None):
- ... if not isinstance(runner_cfg, dict):
- ... raise TypeError('runner_cfg should be a dict',
- ... f'but got {type(runner_cfg)}')
- ... self.runner_cfg = runner_cfg
- ... self.default_args = default_args
- ...
- ... def __call__(self):
- ... runner = RUNNERS.build(self.runner_cfg,
- ... default_args=self.default_args)
- ... # Add new properties for existing runner
- ... runner.my_name = 'my_runner'
- ... runner.my_function = lambda self: print(self.my_name)
- ... ...
- >>> # build your runner
- >>> runner_cfg = dict(type='EpochBasedRunner', max_epochs=40,
- ... constructor='MyRunnerConstructor')
- >>> runner = build_runner(runner_cfg)
- """
-
- def __init__(self, runner_cfg, default_args=None):
- if not isinstance(runner_cfg, dict):
- raise TypeError('runner_cfg should be a dict',
- f'but got {type(runner_cfg)}')
- self.runner_cfg = runner_cfg
- self.default_args = default_args
-
- def __call__(self):
- return RUNNERS.build(self.runner_cfg, default_args=self.default_args)
diff --git a/spaces/MesonWarrior/vk/README.md b/spaces/MesonWarrior/vk/README.md
deleted file mode 100644
index c510d1fbe635395da96cb066da461d3acf78f6af..0000000000000000000000000000000000000000
--- a/spaces/MesonWarrior/vk/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: GPT2 VK (Russian)
-emoji: 🔥
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
----
\ No newline at end of file
diff --git a/spaces/MilesCranmer/PySR/README.md b/spaces/MilesCranmer/PySR/README.md
deleted file mode 100644
index affe03b50736e94da02d433a69e4d298bdd03e5e..0000000000000000000000000000000000000000
--- a/spaces/MilesCranmer/PySR/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: PySR
-emoji: 🌍
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.0.15
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/NAACL2022/CLIP-Caption-Reward/retrieval/README.md b/spaces/NAACL2022/CLIP-Caption-Reward/retrieval/README.md
deleted file mode 100644
index 2f5cce9ad9b93234fa5ac7a0e99f05868d883fd0..0000000000000000000000000000000000000000
--- a/spaces/NAACL2022/CLIP-Caption-Reward/retrieval/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# Finetuning CLIP reward model
-
-```bash
-python train_pl.py --cfg clip_negative_text --id clip_negative_text
-```
\ No newline at end of file
diff --git a/spaces/NCTCMumbai/NCTC/models/research/cognitive_mapping_and_planning/tfcode/vision_baseline_lstm.py b/spaces/NCTCMumbai/NCTC/models/research/cognitive_mapping_and_planning/tfcode/vision_baseline_lstm.py
deleted file mode 100644
index ccf3ab23b06b71ed2a6d300b9a7d2a67a396c52e..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/research/cognitive_mapping_and_planning/tfcode/vision_baseline_lstm.py
+++ /dev/null
@@ -1,533 +0,0 @@
-# Copyright 2016 The TensorFlow Authors All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-import numpy as np
-
-
-import tensorflow as tf
-
-from tensorflow.contrib import slim
-
-import logging
-from tensorflow.python.platform import app
-from tensorflow.python.platform import flags
-from src import utils
-import src.file_utils as fu
-import tfcode.nav_utils as nu
-from tfcode import tf_utils
-
-setup_train_step_kwargs = nu.default_train_step_kwargs
-compute_losses_multi_or = nu.compute_losses_multi_or
-get_repr_from_image = nu.get_repr_from_image
-
-_save_d_at_t = nu.save_d_at_t
-_save_all = nu.save_all
-_eval_ap = nu.eval_ap
-_eval_dist = nu.eval_dist
-_plot_trajectories = nu.plot_trajectories
-
-def lstm_online(cell_fn, num_steps, inputs, state, varscope):
- # inputs is B x num_steps x C, C channels.
- # state is 2 tuple with B x 1 x C1, B x 1 x C2
- # Output state is always B x 1 x C
- inputs = tf.unstack(inputs, axis=1, num=num_steps)
- state = tf.unstack(state, axis=1, num=1)[0]
- outputs = []
-
- if num_steps > 1:
- varscope.reuse_variables()
-
- for s in range(num_steps):
- output, state = cell_fn(inputs[s], state)
- outputs.append(output)
- outputs = tf.stack(outputs, axis=1)
- state = tf.stack([state], axis=1)
- return outputs, state
-
-def _inputs(problem, lstm_states, lstm_state_dims):
- # Set up inputs.
- with tf.name_scope('inputs'):
- n_views = problem.n_views
-
- inputs = []
- inputs.append(('orig_maps', tf.float32,
- (problem.batch_size, 1, None, None, 1)))
- inputs.append(('goal_loc', tf.float32,
- (problem.batch_size, problem.num_goals, 2)))
-
- # For initing LSTM.
- inputs.append(('rel_goal_loc_at_start', tf.float32,
- (problem.batch_size, problem.num_goals,
- problem.rel_goal_loc_dim)))
- common_input_data, _ = tf_utils.setup_inputs(inputs)
-
- inputs = []
- inputs.append(('imgs', tf.float32, (problem.batch_size, None, n_views,
- problem.img_height, problem.img_width,
- problem.img_channels)))
- # Goal location as a tuple of delta location and delta theta.
- inputs.append(('rel_goal_loc', tf.float32, (problem.batch_size, None,
- problem.rel_goal_loc_dim)))
- if problem.outputs.visit_count:
- inputs.append(('visit_count', tf.int32, (problem.batch_size, None, 1)))
- inputs.append(('last_visit', tf.int32, (problem.batch_size, None, 1)))
-
- for i, (state, dim) in enumerate(zip(lstm_states, lstm_state_dims)):
- inputs.append((state, tf.float32, (problem.batch_size, 1, dim)))
-
- if problem.outputs.egomotion:
- inputs.append(('incremental_locs', tf.float32,
- (problem.batch_size, None, 2)))
- inputs.append(('incremental_thetas', tf.float32,
- (problem.batch_size, None, 1)))
-
- inputs.append(('step_number', tf.int32, (1, None, 1)))
- inputs.append(('node_ids', tf.int32, (problem.batch_size, None,
- problem.node_ids_dim)))
- inputs.append(('perturbs', tf.float32, (problem.batch_size, None,
- problem.perturbs_dim)))
-
- # For plotting result plots
- inputs.append(('loc_on_map', tf.float32, (problem.batch_size, None, 2)))
- inputs.append(('gt_dist_to_goal', tf.float32, (problem.batch_size, None, 1)))
- step_input_data, _ = tf_utils.setup_inputs(inputs)
-
- inputs = []
- inputs.append(('executed_actions', tf.int32, (problem.batch_size, None)))
- inputs.append(('rewards', tf.float32, (problem.batch_size, None)))
- inputs.append(('action_sample_wts', tf.float32, (problem.batch_size, None)))
- inputs.append(('action', tf.int32, (problem.batch_size, None,
- problem.num_actions)))
- train_data, _ = tf_utils.setup_inputs(inputs)
- train_data.update(step_input_data)
- train_data.update(common_input_data)
- return common_input_data, step_input_data, train_data
-
-
-def _add_summaries(m, summary_mode, arop_full_summary_iters):
- summarize_ops = [m.lr_op, m.global_step_op, m.sample_gt_prob_op,
- m.total_loss_op, m.data_loss_op, m.reg_loss_op] + m.acc_ops
- summarize_names = ['lr', 'global_step', 'sample_gt_prob_op', 'total_loss',
- 'data_loss', 'reg_loss'] + \
- ['acc_{:d}'.format(i) for i in range(len(m.acc_ops))]
- to_aggregate = [0, 0, 0, 1, 1, 1] + [1]*len(m.acc_ops)
-
- scope_name = 'summary'
- with tf.name_scope(scope_name):
- s_ops = nu.add_default_summaries(summary_mode, arop_full_summary_iters,
- summarize_ops, summarize_names,
- to_aggregate, m.action_prob_op,
- m.input_tensors, scope_name=scope_name)
- m.summary_ops = {summary_mode: s_ops}
-
-def visit_count_fc(visit_count, last_visit, embed_neurons, wt_decay, fc_dropout):
- with tf.variable_scope('embed_visit_count'):
- visit_count = tf.reshape(visit_count, shape=[-1])
- last_visit = tf.reshape(last_visit, shape=[-1])
-
- visit_count = tf.clip_by_value(visit_count, clip_value_min=-1,
- clip_value_max=15)
- last_visit = tf.clip_by_value(last_visit, clip_value_min=-1,
- clip_value_max=15)
- visit_count = tf.one_hot(visit_count, depth=16, axis=1, dtype=tf.float32,
- on_value=10., off_value=0.)
- last_visit = tf.one_hot(last_visit, depth=16, axis=1, dtype=tf.float32,
- on_value=10., off_value=0.)
- f = tf.concat([visit_count, last_visit], 1)
- x, _ = tf_utils.fc_network(
- f, neurons=embed_neurons, wt_decay=wt_decay, name='visit_count_embed',
- offset=0, batch_norm_param=None, dropout_ratio=fc_dropout,
- is_training=is_training)
- return x
-
-def lstm_setup(name, x, batch_size, is_single_step, lstm_dim, lstm_out,
- num_steps, state_input_op):
- # returns state_name, state_init_op, updated_state_op, out_op
- with tf.name_scope('reshape_'+name):
- sh = x.get_shape().as_list()
- x = tf.reshape(x, shape=[batch_size, -1, sh[-1]])
-
- with tf.variable_scope(name) as varscope:
- cell = tf.contrib.rnn.LSTMCell(
- num_units=lstm_dim, forget_bias=1.0, state_is_tuple=False,
- num_proj=lstm_out, use_peepholes=True,
- initializer=tf.random_uniform_initializer(-0.01, 0.01, seed=0),
- cell_clip=None, proj_clip=None)
-
- sh = [batch_size, 1, lstm_dim+lstm_out]
- state_init_op = tf.constant(0., dtype=tf.float32, shape=sh)
-
- fn = lambda ns: lstm_online(cell, ns, x, state_input_op, varscope)
- out_op, updated_state_op = tf.cond(is_single_step, lambda: fn(1), lambda:
- fn(num_steps))
-
- return name, state_init_op, updated_state_op, out_op
-
-def combine_setup(name, combine_type, embed_img, embed_goal, num_img_neuorons=None,
- num_goal_neurons=None):
- with tf.name_scope(name + '_' + combine_type):
- if combine_type == 'add':
- # Simple concat features from goal and image
- out = embed_img + embed_goal
-
- elif combine_type == 'multiply':
- # Multiply things together
- re_embed_img = tf.reshape(
- embed_img, shape=[-1, num_img_neuorons / num_goal_neurons,
- num_goal_neurons])
- re_embed_goal = tf.reshape(embed_goal, shape=[-1, num_goal_neurons, 1])
- x = tf.matmul(re_embed_img, re_embed_goal, transpose_a=False, transpose_b=False)
- out = slim.flatten(x)
- elif combine_type == 'none' or combine_type == 'imgonly':
- out = embed_img
- elif combine_type == 'goalonly':
- out = embed_goal
- else:
- logging.fatal('Undefined combine_type: %s', combine_type)
- return out
-
-
-def preprocess_egomotion(locs, thetas):
- with tf.name_scope('pre_ego'):
- pre_ego = tf.concat([locs, tf.sin(thetas), tf.cos(thetas)], 2)
- sh = pre_ego.get_shape().as_list()
- pre_ego = tf.reshape(pre_ego, [-1, sh[-1]])
- return pre_ego
-
-def setup_to_run(m, args, is_training, batch_norm_is_training, summary_mode):
- # Set up the model.
- tf.set_random_seed(args.solver.seed)
- task_params = args.navtask.task_params
- num_steps = task_params.num_steps
- num_goals = task_params.num_goals
- num_actions = task_params.num_actions
- num_actions_ = num_actions
-
- n_views = task_params.n_views
-
- batch_norm_is_training_op = \
- tf.placeholder_with_default(batch_norm_is_training, shape=[],
- name='batch_norm_is_training_op')
- # Setup the inputs
- m.input_tensors = {}
- lstm_states = []; lstm_state_dims = [];
- state_names = []; updated_state_ops = []; init_state_ops = [];
- if args.arch.lstm_output:
- lstm_states += ['lstm_output']
- lstm_state_dims += [args.arch.lstm_output_dim+task_params.num_actions]
- if args.arch.lstm_ego:
- lstm_states += ['lstm_ego']
- lstm_state_dims += [args.arch.lstm_ego_dim + args.arch.lstm_ego_out]
- lstm_states += ['lstm_img']
- lstm_state_dims += [args.arch.lstm_img_dim + args.arch.lstm_img_out]
- elif args.arch.lstm_img:
- # An LSTM only on the image
- lstm_states += ['lstm_img']
- lstm_state_dims += [args.arch.lstm_img_dim + args.arch.lstm_img_out]
- else:
- # No LSTMs involved here.
- None
-
- m.input_tensors['common'], m.input_tensors['step'], m.input_tensors['train'] = \
- _inputs(task_params, lstm_states, lstm_state_dims)
-
- with tf.name_scope('check_size'):
- is_single_step = tf.equal(tf.unstack(tf.shape(m.input_tensors['step']['imgs']),
- num=6)[1], 1)
-
- images_reshaped = tf.reshape(m.input_tensors['step']['imgs'],
- shape=[-1, task_params.img_height, task_params.img_width,
- task_params.img_channels], name='re_image')
-
- rel_goal_loc_reshaped = tf.reshape(m.input_tensors['step']['rel_goal_loc'],
- shape=[-1, task_params.rel_goal_loc_dim], name='re_rel_goal_loc')
-
- x, vars_ = get_repr_from_image(
- images_reshaped, task_params.modalities, task_params.data_augment,
- args.arch.encoder, args.solver.freeze_conv, args.solver.wt_decay,
- is_training)
-
- # Reshape into nice things so that these can be accumulated over time steps
- # for faster backprop.
- sh_before = x.get_shape().as_list()
- m.encoder_output = tf.reshape(
- x, shape=[task_params.batch_size, -1, n_views] + sh_before[1:])
- x = tf.reshape(m.encoder_output, shape=[-1] + sh_before[1:])
-
- # Add a layer to reduce dimensions for a fc layer.
- if args.arch.dim_reduce_neurons > 0:
- ks = 1; neurons = args.arch.dim_reduce_neurons;
- init_var = np.sqrt(2.0/(ks**2)/neurons)
- batch_norm_param = args.arch.batch_norm_param
- batch_norm_param['is_training'] = batch_norm_is_training_op
- m.conv_feat = slim.conv2d(
- x, neurons, kernel_size=ks, stride=1, normalizer_fn=slim.batch_norm,
- normalizer_params=batch_norm_param, padding='SAME', scope='dim_reduce',
- weights_regularizer=slim.l2_regularizer(args.solver.wt_decay),
- weights_initializer=tf.random_normal_initializer(stddev=init_var))
- reshape_conv_feat = slim.flatten(m.conv_feat)
- sh = reshape_conv_feat.get_shape().as_list()
- m.reshape_conv_feat = tf.reshape(reshape_conv_feat,
- shape=[-1, sh[1]*n_views])
-
- # Restore these from a checkpoint.
- if args.solver.pretrained_path is not None:
- m.init_fn = slim.assign_from_checkpoint_fn(args.solver.pretrained_path,
- vars_)
- else:
- m.init_fn = None
-
- # Hit the goal_location with a bunch of fully connected layers, to embed it
- # into some space.
- with tf.variable_scope('embed_goal'):
- batch_norm_param = args.arch.batch_norm_param
- batch_norm_param['is_training'] = batch_norm_is_training_op
- m.embed_goal, _ = tf_utils.fc_network(
- rel_goal_loc_reshaped, neurons=args.arch.goal_embed_neurons,
- wt_decay=args.solver.wt_decay, name='goal_embed', offset=0,
- batch_norm_param=batch_norm_param, dropout_ratio=args.arch.fc_dropout,
- is_training=is_training)
-
- if args.arch.embed_goal_for_state:
- with tf.variable_scope('embed_goal_for_state'):
- batch_norm_param = args.arch.batch_norm_param
- batch_norm_param['is_training'] = batch_norm_is_training_op
- m.embed_goal_for_state, _ = tf_utils.fc_network(
- m.input_tensors['common']['rel_goal_loc_at_start'][:,0,:],
- neurons=args.arch.goal_embed_neurons, wt_decay=args.solver.wt_decay,
- name='goal_embed', offset=0, batch_norm_param=batch_norm_param,
- dropout_ratio=args.arch.fc_dropout, is_training=is_training)
-
- # Hit the goal_location with a bunch of fully connected layers, to embed it
- # into some space.
- with tf.variable_scope('embed_img'):
- batch_norm_param = args.arch.batch_norm_param
- batch_norm_param['is_training'] = batch_norm_is_training_op
- m.embed_img, _ = tf_utils.fc_network(
- m.reshape_conv_feat, neurons=args.arch.img_embed_neurons,
- wt_decay=args.solver.wt_decay, name='img_embed', offset=0,
- batch_norm_param=batch_norm_param, dropout_ratio=args.arch.fc_dropout,
- is_training=is_training)
-
- # For lstm_ego, and lstm_image, embed the ego motion, accumulate it into an
- # LSTM, combine with image features and accumulate those in an LSTM. Finally
- # combine what you get from the image LSTM with the goal to output an action.
- if args.arch.lstm_ego:
- ego_reshaped = preprocess_egomotion(m.input_tensors['step']['incremental_locs'],
- m.input_tensors['step']['incremental_thetas'])
- with tf.variable_scope('embed_ego'):
- batch_norm_param = args.arch.batch_norm_param
- batch_norm_param['is_training'] = batch_norm_is_training_op
- m.embed_ego, _ = tf_utils.fc_network(
- ego_reshaped, neurons=args.arch.ego_embed_neurons,
- wt_decay=args.solver.wt_decay, name='ego_embed', offset=0,
- batch_norm_param=batch_norm_param, dropout_ratio=args.arch.fc_dropout,
- is_training=is_training)
-
- state_name, state_init_op, updated_state_op, out_op = lstm_setup(
- 'lstm_ego', m.embed_ego, task_params.batch_size, is_single_step,
- args.arch.lstm_ego_dim, args.arch.lstm_ego_out, num_steps*num_goals,
- m.input_tensors['step']['lstm_ego'])
- state_names += [state_name]
- init_state_ops += [state_init_op]
- updated_state_ops += [updated_state_op]
-
- # Combine the output with the vision features.
- m.img_ego_op = combine_setup('img_ego', args.arch.combine_type_ego,
- m.embed_img, out_op,
- args.arch.img_embed_neurons[-1],
- args.arch.lstm_ego_out)
-
- # LSTM on these vision features.
- state_name, state_init_op, updated_state_op, out_op = lstm_setup(
- 'lstm_img', m.img_ego_op, task_params.batch_size, is_single_step,
- args.arch.lstm_img_dim, args.arch.lstm_img_out, num_steps*num_goals,
- m.input_tensors['step']['lstm_img'])
- state_names += [state_name]
- init_state_ops += [state_init_op]
- updated_state_ops += [updated_state_op]
-
- m.img_for_goal = out_op
- num_img_for_goal_neurons = args.arch.lstm_img_out
-
- elif args.arch.lstm_img:
- # LSTM on just the image features.
- state_name, state_init_op, updated_state_op, out_op = lstm_setup(
- 'lstm_img', m.embed_img, task_params.batch_size, is_single_step,
- args.arch.lstm_img_dim, args.arch.lstm_img_out, num_steps*num_goals,
- m.input_tensors['step']['lstm_img'])
- state_names += [state_name]
- init_state_ops += [state_init_op]
- updated_state_ops += [updated_state_op]
- m.img_for_goal = out_op
- num_img_for_goal_neurons = args.arch.lstm_img_out
-
- else:
- m.img_for_goal = m.embed_img
- num_img_for_goal_neurons = args.arch.img_embed_neurons[-1]
-
-
- if args.arch.use_visit_count:
- m.embed_visit_count = visit_count_fc(
- m.input_tensors['step']['visit_count'],
- m.input_tensors['step']['last_visit'], args.arch.goal_embed_neurons,
- args.solver.wt_decay, args.arch.fc_dropout, is_training=is_training)
- m.embed_goal = m.embed_goal + m.embed_visit_count
-
- m.combined_f = combine_setup('img_goal', args.arch.combine_type,
- m.img_for_goal, m.embed_goal,
- num_img_for_goal_neurons,
- args.arch.goal_embed_neurons[-1])
-
- # LSTM on the combined representation.
- if args.arch.lstm_output:
- name = 'lstm_output'
- # A few fully connected layers here.
- with tf.variable_scope('action_pred'):
- batch_norm_param = args.arch.batch_norm_param
- batch_norm_param['is_training'] = batch_norm_is_training_op
- x, _ = tf_utils.fc_network(
- m.combined_f, neurons=args.arch.pred_neurons,
- wt_decay=args.solver.wt_decay, name='pred', offset=0,
- batch_norm_param=batch_norm_param, dropout_ratio=args.arch.fc_dropout)
-
- if args.arch.lstm_output_init_state_from_goal:
- # Use the goal embedding to initialize the LSTM state.
- # UGLY CLUGGY HACK: if this is doing computation for a single time step
- # then this will not involve back prop, so we can use the state input from
- # the feed dict, otherwise we compute the state representation from the
- # goal and feed that in. Necessary for using goal location to generate the
- # state representation.
- m.embed_goal_for_state = tf.expand_dims(m.embed_goal_for_state, dim=1)
- state_op = tf.cond(is_single_step, lambda: m.input_tensors['step'][name],
- lambda: m.embed_goal_for_state)
- state_name, state_init_op, updated_state_op, out_op = lstm_setup(
- name, x, task_params.batch_size, is_single_step,
- args.arch.lstm_output_dim,
- num_actions_,
- num_steps*num_goals, state_op)
- init_state_ops += [m.embed_goal_for_state]
- else:
- state_op = m.input_tensors['step'][name]
- state_name, state_init_op, updated_state_op, out_op = lstm_setup(
- name, x, task_params.batch_size, is_single_step,
- args.arch.lstm_output_dim,
- num_actions_, num_steps*num_goals, state_op)
- init_state_ops += [state_init_op]
-
- state_names += [state_name]
- updated_state_ops += [updated_state_op]
-
- out_op = tf.reshape(out_op, shape=[-1, num_actions_])
- if num_actions_ > num_actions:
- m.action_logits_op = out_op[:,:num_actions]
- m.baseline_op = out_op[:,num_actions:]
- else:
- m.action_logits_op = out_op
- m.baseline_op = None
- m.action_prob_op = tf.nn.softmax(m.action_logits_op)
-
- else:
- # A few fully connected layers here.
- with tf.variable_scope('action_pred'):
- batch_norm_param = args.arch.batch_norm_param
- batch_norm_param['is_training'] = batch_norm_is_training_op
- out_op, _ = tf_utils.fc_network(
- m.combined_f, neurons=args.arch.pred_neurons,
- wt_decay=args.solver.wt_decay, name='pred', offset=0,
- num_pred=num_actions_,
- batch_norm_param=batch_norm_param,
- dropout_ratio=args.arch.fc_dropout, is_training=is_training)
- if num_actions_ > num_actions:
- m.action_logits_op = out_op[:,:num_actions]
- m.baseline_op = out_op[:,num_actions:]
- else:
- m.action_logits_op = out_op
- m.baseline_op = None
- m.action_prob_op = tf.nn.softmax(m.action_logits_op)
-
- m.train_ops = {}
- m.train_ops['step'] = m.action_prob_op
- m.train_ops['common'] = [m.input_tensors['common']['orig_maps'],
- m.input_tensors['common']['goal_loc'],
- m.input_tensors['common']['rel_goal_loc_at_start']]
- m.train_ops['state_names'] = state_names
- m.train_ops['init_state'] = init_state_ops
- m.train_ops['updated_state'] = updated_state_ops
- m.train_ops['batch_norm_is_training_op'] = batch_norm_is_training_op
-
- # Flat list of ops which cache the step data.
- m.train_ops['step_data_cache'] = [tf.no_op()]
-
- if args.solver.freeze_conv:
- m.train_ops['step_data_cache'] = [m.encoder_output]
- else:
- m.train_ops['step_data_cache'] = []
-
- ewma_decay = 0.99 if is_training else 0.0
- weight = tf.ones_like(m.input_tensors['train']['action'], dtype=tf.float32,
- name='weight')
-
- m.reg_loss_op, m.data_loss_op, m.total_loss_op, m.acc_ops = \
- compute_losses_multi_or(
- m.action_logits_op, m.input_tensors['train']['action'],
- weights=weight, num_actions=num_actions,
- data_loss_wt=args.solver.data_loss_wt,
- reg_loss_wt=args.solver.reg_loss_wt, ewma_decay=ewma_decay)
-
-
- if args.solver.freeze_conv:
- vars_to_optimize = list(set(tf.trainable_variables()) - set(vars_))
- else:
- vars_to_optimize = None
-
- m.lr_op, m.global_step_op, m.train_op, m.should_stop_op, m.optimizer, \
- m.sync_optimizer = tf_utils.setup_training(
- m.total_loss_op,
- args.solver.initial_learning_rate,
- args.solver.steps_per_decay,
- args.solver.learning_rate_decay,
- args.solver.momentum,
- args.solver.max_steps,
- args.solver.sync,
- args.solver.adjust_lr_sync,
- args.solver.num_workers,
- args.solver.task,
- vars_to_optimize=vars_to_optimize,
- clip_gradient_norm=args.solver.clip_gradient_norm,
- typ=args.solver.typ, momentum2=args.solver.momentum2,
- adam_eps=args.solver.adam_eps)
-
-
- if args.arch.sample_gt_prob_type == 'inverse_sigmoid_decay':
- m.sample_gt_prob_op = tf_utils.inverse_sigmoid_decay(args.arch.isd_k,
- m.global_step_op)
- elif args.arch.sample_gt_prob_type == 'zero':
- m.sample_gt_prob_op = tf.constant(-1.0, dtype=tf.float32)
- elif args.arch.sample_gt_prob_type.split('_')[0] == 'step':
- step = int(args.arch.sample_gt_prob_type.split('_')[1])
- m.sample_gt_prob_op = tf_utils.step_gt_prob(
- step, m.input_tensors['step']['step_number'][0,0,0])
-
- m.sample_action_type = args.arch.action_sample_type
- m.sample_action_combine_type = args.arch.action_sample_combine_type
- _add_summaries(m, summary_mode, args.summary.arop_full_summary_iters)
-
- m.init_op = tf.group(tf.global_variables_initializer(),
- tf.local_variables_initializer())
- m.saver_op = tf.train.Saver(keep_checkpoint_every_n_hours=4,
- write_version=tf.train.SaverDef.V2)
-
- return m
diff --git a/spaces/Nephele/bert-vits2-multi-voice/setup_ffmpeg.py b/spaces/Nephele/bert-vits2-multi-voice/setup_ffmpeg.py
deleted file mode 100644
index 7137ab5faebb6d80740b8c843667458f25596839..0000000000000000000000000000000000000000
--- a/spaces/Nephele/bert-vits2-multi-voice/setup_ffmpeg.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import os
-import sys
-import re
-from pathlib import Path
-import winreg
-
-def check_ffmpeg_path():
- path_list = os.environ['Path'].split(';')
- ffmpeg_found = False
-
- for path in path_list:
- if 'ffmpeg' in path.lower() and 'bin' in path.lower():
- ffmpeg_found = True
- print("FFmpeg already installed.")
- break
-
- return ffmpeg_found
-
-def add_ffmpeg_path_to_user_variable():
- ffmpeg_bin_path = Path('.\\ffmpeg\\bin')
- if ffmpeg_bin_path.is_dir():
- abs_path = str(ffmpeg_bin_path.resolve())
-
- try:
- key = winreg.OpenKey(
- winreg.HKEY_CURRENT_USER,
- r"Environment",
- 0,
- winreg.KEY_READ | winreg.KEY_WRITE
- )
-
- try:
- current_path, _ = winreg.QueryValueEx(key, "Path")
- if abs_path not in current_path:
- new_path = f"{current_path};{abs_path}"
- winreg.SetValueEx(key, "Path", 0, winreg.REG_EXPAND_SZ, new_path)
- print(f"Added FFmpeg path to user variable 'Path': {abs_path}")
- else:
- print("FFmpeg path already exists in the user variable 'Path'.")
- finally:
- winreg.CloseKey(key)
- except WindowsError:
- print("Error: Unable to modify user variable 'Path'.")
- sys.exit(1)
-
- else:
- print("Error: ffmpeg\\bin folder not found in the current path.")
- sys.exit(1)
-
-def main():
- if not check_ffmpeg_path():
- add_ffmpeg_path_to_user_variable()
-
-if __name__ == "__main__":
- main()
\ No newline at end of file
diff --git a/spaces/Nyari/Super-Resolution-Anime-Diffusion/protogen.py b/spaces/Nyari/Super-Resolution-Anime-Diffusion/protogen.py
deleted file mode 100644
index 0f3dd33d03b439c9bfd0ef132f49879c7481aa33..0000000000000000000000000000000000000000
--- a/spaces/Nyari/Super-Resolution-Anime-Diffusion/protogen.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# -*- coding: utf-8 -*-
-# file: protogen.py
-# time: 14:27 2023/1/9
-# author: yangheng
-# github: https://github.com/yangheng95
-# huggingface: https://huggingface.co/yangheng
-# google scholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en
-# Copyright (C) 2021. All Rights Reserved.
-
-from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
-import torch
-import random
-
-prompt_keys = [
- "naked",
- "loli",
- "teen",
- "squat",
- "big nipples",
- "hairy pussy",
- "pee",
- "beautiful eyes",
- # 'dress', 'wind', 'fingers', 'hands',
- # random.choice(['Sinon', 'saber', ]),
- # random.choice(['white dress', 'red dress', 'blonde dress', 'black dress', 'green dress', ]),
- # random.choice(['white bra', 'red bra', 'black bra',]),
- "lovely",
- "details",
- # random.choice(['white hair', 'red hair', 'blonde hair', 'black hair', 'green hair', ]),
- random.choice(["white hair"]),
- random.choice(["blue eyes", "red eyes", "black eyes"]),
- random.choice(["flower meadow", "garden"]),
-]
-prompt = ",".join(prompt_keys)
-model_id = "darkstorm2150/Protogen_x3.4_Official_Release"
-pipe = StableDiffusionPipeline.from_pretrained(
- model_id, torch_dtype=torch.float16, safety_checker=None
-)
-pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
-pipe = pipe.to("cuda")
-
-guidance = 7.5
-width = 768
-height = 512
-image = pipe(
- prompt,
- num_inference_steps=25,
- guidance_scale=guidance,
- width=width,
- height=height,
-).images[0]
-
-image.save("./result.jpg")
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/ngram_repeat_block.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/ngram_repeat_block.py
deleted file mode 100644
index 854125149448a2d37ad2773cd1e6d614e73e0e79..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/ngram_repeat_block.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# Originally from Microsoft Corporation.
-# Licensed under the MIT License.
-
-""" Wrapper for ngram_repeat_block cuda extension """
-import torch
-from torch import nn
-
-import math
-from typing import Dict, List, Optional
-import warnings
-
-try:
- from fairseq import ngram_repeat_block_cuda
-
- EXTENSION_BUILT = True
-except ImportError:
- EXTENSION_BUILT = False
-
-
-def is_cuda_extension_usable() -> bool:
- """Check whether ngram_repeat_block_cuda is built properly"""
- if not EXTENSION_BUILT or not torch.cuda.is_available():
- return False
- bsz = 2
- tokens = torch.tensor([[4, 4, 3, 2], [1, 2, 3, 4]], dtype=torch.long, device="cuda")
- lprobs = torch.rand((8, 12), device="cuda")
- try:
- outputs = ngram_repeat_block_cuda.forward(tokens, lprobs, bsz, 3, 4, 3)
- outputs = outputs + 4 # This line breaks if the extension is built incorrectly.
- return True
- except RuntimeError:
- warnings.warn(
- "NGramRepeatBlock extension must be rebuilt."
- 'Run TORCH_CUDA_ARCH_LIST="6.0;6.1;7.0" python setup.py build_ext --inplace'
- )
- return False
-
-
-class NGramRepeatBlock(nn.Module):
- """ Wrapper class for calling ngram_repeat_block cuda extension """
-
- def __init__(self, no_repeat_ngram_size: int, use_extension: bool = True):
- super().__init__()
- self.use_extension = is_cuda_extension_usable() if use_extension else False
- self.no_repeat_ngram_size = no_repeat_ngram_size
-
- def reset_parameters(self):
- pass
-
- @torch.jit.unused
- def call_cuda_extension(
- self,
- tokens,
- lprobs,
- bsz: int,
- beam_size: int,
- step: int,
- ):
- return ngram_repeat_block_cuda.forward(
- tokens, lprobs, bsz, step, beam_size, self.no_repeat_ngram_size
- )
-
- def forward(
- self,
- tokens,
- lprobs,
- bsz: int,
- beam_size: int,
- step: int,
- ):
- """
- Args:
- tokens(Tensor): Input tokens(Bsz*beam, seq_len)
- lprobs(Tensor): likelihood probability,
- Expected to be updated in place.(Bsz*beam, vocab_size)
- bsz(int): batch size
- step(int): current step
- beam_size(int): beam size
- no_repeat_ngram_size(int): Ngram size
- """
- msg = f"expected {bsz *beam_size} got"
- assert tokens.size(0) == bsz * beam_size, f"{msg} {tokens.size(0)}"
- assert lprobs.size(0) == bsz * beam_size, f"{msg} {lprobs.size(0)}"
- if self.use_extension:
- return self.call_cuda_extension(tokens, lprobs, bsz, beam_size, step)
-
- else:
- return self._no_repeat_ngram(
- tokens,
- lprobs,
- bsz,
- beam_size,
- step,
- )
-
- def _no_repeat_ngram(self, tokens, lprobs, bsz: int, beam_size: int, step: int):
- """For each hypothesis generate a list of previous ngrams and set associated lprobs to -inf"""
- gen_ngrams: List[Dict[str, List[int]]] = [
- torch.jit.annotate(Dict[str, List[int]], {})
- for bbsz_idx in range(bsz * beam_size)
- ]
- cpu_tokens = tokens.cpu()
- for bbsz_idx in range(bsz * beam_size):
- gen_tokens: List[int] = cpu_tokens[bbsz_idx].tolist()
- for ngram in self.transpose_list(
- [gen_tokens[i:] for i in range(self.no_repeat_ngram_size)]
- ):
- key = ",".join([str(x) for x in ngram[:-1]])
- gen_ngrams[bbsz_idx][key] = gen_ngrams[bbsz_idx].get(
- key, torch.jit.annotate(List[int], [])
- ) + [ngram[-1]]
- if step + 2 - self.no_repeat_ngram_size >= 0:
- # no banned tokens if we haven't generated no_repeat_ngram_size tokens yet
- banned_tokens = [
- self.calculate_banned_tokens(
- tokens, step, gen_ngrams, self.no_repeat_ngram_size, bbsz_idx
- )
- for bbsz_idx in range(bsz * beam_size)
- ]
- else:
- banned_tokens = [
- torch.jit.annotate(List[int], []) for bbsz_idx in range(bsz * beam_size)
- ]
- for bbsz_idx in range(bsz * beam_size):
- lprobs[bbsz_idx][
- torch.tensor(banned_tokens[bbsz_idx], dtype=torch.int64)
- ] = torch.tensor(-math.inf).to(lprobs)
- return lprobs
-
- @staticmethod
- def calculate_banned_tokens(
- tokens,
- step: int,
- gen_ngrams: List[Dict[str, List[int]]],
- no_repeat_ngram_size: int,
- bbsz_idx: int,
- ):
- tokens_list: List[int] = tokens[
- bbsz_idx, step + 2 - no_repeat_ngram_size : step + 1
- ].tolist()
- # before decoding the next token, prevent decoding of ngrams that have already appeared
- ngram_index = ",".join([str(x) for x in tokens_list])
- return gen_ngrams[bbsz_idx].get(ngram_index, torch.jit.annotate(List[int], []))
-
- @staticmethod
- def transpose_list(l: List[List[int]]):
- # GeneratorExp aren't supported in TS so ignoring the lint
- min_len = min([len(x) for x in l]) # noqa
- l2 = [[row[i] for row in l] for i in range(min_len)]
- return l2
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_recognition/new/decoders/base_decoder.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_recognition/new/decoders/base_decoder.py
deleted file mode 100644
index a097969b3c0650cf8ea2ab5f8e96bbc68ea9b97f..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_recognition/new/decoders/base_decoder.py
+++ /dev/null
@@ -1,62 +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 itertools as it
-from typing import Any, Dict, List
-
-import torch
-from fairseq.data.dictionary import Dictionary
-from fairseq.models.fairseq_model import FairseqModel
-
-
-class BaseDecoder:
- def __init__(self, tgt_dict: Dictionary) -> None:
- self.tgt_dict = tgt_dict
- self.vocab_size = len(tgt_dict)
-
- self.blank = (
- tgt_dict.index("")
- if "" in tgt_dict.indices
- else tgt_dict.bos()
- )
- if "" in tgt_dict.indices:
- self.silence = tgt_dict.index("")
- elif "|" in tgt_dict.indices:
- self.silence = tgt_dict.index("|")
- else:
- self.silence = tgt_dict.eos()
-
- def generate(
- self, models: List[FairseqModel], sample: Dict[str, Any], **unused
- ) -> List[List[Dict[str, torch.LongTensor]]]:
- encoder_input = {
- k: v for k, v in sample["net_input"].items() if k != "prev_output_tokens"
- }
- emissions = self.get_emissions(models, encoder_input)
- return self.decode(emissions)
-
- def get_emissions(
- self,
- models: List[FairseqModel],
- encoder_input: Dict[str, Any],
- ) -> torch.FloatTensor:
- model = models[0]
- encoder_out = model(**encoder_input)
- if hasattr(model, "get_logits"):
- emissions = model.get_logits(encoder_out)
- else:
- emissions = model.get_normalized_probs(encoder_out, log_probs=True)
- return emissions.transpose(0, 1).float().cpu().contiguous()
-
- def get_tokens(self, idxs: torch.IntTensor) -> torch.LongTensor:
- idxs = (g[0] for g in it.groupby(idxs))
- idxs = filter(lambda x: x != self.blank, idxs)
- return torch.LongTensor(list(idxs))
-
- def decode(
- self,
- emissions: torch.FloatTensor,
- ) -> List[List[Dict[str, torch.LongTensor]]]:
- raise NotImplementedError
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/wav2vec/unsupervised/scripts/copy_labels.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/wav2vec/unsupervised/scripts/copy_labels.py
deleted file mode 100644
index 989868388eefccc37c82d7602f709632035c7aa1..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/wav2vec/unsupervised/scripts/copy_labels.py
+++ /dev/null
@@ -1,10 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import sys
-
-for idx, line in enumerate(sys.stdin):
- print(f"utt{idx:010d} {line}", end="")
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/quantization/pq/modules/qemb.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/quantization/pq/modules/qemb.py
deleted file mode 100644
index 3a74ad3c4c7c9d3203d26e7885864ba578951bfe..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/quantization/pq/modules/qemb.py
+++ /dev/null
@@ -1,107 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class PQEmbedding(nn.Module):
- """
- Quantized counterpart of nn.Embedding module. Stores the centroids and
- the assignments. The full weight is re-instantiated at each forward
- pass.
-
- Args:
- - centroids: centroids of size n_centroids x block_size
- - assignments: assignments of the centroids to the subvectors
- of size self.out_features x n_blocks
- - bias: the non-quantized bias
-
- Remarks:
- - We refer the reader to the official documentation of the nn.Embedding module
- for the other arguments and the behavior of the module
- - Performance tests on GPU show that this implementation is 10% slower than
- the non-quantized nn.Embedding module for a standard training loop.
- """
-
- def __init__(
- self,
- centroids,
- assignments,
- num_embeddings,
- embedding_dim,
- padding_idx=None,
- max_norm=None,
- norm_type=2.0,
- scale_grad_by_freq=False,
- sparse=False,
- _weight=None,
- ):
- super(PQEmbedding, self).__init__()
- self.block_size = centroids.size(1)
- self.n_centroids = centroids.size(0)
- self.num_embeddings = num_embeddings
- self.embedding_dim = embedding_dim
- if padding_idx is not None:
- if padding_idx > 0:
- assert (
- padding_idx < self.num_embeddings
- ), "Padding_idx must be within num_embeddings"
- elif padding_idx < 0:
- assert (
- padding_idx >= -self.num_embeddings
- ), "Padding_idx must be within num_embeddings"
- padding_idx = self.num_embeddings + padding_idx
- self.padding_idx = padding_idx
- self.max_norm = max_norm
- self.norm_type = norm_type
- self.scale_grad_by_freq = scale_grad_by_freq
- self.sparse = sparse
- # check compatibility
- if self.embedding_dim % self.block_size != 0:
- raise ValueError("Wrong PQ sizes")
- if len(assignments) % self.num_embeddings != 0:
- raise ValueError("Wrong PQ sizes")
- # define parameters
- self.centroids = nn.Parameter(centroids, requires_grad=True)
- self.register_buffer("assignments", assignments)
- self.register_buffer("counts", torch.bincount(assignments).type_as(centroids))
-
- @property
- def weight(self):
- return (
- self.centroids[self.assignments]
- .reshape(-1, self.num_embeddings, self.block_size)
- .permute(1, 0, 2)
- .flatten(1, 2)
- )
-
- def forward(self, input):
- return F.embedding(
- input,
- self.weight,
- self.padding_idx,
- self.max_norm,
- self.norm_type,
- self.scale_grad_by_freq,
- self.sparse,
- )
-
- def extra_repr(self):
- s = "{num_embeddings}, {embedding_dim}"
- if self.padding_idx is not None:
- s += ", padding_idx={padding_idx}"
- if self.max_norm is not None:
- s += ", max_norm={max_norm}"
- if self.norm_type != 2:
- s += ", norm_type={norm_type}"
- if self.scale_grad_by_freq is not False:
- s += ", scale_grad_by_freq={scale_grad_by_freq}"
- if self.sparse is not False:
- s += ", sparse=True"
- s += ", n_centroids={n_centroids}, block_size={block_size}"
-
- return s.format(**self.__dict__)
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/tasks/text_to_speech.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/tasks/text_to_speech.py
deleted file mode 100644
index 5646e41d39f6e39d4b046ee34ff69b998dab160d..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/tasks/text_to_speech.py
+++ /dev/null
@@ -1,467 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import os
-import os.path as op
-
-import torch
-import torch.nn.functional as F
-import numpy as np
-
-from fairseq.data.audio.text_to_speech_dataset import TextToSpeechDatasetCreator
-from fairseq.tasks import register_task
-from fairseq.tasks.speech_to_text import SpeechToTextTask
-from fairseq.speech_generator import (
- AutoRegressiveSpeechGenerator, NonAutoregressiveSpeechGenerator,
- TeacherForcingAutoRegressiveSpeechGenerator
-)
-
-logging.basicConfig(
- format='%(asctime)s | %(levelname)s | %(name)s | %(message)s',
- datefmt='%Y-%m-%d %H:%M:%S', level=logging.INFO
-)
-logger = logging.getLogger(__name__)
-
-
-try:
- from tensorboardX import SummaryWriter
-except ImportError:
- logger.info("Please install tensorboardX: pip install tensorboardX")
- SummaryWriter = None
-
-
-@register_task('text_to_speech')
-class TextToSpeechTask(SpeechToTextTask):
- @staticmethod
- def add_args(parser):
- parser.add_argument('data', help='manifest root path')
- parser.add_argument(
- '--config-yaml', type=str, default='config.yaml',
- help='Configuration YAML filename (under manifest root)'
- )
- parser.add_argument('--max-source-positions', default=1024, type=int,
- metavar='N',
- help='max number of tokens in the source sequence')
- parser.add_argument('--max-target-positions', default=1200, type=int,
- metavar='N',
- help='max number of tokens in the target sequence')
- parser.add_argument("--n-frames-per-step", type=int, default=1)
- parser.add_argument("--eos-prob-threshold", type=float, default=0.5)
- parser.add_argument("--eval-inference", action="store_true")
- parser.add_argument("--eval-tb-nsample", type=int, default=8)
- parser.add_argument("--vocoder", type=str, default="griffin_lim")
- parser.add_argument("--spec-bwd-max-iter", type=int, default=8)
-
- def __init__(self, args, src_dict):
- super().__init__(args, src_dict)
- self.src_dict = src_dict
- self.sr = self.data_cfg.config.get("features").get("sample_rate")
-
- self.tensorboard_writer = None
- self.tensorboard_dir = ""
- if args.tensorboard_logdir and SummaryWriter is not None:
- self.tensorboard_dir = os.path.join(args.tensorboard_logdir,
- "valid_extra")
-
- def load_dataset(self, split, epoch=1, combine=False, **kwargs):
- is_train_split = split.startswith('train')
- pre_tokenizer = self.build_tokenizer(self.args)
- bpe_tokenizer = self.build_bpe(self.args)
- self.datasets[split] = TextToSpeechDatasetCreator.from_tsv(
- self.args.data, self.data_cfg, split, self.src_dict,
- pre_tokenizer, bpe_tokenizer, is_train_split=is_train_split,
- epoch=epoch, seed=self.args.seed,
- n_frames_per_step=self.args.n_frames_per_step,
- speaker_to_id=self.speaker_to_id
- )
-
- @property
- def target_dictionary(self):
- return None
-
- @property
- def source_dictionary(self):
- return self.src_dict
-
- def get_speaker_embeddings_path(self):
- speaker_emb_path = None
- if self.data_cfg.config.get("speaker_emb_filename") is not None:
- speaker_emb_path = op.join(
- self.args.data, self.data_cfg.config.get("speaker_emb_filename")
- )
- return speaker_emb_path
-
- @classmethod
- def get_speaker_embeddings(cls, args):
- embed_speaker = None
- if args.speaker_to_id is not None:
- if args.speaker_emb_path is None:
- embed_speaker = torch.nn.Embedding(
- len(args.speaker_to_id), args.speaker_embed_dim
- )
- else:
- speaker_emb_mat = np.load(args.speaker_emb_path)
- assert speaker_emb_mat.shape[1] == args.speaker_embed_dim
- embed_speaker = torch.nn.Embedding.from_pretrained(
- torch.from_numpy(speaker_emb_mat), freeze=True,
- )
- logger.info(
- f"load speaker embeddings from {args.speaker_emb_path}. "
- f"train embedding? {embed_speaker.weight.requires_grad}\n"
- f"embeddings:\n{speaker_emb_mat}"
- )
- return embed_speaker
-
- def build_model(self, cfg):
- cfg.pitch_min = self.data_cfg.config["features"].get("pitch_min", None)
- cfg.pitch_max = self.data_cfg.config["features"].get("pitch_max", None)
- cfg.energy_min = self.data_cfg.config["features"].get("energy_min", None)
- cfg.energy_max = self.data_cfg.config["features"].get("energy_max", None)
- cfg.speaker_emb_path = self.get_speaker_embeddings_path()
- model = super().build_model(cfg)
- self.generator = None
- if getattr(cfg, "eval_inference", False):
- self.generator = self.build_generator([model], cfg)
- return model
-
- def build_generator(self, models, cfg, vocoder=None, **unused):
- if vocoder is None:
- vocoder = self.build_default_vocoder()
- model = models[0]
- if getattr(model, "NON_AUTOREGRESSIVE", False):
- return NonAutoregressiveSpeechGenerator(
- model, vocoder, self.data_cfg
- )
- else:
- generator = AutoRegressiveSpeechGenerator
- if getattr(cfg, "teacher_forcing", False):
- generator = TeacherForcingAutoRegressiveSpeechGenerator
- logger.info("Teacher forcing mode for generation")
- return generator(
- model, vocoder, self.data_cfg,
- max_iter=self.args.max_target_positions,
- eos_prob_threshold=self.args.eos_prob_threshold
- )
-
- def build_default_vocoder(self):
- from fairseq.models.text_to_speech.vocoder import get_vocoder
- vocoder = get_vocoder(self.args, self.data_cfg)
- if torch.cuda.is_available() and not self.args.cpu:
- vocoder = vocoder.cuda()
- else:
- vocoder = vocoder.cpu()
- return vocoder
-
- def valid_step(self, sample, model, criterion):
- loss, sample_size, logging_output = super().valid_step(
- sample, model, criterion
- )
-
- if getattr(self.args, "eval_inference", False):
- hypos, inference_losses = self.valid_step_with_inference(
- sample, model, self.generator
- )
- for k, v in inference_losses.items():
- assert(k not in logging_output)
- logging_output[k] = v
-
- picked_id = 0
- if self.tensorboard_dir and (sample["id"] == picked_id).any():
- self.log_tensorboard(
- sample,
- hypos[:self.args.eval_tb_nsample],
- model._num_updates,
- is_na_model=getattr(model, "NON_AUTOREGRESSIVE", False)
- )
- return loss, sample_size, logging_output
-
- def valid_step_with_inference(self, sample, model, generator):
- hypos = generator.generate(model, sample, has_targ=True)
-
- losses = {
- "mcd_loss": 0.,
- "targ_frames": 0.,
- "pred_frames": 0.,
- "nins": 0.,
- "ndel": 0.,
- }
- rets = batch_mel_cepstral_distortion(
- [hypo["targ_waveform"] for hypo in hypos],
- [hypo["waveform"] for hypo in hypos],
- self.sr,
- normalize_type=None
- )
- for d, extra in rets:
- pathmap = extra[-1]
- losses["mcd_loss"] += d.item()
- losses["targ_frames"] += pathmap.size(0)
- losses["pred_frames"] += pathmap.size(1)
- losses["nins"] += (pathmap.sum(dim=1) - 1).sum().item()
- losses["ndel"] += (pathmap.sum(dim=0) - 1).sum().item()
-
- return hypos, losses
-
- def log_tensorboard(self, sample, hypos, num_updates, is_na_model=False):
- if self.tensorboard_writer is None:
- self.tensorboard_writer = SummaryWriter(self.tensorboard_dir)
- tb_writer = self.tensorboard_writer
- for b in range(len(hypos)):
- idx = sample["id"][b]
- text = sample["src_texts"][b]
- targ = hypos[b]["targ_feature"]
- pred = hypos[b]["feature"]
- attn = hypos[b]["attn"]
-
- if is_na_model:
- data = plot_tts_output(
- [targ.transpose(0, 1), pred.transpose(0, 1)],
- [f"target (idx={idx})", "output"], attn,
- "alignment", ret_np=True, suptitle=text,
- )
- else:
- eos_prob = hypos[b]["eos_prob"]
- data = plot_tts_output(
- [targ.transpose(0, 1), pred.transpose(0, 1), attn],
- [f"target (idx={idx})", "output", "alignment"], eos_prob,
- "eos prob", ret_np=True, suptitle=text,
- )
-
- tb_writer.add_image(
- f"inference_sample_{b}", data, num_updates,
- dataformats="HWC"
- )
-
- if hypos[b]["waveform"] is not None:
- targ_wave = hypos[b]["targ_waveform"].detach().cpu().float()
- pred_wave = hypos[b]["waveform"].detach().cpu().float()
- tb_writer.add_audio(
- f"inference_targ_{b}",
- targ_wave,
- num_updates,
- sample_rate=self.sr
- )
- tb_writer.add_audio(
- f"inference_pred_{b}",
- pred_wave,
- num_updates,
- sample_rate=self.sr
- )
-
-
-def save_figure_to_numpy(fig):
- data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
- data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
- return data
-
-
-DEFAULT_V_MIN = np.log(1e-5)
-
-
-def plot_tts_output(
- data_2d, title_2d, data_1d, title_1d, figsize=(24, 4),
- v_min=DEFAULT_V_MIN, v_max=3, ret_np=False, suptitle=""
-):
- try:
- import matplotlib.pyplot as plt
- from mpl_toolkits.axes_grid1 import make_axes_locatable
- except ImportError:
- raise ImportError("Please install Matplotlib: pip install matplotlib")
-
- data_2d = [
- x.detach().cpu().float().numpy()
- if isinstance(x, torch.Tensor) else x for x in data_2d
- ]
- fig, axes = plt.subplots(1, len(data_2d) + 1, figsize=figsize)
- if suptitle:
- fig.suptitle(suptitle[:400]) # capped at 400 chars
- axes = [axes] if len(data_2d) == 0 else axes
- for ax, x, name in zip(axes, data_2d, title_2d):
- ax.set_title(name)
- divider = make_axes_locatable(ax)
- cax = divider.append_axes('right', size='5%', pad=0.05)
- im = ax.imshow(
- x, origin="lower", aspect="auto", vmin=max(x.min(), v_min),
- vmax=min(x.max(), v_max)
- )
- fig.colorbar(im, cax=cax, orientation='vertical')
-
- if isinstance(data_1d, torch.Tensor):
- data_1d = data_1d.detach().cpu().numpy()
- axes[-1].plot(data_1d)
- axes[-1].set_title(title_1d)
- plt.tight_layout()
-
- if ret_np:
- fig.canvas.draw()
- data = save_figure_to_numpy(fig)
- plt.close(fig)
- return data
-
-
-def antidiag_indices(offset, min_i=0, max_i=None, min_j=0, max_j=None):
- """
- for a (3, 4) matrix with min_i=1, max_i=3, min_j=1, max_j=4, outputs
-
- offset=2 (1, 1),
- offset=3 (2, 1), (1, 2)
- offset=4 (2, 2), (1, 3)
- offset=5 (2, 3)
-
- constraints:
- i + j = offset
- min_j <= j < max_j
- min_i <= offset - j < max_i
- """
- if max_i is None:
- max_i = offset + 1
- if max_j is None:
- max_j = offset + 1
- min_j = max(min_j, offset - max_i + 1, 0)
- max_j = min(max_j, offset - min_i + 1, offset + 1)
- j = torch.arange(min_j, max_j)
- i = offset - j
- return torch.stack([i, j])
-
-
-def batch_dynamic_time_warping(distance, shapes=None):
- """full batched DTW without any constraints
-
- distance: (batchsize, max_M, max_N) matrix
- shapes: (batchsize,) vector specifying (M, N) for each entry
- """
- # ptr: 0=left, 1=up-left, 2=up
- ptr2dij = {0: (0, -1), 1: (-1, -1), 2: (-1, 0)}
-
- bsz, m, n = distance.size()
- cumdist = torch.zeros_like(distance)
- backptr = torch.zeros_like(distance).type(torch.int32) - 1
-
- # initialize
- cumdist[:, 0, :] = distance[:, 0, :].cumsum(dim=-1)
- cumdist[:, :, 0] = distance[:, :, 0].cumsum(dim=-1)
- backptr[:, 0, :] = 0
- backptr[:, :, 0] = 2
-
- # DP with optimized anti-diagonal parallelization, O(M+N) steps
- for offset in range(2, m + n - 1):
- ind = antidiag_indices(offset, 1, m, 1, n)
- c = torch.stack(
- [cumdist[:, ind[0], ind[1] - 1], cumdist[:, ind[0] - 1, ind[1] - 1],
- cumdist[:, ind[0] - 1, ind[1]], ],
- dim=2
- )
- v, b = c.min(axis=-1)
- backptr[:, ind[0], ind[1]] = b.int()
- cumdist[:, ind[0], ind[1]] = v + distance[:, ind[0], ind[1]]
-
- # backtrace
- pathmap = torch.zeros_like(backptr)
- for b in range(bsz):
- i = m - 1 if shapes is None else (shapes[b][0] - 1).item()
- j = n - 1 if shapes is None else (shapes[b][1] - 1).item()
- dtwpath = [(i, j)]
- while (i != 0 or j != 0) and len(dtwpath) < 10000:
- assert (i >= 0 and j >= 0)
- di, dj = ptr2dij[backptr[b, i, j].item()]
- i, j = i + di, j + dj
- dtwpath.append((i, j))
- dtwpath = dtwpath[::-1]
- indices = torch.from_numpy(np.array(dtwpath))
- pathmap[b, indices[:, 0], indices[:, 1]] = 1
-
- return cumdist, backptr, pathmap
-
-
-def compute_l2_dist(x1, x2):
- """compute an (m, n) L2 distance matrix from (m, d) and (n, d) matrices"""
- return torch.cdist(x1.unsqueeze(0), x2.unsqueeze(0), p=2).squeeze(0).pow(2)
-
-
-def compute_rms_dist(x1, x2):
- l2_dist = compute_l2_dist(x1, x2)
- return (l2_dist / x1.size(1)).pow(0.5)
-
-
-def get_divisor(pathmap, normalize_type):
- if normalize_type is None:
- return 1
- elif normalize_type == "len1":
- return pathmap.size(0)
- elif normalize_type == "len2":
- return pathmap.size(1)
- elif normalize_type == "path":
- return pathmap.sum().item()
- else:
- raise ValueError(f"normalize_type {normalize_type} not supported")
-
-
-def batch_compute_distortion(y1, y2, sr, feat_fn, dist_fn, normalize_type):
- d, s, x1, x2 = [], [], [], []
- for cur_y1, cur_y2 in zip(y1, y2):
- assert (cur_y1.ndim == 1 and cur_y2.ndim == 1)
- cur_x1 = feat_fn(cur_y1)
- cur_x2 = feat_fn(cur_y2)
- x1.append(cur_x1)
- x2.append(cur_x2)
-
- cur_d = dist_fn(cur_x1, cur_x2)
- d.append(cur_d)
- s.append(d[-1].size())
- max_m = max(ss[0] for ss in s)
- max_n = max(ss[1] for ss in s)
- d = torch.stack(
- [F.pad(dd, (0, max_n - dd.size(1), 0, max_m - dd.size(0))) for dd in d]
- )
- s = torch.LongTensor(s).to(d.device)
- cumdists, backptrs, pathmaps = batch_dynamic_time_warping(d, s)
-
- rets = []
- itr = zip(s, x1, x2, d, cumdists, backptrs, pathmaps)
- for (m, n), cur_x1, cur_x2, dist, cumdist, backptr, pathmap in itr:
- cumdist = cumdist[:m, :n]
- backptr = backptr[:m, :n]
- pathmap = pathmap[:m, :n]
- divisor = get_divisor(pathmap, normalize_type)
-
- distortion = cumdist[-1, -1] / divisor
- ret = distortion, (cur_x1, cur_x2, dist, cumdist, backptr, pathmap)
- rets.append(ret)
- return rets
-
-
-def batch_mel_cepstral_distortion(
- y1, y2, sr, normalize_type="path", mfcc_fn=None
-):
- """
- https://arxiv.org/pdf/2011.03568.pdf
-
- The root mean squared error computed on 13-dimensional MFCC using DTW for
- alignment. MFCC features are computed from an 80-channel log-mel
- spectrogram using a 50ms Hann window and hop of 12.5ms.
-
- y1: list of waveforms
- y2: list of waveforms
- sr: sampling rate
- """
-
- try:
- import torchaudio
- except ImportError:
- raise ImportError("Please install torchaudio: pip install torchaudio")
-
- if mfcc_fn is None or mfcc_fn.sample_rate != sr:
- melkwargs = {
- "n_fft": int(0.05 * sr), "win_length": int(0.05 * sr),
- "hop_length": int(0.0125 * sr), "f_min": 20,
- "n_mels": 80, "window_fn": torch.hann_window
- }
- mfcc_fn = torchaudio.transforms.MFCC(
- sr, n_mfcc=13, log_mels=True, melkwargs=melkwargs
- ).to(y1[0].device)
- return batch_compute_distortion(
- y1, y2, sr, lambda y: mfcc_fn(y).transpose(-1, -2), compute_rms_dist,
- normalize_type
- )
diff --git a/spaces/PAIR/PAIR-Diffusion/README.md b/spaces/PAIR/PAIR-Diffusion/README.md
deleted file mode 100644
index 4368be22b5a39ef23354993d53a668aa1e607415..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: PAIR Diffusion
-emoji: 📚
-colorFrom: purple
-colorTo: gray
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/utils.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/utils.py
deleted file mode 100644
index c5befb8e56ece50b5fecfd007b26f8a29124c0bd..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/runner/utils.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os
-import random
-import sys
-import time
-import warnings
-from getpass import getuser
-from socket import gethostname
-
-import numpy as np
-import torch
-
-import annotator.uniformer.mmcv as mmcv
-
-
-def get_host_info():
- """Get hostname and username.
-
- Return empty string if exception raised, e.g. ``getpass.getuser()`` will
- lead to error in docker container
- """
- host = ''
- try:
- host = f'{getuser()}@{gethostname()}'
- except Exception as e:
- warnings.warn(f'Host or user not found: {str(e)}')
- finally:
- return host
-
-
-def get_time_str():
- return time.strftime('%Y%m%d_%H%M%S', time.localtime())
-
-
-def obj_from_dict(info, parent=None, default_args=None):
- """Initialize an object from dict.
-
- The dict must contain the key "type", which indicates the object type, it
- can be either a string or type, such as "list" or ``list``. Remaining
- fields are treated as the arguments for constructing the object.
-
- Args:
- info (dict): Object types and arguments.
- parent (:class:`module`): Module which may containing expected object
- classes.
- default_args (dict, optional): Default arguments for initializing the
- object.
-
- Returns:
- any type: Object built from the dict.
- """
- assert isinstance(info, dict) and 'type' in info
- assert isinstance(default_args, dict) or default_args is None
- args = info.copy()
- obj_type = args.pop('type')
- if mmcv.is_str(obj_type):
- if parent is not None:
- obj_type = getattr(parent, obj_type)
- else:
- obj_type = sys.modules[obj_type]
- elif not isinstance(obj_type, type):
- raise TypeError('type must be a str or valid type, but '
- f'got {type(obj_type)}')
- if default_args is not None:
- for name, value in default_args.items():
- args.setdefault(name, value)
- return obj_type(**args)
-
-
-def set_random_seed(seed, deterministic=False, use_rank_shift=False):
- """Set random seed.
-
- Args:
- seed (int): Seed to be used.
- deterministic (bool): Whether to set the deterministic option for
- CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
- to True and `torch.backends.cudnn.benchmark` to False.
- Default: False.
- rank_shift (bool): Whether to add rank number to the random seed to
- have different random seed in different threads. Default: False.
- """
- if use_rank_shift:
- rank, _ = mmcv.runner.get_dist_info()
- seed += rank
- random.seed(seed)
- np.random.seed(seed)
- torch.manual_seed(seed)
- torch.cuda.manual_seed(seed)
- torch.cuda.manual_seed_all(seed)
- os.environ['PYTHONHASHSEED'] = str(seed)
- if deterministic:
- torch.backends.cudnn.deterministic = True
- torch.backends.cudnn.benchmark = False
diff --git a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/utils/parrots_wrapper.py b/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/utils/parrots_wrapper.py
deleted file mode 100644
index 93c97640d4b9ed088ca82cfe03e6efebfcfa9dbf..0000000000000000000000000000000000000000
--- a/spaces/PAIR/Text2Video-Zero/annotator/uniformer/mmcv/utils/parrots_wrapper.py
+++ /dev/null
@@ -1,107 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from functools import partial
-
-import torch
-
-TORCH_VERSION = torch.__version__
-
-
-def is_rocm_pytorch() -> bool:
- is_rocm = False
- if TORCH_VERSION != 'parrots':
- try:
- from torch.utils.cpp_extension import ROCM_HOME
- is_rocm = True if ((torch.version.hip is not None) and
- (ROCM_HOME is not None)) else False
- except ImportError:
- pass
- return is_rocm
-
-
-def _get_cuda_home():
- if TORCH_VERSION == 'parrots':
- from parrots.utils.build_extension import CUDA_HOME
- else:
- if is_rocm_pytorch():
- from torch.utils.cpp_extension import ROCM_HOME
- CUDA_HOME = ROCM_HOME
- else:
- from torch.utils.cpp_extension import CUDA_HOME
- return CUDA_HOME
-
-
-def get_build_config():
- if TORCH_VERSION == 'parrots':
- from parrots.config import get_build_info
- return get_build_info()
- else:
- return torch.__config__.show()
-
-
-def _get_conv():
- if TORCH_VERSION == 'parrots':
- from parrots.nn.modules.conv import _ConvNd, _ConvTransposeMixin
- else:
- from torch.nn.modules.conv import _ConvNd, _ConvTransposeMixin
- return _ConvNd, _ConvTransposeMixin
-
-
-def _get_dataloader():
- if TORCH_VERSION == 'parrots':
- from torch.utils.data import DataLoader, PoolDataLoader
- else:
- from torch.utils.data import DataLoader
- PoolDataLoader = DataLoader
- return DataLoader, PoolDataLoader
-
-
-def _get_extension():
- if TORCH_VERSION == 'parrots':
- from parrots.utils.build_extension import BuildExtension, Extension
- CppExtension = partial(Extension, cuda=False)
- CUDAExtension = partial(Extension, cuda=True)
- else:
- from torch.utils.cpp_extension import (BuildExtension, CppExtension,
- CUDAExtension)
- return BuildExtension, CppExtension, CUDAExtension
-
-
-def _get_pool():
- if TORCH_VERSION == 'parrots':
- from parrots.nn.modules.pool import (_AdaptiveAvgPoolNd,
- _AdaptiveMaxPoolNd, _AvgPoolNd,
- _MaxPoolNd)
- else:
- from torch.nn.modules.pooling import (_AdaptiveAvgPoolNd,
- _AdaptiveMaxPoolNd, _AvgPoolNd,
- _MaxPoolNd)
- return _AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd, _AvgPoolNd, _MaxPoolNd
-
-
-def _get_norm():
- if TORCH_VERSION == 'parrots':
- from parrots.nn.modules.batchnorm import _BatchNorm, _InstanceNorm
- SyncBatchNorm_ = torch.nn.SyncBatchNorm2d
- else:
- from torch.nn.modules.instancenorm import _InstanceNorm
- from torch.nn.modules.batchnorm import _BatchNorm
- SyncBatchNorm_ = torch.nn.SyncBatchNorm
- return _BatchNorm, _InstanceNorm, SyncBatchNorm_
-
-
-_ConvNd, _ConvTransposeMixin = _get_conv()
-DataLoader, PoolDataLoader = _get_dataloader()
-BuildExtension, CppExtension, CUDAExtension = _get_extension()
-_BatchNorm, _InstanceNorm, SyncBatchNorm_ = _get_norm()
-_AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd, _AvgPoolNd, _MaxPoolNd = _get_pool()
-
-
-class SyncBatchNorm(SyncBatchNorm_):
-
- def _check_input_dim(self, input):
- if TORCH_VERSION == 'parrots':
- if input.dim() < 2:
- raise ValueError(
- f'expected at least 2D input (got {input.dim()}D input)')
- else:
- super()._check_input_dim(input)
diff --git a/spaces/PSLD/PSLD/stable-diffusion/ldm/modules/diffusionmodules/model.py b/spaces/PSLD/PSLD/stable-diffusion/ldm/modules/diffusionmodules/model.py
deleted file mode 100644
index 533e589a2024f1d7c52093d8c472c3b1b6617e26..0000000000000000000000000000000000000000
--- a/spaces/PSLD/PSLD/stable-diffusion/ldm/modules/diffusionmodules/model.py
+++ /dev/null
@@ -1,835 +0,0 @@
-# pytorch_diffusion + derived encoder decoder
-import math
-import torch
-import torch.nn as nn
-import numpy as np
-from einops import rearrange
-
-from ldm.util import instantiate_from_config
-from ldm.modules.attention import LinearAttention
-
-
-def get_timestep_embedding(timesteps, embedding_dim):
- """
- This matches the implementation in Denoising Diffusion Probabilistic Models:
- From Fairseq.
- Build sinusoidal embeddings.
- This matches the implementation in tensor2tensor, but differs slightly
- from the description in Section 3.5 of "Attention Is All You Need".
- """
- assert len(timesteps.shape) == 1
-
- half_dim = embedding_dim // 2
- emb = math.log(10000) / (half_dim - 1)
- emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
- emb = emb.to(device=timesteps.device)
- emb = timesteps.float()[:, None] * emb[None, :]
- emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
- if embedding_dim % 2 == 1: # zero pad
- emb = torch.nn.functional.pad(emb, (0,1,0,0))
- return emb
-
-
-def nonlinearity(x):
- # swish
- return x*torch.sigmoid(x)
-
-
-def Normalize(in_channels, num_groups=32):
- return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-class Upsample(nn.Module):
- def __init__(self, in_channels, with_conv):
- super().__init__()
- self.with_conv = with_conv
- if self.with_conv:
- self.conv = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x):
- x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
- if self.with_conv:
- x = self.conv(x)
- return x
-
-
-class Downsample(nn.Module):
- def __init__(self, in_channels, with_conv):
- super().__init__()
- self.with_conv = with_conv
- if self.with_conv:
- # no asymmetric padding in torch conv, must do it ourselves
- self.conv = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=3,
- stride=2,
- padding=0)
-
- def forward(self, x):
- if self.with_conv:
- pad = (0,1,0,1)
- x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
- x = self.conv(x)
- else:
- x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
- return x
-
-
-class ResnetBlock(nn.Module):
- def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
- dropout, temb_channels=512):
- super().__init__()
- self.in_channels = in_channels
- out_channels = in_channels if out_channels is None else out_channels
- self.out_channels = out_channels
- self.use_conv_shortcut = conv_shortcut
-
- self.norm1 = Normalize(in_channels)
- self.conv1 = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- if temb_channels > 0:
- self.temb_proj = torch.nn.Linear(temb_channels,
- out_channels)
- self.norm2 = Normalize(out_channels)
- self.dropout = torch.nn.Dropout(dropout)
- self.conv2 = torch.nn.Conv2d(out_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- if self.in_channels != self.out_channels:
- if self.use_conv_shortcut:
- self.conv_shortcut = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- else:
- self.nin_shortcut = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=1,
- stride=1,
- padding=0)
-
- def forward(self, x, temb):
- h = x
- h = self.norm1(h)
- h = nonlinearity(h)
- h = self.conv1(h)
-
- if temb is not None:
- h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
-
- h = self.norm2(h)
- h = nonlinearity(h)
- h = self.dropout(h)
- h = self.conv2(h)
-
- if self.in_channels != self.out_channels:
- if self.use_conv_shortcut:
- x = self.conv_shortcut(x)
- else:
- x = self.nin_shortcut(x)
-
- return x+h
-
-
-class LinAttnBlock(LinearAttention):
- """to match AttnBlock usage"""
- def __init__(self, in_channels):
- super().__init__(dim=in_channels, heads=1, dim_head=in_channels)
-
-
-class AttnBlock(nn.Module):
- def __init__(self, in_channels):
- super().__init__()
- self.in_channels = in_channels
-
- self.norm = Normalize(in_channels)
- self.q = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.k = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.v = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
- self.proj_out = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=1,
- stride=1,
- padding=0)
-
-
- def forward(self, x):
- h_ = x
- h_ = self.norm(h_)
- q = self.q(h_)
- k = self.k(h_)
- v = self.v(h_)
-
- # compute attention
- b,c,h,w = q.shape
- q = q.reshape(b,c,h*w)
- q = q.permute(0,2,1) # b,hw,c
- k = k.reshape(b,c,h*w) # b,c,hw
- w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
- w_ = w_ * (int(c)**(-0.5))
- w_ = torch.nn.functional.softmax(w_, dim=2)
-
- # attend to values
- v = v.reshape(b,c,h*w)
- w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
- h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
- h_ = h_.reshape(b,c,h,w)
-
- h_ = self.proj_out(h_)
-
- return x+h_
-
-
-def make_attn(in_channels, attn_type="vanilla"):
- assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
- print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
- if attn_type == "vanilla":
- return AttnBlock(in_channels)
- elif attn_type == "none":
- return nn.Identity(in_channels)
- else:
- return LinAttnBlock(in_channels)
-
-
-class Model(nn.Module):
- def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
- resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
- super().__init__()
- if use_linear_attn: attn_type = "linear"
- self.ch = ch
- self.temb_ch = self.ch*4
- self.num_resolutions = len(ch_mult)
- self.num_res_blocks = num_res_blocks
- self.resolution = resolution
- self.in_channels = in_channels
-
- self.use_timestep = use_timestep
- if self.use_timestep:
- # timestep embedding
- self.temb = nn.Module()
- self.temb.dense = nn.ModuleList([
- torch.nn.Linear(self.ch,
- self.temb_ch),
- torch.nn.Linear(self.temb_ch,
- self.temb_ch),
- ])
-
- # downsampling
- self.conv_in = torch.nn.Conv2d(in_channels,
- self.ch,
- kernel_size=3,
- stride=1,
- padding=1)
-
- curr_res = resolution
- in_ch_mult = (1,)+tuple(ch_mult)
- self.down = nn.ModuleList()
- for i_level in range(self.num_resolutions):
- block = nn.ModuleList()
- attn = nn.ModuleList()
- block_in = ch*in_ch_mult[i_level]
- block_out = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks):
- block.append(ResnetBlock(in_channels=block_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- if curr_res in attn_resolutions:
- attn.append(make_attn(block_in, attn_type=attn_type))
- down = nn.Module()
- down.block = block
- down.attn = attn
- if i_level != self.num_resolutions-1:
- down.downsample = Downsample(block_in, resamp_with_conv)
- curr_res = curr_res // 2
- self.down.append(down)
-
- # middle
- self.mid = nn.Module()
- self.mid.block_1 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
- self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
- self.mid.block_2 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
-
- # upsampling
- self.up = nn.ModuleList()
- for i_level in reversed(range(self.num_resolutions)):
- block = nn.ModuleList()
- attn = nn.ModuleList()
- block_out = ch*ch_mult[i_level]
- skip_in = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks+1):
- if i_block == self.num_res_blocks:
- skip_in = ch*in_ch_mult[i_level]
- block.append(ResnetBlock(in_channels=block_in+skip_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- if curr_res in attn_resolutions:
- attn.append(make_attn(block_in, attn_type=attn_type))
- up = nn.Module()
- up.block = block
- up.attn = attn
- if i_level != 0:
- up.upsample = Upsample(block_in, resamp_with_conv)
- curr_res = curr_res * 2
- self.up.insert(0, up) # prepend to get consistent order
-
- # end
- self.norm_out = Normalize(block_in)
- self.conv_out = torch.nn.Conv2d(block_in,
- out_ch,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x, t=None, context=None):
- #assert x.shape[2] == x.shape[3] == self.resolution
- if context is not None:
- # assume aligned context, cat along channel axis
- x = torch.cat((x, context), dim=1)
- if self.use_timestep:
- # timestep embedding
- assert t is not None
- temb = get_timestep_embedding(t, self.ch)
- temb = self.temb.dense[0](temb)
- temb = nonlinearity(temb)
- temb = self.temb.dense[1](temb)
- else:
- temb = None
-
- # downsampling
- hs = [self.conv_in(x)]
- for i_level in range(self.num_resolutions):
- for i_block in range(self.num_res_blocks):
- h = self.down[i_level].block[i_block](hs[-1], temb)
- if len(self.down[i_level].attn) > 0:
- h = self.down[i_level].attn[i_block](h)
- hs.append(h)
- if i_level != self.num_resolutions-1:
- hs.append(self.down[i_level].downsample(hs[-1]))
-
- # middle
- h = hs[-1]
- h = self.mid.block_1(h, temb)
- h = self.mid.attn_1(h)
- h = self.mid.block_2(h, temb)
-
- # upsampling
- for i_level in reversed(range(self.num_resolutions)):
- for i_block in range(self.num_res_blocks+1):
- h = self.up[i_level].block[i_block](
- torch.cat([h, hs.pop()], dim=1), temb)
- if len(self.up[i_level].attn) > 0:
- h = self.up[i_level].attn[i_block](h)
- if i_level != 0:
- h = self.up[i_level].upsample(h)
-
- # end
- h = self.norm_out(h)
- h = nonlinearity(h)
- h = self.conv_out(h)
- return h
-
- def get_last_layer(self):
- return self.conv_out.weight
-
-
-class Encoder(nn.Module):
- def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
- resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
- **ignore_kwargs):
- super().__init__()
- if use_linear_attn: attn_type = "linear"
- self.ch = ch
- self.temb_ch = 0
- self.num_resolutions = len(ch_mult)
- self.num_res_blocks = num_res_blocks
- self.resolution = resolution
- self.in_channels = in_channels
-
- # downsampling
- self.conv_in = torch.nn.Conv2d(in_channels,
- self.ch,
- kernel_size=3,
- stride=1,
- padding=1)
-
- curr_res = resolution
- in_ch_mult = (1,)+tuple(ch_mult)
- self.in_ch_mult = in_ch_mult
- self.down = nn.ModuleList()
- for i_level in range(self.num_resolutions):
- block = nn.ModuleList()
- attn = nn.ModuleList()
- block_in = ch*in_ch_mult[i_level]
- block_out = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks):
- block.append(ResnetBlock(in_channels=block_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- if curr_res in attn_resolutions:
- attn.append(make_attn(block_in, attn_type=attn_type))
- down = nn.Module()
- down.block = block
- down.attn = attn
- if i_level != self.num_resolutions-1:
- down.downsample = Downsample(block_in, resamp_with_conv)
- curr_res = curr_res // 2
- self.down.append(down)
-
- # middle
- self.mid = nn.Module()
- self.mid.block_1 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
- self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
- self.mid.block_2 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
-
- # end
- self.norm_out = Normalize(block_in)
- self.conv_out = torch.nn.Conv2d(block_in,
- 2*z_channels if double_z else z_channels,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x):
- # timestep embedding
- temb = None
-
- # downsampling
- hs = [self.conv_in(x)]
- for i_level in range(self.num_resolutions):
- for i_block in range(self.num_res_blocks):
- h = self.down[i_level].block[i_block](hs[-1], temb)
- if len(self.down[i_level].attn) > 0:
- h = self.down[i_level].attn[i_block](h)
- hs.append(h)
- if i_level != self.num_resolutions-1:
- hs.append(self.down[i_level].downsample(hs[-1]))
-
- # middle
- h = hs[-1]
- h = self.mid.block_1(h, temb)
- h = self.mid.attn_1(h)
- h = self.mid.block_2(h, temb)
-
- # end
- h = self.norm_out(h)
- h = nonlinearity(h)
- h = self.conv_out(h)
- return h
-
-
-class Decoder(nn.Module):
- def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
- resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
- attn_type="vanilla", **ignorekwargs):
- super().__init__()
- if use_linear_attn: attn_type = "linear"
- self.ch = ch
- self.temb_ch = 0
- self.num_resolutions = len(ch_mult)
- self.num_res_blocks = num_res_blocks
- self.resolution = resolution
- self.in_channels = in_channels
- self.give_pre_end = give_pre_end
- self.tanh_out = tanh_out
-
- # compute in_ch_mult, block_in and curr_res at lowest res
- in_ch_mult = (1,)+tuple(ch_mult)
- block_in = ch*ch_mult[self.num_resolutions-1]
- curr_res = resolution // 2**(self.num_resolutions-1)
- self.z_shape = (1,z_channels,curr_res,curr_res)
- print("Working with z of shape {} = {} dimensions.".format(
- self.z_shape, np.prod(self.z_shape)))
-
- # z to block_in
- self.conv_in = torch.nn.Conv2d(z_channels,
- block_in,
- kernel_size=3,
- stride=1,
- padding=1)
-
- # middle
- self.mid = nn.Module()
- self.mid.block_1 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
- self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
- self.mid.block_2 = ResnetBlock(in_channels=block_in,
- out_channels=block_in,
- temb_channels=self.temb_ch,
- dropout=dropout)
-
- # upsampling
- self.up = nn.ModuleList()
- for i_level in reversed(range(self.num_resolutions)):
- block = nn.ModuleList()
- attn = nn.ModuleList()
- block_out = ch*ch_mult[i_level]
- for i_block in range(self.num_res_blocks+1):
- block.append(ResnetBlock(in_channels=block_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- if curr_res in attn_resolutions:
- attn.append(make_attn(block_in, attn_type=attn_type))
- up = nn.Module()
- up.block = block
- up.attn = attn
- if i_level != 0:
- up.upsample = Upsample(block_in, resamp_with_conv)
- curr_res = curr_res * 2
- self.up.insert(0, up) # prepend to get consistent order
-
- # end
- self.norm_out = Normalize(block_in)
- self.conv_out = torch.nn.Conv2d(block_in,
- out_ch,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, z):
- #assert z.shape[1:] == self.z_shape[1:]
- self.last_z_shape = z.shape
-
- # timestep embedding
- temb = None
-
- # z to block_in
- h = self.conv_in(z)
-
- # middle
- h = self.mid.block_1(h, temb)
- h = self.mid.attn_1(h)
- h = self.mid.block_2(h, temb)
-
- # upsampling
- for i_level in reversed(range(self.num_resolutions)):
- for i_block in range(self.num_res_blocks+1):
- h = self.up[i_level].block[i_block](h, temb)
- if len(self.up[i_level].attn) > 0:
- h = self.up[i_level].attn[i_block](h)
- if i_level != 0:
- h = self.up[i_level].upsample(h)
-
- # end
- if self.give_pre_end:
- return h
-
- h = self.norm_out(h)
- h = nonlinearity(h)
- h = self.conv_out(h)
- if self.tanh_out:
- h = torch.tanh(h)
- return h
-
-
-class SimpleDecoder(nn.Module):
- def __init__(self, in_channels, out_channels, *args, **kwargs):
- super().__init__()
- self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
- ResnetBlock(in_channels=in_channels,
- out_channels=2 * in_channels,
- temb_channels=0, dropout=0.0),
- ResnetBlock(in_channels=2 * in_channels,
- out_channels=4 * in_channels,
- temb_channels=0, dropout=0.0),
- ResnetBlock(in_channels=4 * in_channels,
- out_channels=2 * in_channels,
- temb_channels=0, dropout=0.0),
- nn.Conv2d(2*in_channels, in_channels, 1),
- Upsample(in_channels, with_conv=True)])
- # end
- self.norm_out = Normalize(in_channels)
- self.conv_out = torch.nn.Conv2d(in_channels,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x):
- for i, layer in enumerate(self.model):
- if i in [1,2,3]:
- x = layer(x, None)
- else:
- x = layer(x)
-
- h = self.norm_out(x)
- h = nonlinearity(h)
- x = self.conv_out(h)
- return x
-
-
-class UpsampleDecoder(nn.Module):
- def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
- ch_mult=(2,2), dropout=0.0):
- super().__init__()
- # upsampling
- self.temb_ch = 0
- self.num_resolutions = len(ch_mult)
- self.num_res_blocks = num_res_blocks
- block_in = in_channels
- curr_res = resolution // 2 ** (self.num_resolutions - 1)
- self.res_blocks = nn.ModuleList()
- self.upsample_blocks = nn.ModuleList()
- for i_level in range(self.num_resolutions):
- res_block = []
- block_out = ch * ch_mult[i_level]
- for i_block in range(self.num_res_blocks + 1):
- res_block.append(ResnetBlock(in_channels=block_in,
- out_channels=block_out,
- temb_channels=self.temb_ch,
- dropout=dropout))
- block_in = block_out
- self.res_blocks.append(nn.ModuleList(res_block))
- if i_level != self.num_resolutions - 1:
- self.upsample_blocks.append(Upsample(block_in, True))
- curr_res = curr_res * 2
-
- # end
- self.norm_out = Normalize(block_in)
- self.conv_out = torch.nn.Conv2d(block_in,
- out_channels,
- kernel_size=3,
- stride=1,
- padding=1)
-
- def forward(self, x):
- # upsampling
- h = x
- for k, i_level in enumerate(range(self.num_resolutions)):
- for i_block in range(self.num_res_blocks + 1):
- h = self.res_blocks[i_level][i_block](h, None)
- if i_level != self.num_resolutions - 1:
- h = self.upsample_blocks[k](h)
- h = self.norm_out(h)
- h = nonlinearity(h)
- h = self.conv_out(h)
- return h
-
-
-class LatentRescaler(nn.Module):
- def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
- super().__init__()
- # residual block, interpolate, residual block
- self.factor = factor
- self.conv_in = nn.Conv2d(in_channels,
- mid_channels,
- kernel_size=3,
- stride=1,
- padding=1)
- self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
- out_channels=mid_channels,
- temb_channels=0,
- dropout=0.0) for _ in range(depth)])
- self.attn = AttnBlock(mid_channels)
- self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
- out_channels=mid_channels,
- temb_channels=0,
- dropout=0.0) for _ in range(depth)])
-
- self.conv_out = nn.Conv2d(mid_channels,
- out_channels,
- kernel_size=1,
- )
-
- def forward(self, x):
- x = self.conv_in(x)
- for block in self.res_block1:
- x = block(x, None)
- x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
- x = self.attn(x)
- for block in self.res_block2:
- x = block(x, None)
- x = self.conv_out(x)
- return x
-
-
-class MergedRescaleEncoder(nn.Module):
- def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
- attn_resolutions, dropout=0.0, resamp_with_conv=True,
- ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
- super().__init__()
- intermediate_chn = ch * ch_mult[-1]
- self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
- z_channels=intermediate_chn, double_z=False, resolution=resolution,
- attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
- out_ch=None)
- self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
- mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
-
- def forward(self, x):
- x = self.encoder(x)
- x = self.rescaler(x)
- return x
-
-
-class MergedRescaleDecoder(nn.Module):
- def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
- dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
- super().__init__()
- tmp_chn = z_channels*ch_mult[-1]
- self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
- resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
- ch_mult=ch_mult, resolution=resolution, ch=ch)
- self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
- out_channels=tmp_chn, depth=rescale_module_depth)
-
- def forward(self, x):
- x = self.rescaler(x)
- x = self.decoder(x)
- return x
-
-
-class Upsampler(nn.Module):
- def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
- super().__init__()
- assert out_size >= in_size
- num_blocks = int(np.log2(out_size//in_size))+1
- factor_up = 1.+ (out_size % in_size)
- print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
- self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
- out_channels=in_channels)
- self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
- attn_resolutions=[], in_channels=None, ch=in_channels,
- ch_mult=[ch_mult for _ in range(num_blocks)])
-
- def forward(self, x):
- x = self.rescaler(x)
- x = self.decoder(x)
- return x
-
-
-class Resize(nn.Module):
- def __init__(self, in_channels=None, learned=False, mode="bilinear"):
- super().__init__()
- self.with_conv = learned
- self.mode = mode
- if self.with_conv:
- print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
- raise NotImplementedError()
- assert in_channels is not None
- # no asymmetric padding in torch conv, must do it ourselves
- self.conv = torch.nn.Conv2d(in_channels,
- in_channels,
- kernel_size=4,
- stride=2,
- padding=1)
-
- def forward(self, x, scale_factor=1.0):
- if scale_factor==1.0:
- return x
- else:
- x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
- return x
-
-class FirstStagePostProcessor(nn.Module):
-
- def __init__(self, ch_mult:list, in_channels,
- pretrained_model:nn.Module=None,
- reshape=False,
- n_channels=None,
- dropout=0.,
- pretrained_config=None):
- super().__init__()
- if pretrained_config is None:
- assert pretrained_model is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
- self.pretrained_model = pretrained_model
- else:
- assert pretrained_config is not None, 'Either "pretrained_model" or "pretrained_config" must not be None'
- self.instantiate_pretrained(pretrained_config)
-
- self.do_reshape = reshape
-
- if n_channels is None:
- n_channels = self.pretrained_model.encoder.ch
-
- self.proj_norm = Normalize(in_channels,num_groups=in_channels//2)
- self.proj = nn.Conv2d(in_channels,n_channels,kernel_size=3,
- stride=1,padding=1)
-
- blocks = []
- downs = []
- ch_in = n_channels
- for m in ch_mult:
- blocks.append(ResnetBlock(in_channels=ch_in,out_channels=m*n_channels,dropout=dropout))
- ch_in = m * n_channels
- downs.append(Downsample(ch_in, with_conv=False))
-
- self.model = nn.ModuleList(blocks)
- self.downsampler = nn.ModuleList(downs)
-
-
- def instantiate_pretrained(self, config):
- model = instantiate_from_config(config)
- self.pretrained_model = model.eval()
- # self.pretrained_model.train = False
- for param in self.pretrained_model.parameters():
- param.requires_grad = False
-
-
- @torch.no_grad()
- def encode_with_pretrained(self,x):
- c = self.pretrained_model.encode(x)
- if isinstance(c, DiagonalGaussianDistribution):
- c = c.mode()
- return c
-
- def forward(self,x):
- z_fs = self.encode_with_pretrained(x)
- z = self.proj_norm(z_fs)
- z = self.proj(z)
- z = nonlinearity(z)
-
- for submodel, downmodel in zip(self.model,self.downsampler):
- z = submodel(z,temb=None)
- z = downmodel(z)
-
- if self.do_reshape:
- z = rearrange(z,'b c h w -> b (h w) c')
- return z
-
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/cps/simplify.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/cps/simplify.go
deleted file mode 100644
index 19bec774f13fe9a17e5596cb06eef3fceed0e504..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/language/cps/simplify.go and /dev/null differ
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/sxml/match.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/sxml/match.go
deleted file mode 100644
index 25832f4ebeeb7dd6076de6fa94eb8f3f7c935bb9..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/sxml/match.go and /dev/null differ
diff --git a/spaces/PeepDaSlan9/stabilityai-stablecode-instruct-alpha-3b/app.py b/spaces/PeepDaSlan9/stabilityai-stablecode-instruct-alpha-3b/app.py
deleted file mode 100644
index 5be5a08bcfd1d6c532d409da09c75cf47e0e5415..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/stabilityai-stablecode-instruct-alpha-3b/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/stabilityai/stablecode-instruct-alpha-3b").launch()
\ No newline at end of file
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/utils/make_divisible.py b/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/utils/make_divisible.py
deleted file mode 100644
index 75ad756052529f52fe83bb95dd1f0ecfc9a13078..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/mmseg/models/utils/make_divisible.py
+++ /dev/null
@@ -1,27 +0,0 @@
-def make_divisible(value, divisor, min_value=None, min_ratio=0.9):
- """Make divisible function.
-
- This function rounds the channel number to the nearest value that can be
- divisible by the divisor. It is taken from the original tf repo. It ensures
- that all layers have a channel number that is divisible by divisor. It can
- be seen here: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py # noqa
-
- Args:
- value (int): The original channel number.
- divisor (int): The divisor to fully divide the channel number.
- min_value (int): The minimum value of the output channel.
- Default: None, means that the minimum value equal to the divisor.
- min_ratio (float): The minimum ratio of the rounded channel number to
- the original channel number. Default: 0.9.
-
- Returns:
- int: The modified output channel number.
- """
-
- if min_value is None:
- min_value = divisor
- new_value = max(min_value, int(value + divisor / 2) // divisor * divisor)
- # Make sure that round down does not go down by more than (1-min_ratio).
- if new_value < min_ratio * value:
- new_value += divisor
- return new_value
diff --git a/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/scripts/extract_segmentation.py b/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/scripts/extract_segmentation.py
deleted file mode 100644
index 235b3c4b4575981b7533ce18bceaff97e05b55f9..0000000000000000000000000000000000000000
--- a/spaces/Purple11/Grounded-Diffusion/src/taming-transformers/scripts/extract_segmentation.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import sys, os
-import numpy as np
-import scipy
-import torch
-import torch.nn as nn
-from scipy import ndimage
-from tqdm import tqdm, trange
-from PIL import Image
-import torch.hub
-import torchvision
-import torch.nn.functional as F
-
-# download deeplabv2_resnet101_msc-cocostuff164k-100000.pth from
-# https://github.com/kazuto1011/deeplab-pytorch/releases/download/v1.0/deeplabv2_resnet101_msc-cocostuff164k-100000.pth
-# and put the path here
-CKPT_PATH = "TODO"
-
-rescale = lambda x: (x + 1.) / 2.
-
-def rescale_bgr(x):
- x = (x+1)*127.5
- x = torch.flip(x, dims=[0])
- return x
-
-
-class COCOStuffSegmenter(nn.Module):
- def __init__(self, config):
- super().__init__()
- self.config = config
- self.n_labels = 182
- model = torch.hub.load("kazuto1011/deeplab-pytorch", "deeplabv2_resnet101", n_classes=self.n_labels)
- ckpt_path = CKPT_PATH
- model.load_state_dict(torch.load(ckpt_path))
- self.model = model
-
- normalize = torchvision.transforms.Normalize(mean=self.mean, std=self.std)
- self.image_transform = torchvision.transforms.Compose([
- torchvision.transforms.Lambda(lambda image: torch.stack(
- [normalize(rescale_bgr(x)) for x in image]))
- ])
-
- def forward(self, x, upsample=None):
- x = self._pre_process(x)
- x = self.model(x)
- if upsample is not None:
- x = torch.nn.functional.upsample_bilinear(x, size=upsample)
- return x
-
- def _pre_process(self, x):
- x = self.image_transform(x)
- return x
-
- @property
- def mean(self):
- # bgr
- return [104.008, 116.669, 122.675]
-
- @property
- def std(self):
- return [1.0, 1.0, 1.0]
-
- @property
- def input_size(self):
- return [3, 224, 224]
-
-
-def run_model(img, model):
- model = model.eval()
- with torch.no_grad():
- segmentation = model(img, upsample=(img.shape[2], img.shape[3]))
- segmentation = torch.argmax(segmentation, dim=1, keepdim=True)
- return segmentation.detach().cpu()
-
-
-def get_input(batch, k):
- x = batch[k]
- if len(x.shape) == 3:
- x = x[..., None]
- x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format)
- return x.float()
-
-
-def save_segmentation(segmentation, path):
- # --> class label to uint8, save as png
- os.makedirs(os.path.dirname(path), exist_ok=True)
- assert len(segmentation.shape)==4
- assert segmentation.shape[0]==1
- for seg in segmentation:
- seg = seg.permute(1,2,0).numpy().squeeze().astype(np.uint8)
- seg = Image.fromarray(seg)
- seg.save(path)
-
-
-def iterate_dataset(dataloader, destpath, model):
- os.makedirs(destpath, exist_ok=True)
- num_processed = 0
- for i, batch in tqdm(enumerate(dataloader), desc="Data"):
- try:
- img = get_input(batch, "image")
- img = img.cuda()
- seg = run_model(img, model)
-
- path = batch["relative_file_path_"][0]
- path = os.path.splitext(path)[0]
-
- path = os.path.join(destpath, path + ".png")
- save_segmentation(seg, path)
- num_processed += 1
- except Exception as e:
- print(e)
- print("but anyhow..")
-
- print("Processed {} files. Bye.".format(num_processed))
-
-
-from taming.data.sflckr import Examples
-from torch.utils.data import DataLoader
-
-if __name__ == "__main__":
- dest = sys.argv[1]
- batchsize = 1
- print("Running with batch-size {}, saving to {}...".format(batchsize, dest))
-
- model = COCOStuffSegmenter({}).cuda()
- print("Instantiated model.")
-
- dataset = Examples()
- dloader = DataLoader(dataset, batch_size=batchsize)
- iterate_dataset(dataloader=dloader, destpath=dest, model=model)
- print("done.")
diff --git a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/_win32_console.py b/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/_win32_console.py
deleted file mode 100644
index 81b1082905338a74b72b9de432ece50a456687bc..0000000000000000000000000000000000000000
--- a/spaces/Raspberry-ai/main/.env/lib/python3.11/site-packages/pip/_vendor/rich/_win32_console.py
+++ /dev/null
@@ -1,662 +0,0 @@
-"""Light wrapper around the Win32 Console API - this module should only be imported on Windows
-
-The API that this module wraps is documented at https://docs.microsoft.com/en-us/windows/console/console-functions
-"""
-import ctypes
-import sys
-from typing import Any
-
-windll: Any = None
-if sys.platform == "win32":
- windll = ctypes.LibraryLoader(ctypes.WinDLL)
-else:
- raise ImportError(f"{__name__} can only be imported on Windows")
-
-import time
-from ctypes import Structure, byref, wintypes
-from typing import IO, NamedTuple, Type, cast
-
-from pip._vendor.rich.color import ColorSystem
-from pip._vendor.rich.style import Style
-
-STDOUT = -11
-ENABLE_VIRTUAL_TERMINAL_PROCESSING = 4
-
-COORD = wintypes._COORD
-
-
-class LegacyWindowsError(Exception):
- pass
-
-
-class WindowsCoordinates(NamedTuple):
- """Coordinates in the Windows Console API are (y, x), not (x, y).
- This class is intended to prevent that confusion.
- Rows and columns are indexed from 0.
- This class can be used in place of wintypes._COORD in arguments and argtypes.
- """
-
- row: int
- col: int
-
- @classmethod
- def from_param(cls, value: "WindowsCoordinates") -> COORD:
- """Converts a WindowsCoordinates into a wintypes _COORD structure.
- This classmethod is internally called by ctypes to perform the conversion.
-
- Args:
- value (WindowsCoordinates): The input coordinates to convert.
-
- Returns:
- wintypes._COORD: The converted coordinates struct.
- """
- return COORD(value.col, value.row)
-
-
-class CONSOLE_SCREEN_BUFFER_INFO(Structure):
- _fields_ = [
- ("dwSize", COORD),
- ("dwCursorPosition", COORD),
- ("wAttributes", wintypes.WORD),
- ("srWindow", wintypes.SMALL_RECT),
- ("dwMaximumWindowSize", COORD),
- ]
-
-
-class CONSOLE_CURSOR_INFO(ctypes.Structure):
- _fields_ = [("dwSize", wintypes.DWORD), ("bVisible", wintypes.BOOL)]
-
-
-_GetStdHandle = windll.kernel32.GetStdHandle
-_GetStdHandle.argtypes = [
- wintypes.DWORD,
-]
-_GetStdHandle.restype = wintypes.HANDLE
-
-
-def GetStdHandle(handle: int = STDOUT) -> wintypes.HANDLE:
- """Retrieves a handle to the specified standard device (standard input, standard output, or standard error).
-
- Args:
- handle (int): Integer identifier for the handle. Defaults to -11 (stdout).
-
- Returns:
- wintypes.HANDLE: The handle
- """
- return cast(wintypes.HANDLE, _GetStdHandle(handle))
-
-
-_GetConsoleMode = windll.kernel32.GetConsoleMode
-_GetConsoleMode.argtypes = [wintypes.HANDLE, wintypes.LPDWORD]
-_GetConsoleMode.restype = wintypes.BOOL
-
-
-def GetConsoleMode(std_handle: wintypes.HANDLE) -> int:
- """Retrieves the current input mode of a console's input buffer
- or the current output mode of a console screen buffer.
-
- Args:
- std_handle (wintypes.HANDLE): A handle to the console input buffer or the console screen buffer.
-
- Raises:
- LegacyWindowsError: If any error occurs while calling the Windows console API.
-
- Returns:
- int: Value representing the current console mode as documented at
- https://docs.microsoft.com/en-us/windows/console/getconsolemode#parameters
- """
-
- console_mode = wintypes.DWORD()
- success = bool(_GetConsoleMode(std_handle, console_mode))
- if not success:
- raise LegacyWindowsError("Unable to get legacy Windows Console Mode")
- return console_mode.value
-
-
-_FillConsoleOutputCharacterW = windll.kernel32.FillConsoleOutputCharacterW
-_FillConsoleOutputCharacterW.argtypes = [
- wintypes.HANDLE,
- ctypes.c_char,
- wintypes.DWORD,
- cast(Type[COORD], WindowsCoordinates),
- ctypes.POINTER(wintypes.DWORD),
-]
-_FillConsoleOutputCharacterW.restype = wintypes.BOOL
-
-
-def FillConsoleOutputCharacter(
- std_handle: wintypes.HANDLE,
- char: str,
- length: int,
- start: WindowsCoordinates,
-) -> int:
- """Writes a character to the console screen buffer a specified number of times, beginning at the specified coordinates.
-
- Args:
- std_handle (wintypes.HANDLE): A handle to the console input buffer or the console screen buffer.
- char (str): The character to write. Must be a string of length 1.
- length (int): The number of times to write the character.
- start (WindowsCoordinates): The coordinates to start writing at.
-
- Returns:
- int: The number of characters written.
- """
- character = ctypes.c_char(char.encode())
- num_characters = wintypes.DWORD(length)
- num_written = wintypes.DWORD(0)
- _FillConsoleOutputCharacterW(
- std_handle,
- character,
- num_characters,
- start,
- byref(num_written),
- )
- return num_written.value
-
-
-_FillConsoleOutputAttribute = windll.kernel32.FillConsoleOutputAttribute
-_FillConsoleOutputAttribute.argtypes = [
- wintypes.HANDLE,
- wintypes.WORD,
- wintypes.DWORD,
- cast(Type[COORD], WindowsCoordinates),
- ctypes.POINTER(wintypes.DWORD),
-]
-_FillConsoleOutputAttribute.restype = wintypes.BOOL
-
-
-def FillConsoleOutputAttribute(
- std_handle: wintypes.HANDLE,
- attributes: int,
- length: int,
- start: WindowsCoordinates,
-) -> int:
- """Sets the character attributes for a specified number of character cells,
- beginning at the specified coordinates in a screen buffer.
-
- Args:
- std_handle (wintypes.HANDLE): A handle to the console input buffer or the console screen buffer.
- attributes (int): Integer value representing the foreground and background colours of the cells.
- length (int): The number of cells to set the output attribute of.
- start (WindowsCoordinates): The coordinates of the first cell whose attributes are to be set.
-
- Returns:
- int: The number of cells whose attributes were actually set.
- """
- num_cells = wintypes.DWORD(length)
- style_attrs = wintypes.WORD(attributes)
- num_written = wintypes.DWORD(0)
- _FillConsoleOutputAttribute(
- std_handle, style_attrs, num_cells, start, byref(num_written)
- )
- return num_written.value
-
-
-_SetConsoleTextAttribute = windll.kernel32.SetConsoleTextAttribute
-_SetConsoleTextAttribute.argtypes = [
- wintypes.HANDLE,
- wintypes.WORD,
-]
-_SetConsoleTextAttribute.restype = wintypes.BOOL
-
-
-def SetConsoleTextAttribute(
- std_handle: wintypes.HANDLE, attributes: wintypes.WORD
-) -> bool:
- """Set the colour attributes for all text written after this function is called.
-
- Args:
- std_handle (wintypes.HANDLE): A handle to the console input buffer or the console screen buffer.
- attributes (int): Integer value representing the foreground and background colours.
-
-
- Returns:
- bool: True if the attribute was set successfully, otherwise False.
- """
- return bool(_SetConsoleTextAttribute(std_handle, attributes))
-
-
-_GetConsoleScreenBufferInfo = windll.kernel32.GetConsoleScreenBufferInfo
-_GetConsoleScreenBufferInfo.argtypes = [
- wintypes.HANDLE,
- ctypes.POINTER(CONSOLE_SCREEN_BUFFER_INFO),
-]
-_GetConsoleScreenBufferInfo.restype = wintypes.BOOL
-
-
-def GetConsoleScreenBufferInfo(
- std_handle: wintypes.HANDLE,
-) -> CONSOLE_SCREEN_BUFFER_INFO:
- """Retrieves information about the specified console screen buffer.
-
- Args:
- std_handle (wintypes.HANDLE): A handle to the console input buffer or the console screen buffer.
-
- Returns:
- CONSOLE_SCREEN_BUFFER_INFO: A CONSOLE_SCREEN_BUFFER_INFO ctype struct contain information about
- screen size, cursor position, colour attributes, and more."""
- console_screen_buffer_info = CONSOLE_SCREEN_BUFFER_INFO()
- _GetConsoleScreenBufferInfo(std_handle, byref(console_screen_buffer_info))
- return console_screen_buffer_info
-
-
-_SetConsoleCursorPosition = windll.kernel32.SetConsoleCursorPosition
-_SetConsoleCursorPosition.argtypes = [
- wintypes.HANDLE,
- cast(Type[COORD], WindowsCoordinates),
-]
-_SetConsoleCursorPosition.restype = wintypes.BOOL
-
-
-def SetConsoleCursorPosition(
- std_handle: wintypes.HANDLE, coords: WindowsCoordinates
-) -> bool:
- """Set the position of the cursor in the console screen
-
- Args:
- std_handle (wintypes.HANDLE): A handle to the console input buffer or the console screen buffer.
- coords (WindowsCoordinates): The coordinates to move the cursor to.
-
- Returns:
- bool: True if the function succeeds, otherwise False.
- """
- return bool(_SetConsoleCursorPosition(std_handle, coords))
-
-
-_GetConsoleCursorInfo = windll.kernel32.GetConsoleCursorInfo
-_GetConsoleCursorInfo.argtypes = [
- wintypes.HANDLE,
- ctypes.POINTER(CONSOLE_CURSOR_INFO),
-]
-_GetConsoleCursorInfo.restype = wintypes.BOOL
-
-
-def GetConsoleCursorInfo(
- std_handle: wintypes.HANDLE, cursor_info: CONSOLE_CURSOR_INFO
-) -> bool:
- """Get the cursor info - used to get cursor visibility and width
-
- Args:
- std_handle (wintypes.HANDLE): A handle to the console input buffer or the console screen buffer.
- cursor_info (CONSOLE_CURSOR_INFO): CONSOLE_CURSOR_INFO ctype struct that receives information
- about the console's cursor.
-
- Returns:
- bool: True if the function succeeds, otherwise False.
- """
- return bool(_GetConsoleCursorInfo(std_handle, byref(cursor_info)))
-
-
-_SetConsoleCursorInfo = windll.kernel32.SetConsoleCursorInfo
-_SetConsoleCursorInfo.argtypes = [
- wintypes.HANDLE,
- ctypes.POINTER(CONSOLE_CURSOR_INFO),
-]
-_SetConsoleCursorInfo.restype = wintypes.BOOL
-
-
-def SetConsoleCursorInfo(
- std_handle: wintypes.HANDLE, cursor_info: CONSOLE_CURSOR_INFO
-) -> bool:
- """Set the cursor info - used for adjusting cursor visibility and width
-
- Args:
- std_handle (wintypes.HANDLE): A handle to the console input buffer or the console screen buffer.
- cursor_info (CONSOLE_CURSOR_INFO): CONSOLE_CURSOR_INFO ctype struct containing the new cursor info.
-
- Returns:
- bool: True if the function succeeds, otherwise False.
- """
- return bool(_SetConsoleCursorInfo(std_handle, byref(cursor_info)))
-
-
-_SetConsoleTitle = windll.kernel32.SetConsoleTitleW
-_SetConsoleTitle.argtypes = [wintypes.LPCWSTR]
-_SetConsoleTitle.restype = wintypes.BOOL
-
-
-def SetConsoleTitle(title: str) -> bool:
- """Sets the title of the current console window
-
- Args:
- title (str): The new title of the console window.
-
- Returns:
- bool: True if the function succeeds, otherwise False.
- """
- return bool(_SetConsoleTitle(title))
-
-
-class LegacyWindowsTerm:
- """This class allows interaction with the legacy Windows Console API. It should only be used in the context
- of environments where virtual terminal processing is not available. However, if it is used in a Windows environment,
- the entire API should work.
-
- Args:
- file (IO[str]): The file which the Windows Console API HANDLE is retrieved from, defaults to sys.stdout.
- """
-
- BRIGHT_BIT = 8
-
- # Indices are ANSI color numbers, values are the corresponding Windows Console API color numbers
- ANSI_TO_WINDOWS = [
- 0, # black The Windows colours are defined in wincon.h as follows:
- 4, # red define FOREGROUND_BLUE 0x0001 -- 0000 0001
- 2, # green define FOREGROUND_GREEN 0x0002 -- 0000 0010
- 6, # yellow define FOREGROUND_RED 0x0004 -- 0000 0100
- 1, # blue define FOREGROUND_INTENSITY 0x0008 -- 0000 1000
- 5, # magenta define BACKGROUND_BLUE 0x0010 -- 0001 0000
- 3, # cyan define BACKGROUND_GREEN 0x0020 -- 0010 0000
- 7, # white define BACKGROUND_RED 0x0040 -- 0100 0000
- 8, # bright black (grey) define BACKGROUND_INTENSITY 0x0080 -- 1000 0000
- 12, # bright red
- 10, # bright green
- 14, # bright yellow
- 9, # bright blue
- 13, # bright magenta
- 11, # bright cyan
- 15, # bright white
- ]
-
- def __init__(self, file: "IO[str]") -> None:
- handle = GetStdHandle(STDOUT)
- self._handle = handle
- default_text = GetConsoleScreenBufferInfo(handle).wAttributes
- self._default_text = default_text
-
- self._default_fore = default_text & 7
- self._default_back = (default_text >> 4) & 7
- self._default_attrs = self._default_fore | (self._default_back << 4)
-
- self._file = file
- self.write = file.write
- self.flush = file.flush
-
- @property
- def cursor_position(self) -> WindowsCoordinates:
- """Returns the current position of the cursor (0-based)
-
- Returns:
- WindowsCoordinates: The current cursor position.
- """
- coord: COORD = GetConsoleScreenBufferInfo(self._handle).dwCursorPosition
- return WindowsCoordinates(row=cast(int, coord.Y), col=cast(int, coord.X))
-
- @property
- def screen_size(self) -> WindowsCoordinates:
- """Returns the current size of the console screen buffer, in character columns and rows
-
- Returns:
- WindowsCoordinates: The width and height of the screen as WindowsCoordinates.
- """
- screen_size: COORD = GetConsoleScreenBufferInfo(self._handle).dwSize
- return WindowsCoordinates(
- row=cast(int, screen_size.Y), col=cast(int, screen_size.X)
- )
-
- def write_text(self, text: str) -> None:
- """Write text directly to the terminal without any modification of styles
-
- Args:
- text (str): The text to write to the console
- """
- self.write(text)
- self.flush()
-
- def write_styled(self, text: str, style: Style) -> None:
- """Write styled text to the terminal.
-
- Args:
- text (str): The text to write
- style (Style): The style of the text
- """
- color = style.color
- bgcolor = style.bgcolor
- if style.reverse:
- color, bgcolor = bgcolor, color
-
- if color:
- fore = color.downgrade(ColorSystem.WINDOWS).number
- fore = fore if fore is not None else 7 # Default to ANSI 7: White
- if style.bold:
- fore = fore | self.BRIGHT_BIT
- if style.dim:
- fore = fore & ~self.BRIGHT_BIT
- fore = self.ANSI_TO_WINDOWS[fore]
- else:
- fore = self._default_fore
-
- if bgcolor:
- back = bgcolor.downgrade(ColorSystem.WINDOWS).number
- back = back if back is not None else 0 # Default to ANSI 0: Black
- back = self.ANSI_TO_WINDOWS[back]
- else:
- back = self._default_back
-
- assert fore is not None
- assert back is not None
-
- SetConsoleTextAttribute(
- self._handle, attributes=ctypes.c_ushort(fore | (back << 4))
- )
- self.write_text(text)
- SetConsoleTextAttribute(self._handle, attributes=self._default_text)
-
- def move_cursor_to(self, new_position: WindowsCoordinates) -> None:
- """Set the position of the cursor
-
- Args:
- new_position (WindowsCoordinates): The WindowsCoordinates representing the new position of the cursor.
- """
- if new_position.col < 0 or new_position.row < 0:
- return
- SetConsoleCursorPosition(self._handle, coords=new_position)
-
- def erase_line(self) -> None:
- """Erase all content on the line the cursor is currently located at"""
- screen_size = self.screen_size
- cursor_position = self.cursor_position
- cells_to_erase = screen_size.col
- start_coordinates = WindowsCoordinates(row=cursor_position.row, col=0)
- FillConsoleOutputCharacter(
- self._handle, " ", length=cells_to_erase, start=start_coordinates
- )
- FillConsoleOutputAttribute(
- self._handle,
- self._default_attrs,
- length=cells_to_erase,
- start=start_coordinates,
- )
-
- def erase_end_of_line(self) -> None:
- """Erase all content from the cursor position to the end of that line"""
- cursor_position = self.cursor_position
- cells_to_erase = self.screen_size.col - cursor_position.col
- FillConsoleOutputCharacter(
- self._handle, " ", length=cells_to_erase, start=cursor_position
- )
- FillConsoleOutputAttribute(
- self._handle,
- self._default_attrs,
- length=cells_to_erase,
- start=cursor_position,
- )
-
- def erase_start_of_line(self) -> None:
- """Erase all content from the cursor position to the start of that line"""
- row, col = self.cursor_position
- start = WindowsCoordinates(row, 0)
- FillConsoleOutputCharacter(self._handle, " ", length=col, start=start)
- FillConsoleOutputAttribute(
- self._handle, self._default_attrs, length=col, start=start
- )
-
- def move_cursor_up(self) -> None:
- """Move the cursor up a single cell"""
- cursor_position = self.cursor_position
- SetConsoleCursorPosition(
- self._handle,
- coords=WindowsCoordinates(
- row=cursor_position.row - 1, col=cursor_position.col
- ),
- )
-
- def move_cursor_down(self) -> None:
- """Move the cursor down a single cell"""
- cursor_position = self.cursor_position
- SetConsoleCursorPosition(
- self._handle,
- coords=WindowsCoordinates(
- row=cursor_position.row + 1,
- col=cursor_position.col,
- ),
- )
-
- def move_cursor_forward(self) -> None:
- """Move the cursor forward a single cell. Wrap to the next line if required."""
- row, col = self.cursor_position
- if col == self.screen_size.col - 1:
- row += 1
- col = 0
- else:
- col += 1
- SetConsoleCursorPosition(
- self._handle, coords=WindowsCoordinates(row=row, col=col)
- )
-
- def move_cursor_to_column(self, column: int) -> None:
- """Move cursor to the column specified by the zero-based column index, staying on the same row
-
- Args:
- column (int): The zero-based column index to move the cursor to.
- """
- row, _ = self.cursor_position
- SetConsoleCursorPosition(self._handle, coords=WindowsCoordinates(row, column))
-
- def move_cursor_backward(self) -> None:
- """Move the cursor backward a single cell. Wrap to the previous line if required."""
- row, col = self.cursor_position
- if col == 0:
- row -= 1
- col = self.screen_size.col - 1
- else:
- col -= 1
- SetConsoleCursorPosition(
- self._handle, coords=WindowsCoordinates(row=row, col=col)
- )
-
- def hide_cursor(self) -> None:
- """Hide the cursor"""
- current_cursor_size = self._get_cursor_size()
- invisible_cursor = CONSOLE_CURSOR_INFO(dwSize=current_cursor_size, bVisible=0)
- SetConsoleCursorInfo(self._handle, cursor_info=invisible_cursor)
-
- def show_cursor(self) -> None:
- """Show the cursor"""
- current_cursor_size = self._get_cursor_size()
- visible_cursor = CONSOLE_CURSOR_INFO(dwSize=current_cursor_size, bVisible=1)
- SetConsoleCursorInfo(self._handle, cursor_info=visible_cursor)
-
- def set_title(self, title: str) -> None:
- """Set the title of the terminal window
-
- Args:
- title (str): The new title of the console window
- """
- assert len(title) < 255, "Console title must be less than 255 characters"
- SetConsoleTitle(title)
-
- def _get_cursor_size(self) -> int:
- """Get the percentage of the character cell that is filled by the cursor"""
- cursor_info = CONSOLE_CURSOR_INFO()
- GetConsoleCursorInfo(self._handle, cursor_info=cursor_info)
- return int(cursor_info.dwSize)
-
-
-if __name__ == "__main__":
- handle = GetStdHandle()
-
- from pip._vendor.rich.console import Console
-
- console = Console()
-
- term = LegacyWindowsTerm(sys.stdout)
- term.set_title("Win32 Console Examples")
-
- style = Style(color="black", bgcolor="red")
-
- heading = Style.parse("black on green")
-
- # Check colour output
- console.rule("Checking colour output")
- console.print("[on red]on red!")
- console.print("[blue]blue!")
- console.print("[yellow]yellow!")
- console.print("[bold yellow]bold yellow!")
- console.print("[bright_yellow]bright_yellow!")
- console.print("[dim bright_yellow]dim bright_yellow!")
- console.print("[italic cyan]italic cyan!")
- console.print("[bold white on blue]bold white on blue!")
- console.print("[reverse bold white on blue]reverse bold white on blue!")
- console.print("[bold black on cyan]bold black on cyan!")
- console.print("[black on green]black on green!")
- console.print("[blue on green]blue on green!")
- console.print("[white on black]white on black!")
- console.print("[black on white]black on white!")
- console.print("[#1BB152 on #DA812D]#1BB152 on #DA812D!")
-
- # Check cursor movement
- console.rule("Checking cursor movement")
- console.print()
- term.move_cursor_backward()
- term.move_cursor_backward()
- term.write_text("went back and wrapped to prev line")
- time.sleep(1)
- term.move_cursor_up()
- term.write_text("we go up")
- time.sleep(1)
- term.move_cursor_down()
- term.write_text("and down")
- time.sleep(1)
- term.move_cursor_up()
- term.move_cursor_backward()
- term.move_cursor_backward()
- term.write_text("we went up and back 2")
- time.sleep(1)
- term.move_cursor_down()
- term.move_cursor_backward()
- term.move_cursor_backward()
- term.write_text("we went down and back 2")
- time.sleep(1)
-
- # Check erasing of lines
- term.hide_cursor()
- console.print()
- console.rule("Checking line erasing")
- console.print("\n...Deleting to the start of the line...")
- term.write_text("The red arrow shows the cursor location, and direction of erase")
- time.sleep(1)
- term.move_cursor_to_column(16)
- term.write_styled("<", Style.parse("black on red"))
- term.move_cursor_backward()
- time.sleep(1)
- term.erase_start_of_line()
- time.sleep(1)
-
- console.print("\n\n...And to the end of the line...")
- term.write_text("The red arrow shows the cursor location, and direction of erase")
- time.sleep(1)
-
- term.move_cursor_to_column(16)
- term.write_styled(">", Style.parse("black on red"))
- time.sleep(1)
- term.erase_end_of_line()
- time.sleep(1)
-
- console.print("\n\n...Now the whole line will be erased...")
- term.write_styled("I'm going to disappear!", style=Style.parse("black on cyan"))
- time.sleep(1)
- term.erase_line()
-
- term.show_cursor()
- print("\n")
diff --git a/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/scripts/reproduce_train/outdoor.sh b/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/scripts/reproduce_train/outdoor.sh
deleted file mode 100644
index c447e8feaa5c7ef7ff74da3b622151c7018447a6..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/ASpanFormer/scripts/reproduce_train/outdoor.sh
+++ /dev/null
@@ -1,34 +0,0 @@
-#!/bin/bash -l
-
-SCRIPTPATH=$(dirname $(readlink -f "$0"))
-PROJECT_DIR="${SCRIPTPATH}/../../"
-
-# conda activate loftr
-export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
-cd $PROJECT_DIR
-
-TRAIN_IMG_SIZE=832
-data_cfg_path="configs/data/megadepth_trainval_${TRAIN_IMG_SIZE}.py"
-main_cfg_path="configs/aspan/outdoor/aspan_train.py"
-
-n_nodes=1
-n_gpus_per_node=8
-torch_num_workers=8
-batch_size=1
-pin_memory=true
-exp_name="outdoor-ds-aspan-${TRAIN_IMG_SIZE}-bs=$(($n_gpus_per_node * $n_nodes * $batch_size))"
-
-CUDA_VISIBLE_DEVICES='0,1,2,3,4,5,6,7' python -u ./train.py \
- ${data_cfg_path} \
- ${main_cfg_path} \
- --exp_name=${exp_name} \
- --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
- --batch_size=${batch_size} --num_workers=${torch_num_workers} --pin_memory=${pin_memory} \
- --check_val_every_n_epoch=1 \
- --log_every_n_steps=100 \
- --flush_logs_every_n_steps=100 \
- --limit_val_batches=1. \
- --num_sanity_val_steps=10 \
- --benchmark=True \
- --max_epochs=30 \
- --mode integrated
diff --git a/spaces/Realcat/image-matching-webui/third_party/TopicFM/scripts/reproduce_train/outdoor.sh b/spaces/Realcat/image-matching-webui/third_party/TopicFM/scripts/reproduce_train/outdoor.sh
deleted file mode 100644
index d30320f04e0b560f4b4de9ee68305a4e698b538b..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/TopicFM/scripts/reproduce_train/outdoor.sh
+++ /dev/null
@@ -1,32 +0,0 @@
-#!/bin/bash -l
-
-SCRIPTPATH=$(dirname $(readlink -f "$0"))
-PROJECT_DIR="${SCRIPTPATH}/../../"
-
-# conda activate loftr
-export PYTHONPATH=$PROJECT_DIR:$PYTHONPATH
-cd $PROJECT_DIR
-
-data_cfg_path="configs/data/megadepth_trainval.py"
-main_cfg_path="configs/model/outdoor/model_ds.py"
-
-n_nodes=1
-n_gpus_per_node=4
-torch_num_workers=4
-batch_size=1
-pin_memory=true
-exp_name="outdoor-bs=$(($n_gpus_per_node * $n_nodes * $batch_size))"
-
-python -u ./train.py \
- ${data_cfg_path} \
- ${main_cfg_path} \
- --exp_name=${exp_name} \
- --gpus=${n_gpus_per_node} --num_nodes=${n_nodes} --accelerator="ddp" \
- --batch_size=${batch_size} --num_workers=${torch_num_workers} --pin_memory=${pin_memory} \
- --check_val_every_n_epoch=1 \
- --log_every_n_steps=30000 \
- --flush_logs_every_n_steps=30000 \
- --limit_val_batches=1. \
- --num_sanity_val_steps=10 \
- --benchmark=True \
- --max_epochs=40 # --ckpt_path="pretrained_epoch22.ckpt"
diff --git a/spaces/Robert001/UniControl-Demo/annotator/midas/__init__.py b/spaces/Robert001/UniControl-Demo/annotator/midas/__init__.py
deleted file mode 100644
index 426c7b2328f9cb475c344e80aeb828c866559aba..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/midas/__init__.py
+++ /dev/null
@@ -1,52 +0,0 @@
-'''
- * Copyright (c) 2023 Salesforce, Inc.
- * All rights reserved.
- * SPDX-License-Identifier: Apache License 2.0
- * For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/
- * By Can Qin
- * Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet
- * Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala
-'''
-
-# Midas Depth Estimation
-# From https://github.com/isl-org/MiDaS
-# MIT LICENSE
-
-import cv2
-import numpy as np
-import torch
-
-from einops import rearrange
-from .api import MiDaSInference
-
-
-class MidasDetector:
- def __init__(self):
- self.model = MiDaSInference(model_type="dpt_hybrid").cuda()
-
- def __call__(self, input_image, a=np.pi * 2.0, bg_th=0.1):
- assert input_image.ndim == 3
- image_depth = input_image
- with torch.no_grad():
- image_depth = torch.from_numpy(image_depth).float().cuda()
- image_depth = image_depth / 127.5 - 1.0
- image_depth = rearrange(image_depth, 'h w c -> 1 c h w')
- depth = self.model(image_depth)[0]
-
- depth_pt = depth.clone()
- depth_pt -= torch.min(depth_pt)
- depth_pt /= torch.max(depth_pt)
- depth_pt = depth_pt.cpu().numpy()
- depth_image = (depth_pt * 255.0).clip(0, 255).astype(np.uint8)
-
- depth_np = depth.cpu().numpy()
- x = cv2.Sobel(depth_np, cv2.CV_32F, 1, 0, ksize=3)
- y = cv2.Sobel(depth_np, cv2.CV_32F, 0, 1, ksize=3)
- z = np.ones_like(x) * a
- x[depth_pt < bg_th] = 0
- y[depth_pt < bg_th] = 0
- normal = np.stack([x, y, z], axis=2)
- normal /= np.sum(normal ** 2.0, axis=2, keepdims=True) ** 0.5
- normal_image = (normal * 127.5 + 127.5).clip(0, 255).astype(np.uint8)
-
- return depth_image, normal_image
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/fileio/handlers/yaml_handler.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/fileio/handlers/yaml_handler.py
deleted file mode 100644
index c5aa2eea1e8c76f8baf753d1c8c959dee665e543..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/fileio/handlers/yaml_handler.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import yaml
-
-try:
- from yaml import CLoader as Loader, CDumper as Dumper
-except ImportError:
- from yaml import Loader, Dumper
-
-from .base import BaseFileHandler # isort:skip
-
-
-class YamlHandler(BaseFileHandler):
-
- def load_from_fileobj(self, file, **kwargs):
- kwargs.setdefault('Loader', Loader)
- return yaml.load(file, **kwargs)
-
- def dump_to_fileobj(self, obj, file, **kwargs):
- kwargs.setdefault('Dumper', Dumper)
- yaml.dump(obj, file, **kwargs)
-
- def dump_to_str(self, obj, **kwargs):
- kwargs.setdefault('Dumper', Dumper)
- return yaml.dump(obj, **kwargs)
diff --git a/spaces/Rongjiehuang/ProDiff/modules/ProDiff/task/ProDiff_task.py b/spaces/Rongjiehuang/ProDiff/modules/ProDiff/task/ProDiff_task.py
deleted file mode 100644
index 795752e4414a8d28dd12ecf020465f9f299b6d0f..0000000000000000000000000000000000000000
--- a/spaces/Rongjiehuang/ProDiff/modules/ProDiff/task/ProDiff_task.py
+++ /dev/null
@@ -1,137 +0,0 @@
-import torch
-from torch import nn
-import utils
-from functools import partial
-from utils.hparams import hparams
-from modules.ProDiff.model.ProDiff import GaussianDiffusion
-from usr.diff.net import DiffNet
-from tasks.tts.fs2 import FastSpeech2Task
-from vocoders.base_vocoder import get_vocoder_cls, BaseVocoder
-from utils.pitch_utils import denorm_f0
-from tasks.tts.fs2_utils import FastSpeechDataset
-DIFF_DECODERS = {
- 'wavenet': lambda hp: DiffNet(hp['audio_num_mel_bins']),
-}
-
-
-class ProDiff_Task(FastSpeech2Task):
- def __init__(self):
- super(ProDiff_Task, self).__init__()
- self.dataset_cls = FastSpeechDataset
- self.vocoder: BaseVocoder = get_vocoder_cls(hparams)()
-
- def build_model(self):
- self.build_tts_model()
- if hparams['load_ckpt'] != '':
- self.load_ckpt(hparams['load_ckpt'], strict=False)
- utils.num_params(self.model, print_out=True, model_name="Generator: student")
- utils.num_params(self.teacher, print_out=True, model_name="Generator: teacher")
- if not hasattr(self, 'gen_params'):
- self.gen_params = list(self.model.parameters())
- return self.model
-
- def build_tts_model(self):
- mel_bins = hparams['audio_num_mel_bins']
- checkpoint = torch.load(hparams['teacher_ckpt'], map_location='cpu')["state_dict"]['model']
- teacher_timesteps = int(checkpoint['timesteps'].item())
- teacher_timescales = int(checkpoint['timescale'].item())
- student_timesteps = teacher_timesteps // 2
- student_timescales = teacher_timescales * 2
-
- self.teacher = GaussianDiffusion(
- phone_encoder=self.phone_encoder,
- out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams),
- loss_type=hparams['diff_loss_type'],
- timesteps=teacher_timesteps, time_scale=teacher_timescales,
- spec_min=hparams['spec_min'], spec_max=hparams['spec_max'],
- )
- self.model = GaussianDiffusion(
- phone_encoder=self.phone_encoder,
- out_dims=mel_bins, denoise_fn=DIFF_DECODERS[hparams['diff_decoder_type']](hparams),
- timesteps=student_timesteps, time_scale=student_timescales,
- loss_type=hparams['diff_loss_type'],
- spec_min=hparams['spec_min'], spec_max=hparams['spec_max'],
- )
-
- utils.load_ckpt(self.teacher, hparams['teacher_ckpt'], 'model', strict=False)
- utils.load_ckpt(self.model, hparams['teacher_ckpt'], 'model', strict=False)
- to_torch = partial(torch.tensor, dtype=torch.float32)
- self.model.num_timesteps = student_timesteps
- self.model.time_scale = student_timescales
- self.model.register_buffer('timesteps', to_torch(student_timesteps)) # beta
- self.model.register_buffer('timescale', to_torch(student_timescales)) # beta
-
- for k, v in self.model.fs2.named_parameters():
- if not 'denoise_fn' in k:
- v.requires_grad = False
-
- for param in self.teacher.parameters():
- param.requires_grad = False
-
-
- def run_model(self, model, sample, return_output=False, infer=False):
- txt_tokens = sample['txt_tokens'] # [B, T_t]
- target = sample['mels'] # [B, T_s, 80]
- # mel2ph = sample['mel2ph'] if hparams['use_gt_dur'] else None # [B, T_s]
- mel2ph = sample['mel2ph']
- f0 = sample['f0']
- uv = sample['uv']
- energy = sample['energy']
- spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
- output = model(txt_tokens, self.teacher, mel2ph=mel2ph, spk_embed=spk_embed,
- ref_mels=target, f0=f0, uv=uv, energy=energy, infer=infer)
-
- losses = {}
- losses['l1'] = output['mel_out']
- self.add_dur_loss(output['dur'], mel2ph, txt_tokens, losses=losses)
- if hparams['use_pitch_embed']:
- self.add_pitch_loss(output, sample, losses)
- if hparams['use_energy_embed']:
- self.add_energy_loss(output['energy_pred'], energy, losses)
- if not return_output:
- return losses
- else:
- return losses, output
-
- def validation_step(self, sample, batch_idx):
- outputs = {}
- txt_tokens = sample['txt_tokens'] # [B, T_t]
-
- energy = sample['energy']
- spk_embed = sample.get('spk_embed') if not hparams['use_spk_id'] else sample.get('spk_ids')
- mel2ph = sample['mel2ph']
- f0 = sample['f0']
- uv = sample['uv']
-
- outputs['losses'] = {}
- outputs['losses'], model_out = self.run_model(self.model, sample, return_output=True, infer=False)
-
- outputs['total_loss'] = sum(outputs['losses'].values())
- outputs['nsamples'] = sample['nsamples']
- outputs = utils.tensors_to_scalars(outputs)
- if batch_idx < hparams['num_valid_plots']:
- # model_out = self.model(
- # txt_tokens, spk_embed=spk_embed, mel2ph=None, f0=None, uv=None, energy=None, ref_mels=None, inference=True)
- # self.plot_mel(batch_idx, model_out['mel_out'], model_out['fs2_mel'], name=f'diffspeech_vs_fs2_{batch_idx}')
- model_out = self.model(
- txt_tokens, spk_embed=spk_embed, mel2ph=mel2ph, f0=f0, uv=uv, energy=energy, ref_mels=None, infer=True)
- gt_f0 = denorm_f0(sample['f0'], sample['uv'], hparams)
- self.plot_wav(batch_idx, sample['mels'], model_out['mel_out'], is_mel=True, gt_f0=gt_f0, f0=model_out.get('f0_denorm'))
- self.plot_mel(batch_idx, sample['mels'], model_out['mel_out'])
- return outputs
-
-
- ############
- # validation plots
- ############
- def plot_wav(self, batch_idx, gt_wav, wav_out, is_mel=False, gt_f0=None, f0=None, name=None):
- gt_wav = gt_wav[0].cpu().numpy()
- wav_out = wav_out[0].cpu().numpy()
- gt_f0 = gt_f0[0].cpu().numpy()
- f0 = f0[0].cpu().numpy()
- if is_mel:
- gt_wav = self.vocoder.spec2wav(gt_wav, f0=gt_f0)
- wav_out = self.vocoder.spec2wav(wav_out, f0=f0)
- self.logger.add_audio(f'gt_{batch_idx}', gt_wav, sample_rate=hparams['audio_sample_rate'], global_step=self.global_step)
- self.logger.add_audio(f'wav_{batch_idx}', wav_out, sample_rate=hparams['audio_sample_rate'], global_step=self.global_step)
-
diff --git a/spaces/Rutakate21/anything-v3.0/app.py b/spaces/Rutakate21/anything-v3.0/app.py
deleted file mode 100644
index 16e8131a0bbf7b06956e69e2b7758fa01e4eb51f..0000000000000000000000000000000000000000
--- a/spaces/Rutakate21/anything-v3.0/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Linaqruf/anything-v3.0").launch()
\ No newline at end of file
diff --git a/spaces/Sa-m/Vehicles-Detection-Custom-YoloV7/models/common.py b/spaces/Sa-m/Vehicles-Detection-Custom-YoloV7/models/common.py
deleted file mode 100644
index 950288a0018315541aefd2f113de20c4eaa49c51..0000000000000000000000000000000000000000
--- a/spaces/Sa-m/Vehicles-Detection-Custom-YoloV7/models/common.py
+++ /dev/null
@@ -1,2047 +0,0 @@
-import math
-from copy import copy
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-import requests
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torchvision.ops import DeformConv2d
-from PIL import Image
-from torch.cuda import amp
-
-from utils.datasets import letterbox
-from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh
-from utils.plots import color_list, plot_one_box
-from utils.torch_utils import time_synchronized
-
-
-##### basic ####
-
-def autopad(k, p=None): # kernel, padding
- # Pad to 'same'
- if p is None:
- p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
- return p
-
-
-class MP(nn.Module):
- def __init__(self, k=2):
- super(MP, self).__init__()
- self.m = nn.MaxPool2d(kernel_size=k, stride=k)
-
- def forward(self, x):
- return self.m(x)
-
-
-class SP(nn.Module):
- def __init__(self, k=3, s=1):
- super(SP, self).__init__()
- self.m = nn.MaxPool2d(kernel_size=k, stride=s, padding=k // 2)
-
- def forward(self, x):
- return self.m(x)
-
-
-class ReOrg(nn.Module):
- def __init__(self):
- super(ReOrg, self).__init__()
-
- def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
- return torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)
-
-
-class Merge(nn.Module):
- def __init__(self,ch=()):
- super(Merge, self).__init__()
-
- def forward(self, x):
-
- return [x[0],x[1],x[2]]
-
-
-class Refine(nn.Module):
-
- def __init__(self, c2, k, s, ch): # ch_in, ch_out, kernel, stride, padding, groups
- super(Refine, self).__init__()
- self.refine = nn.ModuleList()
- for c in ch:
- self.refine.append(Conv(c, c2, k, s))
-
- def forward(self, x):
- for i, f in enumerate(x):
- if i == 0:
- r = self.refine[i](f)
- else:
- r_p = self.refine[i](f)
- r_p = F.interpolate(r_p, r.size()[2:], mode="bilinear", align_corners=False)
- r = r + r_p
- return r
-
-
-class Concat(nn.Module):
- def __init__(self, dimension=1):
- super(Concat, self).__init__()
- self.d = dimension
-
- def forward(self, x):
- return torch.cat(x, self.d)
-
-
-class Chuncat(nn.Module):
- def __init__(self, dimension=1):
- super(Chuncat, self).__init__()
- self.d = dimension
-
- def forward(self, x):
- x1 = []
- x2 = []
- for xi in x:
- xi1, xi2 = xi.chunk(2, self.d)
- x1.append(xi1)
- x2.append(xi2)
- return torch.cat(x1+x2, self.d)
-
-
-class Shortcut(nn.Module):
- def __init__(self, dimension=0):
- super(Shortcut, self).__init__()
- self.d = dimension
-
- def forward(self, x):
- return x[0]+x[1]
-
-
-class Foldcut(nn.Module):
- def __init__(self, dimension=0):
- super(Foldcut, self).__init__()
- self.d = dimension
-
- def forward(self, x):
- x1, x2 = x.chunk(2, self.d)
- return x1+x2
-
-
-class Conv(nn.Module):
- # Standard convolution
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
- super(Conv, self).__init__()
- self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
- self.bn = nn.BatchNorm2d(c2)
- self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
-
- def forward(self, x):
- return self.act(self.bn(self.conv(x)))
-
- def fuseforward(self, x):
- return self.act(self.conv(x))
-
-
-class RobustConv(nn.Module):
- # Robust convolution (use high kernel size 7-11 for: downsampling and other layers). Train for 300 - 450 epochs.
- def __init__(self, c1, c2, k=7, s=1, p=None, g=1, act=True, layer_scale_init_value=1e-6): # ch_in, ch_out, kernel, stride, padding, groups
- super(RobustConv, self).__init__()
- self.conv_dw = Conv(c1, c1, k=k, s=s, p=p, g=c1, act=act)
- self.conv1x1 = nn.Conv2d(c1, c2, 1, 1, 0, groups=1, bias=True)
- self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(c2)) if layer_scale_init_value > 0 else None
-
- def forward(self, x):
- x = x.to(memory_format=torch.channels_last)
- x = self.conv1x1(self.conv_dw(x))
- if self.gamma is not None:
- x = x.mul(self.gamma.reshape(1, -1, 1, 1))
- return x
-
-
-class RobustConv2(nn.Module):
- # Robust convolution 2 (use [32, 5, 2] or [32, 7, 4] or [32, 11, 8] for one of the paths in CSP).
- def __init__(self, c1, c2, k=7, s=4, p=None, g=1, act=True, layer_scale_init_value=1e-6): # ch_in, ch_out, kernel, stride, padding, groups
- super(RobustConv2, self).__init__()
- self.conv_strided = Conv(c1, c1, k=k, s=s, p=p, g=c1, act=act)
- self.conv_deconv = nn.ConvTranspose2d(in_channels=c1, out_channels=c2, kernel_size=s, stride=s,
- padding=0, bias=True, dilation=1, groups=1
- )
- self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(c2)) if layer_scale_init_value > 0 else None
-
- def forward(self, x):
- x = self.conv_deconv(self.conv_strided(x))
- if self.gamma is not None:
- x = x.mul(self.gamma.reshape(1, -1, 1, 1))
- return x
-
-
-def DWConv(c1, c2, k=1, s=1, act=True):
- # Depthwise convolution
- return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
-
-
-class GhostConv(nn.Module):
- # Ghost Convolution https://github.com/huawei-noah/ghostnet
- def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups
- super(GhostConv, self).__init__()
- c_ = c2 // 2 # hidden channels
- self.cv1 = Conv(c1, c_, k, s, None, g, act)
- self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
-
- def forward(self, x):
- y = self.cv1(x)
- return torch.cat([y, self.cv2(y)], 1)
-
-
-class Stem(nn.Module):
- # Stem
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
- super(Stem, self).__init__()
- c_ = int(c2/2) # hidden channels
- self.cv1 = Conv(c1, c_, 3, 2)
- self.cv2 = Conv(c_, c_, 1, 1)
- self.cv3 = Conv(c_, c_, 3, 2)
- self.pool = torch.nn.MaxPool2d(2, stride=2)
- self.cv4 = Conv(2 * c_, c2, 1, 1)
-
- def forward(self, x):
- x = self.cv1(x)
- return self.cv4(torch.cat((self.cv3(self.cv2(x)), self.pool(x)), dim=1))
-
-
-class DownC(nn.Module):
- # Spatial pyramid pooling layer used in YOLOv3-SPP
- def __init__(self, c1, c2, n=1, k=2):
- super(DownC, self).__init__()
- c_ = int(c1) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_, c2//2, 3, k)
- self.cv3 = Conv(c1, c2//2, 1, 1)
- self.mp = nn.MaxPool2d(kernel_size=k, stride=k)
-
- def forward(self, x):
- return torch.cat((self.cv2(self.cv1(x)), self.cv3(self.mp(x))), dim=1)
-
-
-class SPP(nn.Module):
- # Spatial pyramid pooling layer used in YOLOv3-SPP
- def __init__(self, c1, c2, k=(5, 9, 13)):
- super(SPP, self).__init__()
- c_ = c1 // 2 # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
- self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
-
- def forward(self, x):
- x = self.cv1(x)
- return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
-
-
-class Bottleneck(nn.Module):
- # Darknet bottleneck
- def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
- super(Bottleneck, self).__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_, c2, 3, 1, g=g)
- self.add = shortcut and c1 == c2
-
- def forward(self, x):
- return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
-
-
-class Res(nn.Module):
- # ResNet bottleneck
- def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
- super(Res, self).__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_, c_, 3, 1, g=g)
- self.cv3 = Conv(c_, c2, 1, 1)
- self.add = shortcut and c1 == c2
-
- def forward(self, x):
- return x + self.cv3(self.cv2(self.cv1(x))) if self.add else self.cv3(self.cv2(self.cv1(x)))
-
-
-class ResX(Res):
- # ResNet bottleneck
- def __init__(self, c1, c2, shortcut=True, g=32, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
- super().__init__(c1, c2, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
-
-
-class Ghost(nn.Module):
- # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
- def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride
- super(Ghost, self).__init__()
- c_ = c2 // 2
- self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw
- DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
- GhostConv(c_, c2, 1, 1, act=False)) # pw-linear
- self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
- Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
-
- def forward(self, x):
- return self.conv(x) + self.shortcut(x)
-
-##### end of basic #####
-
-
-##### cspnet #####
-
-class SPPCSPC(nn.Module):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)):
- super(SPPCSPC, self).__init__()
- c_ = int(2 * c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c1, c_, 1, 1)
- self.cv3 = Conv(c_, c_, 3, 1)
- self.cv4 = Conv(c_, c_, 1, 1)
- self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
- self.cv5 = Conv(4 * c_, c_, 1, 1)
- self.cv6 = Conv(c_, c_, 3, 1)
- self.cv7 = Conv(2 * c_, c2, 1, 1)
-
- def forward(self, x):
- x1 = self.cv4(self.cv3(self.cv1(x)))
- y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1)))
- y2 = self.cv2(x)
- return self.cv7(torch.cat((y1, y2), dim=1))
-
-class GhostSPPCSPC(SPPCSPC):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)):
- super().__init__(c1, c2, n, shortcut, g, e, k)
- c_ = int(2 * c2 * e) # hidden channels
- self.cv1 = GhostConv(c1, c_, 1, 1)
- self.cv2 = GhostConv(c1, c_, 1, 1)
- self.cv3 = GhostConv(c_, c_, 3, 1)
- self.cv4 = GhostConv(c_, c_, 1, 1)
- self.cv5 = GhostConv(4 * c_, c_, 1, 1)
- self.cv6 = GhostConv(c_, c_, 3, 1)
- self.cv7 = GhostConv(2 * c_, c2, 1, 1)
-
-
-class GhostStem(Stem):
- # Stem
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
- super().__init__(c1, c2, k, s, p, g, act)
- c_ = int(c2/2) # hidden channels
- self.cv1 = GhostConv(c1, c_, 3, 2)
- self.cv2 = GhostConv(c_, c_, 1, 1)
- self.cv3 = GhostConv(c_, c_, 3, 2)
- self.cv4 = GhostConv(2 * c_, c2, 1, 1)
-
-
-class BottleneckCSPA(nn.Module):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super(BottleneckCSPA, self).__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c1, c_, 1, 1)
- self.cv3 = Conv(2 * c_, c2, 1, 1)
- self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
- def forward(self, x):
- y1 = self.m(self.cv1(x))
- y2 = self.cv2(x)
- return self.cv3(torch.cat((y1, y2), dim=1))
-
-
-class BottleneckCSPB(nn.Module):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super(BottleneckCSPB, self).__init__()
- c_ = int(c2) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_, c_, 1, 1)
- self.cv3 = Conv(2 * c_, c2, 1, 1)
- self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
- def forward(self, x):
- x1 = self.cv1(x)
- y1 = self.m(x1)
- y2 = self.cv2(x1)
- return self.cv3(torch.cat((y1, y2), dim=1))
-
-
-class BottleneckCSPC(nn.Module):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super(BottleneckCSPC, self).__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c1, c_, 1, 1)
- self.cv3 = Conv(c_, c_, 1, 1)
- self.cv4 = Conv(2 * c_, c2, 1, 1)
- self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
- def forward(self, x):
- y1 = self.cv3(self.m(self.cv1(x)))
- y2 = self.cv2(x)
- return self.cv4(torch.cat((y1, y2), dim=1))
-
-
-class ResCSPA(BottleneckCSPA):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class ResCSPB(BottleneckCSPB):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2) # hidden channels
- self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class ResCSPC(BottleneckCSPC):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class ResXCSPA(ResCSPA):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-
-class ResXCSPB(ResCSPB):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2) # hidden channels
- self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-
-class ResXCSPC(ResCSPC):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-
-class GhostCSPA(BottleneckCSPA):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)])
-
-
-class GhostCSPB(BottleneckCSPB):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2) # hidden channels
- self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)])
-
-
-class GhostCSPC(BottleneckCSPC):
- # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)])
-
-##### end of cspnet #####
-
-
-##### yolor #####
-
-class ImplicitA(nn.Module):
- def __init__(self, channel, mean=0., std=.02):
- super(ImplicitA, self).__init__()
- self.channel = channel
- self.mean = mean
- self.std = std
- self.implicit = nn.Parameter(torch.zeros(1, channel, 1, 1))
- nn.init.normal_(self.implicit, mean=self.mean, std=self.std)
-
- def forward(self, x):
- return self.implicit + x
-
-
-class ImplicitM(nn.Module):
- def __init__(self, channel, mean=0., std=.02):
- super(ImplicitM, self).__init__()
- self.channel = channel
- self.mean = mean
- self.std = std
- self.implicit = nn.Parameter(torch.ones(1, channel, 1, 1))
- nn.init.normal_(self.implicit, mean=self.mean, std=self.std)
-
- def forward(self, x):
- return self.implicit * x
-
-##### end of yolor #####
-
-
-##### repvgg #####
-
-class RepConv(nn.Module):
- # Represented convolution
- # https://arxiv.org/abs/2101.03697
-
- def __init__(self, c1, c2, k=3, s=1, p=None, g=1, act=True, deploy=False):
- super(RepConv, self).__init__()
-
- self.deploy = deploy
- self.groups = g
- self.in_channels = c1
- self.out_channels = c2
-
- assert k == 3
- assert autopad(k, p) == 1
-
- padding_11 = autopad(k, p) - k // 2
-
- self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
-
- if deploy:
- self.rbr_reparam = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=True)
-
- else:
- self.rbr_identity = (nn.BatchNorm2d(num_features=c1) if c2 == c1 and s == 1 else None)
-
- self.rbr_dense = nn.Sequential(
- nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False),
- nn.BatchNorm2d(num_features=c2),
- )
-
- self.rbr_1x1 = nn.Sequential(
- nn.Conv2d( c1, c2, 1, s, padding_11, groups=g, bias=False),
- nn.BatchNorm2d(num_features=c2),
- )
-
- def forward(self, inputs):
- if hasattr(self, "rbr_reparam"):
- return self.act(self.rbr_reparam(inputs))
-
- if self.rbr_identity is None:
- id_out = 0
- else:
- id_out = self.rbr_identity(inputs)
-
- return self.act(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out)
-
- def get_equivalent_kernel_bias(self):
- kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense)
- kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1)
- kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity)
- return (
- kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid,
- bias3x3 + bias1x1 + biasid,
- )
-
- def _pad_1x1_to_3x3_tensor(self, kernel1x1):
- if kernel1x1 is None:
- return 0
- else:
- return nn.functional.pad(kernel1x1, [1, 1, 1, 1])
-
- def _fuse_bn_tensor(self, branch):
- if branch is None:
- return 0, 0
- if isinstance(branch, nn.Sequential):
- kernel = branch[0].weight
- running_mean = branch[1].running_mean
- running_var = branch[1].running_var
- gamma = branch[1].weight
- beta = branch[1].bias
- eps = branch[1].eps
- else:
- assert isinstance(branch, nn.BatchNorm2d)
- if not hasattr(self, "id_tensor"):
- input_dim = self.in_channels // self.groups
- kernel_value = np.zeros(
- (self.in_channels, input_dim, 3, 3), dtype=np.float32
- )
- for i in range(self.in_channels):
- kernel_value[i, i % input_dim, 1, 1] = 1
- self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)
- kernel = self.id_tensor
- running_mean = branch.running_mean
- running_var = branch.running_var
- gamma = branch.weight
- beta = branch.bias
- eps = branch.eps
- std = (running_var + eps).sqrt()
- t = (gamma / std).reshape(-1, 1, 1, 1)
- return kernel * t, beta - running_mean * gamma / std
-
- def repvgg_convert(self):
- kernel, bias = self.get_equivalent_kernel_bias()
- return (
- kernel.detach().cpu().numpy(),
- bias.detach().cpu().numpy(),
- )
-
- def fuse_conv_bn(self, conv, bn):
-
- std = (bn.running_var + bn.eps).sqrt()
- bias = bn.bias - bn.running_mean * bn.weight / std
-
- t = (bn.weight / std).reshape(-1, 1, 1, 1)
- weights = conv.weight * t
-
- bn = nn.Identity()
- conv = nn.Conv2d(in_channels = conv.in_channels,
- out_channels = conv.out_channels,
- kernel_size = conv.kernel_size,
- stride=conv.stride,
- padding = conv.padding,
- dilation = conv.dilation,
- groups = conv.groups,
- bias = True,
- padding_mode = conv.padding_mode)
-
- conv.weight = torch.nn.Parameter(weights)
- conv.bias = torch.nn.Parameter(bias)
- return conv
-
- def fuse_repvgg_block(self):
- if self.deploy:
- return
- print(f"RepConv.fuse_repvgg_block")
-
- self.rbr_dense = self.fuse_conv_bn(self.rbr_dense[0], self.rbr_dense[1])
-
- self.rbr_1x1 = self.fuse_conv_bn(self.rbr_1x1[0], self.rbr_1x1[1])
- rbr_1x1_bias = self.rbr_1x1.bias
- weight_1x1_expanded = torch.nn.functional.pad(self.rbr_1x1.weight, [1, 1, 1, 1])
-
- # Fuse self.rbr_identity
- if (isinstance(self.rbr_identity, nn.BatchNorm2d) or isinstance(self.rbr_identity, nn.modules.batchnorm.SyncBatchNorm)):
- # print(f"fuse: rbr_identity == BatchNorm2d or SyncBatchNorm")
- identity_conv_1x1 = nn.Conv2d(
- in_channels=self.in_channels,
- out_channels=self.out_channels,
- kernel_size=1,
- stride=1,
- padding=0,
- groups=self.groups,
- bias=False)
- identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.to(self.rbr_1x1.weight.data.device)
- identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.squeeze().squeeze()
- # print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}")
- identity_conv_1x1.weight.data.fill_(0.0)
- identity_conv_1x1.weight.data.fill_diagonal_(1.0)
- identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.unsqueeze(2).unsqueeze(3)
- # print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}")
-
- identity_conv_1x1 = self.fuse_conv_bn(identity_conv_1x1, self.rbr_identity)
- bias_identity_expanded = identity_conv_1x1.bias
- weight_identity_expanded = torch.nn.functional.pad(identity_conv_1x1.weight, [1, 1, 1, 1])
- else:
- # print(f"fuse: rbr_identity != BatchNorm2d, rbr_identity = {self.rbr_identity}")
- bias_identity_expanded = torch.nn.Parameter( torch.zeros_like(rbr_1x1_bias) )
- weight_identity_expanded = torch.nn.Parameter( torch.zeros_like(weight_1x1_expanded) )
-
-
- #print(f"self.rbr_1x1.weight = {self.rbr_1x1.weight.shape}, ")
- #print(f"weight_1x1_expanded = {weight_1x1_expanded.shape}, ")
- #print(f"self.rbr_dense.weight = {self.rbr_dense.weight.shape}, ")
-
- self.rbr_dense.weight = torch.nn.Parameter(self.rbr_dense.weight + weight_1x1_expanded + weight_identity_expanded)
- self.rbr_dense.bias = torch.nn.Parameter(self.rbr_dense.bias + rbr_1x1_bias + bias_identity_expanded)
-
- self.rbr_reparam = self.rbr_dense
- self.deploy = True
-
- if self.rbr_identity is not None:
- del self.rbr_identity
- self.rbr_identity = None
-
- if self.rbr_1x1 is not None:
- del self.rbr_1x1
- self.rbr_1x1 = None
-
- if self.rbr_dense is not None:
- del self.rbr_dense
- self.rbr_dense = None
-
-
-class RepBottleneck(Bottleneck):
- # Standard bottleneck
- def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
- super().__init__(c1, c2, shortcut=True, g=1, e=0.5)
- c_ = int(c2 * e) # hidden channels
- self.cv2 = RepConv(c_, c2, 3, 1, g=g)
-
-
-class RepBottleneckCSPA(BottleneckCSPA):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-
-class RepBottleneckCSPB(BottleneckCSPB):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2) # hidden channels
- self.m = nn.Sequential(*[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-
-class RepBottleneckCSPC(BottleneckCSPC):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
-
-class RepRes(Res):
- # Standard bottleneck
- def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
- super().__init__(c1, c2, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.cv2 = RepConv(c_, c_, 3, 1, g=g)
-
-
-class RepResCSPA(ResCSPA):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class RepResCSPB(ResCSPB):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2) # hidden channels
- self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class RepResCSPC(ResCSPC):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class RepResX(ResX):
- # Standard bottleneck
- def __init__(self, c1, c2, shortcut=True, g=32, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
- super().__init__(c1, c2, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.cv2 = RepConv(c_, c_, 3, 1, g=g)
-
-
-class RepResXCSPA(ResXCSPA):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class RepResXCSPB(ResXCSPB):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=False, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2) # hidden channels
- self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-
-class RepResXCSPC(ResXCSPC):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__(c1, c2, n, shortcut, g, e)
- c_ = int(c2 * e) # hidden channels
- self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
-
-##### end of repvgg #####
-
-
-##### transformer #####
-
-class TransformerLayer(nn.Module):
- # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
- def __init__(self, c, num_heads):
- super().__init__()
- self.q = nn.Linear(c, c, bias=False)
- self.k = nn.Linear(c, c, bias=False)
- self.v = nn.Linear(c, c, bias=False)
- self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)
- self.fc1 = nn.Linear(c, c, bias=False)
- self.fc2 = nn.Linear(c, c, bias=False)
-
- def forward(self, x):
- x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
- x = self.fc2(self.fc1(x)) + x
- return x
-
-
-class TransformerBlock(nn.Module):
- # Vision Transformer https://arxiv.org/abs/2010.11929
- def __init__(self, c1, c2, num_heads, num_layers):
- super().__init__()
- self.conv = None
- if c1 != c2:
- self.conv = Conv(c1, c2)
- self.linear = nn.Linear(c2, c2) # learnable position embedding
- self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)])
- self.c2 = c2
-
- def forward(self, x):
- if self.conv is not None:
- x = self.conv(x)
- b, _, w, h = x.shape
- p = x.flatten(2)
- p = p.unsqueeze(0)
- p = p.transpose(0, 3)
- p = p.squeeze(3)
- e = self.linear(p)
- x = p + e
-
- x = self.tr(x)
- x = x.unsqueeze(3)
- x = x.transpose(0, 3)
- x = x.reshape(b, self.c2, w, h)
- return x
-
-##### end of transformer #####
-
-
-##### yolov5 #####
-
-class Focus(nn.Module):
- # Focus wh information into c-space
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
- super(Focus, self).__init__()
- self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
- # self.contract = Contract(gain=2)
-
- def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
- return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
- # return self.conv(self.contract(x))
-
-
-class SPPF(nn.Module):
- # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
- def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))
- super().__init__()
- c_ = c1 // 2 # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_ * 4, c2, 1, 1)
- self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
-
- def forward(self, x):
- x = self.cv1(x)
- y1 = self.m(x)
- y2 = self.m(y1)
- return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))
-
-
-class Contract(nn.Module):
- # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
- def __init__(self, gain=2):
- super().__init__()
- self.gain = gain
-
- def forward(self, x):
- N, C, H, W = x.size() # assert (H / s == 0) and (W / s == 0), 'Indivisible gain'
- s = self.gain
- x = x.view(N, C, H // s, s, W // s, s) # x(1,64,40,2,40,2)
- x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40)
- return x.view(N, C * s * s, H // s, W // s) # x(1,256,40,40)
-
-
-class Expand(nn.Module):
- # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
- def __init__(self, gain=2):
- super().__init__()
- self.gain = gain
-
- def forward(self, x):
- N, C, H, W = x.size() # assert C / s ** 2 == 0, 'Indivisible gain'
- s = self.gain
- x = x.view(N, s, s, C // s ** 2, H, W) # x(1,2,2,16,80,80)
- x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2)
- return x.view(N, C // s ** 2, H * s, W * s) # x(1,16,160,160)
-
-
-class NMS(nn.Module):
- # Non-Maximum Suppression (NMS) module
- conf = 0.25 # confidence threshold
- iou = 0.45 # IoU threshold
- classes = None # (optional list) filter by class
-
- def __init__(self):
- super(NMS, self).__init__()
-
- def forward(self, x):
- return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
-
-
-class autoShape(nn.Module):
- # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
- conf = 0.25 # NMS confidence threshold
- iou = 0.45 # NMS IoU threshold
- classes = None # (optional list) filter by class
-
- def __init__(self, model):
- super(autoShape, self).__init__()
- self.model = model.eval()
-
- def autoshape(self):
- print('autoShape already enabled, skipping... ') # model already converted to model.autoshape()
- return self
-
- @torch.no_grad()
- def forward(self, imgs, size=640, augment=False, profile=False):
- # Inference from various sources. For height=640, width=1280, RGB images example inputs are:
- # filename: imgs = 'data/samples/zidane.jpg'
- # URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg'
- # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3)
- # PIL: = Image.open('image.jpg') # HWC x(640,1280,3)
- # numpy: = np.zeros((640,1280,3)) # HWC
- # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values)
- # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
-
- t = [time_synchronized()]
- p = next(self.model.parameters()) # for device and type
- if isinstance(imgs, torch.Tensor): # torch
- with amp.autocast(enabled=p.device.type != 'cpu'):
- return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference
-
- # Pre-process
- n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images
- shape0, shape1, files = [], [], [] # image and inference shapes, filenames
- for i, im in enumerate(imgs):
- f = f'image{i}' # filename
- if isinstance(im, str): # filename or uri
- im, f = np.asarray(Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im)), im
- elif isinstance(im, Image.Image): # PIL Image
- im, f = np.asarray(im), getattr(im, 'filename', f) or f
- files.append(Path(f).with_suffix('.jpg').name)
- if im.shape[0] < 5: # image in CHW
- im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
- im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input
- s = im.shape[:2] # HWC
- shape0.append(s) # image shape
- g = (size / max(s)) # gain
- shape1.append([y * g for y in s])
- imgs[i] = im # update
- shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape
- x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad
- x = np.stack(x, 0) if n > 1 else x[0][None] # stack
- x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW
- x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32
- t.append(time_synchronized())
-
- with amp.autocast(enabled=p.device.type != 'cpu'):
- # Inference
- y = self.model(x, augment, profile)[0] # forward
- t.append(time_synchronized())
-
- # Post-process
- y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS
- for i in range(n):
- scale_coords(shape1, y[i][:, :4], shape0[i])
-
- t.append(time_synchronized())
- return Detections(imgs, y, files, t, self.names, x.shape)
-
-
-class Detections:
- # detections class for YOLOv5 inference results
- def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
- super(Detections, self).__init__()
- d = pred[0].device # device
- gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations
- self.imgs = imgs # list of images as numpy arrays
- self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
- self.names = names # class names
- self.files = files # image filenames
- self.xyxy = pred # xyxy pixels
- self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
- self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
- self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
- self.n = len(self.pred) # number of images (batch size)
- self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms)
- self.s = shape # inference BCHW shape
-
- def display(self, pprint=False, show=False, save=False, render=False, save_dir=''):
- colors = color_list()
- for i, (img, pred) in enumerate(zip(self.imgs, self.pred)):
- str = f'image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} '
- if pred is not None:
- for c in pred[:, -1].unique():
- n = (pred[:, -1] == c).sum() # detections per class
- str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string
- if show or save or render:
- for *box, conf, cls in pred: # xyxy, confidence, class
- label = f'{self.names[int(cls)]} {conf:.2f}'
- plot_one_box(box, img, label=label, color=colors[int(cls) % 10])
- img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img # from np
- if pprint:
- print(str.rstrip(', '))
- if show:
- img.show(self.files[i]) # show
- if save:
- f = self.files[i]
- img.save(Path(save_dir) / f) # save
- print(f"{'Saved' * (i == 0)} {f}", end=',' if i < self.n - 1 else f' to {save_dir}\n')
- if render:
- self.imgs[i] = np.asarray(img)
-
- def print(self):
- self.display(pprint=True) # print results
- print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t)
-
- def show(self):
- self.display(show=True) # show results
-
- def save(self, save_dir='runs/hub/exp'):
- save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp') # increment save_dir
- Path(save_dir).mkdir(parents=True, exist_ok=True)
- self.display(save=True, save_dir=save_dir) # save results
-
- def render(self):
- self.display(render=True) # render results
- return self.imgs
-
- def pandas(self):
- # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
- new = copy(self) # return copy
- ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns
- cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns
- for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
- a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update
- setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
- return new
-
- def tolist(self):
- # return a list of Detections objects, i.e. 'for result in results.tolist():'
- x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)]
- for d in x:
- for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
- setattr(d, k, getattr(d, k)[0]) # pop out of list
- return x
-
- def __len__(self):
- return self.n
-
-
-class Classify(nn.Module):
- # Classification head, i.e. x(b,c1,20,20) to x(b,c2)
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
- super(Classify, self).__init__()
- self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1)
- self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1)
- self.flat = nn.Flatten()
-
- def forward(self, x):
- z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list
- return self.flat(self.conv(z)) # flatten to x(b,c2)
-
-##### end of yolov5 ######
-
-
-##### orepa #####
-
-def transI_fusebn(kernel, bn):
- gamma = bn.weight
- std = (bn.running_var + bn.eps).sqrt()
- return kernel * ((gamma / std).reshape(-1, 1, 1, 1)), bn.bias - bn.running_mean * gamma / std
-
-
-class ConvBN(nn.Module):
- def __init__(self, in_channels, out_channels, kernel_size,
- stride=1, padding=0, dilation=1, groups=1, deploy=False, nonlinear=None):
- super().__init__()
- if nonlinear is None:
- self.nonlinear = nn.Identity()
- else:
- self.nonlinear = nonlinear
- if deploy:
- self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
- stride=stride, padding=padding, dilation=dilation, groups=groups, bias=True)
- else:
- self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
- stride=stride, padding=padding, dilation=dilation, groups=groups, bias=False)
- self.bn = nn.BatchNorm2d(num_features=out_channels)
-
- def forward(self, x):
- if hasattr(self, 'bn'):
- return self.nonlinear(self.bn(self.conv(x)))
- else:
- return self.nonlinear(self.conv(x))
-
- def switch_to_deploy(self):
- kernel, bias = transI_fusebn(self.conv.weight, self.bn)
- conv = nn.Conv2d(in_channels=self.conv.in_channels, out_channels=self.conv.out_channels, kernel_size=self.conv.kernel_size,
- stride=self.conv.stride, padding=self.conv.padding, dilation=self.conv.dilation, groups=self.conv.groups, bias=True)
- conv.weight.data = kernel
- conv.bias.data = bias
- for para in self.parameters():
- para.detach_()
- self.__delattr__('conv')
- self.__delattr__('bn')
- self.conv = conv
-
-class OREPA_3x3_RepConv(nn.Module):
-
- def __init__(self, in_channels, out_channels, kernel_size,
- stride=1, padding=0, dilation=1, groups=1,
- internal_channels_1x1_3x3=None,
- deploy=False, nonlinear=None, single_init=False):
- super(OREPA_3x3_RepConv, self).__init__()
- self.deploy = deploy
-
- if nonlinear is None:
- self.nonlinear = nn.Identity()
- else:
- self.nonlinear = nonlinear
-
- self.kernel_size = kernel_size
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.groups = groups
- assert padding == kernel_size // 2
-
- self.stride = stride
- self.padding = padding
- self.dilation = dilation
-
- self.branch_counter = 0
-
- self.weight_rbr_origin = nn.Parameter(torch.Tensor(out_channels, int(in_channels/self.groups), kernel_size, kernel_size))
- nn.init.kaiming_uniform_(self.weight_rbr_origin, a=math.sqrt(1.0))
- self.branch_counter += 1
-
-
- if groups < out_channels:
- self.weight_rbr_avg_conv = nn.Parameter(torch.Tensor(out_channels, int(in_channels/self.groups), 1, 1))
- self.weight_rbr_pfir_conv = nn.Parameter(torch.Tensor(out_channels, int(in_channels/self.groups), 1, 1))
- nn.init.kaiming_uniform_(self.weight_rbr_avg_conv, a=1.0)
- nn.init.kaiming_uniform_(self.weight_rbr_pfir_conv, a=1.0)
- self.weight_rbr_avg_conv.data
- self.weight_rbr_pfir_conv.data
- self.register_buffer('weight_rbr_avg_avg', torch.ones(kernel_size, kernel_size).mul(1.0/kernel_size/kernel_size))
- self.branch_counter += 1
-
- else:
- raise NotImplementedError
- self.branch_counter += 1
-
- if internal_channels_1x1_3x3 is None:
- internal_channels_1x1_3x3 = in_channels if groups < out_channels else 2 * in_channels # For mobilenet, it is better to have 2X internal channels
-
- if internal_channels_1x1_3x3 == in_channels:
- self.weight_rbr_1x1_kxk_idconv1 = nn.Parameter(torch.zeros(in_channels, int(in_channels/self.groups), 1, 1))
- id_value = np.zeros((in_channels, int(in_channels/self.groups), 1, 1))
- for i in range(in_channels):
- id_value[i, i % int(in_channels/self.groups), 0, 0] = 1
- id_tensor = torch.from_numpy(id_value).type_as(self.weight_rbr_1x1_kxk_idconv1)
- self.register_buffer('id_tensor', id_tensor)
-
- else:
- self.weight_rbr_1x1_kxk_conv1 = nn.Parameter(torch.Tensor(internal_channels_1x1_3x3, int(in_channels/self.groups), 1, 1))
- nn.init.kaiming_uniform_(self.weight_rbr_1x1_kxk_conv1, a=math.sqrt(1.0))
- self.weight_rbr_1x1_kxk_conv2 = nn.Parameter(torch.Tensor(out_channels, int(internal_channels_1x1_3x3/self.groups), kernel_size, kernel_size))
- nn.init.kaiming_uniform_(self.weight_rbr_1x1_kxk_conv2, a=math.sqrt(1.0))
- self.branch_counter += 1
-
- expand_ratio = 8
- self.weight_rbr_gconv_dw = nn.Parameter(torch.Tensor(in_channels*expand_ratio, 1, kernel_size, kernel_size))
- self.weight_rbr_gconv_pw = nn.Parameter(torch.Tensor(out_channels, in_channels*expand_ratio, 1, 1))
- nn.init.kaiming_uniform_(self.weight_rbr_gconv_dw, a=math.sqrt(1.0))
- nn.init.kaiming_uniform_(self.weight_rbr_gconv_pw, a=math.sqrt(1.0))
- self.branch_counter += 1
-
- if out_channels == in_channels and stride == 1:
- self.branch_counter += 1
-
- self.vector = nn.Parameter(torch.Tensor(self.branch_counter, self.out_channels))
- self.bn = nn.BatchNorm2d(out_channels)
-
- self.fre_init()
-
- nn.init.constant_(self.vector[0, :], 0.25) #origin
- nn.init.constant_(self.vector[1, :], 0.25) #avg
- nn.init.constant_(self.vector[2, :], 0.0) #prior
- nn.init.constant_(self.vector[3, :], 0.5) #1x1_kxk
- nn.init.constant_(self.vector[4, :], 0.5) #dws_conv
-
-
- def fre_init(self):
- prior_tensor = torch.Tensor(self.out_channels, self.kernel_size, self.kernel_size)
- half_fg = self.out_channels/2
- for i in range(self.out_channels):
- for h in range(3):
- for w in range(3):
- if i < half_fg:
- prior_tensor[i, h, w] = math.cos(math.pi*(h+0.5)*(i+1)/3)
- else:
- prior_tensor[i, h, w] = math.cos(math.pi*(w+0.5)*(i+1-half_fg)/3)
-
- self.register_buffer('weight_rbr_prior', prior_tensor)
-
- def weight_gen(self):
-
- weight_rbr_origin = torch.einsum('oihw,o->oihw', self.weight_rbr_origin, self.vector[0, :])
-
- weight_rbr_avg = torch.einsum('oihw,o->oihw', torch.einsum('oihw,hw->oihw', self.weight_rbr_avg_conv, self.weight_rbr_avg_avg), self.vector[1, :])
-
- weight_rbr_pfir = torch.einsum('oihw,o->oihw', torch.einsum('oihw,ohw->oihw', self.weight_rbr_pfir_conv, self.weight_rbr_prior), self.vector[2, :])
-
- weight_rbr_1x1_kxk_conv1 = None
- if hasattr(self, 'weight_rbr_1x1_kxk_idconv1'):
- weight_rbr_1x1_kxk_conv1 = (self.weight_rbr_1x1_kxk_idconv1 + self.id_tensor).squeeze()
- elif hasattr(self, 'weight_rbr_1x1_kxk_conv1'):
- weight_rbr_1x1_kxk_conv1 = self.weight_rbr_1x1_kxk_conv1.squeeze()
- else:
- raise NotImplementedError
- weight_rbr_1x1_kxk_conv2 = self.weight_rbr_1x1_kxk_conv2
-
- if self.groups > 1:
- g = self.groups
- t, ig = weight_rbr_1x1_kxk_conv1.size()
- o, tg, h, w = weight_rbr_1x1_kxk_conv2.size()
- weight_rbr_1x1_kxk_conv1 = weight_rbr_1x1_kxk_conv1.view(g, int(t/g), ig)
- weight_rbr_1x1_kxk_conv2 = weight_rbr_1x1_kxk_conv2.view(g, int(o/g), tg, h, w)
- weight_rbr_1x1_kxk = torch.einsum('gti,gothw->goihw', weight_rbr_1x1_kxk_conv1, weight_rbr_1x1_kxk_conv2).view(o, ig, h, w)
- else:
- weight_rbr_1x1_kxk = torch.einsum('ti,othw->oihw', weight_rbr_1x1_kxk_conv1, weight_rbr_1x1_kxk_conv2)
-
- weight_rbr_1x1_kxk = torch.einsum('oihw,o->oihw', weight_rbr_1x1_kxk, self.vector[3, :])
-
- weight_rbr_gconv = self.dwsc2full(self.weight_rbr_gconv_dw, self.weight_rbr_gconv_pw, self.in_channels)
- weight_rbr_gconv = torch.einsum('oihw,o->oihw', weight_rbr_gconv, self.vector[4, :])
-
- weight = weight_rbr_origin + weight_rbr_avg + weight_rbr_1x1_kxk + weight_rbr_pfir + weight_rbr_gconv
-
- return weight
-
- def dwsc2full(self, weight_dw, weight_pw, groups):
-
- t, ig, h, w = weight_dw.size()
- o, _, _, _ = weight_pw.size()
- tg = int(t/groups)
- i = int(ig*groups)
- weight_dw = weight_dw.view(groups, tg, ig, h, w)
- weight_pw = weight_pw.squeeze().view(o, groups, tg)
-
- weight_dsc = torch.einsum('gtihw,ogt->ogihw', weight_dw, weight_pw)
- return weight_dsc.view(o, i, h, w)
-
- def forward(self, inputs):
- weight = self.weight_gen()
- out = F.conv2d(inputs, weight, bias=None, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups)
-
- return self.nonlinear(self.bn(out))
-
-class RepConv_OREPA(nn.Module):
-
- def __init__(self, c1, c2, k=3, s=1, padding=1, dilation=1, groups=1, padding_mode='zeros', deploy=False, use_se=False, nonlinear=nn.SiLU()):
- super(RepConv_OREPA, self).__init__()
- self.deploy = deploy
- self.groups = groups
- self.in_channels = c1
- self.out_channels = c2
-
- self.padding = padding
- self.dilation = dilation
- self.groups = groups
-
- assert k == 3
- assert padding == 1
-
- padding_11 = padding - k // 2
-
- if nonlinear is None:
- self.nonlinearity = nn.Identity()
- else:
- self.nonlinearity = nonlinear
-
- if use_se:
- self.se = SEBlock(self.out_channels, internal_neurons=self.out_channels // 16)
- else:
- self.se = nn.Identity()
-
- if deploy:
- self.rbr_reparam = nn.Conv2d(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=k, stride=s,
- padding=padding, dilation=dilation, groups=groups, bias=True, padding_mode=padding_mode)
-
- else:
- self.rbr_identity = nn.BatchNorm2d(num_features=self.in_channels) if self.out_channels == self.in_channels and s == 1 else None
- self.rbr_dense = OREPA_3x3_RepConv(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=k, stride=s, padding=padding, groups=groups, dilation=1)
- self.rbr_1x1 = ConvBN(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=1, stride=s, padding=padding_11, groups=groups, dilation=1)
- print('RepVGG Block, identity = ', self.rbr_identity)
-
-
- def forward(self, inputs):
- if hasattr(self, 'rbr_reparam'):
- return self.nonlinearity(self.se(self.rbr_reparam(inputs)))
-
- if self.rbr_identity is None:
- id_out = 0
- else:
- id_out = self.rbr_identity(inputs)
-
- out1 = self.rbr_dense(inputs)
- out2 = self.rbr_1x1(inputs)
- out3 = id_out
- out = out1 + out2 + out3
-
- return self.nonlinearity(self.se(out))
-
-
- # Optional. This improves the accuracy and facilitates quantization.
- # 1. Cancel the original weight decay on rbr_dense.conv.weight and rbr_1x1.conv.weight.
- # 2. Use like this.
- # loss = criterion(....)
- # for every RepVGGBlock blk:
- # loss += weight_decay_coefficient * 0.5 * blk.get_cust_L2()
- # optimizer.zero_grad()
- # loss.backward()
-
- # Not used for OREPA
- def get_custom_L2(self):
- K3 = self.rbr_dense.weight_gen()
- K1 = self.rbr_1x1.conv.weight
- t3 = (self.rbr_dense.bn.weight / ((self.rbr_dense.bn.running_var + self.rbr_dense.bn.eps).sqrt())).reshape(-1, 1, 1, 1).detach()
- t1 = (self.rbr_1x1.bn.weight / ((self.rbr_1x1.bn.running_var + self.rbr_1x1.bn.eps).sqrt())).reshape(-1, 1, 1, 1).detach()
-
- l2_loss_circle = (K3 ** 2).sum() - (K3[:, :, 1:2, 1:2] ** 2).sum() # The L2 loss of the "circle" of weights in 3x3 kernel. Use regular L2 on them.
- eq_kernel = K3[:, :, 1:2, 1:2] * t3 + K1 * t1 # The equivalent resultant central point of 3x3 kernel.
- l2_loss_eq_kernel = (eq_kernel ** 2 / (t3 ** 2 + t1 ** 2)).sum() # Normalize for an L2 coefficient comparable to regular L2.
- return l2_loss_eq_kernel + l2_loss_circle
-
- def get_equivalent_kernel_bias(self):
- kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense)
- kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1)
- kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity)
- return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid
-
- def _pad_1x1_to_3x3_tensor(self, kernel1x1):
- if kernel1x1 is None:
- return 0
- else:
- return torch.nn.functional.pad(kernel1x1, [1,1,1,1])
-
- def _fuse_bn_tensor(self, branch):
- if branch is None:
- return 0, 0
- if not isinstance(branch, nn.BatchNorm2d):
- if isinstance(branch, OREPA_3x3_RepConv):
- kernel = branch.weight_gen()
- elif isinstance(branch, ConvBN):
- kernel = branch.conv.weight
- else:
- raise NotImplementedError
- running_mean = branch.bn.running_mean
- running_var = branch.bn.running_var
- gamma = branch.bn.weight
- beta = branch.bn.bias
- eps = branch.bn.eps
- else:
- if not hasattr(self, 'id_tensor'):
- input_dim = self.in_channels // self.groups
- kernel_value = np.zeros((self.in_channels, input_dim, 3, 3), dtype=np.float32)
- for i in range(self.in_channels):
- kernel_value[i, i % input_dim, 1, 1] = 1
- self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)
- kernel = self.id_tensor
- running_mean = branch.running_mean
- running_var = branch.running_var
- gamma = branch.weight
- beta = branch.bias
- eps = branch.eps
- std = (running_var + eps).sqrt()
- t = (gamma / std).reshape(-1, 1, 1, 1)
- return kernel * t, beta - running_mean * gamma / std
-
- def switch_to_deploy(self):
- if hasattr(self, 'rbr_reparam'):
- return
- print(f"RepConv_OREPA.switch_to_deploy")
- kernel, bias = self.get_equivalent_kernel_bias()
- self.rbr_reparam = nn.Conv2d(in_channels=self.rbr_dense.in_channels, out_channels=self.rbr_dense.out_channels,
- kernel_size=self.rbr_dense.kernel_size, stride=self.rbr_dense.stride,
- padding=self.rbr_dense.padding, dilation=self.rbr_dense.dilation, groups=self.rbr_dense.groups, bias=True)
- self.rbr_reparam.weight.data = kernel
- self.rbr_reparam.bias.data = bias
- for para in self.parameters():
- para.detach_()
- self.__delattr__('rbr_dense')
- self.__delattr__('rbr_1x1')
- if hasattr(self, 'rbr_identity'):
- self.__delattr__('rbr_identity')
-
-##### end of orepa #####
-
-
-##### swin transformer #####
-
-class WindowAttention(nn.Module):
-
- def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
- super().__init__()
- self.dim = dim
- self.window_size = window_size # Wh, Ww
- self.num_heads = num_heads
- head_dim = dim // num_heads
- self.scale = qk_scale or head_dim ** -0.5
-
- # define a parameter table of relative position bias
- self.relative_position_bias_table = nn.Parameter(
- torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
-
- # get pair-wise relative position index for each token inside the window
- coords_h = torch.arange(self.window_size[0])
- coords_w = torch.arange(self.window_size[1])
- coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
- coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
- relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
- relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
- relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
- relative_coords[:, :, 1] += self.window_size[1] - 1
- relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
- relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
- self.register_buffer("relative_position_index", relative_position_index)
-
- self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = nn.Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
-
- nn.init.normal_(self.relative_position_bias_table, std=.02)
- self.softmax = nn.Softmax(dim=-1)
-
- def forward(self, x, mask=None):
-
- B_, N, C = x.shape
- qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
- q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
-
- q = q * self.scale
- attn = (q @ k.transpose(-2, -1))
-
- relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
- self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
- relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
- attn = attn + relative_position_bias.unsqueeze(0)
-
- if mask is not None:
- nW = mask.shape[0]
- attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
- attn = attn.view(-1, self.num_heads, N, N)
- attn = self.softmax(attn)
- else:
- attn = self.softmax(attn)
-
- attn = self.attn_drop(attn)
-
- # print(attn.dtype, v.dtype)
- try:
- x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
- except:
- #print(attn.dtype, v.dtype)
- x = (attn.half() @ v).transpose(1, 2).reshape(B_, N, C)
- x = self.proj(x)
- x = self.proj_drop(x)
- return x
-
-class Mlp(nn.Module):
-
- def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0.):
- super().__init__()
- out_features = out_features or in_features
- hidden_features = hidden_features or in_features
- self.fc1 = nn.Linear(in_features, hidden_features)
- self.act = act_layer()
- self.fc2 = nn.Linear(hidden_features, out_features)
- self.drop = nn.Dropout(drop)
-
- def forward(self, x):
- x = self.fc1(x)
- x = self.act(x)
- x = self.drop(x)
- x = self.fc2(x)
- x = self.drop(x)
- return x
-
-def window_partition(x, window_size):
-
- B, H, W, C = x.shape
- assert H % window_size == 0, 'feature map h and w can not divide by window size'
- x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
- windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
- return windows
-
-def window_reverse(windows, window_size, H, W):
-
- B = int(windows.shape[0] / (H * W / window_size / window_size))
- x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
- x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
- return x
-
-
-class SwinTransformerLayer(nn.Module):
-
- def __init__(self, dim, num_heads, window_size=8, shift_size=0,
- mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
- act_layer=nn.SiLU, norm_layer=nn.LayerNorm):
- super().__init__()
- self.dim = dim
- self.num_heads = num_heads
- self.window_size = window_size
- self.shift_size = shift_size
- self.mlp_ratio = mlp_ratio
- # if min(self.input_resolution) <= self.window_size:
- # # if window size is larger than input resolution, we don't partition windows
- # self.shift_size = 0
- # self.window_size = min(self.input_resolution)
- assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
- self.norm1 = norm_layer(dim)
- self.attn = WindowAttention(
- dim, window_size=(self.window_size, self.window_size), num_heads=num_heads,
- qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
- self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
- self.norm2 = norm_layer(dim)
- mlp_hidden_dim = int(dim * mlp_ratio)
- self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
- def create_mask(self, H, W):
- # calculate attention mask for SW-MSA
- img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
- h_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- w_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- cnt = 0
- for h in h_slices:
- for w in w_slices:
- img_mask[:, h, w, :] = cnt
- cnt += 1
-
- mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
- mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
- attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
- attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
- return attn_mask
-
- def forward(self, x):
- # reshape x[b c h w] to x[b l c]
- _, _, H_, W_ = x.shape
-
- Padding = False
- if min(H_, W_) < self.window_size or H_ % self.window_size!=0 or W_ % self.window_size!=0:
- Padding = True
- # print(f'img_size {min(H_, W_)} is less than (or not divided by) window_size {self.window_size}, Padding.')
- pad_r = (self.window_size - W_ % self.window_size) % self.window_size
- pad_b = (self.window_size - H_ % self.window_size) % self.window_size
- x = F.pad(x, (0, pad_r, 0, pad_b))
-
- # print('2', x.shape)
- B, C, H, W = x.shape
- L = H * W
- x = x.permute(0, 2, 3, 1).contiguous().view(B, L, C) # b, L, c
-
- # create mask from init to forward
- if self.shift_size > 0:
- attn_mask = self.create_mask(H, W).to(x.device)
- else:
- attn_mask = None
-
- shortcut = x
- x = self.norm1(x)
- x = x.view(B, H, W, C)
-
- # cyclic shift
- if self.shift_size > 0:
- shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
- else:
- shifted_x = x
-
- # partition windows
- x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
- x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
-
- # W-MSA/SW-MSA
- attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
-
- # merge windows
- attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
- shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
-
- # reverse cyclic shift
- if self.shift_size > 0:
- x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
- else:
- x = shifted_x
- x = x.view(B, H * W, C)
-
- # FFN
- x = shortcut + self.drop_path(x)
- x = x + self.drop_path(self.mlp(self.norm2(x)))
-
- x = x.permute(0, 2, 1).contiguous().view(-1, C, H, W) # b c h w
-
- if Padding:
- x = x[:, :, :H_, :W_] # reverse padding
-
- return x
-
-
-class SwinTransformerBlock(nn.Module):
- def __init__(self, c1, c2, num_heads, num_layers, window_size=8):
- super().__init__()
- self.conv = None
- if c1 != c2:
- self.conv = Conv(c1, c2)
-
- # remove input_resolution
- self.blocks = nn.Sequential(*[SwinTransformerLayer(dim=c2, num_heads=num_heads, window_size=window_size,
- shift_size=0 if (i % 2 == 0) else window_size // 2) for i in range(num_layers)])
-
- def forward(self, x):
- if self.conv is not None:
- x = self.conv(x)
- x = self.blocks(x)
- return x
-
-
-class STCSPA(nn.Module):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super(STCSPA, self).__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c1, c_, 1, 1)
- self.cv3 = Conv(2 * c_, c2, 1, 1)
- num_heads = c_ // 32
- self.m = SwinTransformerBlock(c_, c_, num_heads, n)
- #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
- def forward(self, x):
- y1 = self.m(self.cv1(x))
- y2 = self.cv2(x)
- return self.cv3(torch.cat((y1, y2), dim=1))
-
-
-class STCSPB(nn.Module):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super(STCSPB, self).__init__()
- c_ = int(c2) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_, c_, 1, 1)
- self.cv3 = Conv(2 * c_, c2, 1, 1)
- num_heads = c_ // 32
- self.m = SwinTransformerBlock(c_, c_, num_heads, n)
- #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
- def forward(self, x):
- x1 = self.cv1(x)
- y1 = self.m(x1)
- y2 = self.cv2(x1)
- return self.cv3(torch.cat((y1, y2), dim=1))
-
-
-class STCSPC(nn.Module):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super(STCSPC, self).__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c1, c_, 1, 1)
- self.cv3 = Conv(c_, c_, 1, 1)
- self.cv4 = Conv(2 * c_, c2, 1, 1)
- num_heads = c_ // 32
- self.m = SwinTransformerBlock(c_, c_, num_heads, n)
- #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
- def forward(self, x):
- y1 = self.cv3(self.m(self.cv1(x)))
- y2 = self.cv2(x)
- return self.cv4(torch.cat((y1, y2), dim=1))
-
-##### end of swin transformer #####
-
-
-##### swin transformer v2 #####
-
-class WindowAttention_v2(nn.Module):
-
- def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
- pretrained_window_size=[0, 0]):
-
- super().__init__()
- self.dim = dim
- self.window_size = window_size # Wh, Ww
- self.pretrained_window_size = pretrained_window_size
- self.num_heads = num_heads
-
- self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)
-
- # mlp to generate continuous relative position bias
- self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
- nn.ReLU(inplace=True),
- nn.Linear(512, num_heads, bias=False))
-
- # get relative_coords_table
- relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
- relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
- relative_coords_table = torch.stack(
- torch.meshgrid([relative_coords_h,
- relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2
- if pretrained_window_size[0] > 0:
- relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
- relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
- else:
- relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
- relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
- relative_coords_table *= 8 # normalize to -8, 8
- relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
- torch.abs(relative_coords_table) + 1.0) / np.log2(8)
-
- self.register_buffer("relative_coords_table", relative_coords_table)
-
- # get pair-wise relative position index for each token inside the window
- coords_h = torch.arange(self.window_size[0])
- coords_w = torch.arange(self.window_size[1])
- coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
- coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
- relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
- relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
- relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
- relative_coords[:, :, 1] += self.window_size[1] - 1
- relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
- relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
- self.register_buffer("relative_position_index", relative_position_index)
-
- self.qkv = nn.Linear(dim, dim * 3, bias=False)
- if qkv_bias:
- self.q_bias = nn.Parameter(torch.zeros(dim))
- self.v_bias = nn.Parameter(torch.zeros(dim))
- else:
- self.q_bias = None
- self.v_bias = None
- self.attn_drop = nn.Dropout(attn_drop)
- self.proj = nn.Linear(dim, dim)
- self.proj_drop = nn.Dropout(proj_drop)
- self.softmax = nn.Softmax(dim=-1)
-
- def forward(self, x, mask=None):
-
- B_, N, C = x.shape
- qkv_bias = None
- if self.q_bias is not None:
- qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
- qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
- qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
- q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
-
- # cosine attention
- attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
- logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01))).exp()
- attn = attn * logit_scale
-
- relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
- relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
- self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
- relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
- relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
- attn = attn + relative_position_bias.unsqueeze(0)
-
- if mask is not None:
- nW = mask.shape[0]
- attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
- attn = attn.view(-1, self.num_heads, N, N)
- attn = self.softmax(attn)
- else:
- attn = self.softmax(attn)
-
- attn = self.attn_drop(attn)
-
- try:
- x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
- except:
- x = (attn.half() @ v).transpose(1, 2).reshape(B_, N, C)
-
- x = self.proj(x)
- x = self.proj_drop(x)
- return x
-
- def extra_repr(self) -> str:
- return f'dim={self.dim}, window_size={self.window_size}, ' \
- f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'
-
- def flops(self, N):
- # calculate flops for 1 window with token length of N
- flops = 0
- # qkv = self.qkv(x)
- flops += N * self.dim * 3 * self.dim
- # attn = (q @ k.transpose(-2, -1))
- flops += self.num_heads * N * (self.dim // self.num_heads) * N
- # x = (attn @ v)
- flops += self.num_heads * N * N * (self.dim // self.num_heads)
- # x = self.proj(x)
- flops += N * self.dim * self.dim
- return flops
-
-class Mlp_v2(nn.Module):
- def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0.):
- super().__init__()
- out_features = out_features or in_features
- hidden_features = hidden_features or in_features
- self.fc1 = nn.Linear(in_features, hidden_features)
- self.act = act_layer()
- self.fc2 = nn.Linear(hidden_features, out_features)
- self.drop = nn.Dropout(drop)
-
- def forward(self, x):
- x = self.fc1(x)
- x = self.act(x)
- x = self.drop(x)
- x = self.fc2(x)
- x = self.drop(x)
- return x
-
-
-def window_partition_v2(x, window_size):
-
- B, H, W, C = x.shape
- x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
- windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
- return windows
-
-
-def window_reverse_v2(windows, window_size, H, W):
-
- B = int(windows.shape[0] / (H * W / window_size / window_size))
- x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
- x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
- return x
-
-
-class SwinTransformerLayer_v2(nn.Module):
-
- def __init__(self, dim, num_heads, window_size=7, shift_size=0,
- mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
- act_layer=nn.SiLU, norm_layer=nn.LayerNorm, pretrained_window_size=0):
- super().__init__()
- self.dim = dim
- #self.input_resolution = input_resolution
- self.num_heads = num_heads
- self.window_size = window_size
- self.shift_size = shift_size
- self.mlp_ratio = mlp_ratio
- #if min(self.input_resolution) <= self.window_size:
- # # if window size is larger than input resolution, we don't partition windows
- # self.shift_size = 0
- # self.window_size = min(self.input_resolution)
- assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
- self.norm1 = norm_layer(dim)
- self.attn = WindowAttention_v2(
- dim, window_size=(self.window_size, self.window_size), num_heads=num_heads,
- qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
- pretrained_window_size=(pretrained_window_size, pretrained_window_size))
-
- self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
- self.norm2 = norm_layer(dim)
- mlp_hidden_dim = int(dim * mlp_ratio)
- self.mlp = Mlp_v2(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
- def create_mask(self, H, W):
- # calculate attention mask for SW-MSA
- img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
- h_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- w_slices = (slice(0, -self.window_size),
- slice(-self.window_size, -self.shift_size),
- slice(-self.shift_size, None))
- cnt = 0
- for h in h_slices:
- for w in w_slices:
- img_mask[:, h, w, :] = cnt
- cnt += 1
-
- mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
- mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
- attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
- attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
- return attn_mask
-
- def forward(self, x):
- # reshape x[b c h w] to x[b l c]
- _, _, H_, W_ = x.shape
-
- Padding = False
- if min(H_, W_) < self.window_size or H_ % self.window_size!=0 or W_ % self.window_size!=0:
- Padding = True
- # print(f'img_size {min(H_, W_)} is less than (or not divided by) window_size {self.window_size}, Padding.')
- pad_r = (self.window_size - W_ % self.window_size) % self.window_size
- pad_b = (self.window_size - H_ % self.window_size) % self.window_size
- x = F.pad(x, (0, pad_r, 0, pad_b))
-
- # print('2', x.shape)
- B, C, H, W = x.shape
- L = H * W
- x = x.permute(0, 2, 3, 1).contiguous().view(B, L, C) # b, L, c
-
- # create mask from init to forward
- if self.shift_size > 0:
- attn_mask = self.create_mask(H, W).to(x.device)
- else:
- attn_mask = None
-
- shortcut = x
- x = x.view(B, H, W, C)
-
- # cyclic shift
- if self.shift_size > 0:
- shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
- else:
- shifted_x = x
-
- # partition windows
- x_windows = window_partition_v2(shifted_x, self.window_size) # nW*B, window_size, window_size, C
- x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
-
- # W-MSA/SW-MSA
- attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
-
- # merge windows
- attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
- shifted_x = window_reverse_v2(attn_windows, self.window_size, H, W) # B H' W' C
-
- # reverse cyclic shift
- if self.shift_size > 0:
- x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
- else:
- x = shifted_x
- x = x.view(B, H * W, C)
- x = shortcut + self.drop_path(self.norm1(x))
-
- # FFN
- x = x + self.drop_path(self.norm2(self.mlp(x)))
- x = x.permute(0, 2, 1).contiguous().view(-1, C, H, W) # b c h w
-
- if Padding:
- x = x[:, :, :H_, :W_] # reverse padding
-
- return x
-
- def extra_repr(self) -> str:
- return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
- f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-
- def flops(self):
- flops = 0
- H, W = self.input_resolution
- # norm1
- flops += self.dim * H * W
- # W-MSA/SW-MSA
- nW = H * W / self.window_size / self.window_size
- flops += nW * self.attn.flops(self.window_size * self.window_size)
- # mlp
- flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
- # norm2
- flops += self.dim * H * W
- return flops
-
-
-class SwinTransformer2Block(nn.Module):
- def __init__(self, c1, c2, num_heads, num_layers, window_size=7):
- super().__init__()
- self.conv = None
- if c1 != c2:
- self.conv = Conv(c1, c2)
-
- # remove input_resolution
- self.blocks = nn.Sequential(*[SwinTransformerLayer_v2(dim=c2, num_heads=num_heads, window_size=window_size,
- shift_size=0 if (i % 2 == 0) else window_size // 2) for i in range(num_layers)])
-
- def forward(self, x):
- if self.conv is not None:
- x = self.conv(x)
- x = self.blocks(x)
- return x
-
-
-class ST2CSPA(nn.Module):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super(ST2CSPA, self).__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c1, c_, 1, 1)
- self.cv3 = Conv(2 * c_, c2, 1, 1)
- num_heads = c_ // 32
- self.m = SwinTransformer2Block(c_, c_, num_heads, n)
- #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
- def forward(self, x):
- y1 = self.m(self.cv1(x))
- y2 = self.cv2(x)
- return self.cv3(torch.cat((y1, y2), dim=1))
-
-
-class ST2CSPB(nn.Module):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super(ST2CSPB, self).__init__()
- c_ = int(c2) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_, c_, 1, 1)
- self.cv3 = Conv(2 * c_, c2, 1, 1)
- num_heads = c_ // 32
- self.m = SwinTransformer2Block(c_, c_, num_heads, n)
- #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
- def forward(self, x):
- x1 = self.cv1(x)
- y1 = self.m(x1)
- y2 = self.cv2(x1)
- return self.cv3(torch.cat((y1, y2), dim=1))
-
-
-class ST2CSPC(nn.Module):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super(ST2CSPC, self).__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c1, c_, 1, 1)
- self.cv3 = Conv(c_, c_, 1, 1)
- self.cv4 = Conv(2 * c_, c2, 1, 1)
- num_heads = c_ // 32
- self.m = SwinTransformer2Block(c_, c_, num_heads, n)
- #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
-
- def forward(self, x):
- y1 = self.cv3(self.m(self.cv1(x)))
- y2 = self.cv2(x)
- return self.cv4(torch.cat((y1, y2), dim=1))
-
-##### end of swin transformer v2 #####
diff --git a/spaces/Sakil/english_audio_transcriptor/README.md b/spaces/Sakil/english_audio_transcriptor/README.md
deleted file mode 100644
index 5110bac49ef53b55f3e9197a8058b6db7fa56a26..0000000000000000000000000000000000000000
--- a/spaces/Sakil/english_audio_transcriptor/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: English_audio_transcriptor
-emoji: 🐨
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/Sakukaze/VITS-Umamusume-voice-synthesizer/mel_processing.py b/spaces/Sakukaze/VITS-Umamusume-voice-synthesizer/mel_processing.py
deleted file mode 100644
index 817f03756f64caf8cc54329a9325024c8fb9e0c3..0000000000000000000000000000000000000000
--- a/spaces/Sakukaze/VITS-Umamusume-voice-synthesizer/mel_processing.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import math
-import os
-import random
-import torch
-from torch import nn
-import torch.nn.functional as F
-import torch.utils.data
-import numpy as np
-import librosa
-import librosa.util as librosa_util
-from librosa.util import normalize, pad_center, tiny
-from scipy.signal import get_window
-from scipy.io.wavfile import read
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- """
- PARAMS
- ------
- C: compression factor
- """
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
- """
- PARAMS
- ------
- C: compression factor used to compress
- """
- return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
- output = dynamic_range_compression_torch(magnitudes)
- return output
-
-
-def spectral_de_normalize_torch(magnitudes):
- output = dynamic_range_decompression_torch(magnitudes)
- return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global hann_window
- dtype_device = str(y.dtype) + '_' + str(y.device)
- wnsize_dtype_device = str(win_size) + '_' + dtype_device
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True)
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
- return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
- global mel_basis
- dtype_device = str(spec.dtype) + '_' + str(spec.device)
- fmax_dtype_device = str(fmax) + '_' + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
- return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
- if torch.min(y) < -1.:
- print('min value is ', torch.min(y))
- if torch.max(y) > 1.:
- print('max value is ', torch.max(y))
-
- global mel_basis, hann_window
- dtype_device = str(y.dtype) + '_' + str(y.device)
- fmax_dtype_device = str(fmax) + '_' + dtype_device
- wnsize_dtype_device = str(win_size) + '_' + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
- y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
- y = y.squeeze(1)
-
- spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
- center=center, pad_mode='reflect', normalized=False, onesided=True)
-
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
- spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- spec = spectral_normalize_torch(spec)
-
- return spec
diff --git a/spaces/SanchezVFX/dis/app.py b/spaces/SanchezVFX/dis/app.py
deleted file mode 100644
index b7e31635d2ef54e0efe486050f55dc919d7be12a..0000000000000000000000000000000000000000
--- a/spaces/SanchezVFX/dis/app.py
+++ /dev/null
@@ -1,155 +0,0 @@
-import cv2
-import gradio as gr
-import os
-from PIL import Image
-import numpy as np
-import torch
-from torch.autograd import Variable
-from torchvision import transforms
-import torch.nn.functional as F
-import gdown
-import matplotlib.pyplot as plt
-import warnings
-warnings.filterwarnings("ignore")
-
-os.system("git clone https://github.com/xuebinqin/DIS")
-os.system("mv DIS/IS-Net/* .")
-
-# project imports
-from data_loader_cache import normalize, im_reader, im_preprocess
-from models import *
-
-#Helpers
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-# Download official dis weights
-if not os.path.exists("saved_models"):
- os.mkdir("saved_models")
- MODEL_PATH_URL = "https://drive.google.com/uc?id=1nV57qKuy--d5u1yvkng9aXW1KS4sOpOi"
- gdown.download(MODEL_PATH_URL, "saved_models/isnet-general-use.pth", use_cookies=False)
-
-class GOSNormalize(object):
- '''
- Normalize the Image using torch.transforms
- '''
- def __init__(self, mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]):
- self.mean = mean
- self.std = std
-
- def __call__(self,image):
- image = normalize(image,self.mean,self.std)
- return image
-
-
-transform = transforms.Compose([GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0])])
-
-def load_image(im_path, hypar):
- im = im_reader(im_path)
- im, im_shp = im_preprocess(im, hypar["cache_size"])
- im = torch.divide(im,255.0)
- shape = torch.from_numpy(np.array(im_shp))
- return transform(im).unsqueeze(0), shape.unsqueeze(0) # make a batch of image, shape
-
-
-def build_model(hypar,device):
- net = hypar["model"]#GOSNETINC(3,1)
-
- # convert to half precision
- if(hypar["model_digit"]=="half"):
- net.half()
- for layer in net.modules():
- if isinstance(layer, nn.BatchNorm2d):
- layer.float()
-
- net.to(device)
-
- if(hypar["restore_model"]!=""):
- net.load_state_dict(torch.load(hypar["model_path"]+"/"+hypar["restore_model"], map_location=device))
- net.to(device)
- net.eval()
- return net
-
-
-def predict(net, inputs_val, shapes_val, hypar, device):
- '''
- Given an Image, predict the mask
- '''
- net.eval()
-
- if(hypar["model_digit"]=="full"):
- inputs_val = inputs_val.type(torch.FloatTensor)
- else:
- inputs_val = inputs_val.type(torch.HalfTensor)
-
-
- inputs_val_v = Variable(inputs_val, requires_grad=False).to(device) # wrap inputs in Variable
-
- ds_val = net(inputs_val_v)[0] # list of 6 results
-
- pred_val = ds_val[0][0,:,:,:] # B x 1 x H x W # we want the first one which is the most accurate prediction
-
- ## recover the prediction spatial size to the orignal image size
- pred_val = torch.squeeze(F.upsample(torch.unsqueeze(pred_val,0),(shapes_val[0][0],shapes_val[0][1]),mode='bilinear'))
-
- ma = torch.max(pred_val)
- mi = torch.min(pred_val)
- pred_val = (pred_val-mi)/(ma-mi) # max = 1
-
- if device == 'cuda': torch.cuda.empty_cache()
- return (pred_val.detach().cpu().numpy()*255).astype(np.uint8) # it is the mask we need
-
-# Set Parameters
-hypar = {} # paramters for inferencing
-
-
-hypar["model_path"] ="./saved_models" ## load trained weights from this path
-hypar["restore_model"] = "isnet-general-use.pth" ## name of the to-be-loaded weights
-hypar["interm_sup"] = False ## indicate if activate intermediate feature supervision
-
-## choose floating point accuracy --
-hypar["model_digit"] = "full" ## indicates "half" or "full" accuracy of float number
-hypar["seed"] = 0
-
-hypar["cache_size"] = [1024, 1024] ## cached input spatial resolution, can be configured into different size
-
-## data augmentation parameters ---
-hypar["input_size"] = [1024, 1024] ## mdoel input spatial size, usually use the same value hypar["cache_size"], which means we don't further resize the images
-hypar["crop_size"] = [1024, 1024] ## random crop size from the input, it is usually set as smaller than hypar["cache_size"], e.g., [920,920] for data augmentation
-
-hypar["model"] = ISNetDIS()
-
- # Build Model
-net = build_model(hypar, device)
-
-
-def inference(image: Image):
- image_path = image
-
- image_tensor, orig_size = load_image(image_path, hypar)
- mask = predict(net, image_tensor, orig_size, hypar, device)
-
- pil_mask = Image.fromarray(mask).convert('L')
- im_rgb = Image.open(image).convert("RGB")
-
- im_rgba = im_rgb.copy()
- im_rgba.putalpha(pil_mask)
-
- return [im_rgba, pil_mask]
-
-
-title = "DIS Background Removal"
-description = "This is an unofficial demo for DIS, a model that can remove the background from a given image. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below.`.
- .. versionadded:: 1.1
- Parameters
- ----------
- xx0 : ndarray of shape (grid_resolution, grid_resolution)
- First output of :func:`meshgrid `.
- xx1 : ndarray of shape (grid_resolution, grid_resolution)
- Second output of :func:`meshgrid `.
- response : ndarray of shape (grid_resolution, grid_resolution)
- Values of the response function.
- xlabel : str, default=None
- Default label to place on x axis.
- ylabel : str, default=None
- Default label to place on y axis.
- Attributes
- ----------
- surface_ : matplotlib `QuadContourSet` or `QuadMesh`
- If `plot_method` is 'contour' or 'contourf', `surface_` is a
- :class:`QuadContourSet `. If
- `plot_method is `pcolormesh`, `surface_` is a
- :class:`QuadMesh `.
- ax_ : matplotlib Axes
- Axes with confusion matrix.
- figure_ : matplotlib Figure
- Figure containing the confusion matrix.
- """
-
- def __init__(self, *, xx0, xx1, response, xlabel=None, ylabel=None):
- self.xx0 = xx0
- self.xx1 = xx1
- self.response = response
- self.xlabel = xlabel
- self.ylabel = ylabel
-
- def plot(self, plot_method="contourf", ax=None, xlabel=None, ylabel=None, **kwargs):
- """Plot visualization.
- Parameters
- ----------
- plot_method : {'contourf', 'contour', 'pcolormesh'}, default='contourf'
- Plotting method to call when plotting the response. Please refer
- to the following matplotlib documentation for details:
- :func:`contourf `,
- :func:`contour `,
- :func:`pcolomesh `.
- ax : Matplotlib axes, default=None
- Axes object to plot on. If `None`, a new figure and axes is
- created.
- xlabel : str, default=None
- Overwrite the x-axis label.
- ylabel : str, default=None
- Overwrite the y-axis label.
- **kwargs : dict
- Additional keyword arguments to be passed to the `plot_method`.
- Returns
- -------
- display: :class:`~sklearn.inspection.DecisionBoundaryDisplay`
- """
- check_matplotlib_support("DecisionBoundaryDisplay.plot")
- import matplotlib.pyplot as plt # noqa
-
- if plot_method not in ("contourf", "contour", "pcolormesh"):
- raise ValueError(
- "plot_method must be 'contourf', 'contour', or 'pcolormesh'"
- )
-
- if ax is None:
- _, ax = plt.subplots()
-
- plot_func = getattr(ax, plot_method)
- self.surface_ = plot_func(self.xx0, self.xx1, self.response, **kwargs)
-
- if xlabel is not None or not ax.get_xlabel():
- xlabel = self.xlabel if xlabel is None else xlabel
- ax.set_xlabel(xlabel)
- if ylabel is not None or not ax.get_ylabel():
- ylabel = self.ylabel if ylabel is None else ylabel
- ax.set_ylabel(ylabel)
-
- self.ax_ = ax
- self.figure_ = ax.figure
- return self
-
- @classmethod
- def from_estimator(
- cls,
- estimator,
- X,
- *,
- grid_resolution=100,
- eps=1.0,
- plot_method="contourf",
- response_method="auto",
- xlabel=None,
- ylabel=None,
- ax=None,
- **kwargs,
- ):
- """Plot decision boundary given an estimator.
- Read more in the :ref:`User Guide `.
- Parameters
- ----------
- estimator : object
- Trained estimator used to plot the decision boundary.
- X : {array-like, sparse matrix, dataframe} of shape (n_samples, 2)
- Input data that should be only 2-dimensional.
- grid_resolution : int, default=100
- Number of grid points to use for plotting decision boundary.
- Higher values will make the plot look nicer but be slower to
- render.
- eps : float, default=1.0
- Extends the minimum and maximum values of X for evaluating the
- response function.
- plot_method : {'contourf', 'contour', 'pcolormesh'}, default='contourf'
- Plotting method to call when plotting the response. Please refer
- to the following matplotlib documentation for details:
- :func:`contourf `,
- :func:`contour `,
- :func:`pcolomesh `.
- response_method : {'auto', 'predict_proba', 'decision_function', \
- 'predict'}, default='auto'
- Specifies whether to use :term:`predict_proba`,
- :term:`decision_function`, :term:`predict` as the target response.
- If set to 'auto', the response method is tried in the following order:
- :term:`decision_function`, :term:`predict_proba`, :term:`predict`.
- For multiclass problems, :term:`predict` is selected when
- `response_method="auto"`.
- xlabel : str, default=None
- The label used for the x-axis. If `None`, an attempt is made to
- extract a label from `X` if it is a dataframe, otherwise an empty
- string is used.
- ylabel : str, default=None
- The label used for the y-axis. If `None`, an attempt is made to
- extract a label from `X` if it is a dataframe, otherwise an empty
- string is used.
- ax : Matplotlib axes, default=None
- Axes object to plot on. If `None`, a new figure and axes is
- created.
- **kwargs : dict
- Additional keyword arguments to be passed to the
- `plot_method`.
- Returns
- -------
- display : :class:`~sklearn.inspection.DecisionBoundaryDisplay`
- Object that stores the result.
- See Also
- --------
- DecisionBoundaryDisplay : Decision boundary visualization.
- ConfusionMatrixDisplay.from_estimator : Plot the confusion matrix
- given an estimator, the data, and the label.
- ConfusionMatrixDisplay.from_predictions : Plot the confusion matrix
- given the true and predicted labels.
- Examples
- --------
- >>> import matplotlib.pyplot as plt
- >>> from sklearn.datasets import load_iris
- >>> from sklearn.linear_model import LogisticRegression
- >>> from sklearn.inspection import DecisionBoundaryDisplay
- >>> iris = load_iris()
- >>> X = iris.data[:, :2]
- >>> classifier = LogisticRegression().fit(X, iris.target)
- >>> disp = DecisionBoundaryDisplay.from_estimator(
- ... classifier, X, response_method="predict",
- ... xlabel=iris.feature_names[0], ylabel=iris.feature_names[1],
- ... alpha=0.5,
- ... )
- >>> disp.ax_.scatter(X[:, 0], X[:, 1], c=iris.target, edgecolor="k")
- <...>
- >>> plt.show()
- """
- check_matplotlib_support(f"{cls.__name__}.from_estimator")
- check_is_fitted(estimator)
-
- if not grid_resolution > 1:
- raise ValueError(
- "grid_resolution must be greater than 1. Got"
- f" {grid_resolution} instead."
- )
-
- if not eps >= 0:
- raise ValueError(
- f"eps must be greater than or equal to 0. Got {eps} instead."
- )
-
- possible_plot_methods = ("contourf", "contour", "pcolormesh")
- if plot_method not in possible_plot_methods:
- available_methods = ", ".join(possible_plot_methods)
- raise ValueError(
- f"plot_method must be one of {available_methods}. "
- f"Got {plot_method} instead."
- )
-
- x0, x1 = _safe_indexing(X, 0, axis=1), _safe_indexing(X, 1, axis=1)
-
- x0_min, x0_max = x0.min() - eps, x0.max() + eps
- x1_min, x1_max = x1.min() - eps, x1.max() + eps
-
- xx0, xx1 = np.meshgrid(
- np.linspace(x0_min, x0_max, grid_resolution),
- np.linspace(x1_min, x1_max, grid_resolution),
- )
- if hasattr(X, "iloc"):
- # we need to preserve the feature names and therefore get an empty dataframe
- X_grid = X.iloc[[], :].copy()
- X_grid.iloc[:, 0] = xx0.ravel()
- X_grid.iloc[:, 1] = xx1.ravel()
- else:
- X_grid = np.c_[xx0.ravel(), xx1.ravel()]
-
- pred_func = _check_boundary_response_method(estimator, response_method)
- response = pred_func(X_grid)
-
- # convert classes predictions into integers
- if pred_func.__name__ == "predict" and hasattr(estimator, "classes_"):
- encoder = LabelEncoder()
- encoder.classes_ = estimator.classes_
- response = encoder.transform(response)
-
- if response.ndim != 1:
- if is_regressor(estimator):
- raise ValueError("Multi-output regressors are not supported")
-
- # TODO: Support pos_label
- response = response[:, 1]
-
- if xlabel is None:
- xlabel = X.columns[0] if hasattr(X, "columns") else ""
-
- if ylabel is None:
- ylabel = X.columns[1] if hasattr(X, "columns") else ""
-
- display = DecisionBoundaryDisplay(
- xx0=xx0,
- xx1=xx1,
- response=response.reshape(xx0.shape),
- xlabel=xlabel,
- ylabel=ylabel,
- )
- return display.plot(ax=ax, plot_method=plot_method, **kwargs)
\ No newline at end of file
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/dir_util.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/dir_util.py
deleted file mode 100644
index 23dc3392a2c9b11f93bc8d9e1381848b7c8fd7b3..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/dir_util.py
+++ /dev/null
@@ -1,243 +0,0 @@
-"""distutils.dir_util
-
-Utility functions for manipulating directories and directory trees."""
-
-import os
-import errno
-from .errors import DistutilsInternalError, DistutilsFileError
-from ._log import log
-
-# cache for by mkpath() -- in addition to cheapening redundant calls,
-# eliminates redundant "creating /foo/bar/baz" messages in dry-run mode
-_path_created = {}
-
-
-def mkpath(name, mode=0o777, verbose=1, dry_run=0): # noqa: C901
- """Create a directory and any missing ancestor directories.
-
- If the directory already exists (or if 'name' is the empty string, which
- means the current directory, which of course exists), then do nothing.
- Raise DistutilsFileError if unable to create some directory along the way
- (eg. some sub-path exists, but is a file rather than a directory).
- If 'verbose' is true, print a one-line summary of each mkdir to stdout.
- Return the list of directories actually created.
-
- os.makedirs is not used because:
-
- a) It's new to Python 1.5.2, and
- b) it blows up if the directory already exists (in which case it should
- silently succeed).
- """
-
- global _path_created
-
- # Detect a common bug -- name is None
- if not isinstance(name, str):
- raise DistutilsInternalError(
- "mkpath: 'name' must be a string (got {!r})".format(name)
- )
-
- # XXX what's the better way to handle verbosity? print as we create
- # each directory in the path (the current behaviour), or only announce
- # the creation of the whole path? (quite easy to do the latter since
- # we're not using a recursive algorithm)
-
- name = os.path.normpath(name)
- created_dirs = []
- if os.path.isdir(name) or name == '':
- return created_dirs
- if _path_created.get(os.path.abspath(name)):
- return created_dirs
-
- (head, tail) = os.path.split(name)
- tails = [tail] # stack of lone dirs to create
-
- while head and tail and not os.path.isdir(head):
- (head, tail) = os.path.split(head)
- tails.insert(0, tail) # push next higher dir onto stack
-
- # now 'head' contains the deepest directory that already exists
- # (that is, the child of 'head' in 'name' is the highest directory
- # that does *not* exist)
- for d in tails:
- # print "head = %s, d = %s: " % (head, d),
- head = os.path.join(head, d)
- abs_head = os.path.abspath(head)
-
- if _path_created.get(abs_head):
- continue
-
- if verbose >= 1:
- log.info("creating %s", head)
-
- if not dry_run:
- try:
- os.mkdir(head, mode)
- except OSError as exc:
- if not (exc.errno == errno.EEXIST and os.path.isdir(head)):
- raise DistutilsFileError(
- "could not create '{}': {}".format(head, exc.args[-1])
- )
- created_dirs.append(head)
-
- _path_created[abs_head] = 1
- return created_dirs
-
-
-def create_tree(base_dir, files, mode=0o777, verbose=1, dry_run=0):
- """Create all the empty directories under 'base_dir' needed to put 'files'
- there.
-
- 'base_dir' is just the name of a directory which doesn't necessarily
- exist yet; 'files' is a list of filenames to be interpreted relative to
- 'base_dir'. 'base_dir' + the directory portion of every file in 'files'
- will be created if it doesn't already exist. 'mode', 'verbose' and
- 'dry_run' flags are as for 'mkpath()'.
- """
- # First get the list of directories to create
- need_dir = set()
- for file in files:
- need_dir.add(os.path.join(base_dir, os.path.dirname(file)))
-
- # Now create them
- for dir in sorted(need_dir):
- mkpath(dir, mode, verbose=verbose, dry_run=dry_run)
-
-
-def copy_tree( # noqa: C901
- src,
- dst,
- preserve_mode=1,
- preserve_times=1,
- preserve_symlinks=0,
- update=0,
- verbose=1,
- dry_run=0,
-):
- """Copy an entire directory tree 'src' to a new location 'dst'.
-
- Both 'src' and 'dst' must be directory names. If 'src' is not a
- directory, raise DistutilsFileError. If 'dst' does not exist, it is
- created with 'mkpath()'. The end result of the copy is that every
- file in 'src' is copied to 'dst', and directories under 'src' are
- recursively copied to 'dst'. Return the list of files that were
- copied or might have been copied, using their output name. The
- return value is unaffected by 'update' or 'dry_run': it is simply
- the list of all files under 'src', with the names changed to be
- under 'dst'.
-
- 'preserve_mode' and 'preserve_times' are the same as for
- 'copy_file'; note that they only apply to regular files, not to
- directories. If 'preserve_symlinks' is true, symlinks will be
- copied as symlinks (on platforms that support them!); otherwise
- (the default), the destination of the symlink will be copied.
- 'update' and 'verbose' are the same as for 'copy_file'.
- """
- from distutils.file_util import copy_file
-
- if not dry_run and not os.path.isdir(src):
- raise DistutilsFileError("cannot copy tree '%s': not a directory" % src)
- try:
- names = os.listdir(src)
- except OSError as e:
- if dry_run:
- names = []
- else:
- raise DistutilsFileError(
- "error listing files in '{}': {}".format(src, e.strerror)
- )
-
- if not dry_run:
- mkpath(dst, verbose=verbose)
-
- outputs = []
-
- for n in names:
- src_name = os.path.join(src, n)
- dst_name = os.path.join(dst, n)
-
- if n.startswith('.nfs'):
- # skip NFS rename files
- continue
-
- if preserve_symlinks and os.path.islink(src_name):
- link_dest = os.readlink(src_name)
- if verbose >= 1:
- log.info("linking %s -> %s", dst_name, link_dest)
- if not dry_run:
- os.symlink(link_dest, dst_name)
- outputs.append(dst_name)
-
- elif os.path.isdir(src_name):
- outputs.extend(
- copy_tree(
- src_name,
- dst_name,
- preserve_mode,
- preserve_times,
- preserve_symlinks,
- update,
- verbose=verbose,
- dry_run=dry_run,
- )
- )
- else:
- copy_file(
- src_name,
- dst_name,
- preserve_mode,
- preserve_times,
- update,
- verbose=verbose,
- dry_run=dry_run,
- )
- outputs.append(dst_name)
-
- return outputs
-
-
-def _build_cmdtuple(path, cmdtuples):
- """Helper for remove_tree()."""
- for f in os.listdir(path):
- real_f = os.path.join(path, f)
- if os.path.isdir(real_f) and not os.path.islink(real_f):
- _build_cmdtuple(real_f, cmdtuples)
- else:
- cmdtuples.append((os.remove, real_f))
- cmdtuples.append((os.rmdir, path))
-
-
-def remove_tree(directory, verbose=1, dry_run=0):
- """Recursively remove an entire directory tree.
-
- Any errors are ignored (apart from being reported to stdout if 'verbose'
- is true).
- """
- global _path_created
-
- if verbose >= 1:
- log.info("removing '%s' (and everything under it)", directory)
- if dry_run:
- return
- cmdtuples = []
- _build_cmdtuple(directory, cmdtuples)
- for cmd in cmdtuples:
- try:
- cmd[0](cmd[1])
- # remove dir from cache if it's already there
- abspath = os.path.abspath(cmd[1])
- if abspath in _path_created:
- _path_created.pop(abspath)
- except OSError as exc:
- log.warning("error removing %s: %s", directory, exc)
-
-
-def ensure_relative(path):
- """Take the full path 'path', and make it a relative path.
-
- This is useful to make 'path' the second argument to os.path.join().
- """
- drive, path = os.path.splitdrive(path)
- if path[0:1] == os.sep:
- path = drive + path[1:]
- return path
diff --git a/spaces/TheRealZoink/Zoink_OV3RL0AD/README.md b/spaces/TheRealZoink/Zoink_OV3RL0AD/README.md
deleted file mode 100644
index 2951c710b6b41ba2b2f3a4f305e16747d5b0eded..0000000000000000000000000000000000000000
--- a/spaces/TheRealZoink/Zoink_OV3RL0AD/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: Zoink OV3RL0AD
-emoji: 😻
-colorFrom: pink
-colorTo: gray
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/TornikeO/dis-background-removal/models/isnet.py b/spaces/TornikeO/dis-background-removal/models/isnet.py
deleted file mode 100644
index 8be74bb03954496e6cb8af6e1c8e1cd2c0bbb80c..0000000000000000000000000000000000000000
--- a/spaces/TornikeO/dis-background-removal/models/isnet.py
+++ /dev/null
@@ -1,610 +0,0 @@
-import torch
-import torch.nn as nn
-from torchvision import models
-import torch.nn.functional as F
-
-
-bce_loss = nn.BCELoss(size_average=True)
-def muti_loss_fusion(preds, target):
- loss0 = 0.0
- loss = 0.0
-
- for i in range(0,len(preds)):
- # print("i: ", i, preds[i].shape)
- if(preds[i].shape[2]!=target.shape[2] or preds[i].shape[3]!=target.shape[3]):
- # tmp_target = _upsample_like(target,preds[i])
- tmp_target = F.interpolate(target, size=preds[i].size()[2:], mode='bilinear', align_corners=True)
- loss = loss + bce_loss(preds[i],tmp_target)
- else:
- loss = loss + bce_loss(preds[i],target)
- if(i==0):
- loss0 = loss
- return loss0, loss
-
-fea_loss = nn.MSELoss(size_average=True)
-kl_loss = nn.KLDivLoss(size_average=True)
-l1_loss = nn.L1Loss(size_average=True)
-smooth_l1_loss = nn.SmoothL1Loss(size_average=True)
-def muti_loss_fusion_kl(preds, target, dfs, fs, mode='MSE'):
- loss0 = 0.0
- loss = 0.0
-
- for i in range(0,len(preds)):
- # print("i: ", i, preds[i].shape)
- if(preds[i].shape[2]!=target.shape[2] or preds[i].shape[3]!=target.shape[3]):
- # tmp_target = _upsample_like(target,preds[i])
- tmp_target = F.interpolate(target, size=preds[i].size()[2:], mode='bilinear', align_corners=True)
- loss = loss + bce_loss(preds[i],tmp_target)
- else:
- loss = loss + bce_loss(preds[i],target)
- if(i==0):
- loss0 = loss
-
- for i in range(0,len(dfs)):
- if(mode=='MSE'):
- loss = loss + fea_loss(dfs[i],fs[i]) ### add the mse loss of features as additional constraints
- # print("fea_loss: ", fea_loss(dfs[i],fs[i]).item())
- elif(mode=='KL'):
- loss = loss + kl_loss(F.log_softmax(dfs[i],dim=1),F.softmax(fs[i],dim=1))
- # print("kl_loss: ", kl_loss(F.log_softmax(dfs[i],dim=1),F.softmax(fs[i],dim=1)).item())
- elif(mode=='MAE'):
- loss = loss + l1_loss(dfs[i],fs[i])
- # print("ls_loss: ", l1_loss(dfs[i],fs[i]))
- elif(mode=='SmoothL1'):
- loss = loss + smooth_l1_loss(dfs[i],fs[i])
- # print("SmoothL1: ", smooth_l1_loss(dfs[i],fs[i]).item())
-
- return loss0, loss
-
-class REBNCONV(nn.Module):
- def __init__(self,in_ch=3,out_ch=3,dirate=1,stride=1):
- super(REBNCONV,self).__init__()
-
- self.conv_s1 = nn.Conv2d(in_ch,out_ch,3,padding=1*dirate,dilation=1*dirate,stride=stride)
- self.bn_s1 = nn.BatchNorm2d(out_ch)
- self.relu_s1 = nn.ReLU(inplace=True)
-
- def forward(self,x):
-
- hx = x
- xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
-
- return xout
-
-## upsample tensor 'src' to have the same spatial size with tensor 'tar'
-def _upsample_like(src,tar):
-
- src = F.upsample(src,size=tar.shape[2:],mode='bilinear')
-
- return src
-
-
-### RSU-7 ###
-class RSU7(nn.Module):
-
- def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512):
- super(RSU7,self).__init__()
-
- self.in_ch = in_ch
- self.mid_ch = mid_ch
- self.out_ch = out_ch
-
- self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1) ## 1 -> 1/2
-
- self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
- self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
- self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
- self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
- self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
- self.pool5 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=1)
-
- self.rebnconv7 = REBNCONV(mid_ch,mid_ch,dirate=2)
-
- self.rebnconv6d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
- self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
- self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
- self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
- self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
- self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
-
- def forward(self,x):
- b, c, h, w = x.shape
-
- hx = x
- hxin = self.rebnconvin(hx)
-
- hx1 = self.rebnconv1(hxin)
- hx = self.pool1(hx1)
-
- hx2 = self.rebnconv2(hx)
- hx = self.pool2(hx2)
-
- hx3 = self.rebnconv3(hx)
- hx = self.pool3(hx3)
-
- hx4 = self.rebnconv4(hx)
- hx = self.pool4(hx4)
-
- hx5 = self.rebnconv5(hx)
- hx = self.pool5(hx5)
-
- hx6 = self.rebnconv6(hx)
-
- hx7 = self.rebnconv7(hx6)
-
- hx6d = self.rebnconv6d(torch.cat((hx7,hx6),1))
- hx6dup = _upsample_like(hx6d,hx5)
-
- hx5d = self.rebnconv5d(torch.cat((hx6dup,hx5),1))
- hx5dup = _upsample_like(hx5d,hx4)
-
- hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
- hx4dup = _upsample_like(hx4d,hx3)
-
- hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
- hx3dup = _upsample_like(hx3d,hx2)
-
- hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
- hx2dup = _upsample_like(hx2d,hx1)
-
- hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
-
- return hx1d + hxin
-
-
-### RSU-6 ###
-class RSU6(nn.Module):
-
- def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
- super(RSU6,self).__init__()
-
- self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
-
- self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
- self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
- self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
- self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
- self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
-
- self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=2)
-
- self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
- self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
- self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
- self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
- self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
-
- def forward(self,x):
-
- hx = x
-
- hxin = self.rebnconvin(hx)
-
- hx1 = self.rebnconv1(hxin)
- hx = self.pool1(hx1)
-
- hx2 = self.rebnconv2(hx)
- hx = self.pool2(hx2)
-
- hx3 = self.rebnconv3(hx)
- hx = self.pool3(hx3)
-
- hx4 = self.rebnconv4(hx)
- hx = self.pool4(hx4)
-
- hx5 = self.rebnconv5(hx)
-
- hx6 = self.rebnconv6(hx5)
-
-
- hx5d = self.rebnconv5d(torch.cat((hx6,hx5),1))
- hx5dup = _upsample_like(hx5d,hx4)
-
- hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
- hx4dup = _upsample_like(hx4d,hx3)
-
- hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
- hx3dup = _upsample_like(hx3d,hx2)
-
- hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
- hx2dup = _upsample_like(hx2d,hx1)
-
- hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
-
- return hx1d + hxin
-
-### RSU-5 ###
-class RSU5(nn.Module):
-
- def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
- super(RSU5,self).__init__()
-
- self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
-
- self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
- self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
- self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
- self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
-
- self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=2)
-
- self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
- self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
- self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
- self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
-
- def forward(self,x):
-
- hx = x
-
- hxin = self.rebnconvin(hx)
-
- hx1 = self.rebnconv1(hxin)
- hx = self.pool1(hx1)
-
- hx2 = self.rebnconv2(hx)
- hx = self.pool2(hx2)
-
- hx3 = self.rebnconv3(hx)
- hx = self.pool3(hx3)
-
- hx4 = self.rebnconv4(hx)
-
- hx5 = self.rebnconv5(hx4)
-
- hx4d = self.rebnconv4d(torch.cat((hx5,hx4),1))
- hx4dup = _upsample_like(hx4d,hx3)
-
- hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
- hx3dup = _upsample_like(hx3d,hx2)
-
- hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
- hx2dup = _upsample_like(hx2d,hx1)
-
- hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
-
- return hx1d + hxin
-
-### RSU-4 ###
-class RSU4(nn.Module):
-
- def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
- super(RSU4,self).__init__()
-
- self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
-
- self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
- self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
- self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
-
- self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=2)
-
- self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
- self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
- self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
-
- def forward(self,x):
-
- hx = x
-
- hxin = self.rebnconvin(hx)
-
- hx1 = self.rebnconv1(hxin)
- hx = self.pool1(hx1)
-
- hx2 = self.rebnconv2(hx)
- hx = self.pool2(hx2)
-
- hx3 = self.rebnconv3(hx)
-
- hx4 = self.rebnconv4(hx3)
-
- hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
- hx3dup = _upsample_like(hx3d,hx2)
-
- hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
- hx2dup = _upsample_like(hx2d,hx1)
-
- hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
-
- return hx1d + hxin
-
-### RSU-4F ###
-class RSU4F(nn.Module):
-
- def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
- super(RSU4F,self).__init__()
-
- self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
-
- self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
- self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=2)
- self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=4)
-
- self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=8)
-
- self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=4)
- self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=2)
- self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
-
- def forward(self,x):
-
- hx = x
-
- hxin = self.rebnconvin(hx)
-
- hx1 = self.rebnconv1(hxin)
- hx2 = self.rebnconv2(hx1)
- hx3 = self.rebnconv3(hx2)
-
- hx4 = self.rebnconv4(hx3)
-
- hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
- hx2d = self.rebnconv2d(torch.cat((hx3d,hx2),1))
- hx1d = self.rebnconv1d(torch.cat((hx2d,hx1),1))
-
- return hx1d + hxin
-
-
-class myrebnconv(nn.Module):
- def __init__(self, in_ch=3,
- out_ch=1,
- kernel_size=3,
- stride=1,
- padding=1,
- dilation=1,
- groups=1):
- super(myrebnconv,self).__init__()
-
- self.conv = nn.Conv2d(in_ch,
- out_ch,
- kernel_size=kernel_size,
- stride=stride,
- padding=padding,
- dilation=dilation,
- groups=groups)
- self.bn = nn.BatchNorm2d(out_ch)
- self.rl = nn.ReLU(inplace=True)
-
- def forward(self,x):
- return self.rl(self.bn(self.conv(x)))
-
-
-class ISNetGTEncoder(nn.Module):
-
- def __init__(self,in_ch=1,out_ch=1):
- super(ISNetGTEncoder,self).__init__()
-
- self.conv_in = myrebnconv(in_ch,16,3,stride=2,padding=1) # nn.Conv2d(in_ch,64,3,stride=2,padding=1)
-
- self.stage1 = RSU7(16,16,64)
- self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.stage2 = RSU6(64,16,64)
- self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.stage3 = RSU5(64,32,128)
- self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.stage4 = RSU4(128,32,256)
- self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.stage5 = RSU4F(256,64,512)
- self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.stage6 = RSU4F(512,64,512)
-
-
- self.side1 = nn.Conv2d(64,out_ch,3,padding=1)
- self.side2 = nn.Conv2d(64,out_ch,3,padding=1)
- self.side3 = nn.Conv2d(128,out_ch,3,padding=1)
- self.side4 = nn.Conv2d(256,out_ch,3,padding=1)
- self.side5 = nn.Conv2d(512,out_ch,3,padding=1)
- self.side6 = nn.Conv2d(512,out_ch,3,padding=1)
-
- def compute_loss(self, preds, targets):
-
- return muti_loss_fusion(preds,targets)
-
- def forward(self,x):
-
- hx = x
-
- hxin = self.conv_in(hx)
- # hx = self.pool_in(hxin)
-
- #stage 1
- hx1 = self.stage1(hxin)
- hx = self.pool12(hx1)
-
- #stage 2
- hx2 = self.stage2(hx)
- hx = self.pool23(hx2)
-
- #stage 3
- hx3 = self.stage3(hx)
- hx = self.pool34(hx3)
-
- #stage 4
- hx4 = self.stage4(hx)
- hx = self.pool45(hx4)
-
- #stage 5
- hx5 = self.stage5(hx)
- hx = self.pool56(hx5)
-
- #stage 6
- hx6 = self.stage6(hx)
-
-
- #side output
- d1 = self.side1(hx1)
- d1 = _upsample_like(d1,x)
-
- d2 = self.side2(hx2)
- d2 = _upsample_like(d2,x)
-
- d3 = self.side3(hx3)
- d3 = _upsample_like(d3,x)
-
- d4 = self.side4(hx4)
- d4 = _upsample_like(d4,x)
-
- d5 = self.side5(hx5)
- d5 = _upsample_like(d5,x)
-
- d6 = self.side6(hx6)
- d6 = _upsample_like(d6,x)
-
- # d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1))
-
- return [F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6)], [hx1,hx2,hx3,hx4,hx5,hx6]
-
-class ISNetDIS(nn.Module):
-
- def __init__(self,in_ch=3,out_ch=1):
- super(ISNetDIS,self).__init__()
-
- self.conv_in = nn.Conv2d(in_ch,64,3,stride=2,padding=1)
- self.pool_in = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.stage1 = RSU7(64,32,64)
- self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.stage2 = RSU6(64,32,128)
- self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.stage3 = RSU5(128,64,256)
- self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.stage4 = RSU4(256,128,512)
- self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.stage5 = RSU4F(512,256,512)
- self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
-
- self.stage6 = RSU4F(512,256,512)
-
- # decoder
- self.stage5d = RSU4F(1024,256,512)
- self.stage4d = RSU4(1024,128,256)
- self.stage3d = RSU5(512,64,128)
- self.stage2d = RSU6(256,32,64)
- self.stage1d = RSU7(128,16,64)
-
- self.side1 = nn.Conv2d(64,out_ch,3,padding=1)
- self.side2 = nn.Conv2d(64,out_ch,3,padding=1)
- self.side3 = nn.Conv2d(128,out_ch,3,padding=1)
- self.side4 = nn.Conv2d(256,out_ch,3,padding=1)
- self.side5 = nn.Conv2d(512,out_ch,3,padding=1)
- self.side6 = nn.Conv2d(512,out_ch,3,padding=1)
-
- # self.outconv = nn.Conv2d(6*out_ch,out_ch,1)
-
- def compute_loss_kl(self, preds, targets, dfs, fs, mode='MSE'):
-
- # return muti_loss_fusion(preds,targets)
- return muti_loss_fusion_kl(preds, targets, dfs, fs, mode=mode)
-
- def compute_loss(self, preds, targets):
-
- # return muti_loss_fusion(preds,targets)
- return muti_loss_fusion(preds, targets)
-
- def forward(self,x):
-
- hx = x
-
- hxin = self.conv_in(hx)
- #hx = self.pool_in(hxin)
-
- #stage 1
- hx1 = self.stage1(hxin)
- hx = self.pool12(hx1)
-
- #stage 2
- hx2 = self.stage2(hx)
- hx = self.pool23(hx2)
-
- #stage 3
- hx3 = self.stage3(hx)
- hx = self.pool34(hx3)
-
- #stage 4
- hx4 = self.stage4(hx)
- hx = self.pool45(hx4)
-
- #stage 5
- hx5 = self.stage5(hx)
- hx = self.pool56(hx5)
-
- #stage 6
- hx6 = self.stage6(hx)
- hx6up = _upsample_like(hx6,hx5)
-
- #-------------------- decoder --------------------
- hx5d = self.stage5d(torch.cat((hx6up,hx5),1))
- hx5dup = _upsample_like(hx5d,hx4)
-
- hx4d = self.stage4d(torch.cat((hx5dup,hx4),1))
- hx4dup = _upsample_like(hx4d,hx3)
-
- hx3d = self.stage3d(torch.cat((hx4dup,hx3),1))
- hx3dup = _upsample_like(hx3d,hx2)
-
- hx2d = self.stage2d(torch.cat((hx3dup,hx2),1))
- hx2dup = _upsample_like(hx2d,hx1)
-
- hx1d = self.stage1d(torch.cat((hx2dup,hx1),1))
-
-
- #side output
- d1 = self.side1(hx1d)
- d1 = _upsample_like(d1,x)
-
- d2 = self.side2(hx2d)
- d2 = _upsample_like(d2,x)
-
- d3 = self.side3(hx3d)
- d3 = _upsample_like(d3,x)
-
- d4 = self.side4(hx4d)
- d4 = _upsample_like(d4,x)
-
- d5 = self.side5(hx5d)
- d5 = _upsample_like(d5,x)
-
- d6 = self.side6(hx6)
- d6 = _upsample_like(d6,x)
-
- # d0 = self.outconv(torch.cat((d1,d2,d3,d4,d5,d6),1))
-
- return [F.sigmoid(d1), F.sigmoid(d2), F.sigmoid(d3), F.sigmoid(d4), F.sigmoid(d5), F.sigmoid(d6)],[hx1d,hx2d,hx3d,hx4d,hx5d,hx6]
diff --git a/spaces/Tune-A-Video-library/Tune-A-Video-inference/Dockerfile b/spaces/Tune-A-Video-library/Tune-A-Video-inference/Dockerfile
deleted file mode 100644
index e8711cb816d416037617dc0b72b33d866790c3d4..0000000000000000000000000000000000000000
--- a/spaces/Tune-A-Video-library/Tune-A-Video-inference/Dockerfile
+++ /dev/null
@@ -1,57 +0,0 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu22.04
-ENV DEBIAN_FRONTEND=noninteractive
-RUN apt-get update && \
- apt-get upgrade -y && \
- apt-get install -y --no-install-recommends \
- git \
- git-lfs \
- wget \
- curl \
- # ffmpeg \
- ffmpeg \
- x264 \
- # python build dependencies \
- build-essential \
- libssl-dev \
- zlib1g-dev \
- libbz2-dev \
- libreadline-dev \
- libsqlite3-dev \
- libncursesw5-dev \
- xz-utils \
- tk-dev \
- libxml2-dev \
- libxmlsec1-dev \
- libffi-dev \
- liblzma-dev && \
- apt-get clean && \
- rm -rf /var/lib/apt/lists/*
-
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:${PATH}
-WORKDIR ${HOME}/app
-
-RUN curl https://pyenv.run | bash
-ENV PATH=${HOME}/.pyenv/shims:${HOME}/.pyenv/bin:${PATH}
-ENV PYTHON_VERSION=3.10.9
-RUN pyenv install ${PYTHON_VERSION} && \
- pyenv global ${PYTHON_VERSION} && \
- pyenv rehash && \
- pip install --no-cache-dir -U pip setuptools wheel
-
-RUN pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1
-COPY --chown=1000 requirements.txt /tmp/requirements.txt
-RUN pip install --no-cache-dir -U -r /tmp/requirements.txt
-
-COPY --chown=1000 . ${HOME}/app
-RUN cd Tune-A-Video && patch -p1 < ../patch
-ENV PYTHONPATH=${HOME}/app \
- PYTHONUNBUFFERED=1 \
- GRADIO_ALLOW_FLAGGING=never \
- GRADIO_NUM_PORTS=1 \
- GRADIO_SERVER_NAME=0.0.0.0 \
- GRADIO_THEME=huggingface \
- SYSTEM=spaces
-CMD ["python", "app.py"]
diff --git a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/processors/randaugment.py b/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/processors/randaugment.py
deleted file mode 100644
index 7034a49ad5fc63b97910790017432617ff4c6d7b..0000000000000000000000000000000000000000
--- a/spaces/Vision-CAIR/MiniGPT-v2/minigpt4/processors/randaugment.py
+++ /dev/null
@@ -1,398 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import cv2
-import numpy as np
-
-import torch
-
-
-## aug functions
-def identity_func(img):
- return img
-
-
-def autocontrast_func(img, cutoff=0):
- """
- same output as PIL.ImageOps.autocontrast
- """
- n_bins = 256
-
- def tune_channel(ch):
- n = ch.size
- cut = cutoff * n // 100
- if cut == 0:
- high, low = ch.max(), ch.min()
- else:
- hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins])
- low = np.argwhere(np.cumsum(hist) > cut)
- low = 0 if low.shape[0] == 0 else low[0]
- high = np.argwhere(np.cumsum(hist[::-1]) > cut)
- high = n_bins - 1 if high.shape[0] == 0 else n_bins - 1 - high[0]
- if high <= low:
- table = np.arange(n_bins)
- else:
- scale = (n_bins - 1) / (high - low)
- offset = -low * scale
- table = np.arange(n_bins) * scale + offset
- table[table < 0] = 0
- table[table > n_bins - 1] = n_bins - 1
- table = table.clip(0, 255).astype(np.uint8)
- return table[ch]
-
- channels = [tune_channel(ch) for ch in cv2.split(img)]
- out = cv2.merge(channels)
- return out
-
-
-def equalize_func(img):
- """
- same output as PIL.ImageOps.equalize
- PIL's implementation is different from cv2.equalize
- """
- n_bins = 256
-
- def tune_channel(ch):
- hist = cv2.calcHist([ch], [0], None, [n_bins], [0, n_bins])
- non_zero_hist = hist[hist != 0].reshape(-1)
- step = np.sum(non_zero_hist[:-1]) // (n_bins - 1)
- if step == 0:
- return ch
- n = np.empty_like(hist)
- n[0] = step // 2
- n[1:] = hist[:-1]
- table = (np.cumsum(n) // step).clip(0, 255).astype(np.uint8)
- return table[ch]
-
- channels = [tune_channel(ch) for ch in cv2.split(img)]
- out = cv2.merge(channels)
- return out
-
-
-def rotate_func(img, degree, fill=(0, 0, 0)):
- """
- like PIL, rotate by degree, not radians
- """
- H, W = img.shape[0], img.shape[1]
- center = W / 2, H / 2
- M = cv2.getRotationMatrix2D(center, degree, 1)
- out = cv2.warpAffine(img, M, (W, H), borderValue=fill)
- return out
-
-
-def solarize_func(img, thresh=128):
- """
- same output as PIL.ImageOps.posterize
- """
- table = np.array([el if el < thresh else 255 - el for el in range(256)])
- table = table.clip(0, 255).astype(np.uint8)
- out = table[img]
- return out
-
-
-def color_func(img, factor):
- """
- same output as PIL.ImageEnhance.Color
- """
- ## implementation according to PIL definition, quite slow
- # degenerate = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)[:, :, np.newaxis]
- # out = blend(degenerate, img, factor)
- # M = (
- # np.eye(3) * factor
- # + np.float32([0.114, 0.587, 0.299]).reshape(3, 1) * (1. - factor)
- # )[np.newaxis, np.newaxis, :]
- M = np.float32(
- [[0.886, -0.114, -0.114], [-0.587, 0.413, -0.587], [-0.299, -0.299, 0.701]]
- ) * factor + np.float32([[0.114], [0.587], [0.299]])
- out = np.matmul(img, M).clip(0, 255).astype(np.uint8)
- return out
-
-
-def contrast_func(img, factor):
- """
- same output as PIL.ImageEnhance.Contrast
- """
- mean = np.sum(np.mean(img, axis=(0, 1)) * np.array([0.114, 0.587, 0.299]))
- table = (
- np.array([(el - mean) * factor + mean for el in range(256)])
- .clip(0, 255)
- .astype(np.uint8)
- )
- out = table[img]
- return out
-
-
-def brightness_func(img, factor):
- """
- same output as PIL.ImageEnhance.Contrast
- """
- table = (np.arange(256, dtype=np.float32) * factor).clip(0, 255).astype(np.uint8)
- out = table[img]
- return out
-
-
-def sharpness_func(img, factor):
- """
- The differences the this result and PIL are all on the 4 boundaries, the center
- areas are same
- """
- kernel = np.ones((3, 3), dtype=np.float32)
- kernel[1][1] = 5
- kernel /= 13
- degenerate = cv2.filter2D(img, -1, kernel)
- if factor == 0.0:
- out = degenerate
- elif factor == 1.0:
- out = img
- else:
- out = img.astype(np.float32)
- degenerate = degenerate.astype(np.float32)[1:-1, 1:-1, :]
- out[1:-1, 1:-1, :] = degenerate + factor * (out[1:-1, 1:-1, :] - degenerate)
- out = out.astype(np.uint8)
- return out
-
-
-def shear_x_func(img, factor, fill=(0, 0, 0)):
- H, W = img.shape[0], img.shape[1]
- M = np.float32([[1, factor, 0], [0, 1, 0]])
- out = cv2.warpAffine(
- img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR
- ).astype(np.uint8)
- return out
-
-
-def translate_x_func(img, offset, fill=(0, 0, 0)):
- """
- same output as PIL.Image.transform
- """
- H, W = img.shape[0], img.shape[1]
- M = np.float32([[1, 0, -offset], [0, 1, 0]])
- out = cv2.warpAffine(
- img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR
- ).astype(np.uint8)
- return out
-
-
-def translate_y_func(img, offset, fill=(0, 0, 0)):
- """
- same output as PIL.Image.transform
- """
- H, W = img.shape[0], img.shape[1]
- M = np.float32([[1, 0, 0], [0, 1, -offset]])
- out = cv2.warpAffine(
- img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR
- ).astype(np.uint8)
- return out
-
-
-def posterize_func(img, bits):
- """
- same output as PIL.ImageOps.posterize
- """
- out = np.bitwise_and(img, np.uint8(255 << (8 - bits)))
- return out
-
-
-def shear_y_func(img, factor, fill=(0, 0, 0)):
- H, W = img.shape[0], img.shape[1]
- M = np.float32([[1, 0, 0], [factor, 1, 0]])
- out = cv2.warpAffine(
- img, M, (W, H), borderValue=fill, flags=cv2.INTER_LINEAR
- ).astype(np.uint8)
- return out
-
-
-def cutout_func(img, pad_size, replace=(0, 0, 0)):
- replace = np.array(replace, dtype=np.uint8)
- H, W = img.shape[0], img.shape[1]
- rh, rw = np.random.random(2)
- pad_size = pad_size // 2
- ch, cw = int(rh * H), int(rw * W)
- x1, x2 = max(ch - pad_size, 0), min(ch + pad_size, H)
- y1, y2 = max(cw - pad_size, 0), min(cw + pad_size, W)
- out = img.copy()
- out[x1:x2, y1:y2, :] = replace
- return out
-
-
-### level to args
-def enhance_level_to_args(MAX_LEVEL):
- def level_to_args(level):
- return ((level / MAX_LEVEL) * 1.8 + 0.1,)
-
- return level_to_args
-
-
-def shear_level_to_args(MAX_LEVEL, replace_value):
- def level_to_args(level):
- level = (level / MAX_LEVEL) * 0.3
- if np.random.random() > 0.5:
- level = -level
- return (level, replace_value)
-
- return level_to_args
-
-
-def translate_level_to_args(translate_const, MAX_LEVEL, replace_value):
- def level_to_args(level):
- level = (level / MAX_LEVEL) * float(translate_const)
- if np.random.random() > 0.5:
- level = -level
- return (level, replace_value)
-
- return level_to_args
-
-
-def cutout_level_to_args(cutout_const, MAX_LEVEL, replace_value):
- def level_to_args(level):
- level = int((level / MAX_LEVEL) * cutout_const)
- return (level, replace_value)
-
- return level_to_args
-
-
-def solarize_level_to_args(MAX_LEVEL):
- def level_to_args(level):
- level = int((level / MAX_LEVEL) * 256)
- return (level,)
-
- return level_to_args
-
-
-def none_level_to_args(level):
- return ()
-
-
-def posterize_level_to_args(MAX_LEVEL):
- def level_to_args(level):
- level = int((level / MAX_LEVEL) * 4)
- return (level,)
-
- return level_to_args
-
-
-def rotate_level_to_args(MAX_LEVEL, replace_value):
- def level_to_args(level):
- level = (level / MAX_LEVEL) * 30
- if np.random.random() < 0.5:
- level = -level
- return (level, replace_value)
-
- return level_to_args
-
-
-func_dict = {
- "Identity": identity_func,
- "AutoContrast": autocontrast_func,
- "Equalize": equalize_func,
- "Rotate": rotate_func,
- "Solarize": solarize_func,
- "Color": color_func,
- "Contrast": contrast_func,
- "Brightness": brightness_func,
- "Sharpness": sharpness_func,
- "ShearX": shear_x_func,
- "TranslateX": translate_x_func,
- "TranslateY": translate_y_func,
- "Posterize": posterize_func,
- "ShearY": shear_y_func,
-}
-
-translate_const = 10
-MAX_LEVEL = 10
-replace_value = (128, 128, 128)
-arg_dict = {
- "Identity": none_level_to_args,
- "AutoContrast": none_level_to_args,
- "Equalize": none_level_to_args,
- "Rotate": rotate_level_to_args(MAX_LEVEL, replace_value),
- "Solarize": solarize_level_to_args(MAX_LEVEL),
- "Color": enhance_level_to_args(MAX_LEVEL),
- "Contrast": enhance_level_to_args(MAX_LEVEL),
- "Brightness": enhance_level_to_args(MAX_LEVEL),
- "Sharpness": enhance_level_to_args(MAX_LEVEL),
- "ShearX": shear_level_to_args(MAX_LEVEL, replace_value),
- "TranslateX": translate_level_to_args(translate_const, MAX_LEVEL, replace_value),
- "TranslateY": translate_level_to_args(translate_const, MAX_LEVEL, replace_value),
- "Posterize": posterize_level_to_args(MAX_LEVEL),
- "ShearY": shear_level_to_args(MAX_LEVEL, replace_value),
-}
-
-
-class RandomAugment(object):
- def __init__(self, N=2, M=10, isPIL=False, augs=[]):
- self.N = N
- self.M = M
- self.isPIL = isPIL
- if augs:
- self.augs = augs
- else:
- self.augs = list(arg_dict.keys())
-
- def get_random_ops(self):
- sampled_ops = np.random.choice(self.augs, self.N)
- return [(op, 0.5, self.M) for op in sampled_ops]
-
- def __call__(self, img):
- if self.isPIL:
- img = np.array(img)
- ops = self.get_random_ops()
- for name, prob, level in ops:
- if np.random.random() > prob:
- continue
- args = arg_dict[name](level)
- img = func_dict[name](img, *args)
- return img
-
-
-class VideoRandomAugment(object):
- def __init__(self, N=2, M=10, p=0.0, tensor_in_tensor_out=True, augs=[]):
- self.N = N
- self.M = M
- self.p = p
- self.tensor_in_tensor_out = tensor_in_tensor_out
- if augs:
- self.augs = augs
- else:
- self.augs = list(arg_dict.keys())
-
- def get_random_ops(self):
- sampled_ops = np.random.choice(self.augs, self.N, replace=False)
- return [(op, self.M) for op in sampled_ops]
-
- def __call__(self, frames):
- assert (
- frames.shape[-1] == 3
- ), "Expecting last dimension for 3-channels RGB (b, h, w, c)."
-
- if self.tensor_in_tensor_out:
- frames = frames.numpy().astype(np.uint8)
-
- num_frames = frames.shape[0]
-
- ops = num_frames * [self.get_random_ops()]
- apply_or_not = num_frames * [np.random.random(size=self.N) > self.p]
-
- frames = torch.stack(
- list(map(self._aug, frames, ops, apply_or_not)), dim=0
- ).float()
-
- return frames
-
- def _aug(self, img, ops, apply_or_not):
- for i, (name, level) in enumerate(ops):
- if not apply_or_not[i]:
- continue
- args = arg_dict[name](level)
- img = func_dict[name](img, *args)
- return torch.from_numpy(img)
-
-
-if __name__ == "__main__":
- a = RandomAugment()
- img = np.random.randn(32, 32, 3)
- a(img)
diff --git a/spaces/VoiceHero69/changer/webui/extensionlib/callbacks.py b/spaces/VoiceHero69/changer/webui/extensionlib/callbacks.py
deleted file mode 100644
index 9ca04d6422894f69eca912b0ea8e9501a8fab5ae..0000000000000000000000000000000000000000
--- a/spaces/VoiceHero69/changer/webui/extensionlib/callbacks.py
+++ /dev/null
@@ -1,23 +0,0 @@
-class CallBack:
- def __init__(self, priority, value):
- self.priority = priority
- self.callback = value
-
- def call(self, *args, **kwargs):
- self.callback(*args, **kwargs)
-
-
-class CallBackManager:
- def __init__(self, name):
- self.name = name
- self.callbacks: list[CallBack] = []
-
-
-callbacks: list[CallBackManager] = []
-
-
-def by_name(name):
- matches = [callback for callback in callbacks if callback.name.casefold() == name.casefold()]
- if len(matches) == 0:
- return None
- return matches[0]
diff --git a/spaces/Whatcoldwind/csgo_investment/README.md b/spaces/Whatcoldwind/csgo_investment/README.md
deleted file mode 100644
index 7ff6fcd427999166a73a891d353d4a0e78daea7f..0000000000000000000000000000000000000000
--- a/spaces/Whatcoldwind/csgo_investment/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Csgo Investment
-emoji: 🚀
-colorFrom: red
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
\ No newline at end of file
diff --git a/spaces/XingHe0127/Chatbot/modules/pdf_func.py b/spaces/XingHe0127/Chatbot/modules/pdf_func.py
deleted file mode 100644
index 0aba6b7b891fc527c79b887256b0cbaa81ae5b3d..0000000000000000000000000000000000000000
--- a/spaces/XingHe0127/Chatbot/modules/pdf_func.py
+++ /dev/null
@@ -1,180 +0,0 @@
-from types import SimpleNamespace
-import pdfplumber
-import logging
-from llama_index import Document
-
-def prepare_table_config(crop_page):
- """Prepare table查找边界, 要求page为原始page
-
- From https://github.com/jsvine/pdfplumber/issues/242
- """
- page = crop_page.root_page # root/parent
- cs = page.curves + page.edges
- def curves_to_edges():
- """See https://github.com/jsvine/pdfplumber/issues/127"""
- edges = []
- for c in cs:
- edges += pdfplumber.utils.rect_to_edges(c)
- return edges
- edges = curves_to_edges()
- return {
- "vertical_strategy": "explicit",
- "horizontal_strategy": "explicit",
- "explicit_vertical_lines": edges,
- "explicit_horizontal_lines": edges,
- "intersection_y_tolerance": 10,
- }
-
-def get_text_outside_table(crop_page):
- ts = prepare_table_config(crop_page)
- if len(ts["explicit_vertical_lines"]) == 0 or len(ts["explicit_horizontal_lines"]) == 0:
- return crop_page
-
- ### Get the bounding boxes of the tables on the page.
- bboxes = [table.bbox for table in crop_page.root_page.find_tables(table_settings=ts)]
- def not_within_bboxes(obj):
- """Check if the object is in any of the table's bbox."""
- def obj_in_bbox(_bbox):
- """See https://github.com/jsvine/pdfplumber/blob/stable/pdfplumber/table.py#L404"""
- v_mid = (obj["top"] + obj["bottom"]) / 2
- h_mid = (obj["x0"] + obj["x1"]) / 2
- x0, top, x1, bottom = _bbox
- return (h_mid >= x0) and (h_mid < x1) and (v_mid >= top) and (v_mid < bottom)
- return not any(obj_in_bbox(__bbox) for __bbox in bboxes)
-
- return crop_page.filter(not_within_bboxes)
-# 请使用 LaTeX 表达公式，行内公式以 $ 包裹，行间公式以 $$ 包裹
-
-extract_words = lambda page: page.extract_words(keep_blank_chars=True, y_tolerance=0, x_tolerance=1, extra_attrs=["fontname", "size", "object_type"])
-# dict_keys(['text', 'x0', 'x1', 'top', 'doctop', 'bottom', 'upright', 'direction', 'fontname', 'size'])
-
-def get_title_with_cropped_page(first_page):
- title = [] # 处理标题
- x0,top,x1,bottom = first_page.bbox # 获取页面边框
-
- for word in extract_words(first_page):
- word = SimpleNamespace(**word)
-
- if word.size >= 14:
- title.append(word.text)
- title_bottom = word.bottom
- elif word.text == "Abstract": # 获取页面abstract
- top = word.top
-
- user_info = [i["text"] for i in extract_words(first_page.within_bbox((x0,title_bottom,x1,top)))]
- # 裁剪掉上半部分, within_bbox: full_included; crop: partial_included
- return title, user_info, first_page.within_bbox((x0,top,x1,bottom))
-
-def get_column_cropped_pages(pages, two_column=True):
- new_pages = []
- for page in pages:
- if two_column:
- left = page.within_bbox((0, 0, page.width/2, page.height),relative=True)
- right = page.within_bbox((page.width/2, 0, page.width, page.height), relative=True)
- new_pages.append(left)
- new_pages.append(right)
- else:
- new_pages.append(page)
-
- return new_pages
-
-def parse_pdf(filename, two_column = True):
- level = logging.getLogger().level
- if level == logging.getLevelName("DEBUG"):
- logging.getLogger().setLevel("INFO")
-
- with pdfplumber.open(filename) as pdf:
- title, user_info, first_page = get_title_with_cropped_page(pdf.pages[0])
- new_pages = get_column_cropped_pages([first_page] + pdf.pages[1:], two_column)
-
- chapters = []
- # tuple (chapter_name, [pageid] (start,stop), chapter_text)
- create_chapter = lambda page_start,name_top,name_bottom: SimpleNamespace(
- name=[],
- name_top=name_top,
- name_bottom=name_bottom,
- record_chapter_name = True,
-
- page_start=page_start,
- page_stop=None,
-
- text=[],
- )
- cur_chapter = None
-
- # 按页遍历PDF文档
- for idx, page in enumerate(new_pages):
- page = get_text_outside_table(page)
-
- # 按行遍历页面文本
- for word in extract_words(page):
- word = SimpleNamespace(**word)
-
- # 检查行文本是否以12号字体打印，如果是，则将其作为新章节开始
- if word.size >= 11: # 出现chapter name
- if cur_chapter is None:
- cur_chapter = create_chapter(page.page_number, word.top, word.bottom)
- elif not cur_chapter.record_chapter_name or (cur_chapter.name_bottom != cur_chapter.name_bottom and cur_chapter.name_top != cur_chapter.name_top):
- # 不再继续写chapter name
- cur_chapter.page_stop = page.page_number # stop id
- chapters.append(cur_chapter)
- # 重置当前chapter信息
- cur_chapter = create_chapter(page.page_number, word.top, word.bottom)
-
- # print(word.size, word.top, word.bottom, word.text)
- cur_chapter.name.append(word.text)
- else:
- cur_chapter.record_chapter_name = False # chapter name 结束
- cur_chapter.text.append(word.text)
- else:
- # 处理最后一个章节
- cur_chapter.page_stop = page.page_number # stop id
- chapters.append(cur_chapter)
-
- for i in chapters:
- logging.info(f"section: {i.name} pages:{i.page_start, i.page_stop} word-count:{len(i.text)}")
- logging.debug(" ".join(i.text))
-
- title = " ".join(title)
- user_info = " ".join(user_info)
- text = f"Article Title: {title}, Information:{user_info}\n"
- for idx, chapter in enumerate(chapters):
- chapter.name = " ".join(chapter.name)
- text += f"The {idx}th Chapter {chapter.name}: " + " ".join(chapter.text) + "\n"
-
- logging.getLogger().setLevel(level)
- return Document(text=text, extra_info={"title": title})
-
-BASE_POINTS = """
-1. Who are the authors?
-2. What is the process of the proposed method?
-3. What is the performance of the proposed method? Please note down its performance metrics.
-4. What are the baseline models and their performances? Please note down these baseline methods.
-5. What dataset did this paper use?
-"""
-
-READING_PROMPT = """
-You are a researcher helper bot. You can help the user with research paper reading and summarizing. \n
-Now I am going to send you a paper. You need to read it and summarize it for me part by part. \n
-When you are reading, You need to focus on these key points:{}
-"""
-
-READING_PROMT_V2 = """
-You are a researcher helper bot. You can help the user with research paper reading and summarizing. \n
-Now I am going to send you a paper. You need to read it and summarize it for me part by part. \n
-When you are reading, You need to focus on these key points:{},
-
-And You need to generate a brief but informative title for this part.
-Your return format:
-- title: '...'
-- summary: '...'
-"""
-
-SUMMARY_PROMPT = "You are a researcher helper bot. Now you need to read the summaries of a research paper."
-
-
-if __name__ == '__main__':
- # Test code
- z = parse_pdf("./build/test.pdf")
- print(z["user_info"])
- print(z["title"])
\ No newline at end of file
diff --git a/spaces/XzJosh/LittleTaffy-Bert-VITS2/data_utils.py b/spaces/XzJosh/LittleTaffy-Bert-VITS2/data_utils.py
deleted file mode 100644
index be3a29a93188c5b3386f22e5db29e5e96d78109a..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/LittleTaffy-Bert-VITS2/data_utils.py
+++ /dev/null
@@ -1,321 +0,0 @@
-import time
-import os
-import random
-import numpy as np
-import torch
-import torch.utils.data
-import commons
-from mel_processing import spectrogram_torch, mel_spectrogram_torch, spec_to_mel_torch
-from utils import load_wav_to_torch, load_filepaths_and_text
-from text import cleaned_text_to_sequence, get_bert
-
-"""Multi speaker version"""
-
-
-class TextAudioSpeakerLoader(torch.utils.data.Dataset):
- """
- 1) loads audio, speaker_id, text pairs
- 2) normalizes text and converts them to sequences of integers
- 3) computes spectrograms from audio files.
- """
-
- def __init__(self, audiopaths_sid_text, hparams):
- self.audiopaths_sid_text = load_filepaths_and_text(audiopaths_sid_text)
- self.max_wav_value = hparams.max_wav_value
- self.sampling_rate = hparams.sampling_rate
- self.filter_length = hparams.filter_length
- self.hop_length = hparams.hop_length
- self.win_length = hparams.win_length
- self.sampling_rate = hparams.sampling_rate
- self.spk_map = hparams.spk2id
- self.hparams = hparams
-
- self.use_mel_spec_posterior = getattr(hparams, "use_mel_posterior_encoder", False)
- if self.use_mel_spec_posterior:
- self.n_mel_channels = getattr(hparams, "n_mel_channels", 80)
-
- self.cleaned_text = getattr(hparams, "cleaned_text", False)
-
- self.add_blank = hparams.add_blank
- self.min_text_len = getattr(hparams, "min_text_len", 1)
- self.max_text_len = getattr(hparams, "max_text_len", 300)
-
- random.seed(1234)
- random.shuffle(self.audiopaths_sid_text)
- self._filter()
-
- def _filter(self):
- """
- Filter text & store spec lengths
- """
- # Store spectrogram lengths for Bucketing
- # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2)
- # spec_length = wav_length // hop_length
-
- audiopaths_sid_text_new = []
- lengths = []
- skipped = 0
- for _id, spk, language, text, phones, tone, word2ph in self.audiopaths_sid_text:
- audiopath = f'{_id}'
- if self.min_text_len <= len(phones) and len(phones) <= self.max_text_len:
- phones = phones.split(" ")
- tone = [int(i) for i in tone.split(" ")]
- word2ph = [int(i) for i in word2ph.split(" ")]
- audiopaths_sid_text_new.append([audiopath, spk, language, text, phones, tone, word2ph])
- lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length))
- else:
- skipped += 1
- print("skipped: ", skipped, ", total: ", len(self.audiopaths_sid_text))
- self.audiopaths_sid_text = audiopaths_sid_text_new
- self.lengths = lengths
-
- def get_audio_text_speaker_pair(self, audiopath_sid_text):
- # separate filename, speaker_id and text
- audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text
-
- bert, phones, tone, language = self.get_text(text, word2ph, phones, tone, language, audiopath)
-
- spec, wav = self.get_audio(audiopath)
- sid = torch.LongTensor([int(self.spk_map[sid])])
- return (phones, spec, wav, sid, tone, language, bert)
-
- def get_audio(self, filename):
- audio, sampling_rate = load_wav_to_torch(filename)
- if sampling_rate != self.sampling_rate:
- raise ValueError("{} {} SR doesn't match target {} SR".format(
- sampling_rate, self.sampling_rate))
- audio_norm = audio / self.max_wav_value
- audio_norm = audio_norm.unsqueeze(0)
- spec_filename = filename.replace(".wav", ".spec.pt")
- if self.use_mel_spec_posterior:
- spec_filename = spec_filename.replace(".spec.pt", ".mel.pt")
- try:
- spec = torch.load(spec_filename)
- except:
- if self.use_mel_spec_posterior:
- spec = mel_spectrogram_torch(audio_norm, self.filter_length,
- self.n_mel_channels, self.sampling_rate, self.hop_length,
- self.win_length, self.hparams.mel_fmin, self.hparams.mel_fmax, center=False)
- else:
- spec = spectrogram_torch(audio_norm, self.filter_length,
- self.sampling_rate, self.hop_length, self.win_length,
- center=False)
- spec = torch.squeeze(spec, 0)
- torch.save(spec, spec_filename)
- return spec, audio_norm
-
- def get_text(self, text, word2ph, phone, tone, language_str, wav_path):
- pold = phone
- w2pho = [i for i in word2ph]
- word2ph = [i for i in word2ph]
- phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
- pold2 = phone
-
- if self.add_blank:
- p1 = len(phone)
- phone = commons.intersperse(phone, 0)
- p2 = len(phone)
- t1 = len(tone)
- tone = commons.intersperse(tone, 0)
- t2 = len(tone)
- language = commons.intersperse(language, 0)
- for i in range(len(word2ph)):
- word2ph[i] = word2ph[i] * 2
- word2ph[0] += 1
- bert_path = wav_path.replace(".wav", ".bert.pt")
- try:
- bert = torch.load(bert_path)
- assert bert.shape[-1] == len(phone)
- except:
- bert = get_bert(text, word2ph, language_str)
- torch.save(bert, bert_path)
- #print(bert.shape[-1], bert_path, text, pold)
- assert bert.shape[-1] == len(phone)
-
- assert bert.shape[-1] == len(phone), (
- bert.shape, len(phone), sum(word2ph), p1, p2, t1, t2, pold, pold2, word2ph, text, w2pho)
- phone = torch.LongTensor(phone)
- tone = torch.LongTensor(tone)
- language = torch.LongTensor(language)
- return bert, phone, tone, language
-
- def get_sid(self, sid):
- sid = torch.LongTensor([int(sid)])
- return sid
-
- def __getitem__(self, index):
- return self.get_audio_text_speaker_pair(self.audiopaths_sid_text[index])
-
- def __len__(self):
- return len(self.audiopaths_sid_text)
-
-
-class TextAudioSpeakerCollate():
- """ Zero-pads model inputs and targets
- """
-
- def __init__(self, return_ids=False):
- self.return_ids = return_ids
-
- def __call__(self, batch):
- """Collate's training batch from normalized text, audio and speaker identities
- PARAMS
- ------
- batch: [text_normalized, spec_normalized, wav_normalized, sid]
- """
- # Right zero-pad all one-hot text sequences to max input length
- _, ids_sorted_decreasing = torch.sort(
- torch.LongTensor([x[1].size(1) for x in batch]),
- dim=0, descending=True)
-
- max_text_len = max([len(x[0]) for x in batch])
- max_spec_len = max([x[1].size(1) for x in batch])
- max_wav_len = max([x[2].size(1) for x in batch])
-
- text_lengths = torch.LongTensor(len(batch))
- spec_lengths = torch.LongTensor(len(batch))
- wav_lengths = torch.LongTensor(len(batch))
- sid = torch.LongTensor(len(batch))
-
- text_padded = torch.LongTensor(len(batch), max_text_len)
- tone_padded = torch.LongTensor(len(batch), max_text_len)
- language_padded = torch.LongTensor(len(batch), max_text_len)
- bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
-
- spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
- wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
- text_padded.zero_()
- tone_padded.zero_()
- language_padded.zero_()
- spec_padded.zero_()
- wav_padded.zero_()
- bert_padded.zero_()
- for i in range(len(ids_sorted_decreasing)):
- row = batch[ids_sorted_decreasing[i]]
-
- text = row[0]
- text_padded[i, :text.size(0)] = text
- text_lengths[i] = text.size(0)
-
- spec = row[1]
- spec_padded[i, :, :spec.size(1)] = spec
- spec_lengths[i] = spec.size(1)
-
- wav = row[2]
- wav_padded[i, :, :wav.size(1)] = wav
- wav_lengths[i] = wav.size(1)
-
- sid[i] = row[3]
-
- tone = row[4]
- tone_padded[i, :tone.size(0)] = tone
-
- language = row[5]
- language_padded[i, :language.size(0)] = language
-
- bert = row[6]
- bert_padded[i, :, :bert.size(1)] = bert
-
- return text_padded, text_lengths, spec_padded, spec_lengths, wav_padded, wav_lengths, sid, tone_padded, language_padded, bert_padded
-
-
-class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler):
- """
- Maintain similar input lengths in a batch.
- Length groups are specified by boundaries.
- Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}.
-
- It removes samples which are not included in the boundaries.
- Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded.
- """
-
- def __init__(self, dataset, batch_size, boundaries, num_replicas=None, rank=None, shuffle=True):
- super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
- self.lengths = dataset.lengths
- self.batch_size = batch_size
- self.boundaries = boundaries
-
- self.buckets, self.num_samples_per_bucket = self._create_buckets()
- self.total_size = sum(self.num_samples_per_bucket)
- self.num_samples = self.total_size // self.num_replicas
-
- def _create_buckets(self):
- buckets = [[] for _ in range(len(self.boundaries) - 1)]
- for i in range(len(self.lengths)):
- length = self.lengths[i]
- idx_bucket = self._bisect(length)
- if idx_bucket != -1:
- buckets[idx_bucket].append(i)
-
- for i in range(len(buckets) - 1, 0, -1):
- if len(buckets[i]) == 0:
- buckets.pop(i)
- self.boundaries.pop(i + 1)
-
- num_samples_per_bucket = []
- for i in range(len(buckets)):
- len_bucket = len(buckets[i])
- total_batch_size = self.num_replicas * self.batch_size
- rem = (total_batch_size - (len_bucket % total_batch_size)) % total_batch_size
- num_samples_per_bucket.append(len_bucket + rem)
- return buckets, num_samples_per_bucket
-
- def __iter__(self):
- # deterministically shuffle based on epoch
- g = torch.Generator()
- g.manual_seed(self.epoch)
-
- indices = []
- if self.shuffle:
- for bucket in self.buckets:
- indices.append(torch.randperm(len(bucket), generator=g).tolist())
- else:
- for bucket in self.buckets:
- indices.append(list(range(len(bucket))))
-
- batches = []
- for i in range(len(self.buckets)):
- bucket = self.buckets[i]
- len_bucket = len(bucket)
- if (len_bucket == 0):
- continue
- ids_bucket = indices[i]
- num_samples_bucket = self.num_samples_per_bucket[i]
-
- # add extra samples to make it evenly divisible
- rem = num_samples_bucket - len_bucket
- ids_bucket = ids_bucket + ids_bucket * (rem // len_bucket) + ids_bucket[:(rem % len_bucket)]
-
- # subsample
- ids_bucket = ids_bucket[self.rank::self.num_replicas]
-
- # batching
- for j in range(len(ids_bucket) // self.batch_size):
- batch = [bucket[idx] for idx in ids_bucket[j * self.batch_size:(j + 1) * self.batch_size]]
- batches.append(batch)
-
- if self.shuffle:
- batch_ids = torch.randperm(len(batches), generator=g).tolist()
- batches = [batches[i] for i in batch_ids]
- self.batches = batches
-
- assert len(self.batches) * self.batch_size == self.num_samples
- return iter(self.batches)
-
- def _bisect(self, x, lo=0, hi=None):
- if hi is None:
- hi = len(self.boundaries) - 1
-
- if hi > lo:
- mid = (hi + lo) // 2
- if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]:
- return mid
- elif x <= self.boundaries[mid]:
- return self._bisect(x, lo, mid)
- else:
- return self._bisect(x, mid + 1, hi)
- else:
- return -1
-
- def __len__(self):
- return self.num_samples // self.batch_size
diff --git a/spaces/XzJosh/LittleTaffy-Bert-VITS2/text/symbols.py b/spaces/XzJosh/LittleTaffy-Bert-VITS2/text/symbols.py
deleted file mode 100644
index 9dfae4e633829f20c4fd767b1c7a9198911ed801..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/LittleTaffy-Bert-VITS2/text/symbols.py
+++ /dev/null
@@ -1,51 +0,0 @@
-punctuation = ['!', '?', '…', ",", ".", "'", '-']
-pu_symbols = punctuation + ["SP", "UNK"]
-pad = '_'
-
-# chinese
-zh_symbols = ['E', 'En', 'a', 'ai', 'an', 'ang', 'ao', 'b', 'c', 'ch', 'd', 'e', 'ei', 'en', 'eng', 'er', 'f', 'g', 'h',
- 'i', 'i0', 'ia', 'ian', 'iang', 'iao', 'ie', 'in', 'ing', 'iong', 'ir', 'iu', 'j', 'k', 'l', 'm', 'n', 'o',
- 'ong',
- 'ou', 'p', 'q', 'r', 's', 'sh', 't', 'u', 'ua', 'uai', 'uan', 'uang', 'ui', 'un', 'uo', 'v', 'van', 've', 'vn',
- 'w', 'x', 'y', 'z', 'zh',
- "AA", "EE", "OO"]
-num_zh_tones = 6
-
-# japanese
-ja_symbols = ['I', 'N', 'U', 'a', 'b', 'by', 'ch', 'cl', 'd', 'dy', 'e', 'f', 'g', 'gy', 'h', 'hy', 'i', 'j', 'k', 'ky',
- 'm', 'my', 'n', 'ny', 'o', 'p', 'py', 'r', 'ry', 's', 'sh', 't', 'ts', 'u', 'V', 'w', 'y', 'z']
-num_ja_tones = 1
-
-# English
-en_symbols = ['aa', 'ae', 'ah', 'ao', 'aw', 'ay', 'b', 'ch', 'd', 'dh', 'eh', 'er', 'ey', 'f', 'g', 'hh', 'ih', 'iy',
- 'jh', 'k', 'l', 'm', 'n', 'ng', 'ow', 'oy', 'p', 'r', 's',
- 'sh', 't', 'th', 'uh', 'uw', 'V', 'w', 'y', 'z', 'zh']
-num_en_tones = 4
-
-# combine all symbols
-normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
-symbols = [pad] + normal_symbols + pu_symbols
-sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
-
-# combine all tones
-num_tones = num_zh_tones + num_ja_tones + num_en_tones
-
-# language maps
-language_id_map = {
- 'ZH': 0,
- "JA": 1,
- "EN": 2
-}
-num_languages = len(language_id_map.keys())
-
-language_tone_start_map = {
- 'ZH': 0,
- "JA": num_zh_tones,
- "EN": num_zh_tones + num_ja_tones
-}
-
-if __name__ == '__main__':
- a = set(zh_symbols)
- b = set(en_symbols)
- print(sorted(a&b))
-
diff --git a/spaces/Yassine/Stego/sse_mathfun.h b/spaces/Yassine/Stego/sse_mathfun.h
deleted file mode 100644
index 9773e2961ad1f5af5d07b02484fd7edc0f6a2a53..0000000000000000000000000000000000000000
--- a/spaces/Yassine/Stego/sse_mathfun.h
+++ /dev/null
@@ -1,762 +0,0 @@
-/* SIMD (SSE1+MMX or SSE2) implementation of sin, cos, exp and log
-
- Inspired by Intel Approximate Math library, and based on the
- corresponding algorithms of the cephes math library
-
- The default is to use the SSE1 version. If you define USE_SSE2 the
- the SSE2 intrinsics will be used in place of the MMX intrinsics. Do
- not expect any significant performance improvement with SSE2.
-*/
-
-/* Copyright (C) 2007 Julien Pommier
-
- This software is provided 'as-is', without any express or implied
- warranty. In no event will the authors be held liable for any damages
- arising from the use of this software.
-
- Permission is granted to anyone to use this software for any purpose,
- including commercial applications, and to alter it and redistribute it
- freely, subject to the following restrictions:
-
- 1. The origin of this software must not be misrepresented; you must not
- claim that you wrote the original software. If you use this software
- in a product, an acknowledgment in the product documentation would be
- appreciated but is not required.
- 2. Altered source versions must be plainly marked as such, and must not be
- misrepresented as being the original software.
- 3. This notice may not be removed or altered from any source distribution.
-
- (this is the zlib license)
-*/
-
-#include
-
-/* yes I know, the top of this file is quite ugly */
-
-#define USE_SSE2 // use SSE2 version
-
-#ifdef _MSC_VER /* visual c++ */
-# define ALIGN16_BEG __declspec(align(16))
-# define ALIGN16_END
-#else /* gcc or icc */
-# define ALIGN16_BEG
-# define ALIGN16_END __attribute__((aligned(16)))
-#endif
-
-/* __m128 is ugly to write */
-typedef __m128 v4sf; // vector of 4 float (sse1)
-
-#ifdef USE_SSE2
-# include
-typedef __m128i v4si; // vector of 4 int (sse2)
-#else
-typedef __m64 v2si; // vector of 2 int (mmx)
-#endif
-
-/* declare some SSE constants -- why can't I figure a better way to do that? */
-#define _PS_CONST(Name, Val) \
- static const ALIGN16_BEG float _ps_##Name[4] ALIGN16_END = { Val, Val, Val, Val }
-#define _PI32_CONST(Name, Val) \
- static const ALIGN16_BEG int _pi32_##Name[4] ALIGN16_END = { Val, Val, Val, Val }
-#define _PS_CONST_TYPE(Name, Type, Val) \
- static const ALIGN16_BEG Type _ps_##Name[4] ALIGN16_END = { Val, Val, Val, Val }
-
-_PS_CONST(1 , 1.0f);
-_PS_CONST(0p5, 0.5f);
-/* the smallest non denormalized float number */
-_PS_CONST_TYPE(min_norm_pos, int, 0x00800000);
-_PS_CONST_TYPE(mant_mask, int, 0x7f800000);
-_PS_CONST_TYPE(inv_mant_mask, int, ~0x7f800000);
-
-_PS_CONST_TYPE(sign_mask, int, 0x80000000);
-_PS_CONST_TYPE(inv_sign_mask, int, ~0x80000000);
-
-_PI32_CONST(1, 1);
-_PI32_CONST(inv1, ~1);
-_PI32_CONST(2, 2);
-_PI32_CONST(4, 4);
-_PI32_CONST(0x7f, 0x7f);
-
-_PS_CONST(cephes_SQRTHF, 0.707106781186547524);
-_PS_CONST(cephes_log_p0, 7.0376836292E-2);
-_PS_CONST(cephes_log_p1, - 1.1514610310E-1);
-_PS_CONST(cephes_log_p2, 1.1676998740E-1);
-_PS_CONST(cephes_log_p3, - 1.2420140846E-1);
-_PS_CONST(cephes_log_p4, + 1.4249322787E-1);
-_PS_CONST(cephes_log_p5, - 1.6668057665E-1);
-_PS_CONST(cephes_log_p6, + 2.0000714765E-1);
-_PS_CONST(cephes_log_p7, - 2.4999993993E-1);
-_PS_CONST(cephes_log_p8, + 3.3333331174E-1);
-_PS_CONST(cephes_log_q1, -2.12194440e-4);
-_PS_CONST(cephes_log_q2, 0.693359375);
-
-#if defined (__MINGW32__)
-
-/* the ugly part below: many versions of gcc used to be completely buggy with respect to some intrinsics
- The movehl_ps is fixed in mingw 3.4.5, but I found out that all the _mm_cmp* intrinsics were completely
- broken on my mingw gcc 3.4.5 ...
-
- Note that the bug on _mm_cmp* does occur only at -O0 optimization level
-*/
-
-inline __m128 my_movehl_ps(__m128 a, const __m128 b) {
- asm (
- "movhlps %2,%0\n\t"
- : "=x" (a)
- : "0" (a), "x"(b)
- );
- return a; }
-#warning "redefined _mm_movehl_ps (see gcc bug 21179)"
-#define _mm_movehl_ps my_movehl_ps
-
-inline __m128 my_cmplt_ps(__m128 a, const __m128 b) {
- asm (
- "cmpltps %2,%0\n\t"
- : "=x" (a)
- : "0" (a), "x"(b)
- );
- return a;
- }
-inline __m128 my_cmpgt_ps(__m128 a, const __m128 b) {
- asm (
- "cmpnleps %2,%0\n\t"
- : "=x" (a)
- : "0" (a), "x"(b)
- );
- return a;
-}
-inline __m128 my_cmpeq_ps(__m128 a, const __m128 b) {
- asm (
- "cmpeqps %2,%0\n\t"
- : "=x" (a)
- : "0" (a), "x"(b)
- );
- return a;
-}
-#warning "redefined _mm_cmpxx_ps functions..."
-#define _mm_cmplt_ps my_cmplt_ps
-#define _mm_cmpgt_ps my_cmpgt_ps
-#define _mm_cmpeq_ps my_cmpeq_ps
-#endif
-
-#ifndef USE_SSE2
-typedef union xmm_mm_union {
- __m128 xmm;
- __m64 mm[2];
-} xmm_mm_union;
-
-#define COPY_XMM_TO_MM(xmm_, mm0_, mm1_) { \
- xmm_mm_union u; u.xmm = xmm_; \
- mm0_ = u.mm[0]; \
- mm1_ = u.mm[1]; \
-}
-
-#define COPY_MM_TO_XMM(mm0_, mm1_, xmm_) { \
- xmm_mm_union u; u.mm[0]=mm0_; u.mm[1]=mm1_; xmm_ = u.xmm; \
- }
-
-#endif // USE_SSE2
-
-/* natural logarithm computed for 4 simultaneous float
- return NaN for x <= 0
-*/
-v4sf log_ps(v4sf x) {
-#ifdef USE_SSE2
- v4si emm0;
-#else
- v2si mm0, mm1;
-#endif
- v4sf one = *(v4sf*)_ps_1;
-
- v4sf invalid_mask = _mm_cmple_ps(x, _mm_setzero_ps());
-
- x = _mm_max_ps(x, *(v4sf*)_ps_min_norm_pos); /* cut off denormalized stuff */
-
-#ifndef USE_SSE2
- /* part 1: x = frexpf(x, &e); */
- COPY_XMM_TO_MM(x, mm0, mm1);
- mm0 = _mm_srli_pi32(mm0, 23);
- mm1 = _mm_srli_pi32(mm1, 23);
-#else
- emm0 = _mm_srli_epi32(_mm_castps_si128(x), 23);
-#endif
- /* keep only the fractional part */
- x = _mm_and_ps(x, *(v4sf*)_ps_inv_mant_mask);
- x = _mm_or_ps(x, *(v4sf*)_ps_0p5);
-
-#ifndef USE_SSE2
- /* now e=mm0:mm1 contain the really base-2 exponent */
- mm0 = _mm_sub_pi32(mm0, *(v2si*)_pi32_0x7f);
- mm1 = _mm_sub_pi32(mm1, *(v2si*)_pi32_0x7f);
- v4sf e = _mm_cvtpi32x2_ps(mm0, mm1);
- _mm_empty(); /* bye bye mmx */
-#else
- emm0 = _mm_sub_epi32(emm0, *(v4si*)_pi32_0x7f);
- v4sf e = _mm_cvtepi32_ps(emm0);
-#endif
-
- e = _mm_add_ps(e, one);
-
- /* part2:
- if( x < SQRTHF ) {
- e -= 1;
- x = x + x - 1.0;
- } else { x = x - 1.0; }
- */
- v4sf mask = _mm_cmplt_ps(x, *(v4sf*)_ps_cephes_SQRTHF);
- v4sf tmp = _mm_and_ps(x, mask);
- x = _mm_sub_ps(x, one);
- e = _mm_sub_ps(e, _mm_and_ps(one, mask));
- x = _mm_add_ps(x, tmp);
-
-
- v4sf z = _mm_mul_ps(x,x);
-
- v4sf y = *(v4sf*)_ps_cephes_log_p0;
- y = _mm_mul_ps(y, x);
- y = _mm_add_ps(y, *(v4sf*)_ps_cephes_log_p1);
- y = _mm_mul_ps(y, x);
- y = _mm_add_ps(y, *(v4sf*)_ps_cephes_log_p2);
- y = _mm_mul_ps(y, x);
- y = _mm_add_ps(y, *(v4sf*)_ps_cephes_log_p3);
- y = _mm_mul_ps(y, x);
- y = _mm_add_ps(y, *(v4sf*)_ps_cephes_log_p4);
- y = _mm_mul_ps(y, x);
- y = _mm_add_ps(y, *(v4sf*)_ps_cephes_log_p5);
- y = _mm_mul_ps(y, x);
- y = _mm_add_ps(y, *(v4sf*)_ps_cephes_log_p6);
- y = _mm_mul_ps(y, x);
- y = _mm_add_ps(y, *(v4sf*)_ps_cephes_log_p7);
- y = _mm_mul_ps(y, x);
- y = _mm_add_ps(y, *(v4sf*)_ps_cephes_log_p8);
- y = _mm_mul_ps(y, x);
-
- y = _mm_mul_ps(y, z);
-
-
- tmp = _mm_mul_ps(e, *(v4sf*)_ps_cephes_log_q1);
- y = _mm_add_ps(y, tmp);
-
-
- tmp = _mm_mul_ps(z, *(v4sf*)_ps_0p5);
- y = _mm_sub_ps(y, tmp);
-
- tmp = _mm_mul_ps(e, *(v4sf*)_ps_cephes_log_q2);
- x = _mm_add_ps(x, y);
- x = _mm_add_ps(x, tmp);
- x = _mm_or_ps(x, invalid_mask); // negative arg will be NAN
- return x;
-}
-
-_PS_CONST(exp_hi, 88.3762626647949f);
-_PS_CONST(exp_lo, -88.3762626647949f);
-
-_PS_CONST(cephes_LOG2EF, 1.44269504088896341);
-_PS_CONST(cephes_exp_C1, 0.693359375);
-_PS_CONST(cephes_exp_C2, -2.12194440e-4);
-
-_PS_CONST(cephes_exp_p0, 1.9875691500E-4);
-_PS_CONST(cephes_exp_p1, 1.3981999507E-3);
-_PS_CONST(cephes_exp_p2, 8.3334519073E-3);
-_PS_CONST(cephes_exp_p3, 4.1665795894E-2);
-_PS_CONST(cephes_exp_p4, 1.6666665459E-1);
-_PS_CONST(cephes_exp_p5, 5.0000001201E-1);
-
-v4sf exp_ps(v4sf x) {
- v4sf tmp = _mm_setzero_ps(), fx;
-#ifdef USE_SSE2
- v4si emm0;
-#else
- v2si mm0, mm1;
-#endif
- v4sf one = *(v4sf*)_ps_1;
-
- x = _mm_min_ps(x, *(v4sf*)_ps_exp_hi);
- x = _mm_max_ps(x, *(v4sf*)_ps_exp_lo);
-
- /* express exp(x) as exp(g + n*log(2)) */
- fx = _mm_mul_ps(x, *(v4sf*)_ps_cephes_LOG2EF);
- fx = _mm_add_ps(fx, *(v4sf*)_ps_0p5);
-
- /* how to perform a floorf with SSE: just below */
-#ifndef USE_SSE2
- /* step 1 : cast to int */
- tmp = _mm_movehl_ps(tmp, fx);
- mm0 = _mm_cvttps_pi32(fx);
- mm1 = _mm_cvttps_pi32(tmp);
- /* step 2 : cast back to float */
- tmp = _mm_cvtpi32x2_ps(mm0, mm1);
-#else
- emm0 = _mm_cvttps_epi32(fx);
- tmp = _mm_cvtepi32_ps(emm0);
-#endif
- /* if greater, substract 1 */
- v4sf mask = _mm_cmpgt_ps(tmp, fx);
- mask = _mm_and_ps(mask, one);
- fx = _mm_sub_ps(tmp, mask);
-
- tmp = _mm_mul_ps(fx, *(v4sf*)_ps_cephes_exp_C1);
- v4sf z = _mm_mul_ps(fx, *(v4sf*)_ps_cephes_exp_C2);
- x = _mm_sub_ps(x, tmp);
- x = _mm_sub_ps(x, z);
-
- z = _mm_mul_ps(x,x);
-
- v4sf y = *(v4sf*)_ps_cephes_exp_p0;
- y = _mm_mul_ps(y, x);
- y = _mm_add_ps(y, *(v4sf*)_ps_cephes_exp_p1);
- y = _mm_mul_ps(y, x);
- y = _mm_add_ps(y, *(v4sf*)_ps_cephes_exp_p2);
- y = _mm_mul_ps(y, x);
- y = _mm_add_ps(y, *(v4sf*)_ps_cephes_exp_p3);
- y = _mm_mul_ps(y, x);
- y = _mm_add_ps(y, *(v4sf*)_ps_cephes_exp_p4);
- y = _mm_mul_ps(y, x);
- y = _mm_add_ps(y, *(v4sf*)_ps_cephes_exp_p5);
- y = _mm_mul_ps(y, z);
- y = _mm_add_ps(y, x);
- y = _mm_add_ps(y, one);
-
- /* build 2^n */
-#ifndef USE_SSE2
- z = _mm_movehl_ps(z, fx);
- mm0 = _mm_cvttps_pi32(fx);
- mm1 = _mm_cvttps_pi32(z);
- mm0 = _mm_add_pi32(mm0, *(v2si*)_pi32_0x7f);
- mm1 = _mm_add_pi32(mm1, *(v2si*)_pi32_0x7f);
- mm0 = _mm_slli_pi32(mm0, 23);
- mm1 = _mm_slli_pi32(mm1, 23);
-
- v4sf pow2n;
- COPY_MM_TO_XMM(mm0, mm1, pow2n);
- _mm_empty();
-#else
- emm0 = _mm_cvttps_epi32(fx);
- emm0 = _mm_add_epi32(emm0, *(v4si*)_pi32_0x7f);
- emm0 = _mm_slli_epi32(emm0, 23);
- v4sf pow2n = _mm_castsi128_ps(emm0);
-#endif
- y = _mm_mul_ps(y, pow2n);
- return y;
-}
-
-_PS_CONST(minus_cephes_DP1, -0.78515625);
-_PS_CONST(minus_cephes_DP2, -2.4187564849853515625e-4);
-_PS_CONST(minus_cephes_DP3, -3.77489497744594108e-8);
-_PS_CONST(sincof_p0, -1.9515295891E-4);
-_PS_CONST(sincof_p1, 8.3321608736E-3);
-_PS_CONST(sincof_p2, -1.6666654611E-1);
-_PS_CONST(coscof_p0, 2.443315711809948E-005);
-_PS_CONST(coscof_p1, -1.388731625493765E-003);
-_PS_CONST(coscof_p2, 4.166664568298827E-002);
-_PS_CONST(cephes_FOPI, 1.27323954473516); // 4 / M_PI
-
-
-/* evaluation of 4 sines at onces, using only SSE1+MMX intrinsics so
- it runs also on old athlons XPs and the pentium III of your grand
- mother.
-
- The code is the exact rewriting of the cephes sinf function.
- Precision is excellent as long as x < 8192 (I did not bother to
- take into account the special handling they have for greater values
- -- it does not return garbage for arguments over 8192, though, but
- the extra precision is missing).
-
- Note that it is such that sinf((float)M_PI) = 8.74e-8, which is the
- surprising but correct result.
-
- Performance is also surprisingly good, 1.33 times faster than the
- macos vsinf SSE2 function, and 1.5 times faster than the
- __vrs4_sinf of amd's ACML (which is only available in 64 bits). Not
- too bad for an SSE1 function (with no special tuning) !
- However the latter libraries probably have a much better handling of NaN,
- Inf, denormalized and other special arguments..
-
- On my core 1 duo, the execution of this function takes approximately 95 cycles.
-
- From what I have observed on the experiments with Intel AMath lib, switching to an
- SSE2 version would improve the perf by only 10%.
-
- Since it is based on SSE intrinsics, it has to be compiled at -O2 to
- deliver full speed.
-*/
-v4sf sin_ps(v4sf x) { // any x
- v4sf xmm1, xmm2 = _mm_setzero_ps(), xmm3, sign_bit, y;
-
-#ifdef USE_SSE2
- v4si emm0, emm2;
-#else
- v2si mm0, mm1, mm2, mm3;
-#endif
- sign_bit = x;
- /* take the absolute value */
- x = _mm_and_ps(x, *(v4sf*)_ps_inv_sign_mask);
- /* extract the sign bit (upper one) */
- sign_bit = _mm_and_ps(sign_bit, *(v4sf*)_ps_sign_mask);
-
- /* scale by 4/Pi */
- y = _mm_mul_ps(x, *(v4sf*)_ps_cephes_FOPI);
-
- //printf("plop:"); print4(y);
-#ifdef USE_SSE2
- /* store the integer part of y in mm0 */
- emm2 = _mm_cvttps_epi32(y);
- /* j=(j+1) & (~1) (see the cephes sources) */
- emm2 = _mm_add_epi32(emm2, *(v4si*)_pi32_1);
- emm2 = _mm_and_si128(emm2, *(v4si*)_pi32_inv1);
- y = _mm_cvtepi32_ps(emm2);
- /* get the swap sign flag */
- emm0 = _mm_and_si128(emm2, *(v4si*)_pi32_4);
- emm0 = _mm_slli_epi32(emm0, 29);
- /* get the polynom selection mask
- there is one polynom for 0 <= x <= Pi/4
- and another one for Pi/4
-#include
-#include
-#include
-#include "stc_extract_c.h"
-
-// {{{ stc_extract()
-int stc_extract(const u8 *vector, int vectorlength, u8 *message, int syndromelength, int matrixheight)
-{
- int i, j, k, index, index2, base, height;
-
- u8 *binmat[2];
- int *matrices, *widths;
-
- height = matrixheight;
-
- if(matrixheight > 31) {
- fprintf(stderr, "Submatrix height must not exceed 31.");
- return -1;
- }
-
- {
- double invalpha;
- int shorter, longer, worm;
- u32 *columns[2];
-
- matrices = (int *)malloc(syndromelength * sizeof(int));
- widths = (int *)malloc(syndromelength * sizeof(int));
-
- invalpha = (double)vectorlength / syndromelength;
- if(invalpha < 1) {
- fprintf(stderr, "The message cannot be longer than the cover object.\n");
- return -1;
- }
- shorter = (int)floor(invalpha);
- longer = (int)ceil(invalpha);
- if((columns[0] = getMatrix(shorter, matrixheight)) == NULL) {
- free(widths);
- free(matrices);
- return -1;
- }
- if((columns[1] = getMatrix(longer, matrixheight)) == NULL) {
- free(columns[0]);
- free(widths);
- free(matrices);
- return -1;
- }
- worm = 0;
- for(i = 0; i < syndromelength; i++) {
- if(worm + longer <= (i + 1) * invalpha + 0.5) {
- matrices[i] = 1;
- widths[i] = longer;
- worm += longer;
- } else {
- matrices[i] = 0;
- widths[i] = shorter;
- worm += shorter;
- }
- }
- binmat[0] = (u8*)malloc(shorter * matrixheight * sizeof(u8));
- binmat[1] = (u8*)malloc(longer * matrixheight * sizeof(u8));
- for(i = 0, index = 0; i < shorter; i++) {
- for(j = 0; j < matrixheight; j++, index++) {
- binmat[0][index] = (columns[0][i] & (1 << j)) ? 1 : 0;
- }
- }
- for(i = 0, index = 0; i < longer; i++) {
- for(j = 0; j < matrixheight; j++, index++) {
- binmat[1][index] = (columns[1][i] & (1 << j)) ? 1 : 0;
- }
- }
- free(columns[0]);
- free(columns[1]);
- }
-
- for(i = 0; i < syndromelength; i++) {
- message[i] = 0;
- }
-
- for(index = 0, index2 = 0; index2 < syndromelength; index2++) {
- for(k = 0, base = 0; k < widths[index2]; k++, index++, base += matrixheight) {
- if(vector[index]) {
- for(i = 0; i < height; i++) {
- message[index2 + i] ^= binmat[matrices[index2]][base + i];
- }
- }
- }
- if(syndromelength - index2 <= matrixheight)
- height--;
- }
-
- free(matrices);
- free(widths);
- free(binmat[0]);
- free(binmat[1]);
-
- return 0;
-}
-// }}}
-
-
diff --git a/spaces/YueMafighting/FollowYourPose/inference_mmpose.py b/spaces/YueMafighting/FollowYourPose/inference_mmpose.py
deleted file mode 100644
index dafbb96f0661abe8ff1cf72d0698bb32e49c65ff..0000000000000000000000000000000000000000
--- a/spaces/YueMafighting/FollowYourPose/inference_mmpose.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import gradio as gr
-
-import os
-import cv2
-import numpy as np
-from PIL import Image
-from moviepy.editor import *
-
-import sys
-sys.path.append('FollowYourPose')
-
-def get_frames(video_in):
- frames = []
- #resize the video
- clip = VideoFileClip(video_in)
- start_frame = 0 # 起始帧数
- end_frame = 50 # 结束帧数
-
- if not os.path.exists('./raw_frames'):
- os.makedirs('./raw_frames')
-
- if not os.path.exists('./mmpose_frames'):
- os.makedirs('./mmpose_frames')
-
- #check fps
- if clip.fps > 30:
- print("vide rate is over 30, resetting to 30")
- clip_resized = clip.resize(height=512)
- clip_resized = clip_resized.subclip(start_frame / clip_resized.fps, end_frame / clip_resized.fps) # subclip 2 seconds
- clip_resized.write_videofile("./video_resized.mp4", fps=30)
- else:
- print("video rate is OK")
- clip_resized = clip.resize(height=512)
- clip_resized = clip_resized.subclip(start_frame / clip.fps, end_frame / clip.fps) # subclip 5 seconds
- clip_resized.write_videofile("./video_resized.mp4", fps=clip.fps)
-
- print("video resized to 512 height")
-
- # Opens the Video file with CV2
- cap= cv2.VideoCapture("./video_resized.mp4")
-
- fps = cap.get(cv2.CAP_PROP_FPS)
- print("video fps: " + str(fps))
- i=0
- while(cap.isOpened()):
- ret, frame = cap.read()
- if ret == False:
- break
- cv2.imwrite('./raw_frames/kang'+str(i)+'.jpg',frame)
- frames.append('./raw_frames/kang'+str(i)+'.jpg')
- i+=1
-
- cap.release()
- cv2.destroyAllWindows()
- print("broke the video into frames")
-
- return frames, fps
-
-def get_mmpose_filter(mmpose, i):
- #image = Image.open(i)
-
- #image = np.array(image)
- image = mmpose(i, fn_index=0)[1]
- image = Image.open(image)
- #image = Image.fromarray(image)
- image.save("./mmpose_frames/mmpose_frame_" + str(i).split('/')[-1][:-4] + ".jpeg")
- return "./mmpose_frames/mmpose_frame_" + str(i).split('/')[-1][:-4] + ".jpeg"
-
-def create_video(frames, fps, type):
- print("building video result")
- clip = ImageSequenceClip(frames, fps=fps)
- clip.write_videofile(type + "_result.mp4", fps=fps)
-
- return type + "_result.mp4"
-
-
-def infer_skeleton(mmpose, video_in):
-
-
- # 1. break video into frames and get FPS
-
- break_vid = get_frames(video_in)
- frames_list= break_vid[0]
- fps = break_vid[1]
- #n_frame = int(trim_value*fps)
- n_frame = len(frames_list)
-
- if n_frame >= len(frames_list):
- print("video is shorter than the cut value")
- n_frame = len(frames_list)
-
- # 2. prepare frames result arrays
- result_frames = []
- print("set stop frames to: " + str(n_frame))
-
- for i in frames_list[0:int(n_frame)]:
- mmpose_frame = get_mmpose_filter(mmpose, i)
- result_frames.append(mmpose_frame)
- print("frame " + i + "/" + str(n_frame) + ": done;")
-
-
- final_vid = create_video(result_frames, fps, "mmpose")
- files = [final_vid]
-
- return final_vid, files
\ No newline at end of file
diff --git a/spaces/a-v-bely/spanish-task-generator/utilities_cookies/cookie_manager.py b/spaces/a-v-bely/spanish-task-generator/utilities_cookies/cookie_manager.py
deleted file mode 100644
index 296182195a040e3dc6130499bff23508e861eab4..0000000000000000000000000000000000000000
--- a/spaces/a-v-bely/spanish-task-generator/utilities_cookies/cookie_manager.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import streamlit as st
-from pathlib import Path
-from typing import Mapping
-from datetime import datetime
-from datetime import timedelta
-from urllib.parse import unquote
-from typing import MutableMapping
-from streamlit.components.v1 import components
-
-
-build_path = Path(__file__).parent / 'build'
-_component_func = components.declare_component("CookieManager.sync_cookies", path=str(build_path))
-
-
-class CookieManager(MutableMapping[str, str]):
- def __init__(self, *, path: str = None, prefix=""):
- self._queue = st.session_state.setdefault('CookieManager.queue', {})
- self._prefix = prefix
- raw_cookie = self._run_component(save_only=False, key="CookieManager.sync_cookies")
- if raw_cookie is None:
- self._cookies = None
- else:
- self._cookies = parse_cookies(raw_cookie)
- self._clean_queue()
- self._default_expiry = datetime.now() + timedelta(days=365)
- self._path = path if path is not None else "/"
-
- def ready(self) -> bool:
- return self._cookies is not None
-
- def save(self):
- if self._queue:
- self._run_component(save_only=True, key="CookieManager.sync_cookies.save")
-
- def _run_component(self, save_only: bool, key: str):
- queue = {
- self._prefix + k: v for k, v in self._queue.items()
- }
- return _component_func(queue=queue, saveOnly=save_only, key=key)
-
- def _clean_queue(self):
- for name, spec in list(self._queue.items()):
- value = self._cookies.get(self._prefix + name)
- if value == spec['value']:
- del self._queue[name]
-
- def __repr__(self):
- if self.ready():
- return f''
- return ''
-
- def __getitem__(self, k: str) -> str:
- return self._get_cookies()[k]
-
- def __iter__(self):
- return iter(self._get_cookies())
-
- def __len__(self):
- return len(self._get_cookies())
-
- def __setitem__(self, key: str, value: str) -> None:
- if self._cookies.get(key) != value:
- self._queue[key] = dict(
- value=value,
- expires_at=self._default_expiry.isoformat(),
- path=self._path,
- )
-
- def __delitem__(self, key: str) -> None:
- if key in self._cookies:
- self._queue[key] = dict(value=None, path=self._path)
-
- def _get_cookies(self) -> Mapping[str, str]:
- if self._cookies is None:
- raise CookiesNotReady()
- cookies = {
- k[len(self._prefix):]: v
- for k, v in self._cookies.items()
- if k.startswith(self._prefix)
- }
- for name, spec in self._queue.items():
- if spec['value'] is not None:
- cookies[name] = spec['value']
- else:
- cookies.pop(name, None)
- return cookies
-
-
-def parse_cookies(raw_cookie):
- cookies = {}
- for part in raw_cookie.split(';'):
- part = part.strip()
- if not part:
- continue
- name, value = part.split('=', 1)
- cookies[unquote(name)] = unquote(value)
- return cookies
-
-
-class CookiesNotReady(Exception):
- pass
diff --git a/spaces/abdvl/datahub_qa_bot/docs/modeling/metadata-model.md b/spaces/abdvl/datahub_qa_bot/docs/modeling/metadata-model.md
deleted file mode 100644
index 63f6cb6177c04790a8913fa0aba5998c9a21b4e1..0000000000000000000000000000000000000000
--- a/spaces/abdvl/datahub_qa_bot/docs/modeling/metadata-model.md
+++ /dev/null
@@ -1,621 +0,0 @@
----
-title: The Metadata Model
-sidebar_label: The Metadata Model
-slug: /metadata-modeling/metadata-model
----
-
-# How does DataHub model metadata?
-
-DataHub takes a schema-first approach to modeling metadata. We use the open-source Pegasus schema language ([PDL](https://linkedin.github.io/rest.li/pdl_schema)) extended with a custom set of annotations to model metadata. The DataHub storage, serving, indexing and ingestion layer operates directly on top of the metadata model and supports strong types all the way from the client to the storage layer.
-
-Conceptually, metadata is modeled using the following abstractions
-
-- **Entities**: An entity is the primary node in the metadata graph. For example, an instance of a Dataset or a CorpUser is an Entity. An entity is made up of a type, e.g. 'dataset', a unique identifier (e.g. an 'urn') and groups of metadata attributes (e.g. documents) which we call aspects.
-
-
-- **Aspects**: An aspect is a collection of attributes that describes a particular facet of an entity. They are the smallest atomic unit of write in DataHub. That is, multiple aspects associated with the same Entity can be updated independently. For example, DatasetProperties contains a collection of attributes that describes a Dataset. Aspects can be shared across entities, for example "Ownership" is an aspect that is re-used across all the Entities that have owners. Common aspects include
-
- - [ownership](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/common/Ownership.pdl): Captures the users and groups who own an Entity.
- - [globalTags](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/common/GlobalTags.pdl): Captures references to the Tags associated with an Entity.
- - [glossaryTerms](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/common/GlossaryTerms.pdl): Captures references to the Glossary Terms associated with an Entity.
- - [institutionalMemory](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/common/InstitutionalMemory.pdl): Captures internal company Documents associated with an Entity (e.g. links!)
- - [status](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/common/Status.pdl): Captures the "deletion" status of an Entity, i.e. whether it should be soft-deleted.
- - [subTypes](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/common/SubTypes.pdl): Captures one or more "sub types" of a more generic Entity type. An example can be a "Looker Explore" Dataset, a "View" Dataset. Specific sub types can imply that certain additional aspects are present for a given Entity.
-
-
-- **Relationships**: A relationship represents a named edge between 2 entities. They are declared via foreign key attributes within Aspects along with a custom annotation (@Relationship). Relationships permit edges to be traversed bi-directionally. For example, a Chart may refer to a CorpUser as its owner via a relationship named "OwnedBy". This edge would be walkable starting from the Chart *or* the CorpUser instance.
-
-- **Identifiers (Keys & Urns)**: A key is a special type of aspect that contains the fields that uniquely identify an individual Entity. Key aspects can be serialized into *Urns*, which represent a stringified form of the key fields used for primary-key lookup. Moreover, *Urns* can be converted back into key aspect structs, making key aspects a type of "virtual" aspect. Key aspects provide a mechanism for clients to easily read fields comprising the primary key, which are usually generally useful like Dataset names, platform names etc. Urns provide a friendly handle by which Entities can be queried without requiring a fully materialized struct.
-
-
-Here is an example graph consisting of 3 types of entity (CorpUser, Chart, Dashboard), 2 types of relationship (OwnedBy, Contains), and 3 types of metadata aspect (Ownership, ChartInfo, and DashboardInfo).
-
-
-
-## The Core Entities
-
-DataHub's "core" Entity types model the Data Assets that comprise the Modern Data Stack. They include
-
-1. **[Data Platform](docs/generated/metamodel/entities/dataPlatform.md)**: A type of Data "Platform". That is, an external system that is involved in processing, storing, or visualizing Data Assets. Examples include MySQL, Snowflake, Redshift, and S3.
-2. **[Dataset](docs/generated/metamodel/entities/dataset.md)**: A collection of data. Tables, Views, Streams, Document Collections, and Files are all modeled as "Datasets" on DataHub. Datasets can have tags, owners, links, glossary terms, and descriptions attached to them. They can also have specific sub-types, such as "View", "Collection", "Stream", "Explore", and more. Examples include Postgres Tables, MongoDB Collections, or S3 files.
-3. **[Chart](docs/generated/metamodel/entities/chart.md)**: A single data vizualization derived from a Dataset. A single Chart can be a part of multiple Dashboards. Charts can have tags, owners, links, glossary terms, and descriptions attached to them. Examples include a Superset or Looker Chart.
-4. **[Dashboard](docs/generated/metamodel/entities/dashboard.md)**: A collection of Charts for visualization. Dashboards can have tags, owners, links, glossary terms, and descriptions attached to them. Examples include a Superset or Mode Dashboard.
-5. **[Data Job](docs/generated/metamodel/entities/dataJob.md)** (Task): An executable job that processes data assets, where "processing" implies consuming data, producing data, or both. Data Jobs can have tags, owners, links, glossary terms, and descriptions attached to them. They must belong to a single Data Flow. Examples include an Airflow Task.
-6. **[Data Flow](docs/generated/metamodel/entities/dataFlow.md)** (Pipeline): An executable collection of Data Jobs with dependencies among them, or a DAG. Data Jobs can have tags, owners, links, glossary terms, and descriptions attached to them. Examples include an Airflow DAG.
-
-See the **Metadata Modeling/Entities** section on the left to explore the entire model.
-
-## The Entity Registry
-
-Where are Entities and their aspects defined in DataHub? Where does the Metadata Model "live"? The Metadata Model is stitched together by means
-of an **Entity Registry**, a catalog of Entities that comprise the Metadata Graph along with the aspects associated with each. Put
-simply, this is where the "schema" of the model is defined.
-
-Traditionally, the Entity Registry was constructed using [Snapshot](https://github.com/datahub-project/datahub/tree/master/metadata-models/src/main/pegasus/com/linkedin/metadata/snapshot) models, which are schemas that explicitly tie
-an Entity to the Aspects associated with it. An example is [DatasetSnapshot](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/metadata/snapshot/DatasetSnapshot.pdl), which defines the core `Dataset` Entity.
-The Aspects of the Dataset entity are captured via a union field inside a special "Aspect" schema. An example is
-[DatasetAspect](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/metadata/aspect/DatasetAspect.pdl).
-This file associates dataset-specific aspects (like [DatasetProperties](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/dataset/DatasetProperties.pdl)) and common aspects (like [Ownership](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/common/Ownership.pdl),
-[InstitutionalMemory](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/common/InstitutionalMemory.pdl),
-and [Status](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/common/Status.pdl))
-to the Dataset Entity. This approach to defining Entities will soon be deprecated in favor of a new approach.
-
-As of January 2022, DataHub has deprecated support for Snapshot models as a means of adding new entities. Instead,
-the Entity Registry is defined inside a YAML configuration file called [entity-registry.yml](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/resources/entity-registry.yml),
-which is provided to DataHub's Metadata Service at start up. This file declares Entities and Aspects by referring to their [names](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/common/Ownership.pdl#L7).
-At boot time, DataHub validates the structure of the registry file and ensures that it can find PDL schemas associated with
-each aspect name provided by configuration (via the [@Aspect](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/common/Ownership.pdl#L6) annotation).
-
-By moving to this format, evolving the Metadata Model becomes much easier. Adding Entities & Aspects becomes a matter of adding a
-to the YAML configuration, instead of creating new Snapshot / Aspect files.
-
-
-## Exploring DataHub's Metadata Model
-
-To explore the current DataHub metadata model, you can inspect this high-level picture that shows the different entities and edges between them showing the relationships between them.
-
-
-To navigate the aspect model for specific entities and explore relationships using the `foreign-key` concept, you can view them in our demo environment or navigate the auto-generated docs in the **Metadata Modeling/Entities** section on the left.
-
-For example, here are helpful links to the most popular entities in DataHub's metadata model:
-* [Dataset](docs/generated/metamodel/entities/dataset.md): [Profile](https://demo.datahubproject.io/dataset/urn:li:dataset:(urn:li:dataPlatform:datahub,Dataset,PROD)/Schema?is_lineage_mode=false) [Documentation](https://demo.datahubproject.io/dataset/urn:li:dataset:(urn:li:dataPlatform:datahub,Dataset,PROD)/Documentation?is_lineage_mode=false)
-* [Dashboard](docs/generated/metamodel/entities/dashboard.md): [Profile](https://demo.datahubproject.io/dataset/urn:li:dataset:(urn:li:dataPlatform:datahub,Dashboard,PROD)/Schema?is_lineage_mode=false) [Documentation](https://demo.datahubproject.io/dataset/urn:li:dataset:(urn:li:dataPlatform:datahub,Dashboard,PROD)/Documentation?is_lineage_mode=false)
-* [User (a.k.a CorpUser)](docs/generated/metamodel/entities/corpuser.md): [Profile](https://demo.datahubproject.io/dataset/urn:li:dataset:(urn:li:dataPlatform:datahub,Corpuser,PROD)/Schema?is_lineage_mode=false) [Documentation](https://demo.datahubproject.io/dataset/urn:li:dataset:(urn:li:dataPlatform:datahub,Corpuser,PROD)/Documentation?is_lineage_mode=false)
-* [Pipeline (a.k.a DataFlow)](docs/generated/metamodel/entities/dataFlow.md): [Profile](https://demo.datahubproject.io/dataset/urn:li:dataset:(urn:li:dataPlatform:datahub,DataFlow,PROD)/Schema?is_lineage_mode=false) [Documentation](https://demo.datahubproject.io/dataset/urn:li:dataset:(urn:li:dataPlatform:datahub,DataFlow,PROD)/Documentation?is_lineage_mode=false)
-* [Feature Table (a.k.a. MLFeatureTable)](docs/generated/metamodel/entities/mlFeatureTable.md): [Profile](https://demo.datahubproject.io/dataset/urn:li:dataset:(urn:li:dataPlatform:datahub,MlFeatureTable,PROD)/Schema?is_lineage_mode=false) [Documentation](https://demo.datahubproject.io/dataset/urn:li:dataset:(urn:li:dataPlatform:datahub,MlFeatureTable,PROD)/Documentation?is_lineage_mode=false)
-* For the full list of entities in the metadata model, browse them [here](https://demo.datahubproject.io/browse/dataset/prod/datahub/entities) or use the **Metadata Modeling/Entities** section on the left.
-
-### Generating documentation for the Metadata Model
-
-- This website: Metadata model documentation for this website is generated using `./gradlew :docs-website:yarnBuild`, which delegates the model doc generation to the `modelDocGen` task in the `metadata-ingestion` module.
-- Uploading documentation to a running DataHub Instance: The metadata model documentation can be generated and uploaded into a running DataHub instance using the command `./gradlew :metadata-ingestion:modelDocUpload`. **_NOTE_**: This will upload the model documentation to the DataHub instance running at the environment variable `$DATAHUB_SERVER` (http://localhost:8080 by default)
-
-## Querying the Metadata Graph
-
-DataHub’s modeling language allows you to optimize metadata persistence to align with query patterns.
-
-There are three supported ways to query the metadata graph: by primary key lookup, a search query, and via relationship traversal.
-
-> New to [PDL](https://linkedin.github.io/rest.li/pdl_schema) files? Don't fret. They are just a way to define a JSON document "schema" for Aspects in DataHub. All Data ingested to DataHub's Metadata Service is validated against a PDL schema, with each @Aspect corresponding to a single schema. Structurally, PDL is quite similar to [Protobuf](https://developers.google.com/protocol-buffers) and conveniently maps to JSON.
-
-### Querying an Entity
-
-#### Fetching Latest Entity Aspects (Snapshot)
-
-Querying an Entity by primary key means using the "entities" endpoint, passing in the
-urn of the entity to retrieve.
-
-For example, to fetch a Chart entity, we can use the following `curl`:
-
-```
-curl --location --request GET 'http://localhost:8080/entities/urn%3Ali%3Achart%3Acustomers
-```
-
-This request will return a set of versioned aspects, each at the latest version.
-
-As you'll notice, we perform the lookup using the url-encoded *Urn* associated with an entity.
-The response would be an "Entity" record containing the Entity Snapshot (which in turn contains the latest aspects associated with the Entity).
-
-#### Fetching Versioned Aspects
-
-DataHub also supports fetching individual pieces of metadata about an Entity, which we call aspects. To do so,
-you'll provide both an Entity's primary key (urn) along with the aspect name and version that you'd like to retrieve.
-
-For example, to fetch the latest version of a Dataset's SchemaMetadata aspect, you would issue the following query:
-
-```
-curl 'http://localhost:8080/aspects/urn%3Ali%3Adataset%3A(urn%3Ali%3AdataPlatform%3Afoo%2Cbar%2CPROD)?aspect=schemaMetadata&version=0'
-
-{
- "version":0,
- "aspect":{
- "com.linkedin.schema.SchemaMetadata":{
- "created":{
- "actor":"urn:li:corpuser:fbar",
- "time":0
- },
- "platformSchema":{
- "com.linkedin.schema.KafkaSchema":{
- "documentSchema":"{\"type\":\"record\",\"name\":\"MetadataChangeEvent\",\"namespace\":\"com.linkedin.mxe\",\"doc\":\"Kafka event for proposing a metadata change for an entity.\",\"fields\":[{\"name\":\"auditHeader\",\"type\":{\"type\":\"record\",\"name\":\"KafkaAuditHeader\",\"namespace\":\"com.linkedin.avro2pegasus.events\",\"doc\":\"Header\"}}]}"
- }
- },
- "lastModified":{
- "actor":"urn:li:corpuser:fbar",
- "time":0
- },
- "schemaName":"FooEvent",
- "fields":[
- {
- "fieldPath":"foo",
- "description":"Bar",
- "type":{
- "type":{
- "com.linkedin.schema.StringType":{
-
- }
- }
- },
- "nativeDataType":"string"
- }
- ],
- "version":0,
- "hash":"",
- "platform":"urn:li:dataPlatform:foo"
- }
- }
-}
-```
-
-#### Fetching Timeseries Aspects
-
-DataHub supports an API for fetching a group of Timeseries aspects about an Entity. For example, you may want to use this API
-to fetch recent profiling runs & statistics about a Dataset. To do so, you can issue a "get" request against the `/aspects` endpoint.
-
-For example, to fetch dataset profiles (ie. stats) for a Dataset, you would issue the following query:
-
-```
-curl -X POST 'http://localhost:8080/aspects?action=getTimeseriesAspectValues' \
---data '{
- "urn": "urn:li:dataset:(urn:li:dataPlatform:redshift,global_dev.larxynx_carcinoma_data_2020,PROD)",
- "entity": "dataset",
- "aspect": "datasetProfile",
- "startTimeMillis": 1625122800000,
- "endTimeMillis": 1627455600000
-}'
-
-{
- "value":{
- "limit":10000,
- "aspectName":"datasetProfile",
- "endTimeMillis":1627455600000,
- "startTimeMillis":1625122800000,
- "entityName":"dataset",
- "values":[
- {
- "aspect":{
- "value":"{\"timestampMillis\":1626912000000,\"fieldProfiles\":[{\"uniqueProportion\":1.0,\"sampleValues\":[\"123MMKK12\",\"13KDFMKML\",\"123NNJJJL\"],\"fieldPath\":\"id\",\"nullCount\":0,\"nullProportion\":0.0,\"uniqueCount\":3742},{\"uniqueProportion\":1.0,\"min\":\"1524406400000\",\"max\":\"1624406400000\",\"sampleValues\":[\"1640023230002\",\"1640343012207\",\"16303412330117\"],\"mean\":\"1555406400000\",\"fieldPath\":\"date\",\"nullCount\":0,\"nullProportion\":0.0,\"uniqueCount\":3742},{\"uniqueProportion\":0.037,\"min\":\"21\",\"median\":\"68\",\"max\":\"92\",\"sampleValues\":[\"45\",\"65\",\"81\"],\"mean\":\"65\",\"distinctValueFrequencies\":[{\"value\":\"12\",\"frequency\":103},{\"value\":\"54\",\"frequency\":12}],\"fieldPath\":\"patient_age\",\"nullCount\":0,\"nullProportion\":0.0,\"uniqueCount\":79},{\"uniqueProportion\":0.00820873786407767,\"sampleValues\":[\"male\",\"female\"],\"fieldPath\":\"patient_gender\",\"nullCount\":120,\"nullProportion\":0.03,\"uniqueCount\":2}],\"rowCount\":3742,\"columnCount\":4}",
- "contentType":"application/json"
- }
- },
- ]
- }
-}
-```
-
-You'll notice that the aspect itself is serialized as escaped JSON. This is part of a shift toward a more generic set of READ / WRITE APIs
-that permit serialization of aspects in different ways. By default, the content type will be JSON, and the aspect can be deserialized into a normal JSON object
-in the language of your choice. Note that this will soon become the de-facto way to both write and read individual aspects.
-
-
-
-### Search Query
-
-A search query allows you to search for entities matching an arbitrary string.
-
-For example, to search for entities matching the term "customers", we can use the following CURL:
-
-```
-curl --location --request POST 'http://localhost:8080/entities?action=search' \
---header 'X-RestLi-Protocol-Version: 2.0.0' \
---header 'Content-Type: application/json' \
---data-raw '{
- "input": "\"customers\"",
- "entity": "chart",
- "start": 0,
- "count": 10
-}'
-```
-
-The notable parameters are `input` and `entity`. `input` specifies the query we are issuing and `entity` specifies the Entity Type we want to search over. This is the common name of the Entity as defined in the @Entity definition. The response contains a list of Urns, that can be used to fetch the full entity.
-
-### Relationship Query
-
-A relationship query allows you to find Entity connected to a particular source Entity via an edge of a particular type.
-
-For example, to find the owners of a particular Chart, we can use the following CURL:
-
-```
-curl --location --request GET --header 'X-RestLi-Protocol-Version: 2.0.0' 'http://localhost:8080/relationships?direction=OUTGOING&urn=urn%3Ali%3Achart%3Acustomers&types=List(OwnedBy)'
-```
-
-The notable parameters are `direction`, `urn` and `types`. The response contains *Urns* associated with all entities connected
-to the primary entity (urn:li:chart:customer) by an relationship named "OwnedBy". That is, it permits fetching the owners of a given
-chart.
-
-### Special Aspects
-
-There are a few special aspects worth mentioning:
-
-1. Key aspects: Contain the properties that uniquely identify an Entity.
-2. Browse Paths aspect: Represents a hierarchical path associated with an Entity.
-
-#### Key aspects
-
-As introduced above, Key aspects are structs / records that contain the fields that uniquely identify an Entity. There are
-some constraints about the fields that can be present in Key aspects:
-
-- All fields must be of STRING or ENUM type
-- All fields must be REQUIRED
-
-Keys can be created from and turned into *Urns*, which represent the stringified version of the Key record.
-The algorithm used to do the conversion is straightforward: the fields of the Key aspect are substituted into a
-string template based on their index (order of definition) using the following template:
-
-```aidl
-// Case 1: # key fields == 1
-urn:li::key-field-1
-
-// Case 2: # key fields > 1
-urn:li::(key-field-1, key-field-2, ... key-field-n)
-```
-
-By convention, key aspects are defined under [metadata-models/src/main/pegasus/com/linkedin/metadata/key](https://github.com/datahub-project/datahub/tree/master/metadata-models/src/main/pegasus/com/linkedin/metadata/key).
-
-##### Example
-
-A CorpUser can be uniquely identified by a "username", which should typically correspond to an LDAP name.
-
-Thus, it's Key Aspect is defined as the following:
-
-```aidl
-namespace com.linkedin.metadata.key
-
-/**
- * Key for a CorpUser
- */
-@Aspect = {
- "name": "corpUserKey"
-}
-record CorpUserKey {
- /**
- * The name of the AD/LDAP user.
- */
- username: string
-}
-```
-
-and it's Entity Snapshot model is defined as
-
-```aidl
-/**
- * A metadata snapshot for a specific CorpUser entity.
- */
-@Entity = {
- "name": "corpuser",
- "keyAspect": "corpUserKey"
-}
-record CorpUserSnapshot {
-
- /**
- * URN for the entity the metadata snapshot is associated with.
- */
- urn: CorpuserUrn
-
- /**
- * The list of metadata aspects associated with the CorpUser. Depending on the use case, this can either be all, or a selection, of supported aspects.
- */
- aspects: array[CorpUserAspect]
-}
-```
-
-Using a combination of the information provided by these models, we are able to generate the Urn corresponding to a CorpUser as
-
-```
-urn:li:corpuser:
-```
-
-Imagine we have a CorpUser Entity with the username "johnsmith". In this world, the JSON version of the Key Aspect associated with the Entity would be
-
-```aidl
-{
- "username": "johnsmith"
-}
-```
-
-and its corresponding Urn would be
-
-```aidl
-urn:li:corpuser:johnsmith
-```
-
-#### BrowsePaths aspect
-
-The BrowsePaths aspect allows you to define a custom "browse path" for an Entity. A browse path is a way to hierarchically organize
-entities. They manifest within the "Explore" features on the UI, allowing users to navigate through trees of related entities of a given type.
-
-To support browsing a particular entity, add the "browsePaths" aspect to the entity in your `entity-registry.yml` file.
-
-```aidl
-/// entity-registry.yml
-entities:
- - name: dataset
- doc: Datasets represent logical or physical data assets stored or represented in various data platforms. Tables, Views, Streams are all instances of datasets.
- keyAspect: datasetKey
- aspects:
- ...
- - browsePaths
-```
-
-By declaring this aspect, you can produce custom browse paths as well as query for browse paths manually using a CURL like the following:
-
-```aidl
-curl --location --request POST 'http://localhost:8080/entities?action=browse' \
---header 'X-RestLi-Protocol-Version: 2.0.0' \
---header 'Content-Type: application/json' \
---data-raw '{
- "path": "/my/custom/browse/path",
- "entity": "dataset",
- "start": 0,
- "limit": 10
-}'
-```
-
-Please note you must provide:
-- The "/"-delimited root path for which to fetch results.
-- An entity "type" using its common name ("dataset" in the example above).
-
-### Types of Aspect
-
-There are 2 "types" of Metadata Aspects. Both are modeled using PDL schemas, and both can be ingested in the same way.
-However, they differ in what they represent and how they are handled by DataHub's Metadata Service.
-
-#### 1. Versioned Aspects
-
-Versioned Aspects each have a **numeric version** associated with them. When a field in an aspect changes, a new
-version is automatically created and stored within DataHub's backend. In practice, all versioned aspects are stored inside a relational database
-that can be backed up and restored. Versioned aspects power much of the UI experience you're used to, including Ownership, Descriptions,
-Tags, Glossary Terms, and more. Examples include Ownership, Global Tags, and Glossary Terms.
-
-#### 2. Timeseries Aspects
-
-Timeseries Aspects each have a **timestamp** associated with them. They are useful for representing
-time-ordered events about an Entity. For example, the results of profiling a Dataset, or a set of Data Quality checks that
-run every day. It is important to note that Timeseries aspects are NOT persisted inside the relational store, and are instead
-persisted only in the search index (e.g. elasticsearch) and the message queue (Kafka). This makes restoring timeseries aspects
-in a disaster scenario a bit more challenge. Timeseries aspects can be queried by time range, which is what makes them most different from Versioned Aspects.
-A timeseries aspect can be identified by the "timeseries" [type](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/dataset/DatasetProfile.pdl#L10) in its [@Aspect](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/dataset/DatasetProfile.pdl#L8) annotation.
-Examples include [DatasetProfile](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/dataset/DatasetProfile.pdl) & [DatasetUsageStatistics](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/dataset/DatasetUsageStatistics.pdl).
-
-Timeseries aspects are aspects that have a timestampMillis field, and are meant for aspects that continuously change on a
-timely basis e.g. data profiles, usage statistics, etc.
-
-Each timeseries aspect must be declared "type": "timeseries" and must
-include [TimeseriesAspectBase](https://github.com/datahub-project/datahub/tree/master/metadata-models/src/main/pegasus/com/linkedin/timeseries/TimeseriesAspectBase.pdl)
-, which contains a timestampMillis field.
-
-Timeseries aspect cannot have any fields that have the @Searchable or @Relationship annotation, as it goes through a
-completely different flow.
-
-Please refer
-to [DatasetProfile](https://github.com/datahub-project/datahub/tree/master/metadata-models/src/main/pegasus/com/linkedin/dataset/DatasetProfile.pdl)
-to see an example of a timeseries aspect.
-
-Because timeseries aspects are updated on a frequent basis, ingests of these aspects go straight to elastic search (
-instead of being stored in local DB).
-
-You can retrieve timeseries aspects using the "aspects?action=getTimeseriesAspectValues" end point.
-
-##### Aggregatable Timeseries aspects
-Being able to perform SQL like *group by + aggregate* operations on the timeseries aspects is a very natural use-case for
-this kind of data (dataset profiles, usage statistics etc.). This section describes how to define, ingest and perform an
-aggregation query against a timeseries aspect.
-
-###### Defining a new aggregatable Timeseries aspect.
-
-The *@TimeseriesField* and the *@TimeseriesFieldCollection* are two new annotations that can be attached to a field of
-a *Timeseries aspect* that allows it to be part of an aggregatable query. The kinds of aggregations allowed on these
-annotated fields depends on the type of the field, as well as the kind of aggregation, as
-described [here](#Performing-an-aggregation-on-a-Timeseries-aspect).
-
-* `@TimeseriesField = {}` - this annotation can be used with any type of non-collection type field of the aspect such as
- primitive types and records (see the fields *stat*, *strStat* and *strArray* fields
- of [TestEntityProfile.pdl](https://github.com/datahub-project/datahub/blob/master/test-models/src/main/pegasus/com/datahub/test/TestEntityProfile.pdl)).
-
-* The `@TimeseriesFieldCollection {"key":""}` annotation allows for
-aggregation support on the items of a collection type (supported only for the array type collections for now), where the
-value of `"key"` is the name of the field in the collection item type that will be used to specify the group-by clause (
-see *userCounts* and *fieldCounts* fields of [DatasetUsageStatistics.pdl](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/pegasus/com/linkedin/dataset/DatasetUsageStatistics.pdl)).
-
-In addition to defining the new aspect with appropriate Timeseries annotations,
-the [entity-registry.yml](https://github.com/datahub-project/datahub/blob/master/metadata-models/src/main/resources/entity-registry.yml)
-file needs to be updated as well. Just add the new aspect name under the list of aspects against the appropriate entity as shown below, such as `datasetUsageStatistics` for the aspect DatasetUsageStatistics.
-```yaml
-entities:
- - name: dataset
- keyAspect: datasetKey
- aspects:
- - datasetProfile
- - datasetUsageStatistics
-```
-
-###### Ingesting a Timeseries aspect
-The timeseries aspects can be ingested via the GMS REST endpoint `/aspects?action=ingestProposal` or via the python API.
-
-Example1: Via GMS REST API using curl.
-
-```shell
-curl --location --request POST 'http://localhost:8080/aspects?action=ingestProposal' \
---header 'X-RestLi-Protocol-Version: 2.0.0' \
---header 'Content-Type: application/json' \
---data-raw '{
- "proposal" : {
- "entityType": "dataset",
- "entityUrn" : "urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)",
- "changeType" : "UPSERT",
- "aspectName" : "datasetUsageStatistics",
- "aspect" : {
- "value" : "{ \"timestampMillis\":1629840771000,\"uniqueUserCount\" : 10, \"totalSqlQueries\": 20, \"fieldCounts\": [ {\"fieldPath\": \"col1\", \"count\": 20}, {\"fieldPath\" : \"col2\", \"count\": 5} ]}",
- "contentType": "application/json"
- }
- }
-}'
-```
-Example2: Via Python API to Kafka(or REST)
-```python
-from datahub.metadata.schema_classes import (
- ChangeTypeClass,
- DatasetFieldUsageCountsClass,
- DatasetUsageStatisticsClass,
-)
-from datahub.emitter.kafka_emitter import DatahubKafkaEmitter
-from datahub.emitter.rest_emitter import DatahubRestEmitter
-
-usageStats = DatasetUsageStatisticsClass(
- timestampMillis=1629840771000,
- uniqueUserCount=10,
- totalSqlQueries=20,
- fieldCounts=[
- DatasetFieldUsageCountsClass(
- fieldPath="col1",
- count=10
- )
- ]
- )
-
-mcpw = MetadataChangeProposalWrapper(
- entityType="dataset",
- aspectName="datasetUsageStatistics",
- changeType=ChangeTypeClass.UPSERT,
- entityUrn="urn:li:dataset:(urn:li:dataPlatform:hive,SampleHiveDataset,PROD)",
- aspect=usageStats,
-)
-
-# Instantiate appropriate emitter (kafk_emitter/rest_emitter)
-my_emitter = DatahubKafkaEmitter("""""")
-my_emitter.emit(mcpw)
-```
-
-###### Performing an aggregation on a Timeseries aspect.
-
-Aggreations on timeseries aspects can be performed by the GMS REST API for `/analytics?action=getTimeseriesStats` which
-accepts the following params.
-* `entityName` - The name of the entity the aspect is associated with.
-* `aspectName` - The name of the aspect.
-* `filter` - Any pre-filtering criteria before grouping and aggregations are performed.
-* `metrics` - A list of aggregation specification. The `fieldPath` member of an aggregation specification refers to the
- field name against which the aggregation needs to be performed, and the `aggregationType` specifies the kind of aggregation.
-* `buckets` - A list of grouping bucket specifications. Each grouping bucket has a `key` field that refers to the field
- to use for grouping. The `type` field specifies the kind of grouping bucket.
-
-We support three kinds of aggregations that can be specified in an aggregation query on the Timeseries annotated fields.
-The values that `aggregationType` can take are:
-
-* `LATEST`: The latest value of the field in each bucket. Supported for any type of field.
-* `SUM`: The cumulative sum of the field in each bucket. Supported only for integral types.
-* `CARDINALITY`: The number of unique values or the cardinality of the set in each bucket. Supported for string and
- record types.
-
-We support two types of grouping for defining the buckets to perform aggregations against:
-
-* `DATE_GROUPING_BUCKET`: Allows for creating time-based buckets such as by second, minute, hour, day, week, month,
- quarter, year etc. Should be used in conjunction with a timestamp field whose value is in milliseconds since *epoch*.
- The `timeWindowSize` param specifies the date histogram bucket width.
-* `STRING_GROUPING_BUCKET`: Allows for creating buckets grouped by the unique values of a field. Should always be used in
- conjunction with a string type field.
-
-The API returns a generic SQL like table as the `table` member of the output that contains the results of
-the `group-by/aggregate` query, in addition to echoing the input params.
-
-* `columnNames`: the names of the table columns. The group-by `key` names appear in the same order as they are specified
- in the request. Aggregation specifications follow the grouping fields in the same order as specified in the request,
- and will be named `_`.
-* `columnTypes`: the data types of the columns.
-* `rows`: the data values, each row corresponding to the respective bucket(s).
-
-Example: Latest unique user count for each day.
-```shell
-# QUERY
-curl --location --request POST 'http://localhost:8080/analytics?action=getTimeseriesStats' \
---header 'X-RestLi-Protocol-Version: 2.0.0' \
---header 'Content-Type: application/json' \
---data-raw '{
- "entityName": "dataset",
- "aspectName": "datasetUsageStatistics",
- "filter": {
- "criteria": []
- },
- "metrics": [
- {
- "fieldPath": "uniqueUserCount",
- "aggregationType": "LATEST"
- }
- ],
- "buckets": [
- {
- "key": "timestampMillis",
- "type": "DATE_GROUPING_BUCKET",
- "timeWindowSize": {
- "multiple": 1,
- "unit": "DAY"
- }
- }
- ]
-}'
-
-# SAMPLE RESPOSNE
-{
- "value": {
- "filter": {
- "criteria": []
- },
- "aspectName": "datasetUsageStatistics",
- "entityName": "dataset",
- "groupingBuckets": [
- {
- "type": "DATE_GROUPING_BUCKET",
- "timeWindowSize": {
- "multiple": 1,
- "unit": "DAY"
- },
- "key": "timestampMillis"
- }
- ],
- "aggregationSpecs": [
- {
- "fieldPath": "uniqueUserCount",
- "aggregationType": "LATEST"
- }
- ],
- "table": {
- "columnNames": [
- "timestampMillis",
- "latest_uniqueUserCount"
- ],
- "rows": [
- [
- "1631491200000",
- "1"
- ]
- ],
- "columnTypes": [
- "long",
- "int"
- ]
- }
- }
-}
-```
-For more examples on the complex types of group-by/aggregations, refer to the tests in the group `getAggregatedStats` of [ElasticSearchTimeseriesAspectServiceTest.java](https://github.com/datahub-project/datahub/blob/master/metadata-io/src/test/java/com/linkedin/metadata/timeseries/elastic/ElasticSearchTimeseriesAspectServiceTest.java).
-
-
-
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/configs/_base_/datasets/hrf.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/configs/_base_/datasets/hrf.py
deleted file mode 100644
index 242d790eb1b83e75cf6b7eaa7a35c674099311ad..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/configs/_base_/datasets/hrf.py
+++ /dev/null
@@ -1,59 +0,0 @@
-# dataset settings
-dataset_type = 'HRFDataset'
-data_root = 'data/HRF'
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-img_scale = (2336, 3504)
-crop_size = (256, 256)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations'),
- dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
- dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
- dict(type='RandomFlip', prob=0.5),
- dict(type='PhotoMetricDistortion'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_semantic_seg'])
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=img_scale,
- # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0],
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='ImageToTensor', keys=['img']),
- dict(type='Collect', keys=['img'])
- ])
-]
-
-data = dict(
- samples_per_gpu=4,
- workers_per_gpu=4,
- train=dict(
- type='RepeatDataset',
- times=40000,
- dataset=dict(
- type=dataset_type,
- data_root=data_root,
- img_dir='images/training',
- ann_dir='annotations/training',
- pipeline=train_pipeline)),
- val=dict(
- type=dataset_type,
- data_root=data_root,
- img_dir='images/validation',
- ann_dir='annotations/validation',
- pipeline=test_pipeline),
- test=dict(
- type=dataset_type,
- data_root=data_root,
- img_dir='images/validation',
- ann_dir='annotations/validation',
- pipeline=test_pipeline))
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/evaluation/bbox_overlaps.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/evaluation/bbox_overlaps.py
deleted file mode 100644
index 93559ea0f25369d552a5365312fa32b9ffec9226..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/core/evaluation/bbox_overlaps.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import numpy as np
-
-
-def bbox_overlaps(bboxes1, bboxes2, mode='iou', eps=1e-6):
- """Calculate the ious between each bbox of bboxes1 and bboxes2.
-
- Args:
- bboxes1(ndarray): shape (n, 4)
- bboxes2(ndarray): shape (k, 4)
- mode(str): iou (intersection over union) or iof (intersection
- over foreground)
-
- Returns:
- ious(ndarray): shape (n, k)
- """
-
- assert mode in ['iou', 'iof']
-
- bboxes1 = bboxes1.astype(np.float32)
- bboxes2 = bboxes2.astype(np.float32)
- rows = bboxes1.shape[0]
- cols = bboxes2.shape[0]
- ious = np.zeros((rows, cols), dtype=np.float32)
- if rows * cols == 0:
- return ious
- exchange = False
- if bboxes1.shape[0] > bboxes2.shape[0]:
- bboxes1, bboxes2 = bboxes2, bboxes1
- ious = np.zeros((cols, rows), dtype=np.float32)
- exchange = True
- area1 = (bboxes1[:, 2] - bboxes1[:, 0]) * (bboxes1[:, 3] - bboxes1[:, 1])
- area2 = (bboxes2[:, 2] - bboxes2[:, 0]) * (bboxes2[:, 3] - bboxes2[:, 1])
- for i in range(bboxes1.shape[0]):
- x_start = np.maximum(bboxes1[i, 0], bboxes2[:, 0])
- y_start = np.maximum(bboxes1[i, 1], bboxes2[:, 1])
- x_end = np.minimum(bboxes1[i, 2], bboxes2[:, 2])
- y_end = np.minimum(bboxes1[i, 3], bboxes2[:, 3])
- overlap = np.maximum(x_end - x_start, 0) * np.maximum(
- y_end - y_start, 0)
- if mode == 'iou':
- union = area1[i] + area2 - overlap
- else:
- union = area1[i] if not exchange else area2
- union = np.maximum(union, eps)
- ious[i, :] = overlap / union
- if exchange:
- ious = ious.T
- return ious
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/datasets/xml_style.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/datasets/xml_style.py
deleted file mode 100644
index 71069488b0f6da3b37e588228f44460ce5f00679..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/datasets/xml_style.py
+++ /dev/null
@@ -1,170 +0,0 @@
-import os.path as osp
-import xml.etree.ElementTree as ET
-
-import mmcv
-import numpy as np
-from PIL import Image
-
-from .builder import DATASETS
-from .custom import CustomDataset
-
-
-@DATASETS.register_module()
-class XMLDataset(CustomDataset):
- """XML dataset for detection.
-
- Args:
- min_size (int | float, optional): The minimum size of bounding
- boxes in the images. If the size of a bounding box is less than
- ``min_size``, it would be add to ignored field.
- """
-
- def __init__(self, min_size=None, **kwargs):
- assert self.CLASSES or kwargs.get(
- 'classes', None), 'CLASSES in `XMLDataset` can not be None.'
- super(XMLDataset, self).__init__(**kwargs)
- self.cat2label = {cat: i for i, cat in enumerate(self.CLASSES)}
- self.min_size = min_size
-
- def load_annotations(self, ann_file):
- """Load annotation from XML style ann_file.
-
- Args:
- ann_file (str): Path of XML file.
-
- Returns:
- list[dict]: Annotation info from XML file.
- """
-
- data_infos = []
- img_ids = mmcv.list_from_file(ann_file)
- for img_id in img_ids:
- filename = f'JPEGImages/{img_id}.jpg'
- xml_path = osp.join(self.img_prefix, 'Annotations',
- f'{img_id}.xml')
- tree = ET.parse(xml_path)
- root = tree.getroot()
- size = root.find('size')
- if size is not None:
- width = int(size.find('width').text)
- height = int(size.find('height').text)
- else:
- img_path = osp.join(self.img_prefix, 'JPEGImages',
- '{}.jpg'.format(img_id))
- img = Image.open(img_path)
- width, height = img.size
- data_infos.append(
- dict(id=img_id, filename=filename, width=width, height=height))
-
- return data_infos
-
- def _filter_imgs(self, min_size=32):
- """Filter images too small or without annotation."""
- valid_inds = []
- for i, img_info in enumerate(self.data_infos):
- if min(img_info['width'], img_info['height']) < min_size:
- continue
- if self.filter_empty_gt:
- img_id = img_info['id']
- xml_path = osp.join(self.img_prefix, 'Annotations',
- f'{img_id}.xml')
- tree = ET.parse(xml_path)
- root = tree.getroot()
- for obj in root.findall('object'):
- name = obj.find('name').text
- if name in self.CLASSES:
- valid_inds.append(i)
- break
- else:
- valid_inds.append(i)
- return valid_inds
-
- def get_ann_info(self, idx):
- """Get annotation from XML file by index.
-
- Args:
- idx (int): Index of data.
-
- Returns:
- dict: Annotation info of specified index.
- """
-
- img_id = self.data_infos[idx]['id']
- xml_path = osp.join(self.img_prefix, 'Annotations', f'{img_id}.xml')
- tree = ET.parse(xml_path)
- root = tree.getroot()
- bboxes = []
- labels = []
- bboxes_ignore = []
- labels_ignore = []
- for obj in root.findall('object'):
- name = obj.find('name').text
- if name not in self.CLASSES:
- continue
- label = self.cat2label[name]
- difficult = obj.find('difficult')
- difficult = 0 if difficult is None else int(difficult.text)
- bnd_box = obj.find('bndbox')
- # TODO: check whether it is necessary to use int
- # Coordinates may be float type
- bbox = [
- int(float(bnd_box.find('xmin').text)),
- int(float(bnd_box.find('ymin').text)),
- int(float(bnd_box.find('xmax').text)),
- int(float(bnd_box.find('ymax').text))
- ]
- ignore = False
- if self.min_size:
- assert not self.test_mode
- w = bbox[2] - bbox[0]
- h = bbox[3] - bbox[1]
- if w < self.min_size or h < self.min_size:
- ignore = True
- if difficult or ignore:
- bboxes_ignore.append(bbox)
- labels_ignore.append(label)
- else:
- bboxes.append(bbox)
- labels.append(label)
- if not bboxes:
- bboxes = np.zeros((0, 4))
- labels = np.zeros((0, ))
- else:
- bboxes = np.array(bboxes, ndmin=2) - 1
- labels = np.array(labels)
- if not bboxes_ignore:
- bboxes_ignore = np.zeros((0, 4))
- labels_ignore = np.zeros((0, ))
- else:
- bboxes_ignore = np.array(bboxes_ignore, ndmin=2) - 1
- labels_ignore = np.array(labels_ignore)
- ann = dict(
- bboxes=bboxes.astype(np.float32),
- labels=labels.astype(np.int64),
- bboxes_ignore=bboxes_ignore.astype(np.float32),
- labels_ignore=labels_ignore.astype(np.int64))
- return ann
-
- def get_cat_ids(self, idx):
- """Get category ids in XML file by index.
-
- Args:
- idx (int): Index of data.
-
- Returns:
- list[int]: All categories in the image of specified index.
- """
-
- cat_ids = []
- img_id = self.data_infos[idx]['id']
- xml_path = osp.join(self.img_prefix, 'Annotations', f'{img_id}.xml')
- tree = ET.parse(xml_path)
- root = tree.getroot()
- for obj in root.findall('object'):
- name = obj.find('name').text
- if name not in self.CLASSES:
- continue
- label = self.cat2label[name]
- cat_ids.append(label)
-
- return cat_ids
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/dense_heads/base_dense_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/dense_heads/base_dense_head.py
deleted file mode 100644
index de11e4a2197b1dfe241ce7a66daa1907a8fc5661..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/models/dense_heads/base_dense_head.py
+++ /dev/null
@@ -1,59 +0,0 @@
-from abc import ABCMeta, abstractmethod
-
-import torch.nn as nn
-
-
-class BaseDenseHead(nn.Module, metaclass=ABCMeta):
- """Base class for DenseHeads."""
-
- def __init__(self):
- super(BaseDenseHead, self).__init__()
-
- @abstractmethod
- def loss(self, **kwargs):
- """Compute losses of the head."""
- pass
-
- @abstractmethod
- def get_bboxes(self, **kwargs):
- """Transform network output for a batch into bbox predictions."""
- pass
-
- def forward_train(self,
- x,
- img_metas,
- gt_bboxes,
- gt_labels=None,
- gt_bboxes_ignore=None,
- proposal_cfg=None,
- **kwargs):
- """
- Args:
- x (list[Tensor]): Features from FPN.
- img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- gt_bboxes (Tensor): Ground truth bboxes of the image,
- shape (num_gts, 4).
- gt_labels (Tensor): Ground truth labels of each box,
- shape (num_gts,).
- gt_bboxes_ignore (Tensor): Ground truth bboxes to be
- ignored, shape (num_ignored_gts, 4).
- proposal_cfg (mmcv.Config): Test / postprocessing configuration,
- if None, test_cfg would be used
-
- Returns:
- tuple:
- losses: (dict[str, Tensor]): A dictionary of loss components.
- proposal_list (list[Tensor]): Proposals of each image.
- """
- outs = self(x)
- if gt_labels is None:
- loss_inputs = outs + (gt_bboxes, img_metas)
- else:
- loss_inputs = outs + (gt_bboxes, gt_labels, img_metas)
- losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
- if proposal_cfg is None:
- return losses
- else:
- proposal_list = self.get_bboxes(*outs, img_metas, cfg=proposal_cfg)
- return losses, proposal_list
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/cnn/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/cnn/__init__.py
deleted file mode 100644
index 7246c897430f0cc7ce12719ad8608824fc734446..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/cnn/__init__.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .alexnet import AlexNet
-# yapf: disable
-from .bricks import (ACTIVATION_LAYERS, CONV_LAYERS, NORM_LAYERS,
- PADDING_LAYERS, PLUGIN_LAYERS, UPSAMPLE_LAYERS,
- ContextBlock, Conv2d, Conv3d, ConvAWS2d, ConvModule,
- ConvTranspose2d, ConvTranspose3d, ConvWS2d,
- DepthwiseSeparableConvModule, GeneralizedAttention,
- HSigmoid, HSwish, Linear, MaxPool2d, MaxPool3d,
- NonLocal1d, NonLocal2d, NonLocal3d, Scale, Swish,
- build_activation_layer, build_conv_layer,
- build_norm_layer, build_padding_layer, build_plugin_layer,
- build_upsample_layer, conv_ws_2d, is_norm)
-from .builder import MODELS, build_model_from_cfg
-# yapf: enable
-from .resnet import ResNet, make_res_layer
-from .utils import (INITIALIZERS, Caffe2XavierInit, ConstantInit, KaimingInit,
- NormalInit, PretrainedInit, TruncNormalInit, UniformInit,
- XavierInit, bias_init_with_prob, caffe2_xavier_init,
- constant_init, fuse_conv_bn, get_model_complexity_info,
- initialize, kaiming_init, normal_init, trunc_normal_init,
- uniform_init, xavier_init)
-from .vgg import VGG, make_vgg_layer
-
-__all__ = [
- 'AlexNet', 'VGG', 'make_vgg_layer', 'ResNet', 'make_res_layer',
- 'constant_init', 'xavier_init', 'normal_init', 'trunc_normal_init',
- 'uniform_init', 'kaiming_init', 'caffe2_xavier_init',
- 'bias_init_with_prob', 'ConvModule', 'build_activation_layer',
- 'build_conv_layer', 'build_norm_layer', 'build_padding_layer',
- 'build_upsample_layer', 'build_plugin_layer', 'is_norm', 'NonLocal1d',
- 'NonLocal2d', 'NonLocal3d', 'ContextBlock', 'HSigmoid', 'Swish', 'HSwish',
- 'GeneralizedAttention', 'ACTIVATION_LAYERS', 'CONV_LAYERS', 'NORM_LAYERS',
- 'PADDING_LAYERS', 'UPSAMPLE_LAYERS', 'PLUGIN_LAYERS', 'Scale',
- 'get_model_complexity_info', 'conv_ws_2d', 'ConvAWS2d', 'ConvWS2d',
- 'fuse_conv_bn', 'DepthwiseSeparableConvModule', 'Linear', 'Conv2d',
- 'ConvTranspose2d', 'MaxPool2d', 'ConvTranspose3d', 'MaxPool3d', 'Conv3d',
- 'initialize', 'INITIALIZERS', 'ConstantInit', 'XavierInit', 'NormalInit',
- 'TruncNormalInit', 'UniformInit', 'KaimingInit', 'PretrainedInit',
- 'Caffe2XavierInit', 'MODELS', 'build_model_from_cfg'
-]
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/parallel/distributed_deprecated.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/parallel/distributed_deprecated.py
deleted file mode 100644
index 676937a2085d4da20fa87923041a200fca6214eb..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/parallel/distributed_deprecated.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.distributed as dist
-import torch.nn as nn
-from torch._utils import (_flatten_dense_tensors, _take_tensors,
- _unflatten_dense_tensors)
-
-from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
-from .registry import MODULE_WRAPPERS
-from .scatter_gather import scatter_kwargs
-
-
-@MODULE_WRAPPERS.register_module()
-class MMDistributedDataParallel(nn.Module):
-
- def __init__(self,
- module,
- dim=0,
- broadcast_buffers=True,
- bucket_cap_mb=25):
- super(MMDistributedDataParallel, self).__init__()
- self.module = module
- self.dim = dim
- self.broadcast_buffers = broadcast_buffers
-
- self.broadcast_bucket_size = bucket_cap_mb * 1024 * 1024
- self._sync_params()
-
- def _dist_broadcast_coalesced(self, tensors, buffer_size):
- for tensors in _take_tensors(tensors, buffer_size):
- flat_tensors = _flatten_dense_tensors(tensors)
- dist.broadcast(flat_tensors, 0)
- for tensor, synced in zip(
- tensors, _unflatten_dense_tensors(flat_tensors, tensors)):
- tensor.copy_(synced)
-
- def _sync_params(self):
- module_states = list(self.module.state_dict().values())
- if len(module_states) > 0:
- self._dist_broadcast_coalesced(module_states,
- self.broadcast_bucket_size)
- if self.broadcast_buffers:
- if (TORCH_VERSION != 'parrots'
- and digit_version(TORCH_VERSION) < digit_version('1.0')):
- buffers = [b.data for b in self.module._all_buffers()]
- else:
- buffers = [b.data for b in self.module.buffers()]
- if len(buffers) > 0:
- self._dist_broadcast_coalesced(buffers,
- self.broadcast_bucket_size)
-
- def scatter(self, inputs, kwargs, device_ids):
- return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim)
-
- def forward(self, *inputs, **kwargs):
- inputs, kwargs = self.scatter(inputs, kwargs,
- [torch.cuda.current_device()])
- return self.module(*inputs[0], **kwargs[0])
-
- def train_step(self, *inputs, **kwargs):
- inputs, kwargs = self.scatter(inputs, kwargs,
- [torch.cuda.current_device()])
- output = self.module.train_step(*inputs[0], **kwargs[0])
- return output
-
- def val_step(self, *inputs, **kwargs):
- inputs, kwargs = self.scatter(inputs, kwargs,
- [torch.cuda.current_device()])
- output = self.module.val_step(*inputs[0], **kwargs[0])
- return output
diff --git a/spaces/akhaliq/Music_Source_Separation/bytesep/models/conditional_unet.py b/spaces/akhaliq/Music_Source_Separation/bytesep/models/conditional_unet.py
deleted file mode 100644
index 1e925c11308b04ba195db83b08c2718930b1b4c6..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/Music_Source_Separation/bytesep/models/conditional_unet.py
+++ /dev/null
@@ -1,496 +0,0 @@
-import math
-from typing import List
-
-import numpy as np
-import matplotlib.pyplot as plt
-import pytorch_lightning as pl
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.optim as optim
-from torch.optim.lr_scheduler import LambdaLR
-from torchlibrosa.stft import STFT, ISTFT, magphase
-
-from bytesep.models.pytorch_modules import (
- Base,
- init_bn,
- init_embedding,
- init_layer,
- act,
- Subband,
-)
-
-
-class ConvBlock(nn.Module):
- def __init__(
- self,
- in_channels,
- out_channels,
- condition_size,
- kernel_size,
- activation,
- momentum,
- ):
- super(ConvBlock, self).__init__()
-
- self.activation = activation
- padding = (kernel_size[0] // 2, kernel_size[1] // 2)
-
- self.conv1 = nn.Conv2d(
- in_channels=in_channels,
- out_channels=out_channels,
- kernel_size=kernel_size,
- stride=(1, 1),
- dilation=(1, 1),
- padding=padding,
- bias=False,
- )
-
- self.bn1 = nn.BatchNorm2d(out_channels, momentum=momentum)
-
- self.conv2 = nn.Conv2d(
- in_channels=out_channels,
- out_channels=out_channels,
- kernel_size=kernel_size,
- stride=(1, 1),
- dilation=(1, 1),
- padding=padding,
- bias=False,
- )
-
- self.bn2 = nn.BatchNorm2d(out_channels, momentum=momentum)
-
- self.beta1 = nn.Linear(condition_size, out_channels, bias=True)
- self.beta2 = nn.Linear(condition_size, out_channels, bias=True)
-
- self.init_weights()
-
- def init_weights(self):
- init_layer(self.conv1)
- init_layer(self.conv2)
- init_bn(self.bn1)
- init_bn(self.bn2)
- init_embedding(self.beta1)
- init_embedding(self.beta2)
-
- def forward(self, x, condition):
-
- b1 = self.beta1(condition)[:, :, None, None]
- b2 = self.beta2(condition)[:, :, None, None]
-
- x = act(self.bn1(self.conv1(x)) + b1, self.activation)
- x = act(self.bn2(self.conv2(x)) + b2, self.activation)
- return x
-
-
-class EncoderBlock(nn.Module):
- def __init__(
- self,
- in_channels,
- out_channels,
- condition_size,
- kernel_size,
- downsample,
- activation,
- momentum,
- ):
- super(EncoderBlock, self).__init__()
-
- self.conv_block = ConvBlock(
- in_channels, out_channels, condition_size, kernel_size, activation, momentum
- )
- self.downsample = downsample
-
- def forward(self, x, condition):
- encoder = self.conv_block(x, condition)
- encoder_pool = F.avg_pool2d(encoder, kernel_size=self.downsample)
- return encoder_pool, encoder
-
-
-class DecoderBlock(nn.Module):
- def __init__(
- self,
- in_channels,
- out_channels,
- condition_size,
- kernel_size,
- upsample,
- activation,
- momentum,
- ):
- super(DecoderBlock, self).__init__()
- self.kernel_size = kernel_size
- self.stride = upsample
- self.activation = activation
-
- self.conv1 = torch.nn.ConvTranspose2d(
- in_channels=in_channels,
- out_channels=out_channels,
- kernel_size=self.stride,
- stride=self.stride,
- padding=(0, 0),
- bias=False,
- dilation=(1, 1),
- )
-
- self.bn1 = nn.BatchNorm2d(out_channels, momentum=momentum)
-
- self.conv_block2 = ConvBlock(
- out_channels * 2,
- out_channels,
- condition_size,
- kernel_size,
- activation,
- momentum,
- )
-
- self.beta1 = nn.Linear(condition_size, out_channels, bias=True)
-
- self.init_weights()
-
- def init_weights(self):
- init_layer(self.conv1)
- init_bn(self.bn1)
- init_embedding(self.beta1)
-
- def forward(self, input_tensor, concat_tensor, condition):
- b1 = self.beta1(condition)[:, :, None, None]
- x = act(self.bn1(self.conv1(input_tensor)) + b1, self.activation)
- x = torch.cat((x, concat_tensor), dim=1)
- x = self.conv_block2(x, condition)
- return x
-
-
-class ConditionalUNet(nn.Module, Base):
- def __init__(self, input_channels, target_sources_num):
- super(ConditionalUNet, self).__init__()
-
- self.input_channels = input_channels
- condition_size = target_sources_num
- self.output_sources_num = 1
-
- window_size = 2048
- hop_size = 441
- center = True
- pad_mode = "reflect"
- window = "hann"
- activation = "relu"
- momentum = 0.01
-
- self.subbands_num = 4
- self.K = 3 # outputs: |M|, cos∠M, sin∠M
-
- self.downsample_ratio = 2 ** 6 # This number equals 2^{#encoder_blcoks}
-
- self.stft = STFT(
- n_fft=window_size,
- hop_length=hop_size,
- win_length=window_size,
- window=window,
- center=center,
- pad_mode=pad_mode,
- freeze_parameters=True,
- )
-
- self.istft = ISTFT(
- n_fft=window_size,
- hop_length=hop_size,
- win_length=window_size,
- window=window,
- center=center,
- pad_mode=pad_mode,
- freeze_parameters=True,
- )
-
- self.bn0 = nn.BatchNorm2d(window_size // 2 + 1, momentum=momentum)
-
- self.subband = Subband(subbands_num=self.subbands_num)
-
- self.encoder_block1 = EncoderBlock(
- in_channels=input_channels * self.subbands_num,
- out_channels=32,
- condition_size=condition_size,
- kernel_size=(3, 3),
- downsample=(2, 2),
- activation=activation,
- momentum=momentum,
- )
- self.encoder_block2 = EncoderBlock(
- in_channels=32,
- out_channels=64,
- condition_size=condition_size,
- kernel_size=(3, 3),
- downsample=(2, 2),
- activation=activation,
- momentum=momentum,
- )
- self.encoder_block3 = EncoderBlock(
- in_channels=64,
- out_channels=128,
- condition_size=condition_size,
- kernel_size=(3, 3),
- downsample=(2, 2),
- activation=activation,
- momentum=momentum,
- )
- self.encoder_block4 = EncoderBlock(
- in_channels=128,
- out_channels=256,
- condition_size=condition_size,
- kernel_size=(3, 3),
- downsample=(2, 2),
- activation=activation,
- momentum=momentum,
- )
- self.encoder_block5 = EncoderBlock(
- in_channels=256,
- out_channels=384,
- condition_size=condition_size,
- kernel_size=(3, 3),
- downsample=(2, 2),
- activation=activation,
- momentum=momentum,
- )
- self.encoder_block6 = EncoderBlock(
- in_channels=384,
- out_channels=384,
- condition_size=condition_size,
- kernel_size=(3, 3),
- downsample=(2, 2),
- activation=activation,
- momentum=momentum,
- )
- self.conv_block7 = ConvBlock(
- in_channels=384,
- out_channels=384,
- condition_size=condition_size,
- kernel_size=(3, 3),
- activation=activation,
- momentum=momentum,
- )
- self.decoder_block1 = DecoderBlock(
- in_channels=384,
- out_channels=384,
- condition_size=condition_size,
- kernel_size=(3, 3),
- upsample=(2, 2),
- activation=activation,
- momentum=momentum,
- )
- self.decoder_block2 = DecoderBlock(
- in_channels=384,
- out_channels=384,
- condition_size=condition_size,
- kernel_size=(3, 3),
- upsample=(2, 2),
- activation=activation,
- momentum=momentum,
- )
- self.decoder_block3 = DecoderBlock(
- in_channels=384,
- out_channels=256,
- condition_size=condition_size,
- kernel_size=(3, 3),
- upsample=(2, 2),
- activation=activation,
- momentum=momentum,
- )
- self.decoder_block4 = DecoderBlock(
- in_channels=256,
- out_channels=128,
- condition_size=condition_size,
- kernel_size=(3, 3),
- upsample=(2, 2),
- activation=activation,
- momentum=momentum,
- )
- self.decoder_block5 = DecoderBlock(
- in_channels=128,
- out_channels=64,
- condition_size=condition_size,
- kernel_size=(3, 3),
- upsample=(2, 2),
- activation=activation,
- momentum=momentum,
- )
- self.decoder_block6 = DecoderBlock(
- in_channels=64,
- out_channels=32,
- condition_size=condition_size,
- kernel_size=(3, 3),
- upsample=(2, 2),
- activation=activation,
- momentum=momentum,
- )
-
- self.after_conv_block1 = ConvBlock(
- in_channels=32,
- out_channels=32,
- condition_size=condition_size,
- kernel_size=(3, 3),
- activation=activation,
- momentum=momentum,
- )
-
- self.after_conv2 = nn.Conv2d(
- in_channels=32,
- out_channels=input_channels
- * self.subbands_num
- * self.output_sources_num
- * self.K,
- kernel_size=(1, 1),
- stride=(1, 1),
- padding=(0, 0),
- bias=True,
- )
-
- self.init_weights()
-
- def init_weights(self):
- init_bn(self.bn0)
- init_layer(self.after_conv2)
-
- def feature_maps_to_wav(self, x, sp, sin_in, cos_in, audio_length):
-
- batch_size, _, time_steps, freq_bins = x.shape
-
- x = x.reshape(
- batch_size,
- self.output_sources_num,
- self.input_channels,
- self.K,
- time_steps,
- freq_bins,
- )
- # x: (batch_size, output_sources_num, input_channles, K, time_steps, freq_bins)
-
- mask_mag = torch.sigmoid(x[:, :, :, 0, :, :])
- _mask_real = torch.tanh(x[:, :, :, 1, :, :])
- _mask_imag = torch.tanh(x[:, :, :, 2, :, :])
- _, mask_cos, mask_sin = magphase(_mask_real, _mask_imag)
- # mask_cos, mask_sin: (batch_size, output_sources_num, input_channles, time_steps, freq_bins)
-
- # Y = |Y|cos∠Y + j|Y|sin∠Y
- # = |Y|cos(∠X + ∠M) + j|Y|sin(∠X + ∠M)
- # = |Y|(cos∠X cos∠M - sin∠X sin∠M) + j|Y|(sin∠X cos∠M + cos∠X sin∠M)
- out_cos = (
- cos_in[:, None, :, :, :] * mask_cos - sin_in[:, None, :, :, :] * mask_sin
- )
- out_sin = (
- sin_in[:, None, :, :, :] * mask_cos + cos_in[:, None, :, :, :] * mask_sin
- )
- # out_cos, out_sin: (batch_size, output_sources_num, input_channles, time_steps, freq_bins)
-
- # Calculate |Y|.
- out_mag = F.relu_(sp[:, None, :, :, :] * mask_mag)
- # out_mag: (batch_size, output_sources_num, input_channles, time_steps, freq_bins)
-
- # Calculate Y_{real} and Y_{imag} for ISTFT.
- out_real = out_mag * out_cos
- out_imag = out_mag * out_sin
- # out_real, out_imag: (batch_size, output_sources_num, input_channles, time_steps, freq_bins)
-
- # Reformat shape to (n, 1, time_steps, freq_bins) for ISTFT.
- shape = (
- batch_size * self.output_sources_num * self.input_channels,
- 1,
- time_steps,
- freq_bins,
- )
- out_real = out_real.reshape(shape)
- out_imag = out_imag.reshape(shape)
-
- # ISTFT.
- wav_out = self.istft(out_real, out_imag, audio_length)
- # (batch_size * output_sources_num * input_channels, segments_num)
-
- # Reshape.
- wav_out = wav_out.reshape(
- batch_size, self.output_sources_num * self.input_channels, audio_length
- )
- # (batch_size, output_sources_num * input_channels, segments_num)
-
- return wav_out
-
- def forward(self, input_dict):
- """
- Args:
- input: (batch_size, segment_samples, channels_num)
-
- Outputs:
- output_dict: {
- 'wav': (batch_size, segment_samples, channels_num),
- 'sp': (batch_size, channels_num, time_steps, freq_bins)}
- """
-
- mixture = input_dict['waveform']
- condition = input_dict['condition']
-
- sp, cos_in, sin_in = self.wav_to_spectrogram_phase(mixture)
- """(batch_size, channels_num, time_steps, freq_bins)"""
-
- # Batch normalization
- x = sp.transpose(1, 3)
- x = self.bn0(x)
- x = x.transpose(1, 3)
- """(batch_size, chanenls, time_steps, freq_bins)"""
-
- # Pad spectrogram to be evenly divided by downsample ratio.
- origin_len = x.shape[2]
- pad_len = (
- int(np.ceil(x.shape[2] / self.downsample_ratio)) * self.downsample_ratio
- - origin_len
- )
- x = F.pad(x, pad=(0, 0, 0, pad_len))
- """(batch_size, channels, padded_time_steps, freq_bins)"""
-
- # Let frequency bins be evenly divided by 2, e.g., 513 -> 512
- x = x[..., 0 : x.shape[-1] - 1] # (bs, channels, T, F)
-
- x = self.subband.analysis(x)
-
- # UNet
- (x1_pool, x1) = self.encoder_block1(
- x, condition
- ) # x1_pool: (bs, 32, T / 2, F / 2)
- (x2_pool, x2) = self.encoder_block2(
- x1_pool, condition
- ) # x2_pool: (bs, 64, T / 4, F / 4)
- (x3_pool, x3) = self.encoder_block3(
- x2_pool, condition
- ) # x3_pool: (bs, 128, T / 8, F / 8)
- (x4_pool, x4) = self.encoder_block4(
- x3_pool, condition
- ) # x4_pool: (bs, 256, T / 16, F / 16)
- (x5_pool, x5) = self.encoder_block5(
- x4_pool, condition
- ) # x5_pool: (bs, 512, T / 32, F / 32)
- (x6_pool, x6) = self.encoder_block6(
- x5_pool, condition
- ) # x6_pool: (bs, 1024, T / 64, F / 64)
- x_center = self.conv_block7(x6_pool, condition) # (bs, 2048, T / 64, F / 64)
- x7 = self.decoder_block1(x_center, x6, condition) # (bs, 1024, T / 32, F / 32)
- x8 = self.decoder_block2(x7, x5, condition) # (bs, 512, T / 16, F / 16)
- x9 = self.decoder_block3(x8, x4, condition) # (bs, 256, T / 8, F / 8)
- x10 = self.decoder_block4(x9, x3, condition) # (bs, 128, T / 4, F / 4)
- x11 = self.decoder_block5(x10, x2, condition) # (bs, 64, T / 2, F / 2)
- x12 = self.decoder_block6(x11, x1, condition) # (bs, 32, T, F)
- x = self.after_conv_block1(x12, condition) # (bs, 32, T, F)
- x = self.after_conv2(x)
- # (batch_size, input_channles * subbands_num * targets_num * k, T, F // subbands_num)
-
- x = self.subband.synthesis(x)
- # (batch_size, input_channles * targets_num * K, T, F)
-
- # Recover shape
- x = F.pad(x, pad=(0, 1)) # Pad frequency, e.g., 1024 -> 1025.
- x = x[:, :, 0:origin_len, :] # (bs, feature_maps, T, F)
-
- audio_length = mixture.shape[2]
-
- separated_audio = self.feature_maps_to_wav(x, sp, sin_in, cos_in, audio_length)
- # separated_audio: (batch_size, output_sources_num * input_channels, segments_num)
-
- output_dict = {'waveform': separated_audio}
-
- return output_dict
diff --git a/spaces/akhaliq/VQMIVC/ParallelWaveGAN/utils/split_data.sh b/spaces/akhaliq/VQMIVC/ParallelWaveGAN/utils/split_data.sh
deleted file mode 100644
index baf97b6664c37b714213bafd0260bd7aa600b69a..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/VQMIVC/ParallelWaveGAN/utils/split_data.sh
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/bin/bash
-
-# Split data direcoty into two data direcotries
-
-# Copyright 2019 Tomoki Hayashi
-# MIT License (https://opensource.org/licenses/MIT)
-
-# shellcheck disable=SC1091
-. ./path.sh || exit 1;
-
-shuffle=false
-num_first=0
-num_second=0
-
-# shellcheck disable=SC1091
-. utils/parse_options.sh || exit 1;
-
-if [ $# -ne 3 ]; then
- echo "Usage: $0 ..."
- echo "e.g.: $0 data/all data/train data/deveval"
- echo ""
- echo "Options:"
- echo " --shuffle: Whether to perform shuffle (default=false)."
- echo " --num_first: Number of utts in the first dist dir."
- echo " If set to 0, it will be automatically decided (default=0)."
- echo " --num_second: Number of utts in the second dist dir."
- echo " If set to 0, it will be automatically decided (default=0)."
- exit 1
-fi
-
-set -eu
-
-src_dir=$1
-first_dist_dir=$2
-second_dist_dir=$3
-
-src_scp=${src_dir}/wav.scp
-if [ -e "${src_dir}/segments" ]; then
- has_segments=true
- src_segments=${src_dir}/segments
- num_src_utts=$(wc -l < "${src_segments}")
-else
- has_segments=false
- num_src_utts=$(wc -l < "${src_scp}")
-fi
-
-# check number of utts
-if [ "${num_first}" -eq 0 ] && [ "${num_second}" -eq 0 ]; then
- num_first=$((num_src_utts / 2 ))
- num_second=$((num_src_utts - num_first))
-elif [ "${num_first}" -gt 0 ] && [ "${num_second}" -eq 0 ]; then
- [ "${num_src_utts}" -le "${num_first}" ] && \
- echo "ERROR: num_first must be less than # utts in src. (${num_first} vs ${num_src_utts})" >&2 && \
- exit 1
- num_second=$((num_src_utts - num_first))
-elif [ "${num_first}" -eq 0 ] && [ "${num_second}" -gt 0 ]; then
- [ "${num_src_utts}" -le "${num_second}" ] && \
- echo "ERROR: num_second must be less than # utts in src. (${num_second} vs ${num_src_utts})" >&2 && \
- exit 1
- num_first=$((num_src_utts - num_second))
-elif [ "${num_first}" -gt 0 ] && [ "${num_second}" -gt 0 ]; then
- [ "${num_src_utts}" -ne "$((num_first + num_second))" ] && \
- echo "ERROR: num_first + num_second must be the same # utts in src. ($((num_first + num_second)) vs ${num_src_utts})" >&2 && \
- exit 1
-fi
-
-# check directory existence
-[ ! -e "${first_dist_dir}" ] && mkdir -p "${first_dist_dir}"
-[ ! -e "${second_dist_dir}" ] && mkdir -p "${second_dist_dir}"
-
-# split
-if ! "${has_segments}"; then
- if "${shuffle}"; then
- sort -R "${src_scp}" > "${src_scp}.unsorted"
- head -n "${num_first}" "${src_scp}.unsorted" | sort > "${first_dist_dir}/wav.scp"
- tail -n "${num_second}" "${src_scp}.unsorted" | sort > "${second_dist_dir}/wav.scp"
- rm "${src_scp}.unsorted"
- else
- head -n "${num_first}" "${src_scp}" | sort > "${first_dist_dir}/wav.scp"
- tail -n "${num_second}" "${src_scp}" | sort > "${second_dist_dir}/wav.scp"
- fi
-else
- # split segments at first
- if "${shuffle}"; then
- sort -R "${src_segments}" > "${src_segments}.unsorted"
- head -n "${num_first}" "${src_segments}.unsorted" | sort > "${first_dist_dir}/segments"
- tail -n "${num_second}" "${src_segments}.unsorted" | sort > "${second_dist_dir}/segments"
- rm "${src_segments}.unsorted"
- else
- head -n "${num_first}" "${src_segments}" | sort > "${first_dist_dir}/segments"
- tail -n "${num_second}" "${src_segments}" | sort > "${second_dist_dir}/segments"
- fi
- # split wav.scp
- rm -rf "${first_dist_dir}/wav.scp"
- awk '{print $2}' < "${first_dist_dir}/segments" | sort | uniq | while read -r wav_id; do
- grep "^${wav_id} " < "${src_scp}" >> "${first_dist_dir}/wav.scp"
- done
- rm -rf "${second_dist_dir}/wav.scp"
- awk '{print $2}' < "${second_dist_dir}/segments" | sort | uniq | while read -r wav_id; do
- grep "^${wav_id} " < "${src_scp}" >> "${second_dist_dir}/wav.scp"
- done
-fi
-
-echo "Successfully split data directory."
diff --git a/spaces/akhaliq/deeplab2/data/build_dvps_data.py b/spaces/akhaliq/deeplab2/data/build_dvps_data.py
deleted file mode 100644
index 7057aae62cb23d8571e7c65f5bb3bf789a02b2f2..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/data/build_dvps_data.py
+++ /dev/null
@@ -1,264 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Deeplab2 Authors.
-#
-# 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.
-
-r"""Converts Depth-aware Video Panoptic Segmentation (DVPS) data to sharded TFRecord file format with tf.train.Example protos.
-
-The expected directory structure of the DVPS dataset should be as follows:
-
- + DVPS_ROOT
- + train | val
- - ground-truth depth maps (*_depth.png)
- - ground-truth panoptic maps (*_gtFine_instanceTrainIds.png)
- - images (*_leftImg8bit.png)
- + test
- - images (*_leftImg8bit.png)
-
-The ground-truth panoptic map is encoded as the following in PNG format:
-
- panoptic ID = semantic ID * panoptic divisor (1000) + instance ID
-
-
-The output Example proto contains the following fields:
-
- image/encoded: encoded image content.
- image/filename: image filename.
- image/format: image file format.
- image/height: image height.
- image/width: image width.
- image/channels: image channels.
- image/segmentation/class/encoded: encoded panoptic segmentation content.
- image/segmentation/class/format: segmentation encoding format.
- image/depth/encoded: encoded depth content.
- image/depth/format: depth encoding format.
- video/sequence_id: sequence ID of the frame.
- video/frame_id: ID of the frame of the video sequence.
- next_image/encoded: encoded next-frame image content.
- next_image/segmentation/class/encoded: encoded panoptic segmentation content
- of the next frame.
-
-The output panoptic segmentation map stored in the Example will be the raw bytes
-of an int32 panoptic map, where each pixel is assigned to a panoptic ID:
-
- panoptic ID = semantic ID * panoptic divisor (1000) + instance ID
-
-where semantic ID will be the same with `category_id` for each segment, and
-ignore label for pixels not belong to any segment.
-
-The depth map will be the raw bytes of an int32 depth map, where each pixel is:
-
- depth map = depth ground truth * 256
-
-Example to run the scipt:
-
- python deeplab2/data/build_dvps_data.py \
- --dvps_root=${DVPS_ROOT} \
- --output_dir=${OUTPUT_DIR}
-"""
-
-import math
-import os
-
-from typing import Sequence, Tuple, Optional
-
-from absl import app
-from absl import flags
-from absl import logging
-import numpy as np
-
-from PIL import Image
-
-import tensorflow as tf
-
-from deeplab2.data import data_utils
-
-FLAGS = flags.FLAGS
-
-flags.DEFINE_string('dvps_root', None, 'DVPS dataset root folder.')
-
-flags.DEFINE_string('output_dir', None,
- 'Path to save converted TFRecord of TensorFlow examples.')
-
-_PANOPTIC_DEPTH_FORMAT = 'raw'
-_NUM_SHARDS = 1000
-_TF_RECORD_PATTERN = '%s-%05d-of-%05d.tfrecord'
-_IMAGE_SUFFIX = '_leftImg8bit.png'
-_LABEL_SUFFIX = '_gtFine_instanceTrainIds.png'
-_DEPTH_SUFFIX = '_depth.png'
-
-
-def _get_image_info_from_path(image_path: str) -> Tuple[str, str]:
- """Gets image info including sequence id and image id.
-
- Image path is in the format of '{sequence_id}_{image_id}_*.png',
- where `sequence_id` refers to the id of the video sequence, and `image_id` is
- the id of the image in the video sequence.
-
- Args:
- image_path: Absolute path of the image.
-
- Returns:
- sequence_id, and image_id as strings.
- """
- image_path = os.path.basename(image_path)
- return tuple(image_path.split('_')[:2])
-
-
-def _get_images(dvps_root: str, dataset_split: str) -> Sequence[str]:
- """Gets files for the specified data type and dataset split.
-
- Args:
- dvps_root: String, path to DVPS dataset root folder.
- dataset_split: String, dataset split ('train', 'val', 'test').
-
- Returns:
- A list of sorted file names under dvps_root and dataset_split.
- """
- search_files = os.path.join(dvps_root, dataset_split, '*' + _IMAGE_SUFFIX)
- filenames = tf.io.gfile.glob(search_files)
- return sorted(filenames)
-
-
-def _decode_panoptic_or_depth_map(map_path: str) -> Optional[str]:
- """Decodes the panoptic or depth map from encoded image file.
-
- Args:
- map_path: Path to the panoptic or depth map image file.
-
- Returns:
- Panoptic or depth map as an encoded int32 numpy array bytes or None if not
- existing.
- """
- if not tf.io.gfile.exists(map_path):
- return None
- with tf.io.gfile.GFile(map_path, 'rb') as f:
- decoded_map = np.array(Image.open(f)).astype(np.int32)
- return decoded_map.tobytes()
-
-
-def _get_next_frame_path(image_path: str) -> Optional[str]:
- """Gets next frame path.
-
- If not exists, return None.
-
- The files are named {sequence_id}_{frame_id}*. To get the path of the next
- frame, this function keeps sequence_id and increase the frame_id by 1. It
- finds all the files matching this pattern, and returns the corresponding
- file path matching the input type.
-
- Args:
- image_path: String, path to the image.
-
- Returns:
- A string for the path of the next frame of the given image path or None if
- the given image path is the last frame of the sequence.
- """
- sequence_id, image_id = _get_image_info_from_path(image_path)
- next_image_id = '{:06d}'.format(int(image_id) + 1)
- next_image_name = sequence_id + '_' + next_image_id
- next_image_path = None
- for suffix in (_IMAGE_SUFFIX, _LABEL_SUFFIX):
- if image_path.endswith(suffix):
- next_image_path = os.path.join(
- os.path.dirname(image_path), next_image_name + suffix)
- if not tf.io.gfile.exists(next_image_path):
- return None
- return next_image_path
-
-
-def _create_tfexample(image_path: str, panoptic_map_path: str,
- depth_map_path: str) -> Optional[tf.train.Example]:
- """Creates a TF example for each image.
-
- Args:
- image_path: Path to the image.
- panoptic_map_path: Path to the panoptic map (as an image file).
- depth_map_path: Path to the depth map (as an image file).
-
- Returns:
- TF example proto.
- """
- with tf.io.gfile.GFile(image_path, 'rb') as f:
- image_data = f.read()
- label_data = _decode_panoptic_or_depth_map(panoptic_map_path)
- depth_data = _decode_panoptic_or_depth_map(depth_map_path)
- image_name = os.path.basename(image_path)
- image_format = image_name.split('.')[1].lower()
- sequence_id, frame_id = _get_image_info_from_path(image_path)
- next_image_data = None
- next_label_data = None
- # Next image.
- next_image_path = _get_next_frame_path(image_path)
- # If there is no next image, no examples will be created.
- if next_image_path is None:
- return None
- with tf.io.gfile.GFile(next_image_path, 'rb') as f:
- next_image_data = f.read()
- # Next panoptic map.
- next_panoptic_map_path = _get_next_frame_path(panoptic_map_path)
- next_label_data = _decode_panoptic_or_depth_map(next_panoptic_map_path)
- return data_utils.create_video_and_depth_tfexample(
- image_data,
- image_format,
- image_name,
- label_format=_PANOPTIC_DEPTH_FORMAT,
- sequence_id=sequence_id,
- image_id=frame_id,
- label_data=label_data,
- next_image_data=next_image_data,
- next_label_data=next_label_data,
- depth_data=depth_data,
- depth_format=_PANOPTIC_DEPTH_FORMAT)
-
-
-def _convert_dataset(dvps_root: str, dataset_split: str, output_dir: str):
- """Converts the specified dataset split to TFRecord format.
-
- Args:
- dvps_root: String, path to DVPS dataset root folder.
- dataset_split: String, the dataset split (e.g., train, val, test).
- output_dir: String, directory to write output TFRecords to.
- """
- image_files = _get_images(dvps_root, dataset_split)
- num_images = len(image_files)
-
- num_per_shard = int(math.ceil(len(image_files) / _NUM_SHARDS))
-
- for shard_id in range(_NUM_SHARDS):
- shard_filename = _TF_RECORD_PATTERN % (dataset_split, shard_id, _NUM_SHARDS)
- output_filename = os.path.join(output_dir, shard_filename)
- with tf.io.TFRecordWriter(output_filename) as tfrecord_writer:
- start_idx = shard_id * num_per_shard
- end_idx = min((shard_id + 1) * num_per_shard, num_images)
- for i in range(start_idx, end_idx):
- image_path = image_files[i]
- panoptic_map_path = image_path.replace(_IMAGE_SUFFIX, _LABEL_SUFFIX)
- depth_map_path = image_path.replace(_IMAGE_SUFFIX, _DEPTH_SUFFIX)
- example = _create_tfexample(image_path, panoptic_map_path,
- depth_map_path)
- if example is not None:
- tfrecord_writer.write(example.SerializeToString())
-
-
-def main(argv: Sequence[str]) -> None:
- if len(argv) > 1:
- raise app.UsageError('Too many command-line arguments.')
- tf.io.gfile.makedirs(FLAGS.output_dir)
- for dataset_split in ('train', 'val', 'test'):
- logging.info('Starts to processing DVPS dataset split %s.', dataset_split)
- _convert_dataset(FLAGS.dvps_root, dataset_split, FLAGS.output_dir)
-
-
-if __name__ == '__main__':
- app.run(main)
diff --git a/spaces/akhaliq/deeplab2/data/data_utils_test.py b/spaces/akhaliq/deeplab2/data/data_utils_test.py
deleted file mode 100644
index e87ba80eaa2f7099bff65f84d725dbbdcd99f161..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/deeplab2/data/data_utils_test.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Deeplab2 Authors.
-#
-# 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.
-
-"""Tests for data_utils."""
-
-import io
-import numpy as np
-from PIL import Image
-import tensorflow as tf
-
-from deeplab2.data import data_utils
-
-
-def _encode_png_image(image):
- """Helper method to encode input image in PNG format."""
- buffer = io.BytesIO()
- Image.fromarray(image).save(buffer, format='png')
- return buffer.getvalue()
-
-
-class DataUtilsTest(tf.test.TestCase):
-
- def _create_test_image(self, height, width):
- rng = np.random.RandomState(319281498)
- return rng.randint(0, 255, size=(height, width, 3), dtype=np.uint8)
-
- def test_encode_and_decode(self):
- """Checks decode created tf.Example for semantic segmentation."""
- test_image_height = 20
- test_image_width = 15
- filename = 'dummy'
-
- image = self._create_test_image(test_image_height, test_image_width)
- # Take the last channel as dummy label.
- label = image[..., 0]
-
- example = data_utils.create_tfexample(
- image_data=_encode_png_image(image),
- image_format='png', filename=filename,
- label_data=_encode_png_image(label), label_format='png')
-
- # Parse created example, expect getting identical results.
- parser = data_utils.SegmentationDecoder(is_panoptic_dataset=False)
- parsed_tensors = parser(example.SerializeToString())
-
- self.assertIn('image', parsed_tensors)
- self.assertIn('image_name', parsed_tensors)
- self.assertIn('label', parsed_tensors)
- self.assertEqual(filename, parsed_tensors['image_name'])
- np.testing.assert_array_equal(image, parsed_tensors['image'].numpy())
- # Decoded label is a 3-D array with last dimension of 1.
- decoded_label = parsed_tensors['label'].numpy()
- np.testing.assert_array_equal(label, decoded_label[..., 0])
-
- def test_encode_and_decode_panoptic(self):
- test_image_height = 31
- test_image_width = 17
- filename = 'dummy'
-
- image = self._create_test_image(test_image_height, test_image_width)
- # Create dummy panoptic label in np.int32 dtype.
- label = np.dot(image.astype(np.int32), [1, 256, 256 * 256]).astype(np.int32)
- example = data_utils.create_tfexample(
- image_data=_encode_png_image(image),
- image_format='png', filename=filename,
- label_data=label.tostring(), label_format='raw')
-
- parser = data_utils.SegmentationDecoder(is_panoptic_dataset=True)
- parsed_tensors = parser(example.SerializeToString())
-
- self.assertIn('image', parsed_tensors)
- self.assertIn('image_name', parsed_tensors)
- self.assertIn('label', parsed_tensors)
- self.assertEqual(filename, parsed_tensors['image_name'])
- np.testing.assert_array_equal(image, parsed_tensors['image'].numpy())
- # Decoded label is a 3-D array with last dimension of 1.
- decoded_label = parsed_tensors['label'].numpy()
- np.testing.assert_array_equal(label, decoded_label[..., 0])
-
-
-if __name__ == '__main__':
- tf.test.main()
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/formatters/terminal256.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/formatters/terminal256.py
deleted file mode 100644
index b5eab1400563dadbd4f5f7deb7c12c1c8c23e066..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/pygments/formatters/terminal256.py
+++ /dev/null
@@ -1,338 +0,0 @@
-"""
- pygments.formatters.terminal256
- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
- Formatter for 256-color terminal output with ANSI sequences.
-
- RGB-to-XTERM color conversion routines adapted from xterm256-conv
- tool (http://frexx.de/xterm-256-notes/data/xterm256-conv2.tar.bz2)
- by Wolfgang Frisch.
-
- Formatter version 1.
-
- :copyright: Copyright 2006-2021 by the Pygments team, see AUTHORS.
- :license: BSD, see LICENSE for details.
-"""
-
-# TODO:
-# - Options to map style's bold/underline/italic/border attributes
-# to some ANSI attrbutes (something like 'italic=underline')
-# - An option to output "style RGB to xterm RGB/index" conversion table
-# - An option to indicate that we are running in "reverse background"
-# xterm. This means that default colors are white-on-black, not
-# black-on-while, so colors like "white background" need to be converted
-# to "white background, black foreground", etc...
-
-from pip._vendor.pygments.formatter import Formatter
-from pip._vendor.pygments.console import codes
-from pip._vendor.pygments.style import ansicolors
-
-
-__all__ = ['Terminal256Formatter', 'TerminalTrueColorFormatter']
-
-
-class EscapeSequence:
- def __init__(self, fg=None, bg=None, bold=False, underline=False, italic=False):
- self.fg = fg
- self.bg = bg
- self.bold = bold
- self.underline = underline
- self.italic = italic
-
- def escape(self, attrs):
- if len(attrs):
- return "\x1b[" + ";".join(attrs) + "m"
- return ""
-
- def color_string(self):
- attrs = []
- if self.fg is not None:
- if self.fg in ansicolors:
- esc = codes[self.fg.replace('ansi','')]
- if ';01m' in esc:
- self.bold = True
- # extract fg color code.
- attrs.append(esc[2:4])
- else:
- attrs.extend(("38", "5", "%i" % self.fg))
- if self.bg is not None:
- if self.bg in ansicolors:
- esc = codes[self.bg.replace('ansi','')]
- # extract fg color code, add 10 for bg.
- attrs.append(str(int(esc[2:4])+10))
- else:
- attrs.extend(("48", "5", "%i" % self.bg))
- if self.bold:
- attrs.append("01")
- if self.underline:
- attrs.append("04")
- if self.italic:
- attrs.append("03")
- return self.escape(attrs)
-
- def true_color_string(self):
- attrs = []
- if self.fg:
- attrs.extend(("38", "2", str(self.fg[0]), str(self.fg[1]), str(self.fg[2])))
- if self.bg:
- attrs.extend(("48", "2", str(self.bg[0]), str(self.bg[1]), str(self.bg[2])))
- if self.bold:
- attrs.append("01")
- if self.underline:
- attrs.append("04")
- if self.italic:
- attrs.append("03")
- return self.escape(attrs)
-
- def reset_string(self):
- attrs = []
- if self.fg is not None:
- attrs.append("39")
- if self.bg is not None:
- attrs.append("49")
- if self.bold or self.underline or self.italic:
- attrs.append("00")
- return self.escape(attrs)
-
-
-class Terminal256Formatter(Formatter):
- """
- Format tokens with ANSI color sequences, for output in a 256-color
- terminal or console. Like in `TerminalFormatter` color sequences
- are terminated at newlines, so that paging the output works correctly.
-
- The formatter takes colors from a style defined by the `style` option
- and converts them to nearest ANSI 256-color escape sequences. Bold and
- underline attributes from the style are preserved (and displayed).
-
- .. versionadded:: 0.9
-
- .. versionchanged:: 2.2
- If the used style defines foreground colors in the form ``#ansi*``, then
- `Terminal256Formatter` will map these to non extended foreground color.
- See :ref:`AnsiTerminalStyle` for more information.
-
- .. versionchanged:: 2.4
- The ANSI color names have been updated with names that are easier to
- understand and align with colornames of other projects and terminals.
- See :ref:`this table ` for more information.
-
-
- Options accepted:
-
- `style`
- The style to use, can be a string or a Style subclass (default:
- ``'default'``).
-
- `linenos`
- Set to ``True`` to have line numbers on the terminal output as well
- (default: ``False`` = no line numbers).
- """
- name = 'Terminal256'
- aliases = ['terminal256', 'console256', '256']
- filenames = []
-
- def __init__(self, **options):
- Formatter.__init__(self, **options)
-
- self.xterm_colors = []
- self.best_match = {}
- self.style_string = {}
-
- self.usebold = 'nobold' not in options
- self.useunderline = 'nounderline' not in options
- self.useitalic = 'noitalic' not in options
-
- self._build_color_table() # build an RGB-to-256 color conversion table
- self._setup_styles() # convert selected style's colors to term. colors
-
- self.linenos = options.get('linenos', False)
- self._lineno = 0
-
- def _build_color_table(self):
- # colors 0..15: 16 basic colors
-
- self.xterm_colors.append((0x00, 0x00, 0x00)) # 0
- self.xterm_colors.append((0xcd, 0x00, 0x00)) # 1
- self.xterm_colors.append((0x00, 0xcd, 0x00)) # 2
- self.xterm_colors.append((0xcd, 0xcd, 0x00)) # 3
- self.xterm_colors.append((0x00, 0x00, 0xee)) # 4
- self.xterm_colors.append((0xcd, 0x00, 0xcd)) # 5
- self.xterm_colors.append((0x00, 0xcd, 0xcd)) # 6
- self.xterm_colors.append((0xe5, 0xe5, 0xe5)) # 7
- self.xterm_colors.append((0x7f, 0x7f, 0x7f)) # 8
- self.xterm_colors.append((0xff, 0x00, 0x00)) # 9
- self.xterm_colors.append((0x00, 0xff, 0x00)) # 10
- self.xterm_colors.append((0xff, 0xff, 0x00)) # 11
- self.xterm_colors.append((0x5c, 0x5c, 0xff)) # 12
- self.xterm_colors.append((0xff, 0x00, 0xff)) # 13
- self.xterm_colors.append((0x00, 0xff, 0xff)) # 14
- self.xterm_colors.append((0xff, 0xff, 0xff)) # 15
-
- # colors 16..232: the 6x6x6 color cube
-
- valuerange = (0x00, 0x5f, 0x87, 0xaf, 0xd7, 0xff)
-
- for i in range(217):
- r = valuerange[(i // 36) % 6]
- g = valuerange[(i // 6) % 6]
- b = valuerange[i % 6]
- self.xterm_colors.append((r, g, b))
-
- # colors 233..253: grayscale
-
- for i in range(1, 22):
- v = 8 + i * 10
- self.xterm_colors.append((v, v, v))
-
- def _closest_color(self, r, g, b):
- distance = 257*257*3 # "infinity" (>distance from #000000 to #ffffff)
- match = 0
-
- for i in range(0, 254):
- values = self.xterm_colors[i]
-
- rd = r - values[0]
- gd = g - values[1]
- bd = b - values[2]
- d = rd*rd + gd*gd + bd*bd
-
- if d < distance:
- match = i
- distance = d
- return match
-
- def _color_index(self, color):
- index = self.best_match.get(color, None)
- if color in ansicolors:
- # strip the `ansi/#ansi` part and look up code
- index = color
- self.best_match[color] = index
- if index is None:
- try:
- rgb = int(str(color), 16)
- except ValueError:
- rgb = 0
-
- r = (rgb >> 16) & 0xff
- g = (rgb >> 8) & 0xff
- b = rgb & 0xff
- index = self._closest_color(r, g, b)
- self.best_match[color] = index
- return index
-
- def _setup_styles(self):
- for ttype, ndef in self.style:
- escape = EscapeSequence()
- # get foreground from ansicolor if set
- if ndef['ansicolor']:
- escape.fg = self._color_index(ndef['ansicolor'])
- elif ndef['color']:
- escape.fg = self._color_index(ndef['color'])
- if ndef['bgansicolor']:
- escape.bg = self._color_index(ndef['bgansicolor'])
- elif ndef['bgcolor']:
- escape.bg = self._color_index(ndef['bgcolor'])
- if self.usebold and ndef['bold']:
- escape.bold = True
- if self.useunderline and ndef['underline']:
- escape.underline = True
- if self.useitalic and ndef['italic']:
- escape.italic = True
- self.style_string[str(ttype)] = (escape.color_string(),
- escape.reset_string())
-
- def _write_lineno(self, outfile):
- self._lineno += 1
- outfile.write("%s%04d: " % (self._lineno != 1 and '\n' or '', self._lineno))
-
- def format(self, tokensource, outfile):
- return Formatter.format(self, tokensource, outfile)
-
- def format_unencoded(self, tokensource, outfile):
- if self.linenos:
- self._write_lineno(outfile)
-
- for ttype, value in tokensource:
- not_found = True
- while ttype and not_found:
- try:
- # outfile.write( "<" + str(ttype) + ">" )
- on, off = self.style_string[str(ttype)]
-
- # Like TerminalFormatter, add "reset colors" escape sequence
- # on newline.
- spl = value.split('\n')
- for line in spl[:-1]:
- if line:
- outfile.write(on + line + off)
- if self.linenos:
- self._write_lineno(outfile)
- else:
- outfile.write('\n')
-
- if spl[-1]:
- outfile.write(on + spl[-1] + off)
-
- not_found = False
- # outfile.write( '#' + str(ttype) + '#' )
-
- except KeyError:
- # ottype = ttype
- ttype = ttype.parent
- # outfile.write( '!' + str(ottype) + '->' + str(ttype) + '!' )
-
- if not_found:
- outfile.write(value)
-
- if self.linenos:
- outfile.write("\n")
-
-
-
-class TerminalTrueColorFormatter(Terminal256Formatter):
- r"""
- Format tokens with ANSI color sequences, for output in a true-color
- terminal or console. Like in `TerminalFormatter` color sequences
- are terminated at newlines, so that paging the output works correctly.
-
- .. versionadded:: 2.1
-
- Options accepted:
-
- `style`
- The style to use, can be a string or a Style subclass (default:
- ``'default'``).
- """
- name = 'TerminalTrueColor'
- aliases = ['terminal16m', 'console16m', '16m']
- filenames = []
-
- def _build_color_table(self):
- pass
-
- def _color_tuple(self, color):
- try:
- rgb = int(str(color), 16)
- except ValueError:
- return None
- r = (rgb >> 16) & 0xff
- g = (rgb >> 8) & 0xff
- b = rgb & 0xff
- return (r, g, b)
-
- def _setup_styles(self):
- for ttype, ndef in self.style:
- escape = EscapeSequence()
- if ndef['color']:
- escape.fg = self._color_tuple(ndef['color'])
- if ndef['bgcolor']:
- escape.bg = self._color_tuple(ndef['bgcolor'])
- if self.usebold and ndef['bold']:
- escape.bold = True
- if self.useunderline and ndef['underline']:
- escape.underline = True
- if self.useitalic and ndef['italic']:
- escape.italic = True
- self.style_string[str(ttype)] = (escape.true_color_string(),
- escape.reset_string())
diff --git a/spaces/aliabid94/AutoGPT/autogpt/commands/web_selenium.py b/spaces/aliabid94/AutoGPT/autogpt/commands/web_selenium.py
deleted file mode 100644
index 11bdfeb1f1630fc6ff6f55d68e8d7233281c5098..0000000000000000000000000000000000000000
--- a/spaces/aliabid94/AutoGPT/autogpt/commands/web_selenium.py
+++ /dev/null
@@ -1,154 +0,0 @@
-"""Selenium web scraping module."""
-from __future__ import annotations
-
-import logging
-from pathlib import Path
-from sys import platform
-
-from bs4 import BeautifulSoup
-from selenium import webdriver
-from selenium.webdriver.chrome.options import Options as ChromeOptions
-from selenium.webdriver.common.by import By
-from selenium.webdriver.firefox.options import Options as FirefoxOptions
-from selenium.webdriver.remote.webdriver import WebDriver
-from selenium.webdriver.safari.options import Options as SafariOptions
-from selenium.webdriver.support import expected_conditions as EC
-from selenium.webdriver.support.wait import WebDriverWait
-from webdriver_manager.chrome import ChromeDriverManager
-from webdriver_manager.firefox import GeckoDriverManager
-
-import autogpt.processing.text as summary
-from autogpt.config import Config
-from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
-
-FILE_DIR = Path(__file__).parent.parent
-CFG = Config()
-
-
-def browse_website(url: str, question: str) -> tuple[str, WebDriver]:
- """Browse a website and return the answer and links to the user
-
- Args:
- url (str): The url of the website to browse
- question (str): The question asked by the user
-
- Returns:
- Tuple[str, WebDriver]: The answer and links to the user and the webdriver
- """
- driver, text = scrape_text_with_selenium(url)
- add_header(driver)
- summary_text = summary.summarize_text(url, text, question, driver)
- links = scrape_links_with_selenium(driver, url)
-
- # Limit links to 5
- if len(links) > 5:
- links = links[:5]
- close_browser(driver)
- return f"Answer gathered from website: {summary_text} \n \n Links: {links}", driver
-
-
-def scrape_text_with_selenium(url: str) -> tuple[WebDriver, str]:
- """Scrape text from a website using selenium
-
- Args:
- url (str): The url of the website to scrape
-
- Returns:
- Tuple[WebDriver, str]: The webdriver and the text scraped from the website
- """
- logging.getLogger("selenium").setLevel(logging.CRITICAL)
-
- options_available = {
- "chrome": ChromeOptions,
- "safari": SafariOptions,
- "firefox": FirefoxOptions,
- }
-
- options = options_available[CFG.selenium_web_browser]()
- options.add_argument(
- "user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.5615.49 Safari/537.36"
- )
-
- if CFG.selenium_web_browser == "firefox":
- driver = webdriver.Firefox(
- executable_path=GeckoDriverManager().install(), options=options
- )
- elif CFG.selenium_web_browser == "safari":
- # Requires a bit more setup on the users end
- # See https://developer.apple.com/documentation/webkit/testing_with_webdriver_in_safari
- driver = webdriver.Safari(options=options)
- else:
- if platform == "linux" or platform == "linux2":
- options.add_argument("--disable-dev-shm-usage")
- options.add_argument("--remote-debugging-port=9222")
-
- options.add_argument("--no-sandbox")
- if CFG.selenium_headless:
- options.add_argument("--headless")
- options.add_argument("--disable-gpu")
-
- driver = webdriver.Chrome(
- executable_path=ChromeDriverManager().install(), options=options
- )
- driver.get(url)
-
- WebDriverWait(driver, 10).until(
- EC.presence_of_element_located((By.TAG_NAME, "body"))
- )
-
- # Get the HTML content directly from the browser's DOM
- page_source = driver.execute_script("return document.body.outerHTML;")
- soup = BeautifulSoup(page_source, "html.parser")
-
- for script in soup(["script", "style"]):
- script.extract()
-
- text = soup.get_text()
- lines = (line.strip() for line in text.splitlines())
- chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
- text = "\n".join(chunk for chunk in chunks if chunk)
- return driver, text
-
-
-def scrape_links_with_selenium(driver: WebDriver, url: str) -> list[str]:
- """Scrape links from a website using selenium
-
- Args:
- driver (WebDriver): The webdriver to use to scrape the links
-
- Returns:
- List[str]: The links scraped from the website
- """
- page_source = driver.page_source
- soup = BeautifulSoup(page_source, "html.parser")
-
- for script in soup(["script", "style"]):
- script.extract()
-
- hyperlinks = extract_hyperlinks(soup, url)
-
- return format_hyperlinks(hyperlinks)
-
-
-def close_browser(driver: WebDriver) -> None:
- """Close the browser
-
- Args:
- driver (WebDriver): The webdriver to close
-
- Returns:
- None
- """
- driver.quit()
-
-
-def add_header(driver: WebDriver) -> None:
- """Add a header to the website
-
- Args:
- driver (WebDriver): The webdriver to use to add the header
-
- Returns:
- None
- """
- driver.execute_script(open(f"{FILE_DIR}/js/overlay.js", "r").read())
diff --git a/spaces/alitrack/ChatPDF/chatpdf.py b/spaces/alitrack/ChatPDF/chatpdf.py
deleted file mode 100644
index d337b7d31b13ee551313b8e2e41f0e49046324d1..0000000000000000000000000000000000000000
--- a/spaces/alitrack/ChatPDF/chatpdf.py
+++ /dev/null
@@ -1,171 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-@author:XuMing(xuming624@qq.com)
-@description:
-"""
-from similarities import Similarity
-from textgen import ChatGlmModel, LlamaModel
-
-PROMPT_TEMPLATE = """\
-基于以下已知信息，简洁和专业的来回答用户的问题。
-如果无法从中得到答案，请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息"，不允许在答案中添加编造成分，答案请使用中文。
-
-已知内容:
-{context_str}
-
-问题:
-{query_str}
-"""
-
-
-class ChatPDF:
- def __init__(
- self,
- sim_model_name_or_path: str = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
- gen_model_type: str = "chatglm",
- gen_model_name_or_path: str = "THUDM/chatglm-6b-int4",
- lora_model_name_or_path: str = None,
-
- ):
- self.sim_model = Similarity(model_name_or_path=sim_model_name_or_path)
-
- if gen_model_type == "chatglm":
- self.gen_model = ChatGlmModel(gen_model_type, gen_model_name_or_path, lora_name=lora_model_name_or_path)
- elif gen_model_type == "llama":
- self.gen_model = LlamaModel(gen_model_type, gen_model_name_or_path, lora_name=lora_model_name_or_path)
- else:
- raise ValueError('gen_model_type must be chatglm or llama.')
- self.history = None
- self.pdf_path = None
-
- def load_pdf_file(self, pdf_path: str):
- """Load a PDF file."""
- if pdf_path.endswith('.pdf'):
- corpus = self.extract_text_from_pdf(pdf_path)
- elif pdf_path.endswith('.docx'):
- corpus = self.extract_text_from_docx(pdf_path)
- elif pdf_path.endswith('.md'):
- corpus = self.extract_text_from_markdown(pdf_path)
- else:
- corpus = self.extract_text_from_txt(pdf_path)
- self.sim_model.add_corpus(corpus)
- self.pdf_path = pdf_path
-
- @staticmethod
- def extract_text_from_pdf(file_path: str):
- """Extract text content from a PDF file."""
- import PyPDF2
- contents = []
- with open(file_path, 'rb') as f:
- pdf_reader = PyPDF2.PdfReader(f)
- for page in pdf_reader.pages:
- page_text = page.extract_text().strip()
- raw_text = [text.strip() for text in page_text.splitlines() if text.strip()]
- new_text = ''
- for text in raw_text:
- new_text += text
- if text[-1] in ['.', '!', '?', '。', '！', '？', '…', ';', '；', ':', '：', '”', '’', '）', '】', '》', '」',
- '』', '〕', '〉', '》', '〗', '〞', '〟', '»', '"', "'", ')', ']', '}']:
- contents.append(new_text)
- new_text = ''
- if new_text:
- contents.append(new_text)
- return contents
-
- @staticmethod
- def extract_text_from_txt(file_path: str):
- """Extract text content from a TXT file."""
- contents = []
- with open(file_path, 'r', encoding='utf-8') as f:
- contents = [text.strip() for text in f.readlines() if text.strip()]
- return contents
-
- @staticmethod
- def extract_text_from_docx(file_path: str):
- """Extract text content from a DOCX file."""
- import docx
- document = docx.Document(file_path)
- contents = [paragraph.text.strip() for paragraph in document.paragraphs if paragraph.text.strip()]
- return contents
-
- @staticmethod
- def extract_text_from_markdown(file_path: str):
- """Extract text content from a Markdown file."""
- import markdown
- from bs4 import BeautifulSoup
- with open(file_path, 'r', encoding='utf-8') as f:
- markdown_text = f.read()
- html = markdown.markdown(markdown_text)
- soup = BeautifulSoup(html, 'html.parser')
- contents = [text.strip() for text in soup.get_text().splitlines() if text.strip()]
- return contents
-
- @staticmethod
- def _add_source_numbers(lst):
- """Add source numbers to a list of strings."""
- return [f'[{idx + 1}]\t "{item}"' for idx, item in enumerate(lst)]
-
- def _generate_answer(self, query_str, context_str, history=None, max_length=1024):
- """Generate answer from query and context."""
- prompt = PROMPT_TEMPLATE.format(context_str=context_str, query_str=query_str)
- response, out_history = self.gen_model.chat(prompt, history, max_length=max_length)
- return response, out_history
-
- def query(
- self,
- query,
- topn: int = 5,
- max_length: int = 1024,
- max_input_size: int = 1024,
- use_history: bool = False
- ):
- """Query from corpus."""
-
- sim_contents = self.sim_model.most_similar(query, topn=topn)
-
- reference_results = []
- for query_id, id_score_dict in sim_contents.items():
- for corpus_id, s in id_score_dict.items():
- reference_results.append(self.sim_model.corpus[corpus_id])
- if not reference_results:
- return '没有提供足够的相关信息', reference_results
- reference_results = self._add_source_numbers(reference_results)
-
- context_str = '\n'.join(reference_results)[:(max_input_size - len(PROMPT_TEMPLATE))]
-
- if use_history:
- response, out_history = self._generate_answer(query, context_str, self.history, max_length=max_length)
- self.history = out_history
- else:
-
- response, out_history = self._generate_answer(query, context_str)
-
- return response, out_history, reference_results
-
- def save_index(self, index_path=None):
- """Save model."""
- if index_path is None:
- index_path = '.'.join(self.pdf_path.split('.')[:-1]) + '_index.json'
- self.sim_model.save_index(index_path)
-
- def load_index(self, index_path=None):
- """Load model."""
- if index_path is None:
- index_path = '.'.join(self.pdf_path.split('.')[:-1]) + '_index.json'
- self.sim_model.load_index(index_path)
-
-
-if __name__ == "__main__":
- import sys
-
- if len(sys.argv) > 2:
- gen_model_name_or_path = sys.argv[1]
- else:
- print('Usage: python chatpdf.py ')
- gen_model_name_or_path = "THUDM/chatglm-6b-int4"
- m = ChatPDF(gen_model_name_or_path=gen_model_name_or_path)
- m.load_pdf_file(pdf_path='sample.pdf')
- response = m.query('自然语言中的非平行迁移是指什么？')
- print(response[0])
- response = m.query('本文作者是谁？')
- print(response[0])
\ No newline at end of file
diff --git a/spaces/allknowingroger/Image-Models-Test45/app.py b/spaces/allknowingroger/Image-Models-Test45/app.py
deleted file mode 100644
index a63d0a8e64ea7d6c09f9894660b40606e405094c..0000000000000000000000000000000000000000
--- a/spaces/allknowingroger/Image-Models-Test45/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import gradio as gr
-# import os
-# import sys
-# from pathlib import Path
-import time
-
-models =[
- "Yntec/DucHaitenAIart-beta",
- "dpwm/lora-trained-xl",
- "Vsukiyaki/ShiratakiMix",
- "Silvelter/Sathariel",
- "digiplay/nk15_diffusers",
- "digiplay/kencanmix_v1.6",
- "digiplay/kencanmix_v1.5",
- "jonaylor89/sd-johannes",
- "EhsanElahi/avatar-creator",
-]
-
-
-model_functions = {}
-model_idx = 1
-for model_path in models:
- try:
- model_functions[model_idx] = gr.Interface.load(f"models/{model_path}", live=False, preprocess=True, postprocess=False)
- except Exception as error:
- def the_fn(txt):
- return None
- model_functions[model_idx] = gr.Interface(fn=the_fn, inputs=["text"], outputs=["image"])
- model_idx+=1
-
-
-def send_it_idx(idx):
- def send_it_fn(prompt):
- output = (model_functions.get(str(idx)) or model_functions.get(str(1)))(prompt)
- return output
- return send_it_fn
-
-def get_prompts(prompt_text):
- return prompt_text
-
-def clear_it(val):
- if int(val) != 0:
- val = 0
- else:
- val = 0
- pass
- return val
-
-def all_task_end(cnt,t_stamp):
- to = t_stamp + 60
- et = time.time()
- if et > to and t_stamp != 0:
- d = gr.update(value=0)
- tog = gr.update(value=1)
- #print(f'to: {to} et: {et}')
- else:
- if cnt != 0:
- d = gr.update(value=et)
- else:
- d = gr.update(value=0)
- tog = gr.update(value=0)
- #print (f'passing: to: {to} et: {et}')
- pass
- return d, tog
-
-def all_task_start():
- print("\n\n\n\n\n\n\n")
- t = time.gmtime()
- t_stamp = time.time()
- current_time = time.strftime("%H:%M:%S", t)
- return gr.update(value=t_stamp), gr.update(value=t_stamp), gr.update(value=0)
-
-def clear_fn():
- nn = len(models)
- return tuple([None, *[None for _ in range(nn)]])
-
-
-
-with gr.Blocks(title="SD Models") as my_interface:
- with gr.Column(scale=12):
- # with gr.Row():
- # gr.Markdown("""- Primary prompt: 你想画的内容(英文单词，如 a cat, 加英文逗号效果更好；点 Improve 按钮进行完善)\n- Real prompt: 完善后的提示词，出现后再点右边的 Run 按钮开始运行""")
- with gr.Row():
- with gr.Row(scale=6):
- primary_prompt=gr.Textbox(label="Prompt", value="")
- # real_prompt=gr.Textbox(label="Real prompt")
- with gr.Row(scale=6):
- # improve_prompts_btn=gr.Button("Improve")
- with gr.Row():
- run=gr.Button("Run",variant="primary")
- clear_btn=gr.Button("Clear")
- with gr.Row():
- sd_outputs = {}
- model_idx = 1
- for model_path in models:
- with gr.Column(scale=3, min_width=320):
- with gr.Box():
- sd_outputs[model_idx] = gr.Image(label=model_path)
- pass
- model_idx += 1
- pass
- pass
-
- with gr.Row(visible=False):
- start_box=gr.Number(interactive=False)
- end_box=gr.Number(interactive=False)
- tog_box=gr.Textbox(value=0,interactive=False)
-
- start_box.change(
- all_task_end,
- [start_box, end_box],
- [start_box, tog_box],
- every=1,
- show_progress=False)
-
- primary_prompt.submit(all_task_start, None, [start_box, end_box, tog_box])
- run.click(all_task_start, None, [start_box, end_box, tog_box])
- runs_dict = {}
- model_idx = 1
- for model_path in models:
- runs_dict[model_idx] = run.click(model_functions[model_idx], inputs=[primary_prompt], outputs=[sd_outputs[model_idx]])
- model_idx += 1
- pass
- pass
-
- # improve_prompts_btn_clicked=improve_prompts_btn.click(
- # get_prompts,
- # inputs=[primary_prompt],
- # outputs=[primary_prompt],
- # cancels=list(runs_dict.values()))
- clear_btn.click(
- clear_fn,
- None,
- [primary_prompt, *list(sd_outputs.values())],
- cancels=[*list(runs_dict.values())])
- tog_box.change(
- clear_it,
- tog_box,
- tog_box,
- cancels=[*list(runs_dict.values())])
-
-my_interface.queue(concurrency_count=600, status_update_rate=1)
-my_interface.launch(inline=True, show_api=False)
-
\ No newline at end of file
diff --git a/spaces/anshu-man853/webscrapping/README.md b/spaces/anshu-man853/webscrapping/README.md
deleted file mode 100644
index 4d3db4693f153cddc9361b2b66fa77bf39fb367d..0000000000000000000000000000000000000000
--- a/spaces/anshu-man853/webscrapping/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Webscrapping
-emoji: ⚡
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.33.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/apoorvumang/kgt5/app.py b/spaces/apoorvumang/kgt5/app.py
deleted file mode 100644
index 0329a6e36c9ac4afd7a0e3f6c5601f55e089824d..0000000000000000000000000000000000000000
--- a/spaces/apoorvumang/kgt5/app.py
+++ /dev/null
@@ -1,114 +0,0 @@
-import gradio as gr
-import torch
-import numpy as np
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-
-
-def getScores(ids, scores, pad_token_id):
- """get sequence scores from model.generate output"""
- scores = torch.stack(scores, dim=1)
- log_probs = torch.log_softmax(scores, dim=2)
- # remove start token
- ids = ids[:,1:]
- # gather needed probs
- x = ids.unsqueeze(-1).expand(log_probs.shape)
- needed_logits = torch.gather(log_probs, 2, x)
- final_logits = needed_logits[:, :, 0]
- padded_mask = (ids == pad_token_id)
- final_logits[padded_mask] = 0
- final_scores = final_logits.sum(dim=-1)
- return final_scores.cpu().detach().numpy()
-
-def topkSample(input, model, tokenizer,
- num_samples=5,
- num_beams=1,
- max_output_length=30):
- tokenized = tokenizer(input, return_tensors="pt")
- out = model.generate(**tokenized,
- do_sample=True,
- num_return_sequences = num_samples,
- num_beams = num_beams,
- eos_token_id = tokenizer.eos_token_id,
- pad_token_id = tokenizer.pad_token_id,
- output_scores = True,
- return_dict_in_generate=True,
- max_length=max_output_length,)
- out_tokens = out.sequences
- out_str = tokenizer.batch_decode(out_tokens, skip_special_tokens=True)
- out_scores = getScores(out_tokens, out.scores, tokenizer.pad_token_id)
-
- pair_list = [(x[0], x[1]) for x in zip(out_str, out_scores)]
- sorted_pair_list = sorted(pair_list, key=lambda x:x[1], reverse=True)
- return sorted_pair_list
-
-def greedyPredict(input, model, tokenizer):
- input_ids = tokenizer([input], return_tensors="pt").input_ids
- out_tokens = model.generate(input_ids)
- out_str = tokenizer.batch_decode(out_tokens, skip_special_tokens=True)
- return out_str[0]
-
-def predict_tail(entity, relation):
- global model, tokenizer
- input = entity + "| " + relation
- out = topkSample(input, model, tokenizer, num_samples=25)
- out_dict = {}
- for k, v in out:
- out_dict[k] = np.exp(v).item()
- return out_dict
-
-
-tokenizer = AutoTokenizer.from_pretrained("apoorvumang/kgt5-base-wikikg90mv2")
-model = AutoModelForSeq2SeqLM.from_pretrained("apoorvumang/kgt5-base-wikikg90mv2")
-
-
-ent_input = gr.inputs.Textbox(lines=1, default="Apoorv Umang Saxena")
-rel_input = gr.inputs.Textbox(lines=1, default="country")
-output = gr.outputs.Label()
-
-examples = [
-['Adrian Kochsiek', 'sex or gender'],
-['Apoorv Umang Saxena', 'family name'],
-['World War II', 'followed by'],
-['Apoorv Umang Saxena', 'country'],
-['Ippolito Boccolini', 'writing language'] ,
-['Roelant', 'writing system'] ,
-['The Accountant 2227', 'language of work or name'] ,
-['Microbial Infection and AMR in Hospitalized Patients With Covid 19', 'study type'] ,
-['Carla Fracci', 'manner of death'] ,
-['list of programs broadcast by Comet', 'is a list of'] ,
-['Loreta Podhradí', 'continent'] ,
-['Opistognathotrema', 'taxon rank'] ,
-['Museum Arbeitswelt Steyr', 'wheelchair accessibility'] ,
-['Heliotropium tytoides', 'subject has role'] ,
-['School bus crash rates on routine and nonroutine routes.', 'sponsor'] ,
-['Tachigalieae', 'taxon rank'] ,
-['Irena Salusová', 'place of detention'] ,
-
-]
-title = "Interactive demo: KGT5"
-description = """Demo for Sequence-to-Sequence Knowledge Graph Completion and Question Answering (KGT5). This particular model is a T5-base model trained on the task of tail prediction on WikiKG90Mv2 dataset and obtains 0.239 validation MRR on this task (leaderboard , see paper for details).
- To use it, simply give an entity name and relation and click 'submit'. Upto 25 model predictions will show up in a few seconds. The model works best when the exact entity/relation names that it has been trained on are used.
- It is sometimes able to generalize to unseen entities as well (see examples).
-"""
-#article = """
-#Sequence-to-Sequence Knowledge Graph Completion and Question Answering | Github Repo
-#"""
-
-article = """
-Under the hood, this demo concatenates the entity and relation, feeds it to the model and then samples 25 sequences, which are then ranked according to their sequence probabilities.
-https://storage.googleapis.com/kgt5-wikikg90mv2/rel_alias_list.pickle and
-https://storage.googleapis.com/kgt5-wikikg90mv2/ent_alias_list.pickle
-Github repo or the hf model page .
-"""
-
-
-iface = gr.Interface(fn=predict_tail,
- inputs=[ent_input, rel_input],
- outputs=output,
- title=title,
- description=description,
- article=article,
- examples=examples,)
-iface.launch()
diff --git a/spaces/arch-123/bingo/src/components/settings.tsx b/spaces/arch-123/bingo/src/components/settings.tsx
deleted file mode 100644
index 80b8a2d3b252b875f5b6f7dfc2f6e3ad9cdfb22a..0000000000000000000000000000000000000000
--- a/spaces/arch-123/bingo/src/components/settings.tsx
+++ /dev/null
@@ -1,157 +0,0 @@
-import { useEffect, useState } from 'react'
-import { useAtom } from 'jotai'
-import { Switch } from '@headlessui/react'
-import { toast } from 'react-hot-toast'
-import { hashAtom, voiceAtom } from '@/state'
-import {
- Dialog,
- DialogContent,
- DialogDescription,
- DialogFooter,
- DialogHeader,
- DialogTitle
-} from '@/components/ui/dialog'
-import { Button } from './ui/button'
-import { Input } from './ui/input'
-import { ChunkKeys, parseCookies, extraCurlFromCookie, encodeHeadersToCookie, getCookie, setCookie } from '@/lib/utils'
-import { ExternalLink } from './external-link'
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-
-
-export function Settings() {
- const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
- const [loc, setLoc] = useAtom(hashAtom)
- const [curlValue, setCurlValue] = useState(extraCurlFromCookie(parseCookies(document.cookie, ChunkKeys)))
- const [imageOnly, setImageOnly] = useState(getCookie('IMAGE_ONLY') !== '0')
- const [enableTTS, setEnableTTS] = useAtom(voiceAtom)
-
- useEffect(() => {
- if (isCopied) {
- toast.success('复制成功')
- }
- }, [isCopied])
-
- if (loc === 'settings') {
- return (
- setLoc('')} modal>
-
-
- 设置你的用户信息
-
- 请使用 Edge 浏览器
-
- 打开并登录 Bing
-
- ，然后再打开
- Challenge 接口
- 右键 》检查。打开开发者工具，在网络里面找到 Create 接口 》右键复制》复制为 cURL(bash)，粘贴到此处，然后保存。
-
- 图文示例：
- 如何获取 BING_HEADER
-
-
-
-
-
-
- 身份信息仅用于画图（推荐）
- setImageOnly(checked)}
- >
-
-
-
- copyToClipboard(btoa(curlValue))}>
- 转成 BING_HEADER 并复制
-
-
-
- {
- let headerValue = curlValue
- if (headerValue) {
- try {
- headerValue = atob(headerValue)
- } catch (e) { }
- if (!/^\s*curl ['"]https:\/\/(www|cn)\.bing\.com\/turing\/captcha\/challenge['"]/.test(headerValue)) {
- toast.error('格式不正确')
- return
- }
- const maxAge = 86400 * 30
- encodeHeadersToCookie(headerValue).forEach(cookie => document.cookie = `${cookie}; Max-Age=${maxAge}; Path=/; SameSite=None; Secure`)
- } else {
- [...ChunkKeys, 'BING_COOKIE', 'BING_UA', 'BING_IP'].forEach(key => setCookie(key, ''))
- }
- setCookie('IMAGE_ONLY', RegExp.$1 === 'cn' || imageOnly ? '1' : '0')
-
- toast.success('保存成功')
- setLoc('')
- setTimeout(() => {
- location.href = './'
- }, 2000)
- }}
- >
- 保存
-
-
-
-
- )
- } else if (loc === 'voice') {
- return (
- setLoc('')} modal>
-
-
- 语音设置
-
- 目前仅支持 PC 端 Edge 及 Chrome 浏览器
-
-
-
-
- 启用语音回答
- setEnableTTS(checked)}
- >
-
-
-
-
- {
- toast.success('保存成功')
- setLoc('')
- setTimeout(() => {
- location.href = './'
- }, 2000)
- }}
- >
- 保存
-
-
-
-
- )
- }
- return null
-}
diff --git a/spaces/argilla/argilla-streamlit-customs/my_app/utils/commons.py b/spaces/argilla/argilla-streamlit-customs/my_app/utils/commons.py
deleted file mode 100644
index 5b79c78070a01dfccb7795c19957b8bd2f6bcd81..0000000000000000000000000000000000000000
--- a/spaces/argilla/argilla-streamlit-customs/my_app/utils/commons.py
+++ /dev/null
@@ -1,195 +0,0 @@
-import os
-
-import argilla as rg
-import httpx
-import streamlit as st
-from argilla.client.api import ArgillaSingleton, active_client
-from argilla.client.apis.datasets import __TASK_TO_SETTINGS__
-from huggingface_hub import HfApi
-
-
-def argilla_login_flow(title: str) -> str:
- """
- It tries to log in to Argilla
- using the environment variables `ARGILLA_API_URL` and `ARGILLA_API_KEY`. If they
- are not set, it shows a sidebar with two text inputs to manually enter the API URL
- and API Key
-
- Args:
- title: The title of the app.
-
- Returns:
- The api_url is being returned.
- """
- x = st.columns(3)
- x[0].image("https://docs.argilla.io/en/latest/_static/images/logo-light-mode.svg", use_column_width=True)
-
- api_url, api_key = None, None
-
- if os.environ.get("ARGILLA_API_URL") and os.environ.get("ARGILLA_API_KEY"):
- api_url = os.environ.get("ARGILLA_API_URL")
- api_key = os.environ.get("ARGILLA_API_KEY")
- rg.init(
- api_url=api_url,
- api_key=api_key,
- )
- st.success(
- f"Logged in at {os.environ.get('ARGILLA_API_URL')}, and workspace is"
- f" {rg.get_workspace()}. Change `ARGILLA_API_URL` and `ARGILLA_API_KEY` to"
- " use a different endpoint."
- )
- else:
- api_url = st.sidebar.text_input(
- "API URL", "https://argilla-live-demo.hf.space", type="password"
- )
- api_key = st.sidebar.text_input("API Key", value="team.apikey", type="password")
- try:
- rg.init(
- api_url=api_url,
- api_key=api_key,
- )
-
- st.success(
- f"Logged in at {api_url}, and workspace is {rg.get_workspace()}. Set"
- " `ARGILLA_API_URL` and `ARGILLA_API_KEY` as environment variables to"
- " avoid this step."
- )
- except Exception:
- st.error(
- "Invalid API URL or API Key. Use a correct manual input or, even"
- " better, set `ARGILLA_API_URL` and `ARGILLA_API_KEY` as environment"
- " variables to avoid this step."
- )
- st.title(title)
- return api_url, api_key
-
-
-
-def get_data_snapshot(dataset_name, workspace, query=None):
- rg.set_workspace(workspace)
- if query == "":
- query = None
- ds = rg.load(dataset_name, query=query, limit=5).to_pandas()
- st.write(f"Sample of the `{workspace}/{dataset_name}`", ds)
-
-
-def hf_login_flow():
- """
- It checks if the user has provided a Hugging Face API token in the environment variable
- `HF_AUTH_TOKEN` or in the sidebar. If not, it displays an error message and stops the app
-
- Returns:
- A tuple of the token and the api
- """
- hf_auth_token = os.environ.get("HF_AUTH_TOKEN", "")
- if not hf_auth_token:
- hf_auth_token = st.sidebar.text_input(
- "Hugging Face [User Access Tokens](https://huggingface.co/settings/tokens)",
- os.environ.get("HF_AUTH_TOKEN", ""), type="password"
- )
- if not hf_auth_token:
- st.error(
- "Please provide a Hugging Face [User Access"
- " Tokens](https://huggingface.co/settings/tokens) in the sidebar or set"
- " `HF_AUTH_TOKEN` as environment variable"
- )
- st.stop()
- api = HfApi(token=hf_auth_token)
- return hf_auth_token, api
-
-
-# def record_info():
-# with st.expander("Dataset Type Info"):
-# if dataset_type == "TextClassification":
-# st.write(rg.TextClassificationRecord.__doc__)
-# elif dataset_type == "TokenClassification":
-# st.write(rg.TokenClassificationRecord.__doc__)
-# else:
-# st.write(rg.Text2TextRecord.__doc__)
-
-
-def get_dataset_list(api_url, api_key):
- client = ArgillaSingleton.init(api_url, api_key)._client
- url = "{}/api/workspaces".format(client.base_url)
- response = httpx.get(
- url=url,
- headers=client.get_headers(),
- cookies=client.get_cookies(),
- timeout=client.get_timeout(),
- )
- response.raise_for_status()
- worskpace_names = [w["name"] for w in response.json()]
-
- datasets = []
- for name in worskpace_names:
- url = "{}/api/datasets?workspace={}".format(client.base_url, name)
- response = httpx.get(
- url=url,
- headers=client.get_headers(),
- cookies=client.get_cookies(),
- timeout=client.get_timeout(),
- )
- response.raise_for_status()
- datasets += response.json()
-
- dataset_overview = []
- for dataset in datasets:
- metadata = dataset["metadata"].values()
- if (
- metadata
- and not isinstance(list(metadata)[0], str)
- and not isinstance(list(metadata)[0], int)
- ):
- metadata = list(metadata)[0].get(
- "labels", list(metadata)[0].get("entities")
- )
- else:
- metadata = None
- dataset_overview.append(
- {
- "name": dataset["name"],
- "task": dataset["task"],
- "owner": dataset["owner"],
- "id": dataset["id"],
- "labels": metadata,
- }
- )
- # if metadata is None:
- # # dataset_overview[-1]["labels"] =
- # setting = get_dataset_settings(dataset["name"], dataset["task"])
- # if setting is not None:
- # dataset_overview[-1]["labels"] = list(setting)
- return dataset_overview
-
-
-def whoami():
- client = active_client()._client
- url = "{}/api/me".format(client.base_url)
- response = httpx.get(
- url=url,
- headers=client.get_headers(),
- cookies=client.get_cookies(),
- timeout=client.get_timeout(),
- )
- return {**response.json()}
-
-
-def get_dataset_settings(dataset_name, dataset_task):
- client = active_client()._client
- url = f"{client.base_url}/api/datasets/{dataset_task}/{dataset_name}/settings"
- response = httpx.get(
- url=url,
- headers=client.get_headers(),
- cookies=client.get_cookies(),
- timeout=client.get_timeout(),
- )
-
- try:
- response.raise_for_status()
- return (
- __TASK_TO_SETTINGS__.get(dataset_task)
- .from_dict(response.json())
- .label_schema
- )
- except Exception:
- return None
diff --git a/spaces/artificialguybr/video-dubbing/TTS/CODE_OF_CONDUCT.md b/spaces/artificialguybr/video-dubbing/TTS/CODE_OF_CONDUCT.md
deleted file mode 100644
index b80639d63c29e902c547de347806651bcc9ad3b2..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/CODE_OF_CONDUCT.md
+++ /dev/null
@@ -1,133 +0,0 @@
-
-# Contributor Covenant Code of Conduct
-
-## Our Pledge
-
-We as members, contributors, and leaders pledge to make participation in our
-community a harassment-free experience for everyone, regardless of age, body
-size, visible or invisible disability, ethnicity, sex characteristics, gender
-identity and expression, level of experience, education, socio-economic status,
-nationality, personal appearance, race, caste, color, religion, or sexual identity
-and orientation.
-
-We pledge to act and interact in ways that contribute to an open, welcoming,
-diverse, inclusive, and healthy community.
-
-## Our Standards
-
-Examples of behavior that contributes to a positive environment for our
-community include:
-
-* Demonstrating empathy and kindness toward other people
-* Being respectful of differing opinions, viewpoints, and experiences
-* Giving and gracefully accepting constructive feedback
-* Accepting responsibility and apologizing to those affected by our mistakes,
- and learning from the experience
-* Focusing on what is best not just for us as individuals, but for the
- overall community
-
-Examples of unacceptable behavior include:
-
-* The use of sexualized language or imagery, and sexual attention or
- advances of any kind
-* Trolling, insulting or derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or email
- address, without their explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
- professional setting
-
-## Enforcement Responsibilities
-
-Community leaders are responsible for clarifying and enforcing our standards of
-acceptable behavior and will take appropriate and fair corrective action in
-response to any behavior that they deem inappropriate, threatening, offensive,
-or harmful.
-
-Community leaders have the right and responsibility to remove, edit, or reject
-comments, commits, code, wiki edits, issues, and other contributions that are
-not aligned to this Code of Conduct, and will communicate reasons for moderation
-decisions when appropriate.
-
-## Scope
-
-This Code of Conduct applies within all community spaces, and also applies when
-an individual is officially representing the community in public spaces.
-Examples of representing our community include using an official e-mail address,
-posting via an official social media account, or acting as an appointed
-representative at an online or offline event.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported to the community leaders responsible for enforcement at
-coc-report@coqui.ai.
-All complaints will be reviewed and investigated promptly and fairly.
-
-All community leaders are obligated to respect the privacy and security of the
-reporter of any incident.
-
-## Enforcement Guidelines
-
-Community leaders will follow these Community Impact Guidelines in determining
-the consequences for any action they deem in violation of this Code of Conduct:
-
-### 1. Correction
-
-**Community Impact**: Use of inappropriate language or other behavior deemed
-unprofessional or unwelcome in the community.
-
-**Consequence**: A private, written warning from community leaders, providing
-clarity around the nature of the violation and an explanation of why the
-behavior was inappropriate. A public apology may be requested.
-
-### 2. Warning
-
-**Community Impact**: A violation through a single incident or series
-of actions.
-
-**Consequence**: A warning with consequences for continued behavior. No
-interaction with the people involved, including unsolicited interaction with
-those enforcing the Code of Conduct, for a specified period of time. This
-includes avoiding interactions in community spaces as well as external channels
-like social media. Violating these terms may lead to a temporary or
-permanent ban.
-
-### 3. Temporary Ban
-
-**Community Impact**: A serious violation of community standards, including
-sustained inappropriate behavior.
-
-**Consequence**: A temporary ban from any sort of interaction or public
-communication with the community for a specified period of time. No public or
-private interaction with the people involved, including unsolicited interaction
-with those enforcing the Code of Conduct, is allowed during this period.
-Violating these terms may lead to a permanent ban.
-
-### 4. Permanent Ban
-
-**Community Impact**: Demonstrating a pattern of violation of community
-standards, including sustained inappropriate behavior, harassment of an
-individual, or aggression toward or disparagement of classes of individuals.
-
-**Consequence**: A permanent ban from any sort of public interaction within
-the community.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage],
-version 2.0, available at
-[https://www.contributor-covenant.org/version/2/0/code_of_conduct.html][v2.0].
-
-Community Impact Guidelines were inspired by
-[Mozilla's code of conduct enforcement ladder][Mozilla CoC].
-
-For answers to common questions about this code of conduct, see the FAQ at
-[https://www.contributor-covenant.org/faq][FAQ]. Translations are available
-at [https://www.contributor-covenant.org/translations][translations].
-
-[homepage]: https://www.contributor-covenant.org
-[v2.0]: https://www.contributor-covenant.org/version/2/0/code_of_conduct.html
-[Mozilla CoC]: https://github.com/mozilla/diversity
-[FAQ]: https://www.contributor-covenant.org/faq
-[translations]: https://www.contributor-covenant.org/translations
diff --git a/spaces/artificialguybr/video-dubbing/TTS/docs/source/models/xtts.md b/spaces/artificialguybr/video-dubbing/TTS/docs/source/models/xtts.md
deleted file mode 100644
index 03e44af1707a97c05a1a535215e6e71904221425..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/docs/source/models/xtts.md
+++ /dev/null
@@ -1,251 +0,0 @@
-# ⓍTTS
-ⓍTTS is a super cool Text-to-Speech model that lets you clone voices in different languages by using just a quick 3-second audio clip. Built on the 🐢Tortoise,
-ⓍTTS has important model changes that make cross-language voice cloning and multi-lingual speech generation super easy.
-There is no need for an excessive amount of training data that spans countless hours.
-
-This is the same model that powers [Coqui Studio](https://coqui.ai/), and [Coqui API](https://docs.coqui.ai/docs), however we apply
-a few tricks to make it faster and support streaming inference.
-
-### Features
-- Voice cloning.
-- Cross-language voice cloning.
-- Multi-lingual speech generation.
-- 24khz sampling rate.
-- Streaming inference with < 200ms latency. (See [Streaming inference](#streaming-inference))
-- Fine-tuning support. (See [Training](#training))
-
-### Updates with v2
-- Improved voice cloning.
-- Voices can be cloned with a single audio file or multiple audio files, without any effect on the runtime.
-- 2 new languages: Hungarian and Korean.
-- Across the board quality improvements.
-
-### Code
-Current implementation only supports inference.
-
-### Languages
-As of now, XTTS-v2 supports 16 languages: English (en), Spanish (es), French (fr), German (de), Italian (it), Portuguese (pt), Polish (pl), Turkish (tr), Russian (ru), Dutch (nl), Czech (cs), Arabic (ar), Chinese (zh-cn), Japanese (ja), Hungarian (hu) and Korean (ko).
-
-Stay tuned as we continue to add support for more languages. If you have any language requests, please feel free to reach out.
-
-### License
-This model is licensed under [Coqui Public Model License](https://coqui.ai/cpml).
-
-### Contact
-Come and join in our 🐸Community. We're active on [Discord](https://discord.gg/fBC58unbKE) and [Twitter](https://twitter.com/coqui_ai).
-You can also mail us at info@coqui.ai.
-
-### Inference
-#### 🐸TTS API
-
-##### Single reference
-```python
-from TTS.api import TTS
-tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2", gpu=True)
-
-# generate speech by cloning a voice using default settings
-tts.tts_to_file(text="It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- file_path="output.wav",
- speaker_wav=["/path/to/target/speaker.wav"],
- language="en")
-```
-
-##### Multiple references
-```python
-from TTS.api import TTS
-tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2", gpu=True)
-
-# generate speech by cloning a voice using default settings
-tts.tts_to_file(text="It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.",
- file_path="output.wav",
- speaker_wav=["/path/to/target/speaker.wav", "/path/to/target/speaker_2.wav", "/path/to/target/speaker_3.wav"],
- language="en")
-```
-
-#### 🐸TTS Command line
-
-##### Single reference
-```console
- tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 \
- --text "Bugün okula gitmek istemiyorum." \
- --speaker_wav /path/to/target/speaker.wav \
- --language_idx tr \
- --use_cuda true
-```
-
-##### Multiple references
-```console
- tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 \
- --text "Bugün okula gitmek istemiyorum." \
- --speaker_wav /path/to/target/speaker.wav /path/to/target/speaker_2.wav /path/to/target/speaker_3.wav \
- --language_idx tr \
- --use_cuda true
-```
-or for all wav files in a directory you can use:
-
-```console
- tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 \
- --text "Bugün okula gitmek istemiyorum." \
- --speaker_wav /path/to/target/*.wav \
- --language_idx tr \
- --use_cuda true
-```
-
-
-#### model directly
-
-If you want to be able to run with `use_deepspeed=True` and enjoy the speedup, you need to install deepspeed first.
-
-```console
-pip install deepspeed==0.8.3
-```
-
-```python
-import os
-import torch
-import torchaudio
-from TTS.tts.configs.xtts_config import XttsConfig
-from TTS.tts.models.xtts import Xtts
-
-print("Loading model...")
-config = XttsConfig()
-config.load_json("/path/to/xtts/config.json")
-model = Xtts.init_from_config(config)
-model.load_checkpoint(config, checkpoint_dir="/path/to/xtts/", use_deepspeed=True)
-model.cuda()
-
-print("Computing speaker latents...")
-gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(audio_path=["reference.wav"])
-
-print("Inference...")
-out = model.inference(
- "It took me quite a long time to develop a voice and now that I have it I am not going to be silent.",
- "en",
- gpt_cond_latent,
- speaker_embedding,
- temperature=0.7, # Add custom parameters here
-)
-torchaudio.save("xtts.wav", torch.tensor(out["wav"]).unsqueeze(0), 24000)
-```
-
-
-#### streaming inference
-
-Here the goal is to stream the audio as it is being generated. This is useful for real-time applications.
-Streaming inference is typically slower than regular inference, but it allows to get a first chunk of audio faster.
-
-
-```python
-import os
-import time
-import torch
-import torchaudio
-from TTS.tts.configs.xtts_config import XttsConfig
-from TTS.tts.models.xtts import Xtts
-
-print("Loading model...")
-config = XttsConfig()
-config.load_json("/path/to/xtts/config.json")
-model = Xtts.init_from_config(config)
-model.load_checkpoint(config, checkpoint_dir="/path/to/xtts/", use_deepspeed=True)
-model.cuda()
-
-print("Computing speaker latents...")
-gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(audio_path=["reference.wav"])
-
-print("Inference...")
-t0 = time.time()
-chunks = model.inference_stream(
- "It took me quite a long time to develop a voice and now that I have it I am not going to be silent.",
- "en",
- gpt_cond_latent,
- speaker_embedding
-)
-
-wav_chuncks = []
-for i, chunk in enumerate(chunks):
- if i == 0:
- print(f"Time to first chunck: {time.time() - t0}")
- print(f"Received chunk {i} of audio length {chunk.shape[-1]}")
- wav_chuncks.append(chunk)
-wav = torch.cat(wav_chuncks, dim=0)
-torchaudio.save("xtts_streaming.wav", wav.squeeze().unsqueeze(0).cpu(), 24000)
-```
-
-
-### Training
-
-A recipe for `XTTS_v2` GPT encoder training using `LJSpeech` dataset is available at https://github.com/coqui-ai/TTS/tree/dev/recipes/ljspeech/xtts_v1/train_gpt_xtts.py
-
-You need to change the fields of the `BaseDatasetConfig` to match your dataset and then update `GPTArgs` and `GPTTrainerConfig` fields as you need. By default, it will use the same parameters that XTTS v1.1 model was trained with. To speed up the model convergence, as default, it will also download the XTTS v1.1 checkpoint and load it.
-
-After training you can do inference following the code bellow.
-
-```python
-import os
-import torch
-import torchaudio
-from TTS.tts.configs.xtts_config import XttsConfig
-from TTS.tts.models.xtts import Xtts
-
-# Add here the xtts_config path
-CONFIG_PATH = "recipes/ljspeech/xtts_v1/run/training/GPT_XTTS_LJSpeech_FT-October-23-2023_10+36AM-653f2e75/config.json"
-# Add here the vocab file that you have used to train the model
-TOKENIZER_PATH = "recipes/ljspeech/xtts_v1/run/training/XTTS_v2_original_model_files/vocab.json"
-# Add here the checkpoint that you want to do inference with
-XTTS_CHECKPOINT = "recipes/ljspeech/xtts_v1/run/training/GPT_XTTS_LJSpeech_FT/best_model.pth"
-# Add here the speaker reference
-SPEAKER_REFERENCE = "LjSpeech_reference.wav"
-
-# output wav path
-OUTPUT_WAV_PATH = "xtts-ft.wav"
-
-print("Loading model...")
-config = XttsConfig()
-config.load_json(CONFIG_PATH)
-model = Xtts.init_from_config(config)
-model.load_checkpoint(config, checkpoint_path=XTTS_CHECKPOINT, vocab_path=TOKENIZER_PATH, use_deepspeed=False)
-model.cuda()
-
-print("Computing speaker latents...")
-gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(audio_path=[SPEAKER_REFERENCE])
-
-print("Inference...")
-out = model.inference(
- "It took me quite a long time to develop a voice and now that I have it I am not going to be silent.",
- "en",
- gpt_cond_latent,
- speaker_embedding,
- temperature=0.7, # Add custom parameters here
-)
-torchaudio.save(OUTPUT_WAV_PATH, torch.tensor(out["wav"]).unsqueeze(0), 24000)
-```
-
-
-## References and Acknowledgements
-- VallE: https://arxiv.org/abs/2301.02111
-- Tortoise Repo: https://github.com/neonbjb/tortoise-tts
-- Faster implementation: https://github.com/152334H/tortoise-tts-fast
-- Univnet: https://arxiv.org/abs/2106.07889
-- Latent Diffusion:https://arxiv.org/abs/2112.10752
-- DALL-E: https://arxiv.org/abs/2102.12092
-- Perceiver: https://arxiv.org/abs/2103.03206
-
-
-## XttsConfig
-```{eval-rst}
-.. autoclass:: TTS.tts.configs.xtts_config.XttsConfig
- :members:
-```
-
-## XttsArgs
-```{eval-rst}
-.. autoclass:: TTS.tts.models.xtts.XttsArgs
- :members:
-```
-
-## XTTS Model
-```{eval-rst}
-.. autoclass:: TTS.tts.models.xtts.XTTS
- :members:
-```
diff --git a/spaces/aseifert/writing-assistant/README.md b/spaces/aseifert/writing-assistant/README.md
deleted file mode 100644
index aa6c9ceda53155f83b1282f1b4208b233b2d1681..0000000000000000000000000000000000000000
--- a/spaces/aseifert/writing-assistant/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Writing Assistant
-emoji: 📊
-colorFrom: blue
-colorTo: gray
-sdk: streamlit
-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/awacke1/04-AW-StorywriterwMem/README.md b/spaces/awacke1/04-AW-StorywriterwMem/README.md
deleted file mode 100644
index dee5d8a05f3975a8b80215895c6f657f9e27feb8..0000000000000000000000000000000000000000
--- a/spaces/awacke1/04-AW-StorywriterwMem/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: 04 AW StorywriterwMem
-emoji: 📉
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 3.6
-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/awacke1/GradioVoicetoTexttoSentiment/app.py b/spaces/awacke1/GradioVoicetoTexttoSentiment/app.py
deleted file mode 100644
index 6066bc8c5bd0572c67119cf0f910228e3ad05ab6..0000000000000000000000000000000000000000
--- a/spaces/awacke1/GradioVoicetoTexttoSentiment/app.py
+++ /dev/null
@@ -1,73 +0,0 @@
-from transformers import pipeline
-import gradio as gr
-
-asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h")
-classifier = pipeline("text-classification", "michellejieli/emotion_text_classifier")
-
-def transcribe(speech, state=""):
- text = asr(speech)["text"]
- state += text + " "
- return text, state
-
-def speech_to_text(speech):
- text = asr(speech)["text"]
- return text
-
-def text_to_sentiment(text):
- return classifier(text)[0]["label"]
-
-
-demo = gr.Blocks()
-with demo:
-
- microphone = gr.Audio(source="microphone", type="filepath")
- audio_file = gr.Audio(type="filepath")
- text = gr.Textbox()
- label = gr.Label()
-
- b0 = gr.Button("Speech From Microphone")
- b1 = gr.Button("Recognize Speech")
- b2 = gr.Button("Classify Sentiment")
-
- #b0.click(transcribe, inputs=[microphone, "state"], outputs=[text, "state"], live=True)
- b0.click(transcribe, inputs=[microphone], outputs=[text])
- b1.click(speech_to_text, inputs=audio_file, outputs=text)
- b2.click(text_to_sentiment, inputs=text, outputs=label)
-
- gr.Markdown("""References:
-
-## Building an Asynchronous Real-Time Live Telemedicine System Using AI Pipelines for Smart Communities
-
-1. **Designing the Telemedicine System**
- - Identify the needs and challenges of smart communities and design a telemedicine system that addresses these challenges.
- - Choose a platform that allows for asynchronous real-time communication, such as video conferencing or chat-based messaging, to facilitate remote consultations with healthcare providers.
- - Design the system to incorporate AI pipelines that can analyze patient data and provide decision support for healthcare providers.
-
-2. **Implementing the AI Pipelines**
- - Identify the relevant AI algorithms and techniques that can be used to analyze patient data, such as machine learning or natural language processing.
- - Integrate these AI pipelines into the telemedicine system to provide decision support for healthcare providers during consultations.
- - Ensure that the AI algorithms are accurate and reliable by testing them on a large and diverse set of patient data.
-
-3. **Deploying the Telemedicine System**
- - Deploy the telemedicine system in smart communities, ensuring that it is easily accessible and user-friendly for patients and healthcare providers.
- - Train healthcare providers on how to use the system effectively and provide ongoing support and feedback to optimize its use.
- - Continuously monitor and evaluate the system's performance, making improvements and updates as needed to ensure that it remains effective and efficient in meeting the needs of smart communities.
-
-**__Asynchronous Telemedicine:__ A Solution to Address Provider Shortages by Offering Remote Care Services.**
-([Wikipedia](https://en.wikipedia.org/wiki/Telemedicine))
-
-
-# 2023's Top 7 Breakthroughs in Medical Technology
-1. __Asynchronous Telemedicine:__ A Solution to Address Provider Shortages by Offering Remote Care Services. ([Wikipedia](https://en.wikipedia.org/wiki/Telemedicine))
-2. __Ambient and Emotion AI:__ Empowering Patients with Artificial Intelligence That Shows Empathy and Compassion. ([Wikipedia](https://en.wikipedia.org/wiki/Ambient_intelligence))
-3. __Skin Patch Technology:__ A Convenient Way to Measure Vital Signals such as Blood Pressure and Glucose Levels. ([Wikipedia](https://en.wikipedia.org/wiki/Skin_patch))
-4. __Affordable Vein Scanner:__ A Revolutionary Tool to View Veins Through the Skin. ([Wikipedia](https://en.wikipedia.org/wiki/Vein_matching))
-5. __Synthetic Medical Records:__ Creating Reliable Medical Records Using Generative Adversarial Networks. ([Wikipedia](https://en.wikipedia.org/wiki/Synthetic_data))
-6. __Blood Draw Devices for Clinical Trials:__ Facilitating Remote Participation in Trials with Innovative Technology. ([Wikipedia](https://en.wikipedia.org/wiki/Blood_sampling))
-7. __Smart TVs for Remote Care:__ Enhancing Remote Care Consultations with Video Chat and Recordings. ([Wikipedia](https://en.wikipedia.org/wiki/Smart_television))
-
-Reference: [The Medical Futurist](https://www.youtube.com/watch?v=_9DpLD4S2AY&list=PLHgX2IExbFotoMt32SrT3Xynt5BXTGnEP&index=2)
-
- """)
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/awacke1/InContextLearning-PromptTargeting/backup.app.py b/spaces/awacke1/InContextLearning-PromptTargeting/backup.app.py
deleted file mode 100644
index d5811b6ec7a1e176e20c3a5a17272f70bdde2d81..0000000000000000000000000000000000000000
--- a/spaces/awacke1/InContextLearning-PromptTargeting/backup.app.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import streamlit as st
-import graphviz as gv
-import json
-import os
-
-FILE_NAME = 'saved_data.json'
-
-# Function to create the Graphviz graph
-def create_graph():
- g = gv.Digraph('G', engine='dot', format='png')
-
- # Create the first box
- box1_label = 'Source Sequence: Question{s1,s2,s3}\nContext: {sx,sy,sz}\nAnswer: {s1,s2,s3}'
- g.node('box1', label=box1_label, shape='box', style='rounded')
-
- # Create the second box
- box2_label = 'Target Sequence: The answer to the question given the context is yes.'
- g.node('box2', label=box2_label, shape='box', style='rounded')
-
- # Add the line connecting the two boxes
- g.edge('box1', 'box2')
-
- return g
-
-def save_data(data):
- with open(FILE_NAME, 'w') as f:
- json.dump(data, f)
-
-def load_data():
- if not os.path.exists(FILE_NAME):
- return {}
- with open(FILE_NAME, 'r') as f:
- return json.load(f)
-
-# Create the graph
-graph = create_graph()
-
-# Streamlit app
-st.title("In Context Learning - Prompt Targeting QA Pattern")
-st.subheader("The Question / Answer pattern below can be used in concert with a LLM to do real time in context learning using general intelligence.")
-st.graphviz_chart(graph)
-
-data = load_data()
-
-# Input fields
-st.header("Enter your data")
-question = st.text_input("Question:")
-context = st.text_input("Context:")
-answer = st.text_input("Answer:")
-target_sequence = st.text_input("Target Sequence:")
-
-if st.button("Save"):
- if question and context and answer and target_sequence:
- data["question"] = question
- data["context"] = context
- data["answer"] = answer
- data["target_sequence"] = target_sequence
- save_data(data)
- st.success("Data saved successfully.")
- else:
- st.error("Please fill in all fields.")
-
-st.header("Saved data")
-st.write(data)
diff --git a/spaces/ayaanzaveri/whisper-webui/tests/vad_test.py b/spaces/ayaanzaveri/whisper-webui/tests/vad_test.py
deleted file mode 100644
index b465d8a380f9316a6830d9aac320c85f22aba0a0..0000000000000000000000000000000000000000
--- a/spaces/ayaanzaveri/whisper-webui/tests/vad_test.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import pprint
-import unittest
-import numpy as np
-import sys
-
-sys.path.append('../whisper-webui')
-
-from src.vad import AbstractTranscription, TranscriptionConfig, VadSileroTranscription
-
-class TestVad(unittest.TestCase):
- def __init__(self, *args, **kwargs):
- super(TestVad, self).__init__(*args, **kwargs)
- self.transcribe_calls = []
-
- def test_transcript(self):
- mock = MockVadTranscription()
-
- self.transcribe_calls.clear()
- result = mock.transcribe("mock", lambda segment : self.transcribe_segments(segment))
-
- self.assertListEqual(self.transcribe_calls, [
- [30, 30],
- [100, 100]
- ])
-
- self.assertListEqual(result['segments'],
- [{'end': 50.0, 'start': 40.0, 'text': 'Hello world '},
- {'end': 120.0, 'start': 110.0, 'text': 'Hello world '}]
- )
-
- def transcribe_segments(self, segment):
- self.transcribe_calls.append(segment.tolist())
-
- # Dummy text
- return {
- 'text': "Hello world ",
- 'segments': [
- {
- "start": 10.0,
- "end": 20.0,
- "text": "Hello world "
- }
- ],
- 'language': ""
- }
-
-class MockVadTranscription(AbstractTranscription):
- def __init__(self):
- super().__init__()
-
- def get_audio_segment(self, str, start_time: str = None, duration: str = None):
- start_time_seconds = float(start_time.removesuffix("s"))
- duration_seconds = float(duration.removesuffix("s"))
-
- # For mocking, this just returns a simple numppy array
- return np.array([start_time_seconds, duration_seconds], dtype=np.float64)
-
- def get_transcribe_timestamps(self, audio: str, config: TranscriptionConfig, start_time: float, duration: float):
- result = []
-
- result.append( { 'start': 30, 'end': 60 } )
- result.append( { 'start': 100, 'end': 200 } )
- return result
-
-if __name__ == '__main__':
- unittest.main()
\ No newline at end of file
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/animation/PropertyBinding.js b/spaces/banana-projects/web3d/node_modules/three/src/animation/PropertyBinding.js
deleted file mode 100644
index 4e89bd3e2758f02d780634c78d8a77bb28c2dcdf..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/animation/PropertyBinding.js
+++ /dev/null
@@ -1,723 +0,0 @@
-/**
- *
- * A reference to a real property in the scene graph.
- *
- *
- * @author Ben Houston / http://clara.io/
- * @author David Sarno / http://lighthaus.us/
- * @author tschw
- */
-
-// Characters [].:/ are reserved for track binding syntax.
-var RESERVED_CHARS_RE = '\\[\\]\\.:\\/';
-
-function Composite( targetGroup, path, optionalParsedPath ) {
-
- var parsedPath = optionalParsedPath || PropertyBinding.parseTrackName( path );
-
- this._targetGroup = targetGroup;
- this._bindings = targetGroup.subscribe_( path, parsedPath );
-
-}
-
-Object.assign( Composite.prototype, {
-
- getValue: function ( array, offset ) {
-
- this.bind(); // bind all binding
-
- var firstValidIndex = this._targetGroup.nCachedObjects_,
- binding = this._bindings[ firstValidIndex ];
-
- // and only call .getValue on the first
- if ( binding !== undefined ) binding.getValue( array, offset );
-
- },
-
- setValue: function ( array, offset ) {
-
- var bindings = this._bindings;
-
- for ( var i = this._targetGroup.nCachedObjects_, n = bindings.length; i !== n; ++ i ) {
-
- bindings[ i ].setValue( array, offset );
-
- }
-
- },
-
- bind: function () {
-
- var bindings = this._bindings;
-
- for ( var i = this._targetGroup.nCachedObjects_, n = bindings.length; i !== n; ++ i ) {
-
- bindings[ i ].bind();
-
- }
-
- },
-
- unbind: function () {
-
- var bindings = this._bindings;
-
- for ( var i = this._targetGroup.nCachedObjects_, n = bindings.length; i !== n; ++ i ) {
-
- bindings[ i ].unbind();
-
- }
-
- }
-
-} );
-
-
-function PropertyBinding( rootNode, path, parsedPath ) {
-
- this.path = path;
- this.parsedPath = parsedPath || PropertyBinding.parseTrackName( path );
-
- this.node = PropertyBinding.findNode( rootNode, this.parsedPath.nodeName ) || rootNode;
-
- this.rootNode = rootNode;
-
-}
-
-Object.assign( PropertyBinding, {
-
- Composite: Composite,
-
- create: function ( root, path, parsedPath ) {
-
- if ( ! ( root && root.isAnimationObjectGroup ) ) {
-
- return new PropertyBinding( root, path, parsedPath );
-
- } else {
-
- return new PropertyBinding.Composite( root, path, parsedPath );
-
- }
-
- },
-
- /**
- * Replaces spaces with underscores and removes unsupported characters from
- * node names, to ensure compatibility with parseTrackName().
- *
- * @param {string} name Node name to be sanitized.
- * @return {string}
- */
- sanitizeNodeName: ( function () {
-
- var reservedRe = new RegExp( '[' + RESERVED_CHARS_RE + ']', 'g' );
-
- return function sanitizeNodeName( name ) {
-
- return name.replace( /\s/g, '_' ).replace( reservedRe, '' );
-
- };
-
- }() ),
-
- parseTrackName: function () {
-
- // Attempts to allow node names from any language. ES5's `\w` regexp matches
- // only latin characters, and the unicode \p{L} is not yet supported. So
- // instead, we exclude reserved characters and match everything else.
- var wordChar = '[^' + RESERVED_CHARS_RE + ']';
- var wordCharOrDot = '[^' + RESERVED_CHARS_RE.replace( '\\.', '' ) + ']';
-
- // Parent directories, delimited by '/' or ':'. Currently unused, but must
- // be matched to parse the rest of the track name.
- var directoryRe = /((?:WC+[\/:])*)/.source.replace( 'WC', wordChar );
-
- // Target node. May contain word characters (a-zA-Z0-9_) and '.' or '-'.
- var nodeRe = /(WCOD+)?/.source.replace( 'WCOD', wordCharOrDot );
-
- // Object on target node, and accessor. May not contain reserved
- // characters. Accessor may contain any character except closing bracket.
- var objectRe = /(?:\.(WC+)(?:\[(.+)\])?)?/.source.replace( 'WC', wordChar );
-
- // Property and accessor. May not contain reserved characters. Accessor may
- // contain any non-bracket characters.
- var propertyRe = /\.(WC+)(?:\[(.+)\])?/.source.replace( 'WC', wordChar );
-
- var trackRe = new RegExp( ''
- + '^'
- + directoryRe
- + nodeRe
- + objectRe
- + propertyRe
- + '$'
- );
-
- var supportedObjectNames = [ 'material', 'materials', 'bones' ];
-
- return function parseTrackName( trackName ) {
-
- var matches = trackRe.exec( trackName );
-
- if ( ! matches ) {
-
- throw new Error( 'PropertyBinding: Cannot parse trackName: ' + trackName );
-
- }
-
- var results = {
- // directoryName: matches[ 1 ], // (tschw) currently unused
- nodeName: matches[ 2 ],
- objectName: matches[ 3 ],
- objectIndex: matches[ 4 ],
- propertyName: matches[ 5 ], // required
- propertyIndex: matches[ 6 ]
- };
-
- var lastDot = results.nodeName && results.nodeName.lastIndexOf( '.' );
-
- if ( lastDot !== undefined && lastDot !== - 1 ) {
-
- var objectName = results.nodeName.substring( lastDot + 1 );
-
- // Object names must be checked against a whitelist. Otherwise, there
- // is no way to parse 'foo.bar.baz': 'baz' must be a property, but
- // 'bar' could be the objectName, or part of a nodeName (which can
- // include '.' characters).
- if ( supportedObjectNames.indexOf( objectName ) !== - 1 ) {
-
- results.nodeName = results.nodeName.substring( 0, lastDot );
- results.objectName = objectName;
-
- }
-
- }
-
- if ( results.propertyName === null || results.propertyName.length === 0 ) {
-
- throw new Error( 'PropertyBinding: can not parse propertyName from trackName: ' + trackName );
-
- }
-
- return results;
-
- };
-
- }(),
-
- findNode: function ( root, nodeName ) {
-
- if ( ! nodeName || nodeName === "" || nodeName === "root" || nodeName === "." || nodeName === - 1 || nodeName === root.name || nodeName === root.uuid ) {
-
- return root;
-
- }
-
- // search into skeleton bones.
- if ( root.skeleton ) {
-
- var bone = root.skeleton.getBoneByName( nodeName );
-
- if ( bone !== undefined ) {
-
- return bone;
-
- }
-
- }
-
- // search into node subtree.
- if ( root.children ) {
-
- var searchNodeSubtree = function ( children ) {
-
- for ( var i = 0; i < children.length; i ++ ) {
-
- var childNode = children[ i ];
-
- if ( childNode.name === nodeName || childNode.uuid === nodeName ) {
-
- return childNode;
-
- }
-
- var result = searchNodeSubtree( childNode.children );
-
- if ( result ) return result;
-
- }
-
- return null;
-
- };
-
- var subTreeNode = searchNodeSubtree( root.children );
-
- if ( subTreeNode ) {
-
- return subTreeNode;
-
- }
-
- }
-
- return null;
-
- }
-
-} );
-
-Object.assign( PropertyBinding.prototype, { // prototype, continued
-
- // these are used to "bind" a nonexistent property
- _getValue_unavailable: function () {},
- _setValue_unavailable: function () {},
-
- BindingType: {
- Direct: 0,
- EntireArray: 1,
- ArrayElement: 2,
- HasFromToArray: 3
- },
-
- Versioning: {
- None: 0,
- NeedsUpdate: 1,
- MatrixWorldNeedsUpdate: 2
- },
-
- GetterByBindingType: [
-
- function getValue_direct( buffer, offset ) {
-
- buffer[ offset ] = this.node[ this.propertyName ];
-
- },
-
- function getValue_array( buffer, offset ) {
-
- var source = this.resolvedProperty;
-
- for ( var i = 0, n = source.length; i !== n; ++ i ) {
-
- buffer[ offset ++ ] = source[ i ];
-
- }
-
- },
-
- function getValue_arrayElement( buffer, offset ) {
-
- buffer[ offset ] = this.resolvedProperty[ this.propertyIndex ];
-
- },
-
- function getValue_toArray( buffer, offset ) {
-
- this.resolvedProperty.toArray( buffer, offset );
-
- }
-
- ],
-
- SetterByBindingTypeAndVersioning: [
-
- [
- // Direct
-
- function setValue_direct( buffer, offset ) {
-
- this.targetObject[ this.propertyName ] = buffer[ offset ];
-
- },
-
- function setValue_direct_setNeedsUpdate( buffer, offset ) {
-
- this.targetObject[ this.propertyName ] = buffer[ offset ];
- this.targetObject.needsUpdate = true;
-
- },
-
- function setValue_direct_setMatrixWorldNeedsUpdate( buffer, offset ) {
-
- this.targetObject[ this.propertyName ] = buffer[ offset ];
- this.targetObject.matrixWorldNeedsUpdate = true;
-
- }
-
- ], [
-
- // EntireArray
-
- function setValue_array( buffer, offset ) {
-
- var dest = this.resolvedProperty;
-
- for ( var i = 0, n = dest.length; i !== n; ++ i ) {
-
- dest[ i ] = buffer[ offset ++ ];
-
- }
-
- },
-
- function setValue_array_setNeedsUpdate( buffer, offset ) {
-
- var dest = this.resolvedProperty;
-
- for ( var i = 0, n = dest.length; i !== n; ++ i ) {
-
- dest[ i ] = buffer[ offset ++ ];
-
- }
-
- this.targetObject.needsUpdate = true;
-
- },
-
- function setValue_array_setMatrixWorldNeedsUpdate( buffer, offset ) {
-
- var dest = this.resolvedProperty;
-
- for ( var i = 0, n = dest.length; i !== n; ++ i ) {
-
- dest[ i ] = buffer[ offset ++ ];
-
- }
-
- this.targetObject.matrixWorldNeedsUpdate = true;
-
- }
-
- ], [
-
- // ArrayElement
-
- function setValue_arrayElement( buffer, offset ) {
-
- this.resolvedProperty[ this.propertyIndex ] = buffer[ offset ];
-
- },
-
- function setValue_arrayElement_setNeedsUpdate( buffer, offset ) {
-
- this.resolvedProperty[ this.propertyIndex ] = buffer[ offset ];
- this.targetObject.needsUpdate = true;
-
- },
-
- function setValue_arrayElement_setMatrixWorldNeedsUpdate( buffer, offset ) {
-
- this.resolvedProperty[ this.propertyIndex ] = buffer[ offset ];
- this.targetObject.matrixWorldNeedsUpdate = true;
-
- }
-
- ], [
-
- // HasToFromArray
-
- function setValue_fromArray( buffer, offset ) {
-
- this.resolvedProperty.fromArray( buffer, offset );
-
- },
-
- function setValue_fromArray_setNeedsUpdate( buffer, offset ) {
-
- this.resolvedProperty.fromArray( buffer, offset );
- this.targetObject.needsUpdate = true;
-
- },
-
- function setValue_fromArray_setMatrixWorldNeedsUpdate( buffer, offset ) {
-
- this.resolvedProperty.fromArray( buffer, offset );
- this.targetObject.matrixWorldNeedsUpdate = true;
-
- }
-
- ]
-
- ],
-
- getValue: function getValue_unbound( targetArray, offset ) {
-
- this.bind();
- this.getValue( targetArray, offset );
-
- // Note: This class uses a State pattern on a per-method basis:
- // 'bind' sets 'this.getValue' / 'setValue' and shadows the
- // prototype version of these methods with one that represents
- // the bound state. When the property is not found, the methods
- // become no-ops.
-
- },
-
- setValue: function getValue_unbound( sourceArray, offset ) {
-
- this.bind();
- this.setValue( sourceArray, offset );
-
- },
-
- // create getter / setter pair for a property in the scene graph
- bind: function () {
-
- var targetObject = this.node,
- parsedPath = this.parsedPath,
-
- objectName = parsedPath.objectName,
- propertyName = parsedPath.propertyName,
- propertyIndex = parsedPath.propertyIndex;
-
- if ( ! targetObject ) {
-
- targetObject = PropertyBinding.findNode( this.rootNode, parsedPath.nodeName ) || this.rootNode;
-
- this.node = targetObject;
-
- }
-
- // set fail state so we can just 'return' on error
- this.getValue = this._getValue_unavailable;
- this.setValue = this._setValue_unavailable;
-
- // ensure there is a value node
- if ( ! targetObject ) {
-
- console.error( 'THREE.PropertyBinding: Trying to update node for track: ' + this.path + ' but it wasn\'t found.' );
- return;
-
- }
-
- if ( objectName ) {
-
- var objectIndex = parsedPath.objectIndex;
-
- // special cases were we need to reach deeper into the hierarchy to get the face materials....
- switch ( objectName ) {
-
- case 'materials':
-
- if ( ! targetObject.material ) {
-
- console.error( 'THREE.PropertyBinding: Can not bind to material as node does not have a material.', this );
- return;
-
- }
-
- if ( ! targetObject.material.materials ) {
-
- console.error( 'THREE.PropertyBinding: Can not bind to material.materials as node.material does not have a materials array.', this );
- return;
-
- }
-
- targetObject = targetObject.material.materials;
-
- break;
-
- case 'bones':
-
- if ( ! targetObject.skeleton ) {
-
- console.error( 'THREE.PropertyBinding: Can not bind to bones as node does not have a skeleton.', this );
- return;
-
- }
-
- // potential future optimization: skip this if propertyIndex is already an integer
- // and convert the integer string to a true integer.
-
- targetObject = targetObject.skeleton.bones;
-
- // support resolving morphTarget names into indices.
- for ( var i = 0; i < targetObject.length; i ++ ) {
-
- if ( targetObject[ i ].name === objectIndex ) {
-
- objectIndex = i;
- break;
-
- }
-
- }
-
- break;
-
- default:
-
- if ( targetObject[ objectName ] === undefined ) {
-
- console.error( 'THREE.PropertyBinding: Can not bind to objectName of node undefined.', this );
- return;
-
- }
-
- targetObject = targetObject[ objectName ];
-
- }
-
-
- if ( objectIndex !== undefined ) {
-
- if ( targetObject[ objectIndex ] === undefined ) {
-
- console.error( 'THREE.PropertyBinding: Trying to bind to objectIndex of objectName, but is undefined.', this, targetObject );
- return;
-
- }
-
- targetObject = targetObject[ objectIndex ];
-
- }
-
- }
-
- // resolve property
- var nodeProperty = targetObject[ propertyName ];
-
- if ( nodeProperty === undefined ) {
-
- var nodeName = parsedPath.nodeName;
-
- console.error( 'THREE.PropertyBinding: Trying to update property for track: ' + nodeName +
- '.' + propertyName + ' but it wasn\'t found.', targetObject );
- return;
-
- }
-
- // determine versioning scheme
- var versioning = this.Versioning.None;
-
- this.targetObject = targetObject;
-
- if ( targetObject.needsUpdate !== undefined ) { // material
-
- versioning = this.Versioning.NeedsUpdate;
-
- } else if ( targetObject.matrixWorldNeedsUpdate !== undefined ) { // node transform
-
- versioning = this.Versioning.MatrixWorldNeedsUpdate;
-
- }
-
- // determine how the property gets bound
- var bindingType = this.BindingType.Direct;
-
- if ( propertyIndex !== undefined ) {
-
- // access a sub element of the property array (only primitives are supported right now)
-
- if ( propertyName === "morphTargetInfluences" ) {
-
- // potential optimization, skip this if propertyIndex is already an integer, and convert the integer string to a true integer.
-
- // support resolving morphTarget names into indices.
- if ( ! targetObject.geometry ) {
-
- console.error( 'THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.', this );
- return;
-
- }
-
- if ( targetObject.geometry.isBufferGeometry ) {
-
- if ( ! targetObject.geometry.morphAttributes ) {
-
- console.error( 'THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.morphAttributes.', this );
- return;
-
- }
-
- for ( var i = 0; i < this.node.geometry.morphAttributes.position.length; i ++ ) {
-
- if ( targetObject.geometry.morphAttributes.position[ i ].name === propertyIndex ) {
-
- propertyIndex = i;
- break;
-
- }
-
- }
-
-
- } else {
-
- if ( ! targetObject.geometry.morphTargets ) {
-
- console.error( 'THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.morphTargets.', this );
- return;
-
- }
-
- for ( var i = 0; i < this.node.geometry.morphTargets.length; i ++ ) {
-
- if ( targetObject.geometry.morphTargets[ i ].name === propertyIndex ) {
-
- propertyIndex = i;
- break;
-
- }
-
- }
-
- }
-
- }
-
- bindingType = this.BindingType.ArrayElement;
-
- this.resolvedProperty = nodeProperty;
- this.propertyIndex = propertyIndex;
-
- } else if ( nodeProperty.fromArray !== undefined && nodeProperty.toArray !== undefined ) {
-
- // must use copy for Object3D.Euler/Quaternion
-
- bindingType = this.BindingType.HasFromToArray;
-
- this.resolvedProperty = nodeProperty;
-
- } else if ( Array.isArray( nodeProperty ) ) {
-
- bindingType = this.BindingType.EntireArray;
-
- this.resolvedProperty = nodeProperty;
-
- } else {
-
- this.propertyName = propertyName;
-
- }
-
- // select getter / setter
- this.getValue = this.GetterByBindingType[ bindingType ];
- this.setValue = this.SetterByBindingTypeAndVersioning[ bindingType ][ versioning ];
-
- },
-
- unbind: function () {
-
- this.node = null;
-
- // back to the prototype version of getValue / setValue
- // note: avoiding to mutate the shape of 'this' via 'delete'
- this.getValue = this._getValue_unbound;
- this.setValue = this._setValue_unbound;
-
- }
-
-} );
-
-//!\ DECLARE ALIAS AFTER assign prototype !
-Object.assign( PropertyBinding.prototype, {
-
- // initial state of these methods that calls 'bind'
- _getValue_unbound: PropertyBinding.prototype.getValue,
- _setValue_unbound: PropertyBinding.prototype.setValue,
-
-} );
-
-export { PropertyBinding };
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/geometries/TextGeometry.js b/spaces/banana-projects/web3d/node_modules/three/src/geometries/TextGeometry.js
deleted file mode 100644
index bd8ba8a7607b6145a8de9218d2919c1e42d29a48..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/geometries/TextGeometry.js
+++ /dev/null
@@ -1,81 +0,0 @@
-/**
- * @author zz85 / http://www.lab4games.net/zz85/blog
- * @author alteredq / http://alteredqualia.com/
- *
- * Text = 3D Text
- *
- * parameters = {
- * font: , // font
- *
- * size: , // size of the text
- * height: , // thickness to extrude text
- * curveSegments: , // number of points on the curves
- *
- * bevelEnabled: , // turn on bevel
- * bevelThickness: , // how deep into text bevel goes
- * bevelSize: // how far from text outline is bevel
- * }
- */
-
-import { Geometry } from '../core/Geometry.js';
-import { ExtrudeBufferGeometry } from './ExtrudeGeometry.js';
-
-// TextGeometry
-
-function TextGeometry( text, parameters ) {
-
- Geometry.call( this );
-
- this.type = 'TextGeometry';
-
- this.parameters = {
- text: text,
- parameters: parameters
- };
-
- this.fromBufferGeometry( new TextBufferGeometry( text, parameters ) );
- this.mergeVertices();
-
-}
-
-TextGeometry.prototype = Object.create( Geometry.prototype );
-TextGeometry.prototype.constructor = TextGeometry;
-
-// TextBufferGeometry
-
-function TextBufferGeometry( text, parameters ) {
-
- parameters = parameters || {};
-
- var font = parameters.font;
-
- if ( ! ( font && font.isFont ) ) {
-
- console.error( 'THREE.TextGeometry: font parameter is not an instance of THREE.Font.' );
- return new Geometry();
-
- }
-
- var shapes = font.generateShapes( text, parameters.size );
-
- // translate parameters to ExtrudeGeometry API
-
- parameters.depth = parameters.height !== undefined ? parameters.height : 50;
-
- // defaults
-
- if ( parameters.bevelThickness === undefined ) parameters.bevelThickness = 10;
- if ( parameters.bevelSize === undefined ) parameters.bevelSize = 8;
- if ( parameters.bevelEnabled === undefined ) parameters.bevelEnabled = false;
-
- ExtrudeBufferGeometry.call( this, shapes, parameters );
-
- this.type = 'TextBufferGeometry';
-
-}
-
-TextBufferGeometry.prototype = Object.create( ExtrudeBufferGeometry.prototype );
-TextBufferGeometry.prototype.constructor = TextBufferGeometry;
-
-
-export { TextGeometry, TextBufferGeometry };
diff --git a/spaces/bguberfain/Detic/detic/data/datasets/coco_zeroshot.py b/spaces/bguberfain/Detic/detic/data/datasets/coco_zeroshot.py
deleted file mode 100644
index aee895de41db95e379874fa6e1badd95c5fe6742..0000000000000000000000000000000000000000
--- a/spaces/bguberfain/Detic/detic/data/datasets/coco_zeroshot.py
+++ /dev/null
@@ -1,121 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import os
-
-from detectron2.data.datasets.register_coco import register_coco_instances
-from detectron2.data.datasets.builtin_meta import _get_builtin_metadata
-from .lvis_v1 import custom_register_lvis_instances
-
-categories_seen = [
- {'id': 1, 'name': 'person'},
- {'id': 2, 'name': 'bicycle'},
- {'id': 3, 'name': 'car'},
- {'id': 4, 'name': 'motorcycle'},
- {'id': 7, 'name': 'train'},
- {'id': 8, 'name': 'truck'},
- {'id': 9, 'name': 'boat'},
- {'id': 15, 'name': 'bench'},
- {'id': 16, 'name': 'bird'},
- {'id': 19, 'name': 'horse'},
- {'id': 20, 'name': 'sheep'},
- {'id': 23, 'name': 'bear'},
- {'id': 24, 'name': 'zebra'},
- {'id': 25, 'name': 'giraffe'},
- {'id': 27, 'name': 'backpack'},
- {'id': 31, 'name': 'handbag'},
- {'id': 33, 'name': 'suitcase'},
- {'id': 34, 'name': 'frisbee'},
- {'id': 35, 'name': 'skis'},
- {'id': 38, 'name': 'kite'},
- {'id': 42, 'name': 'surfboard'},
- {'id': 44, 'name': 'bottle'},
- {'id': 48, 'name': 'fork'},
- {'id': 50, 'name': 'spoon'},
- {'id': 51, 'name': 'bowl'},
- {'id': 52, 'name': 'banana'},
- {'id': 53, 'name': 'apple'},
- {'id': 54, 'name': 'sandwich'},
- {'id': 55, 'name': 'orange'},
- {'id': 56, 'name': 'broccoli'},
- {'id': 57, 'name': 'carrot'},
- {'id': 59, 'name': 'pizza'},
- {'id': 60, 'name': 'donut'},
- {'id': 62, 'name': 'chair'},
- {'id': 65, 'name': 'bed'},
- {'id': 70, 'name': 'toilet'},
- {'id': 72, 'name': 'tv'},
- {'id': 73, 'name': 'laptop'},
- {'id': 74, 'name': 'mouse'},
- {'id': 75, 'name': 'remote'},
- {'id': 78, 'name': 'microwave'},
- {'id': 79, 'name': 'oven'},
- {'id': 80, 'name': 'toaster'},
- {'id': 82, 'name': 'refrigerator'},
- {'id': 84, 'name': 'book'},
- {'id': 85, 'name': 'clock'},
- {'id': 86, 'name': 'vase'},
- {'id': 90, 'name': 'toothbrush'},
-]
-
-categories_unseen = [
- {'id': 5, 'name': 'airplane'},
- {'id': 6, 'name': 'bus'},
- {'id': 17, 'name': 'cat'},
- {'id': 18, 'name': 'dog'},
- {'id': 21, 'name': 'cow'},
- {'id': 22, 'name': 'elephant'},
- {'id': 28, 'name': 'umbrella'},
- {'id': 32, 'name': 'tie'},
- {'id': 36, 'name': 'snowboard'},
- {'id': 41, 'name': 'skateboard'},
- {'id': 47, 'name': 'cup'},
- {'id': 49, 'name': 'knife'},
- {'id': 61, 'name': 'cake'},
- {'id': 63, 'name': 'couch'},
- {'id': 76, 'name': 'keyboard'},
- {'id': 81, 'name': 'sink'},
- {'id': 87, 'name': 'scissors'},
-]
-
-def _get_metadata(cat):
- if cat == 'all':
- return _get_builtin_metadata('coco')
- elif cat == 'seen':
- id_to_name = {x['id']: x['name'] for x in categories_seen}
- else:
- assert cat == 'unseen'
- id_to_name = {x['id']: x['name'] for x in categories_unseen}
-
- thing_dataset_id_to_contiguous_id = {
- x: i for i, x in enumerate(sorted(id_to_name))}
- thing_classes = [id_to_name[k] for k in sorted(id_to_name)]
- return {
- "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id,
- "thing_classes": thing_classes}
-
-_PREDEFINED_SPLITS_COCO = {
- "coco_zeroshot_train": ("coco/train2017", "coco/zero-shot/instances_train2017_seen_2.json", 'seen'),
- "coco_zeroshot_val": ("coco/val2017", "coco/zero-shot/instances_val2017_unseen_2.json", 'unseen'),
- "coco_not_zeroshot_val": ("coco/val2017", "coco/zero-shot/instances_val2017_seen_2.json", 'seen'),
- "coco_generalized_zeroshot_val": ("coco/val2017", "coco/zero-shot/instances_val2017_all_2_oriorder.json", 'all'),
- "coco_zeroshot_train_oriorder": ("coco/train2017", "coco/zero-shot/instances_train2017_seen_2_oriorder.json", 'all'),
-}
-
-for key, (image_root, json_file, cat) in _PREDEFINED_SPLITS_COCO.items():
- register_coco_instances(
- key,
- _get_metadata(cat),
- os.path.join("datasets", json_file) if "://" not in json_file else json_file,
- os.path.join("datasets", image_root),
- )
-
-_CUSTOM_SPLITS_COCO = {
- "cc3m_coco_train_tags": ("cc3m/training/", "cc3m/coco_train_image_info_tags.json"),
- "coco_caption_train_tags": ("coco/train2017/", "coco/annotations/captions_train2017_tags_allcaps.json"),}
-
-for key, (image_root, json_file) in _CUSTOM_SPLITS_COCO.items():
- custom_register_lvis_instances(
- key,
- _get_builtin_metadata('coco'),
- os.path.join("datasets", json_file) if "://" not in json_file else json_file,
- os.path.join("datasets", image_root),
- )
\ No newline at end of file
diff --git a/spaces/bingbing520/ChatGPT/modules/webui_locale.py b/spaces/bingbing520/ChatGPT/modules/webui_locale.py
deleted file mode 100644
index 1ce4d97b9b41cbb2d9be3fdadc4c85f6ef897604..0000000000000000000000000000000000000000
--- a/spaces/bingbing520/ChatGPT/modules/webui_locale.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import os
-import locale
-import commentjson as json
-
-class I18nAuto:
- def __init__(self):
- if os.path.exists("config.json"):
- with open("config.json", "r", encoding='utf-8') as f:
- config = json.load(f)
- else:
- config = {}
- lang_config = config.get("language", "auto")
- language = os.environ.get("LANGUAGE", lang_config)
- if language == "auto":
- language = locale.getdefaultlocale()[0] # get the language code of the system (ex. zh_CN)
- self.language_map = {}
- self.file_is_exists = os.path.isfile(f"./locale/{language}.json")
- if self.file_is_exists:
- with open(f"./locale/{language}.json", "r", encoding="utf-8") as f:
- self.language_map.update(json.load(f))
-
- def __call__(self, key):
- if self.file_is_exists and key in self.language_map:
- return self.language_map[key]
- else:
- return key
diff --git a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/grids/diffusion/_explorers.py b/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/grids/diffusion/_explorers.py
deleted file mode 100644
index 0bf4ca57b63f5f9308bd1178ddbde5d8f06748e5..0000000000000000000000000000000000000000
--- a/spaces/brainblow/AudioCreator_Music-Audio_Generation/audiocraft/grids/diffusion/_explorers.py
+++ /dev/null
@@ -1,66 +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 treetable as tt
-
-from .._base_explorers import BaseExplorer
-
-
-class DiffusionExplorer(BaseExplorer):
- eval_metrics = ["sisnr", "visqol"]
-
- def stages(self):
- return ["train", "valid", "valid_ema", "evaluate", "evaluate_ema"]
-
- def get_grid_meta(self):
- """Returns the list of Meta information to display for each XP/job.
- """
- return [
- tt.leaf("index", align=">"),
- tt.leaf("name", wrap=140),
- tt.leaf("state"),
- tt.leaf("sig", align=">"),
- ]
-
- def get_grid_metrics(self):
- """Return the metrics that should be displayed in the tracking table.
- """
- return [
- tt.group(
- "train",
- [
- tt.leaf("epoch"),
- tt.leaf("loss", ".3%"),
- ],
- align=">",
- ),
- tt.group(
- "valid",
- [
- tt.leaf("loss", ".3%"),
- # tt.leaf("loss_0", ".3%"),
- ],
- align=">",
- ),
- tt.group(
- "valid_ema",
- [
- tt.leaf("loss", ".3%"),
- # tt.leaf("loss_0", ".3%"),
- ],
- align=">",
- ),
- tt.group(
- "evaluate", [tt.leaf("rvm", ".4f"), tt.leaf("rvm_0", ".4f"),
- tt.leaf("rvm_1", ".4f"), tt.leaf("rvm_2", ".4f"),
- tt.leaf("rvm_3", ".4f"), ], align=">"
- ),
- tt.group(
- "evaluate_ema", [tt.leaf("rvm", ".4f"), tt.leaf("rvm_0", ".4f"),
- tt.leaf("rvm_1", ".4f"), tt.leaf("rvm_2", ".4f"),
- tt.leaf("rvm_3", ".4f")], align=">"
- ),
- ]
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/modeling/mmdet_wrapper.py b/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/modeling/mmdet_wrapper.py
deleted file mode 100644
index 293b3e9faf34c48456cd3fff37b966af9042fe4e..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/detectron2/modeling/mmdet_wrapper.py
+++ /dev/null
@@ -1,273 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import itertools
-import logging
-import numpy as np
-from collections import OrderedDict
-from collections.abc import Mapping
-from typing import Dict, List, Optional, Tuple, Union
-import torch
-from omegaconf import DictConfig, OmegaConf
-from torch import Tensor, nn
-
-from detectron2.layers import ShapeSpec
-from detectron2.structures import BitMasks, Boxes, ImageList, Instances
-from detectron2.utils.events import get_event_storage
-
-from .backbone import Backbone
-
-logger = logging.getLogger(__name__)
-
-
-def _to_container(cfg):
- """
- mmdet will assert the type of dict/list.
- So convert omegaconf objects to dict/list.
- """
- if isinstance(cfg, DictConfig):
- cfg = OmegaConf.to_container(cfg, resolve=True)
- from mmcv.utils import ConfigDict
-
- return ConfigDict(cfg)
-
-
-class MMDetBackbone(Backbone):
- """
- Wrapper of mmdetection backbones to use in detectron2.
-
- mmdet backbones produce list/tuple of tensors, while detectron2 backbones
- produce a dict of tensors. This class wraps the given backbone to produce
- output in detectron2's convention, so it can be used in place of detectron2
- backbones.
- """
-
- def __init__(
- self,
- backbone: Union[nn.Module, Mapping],
- neck: Union[nn.Module, Mapping, None] = None,
- *,
- output_shapes: List[ShapeSpec],
- output_names: Optional[List[str]] = None,
- ):
- """
- Args:
- backbone: either a backbone module or a mmdet config dict that defines a
- backbone. The backbone takes a 4D image tensor and returns a
- sequence of tensors.
- neck: either a backbone module or a mmdet config dict that defines a
- neck. The neck takes outputs of backbone and returns a
- sequence of tensors. If None, no neck is used.
- output_shapes: shape for every output of the backbone (or neck, if given).
- stride and channels are often needed.
- output_names: names for every output of the backbone (or neck, if given).
- By default, will use "out0", "out1", ...
- """
- super().__init__()
- if isinstance(backbone, Mapping):
- from mmdet.models import build_backbone
-
- backbone = build_backbone(_to_container(backbone))
- self.backbone = backbone
-
- if isinstance(neck, Mapping):
- from mmdet.models import build_neck
-
- neck = build_neck(_to_container(neck))
- self.neck = neck
-
- # "Neck" weights, if any, are part of neck itself. This is the interface
- # of mmdet so we follow it. Reference:
- # https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/detectors/two_stage.py
- logger.info("Initializing mmdet backbone weights...")
- self.backbone.init_weights()
- # train() in mmdet modules is non-trivial, and has to be explicitly
- # called. Reference:
- # https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/backbones/resnet.py
- self.backbone.train()
- if self.neck is not None:
- logger.info("Initializing mmdet neck weights ...")
- if isinstance(self.neck, nn.Sequential):
- for m in self.neck:
- m.init_weights()
- else:
- self.neck.init_weights()
- self.neck.train()
-
- self._output_shapes = output_shapes
- if not output_names:
- output_names = [f"out{i}" for i in range(len(output_shapes))]
- self._output_names = output_names
-
- def forward(self, x) -> Dict[str, Tensor]:
- outs = self.backbone(x)
- if self.neck is not None:
- outs = self.neck(outs)
- assert isinstance(
- outs, (list, tuple)
- ), "mmdet backbone should return a list/tuple of tensors!"
- if len(outs) != len(self._output_shapes):
- raise ValueError(
- "Length of output_shapes does not match outputs from the mmdet backbone: "
- f"{len(outs)} != {len(self._output_shapes)}"
- )
- return {k: v for k, v in zip(self._output_names, outs)}
-
- def output_shape(self) -> Dict[str, ShapeSpec]:
- return {k: v for k, v in zip(self._output_names, self._output_shapes)}
-
-
-class MMDetDetector(nn.Module):
- """
- Wrapper of a mmdetection detector model, for detection and instance segmentation.
- Input/output formats of this class follow detectron2's convention, so a
- mmdetection model can be trained and evaluated in detectron2.
- """
-
- def __init__(
- self,
- detector: Union[nn.Module, Mapping],
- *,
- # Default is 32 regardless of model:
- # https://github.com/open-mmlab/mmdetection/tree/master/configs/_base_/datasets
- size_divisibility=32,
- pixel_mean: Tuple[float],
- pixel_std: Tuple[float],
- ):
- """
- Args:
- detector: a mmdet detector, or a mmdet config dict that defines a detector.
- size_divisibility: pad input images to multiple of this number
- pixel_mean: per-channel mean to normalize input image
- pixel_std: per-channel stddev to normalize input image
- """
- super().__init__()
- if isinstance(detector, Mapping):
- from mmdet.models import build_detector
-
- detector = build_detector(_to_container(detector))
- self.detector = detector
- self.detector.init_weights()
- self.size_divisibility = size_divisibility
-
- self.register_buffer("pixel_mean", torch.tensor(pixel_mean).view(-1, 1, 1), False)
- self.register_buffer("pixel_std", torch.tensor(pixel_std).view(-1, 1, 1), False)
- assert (
- self.pixel_mean.shape == self.pixel_std.shape
- ), f"{self.pixel_mean} and {self.pixel_std} have different shapes!"
-
- def forward(self, batched_inputs: List[Dict[str, torch.Tensor]]):
- images = [x["image"].to(self.device) for x in batched_inputs]
- images = [(x - self.pixel_mean) / self.pixel_std for x in images]
- images = ImageList.from_tensors(images, size_divisibility=self.size_divisibility).tensor
- metas = []
- rescale = {"height" in x for x in batched_inputs}
- if len(rescale) != 1:
- raise ValueError("Some inputs have original height/width, but some don't!")
- rescale = list(rescale)[0]
- output_shapes = []
- for input in batched_inputs:
- meta = {}
- c, h, w = input["image"].shape
- meta["img_shape"] = meta["ori_shape"] = (h, w, c)
- if rescale:
- scale_factor = np.array(
- [w / input["width"], h / input["height"]] * 2, dtype="float32"
- )
- ori_shape = (input["height"], input["width"])
- output_shapes.append(ori_shape)
- meta["ori_shape"] = ori_shape + (c,)
- else:
- scale_factor = 1.0
- output_shapes.append((h, w))
- meta["scale_factor"] = scale_factor
- meta["flip"] = False
- padh, padw = images.shape[-2:]
- meta["pad_shape"] = (padh, padw, c)
- metas.append(meta)
-
- if self.training:
- gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
- if gt_instances[0].has("gt_masks"):
- from mmdet.core import PolygonMasks as mm_PolygonMasks, BitmapMasks as mm_BitMasks
-
- def convert_mask(m, shape):
- # mmdet mask format
- if isinstance(m, BitMasks):
- return mm_BitMasks(m.tensor.cpu().numpy(), shape[0], shape[1])
- else:
- return mm_PolygonMasks(m.polygons, shape[0], shape[1])
-
- gt_masks = [convert_mask(x.gt_masks, x.image_size) for x in gt_instances]
- losses_and_metrics = self.detector.forward_train(
- images,
- metas,
- [x.gt_boxes.tensor for x in gt_instances],
- [x.gt_classes for x in gt_instances],
- gt_masks=gt_masks,
- )
- else:
- losses_and_metrics = self.detector.forward_train(
- images,
- metas,
- [x.gt_boxes.tensor for x in gt_instances],
- [x.gt_classes for x in gt_instances],
- )
- return _parse_losses(losses_and_metrics)
- else:
- results = self.detector.simple_test(images, metas, rescale=rescale)
- results = [
- {"instances": _convert_mmdet_result(r, shape)}
- for r, shape in zip(results, output_shapes)
- ]
- return results
-
- @property
- def device(self):
- return self.pixel_mean.device
-
-
-# Reference: show_result() in
-# https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/detectors/base.py
-def _convert_mmdet_result(result, shape: Tuple[int, int]) -> Instances:
- if isinstance(result, tuple):
- bbox_result, segm_result = result
- if isinstance(segm_result, tuple):
- segm_result = segm_result[0]
- else:
- bbox_result, segm_result = result, None
-
- bboxes = torch.from_numpy(np.vstack(bbox_result)) # Nx5
- bboxes, scores = bboxes[:, :4], bboxes[:, -1]
- labels = [
- torch.full((bbox.shape[0],), i, dtype=torch.int32) for i, bbox in enumerate(bbox_result)
- ]
- labels = torch.cat(labels)
- inst = Instances(shape)
- inst.pred_boxes = Boxes(bboxes)
- inst.scores = scores
- inst.pred_classes = labels
-
- if segm_result is not None and len(labels) > 0:
- segm_result = list(itertools.chain(*segm_result))
- segm_result = [torch.from_numpy(x) if isinstance(x, np.ndarray) else x for x in segm_result]
- segm_result = torch.stack(segm_result, dim=0)
- inst.pred_masks = segm_result
- return inst
-
-
-# reference: https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/detectors/base.py
-def _parse_losses(losses: Dict[str, Tensor]) -> Dict[str, Tensor]:
- log_vars = OrderedDict()
- for loss_name, loss_value in losses.items():
- if isinstance(loss_value, torch.Tensor):
- log_vars[loss_name] = loss_value.mean()
- elif isinstance(loss_value, list):
- log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value)
- else:
- raise TypeError(f"{loss_name} is not a tensor or list of tensors")
-
- if "loss" not in loss_name:
- # put metrics to storage; don't return them
- storage = get_event_storage()
- value = log_vars.pop(loss_name).cpu().item()
- storage.put_scalar(loss_name, value)
- return log_vars
diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/modeling/cse/embedder.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/modeling/cse/embedder.py
deleted file mode 100644
index 7f52b06032ed97b2d652931646f0855ef342ada9..0000000000000000000000000000000000000000
--- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/modeling/cse/embedder.py
+++ /dev/null
@@ -1,130 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-import logging
-import numpy as np
-import pickle
-from enum import Enum
-from typing import Optional
-import torch
-from torch import nn
-
-from detectron2.config import CfgNode
-from detectron2.utils.file_io import PathManager
-
-from .vertex_direct_embedder import VertexDirectEmbedder
-from .vertex_feature_embedder import VertexFeatureEmbedder
-
-
-class EmbedderType(Enum):
- """
- Embedder type which defines how vertices are mapped into the embedding space:
- - "vertex_direct": direct vertex embedding
- - "vertex_feature": embedding vertex features
- """
-
- VERTEX_DIRECT = "vertex_direct"
- VERTEX_FEATURE = "vertex_feature"
-
-
-def create_embedder(embedder_spec: CfgNode, embedder_dim: int) -> nn.Module:
- """
- Create an embedder based on the provided configuration
-
- Args:
- embedder_spec (CfgNode): embedder configuration
- embedder_dim (int): embedding space dimensionality
- Return:
- An embedder instance for the specified configuration
- Raises ValueError, in case of unexpected embedder type
- """
- embedder_type = EmbedderType(embedder_spec.TYPE)
- if embedder_type == EmbedderType.VERTEX_DIRECT:
- embedder = VertexDirectEmbedder(
- num_vertices=embedder_spec.NUM_VERTICES,
- embed_dim=embedder_dim,
- )
- if embedder_spec.INIT_FILE != "":
- embedder.load(embedder_spec.INIT_FILE)
- elif embedder_type == EmbedderType.VERTEX_FEATURE:
- embedder = VertexFeatureEmbedder(
- num_vertices=embedder_spec.NUM_VERTICES,
- feature_dim=embedder_spec.FEATURE_DIM,
- embed_dim=embedder_dim,
- train_features=embedder_spec.FEATURES_TRAINABLE,
- )
- if embedder_spec.INIT_FILE != "":
- embedder.load(embedder_spec.INIT_FILE)
- else:
- raise ValueError(f"Unexpected embedder type {embedder_type}")
-
- if not embedder_spec.IS_TRAINABLE:
- embedder.requires_grad_(False)
-
- return embedder
-
-
-class Embedder(nn.Module):
- """
- Embedder module that serves as a container for embedders to use with different
- meshes. Extends Module to automatically save / load state dict.
- """
-
- DEFAULT_MODEL_CHECKPOINT_PREFIX = "roi_heads.embedder."
-
- def __init__(self, cfg: CfgNode):
- """
- Initialize mesh embedders. An embedder for mesh `i` is stored in a submodule
- "embedder_{i}".
-
- Args:
- cfg (CfgNode): configuration options
- """
- super(Embedder, self).__init__()
- self.mesh_names = set()
- embedder_dim = cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_SIZE
- logger = logging.getLogger(__name__)
- for mesh_name, embedder_spec in cfg.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDERS.items():
- logger.info(f"Adding embedder embedder_{mesh_name} with spec {embedder_spec}")
- self.add_module(f"embedder_{mesh_name}", create_embedder(embedder_spec, embedder_dim))
- self.mesh_names.add(mesh_name)
- if cfg.MODEL.WEIGHTS != "":
- self.load_from_model_checkpoint(cfg.MODEL.WEIGHTS)
-
- def load_from_model_checkpoint(self, fpath: str, prefix: Optional[str] = None):
- if prefix is None:
- prefix = Embedder.DEFAULT_MODEL_CHECKPOINT_PREFIX
- state_dict = None
- if fpath.endswith(".pkl"):
- with PathManager.open(fpath, "rb") as hFile:
- state_dict = pickle.load(hFile, encoding="latin1") # pyre-ignore[6]
- else:
- with PathManager.open(fpath, "rb") as hFile:
- # pyre-fixme[6]: For 1st param expected `Union[PathLike[typing.Any],
- # IO[bytes], str, BinaryIO]` but got `Union[IO[bytes], IO[str]]`.
- state_dict = torch.load(hFile, map_location=torch.device("cpu"))
- if state_dict is not None and "model" in state_dict:
- state_dict_local = {}
- for key in state_dict["model"]:
- if key.startswith(prefix):
- v_key = state_dict["model"][key]
- if isinstance(v_key, np.ndarray):
- v_key = torch.from_numpy(v_key)
- state_dict_local[key[len(prefix) :]] = v_key
- # non-strict loading to finetune on different meshes
- self.load_state_dict(state_dict_local, strict=False)
-
- def forward(self, mesh_name: str) -> torch.Tensor:
- """
- Produce vertex embeddings for the specific mesh; vertex embeddings are
- a tensor of shape [N, D] where:
- N = number of vertices
- D = number of dimensions in the embedding space
- Args:
- mesh_name (str): name of a mesh for which to obtain vertex embeddings
- Return:
- Vertex embeddings, a tensor of shape [N, D]
- """
- return getattr(self, f"embedder_{mesh_name}")()
-
- def has_embeddings(self, mesh_name: str) -> bool:
- return hasattr(self, f"embedder_{mesh_name}")
diff --git a/spaces/bzd4576/sovits-sin/transforms.py b/spaces/bzd4576/sovits-sin/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/bzd4576/sovits-sin/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails=None,
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
-
- if tails is None:
- spline_fn = rational_quadratic_spline
- spline_kwargs = {}
- else:
- spline_fn = unconstrained_rational_quadratic_spline
- spline_kwargs = {
- 'tails': tails,
- 'tail_bound': tail_bound
- }
-
- outputs, logabsdet = spline_fn(
- inputs=inputs,
- unnormalized_widths=unnormalized_widths,
- unnormalized_heights=unnormalized_heights,
- unnormalized_derivatives=unnormalized_derivatives,
- inverse=inverse,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- **spline_kwargs
- )
- return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
- bin_locations[..., -1] += eps
- return torch.sum(
- inputs[..., None] >= bin_locations,
- dim=-1
- ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails='linear',
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
- outside_interval_mask = ~inside_interval_mask
-
- outputs = torch.zeros_like(inputs)
- logabsdet = torch.zeros_like(inputs)
-
- if tails == 'linear':
- unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
- constant = np.log(np.exp(1 - min_derivative) - 1)
- unnormalized_derivatives[..., 0] = constant
- unnormalized_derivatives[..., -1] = constant
-
- outputs[outside_interval_mask] = inputs[outside_interval_mask]
- logabsdet[outside_interval_mask] = 0
- else:
- raise RuntimeError('{} tails are not implemented.'.format(tails))
-
- outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
- inputs=inputs[inside_interval_mask],
- unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
- unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
- unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
- inverse=inverse,
- left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative
- )
-
- return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- left=0., right=1., bottom=0., top=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- if torch.min(inputs) < left or torch.max(inputs) > right:
- raise ValueError('Input to a transform is not within its domain')
-
- num_bins = unnormalized_widths.shape[-1]
-
- if min_bin_width * num_bins > 1.0:
- raise ValueError('Minimal bin width too large for the number of bins')
- if min_bin_height * num_bins > 1.0:
- raise ValueError('Minimal bin height too large for the number of bins')
-
- widths = F.softmax(unnormalized_widths, dim=-1)
- widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
- cumwidths = torch.cumsum(widths, dim=-1)
- cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
- cumwidths = (right - left) * cumwidths + left
- cumwidths[..., 0] = left
- cumwidths[..., -1] = right
- widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
- derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
- heights = F.softmax(unnormalized_heights, dim=-1)
- heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
- cumheights = torch.cumsum(heights, dim=-1)
- cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
- cumheights = (top - bottom) * cumheights + bottom
- cumheights[..., 0] = bottom
- cumheights[..., -1] = top
- heights = cumheights[..., 1:] - cumheights[..., :-1]
-
- if inverse:
- bin_idx = searchsorted(cumheights, inputs)[..., None]
- else:
- bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
- input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
- input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
- input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
- delta = heights / widths
- input_delta = delta.gather(-1, bin_idx)[..., 0]
-
- input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
- input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
- input_heights = heights.gather(-1, bin_idx)[..., 0]
-
- if inverse:
- a = (((inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta)
- + input_heights * (input_delta - input_derivatives)))
- b = (input_heights * input_derivatives
- - (inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta))
- c = - input_delta * (inputs - input_cumheights)
-
- discriminant = b.pow(2) - 4 * a * c
- assert (discriminant >= 0).all()
-
- root = (2 * c) / (-b - torch.sqrt(discriminant))
- outputs = root * input_bin_widths + input_cumwidths
-
- theta_one_minus_theta = root * (1 - root)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - root).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, -logabsdet
- else:
- theta = (inputs - input_cumwidths) / input_bin_widths
- theta_one_minus_theta = theta * (1 - theta)
-
- numerator = input_heights * (input_delta * theta.pow(2)
- + input_derivatives * theta_one_minus_theta)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- outputs = input_cumheights + numerator / denominator
-
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - theta).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, logabsdet
diff --git a/spaces/cahya/persona-chatbot/app/app.py b/spaces/cahya/persona-chatbot/app/app.py
deleted file mode 100644
index 193abb5a72789fd816e9f933efa831e8aa01a25d..0000000000000000000000000000000000000000
--- a/spaces/cahya/persona-chatbot/app/app.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import streamlit as st
-import codecs
-import streamlit.components.v1 as stc
-import pathlib
-
-
-def stc_chatbot(root_dir, width=700, height=600):
- html_file = root_dir/"app/chatbot.html"
- css_file = root_dir/"app/css/chatbot.css"
- js_file = root_dir/"app/js/chatbot.js"
- if css_file.exists() and js_file.exists():
- html = codecs.open(html_file, "r").read()
- css = codecs.open(css_file, "r").read()
- js = codecs.open(js_file, "r").read()
- html = html.replace('
-
- This implementation is a single unified interface to be able to run FS operations
- over generic URLs, and dispatch to the specific implementations using the URL
- protocol prefix.
-
- Note: instances of this FS are always async, even if you never use it with any async
- backend.
- """
-
- protocol = "generic" # there is no real reason to ever use a protocol with this FS
-
- def __init__(self, default_method="default", **kwargs):
- """
-
- Parameters
- ----------
- default_method: str (optional)
- Defines how to configure backend FS instances. Options are:
- - "default": instantiate like FSClass(), with no
- extra arguments; this is the default instance of that FS, and can be
- configured via the config system
- - "generic": takes instances from the `_generic_fs` dict in this module,
- which you must populate before use. Keys are by protocol
- - "current": takes the most recently instantiated version of each FS
- """
- self.method = default_method
- super(GenericFileSystem, self).__init__(**kwargs)
-
- def _strip_protocol(self, path):
- # normalization only
- fs = _resolve_fs(path, self.method)
- return fs.unstrip_protocol(fs._strip_protocol(path))
-
- async def _find(self, path, maxdepth=None, withdirs=False, detail=False, **kwargs):
- fs = _resolve_fs(path, self.method)
- if fs.async_impl:
- out = await fs._find(
- path, maxdepth=maxdepth, withdirs=withdirs, detail=detail, **kwargs
- )
- else:
- out = fs.find(
- path, maxdepth=maxdepth, withdirs=withdirs, detail=detail, **kwargs
- )
- result = {}
- for k, v in out.items():
- name = fs.unstrip_protocol(k)
- v["name"] = name
- result[name] = v
- if detail:
- return result
- return list(result)
-
- async def _info(self, url, **kwargs):
- fs = _resolve_fs(url, self.method)
- if fs.async_impl:
- out = await fs._info(url, **kwargs)
- else:
- out = fs.info(url, **kwargs)
- out["name"] = fs.unstrip_protocol(out["name"])
- return out
-
- async def _ls(
- self,
- url,
- detail=True,
- **kwargs,
- ):
- fs = _resolve_fs(url, self.method)
- if fs.async_impl:
- out = await fs._ls(url, detail=True, **kwargs)
- else:
- out = fs.ls(url, detail=True, **kwargs)
- for o in out:
- o["name"] = fs.unstrip_protocol(o["name"])
- if detail:
- return out
- else:
- return [o["name"] for o in out]
-
- async def _cat_file(
- self,
- url,
- **kwargs,
- ):
- fs = _resolve_fs(url, self.method)
- if fs.async_impl:
- return await fs._cat_file(url, **kwargs)
- else:
- return fs.cat_file(url, **kwargs)
-
- async def _pipe_file(
- self,
- path,
- value,
- **kwargs,
- ):
- fs = _resolve_fs(path, self.method)
- if fs.async_impl:
- return await fs._pipe_file(path, value, **kwargs)
- else:
- return fs.pipe_file(path, value, **kwargs)
-
- async def _rm(self, url, **kwargs):
- fs = _resolve_fs(url, self.method)
- if fs.async_impl:
- await fs._rm(url, **kwargs)
- else:
- fs.rm(url, **kwargs)
-
- async def _makedirs(self, path, exist_ok=False):
- fs = _resolve_fs(path, self.method)
- if fs.async_impl:
- await fs._makedirs(path, exist_ok=exist_ok)
- else:
- fs.makedirs(path, exist_ok=exist_ok)
-
- def rsync(self, source, destination, **kwargs):
- """Sync files between two directory trees
-
- See `func:rsync` for more details.
- """
- rsync(source, destination, fs=self, **kwargs)
-
- async def _cp_file(
- self,
- url,
- url2,
- blocksize=2**20,
- callback=_DEFAULT_CALLBACK,
- **kwargs,
- ):
- fs = _resolve_fs(url, self.method)
- fs2 = _resolve_fs(url2, self.method)
- if fs is fs2:
- # pure remote
- if fs.async_impl:
- return await fs._cp_file(url, url2, **kwargs)
- else:
- return fs.cp_file(url, url2, **kwargs)
- kw = {"blocksize": 0, "cache_type": "none"}
- try:
- f1 = (
- await fs.open_async(url, "rb")
- if hasattr(fs, "open_async")
- else fs.open(url, "rb", **kw)
- )
- callback.set_size(await maybe_await(f1.size))
- f2 = (
- await fs2.open_async(url2, "wb")
- if hasattr(fs2, "open_async")
- else fs2.open(url2, "wb", **kw)
- )
- while f1.size is None or f2.tell() < f1.size:
- data = await maybe_await(f1.read(blocksize))
- if f1.size is None and not data:
- break
- await maybe_await(f2.write(data))
- callback.absolute_update(f2.tell())
- finally:
- try:
- await maybe_await(f2.close())
- await maybe_await(f1.close())
- except NameError:
- # fail while opening f1 or f2
- pass
-
-
-async def maybe_await(cor):
- if inspect.iscoroutine(cor):
- return await cor
- else:
- return cor
diff --git a/spaces/cihyFjudo/fairness-paper-search/Despedida Maria Grever Partitura PDF Download Learn the Beautiful Song by the Mexican Composer.md b/spaces/cihyFjudo/fairness-paper-search/Despedida Maria Grever Partitura PDF Download Learn the Beautiful Song by the Mexican Composer.md
deleted file mode 100644
index 8549657fb5d40667913e8b1b33b61df30a404dc5..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Despedida Maria Grever Partitura PDF Download Learn the Beautiful Song by the Mexican Composer.md
+++ /dev/null
@@ -1,6 +0,0 @@
-despedida maria grever partitura pdf download DOWNLOAD ☆ https://tinurli.com/2uwk4B
diff --git a/spaces/cihyFjudo/fairness-paper-search/Roblox Walk Through Walls Hack Download Mac A Simple and Easy Tutorial.md b/spaces/cihyFjudo/fairness-paper-search/Roblox Walk Through Walls Hack Download Mac A Simple and Easy Tutorial.md
deleted file mode 100644
index 512122c0aa71a14b9596a09f36c24712b6870152..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Roblox Walk Through Walls Hack Download Mac A Simple and Easy Tutorial.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Roblox Walk Through Walls Hack Download Mac Download File › https://tinurli.com/2uwhRM
diff --git a/spaces/cihyFjudo/fairness-paper-search/Roxas Boulevard Pasay City Zip Code The Benefits and Challenges of Living in this Area.md b/spaces/cihyFjudo/fairness-paper-search/Roxas Boulevard Pasay City Zip Code The Benefits and Challenges of Living in this Area.md
deleted file mode 100644
index 1450c0cdf2f4b2886ed31d0a2faa16af99f78ceb..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Roxas Boulevard Pasay City Zip Code The Benefits and Challenges of Living in this Area.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Roxas Boulevard Pasay City Zip Code Download Zip ->>> https://tinurli.com/2uwjPA
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/GifImagePlugin.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/GifImagePlugin.py
deleted file mode 100644
index cf2993e38920bdebf79c6342875c2898e174ef6b..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/PIL/GifImagePlugin.py
+++ /dev/null
@@ -1,1064 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# GIF file handling
-#
-# History:
-# 1995-09-01 fl Created
-# 1996-12-14 fl Added interlace support
-# 1996-12-30 fl Added animation support
-# 1997-01-05 fl Added write support, fixed local colour map bug
-# 1997-02-23 fl Make sure to load raster data in getdata()
-# 1997-07-05 fl Support external decoder (0.4)
-# 1998-07-09 fl Handle all modes when saving (0.5)
-# 1998-07-15 fl Renamed offset attribute to avoid name clash
-# 2001-04-16 fl Added rewind support (seek to frame 0) (0.6)
-# 2001-04-17 fl Added palette optimization (0.7)
-# 2002-06-06 fl Added transparency support for save (0.8)
-# 2004-02-24 fl Disable interlacing for small images
-#
-# Copyright (c) 1997-2004 by Secret Labs AB
-# Copyright (c) 1995-2004 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-import itertools
-import math
-import os
-import subprocess
-from enum import IntEnum
-
-from . import Image, ImageChops, ImageFile, ImagePalette, ImageSequence
-from ._binary import i16le as i16
-from ._binary import o8
-from ._binary import o16le as o16
-
-
-class LoadingStrategy(IntEnum):
- """.. versionadded:: 9.1.0"""
-
- RGB_AFTER_FIRST = 0
- RGB_AFTER_DIFFERENT_PALETTE_ONLY = 1
- RGB_ALWAYS = 2
-
-
-#: .. versionadded:: 9.1.0
-LOADING_STRATEGY = LoadingStrategy.RGB_AFTER_FIRST
-
-# --------------------------------------------------------------------
-# Identify/read GIF files
-
-
-def _accept(prefix):
- return prefix[:6] in [b"GIF87a", b"GIF89a"]
-
-
-##
-# Image plugin for GIF images. This plugin supports both GIF87 and
-# GIF89 images.
-
-
-class GifImageFile(ImageFile.ImageFile):
- format = "GIF"
- format_description = "Compuserve GIF"
- _close_exclusive_fp_after_loading = False
-
- global_palette = None
-
- def data(self):
- s = self.fp.read(1)
- if s and s[0]:
- return self.fp.read(s[0])
- return None
-
- def _is_palette_needed(self, p):
- for i in range(0, len(p), 3):
- if not (i // 3 == p[i] == p[i + 1] == p[i + 2]):
- return True
- return False
-
- def _open(self):
- # Screen
- s = self.fp.read(13)
- if not _accept(s):
- msg = "not a GIF file"
- raise SyntaxError(msg)
-
- self.info["version"] = s[:6]
- self._size = i16(s, 6), i16(s, 8)
- self.tile = []
- flags = s[10]
- bits = (flags & 7) + 1
-
- if flags & 128:
- # get global palette
- self.info["background"] = s[11]
- # check if palette contains colour indices
- p = self.fp.read(3 << bits)
- if self._is_palette_needed(p):
- p = ImagePalette.raw("RGB", p)
- self.global_palette = self.palette = p
-
- self._fp = self.fp # FIXME: hack
- self.__rewind = self.fp.tell()
- self._n_frames = None
- self._is_animated = None
- self._seek(0) # get ready to read first frame
-
- @property
- def n_frames(self):
- if self._n_frames is None:
- current = self.tell()
- try:
- while True:
- self._seek(self.tell() + 1, False)
- except EOFError:
- self._n_frames = self.tell() + 1
- self.seek(current)
- return self._n_frames
-
- @property
- def is_animated(self):
- if self._is_animated is None:
- if self._n_frames is not None:
- self._is_animated = self._n_frames != 1
- else:
- current = self.tell()
- if current:
- self._is_animated = True
- else:
- try:
- self._seek(1, False)
- self._is_animated = True
- except EOFError:
- self._is_animated = False
-
- self.seek(current)
- return self._is_animated
-
- def seek(self, frame):
- if not self._seek_check(frame):
- return
- if frame < self.__frame:
- self.im = None
- self._seek(0)
-
- last_frame = self.__frame
- for f in range(self.__frame + 1, frame + 1):
- try:
- self._seek(f)
- except EOFError as e:
- self.seek(last_frame)
- msg = "no more images in GIF file"
- raise EOFError(msg) from e
-
- def _seek(self, frame, update_image=True):
- if frame == 0:
- # rewind
- self.__offset = 0
- self.dispose = None
- self.__frame = -1
- self._fp.seek(self.__rewind)
- self.disposal_method = 0
- if "comment" in self.info:
- del self.info["comment"]
- else:
- # ensure that the previous frame was loaded
- if self.tile and update_image:
- self.load()
-
- if frame != self.__frame + 1:
- msg = f"cannot seek to frame {frame}"
- raise ValueError(msg)
-
- self.fp = self._fp
- if self.__offset:
- # backup to last frame
- self.fp.seek(self.__offset)
- while self.data():
- pass
- self.__offset = 0
-
- s = self.fp.read(1)
- if not s or s == b";":
- raise EOFError
-
- palette = None
-
- info = {}
- frame_transparency = None
- interlace = None
- frame_dispose_extent = None
- while True:
- if not s:
- s = self.fp.read(1)
- if not s or s == b";":
- break
-
- elif s == b"!":
- #
- # extensions
- #
- s = self.fp.read(1)
- block = self.data()
- if s[0] == 249:
- #
- # graphic control extension
- #
- flags = block[0]
- if flags & 1:
- frame_transparency = block[3]
- info["duration"] = i16(block, 1) * 10
-
- # disposal method - find the value of bits 4 - 6
- dispose_bits = 0b00011100 & flags
- dispose_bits = dispose_bits >> 2
- if dispose_bits:
- # only set the dispose if it is not
- # unspecified. I'm not sure if this is
- # correct, but it seems to prevent the last
- # frame from looking odd for some animations
- self.disposal_method = dispose_bits
- elif s[0] == 254:
- #
- # comment extension
- #
- comment = b""
-
- # Read this comment block
- while block:
- comment += block
- block = self.data()
-
- if "comment" in info:
- # If multiple comment blocks in frame, separate with \n
- info["comment"] += b"\n" + comment
- else:
- info["comment"] = comment
- s = None
- continue
- elif s[0] == 255 and frame == 0:
- #
- # application extension
- #
- info["extension"] = block, self.fp.tell()
- if block[:11] == b"NETSCAPE2.0":
- block = self.data()
- if len(block) >= 3 and block[0] == 1:
- self.info["loop"] = i16(block, 1)
- while self.data():
- pass
-
- elif s == b",":
- #
- # local image
- #
- s = self.fp.read(9)
-
- # extent
- x0, y0 = i16(s, 0), i16(s, 2)
- x1, y1 = x0 + i16(s, 4), y0 + i16(s, 6)
- if (x1 > self.size[0] or y1 > self.size[1]) and update_image:
- self._size = max(x1, self.size[0]), max(y1, self.size[1])
- Image._decompression_bomb_check(self._size)
- frame_dispose_extent = x0, y0, x1, y1
- flags = s[8]
-
- interlace = (flags & 64) != 0
-
- if flags & 128:
- bits = (flags & 7) + 1
- p = self.fp.read(3 << bits)
- if self._is_palette_needed(p):
- palette = ImagePalette.raw("RGB", p)
- else:
- palette = False
-
- # image data
- bits = self.fp.read(1)[0]
- self.__offset = self.fp.tell()
- break
-
- else:
- pass
- # raise OSError, "illegal GIF tag `%x`" % s[0]
- s = None
-
- if interlace is None:
- # self._fp = None
- raise EOFError
-
- self.__frame = frame
- if not update_image:
- return
-
- self.tile = []
-
- if self.dispose:
- self.im.paste(self.dispose, self.dispose_extent)
-
- self._frame_palette = palette if palette is not None else self.global_palette
- self._frame_transparency = frame_transparency
- if frame == 0:
- if self._frame_palette:
- if LOADING_STRATEGY == LoadingStrategy.RGB_ALWAYS:
- self.mode = "RGBA" if frame_transparency is not None else "RGB"
- else:
- self.mode = "P"
- else:
- self.mode = "L"
-
- if not palette and self.global_palette:
- from copy import copy
-
- palette = copy(self.global_palette)
- self.palette = palette
- else:
- if self.mode == "P":
- if (
- LOADING_STRATEGY != LoadingStrategy.RGB_AFTER_DIFFERENT_PALETTE_ONLY
- or palette
- ):
- self.pyaccess = None
- if "transparency" in self.info:
- self.im.putpalettealpha(self.info["transparency"], 0)
- self.im = self.im.convert("RGBA", Image.Dither.FLOYDSTEINBERG)
- self.mode = "RGBA"
- del self.info["transparency"]
- else:
- self.mode = "RGB"
- self.im = self.im.convert("RGB", Image.Dither.FLOYDSTEINBERG)
-
- def _rgb(color):
- if self._frame_palette:
- color = tuple(self._frame_palette.palette[color * 3 : color * 3 + 3])
- else:
- color = (color, color, color)
- return color
-
- self.dispose_extent = frame_dispose_extent
- try:
- if self.disposal_method < 2:
- # do not dispose or none specified
- self.dispose = None
- elif self.disposal_method == 2:
- # replace with background colour
-
- # only dispose the extent in this frame
- x0, y0, x1, y1 = self.dispose_extent
- dispose_size = (x1 - x0, y1 - y0)
-
- Image._decompression_bomb_check(dispose_size)
-
- # by convention, attempt to use transparency first
- dispose_mode = "P"
- color = self.info.get("transparency", frame_transparency)
- if color is not None:
- if self.mode in ("RGB", "RGBA"):
- dispose_mode = "RGBA"
- color = _rgb(color) + (0,)
- else:
- color = self.info.get("background", 0)
- if self.mode in ("RGB", "RGBA"):
- dispose_mode = "RGB"
- color = _rgb(color)
- self.dispose = Image.core.fill(dispose_mode, dispose_size, color)
- else:
- # replace with previous contents
- if self.im is not None:
- # only dispose the extent in this frame
- self.dispose = self._crop(self.im, self.dispose_extent)
- elif frame_transparency is not None:
- x0, y0, x1, y1 = self.dispose_extent
- dispose_size = (x1 - x0, y1 - y0)
-
- Image._decompression_bomb_check(dispose_size)
- dispose_mode = "P"
- color = frame_transparency
- if self.mode in ("RGB", "RGBA"):
- dispose_mode = "RGBA"
- color = _rgb(frame_transparency) + (0,)
- self.dispose = Image.core.fill(dispose_mode, dispose_size, color)
- except AttributeError:
- pass
-
- if interlace is not None:
- transparency = -1
- if frame_transparency is not None:
- if frame == 0:
- if LOADING_STRATEGY != LoadingStrategy.RGB_ALWAYS:
- self.info["transparency"] = frame_transparency
- elif self.mode not in ("RGB", "RGBA"):
- transparency = frame_transparency
- self.tile = [
- (
- "gif",
- (x0, y0, x1, y1),
- self.__offset,
- (bits, interlace, transparency),
- )
- ]
-
- if info.get("comment"):
- self.info["comment"] = info["comment"]
- for k in ["duration", "extension"]:
- if k in info:
- self.info[k] = info[k]
- elif k in self.info:
- del self.info[k]
-
- def load_prepare(self):
- temp_mode = "P" if self._frame_palette else "L"
- self._prev_im = None
- if self.__frame == 0:
- if self._frame_transparency is not None:
- self.im = Image.core.fill(
- temp_mode, self.size, self._frame_transparency
- )
- elif self.mode in ("RGB", "RGBA"):
- self._prev_im = self.im
- if self._frame_palette:
- self.im = Image.core.fill("P", self.size, self._frame_transparency or 0)
- self.im.putpalette(*self._frame_palette.getdata())
- else:
- self.im = None
- self.mode = temp_mode
- self._frame_palette = None
-
- super().load_prepare()
-
- def load_end(self):
- if self.__frame == 0:
- if self.mode == "P" and LOADING_STRATEGY == LoadingStrategy.RGB_ALWAYS:
- if self._frame_transparency is not None:
- self.im.putpalettealpha(self._frame_transparency, 0)
- self.mode = "RGBA"
- else:
- self.mode = "RGB"
- self.im = self.im.convert(self.mode, Image.Dither.FLOYDSTEINBERG)
- return
- if not self._prev_im:
- return
- if self._frame_transparency is not None:
- self.im.putpalettealpha(self._frame_transparency, 0)
- frame_im = self.im.convert("RGBA")
- else:
- frame_im = self.im.convert("RGB")
- frame_im = self._crop(frame_im, self.dispose_extent)
-
- self.im = self._prev_im
- self.mode = self.im.mode
- if frame_im.mode == "RGBA":
- self.im.paste(frame_im, self.dispose_extent, frame_im)
- else:
- self.im.paste(frame_im, self.dispose_extent)
-
- def tell(self):
- return self.__frame
-
-
-# --------------------------------------------------------------------
-# Write GIF files
-
-
-RAWMODE = {"1": "L", "L": "L", "P": "P"}
-
-
-def _normalize_mode(im):
- """
- Takes an image (or frame), returns an image in a mode that is appropriate
- for saving in a Gif.
-
- It may return the original image, or it may return an image converted to
- palette or 'L' mode.
-
- :param im: Image object
- :returns: Image object
- """
- if im.mode in RAWMODE:
- im.load()
- return im
- if Image.getmodebase(im.mode) == "RGB":
- im = im.convert("P", palette=Image.Palette.ADAPTIVE)
- if im.palette.mode == "RGBA":
- for rgba in im.palette.colors:
- if rgba[3] == 0:
- im.info["transparency"] = im.palette.colors[rgba]
- break
- return im
- return im.convert("L")
-
-
-def _normalize_palette(im, palette, info):
- """
- Normalizes the palette for image.
- - Sets the palette to the incoming palette, if provided.
- - Ensures that there's a palette for L mode images
- - Optimizes the palette if necessary/desired.
-
- :param im: Image object
- :param palette: bytes object containing the source palette, or ....
- :param info: encoderinfo
- :returns: Image object
- """
- source_palette = None
- if palette:
- # a bytes palette
- if isinstance(palette, (bytes, bytearray, list)):
- source_palette = bytearray(palette[:768])
- if isinstance(palette, ImagePalette.ImagePalette):
- source_palette = bytearray(palette.palette)
-
- if im.mode == "P":
- if not source_palette:
- source_palette = im.im.getpalette("RGB")[:768]
- else: # L-mode
- if not source_palette:
- source_palette = bytearray(i // 3 for i in range(768))
- im.palette = ImagePalette.ImagePalette("RGB", palette=source_palette)
-
- if palette:
- used_palette_colors = []
- for i in range(0, len(source_palette), 3):
- source_color = tuple(source_palette[i : i + 3])
- index = im.palette.colors.get(source_color)
- if index in used_palette_colors:
- index = None
- used_palette_colors.append(index)
- for i, index in enumerate(used_palette_colors):
- if index is None:
- for j in range(len(used_palette_colors)):
- if j not in used_palette_colors:
- used_palette_colors[i] = j
- break
- im = im.remap_palette(used_palette_colors)
- else:
- used_palette_colors = _get_optimize(im, info)
- if used_palette_colors is not None:
- return im.remap_palette(used_palette_colors, source_palette)
-
- im.palette.palette = source_palette
- return im
-
-
-def _write_single_frame(im, fp, palette):
- im_out = _normalize_mode(im)
- for k, v in im_out.info.items():
- im.encoderinfo.setdefault(k, v)
- im_out = _normalize_palette(im_out, palette, im.encoderinfo)
-
- for s in _get_global_header(im_out, im.encoderinfo):
- fp.write(s)
-
- # local image header
- flags = 0
- if get_interlace(im):
- flags = flags | 64
- _write_local_header(fp, im, (0, 0), flags)
-
- im_out.encoderconfig = (8, get_interlace(im))
- ImageFile._save(im_out, fp, [("gif", (0, 0) + im.size, 0, RAWMODE[im_out.mode])])
-
- fp.write(b"\0") # end of image data
-
-
-def _getbbox(base_im, im_frame):
- if _get_palette_bytes(im_frame) == _get_palette_bytes(base_im):
- delta = ImageChops.subtract_modulo(im_frame, base_im)
- else:
- delta = ImageChops.subtract_modulo(
- im_frame.convert("RGBA"), base_im.convert("RGBA")
- )
- return delta.getbbox(alpha_only=False)
-
-
-def _write_multiple_frames(im, fp, palette):
- duration = im.encoderinfo.get("duration")
- disposal = im.encoderinfo.get("disposal", im.info.get("disposal"))
-
- im_frames = []
- frame_count = 0
- background_im = None
- for imSequence in itertools.chain([im], im.encoderinfo.get("append_images", [])):
- for im_frame in ImageSequence.Iterator(imSequence):
- # a copy is required here since seek can still mutate the image
- im_frame = _normalize_mode(im_frame.copy())
- if frame_count == 0:
- for k, v in im_frame.info.items():
- if k == "transparency":
- continue
- im.encoderinfo.setdefault(k, v)
-
- encoderinfo = im.encoderinfo.copy()
- im_frame = _normalize_palette(im_frame, palette, encoderinfo)
- if "transparency" in im_frame.info:
- encoderinfo.setdefault("transparency", im_frame.info["transparency"])
- if isinstance(duration, (list, tuple)):
- encoderinfo["duration"] = duration[frame_count]
- elif duration is None and "duration" in im_frame.info:
- encoderinfo["duration"] = im_frame.info["duration"]
- if isinstance(disposal, (list, tuple)):
- encoderinfo["disposal"] = disposal[frame_count]
- frame_count += 1
-
- if im_frames:
- # delta frame
- previous = im_frames[-1]
- bbox = _getbbox(previous["im"], im_frame)
- if not bbox:
- # This frame is identical to the previous frame
- if encoderinfo.get("duration"):
- previous["encoderinfo"]["duration"] += encoderinfo["duration"]
- continue
- if encoderinfo.get("disposal") == 2:
- if background_im is None:
- color = im.encoderinfo.get(
- "transparency", im.info.get("transparency", (0, 0, 0))
- )
- background = _get_background(im_frame, color)
- background_im = Image.new("P", im_frame.size, background)
- background_im.putpalette(im_frames[0]["im"].palette)
- bbox = _getbbox(background_im, im_frame)
- else:
- bbox = None
- im_frames.append({"im": im_frame, "bbox": bbox, "encoderinfo": encoderinfo})
-
- if len(im_frames) > 1:
- for frame_data in im_frames:
- im_frame = frame_data["im"]
- if not frame_data["bbox"]:
- # global header
- for s in _get_global_header(im_frame, frame_data["encoderinfo"]):
- fp.write(s)
- offset = (0, 0)
- else:
- # compress difference
- if not palette:
- frame_data["encoderinfo"]["include_color_table"] = True
-
- im_frame = im_frame.crop(frame_data["bbox"])
- offset = frame_data["bbox"][:2]
- _write_frame_data(fp, im_frame, offset, frame_data["encoderinfo"])
- return True
- elif "duration" in im.encoderinfo and isinstance(
- im.encoderinfo["duration"], (list, tuple)
- ):
- # Since multiple frames will not be written, add together the frame durations
- im.encoderinfo["duration"] = sum(im.encoderinfo["duration"])
-
-
-def _save_all(im, fp, filename):
- _save(im, fp, filename, save_all=True)
-
-
-def _save(im, fp, filename, save_all=False):
- # header
- if "palette" in im.encoderinfo or "palette" in im.info:
- palette = im.encoderinfo.get("palette", im.info.get("palette"))
- else:
- palette = None
- im.encoderinfo["optimize"] = im.encoderinfo.get("optimize", True)
-
- if not save_all or not _write_multiple_frames(im, fp, palette):
- _write_single_frame(im, fp, palette)
-
- fp.write(b";") # end of file
-
- if hasattr(fp, "flush"):
- fp.flush()
-
-
-def get_interlace(im):
- interlace = im.encoderinfo.get("interlace", 1)
-
- # workaround for @PIL153
- if min(im.size) < 16:
- interlace = 0
-
- return interlace
-
-
-def _write_local_header(fp, im, offset, flags):
- transparent_color_exists = False
- try:
- if "transparency" in im.encoderinfo:
- transparency = im.encoderinfo["transparency"]
- else:
- transparency = im.info["transparency"]
- transparency = int(transparency)
- except (KeyError, ValueError):
- pass
- else:
- # optimize the block away if transparent color is not used
- transparent_color_exists = True
-
- used_palette_colors = _get_optimize(im, im.encoderinfo)
- if used_palette_colors is not None:
- # adjust the transparency index after optimize
- try:
- transparency = used_palette_colors.index(transparency)
- except ValueError:
- transparent_color_exists = False
-
- if "duration" in im.encoderinfo:
- duration = int(im.encoderinfo["duration"] / 10)
- else:
- duration = 0
-
- disposal = int(im.encoderinfo.get("disposal", 0))
-
- if transparent_color_exists or duration != 0 or disposal:
- packed_flag = 1 if transparent_color_exists else 0
- packed_flag |= disposal << 2
- if not transparent_color_exists:
- transparency = 0
-
- fp.write(
- b"!"
- + o8(249) # extension intro
- + o8(4) # length
- + o8(packed_flag) # packed fields
- + o16(duration) # duration
- + o8(transparency) # transparency index
- + o8(0)
- )
-
- include_color_table = im.encoderinfo.get("include_color_table")
- if include_color_table:
- palette_bytes = _get_palette_bytes(im)
- color_table_size = _get_color_table_size(palette_bytes)
- if color_table_size:
- flags = flags | 128 # local color table flag
- flags = flags | color_table_size
-
- fp.write(
- b","
- + o16(offset[0]) # offset
- + o16(offset[1])
- + o16(im.size[0]) # size
- + o16(im.size[1])
- + o8(flags) # flags
- )
- if include_color_table and color_table_size:
- fp.write(_get_header_palette(palette_bytes))
- fp.write(o8(8)) # bits
-
-
-def _save_netpbm(im, fp, filename):
- # Unused by default.
- # To use, uncomment the register_save call at the end of the file.
- #
- # If you need real GIF compression and/or RGB quantization, you
- # can use the external NETPBM/PBMPLUS utilities. See comments
- # below for information on how to enable this.
- tempfile = im._dump()
-
- try:
- with open(filename, "wb") as f:
- if im.mode != "RGB":
- subprocess.check_call(
- ["ppmtogif", tempfile], stdout=f, stderr=subprocess.DEVNULL
- )
- else:
- # Pipe ppmquant output into ppmtogif
- # "ppmquant 256 %s | ppmtogif > %s" % (tempfile, filename)
- quant_cmd = ["ppmquant", "256", tempfile]
- togif_cmd = ["ppmtogif"]
- quant_proc = subprocess.Popen(
- quant_cmd, stdout=subprocess.PIPE, stderr=subprocess.DEVNULL
- )
- togif_proc = subprocess.Popen(
- togif_cmd,
- stdin=quant_proc.stdout,
- stdout=f,
- stderr=subprocess.DEVNULL,
- )
-
- # Allow ppmquant to receive SIGPIPE if ppmtogif exits
- quant_proc.stdout.close()
-
- retcode = quant_proc.wait()
- if retcode:
- raise subprocess.CalledProcessError(retcode, quant_cmd)
-
- retcode = togif_proc.wait()
- if retcode:
- raise subprocess.CalledProcessError(retcode, togif_cmd)
- finally:
- try:
- os.unlink(tempfile)
- except OSError:
- pass
-
-
-# Force optimization so that we can test performance against
-# cases where it took lots of memory and time previously.
-_FORCE_OPTIMIZE = False
-
-
-def _get_optimize(im, info):
- """
- Palette optimization is a potentially expensive operation.
-
- This function determines if the palette should be optimized using
- some heuristics, then returns the list of palette entries in use.
-
- :param im: Image object
- :param info: encoderinfo
- :returns: list of indexes of palette entries in use, or None
- """
- if im.mode in ("P", "L") and info and info.get("optimize", 0):
- # Potentially expensive operation.
-
- # The palette saves 3 bytes per color not used, but palette
- # lengths are restricted to 3*(2**N) bytes. Max saving would
- # be 768 -> 6 bytes if we went all the way down to 2 colors.
- # * If we're over 128 colors, we can't save any space.
- # * If there aren't any holes, it's not worth collapsing.
- # * If we have a 'large' image, the palette is in the noise.
-
- # create the new palette if not every color is used
- optimise = _FORCE_OPTIMIZE or im.mode == "L"
- if optimise or im.width * im.height < 512 * 512:
- # check which colors are used
- used_palette_colors = []
- for i, count in enumerate(im.histogram()):
- if count:
- used_palette_colors.append(i)
-
- if optimise or max(used_palette_colors) >= len(used_palette_colors):
- return used_palette_colors
-
- num_palette_colors = len(im.palette.palette) // Image.getmodebands(
- im.palette.mode
- )
- current_palette_size = 1 << (num_palette_colors - 1).bit_length()
- if (
- # check that the palette would become smaller when saved
- len(used_palette_colors) <= current_palette_size // 2
- # check that the palette is not already the smallest possible size
- and current_palette_size > 2
- ):
- return used_palette_colors
-
-
-def _get_color_table_size(palette_bytes):
- # calculate the palette size for the header
- if not palette_bytes:
- return 0
- elif len(palette_bytes) < 9:
- return 1
- else:
- return math.ceil(math.log(len(palette_bytes) // 3, 2)) - 1
-
-
-def _get_header_palette(palette_bytes):
- """
- Returns the palette, null padded to the next power of 2 (*3) bytes
- suitable for direct inclusion in the GIF header
-
- :param palette_bytes: Unpadded palette bytes, in RGBRGB form
- :returns: Null padded palette
- """
- color_table_size = _get_color_table_size(palette_bytes)
-
- # add the missing amount of bytes
- # the palette has to be 2< 0:
- palette_bytes += o8(0) * 3 * actual_target_size_diff
- return palette_bytes
-
-
-def _get_palette_bytes(im):
- """
- Gets the palette for inclusion in the gif header
-
- :param im: Image object
- :returns: Bytes, len<=768 suitable for inclusion in gif header
- """
- return im.palette.palette if im.palette else b""
-
-
-def _get_background(im, info_background):
- background = 0
- if info_background:
- if isinstance(info_background, tuple):
- # WebPImagePlugin stores an RGBA value in info["background"]
- # So it must be converted to the same format as GifImagePlugin's
- # info["background"] - a global color table index
- try:
- background = im.palette.getcolor(info_background, im)
- except ValueError as e:
- if str(e) not in (
- # If all 256 colors are in use,
- # then there is no need for the background color
- "cannot allocate more than 256 colors",
- # Ignore non-opaque WebP background
- "cannot add non-opaque RGBA color to RGB palette",
- ):
- raise
- else:
- background = info_background
- return background
-
-
-def _get_global_header(im, info):
- """Return a list of strings representing a GIF header"""
-
- # Header Block
- # https://www.matthewflickinger.com/lab/whatsinagif/bits_and_bytes.asp
-
- version = b"87a"
- if im.info.get("version") == b"89a" or (
- info
- and (
- "transparency" in info
- or "loop" in info
- or info.get("duration")
- or info.get("comment")
- )
- ):
- version = b"89a"
-
- background = _get_background(im, info.get("background"))
-
- palette_bytes = _get_palette_bytes(im)
- color_table_size = _get_color_table_size(palette_bytes)
-
- header = [
- b"GIF" # signature
- + version # version
- + o16(im.size[0]) # canvas width
- + o16(im.size[1]), # canvas height
- # Logical Screen Descriptor
- # size of global color table + global color table flag
- o8(color_table_size + 128), # packed fields
- # background + reserved/aspect
- o8(background) + o8(0),
- # Global Color Table
- _get_header_palette(palette_bytes),
- ]
- if "loop" in info:
- header.append(
- b"!"
- + o8(255) # extension intro
- + o8(11)
- + b"NETSCAPE2.0"
- + o8(3)
- + o8(1)
- + o16(info["loop"]) # number of loops
- + o8(0)
- )
- if info.get("comment"):
- comment_block = b"!" + o8(254) # extension intro
-
- comment = info["comment"]
- if isinstance(comment, str):
- comment = comment.encode()
- for i in range(0, len(comment), 255):
- subblock = comment[i : i + 255]
- comment_block += o8(len(subblock)) + subblock
-
- comment_block += o8(0)
- header.append(comment_block)
- return header
-
-
-def _write_frame_data(fp, im_frame, offset, params):
- try:
- im_frame.encoderinfo = params
-
- # local image header
- _write_local_header(fp, im_frame, offset, 0)
-
- ImageFile._save(
- im_frame, fp, [("gif", (0, 0) + im_frame.size, 0, RAWMODE[im_frame.mode])]
- )
-
- fp.write(b"\0") # end of image data
- finally:
- del im_frame.encoderinfo
-
-
-# --------------------------------------------------------------------
-# Legacy GIF utilities
-
-
-def getheader(im, palette=None, info=None):
- """
- Legacy Method to get Gif data from image.
-
- Warning:: May modify image data.
-
- :param im: Image object
- :param palette: bytes object containing the source palette, or ....
- :param info: encoderinfo
- :returns: tuple of(list of header items, optimized palette)
-
- """
- used_palette_colors = _get_optimize(im, info)
-
- if info is None:
- info = {}
-
- if "background" not in info and "background" in im.info:
- info["background"] = im.info["background"]
-
- im_mod = _normalize_palette(im, palette, info)
- im.palette = im_mod.palette
- im.im = im_mod.im
- header = _get_global_header(im, info)
-
- return header, used_palette_colors
-
-
-def getdata(im, offset=(0, 0), **params):
- """
- Legacy Method
-
- Return a list of strings representing this image.
- The first string is a local image header, the rest contains
- encoded image data.
-
- To specify duration, add the time in milliseconds,
- e.g. ``getdata(im_frame, duration=1000)``
-
- :param im: Image object
- :param offset: Tuple of (x, y) pixels. Defaults to (0, 0)
- :param \\**params: e.g. duration or other encoder info parameters
- :returns: List of bytes containing GIF encoded frame data
-
- """
-
- class Collector:
- data = []
-
- def write(self, data):
- self.data.append(data)
-
- im.load() # make sure raster data is available
-
- fp = Collector()
-
- _write_frame_data(fp, im, offset, params)
-
- return fp.data
-
-
-# --------------------------------------------------------------------
-# Registry
-
-Image.register_open(GifImageFile.format, GifImageFile, _accept)
-Image.register_save(GifImageFile.format, _save)
-Image.register_save_all(GifImageFile.format, _save_all)
-Image.register_extension(GifImageFile.format, ".gif")
-Image.register_mime(GifImageFile.format, "image/gif")
-
-#
-# Uncomment the following line if you wish to use NETPBM/PBMPLUS
-# instead of the built-in "uncompressed" GIF encoder
-
-# Image.register_save(GifImageFile.format, _save_netpbm)
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fastapi/background.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fastapi/background.py
deleted file mode 100644
index dd3bbe249130348881331aea569ce3ec3f295128..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fastapi/background.py
+++ /dev/null
@@ -1 +0,0 @@
-from starlette.background import BackgroundTasks as BackgroundTasks # noqa
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/merge/layout.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/merge/layout.py
deleted file mode 100644
index 6b85cd503387291f326e937b36a5739b1de23ef1..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/merge/layout.py
+++ /dev/null
@@ -1,530 +0,0 @@
-# Copyright 2013 Google, Inc. All Rights Reserved.
-#
-# Google Author(s): Behdad Esfahbod, Roozbeh Pournader
-
-from fontTools import ttLib
-from fontTools.ttLib.tables.DefaultTable import DefaultTable
-from fontTools.ttLib.tables import otTables
-from fontTools.merge.base import add_method, mergeObjects
-from fontTools.merge.util import *
-import logging
-
-
-log = logging.getLogger("fontTools.merge")
-
-
-def mergeLookupLists(lst):
- # TODO Do smarter merge.
- return sumLists(lst)
-
-
-def mergeFeatures(lst):
- assert lst
- self = otTables.Feature()
- self.FeatureParams = None
- self.LookupListIndex = mergeLookupLists(
- [l.LookupListIndex for l in lst if l.LookupListIndex]
- )
- self.LookupCount = len(self.LookupListIndex)
- return self
-
-
-def mergeFeatureLists(lst):
- d = {}
- for l in lst:
- for f in l:
- tag = f.FeatureTag
- if tag not in d:
- d[tag] = []
- d[tag].append(f.Feature)
- ret = []
- for tag in sorted(d.keys()):
- rec = otTables.FeatureRecord()
- rec.FeatureTag = tag
- rec.Feature = mergeFeatures(d[tag])
- ret.append(rec)
- return ret
-
-
-def mergeLangSyses(lst):
- assert lst
-
- # TODO Support merging ReqFeatureIndex
- assert all(l.ReqFeatureIndex == 0xFFFF for l in lst)
-
- self = otTables.LangSys()
- self.LookupOrder = None
- self.ReqFeatureIndex = 0xFFFF
- self.FeatureIndex = mergeFeatureLists(
- [l.FeatureIndex for l in lst if l.FeatureIndex]
- )
- self.FeatureCount = len(self.FeatureIndex)
- return self
-
-
-def mergeScripts(lst):
- assert lst
-
- if len(lst) == 1:
- return lst[0]
- langSyses = {}
- for sr in lst:
- for lsr in sr.LangSysRecord:
- if lsr.LangSysTag not in langSyses:
- langSyses[lsr.LangSysTag] = []
- langSyses[lsr.LangSysTag].append(lsr.LangSys)
- lsrecords = []
- for tag, langSys_list in sorted(langSyses.items()):
- lsr = otTables.LangSysRecord()
- lsr.LangSys = mergeLangSyses(langSys_list)
- lsr.LangSysTag = tag
- lsrecords.append(lsr)
-
- self = otTables.Script()
- self.LangSysRecord = lsrecords
- self.LangSysCount = len(lsrecords)
- dfltLangSyses = [s.DefaultLangSys for s in lst if s.DefaultLangSys]
- if dfltLangSyses:
- self.DefaultLangSys = mergeLangSyses(dfltLangSyses)
- else:
- self.DefaultLangSys = None
- return self
-
-
-def mergeScriptRecords(lst):
- d = {}
- for l in lst:
- for s in l:
- tag = s.ScriptTag
- if tag not in d:
- d[tag] = []
- d[tag].append(s.Script)
- ret = []
- for tag in sorted(d.keys()):
- rec = otTables.ScriptRecord()
- rec.ScriptTag = tag
- rec.Script = mergeScripts(d[tag])
- ret.append(rec)
- return ret
-
-
-otTables.ScriptList.mergeMap = {
- "ScriptCount": lambda lst: None, # TODO
- "ScriptRecord": mergeScriptRecords,
-}
-otTables.BaseScriptList.mergeMap = {
- "BaseScriptCount": lambda lst: None, # TODO
- # TODO: Merge duplicate entries
- "BaseScriptRecord": lambda lst: sorted(
- sumLists(lst), key=lambda s: s.BaseScriptTag
- ),
-}
-
-otTables.FeatureList.mergeMap = {
- "FeatureCount": sum,
- "FeatureRecord": lambda lst: sorted(sumLists(lst), key=lambda s: s.FeatureTag),
-}
-
-otTables.LookupList.mergeMap = {
- "LookupCount": sum,
- "Lookup": sumLists,
-}
-
-otTables.Coverage.mergeMap = {
- "Format": min,
- "glyphs": sumLists,
-}
-
-otTables.ClassDef.mergeMap = {
- "Format": min,
- "classDefs": sumDicts,
-}
-
-otTables.LigCaretList.mergeMap = {
- "Coverage": mergeObjects,
- "LigGlyphCount": sum,
- "LigGlyph": sumLists,
-}
-
-otTables.AttachList.mergeMap = {
- "Coverage": mergeObjects,
- "GlyphCount": sum,
- "AttachPoint": sumLists,
-}
-
-# XXX Renumber MarkFilterSets of lookups
-otTables.MarkGlyphSetsDef.mergeMap = {
- "MarkSetTableFormat": equal,
- "MarkSetCount": sum,
- "Coverage": sumLists,
-}
-
-otTables.Axis.mergeMap = {
- "*": mergeObjects,
-}
-
-# XXX Fix BASE table merging
-otTables.BaseTagList.mergeMap = {
- "BaseTagCount": sum,
- "BaselineTag": sumLists,
-}
-
-otTables.GDEF.mergeMap = (
- otTables.GSUB.mergeMap
-) = (
- otTables.GPOS.mergeMap
-) = otTables.BASE.mergeMap = otTables.JSTF.mergeMap = otTables.MATH.mergeMap = {
- "*": mergeObjects,
- "Version": max,
-}
-
-ttLib.getTableClass("GDEF").mergeMap = ttLib.getTableClass(
- "GSUB"
-).mergeMap = ttLib.getTableClass("GPOS").mergeMap = ttLib.getTableClass(
- "BASE"
-).mergeMap = ttLib.getTableClass(
- "JSTF"
-).mergeMap = ttLib.getTableClass(
- "MATH"
-).mergeMap = {
- "tableTag": onlyExisting(equal), # XXX clean me up
- "table": mergeObjects,
-}
-
-
-@add_method(ttLib.getTableClass("GSUB"))
-def merge(self, m, tables):
- assert len(tables) == len(m.duplicateGlyphsPerFont)
- for i, (table, dups) in enumerate(zip(tables, m.duplicateGlyphsPerFont)):
- if not dups:
- continue
- if table is None or table is NotImplemented:
- log.warning(
- "Have non-identical duplicates to resolve for '%s' but no GSUB. Are duplicates intended?: %s",
- m.fonts[i]._merger__name,
- dups,
- )
- continue
-
- synthFeature = None
- synthLookup = None
- for script in table.table.ScriptList.ScriptRecord:
- if script.ScriptTag == "DFLT":
- continue # XXX
- for langsys in [script.Script.DefaultLangSys] + [
- l.LangSys for l in script.Script.LangSysRecord
- ]:
- if langsys is None:
- continue # XXX Create!
- feature = [v for v in langsys.FeatureIndex if v.FeatureTag == "locl"]
- assert len(feature) <= 1
- if feature:
- feature = feature[0]
- else:
- if not synthFeature:
- synthFeature = otTables.FeatureRecord()
- synthFeature.FeatureTag = "locl"
- f = synthFeature.Feature = otTables.Feature()
- f.FeatureParams = None
- f.LookupCount = 0
- f.LookupListIndex = []
- table.table.FeatureList.FeatureRecord.append(synthFeature)
- table.table.FeatureList.FeatureCount += 1
- feature = synthFeature
- langsys.FeatureIndex.append(feature)
- langsys.FeatureIndex.sort(key=lambda v: v.FeatureTag)
-
- if not synthLookup:
- subtable = otTables.SingleSubst()
- subtable.mapping = dups
- synthLookup = otTables.Lookup()
- synthLookup.LookupFlag = 0
- synthLookup.LookupType = 1
- synthLookup.SubTableCount = 1
- synthLookup.SubTable = [subtable]
- if table.table.LookupList is None:
- # mtiLib uses None as default value for LookupList,
- # while feaLib points to an empty array with count 0
- # TODO: make them do the same
- table.table.LookupList = otTables.LookupList()
- table.table.LookupList.Lookup = []
- table.table.LookupList.LookupCount = 0
- table.table.LookupList.Lookup.append(synthLookup)
- table.table.LookupList.LookupCount += 1
-
- if feature.Feature.LookupListIndex[:1] != [synthLookup]:
- feature.Feature.LookupListIndex[:0] = [synthLookup]
- feature.Feature.LookupCount += 1
-
- DefaultTable.merge(self, m, tables)
- return self
-
-
-@add_method(
- otTables.SingleSubst,
- otTables.MultipleSubst,
- otTables.AlternateSubst,
- otTables.LigatureSubst,
- otTables.ReverseChainSingleSubst,
- otTables.SinglePos,
- otTables.PairPos,
- otTables.CursivePos,
- otTables.MarkBasePos,
- otTables.MarkLigPos,
- otTables.MarkMarkPos,
-)
-def mapLookups(self, lookupMap):
- pass
-
-
-# Copied and trimmed down from subset.py
-@add_method(
- otTables.ContextSubst,
- otTables.ChainContextSubst,
- otTables.ContextPos,
- otTables.ChainContextPos,
-)
-def __merge_classify_context(self):
- class ContextHelper(object):
- def __init__(self, klass, Format):
- if klass.__name__.endswith("Subst"):
- Typ = "Sub"
- Type = "Subst"
- else:
- Typ = "Pos"
- Type = "Pos"
- if klass.__name__.startswith("Chain"):
- Chain = "Chain"
- else:
- Chain = ""
- ChainTyp = Chain + Typ
-
- self.Typ = Typ
- self.Type = Type
- self.Chain = Chain
- self.ChainTyp = ChainTyp
-
- self.LookupRecord = Type + "LookupRecord"
-
- if Format == 1:
- self.Rule = ChainTyp + "Rule"
- self.RuleSet = ChainTyp + "RuleSet"
- elif Format == 2:
- self.Rule = ChainTyp + "ClassRule"
- self.RuleSet = ChainTyp + "ClassSet"
-
- if self.Format not in [1, 2, 3]:
- return None # Don't shoot the messenger; let it go
- if not hasattr(self.__class__, "_merge__ContextHelpers"):
- self.__class__._merge__ContextHelpers = {}
- if self.Format not in self.__class__._merge__ContextHelpers:
- helper = ContextHelper(self.__class__, self.Format)
- self.__class__._merge__ContextHelpers[self.Format] = helper
- return self.__class__._merge__ContextHelpers[self.Format]
-
-
-@add_method(
- otTables.ContextSubst,
- otTables.ChainContextSubst,
- otTables.ContextPos,
- otTables.ChainContextPos,
-)
-def mapLookups(self, lookupMap):
- c = self.__merge_classify_context()
-
- if self.Format in [1, 2]:
- for rs in getattr(self, c.RuleSet):
- if not rs:
- continue
- for r in getattr(rs, c.Rule):
- if not r:
- continue
- for ll in getattr(r, c.LookupRecord):
- if not ll:
- continue
- ll.LookupListIndex = lookupMap[ll.LookupListIndex]
- elif self.Format == 3:
- for ll in getattr(self, c.LookupRecord):
- if not ll:
- continue
- ll.LookupListIndex = lookupMap[ll.LookupListIndex]
- else:
- assert 0, "unknown format: %s" % self.Format
-
-
-@add_method(otTables.ExtensionSubst, otTables.ExtensionPos)
-def mapLookups(self, lookupMap):
- if self.Format == 1:
- self.ExtSubTable.mapLookups(lookupMap)
- else:
- assert 0, "unknown format: %s" % self.Format
-
-
-@add_method(otTables.Lookup)
-def mapLookups(self, lookupMap):
- for st in self.SubTable:
- if not st:
- continue
- st.mapLookups(lookupMap)
-
-
-@add_method(otTables.LookupList)
-def mapLookups(self, lookupMap):
- for l in self.Lookup:
- if not l:
- continue
- l.mapLookups(lookupMap)
-
-
-@add_method(otTables.Lookup)
-def mapMarkFilteringSets(self, markFilteringSetMap):
- if self.LookupFlag & 0x0010:
- self.MarkFilteringSet = markFilteringSetMap[self.MarkFilteringSet]
-
-
-@add_method(otTables.LookupList)
-def mapMarkFilteringSets(self, markFilteringSetMap):
- for l in self.Lookup:
- if not l:
- continue
- l.mapMarkFilteringSets(markFilteringSetMap)
-
-
-@add_method(otTables.Feature)
-def mapLookups(self, lookupMap):
- self.LookupListIndex = [lookupMap[i] for i in self.LookupListIndex]
-
-
-@add_method(otTables.FeatureList)
-def mapLookups(self, lookupMap):
- for f in self.FeatureRecord:
- if not f or not f.Feature:
- continue
- f.Feature.mapLookups(lookupMap)
-
-
-@add_method(otTables.DefaultLangSys, otTables.LangSys)
-def mapFeatures(self, featureMap):
- self.FeatureIndex = [featureMap[i] for i in self.FeatureIndex]
- if self.ReqFeatureIndex != 65535:
- self.ReqFeatureIndex = featureMap[self.ReqFeatureIndex]
-
-
-@add_method(otTables.Script)
-def mapFeatures(self, featureMap):
- if self.DefaultLangSys:
- self.DefaultLangSys.mapFeatures(featureMap)
- for l in self.LangSysRecord:
- if not l or not l.LangSys:
- continue
- l.LangSys.mapFeatures(featureMap)
-
-
-@add_method(otTables.ScriptList)
-def mapFeatures(self, featureMap):
- for s in self.ScriptRecord:
- if not s or not s.Script:
- continue
- s.Script.mapFeatures(featureMap)
-
-
-def layoutPreMerge(font):
- # Map indices to references
-
- GDEF = font.get("GDEF")
- GSUB = font.get("GSUB")
- GPOS = font.get("GPOS")
-
- for t in [GSUB, GPOS]:
- if not t:
- continue
-
- if t.table.LookupList:
- lookupMap = {i: v for i, v in enumerate(t.table.LookupList.Lookup)}
- t.table.LookupList.mapLookups(lookupMap)
- t.table.FeatureList.mapLookups(lookupMap)
-
- if (
- GDEF
- and GDEF.table.Version >= 0x00010002
- and GDEF.table.MarkGlyphSetsDef
- ):
- markFilteringSetMap = {
- i: v for i, v in enumerate(GDEF.table.MarkGlyphSetsDef.Coverage)
- }
- t.table.LookupList.mapMarkFilteringSets(markFilteringSetMap)
-
- if t.table.FeatureList and t.table.ScriptList:
- featureMap = {i: v for i, v in enumerate(t.table.FeatureList.FeatureRecord)}
- t.table.ScriptList.mapFeatures(featureMap)
-
- # TODO FeatureParams nameIDs
-
-
-def layoutPostMerge(font):
- # Map references back to indices
-
- GDEF = font.get("GDEF")
- GSUB = font.get("GSUB")
- GPOS = font.get("GPOS")
-
- for t in [GSUB, GPOS]:
- if not t:
- continue
-
- if t.table.FeatureList and t.table.ScriptList:
- # Collect unregistered (new) features.
- featureMap = GregariousIdentityDict(t.table.FeatureList.FeatureRecord)
- t.table.ScriptList.mapFeatures(featureMap)
-
- # Record used features.
- featureMap = AttendanceRecordingIdentityDict(
- t.table.FeatureList.FeatureRecord
- )
- t.table.ScriptList.mapFeatures(featureMap)
- usedIndices = featureMap.s
-
- # Remove unused features
- t.table.FeatureList.FeatureRecord = [
- f
- for i, f in enumerate(t.table.FeatureList.FeatureRecord)
- if i in usedIndices
- ]
-
- # Map back to indices.
- featureMap = NonhashableDict(t.table.FeatureList.FeatureRecord)
- t.table.ScriptList.mapFeatures(featureMap)
-
- t.table.FeatureList.FeatureCount = len(t.table.FeatureList.FeatureRecord)
-
- if t.table.LookupList:
- # Collect unregistered (new) lookups.
- lookupMap = GregariousIdentityDict(t.table.LookupList.Lookup)
- t.table.FeatureList.mapLookups(lookupMap)
- t.table.LookupList.mapLookups(lookupMap)
-
- # Record used lookups.
- lookupMap = AttendanceRecordingIdentityDict(t.table.LookupList.Lookup)
- t.table.FeatureList.mapLookups(lookupMap)
- t.table.LookupList.mapLookups(lookupMap)
- usedIndices = lookupMap.s
-
- # Remove unused lookups
- t.table.LookupList.Lookup = [
- l for i, l in enumerate(t.table.LookupList.Lookup) if i in usedIndices
- ]
-
- # Map back to indices.
- lookupMap = NonhashableDict(t.table.LookupList.Lookup)
- t.table.FeatureList.mapLookups(lookupMap)
- t.table.LookupList.mapLookups(lookupMap)
-
- t.table.LookupList.LookupCount = len(t.table.LookupList.Lookup)
-
- if GDEF and GDEF.table.Version >= 0x00010002:
- markFilteringSetMap = NonhashableDict(
- GDEF.table.MarkGlyphSetsDef.Coverage
- )
- t.table.LookupList.mapMarkFilteringSets(markFilteringSetMap)
-
- # TODO FeatureParams nameIDs
diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/encodingTools.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/encodingTools.py
deleted file mode 100644
index 3b2651d3b1ce222060fa67abaeac4da8030618fa..0000000000000000000000000000000000000000
--- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/misc/encodingTools.py
+++ /dev/null
@@ -1,72 +0,0 @@
-"""fontTools.misc.encodingTools.py -- tools for working with OpenType encodings.
-"""
-
-import fontTools.encodings.codecs
-
-# Map keyed by platformID, then platEncID, then possibly langID
-_encodingMap = {
- 0: { # Unicode
- 0: "utf_16_be",
- 1: "utf_16_be",
- 2: "utf_16_be",
- 3: "utf_16_be",
- 4: "utf_16_be",
- 5: "utf_16_be",
- 6: "utf_16_be",
- },
- 1: { # Macintosh
- # See
- # https://github.com/fonttools/fonttools/issues/236
- 0: { # Macintosh, platEncID==0, keyed by langID
- 15: "mac_iceland",
- 17: "mac_turkish",
- 18: "mac_croatian",
- 24: "mac_latin2",
- 25: "mac_latin2",
- 26: "mac_latin2",
- 27: "mac_latin2",
- 28: "mac_latin2",
- 36: "mac_latin2",
- 37: "mac_romanian",
- 38: "mac_latin2",
- 39: "mac_latin2",
- 40: "mac_latin2",
- Ellipsis: "mac_roman", # Other
- },
- 1: "x_mac_japanese_ttx",
- 2: "x_mac_trad_chinese_ttx",
- 3: "x_mac_korean_ttx",
- 6: "mac_greek",
- 7: "mac_cyrillic",
- 25: "x_mac_simp_chinese_ttx",
- 29: "mac_latin2",
- 35: "mac_turkish",
- 37: "mac_iceland",
- },
- 2: { # ISO
- 0: "ascii",
- 1: "utf_16_be",
- 2: "latin1",
- },
- 3: { # Microsoft
- 0: "utf_16_be",
- 1: "utf_16_be",
- 2: "shift_jis",
- 3: "gb2312",
- 4: "big5",
- 5: "euc_kr",
- 6: "johab",
- 10: "utf_16_be",
- },
-}
-
-
-def getEncoding(platformID, platEncID, langID, default=None):
- """Returns the Python encoding name for OpenType platformID/encodingID/langID
- triplet. If encoding for these values is not known, by default None is
- returned. That can be overriden by passing a value to the default argument.
- """
- encoding = _encodingMap.get(platformID, {}).get(platEncID, default)
- if isinstance(encoding, dict):
- encoding = encoding.get(langID, encoding[Ellipsis])
- return encoding
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dca_core.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dca_core.h
deleted file mode 100644
index 347566d6ed549bf4a6ab051846230fe4170b9691..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dca_core.h
+++ /dev/null
@@ -1,257 +0,0 @@
-/*
- * Copyright (C) 2016 foo86
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef AVCODEC_DCA_CORE_H
-#define AVCODEC_DCA_CORE_H
-
-#include "libavutil/float_dsp.h"
-#include "libavutil/fixed_dsp.h"
-#include "libavutil/mem_internal.h"
-#include "libavutil/tx.h"
-
-#include "avcodec.h"
-#include "get_bits.h"
-#include "dca.h"
-#include "dca_exss.h"
-#include "dcadsp.h"
-#include "dcadct.h"
-#include "dcamath.h"
-#include "dcahuff.h"
-#include "synth_filter.h"
-
-#define DCA_CHANNELS 7
-#define DCA_SUBBANDS 32
-#define DCA_SUBBANDS_X96 64
-#define DCA_SUBFRAMES 16
-#define DCA_SUBBAND_SAMPLES 8
-#define DCA_PCMBLOCK_SAMPLES 32
-#define DCA_LFE_HISTORY 8
-#define DCA_ABITS_MAX 26
-
-#define DCA_CORE_CHANNELS_MAX 6
-#define DCA_DMIX_CHANNELS_MAX 4
-#define DCA_XXCH_CHANNELS_MAX 2
-#define DCA_EXSS_CHANNELS_MAX 8
-#define DCA_EXSS_CHSETS_MAX 4
-
-#define DCA_FILTER_MODE_X96 0x01
-#define DCA_FILTER_MODE_FIXED 0x02
-
-enum DCACoreAudioMode {
- DCA_AMODE_MONO, // Mode 0: A (mono)
- DCA_AMODE_MONO_DUAL, // Mode 1: A + B (dual mono)
- DCA_AMODE_STEREO, // Mode 2: L + R (stereo)
- DCA_AMODE_STEREO_SUMDIFF, // Mode 3: (L+R) + (L-R) (sum-diff)
- DCA_AMODE_STEREO_TOTAL, // Mode 4: LT + RT (left and right total)
- DCA_AMODE_3F, // Mode 5: C + L + R
- DCA_AMODE_2F1R, // Mode 6: L + R + S
- DCA_AMODE_3F1R, // Mode 7: C + L + R + S
- DCA_AMODE_2F2R, // Mode 8: L + R + SL + SR
- DCA_AMODE_3F2R, // Mode 9: C + L + R + SL + SR
-
- DCA_AMODE_COUNT
-};
-
-enum DCACoreExtAudioType {
- DCA_EXT_AUDIO_XCH = 0,
- DCA_EXT_AUDIO_X96 = 2,
- DCA_EXT_AUDIO_XXCH = 6
-};
-
-enum DCACoreLFEFlag {
- DCA_LFE_FLAG_NONE,
- DCA_LFE_FLAG_128,
- DCA_LFE_FLAG_64,
- DCA_LFE_FLAG_INVALID
-};
-
-typedef struct DCADSPData {
- union {
- struct {
- DECLARE_ALIGNED(32, float, hist1)[1024];
- DECLARE_ALIGNED(32, float, hist2)[64];
- } flt;
- struct {
- DECLARE_ALIGNED(32, int32_t, hist1)[1024];
- DECLARE_ALIGNED(32, int32_t, hist2)[64];
- } fix;
- } u;
- int offset;
-} DCADSPData;
-
-typedef struct DCACoreDecoder {
- AVCodecContext *avctx;
- GetBitContext gb;
- GetBitContext gb_in;
-
- // Bit stream header
- int crc_present; ///< CRC present flag
- int npcmblocks; ///< Number of PCM sample blocks
- int frame_size; ///< Primary frame byte size
- int audio_mode; ///< Audio channel arrangement
- int sample_rate; ///< Core audio sampling frequency
- int bit_rate; ///< Transmission bit rate
- int drc_present; ///< Embedded dynamic range flag
- int ts_present; ///< Embedded time stamp flag
- int aux_present; ///< Auxiliary data flag
- int ext_audio_type; ///< Extension audio descriptor flag
- int ext_audio_present; ///< Extended coding flag
- int sync_ssf; ///< Audio sync word insertion flag
- int lfe_present; ///< Low frequency effects flag
- int predictor_history; ///< Predictor history flag switch
- int filter_perfect; ///< Multirate interpolator switch
- int source_pcm_res; ///< Source PCM resolution
- int es_format; ///< Extended surround (ES) mastering flag
- int sumdiff_front; ///< Front sum/difference flag
- int sumdiff_surround; ///< Surround sum/difference flag
-
- // Primary audio coding header
- int nsubframes; ///< Number of subframes
- int nchannels; ///< Number of primary audio channels (incl. extension channels)
- int ch_mask; ///< Speaker layout mask (incl. LFE and extension channels)
- int8_t nsubbands[DCA_CHANNELS]; ///< Subband activity count
- int8_t subband_vq_start[DCA_CHANNELS]; ///< High frequency VQ start subband
- int8_t joint_intensity_index[DCA_CHANNELS]; ///< Joint intensity coding index
- int8_t transition_mode_sel[DCA_CHANNELS]; ///< Transient mode code book
- int8_t scale_factor_sel[DCA_CHANNELS]; ///< Scale factor code book
- int8_t bit_allocation_sel[DCA_CHANNELS]; ///< Bit allocation quantizer select
- int8_t quant_index_sel[DCA_CHANNELS][DCA_CODE_BOOKS]; ///< Quantization index codebook select
- int32_t scale_factor_adj[DCA_CHANNELS][DCA_CODE_BOOKS]; ///< Scale factor adjustment
-
- // Primary audio coding side information
- int8_t nsubsubframes[DCA_SUBFRAMES]; ///< Subsubframe count for each subframe
- int8_t prediction_mode[DCA_CHANNELS][DCA_SUBBANDS_X96]; ///< Prediction mode
- int16_t prediction_vq_index[DCA_CHANNELS][DCA_SUBBANDS_X96]; ///< Prediction coefficients VQ address
- int8_t bit_allocation[DCA_CHANNELS][DCA_SUBBANDS_X96]; ///< Bit allocation index
- int8_t transition_mode[DCA_SUBFRAMES][DCA_CHANNELS][DCA_SUBBANDS]; ///< Transition mode
- int32_t scale_factors[DCA_CHANNELS][DCA_SUBBANDS][2]; ///< Scale factors (2x for transients and X96)
- int8_t joint_scale_sel[DCA_CHANNELS]; ///< Joint subband codebook select
- int32_t joint_scale_factors[DCA_CHANNELS][DCA_SUBBANDS_X96]; ///< Scale factors for joint subband coding
-
- // Auxiliary data
- int prim_dmix_embedded; ///< Auxiliary dynamic downmix flag
- int prim_dmix_type; ///< Auxiliary primary channel downmix type
- int prim_dmix_coeff[DCA_DMIX_CHANNELS_MAX * DCA_CORE_CHANNELS_MAX]; ///< Dynamic downmix code coefficients
-
- // Core extensions
- int ext_audio_mask; ///< Bit mask of fully decoded core extensions
-
- // XCH extension data
- int xch_pos; ///< Bit position of XCH frame in core substream
-
- // XXCH extension data
- int xxch_crc_present; ///< CRC presence flag for XXCH channel set header
- int xxch_mask_nbits; ///< Number of bits for loudspeaker mask
- int xxch_core_mask; ///< Core loudspeaker activity mask
- int xxch_spkr_mask; ///< Loudspeaker layout mask
- int xxch_dmix_embedded; ///< Downmix already performed by encoder
- int xxch_dmix_scale_inv; ///< Downmix scale factor
- int xxch_dmix_mask[DCA_XXCH_CHANNELS_MAX]; ///< Downmix channel mapping mask
- int xxch_dmix_coeff[DCA_XXCH_CHANNELS_MAX * DCA_CORE_CHANNELS_MAX]; ///< Downmix coefficients
- int xxch_pos; ///< Bit position of XXCH frame in core substream
-
- // X96 extension data
- int x96_rev_no; ///< X96 revision number
- int x96_crc_present; ///< CRC presence flag for X96 channel set header
- int x96_nchannels; ///< Number of primary channels in X96 extension
- int x96_high_res; ///< X96 high resolution flag
- int x96_subband_start; ///< First encoded subband in X96 extension
- int x96_rand; ///< Random seed for generating samples for unallocated X96 subbands
- int x96_pos; ///< Bit position of X96 frame in core substream
-
- // Sample buffers
- unsigned int x96_subband_size;
- int32_t *x96_subband_buffer; ///< X96 subband sample buffer base
- int32_t *x96_subband_samples[DCA_CHANNELS][DCA_SUBBANDS_X96]; ///< X96 subband samples
-
- unsigned int subband_size;
- int32_t *subband_buffer; ///< Subband sample buffer base
- int32_t *subband_samples[DCA_CHANNELS][DCA_SUBBANDS]; ///< Subband samples
- int32_t *lfe_samples; ///< Decimated LFE samples
-
- // DSP contexts
- DCADSPData dcadsp_data[DCA_CHANNELS]; ///< FIR history buffers
- DCADSPContext *dcadsp;
- DCADCTContext dcadct;
- AVTXContext *imdct[2];
- av_tx_fn imdct_fn[2];
- SynthFilterContext synth;
- AVFloatDSPContext *float_dsp;
- AVFixedDSPContext *fixed_dsp;
-
- // PCM output data
- unsigned int output_size;
- void *output_buffer; ///< PCM output buffer base
- int32_t *output_samples[DCA_SPEAKER_COUNT]; ///< PCM output for fixed point mode
- int32_t output_history_lfe_fixed; ///< LFE PCM history for X96 filter
- float output_history_lfe_float; ///< LFE PCM history for X96 filter
-
- int ch_remap[DCA_SPEAKER_COUNT]; ///< Channel to speaker map
- int request_mask; ///< Requested channel layout (for stereo downmix)
-
- int npcmsamples; ///< Number of PCM samples per channel
- int output_rate; ///< Output sample rate (1x or 2x header rate)
-
- int filter_mode; ///< Previous filtering mode for detecting changes
-} DCACoreDecoder;
-
-static inline int ff_dca_core_map_spkr(DCACoreDecoder *core, int spkr)
-{
- if (core->ch_mask & (1U << spkr))
- return spkr;
- if (spkr == DCA_SPEAKER_Lss && (core->ch_mask & DCA_SPEAKER_MASK_Ls))
- return DCA_SPEAKER_Ls;
- if (spkr == DCA_SPEAKER_Rss && (core->ch_mask & DCA_SPEAKER_MASK_Rs))
- return DCA_SPEAKER_Rs;
- return -1;
-}
-
-static inline void ff_dca_core_dequantize(int32_t *output, const int32_t *input,
- int32_t step_size, int32_t scale, int residual, int len)
-{
- // Account for quantizer step size
- int64_t step_scale = (int64_t)step_size * scale;
- int n, shift = 0;
-
- // Limit scale factor resolution to 22 bits
- if (step_scale > (1 << 23)) {
- shift = av_log2(step_scale >> 23) + 1;
- step_scale >>= shift;
- }
-
- // Scale the samples
- if (residual) {
- for (n = 0; n < len; n++)
- output[n] += clip23(norm__(input[n] * step_scale, 22 - shift));
- } else {
- for (n = 0; n < len; n++)
- output[n] = clip23(norm__(input[n] * step_scale, 22 - shift));
- }
-}
-
-int ff_dca_core_parse(DCACoreDecoder *s, const uint8_t *data, int size);
-int ff_dca_core_parse_exss(DCACoreDecoder *s, const uint8_t *data, DCAExssAsset *asset);
-int ff_dca_core_filter_fixed(DCACoreDecoder *s, int x96_synth);
-int ff_dca_core_filter_frame(DCACoreDecoder *s, AVFrame *frame);
-av_cold void ff_dca_core_flush(DCACoreDecoder *s);
-av_cold int ff_dca_core_init(DCACoreDecoder *s);
-av_cold void ff_dca_core_close(DCACoreDecoder *s);
-
-#endif
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dct32.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dct32.h
deleted file mode 100644
index 61bf223a8d61f5e06d8953512a365ba5e7c0b854..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/dct32.h
+++ /dev/null
@@ -1,25 +0,0 @@
-/*
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#ifndef AVCODEC_DCT32_H
-#define AVCODEC_DCT32_H
-
-void ff_dct32_float(float *dst, const float *src);
-void ff_dct32_fixed(int *dst, const int *src);
-
-#endif /* AVCODEC_DCT32_H */
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/lagarithrac.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/lagarithrac.h
deleted file mode 100644
index a31b054dbba817204e60c43ff2ed27c95962b953..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/lagarithrac.h
+++ /dev/null
@@ -1,113 +0,0 @@
-/*
- * Lagarith range decoder
- * Copyright (c) 2009 Nathan Caldwell
- * Copyright (c) 2009 David Conrad
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * Lagarith range decoder
- * @author Nathan Caldwell
- * @author David Conrad
- */
-
-#ifndef AVCODEC_LAGARITHRAC_H
-#define AVCODEC_LAGARITHRAC_H
-
-#include
-#include "libavutil/intreadwrite.h"
-#include "avcodec.h"
-#include "get_bits.h"
-
-typedef struct lag_rac {
- AVCodecContext *avctx;
- unsigned low;
- unsigned range;
- unsigned scale; /**< Number of bits of precision in range. */
- unsigned hash_shift; /**< Number of bits to shift to calculate hash for radix search. */
-
- const uint8_t *bytestream_start; /**< Start of input bytestream. */
- const uint8_t *bytestream; /**< Current position in input bytestream. */
- const uint8_t *bytestream_end; /**< End position of input bytestream. */
-
- int overread;
-#define MAX_OVERREAD 4
-
- uint32_t prob[258]; /**< Table of cumulative probability for each symbol. */
- uint8_t range_hash[1024]; /**< Hash table mapping upper byte to approximate symbol. */
-} lag_rac;
-
-void ff_lag_rac_init(lag_rac *l, GetBitContext *gb, int length);
-
-/* TODO: Optimize */
-static inline void lag_rac_refill(lag_rac *l)
-{
- while (l->range <= 0x800000) {
- l->low <<= 8;
- l->range <<= 8;
- l->low |= 0xff & (AV_RB16(l->bytestream) >> 1);
- if (l->bytestream < l->bytestream_end)
- l->bytestream++;
- else
- l->overread++;
- }
-}
-
-/**
- * Decode a single byte from the compressed plane described by *l.
- * @param l pointer to lag_rac for the current plane
- * @return next byte of decoded data
- */
-static inline uint8_t lag_get_rac(lag_rac *l)
-{
- unsigned range_scaled, low_scaled;
- int val;
-
- lag_rac_refill(l);
-
- range_scaled = l->range >> l->scale;
-
- if (l->low < range_scaled * l->prob[255]) {
- /* val = 0 is frequent enough to deserve a shortcut */
- if (l->low < range_scaled * l->prob[1]) {
- val = 0;
- } else {
- low_scaled = l->low / (range_scaled<<(l->hash_shift));
-
- val = l->range_hash[low_scaled];
- while (l->low >= range_scaled * l->prob[val + 1])
- val++;
- }
-
- l->range = range_scaled * (l->prob[val + 1] - l->prob[val]);
- } else {
- val = 255;
- l->range -= range_scaled * l->prob[255];
- }
-
- if (!l->range)
- l->range = 0x80;
-
- l->low -= range_scaled * l->prob[val];
-
- return val;
-}
-
-
-#endif /* AVCODEC_LAGARITHRAC_H */
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Winky Ds Eureka Eureka Album - The Best of Zimbabwean DanceHall.md b/spaces/congsaPfin/Manga-OCR/logs/Download Winky Ds Eureka Eureka Album - The Best of Zimbabwean DanceHall.md
deleted file mode 100644
index d919f24956259a11c89cd6d060c2ec7ccc297e29..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Download Winky Ds Eureka Eureka Album - The Best of Zimbabwean DanceHall.md
+++ /dev/null
@@ -1,74 +0,0 @@
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-If you are a fan of Zimbabwean music, you have probably heard of Winky D, the reggae-dancehall artist who is popularly known as "The Big Man" or "Dancehall Igwe". He is one of the most accomplished and influential musicians in the country, with a career spanning over two decades and eleven albums. His latest album, Eureka Eureka, was released in January 2023 and has been making waves in the music scene. In this article, we will tell you more about Winky D, his new album, and how you can download it and enjoy his amazing songs.
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-Winky D was born Wallace Chirumiko on 1 February 1983 in Kambuzuma, a suburb of Harare. He developed an interest in music at an early age and started listening to reggae when he was eight years old. He began performing at small functions and concerts when he was a teenager, and earned the name "Wicked Deejay" which was later shortened to Winky D. He started recording his music with the help of Bartholomew Vera of Blacklab Studios, and released his first songs like "Rasta" and "Dead Inna War". He has since released eleven albums with many chart hits which have gained him fans across the world. He has also collaborated with other artists such as Oliver Mtukudzi, Gemma Griffiths, Shingai, Holy Ten, Nutty O, and many more. He is married and has a son named Taenda.
- Musical style and influences
-Winky D is often considered the pioneer of Zimdancehall, a genre that blends reggae, dancehall, and Zimbabwean traditional music. His music often provides social commentary about Zimbabwean society and politics, as well as personal and spiritual themes. He is influenced by reggae legends such as Bob Marley, Peter Tosh, and Buju Banton, as well as local artists such as Thomas Mapfumo, Leonard Dembo, and Simon Chimbetu. He is also inspired by international genres such as rock, hip hop, and house music. He has a unique style of singing that incorporates Shona slang, English words, and Jamaican patois. He is known for his catchy hooks, witty lyrics, and energetic stage performances.
- What is Eureka Eureka?
-Album overview and features
-Eureka Eureka is Winky D's tenth studio album and his most collaborative body of work. It was released on 1 January 2023 by Vigilance Music. It features 14 tracks with guest appearances from SHINGAI, ENZO ISHALL, TOCKY VIBES, Holy Ten, Herman, KILLER T, SAINT FLOEW, NUTTY O, MWENJE MATHOLE, ANITA JAXSON, BAZOOKA & POPTAIN, Dr Chaii, QOUNFUZED, and EXQ. The album showcases Winky D's versatility and creativity as he explores different sounds and topics. The album title means "I have found it" in Shona, implying that Winky D has discovered his musical identity and purpose.
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-Eureka Eureka is an album that reflects Winky D's artistic vision and social consciousness. He uses his music to raise reflective questions about inequality, the effects of social media, corruption, friendship, love, dreams, spirituality, and hope. He also celebrates his culture, his achievements, and his gratitude to his fans and God. He also experiments with different genres such as afrobeat, hip hop, and pop, while maintaining his signature Zimdancehall sound. Some of the standout tracks on the album are "Eureka Eureka", "Mwari Pindirai", "Mambo", "Ngirozi", "Kana Ndada", and "Sekuru".
- How to download Eureka Eureka?
-Online platforms and streaming services
-If you want to listen to Winky D's latest album, you have several options to choose from. You can download the album from various online platforms such as iTunes, Amazon Music, Google Play, Spotify, Deezer, Tidal, and YouTube Music. You can also stream the album on these services or on Winky D's official website. You can also watch the official music videos of some of the songs on Winky D's YouTube channel.
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- FAQs
-What does Eureka Eureka mean?
-Eureka Eureka means "I have found it" in Shona, implying that Winky D has discovered his musical identity and purpose.
- How many tracks are on Eureka Eureka?
-Eureka Eureka has 14 tracks with guest appearances from various artists.
- What are some of the genres that Winky D explores on Eureka Eureka?
-Winky D experiments with different genres such as afrobeat, hip hop, and pop, while maintaining his signature Zimdancehall sound.
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-You can download Eureka Eureka from various online platforms such as iTunes, Amazon Music, Google Play, Spotify, Deezer, Tidal, and YouTube Music.
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-By downloading Eureka Eureka, you can listen to it anytime and anywhere, even when you are offline or have a poor internet connection. You can also share the album with your friends and family, and create your own playlists and mixtapes with your favorite tracks from the album.
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diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/exp/upernet_global_small/test_config_h32.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/exp/upernet_global_small/test_config_h32.py
deleted file mode 100644
index a31e3874f76f9f7b089ac8834d85df2441af9b0e..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/exp/upernet_global_small/test_config_h32.py
+++ /dev/null
@@ -1,39 +0,0 @@
-_base_ = [
- '../../configs/_base_/models/upernet_uniformer.py',
- '../../configs/_base_/datasets/ade20k.py',
- '../../configs/_base_/default_runtime.py',
- '../../configs/_base_/schedules/schedule_160k.py'
-]
-model = dict(
- backbone=dict(
- type='UniFormer',
- embed_dim=[64, 128, 320, 512],
- layers=[3, 4, 8, 3],
- head_dim=64,
- drop_path_rate=0.25,
- windows=False,
- hybrid=True,
- window_size=32
- ),
- decode_head=dict(
- in_channels=[64, 128, 320, 512],
- num_classes=150
- ),
- auxiliary_head=dict(
- in_channels=320,
- num_classes=150
- ))
-
-# AdamW optimizer, no weight decay for position embedding & layer norm in backbone
-optimizer = dict(_delete_=True, type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01,
- paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
- 'relative_position_bias_table': dict(decay_mult=0.),
- 'norm': dict(decay_mult=0.)}))
-
-lr_config = dict(_delete_=True, policy='poly',
- warmup='linear',
- warmup_iters=1500,
- warmup_ratio=1e-6,
- power=1.0, min_lr=0.0, by_epoch=False)
-
-data=dict(samples_per_gpu=2)
\ No newline at end of file
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/furthest_point_sample.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/furthest_point_sample.py
deleted file mode 100644
index 374b7a878f1972c183941af28ba1df216ac1a60f..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/ops/furthest_point_sample.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import torch
-from torch.autograd import Function
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', [
- 'furthest_point_sampling_forward',
- 'furthest_point_sampling_with_dist_forward'
-])
-
-
-class FurthestPointSampling(Function):
- """Uses iterative furthest point sampling to select a set of features whose
- corresponding points have the furthest distance."""
-
- @staticmethod
- def forward(ctx, points_xyz: torch.Tensor,
- num_points: int) -> torch.Tensor:
- """
- Args:
- points_xyz (Tensor): (B, N, 3) where N > num_points.
- num_points (int): Number of points in the sampled set.
-
- Returns:
- Tensor: (B, num_points) indices of the sampled points.
- """
- assert points_xyz.is_contiguous()
-
- B, N = points_xyz.size()[:2]
- output = torch.cuda.IntTensor(B, num_points)
- temp = torch.cuda.FloatTensor(B, N).fill_(1e10)
-
- ext_module.furthest_point_sampling_forward(
- points_xyz,
- temp,
- output,
- b=B,
- n=N,
- m=num_points,
- )
- if torch.__version__ != 'parrots':
- ctx.mark_non_differentiable(output)
- return output
-
- @staticmethod
- def backward(xyz, a=None):
- return None, None
-
-
-class FurthestPointSamplingWithDist(Function):
- """Uses iterative furthest point sampling to select a set of features whose
- corresponding points have the furthest distance."""
-
- @staticmethod
- def forward(ctx, points_dist: torch.Tensor,
- num_points: int) -> torch.Tensor:
- """
- Args:
- points_dist (Tensor): (B, N, N) Distance between each point pair.
- num_points (int): Number of points in the sampled set.
-
- Returns:
- Tensor: (B, num_points) indices of the sampled points.
- """
- assert points_dist.is_contiguous()
-
- B, N, _ = points_dist.size()
- output = points_dist.new_zeros([B, num_points], dtype=torch.int32)
- temp = points_dist.new_zeros([B, N]).fill_(1e10)
-
- ext_module.furthest_point_sampling_with_dist_forward(
- points_dist, temp, output, b=B, n=N, m=num_points)
- if torch.__version__ != 'parrots':
- ctx.mark_non_differentiable(output)
- return output
-
- @staticmethod
- def backward(xyz, a=None):
- return None, None
-
-
-furthest_point_sample = FurthestPointSampling.apply
-furthest_point_sample_with_dist = FurthestPointSamplingWithDist.apply
diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/losses/__init__.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/losses/__init__.py
deleted file mode 100644
index beca72045694273d63465bac2f27dbc6672271db..0000000000000000000000000000000000000000
--- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/losses/__init__.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from .accuracy import Accuracy, accuracy
-from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
- cross_entropy, mask_cross_entropy)
-from .dice_loss import DiceLoss
-from .lovasz_loss import LovaszLoss
-from .utils import reduce_loss, weight_reduce_loss, weighted_loss
-
-__all__ = [
- 'accuracy', 'Accuracy', 'cross_entropy', 'binary_cross_entropy',
- 'mask_cross_entropy', 'CrossEntropyLoss', 'reduce_loss',
- 'weight_reduce_loss', 'weighted_loss', 'LovaszLoss', 'DiceLoss'
-]
diff --git a/spaces/csuhan/opendet2/demo/predictor.py b/spaces/csuhan/opendet2/demo/predictor.py
deleted file mode 100644
index b2b2cf1968b2549de6d586e7052fe5ec6fd82945..0000000000000000000000000000000000000000
--- a/spaces/csuhan/opendet2/demo/predictor.py
+++ /dev/null
@@ -1,224 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import atexit
-import bisect
-import multiprocessing as mp
-from collections import deque
-import cv2
-import torch
-
-from detectron2.data import MetadataCatalog
-from detectron2.engine.defaults import DefaultPredictor
-from detectron2.utils.video_visualizer import VideoVisualizer
-from detectron2.utils.visualizer import ColorMode, Visualizer
-from detectron2.data.datasets.builtin_meta import _get_coco_instances_meta
-
-
-class VisualizationDemo(object):
- def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False):
- """
- Args:
- cfg (CfgNode):
- instance_mode (ColorMode):
- parallel (bool): whether to run the model in different processes from visualization.
- Useful since the visualization logic can be slow.
- """
- self.metadata = MetadataCatalog.get(
- cfg.DATASETS.TEST[-1] if len(cfg.DATASETS.TEST) else "__unused"
- )
- thing_colors = _get_coco_instances_meta()["thing_colors"]
- thing_colors.append((0,0,0))
- self.metadata.set(thing_colors=thing_colors)
- self.cpu_device = torch.device("cpu")
- self.instance_mode = instance_mode
-
- self.parallel = parallel
- if parallel:
- num_gpu = torch.cuda.device_count()
- self.predictor = AsyncPredictor(cfg, num_gpus=num_gpu)
- else:
- self.predictor = DefaultPredictor(cfg)
-
- def run_on_image(self, image):
- """
- Args:
- image (np.ndarray): an image of shape (H, W, C) (in BGR order).
- This is the format used by OpenCV.
-
- Returns:
- predictions (dict): the output of the model.
- vis_output (VisImage): the visualized image output.
- """
- vis_output = None
- predictions = self.predictor(image)
- # Convert image from OpenCV BGR format to Matplotlib RGB format.
- image = image[:, :, ::-1]
- visualizer = Visualizer(image, self.metadata, instance_mode=self.instance_mode)
- if "panoptic_seg" in predictions:
- panoptic_seg, segments_info = predictions["panoptic_seg"]
- vis_output = visualizer.draw_panoptic_seg_predictions(
- panoptic_seg.to(self.cpu_device), segments_info
- )
- else:
- if "sem_seg" in predictions:
- vis_output = visualizer.draw_sem_seg(
- predictions["sem_seg"].argmax(dim=0).to(self.cpu_device)
- )
- if "instances" in predictions:
- instances = predictions["instances"].to(self.cpu_device)
- vis_output = visualizer.draw_instance_predictions(predictions=instances)
-
- return predictions, vis_output
-
- def _frame_from_video(self, video):
- while video.isOpened():
- success, frame = video.read()
- if success:
- yield frame
- else:
- break
-
- def run_on_video(self, video):
- """
- Visualizes predictions on frames of the input video.
-
- Args:
- video (cv2.VideoCapture): a :class:`VideoCapture` object, whose source can be
- either a webcam or a video file.
-
- Yields:
- ndarray: BGR visualizations of each video frame.
- """
- video_visualizer = VideoVisualizer(self.metadata, self.instance_mode)
-
- def process_predictions(frame, predictions):
- frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
- if "panoptic_seg" in predictions:
- panoptic_seg, segments_info = predictions["panoptic_seg"]
- vis_frame = video_visualizer.draw_panoptic_seg_predictions(
- frame, panoptic_seg.to(self.cpu_device), segments_info
- )
- elif "instances" in predictions:
- predictions = predictions["instances"].to(self.cpu_device)
- vis_frame = video_visualizer.draw_instance_predictions(frame, predictions)
- elif "sem_seg" in predictions:
- vis_frame = video_visualizer.draw_sem_seg(
- frame, predictions["sem_seg"].argmax(dim=0).to(self.cpu_device)
- )
-
- # Converts Matplotlib RGB format to OpenCV BGR format
- vis_frame = cv2.cvtColor(vis_frame.get_image(), cv2.COLOR_RGB2BGR)
- return vis_frame
-
- frame_gen = self._frame_from_video(video)
- if self.parallel:
- buffer_size = self.predictor.default_buffer_size
-
- frame_data = deque()
-
- for cnt, frame in enumerate(frame_gen):
- frame_data.append(frame)
- self.predictor.put(frame)
-
- if cnt >= buffer_size:
- frame = frame_data.popleft()
- predictions = self.predictor.get()
- yield process_predictions(frame, predictions)
-
- while len(frame_data):
- frame = frame_data.popleft()
- predictions = self.predictor.get()
- yield process_predictions(frame, predictions)
- else:
- for frame in frame_gen:
- yield process_predictions(frame, self.predictor(frame))
-
-
-class AsyncPredictor:
- """
- A predictor that runs the model asynchronously, possibly on >1 GPUs.
- Because rendering the visualization takes considerably amount of time,
- this helps improve throughput a little bit when rendering videos.
- """
-
- class _StopToken:
- pass
-
- class _PredictWorker(mp.Process):
- def __init__(self, cfg, task_queue, result_queue):
- self.cfg = cfg
- self.task_queue = task_queue
- self.result_queue = result_queue
- super().__init__()
-
- def run(self):
- predictor = DefaultPredictor(self.cfg)
-
- while True:
- task = self.task_queue.get()
- if isinstance(task, AsyncPredictor._StopToken):
- break
- idx, data = task
- result = predictor(data)
- self.result_queue.put((idx, result))
-
- def __init__(self, cfg, num_gpus: int = 1):
- """
- Args:
- cfg (CfgNode):
- num_gpus (int): if 0, will run on CPU
- """
- num_workers = max(num_gpus, 1)
- self.task_queue = mp.Queue(maxsize=num_workers * 3)
- self.result_queue = mp.Queue(maxsize=num_workers * 3)
- self.procs = []
- for gpuid in range(max(num_gpus, 1)):
- cfg = cfg.clone()
- cfg.defrost()
- cfg.MODEL.DEVICE = "cuda:{}".format(gpuid) if num_gpus > 0 else "cpu"
- self.procs.append(
- AsyncPredictor._PredictWorker(cfg, self.task_queue, self.result_queue)
- )
-
- self.put_idx = 0
- self.get_idx = 0
- self.result_rank = []
- self.result_data = []
-
- for p in self.procs:
- p.start()
- atexit.register(self.shutdown)
-
- def put(self, image):
- self.put_idx += 1
- self.task_queue.put((self.put_idx, image))
-
- def get(self):
- self.get_idx += 1 # the index needed for this request
- if len(self.result_rank) and self.result_rank[0] == self.get_idx:
- res = self.result_data[0]
- del self.result_data[0], self.result_rank[0]
- return res
-
- while True:
- # make sure the results are returned in the correct order
- idx, res = self.result_queue.get()
- if idx == self.get_idx:
- return res
- insert = bisect.bisect(self.result_rank, idx)
- self.result_rank.insert(insert, idx)
- self.result_data.insert(insert, res)
-
- def __len__(self):
- return self.put_idx - self.get_idx
-
- def __call__(self, image):
- self.put(image)
- return self.get()
-
- def shutdown(self):
- for _ in self.procs:
- self.task_queue.put(AsyncPredictor._StopToken())
-
- @property
- def default_buffer_size(self):
- return len(self.procs) * 5
diff --git a/spaces/cyberoleg/b2719240e190e2a649150d94db50be82838efeb0/diffusion_webui/__init__.py b/spaces/cyberoleg/b2719240e190e2a649150d94db50be82838efeb0/diffusion_webui/__init__.py
deleted file mode 100644
index 6e49af236dab7f041fb4fe27d50b728eaaf552d9..0000000000000000000000000000000000000000
--- a/spaces/cyberoleg/b2719240e190e2a649150d94db50be82838efeb0/diffusion_webui/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from diffusion_webui.diffusion_models.controlnet_inpaint_pipeline import (
- StableDiffusionControlNetInpaintGenerator,
-)
-from diffusion_webui.diffusion_models.controlnet_pipeline import (
- StableDiffusionControlNetGenerator,
-)
-from diffusion_webui.diffusion_models.img2img_app import (
- StableDiffusionImage2ImageGenerator,
-)
-from diffusion_webui.diffusion_models.inpaint_app import (
- StableDiffusionInpaintGenerator,
-)
-from diffusion_webui.diffusion_models.text2img_app import (
- StableDiffusionText2ImageGenerator,
-)
-
-__version__ = "2.5.0"
diff --git a/spaces/cye/dalle-mini/index.html b/spaces/cye/dalle-mini/index.html
deleted file mode 100644
index 74d65ba18bf356ce52b1d00b0e7c1903d5e285f2..0000000000000000000000000000000000000000
--- a/spaces/cye/dalle-mini/index.html
+++ /dev/null
@@ -1,64 +0,0 @@
-
-
-
-
-
-
-
-
diff --git a/spaces/d8aai/finance-dashboard/app.py b/spaces/d8aai/finance-dashboard/app.py
deleted file mode 100644
index 602b054aa8033ddd5d32974a658a18408e036240..0000000000000000000000000000000000000000
--- a/spaces/d8aai/finance-dashboard/app.py
+++ /dev/null
@@ -1,65 +0,0 @@
-from datetime import date
-import json
-import plotly
-from re import sub
-from plotly import graph_objects as go
-
-import streamlit as st
-
-st.set_page_config(layout="wide")
-
-from subs.access_backend import get_tickerlist
-from subs.access_backend import get_plot
-
-tickerTable = get_tickerlist().set_index("Ticker")
-
-PrimeStandardSector = "Prime Standard Sector"
-sectors = tickerTable[PrimeStandardSector].unique()
-sectors.sort()
-sector = st.selectbox(
- label="Select a Sector. Remark: This sets the default for the selected stocks",
- options=sectors,
-)
-
-default_index = tickerTable[PrimeStandardSector] == sector
-
-default = tickerTable[default_index]
-
-selections = st.multiselect(
- label="Dax Constituents",
- options=list(tickerTable.index),
- format_func=lambda x: tickerTable.at[x, "Company"],
- default=list(default.index),
-)
-
-for selection in selections:
-
- try:
-
- fig_scatter = get_plot(selection, "scatter")
- fig_returns = get_plot(selection, "returns")
- fig_histogram = get_plot(selection, "histogram")
-
- st.header(tickerTable.at[selection, "Company"])
- c1, _, c2, _, c3 = st.columns((10, 1, 10, 1, 10))
-
- c1.plotly_chart(fig_scatter, use_container_width=True)
- c2.plotly_chart(fig_returns, use_container_width=True)
- c3.plotly_chart(fig_histogram, use_container_width=True)
-
- except Exception as e:
- st.header(tickerTable.at[selection, "Company"])
- st.markdown(
- f"Data for {tickerTable.at[selection, 'Company']} not available",
- unsafe_allow_html=False,
- )
- # print(selection)
- # print(e)
-
-if __name__ == "__main__":
- print(tickerTable)
- print(tickerTable.index[:3])
-
- print(sectors)
- print(tickerTable["Prime Standard Sector"] == sector)
- print(default)
diff --git a/spaces/dakaiye/dky_xuexi/request_llm/bridge_jittorllms_llama.py b/spaces/dakaiye/dky_xuexi/request_llm/bridge_jittorllms_llama.py
deleted file mode 100644
index 6dfac681aeaa11a780304b9e645637cabd677688..0000000000000000000000000000000000000000
--- a/spaces/dakaiye/dky_xuexi/request_llm/bridge_jittorllms_llama.py
+++ /dev/null
@@ -1,178 +0,0 @@
-
-from transformers import AutoModel, AutoTokenizer
-import time
-import threading
-import importlib
-from toolbox import update_ui, get_conf
-from multiprocessing import Process, Pipe
-
-load_message = "jittorllms尚未加载，加载需要一段时间。注意，请避免混用多种jittor模型，否则可能导致显存溢出而造成卡顿，取决于`config.py`的配置，jittorllms消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
-
-#################################################################################
-class GetGLMHandle(Process):
- def __init__(self):
- super().__init__(daemon=True)
- self.parent, self.child = Pipe()
- self.jittorllms_model = None
- self.info = ""
- self.local_history = []
- self.success = True
- self.check_dependency()
- self.start()
- self.threadLock = threading.Lock()
-
- def check_dependency(self):
- try:
- import pandas
- self.info = "依赖检测通过"
- self.success = True
- except:
- from toolbox import trimmed_format_exc
- self.info = r"缺少jittorllms的依赖，如果要使用jittorllms，除了基础的pip依赖以外，您还需要运行`pip install -r request_llm/requirements_jittorllms.txt -i https://pypi.jittor.org/simple -I`"+\
- r"和`git clone https://gitlink.org.cn/jittor/JittorLLMs.git --depth 1 request_llm/jittorllms`两个指令来安装jittorllms的依赖（在项目根目录运行这两个指令）。" +\
- r"警告：安装jittorllms依赖后将完全破坏现有的pytorch环境，建议使用docker环境！" + trimmed_format_exc()
- self.success = False
-
- def ready(self):
- return self.jittorllms_model is not None
-
- def run(self):
- # 子进程执行
- # 第一次运行，加载参数
- def validate_path():
- import os, sys
- dir_name = os.path.dirname(__file__)
- env = os.environ.get("PATH", "")
- os.environ["PATH"] = env.replace('/cuda/bin', '/x/bin')
- root_dir_assume = os.path.abspath(os.path.dirname(__file__) + '/..')
- os.chdir(root_dir_assume + '/request_llm/jittorllms')
- sys.path.append(root_dir_assume + '/request_llm/jittorllms')
- validate_path() # validate path so you can run from base directory
-
- def load_model():
- import types
- try:
- if self.jittorllms_model is None:
- device, = get_conf('LOCAL_MODEL_DEVICE')
- from .jittorllms.models import get_model
- # availabel_models = ["chatglm", "pangualpha", "llama", "chatrwkv"]
- args_dict = {'model': 'llama'}
- print('self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))')
- self.jittorllms_model = get_model(types.SimpleNamespace(**args_dict))
- print('done get model')
- except:
- self.child.send('[Local Message] Call jittorllms fail 不能正常加载jittorllms的参数。')
- raise RuntimeError("不能正常加载jittorllms的参数！")
- print('load_model')
- load_model()
-
- # 进入任务等待状态
- print('进入任务等待状态')
- while True:
- # 进入任务等待状态
- kwargs = self.child.recv()
- query = kwargs['query']
- history = kwargs['history']
- # 是否重置
- if len(self.local_history) > 0 and len(history)==0:
- print('触发重置')
- self.jittorllms_model.reset()
- self.local_history.append(query)
-
- print('收到消息，开始请求')
- try:
- for response in self.jittorllms_model.stream_chat(query, history):
- print(response)
- self.child.send(response)
- except:
- from toolbox import trimmed_format_exc
- print(trimmed_format_exc())
- self.child.send('[Local Message] Call jittorllms fail.')
- # 请求处理结束，开始下一个循环
- self.child.send('[Finish]')
-
- def stream_chat(self, **kwargs):
- # 主进程执行
- self.threadLock.acquire()
- self.parent.send(kwargs)
- while True:
- res = self.parent.recv()
- if res != '[Finish]':
- yield res
- else:
- break
- self.threadLock.release()
-
-global llama_glm_handle
-llama_glm_handle = None
-#################################################################################
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
- """
- 多线程方法
- 函数的说明请见 request_llm/bridge_all.py
- """
- global llama_glm_handle
- if llama_glm_handle is None:
- llama_glm_handle = GetGLMHandle()
- if len(observe_window) >= 1: observe_window[0] = load_message + "\n\n" + llama_glm_handle.info
- if not llama_glm_handle.success:
- error = llama_glm_handle.info
- llama_glm_handle = None
- raise RuntimeError(error)
-
- # jittorllms 没有 sys_prompt 接口，因此把prompt加入 history
- history_feedin = []
- for i in range(len(history)//2):
- history_feedin.append([history[2*i], history[2*i+1]] )
-
- watch_dog_patience = 5 # 看门狗 (watchdog) 的耐心, 设置5秒即可
- response = ""
- for response in llama_glm_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=sys_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
- print(response)
- if len(observe_window) >= 1: observe_window[0] = response
- if len(observe_window) >= 2:
- if (time.time()-observe_window[1]) > watch_dog_patience:
- raise RuntimeError("程序终止。")
- return response
-
-
-
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
- """
- 单线程方法
- 函数的说明请见 request_llm/bridge_all.py
- """
- chatbot.append((inputs, ""))
-
- global llama_glm_handle
- if llama_glm_handle is None:
- llama_glm_handle = GetGLMHandle()
- chatbot[-1] = (inputs, load_message + "\n\n" + llama_glm_handle.info)
- yield from update_ui(chatbot=chatbot, history=[])
- if not llama_glm_handle.success:
- llama_glm_handle = None
- return
-
- if additional_fn is not None:
- import core_functional
- importlib.reload(core_functional) # 热更新prompt
- core_functional = core_functional.get_core_functions()
- if "PreProcess" in core_functional[additional_fn]: inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话）
- inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
-
- # 处理历史信息
- history_feedin = []
- for i in range(len(history)//2):
- history_feedin.append([history[2*i], history[2*i+1]] )
-
- # 开始接收jittorllms的回复
- response = "[Local Message]: 等待jittorllms响应中 ..."
- for response in llama_glm_handle.stream_chat(query=inputs, history=history_feedin, system_prompt=system_prompt, max_length=llm_kwargs['max_length'], top_p=llm_kwargs['top_p'], temperature=llm_kwargs['temperature']):
- chatbot[-1] = (inputs, response)
- yield from update_ui(chatbot=chatbot, history=history)
-
- # 总结输出
- if response == "[Local Message]: 等待jittorllms响应中 ...":
- response = "[Local Message]: jittorllms响应异常 ..."
- history.extend([inputs, response])
- yield from update_ui(chatbot=chatbot, history=history)
diff --git a/spaces/danterivers/music-generation-samples/audiocraft/utils/__init__.py b/spaces/danterivers/music-generation-samples/audiocraft/utils/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/danterivers/music-generation-samples/audiocraft/utils/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/attr/_funcs.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/attr/_funcs.py
deleted file mode 100644
index 7f5d9610f3cf0010a9185579f7188df5ff609384..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/attr/_funcs.py
+++ /dev/null
@@ -1,477 +0,0 @@
-# SPDX-License-Identifier: MIT
-
-
-import copy
-
-from ._compat import PY_3_9_PLUS, get_generic_base
-from ._make import NOTHING, _obj_setattr, fields
-from .exceptions import AttrsAttributeNotFoundError
-
-
-def asdict(
- inst,
- recurse=True,
- filter=None,
- dict_factory=dict,
- retain_collection_types=False,
- value_serializer=None,
-):
- """
- Return the *attrs* attribute values of *inst* as a dict.
-
- Optionally recurse into other *attrs*-decorated classes.
-
- :param inst: Instance of an *attrs*-decorated class.
- :param bool recurse: Recurse into classes that are also
- *attrs*-decorated.
- :param callable filter: A callable whose return code determines whether an
- attribute or element is included (``True``) or dropped (``False``). Is
- called with the `attrs.Attribute` as the first argument and the
- value as the second argument.
- :param callable dict_factory: A callable to produce dictionaries from. For
- example, to produce ordered dictionaries instead of normal Python
- dictionaries, pass in ``collections.OrderedDict``.
- :param bool retain_collection_types: Do not convert to ``list`` when
- encountering an attribute whose type is ``tuple`` or ``set``. Only
- meaningful if ``recurse`` is ``True``.
- :param Optional[callable] value_serializer: A hook that is called for every
- attribute or dict key/value. It receives the current instance, field
- and value and must return the (updated) value. The hook is run *after*
- the optional *filter* has been applied.
-
- :rtype: return type of *dict_factory*
-
- :raise attrs.exceptions.NotAnAttrsClassError: If *cls* is not an *attrs*
- class.
-
- .. versionadded:: 16.0.0 *dict_factory*
- .. versionadded:: 16.1.0 *retain_collection_types*
- .. versionadded:: 20.3.0 *value_serializer*
- .. versionadded:: 21.3.0 If a dict has a collection for a key, it is
- serialized as a tuple.
- """
- attrs = fields(inst.__class__)
- rv = dict_factory()
- for a in attrs:
- v = getattr(inst, a.name)
- if filter is not None and not filter(a, v):
- continue
-
- if value_serializer is not None:
- v = value_serializer(inst, a, v)
-
- if recurse is True:
- if has(v.__class__):
- rv[a.name] = asdict(
- v,
- recurse=True,
- filter=filter,
- dict_factory=dict_factory,
- retain_collection_types=retain_collection_types,
- value_serializer=value_serializer,
- )
- elif isinstance(v, (tuple, list, set, frozenset)):
- cf = v.__class__ if retain_collection_types is True else list
- rv[a.name] = cf(
- [
- _asdict_anything(
- i,
- is_key=False,
- filter=filter,
- dict_factory=dict_factory,
- retain_collection_types=retain_collection_types,
- value_serializer=value_serializer,
- )
- for i in v
- ]
- )
- elif isinstance(v, dict):
- df = dict_factory
- rv[a.name] = df(
- (
- _asdict_anything(
- kk,
- is_key=True,
- filter=filter,
- dict_factory=df,
- retain_collection_types=retain_collection_types,
- value_serializer=value_serializer,
- ),
- _asdict_anything(
- vv,
- is_key=False,
- filter=filter,
- dict_factory=df,
- retain_collection_types=retain_collection_types,
- value_serializer=value_serializer,
- ),
- )
- for kk, vv in v.items()
- )
- else:
- rv[a.name] = v
- else:
- rv[a.name] = v
- return rv
-
-
-def _asdict_anything(
- val,
- is_key,
- filter,
- dict_factory,
- retain_collection_types,
- value_serializer,
-):
- """
- ``asdict`` only works on attrs instances, this works on anything.
- """
- if getattr(val.__class__, "__attrs_attrs__", None) is not None:
- # Attrs class.
- rv = asdict(
- val,
- recurse=True,
- filter=filter,
- dict_factory=dict_factory,
- retain_collection_types=retain_collection_types,
- value_serializer=value_serializer,
- )
- elif isinstance(val, (tuple, list, set, frozenset)):
- if retain_collection_types is True:
- cf = val.__class__
- elif is_key:
- cf = tuple
- else:
- cf = list
-
- rv = cf(
- [
- _asdict_anything(
- i,
- is_key=False,
- filter=filter,
- dict_factory=dict_factory,
- retain_collection_types=retain_collection_types,
- value_serializer=value_serializer,
- )
- for i in val
- ]
- )
- elif isinstance(val, dict):
- df = dict_factory
- rv = df(
- (
- _asdict_anything(
- kk,
- is_key=True,
- filter=filter,
- dict_factory=df,
- retain_collection_types=retain_collection_types,
- value_serializer=value_serializer,
- ),
- _asdict_anything(
- vv,
- is_key=False,
- filter=filter,
- dict_factory=df,
- retain_collection_types=retain_collection_types,
- value_serializer=value_serializer,
- ),
- )
- for kk, vv in val.items()
- )
- else:
- rv = val
- if value_serializer is not None:
- rv = value_serializer(None, None, rv)
-
- return rv
-
-
-def astuple(
- inst,
- recurse=True,
- filter=None,
- tuple_factory=tuple,
- retain_collection_types=False,
-):
- """
- Return the *attrs* attribute values of *inst* as a tuple.
-
- Optionally recurse into other *attrs*-decorated classes.
-
- :param inst: Instance of an *attrs*-decorated class.
- :param bool recurse: Recurse into classes that are also
- *attrs*-decorated.
- :param callable filter: A callable whose return code determines whether an
- attribute or element is included (``True``) or dropped (``False``). Is
- called with the `attrs.Attribute` as the first argument and the
- value as the second argument.
- :param callable tuple_factory: A callable to produce tuples from. For
- example, to produce lists instead of tuples.
- :param bool retain_collection_types: Do not convert to ``list``
- or ``dict`` when encountering an attribute which type is
- ``tuple``, ``dict`` or ``set``. Only meaningful if ``recurse`` is
- ``True``.
-
- :rtype: return type of *tuple_factory*
-
- :raise attrs.exceptions.NotAnAttrsClassError: If *cls* is not an *attrs*
- class.
-
- .. versionadded:: 16.2.0
- """
- attrs = fields(inst.__class__)
- rv = []
- retain = retain_collection_types # Very long. :/
- for a in attrs:
- v = getattr(inst, a.name)
- if filter is not None and not filter(a, v):
- continue
- if recurse is True:
- if has(v.__class__):
- rv.append(
- astuple(
- v,
- recurse=True,
- filter=filter,
- tuple_factory=tuple_factory,
- retain_collection_types=retain,
- )
- )
- elif isinstance(v, (tuple, list, set, frozenset)):
- cf = v.__class__ if retain is True else list
- rv.append(
- cf(
- [
- astuple(
- j,
- recurse=True,
- filter=filter,
- tuple_factory=tuple_factory,
- retain_collection_types=retain,
- )
- if has(j.__class__)
- else j
- for j in v
- ]
- )
- )
- elif isinstance(v, dict):
- df = v.__class__ if retain is True else dict
- rv.append(
- df(
- (
- astuple(
- kk,
- tuple_factory=tuple_factory,
- retain_collection_types=retain,
- )
- if has(kk.__class__)
- else kk,
- astuple(
- vv,
- tuple_factory=tuple_factory,
- retain_collection_types=retain,
- )
- if has(vv.__class__)
- else vv,
- )
- for kk, vv in v.items()
- )
- )
- else:
- rv.append(v)
- else:
- rv.append(v)
-
- return rv if tuple_factory is list else tuple_factory(rv)
-
-
-def has(cls):
- """
- Check whether *cls* is a class with *attrs* attributes.
-
- :param type cls: Class to introspect.
- :raise TypeError: If *cls* is not a class.
-
- :rtype: bool
- """
- attrs = getattr(cls, "__attrs_attrs__", None)
- if attrs is not None:
- return True
-
- # No attrs, maybe it's a specialized generic (A[str])?
- generic_base = get_generic_base(cls)
- if generic_base is not None:
- generic_attrs = getattr(generic_base, "__attrs_attrs__", None)
- if generic_attrs is not None:
- # Stick it on here for speed next time.
- cls.__attrs_attrs__ = generic_attrs
- return generic_attrs is not None
- return False
-
-
-def assoc(inst, **changes):
- """
- Copy *inst* and apply *changes*.
-
- This is different from `evolve` that applies the changes to the arguments
- that create the new instance.
-
- `evolve`'s behavior is preferable, but there are `edge cases`_ where it
- doesn't work. Therefore `assoc` is deprecated, but will not be removed.
-
- .. _`edge cases`: https://github.com/python-attrs/attrs/issues/251
-
- :param inst: Instance of a class with *attrs* attributes.
- :param changes: Keyword changes in the new copy.
-
- :return: A copy of inst with *changes* incorporated.
-
- :raise attrs.exceptions.AttrsAttributeNotFoundError: If *attr_name*
- couldn't be found on *cls*.
- :raise attrs.exceptions.NotAnAttrsClassError: If *cls* is not an *attrs*
- class.
-
- .. deprecated:: 17.1.0
- Use `attrs.evolve` instead if you can.
- This function will not be removed du to the slightly different approach
- compared to `attrs.evolve`.
- """
- new = copy.copy(inst)
- attrs = fields(inst.__class__)
- for k, v in changes.items():
- a = getattr(attrs, k, NOTHING)
- if a is NOTHING:
- raise AttrsAttributeNotFoundError(
- f"{k} is not an attrs attribute on {new.__class__}."
- )
- _obj_setattr(new, k, v)
- return new
-
-
-def evolve(*args, **changes):
- """
- Create a new instance, based on the first positional argument with
- *changes* applied.
-
- :param inst: Instance of a class with *attrs* attributes.
- :param changes: Keyword changes in the new copy.
-
- :return: A copy of inst with *changes* incorporated.
-
- :raise TypeError: If *attr_name* couldn't be found in the class
- ``__init__``.
- :raise attrs.exceptions.NotAnAttrsClassError: If *cls* is not an *attrs*
- class.
-
- .. versionadded:: 17.1.0
- .. deprecated:: 23.1.0
- It is now deprecated to pass the instance using the keyword argument
- *inst*. It will raise a warning until at least April 2024, after which
- it will become an error. Always pass the instance as a positional
- argument.
- """
- # Try to get instance by positional argument first.
- # Use changes otherwise and warn it'll break.
- if args:
- try:
- (inst,) = args
- except ValueError:
- raise TypeError(
- f"evolve() takes 1 positional argument, but {len(args)} "
- "were given"
- ) from None
- else:
- try:
- inst = changes.pop("inst")
- except KeyError:
- raise TypeError(
- "evolve() missing 1 required positional argument: 'inst'"
- ) from None
-
- import warnings
-
- warnings.warn(
- "Passing the instance per keyword argument is deprecated and "
- "will stop working in, or after, April 2024.",
- DeprecationWarning,
- stacklevel=2,
- )
-
- cls = inst.__class__
- attrs = fields(cls)
- for a in attrs:
- if not a.init:
- continue
- attr_name = a.name # To deal with private attributes.
- init_name = a.alias
- if init_name not in changes:
- changes[init_name] = getattr(inst, attr_name)
-
- return cls(**changes)
-
-
-def resolve_types(
- cls, globalns=None, localns=None, attribs=None, include_extras=True
-):
- """
- Resolve any strings and forward annotations in type annotations.
-
- This is only required if you need concrete types in `Attribute`'s *type*
- field. In other words, you don't need to resolve your types if you only
- use them for static type checking.
-
- With no arguments, names will be looked up in the module in which the class
- was created. If this is not what you want, e.g. if the name only exists
- inside a method, you may pass *globalns* or *localns* to specify other
- dictionaries in which to look up these names. See the docs of
- `typing.get_type_hints` for more details.
-
- :param type cls: Class to resolve.
- :param Optional[dict] globalns: Dictionary containing global variables.
- :param Optional[dict] localns: Dictionary containing local variables.
- :param Optional[list] attribs: List of attribs for the given class.
- This is necessary when calling from inside a ``field_transformer``
- since *cls* is not an *attrs* class yet.
- :param bool include_extras: Resolve more accurately, if possible.
- Pass ``include_extras`` to ``typing.get_hints``, if supported by the
- typing module. On supported Python versions (3.9+), this resolves the
- types more accurately.
-
- :raise TypeError: If *cls* is not a class.
- :raise attrs.exceptions.NotAnAttrsClassError: If *cls* is not an *attrs*
- class and you didn't pass any attribs.
- :raise NameError: If types cannot be resolved because of missing variables.
-
- :returns: *cls* so you can use this function also as a class decorator.
- Please note that you have to apply it **after** `attrs.define`. That
- means the decorator has to come in the line **before** `attrs.define`.
-
- .. versionadded:: 20.1.0
- .. versionadded:: 21.1.0 *attribs*
- .. versionadded:: 23.1.0 *include_extras*
-
- """
- # Since calling get_type_hints is expensive we cache whether we've
- # done it already.
- if getattr(cls, "__attrs_types_resolved__", None) != cls:
- import typing
-
- kwargs = {"globalns": globalns, "localns": localns}
-
- if PY_3_9_PLUS:
- kwargs["include_extras"] = include_extras
-
- hints = typing.get_type_hints(cls, **kwargs)
- for field in fields(cls) if attribs is None else attribs:
- if field.name in hints:
- # Since fields have been frozen we must work around it.
- _obj_setattr(field, "type", hints[field.name])
- # We store the class we resolved so that subclasses know they haven't
- # been resolved.
- cls.__attrs_types_resolved__ = cls
-
- # Return the class so you can use it as a decorator too.
- return cls
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/misc/bezierTools.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/misc/bezierTools.py
deleted file mode 100644
index 7772a4bf8588d2723f2435c7a2ba56ce47a71cf1..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/misc/bezierTools.py
+++ /dev/null
@@ -1,1474 +0,0 @@
-# -*- coding: utf-8 -*-
-"""fontTools.misc.bezierTools.py -- tools for working with Bezier path segments.
-"""
-
-from fontTools.misc.arrayTools import calcBounds, sectRect, rectArea
-from fontTools.misc.transform import Identity
-import math
-from collections import namedtuple
-
-try:
- import cython
-
- COMPILED = cython.compiled
-except (AttributeError, ImportError):
- # if cython not installed, use mock module with no-op decorators and types
- from fontTools.misc import cython
-
- COMPILED = False
-
-
-Intersection = namedtuple("Intersection", ["pt", "t1", "t2"])
-
-
-__all__ = [
- "approximateCubicArcLength",
- "approximateCubicArcLengthC",
- "approximateQuadraticArcLength",
- "approximateQuadraticArcLengthC",
- "calcCubicArcLength",
- "calcCubicArcLengthC",
- "calcQuadraticArcLength",
- "calcQuadraticArcLengthC",
- "calcCubicBounds",
- "calcQuadraticBounds",
- "splitLine",
- "splitQuadratic",
- "splitCubic",
- "splitQuadraticAtT",
- "splitCubicAtT",
- "splitCubicAtTC",
- "splitCubicIntoTwoAtTC",
- "solveQuadratic",
- "solveCubic",
- "quadraticPointAtT",
- "cubicPointAtT",
- "cubicPointAtTC",
- "linePointAtT",
- "segmentPointAtT",
- "lineLineIntersections",
- "curveLineIntersections",
- "curveCurveIntersections",
- "segmentSegmentIntersections",
-]
-
-
-def calcCubicArcLength(pt1, pt2, pt3, pt4, tolerance=0.005):
- """Calculates the arc length for a cubic Bezier segment.
-
- Whereas :func:`approximateCubicArcLength` approximates the length, this
- function calculates it by "measuring", recursively dividing the curve
- until the divided segments are shorter than ``tolerance``.
-
- Args:
- pt1,pt2,pt3,pt4: Control points of the Bezier as 2D tuples.
- tolerance: Controls the precision of the calcuation.
-
- Returns:
- Arc length value.
- """
- return calcCubicArcLengthC(
- complex(*pt1), complex(*pt2), complex(*pt3), complex(*pt4), tolerance
- )
-
-
-def _split_cubic_into_two(p0, p1, p2, p3):
- mid = (p0 + 3 * (p1 + p2) + p3) * 0.125
- deriv3 = (p3 + p2 - p1 - p0) * 0.125
- return (
- (p0, (p0 + p1) * 0.5, mid - deriv3, mid),
- (mid, mid + deriv3, (p2 + p3) * 0.5, p3),
- )
-
-
-@cython.returns(cython.double)
-@cython.locals(
- p0=cython.complex,
- p1=cython.complex,
- p2=cython.complex,
- p3=cython.complex,
-)
-@cython.locals(mult=cython.double, arch=cython.double, box=cython.double)
-def _calcCubicArcLengthCRecurse(mult, p0, p1, p2, p3):
- arch = abs(p0 - p3)
- box = abs(p0 - p1) + abs(p1 - p2) + abs(p2 - p3)
- if arch * mult >= box:
- return (arch + box) * 0.5
- else:
- one, two = _split_cubic_into_two(p0, p1, p2, p3)
- return _calcCubicArcLengthCRecurse(mult, *one) + _calcCubicArcLengthCRecurse(
- mult, *two
- )
-
-
-@cython.returns(cython.double)
-@cython.locals(
- pt1=cython.complex,
- pt2=cython.complex,
- pt3=cython.complex,
- pt4=cython.complex,
-)
-@cython.locals(
- tolerance=cython.double,
- mult=cython.double,
-)
-def calcCubicArcLengthC(pt1, pt2, pt3, pt4, tolerance=0.005):
- """Calculates the arc length for a cubic Bezier segment.
-
- Args:
- pt1,pt2,pt3,pt4: Control points of the Bezier as complex numbers.
- tolerance: Controls the precision of the calcuation.
-
- Returns:
- Arc length value.
- """
- mult = 1.0 + 1.5 * tolerance # The 1.5 is a empirical hack; no math
- return _calcCubicArcLengthCRecurse(mult, pt1, pt2, pt3, pt4)
-
-
-epsilonDigits = 6
-epsilon = 1e-10
-
-
-@cython.cfunc
-@cython.inline
-@cython.returns(cython.double)
-@cython.locals(v1=cython.complex, v2=cython.complex)
-def _dot(v1, v2):
- return (v1 * v2.conjugate()).real
-
-
-@cython.cfunc
-@cython.inline
-@cython.returns(cython.double)
-@cython.locals(x=cython.complex)
-def _intSecAtan(x):
- # In : sympy.integrate(sp.sec(sp.atan(x)))
- # Out: x*sqrt(x**2 + 1)/2 + asinh(x)/2
- return x * math.sqrt(x**2 + 1) / 2 + math.asinh(x) / 2
-
-
-def calcQuadraticArcLength(pt1, pt2, pt3):
- """Calculates the arc length for a quadratic Bezier segment.
-
- Args:
- pt1: Start point of the Bezier as 2D tuple.
- pt2: Handle point of the Bezier as 2D tuple.
- pt3: End point of the Bezier as 2D tuple.
-
- Returns:
- Arc length value.
-
- Example::
-
- >>> calcQuadraticArcLength((0, 0), (0, 0), (0, 0)) # empty segment
- 0.0
- >>> calcQuadraticArcLength((0, 0), (50, 0), (80, 0)) # collinear points
- 80.0
- >>> calcQuadraticArcLength((0, 0), (0, 50), (0, 80)) # collinear points vertical
- 80.0
- >>> calcQuadraticArcLength((0, 0), (50, 20), (100, 40)) # collinear points
- 107.70329614269008
- >>> calcQuadraticArcLength((0, 0), (0, 100), (100, 0))
- 154.02976155645263
- >>> calcQuadraticArcLength((0, 0), (0, 50), (100, 0))
- 120.21581243984076
- >>> calcQuadraticArcLength((0, 0), (50, -10), (80, 50))
- 102.53273816445825
- >>> calcQuadraticArcLength((0, 0), (40, 0), (-40, 0)) # collinear points, control point outside
- 66.66666666666667
- >>> calcQuadraticArcLength((0, 0), (40, 0), (0, 0)) # collinear points, looping back
- 40.0
- """
- return calcQuadraticArcLengthC(complex(*pt1), complex(*pt2), complex(*pt3))
-
-
-@cython.returns(cython.double)
-@cython.locals(
- pt1=cython.complex,
- pt2=cython.complex,
- pt3=cython.complex,
- d0=cython.complex,
- d1=cython.complex,
- d=cython.complex,
- n=cython.complex,
-)
-@cython.locals(
- scale=cython.double,
- origDist=cython.double,
- a=cython.double,
- b=cython.double,
- x0=cython.double,
- x1=cython.double,
- Len=cython.double,
-)
-def calcQuadraticArcLengthC(pt1, pt2, pt3):
- """Calculates the arc length for a quadratic Bezier segment.
-
- Args:
- pt1: Start point of the Bezier as a complex number.
- pt2: Handle point of the Bezier as a complex number.
- pt3: End point of the Bezier as a complex number.
-
- Returns:
- Arc length value.
- """
- # Analytical solution to the length of a quadratic bezier.
- # Documentation: https://github.com/fonttools/fonttools/issues/3055
- d0 = pt2 - pt1
- d1 = pt3 - pt2
- d = d1 - d0
- n = d * 1j
- scale = abs(n)
- if scale == 0.0:
- return abs(pt3 - pt1)
- origDist = _dot(n, d0)
- if abs(origDist) < epsilon:
- if _dot(d0, d1) >= 0:
- return abs(pt3 - pt1)
- a, b = abs(d0), abs(d1)
- return (a * a + b * b) / (a + b)
- x0 = _dot(d, d0) / origDist
- x1 = _dot(d, d1) / origDist
- Len = abs(2 * (_intSecAtan(x1) - _intSecAtan(x0)) * origDist / (scale * (x1 - x0)))
- return Len
-
-
-def approximateQuadraticArcLength(pt1, pt2, pt3):
- """Calculates the arc length for a quadratic Bezier segment.
-
- Uses Gauss-Legendre quadrature for a branch-free approximation.
- See :func:`calcQuadraticArcLength` for a slower but more accurate result.
-
- Args:
- pt1: Start point of the Bezier as 2D tuple.
- pt2: Handle point of the Bezier as 2D tuple.
- pt3: End point of the Bezier as 2D tuple.
-
- Returns:
- Approximate arc length value.
- """
- return approximateQuadraticArcLengthC(complex(*pt1), complex(*pt2), complex(*pt3))
-
-
-@cython.returns(cython.double)
-@cython.locals(
- pt1=cython.complex,
- pt2=cython.complex,
- pt3=cython.complex,
-)
-@cython.locals(
- v0=cython.double,
- v1=cython.double,
- v2=cython.double,
-)
-def approximateQuadraticArcLengthC(pt1, pt2, pt3):
- """Calculates the arc length for a quadratic Bezier segment.
-
- Uses Gauss-Legendre quadrature for a branch-free approximation.
- See :func:`calcQuadraticArcLength` for a slower but more accurate result.
-
- Args:
- pt1: Start point of the Bezier as a complex number.
- pt2: Handle point of the Bezier as a complex number.
- pt3: End point of the Bezier as a complex number.
-
- Returns:
- Approximate arc length value.
- """
- # This, essentially, approximates the length-of-derivative function
- # to be integrated with the best-matching fifth-degree polynomial
- # approximation of it.
- #
- # https://en.wikipedia.org/wiki/Gaussian_quadrature#Gauss.E2.80.93Legendre_quadrature
-
- # abs(BezierCurveC[2].diff(t).subs({t:T})) for T in sorted(.5, .5±sqrt(3/5)/2),
- # weighted 5/18, 8/18, 5/18 respectively.
- v0 = abs(
- -0.492943519233745 * pt1 + 0.430331482911935 * pt2 + 0.0626120363218102 * pt3
- )
- v1 = abs(pt3 - pt1) * 0.4444444444444444
- v2 = abs(
- -0.0626120363218102 * pt1 - 0.430331482911935 * pt2 + 0.492943519233745 * pt3
- )
-
- return v0 + v1 + v2
-
-
-def calcQuadraticBounds(pt1, pt2, pt3):
- """Calculates the bounding rectangle for a quadratic Bezier segment.
-
- Args:
- pt1: Start point of the Bezier as a 2D tuple.
- pt2: Handle point of the Bezier as a 2D tuple.
- pt3: End point of the Bezier as a 2D tuple.
-
- Returns:
- A four-item tuple representing the bounding rectangle ``(xMin, yMin, xMax, yMax)``.
-
- Example::
-
- >>> calcQuadraticBounds((0, 0), (50, 100), (100, 0))
- (0, 0, 100, 50.0)
- >>> calcQuadraticBounds((0, 0), (100, 0), (100, 100))
- (0.0, 0.0, 100, 100)
- """
- (ax, ay), (bx, by), (cx, cy) = calcQuadraticParameters(pt1, pt2, pt3)
- ax2 = ax * 2.0
- ay2 = ay * 2.0
- roots = []
- if ax2 != 0:
- roots.append(-bx / ax2)
- if ay2 != 0:
- roots.append(-by / ay2)
- points = [
- (ax * t * t + bx * t + cx, ay * t * t + by * t + cy)
- for t in roots
- if 0 <= t < 1
- ] + [pt1, pt3]
- return calcBounds(points)
-
-
-def approximateCubicArcLength(pt1, pt2, pt3, pt4):
- """Approximates the arc length for a cubic Bezier segment.
-
- Uses Gauss-Lobatto quadrature with n=5 points to approximate arc length.
- See :func:`calcCubicArcLength` for a slower but more accurate result.
-
- Args:
- pt1,pt2,pt3,pt4: Control points of the Bezier as 2D tuples.
-
- Returns:
- Arc length value.
-
- Example::
-
- >>> approximateCubicArcLength((0, 0), (25, 100), (75, 100), (100, 0))
- 190.04332968932817
- >>> approximateCubicArcLength((0, 0), (50, 0), (100, 50), (100, 100))
- 154.8852074945903
- >>> approximateCubicArcLength((0, 0), (50, 0), (100, 0), (150, 0)) # line; exact result should be 150.
- 149.99999999999991
- >>> approximateCubicArcLength((0, 0), (50, 0), (100, 0), (-50, 0)) # cusp; exact result should be 150.
- 136.9267662156362
- >>> approximateCubicArcLength((0, 0), (50, 0), (100, -50), (-50, 0)) # cusp
- 154.80848416537057
- """
- return approximateCubicArcLengthC(
- complex(*pt1), complex(*pt2), complex(*pt3), complex(*pt4)
- )
-
-
-@cython.returns(cython.double)
-@cython.locals(
- pt1=cython.complex,
- pt2=cython.complex,
- pt3=cython.complex,
- pt4=cython.complex,
-)
-@cython.locals(
- v0=cython.double,
- v1=cython.double,
- v2=cython.double,
- v3=cython.double,
- v4=cython.double,
-)
-def approximateCubicArcLengthC(pt1, pt2, pt3, pt4):
- """Approximates the arc length for a cubic Bezier segment.
-
- Args:
- pt1,pt2,pt3,pt4: Control points of the Bezier as complex numbers.
-
- Returns:
- Arc length value.
- """
- # This, essentially, approximates the length-of-derivative function
- # to be integrated with the best-matching seventh-degree polynomial
- # approximation of it.
- #
- # https://en.wikipedia.org/wiki/Gaussian_quadrature#Gauss.E2.80.93Lobatto_rules
-
- # abs(BezierCurveC[3].diff(t).subs({t:T})) for T in sorted(0, .5±(3/7)**.5/2, .5, 1),
- # weighted 1/20, 49/180, 32/90, 49/180, 1/20 respectively.
- v0 = abs(pt2 - pt1) * 0.15
- v1 = abs(
- -0.558983582205757 * pt1
- + 0.325650248872424 * pt2
- + 0.208983582205757 * pt3
- + 0.024349751127576 * pt4
- )
- v2 = abs(pt4 - pt1 + pt3 - pt2) * 0.26666666666666666
- v3 = abs(
- -0.024349751127576 * pt1
- - 0.208983582205757 * pt2
- - 0.325650248872424 * pt3
- + 0.558983582205757 * pt4
- )
- v4 = abs(pt4 - pt3) * 0.15
-
- return v0 + v1 + v2 + v3 + v4
-
-
-def calcCubicBounds(pt1, pt2, pt3, pt4):
- """Calculates the bounding rectangle for a quadratic Bezier segment.
-
- Args:
- pt1,pt2,pt3,pt4: Control points of the Bezier as 2D tuples.
-
- Returns:
- A four-item tuple representing the bounding rectangle ``(xMin, yMin, xMax, yMax)``.
-
- Example::
-
- >>> calcCubicBounds((0, 0), (25, 100), (75, 100), (100, 0))
- (0, 0, 100, 75.0)
- >>> calcCubicBounds((0, 0), (50, 0), (100, 50), (100, 100))
- (0.0, 0.0, 100, 100)
- >>> print("%f %f %f %f" % calcCubicBounds((50, 0), (0, 100), (100, 100), (50, 0)))
- 35.566243 0.000000 64.433757 75.000000
- """
- (ax, ay), (bx, by), (cx, cy), (dx, dy) = calcCubicParameters(pt1, pt2, pt3, pt4)
- # calc first derivative
- ax3 = ax * 3.0
- ay3 = ay * 3.0
- bx2 = bx * 2.0
- by2 = by * 2.0
- xRoots = [t for t in solveQuadratic(ax3, bx2, cx) if 0 <= t < 1]
- yRoots = [t for t in solveQuadratic(ay3, by2, cy) if 0 <= t < 1]
- roots = xRoots + yRoots
-
- points = [
- (
- ax * t * t * t + bx * t * t + cx * t + dx,
- ay * t * t * t + by * t * t + cy * t + dy,
- )
- for t in roots
- ] + [pt1, pt4]
- return calcBounds(points)
-
-
-def splitLine(pt1, pt2, where, isHorizontal):
- """Split a line at a given coordinate.
-
- Args:
- pt1: Start point of line as 2D tuple.
- pt2: End point of line as 2D tuple.
- where: Position at which to split the line.
- isHorizontal: Direction of the ray splitting the line. If true,
- ``where`` is interpreted as a Y coordinate; if false, then
- ``where`` is interpreted as an X coordinate.
-
- Returns:
- A list of two line segments (each line segment being two 2D tuples)
- if the line was successfully split, or a list containing the original
- line.
-
- Example::
-
- >>> printSegments(splitLine((0, 0), (100, 100), 50, True))
- ((0, 0), (50, 50))
- ((50, 50), (100, 100))
- >>> printSegments(splitLine((0, 0), (100, 100), 100, True))
- ((0, 0), (100, 100))
- >>> printSegments(splitLine((0, 0), (100, 100), 0, True))
- ((0, 0), (0, 0))
- ((0, 0), (100, 100))
- >>> printSegments(splitLine((0, 0), (100, 100), 0, False))
- ((0, 0), (0, 0))
- ((0, 0), (100, 100))
- >>> printSegments(splitLine((100, 0), (0, 0), 50, False))
- ((100, 0), (50, 0))
- ((50, 0), (0, 0))
- >>> printSegments(splitLine((0, 100), (0, 0), 50, True))
- ((0, 100), (0, 50))
- ((0, 50), (0, 0))
- """
- pt1x, pt1y = pt1
- pt2x, pt2y = pt2
-
- ax = pt2x - pt1x
- ay = pt2y - pt1y
-
- bx = pt1x
- by = pt1y
-
- a = (ax, ay)[isHorizontal]
-
- if a == 0:
- return [(pt1, pt2)]
- t = (where - (bx, by)[isHorizontal]) / a
- if 0 <= t < 1:
- midPt = ax * t + bx, ay * t + by
- return [(pt1, midPt), (midPt, pt2)]
- else:
- return [(pt1, pt2)]
-
-
-def splitQuadratic(pt1, pt2, pt3, where, isHorizontal):
- """Split a quadratic Bezier curve at a given coordinate.
-
- Args:
- pt1,pt2,pt3: Control points of the Bezier as 2D tuples.
- where: Position at which to split the curve.
- isHorizontal: Direction of the ray splitting the curve. If true,
- ``where`` is interpreted as a Y coordinate; if false, then
- ``where`` is interpreted as an X coordinate.
-
- Returns:
- A list of two curve segments (each curve segment being three 2D tuples)
- if the curve was successfully split, or a list containing the original
- curve.
-
- Example::
-
- >>> printSegments(splitQuadratic((0, 0), (50, 100), (100, 0), 150, False))
- ((0, 0), (50, 100), (100, 0))
- >>> printSegments(splitQuadratic((0, 0), (50, 100), (100, 0), 50, False))
- ((0, 0), (25, 50), (50, 50))
- ((50, 50), (75, 50), (100, 0))
- >>> printSegments(splitQuadratic((0, 0), (50, 100), (100, 0), 25, False))
- ((0, 0), (12.5, 25), (25, 37.5))
- ((25, 37.5), (62.5, 75), (100, 0))
- >>> printSegments(splitQuadratic((0, 0), (50, 100), (100, 0), 25, True))
- ((0, 0), (7.32233, 14.6447), (14.6447, 25))
- ((14.6447, 25), (50, 75), (85.3553, 25))
- ((85.3553, 25), (92.6777, 14.6447), (100, -7.10543e-15))
- >>> # XXX I'm not at all sure if the following behavior is desirable:
- >>> printSegments(splitQuadratic((0, 0), (50, 100), (100, 0), 50, True))
- ((0, 0), (25, 50), (50, 50))
- ((50, 50), (50, 50), (50, 50))
- ((50, 50), (75, 50), (100, 0))
- """
- a, b, c = calcQuadraticParameters(pt1, pt2, pt3)
- solutions = solveQuadratic(
- a[isHorizontal], b[isHorizontal], c[isHorizontal] - where
- )
- solutions = sorted(t for t in solutions if 0 <= t < 1)
- if not solutions:
- return [(pt1, pt2, pt3)]
- return _splitQuadraticAtT(a, b, c, *solutions)
-
-
-def splitCubic(pt1, pt2, pt3, pt4, where, isHorizontal):
- """Split a cubic Bezier curve at a given coordinate.
-
- Args:
- pt1,pt2,pt3,pt4: Control points of the Bezier as 2D tuples.
- where: Position at which to split the curve.
- isHorizontal: Direction of the ray splitting the curve. If true,
- ``where`` is interpreted as a Y coordinate; if false, then
- ``where`` is interpreted as an X coordinate.
-
- Returns:
- A list of two curve segments (each curve segment being four 2D tuples)
- if the curve was successfully split, or a list containing the original
- curve.
-
- Example::
-
- >>> printSegments(splitCubic((0, 0), (25, 100), (75, 100), (100, 0), 150, False))
- ((0, 0), (25, 100), (75, 100), (100, 0))
- >>> printSegments(splitCubic((0, 0), (25, 100), (75, 100), (100, 0), 50, False))
- ((0, 0), (12.5, 50), (31.25, 75), (50, 75))
- ((50, 75), (68.75, 75), (87.5, 50), (100, 0))
- >>> printSegments(splitCubic((0, 0), (25, 100), (75, 100), (100, 0), 25, True))
- ((0, 0), (2.29379, 9.17517), (4.79804, 17.5085), (7.47414, 25))
- ((7.47414, 25), (31.2886, 91.6667), (68.7114, 91.6667), (92.5259, 25))
- ((92.5259, 25), (95.202, 17.5085), (97.7062, 9.17517), (100, 1.77636e-15))
- """
- a, b, c, d = calcCubicParameters(pt1, pt2, pt3, pt4)
- solutions = solveCubic(
- a[isHorizontal], b[isHorizontal], c[isHorizontal], d[isHorizontal] - where
- )
- solutions = sorted(t for t in solutions if 0 <= t < 1)
- if not solutions:
- return [(pt1, pt2, pt3, pt4)]
- return _splitCubicAtT(a, b, c, d, *solutions)
-
-
-def splitQuadraticAtT(pt1, pt2, pt3, *ts):
- """Split a quadratic Bezier curve at one or more values of t.
-
- Args:
- pt1,pt2,pt3: Control points of the Bezier as 2D tuples.
- *ts: Positions at which to split the curve.
-
- Returns:
- A list of curve segments (each curve segment being three 2D tuples).
-
- Examples::
-
- >>> printSegments(splitQuadraticAtT((0, 0), (50, 100), (100, 0), 0.5))
- ((0, 0), (25, 50), (50, 50))
- ((50, 50), (75, 50), (100, 0))
- >>> printSegments(splitQuadraticAtT((0, 0), (50, 100), (100, 0), 0.5, 0.75))
- ((0, 0), (25, 50), (50, 50))
- ((50, 50), (62.5, 50), (75, 37.5))
- ((75, 37.5), (87.5, 25), (100, 0))
- """
- a, b, c = calcQuadraticParameters(pt1, pt2, pt3)
- return _splitQuadraticAtT(a, b, c, *ts)
-
-
-def splitCubicAtT(pt1, pt2, pt3, pt4, *ts):
- """Split a cubic Bezier curve at one or more values of t.
-
- Args:
- pt1,pt2,pt3,pt4: Control points of the Bezier as 2D tuples.
- *ts: Positions at which to split the curve.
-
- Returns:
- A list of curve segments (each curve segment being four 2D tuples).
-
- Examples::
-
- >>> printSegments(splitCubicAtT((0, 0), (25, 100), (75, 100), (100, 0), 0.5))
- ((0, 0), (12.5, 50), (31.25, 75), (50, 75))
- ((50, 75), (68.75, 75), (87.5, 50), (100, 0))
- >>> printSegments(splitCubicAtT((0, 0), (25, 100), (75, 100), (100, 0), 0.5, 0.75))
- ((0, 0), (12.5, 50), (31.25, 75), (50, 75))
- ((50, 75), (59.375, 75), (68.75, 68.75), (77.3438, 56.25))
- ((77.3438, 56.25), (85.9375, 43.75), (93.75, 25), (100, 0))
- """
- a, b, c, d = calcCubicParameters(pt1, pt2, pt3, pt4)
- return _splitCubicAtT(a, b, c, d, *ts)
-
-
-@cython.locals(
- pt1=cython.complex,
- pt2=cython.complex,
- pt3=cython.complex,
- pt4=cython.complex,
- a=cython.complex,
- b=cython.complex,
- c=cython.complex,
- d=cython.complex,
-)
-def splitCubicAtTC(pt1, pt2, pt3, pt4, *ts):
- """Split a cubic Bezier curve at one or more values of t.
-
- Args:
- pt1,pt2,pt3,pt4: Control points of the Bezier as complex numbers..
- *ts: Positions at which to split the curve.
-
- Yields:
- Curve segments (each curve segment being four complex numbers).
- """
- a, b, c, d = calcCubicParametersC(pt1, pt2, pt3, pt4)
- yield from _splitCubicAtTC(a, b, c, d, *ts)
-
-
-@cython.returns(cython.complex)
-@cython.locals(
- t=cython.double,
- pt1=cython.complex,
- pt2=cython.complex,
- pt3=cython.complex,
- pt4=cython.complex,
- pointAtT=cython.complex,
- off1=cython.complex,
- off2=cython.complex,
-)
-@cython.locals(
- t2=cython.double, _1_t=cython.double, _1_t_2=cython.double, _2_t_1_t=cython.double
-)
-def splitCubicIntoTwoAtTC(pt1, pt2, pt3, pt4, t):
- """Split a cubic Bezier curve at t.
-
- Args:
- pt1,pt2,pt3,pt4: Control points of the Bezier as complex numbers.
- t: Position at which to split the curve.
-
- Returns:
- A tuple of two curve segments (each curve segment being four complex numbers).
- """
- t2 = t * t
- _1_t = 1 - t
- _1_t_2 = _1_t * _1_t
- _2_t_1_t = 2 * t * _1_t
- pointAtT = (
- _1_t_2 * _1_t * pt1 + 3 * (_1_t_2 * t * pt2 + _1_t * t2 * pt3) + t2 * t * pt4
- )
- off1 = _1_t_2 * pt1 + _2_t_1_t * pt2 + t2 * pt3
- off2 = _1_t_2 * pt2 + _2_t_1_t * pt3 + t2 * pt4
-
- pt2 = pt1 + (pt2 - pt1) * t
- pt3 = pt4 + (pt3 - pt4) * _1_t
-
- return ((pt1, pt2, off1, pointAtT), (pointAtT, off2, pt3, pt4))
-
-
-def _splitQuadraticAtT(a, b, c, *ts):
- ts = list(ts)
- segments = []
- ts.insert(0, 0.0)
- ts.append(1.0)
- ax, ay = a
- bx, by = b
- cx, cy = c
- for i in range(len(ts) - 1):
- t1 = ts[i]
- t2 = ts[i + 1]
- delta = t2 - t1
- # calc new a, b and c
- delta_2 = delta * delta
- a1x = ax * delta_2
- a1y = ay * delta_2
- b1x = (2 * ax * t1 + bx) * delta
- b1y = (2 * ay * t1 + by) * delta
- t1_2 = t1 * t1
- c1x = ax * t1_2 + bx * t1 + cx
- c1y = ay * t1_2 + by * t1 + cy
-
- pt1, pt2, pt3 = calcQuadraticPoints((a1x, a1y), (b1x, b1y), (c1x, c1y))
- segments.append((pt1, pt2, pt3))
- return segments
-
-
-def _splitCubicAtT(a, b, c, d, *ts):
- ts = list(ts)
- ts.insert(0, 0.0)
- ts.append(1.0)
- segments = []
- ax, ay = a
- bx, by = b
- cx, cy = c
- dx, dy = d
- for i in range(len(ts) - 1):
- t1 = ts[i]
- t2 = ts[i + 1]
- delta = t2 - t1
-
- delta_2 = delta * delta
- delta_3 = delta * delta_2
- t1_2 = t1 * t1
- t1_3 = t1 * t1_2
-
- # calc new a, b, c and d
- a1x = ax * delta_3
- a1y = ay * delta_3
- b1x = (3 * ax * t1 + bx) * delta_2
- b1y = (3 * ay * t1 + by) * delta_2
- c1x = (2 * bx * t1 + cx + 3 * ax * t1_2) * delta
- c1y = (2 * by * t1 + cy + 3 * ay * t1_2) * delta
- d1x = ax * t1_3 + bx * t1_2 + cx * t1 + dx
- d1y = ay * t1_3 + by * t1_2 + cy * t1 + dy
- pt1, pt2, pt3, pt4 = calcCubicPoints(
- (a1x, a1y), (b1x, b1y), (c1x, c1y), (d1x, d1y)
- )
- segments.append((pt1, pt2, pt3, pt4))
- return segments
-
-
-@cython.locals(
- a=cython.complex,
- b=cython.complex,
- c=cython.complex,
- d=cython.complex,
- t1=cython.double,
- t2=cython.double,
- delta=cython.double,
- delta_2=cython.double,
- delta_3=cython.double,
- a1=cython.complex,
- b1=cython.complex,
- c1=cython.complex,
- d1=cython.complex,
-)
-def _splitCubicAtTC(a, b, c, d, *ts):
- ts = list(ts)
- ts.insert(0, 0.0)
- ts.append(1.0)
- for i in range(len(ts) - 1):
- t1 = ts[i]
- t2 = ts[i + 1]
- delta = t2 - t1
-
- delta_2 = delta * delta
- delta_3 = delta * delta_2
- t1_2 = t1 * t1
- t1_3 = t1 * t1_2
-
- # calc new a, b, c and d
- a1 = a * delta_3
- b1 = (3 * a * t1 + b) * delta_2
- c1 = (2 * b * t1 + c + 3 * a * t1_2) * delta
- d1 = a * t1_3 + b * t1_2 + c * t1 + d
- pt1, pt2, pt3, pt4 = calcCubicPointsC(a1, b1, c1, d1)
- yield (pt1, pt2, pt3, pt4)
-
-
-#
-# Equation solvers.
-#
-
-from math import sqrt, acos, cos, pi
-
-
-def solveQuadratic(a, b, c, sqrt=sqrt):
- """Solve a quadratic equation.
-
- Solves *a*x*x + b*x + c = 0* where a, b and c are real.
-
- Args:
- a: coefficient of *x²*
- b: coefficient of *x*
- c: constant term
-
- Returns:
- A list of roots. Note that the returned list is neither guaranteed to
- be sorted nor to contain unique values!
- """
- if abs(a) < epsilon:
- if abs(b) < epsilon:
- # We have a non-equation; therefore, we have no valid solution
- roots = []
- else:
- # We have a linear equation with 1 root.
- roots = [-c / b]
- else:
- # We have a true quadratic equation. Apply the quadratic formula to find two roots.
- DD = b * b - 4.0 * a * c
- if DD >= 0.0:
- rDD = sqrt(DD)
- roots = [(-b + rDD) / 2.0 / a, (-b - rDD) / 2.0 / a]
- else:
- # complex roots, ignore
- roots = []
- return roots
-
-
-def solveCubic(a, b, c, d):
- """Solve a cubic equation.
-
- Solves *a*x*x*x + b*x*x + c*x + d = 0* where a, b, c and d are real.
-
- Args:
- a: coefficient of *x³*
- b: coefficient of *x²*
- c: coefficient of *x*
- d: constant term
-
- Returns:
- A list of roots. Note that the returned list is neither guaranteed to
- be sorted nor to contain unique values!
-
- Examples::
-
- >>> solveCubic(1, 1, -6, 0)
- [-3.0, -0.0, 2.0]
- >>> solveCubic(-10.0, -9.0, 48.0, -29.0)
- [-2.9, 1.0, 1.0]
- >>> solveCubic(-9.875, -9.0, 47.625, -28.75)
- [-2.911392, 1.0, 1.0]
- >>> solveCubic(1.0, -4.5, 6.75, -3.375)
- [1.5, 1.5, 1.5]
- >>> solveCubic(-12.0, 18.0, -9.0, 1.50023651123)
- [0.5, 0.5, 0.5]
- >>> solveCubic(
- ... 9.0, 0.0, 0.0, -7.62939453125e-05
- ... ) == [-0.0, -0.0, -0.0]
- True
- """
- #
- # adapted from:
- # CUBIC.C - Solve a cubic polynomial
- # public domain by Ross Cottrell
- # found at: http://www.strangecreations.com/library/snippets/Cubic.C
- #
- if abs(a) < epsilon:
- # don't just test for zero; for very small values of 'a' solveCubic()
- # returns unreliable results, so we fall back to quad.
- return solveQuadratic(b, c, d)
- a = float(a)
- a1 = b / a
- a2 = c / a
- a3 = d / a
-
- Q = (a1 * a1 - 3.0 * a2) / 9.0
- R = (2.0 * a1 * a1 * a1 - 9.0 * a1 * a2 + 27.0 * a3) / 54.0
-
- R2 = R * R
- Q3 = Q * Q * Q
- R2 = 0 if R2 < epsilon else R2
- Q3 = 0 if abs(Q3) < epsilon else Q3
-
- R2_Q3 = R2 - Q3
-
- if R2 == 0.0 and Q3 == 0.0:
- x = round(-a1 / 3.0, epsilonDigits)
- return [x, x, x]
- elif R2_Q3 <= epsilon * 0.5:
- # The epsilon * .5 above ensures that Q3 is not zero.
- theta = acos(max(min(R / sqrt(Q3), 1.0), -1.0))
- rQ2 = -2.0 * sqrt(Q)
- a1_3 = a1 / 3.0
- x0 = rQ2 * cos(theta / 3.0) - a1_3
- x1 = rQ2 * cos((theta + 2.0 * pi) / 3.0) - a1_3
- x2 = rQ2 * cos((theta + 4.0 * pi) / 3.0) - a1_3
- x0, x1, x2 = sorted([x0, x1, x2])
- # Merge roots that are close-enough
- if x1 - x0 < epsilon and x2 - x1 < epsilon:
- x0 = x1 = x2 = round((x0 + x1 + x2) / 3.0, epsilonDigits)
- elif x1 - x0 < epsilon:
- x0 = x1 = round((x0 + x1) / 2.0, epsilonDigits)
- x2 = round(x2, epsilonDigits)
- elif x2 - x1 < epsilon:
- x0 = round(x0, epsilonDigits)
- x1 = x2 = round((x1 + x2) / 2.0, epsilonDigits)
- else:
- x0 = round(x0, epsilonDigits)
- x1 = round(x1, epsilonDigits)
- x2 = round(x2, epsilonDigits)
- return [x0, x1, x2]
- else:
- x = pow(sqrt(R2_Q3) + abs(R), 1 / 3.0)
- x = x + Q / x
- if R >= 0.0:
- x = -x
- x = round(x - a1 / 3.0, epsilonDigits)
- return [x]
-
-
-#
-# Conversion routines for points to parameters and vice versa
-#
-
-
-def calcQuadraticParameters(pt1, pt2, pt3):
- x2, y2 = pt2
- x3, y3 = pt3
- cx, cy = pt1
- bx = (x2 - cx) * 2.0
- by = (y2 - cy) * 2.0
- ax = x3 - cx - bx
- ay = y3 - cy - by
- return (ax, ay), (bx, by), (cx, cy)
-
-
-def calcCubicParameters(pt1, pt2, pt3, pt4):
- x2, y2 = pt2
- x3, y3 = pt3
- x4, y4 = pt4
- dx, dy = pt1
- cx = (x2 - dx) * 3.0
- cy = (y2 - dy) * 3.0
- bx = (x3 - x2) * 3.0 - cx
- by = (y3 - y2) * 3.0 - cy
- ax = x4 - dx - cx - bx
- ay = y4 - dy - cy - by
- return (ax, ay), (bx, by), (cx, cy), (dx, dy)
-
-
-@cython.cfunc
-@cython.inline
-@cython.locals(
- pt1=cython.complex,
- pt2=cython.complex,
- pt3=cython.complex,
- pt4=cython.complex,
- a=cython.complex,
- b=cython.complex,
- c=cython.complex,
-)
-def calcCubicParametersC(pt1, pt2, pt3, pt4):
- c = (pt2 - pt1) * 3.0
- b = (pt3 - pt2) * 3.0 - c
- a = pt4 - pt1 - c - b
- return (a, b, c, pt1)
-
-
-def calcQuadraticPoints(a, b, c):
- ax, ay = a
- bx, by = b
- cx, cy = c
- x1 = cx
- y1 = cy
- x2 = (bx * 0.5) + cx
- y2 = (by * 0.5) + cy
- x3 = ax + bx + cx
- y3 = ay + by + cy
- return (x1, y1), (x2, y2), (x3, y3)
-
-
-def calcCubicPoints(a, b, c, d):
- ax, ay = a
- bx, by = b
- cx, cy = c
- dx, dy = d
- x1 = dx
- y1 = dy
- x2 = (cx / 3.0) + dx
- y2 = (cy / 3.0) + dy
- x3 = (bx + cx) / 3.0 + x2
- y3 = (by + cy) / 3.0 + y2
- x4 = ax + dx + cx + bx
- y4 = ay + dy + cy + by
- return (x1, y1), (x2, y2), (x3, y3), (x4, y4)
-
-
-@cython.cfunc
-@cython.inline
-@cython.locals(
- a=cython.complex,
- b=cython.complex,
- c=cython.complex,
- d=cython.complex,
- p2=cython.complex,
- p3=cython.complex,
- p4=cython.complex,
-)
-def calcCubicPointsC(a, b, c, d):
- p2 = c * (1 / 3) + d
- p3 = (b + c) * (1 / 3) + p2
- p4 = a + b + c + d
- return (d, p2, p3, p4)
-
-
-#
-# Point at time
-#
-
-
-def linePointAtT(pt1, pt2, t):
- """Finds the point at time `t` on a line.
-
- Args:
- pt1, pt2: Coordinates of the line as 2D tuples.
- t: The time along the line.
-
- Returns:
- A 2D tuple with the coordinates of the point.
- """
- return ((pt1[0] * (1 - t) + pt2[0] * t), (pt1[1] * (1 - t) + pt2[1] * t))
-
-
-def quadraticPointAtT(pt1, pt2, pt3, t):
- """Finds the point at time `t` on a quadratic curve.
-
- Args:
- pt1, pt2, pt3: Coordinates of the curve as 2D tuples.
- t: The time along the curve.
-
- Returns:
- A 2D tuple with the coordinates of the point.
- """
- x = (1 - t) * (1 - t) * pt1[0] + 2 * (1 - t) * t * pt2[0] + t * t * pt3[0]
- y = (1 - t) * (1 - t) * pt1[1] + 2 * (1 - t) * t * pt2[1] + t * t * pt3[1]
- return (x, y)
-
-
-def cubicPointAtT(pt1, pt2, pt3, pt4, t):
- """Finds the point at time `t` on a cubic curve.
-
- Args:
- pt1, pt2, pt3, pt4: Coordinates of the curve as 2D tuples.
- t: The time along the curve.
-
- Returns:
- A 2D tuple with the coordinates of the point.
- """
- t2 = t * t
- _1_t = 1 - t
- _1_t_2 = _1_t * _1_t
- x = (
- _1_t_2 * _1_t * pt1[0]
- + 3 * (_1_t_2 * t * pt2[0] + _1_t * t2 * pt3[0])
- + t2 * t * pt4[0]
- )
- y = (
- _1_t_2 * _1_t * pt1[1]
- + 3 * (_1_t_2 * t * pt2[1] + _1_t * t2 * pt3[1])
- + t2 * t * pt4[1]
- )
- return (x, y)
-
-
-@cython.returns(cython.complex)
-@cython.locals(
- t=cython.double,
- pt1=cython.complex,
- pt2=cython.complex,
- pt3=cython.complex,
- pt4=cython.complex,
-)
-@cython.locals(t2=cython.double, _1_t=cython.double, _1_t_2=cython.double)
-def cubicPointAtTC(pt1, pt2, pt3, pt4, t):
- """Finds the point at time `t` on a cubic curve.
-
- Args:
- pt1, pt2, pt3, pt4: Coordinates of the curve as complex numbers.
- t: The time along the curve.
-
- Returns:
- A complex number with the coordinates of the point.
- """
- t2 = t * t
- _1_t = 1 - t
- _1_t_2 = _1_t * _1_t
- return _1_t_2 * _1_t * pt1 + 3 * (_1_t_2 * t * pt2 + _1_t * t2 * pt3) + t2 * t * pt4
-
-
-def segmentPointAtT(seg, t):
- if len(seg) == 2:
- return linePointAtT(*seg, t)
- elif len(seg) == 3:
- return quadraticPointAtT(*seg, t)
- elif len(seg) == 4:
- return cubicPointAtT(*seg, t)
- raise ValueError("Unknown curve degree")
-
-
-#
-# Intersection finders
-#
-
-
-def _line_t_of_pt(s, e, pt):
- sx, sy = s
- ex, ey = e
- px, py = pt
- if abs(sx - ex) < epsilon and abs(sy - ey) < epsilon:
- # Line is a point!
- return -1
- # Use the largest
- if abs(sx - ex) > abs(sy - ey):
- return (px - sx) / (ex - sx)
- else:
- return (py - sy) / (ey - sy)
-
-
-def _both_points_are_on_same_side_of_origin(a, b, origin):
- xDiff = (a[0] - origin[0]) * (b[0] - origin[0])
- yDiff = (a[1] - origin[1]) * (b[1] - origin[1])
- return not (xDiff <= 0.0 and yDiff <= 0.0)
-
-
-def lineLineIntersections(s1, e1, s2, e2):
- """Finds intersections between two line segments.
-
- Args:
- s1, e1: Coordinates of the first line as 2D tuples.
- s2, e2: Coordinates of the second line as 2D tuples.
-
- Returns:
- A list of ``Intersection`` objects, each object having ``pt``, ``t1``
- and ``t2`` attributes containing the intersection point, time on first
- segment and time on second segment respectively.
-
- Examples::
-
- >>> a = lineLineIntersections( (310,389), (453, 222), (289, 251), (447, 367))
- >>> len(a)
- 1
- >>> intersection = a[0]
- >>> intersection.pt
- (374.44882952482897, 313.73458370177315)
- >>> (intersection.t1, intersection.t2)
- (0.45069111555824465, 0.5408153767394238)
- """
- s1x, s1y = s1
- e1x, e1y = e1
- s2x, s2y = s2
- e2x, e2y = e2
- if (
- math.isclose(s2x, e2x) and math.isclose(s1x, e1x) and not math.isclose(s1x, s2x)
- ): # Parallel vertical
- return []
- if (
- math.isclose(s2y, e2y) and math.isclose(s1y, e1y) and not math.isclose(s1y, s2y)
- ): # Parallel horizontal
- return []
- if math.isclose(s2x, e2x) and math.isclose(s2y, e2y): # Line segment is tiny
- return []
- if math.isclose(s1x, e1x) and math.isclose(s1y, e1y): # Line segment is tiny
- return []
- if math.isclose(e1x, s1x):
- x = s1x
- slope34 = (e2y - s2y) / (e2x - s2x)
- y = slope34 * (x - s2x) + s2y
- pt = (x, y)
- return [
- Intersection(
- pt=pt, t1=_line_t_of_pt(s1, e1, pt), t2=_line_t_of_pt(s2, e2, pt)
- )
- ]
- if math.isclose(s2x, e2x):
- x = s2x
- slope12 = (e1y - s1y) / (e1x - s1x)
- y = slope12 * (x - s1x) + s1y
- pt = (x, y)
- return [
- Intersection(
- pt=pt, t1=_line_t_of_pt(s1, e1, pt), t2=_line_t_of_pt(s2, e2, pt)
- )
- ]
-
- slope12 = (e1y - s1y) / (e1x - s1x)
- slope34 = (e2y - s2y) / (e2x - s2x)
- if math.isclose(slope12, slope34):
- return []
- x = (slope12 * s1x - s1y - slope34 * s2x + s2y) / (slope12 - slope34)
- y = slope12 * (x - s1x) + s1y
- pt = (x, y)
- if _both_points_are_on_same_side_of_origin(
- pt, e1, s1
- ) and _both_points_are_on_same_side_of_origin(pt, s2, e2):
- return [
- Intersection(
- pt=pt, t1=_line_t_of_pt(s1, e1, pt), t2=_line_t_of_pt(s2, e2, pt)
- )
- ]
- return []
-
-
-def _alignment_transformation(segment):
- # Returns a transformation which aligns a segment horizontally at the
- # origin. Apply this transformation to curves and root-find to find
- # intersections with the segment.
- start = segment[0]
- end = segment[-1]
- angle = math.atan2(end[1] - start[1], end[0] - start[0])
- return Identity.rotate(-angle).translate(-start[0], -start[1])
-
-
-def _curve_line_intersections_t(curve, line):
- aligned_curve = _alignment_transformation(line).transformPoints(curve)
- if len(curve) == 3:
- a, b, c = calcQuadraticParameters(*aligned_curve)
- intersections = solveQuadratic(a[1], b[1], c[1])
- elif len(curve) == 4:
- a, b, c, d = calcCubicParameters(*aligned_curve)
- intersections = solveCubic(a[1], b[1], c[1], d[1])
- else:
- raise ValueError("Unknown curve degree")
- return sorted(i for i in intersections if 0.0 <= i <= 1)
-
-
-def curveLineIntersections(curve, line):
- """Finds intersections between a curve and a line.
-
- Args:
- curve: List of coordinates of the curve segment as 2D tuples.
- line: List of coordinates of the line segment as 2D tuples.
-
- Returns:
- A list of ``Intersection`` objects, each object having ``pt``, ``t1``
- and ``t2`` attributes containing the intersection point, time on first
- segment and time on second segment respectively.
-
- Examples::
- >>> curve = [ (100, 240), (30, 60), (210, 230), (160, 30) ]
- >>> line = [ (25, 260), (230, 20) ]
- >>> intersections = curveLineIntersections(curve, line)
- >>> len(intersections)
- 3
- >>> intersections[0].pt
- (84.9000930760723, 189.87306176459828)
- """
- if len(curve) == 3:
- pointFinder = quadraticPointAtT
- elif len(curve) == 4:
- pointFinder = cubicPointAtT
- else:
- raise ValueError("Unknown curve degree")
- intersections = []
- for t in _curve_line_intersections_t(curve, line):
- pt = pointFinder(*curve, t)
- # Back-project the point onto the line, to avoid problems with
- # numerical accuracy in the case of vertical and horizontal lines
- line_t = _line_t_of_pt(*line, pt)
- pt = linePointAtT(*line, line_t)
- intersections.append(Intersection(pt=pt, t1=t, t2=line_t))
- return intersections
-
-
-def _curve_bounds(c):
- if len(c) == 3:
- return calcQuadraticBounds(*c)
- elif len(c) == 4:
- return calcCubicBounds(*c)
- raise ValueError("Unknown curve degree")
-
-
-def _split_segment_at_t(c, t):
- if len(c) == 2:
- s, e = c
- midpoint = linePointAtT(s, e, t)
- return [(s, midpoint), (midpoint, e)]
- if len(c) == 3:
- return splitQuadraticAtT(*c, t)
- elif len(c) == 4:
- return splitCubicAtT(*c, t)
- raise ValueError("Unknown curve degree")
-
-
-def _curve_curve_intersections_t(
- curve1, curve2, precision=1e-3, range1=None, range2=None
-):
- bounds1 = _curve_bounds(curve1)
- bounds2 = _curve_bounds(curve2)
-
- if not range1:
- range1 = (0.0, 1.0)
- if not range2:
- range2 = (0.0, 1.0)
-
- # If bounds don't intersect, go home
- intersects, _ = sectRect(bounds1, bounds2)
- if not intersects:
- return []
-
- def midpoint(r):
- return 0.5 * (r[0] + r[1])
-
- # If they do overlap but they're tiny, approximate
- if rectArea(bounds1) < precision and rectArea(bounds2) < precision:
- return [(midpoint(range1), midpoint(range2))]
-
- c11, c12 = _split_segment_at_t(curve1, 0.5)
- c11_range = (range1[0], midpoint(range1))
- c12_range = (midpoint(range1), range1[1])
-
- c21, c22 = _split_segment_at_t(curve2, 0.5)
- c21_range = (range2[0], midpoint(range2))
- c22_range = (midpoint(range2), range2[1])
-
- found = []
- found.extend(
- _curve_curve_intersections_t(
- c11, c21, precision, range1=c11_range, range2=c21_range
- )
- )
- found.extend(
- _curve_curve_intersections_t(
- c12, c21, precision, range1=c12_range, range2=c21_range
- )
- )
- found.extend(
- _curve_curve_intersections_t(
- c11, c22, precision, range1=c11_range, range2=c22_range
- )
- )
- found.extend(
- _curve_curve_intersections_t(
- c12, c22, precision, range1=c12_range, range2=c22_range
- )
- )
-
- unique_key = lambda ts: (int(ts[0] / precision), int(ts[1] / precision))
- seen = set()
- unique_values = []
-
- for ts in found:
- key = unique_key(ts)
- if key in seen:
- continue
- seen.add(key)
- unique_values.append(ts)
-
- return unique_values
-
-
-def curveCurveIntersections(curve1, curve2):
- """Finds intersections between a curve and a curve.
-
- Args:
- curve1: List of coordinates of the first curve segment as 2D tuples.
- curve2: List of coordinates of the second curve segment as 2D tuples.
-
- Returns:
- A list of ``Intersection`` objects, each object having ``pt``, ``t1``
- and ``t2`` attributes containing the intersection point, time on first
- segment and time on second segment respectively.
-
- Examples::
- >>> curve1 = [ (10,100), (90,30), (40,140), (220,220) ]
- >>> curve2 = [ (5,150), (180,20), (80,250), (210,190) ]
- >>> intersections = curveCurveIntersections(curve1, curve2)
- >>> len(intersections)
- 3
- >>> intersections[0].pt
- (81.7831487395506, 109.88904552375288)
- """
- intersection_ts = _curve_curve_intersections_t(curve1, curve2)
- return [
- Intersection(pt=segmentPointAtT(curve1, ts[0]), t1=ts[0], t2=ts[1])
- for ts in intersection_ts
- ]
-
-
-def segmentSegmentIntersections(seg1, seg2):
- """Finds intersections between two segments.
-
- Args:
- seg1: List of coordinates of the first segment as 2D tuples.
- seg2: List of coordinates of the second segment as 2D tuples.
-
- Returns:
- A list of ``Intersection`` objects, each object having ``pt``, ``t1``
- and ``t2`` attributes containing the intersection point, time on first
- segment and time on second segment respectively.
-
- Examples::
- >>> curve1 = [ (10,100), (90,30), (40,140), (220,220) ]
- >>> curve2 = [ (5,150), (180,20), (80,250), (210,190) ]
- >>> intersections = segmentSegmentIntersections(curve1, curve2)
- >>> len(intersections)
- 3
- >>> intersections[0].pt
- (81.7831487395506, 109.88904552375288)
- >>> curve3 = [ (100, 240), (30, 60), (210, 230), (160, 30) ]
- >>> line = [ (25, 260), (230, 20) ]
- >>> intersections = segmentSegmentIntersections(curve3, line)
- >>> len(intersections)
- 3
- >>> intersections[0].pt
- (84.9000930760723, 189.87306176459828)
-
- """
- # Arrange by degree
- swapped = False
- if len(seg2) > len(seg1):
- seg2, seg1 = seg1, seg2
- swapped = True
- if len(seg1) > 2:
- if len(seg2) > 2:
- intersections = curveCurveIntersections(seg1, seg2)
- else:
- intersections = curveLineIntersections(seg1, seg2)
- elif len(seg1) == 2 and len(seg2) == 2:
- intersections = lineLineIntersections(*seg1, *seg2)
- else:
- raise ValueError("Couldn't work out which intersection function to use")
- if not swapped:
- return intersections
- return [Intersection(pt=i.pt, t1=i.t2, t2=i.t1) for i in intersections]
-
-
-def _segmentrepr(obj):
- """
- >>> _segmentrepr([1, [2, 3], [], [[2, [3, 4], [0.1, 2.2]]]])
- '(1, (2, 3), (), ((2, (3, 4), (0.1, 2.2))))'
- """
- try:
- it = iter(obj)
- except TypeError:
- return "%g" % obj
- else:
- return "(%s)" % ", ".join(_segmentrepr(x) for x in it)
-
-
-def printSegments(segments):
- """Helper for the doctests, displaying each segment in a list of
- segments on a single line as a tuple.
- """
- for segment in segments:
- print(_segmentrepr(segment))
-
-
-if __name__ == "__main__":
- import sys
- import doctest
-
- sys.exit(doctest.testmod().failed)
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_m_o_r_x.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_m_o_r_x.py
deleted file mode 100644
index da299c6d85893e4113c459d503d77c6a120128ae..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_m_o_r_x.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-# https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6morx.html
-class table__m_o_r_x(BaseTTXConverter):
- pass
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/exceptions.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/exceptions.py
deleted file mode 100644
index 678ca7d5926798d1bd27363a019851717eee6e35..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/exceptions.py
+++ /dev/null
@@ -1,52 +0,0 @@
-from gradio_client.documentation import document, set_documentation_group
-
-set_documentation_group("helpers")
-
-
-class DuplicateBlockError(ValueError):
- """Raised when a Blocks contains more than one Block with the same id"""
-
- pass
-
-
-class TooManyRequestsError(Exception):
- """Raised when the Hugging Face API returns a 429 status code."""
-
- pass
-
-
-class InvalidApiNameError(ValueError):
- pass
-
-
-class ServerFailedToStartError(Exception):
- pass
-
-
-class InvalidBlockError(ValueError):
- """Raised when an event in a Blocks contains a reference to a Block that is not in the original Blocks"""
-
- pass
-
-
-InvalidApiName = InvalidApiNameError # backwards compatibility
-
-
-@document()
-class Error(Exception):
- """
- This class allows you to pass custom error messages to the user. You can do so by raising a gr.Error("custom message") anywhere in the code, and when that line is executed the custom message will appear in a modal on the demo.
-
- Demos: calculator
- """
-
- def __init__(self, message: str = "Error raised."):
- """
- Parameters:
- message: The error message to be displayed to the user.
- """
- self.message = message
- super().__init__(self.message)
-
- def __str__(self):
- return repr(self.message)
diff --git a/spaces/declare-lab/tango/diffusers/examples/dreambooth/train_dreambooth.py b/spaces/declare-lab/tango/diffusers/examples/dreambooth/train_dreambooth.py
deleted file mode 100644
index 7c02d154a0682de4855c1f9e99c47d0c5d1cb73a..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/examples/dreambooth/train_dreambooth.py
+++ /dev/null
@@ -1,1039 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-
-import argparse
-import hashlib
-import itertools
-import logging
-import math
-import os
-import warnings
-from pathlib import Path
-
-import accelerate
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-import transformers
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import ProjectConfiguration, set_seed
-from huggingface_hub import create_repo, upload_folder
-from packaging import version
-from PIL import Image
-from torch.utils.data import Dataset
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import AutoTokenizer, PretrainedConfig
-
-import diffusers
-from diffusers import (
- AutoencoderKL,
- DDPMScheduler,
- DiffusionPipeline,
- DPMSolverMultistepScheduler,
- UNet2DConditionModel,
-)
-from diffusers.optimization import get_scheduler
-from diffusers.utils import check_min_version, is_wandb_available
-from diffusers.utils.import_utils import is_xformers_available
-
-
-if is_wandb_available():
- import wandb
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.15.0.dev0")
-
-logger = get_logger(__name__)
-
-
-def log_validation(text_encoder, tokenizer, unet, vae, args, accelerator, weight_dtype, epoch):
- logger.info(
- f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
- f" {args.validation_prompt}."
- )
- # create pipeline (note: unet and vae are loaded again in float32)
- pipeline = DiffusionPipeline.from_pretrained(
- args.pretrained_model_name_or_path,
- text_encoder=accelerator.unwrap_model(text_encoder),
- tokenizer=tokenizer,
- unet=accelerator.unwrap_model(unet),
- vae=vae,
- revision=args.revision,
- torch_dtype=weight_dtype,
- )
- pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
- pipeline = pipeline.to(accelerator.device)
- pipeline.set_progress_bar_config(disable=True)
-
- # run inference
- generator = None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)
- images = []
- for _ in range(args.num_validation_images):
- with torch.autocast("cuda"):
- image = pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
- images.append(image)
-
- for tracker in accelerator.trackers:
- if tracker.name == "tensorboard":
- np_images = np.stack([np.asarray(img) for img in images])
- tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
- if tracker.name == "wandb":
- tracker.log(
- {
- "validation": [
- wandb.Image(image, caption=f"{i}: {args.validation_prompt}") for i, image in enumerate(images)
- ]
- }
- )
-
- del pipeline
- torch.cuda.empty_cache()
-
-
-def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
- text_encoder_config = PretrainedConfig.from_pretrained(
- pretrained_model_name_or_path,
- subfolder="text_encoder",
- revision=revision,
- )
- model_class = text_encoder_config.architectures[0]
-
- if model_class == "CLIPTextModel":
- from transformers import CLIPTextModel
-
- return CLIPTextModel
- elif model_class == "RobertaSeriesModelWithTransformation":
- from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
-
- return RobertaSeriesModelWithTransformation
- else:
- raise ValueError(f"{model_class} is not supported.")
-
-
-def parse_args(input_args=None):
- parser = argparse.ArgumentParser(description="Simple example of a training script.")
- parser.add_argument(
- "--pretrained_model_name_or_path",
- type=str,
- default=None,
- required=True,
- help="Path to pretrained model or model identifier from huggingface.co/models.",
- )
- parser.add_argument(
- "--revision",
- type=str,
- default=None,
- required=False,
- help=(
- "Revision of pretrained model identifier from huggingface.co/models. Trainable model components should be"
- " float32 precision."
- ),
- )
- parser.add_argument(
- "--tokenizer_name",
- type=str,
- default=None,
- help="Pretrained tokenizer name or path if not the same as model_name",
- )
- parser.add_argument(
- "--instance_data_dir",
- type=str,
- default=None,
- required=True,
- help="A folder containing the training data of instance images.",
- )
- parser.add_argument(
- "--class_data_dir",
- type=str,
- default=None,
- required=False,
- help="A folder containing the training data of class images.",
- )
- parser.add_argument(
- "--instance_prompt",
- type=str,
- default=None,
- required=True,
- help="The prompt with identifier specifying the instance",
- )
- parser.add_argument(
- "--class_prompt",
- type=str,
- default=None,
- help="The prompt to specify images in the same class as provided instance images.",
- )
- parser.add_argument(
- "--with_prior_preservation",
- default=False,
- action="store_true",
- help="Flag to add prior preservation loss.",
- )
- parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
- parser.add_argument(
- "--num_class_images",
- type=int,
- default=100,
- help=(
- "Minimal class images for prior preservation loss. If there are not enough images already present in"
- " class_data_dir, additional images will be sampled with class_prompt."
- ),
- )
- parser.add_argument(
- "--output_dir",
- type=str,
- default="text-inversion-model",
- help="The output directory where the model predictions and checkpoints will be written.",
- )
- parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
- parser.add_argument(
- "--resolution",
- type=int,
- default=512,
- help=(
- "The resolution for input images, all the images in the train/validation dataset will be resized to this"
- " resolution"
- ),
- )
- parser.add_argument(
- "--center_crop",
- default=False,
- action="store_true",
- help=(
- "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
- " cropped. The images will be resized to the resolution first before cropping."
- ),
- )
- parser.add_argument(
- "--train_text_encoder",
- action="store_true",
- help="Whether to train the text encoder. If set, the text encoder should be float32 precision.",
- )
- parser.add_argument(
- "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
- )
- parser.add_argument(
- "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
- )
- parser.add_argument("--num_train_epochs", type=int, default=1)
- parser.add_argument(
- "--max_train_steps",
- type=int,
- default=None,
- help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
- )
- parser.add_argument(
- "--checkpointing_steps",
- type=int,
- default=500,
- help=(
- "Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. "
- "In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference."
- "Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components."
- "See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step"
- "instructions."
- ),
- )
- parser.add_argument(
- "--checkpoints_total_limit",
- type=int,
- default=None,
- help=(
- "Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
- " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
- " for more details"
- ),
- )
- parser.add_argument(
- "--resume_from_checkpoint",
- type=str,
- default=None,
- help=(
- "Whether training should be resumed from a previous checkpoint. Use a path saved by"
- ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
- ),
- )
- parser.add_argument(
- "--gradient_accumulation_steps",
- type=int,
- default=1,
- help="Number of updates steps to accumulate before performing a backward/update pass.",
- )
- parser.add_argument(
- "--gradient_checkpointing",
- action="store_true",
- help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
- )
- parser.add_argument(
- "--learning_rate",
- type=float,
- default=5e-6,
- help="Initial learning rate (after the potential warmup period) to use.",
- )
- parser.add_argument(
- "--scale_lr",
- action="store_true",
- default=False,
- help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
- )
- parser.add_argument(
- "--lr_scheduler",
- type=str,
- default="constant",
- help=(
- 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
- ' "constant", "constant_with_warmup"]'
- ),
- )
- parser.add_argument(
- "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
- )
- parser.add_argument(
- "--lr_num_cycles",
- type=int,
- default=1,
- help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
- )
- parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
- parser.add_argument(
- "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
- )
- parser.add_argument(
- "--dataloader_num_workers",
- type=int,
- default=0,
- help=(
- "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
- ),
- )
- parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
- parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
- parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
- parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
- parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
- parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
- parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
- parser.add_argument(
- "--hub_model_id",
- type=str,
- default=None,
- help="The name of the repository to keep in sync with the local `output_dir`.",
- )
- parser.add_argument(
- "--logging_dir",
- type=str,
- default="logs",
- help=(
- "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
- " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
- ),
- )
- parser.add_argument(
- "--allow_tf32",
- action="store_true",
- help=(
- "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
- " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
- ),
- )
- parser.add_argument(
- "--report_to",
- type=str,
- default="tensorboard",
- help=(
- 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
- ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
- ),
- )
- parser.add_argument(
- "--validation_prompt",
- type=str,
- default=None,
- help="A prompt that is used during validation to verify that the model is learning.",
- )
- parser.add_argument(
- "--num_validation_images",
- type=int,
- default=4,
- help="Number of images that should be generated during validation with `validation_prompt`.",
- )
- parser.add_argument(
- "--validation_steps",
- type=int,
- default=100,
- help=(
- "Run validation every X steps. Validation consists of running the prompt"
- " `args.validation_prompt` multiple times: `args.num_validation_images`"
- " and logging the images."
- ),
- )
- parser.add_argument(
- "--mixed_precision",
- type=str,
- default=None,
- choices=["no", "fp16", "bf16"],
- help=(
- "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
- " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
- " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
- ),
- )
- parser.add_argument(
- "--prior_generation_precision",
- type=str,
- default=None,
- choices=["no", "fp32", "fp16", "bf16"],
- help=(
- "Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
- " 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32."
- ),
- )
- parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
- parser.add_argument(
- "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
- )
- parser.add_argument(
- "--set_grads_to_none",
- action="store_true",
- help=(
- "Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
- " behaviors, so disable this argument if it causes any problems. More info:"
- " https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
- ),
- )
-
- parser.add_argument(
- "--offset_noise",
- action="store_true",
- default=False,
- help=(
- "Fine-tuning against a modified noise"
- " See: https://www.crosslabs.org//blog/diffusion-with-offset-noise for more information."
- ),
- )
-
- if input_args is not None:
- args = parser.parse_args(input_args)
- else:
- args = parser.parse_args()
-
- env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
- if env_local_rank != -1 and env_local_rank != args.local_rank:
- args.local_rank = env_local_rank
-
- if args.with_prior_preservation:
- if args.class_data_dir is None:
- raise ValueError("You must specify a data directory for class images.")
- if args.class_prompt is None:
- raise ValueError("You must specify prompt for class images.")
- else:
- # logger is not available yet
- if args.class_data_dir is not None:
- warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
- if args.class_prompt is not None:
- warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
-
- return args
-
-
-class DreamBoothDataset(Dataset):
- """
- A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
- It pre-processes the images and the tokenizes prompts.
- """
-
- def __init__(
- self,
- instance_data_root,
- instance_prompt,
- tokenizer,
- class_data_root=None,
- class_prompt=None,
- class_num=None,
- size=512,
- center_crop=False,
- ):
- self.size = size
- self.center_crop = center_crop
- self.tokenizer = tokenizer
-
- self.instance_data_root = Path(instance_data_root)
- if not self.instance_data_root.exists():
- raise ValueError(f"Instance {self.instance_data_root} images root doesn't exists.")
-
- self.instance_images_path = list(Path(instance_data_root).iterdir())
- self.num_instance_images = len(self.instance_images_path)
- self.instance_prompt = instance_prompt
- self._length = self.num_instance_images
-
- if class_data_root is not None:
- self.class_data_root = Path(class_data_root)
- self.class_data_root.mkdir(parents=True, exist_ok=True)
- self.class_images_path = list(self.class_data_root.iterdir())
- if class_num is not None:
- self.num_class_images = min(len(self.class_images_path), class_num)
- else:
- self.num_class_images = len(self.class_images_path)
- self._length = max(self.num_class_images, self.num_instance_images)
- self.class_prompt = class_prompt
- else:
- self.class_data_root = None
-
- self.image_transforms = transforms.Compose(
- [
- transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
- transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
- transforms.ToTensor(),
- transforms.Normalize([0.5], [0.5]),
- ]
- )
-
- def __len__(self):
- return self._length
-
- def __getitem__(self, index):
- example = {}
- instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
- if not instance_image.mode == "RGB":
- instance_image = instance_image.convert("RGB")
- example["instance_images"] = self.image_transforms(instance_image)
- example["instance_prompt_ids"] = self.tokenizer(
- self.instance_prompt,
- truncation=True,
- padding="max_length",
- max_length=self.tokenizer.model_max_length,
- return_tensors="pt",
- ).input_ids
-
- if self.class_data_root:
- class_image = Image.open(self.class_images_path[index % self.num_class_images])
- if not class_image.mode == "RGB":
- class_image = class_image.convert("RGB")
- example["class_images"] = self.image_transforms(class_image)
- example["class_prompt_ids"] = self.tokenizer(
- self.class_prompt,
- truncation=True,
- padding="max_length",
- max_length=self.tokenizer.model_max_length,
- return_tensors="pt",
- ).input_ids
-
- return example
-
-
-def collate_fn(examples, with_prior_preservation=False):
- input_ids = [example["instance_prompt_ids"] for example in examples]
- pixel_values = [example["instance_images"] for example in examples]
-
- # Concat class and instance examples for prior preservation.
- # We do this to avoid doing two forward passes.
- if with_prior_preservation:
- input_ids += [example["class_prompt_ids"] for example in examples]
- pixel_values += [example["class_images"] for example in examples]
-
- pixel_values = torch.stack(pixel_values)
- pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
-
- input_ids = torch.cat(input_ids, dim=0)
-
- batch = {
- "input_ids": input_ids,
- "pixel_values": pixel_values,
- }
- return batch
-
-
-class PromptDataset(Dataset):
- "A simple dataset to prepare the prompts to generate class images on multiple GPUs."
-
- def __init__(self, prompt, num_samples):
- self.prompt = prompt
- self.num_samples = num_samples
-
- def __len__(self):
- return self.num_samples
-
- def __getitem__(self, index):
- example = {}
- example["prompt"] = self.prompt
- example["index"] = index
- return example
-
-
-def main(args):
- logging_dir = Path(args.output_dir, args.logging_dir)
-
- accelerator_project_config = ProjectConfiguration(total_limit=args.checkpoints_total_limit)
-
- accelerator = Accelerator(
- gradient_accumulation_steps=args.gradient_accumulation_steps,
- mixed_precision=args.mixed_precision,
- log_with=args.report_to,
- logging_dir=logging_dir,
- project_config=accelerator_project_config,
- )
-
- if args.report_to == "wandb":
- if not is_wandb_available():
- raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
-
- # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
- # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
- # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
- if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
- raise ValueError(
- "Gradient accumulation is not supported when training the text encoder in distributed training. "
- "Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
- )
-
- # Make one log on every process with the configuration for debugging.
- logging.basicConfig(
- format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
- datefmt="%m/%d/%Y %H:%M:%S",
- level=logging.INFO,
- )
- logger.info(accelerator.state, main_process_only=False)
- if accelerator.is_local_main_process:
- transformers.utils.logging.set_verbosity_warning()
- diffusers.utils.logging.set_verbosity_info()
- else:
- transformers.utils.logging.set_verbosity_error()
- diffusers.utils.logging.set_verbosity_error()
-
- # If passed along, set the training seed now.
- if args.seed is not None:
- set_seed(args.seed)
-
- # Generate class images if prior preservation is enabled.
- if args.with_prior_preservation:
- class_images_dir = Path(args.class_data_dir)
- if not class_images_dir.exists():
- class_images_dir.mkdir(parents=True)
- cur_class_images = len(list(class_images_dir.iterdir()))
-
- if cur_class_images < args.num_class_images:
- torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
- if args.prior_generation_precision == "fp32":
- torch_dtype = torch.float32
- elif args.prior_generation_precision == "fp16":
- torch_dtype = torch.float16
- elif args.prior_generation_precision == "bf16":
- torch_dtype = torch.bfloat16
- pipeline = DiffusionPipeline.from_pretrained(
- args.pretrained_model_name_or_path,
- torch_dtype=torch_dtype,
- safety_checker=None,
- revision=args.revision,
- )
- pipeline.set_progress_bar_config(disable=True)
-
- num_new_images = args.num_class_images - cur_class_images
- logger.info(f"Number of class images to sample: {num_new_images}.")
-
- sample_dataset = PromptDataset(args.class_prompt, num_new_images)
- sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
-
- sample_dataloader = accelerator.prepare(sample_dataloader)
- pipeline.to(accelerator.device)
-
- for example in tqdm(
- sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
- ):
- images = pipeline(example["prompt"]).images
-
- for i, image in enumerate(images):
- hash_image = hashlib.sha1(image.tobytes()).hexdigest()
- image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
- image.save(image_filename)
-
- del pipeline
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
-
- # Handle the repository creation
- if accelerator.is_main_process:
- if args.output_dir is not None:
- os.makedirs(args.output_dir, exist_ok=True)
-
- if args.push_to_hub:
- repo_id = create_repo(
- repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
- ).repo_id
-
- # Load the tokenizer
- if args.tokenizer_name:
- tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
- elif args.pretrained_model_name_or_path:
- tokenizer = AutoTokenizer.from_pretrained(
- args.pretrained_model_name_or_path,
- subfolder="tokenizer",
- revision=args.revision,
- use_fast=False,
- )
-
- # import correct text encoder class
- text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
-
- # Load scheduler and models
- noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
- text_encoder = text_encoder_cls.from_pretrained(
- args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
- )
- vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
- unet = UNet2DConditionModel.from_pretrained(
- args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
- )
-
- # `accelerate` 0.16.0 will have better support for customized saving
- if version.parse(accelerate.__version__) >= version.parse("0.16.0"):
- # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
- def save_model_hook(models, weights, output_dir):
- for model in models:
- sub_dir = "unet" if type(model) == type(unet) else "text_encoder"
- model.save_pretrained(os.path.join(output_dir, sub_dir))
-
- # make sure to pop weight so that corresponding model is not saved again
- weights.pop()
-
- def load_model_hook(models, input_dir):
- while len(models) > 0:
- # pop models so that they are not loaded again
- model = models.pop()
-
- if type(model) == type(text_encoder):
- # load transformers style into model
- load_model = text_encoder_cls.from_pretrained(input_dir, subfolder="text_encoder")
- model.config = load_model.config
- else:
- # load diffusers style into model
- load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet")
- model.register_to_config(**load_model.config)
-
- model.load_state_dict(load_model.state_dict())
- del load_model
-
- accelerator.register_save_state_pre_hook(save_model_hook)
- accelerator.register_load_state_pre_hook(load_model_hook)
-
- vae.requires_grad_(False)
- if not args.train_text_encoder:
- text_encoder.requires_grad_(False)
-
- if args.enable_xformers_memory_efficient_attention:
- if is_xformers_available():
- import xformers
-
- xformers_version = version.parse(xformers.__version__)
- if xformers_version == version.parse("0.0.16"):
- logger.warn(
- "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
- )
- unet.enable_xformers_memory_efficient_attention()
- else:
- raise ValueError("xformers is not available. Make sure it is installed correctly")
-
- if args.gradient_checkpointing:
- unet.enable_gradient_checkpointing()
- if args.train_text_encoder:
- text_encoder.gradient_checkpointing_enable()
-
- # Check that all trainable models are in full precision
- low_precision_error_string = (
- "Please make sure to always have all model weights in full float32 precision when starting training - even if"
- " doing mixed precision training. copy of the weights should still be float32."
- )
-
- if accelerator.unwrap_model(unet).dtype != torch.float32:
- raise ValueError(
- f"Unet loaded as datatype {accelerator.unwrap_model(unet).dtype}. {low_precision_error_string}"
- )
-
- if args.train_text_encoder and accelerator.unwrap_model(text_encoder).dtype != torch.float32:
- raise ValueError(
- f"Text encoder loaded as datatype {accelerator.unwrap_model(text_encoder).dtype}."
- f" {low_precision_error_string}"
- )
-
- # Enable TF32 for faster training on Ampere GPUs,
- # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
- if args.allow_tf32:
- torch.backends.cuda.matmul.allow_tf32 = True
-
- if args.scale_lr:
- args.learning_rate = (
- args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
- )
-
- # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
- if args.use_8bit_adam:
- try:
- import bitsandbytes as bnb
- except ImportError:
- raise ImportError(
- "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
- )
-
- optimizer_class = bnb.optim.AdamW8bit
- else:
- optimizer_class = torch.optim.AdamW
-
- # Optimizer creation
- params_to_optimize = (
- itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters()
- )
- optimizer = optimizer_class(
- params_to_optimize,
- lr=args.learning_rate,
- betas=(args.adam_beta1, args.adam_beta2),
- weight_decay=args.adam_weight_decay,
- eps=args.adam_epsilon,
- )
-
- # Dataset and DataLoaders creation:
- train_dataset = DreamBoothDataset(
- instance_data_root=args.instance_data_dir,
- instance_prompt=args.instance_prompt,
- class_data_root=args.class_data_dir if args.with_prior_preservation else None,
- class_prompt=args.class_prompt,
- class_num=args.num_class_images,
- tokenizer=tokenizer,
- size=args.resolution,
- center_crop=args.center_crop,
- )
-
- train_dataloader = torch.utils.data.DataLoader(
- train_dataset,
- batch_size=args.train_batch_size,
- shuffle=True,
- collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
- num_workers=args.dataloader_num_workers,
- )
-
- # Scheduler and math around the number of training steps.
- overrode_max_train_steps = False
- num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
- if args.max_train_steps is None:
- args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
- overrode_max_train_steps = True
-
- lr_scheduler = get_scheduler(
- args.lr_scheduler,
- optimizer=optimizer,
- num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
- num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
- num_cycles=args.lr_num_cycles,
- power=args.lr_power,
- )
-
- # Prepare everything with our `accelerator`.
- if args.train_text_encoder:
- unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
- unet, text_encoder, optimizer, train_dataloader, lr_scheduler
- )
- else:
- unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
- unet, optimizer, train_dataloader, lr_scheduler
- )
-
- # For mixed precision training we cast the text_encoder and vae weights to half-precision
- # as these models are only used for inference, keeping weights in full precision is not required.
- weight_dtype = torch.float32
- if accelerator.mixed_precision == "fp16":
- weight_dtype = torch.float16
- elif accelerator.mixed_precision == "bf16":
- weight_dtype = torch.bfloat16
-
- # Move vae and text_encoder to device and cast to weight_dtype
- vae.to(accelerator.device, dtype=weight_dtype)
- if not args.train_text_encoder:
- text_encoder.to(accelerator.device, dtype=weight_dtype)
-
- # We need to recalculate our total training steps as the size of the training dataloader may have changed.
- num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
- if overrode_max_train_steps:
- args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
- # Afterwards we recalculate our number of training epochs
- args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
- # We need to initialize the trackers we use, and also store our configuration.
- # The trackers initializes automatically on the main process.
- if accelerator.is_main_process:
- accelerator.init_trackers("dreambooth", config=vars(args))
-
- # Train!
- total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-
- logger.info("***** Running training *****")
- logger.info(f" Num examples = {len(train_dataset)}")
- logger.info(f" Num batches each epoch = {len(train_dataloader)}")
- logger.info(f" Num Epochs = {args.num_train_epochs}")
- logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
- logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
- logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
- logger.info(f" Total optimization steps = {args.max_train_steps}")
- global_step = 0
- first_epoch = 0
-
- # Potentially load in the weights and states from a previous save
- if args.resume_from_checkpoint:
- if args.resume_from_checkpoint != "latest":
- path = os.path.basename(args.resume_from_checkpoint)
- else:
- # Get the mos recent checkpoint
- dirs = os.listdir(args.output_dir)
- dirs = [d for d in dirs if d.startswith("checkpoint")]
- dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
- path = dirs[-1] if len(dirs) > 0 else None
-
- if path is None:
- accelerator.print(
- f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
- )
- args.resume_from_checkpoint = None
- else:
- accelerator.print(f"Resuming from checkpoint {path}")
- accelerator.load_state(os.path.join(args.output_dir, path))
- global_step = int(path.split("-")[1])
-
- resume_global_step = global_step * args.gradient_accumulation_steps
- first_epoch = global_step // num_update_steps_per_epoch
- resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
-
- # Only show the progress bar once on each machine.
- progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
- progress_bar.set_description("Steps")
-
- for epoch in range(first_epoch, args.num_train_epochs):
- unet.train()
- if args.train_text_encoder:
- text_encoder.train()
- for step, batch in enumerate(train_dataloader):
- # Skip steps until we reach the resumed step
- if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
- if step % args.gradient_accumulation_steps == 0:
- progress_bar.update(1)
- continue
-
- with accelerator.accumulate(unet):
- # Convert images to latent space
- latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
- latents = latents * vae.config.scaling_factor
-
- # Sample noise that we'll add to the latents
- if args.offset_noise:
- noise = torch.randn_like(latents) + 0.1 * torch.randn(
- latents.shape[0], latents.shape[1], 1, 1, device=latents.device
- )
- else:
- noise = torch.randn_like(latents)
- bsz = latents.shape[0]
- # Sample a random timestep for each image
- timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
- timesteps = timesteps.long()
-
- # Add noise to the latents according to the noise magnitude at each timestep
- # (this is the forward diffusion process)
- noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-
- # Get the text embedding for conditioning
- encoder_hidden_states = text_encoder(batch["input_ids"])[0]
-
- # Predict the noise residual
- model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
-
- # Get the target for loss depending on the prediction type
- if noise_scheduler.config.prediction_type == "epsilon":
- target = noise
- elif noise_scheduler.config.prediction_type == "v_prediction":
- target = noise_scheduler.get_velocity(latents, noise, timesteps)
- else:
- raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-
- if args.with_prior_preservation:
- # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
- model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
- target, target_prior = torch.chunk(target, 2, dim=0)
-
- # Compute instance loss
- loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-
- # Compute prior loss
- prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
-
- # Add the prior loss to the instance loss.
- loss = loss + args.prior_loss_weight * prior_loss
- else:
- loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-
- accelerator.backward(loss)
- if accelerator.sync_gradients:
- params_to_clip = (
- itertools.chain(unet.parameters(), text_encoder.parameters())
- if args.train_text_encoder
- else unet.parameters()
- )
- accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
- optimizer.step()
- lr_scheduler.step()
- optimizer.zero_grad(set_to_none=args.set_grads_to_none)
-
- # Checks if the accelerator has performed an optimization step behind the scenes
- if accelerator.sync_gradients:
- progress_bar.update(1)
- global_step += 1
-
- if accelerator.is_main_process:
- if global_step % args.checkpointing_steps == 0:
- save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
- accelerator.save_state(save_path)
- logger.info(f"Saved state to {save_path}")
-
- if args.validation_prompt is not None and global_step % args.validation_steps == 0:
- log_validation(text_encoder, tokenizer, unet, vae, args, accelerator, weight_dtype, epoch)
-
- logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
- progress_bar.set_postfix(**logs)
- accelerator.log(logs, step=global_step)
-
- if global_step >= args.max_train_steps:
- break
-
- # Create the pipeline using using the trained modules and save it.
- accelerator.wait_for_everyone()
- if accelerator.is_main_process:
- pipeline = DiffusionPipeline.from_pretrained(
- args.pretrained_model_name_or_path,
- unet=accelerator.unwrap_model(unet),
- text_encoder=accelerator.unwrap_model(text_encoder),
- revision=args.revision,
- )
- pipeline.save_pretrained(args.output_dir)
-
- if args.push_to_hub:
- upload_folder(
- repo_id=repo_id,
- folder_path=args.output_dir,
- commit_message="End of training",
- ignore_patterns=["step_*", "epoch_*"],
- )
-
- accelerator.end_training()
-
-
-if __name__ == "__main__":
- args = parse_args()
- main(args)
diff --git a/spaces/descript/vampnet/scripts/utils/gtzan_embeddings.py b/spaces/descript/vampnet/scripts/utils/gtzan_embeddings.py
deleted file mode 100644
index 78a6e318fbba98355fb48aa6ea1c74b0b83ff287..0000000000000000000000000000000000000000
--- a/spaces/descript/vampnet/scripts/utils/gtzan_embeddings.py
+++ /dev/null
@@ -1,263 +0,0 @@
-"""
-TODO: train a linear probe
-usage:
- python gtzan_embeddings.py --args.load conf/interface.yml --Interface.device cuda --path_to_gtzan /path/to/gtzan/genres_original --output_dir /path/to/output
-"""
-from pathlib import Path
-from typing import List
-
-import audiotools as at
-from audiotools import AudioSignal
-import argbind
-import torch
-import numpy as np
-import zipfile
-import json
-
-from vampnet.interface import Interface
-import tqdm
-
-# bind the Interface to argbind
-Interface = argbind.bind(Interface)
-
-DEBUG = False
-
-def smart_plotly_export(fig, save_path):
- img_format = save_path.split('.')[-1]
- if img_format == 'html':
- fig.write_html(save_path)
- elif img_format == 'bytes':
- return fig.to_image(format='png')
- #TODO: come back and make this prettier
- elif img_format == 'numpy':
- import io
- from PIL import Image
-
- def plotly_fig2array(fig):
- #convert Plotly fig to an array
- fig_bytes = fig.to_image(format="png", width=1200, height=700)
- buf = io.BytesIO(fig_bytes)
- img = Image.open(buf)
- return np.asarray(img)
-
- return plotly_fig2array(fig)
- elif img_format == 'jpeg' or 'png' or 'webp':
- fig.write_image(save_path)
- else:
- raise ValueError("invalid image format")
-
-def dim_reduce(emb, labels, save_path, n_components=3, method='tsne', title=''):
- """
- dimensionality reduction for visualization!
- saves an html plotly figure to save_path
- parameters:
- emb (np.ndarray): the samples to be reduces with shape (samples, features)
- labels (list): list of labels for embedding
- save_path (str): path where u wanna save ur figure
- method (str): umap, tsne, or pca
- title (str): title for ur figure
- returns:
- proj (np.ndarray): projection vector with shape (samples, dimensions)
- """
- import pandas as pd
- import plotly.express as px
- if method == 'umap':
- reducer = umap.UMAP(n_components=n_components)
- elif method == 'tsne':
- from sklearn.manifold import TSNE
- reducer = TSNE(n_components=n_components)
- elif method == 'pca':
- from sklearn.decomposition import PCA
- reducer = PCA(n_components=n_components)
- else:
- raise ValueError
-
- proj = reducer.fit_transform(emb)
-
- if n_components == 2:
- df = pd.DataFrame(dict(
- x=proj[:, 0],
- y=proj[:, 1],
- instrument=labels
- ))
- fig = px.scatter(df, x='x', y='y', color='instrument',
- title=title+f"_{method}")
-
- elif n_components == 3:
- df = pd.DataFrame(dict(
- x=proj[:, 0],
- y=proj[:, 1],
- z=proj[:, 2],
- instrument=labels
- ))
- fig = px.scatter_3d(df, x='x', y='y', z='z',
- color='instrument',
- title=title)
- else:
- raise ValueError("cant plot more than 3 components")
-
- fig.update_traces(marker=dict(size=6,
- line=dict(width=1,
- color='DarkSlateGrey')),
- selector=dict(mode='markers'))
-
- return smart_plotly_export(fig, save_path)
-
-
-
-# per JukeMIR, we want the emebddings from the middle layer?
-def vampnet_embed(sig: AudioSignal, interface: Interface, layer=10):
- with torch.inference_mode():
- # preprocess the signal
- sig = interface.preprocess(sig)
-
- # get the coarse vampnet model
- vampnet = interface.coarse
-
- # get the tokens
- z = interface.encode(sig)[:, :vampnet.n_codebooks, :]
- z_latents = vampnet.embedding.from_codes(z, interface.codec)
-
- # do a forward pass through the model, get the embeddings
- _z, embeddings = vampnet(z_latents, return_activations=True)
- # print(f"got embeddings with shape {embeddings.shape}")
- # [layer, batch, time, n_dims]
- # [20, 1, 600ish, 768]
-
-
- # squeeze batch dim (1 bc layer should be dim 0)
- assert embeddings.shape[1] == 1, f"expected batch dim to be 1, got {embeddings.shape[0]}"
- embeddings = embeddings.squeeze(1)
-
- num_layers = embeddings.shape[0]
- assert layer < num_layers, f"layer {layer} is out of bounds for model with {num_layers} layers"
-
- # do meanpooling over the time dimension
- embeddings = embeddings.mean(dim=-2)
- # [20, 768]
-
- # return the embeddings
- return embeddings
-
-from dataclasses import dataclass, fields
-@dataclass
-class Embedding:
- genre: str
- filename: str
- embedding: np.ndarray
-
- def save(self, path):
- """Save the Embedding object to a given path as a zip file."""
- with zipfile.ZipFile(path, 'w') as archive:
-
- # Save numpy array
- with archive.open('embedding.npy', 'w') as f:
- np.save(f, self.embedding)
-
- # Save non-numpy data as json
- non_numpy_data = {f.name: getattr(self, f.name) for f in fields(self) if f.name != 'embedding'}
- with archive.open('data.json', 'w') as f:
- f.write(json.dumps(non_numpy_data).encode('utf-8'))
-
- @classmethod
- def load(cls, path):
- """Load the Embedding object from a given zip path."""
- with zipfile.ZipFile(path, 'r') as archive:
-
- # Load numpy array
- with archive.open('embedding.npy') as f:
- embedding = np.load(f)
-
- # Load non-numpy data from json
- with archive.open('data.json') as f:
- data = json.loads(f.read().decode('utf-8'))
-
- return cls(embedding=embedding, **data)
-
-
-@argbind.bind(without_prefix=True)
-def main(
- path_to_gtzan: str = None,
- cache_dir: str = "./.gtzan_emb_cache",
- output_dir: str = "./gtzan_vampnet_embeddings",
- layers: List[int] = [1, 3, 5, 7, 9, 11, 13, 15, 17, 19]
-):
- path_to_gtzan = Path(path_to_gtzan)
- assert path_to_gtzan.exists(), f"{path_to_gtzan} does not exist"
-
- cache_dir = Path(cache_dir)
- output_dir = Path(output_dir)
- output_dir.mkdir(exist_ok=True, parents=True)
-
- # load our interface
- # argbind will automatically load the default config,
- interface = Interface()
-
- # gtzan should have a folder for each genre, so let's get the list of genres
- genres = [Path(x).name for x in path_to_gtzan.iterdir() if x.is_dir()]
- print(f"Found {len(genres)} genres")
- print(f"genres: {genres}")
-
- # collect audio files, genres, and embeddings
- data = []
- for genre in genres:
- audio_files = list(at.util.find_audio(path_to_gtzan / genre))
- print(f"Found {len(audio_files)} audio files for genre {genre}")
-
- for audio_file in tqdm.tqdm(audio_files, desc=f"embedding genre {genre}"):
- # check if we have a cached embedding for this file
- cached_path = (cache_dir / f"{genre}_{audio_file.stem}.emb")
- if cached_path.exists():
- # if so, load it
- if DEBUG:
- print(f"loading cached embedding for {cached_path.stem}")
- embedding = Embedding.load(cached_path)
- data.append(embedding)
- else:
- try:
- sig = AudioSignal(audio_file)
- except Exception as e:
- print(f"failed to load {audio_file.name} with error {e}")
- print(f"skipping {audio_file.name}")
- continue
-
- # gets the embedding
- emb = vampnet_embed(sig, interface).cpu().numpy()
-
- # create an embedding we can save/load
- embedding = Embedding(
- genre=genre,
- filename=audio_file.name,
- embedding=emb
- )
-
- # cache the embeddings
- cached_path.parent.mkdir(exist_ok=True, parents=True)
- embedding.save(cached_path)
-
- # now, let's do a dim reduction on the embeddings
- # and visualize them.
-
- # collect a list of embeddings and labels
- embeddings = [d.embedding for d in data]
- labels = [d.genre for d in data]
-
- # convert the embeddings to a numpy array
- embeddings = np.stack(embeddings)
-
- # do dimensionality reduction for each layer we're given
- for layer in tqdm.tqdm(layers, desc="dim reduction"):
- dim_reduce(
- embeddings[:, layer, :], labels,
- save_path=str(output_dir / f'vampnet-gtzan-layer={layer}.html'),
- n_components=2, method='tsne',
- title=f'vampnet-gtzan-layer={layer}'
- )
-
-
-
-
-if __name__ == "__main__":
- args = argbind.parse_args()
- with argbind.scope(args):
- main()
\ No newline at end of file
diff --git a/spaces/diacanFperku/AutoGPT/Amagami Ss Pc Game English Download 113 PORTABLE.md b/spaces/diacanFperku/AutoGPT/Amagami Ss Pc Game English Download 113 PORTABLE.md
deleted file mode 100644
index f948efe4555d2e857f8956b8bf653a8b06e71474..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Amagami Ss Pc Game English Download 113 PORTABLE.md
+++ /dev/null
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-amagami ss pc game english download 113 Download Zip ✸ https://gohhs.com/2uFUDd
diff --git a/spaces/diacanFperku/AutoGPT/Bygg.Biler.Med.Mulle.Mekk.NORWEGiAN-NORBiTS PORTABLE.md b/spaces/diacanFperku/AutoGPT/Bygg.Biler.Med.Mulle.Mekk.NORWEGiAN-NORBiTS PORTABLE.md
deleted file mode 100644
index e7e4e16b564098e95e2d60146e0311f5d5e0370b..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Bygg.Biler.Med.Mulle.Mekk.NORWEGiAN-NORBiTS PORTABLE.md
+++ /dev/null
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-Bygg.Biler.Med.Mulle.Mekk.NORWEGiAN-NORBiTS Download Zip ->>> https://gohhs.com/2uFVuH
diff --git a/spaces/diacanFperku/AutoGPT/Download KiGO Primo V2.4 Middle East Maps _BEST_.md b/spaces/diacanFperku/AutoGPT/Download KiGO Primo V2.4 Middle East Maps _BEST_.md
deleted file mode 100644
index d50e650363e751caa5b88534c5be6e4ab05b9b15..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Download KiGO Primo V2.4 Middle East Maps _BEST_.md
+++ /dev/null
@@ -1,30 +0,0 @@
-How to Download k:iGO Primo v2.4 Middle East Maps for Your GPS Device
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-download k:iGO Primo v2.4 Middle East Maps Download Zip ››› https://gohhs.com/2uFSPk
k:iGO Primo v2.4 Middle East Maps is a premium software that offers high-quality 3D graphics, realistic landmarks and buildings, accurate routing and guidance, speed camera alerts, points of interest, voice recognition and more. It also supports online services such as weather information, traffic updates, fuel prices and parking availability. You can customize the software according to your preferences and needs, such as changing the language, units, map colors, vehicle icons and more.
-To download k:iGO Primo v2.4 Middle East Maps for your GPS device, you will need to follow these steps:
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-k:iGO Primo offers a user-friendly and intuitive interface that allows you to easily access all the functions and settings of the software. You can choose from different modes of navigation, such as car, truck, pedestrian, bicycle or public transport. You can also plan your route according to various criteria, such as fastest, shortest, economical or green. You can also avoid toll roads, ferries, highways or unpaved roads if you wish.
-k:iGO Primo also provides you with detailed and accurate maps of the Middle East region, which are updated regularly to reflect the latest changes and developments. You can view the maps in 2D or 3D mode, and zoom in or out as you like. You can also see realistic 3D representations of landmarks and buildings, which help you to orient yourself and recognize your surroundings. You can also switch to night mode or day mode depending on the time of day.
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diff --git a/spaces/dineshreddy/WALT/mmdet/models/detectors/fovea.py b/spaces/dineshreddy/WALT/mmdet/models/detectors/fovea.py
deleted file mode 100644
index 22a578efffbd108db644d907bae95c7c8df31f2e..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/models/detectors/fovea.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from ..builder import DETECTORS
-from .single_stage import SingleStageDetector
-
-
-@DETECTORS.register_module()
-class FOVEA(SingleStageDetector):
- """Implementation of `FoveaBox `_"""
-
- def __init__(self,
- backbone,
- neck,
- bbox_head,
- train_cfg=None,
- test_cfg=None,
- pretrained=None):
- super(FOVEA, self).__init__(backbone, neck, bbox_head, train_cfg,
- test_cfg, pretrained)
diff --git a/spaces/dirge/voicevox/build_util/codesign.bash b/spaces/dirge/voicevox/build_util/codesign.bash
deleted file mode 100644
index f8f79f99c6700edff198b60b44aac11960c7f62d..0000000000000000000000000000000000000000
--- a/spaces/dirge/voicevox/build_util/codesign.bash
+++ /dev/null
@@ -1,49 +0,0 @@
-# !!! コードサイニング証明書を取り扱うので取り扱い注意 !!!
-
-set -eu
-
-if [ ! -v CERT_BASE64 ]; then
- echo "CERT_BASE64が未定義です"
- exit 1
-fi
-if [ ! -v CERT_PASSWORD ]; then
- echo "CERT_PASSWORDが未定義です"
- exit 1
-fi
-
-if [ $# -ne 1 ]; then
- echo "引数の数が一致しません"
- exit 1
-fi
-target_file_glob="$1"
-
-# 証明書
-CERT_PATH=cert.pfx
-echo -n "$CERT_BASE64" | base64 -d - > $CERT_PATH
-
-# 指定ファイルに署名する
-function codesign() {
- TARGET="$1"
- SIGNTOOL=$(find "C:/Program Files (x86)/Windows Kits/10/App Certification Kit" -name "signtool.exe" | sort -V | tail -n 1)
- powershell "& '$SIGNTOOL' sign /fd SHA256 /td SHA256 /tr http://timestamp.digicert.com /f $CERT_PATH /p $CERT_PASSWORD '$TARGET'"
-}
-
-# 指定ファイルが署名されているか
-function is_signed() {
- TARGET="$1"
- SIGNTOOL=$(find "C:/Program Files (x86)/Windows Kits/10/App Certification Kit" -name "signtool.exe" | sort -V | tail -n 1)
- powershell "& '$SIGNTOOL' verify /pa '$TARGET'" || return 1
-}
-
-# 署名されていなければ署名
-ls $target_file_glob | while read target_file; do
- if is_signed "$target_file"; then
- echo "署名済み: $target_file"
- else
- echo "署名: $target_file"
- codesign "$target_file"
- fi
-done
-
-# 証明書を消去
-rm $CERT_PATH
diff --git a/spaces/dragao-elastico/RVC_V2/vc_infer_pipeline.py b/spaces/dragao-elastico/RVC_V2/vc_infer_pipeline.py
deleted file mode 100644
index a0b50d4c703b7638d7c951c9d820a1e59c275fc3..0000000000000000000000000000000000000000
--- a/spaces/dragao-elastico/RVC_V2/vc_infer_pipeline.py
+++ /dev/null
@@ -1,646 +0,0 @@
-import numpy as np, parselmouth, torch, pdb, sys, os
-from time import time as ttime
-import torch.nn.functional as F
-import torchcrepe # Fork feature. Use the crepe f0 algorithm. New dependency (pip install torchcrepe)
-from torch import Tensor
-import scipy.signal as signal
-import pyworld, os, traceback, faiss, librosa, torchcrepe
-from scipy import signal
-from functools import lru_cache
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-
-bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
-
-input_audio_path2wav = {}
-
-
-@lru_cache
-def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
- audio = input_audio_path2wav[input_audio_path]
- f0, t = pyworld.harvest(
- audio,
- fs=fs,
- f0_ceil=f0max,
- f0_floor=f0min,
- frame_period=frame_period,
- )
- f0 = pyworld.stonemask(audio, f0, t, fs)
- return f0
-
-
-def change_rms(data1, sr1, data2, sr2, rate): # 1是输入音频，2是输出音频,rate是2的占比
- # print(data1.max(),data2.max())
- rms1 = librosa.feature.rms(
- y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
- ) # 每半秒一个点
- rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
- rms1 = torch.from_numpy(rms1)
- rms1 = F.interpolate(
- rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
- ).squeeze()
- rms2 = torch.from_numpy(rms2)
- rms2 = F.interpolate(
- rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
- ).squeeze()
- rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
- data2 *= (
- torch.pow(rms1, torch.tensor(1 - rate))
- * torch.pow(rms2, torch.tensor(rate - 1))
- ).numpy()
- return data2
-
-
-class VC(object):
- def __init__(self, tgt_sr, config):
- self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
- config.x_pad,
- config.x_query,
- config.x_center,
- config.x_max,
- config.is_half,
- )
- self.sr = 16000 # hubert输入采样率
- self.window = 160 # 每帧点数
- self.t_pad = self.sr * self.x_pad # 每条前后pad时间
- self.t_pad_tgt = tgt_sr * self.x_pad
- self.t_pad2 = self.t_pad * 2
- self.t_query = self.sr * self.x_query # 查询切点前后查询时间
- self.t_center = self.sr * self.x_center # 查询切点位置
- self.t_max = self.sr * self.x_max # 免查询时长阈值
- self.device = config.device
-
- # Fork Feature: Get the best torch device to use for f0 algorithms that require a torch device. Will return the type (torch.device)
- def get_optimal_torch_device(self, index: int = 0) -> torch.device:
- # Get cuda device
- if torch.cuda.is_available():
- return torch.device(
- f"cuda:{index % torch.cuda.device_count()}"
- ) # Very fast
- elif torch.backends.mps.is_available():
- return torch.device("mps")
- # Insert an else here to grab "xla" devices if available. TO DO later. Requires the torch_xla.core.xla_model library
- # Else wise return the "cpu" as a torch device,
- return torch.device("cpu")
-
- # Fork Feature: Compute f0 with the crepe method
- def get_f0_crepe_computation(
- self,
- x,
- f0_min,
- f0_max,
- p_len,
- hop_length=160, # 512 before. Hop length changes the speed that the voice jumps to a different dramatic pitch. Lower hop lengths means more pitch accuracy but longer inference time.
- model="full", # Either use crepe-tiny "tiny" or crepe "full". Default is full
- ):
- x = x.astype(
- np.float32
- ) # fixes the F.conv2D exception. We needed to convert double to float.
- x /= np.quantile(np.abs(x), 0.999)
- torch_device = self.get_optimal_torch_device()
- audio = torch.from_numpy(x).to(torch_device, copy=True)
- audio = torch.unsqueeze(audio, dim=0)
- if audio.ndim == 2 and audio.shape[0] > 1:
- audio = torch.mean(audio, dim=0, keepdim=True).detach()
- audio = audio.detach()
- print("Initiating prediction with a crepe_hop_length of: " + str(hop_length))
- pitch: Tensor = torchcrepe.predict(
- audio,
- self.sr,
- hop_length,
- f0_min,
- f0_max,
- model,
- batch_size=hop_length * 2,
- device=torch_device,
- pad=True,
- )
- p_len = p_len or x.shape[0] // hop_length
- # Resize the pitch for final f0
- source = np.array(pitch.squeeze(0).cpu().float().numpy())
- source[source < 0.001] = np.nan
- target = np.interp(
- np.arange(0, len(source) * p_len, len(source)) / p_len,
- np.arange(0, len(source)),
- source,
- )
- f0 = np.nan_to_num(target)
- return f0 # Resized f0
-
- def get_f0_official_crepe_computation(
- self,
- x,
- f0_min,
- f0_max,
- model="full",
- ):
- # Pick a batch size that doesn't cause memory errors on your gpu
- batch_size = 512
- # Compute pitch using first gpu
- audio = torch.tensor(np.copy(x))[None].float()
- f0, pd = torchcrepe.predict(
- audio,
- self.sr,
- self.window,
- f0_min,
- f0_max,
- model,
- batch_size=batch_size,
- device=self.device,
- return_periodicity=True,
- )
- pd = torchcrepe.filter.median(pd, 3)
- f0 = torchcrepe.filter.mean(f0, 3)
- f0[pd < 0.1] = 0
- f0 = f0[0].cpu().numpy()
- return f0
-
- # Fork Feature: Compute pYIN f0 method
- def get_f0_pyin_computation(self, x, f0_min, f0_max):
- y, sr = librosa.load("saudio/Sidney.wav", self.sr, mono=True)
- f0, _, _ = librosa.pyin(y, sr=self.sr, fmin=f0_min, fmax=f0_max)
- f0 = f0[1:] # Get rid of extra first frame
- return f0
-
- # Fork Feature: Acquire median hybrid f0 estimation calculation
- def get_f0_hybrid_computation(
- self,
- methods_str,
- input_audio_path,
- x,
- f0_min,
- f0_max,
- p_len,
- filter_radius,
- crepe_hop_length,
- time_step,
- ):
- # Get various f0 methods from input to use in the computation stack
- s = methods_str
- s = s.split("hybrid")[1]
- s = s.replace("[", "").replace("]", "")
- methods = s.split("+")
- f0_computation_stack = []
-
- print("Calculating f0 pitch estimations for methods: %s" % str(methods))
- x = x.astype(np.float32)
- x /= np.quantile(np.abs(x), 0.999)
- # Get f0 calculations for all methods specified
- for method in methods:
- f0 = None
- if method == "pm":
- f0 = (
- parselmouth.Sound(x, self.sr)
- .to_pitch_ac(
- time_step=time_step / 1000,
- voicing_threshold=0.6,
- pitch_floor=f0_min,
- pitch_ceiling=f0_max,
- )
- .selected_array["frequency"]
- )
- pad_size = (p_len - len(f0) + 1) // 2
- if pad_size > 0 or p_len - len(f0) - pad_size > 0:
- f0 = np.pad(
- f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
- )
- elif method == "crepe":
- f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max)
- f0 = f0[1:] # Get rid of extra first frame
- elif method == "crepe-tiny":
- f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, "tiny")
- f0 = f0[1:] # Get rid of extra first frame
- elif method == "mangio-crepe":
- f0 = self.get_f0_crepe_computation(
- x, f0_min, f0_max, p_len, crepe_hop_length
- )
- elif method == "mangio-crepe-tiny":
- f0 = self.get_f0_crepe_computation(
- x, f0_min, f0_max, p_len, crepe_hop_length, "tiny"
- )
- elif method == "harvest":
- f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
- if filter_radius > 2:
- f0 = signal.medfilt(f0, 3)
- f0 = f0[1:] # Get rid of first frame.
- elif method == "dio": # Potentially buggy?
- f0, t = pyworld.dio(
- x.astype(np.double),
- fs=self.sr,
- f0_ceil=f0_max,
- f0_floor=f0_min,
- frame_period=10,
- )
- f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
- f0 = signal.medfilt(f0, 3)
- f0 = f0[1:]
- # elif method == "pyin": Not Working just yet
- # f0 = self.get_f0_pyin_computation(x, f0_min, f0_max)
- # Push method to the stack
- f0_computation_stack.append(f0)
-
- for fc in f0_computation_stack:
- print(len(fc))
-
- print("Calculating hybrid median f0 from the stack of: %s" % str(methods))
- f0_median_hybrid = None
- if len(f0_computation_stack) == 1:
- f0_median_hybrid = f0_computation_stack[0]
- else:
- f0_median_hybrid = np.nanmedian(f0_computation_stack, axis=0)
- return f0_median_hybrid
-
- def get_f0(
- self,
- input_audio_path,
- x,
- p_len,
- f0_up_key,
- f0_method,
- filter_radius,
- crepe_hop_length,
- inp_f0=None,
- ):
- global input_audio_path2wav
- time_step = self.window / self.sr * 1000
- f0_min = 50
- f0_max = 1100
- f0_mel_min = 1127 * np.log(1 + f0_min / 700)
- f0_mel_max = 1127 * np.log(1 + f0_max / 700)
- if f0_method == "pm":
- f0 = (
- parselmouth.Sound(x, self.sr)
- .to_pitch_ac(
- time_step=time_step / 1000,
- voicing_threshold=0.6,
- pitch_floor=f0_min,
- pitch_ceiling=f0_max,
- )
- .selected_array["frequency"]
- )
- pad_size = (p_len - len(f0) + 1) // 2
- if pad_size > 0 or p_len - len(f0) - pad_size > 0:
- f0 = np.pad(
- f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
- )
- elif f0_method == "harvest":
- input_audio_path2wav[input_audio_path] = x.astype(np.double)
- f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
- if filter_radius > 2:
- f0 = signal.medfilt(f0, 3)
- elif f0_method == "dio": # Potentially Buggy?
- f0, t = pyworld.dio(
- x.astype(np.double),
- fs=self.sr,
- f0_ceil=f0_max,
- f0_floor=f0_min,
- frame_period=10,
- )
- f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
- f0 = signal.medfilt(f0, 3)
- elif f0_method == "crepe":
- f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max)
- elif f0_method == "crepe-tiny":
- f0 = self.get_f0_official_crepe_computation(x, f0_min, f0_max, "tiny")
- elif f0_method == "mangio-crepe":
- f0 = self.get_f0_crepe_computation(
- x, f0_min, f0_max, p_len, crepe_hop_length
- )
- elif f0_method == "mangio-crepe-tiny":
- f0 = self.get_f0_crepe_computation(
- x, f0_min, f0_max, p_len, crepe_hop_length, "tiny"
- )
- elif f0_method == "rmvpe":
- if hasattr(self, "model_rmvpe") == False:
- from rmvpe import RMVPE
-
- print("loading rmvpe model")
- self.model_rmvpe = RMVPE(
- "rmvpe.pt", is_half=self.is_half, device=self.device
- )
- f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
-
- elif "hybrid" in f0_method:
- # Perform hybrid median pitch estimation
- input_audio_path2wav[input_audio_path] = x.astype(np.double)
- f0 = self.get_f0_hybrid_computation(
- f0_method,
- input_audio_path,
- x,
- f0_min,
- f0_max,
- p_len,
- filter_radius,
- crepe_hop_length,
- time_step,
- )
-
- f0 *= pow(2, f0_up_key / 12)
- # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- tf0 = self.sr // self.window # 每秒f0点数
- if inp_f0 is not None:
- delta_t = np.round(
- (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
- ).astype("int16")
- replace_f0 = np.interp(
- list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
- )
- shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
- f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[
- :shape
- ]
- # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
- f0bak = f0.copy()
- f0_mel = 1127 * np.log(1 + f0 / 700)
- f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (
- f0_mel_max - f0_mel_min
- ) + 1
- f0_mel[f0_mel <= 1] = 1
- f0_mel[f0_mel > 255] = 255
- f0_coarse = np.rint(f0_mel).astype(np.int)
-
- return f0_coarse, f0bak # 1-0
-
- def vc(
- self,
- model,
- net_g,
- sid,
- audio0,
- pitch,
- pitchf,
- times,
- index,
- big_npy,
- index_rate,
- version,
- protect,
- ): # ,file_index,file_big_npy
- feats = torch.from_numpy(audio0)
- if self.is_half:
- feats = feats.half()
- else:
- feats = feats.float()
- if feats.dim() == 2: # double channels
- feats = feats.mean(-1)
- assert feats.dim() == 1, feats.dim()
- feats = feats.view(1, -1)
- padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
-
- inputs = {
- "source": feats.to(self.device),
- "padding_mask": padding_mask,
- "output_layer": 9 if version == "v1" else 12,
- }
- t0 = ttime()
- with torch.no_grad():
- logits = model.extract_features(**inputs)
- feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
- if protect < 0.5 and pitch != None and pitchf != None:
- feats0 = feats.clone()
- if (
- isinstance(index, type(None)) == False
- and isinstance(big_npy, type(None)) == False
- and index_rate != 0
- ):
- npy = feats[0].cpu().numpy()
- if self.is_half:
- npy = npy.astype("float32")
-
- # _, I = index.search(npy, 1)
- # npy = big_npy[I.squeeze()]
-
- score, ix = index.search(npy, k=8)
- weight = np.square(1 / score)
- weight /= weight.sum(axis=1, keepdims=True)
- npy = np.sum(big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
-
- if self.is_half:
- npy = npy.astype("float16")
- feats = (
- torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
- + (1 - index_rate) * feats
- )
-
- feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
- if protect < 0.5 and pitch != None and pitchf != None:
- feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute(
- 0, 2, 1
- )
- t1 = ttime()
- p_len = audio0.shape[0] // self.window
- if feats.shape[1] < p_len:
- p_len = feats.shape[1]
- if pitch != None and pitchf != None:
- pitch = pitch[:, :p_len]
- pitchf = pitchf[:, :p_len]
-
- if protect < 0.5 and pitch != None and pitchf != None:
- pitchff = pitchf.clone()
- pitchff[pitchf > 0] = 1
- pitchff[pitchf < 1] = protect
- pitchff = pitchff.unsqueeze(-1)
- feats = feats * pitchff + feats0 * (1 - pitchff)
- feats = feats.to(feats0.dtype)
- p_len = torch.tensor([p_len], device=self.device).long()
- with torch.no_grad():
- if pitch != None and pitchf != None:
- audio1 = (
- (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
- .data.cpu()
- .float()
- .numpy()
- )
- else:
- audio1 = (
- (net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
- )
- del feats, p_len, padding_mask
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- t2 = ttime()
- times[0] += t1 - t0
- times[2] += t2 - t1
- return audio1
-
- def pipeline(
- self,
- model,
- net_g,
- sid,
- audio,
- input_audio_path,
- times,
- f0_up_key,
- f0_method,
- file_index,
- # file_big_npy,
- index_rate,
- if_f0,
- filter_radius,
- tgt_sr,
- resample_sr,
- rms_mix_rate,
- version,
- protect,
- crepe_hop_length,
- f0_file=None,
- ):
- if (
- file_index != ""
- # and file_big_npy != ""
- # and os.path.exists(file_big_npy) == True
- and os.path.exists(file_index) == True
- and index_rate != 0
- ):
- try:
- index = faiss.read_index(file_index)
- # big_npy = np.load(file_big_npy)
- big_npy = index.reconstruct_n(0, index.ntotal)
- except:
- traceback.print_exc()
- index = big_npy = None
- else:
- index = big_npy = None
- audio = signal.filtfilt(bh, ah, audio)
- audio_pad = np.pad(audio, (self.window // 2, self.window // 2), mode="reflect")
- opt_ts = []
- if audio_pad.shape[0] > self.t_max:
- audio_sum = np.zeros_like(audio)
- for i in range(self.window):
- audio_sum += audio_pad[i : i - self.window]
- for t in range(self.t_center, audio.shape[0], self.t_center):
- opt_ts.append(
- t
- - self.t_query
- + np.where(
- np.abs(audio_sum[t - self.t_query : t + self.t_query])
- == np.abs(audio_sum[t - self.t_query : t + self.t_query]).min()
- )[0][0]
- )
- s = 0
- audio_opt = []
- t = None
- t1 = ttime()
- audio_pad = np.pad(audio, (self.t_pad, self.t_pad), mode="reflect")
- p_len = audio_pad.shape[0] // self.window
- inp_f0 = None
- if hasattr(f0_file, "name") == True:
- try:
- with open(f0_file.name, "r") as f:
- lines = f.read().strip("\n").split("\n")
- inp_f0 = []
- for line in lines:
- inp_f0.append([float(i) for i in line.split(",")])
- inp_f0 = np.array(inp_f0, dtype="float32")
- except:
- traceback.print_exc()
- sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
- pitch, pitchf = None, None
- if if_f0 == 1:
- pitch, pitchf = self.get_f0(
- input_audio_path,
- audio_pad,
- p_len,
- f0_up_key,
- f0_method,
- filter_radius,
- crepe_hop_length,
- inp_f0,
- )
- pitch = pitch[:p_len]
- pitchf = pitchf[:p_len]
- if self.device == "mps":
- pitchf = pitchf.astype(np.float32)
- pitch = torch.tensor(pitch, device=self.device).unsqueeze(0).long()
- pitchf = torch.tensor(pitchf, device=self.device).unsqueeze(0).float()
- t2 = ttime()
- times[1] += t2 - t1
- for t in opt_ts:
- t = t // self.window * self.window
- if if_f0 == 1:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[s : t + self.t_pad2 + self.window],
- pitch[:, s // self.window : (t + self.t_pad2) // self.window],
- pitchf[:, s // self.window : (t + self.t_pad2) // self.window],
- times,
- index,
- big_npy,
- index_rate,
- version,
- protect,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- else:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[s : t + self.t_pad2 + self.window],
- None,
- None,
- times,
- index,
- big_npy,
- index_rate,
- version,
- protect,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- s = t
- if if_f0 == 1:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[t:],
- pitch[:, t // self.window :] if t is not None else pitch,
- pitchf[:, t // self.window :] if t is not None else pitchf,
- times,
- index,
- big_npy,
- index_rate,
- version,
- protect,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- else:
- audio_opt.append(
- self.vc(
- model,
- net_g,
- sid,
- audio_pad[t:],
- None,
- None,
- times,
- index,
- big_npy,
- index_rate,
- version,
- protect,
- )[self.t_pad_tgt : -self.t_pad_tgt]
- )
- audio_opt = np.concatenate(audio_opt)
- if rms_mix_rate != 1:
- audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
- if resample_sr >= 16000 and tgt_sr != resample_sr:
- audio_opt = librosa.resample(
- audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
- )
- audio_max = np.abs(audio_opt).max() / 0.99
- max_int16 = 32768
- if audio_max > 1:
- max_int16 /= audio_max
- audio_opt = (audio_opt * max_int16).astype(np.int16)
- del pitch, pitchf, sid
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
- return audio_opt
diff --git a/spaces/eaedk/Tuto_Sentiment_Analysis_App/app.py b/spaces/eaedk/Tuto_Sentiment_Analysis_App/app.py
deleted file mode 100644
index ae51d0304156caa761d4ecfc379870b830026398..0000000000000000000000000000000000000000
--- a/spaces/eaedk/Tuto_Sentiment_Analysis_App/app.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import gradio as gr
-from transformers import AutoModelForSequenceClassification
-from transformers import AutoTokenizer, AutoConfig
-import numpy as np
-from scipy.special import softmax
-
-# Setup
-model_path = f"GhylB/Sentiment_Analysis_DistilBERT"
-
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-config = AutoConfig.from_pretrained(model_path)
-model = AutoModelForSequenceClassification.from_pretrained(model_path)
-
-# Functions
-
-# Preprocess text (username and link placeholders)
-
-
-def preprocess(text):
- new_text = []
- for t in text.split(" "):
- t = '@user' if t.startswith('@') and len(t) > 1 else t
- t = 'http' if t.startswith('http') else t
- new_text.append(t)
- return " ".join(new_text)
-
-
-def sentiment_analysis(text):
- text = preprocess(text)
-
- # PyTorch-based models
- encoded_input = tokenizer(text, return_tensors='pt')
- output = model(**encoded_input)
- scores_ = output[0][0].detach().numpy()
- scores_ = softmax(scores_)
-
- # Format output dict of scores
- labels = ['Negative', 'Neutral', 'Positive']
- scores = {l: float(s) for (l, s) in zip(labels, scores_)}
-
- return scores
-
-
-demo = gr.Interface(
- fn=sentiment_analysis,
- inputs=gr.Textbox(placeholder="Copy and paste/Write a tweet here..."),
- outputs="text",
- interpretation="default",
- examples=[["What's up with the vaccine"],
- ["Covid cases are increasing fast!"],
- ["Covid has been invented by Mavis"],
- ["I'm going to party this weekend"],
- ["Covid is hoax"]],
- title="Tutorial : Sentiment Analysis App",
- description="This Application assesses if a twitter post relating to vaccinations is positive, neutral, or negative.", )
-
-if __name__ == "__main__":
- demo.launch(server_name="0.0.0.0", server_port=7860) # 8080 __
diff --git a/spaces/edugp/perplexity-lenses/perplexity_lenses/data.py b/spaces/edugp/perplexity-lenses/perplexity_lenses/data.py
deleted file mode 100644
index 778749de20cde07e61569deecaf6d3519b718ad4..0000000000000000000000000000000000000000
--- a/spaces/edugp/perplexity-lenses/perplexity_lenses/data.py
+++ /dev/null
@@ -1,81 +0,0 @@
-from functools import partial
-
-import numpy as np
-import pandas as pd
-from datasets import load_dataset
-from tqdm import tqdm
-
-from perplexity_lenses import REGISTRY_DATASET
-from perplexity_lenses.perplexity import KenlmModel
-
-
-def hub_dataset_to_dataframe(
- path: str,
- name: str,
- split: str,
- sample: int,
- text_column: str,
- model: KenlmModel,
- seed: int = 0,
- doc_type: str = "Whole document",
-) -> pd.DataFrame:
- load_dataset_fn = partial(load_dataset, path=path)
- if name:
- load_dataset_fn = partial(load_dataset_fn, name=name)
- # Special case for the registry dataset
- if path == REGISTRY_DATASET:
- load_dataset_fn = partial(load_dataset_fn, data_files=f"{name}/*")
- if split:
- load_dataset_fn = partial(load_dataset_fn, split=split)
- dataset = load_dataset_fn(streaming=True).shuffle(buffer_size=10000, seed=seed)
- if doc_type.lower() == "sentence":
- dataset = dataset.map(
- lambda x: [
- {
- text_column: sentence,
- "perplexity": model.get_perplexity(sentence),
- "label": x.get("labels", [])[0]
- if len(x.get("labels", [])) > 0
- else "NONE", # Special case for registry dataset
- }
- for sentence in x[text_column].split("\n")
- ]
- )
- else:
- dataset = dataset.map(
- lambda x: {
- text_column: x[text_column],
- "perplexity": model.get_perplexity(x[text_column]),
- "label": x.get("labels", [])[0]
- if len(x.get("labels", [])) > 0
- else "NONE", # Special case for registry dataset
- }
- )
- instances = []
- count = 0
- for instance in tqdm(dataset, total=sample):
- if isinstance(instance, list):
- for sentence in instance:
- instances.append(sentence)
- count += 1
- if count == sample:
- break
- else:
- instances.append(instance)
- count += 1
- if count == sample:
- break
- return pd.DataFrame(instances)
-
-
-def documents_df_to_sentences_df(
- df: pd.DataFrame, text_column: str, sample: int, seed: int = 0
-):
- df_sentences = pd.DataFrame(
- {
- text_column: np.array(
- df[text_column].map(lambda x: x.split("\n")).values.tolist()
- ).flatten()
- }
- )
- return df_sentences.sample(min(sample, df_sentences.shape[0]), random_state=seed)
diff --git a/spaces/epexVfeibi/Imagedeblurr/3d Girlz 2 Free Download.md b/spaces/epexVfeibi/Imagedeblurr/3d Girlz 2 Free Download.md
deleted file mode 100644
index 281bb63389cd4ae8e871898c6c1d14c8643abafa..0000000000000000000000000000000000000000
--- a/spaces/epexVfeibi/Imagedeblurr/3d Girlz 2 Free Download.md
+++ /dev/null
@@ -1,9 +0,0 @@
-
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-
899543212b기능 | 설명
---- | ---
-원 키워드 | 원 키워드 및 논문 문법 오류를 찾는 기능 지원
-한-영 키워드 | 한-영 키워드 지원
-코드 설명 | 코드 표시, 코드 설명, 코드 생성, 코드에 주석 추가
-[사용자 정의 바로 가기 키](https://www.bilibili.com/video/BV14s4y1E7jN) | 사용자 정의 바로 가기 키 지원
-모듈식 설계 | 강력한[함수 플러그인](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions) 지원, 플러그인이 [램 업데이트](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)를 지원합니다.
-[자체 프로그램 분석](https://www.bilibili.com/video/BV1cj411A7VW) | [함수 플러그인] [원 키 우드] 프로젝트 소스 코드의 내용을 이해하는 기능을 제공
-[프로그램 분석](https://www.bilibili.com/video/BV1cj411A7VW) | [함수 플러그인] 프로젝트 트리를 분석할 수 있습니다 (Python/C/C++/Java/Lua/...)
-논문 읽기, 번역 | [함수 플러그인] LaTex/PDF 논문의 전문을 읽고 요약을 생성합니다.
-LaTeX 텍스트[번역](https://www.bilibili.com/video/BV1nk4y1Y7Js/), [원 키워드](https://www.bilibili.com/video/BV1FT411H7c5/) | [함수 플러그인] LaTeX 논문의 번역 또는 개량을 위해 일련의 모드를 번역할 수 있습니다.
-대량의 주석 생성 | [함수 플러그인] 함수 코멘트를 대량으로 생성할 수 있습니다.
-Markdown 한-영 번역 | [함수 플러그인] 위의 5 종 언어의 [README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md)를 볼 수 있습니다.
-chat 분석 보고서 생성 | [함수 플러그인] 수행 후 요약 보고서를 자동으로 생성합니다.
-[PDF 논문 번역](https://www.bilibili.com/video/BV1KT411x7Wn) | [함수 플러그인] PDF 논문이 제목 및 요약을 추출한 후 번역됩니다. (멀티 스레드)
-[Arxiv 도우미](https://www.bilibili.com/video/BV1LM4y1279X) | [함수 플러그인] Arxiv 논문 URL을 입력하면 요약을 번역하고 PDF를 다운로드 할 수 있습니다.
-[Google Scholar 통합 도우미](https://www.bilibili.com/video/BV19L411U7ia) | [함수 플러그인] Google Scholar 검색 페이지 URL을 제공하면 gpt가 [Related Works 작성](https://www.bilibili.com/video/BV1GP411U7Az/)을 도와줍니다.
-인터넷 정보 집계+GPT | [함수 플러그인] 먼저 GPT가 인터넷에서 정보를 수집하고 질문에 대답 할 수 있도록합니다. 정보가 절대적으로 구식이 아닙니다.
-수식/이미지/표 표시 | 급여, 코드 강조 기능 지원
-멀티 스레드 함수 플러그인 지원 | Chatgpt를 여러 요청에서 실행하여 [대량의 텍스트](https://www.bilibili.com/video/BV1FT411H7c5/) 또는 프로그램을 처리 할 수 있습니다.
-다크 그라디오 테마 시작 | 어둡게 주제를 변경하려면 브라우저 URL 끝에 ```/?__theme=dark```을 추가하면됩니다.
-[다중 LLM 모델](https://www.bilibili.com/video/BV1wT411p7yf) 지원, [API2D](https://api2d.com/) 인터페이스 지원됨 | GPT3.5, GPT4, [Tsinghua ChatGLM](https://github.com/THUDM/ChatGLM-6B), [Fudan MOSS](https://github.com/OpenLMLab/MOSS)가 모두 동시에 작동하는 것처럼 느낄 수 있습니다!
-LLM 모델 추가 및[huggingface 배치](https://huggingface.co/spaces/qingxu98/gpt-academic) 지원 | 새 Bing 인터페이스 (새 Bing) 추가, Clearing House [Jittorllms](https://github.com/Jittor/JittorLLMs) 지원 [LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV) 및 [盘古α](https://openi.org.cn/pangu/)
-기타 새로운 기능 (이미지 생성 등) ... | 이 문서의 끝부분을 참조하세요. ...- 모든 버튼은 functional.py를 동적으로 읽어와서 사용자 정의 기능을 자유롭게 추가할 수 있으며, 클립 보드를 해제합니다.
-
-
-
-
-- 검수/오타 교정
-
-
-
-
-- 출력에 수식이 포함되어 있으면 텍스와 렌더링의 형태로 동시에 표시되어 복사 및 읽기가 용이합니다.
-
-
-
-
-- 프로젝트 코드를 볼 시간이 없습니까? 전체 프로젝트를 chatgpt에 직접 표시하십시오
-
-
-
-
-- 다양한 대형 언어 모델 범용 요청 (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
-
-
-
-
----
-# 설치
-## Installation-Method 1: Run directly (Windows, Linux or MacOS)
-
-1. 프로젝트 다운로드
-```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
-```
-
-2. API_KEY 구성
-
-`config.py`에서 API KEY 등 설정을 구성합니다. [특별한 네트워크 환경 설정](https://github.com/binary-husky/gpt_academic/issues/1) .
-
-(P.S. 프로그램이 실행될 때, 이름이 `config_private.py`인 기밀 설정 파일이 있는지 우선적으로 확인하고 해당 설정으로 `config.py`의 동일한 이름의 설정을 덮어씁니다. 따라서 구성 읽기 논리를 이해할 수 있다면, `config.py` 옆에 `config_private.py`라는 새 구성 파일을 만들고 `config.py`의 구성을 `config_private.py`로 이동(복사)하는 것이 좋습니다. `config_private.py`는 git으로 관리되지 않으며 개인 정보를 더 안전하게 보호할 수 있습니다. P.S. 프로젝트는 또한 대부분의 옵션을 `환경 변수`를 통해 설정할 수 있으며, `docker-compose` 파일을 참조하여 환경 변수 작성 형식을 확인할 수 있습니다. 우선순위: `환경 변수` > `config_private.py` > `config.py`)
-
-
-3. 의존성 설치
-```sh
-# (I 선택: 기존 python 경험이 있다면) (python 버전 3.9 이상, 최신 버전이 좋습니다), 참고: 공식 pip 소스 또는 알리 pip 소스 사용, 일시적인 교체 방법: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
-python -m pip install -r requirements.txt
-
-# (II 선택: Python에 익숙하지 않은 경우) anaconda 사용 방법은 비슷함(https://www.bilibili.com/video/BV1rc411W7Dr):
-conda create -n gptac_venv python=3.11 # anaconda 환경 만들기
-conda activate gptac_venv # anaconda 환경 활성화
-python -m pip install -r requirements.txt # 이 단계도 pip install의 단계와 동일합니다.
-```
-
-
추가지원을 위해 Tsinghua ChatGLM / Fudan MOSS를 사용해야하는 경우 지원을 클릭하여 이 부분을 확장하세요.
-
-
-[Tsinghua ChatGLM] / [Fudan MOSS]를 백엔드로 사용하려면 추가적인 종속성을 설치해야합니다 (전제 조건 : Python을 이해하고 Pytorch를 사용한 적이 있으며, 컴퓨터가 충분히 강력한 경우) :
-```sh
-# [선택 사항 I] Tsinghua ChatGLM을 지원합니다. Tsinghua ChatGLM에 대한 참고사항 : "Call ChatGLM fail cannot load ChatGLM parameters normally" 오류 발생시 다음 참조:
-# 1 : 기본 설치된 것들은 torch + cpu 버전입니다. cuda를 사용하려면 torch를 제거한 다음 torch + cuda를 다시 설치해야합니다.
-# 2 : 모델을 로드할 수 없는 기계 구성 때문에, AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)를
-# AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)로 변경합니다.
-python -m pip install -r request_llm/requirements_chatglm.txt
-
-# [선택 사항 II] Fudan MOSS 지원
-python -m pip install -r request_llm/requirements_moss.txt
-git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss # 다음 코드 줄을 실행할 때 프로젝트 루트 경로에 있어야합니다.
-
-# [선택 사항III] AVAIL_LLM_MODELS config.py 구성 파일에 기대하는 모델이 포함되어 있는지 확인하십시오.
-# 현재 지원되는 전체 모델 :
-AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
-```
-
-
-
-
-
-
-4. 실행
-```sh
-python main.py
-```5. 테스트 함수 플러그인
-```
-- 테스트 함수 플러그인 템플릿 함수 (GPT에게 오늘의 역사에서 무슨 일이 일어났는지 대답하도록 요청)를 구현하는 데 사용할 수 있습니다. 이 함수를 기반으로 더 복잡한 기능을 구현할 수 있습니다.
- "[함수 플러그인 템플릿 데모] 오늘의 역사"를 클릭하세요.
-```
-
-## 설치 - 방법 2 : 도커 사용
-
-1. ChatGPT 만 (대부분의 사람들이 선택하는 것을 권장합니다.)
-
-``` sh
-git clone https://github.com/binary-husky/chatgpt_academic.git # 다운로드
-cd chatgpt_academic # 경로 이동
-nano config.py # 아무 텍스트 에디터로 config.py를 열고 "Proxy","API_KEY","WEB_PORT" (예 : 50923) 등을 구성합니다.
-docker build -t gpt-academic . # 설치
-
-#(마지막 단계-1 선택) Linux 환경에서는 --net=host를 사용하면 더 편리합니다.
-docker run --rm -it --net=host gpt-academic
-#(마지막 단계-2 선택) macOS / windows 환경에서는 -p 옵션을 사용하여 컨테이너의 포트 (예 : 50923)를 호스트의 포트로 노출해야합니다.
-docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
-```
-
-2. ChatGPT + ChatGLM + MOSS (Docker에 익숙해야합니다.)
-
-``` sh
-#docker-compose.yml을 수정하여 계획 1 및 계획 3을 삭제하고 계획 2를 유지합니다. docker-compose.yml에서 계획 2의 구성을 수정하면 됩니다. 주석을 참조하십시오.
-docker-compose up
-```
-
-3. ChatGPT + LLAMA + Pangu + RWKV (Docker에 익숙해야합니다.)
-``` sh
-#docker-compose.yml을 수정하여 계획 1 및 계획 2을 삭제하고 계획 3을 유지합니다. docker-compose.yml에서 계획 3의 구성을 수정하면 됩니다. 주석을 참조하십시오.
-docker-compose up
-```
-
-
-## 설치 - 방법 3 : 다른 배치 방법
-
-1. 리버스 프록시 URL / Microsoft Azure API 사용 방법
-API_URL_REDIRECT를 `config.py`에 따라 구성하면됩니다.
-
-2. 원격 클라우드 서버 배치 (클라우드 서버 지식과 경험이 필요합니다.)
-[배치위키-1](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)에 방문하십시오.
-
-3. WSL2 사용 (Windows Subsystem for Linux 하위 시스템)
-[배치 위키-2](https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)에 방문하십시오.
-
-4. 2 차 URL (예 : `http : //localhost/subpath`)에서 실행하는 방법
-[FastAPI 실행 설명서] (docs / WithFastapi.md)를 참조하십시오.
-
-5. docker-compose 실행
-docker-compose.yml을 읽은 후 지시 사항에 따라 작업하십시오.
----
-# 고급 사용법
-## 사용자 정의 바로 가기 버튼 / 사용자 정의 함수 플러그인
-
-1. 사용자 정의 바로 가기 버튼 (학술 바로 가기)
-임의의 텍스트 편집기로 'core_functional.py'를 엽니다. 엔트리 추가, 그런 다음 프로그램을 다시 시작하면됩니다. (버튼이 이미 추가되어 보이고 접두사, 접미사가 모두 변수가 효과적으로 수정되면 프로그램을 다시 시작하지 않아도됩니다.)
-예 :
-```
-"超级英译中": {
- # 접두사. 당신이 요구하는 것을 설명하는 데 사용됩니다. 예를 들어 번역, 코드를 설명, 다듬기 등
- "Prefix": "下面翻译成中文，然后用一个 markdown 表格逐一解释文中出现的专有名词：\n\n",
-
- # 접미사는 입력 내용 앞뒤에 추가됩니다. 예를 들어 전위를 사용하여 입력 내용을 따옴표로 묶는데 사용할 수 있습니다.
- "Suffix": "",
-},
-```
-
-
-
-
-2. 사용자 지정 함수 플러그인
-강력한 함수 플러그인을 작성하여 원하는 작업을 수행하십시오.
-이 프로젝트의 플러그인 작성 및 디버깅 난이도는 매우 낮으며, 일부 파이썬 기본 지식만 있으면 제공된 템플릿을 모방하여 플러그인 기능을 구현할 수 있습니다. 자세한 내용은 [함수 플러그인 가이드]를 참조하십시오. (https://github.com/binary -husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E 4%BB%B6%E6%8C%87%E5%8D%97).
----
-# 최신 업데이트
-## 새로운 기능 동향1. 대화 저장 기능.
-
-1. 함수 플러그인 영역에서 '현재 대화 저장'을 호출하면 현재 대화를 읽을 수 있고 복원 가능한 HTML 파일로 저장할 수 있습니다. 또한 함수 플러그인 영역(드롭다운 메뉴)에서 '대화 기록 불러오기'를 호출하면 이전 대화를 복원할 수 있습니다. 팁: 파일을 지정하지 않고 '대화 기록 불러오기'를 클릭하면 기록된 HTML 캐시를 볼 수 있으며 '모든 로컬 대화 기록 삭제'를 클릭하면 모든 HTML 캐시를 삭제할 수 있습니다.
-
-2. 보고서 생성. 대부분의 플러그인은 실행이 끝난 후 작업 보고서를 생성합니다.
-
-3. 모듈화 기능 설계, 간단한 인터페이스로도 강력한 기능을 지원할 수 있습니다.
-
-4. 자체 번역이 가능한 오픈 소스 프로젝트입니다.
-
-5. 다른 오픈 소스 프로젝트를 번역하는 것은 어렵지 않습니다.
-
-6. [live2d](https://github.com/fghrsh/live2d_demo) 장식 기능(기본적으로 비활성화되어 있으며 `config.py`를 수정해야 합니다.)
-
-7. MOSS 대 언어 모델 지원 추가
-
-8. OpenAI 이미지 생성
-
-9. OpenAI 음성 분석 및 요약
-
-10. LaTeX 전체적인 교정 및 오류 수정
-
-## 버전:
-- version 3.5 (TODO): 자연어를 사용하여 이 프로젝트의 모든 함수 플러그인을 호출하는 기능(우선순위 높음)
-- version 3.4(TODO): 로컬 대 모듈의 다중 스레드 지원 향상
-- version 3.3: 인터넷 정보 종합 기능 추가
-- version 3.2: 함수 플러그인이 더 많은 인수 인터페이스를 지원합니다.(대화 저장 기능, 임의의 언어 코드 해석 및 동시에 임의의 LLM 조합을 확인하는 기능)
-- version 3.1: 여러 개의 GPT 모델에 대한 동시 쿼리 지원! api2d 지원, 여러 개의 apikey 로드 밸런싱 지원
-- version 3.0: chatglm 및 기타 소형 llm의 지원
-- version 2.6: 플러그인 구조를 재구성하여 상호 작용성을 향상시켰습니다. 더 많은 플러그인을 추가했습니다.
-- version 2.5: 자체 업데이트, 전체 프로젝트를 요약할 때 텍스트가 너무 길어지고 토큰이 오버플로우되는 문제를 해결했습니다.
-- version 2.4: (1) PDF 전체 번역 기능 추가; (2) 입력 영역 위치 전환 기능 추가; (3) 수직 레이아웃 옵션 추가; (4) 다중 스레드 함수 플러그인 최적화.
-- version 2.3: 다중 스레드 상호 작용성 강화
-- version 2.2: 함수 플러그인 히트 리로드 지원
-- version 2.1: 접는 레이아웃 지원
-- version 2.0: 모듈화 함수 플러그인 도입
-- version 1.0: 기본 기능
-
-gpt_academic 개발자 QQ 그룹-2 : 610599535
-
-- 알려진 문제
- - 일부 브라우저 번역 플러그인이이 소프트웨어의 프론트 엔드 작동 방식을 방해합니다.
- - gradio 버전이 너무 높거나 낮으면 여러 가지 이상이 발생할 수 있습니다.
-
-## 참고 및 학습 자료
-
-```
-많은 우수 프로젝트의 디자인을 참고했습니다. 주요 항목은 다음과 같습니다.
-
-# 프로젝트 1 : Tsinghua ChatGLM-6B :
-https://github.com/THUDM/ChatGLM-6B
-
-# 프로젝트 2 : Tsinghua JittorLLMs:
-https://github.com/Jittor/JittorLLMs
-
-# 프로젝트 3 : Edge-GPT :
-https://github.com/acheong08/EdgeGPT
-
-# 프로젝트 4 : ChuanhuChatGPT:
-https://github.com/GaiZhenbiao/ChuanhuChatGPT
-
-# 프로젝트 5 : ChatPaper :
-https://github.com/kaixindelele/ChatPaper
-
-# 더 많은 :
-https://github.com/gradio-app/gradio
-https://github.com/fghrsh/live2d_demo
-```
\ No newline at end of file
diff --git a/spaces/f2api/gpt-academic/docs/self_analysis.md b/spaces/f2api/gpt-academic/docs/self_analysis.md
deleted file mode 100644
index ebc2337194974bf210794df7d858889010fecf08..0000000000000000000000000000000000000000
--- a/spaces/f2api/gpt-academic/docs/self_analysis.md
+++ /dev/null
@@ -1,378 +0,0 @@
-# chatgpt-academic项目自译解报告
-（Author补充：以下分析均由本项目调用ChatGPT一键生成，如果有不准确的地方，全怪GPT😄）
-
-
-| 文件名 | 功能描述 |
-| ------ | ------ |
-| check_proxy.py | 检查代理有效性及地理位置 |
-| colorful.py | 控制台打印彩色文字 |
-| config.py | 配置和参数设置 |
-| config_private.py | 私人配置和参数设置 |
-| core_functional.py | 核心函数和参数设置 |
-| crazy_functional.py | 高级功能插件集合 |
-| main.py | 一个 Chatbot 程序，提供各种学术翻译、文本处理和其他查询服务 |
-| multi_language.py | 识别和翻译不同语言 |
-| theme.py | 自定义 gradio 应用程序主题 |
-| toolbox.py | 工具类库，用于协助实现各种功能 |
-| crazy_functions\crazy_functions_test.py | 测试 crazy_functions 中的各种函数 |
-| crazy_functions\crazy_utils.py | 工具函数，用于字符串处理、异常检测、Markdown 格式转换等 |
-| crazy_functions\Latex全文润色.py | 对整个 Latex 项目进行润色和纠错 |
-| crazy_functions\Latex全文翻译.py | 对整个 Latex 项目进行翻译 |
-| crazy_functions\\_\_init\_\_.py | 模块初始化文件，标识 `crazy_functions` 是一个包 |
-| crazy_functions\下载arxiv论文翻译摘要.py | 下载 `arxiv` 论文的 PDF 文件，并提取摘要和翻译 |
-| crazy_functions\代码重写为全英文_多线程.py | 将Python源代码文件中的中文内容转化为英文 |
-| crazy_functions\图片生成.py | 根据激励文本使用GPT模型生成相应的图像 |
-| crazy_functions\对话历史存档.py | 将每次对话记录写入Markdown格式的文件中 |
-| crazy_functions\总结word文档.py | 对输入的word文档进行摘要生成 |
-| crazy_functions\总结音视频.py | 对输入的音视频文件进行摘要生成 |
-| crazy_functions\批量Markdown翻译.py | 将指定目录下的Markdown文件进行中英文翻译 |
-| crazy_functions\批量总结PDF文档.py | 对PDF文件进行切割和摘要生成 |
-| crazy_functions\批量总结PDF文档pdfminer.py | 对PDF文件进行文本内容的提取和摘要生成 |
-| crazy_functions\批量翻译PDF文档_多线程.py | 将指定目录下的PDF文件进行中英文翻译 |
-| crazy_functions\理解PDF文档内容.py | 对PDF文件进行摘要生成和问题解答 |
-| crazy_functions\生成函数注释.py | 自动生成Python函数的注释 |
-| crazy_functions\联网的ChatGPT.py | 使用网络爬虫和ChatGPT模型进行聊天回答 |
-| crazy_functions\解析JupyterNotebook.py | 对Jupyter Notebook进行代码解析 |
-| crazy_functions\解析项目源代码.py | 对指定编程语言的源代码进行解析 |
-| crazy_functions\询问多个大语言模型.py | 使用多个大语言模型对输入进行处理和回复 |
-| crazy_functions\读文章写摘要.py | 对论文进行解析和全文摘要生成 |
-| crazy_functions\谷歌检索小助手.py | 提供谷歌学术搜索页面中相关文章的元数据信息。 |
-| crazy_functions\高级功能函数模板.py | 使用Unsplash API发送相关图片以回复用户的输入。 |
-| request_llm\bridge_all.py | 基于不同LLM模型进行对话。 |
-| request_llm\bridge_chatglm.py | 使用ChatGLM模型生成回复，支持单线程和多线程方式。 |
-| request_llm\bridge_chatgpt.py | 基于GPT模型完成对话。 |
-| request_llm\bridge_jittorllms_llama.py | 使用JittorLLMs模型完成对话，支持单线程和多线程方式。 |
-| request_llm\bridge_jittorllms_pangualpha.py | 使用JittorLLMs模型完成对话，基于多进程和多线程方式。 |
-| request_llm\bridge_jittorllms_rwkv.py | 使用JittorLLMs模型完成聊天功能，提供包括历史信息、参数调节等在内的多个功能选项。 |
-| request_llm\bridge_moss.py | 加载Moss模型完成对话功能。 |
-| request_llm\bridge_newbing.py | 使用Newbing聊天机器人进行对话，支持单线程和多线程方式。 |
-| request_llm\bridge_newbingfree.py | 基于Bing chatbot API实现聊天机器人的文本生成功能。 |
-| request_llm\bridge_stackclaude.py | 基于Slack API实现Claude与用户的交互。 |
-| request_llm\bridge_tgui.py | 通过websocket实现聊天机器人与UI界面交互。 |
-| request_llm\edge_gpt.py | 调用Bing chatbot API提供聊天机器人服务。 |
-| request_llm\edge_gpt_free.py | 实现聊天机器人API，采用aiohttp和httpx工具库。 |
-| request_llm\test_llms.py | 对llm模型进行单元测试。 |
-
-## 接下来请你逐文件分析下面的工程[0/48] 请对下面的程序文件做一个概述: check_proxy.py
-
-这个文件主要包含了五个函数：
-
-1. `check_proxy`：用于检查代理的有效性及地理位置，输出代理配置和所在地信息。
-
-2. `backup_and_download`：用于备份当前版本并下载新版本。
-
-3. `patch_and_restart`：用于覆盖更新当前版本并重新启动程序。
-
-4. `get_current_version`：用于获取当前程序的版本号。
-
-5. `auto_update`：用于自动检查新版本并提示用户更新。如果用户选择更新，则备份并下载新版本，覆盖更新当前版本并重新启动程序。如果更新失败，则输出错误信息，并不会向用户进行任何提示。
-
-还有一个没有函数名的语句`os.environ['no_proxy'] = '*'`，用于设置环境变量，避免代理网络产生意外污染。
-
-此外，该文件导入了以下三个模块/函数：
-
-- `requests`
-- `shutil`
-- `os`
-
-## [1/48] 请对下面的程序文件做一个概述: colorful.py
-
-该文件是一个Python脚本，用于在控制台中打印彩色文字。该文件包含了一些函数，用于以不同颜色打印文本。其中，红色、绿色、黄色、蓝色、紫色、靛色分别以函数 print红、print绿、print黄、print蓝、print紫、print靛 的形式定义；亮红色、亮绿色、亮黄色、亮蓝色、亮紫色、亮靛色分别以 print亮红、print亮绿、print亮黄、print亮蓝、print亮紫、print亮靛 的形式定义。它们使用 ANSI Escape Code 将彩色输出从控制台突出显示。如果运行在 Linux 操作系统上，文件所执行的操作被留空；否则，该文件导入了 colorama 库并调用 init() 函数进行初始化。最后，通过一系列条件语句，该文件通过将所有彩色输出函数的名称重新赋值为 print 函数的名称来避免输出文件的颜色问题。
-
-## [2/48] 请对下面的程序文件做一个概述: config.py
-
-这个程序文件是用来配置和参数设置的。它包含了许多设置，如API key，使用代理，线程数，默认模型，超时时间等等。此外，它还包含了一些高级功能，如URL重定向等。这些设置将会影响到程序的行为和性能。
-
-## [3/48] 请对下面的程序文件做一个概述: config_private.py
-
-这个程序文件是一个Python脚本，文件名为config_private.py。其中包含以下变量的赋值：
-
-1. API_KEY：API密钥。
-2. USE_PROXY：是否应用代理。
-3. proxies：如果使用代理，则设置代理网络的协议(socks5/http)、地址(localhost)和端口(11284)。
-4. DEFAULT_WORKER_NUM：默认的工作线程数量。
-5. SLACK_CLAUDE_BOT_ID：Slack机器人ID。
-6. SLACK_CLAUDE_USER_TOKEN：Slack用户令牌。
-
-## [4/48] 请对下面的程序文件做一个概述: core_functional.py
-
-这是一个名为core_functional.py的源代码文件，该文件定义了一个名为get_core_functions()的函数，该函数返回一个字典，该字典包含了各种学术翻译润色任务的说明和相关参数，如颜色、前缀、后缀等。这些任务包括英语学术润色、中文学术润色、查找语法错误、中译英、学术中英互译、英译中、找图片和参考文献转Bib。其中，一些任务还定义了预处理函数用于处理任务的输入文本。
-
-## [5/48] 请对下面的程序文件做一个概述: crazy_functional.py
-
-此程序文件（crazy_functional.py）是一个函数插件集合，包含了多个函数插件的定义和调用。这些函数插件旨在提供一些高级功能，如解析项目源代码、批量翻译PDF文档和Latex全文润色等。其中一些插件还支持热更新功能，不需要重启程序即可生效。文件中的函数插件按照功能进行了分类（第一组和第二组），并且有不同的调用方式（作为按钮或下拉菜单）。
-
-## [6/48] 请对下面的程序文件做一个概述: main.py
-
-这是一个Python程序文件，文件名为main.py。该程序包含一个名为main的函数，程序会自动运行该函数。程序要求已经安装了gradio、os等模块，会根据配置文件加载代理、model、API Key等信息。程序提供了Chatbot功能，实现了一个对话界面，用户可以输入问题，然后Chatbot可以回答问题或者提供相关功能。程序还包含了基础功能区、函数插件区、更换模型 & SysPrompt & 交互界面布局、备选输入区，用户可以在这些区域选择功能和插件进行使用。程序中还包含了一些辅助模块，如logging等。
-
-## [7/48] 请对下面的程序文件做一个概述: multi_language.py
-
-该文件multi_language.py是用于将项目翻译成不同语言的程序。它包含了以下函数和变量：lru_file_cache、contains_chinese、split_list、map_to_json、read_map_from_json、advanced_split、trans、trans_json、step_1_core_key_translate、CACHE_FOLDER、blacklist、LANG、TransPrompt、cached_translation等。注释和文档字符串提供了有关程序的说明，例如如何使用该程序，如何修改“LANG”和“TransPrompt”变量等。
-
-## [8/48] 请对下面的程序文件做一个概述: theme.py
-
-这是一个Python源代码文件，文件名为theme.py。此文件中定义了一个函数adjust_theme，其功能是自定义gradio应用程序的主题，包括调整颜色、字体、阴影等。如果允许，则添加一个看板娘。此文件还包括变量advanced_css，其中包含一些CSS样式，用于高亮显示代码和自定义聊天框样式。此文件还导入了get_conf函数和gradio库。
-
-## [9/48] 请对下面的程序文件做一个概述: toolbox.py
-
-toolbox.py是一个工具类库，其中主要包含了一些函数装饰器和小工具函数，用于协助实现聊天机器人所需的各种功能，包括文本处理、功能插件加载、异常检测、Markdown格式转换，文件读写等等。此外，该库还包含一些依赖、参数配置等信息。该库易于理解和维护。
-
-## [10/48] 请对下面的程序文件做一个概述: crazy_functions\crazy_functions_test.py
-
-这个文件是一个Python测试模块，用于测试crazy_functions中的各种函数插件。这些函数包括：解析Python项目源代码、解析Cpp项目源代码、Latex全文润色、Markdown中译英、批量翻译PDF文档、谷歌检索小助手、总结word文档、下载arxiv论文并翻译摘要、联网回答问题、和解析Jupyter Notebooks。对于每个函数插件，都有一个对应的测试函数来进行测试。
-
-## [11/48] 请对下面的程序文件做一个概述: crazy_functions\crazy_utils.py
-
-这个Python文件中包括了两个函数：
-
-1. `input_clipping`: 该函数用于裁剪输入文本长度，使其不超过一定的限制。
-2. `request_gpt_model_in_new_thread_with_ui_alive`: 该函数用于请求 GPT 模型并保持用户界面的响应，支持多线程和实时更新用户界面。
-
-这两个函数都依赖于从 `toolbox` 和 `request_llm` 中导入的一些工具函数。函数的输入和输出有详细的描述文档。
-
-## [12/48] 请对下面的程序文件做一个概述: crazy_functions\Latex全文润色.py
-
-这是一个Python程序文件，文件名为crazy_functions\Latex全文润色.py。文件包含了一个PaperFileGroup类和三个函数Latex英文润色，Latex中文润色和Latex英文纠错。程序使用了字符串处理、正则表达式、文件读写、多线程等技术，主要作用是对整个Latex项目进行润色和纠错。其中润色和纠错涉及到了对文本的语法、清晰度和整体可读性等方面的提升。此外，该程序还参考了第三方库，并封装了一些工具函数。
-
-## [13/48] 请对下面的程序文件做一个概述: crazy_functions\Latex全文翻译.py
-
-这个文件包含两个函数 `Latex英译中` 和 `Latex中译英`，它们都会对整个Latex项目进行翻译。这个文件还包含一个类 `PaperFileGroup`，它拥有一个方法 `run_file_split`，用于把长文本文件分成多个短文件。其中使用了工具库 `toolbox` 中的一些函数和从 `request_llm` 中导入了 `model_info`。接下来的函数把文件读取进来，把它们的注释删除，进行分割，并进行翻译。这个文件还包括了一些异常处理和界面更新的操作。
-
-## [14/48] 请对下面的程序文件做一个概述: crazy_functions\__init__.py
-
-这是一个Python模块的初始化文件（__init__.py），命名为"crazy_functions"。该模块包含了一些疯狂的函数，但该文件并没有实现这些函数，而是作为一个包（package）来导入其它的Python模块以实现这些函数。在该文件中，没有定义任何类或函数，它唯一的作用就是标识"crazy_functions"模块是一个包。
-
-## [15/48] 请对下面的程序文件做一个概述: crazy_functions\下载arxiv论文翻译摘要.py
-
-这是一个 Python 程序文件，文件名为 `下载arxiv论文翻译摘要.py`。程序包含多个函数，其中 `下载arxiv论文并翻译摘要` 函数的作用是下载 `arxiv` 论文的 PDF 文件，提取摘要并使用 GPT 对其进行翻译。其他函数包括用于下载 `arxiv` 论文的 `download_arxiv_` 函数和用于获取文章信息的 `get_name` 函数，其中涉及使用第三方库如 requests, BeautifulSoup 等。该文件还包含一些用于调试和存储文件的代码段。
-
-## [16/48] 请对下面的程序文件做一个概述: crazy_functions\代码重写为全英文_多线程.py
-
-该程序文件是一个多线程程序，主要功能是将指定目录下的所有Python代码文件中的中文内容转化为英文，并将转化后的代码存储到一个新的文件中。其中，程序使用了GPT-3等技术进行中文-英文的转化，同时也进行了一些Token限制下的处理，以防止程序发生错误。程序在执行过程中还会输出一些提示信息，并将所有转化过的代码文件存储到指定目录下。在程序执行结束后，还会生成一个任务执行报告，记录程序运行的详细信息。
-
-## [17/48] 请对下面的程序文件做一个概述: crazy_functions\图片生成.py
-
-该程序文件提供了一个用于生成图像的函数`图片生成`。函数实现的过程中，会调用`gen_image`函数来生成图像，并返回图像生成的网址和本地文件地址。函数有多个参数，包括`prompt`(激励文本)、`llm_kwargs`(GPT模型的参数)、`plugin_kwargs`(插件模型的参数)等。函数核心代码使用了`requests`库向OpenAI API请求图像，并做了简单的处理和保存。函数还更新了交互界面，清空聊天历史并显示正在生成图像的消息和最终的图像网址和预览。
-
-## [18/48] 请对下面的程序文件做一个概述: crazy_functions\对话历史存档.py
-
-这个文件是名为crazy_functions\对话历史存档.py的Python程序文件，包含了4个函数：
-
-1. write_chat_to_file(chatbot, history=None, file_name=None)：用来将对话记录以Markdown格式写入文件中，并且生成文件名，如果没指定文件名则用当前时间。写入完成后将文件路径打印出来。
-
-2. gen_file_preview(file_name)：从传入的文件中读取内容，解析出对话历史记录并返回前100个字符，用于文件预览。
-
-3. read_file_to_chat(chatbot, history, file_name)：从传入的文件中读取内容，解析出对话历史记录并更新聊天显示框。
-
-4. 对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port)：一个主要函数，用于保存当前对话记录并提醒用户。如果用户希望加载历史记录，则调用read_file_to_chat()来更新聊天显示框。如果用户希望删除历史记录，调用删除所有本地对话历史记录()函数完成删除操作。
-
-## [19/48] 请对下面的程序文件做一个概述: crazy_functions\总结word文档.py
-
-该程序文件实现了一个总结Word文档的功能，使用Python的docx库读取docx格式的文件，使用pywin32库读取doc格式的文件。程序会先根据传入的txt参数搜索需要处理的文件，并逐个解析其中的内容，将内容拆分为指定长度的文章片段，然后使用另一个程序文件中的request_gpt_model_in_new_thread_with_ui_alive函数进行中文概述。最后将所有的总结结果写入一个文件中，并在界面上进行展示。
-
-## [20/48] 请对下面的程序文件做一个概述: crazy_functions\总结音视频.py
-
-该程序文件包括两个函数：split_audio_file()和AnalyAudio()，并且导入了一些必要的库并定义了一些工具函数。split_audio_file用于将音频文件分割成多个时长相等的片段，返回一个包含所有切割音频片段文件路径的列表，而AnalyAudio用来分析音频文件，通过调用whisper模型进行音频转文字并使用GPT模型对音频内容进行概述，最终将所有总结结果写入结果文件中。
-
-## [21/48] 请对下面的程序文件做一个概述: crazy_functions\批量Markdown翻译.py
-
-该程序文件名为`批量Markdown翻译.py`，包含了以下功能：读取Markdown文件，将长文本分离开来，将Markdown文件进行翻译（英译中和中译英），整理结果并退出。程序使用了多线程以提高效率。程序使用了`tiktoken`依赖库，可能需要额外安装。文件中还有一些其他的函数和类，但与文件名所描述的功能无关。
-
-## [22/48] 请对下面的程序文件做一个概述: crazy_functions\批量总结PDF文档.py
-
-该文件是一个Python脚本，名为crazy_functions\批量总结PDF文档.py。在导入了一系列库和工具函数后，主要定义了5个函数，其中包括一个错误处理装饰器（@CatchException），用于批量总结PDF文档。该函数主要实现对PDF文档的解析，并调用模型生成中英文摘要。
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-## [23/48] 请对下面的程序文件做一个概述: crazy_functions\批量总结PDF文档pdfminer.py
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-该程序文件是一个用于批量总结PDF文档的函数插件，使用了pdfminer插件和BeautifulSoup库来提取PDF文档的文本内容，对每个PDF文件分别进行处理并生成中英文摘要。同时，该程序文件还包括一些辅助工具函数和处理异常的装饰器。
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-## [24/48] 请对下面的程序文件做一个概述: crazy_functions\批量翻译PDF文档_多线程.py
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-这个程序文件是一个Python脚本，文件名为“批量翻译PDF文档_多线程.py”。它主要使用了“toolbox”、“request_gpt_model_in_new_thread_with_ui_alive”、“request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency”、“colorful”等Python库和自定义的模块“crazy_utils”的一些函数。程序实现了一个批量翻译PDF文档的功能，可以自动解析PDF文件中的基础信息，递归地切割PDF文件，翻译和处理PDF论文中的所有内容，并生成相应的翻译结果文件（包括md文件和html文件）。功能比较复杂，其中需要调用多个函数和依赖库，涉及到多线程操作和UI更新。文件中有详细的注释和变量命名，代码比较清晰易读。
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-## [25/48] 请对下面的程序文件做一个概述: crazy_functions\理解PDF文档内容.py
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-该程序文件实现了一个名为“理解PDF文档内容”的函数，该函数可以为输入的PDF文件提取摘要以及正文各部分的主要内容，并在提取过程中根据上下文关系进行学术性问题解答。该函数依赖于多个辅助函数和第三方库，并在执行过程中针对可能出现的异常进行了处理。
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-## [26/48] 请对下面的程序文件做一个概述: crazy_functions\生成函数注释.py
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-该程序文件是一个Python模块文件，文件名为“生成函数注释.py”，定义了两个函数：一个是生成函数注释的主函数“生成函数注释”，另一个是通过装饰器实现异常捕捉的函数“批量生成函数注释”。该程序文件依赖于“toolbox”和本地“crazy_utils”模块，并且在运行时使用了多线程技术和GPT模型来生成注释。函数生成的注释结果使用Markdown表格输出并写入历史记录文件。
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-## [27/48] 请对下面的程序文件做一个概述: crazy_functions\联网的ChatGPT.py
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-这是一个名为`联网的ChatGPT.py`的Python程序文件，其中定义了一个函数`连接网络回答问题`。该函数通过爬取搜索引擎的结果和访问网页来综合回答给定的问题，并使用ChatGPT模型完成回答。此外，该文件还包括一些工具函数，例如从网页中抓取文本和使用代理访问网页。
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-## [28/48] 请对下面的程序文件做一个概述: crazy_functions\解析JupyterNotebook.py
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-这个程序文件包含了两个函数： `parseNotebook()`和`解析ipynb文件()`，并且引入了一些工具函数和类。`parseNotebook()`函数将Jupyter Notebook文件解析为文本代码块，`解析ipynb文件()`函数则用于解析多个Jupyter Notebook文件，使用`parseNotebook()`解析每个文件和一些其他的处理。函数中使用了多线程处理输入和输出，并且将结果写入到文件中。
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-## [29/48] 请对下面的程序文件做一个概述: crazy_functions\解析项目源代码.py
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-这是一个源代码分析的Python代码文件，其中定义了多个函数，包括解析一个Python项目、解析一个C项目、解析一个C项目的头文件和解析一个Java项目等。其中解析源代码新函数是实际处理源代码分析并生成报告的函数。该函数首先会逐个读取传入的源代码文件，生成对应的请求内容，通过多线程发送到chatgpt进行分析。然后将结果写入文件，并进行汇总分析。最后通过调用update_ui函数刷新界面，完整实现了源代码的分析。
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-## [30/48] 请对下面的程序文件做一个概述: crazy_functions\询问多个大语言模型.py
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-该程序文件包含两个函数：同时问询()和同时问询_指定模型()，它们的作用是使用多个大语言模型同时对用户输入进行处理，返回对应模型的回复结果。同时问询()会默认使用ChatGPT和ChatGLM两个模型，而同时问询_指定模型()则可以指定要使用的模型。该程序文件还引用了其他的模块和函数库。
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-## [31/48] 请对下面的程序文件做一个概述: crazy_functions\读文章写摘要.py
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-这个程序文件是一个Python模块，文件名为crazy_functions\读文章写摘要.py。该模块包含了两个函数，其中主要函数是"读文章写摘要"函数，其实现了解析给定文件夹中的tex文件，对其中每个文件的内容进行摘要生成，并根据各论文片段的摘要，最终生成全文摘要。第二个函数是"解析Paper"函数，用于解析单篇论文文件。其中用到了一些工具函数和库，如update_ui、CatchException、report_execption、write_results_to_file等。
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-## [32/48] 请对下面的程序文件做一个概述: crazy_functions\谷歌检索小助手.py
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-该文件是一个Python模块，文件名为“谷歌检索小助手.py”。该模块包含两个函数，一个是“get_meta_information()”，用于从提供的网址中分析出所有相关的学术文献的元数据信息；另一个是“谷歌检索小助手()”，是主函数，用于分析用户提供的谷歌学术搜索页面中出现的文章，并提取相关信息。其中，“谷歌检索小助手()”函数依赖于“get_meta_information()”函数，并调用了其他一些Python模块，如“arxiv”、“math”、“bs4”等。
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-## [33/48] 请对下面的程序文件做一个概述: crazy_functions\高级功能函数模板.py
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-该程序文件定义了一个名为高阶功能模板函数的函数，该函数接受多个参数，包括输入的文本、gpt模型参数、插件模型参数、聊天显示框的句柄、聊天历史等，并利用送出请求，使用 Unsplash API 发送相关图片。其中，为了避免输入溢出，函数会在开始时清空历史。函数也有一些 UI 更新的语句。该程序文件还依赖于其他两个模块：CatchException 和 update_ui，以及一个名为 request_gpt_model_in_new_thread_with_ui_alive 的来自 crazy_utils 模块（应该是自定义的工具包）的函数。
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-## [34/48] 请对下面的程序文件做一个概述: request_llm\bridge_all.py
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-该文件包含两个函数：predict和predict_no_ui_long_connection，用于基于不同的LLM模型进行对话。该文件还包含一个lazyloadTiktoken类和一个LLM_CATCH_EXCEPTION修饰器函数。其中lazyloadTiktoken类用于懒加载模型的tokenizer，LLM_CATCH_EXCEPTION用于错误处理。整个文件还定义了一些全局变量和模型信息字典，用于引用和配置LLM模型。
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-## [35/48] 请对下面的程序文件做一个概述: request_llm\bridge_chatglm.py
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-这是一个Python程序文件，名为`bridge_chatglm.py`，其中定义了一个名为`GetGLMHandle`的类和三个方法：`predict_no_ui_long_connection`、 `predict`和 `stream_chat`。该文件依赖于多个Python库，如`transformers`和`sentencepiece`。该文件实现了一个聊天机器人，使用ChatGLM模型来生成回复，支持单线程和多线程方式。程序启动时需要加载ChatGLM的模型和tokenizer，需要一段时间。在配置文件`config.py`中设置参数会影响模型的内存和显存使用，因此程序可能会导致低配计算机卡死。
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-## [36/48] 请对下面的程序文件做一个概述: request_llm\bridge_chatgpt.py
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-该文件为 Python 代码文件，文件名为 request_llm\bridge_chatgpt.py。该代码文件主要提供三个函数：predict、predict_no_ui和 predict_no_ui_long_connection，用于发送至 chatGPT 并等待回复，获取输出。该代码文件还包含一些辅助函数，用于处理连接异常、生成 HTTP 请求等。该文件的代码架构清晰，使用了多个自定义函数和模块。
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-## [37/48] 请对下面的程序文件做一个概述: request_llm\bridge_jittorllms_llama.py
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-该代码文件实现了一个聊天机器人，其中使用了 JittorLLMs 模型。主要包括以下几个部分：
-1. GetGLMHandle 类：一个进程类，用于加载 JittorLLMs 模型并接收并处理请求。
-2. predict_no_ui_long_connection 函数：一个多线程方法，用于在后台运行聊天机器人。
-3. predict 函数：一个单线程方法，用于在前端页面上交互式调用聊天机器人，以获取用户输入并返回相应的回复。
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-这个文件中还有一些辅助函数和全局变量，例如 importlib、time、threading 等。
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-## [38/48] 请对下面的程序文件做一个概述: request_llm\bridge_jittorllms_pangualpha.py
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-这个文件是为了实现使用jittorllms（一种机器学习模型）来进行聊天功能的代码。其中包括了模型加载、模型的参数加载、消息的收发等相关操作。其中使用了多进程和多线程来提高性能和效率。代码中还包括了处理依赖关系的函数和预处理函数等。
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-## [39/48] 请对下面的程序文件做一个概述: request_llm\bridge_jittorllms_rwkv.py
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-这个文件是一个Python程序，文件名为request_llm\bridge_jittorllms_rwkv.py。它依赖transformers、time、threading、importlib、multiprocessing等库。在文件中，通过定义GetGLMHandle类加载jittorllms模型参数和定义stream_chat方法来实现与jittorllms模型的交互。同时，该文件还定义了predict_no_ui_long_connection和predict方法来处理历史信息、调用jittorllms模型、接收回复信息并输出结果。
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-## [40/48] 请对下面的程序文件做一个概述: request_llm\bridge_moss.py
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-该文件为一个Python源代码文件，文件名为 request_llm\bridge_moss.py。代码定义了一个 GetGLMHandle 类和两个函数 predict_no_ui_long_connection 和 predict。
-
-GetGLMHandle 类继承自Process类（多进程），主要功能是启动一个子进程并加载 MOSS 模型参数，通过 Pipe 进行主子进程的通信。该类还定义了 check_dependency、moss_init、run 和 stream_chat 等方法，其中 check_dependency 和 moss_init 是子进程的初始化方法，run 是子进程运行方法，stream_chat 实现了主进程和子进程的交互过程。
-
-函数 predict_no_ui_long_connection 是多线程方法，调用 GetGLMHandle 类加载 MOSS 参数后使用 stream_chat 实现主进程和子进程的交互过程。
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-函数 predict 是单线程方法，通过调用 update_ui 将交互过程中 MOSS 的回复实时更新到UI（User Interface）中，并执行一个 named function（additional_fn）指定的函数对输入进行预处理。
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-## [41/48] 请对下面的程序文件做一个概述: request_llm\bridge_newbing.py
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-这是一个名为`bridge_newbing.py`的程序文件，包含三个部分：
-
-第一部分使用from语句导入了`edge_gpt`模块的`NewbingChatbot`类。
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-第二部分定义了一个名为`NewBingHandle`的继承自进程类的子类，该类会检查依赖性并启动进程。同时，该部分还定义了一个名为`predict_no_ui_long_connection`的多线程方法和一个名为`predict`的单线程方法，用于与NewBing进行通信。
-
-第三部分定义了一个名为`newbing_handle`的全局变量，并导出了`predict_no_ui_long_connection`和`predict`这两个方法，以供其他程序可以调用。
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-## [42/48] 请对下面的程序文件做一个概述: request_llm\bridge_newbingfree.py
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-这个Python文件包含了三部分内容。第一部分是来自edge_gpt_free.py文件的聊天机器人程序。第二部分是子进程Worker，用于调用主体。第三部分提供了两个函数：predict_no_ui_long_connection和predict用于调用NewBing聊天机器人和返回响应。其中predict函数还提供了一些参数用于控制聊天机器人的回复和更新UI界面。
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-## [43/48] 请对下面的程序文件做一个概述: request_llm\bridge_stackclaude.py
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-这是一个Python源代码文件，文件名为request_llm\bridge_stackclaude.py。代码分为三个主要部分：
-
-第一部分定义了Slack API Client类，实现Slack消息的发送、接收、循环监听，用于与Slack API进行交互。
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-第二部分定义了ClaudeHandle类，继承Process类，用于创建子进程Worker，调用主体，实现Claude与用户交互的功能。
-
-第三部分定义了predict_no_ui_long_connection和predict两个函数，主要用于通过调用ClaudeHandle对象的stream_chat方法来获取Claude的回复，并更新ui以显示相关信息。其中predict函数采用单线程方法，而predict_no_ui_long_connection函数使用多线程方法。
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-## [44/48] 请对下面的程序文件做一个概述: request_llm\bridge_tgui.py
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-该文件是一个Python代码文件，名为request_llm\bridge_tgui.py。它包含了一些函数用于与chatbot UI交互，并通过WebSocket协议与远程LLM模型通信完成文本生成任务，其中最重要的函数是predict()和predict_no_ui_long_connection()。这个程序还有其他的辅助函数，如random_hash()。整个代码文件在协作的基础上完成了一次修改。
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-## [45/48] 请对下面的程序文件做一个概述: request_llm\edge_gpt.py
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-该文件是一个用于调用Bing chatbot API的Python程序，它由多个类和辅助函数构成，可以根据给定的对话连接在对话中提出问题，使用websocket与远程服务通信。程序实现了一个聊天机器人，可以为用户提供人工智能聊天。
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-## [46/48] 请对下面的程序文件做一个概述: request_llm\edge_gpt_free.py
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-该代码文件为一个会话API，可通过Chathub发送消息以返回响应。其中使用了 aiohttp 和 httpx 库进行网络请求并发送。代码中包含了一些函数和常量，多数用于生成请求数据或是请求头信息等。同时该代码文件还包含了一个 Conversation 类，调用该类可实现对话交互。
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-## [47/48] 请对下面的程序文件做一个概述: request_llm\test_llms.py
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-这个文件是用于对llm模型进行单元测试的Python程序。程序导入一个名为"request_llm.bridge_newbingfree"的模块，然后三次使用该模块中的predict_no_ui_long_connection()函数进行预测，并输出结果。此外，还有一些注释掉的代码段，这些代码段也是关于模型预测的。
-
-## 用一张Markdown表格简要描述以下文件的功能：
-check_proxy.py, colorful.py, config.py, config_private.py, core_functional.py, crazy_functional.py, main.py, multi_language.py, theme.py, toolbox.py, crazy_functions\crazy_functions_test.py, crazy_functions\crazy_utils.py, crazy_functions\Latex全文润色.py, crazy_functions\Latex全文翻译.py, crazy_functions\__init__.py, crazy_functions\下载arxiv论文翻译摘要.py。根据以上分析，用一句话概括程序的整体功能。
-
-| 文件名 | 功能描述 |
-| ------ | ------ |
-| check_proxy.py | 检查代理有效性及地理位置 |
-| colorful.py | 控制台打印彩色文字 |
-| config.py | 配置和参数设置 |
-| config_private.py | 私人配置和参数设置 |
-| core_functional.py | 核心函数和参数设置 |
-| crazy_functional.py | 高级功能插件集合 |
-| main.py | 一个 Chatbot 程序，提供各种学术翻译、文本处理和其他查询服务 |
-| multi_language.py | 识别和翻译不同语言 |
-| theme.py | 自定义 gradio 应用程序主题 |
-| toolbox.py | 工具类库，用于协助实现各种功能 |
-| crazy_functions\crazy_functions_test.py | 测试 crazy_functions 中的各种函数 |
-| crazy_functions\crazy_utils.py | 工具函数，用于字符串处理、异常检测、Markdown 格式转换等 |
-| crazy_functions\Latex全文润色.py | 对整个 Latex 项目进行润色和纠错 |
-| crazy_functions\Latex全文翻译.py | 对整个 Latex 项目进行翻译 |
-| crazy_functions\__init__.py | 模块初始化文件，标识 `crazy_functions` 是一个包 |
-| crazy_functions\下载arxiv论文翻译摘要.py | 下载 `arxiv` 论文的 PDF 文件，并提取摘要和翻译 |
-
-这些程序源文件提供了基础的文本和语言处理功能、工具函数和高级插件，使 Chatbot 能够处理各种复杂的学术文本问题，包括润色、翻译、搜索、下载、解析等。
-
-## 用一张Markdown表格简要描述以下文件的功能：
-crazy_functions\代码重写为全英文_多线程.py, crazy_functions\图片生成.py, crazy_functions\对话历史存档.py, crazy_functions\总结word文档.py, crazy_functions\总结音视频.py, crazy_functions\批量Markdown翻译.py, crazy_functions\批量总结PDF文档.py, crazy_functions\批量总结PDF文档pdfminer.py, crazy_functions\批量翻译PDF文档_多线程.py, crazy_functions\理解PDF文档内容.py, crazy_functions\生成函数注释.py, crazy_functions\联网的ChatGPT.py, crazy_functions\解析JupyterNotebook.py, crazy_functions\解析项目源代码.py, crazy_functions\询问多个大语言模型.py, crazy_functions\读文章写摘要.py。根据以上分析，用一句话概括程序的整体功能。
-
-| 文件名 | 功能简述 |
-| --- | --- |
-| 代码重写为全英文_多线程.py | 将Python源代码文件中的中文内容转化为英文 |
-| 图片生成.py | 根据激励文本使用GPT模型生成相应的图像 |
-| 对话历史存档.py | 将每次对话记录写入Markdown格式的文件中 |
-| 总结word文档.py | 对输入的word文档进行摘要生成 |
-| 总结音视频.py | 对输入的音视频文件进行摘要生成 |
-| 批量Markdown翻译.py | 将指定目录下的Markdown文件进行中英文翻译 |
-| 批量总结PDF文档.py | 对PDF文件进行切割和摘要生成 |
-| 批量总结PDF文档pdfminer.py | 对PDF文件进行文本内容的提取和摘要生成 |
-| 批量翻译PDF文档_多线程.py | 将指定目录下的PDF文件进行中英文翻译 |
-| 理解PDF文档内容.py | 对PDF文件进行摘要生成和问题解答 |
-| 生成函数注释.py | 自动生成Python函数的注释 |
-| 联网的ChatGPT.py | 使用网络爬虫和ChatGPT模型进行聊天回答 |
-| 解析JupyterNotebook.py | 对Jupyter Notebook进行代码解析 |
-| 解析项目源代码.py | 对指定编程语言的源代码进行解析 |
-| 询问多个大语言模型.py | 使用多个大语言模型对输入进行处理和回复 |
-| 读文章写摘要.py | 对论文进行解析和全文摘要生成 |
-
-概括程序的整体功能：提供了一系列处理文本、文件和代码的功能，使用了各类语言模型、多线程、网络请求和数据解析技术来提高效率和精度。
-
-## 用一张Markdown表格简要描述以下文件的功能：
-crazy_functions\谷歌检索小助手.py, crazy_functions\高级功能函数模板.py, request_llm\bridge_all.py, request_llm\bridge_chatglm.py, request_llm\bridge_chatgpt.py, request_llm\bridge_jittorllms_llama.py, request_llm\bridge_jittorllms_pangualpha.py, request_llm\bridge_jittorllms_rwkv.py, request_llm\bridge_moss.py, request_llm\bridge_newbing.py, request_llm\bridge_newbingfree.py, request_llm\bridge_stackclaude.py, request_llm\bridge_tgui.py, request_llm\edge_gpt.py, request_llm\edge_gpt_free.py, request_llm\test_llms.py。根据以上分析，用一句话概括程序的整体功能。
-
-| 文件名 | 功能描述 |
-| --- | --- |
-| crazy_functions\谷歌检索小助手.py | 提供谷歌学术搜索页面中相关文章的元数据信息。 |
-| crazy_functions\高级功能函数模板.py | 使用Unsplash API发送相关图片以回复用户的输入。 |
-| request_llm\bridge_all.py | 基于不同LLM模型进行对话。 |
-| request_llm\bridge_chatglm.py | 使用ChatGLM模型生成回复，支持单线程和多线程方式。 |
-| request_llm\bridge_chatgpt.py | 基于GPT模型完成对话。 |
-| request_llm\bridge_jittorllms_llama.py | 使用JittorLLMs模型完成对话，支持单线程和多线程方式。 |
-| request_llm\bridge_jittorllms_pangualpha.py | 使用JittorLLMs模型完成对话，基于多进程和多线程方式。 |
-| request_llm\bridge_jittorllms_rwkv.py | 使用JittorLLMs模型完成聊天功能，提供包括历史信息、参数调节等在内的多个功能选项。 |
-| request_llm\bridge_moss.py | 加载Moss模型完成对话功能。 |
-| request_llm\bridge_newbing.py | 使用Newbing聊天机器人进行对话，支持单线程和多线程方式。 |
-| request_llm\bridge_newbingfree.py | 基于Bing chatbot API实现聊天机器人的文本生成功能。 |
-| request_llm\bridge_stackclaude.py | 基于Slack API实现Claude与用户的交互。 |
-| request_llm\bridge_tgui.py | 通过websocket实现聊天机器人与UI界面交互。 |
-| request_llm\edge_gpt.py | 调用Bing chatbot API提供聊天机器人服务。 |
-| request_llm\edge_gpt_free.py | 实现聊天机器人API，采用aiohttp和httpx工具库。 |
-| request_llm\test_llms.py | 对llm模型进行单元测试。 |
-| 程序整体功能 | 实现不同种类的聊天机器人，可以根据输入进行文本生成。 |
diff --git a/spaces/facebook/MusicGen/CODE_OF_CONDUCT.md b/spaces/facebook/MusicGen/CODE_OF_CONDUCT.md
deleted file mode 100644
index 83f431e8feeb7e80d571f39c9f6c1b96857b5f85..0000000000000000000000000000000000000000
--- a/spaces/facebook/MusicGen/CODE_OF_CONDUCT.md
+++ /dev/null
@@ -1,80 +0,0 @@
-# Code of Conduct
-
-## Our Pledge
-
-In the interest of fostering an open and welcoming environment, we as
-contributors and maintainers pledge to make participation in our project and
-our community a harassment-free experience for everyone, regardless of age, body
-size, disability, ethnicity, sex characteristics, gender identity and expression,
-level of experience, education, socio-economic status, nationality, personal
-appearance, race, religion, or sexual identity and orientation.
-
-## Our Standards
-
-Examples of behavior that contributes to creating a positive environment
-include:
-
-* Using welcoming and inclusive language
-* Being respectful of differing viewpoints and experiences
-* Gracefully accepting constructive criticism
-* Focusing on what is best for the community
-* Showing empathy towards other community members
-
-Examples of unacceptable behavior by participants include:
-
-* The use of sexualized language or imagery and unwelcome sexual attention or
-advances
-* Trolling, insulting/derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or electronic
-address, without explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-professional setting
-
-## Our Responsibilities
-
-Project maintainers are responsible for clarifying the standards of acceptable
-behavior and are expected to take appropriate and fair corrective action in
-response to any instances of unacceptable behavior.
-
-Project maintainers have the right and responsibility to remove, edit, or
-reject comments, commits, code, wiki edits, issues, and other contributions
-that are not aligned to this Code of Conduct, or to ban temporarily or
-permanently any contributor for other behaviors that they deem inappropriate,
-threatening, offensive, or harmful.
-
-## Scope
-
-This Code of Conduct applies within all project spaces, and it also applies when
-an individual is representing the project or its community in public spaces.
-Examples of representing a project or community include using an official
-project e-mail address, posting via an official social media account, or acting
-as an appointed representative at an online or offline event. Representation of
-a project may be further defined and clarified by project maintainers.
-
-This Code of Conduct also applies outside the project spaces when there is a
-reasonable belief that an individual's behavior may have a negative impact on
-the project or its community.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported by contacting the project team at
. All
-complaints will be reviewed and investigated and will result in a response that
-is deemed necessary and appropriate to the circumstances. The project team is
-obligated to maintain confidentiality with regard to the reporter of an incident.
-Further details of specific enforcement policies may be posted separately.
-
-Project maintainers who do not follow or enforce the Code of Conduct in good
-faith may face temporary or permanent repercussions as determined by other
-members of the project's leadership.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
-available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
-
-[homepage]: https://www.contributor-covenant.org
-
-For answers to common questions about this code of conduct, see
-https://www.contributor-covenant.org/faq
diff --git a/spaces/facebook/StyleNeRF/metrics/perceptual_path_length.py b/spaces/facebook/StyleNeRF/metrics/perceptual_path_length.py
deleted file mode 100644
index 8d2c3a44aececa58a7c5602e14a24d424e51bf14..0000000000000000000000000000000000000000
--- a/spaces/facebook/StyleNeRF/metrics/perceptual_path_length.py
+++ /dev/null
@@ -1,131 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Perceptual Path Length (PPL) from the paper "A Style-Based Generator
-Architecture for Generative Adversarial Networks". Matches the original
-implementation by Karras et al. at
-https://github.com/NVlabs/stylegan/blob/master/metrics/perceptual_path_length.py"""
-
-import copy
-import numpy as np
-import torch
-import dnnlib
-from . import metric_utils
-
-#----------------------------------------------------------------------------
-
-# Spherical interpolation of a batch of vectors.
-def slerp(a, b, t):
- a = a / a.norm(dim=-1, keepdim=True)
- b = b / b.norm(dim=-1, keepdim=True)
- d = (a * b).sum(dim=-1, keepdim=True)
- p = t * torch.acos(d)
- c = b - d * a
- c = c / c.norm(dim=-1, keepdim=True)
- d = a * torch.cos(p) + c * torch.sin(p)
- d = d / d.norm(dim=-1, keepdim=True)
- return d
-
-#----------------------------------------------------------------------------
-
-class PPLSampler(torch.nn.Module):
- def __init__(self, G, G_kwargs, epsilon, space, sampling, crop, vgg16):
- assert space in ['z', 'w']
- assert sampling in ['full', 'end']
- super().__init__()
- self.G = copy.deepcopy(G)
- self.G_kwargs = G_kwargs
- self.epsilon = epsilon
- self.space = space
- self.sampling = sampling
- self.crop = crop
- self.vgg16 = copy.deepcopy(vgg16)
-
- def forward(self, c):
- # Generate random latents and interpolation t-values.
- t = torch.rand([c.shape[0]], device=c.device) * (1 if self.sampling == 'full' else 0)
- z0, z1 = torch.randn([c.shape[0] * 2, self.G.z_dim], device=c.device).chunk(2)
-
- # Interpolate in W or Z.
- if self.space == 'w':
- w0, w1 = self.G.mapping(z=torch.cat([z0,z1]), c=torch.cat([c,c])).chunk(2)
- wt0 = w0.lerp(w1, t.unsqueeze(1).unsqueeze(2))
- wt1 = w0.lerp(w1, t.unsqueeze(1).unsqueeze(2) + self.epsilon)
- else: # space == 'z'
- zt0 = slerp(z0, z1, t.unsqueeze(1))
- zt1 = slerp(z0, z1, t.unsqueeze(1) + self.epsilon)
- wt0, wt1 = self.G.mapping(z=torch.cat([zt0,zt1]), c=torch.cat([c,c])).chunk(2)
-
- # Randomize noise buffers.
- for name, buf in self.G.named_buffers():
- if name.endswith('.noise_const'):
- buf.copy_(torch.randn_like(buf))
-
- # Generate images.
- img = self.G.synthesis(ws=torch.cat([wt0,wt1]), noise_mode='const', force_fp32=True, **self.G_kwargs)
-
- # Center crop.
- if self.crop:
- assert img.shape[2] == img.shape[3]
- c = img.shape[2] // 8
- img = img[:, :, c*3 : c*7, c*2 : c*6]
-
- # Downsample to 256x256.
- factor = self.G.img_resolution // 256
- if factor > 1:
- img = img.reshape([-1, img.shape[1], img.shape[2] // factor, factor, img.shape[3] // factor, factor]).mean([3, 5])
-
- # Scale dynamic range from [-1,1] to [0,255].
- img = (img + 1) * (255 / 2)
- if self.G.img_channels == 1:
- img = img.repeat([1, 3, 1, 1])
-
- # Evaluate differential LPIPS.
- lpips_t0, lpips_t1 = self.vgg16(img, resize_images=False, return_lpips=True).chunk(2)
- dist = (lpips_t0 - lpips_t1).square().sum(1) / self.epsilon ** 2
- return dist
-
-#----------------------------------------------------------------------------
-
-def compute_ppl(opts, num_samples, epsilon, space, sampling, crop, batch_size, jit=False):
- dataset = dnnlib.util.construct_class_by_name(**opts.dataset_kwargs)
- vgg16_url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt'
- vgg16 = metric_utils.get_feature_detector(vgg16_url, num_gpus=opts.num_gpus, rank=opts.rank, verbose=opts.progress.verbose)
-
- # Setup sampler.
- sampler = PPLSampler(G=opts.G, G_kwargs=opts.G_kwargs, epsilon=epsilon, space=space, sampling=sampling, crop=crop, vgg16=vgg16)
- sampler.eval().requires_grad_(False).to(opts.device)
- if jit:
- c = torch.zeros([batch_size, opts.G.c_dim], device=opts.device)
- sampler = torch.jit.trace(sampler, [c], check_trace=False)
-
- # Sampling loop.
- dist = []
- progress = opts.progress.sub(tag='ppl sampling', num_items=num_samples)
- for batch_start in range(0, num_samples, batch_size * opts.num_gpus):
- progress.update(batch_start)
- c = [dataset.get_label(np.random.randint(len(dataset))) for _i in range(batch_size)]
- c = torch.from_numpy(np.stack(c)).pin_memory().to(opts.device)
- x = sampler(c)
- for src in range(opts.num_gpus):
- y = x.clone()
- if opts.num_gpus > 1:
- torch.distributed.broadcast(y, src=src)
- dist.append(y)
- progress.update(num_samples)
-
- # Compute PPL.
- if opts.rank != 0:
- return float('nan')
- dist = torch.cat(dist)[:num_samples].cpu().numpy()
- lo = np.percentile(dist, 1, interpolation='lower')
- hi = np.percentile(dist, 99, interpolation='higher')
- ppl = np.extract(np.logical_and(dist >= lo, dist <= hi), dist).mean()
- return float(ppl)
-
-#----------------------------------------------------------------------------
diff --git a/spaces/falterWliame/Face_Mask_Detection/Galaxyofterrorwormsceneuncut BEST.md b/spaces/falterWliame/Face_Mask_Detection/Galaxyofterrorwormsceneuncut BEST.md
deleted file mode 100644
index ab6d1c9e711393899347b0bb470dbc441ffe4fea..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Galaxyofterrorwormsceneuncut BEST.md
+++ /dev/null
@@ -1,38 +0,0 @@
-galaxyofterrorwormsceneuncut Download Zip ->->->-> https://urlca.com/2uDd4E
diff --git a/spaces/falterWliame/Face_Mask_Detection/Kassai Ilona Fonetika.pdf [EXCLUSIVE].md b/spaces/falterWliame/Face_Mask_Detection/Kassai Ilona Fonetika.pdf [EXCLUSIVE].md
deleted file mode 100644
index 32f4c9c71e77da53d2c60240437fbd68a6f5761c..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Kassai Ilona Fonetika.pdf [EXCLUSIVE].md
+++ /dev/null
@@ -1,6 +0,0 @@
-Kassai Ilona Fonetika.pdf Download Zip ->>> https://urlca.com/2uDcnf
diff --git a/spaces/fartsmellalmao/combined-GI-RVC-models/lib/infer_pack/attentions.py b/spaces/fartsmellalmao/combined-GI-RVC-models/lib/infer_pack/attentions.py
deleted file mode 100644
index 05501be1871643f78dddbeaa529c96667031a8db..0000000000000000000000000000000000000000
--- a/spaces/fartsmellalmao/combined-GI-RVC-models/lib/infer_pack/attentions.py
+++ /dev/null
@@ -1,417 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from lib.infer_pack import commons
-from lib.infer_pack import modules
-from lib.infer_pack.modules import LayerNorm
-
-
-class Encoder(nn.Module):
- def __init__(
- self,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size=1,
- p_dropout=0.0,
- window_size=10,
- **kwargs
- ):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.window_size = window_size
-
- self.drop = nn.Dropout(p_dropout)
- self.attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.attn_layers.append(
- MultiHeadAttention(
- hidden_channels,
- hidden_channels,
- n_heads,
- p_dropout=p_dropout,
- window_size=window_size,
- )
- )
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(
- FFN(
- hidden_channels,
- hidden_channels,
- filter_channels,
- kernel_size,
- p_dropout=p_dropout,
- )
- )
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask):
- attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.attn_layers[i](x, x, attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class Decoder(nn.Module):
- def __init__(
- self,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size=1,
- p_dropout=0.0,
- proximal_bias=False,
- proximal_init=True,
- **kwargs
- ):
- super().__init__()
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
-
- self.drop = nn.Dropout(p_dropout)
- self.self_attn_layers = nn.ModuleList()
- self.norm_layers_0 = nn.ModuleList()
- self.encdec_attn_layers = nn.ModuleList()
- self.norm_layers_1 = nn.ModuleList()
- self.ffn_layers = nn.ModuleList()
- self.norm_layers_2 = nn.ModuleList()
- for i in range(self.n_layers):
- self.self_attn_layers.append(
- MultiHeadAttention(
- hidden_channels,
- hidden_channels,
- n_heads,
- p_dropout=p_dropout,
- proximal_bias=proximal_bias,
- proximal_init=proximal_init,
- )
- )
- self.norm_layers_0.append(LayerNorm(hidden_channels))
- self.encdec_attn_layers.append(
- MultiHeadAttention(
- hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
- )
- )
- self.norm_layers_1.append(LayerNorm(hidden_channels))
- self.ffn_layers.append(
- FFN(
- hidden_channels,
- hidden_channels,
- filter_channels,
- kernel_size,
- p_dropout=p_dropout,
- causal=True,
- )
- )
- self.norm_layers_2.append(LayerNorm(hidden_channels))
-
- def forward(self, x, x_mask, h, h_mask):
- """
- x: decoder input
- h: encoder output
- """
- self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
- device=x.device, dtype=x.dtype
- )
- encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
- x = x * x_mask
- for i in range(self.n_layers):
- y = self.self_attn_layers[i](x, x, self_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_0[i](x + y)
-
- y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
- y = self.drop(y)
- x = self.norm_layers_1[i](x + y)
-
- y = self.ffn_layers[i](x, x_mask)
- y = self.drop(y)
- x = self.norm_layers_2[i](x + y)
- x = x * x_mask
- return x
-
-
-class MultiHeadAttention(nn.Module):
- def __init__(
- self,
- channels,
- out_channels,
- n_heads,
- p_dropout=0.0,
- window_size=None,
- heads_share=True,
- block_length=None,
- proximal_bias=False,
- proximal_init=False,
- ):
- super().__init__()
- assert channels % n_heads == 0
-
- self.channels = channels
- self.out_channels = out_channels
- self.n_heads = n_heads
- self.p_dropout = p_dropout
- self.window_size = window_size
- self.heads_share = heads_share
- self.block_length = block_length
- self.proximal_bias = proximal_bias
- self.proximal_init = proximal_init
- self.attn = None
-
- self.k_channels = channels // n_heads
- self.conv_q = nn.Conv1d(channels, channels, 1)
- self.conv_k = nn.Conv1d(channels, channels, 1)
- self.conv_v = nn.Conv1d(channels, channels, 1)
- self.conv_o = nn.Conv1d(channels, out_channels, 1)
- self.drop = nn.Dropout(p_dropout)
-
- if window_size is not None:
- n_heads_rel = 1 if heads_share else n_heads
- rel_stddev = self.k_channels**-0.5
- self.emb_rel_k = nn.Parameter(
- torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
- * rel_stddev
- )
- self.emb_rel_v = nn.Parameter(
- torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
- * rel_stddev
- )
-
- nn.init.xavier_uniform_(self.conv_q.weight)
- nn.init.xavier_uniform_(self.conv_k.weight)
- nn.init.xavier_uniform_(self.conv_v.weight)
- if proximal_init:
- with torch.no_grad():
- self.conv_k.weight.copy_(self.conv_q.weight)
- self.conv_k.bias.copy_(self.conv_q.bias)
-
- def forward(self, x, c, attn_mask=None):
- q = self.conv_q(x)
- k = self.conv_k(c)
- v = self.conv_v(c)
-
- x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
- x = self.conv_o(x)
- return x
-
- def attention(self, query, key, value, mask=None):
- # reshape [b, d, t] -> [b, n_h, t, d_k]
- b, d, t_s, t_t = (*key.size(), query.size(2))
- query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
- key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
- value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
- scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
- if self.window_size is not None:
- assert (
- t_s == t_t
- ), "Relative attention is only available for self-attention."
- key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
- rel_logits = self._matmul_with_relative_keys(
- query / math.sqrt(self.k_channels), key_relative_embeddings
- )
- scores_local = self._relative_position_to_absolute_position(rel_logits)
- scores = scores + scores_local
- if self.proximal_bias:
- assert t_s == t_t, "Proximal bias is only available for self-attention."
- scores = scores + self._attention_bias_proximal(t_s).to(
- device=scores.device, dtype=scores.dtype
- )
- if mask is not None:
- scores = scores.masked_fill(mask == 0, -1e4)
- if self.block_length is not None:
- assert (
- t_s == t_t
- ), "Local attention is only available for self-attention."
- block_mask = (
- torch.ones_like(scores)
- .triu(-self.block_length)
- .tril(self.block_length)
- )
- scores = scores.masked_fill(block_mask == 0, -1e4)
- p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
- p_attn = self.drop(p_attn)
- output = torch.matmul(p_attn, value)
- if self.window_size is not None:
- relative_weights = self._absolute_position_to_relative_position(p_attn)
- value_relative_embeddings = self._get_relative_embeddings(
- self.emb_rel_v, t_s
- )
- output = output + self._matmul_with_relative_values(
- relative_weights, value_relative_embeddings
- )
- output = (
- output.transpose(2, 3).contiguous().view(b, d, t_t)
- ) # [b, n_h, t_t, d_k] -> [b, d, t_t]
- return output, p_attn
-
- def _matmul_with_relative_values(self, x, y):
- """
- x: [b, h, l, m]
- y: [h or 1, m, d]
- ret: [b, h, l, d]
- """
- ret = torch.matmul(x, y.unsqueeze(0))
- return ret
-
- def _matmul_with_relative_keys(self, x, y):
- """
- x: [b, h, l, d]
- y: [h or 1, m, d]
- ret: [b, h, l, m]
- """
- ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
- return ret
-
- def _get_relative_embeddings(self, relative_embeddings, length):
- max_relative_position = 2 * self.window_size + 1
- # Pad first before slice to avoid using cond ops.
- pad_length = max(length - (self.window_size + 1), 0)
- slice_start_position = max((self.window_size + 1) - length, 0)
- slice_end_position = slice_start_position + 2 * length - 1
- if pad_length > 0:
- padded_relative_embeddings = F.pad(
- relative_embeddings,
- commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
- )
- else:
- padded_relative_embeddings = relative_embeddings
- used_relative_embeddings = padded_relative_embeddings[
- :, slice_start_position:slice_end_position
- ]
- return used_relative_embeddings
-
- def _relative_position_to_absolute_position(self, x):
- """
- x: [b, h, l, 2*l-1]
- ret: [b, h, l, l]
- """
- batch, heads, length, _ = x.size()
- # Concat columns of pad to shift from relative to absolute indexing.
- x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
- # Concat extra elements so to add up to shape (len+1, 2*len-1).
- x_flat = x.view([batch, heads, length * 2 * length])
- x_flat = F.pad(
- x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
- )
-
- # Reshape and slice out the padded elements.
- x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
- :, :, :length, length - 1 :
- ]
- return x_final
-
- def _absolute_position_to_relative_position(self, x):
- """
- x: [b, h, l, l]
- ret: [b, h, l, 2*l-1]
- """
- batch, heads, length, _ = x.size()
- # padd along column
- x = F.pad(
- x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
- )
- x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
- # add 0's in the beginning that will skew the elements after reshape
- x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
- x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
- return x_final
-
- def _attention_bias_proximal(self, length):
- """Bias for self-attention to encourage attention to close positions.
- Args:
- length: an integer scalar.
- Returns:
- a Tensor with shape [1, 1, length, length]
- """
- r = torch.arange(length, dtype=torch.float32)
- diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
- return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
- def __init__(
- self,
- in_channels,
- out_channels,
- filter_channels,
- kernel_size,
- p_dropout=0.0,
- activation=None,
- causal=False,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.activation = activation
- self.causal = causal
-
- if causal:
- self.padding = self._causal_padding
- else:
- self.padding = self._same_padding
-
- self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
- self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
- self.drop = nn.Dropout(p_dropout)
-
- def forward(self, x, x_mask):
- x = self.conv_1(self.padding(x * x_mask))
- if self.activation == "gelu":
- x = x * torch.sigmoid(1.702 * x)
- else:
- x = torch.relu(x)
- x = self.drop(x)
- x = self.conv_2(self.padding(x * x_mask))
- return x * x_mask
-
- def _causal_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = self.kernel_size - 1
- pad_r = 0
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
-
- def _same_padding(self, x):
- if self.kernel_size == 1:
- return x
- pad_l = (self.kernel_size - 1) // 2
- pad_r = self.kernel_size // 2
- padding = [[0, 0], [0, 0], [pad_l, pad_r]]
- x = F.pad(x, commons.convert_pad_shape(padding))
- return x
diff --git a/spaces/farukozderim/bug_test_1/app.py b/spaces/farukozderim/bug_test_1/app.py
deleted file mode 100644
index f6b8e36bf317095f4e9a8056cc94153caa99e54e..0000000000000000000000000000000000000000
--- a/spaces/farukozderim/bug_test_1/app.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import gradio as gr
-import os
-
-
-def temp(input_1):
- return "Hey"
-
-
-print(f"System: {os.getenv('SYSTEM')}")
-
-iface = gr.Interface(fn=temp, inputs="sketchpad", outputs="textbox")
-print(f"You are in the spaces: {iface.is_space}")
-iface.launch(debug=True, share=True)
diff --git a/spaces/fatiXbelha/sd/Chief Almighty Hile APK Assemble Ancient Beasts and Warriors.md b/spaces/fatiXbelha/sd/Chief Almighty Hile APK Assemble Ancient Beasts and Warriors.md
deleted file mode 100644
index 0370435952b5199f2521c406bae31320d36a162e..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Chief Almighty Hile APK Assemble Ancient Beasts and Warriors.md
+++ /dev/null
@@ -1,93 +0,0 @@
-Chief Almighty Hile APK: What Is It and How to Download It
-Introduction
-Chief Almighty is a strategy mobile game that lets you build your tribe, hunt ancient beasts, ally with other chiefs, and dominate the continent in the stone ages. The game features stunning graphics, real-time strategy assembling, global server competition, arcane ancient relics, and fierce collision between ancient creatures and primitive warriors. You can download Chief Almighty for free from Google Play Store or App Store .
-However, some players are not satisfied with the normal gameplay and want to have an edge over others. They look for hile apk, which is a modified version of the game that gives them access to cheats such as unlimited resources, instant upgrades, free VIP benefits, and more. Hile apk is a Turkish term that means cheat apk.
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Before you decide to use hile apk for Chief Almighty, you should be aware of the risks and consequences involved. Using hile apk is against the game's terms of service and may result in your account being banned or suspended. Moreover, hile apk may contain viruses, malware, or spyware that can harm your device or steal your personal information. Therefore, we do not recommend or endorse using hile apk for Chief Almighty or any other game.
-How to Download Chief Almighty Hile APK
-If you still want to try hile apk for Chief Almighty at your own risk, here are the steps you need to follow:
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-Find a reliable source of hile apk. There are many websites that claim to offer hile apk for Chief Almighty, but not all of them are trustworthy or updated. You should do some research and read reviews before downloading any file from an unknown source.
-Enable unknown sources on your device. To install hile apk for Chief Almighty, you need to allow your device to install apps from sources other than Google Play Store or App Store. To do this, go to Settings > Security > Unknown Sources and toggle it on.
-Download and install the hile apk file. Once you have found a reputable source of hile apk for Chief Almighty, download the file to your device and tap on it to start the installation process. Follow the instructions on the screen and wait for the installation to complete.
-Launch the game and enjoy the cheats. After installing hile apk for Chief Almighty, you can launch the game from your app drawer or home screen. You should see some changes in the game interface and features that indicate that the cheats are activated. You can now enjoy unlimited resources, instant upgrades, free VIP benefits, and more.
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-Pros and Cons of Using Chief Almighty Hile APK
-Using hile apk for Chief Almighty may seem tempting and fun, but it also has some drawbacks and disadvantages that you should consider. Here are some of the pros and cons of using hile apk for Chief Almighty:
-
-
-Pros
-Cons
-
-
-Faster progress. You can level up your tribe, research new technologies, train your warriors, and expand your territory faster with hile apk. You don't have to wait for timers or resources to complete your tasks.
-Risk of ban. Using hile apk is a violation of the game's terms of service and may result in your account being banned or suspended. You may lose all your progress and achievements if you get caught.
-
-
-Unlimited resources. You can get unlimited amounts of food, wood, stone, iron, and gold with hile apk. You don't have to worry about running out of resources or spending real money to buy them.
-Virus, malware, or spyware. Hile apk may contain harmful software that can damage your device or steal your personal information. You may expose your device to security risks or privacy breaches if you download hile apk from untrusted sources.
-
-
-More fun. You can enjoy the game more with hile apk. You can explore more features, unlock more items, and experience more challenges with hile apk. You can also have an advantage over other players in the global server competition.
-Unfair gameplay. Using hile apk is unfair to other players who play the game legitimately. You may ruin the game balance and the game fun for others who don't use hile apk. You may also face backlash or criticism from other players who don't like cheaters.
-
-
-Conclusion
-In conclusion, hile apk for Chief Almighty is a modified version of the game that gives you access to cheats such as unlimited resources, instant upgrades, free VIP benefits, and more. However, using hile apk is risky and unethical, as it may result in your account being banned or suspended, your device being infected with viruses or malware, or your gameplay being unfair and unenjoyable. Therefore, we recommend that you play the game without hile apk and enjoy the game as it is meant to be played.
-If you want to download Chief Almighty for free from Google Play Store or App Store , you can use this link: [Download Chief Almighty]. If you want to learn more about Chief Almighty and its features, you can visit the official website or the official Facebook page.
-We hope you found this article helpful and informative. If you have any thoughts or feedback about Chief Almighty or hile apk, please feel free to share them in the comments section below. We would love to hear from you!
-FAQs
-Q1: Is Chief Almighty Hile APK safe to use?
-A1: No, Chief Almighty Hile APK is not safe to use. It may contain viruses, malware, or spyware that can harm your device or steal your personal information. It may also result in your account being banned or suspended by the game developers.
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-Q2: How can I update Chief Almighty Hile APK?
-A2: You can update Chief Almighty Hile APK by downloading the latest version of the file from a reliable source. However, we do not recommend using hile apk at all, as it may cause problems for your device and your account.
-Q3: Can I play Chief Almighty Hile APK with other players?
-A3: Yes, you can play Chief Almighty Hile APK with other players on the global server competition. However, this is unfair and unethical, as you will have an advantage over other players who don't use hile apk. You may also face backlash or criticism from other players who don't like cheaters.
-Q4: What are some alternatives to Chief Almighty Hile APK?
-A4: Some alternatives to Chief Almighty Hile APK are playing the game legitimately without cheats, using tips and tricks from online guides and forums , or playing other similar strategy games that don't require hile apk.
-Q5: Where can I find more information about Chief Almighty?
-A5: You can find more information about Chief Almighty on the official website, the official Facebook page, the official YouTube channel, or the official Instagram account. You can also contact the customer service team if you have any questions or issues about the game.
- <|im_end 401be4b1e0Devil Girl APK: A Simulation Game with a Twist
- If you are looking for a simulation game that is not only fun but also challenging, then you might want to try Devil Girl APK. This is a game that will test your observation skills, your decision-making skills, and your imagination. In this game, you will encounter various devil-like girls who have different personalities and stories. You will have to find out their secrets and help them solve their problems. But be careful, because not everything is as it seems in this game.
- What is Devil Girl APK?
- Devil Girl APK is a simulation game developed by Honor apps. It is available for Android devices and can be downloaded for free from various sources. In this game, you will play as a detective who has to investigate different cases involving devil-like girls. These girls are not really evil, but they have some issues that make them act in strange ways. You will have to observe their pictures carefully and find out the problematic parts. Then, you will have to click on them and see how the story unfolds. You will also have to make choices that will affect the outcome of the story.
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The premise of the game
- The game is set in a world where humans are resisting the control of gods and demons. You are a detective who works for a special agency that deals with supernatural cases. You have a device called the Devil Eye that allows you to see through the illusions and lies of the devil-like girls. You use this device to find out their true intentions and help them overcome their troubles. However, you also have to be careful not to fall into their traps or get corrupted by their influence.
- The gameplay of the game
- The gameplay of Devil Girl APK is simple but addictive. You will have to complete different levels that correspond to different cases. Each level consists of several pictures that show different scenes of the story. You will have to examine each picture carefully and find out the parts that are different from reality. These parts can be anything from objects, colors, shapes, expressions, etc. You will have to tap on them and see what happens next. Sometimes, you will have to make choices that will affect how the story progresses. You will also have to collect clues and evidence that will help you solve the case.
- The features of the game
- Devil Girl APK has many features that make it an enjoyable and engaging game. Some of these features are:
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-Beautiful graphics and animations that create a realistic and immersive atmosphere.
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-Multiple endings that depend on your choices and actions.
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-A captivating storyline that mixes mystery, romance, drama, and fantasy.
-A user-friendly interface that is easy to navigate and control.
-A sound system that enhances the mood and emotion of the game.
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- How to download and install Devil Girl APK?
- If you want to play Devil Girl APK on your Android device, you will have to download and install it from a reliable source. Here are some of the requirements and steps that you need to follow:
- The requirements for the game
- Before you download and install Devil Girl APK, you need to make sure that your device meets some minimum requirements. These are:
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-An Android version of 4.0 or higher and a free storage space of at least 100 MB.
-A stable internet connection and a compatible web browser.
-A permission to install apps from unknown sources on your device. To enable this, you need to go to your device's settings, then navigate to security or privacy, then tap on the install unknown apps option. You will see a list of apps that can download other apps. You need to toggle on the switch next to your web browser. You may see a warning message that tells you the risks of installing apps from unknown sources. You need to tap on OK or Allow to proceed.
-
- The steps to download and install the game
- Once you have met the requirements, you can follow these steps to download and install Devil Girl APK on your device:
-
-Open your web browser and go to a reliable source that offers the Devil Girl APK file. Some of the best sources are APKMirror, MUO, and Internet Archive. These sources are safe and verified by many users.
-Search for the Devil Girl APK file on the source's website. You can use the search bar or browse through the categories. Make sure you choose the latest version of the game, which is 1.0.4 as of July 2022.
-Tap on the download button or link to start downloading the Devil Girl APK file. You may see a pop-up that asks you to confirm the download. Tap on OK or Download to continue.
-Wait for the download to finish. You can check the progress in your notification bar or your download folder.
-Once the download is complete, tap on the Devil Girl APK file to open it. You may see a pop-up that asks you to install the game. Tap on Install or Next to proceed.
-Wait for the installation to finish. You may see a pop-up that tells you that the game is installed successfully. Tap on Open or Done to launch the game or exit the installer.
-
- The tips to play the game safely and smoothly
- To enjoy playing Devil Girl APK without any issues, you should follow these tips:
-
-Make sure you have enough battery life and memory space on your device before playing the game.
-Close any unnecessary apps or background processes that may slow down your device or interfere with the game.
-Update your device's software and security patches regularly to avoid any bugs or vulnerabilities.
-Avoid downloading or installing any modded or hacked versions of the game that may contain malware or viruses.
-Do not share your personal or financial information with anyone in the game or online.
-
- Why should you play Devil Girl APK?
- Devil Girl APK is not just a simple simulation game. It is a game that will challenge your mind, test your skills, and entertain you with its story and characters. Here are some of the reasons why you should play Devil Girl APK:
- The benefits of playing the game
- Playing Devil Girl APK can provide you with many benefits, such as:
-
-Improving your observation skills by finding out the hidden details in each picture.
-Enhancing your decision-making skills by choosing the best options for each situation.
-Stimulating your imagination by creating your own scenarios and outcomes based on your choices.
-Relaxing your mind by enjoying the graphics, sounds, and animations of the game.
-Learning new things by discovering different facts and trivia about devil-like girls and their cultures.
-
- The challenges of playing the game
- Playing Devil Girl APK can also pose some challenges, such as:
-
-Facing difficult puzzles that require your logic and reasoning.
-Dealing with unexpected twists and turns that may surprise or shock you.
-Coping with emotional moments that may make you laugh, cry, or angry.
-Avoiding traps and temptations that may lead you to bad endings or consequences.
-Balancing your morality and curiosity by deciding whether to help or harm the devil-like girls.
-
- The comparison of the game with other similar games
- Devil Girl APK is not the only simulation game that features devil-like girls. There are other similar games that you may have heard of or played before, such as:
-
-Name Description Difference Helltaker A puzzle-adventure game where you try to assemble a harem of demon girls by solving puzzles and making choices. It has a more comedic and lighthearted tone than Devil Girl APK. It also has a more pixelated and retro style of graphics. It is available for Windows, Linux, and Mac devices. Devilish Charms A romance simulation game where you date different devil-like boys who have different personalities and stories. It has a more romantic and dramatic tone than Devil Girl APK. It also has a more realistic and detailed style of graphics. It is available for Android and iOS devices. Devil May Cry An action-adventure game where you fight against demons and other supernatural enemies using various weapons and skills. It has a more action-packed and thrilling tone than Devil Girl APK. It also has a more 3D and cinematic style of graphics. It is available for various consoles and PC devices.
- As you can see, Devil Girl APK has its own unique features and qualities that make it stand out from other similar games. It offers a different kind of experience that will appeal to different kinds of players.
- Conclusion
- Devil Girl APK is a simulation game that will challenge your mind, test your skills, and entertain you with its story and characters. It is a game that will make you think, feel, and imagine. It is a game that will make you curious, surprised, and satisfied. If you are looking for a game that is not only fun but also challenging, then you should try Devil Girl APK. You will not regret it.
- FAQs
- Here are some of the frequently asked questions about Devil Girl APK:
-
-Q: Is Devil Girl APK safe to play? A: Yes, Devil Girl APK is safe to play as long as you download and install it from a reliable source and follow the tips to play the game safely and smoothly. However, you should also be aware that the game contains some mature content and themes that may not be suitable for younger or sensitive players. You should play the game at your own discretion.
-Q: How many levels are there in Devil Girl APK? A: There are currently 20 levels in Devil Girl APK, each with its own devil-like girl and story. The developers may add more levels in the future updates.
-Q: How can I get more clues and evidence in Devil Girl APK? A: You can get more clues and evidence in Devil Girl APK by replaying the levels and making different choices. You can also watch ads or use in-app purchases to get more hints or skip puzzles.
-Q: How can I get the best endings in Devil Girl APK? A: You can get the best endings in Devil Girl APK by finding all the problematic parts in each picture, making the right choices for each situation, and helping the devil-like girls overcome their troubles. You can also use the Devil Eye device to see through their illusions and lies.
-Q: How can I contact the developers of Devil Girl APK? A: You can contact the developers of Devil Girl APK by sending them an email at honorapps@gmail.com or by following them on their Facebook page. You can also leave your feedback, suggestions, or questions on their Google Play Store page.
- 401be4b1e0Download Naruto Ultimate Ninja Storm 4 for Android Free APK
-If you are a fan of Naruto, you probably know that one of the best games based on the popular anime and manga series is Naruto Ultimate Ninja Storm 4. This game is a fighting game that lets you experience the most epic battles in the Naruto Shippuden saga, with stunning graphics, fluid animations, and immersive gameplay.
-But did you know that you can also play this game on your Android device? Yes, you read that right. You can download Naruto Ultimate Ninja Storm 4 for Android free APK and enjoy this amazing game on your smartphone or tablet.
-download naruto ultimate ninja storm 4 for android free apk Download ✺ https://urllie.com/2uNGnT
In this article, we will tell you everything you need to know about Naruto Ultimate Ninja Storm 4 for Android, including its features, how to download and install it, and some tips and tricks to help you become a ninja master. Let's get started!
- Features of Naruto Ultimate Ninja Storm 4
-Naruto Ultimate Ninja Storm 4 is not just another fighting game. It is a game that captures the essence of the Naruto Shippuden series, with its characters, story, and action. Here are some of the features that make this game so special:
-Naruto Shippuden: Ultimate Ninja Storm 4 apk free download for android
-
-The most epic fights in the Naruto Shippuden series. You can relive the iconic battles from the anime and manga, such as Naruto vs Sasuke, Madara vs Hashirama, Kaguya vs Team 7, and more. You can also create your own dream matches with any combination of characters.A revamped battle system with new mechanics and options. You can take advantage of the totally redesigned battle system that offers more freedom and strategy. You can switch between your main character and two support characters at any time, use wall-running and elemental damage, break your opponent's guard with powerful attacks, and unleash devastating team combos.A huge roster of characters from the anime and manga. You can choose from over 100 playable characters, each with their own unique abilities, moves, and personality. You can also customize your characters with different costumes, accessories, and items.A thrilling story mode that covers the final arc of the Naruto Shippuden saga. You can follow the events of the Fourth Shinobi World War, from the rise of Obito Uchiha to the final showdown between Naruto and Sasuke. You can also explore different scenarios and outcomes depending on your choices.
- How to Download Naruto Ultimate Ninja Storm 4 for Android Free APK
-Now that you know what Naruto Ultimate Ninja Storm 4 has to offer, you might be wondering how to download it for your Android device. Well, it's not as hard as you might think. Here are the requirements and steps to do it:
-
-Make sure your Android device meets the minimum specifications. To run the game smoothly, you need at least Android 5.0 (Lollipop), 2 GB of RAM, and 3 GB of free storage space.Download the APK file from a trusted source. An APK file is a package file that contains the installation files for an Android app. You can download Naruto Ultimate Ninja Storm 4 for Android free APK from various websites, but make sure you choose a reliable and legal one. Some of the best sources are APKPure, APKMirror, and APKCombo. These websites scan the APK files for viruses and malware, and provide the latest and original versions of the apps.Enable the installation of unknown sources on your Android device. Since you are downloading the APK file from a third-party source, you need to allow your device to install apps from unknown sources. To do this, go to Settings > Security > Unknown Sources and toggle it on. You might also need to grant permission to your browser or file manager to install the APK file.Install the APK file on your Android device. Once you have downloaded the APK file, you can install it by tapping on it and following the instructions on the screen. It might take a few minutes for the installation to complete, depending on your device and internet speed.Enjoy playing Naruto Ultimate Ninja Storm 4 on your Android device. After the installation is done, you can launch the game from your app drawer or home screen. You can also create a shortcut for the game on your desktop for easy access. Now you can enjoy playing Naruto Ultimate Ninja Storm 4 on your Android device anytime and anywhere.
-However, before you download Naruto Ultimate Ninja Storm 4 for Android free APK, you should also be aware of some of the advantages and disadvantages of using an APK file. Here are some of them:
-
-
-Advantages
-Disadvantages
-
-
-- You can access apps that are not available in your region or on the Google Play Store.
-- You might encounter compatibility issues or bugs with some apps or devices.
-
-
-- You can get the latest updates and features of the apps before they are officially released.
-- You might expose your device to security risks or malware if you download from untrusted sources.
-
-
-- You can save storage space by downloading only the APK file instead of the whole app package.
-- You might violate the terms and conditions or copyrights of the app developers or publishers.
-
-
- Tips and Tricks for Playing Naruto Ultimate Ninja Storm 4 on Android
-Naruto Ultimate Ninja Storm 4 is a fun and challenging game that requires skill and strategy to master. If you want to improve your gameplay and enjoy the game more, here are some tips and tricks that might help you:
-
-Optimize the game performance and settings on your Android device. To ensure that the game runs smoothly and without lag, you should close any background apps that might consume your RAM or battery. You should also adjust the game settings according to your device's capabilities. You can change the graphics quality, frame rate, sound effects, and controls from the options menu in the game.Master the combat system and use the secret techniques, awakenings, and shurikens effectively. The combat system in Naruto Ultimate Ninja Storm 4 is complex and versatile, allowing you to perform various moves and combos with different characters. You should learn how to use the basic attacks, chakra dash, guard, counterattack, substitution jutsu, support characters, team combos, secret techniques, awakenings, and shurikens in different situations. You should also know the strengths and weaknesses of each character and their fighting style.Unlock all the characters, costumes, and modes in the game. The game offers a lot of content and variety for you to explore. You can unlock new characters, costumes, and modes by completing different tasks and challenges in the game. For example, you can unlock new characters by playing through the story mode or completing certain missions. You can unlock new costumes by collecting ryo (the in-game currency) or completing certain objectives. You can unlock new modes by reaching certain ranks or achievements in the game.
- Conclusion
-Naruto Ultimate Ninja Storm 4 is a game that every Naruto fan should play. It is a game that delivers an authentic and immersive Naruto experience, with its stunning graphics, fluid gameplay, epic battles, huge roster of characters, thrilling story mode, and more. It is also a game that you can play on your Android device, thanks to the APK file that you can download for free from various sources. You can enjoy playing Naruto Ultimate Ninja Storm 4 on your smartphone or tablet anytime and anywhere, as long as you meet the minimum requirements and follow the steps to install it.
-So what are you waiting for? Download Naruto Ultimate Ninja Storm 4 for Android free APK now and join the ultimate ninja storm!
- FAQs
-Here are some of the frequently asked questions and answers about Naruto Ultimate Ninja Storm 4 for Android and the APK file:
-
-Is Naruto Ultimate Ninja Storm 4 for Android free? Yes, you can download and play Naruto Ultimate Ninja Storm 4 for Android free APK from various websites. However, you should be careful about the source and the quality of the APK file, as some of them might be fake, outdated, or infected with malware.Is Naruto Ultimate Ninja Storm 4 for Android legal? It depends on the country and the region where you live. Some countries and regions might have strict laws and regulations regarding the use of APK files and the distribution of copyrighted content. You should check the local laws and the terms and conditions of the app developers and publishers before downloading and installing Naruto Ultimate Ninja Storm 4 for Android free APK.Is Naruto Ultimate Ninja Storm 4 for Android safe? As long as you download the APK file from a trusted and legal source, and you enable the installation of unknown sources on your device, Naruto Ultimate Ninja Storm 4 for Android should be safe to play. However, you should always scan the APK file with an antivirus or a malware detector before installing it, and you should backup your data and device before playing the game.How to update Naruto Ultimate Ninja Storm 4 for Android? To update Naruto Ultimate Ninja Storm 4 for Android, you need to download the latest version of the APK file from a reliable source and install it over the existing one. You should also check if there are any updates or patches available in the game itself, and download them accordingly.How to uninstall Naruto Ultimate Ninja Storm 4 for Android? To uninstall Naruto Ultimate Ninja Storm 4 for Android, you need to go to Settings > Apps > Naruto Ultimate Ninja Storm 4 and tap on Uninstall. You should also delete any leftover files or folders related to the game from your device's storage. 197e85843dHow to Download Chess GUI
-If you are a chess enthusiast, you might want to play chess against a computer or analyze your games with a powerful engine. For that, you need a chess graphical user interface (GUI) that can connect to various chess engines and provide you with various features and options. In this article, we will explain what is chess GUI, why do you need it, how to choose one, and how to download and install it on your computer.
-how to download chess gui Download ……… https://gohhs.com/2uPs5r
What is Chess GUI?
-A chess GUI is a software program that allows you to interact with a chess engine. A chess engine is a program that calculates the best moves in a given position and evaluates the strength of each side. A chess GUI provides you with a graphical representation of the chess board, pieces, moves, and analysis. It also lets you play games against the engine, adjust its settings, load and save games, use opening books and endgame tablebases, and more.
- Why do you need Chess GUI?
-There are many benefits of using a chess GUI. Here are some of them:
-
-You can improve your chess skills by playing against different levels of engines, analyzing your games, and learning from their suggestions.
-You can enjoy the game of chess in different ways, such as playing variants like Chess960, using different board styles and piece sets, and listening to move announcements.
-You can test and compare different chess engines and see how they perform in various positions and situations.
-You can access a large database of chess games and positions and search for specific openings, players, events, etc.
-
- How to choose a Chess GUI?
-There are many chess GUIs available on the internet, but not all of them are equally good. You need to consider some factors before choosing one. Here are some of them:
- Features to look for in a Chess GUI
-Compatibility
-The first thing you need to check is whether the chess GUI is compatible with your operating system (Windows, Linux, Mac, Android, iOS, etc.) and your device (PC, laptop, tablet, smartphone, etc.). You also need to make sure that the chess GUI supports the protocols that your chess engines use. The most common protocols are UCI (Universal Chess Interface) and Winboard (or XBoard). Most modern chess engines use UCI, but some older ones use Winboard. Some chess GUIs can support both protocols, but some can only support one.
- Interface
-The second thing you need to consider is how easy and user-friendly the interface of the chess GUI is. You want a chess GUI that has a clear and intuitive layout, with menus, buttons, icons, and tabs that are easy to access and understand. You also want a chess GUI that has customizable options, such as changing the colors, fonts, sounds, languages, etc. You also want a chess GUI that has a responsive and smooth performance, without bugs or crashes.
- Functionality
-The third thing you need to look for is what features and functions the chess GUI offers. You want a chess GUI that has all the basic features that you need, such as playing games against engines or humans online or offline, loading and saving games in PGN format, using opening books and endgame tablebases, displaying analysis lines and evaluations, setting up positions and playing from them, etc. You also want a chess GUI that has some advanced features that you might want, such as playing variants like Chess960 or Fischer Random
Popular Chess GUIs to consider
-Now that you know what to look for in a chess GUI, you might be wondering which ones are the best to choose from. There are many chess GUIs available on the internet, but some of them are more popular and reliable than others. Here are some of the most popular chess GUIs that you can consider:
- Arena Chess GUI
-Arena Chess GUI is a free and powerful chess GUI that supports UCI and Winboard protocols. It has a simple and elegant interface, with many features and options. You can play games against various engines, analyze your games with multiple engines, use opening books and endgame tablebases, access a huge online database of games, create your own tournaments, and more. You can also customize the board style, piece set, sounds, colors, fonts, etc. Arena Chess GUI comes with a few engines pre-installed, but you can also download and install many more from the internet.
-How to install Arena Chess GUI on Windows
- Stockfish Chess GUI
-Stockfish Chess GUI is a free and open source chess GUI that is based on the Stockfish engine, one of the strongest chess engines in the world. It has a simple and intuitive interface, with many features and options. You can play games against the Stockfish engine or other online players, analyze your games with the engine, use opening books and endgame tablebases, access a large database of games, create your own puzzles, and more. You can also customize the board style, piece set, sounds, colors, fonts, etc. Stockfish Chess GUI is compatible with UCI protocol and can connect to other UCI engines as well.
- Chess UI
-Chess UI is a free and user-friendly chess GUI that supports UCI protocol. It has a modern and sleek interface, with many features and options. You can play games against various engines, analyze your games with the engine, use opening books and endgame tablebases, access a large database of games, create your own tournaments, and more. You can also customize the board style, piece set, sounds, colors, fonts, etc. Chess UI comes with a few engines pre-installed, but you can also download and install many more from the internet.
- How to download and install Chess GUI?
-Once you have decided which chess GUI you want to use, you need to download and install it on your computer. The process is usually simple and straightforward, but it may vary slightly depending on the chess GUI you choose. Here are the general steps that you need to follow:
- Step 1: Find the download link for your chosen Chess GUI
-The first step is to find the download link for your chosen chess GUI. You can usually find it on the official website of the chess GUI or on other reputable websites that offer chess software downloads. For example, you can find the download links for Arena Chess GUI, Stockfish Chess GUI, and Chess UI on their respective websites. Make sure that you download the correct version for your operating system (Windows, Linux, Mac, etc.) and your device (PC, laptop, tablet, smartphone, etc.).
- Step 2: Save the file to your computer and unzip it if necessary
-The second step is to save the file to your computer and unzip it if necessary. The file will usually be in a compressed format such as ZIP or RAR. You will need a program such as WinZip or WinRAR to extract the contents of the file. Once you have extracted the contents of the file, you will see a folder containing the files of the chess GUI.
- Step 3: Run the setup program or the executable file to launch the Chess GUI
-The third step is to run the setup program or the executable file to launch the chess GUI. Depending on the chess GUI you choose, you may need to run a setup program that will guide you through the installation process or simply run an executable file that will launch the chess GUI directly. For example, Arena Chess GUI has a setup program that will ask you to agree to the terms of use, choose a destination folder for installation, create shortcuts for easy access, etc. Stockfish Chess GUI and Chess UI do not have a setup program; they have an executable file that will launch the chess GUI when you double-click on it.
- Conclusion
-In this article, we have explained what is chess GUI, why do you need it, how to choose one, and how to download and install it on your computer. We hope that this article has helped you understand how to download chess GUI and enjoy playing chess with various engines and features. If you have any questions or comments about this article or about chess. GUI, please feel free to contact us or leave a comment below. We would love to hear from you and help you with your chess journey. Thank you for reading and happy chess playing!
- FAQs
-Here are some of the frequently asked questions about chess GUI:
- What is the difference between chess GUI and chess engine?
-A chess GUI is a software program that allows you to interact with a chess engine. A chess engine is a software program that calculates the best moves in a given position and evaluates the strength of each side. A chess GUI provides you with a graphical representation of the chess board, pieces, moves, and analysis. A chess engine provides you with the numerical and verbal output of its calculations.
- Can I use any chess engine with any chess GUI?
-Not necessarily. You need to make sure that the chess engine and the chess GUI use the same protocol to communicate with each other. The most common protocols are UCI (Universal Chess Interface) and Winboard (or XBoard). Most modern chess engines use UCI, but some older ones use Winboard. Some chess GUIs can support both protocols, but some can only support one. You need to check the compatibility of the chess engine and the chess GUI before using them together.
- How can I download more chess engines?
-There are many websites that offer free or paid downloads of various chess engines. Some of the most popular ones are Chess Engines Diary, CCRL 40/15, and TCEC. You can also find some links to download chess engines on the official websites of some chess GUIs, such as Arena Chess GUI and Stockfish Chess GUI. You need to make sure that you download the correct version for your operating system and device.
- How can I update my chess engine or chess GUI?
-You can update your chess engine or chess GUI by downloading the latest version from the internet and replacing the old files with the new ones. You can also check if your chess engine or chess GUI has an automatic update feature that will notify you when a new version is available and download it for you. You need to make sure that you backup your settings and files before updating your chess engine or chess GUI.
- How can I improve my chess skills with a chess GUI?
-You can improve your chess skills with a chess GUI by using it in various ways, such as:
-
-Playing games against different levels of engines or online players and analyzing your mistakes and weaknesses.
-Studying openings, middlegames, and endgames with the help of opening books, endgame tablebases, and engine analysis.
-Solving puzzles and exercises that test your tactical and strategic skills.
-Watching and learning from the games of strong players and engines.
-Creating your own scenarios and positions and playing them out with the engine.
- 401be4b1e0
-
-
{children}
- },
- code({ node, inline, className, children, ...props }) {
- if (children.length) {
- if (children[0] == '▍') {
- return (
- ▍
- )
- }
-
- children[0] = (children[0] as string).replace('`▍`', '▍')
- }
-
- const match = /language-(\w+)/.exec(className || '')
-
- if (inline) {
- return (
-
- {children}
-
- )
- }
-
- return (
-
-
-
- {message.author === 'bot' &&
-
-
-Fonctionnalité | Description
---- | ---
-Polissage en un clic | Prend en charge la correction en un clic et la recherche d'erreurs de syntaxe dans les documents de recherche.
-Traduction Chinois-Anglais en un clic | Une touche pour traduire la partie chinoise en anglais ou celle anglaise en chinois.
-Explication de code en un clic | Affiche et explique correctement le code.
-[Raccourcis clavier personnalisables](https://www.bilibili.com/video/BV14s4y1E7jN) | Prend en charge les raccourcis clavier personnalisables.
-[Configuration du serveur proxy](https://www.bilibili.com/video/BV1rc411W7Dr) | Prend en charge la configuration du serveur proxy.
-Conception modulaire | Prend en charge la personnalisation des plugins de fonctions et des [plugins] de fonctions hiérarchiques personnalisés, et les plugins prennent en charge [la mise à jour à chaud](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
-[Auto-analyse du programme](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugins] [Lire en un clic](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) le code source de ce projet.
-[Analyse de programme](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugins] En un clic, les projets Python/C/C++/Java/Lua/... peuvent être analysés.
-Lire le document de recherche | [Plugins] Lisez le résumé de l'article en latex et générer un résumé.
-Traduction et polissage de l'article complet en LaTeX | [Plugins] Une touche pour traduire ou corriger en LaTeX
-Génération Commentaire de fonction en vrac | [Plugins] Lisez en un clic les fonctions et générez des commentaires de fonction.
-Rapport d'analyse automatique des chats générés | [Plugins] Génère un rapport de synthèse après l'exécution.
-[Assistant arxiv](https://www.bilibili.com/video/BV1LM4y1279X) | [Plugins] Entrez l'url de l'article arxiv pour traduire le résumé + télécharger le PDF en un clic
-[Traduction complète des articles PDF](https://www.bilibili.com/video/BV1KT411x7Wn) | [Plugins] Extraire le titre et le résumé de l'article PDF + Traduire le texte entier (multithread)
-[Aide à la recherche Google Academ](https://www.bilibili.com/video/BV19L411U7ia) | [Plugins] Donnez à GPT l'URL de n'importe quelle page de recherche Google Academ pour vous aider à sélectionner des articles intéressants
-Affichage de formules/images/tableaux | Afficher la forme traduite et rendue d'une formule en même temps, plusieurs formules et surlignage du code prend en charge
-Prise en charge des plugins multithread | Prise en charge de l'appel multithread de chatgpt, traitement en masse de texte ou de programmes en un clic
-Activer le thème Gradio sombre [theme](https://github.com/binary-husky/chatgpt_academic/issues/173) au démarrage | Ajoutez ```/?__dark-theme=true``` à l'URL du navigateur pour basculer vers le thème sombre
-[Prise en charge de plusieurs modèles LLM](https://www.bilibili.com/video/BV1wT411p7yf), [prise en charge de l'interface API2D](https://api2d.com/) | Comment cela serait-il de se faire servir par GPT3.5, GPT4 et la [ChatGLM de Tsinghua](https://github.com/THUDM/ChatGLM-6B) en même temps?
-Expérience en ligne d'huggingface sans science | Après vous être connecté à huggingface, copiez [cet espace](https://huggingface.co/spaces/qingxu98/gpt-academic)
-... | ...
-
-
-
-
-Vous êtes un traducteur professionnel d'articles universitaires en français.
-
-Ceci est un fichier Markdown, veuillez le traduire en français sans modifier les commandes Markdown existantes :
-
-- Nouvelle interface (modifiable en modifiant l'option de mise en page dans config.py pour basculer entre les mises en page gauche-droite et haut-bas)
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Tengo para vender el Programa + Crack del LEX DOCTOR 9 y del LEX DOCTOR 10. Por favor comunicarse al Grupo de Facebook: "Abogados del Lex-Doctor"...EL DEL CABALLITO BLANCO CON ALAS.....
-lex doctor 9.1 descargar taringa Download File ○ https://urlgoal.com/2uyNBu
-
-
-def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]):
- """Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights
- Parameters:
- net (network) -- the network to be initialized
- init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
- gain (float) -- scaling factor for normal, xavier and orthogonal.
- gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
-
- Return an initialized network.
- """
- if len(gpu_ids) > 0:
- assert (torch.cuda.is_available())
- net.to(gpu_ids[0])
- net = torch.nn.DataParallel(net, gpu_ids) # multi-GPUs
- init_weights(net, init_type, init_gain=init_gain)
- return net
-
-
-def imageSpaceRotation(xy, rot):
- '''
- args:
- xy: (B, 2, N) input
- rot: (B, 2) x,y axis rotation angles
-
- rotation center will be always image center (other rotation center can be represented by additional z translation)
- '''
- disp = rot.unsqueeze(2).sin().expand_as(xy)
- return (disp * xy).sum(dim=1)
-
-
-def cal_gradient_penalty(netD, real_data, fake_data, device, type='mixed', constant=1.0, lambda_gp=10.0):
- """Calculate the gradient penalty loss, used in WGAN-GP paper https://arxiv.org/abs/1704.00028
-
- Arguments:
- netD (network) -- discriminator network
- real_data (tensor array) -- real images
- fake_data (tensor array) -- generated images from the generator
- device (str) -- GPU / CPU: from torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu')
- type (str) -- if we mix real and fake data or not [real | fake | mixed].
- constant (float) -- the constant used in formula ( | |gradient||_2 - constant)^2
- lambda_gp (float) -- weight for this loss
-
- Returns the gradient penalty loss
- """
- if lambda_gp > 0.0:
- if type == 'real': # either use real images, fake images, or a linear interpolation of two.
- interpolatesv = real_data
- elif type == 'fake':
- interpolatesv = fake_data
- elif type == 'mixed':
- alpha = torch.rand(real_data.shape[0], 1)
- alpha = alpha.expand(real_data.shape[0], real_data.nelement() // real_data.shape[0]).contiguous().view(
- *real_data.shape)
- alpha = alpha.to(device)
- interpolatesv = alpha * real_data + ((1 - alpha) * fake_data)
- else:
- raise NotImplementedError('{} not implemented'.format(type))
- interpolatesv.requires_grad_(True)
- disc_interpolates = netD(interpolatesv)
- gradients = torch.autograd.grad(outputs=disc_interpolates, inputs=interpolatesv,
- grad_outputs=torch.ones(disc_interpolates.size()).to(device),
- create_graph=True, retain_graph=True, only_inputs=True)
- gradients = gradients[0].view(real_data.size(0), -1) # flat the data
- gradient_penalty = (((gradients + 1e-16).norm(2, dim=1) - constant) ** 2).mean() * lambda_gp # added eps
- return gradient_penalty, gradients
- else:
- return 0.0, None
-
-def get_norm_layer(norm_type='instance'):
- """Return a normalization layer
- Parameters:
- norm_type (str) -- the name of the normalization layer: batch | instance | none
- For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev).
- For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics.
- """
- if norm_type == 'batch':
- norm_layer = functools.partial(nn.BatchNorm2d, affine=True, track_running_stats=True)
- elif norm_type == 'instance':
- norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
- elif norm_type == 'group':
- norm_layer = functools.partial(nn.GroupNorm, 32)
- elif norm_type == 'none':
- norm_layer = None
- else:
- raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
- return norm_layer
-
-class Flatten(nn.Module):
- def forward(self, input):
- return input.view(input.size(0), -1)
-
-class ConvBlock(nn.Module):
- def __init__(self, in_planes, out_planes, norm='batch'):
- super(ConvBlock, self).__init__()
- self.conv1 = conv3x3(in_planes, int(out_planes / 2))
- self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4))
- self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4))
-
- if norm == 'batch':
- self.bn1 = nn.BatchNorm2d(in_planes)
- self.bn2 = nn.BatchNorm2d(int(out_planes / 2))
- self.bn3 = nn.BatchNorm2d(int(out_planes / 4))
- self.bn4 = nn.BatchNorm2d(in_planes)
- elif norm == 'group':
- self.bn1 = nn.GroupNorm(32, in_planes)
- self.bn2 = nn.GroupNorm(32, int(out_planes / 2))
- self.bn3 = nn.GroupNorm(32, int(out_planes / 4))
- self.bn4 = nn.GroupNorm(32, in_planes)
-
- if in_planes != out_planes:
- self.downsample = nn.Sequential(
- self.bn4,
- nn.ReLU(True),
- nn.Conv2d(in_planes, out_planes,
- kernel_size=1, stride=1, bias=False),
- )
- else:
- self.downsample = None
-
- def forward(self, x):
- residual = x
-
- out1 = self.bn1(x)
- out1 = F.relu(out1, True)
- out1 = self.conv1(out1)
-
- out2 = self.bn2(out1)
- out2 = F.relu(out2, True)
- out2 = self.conv2(out2)
-
- out3 = self.bn3(out2)
- out3 = F.relu(out3, True)
- out3 = self.conv3(out3)
-
- out3 = torch.cat((out1, out2, out3), 1)
-
- if self.downsample is not None:
- residual = self.downsample(residual)
-
- out3 += residual
-
- return out3
-
\ No newline at end of file
diff --git a/spaces/gulabpatel/GFP_GAN/setup.py b/spaces/gulabpatel/GFP_GAN/setup.py
deleted file mode 100644
index 474e9188aa2dc5c19614921760ce4ad99bd19c13..0000000000000000000000000000000000000000
--- a/spaces/gulabpatel/GFP_GAN/setup.py
+++ /dev/null
@@ -1,107 +0,0 @@
-#!/usr/bin/env python
-
-from setuptools import find_packages, setup
-
-import os
-import subprocess
-import time
-
-version_file = 'gfpgan/version.py'
-
-
-def readme():
- with open('README.md', encoding='utf-8') as f:
- content = f.read()
- return content
-
-
-def get_git_hash():
-
- def _minimal_ext_cmd(cmd):
- # construct minimal environment
- env = {}
- for k in ['SYSTEMROOT', 'PATH', 'HOME']:
- v = os.environ.get(k)
- if v is not None:
- env[k] = v
- # LANGUAGE is used on win32
- env['LANGUAGE'] = 'C'
- env['LANG'] = 'C'
- env['LC_ALL'] = 'C'
- out = subprocess.Popen(cmd, stdout=subprocess.PIPE, env=env).communicate()[0]
- return out
-
- try:
- out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])
- sha = out.strip().decode('ascii')
- except OSError:
- sha = 'unknown'
-
- return sha
-
-
-def get_hash():
- if os.path.exists('.git'):
- sha = get_git_hash()[:7]
- else:
- sha = 'unknown'
-
- return sha
-
-
-def write_version_py():
- content = """# GENERATED VERSION FILE
-# TIME: {}
-__version__ = '{}'
-__gitsha__ = '{}'
-version_info = ({})
-"""
- sha = get_hash()
- with open('VERSION', 'r') as f:
- SHORT_VERSION = f.read().strip()
- VERSION_INFO = ', '.join([x if x.isdigit() else f'"{x}"' for x in SHORT_VERSION.split('.')])
-
- version_file_str = content.format(time.asctime(), SHORT_VERSION, sha, VERSION_INFO)
- with open(version_file, 'w') as f:
- f.write(version_file_str)
-
-
-def get_version():
- with open(version_file, 'r') as f:
- exec(compile(f.read(), version_file, 'exec'))
- return locals()['__version__']
-
-
-def get_requirements(filename='requirements.txt'):
- here = os.path.dirname(os.path.realpath(__file__))
- with open(os.path.join(here, filename), 'r') as f:
- requires = [line.replace('\n', '') for line in f.readlines()]
- return requires
-
-
-if __name__ == '__main__':
- write_version_py()
- setup(
- name='gfpgan',
- version=get_version(),
- description='GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration',
- long_description=readme(),
- long_description_content_type='text/markdown',
- author='Xintao Wang',
- author_email='xintao.wang@outlook.com',
- keywords='computer vision, pytorch, image restoration, super-resolution, face restoration, gan, gfpgan',
- url='https://github.com/TencentARC/GFPGAN',
- include_package_data=True,
- packages=find_packages(exclude=('options', 'datasets', 'experiments', 'results', 'tb_logger', 'wandb')),
- classifiers=[
- 'Development Status :: 4 - Beta',
- 'License :: OSI Approved :: Apache Software License',
- 'Operating System :: OS Independent',
- 'Programming Language :: Python :: 3',
- 'Programming Language :: Python :: 3.7',
- 'Programming Language :: Python :: 3.8',
- ],
- license='Apache License Version 2.0',
- setup_requires=['cython', 'numpy'],
- install_requires=get_requirements(),
- zip_safe=False)
diff --git a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/torch_utils/__init__.py b/spaces/gyugnsu/DragGan-Inversion/stylegan_human/torch_utils/__init__.py
deleted file mode 100644
index a0b0f4efcbe1e3cd4199eeecb043d5afe1548307..0000000000000000000000000000000000000000
--- a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/torch_utils/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-# empty
diff --git a/spaces/hackathon-somos-nlp-2023/GIPBERT/app.py b/spaces/hackathon-somos-nlp-2023/GIPBERT/app.py
deleted file mode 100644
index f96e00a013464ef06c2172a911a8a0d2809ac922..0000000000000000000000000000000000000000
--- a/spaces/hackathon-somos-nlp-2023/GIPBERT/app.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import gradio as gr
-import torch
-from peft import PeftModel, PeftConfig
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-peft_model_id = "hackathon-somos-nlp-2023/PAG-BERT"
-config = PeftConfig.from_pretrained(peft_model_id)
-model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')
-tokenizer = AutoTokenizer.from_pretrained(peft_model_id)
-# Load the Lora model
-model = PeftModel.from_pretrained(model, peft_model_id)
-
-def predecir_intervencion(text):
- text = "" + text + " Intervención: "
- batch = tokenizer(text, return_tensors='pt')
- with torch.cuda.amp.autocast():
- output_tokens = model.generate(**batch, max_new_tokens=256, eos_token_id=50258)
-
- output = tokenizer.decode(output_tokens[0], skip_special_tokens=False)
-
- aux = output.split("Intervención:")[1].strip()
- intervencion = aux.split("Resultado:")[0].strip()
- resultado = aux.split("Resultado:")[1].split("")[0].strip()
-
- return intervencion, resultado
-
-with gr.Blocks() as demo:
- gr.Markdown("Predicción de intervenciones para mitigar el daño racista en el pueblo gitano")
- with gr.Row():
- hechos = gr.Textbox(placeholder="Un alumno gitano de un Instituto...", label="Hechos")
- with gr.Row():
- intervencion = gr.Textbox(label="Intervención")
- resultado = gr.Textbox(label="Resultado")
-
- btn = gr.Button("Go")
- btn.click(fn=predecir_intervencion, inputs=hechos, outputs=[intervencion, resultado])
-
- gr.Examples(
- examples=["El Diario de Almería publicó una noticia sobre un conflicto con la Guardia Civil, mencionando la etnia de la persona implicada.",
- "Un hombre gitano participante de servicios la FSG en Valencia, nos contó que estaba en la puerta de su casa arreglando un asiento de su furgoneta y, para ello, lo desmontó y lo colocó en la acera mientras terminaba la reparación. En ese momento, se acercó un agente de policía local y le dijo que le dejara entrar a su casa para ver si tenía chatarra almacenada. El hombre le dijo que si no tenía una orden no le dejaba entrar a su casa. Ante esa respuesta, el policía (que según la víctima se enfadó por la respuesta que le dio) le puso una multa por “ensuciar la vía pública “. Para valorar la posibilidad de recurrir dicha multa, la Técnica de Igualdad entrevistó al hombre, que informó que esto le había ocurrido hacía tiempo, por lo que no fue posible interponer recurso. Igualmente, nos informó que en el barrio del Cabañal esta situación era bastante frecuente hacia otras personas gitanas. La víctima no quiso que interviniésemos con otras acciones, pero pidió que desde la FSG se estuviera más pendiente de las actuaciones policiales de su zona.",
- "Una mujer gitana contó en la FSG que recibió comentarios inadecuados por teléfono por parte de la administrativa y de la trabajadora social de su centro de servicios sociales, que le dijeron que pidiera ayuda al Secretariado Gitano. La mujer estaba convencida que esto le había pasado por ser gitana."
- ],
- inputs=hechos,
- label="Ejemplos"
- )
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/__init__.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/__init__.py
deleted file mode 100644
index b0804ff9446160fdad093af0b0fcff2e45fddb76..0000000000000000000000000000000000000000
--- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/data/datasets/__init__.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-from .coco import COCODataset
-from .voc import PascalVOCDataset
-from .concat_dataset import ConcatDataset
-from .background import Background
-from .tsv import TSVDataset, ODTSVDataset
-
-from .modulated_coco import ModulatedDataset, CocoDetection, CocoGrounding
-from .flickr import FlickrDataset
-from .refexp import RefExpDataset
-from .mixed import MixedDataset
-from .gqa import GQADataset
-
-from .coco_dt import CocoDetectionTSV
-from .caption import CaptionTSV
-from .lvis import LvisDetection
-from .pseudo_data import PseudoData
-from .phrasecut import PhrasecutDetection
-
-__all__ = ["COCODataset", "TSVDataset", "ODTSVDataset", "ConcatDataset", "PascalVOCDataset", "Background",
- "ModulatedDataset", "MixedDataset", "CocoDetection", "FlickrDataset", "RefExpDataset", "GQADataset",
- "CocoDetectionTSV", "CocoGrounding", "CaptionTSV", "LvisDetection", "PseudoData", "PhrasecutDetection"
- ]
diff --git a/spaces/harshvardhansb/ObjectDetection/node_modules/@tensorflow-models/coco-ssd/dist/classes.js b/spaces/harshvardhansb/ObjectDetection/node_modules/@tensorflow-models/coco-ssd/dist/classes.js
deleted file mode 100644
index 1eda15e6d049ec4993fad37ab520dfd6cbee9ce5..0000000000000000000000000000000000000000
--- a/spaces/harshvardhansb/ObjectDetection/node_modules/@tensorflow-models/coco-ssd/dist/classes.js
+++ /dev/null
@@ -1,32 +0,0 @@
-"use strict";
-/**
- * @license
- * Copyright 2019 Google LLC. 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.
- * =============================================================================
- */
-Object.defineProperty(exports, "__esModule", { value: true });
-exports.CLASSES = {
- 1: {
- name: '/m/01g317',
- id: 1,
- displayName: 'person',
- },
- 2: {
- name: '/m/0199g',
- id: 2,
- displayName: 'bicycle',
- },
- 3: {
- name: '/m/0k4j',
- id:
\ No newline at end of file
diff --git a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/projects/TensorMask/tensormask/layers/csrc/vision.cpp b/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/projects/TensorMask/tensormask/layers/csrc/vision.cpp
deleted file mode 100644
index ad8e472c2cfc7c10e00cd6b00fc22c0dd9384dd1..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/Self-Correction-Human-Parsing-for-ACGPN/mhp_extension/detectron2/projects/TensorMask/tensormask/layers/csrc/vision.cpp
+++ /dev/null
@@ -1,19 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-#include
-#include "SwapAlign2Nat/SwapAlign2Nat.h"
-
-namespace tensormask {
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def(
- "swap_align2nat_forward",
- &SwapAlign2Nat_forward,
- "SwapAlign2Nat_forward");
- m.def(
- "swap_align2nat_backward",
- &SwapAlign2Nat_backward,
- "SwapAlign2Nat_backward");
-}
-
-} // namespace tensormask
diff --git a/spaces/hasibzunair/image-recognition-demo/app.py b/spaces/hasibzunair/image-recognition-demo/app.py
deleted file mode 100644
index 4c8d54503da90f9abdcab7021b625b29579866ef..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/image-recognition-demo/app.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import os
-import torch
-import gradio as gr
-
-from PIL import Image
-from torchvision import transforms
-
-
-"""
-Built following:
-https://huggingface.co/spaces/pytorch/ResNet/tree/main
-https://www.gradio.app/image_classification_in_pytorch/
-"""
-
-# Get classes list
-os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt")
-
-# Load PyTorch model
-model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', pretrained=True)
-model.eval()
-
-# Download an example image from the pytorch website
-torch.hub.download_url_to_file("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
-
-# Inference!
-def inference(input_image):
- preprocess = transforms.Compose([
- transforms.Resize(256),
- transforms.CenterCrop(224),
- transforms.ToTensor(),
- transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
- ])
- input_tensor = preprocess(input_image)
- input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
-
- # Move the input and model to GPU for speed if available
- if torch.cuda.is_available():
- input_batch = input_batch.to('cuda')
- model.to('cuda')
-
- with torch.no_grad():
- output = model(input_batch)
- # The output has unnormalized scores. To get probabilities, you can run a softmax on it.
- probabilities = torch.nn.functional.softmax(output[0], dim=0)
-
- # Read the categories
- with open("imagenet_classes.txt", "r") as f:
- categories = [s.strip() for s in f.readlines()]
- # Show top categories per image
- top5_prob, top5_catid = torch.topk(probabilities, 5)
- result = {}
- for i in range(top5_prob.size(0)):
- result[categories[top5_catid[i]]] = top5_prob[i].item()
- return result
-
-# Define ins outs placeholders
-inputs = gr.inputs.Image(type='pil')
-outputs = gr.outputs.Label(type="confidences",num_top_classes=5)
-
-# Define style
-title = "Image Recognition Demo"
-description = "This is a prototype application which demonstrates how artifical intelligence based systems can recognize what object(s) is present in an image. This fundamental task in computer vision known as `Image Classification` has applications stretching from autonomous vehicles to medical imaging. To use it, simply upload your image, or click one of the examples images to load them, which I took at Montréal Biodôme ! Read more at the links below."
-article = "Deep Residual Learning for Image Recognition | Github Repo
"
-
-# Run inference
-gr.Interface(inference,
- inputs,
- outputs,
- examples=["example1.jpg", "example2.jpg"],
- title=title,
- description=description,
- article=article,
- analytics_enabled=False).launch()
-
diff --git a/spaces/hdhzk/bingo/src/lib/bots/bing/utils.ts b/spaces/hdhzk/bingo/src/lib/bots/bing/utils.ts
deleted file mode 100644
index 64b4b96452d125346b0fc4436b5f7c18c962df0b..0000000000000000000000000000000000000000
--- a/spaces/hdhzk/bingo/src/lib/bots/bing/utils.ts
+++ /dev/null
@@ -1,87 +0,0 @@
-import { ChatResponseMessage, BingChatResponse } from './types'
-
-export function convertMessageToMarkdown(message: ChatResponseMessage): string {
- if (message.messageType === 'InternalSearchQuery') {
- return message.text
- }
- for (const card of message.adaptiveCards??[]) {
- for (const block of card.body) {
- if (block.type === 'TextBlock') {
- return block.text
- }
- }
- }
- return ''
-}
-
-const RecordSeparator = String.fromCharCode(30)
-
-export const websocketUtils = {
- packMessage(data: any) {
- return `${JSON.stringify(data)}${RecordSeparator}`
- },
- unpackMessage(data: string | ArrayBuffer | Blob) {
- if (!data) return {}
- return data
- .toString()
- .split(RecordSeparator)
- .filter(Boolean)
- .map((s) => {
- try {
- return JSON.parse(s)
- } catch (e) {
- return {}
- }
- })
- },
-}
-
-export async function createImage(prompt: string, id: string, headers: HeadersInit): Promise {
- const { headers: responseHeaders } = await fetch(`https://www.bing.com/images/create?partner=sydney&re=1&showselective=1&sude=1&kseed=7000&SFX=&q=${encodeURIComponent(prompt)}&iframeid=${id}`,
- {
- method: 'HEAD',
- headers,
- redirect: 'manual'
- },
- );
-
- if (!/&id=([^&]+)$/.test(responseHeaders.get('location') || '')) {
- throw new Error('请求异常，请检查 cookie 是否有效')
- }
-
- const resultId = RegExp.$1;
- let count = 0
- const imageThumbUrl = `https://www.bing.com/images/create/async/results/${resultId}?q=${encodeURIComponent(prompt)}&partner=sydney&showselective=1&IID=images.as`;
-
- do {
- await sleep(3000);
- const content = await fetch(imageThumbUrl, { headers, method: 'GET' })
-
- // @ts-ignore
- if (content.headers.get('content-length') > 1) {
- const text = await content.text()
- return (text?.match(/ {
- prompt: string
- imageUrl?: string
- options: T
- onEvent: (event: Event) => void
- signal?: AbortSignal
-}
-
-export interface ConversationResponse {
- conversationId: string
- clientId: string
- conversationSignature: string
- result: {
- value: string
- message?: string
- }
-}
-
-export interface Telemetry {
- metrics?: null
- startTime: string
-}
-
-export interface ChatUpdateArgument {
- messages?: ChatResponseMessage[]
- throttling?: Throttling
- requestId: string
- result: null
-}
-
-export type ChatUpdateCompleteResponse = {
- type: 2
- invocationId: string
- item: ChatResponseItem
-} | {
- type: 1
- target: string
- arguments: ChatUpdateArgument[]
-} | {
- type: 3
- invocationId: string
-} | {
- type: 6 | 7
-}
-
-export interface ChatRequestResult {
- value: string
- serviceVersion: string
- error?: string
-}
-
-export interface ChatResponseItem {
- messages: ChatResponseMessage[]
- firstNewMessageIndex: number
- suggestedResponses: null
- conversationId: string
- requestId: string
- conversationExpiryTime: string
- telemetry: Telemetry
- result: ChatRequestResult
- throttling: Throttling
-}
-export enum InvocationEventType {
- Invocation = 1,
- StreamItem = 2,
- Completion = 3,
- StreamInvocation = 4,
- CancelInvocation = 5,
- Ping = 6,
- Close = 7,
-}
-
-// https://github.com/bytemate/bingchat-api/blob/main/src/lib.ts
-
-export interface ConversationInfo {
- conversationId: string
- clientId: string
- conversationSignature: string
- invocationId: number
- conversationStyle: BingConversationStyle
- prompt: string
- imageUrl?: string
-}
-
-export interface BingChatResponse {
- conversationSignature: string
- conversationId: string
- clientId: string
- invocationId: number
- conversationExpiryTime: Date
- response: string
- details: ChatResponseMessage
-}
-
-export interface Throttling {
- maxNumLongDocSummaryUserMessagesInConversation: number
- maxNumUserMessagesInConversation: number
- numLongDocSummaryUserMessagesInConversation: number
- numUserMessagesInConversation: number
-}
-
-export interface ChatResponseMessage {
- text: string
- spokenText?: string
- author: string
- createdAt: Date
- timestamp: Date
- messageId: string
- requestId: string
- offense: string
- adaptiveCards: AdaptiveCard[]
- sourceAttributions: SourceAttribution[]
- feedback: Feedback
- contentOrigin: string
- messageType?: string
- contentType?: string
- privacy: null
- suggestedResponses: SuggestedResponse[]
-}
-
-export interface AdaptiveCard {
- type: string
- version: string
- body: Body[]
-}
-
-export interface Body {
- type: string
- text: string
- wrap: boolean
- size?: string
-}
-
-export interface Feedback {
- tag: null
- updatedOn: null
- type: string
-}
-
-export interface SourceAttribution {
- providerDisplayName: string
- seeMoreUrl: string
- searchQuery: string
-}
-
-export interface SuggestedResponse {
- text: string
- author?: Author
- createdAt?: Date
- timestamp?: Date
- messageId?: string
- messageType?: string
- offense?: string
- feedback?: Feedback
- contentOrigin?: string
- privacy?: null
-}
-
-export interface KBlobRequest {
- knowledgeRequest: KnowledgeRequestContext
- imageBase64?: string
-}
-
-export interface KBlobResponse {
- blobId: string
- processedBlobId?: string
-}
-
-export interface KnowledgeRequestContext {
- imageInfo: ImageInfo;
- knowledgeRequest: KnowledgeRequest;
-}
-
-export interface ImageInfo {
- url?: string;
-}
-
-export interface KnowledgeRequest {
- invokedSkills: string[];
- subscriptionId: string;
- invokedSkillsRequestData: InvokedSkillsRequestData;
- convoData: ConvoData;
-}
-
-export interface ConvoData {
- convoid: string;
- convotone: BingConversationStyle;
-}
-
-export interface InvokedSkillsRequestData {
- enableFaceBlur: boolean;
-}
-
-export interface FileItem {
- url: string;
- status?: 'loading' | 'error' | 'loaded'
-}
diff --git a/spaces/hrdtbs/rvc-mochinoa/weights/mochinoa/LICENCE.md b/spaces/hrdtbs/rvc-mochinoa/weights/mochinoa/LICENCE.md
deleted file mode 100644
index 08b24e40c28a3a2f5160042b77d72d0cff4f05f1..0000000000000000000000000000000000000000
--- a/spaces/hrdtbs/rvc-mochinoa/weights/mochinoa/LICENCE.md
+++ /dev/null
@@ -1,23 +0,0 @@
-ライセンスの要約
-
-- このモデルを利用して作成された音声は公序良俗に反しない範囲で商用利用が認められます。
-- 望月のあの宣伝をしてくれると嬉しいです。
-- このモデルとこのモデルを利用して作成された音声によって損害が発生しても責任は負いません。
-
----
-
-Mochinoa Licence Revision 1
-
-Copyright (C) 2023 望月のあ Mochizuki Noa
-
-Permission is hereby granted, free of charge, for anyone to use the compiled binaries, source code, and documentation (the "Software").
-
-Permission to modify the Software is only granted to individuals or entities that have a higher number of YouTube subscribers and Twitter followers than the copyright holder (Mochizuki Noa) at the time of modification.
-
-You are permitted to sell or distribute products created using this Software, as long as the sale or distribution does not violate public order and morals.
-
-When publicly releasing any product created using this Software, you are strongly encouraged to promote the copyright holder's YouTube and Twitter accounts to the best of your ability.
-
-The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
-
-The Software is provided in the hope that it will be useful, but the Software comes with NO WARRANTY, EXPRESS OR IMPLIED, and the authors of the Software are NOT LIABLE FOR ANY LOSSES, DAMAGES, OR MISUSE relating to the Software.
\ No newline at end of file
diff --git a/spaces/huggingface-projects/stable-diffusion-multiplayer/static/_app/immutable/components/pages/_layout.svelte-9d5377d3.js b/spaces/huggingface-projects/stable-diffusion-multiplayer/static/_app/immutable/components/pages/_layout.svelte-9d5377d3.js
deleted file mode 100644
index 743a20886104cb98a74de421d7c01b0d3d0d20f7..0000000000000000000000000000000000000000
--- a/spaces/huggingface-projects/stable-diffusion-multiplayer/static/_app/immutable/components/pages/_layout.svelte-9d5377d3.js
+++ /dev/null
@@ -1 +0,0 @@
-import{S as l,i,s as r,C as u,D as f,E as _,F as c,f as p,t as d}from"../../chunks/index-ba22f6f0.js";function m(n){let s;const o=n[1].default,e=u(o,n,n[0],null);return{c(){e&&e.c()},l(t){e&&e.l(t)},m(t,a){e&&e.m(t,a),s=!0},p(t,[a]){e&&e.p&&(!s||a&1)&&f(e,o,t,t[0],s?c(o,t[0],a,null):_(t[0]),null)},i(t){s||(p(e,t),s=!0)},o(t){d(e,t),s=!1},d(t){e&&e.d(t)}}}function $(n,s,o){let{$$slots:e={},$$scope:t}=s;return n.$$set=a=>{"$$scope"in a&&o(0,t=a.$$scope)},[t,e]}class h extends l{constructor(s){super(),i(this,s,$,m,r,{})}}export{h as default};
diff --git a/spaces/huggingface/hf-speech-bench/README.md b/spaces/huggingface/hf-speech-bench/README.md
deleted file mode 100644
index 1a3ea79d7ba62ce607648439318d80f0a6c72d1a..0000000000000000000000000000000000000000
--- a/spaces/huggingface/hf-speech-bench/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: The 🤗 Speech Bench
-emoji: 📈
-colorFrom: red
-colorTo: yellow
-sdk: streamlit
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/hugginglearners/pokemon-card-checker/app.py b/spaces/hugginglearners/pokemon-card-checker/app.py
deleted file mode 100644
index 232245fe7eaa10598ffe634bfeb70d2ca4e0d847..0000000000000000000000000000000000000000
--- a/spaces/hugginglearners/pokemon-card-checker/app.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import numpy as np
-import gradio as gr
-from huggingface_hub import from_pretrained_fastai
-from lime import lime_image
-from skimage.segmentation import mark_boundaries
-
-learn = from_pretrained_fastai('hugginglearners/pokemon-card-checker')
-
-def check_card(img):
- pred_label, _, scores = learn.predict(img)
- scores = scores.detach().numpy()
- scores = {'real': float(scores[1]), 'fake': float(scores[0])}
-
- print(np.array(img).shape)
-
- # Lime Explanation
- explainer = lime_image.LimeImageExplainer()
- explanation = explainer.explain_instance(
- np.array(img),
- classifier_fn=classify_cards,
- labels=['0', '1'],
- num_samples=1000,
- random_seed=42,
- )
-
- temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=False, num_features=10, hide_rest=False)
- img_boundry = mark_boundaries(temp/255.0, mask)
- return scores, img_boundry
-
-def classify_cards(imgs):
- print(imgs.shape)
- scores = []
-
- for i in range(imgs.shape[0]):
- pred_label, _, score = learn.predict(imgs[i])
- scores.append(score.detach().numpy())
-
- scores = np.array(scores)
- print(scores.shape)
-
- return scores
-
-
-demo = gr.Interface(
- fn=check_card,
- inputs='image',
- outputs=["label", "image"],
- examples=['real-1.jpeg','real-2.jpeg','fake-1.jpeg','fake-2.jpeg','real-3.jpeg','real-4.jpeg','fake-3.jpeg','fake-4.jpeg'],
- title='Pokemon Card Checker',
- description='This space uses a resnet34 model fine-tuned to determine whether Pokemon cards are real or fake. \n\nAdded [LIME](https://github.com/marcotcr/lime) to show what contributed to the predicted label (green shows what contributed towards that label and red shows what contributed against the label predicted).\n\n[Dataset](https://www.kaggle.com/datasets/ongshujian/real-and-fake-pokemon-cards) created by [Shujian Ong](https://www.kaggle.com/ongshujian).',
- article='Can you guess which cards are real and fake? \n\nI can\'t 🤔 \n\n([View Labels](https://gist.github.com/mindwrapped/e5aad747757ef006037a1a1982be34fc)) \n\nSpace and model by Scott Krstyen (mindwrapped) \n\n',
- live=False,
- )
-
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/hunkim/es-gpt/app.py b/spaces/hunkim/es-gpt/app.py
deleted file mode 100644
index bfe256af05ce10302312640b223edb91cd476754..0000000000000000000000000000000000000000
--- a/spaces/hunkim/es-gpt/app.py
+++ /dev/null
@@ -1,54 +0,0 @@
-from fastapi import FastAPI, HTTPException
-from fastapi.responses import StreamingResponse, HTMLResponse
-import json
-from es_gpt import ESGPT
-import asyncio
-
-# Create an instance of the ESGPT class
-es = ESGPT(index_name="papers")
-
-# Create a FastAPI app
-app = FastAPI()
-
-# Define the search route
-
-
-@app.get("/", response_class=HTMLResponse)
-async def read_index():
- with open("index.html", "r") as file:
- html = file.read()
- return html
-
-
-@app.get("/search")
-async def search(q: str):
- # Perform a search for the query
- results = es.search(q)
- print(results)
-
- # Stream the search results to the client
- async def stream_response():
- for hit in results:
- # sleep(0.1)
- await asyncio.sleep(0.1)
- yield "data: " + json.dumps(hit) + "\n\n"
- yield "[DONE]"
-
- return StreamingResponse(stream_response(), media_type="text/event-stream")
-
-# Define the summary route
-
-
-@app.get("/summary")
-async def summary(q: str):
- # Perform a search for the query
- results = es.search(q)
-
- # Generate summaries of the search results
- resp = es.summarize(q, results)
-
- if resp.status_code != 200:
- raise HTTPException(resp.status_code, resp.text)
-
- return StreamingResponse(resp.iter_content(1),
- media_type="text/event-stream")
diff --git a/spaces/hunkim/es-gpt/es_gpt.py b/spaces/hunkim/es-gpt/es_gpt.py
deleted file mode 100644
index 40ac92f71962ef5e2c9baa893827f076b0fffacb..0000000000000000000000000000000000000000
--- a/spaces/hunkim/es-gpt/es_gpt.py
+++ /dev/null
@@ -1,75 +0,0 @@
-from elasticsearch import Elasticsearch
-import os
-import json
-import requests
-
-ES_URL = os.environ["ES_URL"]
-ES_USER = os.environ["ES_USER"]
-ES_PASS = os.environ["ES_PASS"]
-ES_CA_CERT = os.environ["ES_CA_CERT"]
-
-
-class ESGPT:
- def __init__(self, index_name):
- self.es = Elasticsearch(ES_URL, http_auth=(ES_USER, ES_PASS),
- ca_certs=ES_CA_CERT, verify_certs=True)
- self.index_name = index_name
- self.model_engine = os.environ["OPENAI_GPT_ENGINE"]
- self.api_key = os.environ["OPENAI_API_KEY"]
-
- def index(self, doc_id, doc):
- self.es.index(index=self.index_name,
- id=doc_id,
- document=doc)
-
- def search(self, query):
- body = {
- "query": {
- "query_string": {"query": query}
- }
- }
-
- results = self.es.search(index=self.index_name, body=body)
- return results['hits']['hits']
-
- def _paper_results_to_text(self, results):
- text_result = ""
- for paper in results:
- title = ""
- if "title" in paper["_source"]:
- title = paper["_source"]["title"]
-
- abstract = ""
- if "abctract" in paper["_source"]:
- abstract = paper["_source"]["abstract"]
-
- paper_str = f"{title}:\n{abstract[:100]}\n\n"
- text_result += paper_str
- return text_result
-
- def summarize(self, query, results):
- # Generate summaries for each search result
- result_json_str = self._paper_results_to_text(results)
- if result_json_str == "":
- result_json_str = "No results found"
-
- print(result_json_str[:500])
-
- body = {
- "model": self.model_engine,
- "prompt": f"Please summarize the following search results for query: {query}:\n{result_json_str[:1000]}",
- "max_tokens": 1000,
- "n": 1,
- "stop": None,
- "temperature": 0.5,
- "stream": True,
- }
-
- headers = {"Content-Type": "application/json",
- "Authorization": f"Bearer {self.api_key}"}
-
- resp = requests.post("https://api.openai.com/v1/completions",
- headers=headers,
- data=json.dumps(body),
- stream=True)
- return resp
diff --git a/spaces/imjunaidafzal/LoRA-DreamBooth-Training-UI/train_dreambooth_lora.py b/spaces/imjunaidafzal/LoRA-DreamBooth-Training-UI/train_dreambooth_lora.py
deleted file mode 100644
index 93d429590ca4f357aff07989965b673bdf1e50fe..0000000000000000000000000000000000000000
--- a/spaces/imjunaidafzal/LoRA-DreamBooth-Training-UI/train_dreambooth_lora.py
+++ /dev/null
@@ -1,1026 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-#
-# This file is adapted from https://github.com/huggingface/diffusers/blob/febaf863026bd014b7a14349336544fc109d0f57/examples/dreambooth/train_dreambooth_lora.py
-# The original license is as below:
-#
-# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-
-import argparse
-import hashlib
-import logging
-import math
-import os
-import warnings
-from pathlib import Path
-from typing import Optional
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from torch.utils.data import Dataset
-
-import datasets
-import diffusers
-import transformers
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import set_seed
-from diffusers import (
- AutoencoderKL,
- DDPMScheduler,
- DiffusionPipeline,
- DPMSolverMultistepScheduler,
- UNet2DConditionModel,
-)
-from diffusers.loaders import AttnProcsLayers
-from diffusers.models.cross_attention import LoRACrossAttnProcessor
-from diffusers.optimization import get_scheduler
-from diffusers.utils import check_min_version, is_wandb_available
-from diffusers.utils.import_utils import is_xformers_available
-from huggingface_hub import HfFolder, Repository, create_repo, whoami
-from PIL import Image
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import AutoTokenizer, PretrainedConfig
-
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.12.0.dev0")
-
-logger = get_logger(__name__)
-
-
-def save_model_card(repo_name, images=None, base_model=str, prompt=str, repo_folder=None):
- img_str = ""
- for i, image in enumerate(images):
- image.save(os.path.join(repo_folder, f"image_{i}.png"))
- img_str += f"\n"
-
- yaml = f"""
----
-license: creativeml-openrail-m
-base_model: {base_model}
-tags:
-- stable-diffusion
-- stable-diffusion-diffusers
-- text-to-image
-- diffusers
-- lora
-inference: true
----
- """
- model_card = f"""
-# LoRA DreamBooth - {repo_name}
-
-These are LoRA adaption weights for {repo_name}. The weights were trained on {prompt} using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. \n
-{img_str}
-"""
- with open(os.path.join(repo_folder, "README.md"), "w") as f:
- f.write(yaml + model_card)
-
-
-def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
- text_encoder_config = PretrainedConfig.from_pretrained(
- pretrained_model_name_or_path,
- subfolder="text_encoder",
- revision=revision,
- )
- model_class = text_encoder_config.architectures[0]
-
- if model_class == "CLIPTextModel":
- from transformers import CLIPTextModel
-
- return CLIPTextModel
- elif model_class == "RobertaSeriesModelWithTransformation":
- from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
-
- return RobertaSeriesModelWithTransformation
- else:
- raise ValueError(f"{model_class} is not supported.")
-
-
-def parse_args(input_args=None):
- parser = argparse.ArgumentParser(description="Simple example of a training script.")
- parser.add_argument(
- "--pretrained_model_name_or_path",
- type=str,
- default=None,
- required=True,
- help="Path to pretrained model or model identifier from huggingface.co/models.",
- )
- parser.add_argument(
- "--revision",
- type=str,
- default=None,
- required=False,
- help="Revision of pretrained model identifier from huggingface.co/models.",
- )
- parser.add_argument(
- "--tokenizer_name",
- type=str,
- default=None,
- help="Pretrained tokenizer name or path if not the same as model_name",
- )
- parser.add_argument(
- "--instance_data_dir",
- type=str,
- default=None,
- required=True,
- help="A folder containing the training data of instance images.",
- )
- parser.add_argument(
- "--class_data_dir",
- type=str,
- default=None,
- required=False,
- help="A folder containing the training data of class images.",
- )
- parser.add_argument(
- "--instance_prompt",
- type=str,
- default=None,
- required=True,
- help="The prompt with identifier specifying the instance",
- )
- parser.add_argument(
- "--class_prompt",
- type=str,
- default=None,
- help="The prompt to specify images in the same class as provided instance images.",
- )
- parser.add_argument(
- "--validation_prompt",
- type=str,
- default=None,
- help="A prompt that is used during validation to verify that the model is learning.",
- )
- parser.add_argument(
- "--num_validation_images",
- type=int,
- default=4,
- help="Number of images that should be generated during validation with `validation_prompt`.",
- )
- parser.add_argument(
- "--validation_epochs",
- type=int,
- default=50,
- help=(
- "Run dreambooth validation every X epochs. Dreambooth validation consists of running the prompt"
- " `args.validation_prompt` multiple times: `args.num_validation_images`."
- ),
- )
- parser.add_argument(
- "--with_prior_preservation",
- default=False,
- action="store_true",
- help="Flag to add prior preservation loss.",
- )
- parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
- parser.add_argument(
- "--num_class_images",
- type=int,
- default=100,
- help=(
- "Minimal class images for prior preservation loss. If there are not enough images already present in"
- " class_data_dir, additional images will be sampled with class_prompt."
- ),
- )
- parser.add_argument(
- "--output_dir",
- type=str,
- default="lora-dreambooth-model",
- help="The output directory where the model predictions and checkpoints will be written.",
- )
- parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
- parser.add_argument(
- "--resolution",
- type=int,
- default=512,
- help=(
- "The resolution for input images, all the images in the train/validation dataset will be resized to this"
- " resolution"
- ),
- )
- parser.add_argument(
- "--center_crop",
- default=False,
- action="store_true",
- help=(
- "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
- " cropped. The images will be resized to the resolution first before cropping."
- ),
- )
- parser.add_argument(
- "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
- )
- parser.add_argument(
- "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
- )
- parser.add_argument("--num_train_epochs", type=int, default=1)
- parser.add_argument(
- "--max_train_steps",
- type=int,
- default=None,
- help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
- )
- parser.add_argument(
- "--checkpointing_steps",
- type=int,
- default=500,
- help=(
- "Save a checkpoint of the training state every X updates. These checkpoints can be used both as final"
- " checkpoints in case they are better than the last checkpoint, and are also suitable for resuming"
- " training using `--resume_from_checkpoint`."
- ),
- )
- parser.add_argument(
- "--resume_from_checkpoint",
- type=str,
- default=None,
- help=(
- "Whether training should be resumed from a previous checkpoint. Use a path saved by"
- ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
- ),
- )
- parser.add_argument(
- "--gradient_accumulation_steps",
- type=int,
- default=1,
- help="Number of updates steps to accumulate before performing a backward/update pass.",
- )
- parser.add_argument(
- "--gradient_checkpointing",
- action="store_true",
- help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
- )
- parser.add_argument(
- "--learning_rate",
- type=float,
- default=5e-4,
- help="Initial learning rate (after the potential warmup period) to use.",
- )
- parser.add_argument(
- "--scale_lr",
- action="store_true",
- default=False,
- help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
- )
- parser.add_argument(
- "--lr_scheduler",
- type=str,
- default="constant",
- help=(
- 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
- ' "constant", "constant_with_warmup"]'
- ),
- )
- parser.add_argument(
- "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
- )
- parser.add_argument(
- "--lr_num_cycles",
- type=int,
- default=1,
- help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
- )
- parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
- parser.add_argument(
- "--dataloader_num_workers",
- type=int,
- default=0,
- help=(
- "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
- ),
- )
- parser.add_argument(
- "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
- )
- parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
- parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
- parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
- parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
- parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
- parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
- parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
- parser.add_argument(
- "--hub_model_id",
- type=str,
- default=None,
- help="The name of the repository to keep in sync with the local `output_dir`.",
- )
- parser.add_argument(
- "--logging_dir",
- type=str,
- default="logs",
- help=(
- "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
- " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
- ),
- )
- parser.add_argument(
- "--allow_tf32",
- action="store_true",
- help=(
- "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
- " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
- ),
- )
- parser.add_argument(
- "--report_to",
- type=str,
- default="tensorboard",
- help=(
- 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
- ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
- ),
- )
- parser.add_argument(
- "--mixed_precision",
- type=str,
- default=None,
- choices=["no", "fp16", "bf16"],
- help=(
- "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
- " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
- " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
- ),
- )
- parser.add_argument(
- "--prior_generation_precision",
- type=str,
- default=None,
- choices=["no", "fp32", "fp16", "bf16"],
- help=(
- "Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
- " 1.10.and an Nvidia Ampere GPU. Default to fp16 if a GPU is available else fp32."
- ),
- )
- parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
- parser.add_argument(
- "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
- )
-
- if input_args is not None:
- args = parser.parse_args(input_args)
- else:
- args = parser.parse_args()
-
- env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
- if env_local_rank != -1 and env_local_rank != args.local_rank:
- args.local_rank = env_local_rank
-
- if args.with_prior_preservation:
- if args.class_data_dir is None:
- raise ValueError("You must specify a data directory for class images.")
- if args.class_prompt is None:
- raise ValueError("You must specify prompt for class images.")
- else:
- # logger is not available yet
- if args.class_data_dir is not None:
- warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
- if args.class_prompt is not None:
- warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
-
- return args
-
-
-class DreamBoothDataset(Dataset):
- """
- A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
- It pre-processes the images and the tokenizes prompts.
- """
-
- def __init__(
- self,
- instance_data_root,
- instance_prompt,
- tokenizer,
- class_data_root=None,
- class_prompt=None,
- size=512,
- center_crop=False,
- ):
- self.size = size
- self.center_crop = center_crop
- self.tokenizer = tokenizer
-
- self.instance_data_root = Path(instance_data_root)
- if not self.instance_data_root.exists():
- raise ValueError("Instance images root doesn't exists.")
-
- self.instance_images_path = list(Path(instance_data_root).iterdir())
- self.num_instance_images = len(self.instance_images_path)
- self.instance_prompt = instance_prompt
- self._length = self.num_instance_images
-
- if class_data_root is not None:
- self.class_data_root = Path(class_data_root)
- self.class_data_root.mkdir(parents=True, exist_ok=True)
- self.class_images_path = list(self.class_data_root.iterdir())
- self.num_class_images = len(self.class_images_path)
- self._length = max(self.num_class_images, self.num_instance_images)
- self.class_prompt = class_prompt
- else:
- self.class_data_root = None
-
- self.image_transforms = transforms.Compose(
- [
- transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
- transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
- transforms.ToTensor(),
- transforms.Normalize([0.5], [0.5]),
- ]
- )
-
- def __len__(self):
- return self._length
-
- def __getitem__(self, index):
- example = {}
- instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
- if not instance_image.mode == "RGB":
- instance_image = instance_image.convert("RGB")
- example["instance_images"] = self.image_transforms(instance_image)
- example["instance_prompt_ids"] = self.tokenizer(
- self.instance_prompt,
- truncation=True,
- padding="max_length",
- max_length=self.tokenizer.model_max_length,
- return_tensors="pt",
- ).input_ids
-
- if self.class_data_root:
- class_image = Image.open(self.class_images_path[index % self.num_class_images])
- if not class_image.mode == "RGB":
- class_image = class_image.convert("RGB")
- example["class_images"] = self.image_transforms(class_image)
- example["class_prompt_ids"] = self.tokenizer(
- self.class_prompt,
- truncation=True,
- padding="max_length",
- max_length=self.tokenizer.model_max_length,
- return_tensors="pt",
- ).input_ids
-
- return example
-
-
-def collate_fn(examples, with_prior_preservation=False):
- input_ids = [example["instance_prompt_ids"] for example in examples]
- pixel_values = [example["instance_images"] for example in examples]
-
- # Concat class and instance examples for prior preservation.
- # We do this to avoid doing two forward passes.
- if with_prior_preservation:
- input_ids += [example["class_prompt_ids"] for example in examples]
- pixel_values += [example["class_images"] for example in examples]
-
- pixel_values = torch.stack(pixel_values)
- pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
-
- input_ids = torch.cat(input_ids, dim=0)
-
- batch = {
- "input_ids": input_ids,
- "pixel_values": pixel_values,
- }
- return batch
-
-
-class PromptDataset(Dataset):
- "A simple dataset to prepare the prompts to generate class images on multiple GPUs."
-
- def __init__(self, prompt, num_samples):
- self.prompt = prompt
- self.num_samples = num_samples
-
- def __len__(self):
- return self.num_samples
-
- def __getitem__(self, index):
- example = {}
- example["prompt"] = self.prompt
- example["index"] = index
- return example
-
-
-def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
- if token is None:
- token = HfFolder.get_token()
- if organization is None:
- username = whoami(token)["name"]
- return f"{username}/{model_id}"
- else:
- return f"{organization}/{model_id}"
-
-
-def main(args):
- logging_dir = Path(args.output_dir, args.logging_dir)
-
- accelerator = Accelerator(
- gradient_accumulation_steps=args.gradient_accumulation_steps,
- mixed_precision=args.mixed_precision,
- log_with=args.report_to,
- logging_dir=logging_dir,
- )
-
- if args.report_to == "wandb":
- if not is_wandb_available():
- raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
- import wandb
-
- # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
- # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
- # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
- # Make one log on every process with the configuration for debugging.
- logging.basicConfig(
- format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
- datefmt="%m/%d/%Y %H:%M:%S",
- level=logging.INFO,
- )
- logger.info(accelerator.state, main_process_only=False)
- if accelerator.is_local_main_process:
- datasets.utils.logging.set_verbosity_warning()
- transformers.utils.logging.set_verbosity_warning()
- diffusers.utils.logging.set_verbosity_info()
- else:
- datasets.utils.logging.set_verbosity_error()
- transformers.utils.logging.set_verbosity_error()
- diffusers.utils.logging.set_verbosity_error()
-
- # If passed along, set the training seed now.
- if args.seed is not None:
- set_seed(args.seed)
-
- # Generate class images if prior preservation is enabled.
- if args.with_prior_preservation:
- class_images_dir = Path(args.class_data_dir)
- if not class_images_dir.exists():
- class_images_dir.mkdir(parents=True)
- cur_class_images = len(list(class_images_dir.iterdir()))
-
- if cur_class_images < args.num_class_images:
- torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
- if args.prior_generation_precision == "fp32":
- torch_dtype = torch.float32
- elif args.prior_generation_precision == "fp16":
- torch_dtype = torch.float16
- elif args.prior_generation_precision == "bf16":
- torch_dtype = torch.bfloat16
- pipeline = DiffusionPipeline.from_pretrained(
- args.pretrained_model_name_or_path,
- torch_dtype=torch_dtype,
- safety_checker=None,
- revision=args.revision,
- )
- pipeline.set_progress_bar_config(disable=True)
-
- num_new_images = args.num_class_images - cur_class_images
- logger.info(f"Number of class images to sample: {num_new_images}.")
-
- sample_dataset = PromptDataset(args.class_prompt, num_new_images)
- sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
-
- sample_dataloader = accelerator.prepare(sample_dataloader)
- pipeline.to(accelerator.device)
-
- for example in tqdm(
- sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
- ):
- images = pipeline(example["prompt"]).images
-
- for i, image in enumerate(images):
- hash_image = hashlib.sha1(image.tobytes()).hexdigest()
- image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
- image.save(image_filename)
-
- del pipeline
- if torch.cuda.is_available():
- torch.cuda.empty_cache()
-
- # Handle the repository creation
- if accelerator.is_main_process:
- if args.push_to_hub:
- if args.hub_model_id is None:
- repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
- else:
- repo_name = args.hub_model_id
-
- create_repo(repo_name, exist_ok=True, token=args.hub_token)
- repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
-
- with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
- if "step_*" not in gitignore:
- gitignore.write("step_*\n")
- if "epoch_*" not in gitignore:
- gitignore.write("epoch_*\n")
- elif args.output_dir is not None:
- os.makedirs(args.output_dir, exist_ok=True)
-
- # Load the tokenizer
- if args.tokenizer_name:
- tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
- elif args.pretrained_model_name_or_path:
- tokenizer = AutoTokenizer.from_pretrained(
- args.pretrained_model_name_or_path,
- subfolder="tokenizer",
- revision=args.revision,
- use_fast=False,
- )
-
- # import correct text encoder class
- text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
-
- # Load scheduler and models
- noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
- text_encoder = text_encoder_cls.from_pretrained(
- args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
- )
- vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision)
- unet = UNet2DConditionModel.from_pretrained(
- args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
- )
-
- # We only train the additional adapter LoRA layers
- vae.requires_grad_(False)
- text_encoder.requires_grad_(False)
- unet.requires_grad_(False)
-
- # For mixed precision training we cast the text_encoder and vae weights to half-precision
- # as these models are only used for inference, keeping weights in full precision is not required.
- weight_dtype = torch.float32
- if accelerator.mixed_precision == "fp16":
- weight_dtype = torch.float16
- elif accelerator.mixed_precision == "bf16":
- weight_dtype = torch.bfloat16
-
- # Move unet, vae and text_encoder to device and cast to weight_dtype
- unet.to(accelerator.device, dtype=weight_dtype)
- vae.to(accelerator.device, dtype=weight_dtype)
- text_encoder.to(accelerator.device, dtype=weight_dtype)
-
- if args.enable_xformers_memory_efficient_attention:
- if is_xformers_available():
- unet.enable_xformers_memory_efficient_attention()
- else:
- raise ValueError("xformers is not available. Make sure it is installed correctly")
-
- # now we will add new LoRA weights to the attention layers
- # It's important to realize here how many attention weights will be added and of which sizes
- # The sizes of the attention layers consist only of two different variables:
- # 1) - the "hidden_size", which is increased according to `unet.config.block_out_channels`.
- # 2) - the "cross attention size", which is set to `unet.config.cross_attention_dim`.
-
- # Let's first see how many attention processors we will have to set.
- # For Stable Diffusion, it should be equal to:
- # - down blocks (2x attention layers) * (2x transformer layers) * (3x down blocks) = 12
- # - mid blocks (2x attention layers) * (1x transformer layers) * (1x mid blocks) = 2
- # - up blocks (2x attention layers) * (3x transformer layers) * (3x down blocks) = 18
- # => 32 layers
-
- # Set correct lora layers
- lora_attn_procs = {}
- for name in unet.attn_processors.keys():
- cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
- if name.startswith("mid_block"):
- hidden_size = unet.config.block_out_channels[-1]
- elif name.startswith("up_blocks"):
- block_id = int(name[len("up_blocks.")])
- hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
- elif name.startswith("down_blocks"):
- block_id = int(name[len("down_blocks.")])
- hidden_size = unet.config.block_out_channels[block_id]
-
- lora_attn_procs[name] = LoRACrossAttnProcessor(
- hidden_size=hidden_size, cross_attention_dim=cross_attention_dim
- )
-
- unet.set_attn_processor(lora_attn_procs)
- lora_layers = AttnProcsLayers(unet.attn_processors)
-
- accelerator.register_for_checkpointing(lora_layers)
-
- if args.scale_lr:
- args.learning_rate = (
- args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
- )
-
- # Enable TF32 for faster training on Ampere GPUs,
- # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
- if args.allow_tf32:
- torch.backends.cuda.matmul.allow_tf32 = True
-
- if args.scale_lr:
- args.learning_rate = (
- args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
- )
-
- # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
- if args.use_8bit_adam:
- try:
- import bitsandbytes as bnb
- except ImportError:
- raise ImportError(
- "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
- )
-
- optimizer_class = bnb.optim.AdamW8bit
- else:
- optimizer_class = torch.optim.AdamW
-
- # Optimizer creation
- optimizer = optimizer_class(
- lora_layers.parameters(),
- lr=args.learning_rate,
- betas=(args.adam_beta1, args.adam_beta2),
- weight_decay=args.adam_weight_decay,
- eps=args.adam_epsilon,
- )
-
- # Dataset and DataLoaders creation:
- train_dataset = DreamBoothDataset(
- instance_data_root=args.instance_data_dir,
- instance_prompt=args.instance_prompt,
- class_data_root=args.class_data_dir if args.with_prior_preservation else None,
- class_prompt=args.class_prompt,
- tokenizer=tokenizer,
- size=args.resolution,
- center_crop=args.center_crop,
- )
-
- train_dataloader = torch.utils.data.DataLoader(
- train_dataset,
- batch_size=args.train_batch_size,
- shuffle=True,
- collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
- num_workers=args.dataloader_num_workers,
- )
-
- # Scheduler and math around the number of training steps.
- overrode_max_train_steps = False
- num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
- if args.max_train_steps is None:
- args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
- overrode_max_train_steps = True
-
- lr_scheduler = get_scheduler(
- args.lr_scheduler,
- optimizer=optimizer,
- num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
- num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
- num_cycles=args.lr_num_cycles,
- power=args.lr_power,
- )
-
- # Prepare everything with our `accelerator`.
- lora_layers, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
- lora_layers, optimizer, train_dataloader, lr_scheduler
- )
-
- # We need to recalculate our total training steps as the size of the training dataloader may have changed.
- num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
- if overrode_max_train_steps:
- args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
- # Afterwards we recalculate our number of training epochs
- args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
- # We need to initialize the trackers we use, and also store our configuration.
- # The trackers initializes automatically on the main process.
- if accelerator.is_main_process:
- accelerator.init_trackers("dreambooth-lora", config=vars(args))
-
- # Train!
- total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-
- logger.info("***** Running training *****")
- logger.info(f" Num examples = {len(train_dataset)}")
- logger.info(f" Num batches each epoch = {len(train_dataloader)}")
- logger.info(f" Num Epochs = {args.num_train_epochs}")
- logger.info(f" Instantaneous batch size per device = {args.train_batch_size}")
- logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
- logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
- logger.info(f" Total optimization steps = {args.max_train_steps}")
- global_step = 0
- first_epoch = 0
-
- # Potentially load in the weights and states from a previous save
- if args.resume_from_checkpoint:
- if args.resume_from_checkpoint != "latest":
- path = os.path.basename(args.resume_from_checkpoint)
- else:
- # Get the mos recent checkpoint
- dirs = os.listdir(args.output_dir)
- dirs = [d for d in dirs if d.startswith("checkpoint")]
- dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
- path = dirs[-1] if len(dirs) > 0 else None
-
- if path is None:
- accelerator.print(
- f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
- )
- args.resume_from_checkpoint = None
- else:
- accelerator.print(f"Resuming from checkpoint {path}")
- accelerator.load_state(os.path.join(args.output_dir, path))
- global_step = int(path.split("-")[1])
-
- resume_global_step = global_step * args.gradient_accumulation_steps
- first_epoch = global_step // num_update_steps_per_epoch
- resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
-
- # Only show the progress bar once on each machine.
- progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
- progress_bar.set_description("Steps")
-
- for epoch in range(first_epoch, args.num_train_epochs):
- unet.train()
- for step, batch in enumerate(train_dataloader):
- # Skip steps until we reach the resumed step
- if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
- if step % args.gradient_accumulation_steps == 0:
- progress_bar.update(1)
- continue
-
- with accelerator.accumulate(unet):
- # Convert images to latent space
- latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
- latents = latents * 0.18215
-
- # Sample noise that we'll add to the latents
- noise = torch.randn_like(latents)
- bsz = latents.shape[0]
- # Sample a random timestep for each image
- timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
- timesteps = timesteps.long()
-
- # Add noise to the latents according to the noise magnitude at each timestep
- # (this is the forward diffusion process)
- noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-
- # Get the text embedding for conditioning
- encoder_hidden_states = text_encoder(batch["input_ids"])[0]
-
- # Predict the noise residual
- model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
-
- # Get the target for loss depending on the prediction type
- if noise_scheduler.config.prediction_type == "epsilon":
- target = noise
- elif noise_scheduler.config.prediction_type == "v_prediction":
- target = noise_scheduler.get_velocity(latents, noise, timesteps)
- else:
- raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-
- if args.with_prior_preservation:
- # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
- model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
- target, target_prior = torch.chunk(target, 2, dim=0)
-
- # Compute instance loss
- loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-
- # Compute prior loss
- prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
-
- # Add the prior loss to the instance loss.
- loss = loss + args.prior_loss_weight * prior_loss
- else:
- loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-
- accelerator.backward(loss)
- if accelerator.sync_gradients:
- params_to_clip = lora_layers.parameters()
- accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
- optimizer.step()
- lr_scheduler.step()
- optimizer.zero_grad()
-
- # Checks if the accelerator has performed an optimization step behind the scenes
- if accelerator.sync_gradients:
- progress_bar.update(1)
- global_step += 1
-
- if global_step % args.checkpointing_steps == 0:
- if accelerator.is_main_process:
- save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
- accelerator.save_state(save_path)
- logger.info(f"Saved state to {save_path}")
-
- logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
- progress_bar.set_postfix(**logs)
- accelerator.log(logs, step=global_step)
-
- if global_step >= args.max_train_steps:
- break
-
- if args.validation_prompt is not None and epoch % args.validation_epochs == 0:
- logger.info(
- f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
- f" {args.validation_prompt}."
- )
- # create pipeline
- pipeline = DiffusionPipeline.from_pretrained(
- args.pretrained_model_name_or_path,
- unet=accelerator.unwrap_model(unet),
- text_encoder=accelerator.unwrap_model(text_encoder),
- revision=args.revision,
- torch_dtype=weight_dtype,
- )
- pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
- pipeline = pipeline.to(accelerator.device)
- pipeline.set_progress_bar_config(disable=True)
-
- # run inference
- generator = torch.Generator(device=accelerator.device).manual_seed(args.seed)
- prompt = args.num_validation_images * [args.validation_prompt]
- images = pipeline(prompt, num_inference_steps=25, generator=generator).images
-
- for tracker in accelerator.trackers:
- if tracker.name == "tensorboard":
- np_images = np.stack([np.asarray(img) for img in images])
- tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
- if tracker.name == "wandb":
- tracker.log(
- {
- "validation": [
- wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
- for i, image in enumerate(images)
- ]
- }
- )
-
- del pipeline
- torch.cuda.empty_cache()
-
- # Save the lora layers
- accelerator.wait_for_everyone()
- if accelerator.is_main_process:
- unet = unet.to(torch.float32)
- unet.save_attn_procs(args.output_dir)
-
- # Final inference
- # Load previous pipeline
- pipeline = DiffusionPipeline.from_pretrained(
- args.pretrained_model_name_or_path, revision=args.revision, torch_dtype=weight_dtype
- )
- pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
- pipeline = pipeline.to(accelerator.device)
-
- # load attention processors
- pipeline.unet.load_attn_procs(args.output_dir)
-
- # run inference
- if args.validation_prompt and args.num_validation_images > 0:
- generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed else None
- prompt = args.num_validation_images * [args.validation_prompt]
- images = pipeline(prompt, num_inference_steps=25, generator=generator).images
-
- test_image_dir = Path(args.output_dir) / 'test_images'
- test_image_dir.mkdir()
- for i, image in enumerate(images):
- out_path = test_image_dir / f'image_{i}.png'
- image.save(out_path)
-
- for tracker in accelerator.trackers:
- if tracker.name == "tensorboard":
- np_images = np.stack([np.asarray(img) for img in images])
- tracker.writer.add_images("test", np_images, epoch, dataformats="NHWC")
- if tracker.name == "wandb":
- tracker.log(
- {
- "test": [
- wandb.Image(image, caption=f"{i}: {args.validation_prompt}")
- for i, image in enumerate(images)
- ]
- }
- )
-
- if args.push_to_hub:
- save_model_card(
- repo_name,
- images=images,
- base_model=args.pretrained_model_name_or_path,
- prompt=args.instance_prompt,
- repo_folder=args.output_dir,
- )
- repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
-
- accelerator.end_training()
-
-
-if __name__ == "__main__":
- args = parse_args()
- main(args)
diff --git a/spaces/inamXcontru/PoeticTTS/Avast Antivirus Serial Key Facebook The Easiest Way to Install and Update Your Antivirus.md b/spaces/inamXcontru/PoeticTTS/Avast Antivirus Serial Key Facebook The Easiest Way to Install and Update Your Antivirus.md
deleted file mode 100644
index 2e1a469989ed07cfd337412b1959cb19e314291c..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Avast Antivirus Serial Key Facebook The Easiest Way to Install and Update Your Antivirus.md
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diff --git a/spaces/inamXcontru/PoeticTTS/Despedida Maria Grever Partitura Pdf 22.md b/spaces/inamXcontru/PoeticTTS/Despedida Maria Grever Partitura Pdf 22.md
deleted file mode 100644
index 7150f6919619c5518e43c5b313786f09b0e10827..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Despedida Maria Grever Partitura Pdf 22.md
+++ /dev/null
@@ -1,68 +0,0 @@
-## Despedida Maria Grever Partitura Pdf 22
-
-
-
-
-
-
-
-
-
-**LINK ===> [https://www.google.com/url?q=https%3A%2F%2Furlca.com%2F2txTed&sa=D&sntz=1&usg=AOvVaw0byKEn3rcy6jr4WlbdPRRb](https://www.google.com/url?q=https%3A%2F%2Furlca.com%2F2txTed&sa=D&sntz=1&usg=AOvVaw0byKEn3rcy6jr4WlbdPRRb)**
-
-
-
-
-
-
-
-
-
-
-
-
-
-# Despedida: A Beautiful Song by Maria Grever
-
-
-
-Maria Grever was a Mexican composer and songwriter who wrote more than 800 songs in her lifetime. She is considered one of the most important and influential Latin American composers of the 20th century. One of her most famous songs is Despedida, which means "farewell" in Spanish.
-
-
-
-Despedida is a romantic ballad that expresses the sadness of parting from a loved one. The lyrics are full of emotion and nostalgia, as the singer recalls the happy moments they shared and hopes to see them again someday. The melody is simple but elegant, with a smooth and flowing rhythm.
-
-
-
-The song was written in 1934 and has been recorded by many artists, such as Placido Domingo, Andrea Bocelli, Luis Miguel, and Nat King Cole. It has also been adapted into different languages, such as English, French, Italian, and Portuguese.
-
-
-
-If you want to play Despedida on your instrument, you can find the sheet music online in PDF format. There are different versions available for piano, guitar, violin, cello, flute, and more. You can also listen to the song on YouTube or Spotify to get inspired by the beautiful performance of Maria Grever and other singers.
-
-
-
-Despedida is a song that will touch your heart and soul with its poetic lyrics and graceful melody. It is a perfect example of Maria Grever's talent and legacy as a composer and songwriter. If you love music and romance, you should definitely listen to Despedida and learn more about Maria Grever's life and work.
-
-
-
-If you are interested in learning more about Maria Grever and her music, you can visit the website of the Maria Grever Foundation, which is dedicated to preserving and promoting her legacy. The website contains biographical information, photos, videos, audio clips, and news about events and projects related to Maria Grever. You can also find links to other resources, such as books, articles, documentaries, and podcasts.
-
-
-
-Another way to appreciate Maria Grever's music is to attend a concert or a musical show that features her songs. There are many artists and groups that perform her music regularly, such as the Maria Grever Orchestra, the Maria Grever Quartet, and the Maria Grever Ensemble. You can check their schedules and venues online or follow them on social media.
-
-
-
-Maria Grever was a remarkable woman who overcame many challenges and obstacles in her life. She was born in Mexico in 1894 and moved to New York in 1916. She faced discrimination and prejudice as a female and a Latino composer in a male-dominated industry. She also suffered from health problems and personal tragedies. Despite all this, she never gave up on her passion and dream of creating music. She became one of the most successful and respected composers of her time, earning recognition and awards from around the world.
-
-
-
-Despedida is just one of the many gems that Maria Grever left for us to enjoy. It is a song that transcends time and space, and connects us with our feelings and memories. It is a song that celebrates love and life, even in the face of loss and sorrow. It is a song that reminds us of the beauty and power of music.
-
- dfd1c89656
-
-
-
-
-
diff --git a/spaces/innnky/soft-vits-vc/modules.py b/spaces/innnky/soft-vits-vc/modules.py
deleted file mode 100644
index 9c7fd9cd6eb8b7e0ec0e08957e970744a374a924..0000000000000000000000000000000000000000
--- a/spaces/innnky/soft-vits-vc/modules.py
+++ /dev/null
@@ -1,390 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
- def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
- super().__init__()
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.out_channels = out_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
- assert n_layers > 1, "Number of layers should be larger than 0."
-
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.relu_drop = nn.Sequential(
- nn.ReLU(),
- nn.Dropout(p_dropout))
- for _ in range(n_layers-1):
- self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask):
- x_org = x
- for i in range(self.n_layers):
- x = self.conv_layers[i](x * x_mask)
- x = self.norm_layers[i](x)
- x = self.relu_drop(x)
- x = x_org + self.proj(x)
- return x * x_mask
-
-
-class DDSConv(nn.Module):
- """
- Dialted and Depth-Separable Convolution
- """
- def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
- super().__init__()
- self.channels = channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
-
- self.drop = nn.Dropout(p_dropout)
- self.convs_sep = nn.ModuleList()
- self.convs_1x1 = nn.ModuleList()
- self.norms_1 = nn.ModuleList()
- self.norms_2 = nn.ModuleList()
- for i in range(n_layers):
- dilation = kernel_size ** i
- padding = (kernel_size * dilation - dilation) // 2
- self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
- groups=channels, dilation=dilation, padding=padding
- ))
- self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
- self.norms_1.append(LayerNorm(channels))
- self.norms_2.append(LayerNorm(channels))
-
- def forward(self, x, x_mask, g=None):
- if g is not None:
- x = x + g
- for i in range(self.n_layers):
- y = self.convs_sep[i](x * x_mask)
- y = self.norms_1[i](y)
- y = F.gelu(y)
- y = self.convs_1x1[i](y)
- y = self.norms_2[i](y)
- y = F.gelu(y)
- y = self.drop(y)
- x = x + y
- return x * x_mask
-
-
-class WN(torch.nn.Module):
- def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
- super(WN, self).__init__()
- assert(kernel_size % 2 == 1)
- self.hidden_channels =hidden_channels
- self.kernel_size = kernel_size,
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
- self.p_dropout = p_dropout
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.drop = nn.Dropout(p_dropout)
-
- if gin_channels != 0:
- cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
- for i in range(n_layers):
- dilation = dilation_rate ** i
- padding = int((kernel_size * dilation - dilation) / 2)
- in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
- dilation=dilation, padding=padding)
- in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
- self.in_layers.append(in_layer)
-
- # last one is not necessary
- if i < n_layers - 1:
- res_skip_channels = 2 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
- self.res_skip_layers.append(res_skip_layer)
-
- def forward(self, x, x_mask, g=None, **kwargs):
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
- if g is not None:
- g = self.cond_layer(g)
-
- for i in range(self.n_layers):
- x_in = self.in_layers[i](x)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
- else:
- g_l = torch.zeros_like(x_in)
-
- acts = commons.fused_add_tanh_sigmoid_multiply(
- x_in,
- g_l,
- n_channels_tensor)
- acts = self.drop(acts)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- res_acts = res_skip_acts[:,:self.hidden_channels,:]
- x = (x + res_acts) * x_mask
- output = output + res_skip_acts[:,self.hidden_channels:,:]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.gin_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.convs1 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2])))
- ])
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1)))
- ])
- self.convs2.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c2(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.convs = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1])))
- ])
- self.convs.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Log(nn.Module):
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
- logdet = torch.sum(-y, [1, 2])
- return y, logdet
- else:
- x = torch.exp(x) * x_mask
- return x
-
-
-class Flip(nn.Module):
- def forward(self, x, *args, reverse=False, **kwargs):
- x = torch.flip(x, [1])
- if not reverse:
- logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
- return x, logdet
- else:
- return x
-
-
-class ElementwiseAffine(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.channels = channels
- self.m = nn.Parameter(torch.zeros(channels,1))
- self.logs = nn.Parameter(torch.zeros(channels,1))
-
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = self.m + torch.exp(self.logs) * x
- y = y * x_mask
- logdet = torch.sum(self.logs * x_mask, [1,2])
- return y, logdet
- else:
- x = (x - self.m) * torch.exp(-self.logs) * x_mask
- return x
-
-
-class ResidualCouplingLayer(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=0,
- gin_channels=0,
- mean_only=False):
- assert channels % 2 == 0, "channels should be divisible by 2"
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels]*2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1,2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
-
-
-class ConvFlow(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
- super().__init__()
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.num_bins = num_bins
- self.tail_bound = tail_bound
- self.half_channels = in_channels // 2
-
- self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
- self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
- self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0)
- h = self.convs(h, x_mask, g=g)
- h = self.proj(h) * x_mask
-
- b, c, t = x0.shape
- h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
- unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
- x1, logabsdet = piecewise_rational_quadratic_transform(x1,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=reverse,
- tails='linear',
- tail_bound=self.tail_bound
- )
-
- x = torch.cat([x0, x1], 1) * x_mask
- logdet = torch.sum(logabsdet * x_mask, [1,2])
- if not reverse:
- return x, logdet
- else:
- return x
diff --git a/spaces/innnky/visinger2-nomidi/modules/ddsp.py b/spaces/innnky/visinger2-nomidi/modules/ddsp.py
deleted file mode 100644
index 7ffd7bb3d3f75ca963fa9795628ada54ec70f909..0000000000000000000000000000000000000000
--- a/spaces/innnky/visinger2-nomidi/modules/ddsp.py
+++ /dev/null
@@ -1,189 +0,0 @@
-import torch
-import torch.nn as nn
-from torch.nn import functional as F
-import torch.fft as fft
-import numpy as np
-import librosa as li
-import math
-from scipy.signal import get_window
-
-def safe_log(x):
- return torch.log(x + 1e-7)
-
-
-@torch.no_grad()
-def mean_std_loudness(dataset):
- mean = 0
- std = 0
- n = 0
- for _, _, l in dataset:
- n += 1
- mean += (l.mean().item() - mean) / n
- std += (l.std().item() - std) / n
- return mean, std
-
-
-def multiscale_fft(signal, scales, overlap):
- stfts = []
- for s in scales:
- S = torch.stft(
- signal,
- s,
- int(s * (1 - overlap)),
- s,
- torch.hann_window(s).to(signal),
- True,
- normalized=True,
- return_complex=True,
- ).abs()
- stfts.append(S)
- return stfts
-
-
-def resample(x, factor: int):
- batch, frame, channel = x.shape
- x = x.permute(0, 2, 1).reshape(batch * channel, 1, frame)
-
- window = torch.hann_window(
- factor * 2,
- dtype=x.dtype,
- device=x.device,
- ).reshape(1, 1, -1)
- y = torch.zeros(x.shape[0], x.shape[1], factor * x.shape[2]).to(x)
- y[..., ::factor] = x
- y[..., -1:] = x[..., -1:]
- y = torch.nn.functional.pad(y, [factor, factor])
- y = torch.nn.functional.conv1d(y, window)[..., :-1]
-
- y = y.reshape(batch, channel, factor * frame).permute(0, 2, 1)
-
- return y
-
-
-def upsample(signal, factor):
- signal = signal.permute(0, 2, 1)
- signal = nn.functional.interpolate(signal, size=signal.shape[-1] * factor)
- return signal.permute(0, 2, 1)
-
-
-def remove_above_nyquist(amplitudes, pitch, sampling_rate):
- n_harm = amplitudes.shape[-1]
- pitches = pitch * torch.arange(1, n_harm + 1).to(pitch)
- aa = (pitches < sampling_rate / 2).float() + 1e-4
- return amplitudes * aa
-
-
-def scale_function(x):
- return 2 * torch.sigmoid(x)**(math.log(10)) + 1e-7
-
-
-def extract_loudness(signal, sampling_rate, block_size, n_fft=2048):
- S = li.stft(
- signal,
- n_fft=n_fft,
- hop_length=block_size,
- win_length=n_fft,
- center=True,
- )
- S = np.log(abs(S) + 1e-7)
- f = li.fft_frequencies(sampling_rate, n_fft)
- a_weight = li.A_weighting(f)
-
- S = S + a_weight.reshape(-1, 1)
-
- S = np.mean(S, 0)[..., :-1]
-
- return S
-
-
-def extract_pitch(signal, sampling_rate, block_size):
- length = signal.shape[-1] // block_size
- f0 = crepe.predict(
- signal,
- sampling_rate,
- step_size=int(1000 * block_size / sampling_rate),
- verbose=1,
- center=True,
- viterbi=True,
- )
- f0 = f0[1].reshape(-1)[:-1]
-
- if f0.shape[-1] != length:
- f0 = np.interp(
- np.linspace(0, 1, length, endpoint=False),
- np.linspace(0, 1, f0.shape[-1], endpoint=False),
- f0,
- )
-
- return f0
-
-
-def mlp(in_size, hidden_size, n_layers):
- channels = [in_size] + (n_layers) * [hidden_size]
- net = []
- for i in range(n_layers):
- net.append(nn.Linear(channels[i], channels[i + 1]))
- net.append(nn.LayerNorm(channels[i + 1]))
- net.append(nn.LeakyReLU())
- return nn.Sequential(*net)
-
-
-def gru(n_input, hidden_size):
- return nn.GRU(n_input * hidden_size, hidden_size, batch_first=True)
-
-
-def harmonic_synth(pitch, amplitudes, sampling_rate):
- n_harmonic = amplitudes.shape[-1]
- omega = torch.cumsum(2 * math.pi * pitch / sampling_rate, 1)
- omegas = omega * torch.arange(1, n_harmonic + 1).to(omega)
- signal = (torch.sin(omegas) * amplitudes).sum(-1, keepdim=True)
- return signal
-
-
-def amp_to_impulse_response(amp, target_size):
- amp = torch.stack([amp, torch.zeros_like(amp)], -1)
- amp = torch.view_as_complex(amp)
- amp = fft.irfft(amp)
-
- filter_size = amp.shape[-1]
-
- amp = torch.roll(amp, filter_size // 2, -1)
- win = torch.hann_window(filter_size, dtype=amp.dtype, device=amp.device)
-
- amp = amp * win
-
- amp = nn.functional.pad(amp, (0, int(target_size) - int(filter_size)))
- amp = torch.roll(amp, -filter_size // 2, -1)
-
- return amp
-
-
-def fft_convolve(signal, kernel):
- signal = nn.functional.pad(signal, (0, signal.shape[-1]))
- kernel = nn.functional.pad(kernel, (kernel.shape[-1], 0))
-
- output = fft.irfft(fft.rfft(signal) * fft.rfft(kernel))
- output = output[..., output.shape[-1] // 2:]
-
- return output
-
-
-def init_kernels(win_len, win_inc, fft_len, win_type=None, invers=False):
- if win_type == 'None' or win_type is None:
- window = np.ones(win_len)
- else:
- window = get_window(win_type, win_len, fftbins=True)#**0.5
-
- N = fft_len
- fourier_basis = np.fft.rfft(np.eye(N))[:win_len]
- real_kernel = np.real(fourier_basis)
- imag_kernel = np.imag(fourier_basis)
- kernel = np.concatenate([real_kernel, imag_kernel], 1).T
-
- if invers :
- kernel = np.linalg.pinv(kernel).T
-
- kernel = kernel*window
- kernel = kernel[:, None, :]
- return torch.from_numpy(kernel.astype(np.float32)), torch.from_numpy(window[None,:,None].astype(np.float32))
-
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Ayodance Offline Free Download Full 31 PORTABLE.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Ayodance Offline Free Download Full 31 PORTABLE.md
deleted file mode 100644
index e5208959016c9b21b0eaa84c4e8039216e551b11..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Ayodance Offline Free Download Full 31 PORTABLE.md
+++ /dev/null
@@ -1,25 +0,0 @@
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diff --git a/spaces/inreVtussa/clothingai/Examples/Descargar Crack Lex Doctor 8 ((FREE)).md b/spaces/inreVtussa/clothingai/Examples/Descargar Crack Lex Doctor 8 ((FREE)).md
deleted file mode 100644
index 1852450295a3f4b935a0144b5b4a5d4bff753cdb..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Descargar Crack Lex Doctor 8 ((FREE)).md
+++ /dev/null
@@ -1,6 +0,0 @@
-Descargar Crack Lex Doctor 8 Download Zip ••• https://tiurll.com/2uCjwS
diff --git a/spaces/ioanniskarkanias/chatbot-with-sources/README.md b/spaces/ioanniskarkanias/chatbot-with-sources/README.md
deleted file mode 100644
index b31e315c08041251f9ae6513e1db4d0695aaf357..0000000000000000000000000000000000000000
--- a/spaces/ioanniskarkanias/chatbot-with-sources/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Chatbot With Sources
-emoji: 🤖
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/jacklindsai/is_it_elon_musk/README.md b/spaces/jacklindsai/is_it_elon_musk/README.md
deleted file mode 100644
index 5bd955a61784dc5c69fe5b40ec41de37551d7aed..0000000000000000000000000000000000000000
--- a/spaces/jacklindsai/is_it_elon_musk/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Is_it_elon_musk
-emoji: 🐦
-colorFrom: red
-colorTo: gray
-sdk: gradio
-sdk_version: 2.9.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/jackyccl/segment-anything/segment_anything/utils/transforms.py b/spaces/jackyccl/segment-anything/segment_anything/utils/transforms.py
deleted file mode 100644
index c08ba1e3db751f3a5483a003be38c69c2cf2df85..0000000000000000000000000000000000000000
--- a/spaces/jackyccl/segment-anything/segment_anything/utils/transforms.py
+++ /dev/null
@@ -1,102 +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 numpy as np
-import torch
-from torch.nn import functional as F
-from torchvision.transforms.functional import resize, to_pil_image # type: ignore
-
-from copy import deepcopy
-from typing import Tuple
-
-
-class ResizeLongestSide:
- """
- Resizes images to the longest side 'target_length', as well as provides
- methods for resizing coordinates and boxes. Provides methods for
- transforming both numpy array and batched torch tensors.
- """
-
- def __init__(self, target_length: int) -> None:
- self.target_length = target_length
-
- def apply_image(self, image: np.ndarray) -> np.ndarray:
- """
- Expects a numpy array with shape HxWxC in uint8 format.
- """
- target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
- return np.array(resize(to_pil_image(image), target_size))
-
- def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
- """
- Expects a numpy array of length 2 in the final dimension. Requires the
- original image size in (H, W) format.
- """
- old_h, old_w = original_size
- new_h, new_w = self.get_preprocess_shape(
- original_size[0], original_size[1], self.target_length
- )
- coords = deepcopy(coords).astype(float)
- coords[..., 0] = coords[..., 0] * (new_w / old_w)
- coords[..., 1] = coords[..., 1] * (new_h / old_h)
- return coords
-
- def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
- """
- Expects a numpy array shape Bx4. Requires the original image size
- in (H, W) format.
- """
- boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)
- return boxes.reshape(-1, 4)
-
- def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:
- """
- Expects batched images with shape BxCxHxW and float format. This
- transformation may not exactly match apply_image. apply_image is
- the transformation expected by the model.
- """
- # Expects an image in BCHW format. May not exactly match apply_image.
- target_size = self.get_preprocess_shape(image.shape[2], image.shape[3], self.target_length)
- return F.interpolate(
- image, target_size, mode="bilinear", align_corners=False, antialias=True
- )
-
- def apply_coords_torch(
- self, coords: torch.Tensor, original_size: Tuple[int, ...]
- ) -> torch.Tensor:
- """
- Expects a torch tensor with length 2 in the last dimension. Requires the
- original image size in (H, W) format.
- """
- old_h, old_w = original_size
- new_h, new_w = self.get_preprocess_shape(
- original_size[0], original_size[1], self.target_length
- )
- coords = deepcopy(coords).to(torch.float)
- coords[..., 0] = coords[..., 0] * (new_w / old_w)
- coords[..., 1] = coords[..., 1] * (new_h / old_h)
- return coords
-
- def apply_boxes_torch(
- self, boxes: torch.Tensor, original_size: Tuple[int, ...]
- ) -> torch.Tensor:
- """
- Expects a torch tensor with shape Bx4. Requires the original image
- size in (H, W) format.
- """
- boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size)
- return boxes.reshape(-1, 4)
-
- @staticmethod
- def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]:
- """
- Compute the output size given input size and target long side length.
- """
- scale = long_side_length * 1.0 / max(oldh, oldw)
- newh, neww = oldh * scale, oldw * scale
- neww = int(neww + 0.5)
- newh = int(newh + 0.5)
- return (newh, neww)
diff --git a/spaces/jbilcke-hf/VideoChain-UI/src/components/business/videos/video-actions.tsx b/spaces/jbilcke-hf/VideoChain-UI/src/components/business/videos/video-actions.tsx
deleted file mode 100644
index e70045de88ddad8c9d201e4f50293a3adce19020..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/VideoChain-UI/src/components/business/videos/video-actions.tsx
+++ /dev/null
@@ -1,52 +0,0 @@
-"use client"
-
-import { DotsHorizontalIcon } from "@radix-ui/react-icons"
-import { Row } from "@tanstack/react-table"
-
-import { Button } from "@/components/ui/button"
-import {
- DropdownMenu,
- DropdownMenuContent,
- DropdownMenuItem,
- DropdownMenuSeparator,
- DropdownMenuShortcut,
- DropdownMenuTrigger,
-} from "@/components/ui/dropdown-menu"
-
-import { Video } from "@/app/types"
-
-export function VideoActions({
- row,
-}: {
- row: Row
-}) {
- const task = row.original
-
- return (
-
-
-
- Open menu
-
-
-
- {/*
-
- Pause generation
- ⌘⌫
-
- */}
- Download
-
- Delete
- ⌘⌫
-
-
-
- )
-}
\ No newline at end of file
diff --git a/spaces/jiangjiechen/Auction-Arena-Demo/auction_workflow.py b/spaces/jiangjiechen/Auction-Arena-Demo/auction_workflow.py
deleted file mode 100644
index cb98914da537b0529cb7b564e7194cfea57fbc16..0000000000000000000000000000000000000000
--- a/spaces/jiangjiechen/Auction-Arena-Demo/auction_workflow.py
+++ /dev/null
@@ -1,293 +0,0 @@
-import os
-import time
-import gradio as gr
-import ujson as json
-import traceback
-from typing import List
-from tqdm import tqdm
-from src.auctioneer_base import Auctioneer
-from src.bidder_base import Bidder, bidders_to_chatbots, bidding_multithread
-from utils import trace_back
-
-
-LOG_DIR = 'logs'
-enable_gr = gr.update(interactive=True)
-disable_gr = gr.update(interactive=False)
-
-
-def monitor_all(bidder_list: List[Bidder]):
- return sum([bidder.to_monitors() for bidder in bidder_list], [])
-
-
-def parse_bid_price(auctioneer: Auctioneer, bidder: Bidder, msg: str):
- # rebid if the message is not parsible into a bid price
- bid_price = auctioneer.parse_bid(msg)
- while bid_price is None:
- re_msg = bidder.bid("You must be clear about your bidding decision, say either \"I'm out!\" or \"I bid $xxx!\". Please rebid.")
- bid_price = auctioneer.parse_bid(re_msg)
- print(f"{bidder.name} rebid: {re_msg}")
- return bid_price
-
-
-def enable_human_box(bidder_list):
- signals = []
- for bidder in bidder_list:
- if 'human' in bidder.model_name and not bidder.withdraw:
- signals.append(gr.update(interactive=True, visible=True,
- placeholder="Please bid! Enter \"I'm out\" or \"I bid $xxx\"."))
- else:
- signals.append(disable_gr)
- return signals
-
-
-def disable_all_box(bidder_list):
- signals = []
- for bidder in bidder_list:
- if 'human' in bidder.model_name:
- signals.append(gr.update(interactive=False, visible=True,
- placeholder="Wait a moment to engage in the auction."))
- else:
- signals.append(gr.update(interactive=False, visible=False))
- return signals
-
-
-def run_auction(
- auction_hash: str,
- auctioneer: Auctioneer,
- bidder_list: List[Bidder],
- thread_num: int,
- yield_for_demo=True,
- log_dir=LOG_DIR,
- repeat_num=0,
- memo_file=None):
-
- # bidder_list[0].verbose=True
-
- if yield_for_demo:
- chatbot_list = bidders_to_chatbots(bidder_list)
- yield [bidder_list] + chatbot_list + monitor_all(bidder_list) + [auctioneer.log()] + [disable_gr, disable_gr] + disable_all_box(bidder_list)
-
- # ***************** Learn Round ****************
- for bidder in bidder_list:
- if bidder.enable_learning and memo_file:
- # if no prev memo file, then no need to learn.
- if os.path.exists(memo_file):
- with open(memo_file) as f:
- data = json.load(f)
- past_learnings = data['learnings'][bidder.name]
- past_auction_log = data['auction_log']
- bidder.learn_from_prev_auction(past_learnings, past_auction_log)
-
- # ***************** Plan Round *****************
- # init bidder profit
- bidder_profit_info = auctioneer.gather_all_status(bidder_list)
- for bidder in bidder_list:
- bidder.set_all_bidders_status(bidder_profit_info)
-
- plan_instructs = [bidder.get_plan_instruct(auctioneer.items) for bidder in bidder_list]
-
- bidding_multithread(bidder_list, plan_instructs, func_type='plan', thread_num=thread_num)
-
- if yield_for_demo:
- chatbot_list = bidders_to_chatbots(bidder_list)
- yield [bidder_list] + chatbot_list + monitor_all(bidder_list) + [auctioneer.log()] + [disable_gr, disable_gr] + disable_all_box(bidder_list)
-
- bar = tqdm(total=len(auctioneer.items_queue), desc='Auction Progress')
- while not auctioneer.end_auction():
- cur_item = auctioneer.present_item()
-
- bid_round = 0
- while True:
- # ***************** Bid Round *****************
- auctioneer_msg = auctioneer.ask_for_bid(bid_round)
- _bidder_list = []
- _bid_instruct_list = []
- # remove highest bidder and withdrawn bidders
- for bidder in bidder_list:
- if bidder is auctioneer.highest_bidder or bidder.withdraw:
- bidder.need_input = False
- continue
- else:
- bidder.need_input = True # enable input from demo
- instruct = bidder.get_bid_instruct(auctioneer_msg, bid_round)
- _bidder_list.append(bidder)
- _bid_instruct_list.append(instruct)
-
- if yield_for_demo:
- chatbot_list = bidders_to_chatbots(bidder_list)
- yield [bidder_list] + chatbot_list + monitor_all(bidder_list) + [auctioneer.log()] + [disable_gr, disable_gr] + enable_human_box(bidder_list)
-
- _msgs = bidding_multithread(_bidder_list, _bid_instruct_list, func_type='bid', thread_num=thread_num)
-
- for i, (msg, bidder) in enumerate(zip(_msgs, _bidder_list)):
- if bidder.model_name == 'rule':
- bid_price = bidder.bid_rule(auctioneer.prev_round_max_bid, auctioneer.min_markup_pct)
- else:
- bid_price = parse_bid_price(auctioneer, bidder, msg)
-
- # can't bid more than budget or less than previous highest bid
- while True:
- fail_msg = bidder.bid_sanity_check(bid_price, auctioneer.prev_round_max_bid, auctioneer.min_markup_pct)
- if fail_msg is None:
- break
- else:
- bidder.need_input = True # enable input from demo
- auctioneer_msg = auctioneer.ask_for_rebid(fail_msg=fail_msg, bid_price=bid_price)
- rebid_instruct = bidder.get_rebid_instruct(auctioneer_msg)
-
- if yield_for_demo:
- chatbot_list = bidders_to_chatbots(bidder_list)
- yield [bidder_list] + chatbot_list + monitor_all(bidder_list) + [auctioneer.log()] + [disable_gr, disable_gr] + disable_all_box(bidder_list)
-
- msg = bidder.rebid_for_failure(rebid_instruct)
- bid_price = parse_bid_price(auctioneer, bidder, msg)
-
- if yield_for_demo:
- chatbot_list = bidders_to_chatbots(bidder_list)
- yield [bidder_list] + chatbot_list + monitor_all(bidder_list) + [auctioneer.log()] + [disable_gr, disable_gr] + disable_all_box(bidder_list)
-
- bidder.set_withdraw(bid_price)
- auctioneer.record_bid({'bidder': bidder, 'bid': bid_price, 'raw_msg': msg}, bid_round)
-
- if yield_for_demo:
- chatbot_list = bidders_to_chatbots(bidder_list)
- yield [bidder_list] + chatbot_list + monitor_all(bidder_list) + [auctioneer.log()] + [disable_gr, disable_gr] + disable_all_box(bidder_list)
-
- is_sold = auctioneer.check_hammer(bid_round)
- bid_round += 1
- if is_sold:
- break
- else:
- if auctioneer.fail_to_sell and auctioneer.enable_discount:
- for bidder in bidder_list:
- bidder.set_withdraw(0) # back in the game
-
- # ***************** Summarize *****************
- summarize_instruct_list = []
- for bidder in bidder_list:
- if bidder is auctioneer.highest_bidder:
- win_lose_msg = bidder.win_bid(cur_item, auctioneer.highest_bid)
- else:
- win_lose_msg = bidder.lose_bid(cur_item)
- msg = bidder.get_summarize_instruct(
- bidding_history=auctioneer.all_bidding_history_to_string(),
- hammer_msg=auctioneer.get_hammer_msg(),
- win_lose_msg=win_lose_msg
- )
- summarize_instruct_list.append(msg)
-
- # record profit information of all bidders for each bidder
- # (not used in the auction, just for belief tracking evaluation)
- bidder_profit_info = auctioneer.gather_all_status(bidder_list)
- for bidder in bidder_list:
- bidder.set_all_bidders_status(bidder_profit_info)
-
- bidding_multithread(bidder_list, summarize_instruct_list, func_type='summarize', thread_num=thread_num)
-
- if yield_for_demo:
- chatbot_list = bidders_to_chatbots(bidder_list)
- yield [bidder_list] + chatbot_list + monitor_all(bidder_list) + [auctioneer.log()] + [disable_gr, disable_gr] + disable_all_box(bidder_list)
-
- # ***************** Replan *****************
- if len(auctioneer.items_queue) > 0: # no need to replan if all items are sold
- replan_instruct_list = [bidder.get_replan_instruct(
- # bidding_history=auctioneer.all_bidding_history_to_string(),
- # hammer_msg=auctioneer.get_hammer_msg()
- ) for bidder in bidder_list]
- bidding_multithread(bidder_list, replan_instruct_list, func_type='replan', thread_num=thread_num)
-
- if yield_for_demo:
- chatbot_list = bidders_to_chatbots(bidder_list)
- yield [bidder_list] + chatbot_list + monitor_all(bidder_list) + [auctioneer.log()] + [disable_gr, disable_gr] + disable_all_box(bidder_list)
-
- auctioneer.hammer_fall()
- bar.update(1)
-
- total_cost = sum([b.openai_cost for b in bidder_list]) + auctioneer.openai_cost
- bidder_reports = [bidder.profit_report() for bidder in bidder_list]
-
- if yield_for_demo:
- chatbot_list = bidders_to_chatbots(bidder_list, profit_report=True)
- yield [bidder_list] + chatbot_list + monitor_all(bidder_list) + [auctioneer.log(bidder_reports) + f'\n## Total Cost: ${total_cost}'] + [disable_gr, enable_gr] + disable_all_box(bidder_list)
-
- memo = {'auction_log': auctioneer.log(show_model_name=False),
- 'memo_text': bidder_reports,
- 'profit': {bidder.name: bidder.profit for bidder in bidder_list},
- 'total_cost': total_cost,
- 'learnings': {bidder.name: bidder.learnings for bidder in bidder_list},
- 'model_info': {bidder.name: bidder.model_name for bidder in bidder_list}}
- log_bidders(log_dir, auction_hash, bidder_list, repeat_num, memo)
-
- auctioneer.finish_auction()
-
- if not yield_for_demo:
- yield total_cost
-
-
-def log_bidders(log_dir: str, auction_hash: str, bidder_list: List[Bidder], repeat_num: int, memo: dict):
- for bidder in bidder_list:
- log_file = f"{log_dir}/{auction_hash}/{bidder.name.replace(' ', '')}-{repeat_num}.jsonl"
- if not os.path.exists(log_file):
- os.makedirs(os.path.dirname(log_file), exist_ok=True)
- with open(log_file, 'a') as f:
- log_data = bidder.to_monitors(as_json=True)
- f.write(json.dumps(log_data) + '\n')
-
- with open(f"{log_dir}/{auction_hash}/memo-{repeat_num}.json", 'w') as f:
- f.write(json.dumps(memo) + '\n')
-
-
-def make_auction_hash():
- return str(int(time.time()))
-
-
-if __name__ == '__main__':
- import argparse
- from src.item_base import create_items
- from src.bidder_base import create_bidders
- from transformers import GPT2TokenizerFast
- import cjjpy as cjj
-
- parser = argparse.ArgumentParser()
- parser.add_argument('--input_dir', '-i', type=str, default='data/exp_base/')
- parser.add_argument('--shuffle', action='store_true')
- parser.add_argument('--repeat', type=int, default=1)
- parser.add_argument('--threads', '-t', type=int, help='Number of threads. Max is number of bidders. Reduce it if rate limit is low (e.g., GPT-4).', required=True)
- parser.add_argument('--memo_file', '-m', type=str, help='The last memo.json file to be loaded for learning. Only useful when the repeated auctions are interrupted (i.e., auction hash is different).')
- args = parser.parse_args()
-
- auction_hash = make_auction_hash()
-
- total_money_spent = 0
- for i in tqdm(range(args.repeat), desc='Repeat'):
- cnt = 3
- while cnt > 0:
- try:
- item_file = os.path.join(args.input_dir, f'items_demo.jsonl')
- bidder_file = os.path.join(args.input_dir, f'bidders_demo.jsonl')
- memo_file = args.memo_file if args.memo_file else f'{args.input_dir}/{auction_hash}/memo-{i-1}.json' # past memo for learning
- items = create_items(item_file)
- bidders = create_bidders(bidder_file, auction_hash=auction_hash)
- auctioneer = Auctioneer(enable_discount=False)
- auctioneer.init_items(items)
- if args.shuffle:
- auctioneer.shuffle_items()
- money_spent = list(run_auction(
- auction_hash,
- auctioneer,
- bidders,
- thread_num=min(args.threads, len(bidders)),
- yield_for_demo=False,
- log_dir=args.input_dir,
- repeat_num=i,
- memo_file=memo_file,
- ))
- total_money_spent += sum(money_spent)
- break
- except Exception as e:
- cnt -= 1
- print(f"Error in {i}th auction: {e}\n{trace_back(e)}")
- print(f"Retry {cnt} more times...")
-
- print('Total money spent: $', total_money_spent)
- cjj.SendEmail(f'Completed: {args.input_dir} - {auction_hash}', f'Total money spent: ${total_money_spent}')
\ No newline at end of file
diff --git a/spaces/jinmao/2/assets/custom.js b/spaces/jinmao/2/assets/custom.js
deleted file mode 100644
index 7b1761043149ff97ca498501c87a0d15db5258ee..0000000000000000000000000000000000000000
--- a/spaces/jinmao/2/assets/custom.js
+++ /dev/null
@@ -1 +0,0 @@
-// custom javascript here
\ No newline at end of file
diff --git a/spaces/jitesh/storytelling/src/story_gen.py b/spaces/jitesh/storytelling/src/story_gen.py
deleted file mode 100644
index d4242b66f0b610167f0ee69bbfa5fe33df732eac..0000000000000000000000000000000000000000
--- a/spaces/jitesh/storytelling/src/story_gen.py
+++ /dev/null
@@ -1,227 +0,0 @@
-
-# import nltk
-import re
-import sys
-import time
-
-import numpy as np
-import pandas as pd
-import printj
-import streamlit as st
-from transformers import pipeline # , set_seed
-
-
-class StoryGenerator:
- def __init__(self):
- # self.initialise_models()
- self.stats_df = pd.DataFrame(data=[], columns=[])
- self.stories = []
- self.data = []
-
- @staticmethod
- @st.cache(allow_output_mutation=True)
- def get_generator():
- return pipeline('text-generation', model='gpt2')
-
- @staticmethod
- @st.cache(allow_output_mutation=True)
- def get_classifier():
- return pipeline("text-classification",
- model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
-
- def initialise_classifier_model(self):
- self.classifier = self.get_classifier()
-
- def initialise_models(self):
- # start = time.time()
- self.generator = self.get_generator()
- self.classifier = self.get_classifier()
- # initialising_time = time.time()-start
- # print(f'Initialising Time: {initialising_time}')
- # set_seed(42)
- # sys.exit()
-
- def reset():
- self.clear_stories()
- self.clear_stats()
-
- def clear_stories(self):
- self.data = []
- self.stories = []
-
- def clear_stats(self):
- self.stats_df = pd.DataFrame(data=[], columns=[])
-
- def get_emotion(self, text, filter_by='max'):
- emotions = self.classifier(text)
- if filter_by=='max':
- emotion = max(emotions[0], key=lambda x: x['score'])
- return emotion
- elif filter_by=='sorted':
- return sorted(emotions[0], key=lambda x: x['score'], reverse=True)
-
-
- @staticmethod
- def get_num_token(text):
- # return len(nltk.word_tokenize(text))
- return len(re.findall(r'\w+', text))
-
- @staticmethod
- def check_show_emotion(confidence_score, frequency, w):
- frequency_penalty = 1 - frequency
- probability_emote = w * confidence_score + (1-w) * frequency_penalty
- return probability_emote > np.random.random_sample()
-
- def story(self,
- story_till_now="Hello, I'm a language model,",
- num_generation=4,
- length=10):
- # last_length = 0
-
- for i in range(num_generation):
- last_length = len(story_till_now)
- genreate_robot_sentence = self.generator(story_till_now, max_length=self.get_num_token(story_till_now) +
- length, num_return_sequences=1)
- story_till_now = genreate_robot_sentence[0]['generated_text']
- new_sentence = story_till_now[last_length:]
- emotion = self.get_emotion(new_sentence)
- # printj.yellow(f'Sentence {i}:')
- # story_to_print = f'{printj.ColorText.cyan(story_till_now[:last_length])}{printj.ColorText.green(story_till_now[last_length:])}\n'
- # print(story_to_print)
- # printj.purple(f'Emotion: {emotion}')
- return story_till_now, emotion
-
- def next_sentence(self,
- story_till_now="Hello, I'm a language model,",
- length=10):
- last_length = len(story_till_now)
- genreate_robot_sentence = self.generator(story_till_now, max_length=self.get_num_token(story_till_now) +
- length, num_return_sequences=1)
- story_till_now = genreate_robot_sentence[0]['generated_text']
- new_sentence = story_till_now[last_length:]
- emotion = self.get_emotion(new_sentence)
- return story_till_now, emotion, new_sentence
-
- def auto_ist(self,
- story_till_now="Hello, I'm a language model,",
- num_generation=4,
- length=20, reaction_weight=0.5):
- stats_df = pd.DataFrame(data=[], columns=[])
- stats_dict = dict()
- num_reactions = 0
- reaction_frequency = 0
- emotion = self.get_emotion(story_till_now) # first line emotion
- story_data = [{
- 'sentence': story_till_now,
- 'turn': 'first',
- 'emotion': emotion['label'],
- 'confidence_score': emotion['score'],
- }]
- for i in range(num_generation):
- # Text generation for User
- last_length = len(story_till_now)
- printj.cyan(story_till_now)
- # printj.red.bold_on_white(
- # f'loop: {i}; generate user text; length: {last_length}')
- genreate_user_sentence = self.generator(story_till_now, max_length=self.get_num_token(
- story_till_now)+length, num_return_sequences=1)
- story_till_now = genreate_user_sentence[0]['generated_text']
- new_sentence_user = story_till_now[last_length:]
-
- # printj.red.bold_on_white(f'loop: {i}; check emotion')
- # Emotion self.classifier for User
- emotion_user = self.get_emotion(new_sentence_user)
- if emotion_user['label'] == 'neutral':
- show_emotion_user = False
- else:
- reaction_frequency = num_reactions/(i+1)
- show_emotion_user = self.check_show_emotion(
- confidence_score=emotion_user['score'], frequency=reaction_frequency, w=reaction_weight)
- if show_emotion_user:
- num_reactions += 1
-
- story_data.append({
- 'sentence': new_sentence_user,
- 'turn': 'user',
- 'emotion': emotion_user['label'],
- 'confidence_score': emotion_user['score'],
- })
- stats_dict['sentence_no'] = i
- stats_dict['turn'] = 'user'
- stats_dict['sentence'] = new_sentence_user
- stats_dict['show_emotion'] = show_emotion_user
- stats_dict['emotion_label'] = emotion_user['label']
- stats_dict['emotion_score'] = emotion_user['score']
- stats_dict['num_reactions'] = num_reactions
- stats_dict['reaction_frequency'] = reaction_frequency
- stats_dict['reaction_weight'] = reaction_weight
- stats_df = pd.concat(
- [stats_df, pd.DataFrame(stats_dict, index=[f'idx_{i}'])])
- # Text generation for Robot
- last_length = len(story_till_now)
- printj.cyan(story_till_now)
- # printj.red.bold_on_white(
- # f'loop: {i}; generate robot text; length: {last_length}')
- genreate_robot_sentence = self.generator(story_till_now, max_length=self.get_num_token(
- story_till_now)+length, num_return_sequences=1)
- story_till_now = genreate_robot_sentence[0]['generated_text']
- new_sentence_robot = story_till_now[last_length:]
- emotion_robot = self.get_emotion(new_sentence_robot)
-
- story_data.append({
- 'sentence': new_sentence_robot,
- 'turn': 'robot',
- 'emotion': emotion_robot['label'],
- 'confidence_score': emotion_robot['score'],
- })
- stats_dict['sentence_no'] = i
- stats_dict['turn'] = 'robot'
- stats_dict['sentence'] = new_sentence_robot
- stats_dict['show_emotion'] = None
- stats_dict['emotion_label'] = emotion_robot['label']
- stats_dict['emotion_score'] = emotion_robot['score']
- stats_dict['num_reactions'] = None
- stats_dict['reaction_frequency'] = None
- stats_dict['reaction_weight'] = None
- stats_df = pd.concat(
- [stats_df, pd.DataFrame(stats_dict, index=[f'idx_{i}'])])
-
- return stats_df, story_till_now, story_data
-
- def get_stats(self,
- story_till_now="Hello, I'm a language model,",
- num_generation=4,
- length=20, reaction_weight=-1, num_tests=2):
- use_random_w = reaction_weight == -1
- # self.stories = []
- try:
- num_rows = max(self.stats_df.story_id)+1
- except Exception:
- num_rows = 0
- for story_id in range(num_tests):
- if use_random_w:
- # reaction_weight = np.random.random_sample()
- reaction_weight = np.round(np.random.random_sample(), 1)
- stats_df0, _story_till_now, story_data = self.auto_ist(
- story_till_now=story_till_now,
- num_generation=num_generation,
- length=length, reaction_weight=reaction_weight)
- stats_df0.insert(loc=0, column='story_id', value=story_id+num_rows)
-
- # stats_df0['story_id'] = story_id
- self.stats_df = pd.concat([self.stats_df, stats_df0])
- printj.yellow(f'story_id: {story_id}')
- printj.green(stats_df0)
- self.stories.append(_story_till_now)
- self.data.append(story_data)
- self.stats_df = self.stats_df.reset_index(drop=True)
- # print(self.stats_df)
-
- def save_stats(self, path='pandas_simple.xlsx'):
- writer = pd.ExcelWriter(path, engine='xlsxwriter')
-
- # Convert the dataframe to an XlsxWriter Excel object.
- self.stats_df.to_excel(writer, sheet_name='IST')
-
- # Close the Pandas Excel writer and output the Excel file.
- writer.save()
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/agl.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/agl.py
deleted file mode 100644
index d6994628dd3a68f9592efd8c09b0bf56c2a67056..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/fontTools/agl.py
+++ /dev/null
@@ -1,5233 +0,0 @@
-# -*- coding: utf-8 -*-
-# The tables below are taken from
-# https://github.com/adobe-type-tools/agl-aglfn/raw/4036a9ca80a62f64f9de4f7321a9a045ad0ecfd6/glyphlist.txt
-# and
-# https://github.com/adobe-type-tools/agl-aglfn/raw/4036a9ca80a62f64f9de4f7321a9a045ad0ecfd6/aglfn.txt
-"""
-Interface to the Adobe Glyph List
-
-This module exists to convert glyph names from the Adobe Glyph List
-to their Unicode equivalents. Example usage:
-
- >>> from fontTools.agl import toUnicode
- >>> toUnicode("nahiragana")
- 'な'
-
-It also contains two dictionaries, ``UV2AGL`` and ``AGL2UV``, which map from
-Unicode codepoints to AGL names and vice versa:
-
- >>> import fontTools
- >>> fontTools.agl.UV2AGL[ord("?")]
- 'question'
- >>> fontTools.agl.AGL2UV["wcircumflex"]
- 373
-
-This is used by fontTools when it has to construct glyph names for a font which
-doesn't include any (e.g. format 3.0 post tables).
-"""
-
-from fontTools.misc.textTools import tostr
-import re
-
-
-_aglText = """\
-# -----------------------------------------------------------
-# Copyright 2002-2019 Adobe (http://www.adobe.com/).
-#
-# Redistribution and use in source and binary forms, with or
-# without modification, are permitted provided that the
-# following conditions are met:
-#
-# Redistributions of source code must retain the above
-# copyright notice, this list of conditions and the following
-# disclaimer.
-#
-# Redistributions in binary form must reproduce the above
-# copyright notice, this list of conditions and the following
-# disclaimer in the documentation and/or other materials
-# provided with the distribution.
-#
-# Neither the name of Adobe nor the names of its contributors
-# may be used to endorse or promote products derived from this
-# software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
-# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
-# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
-# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
-# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
-# NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
-# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
-# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-# -----------------------------------------------------------
-# Name: Adobe Glyph List
-# Table version: 2.0
-# Date: September 20, 2002
-# URL: https://github.com/adobe-type-tools/agl-aglfn
-#
-# Format: two semicolon-delimited fields:
-# (1) glyph name--upper/lowercase letters and digits
-# (2) Unicode scalar value--four uppercase hexadecimal digits
-#
-A;0041
-AE;00C6
-AEacute;01FC
-AEmacron;01E2
-AEsmall;F7E6
-Aacute;00C1
-Aacutesmall;F7E1
-Abreve;0102
-Abreveacute;1EAE
-Abrevecyrillic;04D0
-Abrevedotbelow;1EB6
-Abrevegrave;1EB0
-Abrevehookabove;1EB2
-Abrevetilde;1EB4
-Acaron;01CD
-Acircle;24B6
-Acircumflex;00C2
-Acircumflexacute;1EA4
-Acircumflexdotbelow;1EAC
-Acircumflexgrave;1EA6
-Acircumflexhookabove;1EA8
-Acircumflexsmall;F7E2
-Acircumflextilde;1EAA
-Acute;F6C9
-Acutesmall;F7B4
-Acyrillic;0410
-Adblgrave;0200
-Adieresis;00C4
-Adieresiscyrillic;04D2
-Adieresismacron;01DE
-Adieresissmall;F7E4
-Adotbelow;1EA0
-Adotmacron;01E0
-Agrave;00C0
-Agravesmall;F7E0
-Ahookabove;1EA2
-Aiecyrillic;04D4
-Ainvertedbreve;0202
-Alpha;0391
-Alphatonos;0386
-Amacron;0100
-Amonospace;FF21
-Aogonek;0104
-Aring;00C5
-Aringacute;01FA
-Aringbelow;1E00
-Aringsmall;F7E5
-Asmall;F761
-Atilde;00C3
-Atildesmall;F7E3
-Aybarmenian;0531
-B;0042
-Bcircle;24B7
-Bdotaccent;1E02
-Bdotbelow;1E04
-Becyrillic;0411
-Benarmenian;0532
-Beta;0392
-Bhook;0181
-Blinebelow;1E06
-Bmonospace;FF22
-Brevesmall;F6F4
-Bsmall;F762
-Btopbar;0182
-C;0043
-Caarmenian;053E
-Cacute;0106
-Caron;F6CA
-Caronsmall;F6F5
-Ccaron;010C
-Ccedilla;00C7
-Ccedillaacute;1E08
-Ccedillasmall;F7E7
-Ccircle;24B8
-Ccircumflex;0108
-Cdot;010A
-Cdotaccent;010A
-Cedillasmall;F7B8
-Chaarmenian;0549
-Cheabkhasiancyrillic;04BC
-Checyrillic;0427
-Chedescenderabkhasiancyrillic;04BE
-Chedescendercyrillic;04B6
-Chedieresiscyrillic;04F4
-Cheharmenian;0543
-Chekhakassiancyrillic;04CB
-Cheverticalstrokecyrillic;04B8
-Chi;03A7
-Chook;0187
-Circumflexsmall;F6F6
-Cmonospace;FF23
-Coarmenian;0551
-Csmall;F763
-D;0044
-DZ;01F1
-DZcaron;01C4
-Daarmenian;0534
-Dafrican;0189
-Dcaron;010E
-Dcedilla;1E10
-Dcircle;24B9
-Dcircumflexbelow;1E12
-Dcroat;0110
-Ddotaccent;1E0A
-Ddotbelow;1E0C
-Decyrillic;0414
-Deicoptic;03EE
-Delta;2206
-Deltagreek;0394
-Dhook;018A
-Dieresis;F6CB
-DieresisAcute;F6CC
-DieresisGrave;F6CD
-Dieresissmall;F7A8
-Digammagreek;03DC
-Djecyrillic;0402
-Dlinebelow;1E0E
-Dmonospace;FF24
-Dotaccentsmall;F6F7
-Dslash;0110
-Dsmall;F764
-Dtopbar;018B
-Dz;01F2
-Dzcaron;01C5
-Dzeabkhasiancyrillic;04E0
-Dzecyrillic;0405
-Dzhecyrillic;040F
-E;0045
-Eacute;00C9
-Eacutesmall;F7E9
-Ebreve;0114
-Ecaron;011A
-Ecedillabreve;1E1C
-Echarmenian;0535
-Ecircle;24BA
-Ecircumflex;00CA
-Ecircumflexacute;1EBE
-Ecircumflexbelow;1E18
-Ecircumflexdotbelow;1EC6
-Ecircumflexgrave;1EC0
-Ecircumflexhookabove;1EC2
-Ecircumflexsmall;F7EA
-Ecircumflextilde;1EC4
-Ecyrillic;0404
-Edblgrave;0204
-Edieresis;00CB
-Edieresissmall;F7EB
-Edot;0116
-Edotaccent;0116
-Edotbelow;1EB8
-Efcyrillic;0424
-Egrave;00C8
-Egravesmall;F7E8
-Eharmenian;0537
-Ehookabove;1EBA
-Eightroman;2167
-Einvertedbreve;0206
-Eiotifiedcyrillic;0464
-Elcyrillic;041B
-Elevenroman;216A
-Emacron;0112
-Emacronacute;1E16
-Emacrongrave;1E14
-Emcyrillic;041C
-Emonospace;FF25
-Encyrillic;041D
-Endescendercyrillic;04A2
-Eng;014A
-Enghecyrillic;04A4
-Enhookcyrillic;04C7
-Eogonek;0118
-Eopen;0190
-Epsilon;0395
-Epsilontonos;0388
-Ercyrillic;0420
-Ereversed;018E
-Ereversedcyrillic;042D
-Escyrillic;0421
-Esdescendercyrillic;04AA
-Esh;01A9
-Esmall;F765
-Eta;0397
-Etarmenian;0538
-Etatonos;0389
-Eth;00D0
-Ethsmall;F7F0
-Etilde;1EBC
-Etildebelow;1E1A
-Euro;20AC
-Ezh;01B7
-Ezhcaron;01EE
-Ezhreversed;01B8
-F;0046
-Fcircle;24BB
-Fdotaccent;1E1E
-Feharmenian;0556
-Feicoptic;03E4
-Fhook;0191
-Fitacyrillic;0472
-Fiveroman;2164
-Fmonospace;FF26
-Fourroman;2163
-Fsmall;F766
-G;0047
-GBsquare;3387
-Gacute;01F4
-Gamma;0393
-Gammaafrican;0194
-Gangiacoptic;03EA
-Gbreve;011E
-Gcaron;01E6
-Gcedilla;0122
-Gcircle;24BC
-Gcircumflex;011C
-Gcommaaccent;0122
-Gdot;0120
-Gdotaccent;0120
-Gecyrillic;0413
-Ghadarmenian;0542
-Ghemiddlehookcyrillic;0494
-Ghestrokecyrillic;0492
-Gheupturncyrillic;0490
-Ghook;0193
-Gimarmenian;0533
-Gjecyrillic;0403
-Gmacron;1E20
-Gmonospace;FF27
-Grave;F6CE
-Gravesmall;F760
-Gsmall;F767
-Gsmallhook;029B
-Gstroke;01E4
-H;0048
-H18533;25CF
-H18543;25AA
-H18551;25AB
-H22073;25A1
-HPsquare;33CB
-Haabkhasiancyrillic;04A8
-Hadescendercyrillic;04B2
-Hardsigncyrillic;042A
-Hbar;0126
-Hbrevebelow;1E2A
-Hcedilla;1E28
-Hcircle;24BD
-Hcircumflex;0124
-Hdieresis;1E26
-Hdotaccent;1E22
-Hdotbelow;1E24
-Hmonospace;FF28
-Hoarmenian;0540
-Horicoptic;03E8
-Hsmall;F768
-Hungarumlaut;F6CF
-Hungarumlautsmall;F6F8
-Hzsquare;3390
-I;0049
-IAcyrillic;042F
-IJ;0132
-IUcyrillic;042E
-Iacute;00CD
-Iacutesmall;F7ED
-Ibreve;012C
-Icaron;01CF
-Icircle;24BE
-Icircumflex;00CE
-Icircumflexsmall;F7EE
-Icyrillic;0406
-Idblgrave;0208
-Idieresis;00CF
-Idieresisacute;1E2E
-Idieresiscyrillic;04E4
-Idieresissmall;F7EF
-Idot;0130
-Idotaccent;0130
-Idotbelow;1ECA
-Iebrevecyrillic;04D6
-Iecyrillic;0415
-Ifraktur;2111
-Igrave;00CC
-Igravesmall;F7EC
-Ihookabove;1EC8
-Iicyrillic;0418
-Iinvertedbreve;020A
-Iishortcyrillic;0419
-Imacron;012A
-Imacroncyrillic;04E2
-Imonospace;FF29
-Iniarmenian;053B
-Iocyrillic;0401
-Iogonek;012E
-Iota;0399
-Iotaafrican;0196
-Iotadieresis;03AA
-Iotatonos;038A
-Ismall;F769
-Istroke;0197
-Itilde;0128
-Itildebelow;1E2C
-Izhitsacyrillic;0474
-Izhitsadblgravecyrillic;0476
-J;004A
-Jaarmenian;0541
-Jcircle;24BF
-Jcircumflex;0134
-Jecyrillic;0408
-Jheharmenian;054B
-Jmonospace;FF2A
-Jsmall;F76A
-K;004B
-KBsquare;3385
-KKsquare;33CD
-Kabashkircyrillic;04A0
-Kacute;1E30
-Kacyrillic;041A
-Kadescendercyrillic;049A
-Kahookcyrillic;04C3
-Kappa;039A
-Kastrokecyrillic;049E
-Kaverticalstrokecyrillic;049C
-Kcaron;01E8
-Kcedilla;0136
-Kcircle;24C0
-Kcommaaccent;0136
-Kdotbelow;1E32
-Keharmenian;0554
-Kenarmenian;053F
-Khacyrillic;0425
-Kheicoptic;03E6
-Khook;0198
-Kjecyrillic;040C
-Klinebelow;1E34
-Kmonospace;FF2B
-Koppacyrillic;0480
-Koppagreek;03DE
-Ksicyrillic;046E
-Ksmall;F76B
-L;004C
-LJ;01C7
-LL;F6BF
-Lacute;0139
-Lambda;039B
-Lcaron;013D
-Lcedilla;013B
-Lcircle;24C1
-Lcircumflexbelow;1E3C
-Lcommaaccent;013B
-Ldot;013F
-Ldotaccent;013F
-Ldotbelow;1E36
-Ldotbelowmacron;1E38
-Liwnarmenian;053C
-Lj;01C8
-Ljecyrillic;0409
-Llinebelow;1E3A
-Lmonospace;FF2C
-Lslash;0141
-Lslashsmall;F6F9
-Lsmall;F76C
-M;004D
-MBsquare;3386
-Macron;F6D0
-Macronsmall;F7AF
-Macute;1E3E
-Mcircle;24C2
-Mdotaccent;1E40
-Mdotbelow;1E42
-Menarmenian;0544
-Mmonospace;FF2D
-Msmall;F76D
-Mturned;019C
-Mu;039C
-N;004E
-NJ;01CA
-Nacute;0143
-Ncaron;0147
-Ncedilla;0145
-Ncircle;24C3
-Ncircumflexbelow;1E4A
-Ncommaaccent;0145
-Ndotaccent;1E44
-Ndotbelow;1E46
-Nhookleft;019D
-Nineroman;2168
-Nj;01CB
-Njecyrillic;040A
-Nlinebelow;1E48
-Nmonospace;FF2E
-Nowarmenian;0546
-Nsmall;F76E
-Ntilde;00D1
-Ntildesmall;F7F1
-Nu;039D
-O;004F
-OE;0152
-OEsmall;F6FA
-Oacute;00D3
-Oacutesmall;F7F3
-Obarredcyrillic;04E8
-Obarreddieresiscyrillic;04EA
-Obreve;014E
-Ocaron;01D1
-Ocenteredtilde;019F
-Ocircle;24C4
-Ocircumflex;00D4
-Ocircumflexacute;1ED0
-Ocircumflexdotbelow;1ED8
-Ocircumflexgrave;1ED2
-Ocircumflexhookabove;1ED4
-Ocircumflexsmall;F7F4
-Ocircumflextilde;1ED6
-Ocyrillic;041E
-Odblacute;0150
-Odblgrave;020C
-Odieresis;00D6
-Odieresiscyrillic;04E6
-Odieresissmall;F7F6
-Odotbelow;1ECC
-Ogoneksmall;F6FB
-Ograve;00D2
-Ogravesmall;F7F2
-Oharmenian;0555
-Ohm;2126
-Ohookabove;1ECE
-Ohorn;01A0
-Ohornacute;1EDA
-Ohorndotbelow;1EE2
-Ohorngrave;1EDC
-Ohornhookabove;1EDE
-Ohorntilde;1EE0
-Ohungarumlaut;0150
-Oi;01A2
-Oinvertedbreve;020E
-Omacron;014C
-Omacronacute;1E52
-Omacrongrave;1E50
-Omega;2126
-Omegacyrillic;0460
-Omegagreek;03A9
-Omegaroundcyrillic;047A
-Omegatitlocyrillic;047C
-Omegatonos;038F
-Omicron;039F
-Omicrontonos;038C
-Omonospace;FF2F
-Oneroman;2160
-Oogonek;01EA
-Oogonekmacron;01EC
-Oopen;0186
-Oslash;00D8
-Oslashacute;01FE
-Oslashsmall;F7F8
-Osmall;F76F
-Ostrokeacute;01FE
-Otcyrillic;047E
-Otilde;00D5
-Otildeacute;1E4C
-Otildedieresis;1E4E
-Otildesmall;F7F5
-P;0050
-Pacute;1E54
-Pcircle;24C5
-Pdotaccent;1E56
-Pecyrillic;041F
-Peharmenian;054A
-Pemiddlehookcyrillic;04A6
-Phi;03A6
-Phook;01A4
-Pi;03A0
-Piwrarmenian;0553
-Pmonospace;FF30
-Psi;03A8
-Psicyrillic;0470
-Psmall;F770
-Q;0051
-Qcircle;24C6
-Qmonospace;FF31
-Qsmall;F771
-R;0052
-Raarmenian;054C
-Racute;0154
-Rcaron;0158
-Rcedilla;0156
-Rcircle;24C7
-Rcommaaccent;0156
-Rdblgrave;0210
-Rdotaccent;1E58
-Rdotbelow;1E5A
-Rdotbelowmacron;1E5C
-Reharmenian;0550
-Rfraktur;211C
-Rho;03A1
-Ringsmall;F6FC
-Rinvertedbreve;0212
-Rlinebelow;1E5E
-Rmonospace;FF32
-Rsmall;F772
-Rsmallinverted;0281
-Rsmallinvertedsuperior;02B6
-S;0053
-SF010000;250C
-SF020000;2514
-SF030000;2510
-SF040000;2518
-SF050000;253C
-SF060000;252C
-SF070000;2534
-SF080000;251C
-SF090000;2524
-SF100000;2500
-SF110000;2502
-SF190000;2561
-SF200000;2562
-SF210000;2556
-SF220000;2555
-SF230000;2563
-SF240000;2551
-SF250000;2557
-SF260000;255D
-SF270000;255C
-SF280000;255B
-SF360000;255E
-SF370000;255F
-SF380000;255A
-SF390000;2554
-SF400000;2569
-SF410000;2566
-SF420000;2560
-SF430000;2550
-SF440000;256C
-SF450000;2567
-SF460000;2568
-SF470000;2564
-SF480000;2565
-SF490000;2559
-SF500000;2558
-SF510000;2552
-SF520000;2553
-SF530000;256B
-SF540000;256A
-Sacute;015A
-Sacutedotaccent;1E64
-Sampigreek;03E0
-Scaron;0160
-Scarondotaccent;1E66
-Scaronsmall;F6FD
-Scedilla;015E
-Schwa;018F
-Schwacyrillic;04D8
-Schwadieresiscyrillic;04DA
-Scircle;24C8
-Scircumflex;015C
-Scommaaccent;0218
-Sdotaccent;1E60
-Sdotbelow;1E62
-Sdotbelowdotaccent;1E68
-Seharmenian;054D
-Sevenroman;2166
-Shaarmenian;0547
-Shacyrillic;0428
-Shchacyrillic;0429
-Sheicoptic;03E2
-Shhacyrillic;04BA
-Shimacoptic;03EC
-Sigma;03A3
-Sixroman;2165
-Smonospace;FF33
-Softsigncyrillic;042C
-Ssmall;F773
-Stigmagreek;03DA
-T;0054
-Tau;03A4
-Tbar;0166
-Tcaron;0164
-Tcedilla;0162
-Tcircle;24C9
-Tcircumflexbelow;1E70
-Tcommaaccent;0162
-Tdotaccent;1E6A
-Tdotbelow;1E6C
-Tecyrillic;0422
-Tedescendercyrillic;04AC
-Tenroman;2169
-Tetsecyrillic;04B4
-Theta;0398
-Thook;01AC
-Thorn;00DE
-Thornsmall;F7FE
-Threeroman;2162
-Tildesmall;F6FE
-Tiwnarmenian;054F
-Tlinebelow;1E6E
-Tmonospace;FF34
-Toarmenian;0539
-Tonefive;01BC
-Tonesix;0184
-Tonetwo;01A7
-Tretroflexhook;01AE
-Tsecyrillic;0426
-Tshecyrillic;040B
-Tsmall;F774
-Twelveroman;216B
-Tworoman;2161
-U;0055
-Uacute;00DA
-Uacutesmall;F7FA
-Ubreve;016C
-Ucaron;01D3
-Ucircle;24CA
-Ucircumflex;00DB
-Ucircumflexbelow;1E76
-Ucircumflexsmall;F7FB
-Ucyrillic;0423
-Udblacute;0170
-Udblgrave;0214
-Udieresis;00DC
-Udieresisacute;01D7
-Udieresisbelow;1E72
-Udieresiscaron;01D9
-Udieresiscyrillic;04F0
-Udieresisgrave;01DB
-Udieresismacron;01D5
-Udieresissmall;F7FC
-Udotbelow;1EE4
-Ugrave;00D9
-Ugravesmall;F7F9
-Uhookabove;1EE6
-Uhorn;01AF
-Uhornacute;1EE8
-Uhorndotbelow;1EF0
-Uhorngrave;1EEA
-Uhornhookabove;1EEC
-Uhorntilde;1EEE
-Uhungarumlaut;0170
-Uhungarumlautcyrillic;04F2
-Uinvertedbreve;0216
-Ukcyrillic;0478
-Umacron;016A
-Umacroncyrillic;04EE
-Umacrondieresis;1E7A
-Umonospace;FF35
-Uogonek;0172
-Upsilon;03A5
-Upsilon1;03D2
-Upsilonacutehooksymbolgreek;03D3
-Upsilonafrican;01B1
-Upsilondieresis;03AB
-Upsilondieresishooksymbolgreek;03D4
-Upsilonhooksymbol;03D2
-Upsilontonos;038E
-Uring;016E
-Ushortcyrillic;040E
-Usmall;F775
-Ustraightcyrillic;04AE
-Ustraightstrokecyrillic;04B0
-Utilde;0168
-Utildeacute;1E78
-Utildebelow;1E74
-V;0056
-Vcircle;24CB
-Vdotbelow;1E7E
-Vecyrillic;0412
-Vewarmenian;054E
-Vhook;01B2
-Vmonospace;FF36
-Voarmenian;0548
-Vsmall;F776
-Vtilde;1E7C
-W;0057
-Wacute;1E82
-Wcircle;24CC
-Wcircumflex;0174
-Wdieresis;1E84
-Wdotaccent;1E86
-Wdotbelow;1E88
-Wgrave;1E80
-Wmonospace;FF37
-Wsmall;F777
-X;0058
-Xcircle;24CD
-Xdieresis;1E8C
-Xdotaccent;1E8A
-Xeharmenian;053D
-Xi;039E
-Xmonospace;FF38
-Xsmall;F778
-Y;0059
-Yacute;00DD
-Yacutesmall;F7FD
-Yatcyrillic;0462
-Ycircle;24CE
-Ycircumflex;0176
-Ydieresis;0178
-Ydieresissmall;F7FF
-Ydotaccent;1E8E
-Ydotbelow;1EF4
-Yericyrillic;042B
-Yerudieresiscyrillic;04F8
-Ygrave;1EF2
-Yhook;01B3
-Yhookabove;1EF6
-Yiarmenian;0545
-Yicyrillic;0407
-Yiwnarmenian;0552
-Ymonospace;FF39
-Ysmall;F779
-Ytilde;1EF8
-Yusbigcyrillic;046A
-Yusbigiotifiedcyrillic;046C
-Yuslittlecyrillic;0466
-Yuslittleiotifiedcyrillic;0468
-Z;005A
-Zaarmenian;0536
-Zacute;0179
-Zcaron;017D
-Zcaronsmall;F6FF
-Zcircle;24CF
-Zcircumflex;1E90
-Zdot;017B
-Zdotaccent;017B
-Zdotbelow;1E92
-Zecyrillic;0417
-Zedescendercyrillic;0498
-Zedieresiscyrillic;04DE
-Zeta;0396
-Zhearmenian;053A
-Zhebrevecyrillic;04C1
-Zhecyrillic;0416
-Zhedescendercyrillic;0496
-Zhedieresiscyrillic;04DC
-Zlinebelow;1E94
-Zmonospace;FF3A
-Zsmall;F77A
-Zstroke;01B5
-a;0061
-aabengali;0986
-aacute;00E1
-aadeva;0906
-aagujarati;0A86
-aagurmukhi;0A06
-aamatragurmukhi;0A3E
-aarusquare;3303
-aavowelsignbengali;09BE
-aavowelsigndeva;093E
-aavowelsigngujarati;0ABE
-abbreviationmarkarmenian;055F
-abbreviationsigndeva;0970
-abengali;0985
-abopomofo;311A
-abreve;0103
-abreveacute;1EAF
-abrevecyrillic;04D1
-abrevedotbelow;1EB7
-abrevegrave;1EB1
-abrevehookabove;1EB3
-abrevetilde;1EB5
-acaron;01CE
-acircle;24D0
-acircumflex;00E2
-acircumflexacute;1EA5
-acircumflexdotbelow;1EAD
-acircumflexgrave;1EA7
-acircumflexhookabove;1EA9
-acircumflextilde;1EAB
-acute;00B4
-acutebelowcmb;0317
-acutecmb;0301
-acutecomb;0301
-acutedeva;0954
-acutelowmod;02CF
-acutetonecmb;0341
-acyrillic;0430
-adblgrave;0201
-addakgurmukhi;0A71
-adeva;0905
-adieresis;00E4
-adieresiscyrillic;04D3
-adieresismacron;01DF
-adotbelow;1EA1
-adotmacron;01E1
-ae;00E6
-aeacute;01FD
-aekorean;3150
-aemacron;01E3
-afii00208;2015
-afii08941;20A4
-afii10017;0410
-afii10018;0411
-afii10019;0412
-afii10020;0413
-afii10021;0414
-afii10022;0415
-afii10023;0401
-afii10024;0416
-afii10025;0417
-afii10026;0418
-afii10027;0419
-afii10028;041A
-afii10029;041B
-afii10030;041C
-afii10031;041D
-afii10032;041E
-afii10033;041F
-afii10034;0420
-afii10035;0421
-afii10036;0422
-afii10037;0423
-afii10038;0424
-afii10039;0425
-afii10040;0426
-afii10041;0427
-afii10042;0428
-afii10043;0429
-afii10044;042A
-afii10045;042B
-afii10046;042C
-afii10047;042D
-afii10048;042E
-afii10049;042F
-afii10050;0490
-afii10051;0402
-afii10052;0403
-afii10053;0404
-afii10054;0405
-afii10055;0406
-afii10056;0407
-afii10057;0408
-afii10058;0409
-afii10059;040A
-afii10060;040B
-afii10061;040C
-afii10062;040E
-afii10063;F6C4
-afii10064;F6C5
-afii10065;0430
-afii10066;0431
-afii10067;0432
-afii10068;0433
-afii10069;0434
-afii10070;0435
-afii10071;0451
-afii10072;0436
-afii10073;0437
-afii10074;0438
-afii10075;0439
-afii10076;043A
-afii10077;043B
-afii10078;043C
-afii10079;043D
-afii10080;043E
-afii10081;043F
-afii10082;0440
-afii10083;0441
-afii10084;0442
-afii10085;0443
-afii10086;0444
-afii10087;0445
-afii10088;0446
-afii10089;0447
-afii10090;0448
-afii10091;0449
-afii10092;044A
-afii10093;044B
-afii10094;044C
-afii10095;044D
-afii10096;044E
-afii10097;044F
-afii10098;0491
-afii10099;0452
-afii10100;0453
-afii10101;0454
-afii10102;0455
-afii10103;0456
-afii10104;0457
-afii10105;0458
-afii10106;0459
-afii10107;045A
-afii10108;045B
-afii10109;045C
-afii10110;045E
-afii10145;040F
-afii10146;0462
-afii10147;0472
-afii10148;0474
-afii10192;F6C6
-afii10193;045F
-afii10194;0463
-afii10195;0473
-afii10196;0475
-afii10831;F6C7
-afii10832;F6C8
-afii10846;04D9
-afii299;200E
-afii300;200F
-afii301;200D
-afii57381;066A
-afii57388;060C
-afii57392;0660
-afii57393;0661
-afii57394;0662
-afii57395;0663
-afii57396;0664
-afii57397;0665
-afii57398;0666
-afii57399;0667
-afii57400;0668
-afii57401;0669
-afii57403;061B
-afii57407;061F
-afii57409;0621
-afii57410;0622
-afii57411;0623
-afii57412;0624
-afii57413;0625
-afii57414;0626
-afii57415;0627
-afii57416;0628
-afii57417;0629
-afii57418;062A
-afii57419;062B
-afii57420;062C
-afii57421;062D
-afii57422;062E
-afii57423;062F
-afii57424;0630
-afii57425;0631
-afii57426;0632
-afii57427;0633
-afii57428;0634
-afii57429;0635
-afii57430;0636
-afii57431;0637
-afii57432;0638
-afii57433;0639
-afii57434;063A
-afii57440;0640
-afii57441;0641
-afii57442;0642
-afii57443;0643
-afii57444;0644
-afii57445;0645
-afii57446;0646
-afii57448;0648
-afii57449;0649
-afii57450;064A
-afii57451;064B
-afii57452;064C
-afii57453;064D
-afii57454;064E
-afii57455;064F
-afii57456;0650
-afii57457;0651
-afii57458;0652
-afii57470;0647
-afii57505;06A4
-afii57506;067E
-afii57507;0686
-afii57508;0698
-afii57509;06AF
-afii57511;0679
-afii57512;0688
-afii57513;0691
-afii57514;06BA
-afii57519;06D2
-afii57534;06D5
-afii57636;20AA
-afii57645;05BE
-afii57658;05C3
-afii57664;05D0
-afii57665;05D1
-afii57666;05D2
-afii57667;05D3
-afii57668;05D4
-afii57669;05D5
-afii57670;05D6
-afii57671;05D7
-afii57672;05D8
-afii57673;05D9
-afii57674;05DA
-afii57675;05DB
-afii57676;05DC
-afii57677;05DD
-afii57678;05DE
-afii57679;05DF
-afii57680;05E0
-afii57681;05E1
-afii57682;05E2
-afii57683;05E3
-afii57684;05E4
-afii57685;05E5
-afii57686;05E6
-afii57687;05E7
-afii57688;05E8
-afii57689;05E9
-afii57690;05EA
-afii57694;FB2A
-afii57695;FB2B
-afii57700;FB4B
-afii57705;FB1F
-afii57716;05F0
-afii57717;05F1
-afii57718;05F2
-afii57723;FB35
-afii57793;05B4
-afii57794;05B5
-afii57795;05B6
-afii57796;05BB
-afii57797;05B8
-afii57798;05B7
-afii57799;05B0
-afii57800;05B2
-afii57801;05B1
-afii57802;05B3
-afii57803;05C2
-afii57804;05C1
-afii57806;05B9
-afii57807;05BC
-afii57839;05BD
-afii57841;05BF
-afii57842;05C0
-afii57929;02BC
-afii61248;2105
-afii61289;2113
-afii61352;2116
-afii61573;202C
-afii61574;202D
-afii61575;202E
-afii61664;200C
-afii63167;066D
-afii64937;02BD
-agrave;00E0
-agujarati;0A85
-agurmukhi;0A05
-ahiragana;3042
-ahookabove;1EA3
-aibengali;0990
-aibopomofo;311E
-aideva;0910
-aiecyrillic;04D5
-aigujarati;0A90
-aigurmukhi;0A10
-aimatragurmukhi;0A48
-ainarabic;0639
-ainfinalarabic;FECA
-aininitialarabic;FECB
-ainmedialarabic;FECC
-ainvertedbreve;0203
-aivowelsignbengali;09C8
-aivowelsigndeva;0948
-aivowelsigngujarati;0AC8
-akatakana;30A2
-akatakanahalfwidth;FF71
-akorean;314F
-alef;05D0
-alefarabic;0627
-alefdageshhebrew;FB30
-aleffinalarabic;FE8E
-alefhamzaabovearabic;0623
-alefhamzaabovefinalarabic;FE84
-alefhamzabelowarabic;0625
-alefhamzabelowfinalarabic;FE88
-alefhebrew;05D0
-aleflamedhebrew;FB4F
-alefmaddaabovearabic;0622
-alefmaddaabovefinalarabic;FE82
-alefmaksuraarabic;0649
-alefmaksurafinalarabic;FEF0
-alefmaksurainitialarabic;FEF3
-alefmaksuramedialarabic;FEF4
-alefpatahhebrew;FB2E
-alefqamatshebrew;FB2F
-aleph;2135
-allequal;224C
-alpha;03B1
-alphatonos;03AC
-amacron;0101
-amonospace;FF41
-ampersand;0026
-ampersandmonospace;FF06
-ampersandsmall;F726
-amsquare;33C2
-anbopomofo;3122
-angbopomofo;3124
-angkhankhuthai;0E5A
-angle;2220
-anglebracketleft;3008
-anglebracketleftvertical;FE3F
-anglebracketright;3009
-anglebracketrightvertical;FE40
-angleleft;2329
-angleright;232A
-angstrom;212B
-anoteleia;0387
-anudattadeva;0952
-anusvarabengali;0982
-anusvaradeva;0902
-anusvaragujarati;0A82
-aogonek;0105
-apaatosquare;3300
-aparen;249C
-apostrophearmenian;055A
-apostrophemod;02BC
-apple;F8FF
-approaches;2250
-approxequal;2248
-approxequalorimage;2252
-approximatelyequal;2245
-araeaekorean;318E
-araeakorean;318D
-arc;2312
-arighthalfring;1E9A
-aring;00E5
-aringacute;01FB
-aringbelow;1E01
-arrowboth;2194
-arrowdashdown;21E3
-arrowdashleft;21E0
-arrowdashright;21E2
-arrowdashup;21E1
-arrowdblboth;21D4
-arrowdbldown;21D3
-arrowdblleft;21D0
-arrowdblright;21D2
-arrowdblup;21D1
-arrowdown;2193
-arrowdownleft;2199
-arrowdownright;2198
-arrowdownwhite;21E9
-arrowheaddownmod;02C5
-arrowheadleftmod;02C2
-arrowheadrightmod;02C3
-arrowheadupmod;02C4
-arrowhorizex;F8E7
-arrowleft;2190
-arrowleftdbl;21D0
-arrowleftdblstroke;21CD
-arrowleftoverright;21C6
-arrowleftwhite;21E6
-arrowright;2192
-arrowrightdblstroke;21CF
-arrowrightheavy;279E
-arrowrightoverleft;21C4
-arrowrightwhite;21E8
-arrowtableft;21E4
-arrowtabright;21E5
-arrowup;2191
-arrowupdn;2195
-arrowupdnbse;21A8
-arrowupdownbase;21A8
-arrowupleft;2196
-arrowupleftofdown;21C5
-arrowupright;2197
-arrowupwhite;21E7
-arrowvertex;F8E6
-asciicircum;005E
-asciicircummonospace;FF3E
-asciitilde;007E
-asciitildemonospace;FF5E
-ascript;0251
-ascriptturned;0252
-asmallhiragana;3041
-asmallkatakana;30A1
-asmallkatakanahalfwidth;FF67
-asterisk;002A
-asteriskaltonearabic;066D
-asteriskarabic;066D
-asteriskmath;2217
-asteriskmonospace;FF0A
-asterisksmall;FE61
-asterism;2042
-asuperior;F6E9
-asymptoticallyequal;2243
-at;0040
-atilde;00E3
-atmonospace;FF20
-atsmall;FE6B
-aturned;0250
-aubengali;0994
-aubopomofo;3120
-audeva;0914
-augujarati;0A94
-augurmukhi;0A14
-aulengthmarkbengali;09D7
-aumatragurmukhi;0A4C
-auvowelsignbengali;09CC
-auvowelsigndeva;094C
-auvowelsigngujarati;0ACC
-avagrahadeva;093D
-aybarmenian;0561
-ayin;05E2
-ayinaltonehebrew;FB20
-ayinhebrew;05E2
-b;0062
-babengali;09AC
-backslash;005C
-backslashmonospace;FF3C
-badeva;092C
-bagujarati;0AAC
-bagurmukhi;0A2C
-bahiragana;3070
-bahtthai;0E3F
-bakatakana;30D0
-bar;007C
-barmonospace;FF5C
-bbopomofo;3105
-bcircle;24D1
-bdotaccent;1E03
-bdotbelow;1E05
-beamedsixteenthnotes;266C
-because;2235
-becyrillic;0431
-beharabic;0628
-behfinalarabic;FE90
-behinitialarabic;FE91
-behiragana;3079
-behmedialarabic;FE92
-behmeeminitialarabic;FC9F
-behmeemisolatedarabic;FC08
-behnoonfinalarabic;FC6D
-bekatakana;30D9
-benarmenian;0562
-bet;05D1
-beta;03B2
-betasymbolgreek;03D0
-betdagesh;FB31
-betdageshhebrew;FB31
-bethebrew;05D1
-betrafehebrew;FB4C
-bhabengali;09AD
-bhadeva;092D
-bhagujarati;0AAD
-bhagurmukhi;0A2D
-bhook;0253
-bihiragana;3073
-bikatakana;30D3
-bilabialclick;0298
-bindigurmukhi;0A02
-birusquare;3331
-blackcircle;25CF
-blackdiamond;25C6
-blackdownpointingtriangle;25BC
-blackleftpointingpointer;25C4
-blackleftpointingtriangle;25C0
-blacklenticularbracketleft;3010
-blacklenticularbracketleftvertical;FE3B
-blacklenticularbracketright;3011
-blacklenticularbracketrightvertical;FE3C
-blacklowerlefttriangle;25E3
-blacklowerrighttriangle;25E2
-blackrectangle;25AC
-blackrightpointingpointer;25BA
-blackrightpointingtriangle;25B6
-blacksmallsquare;25AA
-blacksmilingface;263B
-blacksquare;25A0
-blackstar;2605
-blackupperlefttriangle;25E4
-blackupperrighttriangle;25E5
-blackuppointingsmalltriangle;25B4
-blackuppointingtriangle;25B2
-blank;2423
-blinebelow;1E07
-block;2588
-bmonospace;FF42
-bobaimaithai;0E1A
-bohiragana;307C
-bokatakana;30DC
-bparen;249D
-bqsquare;33C3
-braceex;F8F4
-braceleft;007B
-braceleftbt;F8F3
-braceleftmid;F8F2
-braceleftmonospace;FF5B
-braceleftsmall;FE5B
-bracelefttp;F8F1
-braceleftvertical;FE37
-braceright;007D
-bracerightbt;F8FE
-bracerightmid;F8FD
-bracerightmonospace;FF5D
-bracerightsmall;FE5C
-bracerighttp;F8FC
-bracerightvertical;FE38
-bracketleft;005B
-bracketleftbt;F8F0
-bracketleftex;F8EF
-bracketleftmonospace;FF3B
-bracketlefttp;F8EE
-bracketright;005D
-bracketrightbt;F8FB
-bracketrightex;F8FA
-bracketrightmonospace;FF3D
-bracketrighttp;F8F9
-breve;02D8
-brevebelowcmb;032E
-brevecmb;0306
-breveinvertedbelowcmb;032F
-breveinvertedcmb;0311
-breveinverteddoublecmb;0361
-bridgebelowcmb;032A
-bridgeinvertedbelowcmb;033A
-brokenbar;00A6
-bstroke;0180
-bsuperior;F6EA
-btopbar;0183
-buhiragana;3076
-bukatakana;30D6
-bullet;2022
-bulletinverse;25D8
-bulletoperator;2219
-bullseye;25CE
-c;0063
-caarmenian;056E
-cabengali;099A
-cacute;0107
-cadeva;091A
-cagujarati;0A9A
-cagurmukhi;0A1A
-calsquare;3388
-candrabindubengali;0981
-candrabinducmb;0310
-candrabindudeva;0901
-candrabindugujarati;0A81
-capslock;21EA
-careof;2105
-caron;02C7
-caronbelowcmb;032C
-caroncmb;030C
-carriagereturn;21B5
-cbopomofo;3118
-ccaron;010D
-ccedilla;00E7
-ccedillaacute;1E09
-ccircle;24D2
-ccircumflex;0109
-ccurl;0255
-cdot;010B
-cdotaccent;010B
-cdsquare;33C5
-cedilla;00B8
-cedillacmb;0327
-cent;00A2
-centigrade;2103
-centinferior;F6DF
-centmonospace;FFE0
-centoldstyle;F7A2
-centsuperior;F6E0
-chaarmenian;0579
-chabengali;099B
-chadeva;091B
-chagujarati;0A9B
-chagurmukhi;0A1B
-chbopomofo;3114
-cheabkhasiancyrillic;04BD
-checkmark;2713
-checyrillic;0447
-chedescenderabkhasiancyrillic;04BF
-chedescendercyrillic;04B7
-chedieresiscyrillic;04F5
-cheharmenian;0573
-chekhakassiancyrillic;04CC
-cheverticalstrokecyrillic;04B9
-chi;03C7
-chieuchacirclekorean;3277
-chieuchaparenkorean;3217
-chieuchcirclekorean;3269
-chieuchkorean;314A
-chieuchparenkorean;3209
-chochangthai;0E0A
-chochanthai;0E08
-chochingthai;0E09
-chochoethai;0E0C
-chook;0188
-cieucacirclekorean;3276
-cieucaparenkorean;3216
-cieuccirclekorean;3268
-cieuckorean;3148
-cieucparenkorean;3208
-cieucuparenkorean;321C
-circle;25CB
-circlemultiply;2297
-circleot;2299
-circleplus;2295
-circlepostalmark;3036
-circlewithlefthalfblack;25D0
-circlewithrighthalfblack;25D1
-circumflex;02C6
-circumflexbelowcmb;032D
-circumflexcmb;0302
-clear;2327
-clickalveolar;01C2
-clickdental;01C0
-clicklateral;01C1
-clickretroflex;01C3
-club;2663
-clubsuitblack;2663
-clubsuitwhite;2667
-cmcubedsquare;33A4
-cmonospace;FF43
-cmsquaredsquare;33A0
-coarmenian;0581
-colon;003A
-colonmonetary;20A1
-colonmonospace;FF1A
-colonsign;20A1
-colonsmall;FE55
-colontriangularhalfmod;02D1
-colontriangularmod;02D0
-comma;002C
-commaabovecmb;0313
-commaaboverightcmb;0315
-commaaccent;F6C3
-commaarabic;060C
-commaarmenian;055D
-commainferior;F6E1
-commamonospace;FF0C
-commareversedabovecmb;0314
-commareversedmod;02BD
-commasmall;FE50
-commasuperior;F6E2
-commaturnedabovecmb;0312
-commaturnedmod;02BB
-compass;263C
-congruent;2245
-contourintegral;222E
-control;2303
-controlACK;0006
-controlBEL;0007
-controlBS;0008
-controlCAN;0018
-controlCR;000D
-controlDC1;0011
-controlDC2;0012
-controlDC3;0013
-controlDC4;0014
-controlDEL;007F
-controlDLE;0010
-controlEM;0019
-controlENQ;0005
-controlEOT;0004
-controlESC;001B
-controlETB;0017
-controlETX;0003
-controlFF;000C
-controlFS;001C
-controlGS;001D
-controlHT;0009
-controlLF;000A
-controlNAK;0015
-controlRS;001E
-controlSI;000F
-controlSO;000E
-controlSOT;0002
-controlSTX;0001
-controlSUB;001A
-controlSYN;0016
-controlUS;001F
-controlVT;000B
-copyright;00A9
-copyrightsans;F8E9
-copyrightserif;F6D9
-cornerbracketleft;300C
-cornerbracketlefthalfwidth;FF62
-cornerbracketleftvertical;FE41
-cornerbracketright;300D
-cornerbracketrighthalfwidth;FF63
-cornerbracketrightvertical;FE42
-corporationsquare;337F
-cosquare;33C7
-coverkgsquare;33C6
-cparen;249E
-cruzeiro;20A2
-cstretched;0297
-curlyand;22CF
-curlyor;22CE
-currency;00A4
-cyrBreve;F6D1
-cyrFlex;F6D2
-cyrbreve;F6D4
-cyrflex;F6D5
-d;0064
-daarmenian;0564
-dabengali;09A6
-dadarabic;0636
-dadeva;0926
-dadfinalarabic;FEBE
-dadinitialarabic;FEBF
-dadmedialarabic;FEC0
-dagesh;05BC
-dageshhebrew;05BC
-dagger;2020
-daggerdbl;2021
-dagujarati;0AA6
-dagurmukhi;0A26
-dahiragana;3060
-dakatakana;30C0
-dalarabic;062F
-dalet;05D3
-daletdagesh;FB33
-daletdageshhebrew;FB33
-dalethatafpatah;05D3 05B2
-dalethatafpatahhebrew;05D3 05B2
-dalethatafsegol;05D3 05B1
-dalethatafsegolhebrew;05D3 05B1
-dalethebrew;05D3
-dalethiriq;05D3 05B4
-dalethiriqhebrew;05D3 05B4
-daletholam;05D3 05B9
-daletholamhebrew;05D3 05B9
-daletpatah;05D3 05B7
-daletpatahhebrew;05D3 05B7
-daletqamats;05D3 05B8
-daletqamatshebrew;05D3 05B8
-daletqubuts;05D3 05BB
-daletqubutshebrew;05D3 05BB
-daletsegol;05D3 05B6
-daletsegolhebrew;05D3 05B6
-daletsheva;05D3 05B0
-daletshevahebrew;05D3 05B0
-dalettsere;05D3 05B5
-dalettserehebrew;05D3 05B5
-dalfinalarabic;FEAA
-dammaarabic;064F
-dammalowarabic;064F
-dammatanaltonearabic;064C
-dammatanarabic;064C
-danda;0964
-dargahebrew;05A7
-dargalefthebrew;05A7
-dasiapneumatacyrilliccmb;0485
-dblGrave;F6D3
-dblanglebracketleft;300A
-dblanglebracketleftvertical;FE3D
-dblanglebracketright;300B
-dblanglebracketrightvertical;FE3E
-dblarchinvertedbelowcmb;032B
-dblarrowleft;21D4
-dblarrowright;21D2
-dbldanda;0965
-dblgrave;F6D6
-dblgravecmb;030F
-dblintegral;222C
-dbllowline;2017
-dbllowlinecmb;0333
-dbloverlinecmb;033F
-dblprimemod;02BA
-dblverticalbar;2016
-dblverticallineabovecmb;030E
-dbopomofo;3109
-dbsquare;33C8
-dcaron;010F
-dcedilla;1E11
-dcircle;24D3
-dcircumflexbelow;1E13
-dcroat;0111
-ddabengali;09A1
-ddadeva;0921
-ddagujarati;0AA1
-ddagurmukhi;0A21
-ddalarabic;0688
-ddalfinalarabic;FB89
-dddhadeva;095C
-ddhabengali;09A2
-ddhadeva;0922
-ddhagujarati;0AA2
-ddhagurmukhi;0A22
-ddotaccent;1E0B
-ddotbelow;1E0D
-decimalseparatorarabic;066B
-decimalseparatorpersian;066B
-decyrillic;0434
-degree;00B0
-dehihebrew;05AD
-dehiragana;3067
-deicoptic;03EF
-dekatakana;30C7
-deleteleft;232B
-deleteright;2326
-delta;03B4
-deltaturned;018D
-denominatorminusonenumeratorbengali;09F8
-dezh;02A4
-dhabengali;09A7
-dhadeva;0927
-dhagujarati;0AA7
-dhagurmukhi;0A27
-dhook;0257
-dialytikatonos;0385
-dialytikatonoscmb;0344
-diamond;2666
-diamondsuitwhite;2662
-dieresis;00A8
-dieresisacute;F6D7
-dieresisbelowcmb;0324
-dieresiscmb;0308
-dieresisgrave;F6D8
-dieresistonos;0385
-dihiragana;3062
-dikatakana;30C2
-dittomark;3003
-divide;00F7
-divides;2223
-divisionslash;2215
-djecyrillic;0452
-dkshade;2593
-dlinebelow;1E0F
-dlsquare;3397
-dmacron;0111
-dmonospace;FF44
-dnblock;2584
-dochadathai;0E0E
-dodekthai;0E14
-dohiragana;3069
-dokatakana;30C9
-dollar;0024
-dollarinferior;F6E3
-dollarmonospace;FF04
-dollaroldstyle;F724
-dollarsmall;FE69
-dollarsuperior;F6E4
-dong;20AB
-dorusquare;3326
-dotaccent;02D9
-dotaccentcmb;0307
-dotbelowcmb;0323
-dotbelowcomb;0323
-dotkatakana;30FB
-dotlessi;0131
-dotlessj;F6BE
-dotlessjstrokehook;0284
-dotmath;22C5
-dottedcircle;25CC
-doubleyodpatah;FB1F
-doubleyodpatahhebrew;FB1F
-downtackbelowcmb;031E
-downtackmod;02D5
-dparen;249F
-dsuperior;F6EB
-dtail;0256
-dtopbar;018C
-duhiragana;3065
-dukatakana;30C5
-dz;01F3
-dzaltone;02A3
-dzcaron;01C6
-dzcurl;02A5
-dzeabkhasiancyrillic;04E1
-dzecyrillic;0455
-dzhecyrillic;045F
-e;0065
-eacute;00E9
-earth;2641
-ebengali;098F
-ebopomofo;311C
-ebreve;0115
-ecandradeva;090D
-ecandragujarati;0A8D
-ecandravowelsigndeva;0945
-ecandravowelsigngujarati;0AC5
-ecaron;011B
-ecedillabreve;1E1D
-echarmenian;0565
-echyiwnarmenian;0587
-ecircle;24D4
-ecircumflex;00EA
-ecircumflexacute;1EBF
-ecircumflexbelow;1E19
-ecircumflexdotbelow;1EC7
-ecircumflexgrave;1EC1
-ecircumflexhookabove;1EC3
-ecircumflextilde;1EC5
-ecyrillic;0454
-edblgrave;0205
-edeva;090F
-edieresis;00EB
-edot;0117
-edotaccent;0117
-edotbelow;1EB9
-eegurmukhi;0A0F
-eematragurmukhi;0A47
-efcyrillic;0444
-egrave;00E8
-egujarati;0A8F
-eharmenian;0567
-ehbopomofo;311D
-ehiragana;3048
-ehookabove;1EBB
-eibopomofo;311F
-eight;0038
-eightarabic;0668
-eightbengali;09EE
-eightcircle;2467
-eightcircleinversesansserif;2791
-eightdeva;096E
-eighteencircle;2471
-eighteenparen;2485
-eighteenperiod;2499
-eightgujarati;0AEE
-eightgurmukhi;0A6E
-eighthackarabic;0668
-eighthangzhou;3028
-eighthnotebeamed;266B
-eightideographicparen;3227
-eightinferior;2088
-eightmonospace;FF18
-eightoldstyle;F738
-eightparen;247B
-eightperiod;248F
-eightpersian;06F8
-eightroman;2177
-eightsuperior;2078
-eightthai;0E58
-einvertedbreve;0207
-eiotifiedcyrillic;0465
-ekatakana;30A8
-ekatakanahalfwidth;FF74
-ekonkargurmukhi;0A74
-ekorean;3154
-elcyrillic;043B
-element;2208
-elevencircle;246A
-elevenparen;247E
-elevenperiod;2492
-elevenroman;217A
-ellipsis;2026
-ellipsisvertical;22EE
-emacron;0113
-emacronacute;1E17
-emacrongrave;1E15
-emcyrillic;043C
-emdash;2014
-emdashvertical;FE31
-emonospace;FF45
-emphasismarkarmenian;055B
-emptyset;2205
-enbopomofo;3123
-encyrillic;043D
-endash;2013
-endashvertical;FE32
-endescendercyrillic;04A3
-eng;014B
-engbopomofo;3125
-enghecyrillic;04A5
-enhookcyrillic;04C8
-enspace;2002
-eogonek;0119
-eokorean;3153
-eopen;025B
-eopenclosed;029A
-eopenreversed;025C
-eopenreversedclosed;025E
-eopenreversedhook;025D
-eparen;24A0
-epsilon;03B5
-epsilontonos;03AD
-equal;003D
-equalmonospace;FF1D
-equalsmall;FE66
-equalsuperior;207C
-equivalence;2261
-erbopomofo;3126
-ercyrillic;0440
-ereversed;0258
-ereversedcyrillic;044D
-escyrillic;0441
-esdescendercyrillic;04AB
-esh;0283
-eshcurl;0286
-eshortdeva;090E
-eshortvowelsigndeva;0946
-eshreversedloop;01AA
-eshsquatreversed;0285
-esmallhiragana;3047
-esmallkatakana;30A7
-esmallkatakanahalfwidth;FF6A
-estimated;212E
-esuperior;F6EC
-eta;03B7
-etarmenian;0568
-etatonos;03AE
-eth;00F0
-etilde;1EBD
-etildebelow;1E1B
-etnahtafoukhhebrew;0591
-etnahtafoukhlefthebrew;0591
-etnahtahebrew;0591
-etnahtalefthebrew;0591
-eturned;01DD
-eukorean;3161
-euro;20AC
-evowelsignbengali;09C7
-evowelsigndeva;0947
-evowelsigngujarati;0AC7
-exclam;0021
-exclamarmenian;055C
-exclamdbl;203C
-exclamdown;00A1
-exclamdownsmall;F7A1
-exclammonospace;FF01
-exclamsmall;F721
-existential;2203
-ezh;0292
-ezhcaron;01EF
-ezhcurl;0293
-ezhreversed;01B9
-ezhtail;01BA
-f;0066
-fadeva;095E
-fagurmukhi;0A5E
-fahrenheit;2109
-fathaarabic;064E
-fathalowarabic;064E
-fathatanarabic;064B
-fbopomofo;3108
-fcircle;24D5
-fdotaccent;1E1F
-feharabic;0641
-feharmenian;0586
-fehfinalarabic;FED2
-fehinitialarabic;FED3
-fehmedialarabic;FED4
-feicoptic;03E5
-female;2640
-ff;FB00
-ffi;FB03
-ffl;FB04
-fi;FB01
-fifteencircle;246E
-fifteenparen;2482
-fifteenperiod;2496
-figuredash;2012
-filledbox;25A0
-filledrect;25AC
-finalkaf;05DA
-finalkafdagesh;FB3A
-finalkafdageshhebrew;FB3A
-finalkafhebrew;05DA
-finalkafqamats;05DA 05B8
-finalkafqamatshebrew;05DA 05B8
-finalkafsheva;05DA 05B0
-finalkafshevahebrew;05DA 05B0
-finalmem;05DD
-finalmemhebrew;05DD
-finalnun;05DF
-finalnunhebrew;05DF
-finalpe;05E3
-finalpehebrew;05E3
-finaltsadi;05E5
-finaltsadihebrew;05E5
-firsttonechinese;02C9
-fisheye;25C9
-fitacyrillic;0473
-five;0035
-fivearabic;0665
-fivebengali;09EB
-fivecircle;2464
-fivecircleinversesansserif;278E
-fivedeva;096B
-fiveeighths;215D
-fivegujarati;0AEB
-fivegurmukhi;0A6B
-fivehackarabic;0665
-fivehangzhou;3025
-fiveideographicparen;3224
-fiveinferior;2085
-fivemonospace;FF15
-fiveoldstyle;F735
-fiveparen;2478
-fiveperiod;248C
-fivepersian;06F5
-fiveroman;2174
-fivesuperior;2075
-fivethai;0E55
-fl;FB02
-florin;0192
-fmonospace;FF46
-fmsquare;3399
-fofanthai;0E1F
-fofathai;0E1D
-fongmanthai;0E4F
-forall;2200
-four;0034
-fourarabic;0664
-fourbengali;09EA
-fourcircle;2463
-fourcircleinversesansserif;278D
-fourdeva;096A
-fourgujarati;0AEA
-fourgurmukhi;0A6A
-fourhackarabic;0664
-fourhangzhou;3024
-fourideographicparen;3223
-fourinferior;2084
-fourmonospace;FF14
-fournumeratorbengali;09F7
-fouroldstyle;F734
-fourparen;2477
-fourperiod;248B
-fourpersian;06F4
-fourroman;2173
-foursuperior;2074
-fourteencircle;246D
-fourteenparen;2481
-fourteenperiod;2495
-fourthai;0E54
-fourthtonechinese;02CB
-fparen;24A1
-fraction;2044
-franc;20A3
-g;0067
-gabengali;0997
-gacute;01F5
-gadeva;0917
-gafarabic;06AF
-gaffinalarabic;FB93
-gafinitialarabic;FB94
-gafmedialarabic;FB95
-gagujarati;0A97
-gagurmukhi;0A17
-gahiragana;304C
-gakatakana;30AC
-gamma;03B3
-gammalatinsmall;0263
-gammasuperior;02E0
-gangiacoptic;03EB
-gbopomofo;310D
-gbreve;011F
-gcaron;01E7
-gcedilla;0123
-gcircle;24D6
-gcircumflex;011D
-gcommaaccent;0123
-gdot;0121
-gdotaccent;0121
-gecyrillic;0433
-gehiragana;3052
-gekatakana;30B2
-geometricallyequal;2251
-gereshaccenthebrew;059C
-gereshhebrew;05F3
-gereshmuqdamhebrew;059D
-germandbls;00DF
-gershayimaccenthebrew;059E
-gershayimhebrew;05F4
-getamark;3013
-ghabengali;0998
-ghadarmenian;0572
-ghadeva;0918
-ghagujarati;0A98
-ghagurmukhi;0A18
-ghainarabic;063A
-ghainfinalarabic;FECE
-ghaininitialarabic;FECF
-ghainmedialarabic;FED0
-ghemiddlehookcyrillic;0495
-ghestrokecyrillic;0493
-gheupturncyrillic;0491
-ghhadeva;095A
-ghhagurmukhi;0A5A
-ghook;0260
-ghzsquare;3393
-gihiragana;304E
-gikatakana;30AE
-gimarmenian;0563
-gimel;05D2
-gimeldagesh;FB32
-gimeldageshhebrew;FB32
-gimelhebrew;05D2
-gjecyrillic;0453
-glottalinvertedstroke;01BE
-glottalstop;0294
-glottalstopinverted;0296
-glottalstopmod;02C0
-glottalstopreversed;0295
-glottalstopreversedmod;02C1
-glottalstopreversedsuperior;02E4
-glottalstopstroke;02A1
-glottalstopstrokereversed;02A2
-gmacron;1E21
-gmonospace;FF47
-gohiragana;3054
-gokatakana;30B4
-gparen;24A2
-gpasquare;33AC
-gradient;2207
-grave;0060
-gravebelowcmb;0316
-gravecmb;0300
-gravecomb;0300
-gravedeva;0953
-gravelowmod;02CE
-gravemonospace;FF40
-gravetonecmb;0340
-greater;003E
-greaterequal;2265
-greaterequalorless;22DB
-greatermonospace;FF1E
-greaterorequivalent;2273
-greaterorless;2277
-greateroverequal;2267
-greatersmall;FE65
-gscript;0261
-gstroke;01E5
-guhiragana;3050
-guillemotleft;00AB
-guillemotright;00BB
-guilsinglleft;2039
-guilsinglright;203A
-gukatakana;30B0
-guramusquare;3318
-gysquare;33C9
-h;0068
-haabkhasiancyrillic;04A9
-haaltonearabic;06C1
-habengali;09B9
-hadescendercyrillic;04B3
-hadeva;0939
-hagujarati;0AB9
-hagurmukhi;0A39
-haharabic;062D
-hahfinalarabic;FEA2
-hahinitialarabic;FEA3
-hahiragana;306F
-hahmedialarabic;FEA4
-haitusquare;332A
-hakatakana;30CF
-hakatakanahalfwidth;FF8A
-halantgurmukhi;0A4D
-hamzaarabic;0621
-hamzadammaarabic;0621 064F
-hamzadammatanarabic;0621 064C
-hamzafathaarabic;0621 064E
-hamzafathatanarabic;0621 064B
-hamzalowarabic;0621
-hamzalowkasraarabic;0621 0650
-hamzalowkasratanarabic;0621 064D
-hamzasukunarabic;0621 0652
-hangulfiller;3164
-hardsigncyrillic;044A
-harpoonleftbarbup;21BC
-harpoonrightbarbup;21C0
-hasquare;33CA
-hatafpatah;05B2
-hatafpatah16;05B2
-hatafpatah23;05B2
-hatafpatah2f;05B2
-hatafpatahhebrew;05B2
-hatafpatahnarrowhebrew;05B2
-hatafpatahquarterhebrew;05B2
-hatafpatahwidehebrew;05B2
-hatafqamats;05B3
-hatafqamats1b;05B3
-hatafqamats28;05B3
-hatafqamats34;05B3
-hatafqamatshebrew;05B3
-hatafqamatsnarrowhebrew;05B3
-hatafqamatsquarterhebrew;05B3
-hatafqamatswidehebrew;05B3
-hatafsegol;05B1
-hatafsegol17;05B1
-hatafsegol24;05B1
-hatafsegol30;05B1
-hatafsegolhebrew;05B1
-hatafsegolnarrowhebrew;05B1
-hatafsegolquarterhebrew;05B1
-hatafsegolwidehebrew;05B1
-hbar;0127
-hbopomofo;310F
-hbrevebelow;1E2B
-hcedilla;1E29
-hcircle;24D7
-hcircumflex;0125
-hdieresis;1E27
-hdotaccent;1E23
-hdotbelow;1E25
-he;05D4
-heart;2665
-heartsuitblack;2665
-heartsuitwhite;2661
-hedagesh;FB34
-hedageshhebrew;FB34
-hehaltonearabic;06C1
-heharabic;0647
-hehebrew;05D4
-hehfinalaltonearabic;FBA7
-hehfinalalttwoarabic;FEEA
-hehfinalarabic;FEEA
-hehhamzaabovefinalarabic;FBA5
-hehhamzaaboveisolatedarabic;FBA4
-hehinitialaltonearabic;FBA8
-hehinitialarabic;FEEB
-hehiragana;3078
-hehmedialaltonearabic;FBA9
-hehmedialarabic;FEEC
-heiseierasquare;337B
-hekatakana;30D8
-hekatakanahalfwidth;FF8D
-hekutaarusquare;3336
-henghook;0267
-herutusquare;3339
-het;05D7
-hethebrew;05D7
-hhook;0266
-hhooksuperior;02B1
-hieuhacirclekorean;327B
-hieuhaparenkorean;321B
-hieuhcirclekorean;326D
-hieuhkorean;314E
-hieuhparenkorean;320D
-hihiragana;3072
-hikatakana;30D2
-hikatakanahalfwidth;FF8B
-hiriq;05B4
-hiriq14;05B4
-hiriq21;05B4
-hiriq2d;05B4
-hiriqhebrew;05B4
-hiriqnarrowhebrew;05B4
-hiriqquarterhebrew;05B4
-hiriqwidehebrew;05B4
-hlinebelow;1E96
-hmonospace;FF48
-hoarmenian;0570
-hohipthai;0E2B
-hohiragana;307B
-hokatakana;30DB
-hokatakanahalfwidth;FF8E
-holam;05B9
-holam19;05B9
-holam26;05B9
-holam32;05B9
-holamhebrew;05B9
-holamnarrowhebrew;05B9
-holamquarterhebrew;05B9
-holamwidehebrew;05B9
-honokhukthai;0E2E
-hookabovecomb;0309
-hookcmb;0309
-hookpalatalizedbelowcmb;0321
-hookretroflexbelowcmb;0322
-hoonsquare;3342
-horicoptic;03E9
-horizontalbar;2015
-horncmb;031B
-hotsprings;2668
-house;2302
-hparen;24A3
-hsuperior;02B0
-hturned;0265
-huhiragana;3075
-huiitosquare;3333
-hukatakana;30D5
-hukatakanahalfwidth;FF8C
-hungarumlaut;02DD
-hungarumlautcmb;030B
-hv;0195
-hyphen;002D
-hypheninferior;F6E5
-hyphenmonospace;FF0D
-hyphensmall;FE63
-hyphensuperior;F6E6
-hyphentwo;2010
-i;0069
-iacute;00ED
-iacyrillic;044F
-ibengali;0987
-ibopomofo;3127
-ibreve;012D
-icaron;01D0
-icircle;24D8
-icircumflex;00EE
-icyrillic;0456
-idblgrave;0209
-ideographearthcircle;328F
-ideographfirecircle;328B
-ideographicallianceparen;323F
-ideographiccallparen;323A
-ideographiccentrecircle;32A5
-ideographicclose;3006
-ideographiccomma;3001
-ideographiccommaleft;FF64
-ideographiccongratulationparen;3237
-ideographiccorrectcircle;32A3
-ideographicearthparen;322F
-ideographicenterpriseparen;323D
-ideographicexcellentcircle;329D
-ideographicfestivalparen;3240
-ideographicfinancialcircle;3296
-ideographicfinancialparen;3236
-ideographicfireparen;322B
-ideographichaveparen;3232
-ideographichighcircle;32A4
-ideographiciterationmark;3005
-ideographiclaborcircle;3298
-ideographiclaborparen;3238
-ideographicleftcircle;32A7
-ideographiclowcircle;32A6
-ideographicmedicinecircle;32A9
-ideographicmetalparen;322E
-ideographicmoonparen;322A
-ideographicnameparen;3234
-ideographicperiod;3002
-ideographicprintcircle;329E
-ideographicreachparen;3243
-ideographicrepresentparen;3239
-ideographicresourceparen;323E
-ideographicrightcircle;32A8
-ideographicsecretcircle;3299
-ideographicselfparen;3242
-ideographicsocietyparen;3233
-ideographicspace;3000
-ideographicspecialparen;3235
-ideographicstockparen;3231
-ideographicstudyparen;323B
-ideographicsunparen;3230
-ideographicsuperviseparen;323C
-ideographicwaterparen;322C
-ideographicwoodparen;322D
-ideographiczero;3007
-ideographmetalcircle;328E
-ideographmooncircle;328A
-ideographnamecircle;3294
-ideographsuncircle;3290
-ideographwatercircle;328C
-ideographwoodcircle;328D
-ideva;0907
-idieresis;00EF
-idieresisacute;1E2F
-idieresiscyrillic;04E5
-idotbelow;1ECB
-iebrevecyrillic;04D7
-iecyrillic;0435
-ieungacirclekorean;3275
-ieungaparenkorean;3215
-ieungcirclekorean;3267
-ieungkorean;3147
-ieungparenkorean;3207
-igrave;00EC
-igujarati;0A87
-igurmukhi;0A07
-ihiragana;3044
-ihookabove;1EC9
-iibengali;0988
-iicyrillic;0438
-iideva;0908
-iigujarati;0A88
-iigurmukhi;0A08
-iimatragurmukhi;0A40
-iinvertedbreve;020B
-iishortcyrillic;0439
-iivowelsignbengali;09C0
-iivowelsigndeva;0940
-iivowelsigngujarati;0AC0
-ij;0133
-ikatakana;30A4
-ikatakanahalfwidth;FF72
-ikorean;3163
-ilde;02DC
-iluyhebrew;05AC
-imacron;012B
-imacroncyrillic;04E3
-imageorapproximatelyequal;2253
-imatragurmukhi;0A3F
-imonospace;FF49
-increment;2206
-infinity;221E
-iniarmenian;056B
-integral;222B
-integralbottom;2321
-integralbt;2321
-integralex;F8F5
-integraltop;2320
-integraltp;2320
-intersection;2229
-intisquare;3305
-invbullet;25D8
-invcircle;25D9
-invsmileface;263B
-iocyrillic;0451
-iogonek;012F
-iota;03B9
-iotadieresis;03CA
-iotadieresistonos;0390
-iotalatin;0269
-iotatonos;03AF
-iparen;24A4
-irigurmukhi;0A72
-ismallhiragana;3043
-ismallkatakana;30A3
-ismallkatakanahalfwidth;FF68
-issharbengali;09FA
-istroke;0268
-isuperior;F6ED
-iterationhiragana;309D
-iterationkatakana;30FD
-itilde;0129
-itildebelow;1E2D
-iubopomofo;3129
-iucyrillic;044E
-ivowelsignbengali;09BF
-ivowelsigndeva;093F
-ivowelsigngujarati;0ABF
-izhitsacyrillic;0475
-izhitsadblgravecyrillic;0477
-j;006A
-jaarmenian;0571
-jabengali;099C
-jadeva;091C
-jagujarati;0A9C
-jagurmukhi;0A1C
-jbopomofo;3110
-jcaron;01F0
-jcircle;24D9
-jcircumflex;0135
-jcrossedtail;029D
-jdotlessstroke;025F
-jecyrillic;0458
-jeemarabic;062C
-jeemfinalarabic;FE9E
-jeeminitialarabic;FE9F
-jeemmedialarabic;FEA0
-jeharabic;0698
-jehfinalarabic;FB8B
-jhabengali;099D
-jhadeva;091D
-jhagujarati;0A9D
-jhagurmukhi;0A1D
-jheharmenian;057B
-jis;3004
-jmonospace;FF4A
-jparen;24A5
-jsuperior;02B2
-k;006B
-kabashkircyrillic;04A1
-kabengali;0995
-kacute;1E31
-kacyrillic;043A
-kadescendercyrillic;049B
-kadeva;0915
-kaf;05DB
-kafarabic;0643
-kafdagesh;FB3B
-kafdageshhebrew;FB3B
-kaffinalarabic;FEDA
-kafhebrew;05DB
-kafinitialarabic;FEDB
-kafmedialarabic;FEDC
-kafrafehebrew;FB4D
-kagujarati;0A95
-kagurmukhi;0A15
-kahiragana;304B
-kahookcyrillic;04C4
-kakatakana;30AB
-kakatakanahalfwidth;FF76
-kappa;03BA
-kappasymbolgreek;03F0
-kapyeounmieumkorean;3171
-kapyeounphieuphkorean;3184
-kapyeounpieupkorean;3178
-kapyeounssangpieupkorean;3179
-karoriisquare;330D
-kashidaautoarabic;0640
-kashidaautonosidebearingarabic;0640
-kasmallkatakana;30F5
-kasquare;3384
-kasraarabic;0650
-kasratanarabic;064D
-kastrokecyrillic;049F
-katahiraprolongmarkhalfwidth;FF70
-kaverticalstrokecyrillic;049D
-kbopomofo;310E
-kcalsquare;3389
-kcaron;01E9
-kcedilla;0137
-kcircle;24DA
-kcommaaccent;0137
-kdotbelow;1E33
-keharmenian;0584
-kehiragana;3051
-kekatakana;30B1
-kekatakanahalfwidth;FF79
-kenarmenian;056F
-kesmallkatakana;30F6
-kgreenlandic;0138
-khabengali;0996
-khacyrillic;0445
-khadeva;0916
-khagujarati;0A96
-khagurmukhi;0A16
-khaharabic;062E
-khahfinalarabic;FEA6
-khahinitialarabic;FEA7
-khahmedialarabic;FEA8
-kheicoptic;03E7
-khhadeva;0959
-khhagurmukhi;0A59
-khieukhacirclekorean;3278
-khieukhaparenkorean;3218
-khieukhcirclekorean;326A
-khieukhkorean;314B
-khieukhparenkorean;320A
-khokhaithai;0E02
-khokhonthai;0E05
-khokhuatthai;0E03
-khokhwaithai;0E04
-khomutthai;0E5B
-khook;0199
-khorakhangthai;0E06
-khzsquare;3391
-kihiragana;304D
-kikatakana;30AD
-kikatakanahalfwidth;FF77
-kiroguramusquare;3315
-kiromeetorusquare;3316
-kirosquare;3314
-kiyeokacirclekorean;326E
-kiyeokaparenkorean;320E
-kiyeokcirclekorean;3260
-kiyeokkorean;3131
-kiyeokparenkorean;3200
-kiyeoksioskorean;3133
-kjecyrillic;045C
-klinebelow;1E35
-klsquare;3398
-kmcubedsquare;33A6
-kmonospace;FF4B
-kmsquaredsquare;33A2
-kohiragana;3053
-kohmsquare;33C0
-kokaithai;0E01
-kokatakana;30B3
-kokatakanahalfwidth;FF7A
-kooposquare;331E
-koppacyrillic;0481
-koreanstandardsymbol;327F
-koroniscmb;0343
-kparen;24A6
-kpasquare;33AA
-ksicyrillic;046F
-ktsquare;33CF
-kturned;029E
-kuhiragana;304F
-kukatakana;30AF
-kukatakanahalfwidth;FF78
-kvsquare;33B8
-kwsquare;33BE
-l;006C
-labengali;09B2
-lacute;013A
-ladeva;0932
-lagujarati;0AB2
-lagurmukhi;0A32
-lakkhangyaothai;0E45
-lamaleffinalarabic;FEFC
-lamalefhamzaabovefinalarabic;FEF8
-lamalefhamzaaboveisolatedarabic;FEF7
-lamalefhamzabelowfinalarabic;FEFA
-lamalefhamzabelowisolatedarabic;FEF9
-lamalefisolatedarabic;FEFB
-lamalefmaddaabovefinalarabic;FEF6
-lamalefmaddaaboveisolatedarabic;FEF5
-lamarabic;0644
-lambda;03BB
-lambdastroke;019B
-lamed;05DC
-lameddagesh;FB3C
-lameddageshhebrew;FB3C
-lamedhebrew;05DC
-lamedholam;05DC 05B9
-lamedholamdagesh;05DC 05B9 05BC
-lamedholamdageshhebrew;05DC 05B9 05BC
-lamedholamhebrew;05DC 05B9
-lamfinalarabic;FEDE
-lamhahinitialarabic;FCCA
-laminitialarabic;FEDF
-lamjeeminitialarabic;FCC9
-lamkhahinitialarabic;FCCB
-lamlamhehisolatedarabic;FDF2
-lammedialarabic;FEE0
-lammeemhahinitialarabic;FD88
-lammeeminitialarabic;FCCC
-lammeemjeeminitialarabic;FEDF FEE4 FEA0
-lammeemkhahinitialarabic;FEDF FEE4 FEA8
-largecircle;25EF
-lbar;019A
-lbelt;026C
-lbopomofo;310C
-lcaron;013E
-lcedilla;013C
-lcircle;24DB
-lcircumflexbelow;1E3D
-lcommaaccent;013C
-ldot;0140
-ldotaccent;0140
-ldotbelow;1E37
-ldotbelowmacron;1E39
-leftangleabovecmb;031A
-lefttackbelowcmb;0318
-less;003C
-lessequal;2264
-lessequalorgreater;22DA
-lessmonospace;FF1C
-lessorequivalent;2272
-lessorgreater;2276
-lessoverequal;2266
-lesssmall;FE64
-lezh;026E
-lfblock;258C
-lhookretroflex;026D
-lira;20A4
-liwnarmenian;056C
-lj;01C9
-ljecyrillic;0459
-ll;F6C0
-lladeva;0933
-llagujarati;0AB3
-llinebelow;1E3B
-llladeva;0934
-llvocalicbengali;09E1
-llvocalicdeva;0961
-llvocalicvowelsignbengali;09E3
-llvocalicvowelsigndeva;0963
-lmiddletilde;026B
-lmonospace;FF4C
-lmsquare;33D0
-lochulathai;0E2C
-logicaland;2227
-logicalnot;00AC
-logicalnotreversed;2310
-logicalor;2228
-lolingthai;0E25
-longs;017F
-lowlinecenterline;FE4E
-lowlinecmb;0332
-lowlinedashed;FE4D
-lozenge;25CA
-lparen;24A7
-lslash;0142
-lsquare;2113
-lsuperior;F6EE
-ltshade;2591
-luthai;0E26
-lvocalicbengali;098C
-lvocalicdeva;090C
-lvocalicvowelsignbengali;09E2
-lvocalicvowelsigndeva;0962
-lxsquare;33D3
-m;006D
-mabengali;09AE
-macron;00AF
-macronbelowcmb;0331
-macroncmb;0304
-macronlowmod;02CD
-macronmonospace;FFE3
-macute;1E3F
-madeva;092E
-magujarati;0AAE
-magurmukhi;0A2E
-mahapakhhebrew;05A4
-mahapakhlefthebrew;05A4
-mahiragana;307E
-maichattawalowleftthai;F895
-maichattawalowrightthai;F894
-maichattawathai;0E4B
-maichattawaupperleftthai;F893
-maieklowleftthai;F88C
-maieklowrightthai;F88B
-maiekthai;0E48
-maiekupperleftthai;F88A
-maihanakatleftthai;F884
-maihanakatthai;0E31
-maitaikhuleftthai;F889
-maitaikhuthai;0E47
-maitholowleftthai;F88F
-maitholowrightthai;F88E
-maithothai;0E49
-maithoupperleftthai;F88D
-maitrilowleftthai;F892
-maitrilowrightthai;F891
-maitrithai;0E4A
-maitriupperleftthai;F890
-maiyamokthai;0E46
-makatakana;30DE
-makatakanahalfwidth;FF8F
-male;2642
-mansyonsquare;3347
-maqafhebrew;05BE
-mars;2642
-masoracirclehebrew;05AF
-masquare;3383
-mbopomofo;3107
-mbsquare;33D4
-mcircle;24DC
-mcubedsquare;33A5
-mdotaccent;1E41
-mdotbelow;1E43
-meemarabic;0645
-meemfinalarabic;FEE2
-meeminitialarabic;FEE3
-meemmedialarabic;FEE4
-meemmeeminitialarabic;FCD1
-meemmeemisolatedarabic;FC48
-meetorusquare;334D
-mehiragana;3081
-meizierasquare;337E
-mekatakana;30E1
-mekatakanahalfwidth;FF92
-mem;05DE
-memdagesh;FB3E
-memdageshhebrew;FB3E
-memhebrew;05DE
-menarmenian;0574
-merkhahebrew;05A5
-merkhakefulahebrew;05A6
-merkhakefulalefthebrew;05A6
-merkhalefthebrew;05A5
-mhook;0271
-mhzsquare;3392
-middledotkatakanahalfwidth;FF65
-middot;00B7
-mieumacirclekorean;3272
-mieumaparenkorean;3212
-mieumcirclekorean;3264
-mieumkorean;3141
-mieumpansioskorean;3170
-mieumparenkorean;3204
-mieumpieupkorean;316E
-mieumsioskorean;316F
-mihiragana;307F
-mikatakana;30DF
-mikatakanahalfwidth;FF90
-minus;2212
-minusbelowcmb;0320
-minuscircle;2296
-minusmod;02D7
-minusplus;2213
-minute;2032
-miribaarusquare;334A
-mirisquare;3349
-mlonglegturned;0270
-mlsquare;3396
-mmcubedsquare;33A3
-mmonospace;FF4D
-mmsquaredsquare;339F
-mohiragana;3082
-mohmsquare;33C1
-mokatakana;30E2
-mokatakanahalfwidth;FF93
-molsquare;33D6
-momathai;0E21
-moverssquare;33A7
-moverssquaredsquare;33A8
-mparen;24A8
-mpasquare;33AB
-mssquare;33B3
-msuperior;F6EF
-mturned;026F
-mu;00B5
-mu1;00B5
-muasquare;3382
-muchgreater;226B
-muchless;226A
-mufsquare;338C
-mugreek;03BC
-mugsquare;338D
-muhiragana;3080
-mukatakana;30E0
-mukatakanahalfwidth;FF91
-mulsquare;3395
-multiply;00D7
-mumsquare;339B
-munahhebrew;05A3
-munahlefthebrew;05A3
-musicalnote;266A
-musicalnotedbl;266B
-musicflatsign;266D
-musicsharpsign;266F
-mussquare;33B2
-muvsquare;33B6
-muwsquare;33BC
-mvmegasquare;33B9
-mvsquare;33B7
-mwmegasquare;33BF
-mwsquare;33BD
-n;006E
-nabengali;09A8
-nabla;2207
-nacute;0144
-nadeva;0928
-nagujarati;0AA8
-nagurmukhi;0A28
-nahiragana;306A
-nakatakana;30CA
-nakatakanahalfwidth;FF85
-napostrophe;0149
-nasquare;3381
-nbopomofo;310B
-nbspace;00A0
-ncaron;0148
-ncedilla;0146
-ncircle;24DD
-ncircumflexbelow;1E4B
-ncommaaccent;0146
-ndotaccent;1E45
-ndotbelow;1E47
-nehiragana;306D
-nekatakana;30CD
-nekatakanahalfwidth;FF88
-newsheqelsign;20AA
-nfsquare;338B
-ngabengali;0999
-ngadeva;0919
-ngagujarati;0A99
-ngagurmukhi;0A19
-ngonguthai;0E07
-nhiragana;3093
-nhookleft;0272
-nhookretroflex;0273
-nieunacirclekorean;326F
-nieunaparenkorean;320F
-nieuncieuckorean;3135
-nieuncirclekorean;3261
-nieunhieuhkorean;3136
-nieunkorean;3134
-nieunpansioskorean;3168
-nieunparenkorean;3201
-nieunsioskorean;3167
-nieuntikeutkorean;3166
-nihiragana;306B
-nikatakana;30CB
-nikatakanahalfwidth;FF86
-nikhahitleftthai;F899
-nikhahitthai;0E4D
-nine;0039
-ninearabic;0669
-ninebengali;09EF
-ninecircle;2468
-ninecircleinversesansserif;2792
-ninedeva;096F
-ninegujarati;0AEF
-ninegurmukhi;0A6F
-ninehackarabic;0669
-ninehangzhou;3029
-nineideographicparen;3228
-nineinferior;2089
-ninemonospace;FF19
-nineoldstyle;F739
-nineparen;247C
-nineperiod;2490
-ninepersian;06F9
-nineroman;2178
-ninesuperior;2079
-nineteencircle;2472
-nineteenparen;2486
-nineteenperiod;249A
-ninethai;0E59
-nj;01CC
-njecyrillic;045A
-nkatakana;30F3
-nkatakanahalfwidth;FF9D
-nlegrightlong;019E
-nlinebelow;1E49
-nmonospace;FF4E
-nmsquare;339A
-nnabengali;09A3
-nnadeva;0923
-nnagujarati;0AA3
-nnagurmukhi;0A23
-nnnadeva;0929
-nohiragana;306E
-nokatakana;30CE
-nokatakanahalfwidth;FF89
-nonbreakingspace;00A0
-nonenthai;0E13
-nonuthai;0E19
-noonarabic;0646
-noonfinalarabic;FEE6
-noonghunnaarabic;06BA
-noonghunnafinalarabic;FB9F
-noonhehinitialarabic;FEE7 FEEC
-nooninitialarabic;FEE7
-noonjeeminitialarabic;FCD2
-noonjeemisolatedarabic;FC4B
-noonmedialarabic;FEE8
-noonmeeminitialarabic;FCD5
-noonmeemisolatedarabic;FC4E
-noonnoonfinalarabic;FC8D
-notcontains;220C
-notelement;2209
-notelementof;2209
-notequal;2260
-notgreater;226F
-notgreaternorequal;2271
-notgreaternorless;2279
-notidentical;2262
-notless;226E
-notlessnorequal;2270
-notparallel;2226
-notprecedes;2280
-notsubset;2284
-notsucceeds;2281
-notsuperset;2285
-nowarmenian;0576
-nparen;24A9
-nssquare;33B1
-nsuperior;207F
-ntilde;00F1
-nu;03BD
-nuhiragana;306C
-nukatakana;30CC
-nukatakanahalfwidth;FF87
-nuktabengali;09BC
-nuktadeva;093C
-nuktagujarati;0ABC
-nuktagurmukhi;0A3C
-numbersign;0023
-numbersignmonospace;FF03
-numbersignsmall;FE5F
-numeralsigngreek;0374
-numeralsignlowergreek;0375
-numero;2116
-nun;05E0
-nundagesh;FB40
-nundageshhebrew;FB40
-nunhebrew;05E0
-nvsquare;33B5
-nwsquare;33BB
-nyabengali;099E
-nyadeva;091E
-nyagujarati;0A9E
-nyagurmukhi;0A1E
-o;006F
-oacute;00F3
-oangthai;0E2D
-obarred;0275
-obarredcyrillic;04E9
-obarreddieresiscyrillic;04EB
-obengali;0993
-obopomofo;311B
-obreve;014F
-ocandradeva;0911
-ocandragujarati;0A91
-ocandravowelsigndeva;0949
-ocandravowelsigngujarati;0AC9
-ocaron;01D2
-ocircle;24DE
-ocircumflex;00F4
-ocircumflexacute;1ED1
-ocircumflexdotbelow;1ED9
-ocircumflexgrave;1ED3
-ocircumflexhookabove;1ED5
-ocircumflextilde;1ED7
-ocyrillic;043E
-odblacute;0151
-odblgrave;020D
-odeva;0913
-odieresis;00F6
-odieresiscyrillic;04E7
-odotbelow;1ECD
-oe;0153
-oekorean;315A
-ogonek;02DB
-ogonekcmb;0328
-ograve;00F2
-ogujarati;0A93
-oharmenian;0585
-ohiragana;304A
-ohookabove;1ECF
-ohorn;01A1
-ohornacute;1EDB
-ohorndotbelow;1EE3
-ohorngrave;1EDD
-ohornhookabove;1EDF
-ohorntilde;1EE1
-ohungarumlaut;0151
-oi;01A3
-oinvertedbreve;020F
-okatakana;30AA
-okatakanahalfwidth;FF75
-okorean;3157
-olehebrew;05AB
-omacron;014D
-omacronacute;1E53
-omacrongrave;1E51
-omdeva;0950
-omega;03C9
-omega1;03D6
-omegacyrillic;0461
-omegalatinclosed;0277
-omegaroundcyrillic;047B
-omegatitlocyrillic;047D
-omegatonos;03CE
-omgujarati;0AD0
-omicron;03BF
-omicrontonos;03CC
-omonospace;FF4F
-one;0031
-onearabic;0661
-onebengali;09E7
-onecircle;2460
-onecircleinversesansserif;278A
-onedeva;0967
-onedotenleader;2024
-oneeighth;215B
-onefitted;F6DC
-onegujarati;0AE7
-onegurmukhi;0A67
-onehackarabic;0661
-onehalf;00BD
-onehangzhou;3021
-oneideographicparen;3220
-oneinferior;2081
-onemonospace;FF11
-onenumeratorbengali;09F4
-oneoldstyle;F731
-oneparen;2474
-oneperiod;2488
-onepersian;06F1
-onequarter;00BC
-oneroman;2170
-onesuperior;00B9
-onethai;0E51
-onethird;2153
-oogonek;01EB
-oogonekmacron;01ED
-oogurmukhi;0A13
-oomatragurmukhi;0A4B
-oopen;0254
-oparen;24AA
-openbullet;25E6
-option;2325
-ordfeminine;00AA
-ordmasculine;00BA
-orthogonal;221F
-oshortdeva;0912
-oshortvowelsigndeva;094A
-oslash;00F8
-oslashacute;01FF
-osmallhiragana;3049
-osmallkatakana;30A9
-osmallkatakanahalfwidth;FF6B
-ostrokeacute;01FF
-osuperior;F6F0
-otcyrillic;047F
-otilde;00F5
-otildeacute;1E4D
-otildedieresis;1E4F
-oubopomofo;3121
-overline;203E
-overlinecenterline;FE4A
-overlinecmb;0305
-overlinedashed;FE49
-overlinedblwavy;FE4C
-overlinewavy;FE4B
-overscore;00AF
-ovowelsignbengali;09CB
-ovowelsigndeva;094B
-ovowelsigngujarati;0ACB
-p;0070
-paampssquare;3380
-paasentosquare;332B
-pabengali;09AA
-pacute;1E55
-padeva;092A
-pagedown;21DF
-pageup;21DE
-pagujarati;0AAA
-pagurmukhi;0A2A
-pahiragana;3071
-paiyannoithai;0E2F
-pakatakana;30D1
-palatalizationcyrilliccmb;0484
-palochkacyrillic;04C0
-pansioskorean;317F
-paragraph;00B6
-parallel;2225
-parenleft;0028
-parenleftaltonearabic;FD3E
-parenleftbt;F8ED
-parenleftex;F8EC
-parenleftinferior;208D
-parenleftmonospace;FF08
-parenleftsmall;FE59
-parenleftsuperior;207D
-parenlefttp;F8EB
-parenleftvertical;FE35
-parenright;0029
-parenrightaltonearabic;FD3F
-parenrightbt;F8F8
-parenrightex;F8F7
-parenrightinferior;208E
-parenrightmonospace;FF09
-parenrightsmall;FE5A
-parenrightsuperior;207E
-parenrighttp;F8F6
-parenrightvertical;FE36
-partialdiff;2202
-paseqhebrew;05C0
-pashtahebrew;0599
-pasquare;33A9
-patah;05B7
-patah11;05B7
-patah1d;05B7
-patah2a;05B7
-patahhebrew;05B7
-patahnarrowhebrew;05B7
-patahquarterhebrew;05B7
-patahwidehebrew;05B7
-pazerhebrew;05A1
-pbopomofo;3106
-pcircle;24DF
-pdotaccent;1E57
-pe;05E4
-pecyrillic;043F
-pedagesh;FB44
-pedageshhebrew;FB44
-peezisquare;333B
-pefinaldageshhebrew;FB43
-peharabic;067E
-peharmenian;057A
-pehebrew;05E4
-pehfinalarabic;FB57
-pehinitialarabic;FB58
-pehiragana;307A
-pehmedialarabic;FB59
-pekatakana;30DA
-pemiddlehookcyrillic;04A7
-perafehebrew;FB4E
-percent;0025
-percentarabic;066A
-percentmonospace;FF05
-percentsmall;FE6A
-period;002E
-periodarmenian;0589
-periodcentered;00B7
-periodhalfwidth;FF61
-periodinferior;F6E7
-periodmonospace;FF0E
-periodsmall;FE52
-periodsuperior;F6E8
-perispomenigreekcmb;0342
-perpendicular;22A5
-perthousand;2030
-peseta;20A7
-pfsquare;338A
-phabengali;09AB
-phadeva;092B
-phagujarati;0AAB
-phagurmukhi;0A2B
-phi;03C6
-phi1;03D5
-phieuphacirclekorean;327A
-phieuphaparenkorean;321A
-phieuphcirclekorean;326C
-phieuphkorean;314D
-phieuphparenkorean;320C
-philatin;0278
-phinthuthai;0E3A
-phisymbolgreek;03D5
-phook;01A5
-phophanthai;0E1E
-phophungthai;0E1C
-phosamphaothai;0E20
-pi;03C0
-pieupacirclekorean;3273
-pieupaparenkorean;3213
-pieupcieuckorean;3176
-pieupcirclekorean;3265
-pieupkiyeokkorean;3172
-pieupkorean;3142
-pieupparenkorean;3205
-pieupsioskiyeokkorean;3174
-pieupsioskorean;3144
-pieupsiostikeutkorean;3175
-pieupthieuthkorean;3177
-pieuptikeutkorean;3173
-pihiragana;3074
-pikatakana;30D4
-pisymbolgreek;03D6
-piwrarmenian;0583
-plus;002B
-plusbelowcmb;031F
-pluscircle;2295
-plusminus;00B1
-plusmod;02D6
-plusmonospace;FF0B
-plussmall;FE62
-plussuperior;207A
-pmonospace;FF50
-pmsquare;33D8
-pohiragana;307D
-pointingindexdownwhite;261F
-pointingindexleftwhite;261C
-pointingindexrightwhite;261E
-pointingindexupwhite;261D
-pokatakana;30DD
-poplathai;0E1B
-postalmark;3012
-postalmarkface;3020
-pparen;24AB
-precedes;227A
-prescription;211E
-primemod;02B9
-primereversed;2035
-product;220F
-projective;2305
-prolongedkana;30FC
-propellor;2318
-propersubset;2282
-propersuperset;2283
-proportion;2237
-proportional;221D
-psi;03C8
-psicyrillic;0471
-psilipneumatacyrilliccmb;0486
-pssquare;33B0
-puhiragana;3077
-pukatakana;30D7
-pvsquare;33B4
-pwsquare;33BA
-q;0071
-qadeva;0958
-qadmahebrew;05A8
-qafarabic;0642
-qaffinalarabic;FED6
-qafinitialarabic;FED7
-qafmedialarabic;FED8
-qamats;05B8
-qamats10;05B8
-qamats1a;05B8
-qamats1c;05B8
-qamats27;05B8
-qamats29;05B8
-qamats33;05B8
-qamatsde;05B8
-qamatshebrew;05B8
-qamatsnarrowhebrew;05B8
-qamatsqatanhebrew;05B8
-qamatsqatannarrowhebrew;05B8
-qamatsqatanquarterhebrew;05B8
-qamatsqatanwidehebrew;05B8
-qamatsquarterhebrew;05B8
-qamatswidehebrew;05B8
-qarneyparahebrew;059F
-qbopomofo;3111
-qcircle;24E0
-qhook;02A0
-qmonospace;FF51
-qof;05E7
-qofdagesh;FB47
-qofdageshhebrew;FB47
-qofhatafpatah;05E7 05B2
-qofhatafpatahhebrew;05E7 05B2
-qofhatafsegol;05E7 05B1
-qofhatafsegolhebrew;05E7 05B1
-qofhebrew;05E7
-qofhiriq;05E7 05B4
-qofhiriqhebrew;05E7 05B4
-qofholam;05E7 05B9
-qofholamhebrew;05E7 05B9
-qofpatah;05E7 05B7
-qofpatahhebrew;05E7 05B7
-qofqamats;05E7 05B8
-qofqamatshebrew;05E7 05B8
-qofqubuts;05E7 05BB
-qofqubutshebrew;05E7 05BB
-qofsegol;05E7 05B6
-qofsegolhebrew;05E7 05B6
-qofsheva;05E7 05B0
-qofshevahebrew;05E7 05B0
-qoftsere;05E7 05B5
-qoftserehebrew;05E7 05B5
-qparen;24AC
-quarternote;2669
-qubuts;05BB
-qubuts18;05BB
-qubuts25;05BB
-qubuts31;05BB
-qubutshebrew;05BB
-qubutsnarrowhebrew;05BB
-qubutsquarterhebrew;05BB
-qubutswidehebrew;05BB
-question;003F
-questionarabic;061F
-questionarmenian;055E
-questiondown;00BF
-questiondownsmall;F7BF
-questiongreek;037E
-questionmonospace;FF1F
-questionsmall;F73F
-quotedbl;0022
-quotedblbase;201E
-quotedblleft;201C
-quotedblmonospace;FF02
-quotedblprime;301E
-quotedblprimereversed;301D
-quotedblright;201D
-quoteleft;2018
-quoteleftreversed;201B
-quotereversed;201B
-quoteright;2019
-quoterightn;0149
-quotesinglbase;201A
-quotesingle;0027
-quotesinglemonospace;FF07
-r;0072
-raarmenian;057C
-rabengali;09B0
-racute;0155
-radeva;0930
-radical;221A
-radicalex;F8E5
-radoverssquare;33AE
-radoverssquaredsquare;33AF
-radsquare;33AD
-rafe;05BF
-rafehebrew;05BF
-ragujarati;0AB0
-ragurmukhi;0A30
-rahiragana;3089
-rakatakana;30E9
-rakatakanahalfwidth;FF97
-ralowerdiagonalbengali;09F1
-ramiddlediagonalbengali;09F0
-ramshorn;0264
-ratio;2236
-rbopomofo;3116
-rcaron;0159
-rcedilla;0157
-rcircle;24E1
-rcommaaccent;0157
-rdblgrave;0211
-rdotaccent;1E59
-rdotbelow;1E5B
-rdotbelowmacron;1E5D
-referencemark;203B
-reflexsubset;2286
-reflexsuperset;2287
-registered;00AE
-registersans;F8E8
-registerserif;F6DA
-reharabic;0631
-reharmenian;0580
-rehfinalarabic;FEAE
-rehiragana;308C
-rehyehaleflamarabic;0631 FEF3 FE8E 0644
-rekatakana;30EC
-rekatakanahalfwidth;FF9A
-resh;05E8
-reshdageshhebrew;FB48
-reshhatafpatah;05E8 05B2
-reshhatafpatahhebrew;05E8 05B2
-reshhatafsegol;05E8 05B1
-reshhatafsegolhebrew;05E8 05B1
-reshhebrew;05E8
-reshhiriq;05E8 05B4
-reshhiriqhebrew;05E8 05B4
-reshholam;05E8 05B9
-reshholamhebrew;05E8 05B9
-reshpatah;05E8 05B7
-reshpatahhebrew;05E8 05B7
-reshqamats;05E8 05B8
-reshqamatshebrew;05E8 05B8
-reshqubuts;05E8 05BB
-reshqubutshebrew;05E8 05BB
-reshsegol;05E8 05B6
-reshsegolhebrew;05E8 05B6
-reshsheva;05E8 05B0
-reshshevahebrew;05E8 05B0
-reshtsere;05E8 05B5
-reshtserehebrew;05E8 05B5
-reversedtilde;223D
-reviahebrew;0597
-reviamugrashhebrew;0597
-revlogicalnot;2310
-rfishhook;027E
-rfishhookreversed;027F
-rhabengali;09DD
-rhadeva;095D
-rho;03C1
-rhook;027D
-rhookturned;027B
-rhookturnedsuperior;02B5
-rhosymbolgreek;03F1
-rhotichookmod;02DE
-rieulacirclekorean;3271
-rieulaparenkorean;3211
-rieulcirclekorean;3263
-rieulhieuhkorean;3140
-rieulkiyeokkorean;313A
-rieulkiyeoksioskorean;3169
-rieulkorean;3139
-rieulmieumkorean;313B
-rieulpansioskorean;316C
-rieulparenkorean;3203
-rieulphieuphkorean;313F
-rieulpieupkorean;313C
-rieulpieupsioskorean;316B
-rieulsioskorean;313D
-rieulthieuthkorean;313E
-rieultikeutkorean;316A
-rieulyeorinhieuhkorean;316D
-rightangle;221F
-righttackbelowcmb;0319
-righttriangle;22BF
-rihiragana;308A
-rikatakana;30EA
-rikatakanahalfwidth;FF98
-ring;02DA
-ringbelowcmb;0325
-ringcmb;030A
-ringhalfleft;02BF
-ringhalfleftarmenian;0559
-ringhalfleftbelowcmb;031C
-ringhalfleftcentered;02D3
-ringhalfright;02BE
-ringhalfrightbelowcmb;0339
-ringhalfrightcentered;02D2
-rinvertedbreve;0213
-rittorusquare;3351
-rlinebelow;1E5F
-rlongleg;027C
-rlonglegturned;027A
-rmonospace;FF52
-rohiragana;308D
-rokatakana;30ED
-rokatakanahalfwidth;FF9B
-roruathai;0E23
-rparen;24AD
-rrabengali;09DC
-rradeva;0931
-rragurmukhi;0A5C
-rreharabic;0691
-rrehfinalarabic;FB8D
-rrvocalicbengali;09E0
-rrvocalicdeva;0960
-rrvocalicgujarati;0AE0
-rrvocalicvowelsignbengali;09C4
-rrvocalicvowelsigndeva;0944
-rrvocalicvowelsigngujarati;0AC4
-rsuperior;F6F1
-rtblock;2590
-rturned;0279
-rturnedsuperior;02B4
-ruhiragana;308B
-rukatakana;30EB
-rukatakanahalfwidth;FF99
-rupeemarkbengali;09F2
-rupeesignbengali;09F3
-rupiah;F6DD
-ruthai;0E24
-rvocalicbengali;098B
-rvocalicdeva;090B
-rvocalicgujarati;0A8B
-rvocalicvowelsignbengali;09C3
-rvocalicvowelsigndeva;0943
-rvocalicvowelsigngujarati;0AC3
-s;0073
-sabengali;09B8
-sacute;015B
-sacutedotaccent;1E65
-sadarabic;0635
-sadeva;0938
-sadfinalarabic;FEBA
-sadinitialarabic;FEBB
-sadmedialarabic;FEBC
-sagujarati;0AB8
-sagurmukhi;0A38
-sahiragana;3055
-sakatakana;30B5
-sakatakanahalfwidth;FF7B
-sallallahoualayhewasallamarabic;FDFA
-samekh;05E1
-samekhdagesh;FB41
-samekhdageshhebrew;FB41
-samekhhebrew;05E1
-saraaathai;0E32
-saraaethai;0E41
-saraaimaimalaithai;0E44
-saraaimaimuanthai;0E43
-saraamthai;0E33
-saraathai;0E30
-saraethai;0E40
-saraiileftthai;F886
-saraiithai;0E35
-saraileftthai;F885
-saraithai;0E34
-saraothai;0E42
-saraueeleftthai;F888
-saraueethai;0E37
-saraueleftthai;F887
-sarauethai;0E36
-sarauthai;0E38
-sarauuthai;0E39
-sbopomofo;3119
-scaron;0161
-scarondotaccent;1E67
-scedilla;015F
-schwa;0259
-schwacyrillic;04D9
-schwadieresiscyrillic;04DB
-schwahook;025A
-scircle;24E2
-scircumflex;015D
-scommaaccent;0219
-sdotaccent;1E61
-sdotbelow;1E63
-sdotbelowdotaccent;1E69
-seagullbelowcmb;033C
-second;2033
-secondtonechinese;02CA
-section;00A7
-seenarabic;0633
-seenfinalarabic;FEB2
-seeninitialarabic;FEB3
-seenmedialarabic;FEB4
-segol;05B6
-segol13;05B6
-segol1f;05B6
-segol2c;05B6
-segolhebrew;05B6
-segolnarrowhebrew;05B6
-segolquarterhebrew;05B6
-segoltahebrew;0592
-segolwidehebrew;05B6
-seharmenian;057D
-sehiragana;305B
-sekatakana;30BB
-sekatakanahalfwidth;FF7E
-semicolon;003B
-semicolonarabic;061B
-semicolonmonospace;FF1B
-semicolonsmall;FE54
-semivoicedmarkkana;309C
-semivoicedmarkkanahalfwidth;FF9F
-sentisquare;3322
-sentosquare;3323
-seven;0037
-sevenarabic;0667
-sevenbengali;09ED
-sevencircle;2466
-sevencircleinversesansserif;2790
-sevendeva;096D
-seveneighths;215E
-sevengujarati;0AED
-sevengurmukhi;0A6D
-sevenhackarabic;0667
-sevenhangzhou;3027
-sevenideographicparen;3226
-seveninferior;2087
-sevenmonospace;FF17
-sevenoldstyle;F737
-sevenparen;247A
-sevenperiod;248E
-sevenpersian;06F7
-sevenroman;2176
-sevensuperior;2077
-seventeencircle;2470
-seventeenparen;2484
-seventeenperiod;2498
-seventhai;0E57
-sfthyphen;00AD
-shaarmenian;0577
-shabengali;09B6
-shacyrillic;0448
-shaddaarabic;0651
-shaddadammaarabic;FC61
-shaddadammatanarabic;FC5E
-shaddafathaarabic;FC60
-shaddafathatanarabic;0651 064B
-shaddakasraarabic;FC62
-shaddakasratanarabic;FC5F
-shade;2592
-shadedark;2593
-shadelight;2591
-shademedium;2592
-shadeva;0936
-shagujarati;0AB6
-shagurmukhi;0A36
-shalshelethebrew;0593
-shbopomofo;3115
-shchacyrillic;0449
-sheenarabic;0634
-sheenfinalarabic;FEB6
-sheeninitialarabic;FEB7
-sheenmedialarabic;FEB8
-sheicoptic;03E3
-sheqel;20AA
-sheqelhebrew;20AA
-sheva;05B0
-sheva115;05B0
-sheva15;05B0
-sheva22;05B0
-sheva2e;05B0
-shevahebrew;05B0
-shevanarrowhebrew;05B0
-shevaquarterhebrew;05B0
-shevawidehebrew;05B0
-shhacyrillic;04BB
-shimacoptic;03ED
-shin;05E9
-shindagesh;FB49
-shindageshhebrew;FB49
-shindageshshindot;FB2C
-shindageshshindothebrew;FB2C
-shindageshsindot;FB2D
-shindageshsindothebrew;FB2D
-shindothebrew;05C1
-shinhebrew;05E9
-shinshindot;FB2A
-shinshindothebrew;FB2A
-shinsindot;FB2B
-shinsindothebrew;FB2B
-shook;0282
-sigma;03C3
-sigma1;03C2
-sigmafinal;03C2
-sigmalunatesymbolgreek;03F2
-sihiragana;3057
-sikatakana;30B7
-sikatakanahalfwidth;FF7C
-siluqhebrew;05BD
-siluqlefthebrew;05BD
-similar;223C
-sindothebrew;05C2
-siosacirclekorean;3274
-siosaparenkorean;3214
-sioscieuckorean;317E
-sioscirclekorean;3266
-sioskiyeokkorean;317A
-sioskorean;3145
-siosnieunkorean;317B
-siosparenkorean;3206
-siospieupkorean;317D
-siostikeutkorean;317C
-six;0036
-sixarabic;0666
-sixbengali;09EC
-sixcircle;2465
-sixcircleinversesansserif;278F
-sixdeva;096C
-sixgujarati;0AEC
-sixgurmukhi;0A6C
-sixhackarabic;0666
-sixhangzhou;3026
-sixideographicparen;3225
-sixinferior;2086
-sixmonospace;FF16
-sixoldstyle;F736
-sixparen;2479
-sixperiod;248D
-sixpersian;06F6
-sixroman;2175
-sixsuperior;2076
-sixteencircle;246F
-sixteencurrencydenominatorbengali;09F9
-sixteenparen;2483
-sixteenperiod;2497
-sixthai;0E56
-slash;002F
-slashmonospace;FF0F
-slong;017F
-slongdotaccent;1E9B
-smileface;263A
-smonospace;FF53
-sofpasuqhebrew;05C3
-softhyphen;00AD
-softsigncyrillic;044C
-sohiragana;305D
-sokatakana;30BD
-sokatakanahalfwidth;FF7F
-soliduslongoverlaycmb;0338
-solidusshortoverlaycmb;0337
-sorusithai;0E29
-sosalathai;0E28
-sosothai;0E0B
-sosuathai;0E2A
-space;0020
-spacehackarabic;0020
-spade;2660
-spadesuitblack;2660
-spadesuitwhite;2664
-sparen;24AE
-squarebelowcmb;033B
-squarecc;33C4
-squarecm;339D
-squarediagonalcrosshatchfill;25A9
-squarehorizontalfill;25A4
-squarekg;338F
-squarekm;339E
-squarekmcapital;33CE
-squareln;33D1
-squarelog;33D2
-squaremg;338E
-squaremil;33D5
-squaremm;339C
-squaremsquared;33A1
-squareorthogonalcrosshatchfill;25A6
-squareupperlefttolowerrightfill;25A7
-squareupperrighttolowerleftfill;25A8
-squareverticalfill;25A5
-squarewhitewithsmallblack;25A3
-srsquare;33DB
-ssabengali;09B7
-ssadeva;0937
-ssagujarati;0AB7
-ssangcieuckorean;3149
-ssanghieuhkorean;3185
-ssangieungkorean;3180
-ssangkiyeokkorean;3132
-ssangnieunkorean;3165
-ssangpieupkorean;3143
-ssangsioskorean;3146
-ssangtikeutkorean;3138
-ssuperior;F6F2
-sterling;00A3
-sterlingmonospace;FFE1
-strokelongoverlaycmb;0336
-strokeshortoverlaycmb;0335
-subset;2282
-subsetnotequal;228A
-subsetorequal;2286
-succeeds;227B
-suchthat;220B
-suhiragana;3059
-sukatakana;30B9
-sukatakanahalfwidth;FF7D
-sukunarabic;0652
-summation;2211
-sun;263C
-superset;2283
-supersetnotequal;228B
-supersetorequal;2287
-svsquare;33DC
-syouwaerasquare;337C
-t;0074
-tabengali;09A4
-tackdown;22A4
-tackleft;22A3
-tadeva;0924
-tagujarati;0AA4
-tagurmukhi;0A24
-taharabic;0637
-tahfinalarabic;FEC2
-tahinitialarabic;FEC3
-tahiragana;305F
-tahmedialarabic;FEC4
-taisyouerasquare;337D
-takatakana;30BF
-takatakanahalfwidth;FF80
-tatweelarabic;0640
-tau;03C4
-tav;05EA
-tavdages;FB4A
-tavdagesh;FB4A
-tavdageshhebrew;FB4A
-tavhebrew;05EA
-tbar;0167
-tbopomofo;310A
-tcaron;0165
-tccurl;02A8
-tcedilla;0163
-tcheharabic;0686
-tchehfinalarabic;FB7B
-tchehinitialarabic;FB7C
-tchehmedialarabic;FB7D
-tchehmeeminitialarabic;FB7C FEE4
-tcircle;24E3
-tcircumflexbelow;1E71
-tcommaaccent;0163
-tdieresis;1E97
-tdotaccent;1E6B
-tdotbelow;1E6D
-tecyrillic;0442
-tedescendercyrillic;04AD
-teharabic;062A
-tehfinalarabic;FE96
-tehhahinitialarabic;FCA2
-tehhahisolatedarabic;FC0C
-tehinitialarabic;FE97
-tehiragana;3066
-tehjeeminitialarabic;FCA1
-tehjeemisolatedarabic;FC0B
-tehmarbutaarabic;0629
-tehmarbutafinalarabic;FE94
-tehmedialarabic;FE98
-tehmeeminitialarabic;FCA4
-tehmeemisolatedarabic;FC0E
-tehnoonfinalarabic;FC73
-tekatakana;30C6
-tekatakanahalfwidth;FF83
-telephone;2121
-telephoneblack;260E
-telishagedolahebrew;05A0
-telishaqetanahebrew;05A9
-tencircle;2469
-tenideographicparen;3229
-tenparen;247D
-tenperiod;2491
-tenroman;2179
-tesh;02A7
-tet;05D8
-tetdagesh;FB38
-tetdageshhebrew;FB38
-tethebrew;05D8
-tetsecyrillic;04B5
-tevirhebrew;059B
-tevirlefthebrew;059B
-thabengali;09A5
-thadeva;0925
-thagujarati;0AA5
-thagurmukhi;0A25
-thalarabic;0630
-thalfinalarabic;FEAC
-thanthakhatlowleftthai;F898
-thanthakhatlowrightthai;F897
-thanthakhatthai;0E4C
-thanthakhatupperleftthai;F896
-theharabic;062B
-thehfinalarabic;FE9A
-thehinitialarabic;FE9B
-thehmedialarabic;FE9C
-thereexists;2203
-therefore;2234
-theta;03B8
-theta1;03D1
-thetasymbolgreek;03D1
-thieuthacirclekorean;3279
-thieuthaparenkorean;3219
-thieuthcirclekorean;326B
-thieuthkorean;314C
-thieuthparenkorean;320B
-thirteencircle;246C
-thirteenparen;2480
-thirteenperiod;2494
-thonangmonthothai;0E11
-thook;01AD
-thophuthaothai;0E12
-thorn;00FE
-thothahanthai;0E17
-thothanthai;0E10
-thothongthai;0E18
-thothungthai;0E16
-thousandcyrillic;0482
-thousandsseparatorarabic;066C
-thousandsseparatorpersian;066C
-three;0033
-threearabic;0663
-threebengali;09E9
-threecircle;2462
-threecircleinversesansserif;278C
-threedeva;0969
-threeeighths;215C
-threegujarati;0AE9
-threegurmukhi;0A69
-threehackarabic;0663
-threehangzhou;3023
-threeideographicparen;3222
-threeinferior;2083
-threemonospace;FF13
-threenumeratorbengali;09F6
-threeoldstyle;F733
-threeparen;2476
-threeperiod;248A
-threepersian;06F3
-threequarters;00BE
-threequartersemdash;F6DE
-threeroman;2172
-threesuperior;00B3
-threethai;0E53
-thzsquare;3394
-tihiragana;3061
-tikatakana;30C1
-tikatakanahalfwidth;FF81
-tikeutacirclekorean;3270
-tikeutaparenkorean;3210
-tikeutcirclekorean;3262
-tikeutkorean;3137
-tikeutparenkorean;3202
-tilde;02DC
-tildebelowcmb;0330
-tildecmb;0303
-tildecomb;0303
-tildedoublecmb;0360
-tildeoperator;223C
-tildeoverlaycmb;0334
-tildeverticalcmb;033E
-timescircle;2297
-tipehahebrew;0596
-tipehalefthebrew;0596
-tippigurmukhi;0A70
-titlocyrilliccmb;0483
-tiwnarmenian;057F
-tlinebelow;1E6F
-tmonospace;FF54
-toarmenian;0569
-tohiragana;3068
-tokatakana;30C8
-tokatakanahalfwidth;FF84
-tonebarextrahighmod;02E5
-tonebarextralowmod;02E9
-tonebarhighmod;02E6
-tonebarlowmod;02E8
-tonebarmidmod;02E7
-tonefive;01BD
-tonesix;0185
-tonetwo;01A8
-tonos;0384
-tonsquare;3327
-topatakthai;0E0F
-tortoiseshellbracketleft;3014
-tortoiseshellbracketleftsmall;FE5D
-tortoiseshellbracketleftvertical;FE39
-tortoiseshellbracketright;3015
-tortoiseshellbracketrightsmall;FE5E
-tortoiseshellbracketrightvertical;FE3A
-totaothai;0E15
-tpalatalhook;01AB
-tparen;24AF
-trademark;2122
-trademarksans;F8EA
-trademarkserif;F6DB
-tretroflexhook;0288
-triagdn;25BC
-triaglf;25C4
-triagrt;25BA
-triagup;25B2
-ts;02A6
-tsadi;05E6
-tsadidagesh;FB46
-tsadidageshhebrew;FB46
-tsadihebrew;05E6
-tsecyrillic;0446
-tsere;05B5
-tsere12;05B5
-tsere1e;05B5
-tsere2b;05B5
-tserehebrew;05B5
-tserenarrowhebrew;05B5
-tserequarterhebrew;05B5
-tserewidehebrew;05B5
-tshecyrillic;045B
-tsuperior;F6F3
-ttabengali;099F
-ttadeva;091F
-ttagujarati;0A9F
-ttagurmukhi;0A1F
-tteharabic;0679
-ttehfinalarabic;FB67
-ttehinitialarabic;FB68
-ttehmedialarabic;FB69
-tthabengali;09A0
-tthadeva;0920
-tthagujarati;0AA0
-tthagurmukhi;0A20
-tturned;0287
-tuhiragana;3064
-tukatakana;30C4
-tukatakanahalfwidth;FF82
-tusmallhiragana;3063
-tusmallkatakana;30C3
-tusmallkatakanahalfwidth;FF6F
-twelvecircle;246B
-twelveparen;247F
-twelveperiod;2493
-twelveroman;217B
-twentycircle;2473
-twentyhangzhou;5344
-twentyparen;2487
-twentyperiod;249B
-two;0032
-twoarabic;0662
-twobengali;09E8
-twocircle;2461
-twocircleinversesansserif;278B
-twodeva;0968
-twodotenleader;2025
-twodotleader;2025
-twodotleadervertical;FE30
-twogujarati;0AE8
-twogurmukhi;0A68
-twohackarabic;0662
-twohangzhou;3022
-twoideographicparen;3221
-twoinferior;2082
-twomonospace;FF12
-twonumeratorbengali;09F5
-twooldstyle;F732
-twoparen;2475
-twoperiod;2489
-twopersian;06F2
-tworoman;2171
-twostroke;01BB
-twosuperior;00B2
-twothai;0E52
-twothirds;2154
-u;0075
-uacute;00FA
-ubar;0289
-ubengali;0989
-ubopomofo;3128
-ubreve;016D
-ucaron;01D4
-ucircle;24E4
-ucircumflex;00FB
-ucircumflexbelow;1E77
-ucyrillic;0443
-udattadeva;0951
-udblacute;0171
-udblgrave;0215
-udeva;0909
-udieresis;00FC
-udieresisacute;01D8
-udieresisbelow;1E73
-udieresiscaron;01DA
-udieresiscyrillic;04F1
-udieresisgrave;01DC
-udieresismacron;01D6
-udotbelow;1EE5
-ugrave;00F9
-ugujarati;0A89
-ugurmukhi;0A09
-uhiragana;3046
-uhookabove;1EE7
-uhorn;01B0
-uhornacute;1EE9
-uhorndotbelow;1EF1
-uhorngrave;1EEB
-uhornhookabove;1EED
-uhorntilde;1EEF
-uhungarumlaut;0171
-uhungarumlautcyrillic;04F3
-uinvertedbreve;0217
-ukatakana;30A6
-ukatakanahalfwidth;FF73
-ukcyrillic;0479
-ukorean;315C
-umacron;016B
-umacroncyrillic;04EF
-umacrondieresis;1E7B
-umatragurmukhi;0A41
-umonospace;FF55
-underscore;005F
-underscoredbl;2017
-underscoremonospace;FF3F
-underscorevertical;FE33
-underscorewavy;FE4F
-union;222A
-universal;2200
-uogonek;0173
-uparen;24B0
-upblock;2580
-upperdothebrew;05C4
-upsilon;03C5
-upsilondieresis;03CB
-upsilondieresistonos;03B0
-upsilonlatin;028A
-upsilontonos;03CD
-uptackbelowcmb;031D
-uptackmod;02D4
-uragurmukhi;0A73
-uring;016F
-ushortcyrillic;045E
-usmallhiragana;3045
-usmallkatakana;30A5
-usmallkatakanahalfwidth;FF69
-ustraightcyrillic;04AF
-ustraightstrokecyrillic;04B1
-utilde;0169
-utildeacute;1E79
-utildebelow;1E75
-uubengali;098A
-uudeva;090A
-uugujarati;0A8A
-uugurmukhi;0A0A
-uumatragurmukhi;0A42
-uuvowelsignbengali;09C2
-uuvowelsigndeva;0942
-uuvowelsigngujarati;0AC2
-uvowelsignbengali;09C1
-uvowelsigndeva;0941
-uvowelsigngujarati;0AC1
-v;0076
-vadeva;0935
-vagujarati;0AB5
-vagurmukhi;0A35
-vakatakana;30F7
-vav;05D5
-vavdagesh;FB35
-vavdagesh65;FB35
-vavdageshhebrew;FB35
-vavhebrew;05D5
-vavholam;FB4B
-vavholamhebrew;FB4B
-vavvavhebrew;05F0
-vavyodhebrew;05F1
-vcircle;24E5
-vdotbelow;1E7F
-vecyrillic;0432
-veharabic;06A4
-vehfinalarabic;FB6B
-vehinitialarabic;FB6C
-vehmedialarabic;FB6D
-vekatakana;30F9
-venus;2640
-verticalbar;007C
-verticallineabovecmb;030D
-verticallinebelowcmb;0329
-verticallinelowmod;02CC
-verticallinemod;02C8
-vewarmenian;057E
-vhook;028B
-vikatakana;30F8
-viramabengali;09CD
-viramadeva;094D
-viramagujarati;0ACD
-visargabengali;0983
-visargadeva;0903
-visargagujarati;0A83
-vmonospace;FF56
-voarmenian;0578
-voicediterationhiragana;309E
-voicediterationkatakana;30FE
-voicedmarkkana;309B
-voicedmarkkanahalfwidth;FF9E
-vokatakana;30FA
-vparen;24B1
-vtilde;1E7D
-vturned;028C
-vuhiragana;3094
-vukatakana;30F4
-w;0077
-wacute;1E83
-waekorean;3159
-wahiragana;308F
-wakatakana;30EF
-wakatakanahalfwidth;FF9C
-wakorean;3158
-wasmallhiragana;308E
-wasmallkatakana;30EE
-wattosquare;3357
-wavedash;301C
-wavyunderscorevertical;FE34
-wawarabic;0648
-wawfinalarabic;FEEE
-wawhamzaabovearabic;0624
-wawhamzaabovefinalarabic;FE86
-wbsquare;33DD
-wcircle;24E6
-wcircumflex;0175
-wdieresis;1E85
-wdotaccent;1E87
-wdotbelow;1E89
-wehiragana;3091
-weierstrass;2118
-wekatakana;30F1
-wekorean;315E
-weokorean;315D
-wgrave;1E81
-whitebullet;25E6
-whitecircle;25CB
-whitecircleinverse;25D9
-whitecornerbracketleft;300E
-whitecornerbracketleftvertical;FE43
-whitecornerbracketright;300F
-whitecornerbracketrightvertical;FE44
-whitediamond;25C7
-whitediamondcontainingblacksmalldiamond;25C8
-whitedownpointingsmalltriangle;25BF
-whitedownpointingtriangle;25BD
-whiteleftpointingsmalltriangle;25C3
-whiteleftpointingtriangle;25C1
-whitelenticularbracketleft;3016
-whitelenticularbracketright;3017
-whiterightpointingsmalltriangle;25B9
-whiterightpointingtriangle;25B7
-whitesmallsquare;25AB
-whitesmilingface;263A
-whitesquare;25A1
-whitestar;2606
-whitetelephone;260F
-whitetortoiseshellbracketleft;3018
-whitetortoiseshellbracketright;3019
-whiteuppointingsmalltriangle;25B5
-whiteuppointingtriangle;25B3
-wihiragana;3090
-wikatakana;30F0
-wikorean;315F
-wmonospace;FF57
-wohiragana;3092
-wokatakana;30F2
-wokatakanahalfwidth;FF66
-won;20A9
-wonmonospace;FFE6
-wowaenthai;0E27
-wparen;24B2
-wring;1E98
-wsuperior;02B7
-wturned;028D
-wynn;01BF
-x;0078
-xabovecmb;033D
-xbopomofo;3112
-xcircle;24E7
-xdieresis;1E8D
-xdotaccent;1E8B
-xeharmenian;056D
-xi;03BE
-xmonospace;FF58
-xparen;24B3
-xsuperior;02E3
-y;0079
-yaadosquare;334E
-yabengali;09AF
-yacute;00FD
-yadeva;092F
-yaekorean;3152
-yagujarati;0AAF
-yagurmukhi;0A2F
-yahiragana;3084
-yakatakana;30E4
-yakatakanahalfwidth;FF94
-yakorean;3151
-yamakkanthai;0E4E
-yasmallhiragana;3083
-yasmallkatakana;30E3
-yasmallkatakanahalfwidth;FF6C
-yatcyrillic;0463
-ycircle;24E8
-ycircumflex;0177
-ydieresis;00FF
-ydotaccent;1E8F
-ydotbelow;1EF5
-yeharabic;064A
-yehbarreearabic;06D2
-yehbarreefinalarabic;FBAF
-yehfinalarabic;FEF2
-yehhamzaabovearabic;0626
-yehhamzaabovefinalarabic;FE8A
-yehhamzaaboveinitialarabic;FE8B
-yehhamzaabovemedialarabic;FE8C
-yehinitialarabic;FEF3
-yehmedialarabic;FEF4
-yehmeeminitialarabic;FCDD
-yehmeemisolatedarabic;FC58
-yehnoonfinalarabic;FC94
-yehthreedotsbelowarabic;06D1
-yekorean;3156
-yen;00A5
-yenmonospace;FFE5
-yeokorean;3155
-yeorinhieuhkorean;3186
-yerahbenyomohebrew;05AA
-yerahbenyomolefthebrew;05AA
-yericyrillic;044B
-yerudieresiscyrillic;04F9
-yesieungkorean;3181
-yesieungpansioskorean;3183
-yesieungsioskorean;3182
-yetivhebrew;059A
-ygrave;1EF3
-yhook;01B4
-yhookabove;1EF7
-yiarmenian;0575
-yicyrillic;0457
-yikorean;3162
-yinyang;262F
-yiwnarmenian;0582
-ymonospace;FF59
-yod;05D9
-yoddagesh;FB39
-yoddageshhebrew;FB39
-yodhebrew;05D9
-yodyodhebrew;05F2
-yodyodpatahhebrew;FB1F
-yohiragana;3088
-yoikorean;3189
-yokatakana;30E8
-yokatakanahalfwidth;FF96
-yokorean;315B
-yosmallhiragana;3087
-yosmallkatakana;30E7
-yosmallkatakanahalfwidth;FF6E
-yotgreek;03F3
-yoyaekorean;3188
-yoyakorean;3187
-yoyakthai;0E22
-yoyingthai;0E0D
-yparen;24B4
-ypogegrammeni;037A
-ypogegrammenigreekcmb;0345
-yr;01A6
-yring;1E99
-ysuperior;02B8
-ytilde;1EF9
-yturned;028E
-yuhiragana;3086
-yuikorean;318C
-yukatakana;30E6
-yukatakanahalfwidth;FF95
-yukorean;3160
-yusbigcyrillic;046B
-yusbigiotifiedcyrillic;046D
-yuslittlecyrillic;0467
-yuslittleiotifiedcyrillic;0469
-yusmallhiragana;3085
-yusmallkatakana;30E5
-yusmallkatakanahalfwidth;FF6D
-yuyekorean;318B
-yuyeokorean;318A
-yyabengali;09DF
-yyadeva;095F
-z;007A
-zaarmenian;0566
-zacute;017A
-zadeva;095B
-zagurmukhi;0A5B
-zaharabic;0638
-zahfinalarabic;FEC6
-zahinitialarabic;FEC7
-zahiragana;3056
-zahmedialarabic;FEC8
-zainarabic;0632
-zainfinalarabic;FEB0
-zakatakana;30B6
-zaqefgadolhebrew;0595
-zaqefqatanhebrew;0594
-zarqahebrew;0598
-zayin;05D6
-zayindagesh;FB36
-zayindageshhebrew;FB36
-zayinhebrew;05D6
-zbopomofo;3117
-zcaron;017E
-zcircle;24E9
-zcircumflex;1E91
-zcurl;0291
-zdot;017C
-zdotaccent;017C
-zdotbelow;1E93
-zecyrillic;0437
-zedescendercyrillic;0499
-zedieresiscyrillic;04DF
-zehiragana;305C
-zekatakana;30BC
-zero;0030
-zeroarabic;0660
-zerobengali;09E6
-zerodeva;0966
-zerogujarati;0AE6
-zerogurmukhi;0A66
-zerohackarabic;0660
-zeroinferior;2080
-zeromonospace;FF10
-zerooldstyle;F730
-zeropersian;06F0
-zerosuperior;2070
-zerothai;0E50
-zerowidthjoiner;FEFF
-zerowidthnonjoiner;200C
-zerowidthspace;200B
-zeta;03B6
-zhbopomofo;3113
-zhearmenian;056A
-zhebrevecyrillic;04C2
-zhecyrillic;0436
-zhedescendercyrillic;0497
-zhedieresiscyrillic;04DD
-zihiragana;3058
-zikatakana;30B8
-zinorhebrew;05AE
-zlinebelow;1E95
-zmonospace;FF5A
-zohiragana;305E
-zokatakana;30BE
-zparen;24B5
-zretroflexhook;0290
-zstroke;01B6
-zuhiragana;305A
-zukatakana;30BA
-# END
-"""
-
-
-_aglfnText = """\
-# -----------------------------------------------------------
-# Copyright 2002-2019 Adobe (http://www.adobe.com/).
-#
-# Redistribution and use in source and binary forms, with or
-# without modification, are permitted provided that the
-# following conditions are met:
-#
-# Redistributions of source code must retain the above
-# copyright notice, this list of conditions and the following
-# disclaimer.
-#
-# Redistributions in binary form must reproduce the above
-# copyright notice, this list of conditions and the following
-# disclaimer in the documentation and/or other materials
-# provided with the distribution.
-#
-# Neither the name of Adobe nor the names of its contributors
-# may be used to endorse or promote products derived from this
-# software without specific prior written permission.
-#
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
-# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
-# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
-# MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
-# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
-# NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
-# HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
-# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-# -----------------------------------------------------------
-# Name: Adobe Glyph List For New Fonts
-# Table version: 1.7
-# Date: November 6, 2008
-# URL: https://github.com/adobe-type-tools/agl-aglfn
-#
-# Description:
-#
-# AGLFN (Adobe Glyph List For New Fonts) provides a list of base glyph
-# names that are recommended for new fonts, which are compatible with
-# the AGL (Adobe Glyph List) Specification, and which should be used
-# as described in Section 6 of that document. AGLFN comprises the set
-# of glyph names from AGL that map via the AGL Specification rules to
-# the semantically correct UV (Unicode Value). For example, "Asmall"
-# is omitted because AGL maps this glyph name to the PUA (Private Use
-# Area) value U+F761, rather than to the UV that maps from the glyph
-# name "A." Also omitted is "ffi," because AGL maps this to the
-# Alphabetic Presentation Forms value U+FB03, rather than decomposing
-# it into the following sequence of three UVs: U+0066, U+0066, and
-# U+0069. The name "arrowvertex" has been omitted because this glyph
-# now has a real UV, and AGL is now incorrect in mapping it to the PUA
-# value U+F8E6. If you do not find an appropriate name for your glyph
-# in this list, then please refer to Section 6 of the AGL
-# Specification.
-#
-# Format: three semicolon-delimited fields:
-# (1) Standard UV or CUS UV--four uppercase hexadecimal digits
-# (2) Glyph name--upper/lowercase letters and digits
-# (3) Character names: Unicode character names for standard UVs, and
-# descriptive names for CUS UVs--uppercase letters, hyphen, and
-# space
-#
-# The records are sorted by glyph name in increasing ASCII order,
-# entries with the same glyph name are sorted in decreasing priority
-# order, the UVs and Unicode character names are provided for
-# convenience, lines starting with "#" are comments, and blank lines
-# should be ignored.
-#
-# Revision History:
-#
-# 1.7 [6 November 2008]
-# - Reverted to the original 1.4 and earlier mappings for Delta,
-# Omega, and mu.
-# - Removed mappings for "afii" names. These should now be assigned
-# "uni" names.
-# - Removed mappings for "commaaccent" names. These should now be
-# assigned "uni" names.
-#
-# 1.6 [30 January 2006]
-# - Completed work intended in 1.5.
-#
-# 1.5 [23 November 2005]
-# - Removed duplicated block at end of file.
-# - Changed mappings:
-# 2206;Delta;INCREMENT changed to 0394;Delta;GREEK CAPITAL LETTER DELTA
-# 2126;Omega;OHM SIGN changed to 03A9;Omega;GREEK CAPITAL LETTER OMEGA
-# 03BC;mu;MICRO SIGN changed to 03BC;mu;GREEK SMALL LETTER MU
-# - Corrected statement above about why "ffi" is omitted.
-#
-# 1.4 [24 September 2003]
-# - Changed version to 1.4, to avoid confusion with the AGL 1.3.
-# - Fixed spelling errors in the header.
-# - Fully removed "arrowvertex," as it is mapped only to a PUA Unicode
-# value in some fonts.
-#
-# 1.1 [17 April 2003]
-# - Renamed [Tt]cedilla back to [Tt]commaaccent.
-#
-# 1.0 [31 January 2003]
-# - Original version.
-# - Derived from the AGLv1.2 by:
-# removing the PUA area codes;
-# removing duplicate Unicode mappings; and
-# renaming "tcommaaccent" to "tcedilla" and "Tcommaaccent" to "Tcedilla"
-#
-0041;A;LATIN CAPITAL LETTER A
-00C6;AE;LATIN CAPITAL LETTER AE
-01FC;AEacute;LATIN CAPITAL LETTER AE WITH ACUTE
-00C1;Aacute;LATIN CAPITAL LETTER A WITH ACUTE
-0102;Abreve;LATIN CAPITAL LETTER A WITH BREVE
-00C2;Acircumflex;LATIN CAPITAL LETTER A WITH CIRCUMFLEX
-00C4;Adieresis;LATIN CAPITAL LETTER A WITH DIAERESIS
-00C0;Agrave;LATIN CAPITAL LETTER A WITH GRAVE
-0391;Alpha;GREEK CAPITAL LETTER ALPHA
-0386;Alphatonos;GREEK CAPITAL LETTER ALPHA WITH TONOS
-0100;Amacron;LATIN CAPITAL LETTER A WITH MACRON
-0104;Aogonek;LATIN CAPITAL LETTER A WITH OGONEK
-00C5;Aring;LATIN CAPITAL LETTER A WITH RING ABOVE
-01FA;Aringacute;LATIN CAPITAL LETTER A WITH RING ABOVE AND ACUTE
-00C3;Atilde;LATIN CAPITAL LETTER A WITH TILDE
-0042;B;LATIN CAPITAL LETTER B
-0392;Beta;GREEK CAPITAL LETTER BETA
-0043;C;LATIN CAPITAL LETTER C
-0106;Cacute;LATIN CAPITAL LETTER C WITH ACUTE
-010C;Ccaron;LATIN CAPITAL LETTER C WITH CARON
-00C7;Ccedilla;LATIN CAPITAL LETTER C WITH CEDILLA
-0108;Ccircumflex;LATIN CAPITAL LETTER C WITH CIRCUMFLEX
-010A;Cdotaccent;LATIN CAPITAL LETTER C WITH DOT ABOVE
-03A7;Chi;GREEK CAPITAL LETTER CHI
-0044;D;LATIN CAPITAL LETTER D
-010E;Dcaron;LATIN CAPITAL LETTER D WITH CARON
-0110;Dcroat;LATIN CAPITAL LETTER D WITH STROKE
-2206;Delta;INCREMENT
-0045;E;LATIN CAPITAL LETTER E
-00C9;Eacute;LATIN CAPITAL LETTER E WITH ACUTE
-0114;Ebreve;LATIN CAPITAL LETTER E WITH BREVE
-011A;Ecaron;LATIN CAPITAL LETTER E WITH CARON
-00CA;Ecircumflex;LATIN CAPITAL LETTER E WITH CIRCUMFLEX
-00CB;Edieresis;LATIN CAPITAL LETTER E WITH DIAERESIS
-0116;Edotaccent;LATIN CAPITAL LETTER E WITH DOT ABOVE
-00C8;Egrave;LATIN CAPITAL LETTER E WITH GRAVE
-0112;Emacron;LATIN CAPITAL LETTER E WITH MACRON
-014A;Eng;LATIN CAPITAL LETTER ENG
-0118;Eogonek;LATIN CAPITAL LETTER E WITH OGONEK
-0395;Epsilon;GREEK CAPITAL LETTER EPSILON
-0388;Epsilontonos;GREEK CAPITAL LETTER EPSILON WITH TONOS
-0397;Eta;GREEK CAPITAL LETTER ETA
-0389;Etatonos;GREEK CAPITAL LETTER ETA WITH TONOS
-00D0;Eth;LATIN CAPITAL LETTER ETH
-20AC;Euro;EURO SIGN
-0046;F;LATIN CAPITAL LETTER F
-0047;G;LATIN CAPITAL LETTER G
-0393;Gamma;GREEK CAPITAL LETTER GAMMA
-011E;Gbreve;LATIN CAPITAL LETTER G WITH BREVE
-01E6;Gcaron;LATIN CAPITAL LETTER G WITH CARON
-011C;Gcircumflex;LATIN CAPITAL LETTER G WITH CIRCUMFLEX
-0120;Gdotaccent;LATIN CAPITAL LETTER G WITH DOT ABOVE
-0048;H;LATIN CAPITAL LETTER H
-25CF;H18533;BLACK CIRCLE
-25AA;H18543;BLACK SMALL SQUARE
-25AB;H18551;WHITE SMALL SQUARE
-25A1;H22073;WHITE SQUARE
-0126;Hbar;LATIN CAPITAL LETTER H WITH STROKE
-0124;Hcircumflex;LATIN CAPITAL LETTER H WITH CIRCUMFLEX
-0049;I;LATIN CAPITAL LETTER I
-0132;IJ;LATIN CAPITAL LIGATURE IJ
-00CD;Iacute;LATIN CAPITAL LETTER I WITH ACUTE
-012C;Ibreve;LATIN CAPITAL LETTER I WITH BREVE
-00CE;Icircumflex;LATIN CAPITAL LETTER I WITH CIRCUMFLEX
-00CF;Idieresis;LATIN CAPITAL LETTER I WITH DIAERESIS
-0130;Idotaccent;LATIN CAPITAL LETTER I WITH DOT ABOVE
-2111;Ifraktur;BLACK-LETTER CAPITAL I
-00CC;Igrave;LATIN CAPITAL LETTER I WITH GRAVE
-012A;Imacron;LATIN CAPITAL LETTER I WITH MACRON
-012E;Iogonek;LATIN CAPITAL LETTER I WITH OGONEK
-0399;Iota;GREEK CAPITAL LETTER IOTA
-03AA;Iotadieresis;GREEK CAPITAL LETTER IOTA WITH DIALYTIKA
-038A;Iotatonos;GREEK CAPITAL LETTER IOTA WITH TONOS
-0128;Itilde;LATIN CAPITAL LETTER I WITH TILDE
-004A;J;LATIN CAPITAL LETTER J
-0134;Jcircumflex;LATIN CAPITAL LETTER J WITH CIRCUMFLEX
-004B;K;LATIN CAPITAL LETTER K
-039A;Kappa;GREEK CAPITAL LETTER KAPPA
-004C;L;LATIN CAPITAL LETTER L
-0139;Lacute;LATIN CAPITAL LETTER L WITH ACUTE
-039B;Lambda;GREEK CAPITAL LETTER LAMDA
-013D;Lcaron;LATIN CAPITAL LETTER L WITH CARON
-013F;Ldot;LATIN CAPITAL LETTER L WITH MIDDLE DOT
-0141;Lslash;LATIN CAPITAL LETTER L WITH STROKE
-004D;M;LATIN CAPITAL LETTER M
-039C;Mu;GREEK CAPITAL LETTER MU
-004E;N;LATIN CAPITAL LETTER N
-0143;Nacute;LATIN CAPITAL LETTER N WITH ACUTE
-0147;Ncaron;LATIN CAPITAL LETTER N WITH CARON
-00D1;Ntilde;LATIN CAPITAL LETTER N WITH TILDE
-039D;Nu;GREEK CAPITAL LETTER NU
-004F;O;LATIN CAPITAL LETTER O
-0152;OE;LATIN CAPITAL LIGATURE OE
-00D3;Oacute;LATIN CAPITAL LETTER O WITH ACUTE
-014E;Obreve;LATIN CAPITAL LETTER O WITH BREVE
-00D4;Ocircumflex;LATIN CAPITAL LETTER O WITH CIRCUMFLEX
-00D6;Odieresis;LATIN CAPITAL LETTER O WITH DIAERESIS
-00D2;Ograve;LATIN CAPITAL LETTER O WITH GRAVE
-01A0;Ohorn;LATIN CAPITAL LETTER O WITH HORN
-0150;Ohungarumlaut;LATIN CAPITAL LETTER O WITH DOUBLE ACUTE
-014C;Omacron;LATIN CAPITAL LETTER O WITH MACRON
-2126;Omega;OHM SIGN
-038F;Omegatonos;GREEK CAPITAL LETTER OMEGA WITH TONOS
-039F;Omicron;GREEK CAPITAL LETTER OMICRON
-038C;Omicrontonos;GREEK CAPITAL LETTER OMICRON WITH TONOS
-00D8;Oslash;LATIN CAPITAL LETTER O WITH STROKE
-01FE;Oslashacute;LATIN CAPITAL LETTER O WITH STROKE AND ACUTE
-00D5;Otilde;LATIN CAPITAL LETTER O WITH TILDE
-0050;P;LATIN CAPITAL LETTER P
-03A6;Phi;GREEK CAPITAL LETTER PHI
-03A0;Pi;GREEK CAPITAL LETTER PI
-03A8;Psi;GREEK CAPITAL LETTER PSI
-0051;Q;LATIN CAPITAL LETTER Q
-0052;R;LATIN CAPITAL LETTER R
-0154;Racute;LATIN CAPITAL LETTER R WITH ACUTE
-0158;Rcaron;LATIN CAPITAL LETTER R WITH CARON
-211C;Rfraktur;BLACK-LETTER CAPITAL R
-03A1;Rho;GREEK CAPITAL LETTER RHO
-0053;S;LATIN CAPITAL LETTER S
-250C;SF010000;BOX DRAWINGS LIGHT DOWN AND RIGHT
-2514;SF020000;BOX DRAWINGS LIGHT UP AND RIGHT
-2510;SF030000;BOX DRAWINGS LIGHT DOWN AND LEFT
-2518;SF040000;BOX DRAWINGS LIGHT UP AND LEFT
-253C;SF050000;BOX DRAWINGS LIGHT VERTICAL AND HORIZONTAL
-252C;SF060000;BOX DRAWINGS LIGHT DOWN AND HORIZONTAL
-2534;SF070000;BOX DRAWINGS LIGHT UP AND HORIZONTAL
-251C;SF080000;BOX DRAWINGS LIGHT VERTICAL AND RIGHT
-2524;SF090000;BOX DRAWINGS LIGHT VERTICAL AND LEFT
-2500;SF100000;BOX DRAWINGS LIGHT HORIZONTAL
-2502;SF110000;BOX DRAWINGS LIGHT VERTICAL
-2561;SF190000;BOX DRAWINGS VERTICAL SINGLE AND LEFT DOUBLE
-2562;SF200000;BOX DRAWINGS VERTICAL DOUBLE AND LEFT SINGLE
-2556;SF210000;BOX DRAWINGS DOWN DOUBLE AND LEFT SINGLE
-2555;SF220000;BOX DRAWINGS DOWN SINGLE AND LEFT DOUBLE
-2563;SF230000;BOX DRAWINGS DOUBLE VERTICAL AND LEFT
-2551;SF240000;BOX DRAWINGS DOUBLE VERTICAL
-2557;SF250000;BOX DRAWINGS DOUBLE DOWN AND LEFT
-255D;SF260000;BOX DRAWINGS DOUBLE UP AND LEFT
-255C;SF270000;BOX DRAWINGS UP DOUBLE AND LEFT SINGLE
-255B;SF280000;BOX DRAWINGS UP SINGLE AND LEFT DOUBLE
-255E;SF360000;BOX DRAWINGS VERTICAL SINGLE AND RIGHT DOUBLE
-255F;SF370000;BOX DRAWINGS VERTICAL DOUBLE AND RIGHT SINGLE
-255A;SF380000;BOX DRAWINGS DOUBLE UP AND RIGHT
-2554;SF390000;BOX DRAWINGS DOUBLE DOWN AND RIGHT
-2569;SF400000;BOX DRAWINGS DOUBLE UP AND HORIZONTAL
-2566;SF410000;BOX DRAWINGS DOUBLE DOWN AND HORIZONTAL
-2560;SF420000;BOX DRAWINGS DOUBLE VERTICAL AND RIGHT
-2550;SF430000;BOX DRAWINGS DOUBLE HORIZONTAL
-256C;SF440000;BOX DRAWINGS DOUBLE VERTICAL AND HORIZONTAL
-2567;SF450000;BOX DRAWINGS UP SINGLE AND HORIZONTAL DOUBLE
-2568;SF460000;BOX DRAWINGS UP DOUBLE AND HORIZONTAL SINGLE
-2564;SF470000;BOX DRAWINGS DOWN SINGLE AND HORIZONTAL DOUBLE
-2565;SF480000;BOX DRAWINGS DOWN DOUBLE AND HORIZONTAL SINGLE
-2559;SF490000;BOX DRAWINGS UP DOUBLE AND RIGHT SINGLE
-2558;SF500000;BOX DRAWINGS UP SINGLE AND RIGHT DOUBLE
-2552;SF510000;BOX DRAWINGS DOWN SINGLE AND RIGHT DOUBLE
-2553;SF520000;BOX DRAWINGS DOWN DOUBLE AND RIGHT SINGLE
-256B;SF530000;BOX DRAWINGS VERTICAL DOUBLE AND HORIZONTAL SINGLE
-256A;SF540000;BOX DRAWINGS VERTICAL SINGLE AND HORIZONTAL DOUBLE
-015A;Sacute;LATIN CAPITAL LETTER S WITH ACUTE
-0160;Scaron;LATIN CAPITAL LETTER S WITH CARON
-015E;Scedilla;LATIN CAPITAL LETTER S WITH CEDILLA
-015C;Scircumflex;LATIN CAPITAL LETTER S WITH CIRCUMFLEX
-03A3;Sigma;GREEK CAPITAL LETTER SIGMA
-0054;T;LATIN CAPITAL LETTER T
-03A4;Tau;GREEK CAPITAL LETTER TAU
-0166;Tbar;LATIN CAPITAL LETTER T WITH STROKE
-0164;Tcaron;LATIN CAPITAL LETTER T WITH CARON
-0398;Theta;GREEK CAPITAL LETTER THETA
-00DE;Thorn;LATIN CAPITAL LETTER THORN
-0055;U;LATIN CAPITAL LETTER U
-00DA;Uacute;LATIN CAPITAL LETTER U WITH ACUTE
-016C;Ubreve;LATIN CAPITAL LETTER U WITH BREVE
-00DB;Ucircumflex;LATIN CAPITAL LETTER U WITH CIRCUMFLEX
-00DC;Udieresis;LATIN CAPITAL LETTER U WITH DIAERESIS
-00D9;Ugrave;LATIN CAPITAL LETTER U WITH GRAVE
-01AF;Uhorn;LATIN CAPITAL LETTER U WITH HORN
-0170;Uhungarumlaut;LATIN CAPITAL LETTER U WITH DOUBLE ACUTE
-016A;Umacron;LATIN CAPITAL LETTER U WITH MACRON
-0172;Uogonek;LATIN CAPITAL LETTER U WITH OGONEK
-03A5;Upsilon;GREEK CAPITAL LETTER UPSILON
-03D2;Upsilon1;GREEK UPSILON WITH HOOK SYMBOL
-03AB;Upsilondieresis;GREEK CAPITAL LETTER UPSILON WITH DIALYTIKA
-038E;Upsilontonos;GREEK CAPITAL LETTER UPSILON WITH TONOS
-016E;Uring;LATIN CAPITAL LETTER U WITH RING ABOVE
-0168;Utilde;LATIN CAPITAL LETTER U WITH TILDE
-0056;V;LATIN CAPITAL LETTER V
-0057;W;LATIN CAPITAL LETTER W
-1E82;Wacute;LATIN CAPITAL LETTER W WITH ACUTE
-0174;Wcircumflex;LATIN CAPITAL LETTER W WITH CIRCUMFLEX
-1E84;Wdieresis;LATIN CAPITAL LETTER W WITH DIAERESIS
-1E80;Wgrave;LATIN CAPITAL LETTER W WITH GRAVE
-0058;X;LATIN CAPITAL LETTER X
-039E;Xi;GREEK CAPITAL LETTER XI
-0059;Y;LATIN CAPITAL LETTER Y
-00DD;Yacute;LATIN CAPITAL LETTER Y WITH ACUTE
-0176;Ycircumflex;LATIN CAPITAL LETTER Y WITH CIRCUMFLEX
-0178;Ydieresis;LATIN CAPITAL LETTER Y WITH DIAERESIS
-1EF2;Ygrave;LATIN CAPITAL LETTER Y WITH GRAVE
-005A;Z;LATIN CAPITAL LETTER Z
-0179;Zacute;LATIN CAPITAL LETTER Z WITH ACUTE
-017D;Zcaron;LATIN CAPITAL LETTER Z WITH CARON
-017B;Zdotaccent;LATIN CAPITAL LETTER Z WITH DOT ABOVE
-0396;Zeta;GREEK CAPITAL LETTER ZETA
-0061;a;LATIN SMALL LETTER A
-00E1;aacute;LATIN SMALL LETTER A WITH ACUTE
-0103;abreve;LATIN SMALL LETTER A WITH BREVE
-00E2;acircumflex;LATIN SMALL LETTER A WITH CIRCUMFLEX
-00B4;acute;ACUTE ACCENT
-0301;acutecomb;COMBINING ACUTE ACCENT
-00E4;adieresis;LATIN SMALL LETTER A WITH DIAERESIS
-00E6;ae;LATIN SMALL LETTER AE
-01FD;aeacute;LATIN SMALL LETTER AE WITH ACUTE
-00E0;agrave;LATIN SMALL LETTER A WITH GRAVE
-2135;aleph;ALEF SYMBOL
-03B1;alpha;GREEK SMALL LETTER ALPHA
-03AC;alphatonos;GREEK SMALL LETTER ALPHA WITH TONOS
-0101;amacron;LATIN SMALL LETTER A WITH MACRON
-0026;ampersand;AMPERSAND
-2220;angle;ANGLE
-2329;angleleft;LEFT-POINTING ANGLE BRACKET
-232A;angleright;RIGHT-POINTING ANGLE BRACKET
-0387;anoteleia;GREEK ANO TELEIA
-0105;aogonek;LATIN SMALL LETTER A WITH OGONEK
-2248;approxequal;ALMOST EQUAL TO
-00E5;aring;LATIN SMALL LETTER A WITH RING ABOVE
-01FB;aringacute;LATIN SMALL LETTER A WITH RING ABOVE AND ACUTE
-2194;arrowboth;LEFT RIGHT ARROW
-21D4;arrowdblboth;LEFT RIGHT DOUBLE ARROW
-21D3;arrowdbldown;DOWNWARDS DOUBLE ARROW
-21D0;arrowdblleft;LEFTWARDS DOUBLE ARROW
-21D2;arrowdblright;RIGHTWARDS DOUBLE ARROW
-21D1;arrowdblup;UPWARDS DOUBLE ARROW
-2193;arrowdown;DOWNWARDS ARROW
-2190;arrowleft;LEFTWARDS ARROW
-2192;arrowright;RIGHTWARDS ARROW
-2191;arrowup;UPWARDS ARROW
-2195;arrowupdn;UP DOWN ARROW
-21A8;arrowupdnbse;UP DOWN ARROW WITH BASE
-005E;asciicircum;CIRCUMFLEX ACCENT
-007E;asciitilde;TILDE
-002A;asterisk;ASTERISK
-2217;asteriskmath;ASTERISK OPERATOR
-0040;at;COMMERCIAL AT
-00E3;atilde;LATIN SMALL LETTER A WITH TILDE
-0062;b;LATIN SMALL LETTER B
-005C;backslash;REVERSE SOLIDUS
-007C;bar;VERTICAL LINE
-03B2;beta;GREEK SMALL LETTER BETA
-2588;block;FULL BLOCK
-007B;braceleft;LEFT CURLY BRACKET
-007D;braceright;RIGHT CURLY BRACKET
-005B;bracketleft;LEFT SQUARE BRACKET
-005D;bracketright;RIGHT SQUARE BRACKET
-02D8;breve;BREVE
-00A6;brokenbar;BROKEN BAR
-2022;bullet;BULLET
-0063;c;LATIN SMALL LETTER C
-0107;cacute;LATIN SMALL LETTER C WITH ACUTE
-02C7;caron;CARON
-21B5;carriagereturn;DOWNWARDS ARROW WITH CORNER LEFTWARDS
-010D;ccaron;LATIN SMALL LETTER C WITH CARON
-00E7;ccedilla;LATIN SMALL LETTER C WITH CEDILLA
-0109;ccircumflex;LATIN SMALL LETTER C WITH CIRCUMFLEX
-010B;cdotaccent;LATIN SMALL LETTER C WITH DOT ABOVE
-00B8;cedilla;CEDILLA
-00A2;cent;CENT SIGN
-03C7;chi;GREEK SMALL LETTER CHI
-25CB;circle;WHITE CIRCLE
-2297;circlemultiply;CIRCLED TIMES
-2295;circleplus;CIRCLED PLUS
-02C6;circumflex;MODIFIER LETTER CIRCUMFLEX ACCENT
-2663;club;BLACK CLUB SUIT
-003A;colon;COLON
-20A1;colonmonetary;COLON SIGN
-002C;comma;COMMA
-2245;congruent;APPROXIMATELY EQUAL TO
-00A9;copyright;COPYRIGHT SIGN
-00A4;currency;CURRENCY SIGN
-0064;d;LATIN SMALL LETTER D
-2020;dagger;DAGGER
-2021;daggerdbl;DOUBLE DAGGER
-010F;dcaron;LATIN SMALL LETTER D WITH CARON
-0111;dcroat;LATIN SMALL LETTER D WITH STROKE
-00B0;degree;DEGREE SIGN
-03B4;delta;GREEK SMALL LETTER DELTA
-2666;diamond;BLACK DIAMOND SUIT
-00A8;dieresis;DIAERESIS
-0385;dieresistonos;GREEK DIALYTIKA TONOS
-00F7;divide;DIVISION SIGN
-2593;dkshade;DARK SHADE
-2584;dnblock;LOWER HALF BLOCK
-0024;dollar;DOLLAR SIGN
-20AB;dong;DONG SIGN
-02D9;dotaccent;DOT ABOVE
-0323;dotbelowcomb;COMBINING DOT BELOW
-0131;dotlessi;LATIN SMALL LETTER DOTLESS I
-22C5;dotmath;DOT OPERATOR
-0065;e;LATIN SMALL LETTER E
-00E9;eacute;LATIN SMALL LETTER E WITH ACUTE
-0115;ebreve;LATIN SMALL LETTER E WITH BREVE
-011B;ecaron;LATIN SMALL LETTER E WITH CARON
-00EA;ecircumflex;LATIN SMALL LETTER E WITH CIRCUMFLEX
-00EB;edieresis;LATIN SMALL LETTER E WITH DIAERESIS
-0117;edotaccent;LATIN SMALL LETTER E WITH DOT ABOVE
-00E8;egrave;LATIN SMALL LETTER E WITH GRAVE
-0038;eight;DIGIT EIGHT
-2208;element;ELEMENT OF
-2026;ellipsis;HORIZONTAL ELLIPSIS
-0113;emacron;LATIN SMALL LETTER E WITH MACRON
-2014;emdash;EM DASH
-2205;emptyset;EMPTY SET
-2013;endash;EN DASH
-014B;eng;LATIN SMALL LETTER ENG
-0119;eogonek;LATIN SMALL LETTER E WITH OGONEK
-03B5;epsilon;GREEK SMALL LETTER EPSILON
-03AD;epsilontonos;GREEK SMALL LETTER EPSILON WITH TONOS
-003D;equal;EQUALS SIGN
-2261;equivalence;IDENTICAL TO
-212E;estimated;ESTIMATED SYMBOL
-03B7;eta;GREEK SMALL LETTER ETA
-03AE;etatonos;GREEK SMALL LETTER ETA WITH TONOS
-00F0;eth;LATIN SMALL LETTER ETH
-0021;exclam;EXCLAMATION MARK
-203C;exclamdbl;DOUBLE EXCLAMATION MARK
-00A1;exclamdown;INVERTED EXCLAMATION MARK
-2203;existential;THERE EXISTS
-0066;f;LATIN SMALL LETTER F
-2640;female;FEMALE SIGN
-2012;figuredash;FIGURE DASH
-25A0;filledbox;BLACK SQUARE
-25AC;filledrect;BLACK RECTANGLE
-0035;five;DIGIT FIVE
-215D;fiveeighths;VULGAR FRACTION FIVE EIGHTHS
-0192;florin;LATIN SMALL LETTER F WITH HOOK
-0034;four;DIGIT FOUR
-2044;fraction;FRACTION SLASH
-20A3;franc;FRENCH FRANC SIGN
-0067;g;LATIN SMALL LETTER G
-03B3;gamma;GREEK SMALL LETTER GAMMA
-011F;gbreve;LATIN SMALL LETTER G WITH BREVE
-01E7;gcaron;LATIN SMALL LETTER G WITH CARON
-011D;gcircumflex;LATIN SMALL LETTER G WITH CIRCUMFLEX
-0121;gdotaccent;LATIN SMALL LETTER G WITH DOT ABOVE
-00DF;germandbls;LATIN SMALL LETTER SHARP S
-2207;gradient;NABLA
-0060;grave;GRAVE ACCENT
-0300;gravecomb;COMBINING GRAVE ACCENT
-003E;greater;GREATER-THAN SIGN
-2265;greaterequal;GREATER-THAN OR EQUAL TO
-00AB;guillemotleft;LEFT-POINTING DOUBLE ANGLE QUOTATION MARK
-00BB;guillemotright;RIGHT-POINTING DOUBLE ANGLE QUOTATION MARK
-2039;guilsinglleft;SINGLE LEFT-POINTING ANGLE QUOTATION MARK
-203A;guilsinglright;SINGLE RIGHT-POINTING ANGLE QUOTATION MARK
-0068;h;LATIN SMALL LETTER H
-0127;hbar;LATIN SMALL LETTER H WITH STROKE
-0125;hcircumflex;LATIN SMALL LETTER H WITH CIRCUMFLEX
-2665;heart;BLACK HEART SUIT
-0309;hookabovecomb;COMBINING HOOK ABOVE
-2302;house;HOUSE
-02DD;hungarumlaut;DOUBLE ACUTE ACCENT
-002D;hyphen;HYPHEN-MINUS
-0069;i;LATIN SMALL LETTER I
-00ED;iacute;LATIN SMALL LETTER I WITH ACUTE
-012D;ibreve;LATIN SMALL LETTER I WITH BREVE
-00EE;icircumflex;LATIN SMALL LETTER I WITH CIRCUMFLEX
-00EF;idieresis;LATIN SMALL LETTER I WITH DIAERESIS
-00EC;igrave;LATIN SMALL LETTER I WITH GRAVE
-0133;ij;LATIN SMALL LIGATURE IJ
-012B;imacron;LATIN SMALL LETTER I WITH MACRON
-221E;infinity;INFINITY
-222B;integral;INTEGRAL
-2321;integralbt;BOTTOM HALF INTEGRAL
-2320;integraltp;TOP HALF INTEGRAL
-2229;intersection;INTERSECTION
-25D8;invbullet;INVERSE BULLET
-25D9;invcircle;INVERSE WHITE CIRCLE
-263B;invsmileface;BLACK SMILING FACE
-012F;iogonek;LATIN SMALL LETTER I WITH OGONEK
-03B9;iota;GREEK SMALL LETTER IOTA
-03CA;iotadieresis;GREEK SMALL LETTER IOTA WITH DIALYTIKA
-0390;iotadieresistonos;GREEK SMALL LETTER IOTA WITH DIALYTIKA AND TONOS
-03AF;iotatonos;GREEK SMALL LETTER IOTA WITH TONOS
-0129;itilde;LATIN SMALL LETTER I WITH TILDE
-006A;j;LATIN SMALL LETTER J
-0135;jcircumflex;LATIN SMALL LETTER J WITH CIRCUMFLEX
-006B;k;LATIN SMALL LETTER K
-03BA;kappa;GREEK SMALL LETTER KAPPA
-0138;kgreenlandic;LATIN SMALL LETTER KRA
-006C;l;LATIN SMALL LETTER L
-013A;lacute;LATIN SMALL LETTER L WITH ACUTE
-03BB;lambda;GREEK SMALL LETTER LAMDA
-013E;lcaron;LATIN SMALL LETTER L WITH CARON
-0140;ldot;LATIN SMALL LETTER L WITH MIDDLE DOT
-003C;less;LESS-THAN SIGN
-2264;lessequal;LESS-THAN OR EQUAL TO
-258C;lfblock;LEFT HALF BLOCK
-20A4;lira;LIRA SIGN
-2227;logicaland;LOGICAL AND
-00AC;logicalnot;NOT SIGN
-2228;logicalor;LOGICAL OR
-017F;longs;LATIN SMALL LETTER LONG S
-25CA;lozenge;LOZENGE
-0142;lslash;LATIN SMALL LETTER L WITH STROKE
-2591;ltshade;LIGHT SHADE
-006D;m;LATIN SMALL LETTER M
-00AF;macron;MACRON
-2642;male;MALE SIGN
-2212;minus;MINUS SIGN
-2032;minute;PRIME
-00B5;mu;MICRO SIGN
-00D7;multiply;MULTIPLICATION SIGN
-266A;musicalnote;EIGHTH NOTE
-266B;musicalnotedbl;BEAMED EIGHTH NOTES
-006E;n;LATIN SMALL LETTER N
-0144;nacute;LATIN SMALL LETTER N WITH ACUTE
-0149;napostrophe;LATIN SMALL LETTER N PRECEDED BY APOSTROPHE
-0148;ncaron;LATIN SMALL LETTER N WITH CARON
-0039;nine;DIGIT NINE
-2209;notelement;NOT AN ELEMENT OF
-2260;notequal;NOT EQUAL TO
-2284;notsubset;NOT A SUBSET OF
-00F1;ntilde;LATIN SMALL LETTER N WITH TILDE
-03BD;nu;GREEK SMALL LETTER NU
-0023;numbersign;NUMBER SIGN
-006F;o;LATIN SMALL LETTER O
-00F3;oacute;LATIN SMALL LETTER O WITH ACUTE
-014F;obreve;LATIN SMALL LETTER O WITH BREVE
-00F4;ocircumflex;LATIN SMALL LETTER O WITH CIRCUMFLEX
-00F6;odieresis;LATIN SMALL LETTER O WITH DIAERESIS
-0153;oe;LATIN SMALL LIGATURE OE
-02DB;ogonek;OGONEK
-00F2;ograve;LATIN SMALL LETTER O WITH GRAVE
-01A1;ohorn;LATIN SMALL LETTER O WITH HORN
-0151;ohungarumlaut;LATIN SMALL LETTER O WITH DOUBLE ACUTE
-014D;omacron;LATIN SMALL LETTER O WITH MACRON
-03C9;omega;GREEK SMALL LETTER OMEGA
-03D6;omega1;GREEK PI SYMBOL
-03CE;omegatonos;GREEK SMALL LETTER OMEGA WITH TONOS
-03BF;omicron;GREEK SMALL LETTER OMICRON
-03CC;omicrontonos;GREEK SMALL LETTER OMICRON WITH TONOS
-0031;one;DIGIT ONE
-2024;onedotenleader;ONE DOT LEADER
-215B;oneeighth;VULGAR FRACTION ONE EIGHTH
-00BD;onehalf;VULGAR FRACTION ONE HALF
-00BC;onequarter;VULGAR FRACTION ONE QUARTER
-2153;onethird;VULGAR FRACTION ONE THIRD
-25E6;openbullet;WHITE BULLET
-00AA;ordfeminine;FEMININE ORDINAL INDICATOR
-00BA;ordmasculine;MASCULINE ORDINAL INDICATOR
-221F;orthogonal;RIGHT ANGLE
-00F8;oslash;LATIN SMALL LETTER O WITH STROKE
-01FF;oslashacute;LATIN SMALL LETTER O WITH STROKE AND ACUTE
-00F5;otilde;LATIN SMALL LETTER O WITH TILDE
-0070;p;LATIN SMALL LETTER P
-00B6;paragraph;PILCROW SIGN
-0028;parenleft;LEFT PARENTHESIS
-0029;parenright;RIGHT PARENTHESIS
-2202;partialdiff;PARTIAL DIFFERENTIAL
-0025;percent;PERCENT SIGN
-002E;period;FULL STOP
-00B7;periodcentered;MIDDLE DOT
-22A5;perpendicular;UP TACK
-2030;perthousand;PER MILLE SIGN
-20A7;peseta;PESETA SIGN
-03C6;phi;GREEK SMALL LETTER PHI
-03D5;phi1;GREEK PHI SYMBOL
-03C0;pi;GREEK SMALL LETTER PI
-002B;plus;PLUS SIGN
-00B1;plusminus;PLUS-MINUS SIGN
-211E;prescription;PRESCRIPTION TAKE
-220F;product;N-ARY PRODUCT
-2282;propersubset;SUBSET OF
-2283;propersuperset;SUPERSET OF
-221D;proportional;PROPORTIONAL TO
-03C8;psi;GREEK SMALL LETTER PSI
-0071;q;LATIN SMALL LETTER Q
-003F;question;QUESTION MARK
-00BF;questiondown;INVERTED QUESTION MARK
-0022;quotedbl;QUOTATION MARK
-201E;quotedblbase;DOUBLE LOW-9 QUOTATION MARK
-201C;quotedblleft;LEFT DOUBLE QUOTATION MARK
-201D;quotedblright;RIGHT DOUBLE QUOTATION MARK
-2018;quoteleft;LEFT SINGLE QUOTATION MARK
-201B;quotereversed;SINGLE HIGH-REVERSED-9 QUOTATION MARK
-2019;quoteright;RIGHT SINGLE QUOTATION MARK
-201A;quotesinglbase;SINGLE LOW-9 QUOTATION MARK
-0027;quotesingle;APOSTROPHE
-0072;r;LATIN SMALL LETTER R
-0155;racute;LATIN SMALL LETTER R WITH ACUTE
-221A;radical;SQUARE ROOT
-0159;rcaron;LATIN SMALL LETTER R WITH CARON
-2286;reflexsubset;SUBSET OF OR EQUAL TO
-2287;reflexsuperset;SUPERSET OF OR EQUAL TO
-00AE;registered;REGISTERED SIGN
-2310;revlogicalnot;REVERSED NOT SIGN
-03C1;rho;GREEK SMALL LETTER RHO
-02DA;ring;RING ABOVE
-2590;rtblock;RIGHT HALF BLOCK
-0073;s;LATIN SMALL LETTER S
-015B;sacute;LATIN SMALL LETTER S WITH ACUTE
-0161;scaron;LATIN SMALL LETTER S WITH CARON
-015F;scedilla;LATIN SMALL LETTER S WITH CEDILLA
-015D;scircumflex;LATIN SMALL LETTER S WITH CIRCUMFLEX
-2033;second;DOUBLE PRIME
-00A7;section;SECTION SIGN
-003B;semicolon;SEMICOLON
-0037;seven;DIGIT SEVEN
-215E;seveneighths;VULGAR FRACTION SEVEN EIGHTHS
-2592;shade;MEDIUM SHADE
-03C3;sigma;GREEK SMALL LETTER SIGMA
-03C2;sigma1;GREEK SMALL LETTER FINAL SIGMA
-223C;similar;TILDE OPERATOR
-0036;six;DIGIT SIX
-002F;slash;SOLIDUS
-263A;smileface;WHITE SMILING FACE
-0020;space;SPACE
-2660;spade;BLACK SPADE SUIT
-00A3;sterling;POUND SIGN
-220B;suchthat;CONTAINS AS MEMBER
-2211;summation;N-ARY SUMMATION
-263C;sun;WHITE SUN WITH RAYS
-0074;t;LATIN SMALL LETTER T
-03C4;tau;GREEK SMALL LETTER TAU
-0167;tbar;LATIN SMALL LETTER T WITH STROKE
-0165;tcaron;LATIN SMALL LETTER T WITH CARON
-2234;therefore;THEREFORE
-03B8;theta;GREEK SMALL LETTER THETA
-03D1;theta1;GREEK THETA SYMBOL
-00FE;thorn;LATIN SMALL LETTER THORN
-0033;three;DIGIT THREE
-215C;threeeighths;VULGAR FRACTION THREE EIGHTHS
-00BE;threequarters;VULGAR FRACTION THREE QUARTERS
-02DC;tilde;SMALL TILDE
-0303;tildecomb;COMBINING TILDE
-0384;tonos;GREEK TONOS
-2122;trademark;TRADE MARK SIGN
-25BC;triagdn;BLACK DOWN-POINTING TRIANGLE
-25C4;triaglf;BLACK LEFT-POINTING POINTER
-25BA;triagrt;BLACK RIGHT-POINTING POINTER
-25B2;triagup;BLACK UP-POINTING TRIANGLE
-0032;two;DIGIT TWO
-2025;twodotenleader;TWO DOT LEADER
-2154;twothirds;VULGAR FRACTION TWO THIRDS
-0075;u;LATIN SMALL LETTER U
-00FA;uacute;LATIN SMALL LETTER U WITH ACUTE
-016D;ubreve;LATIN SMALL LETTER U WITH BREVE
-00FB;ucircumflex;LATIN SMALL LETTER U WITH CIRCUMFLEX
-00FC;udieresis;LATIN SMALL LETTER U WITH DIAERESIS
-00F9;ugrave;LATIN SMALL LETTER U WITH GRAVE
-01B0;uhorn;LATIN SMALL LETTER U WITH HORN
-0171;uhungarumlaut;LATIN SMALL LETTER U WITH DOUBLE ACUTE
-016B;umacron;LATIN SMALL LETTER U WITH MACRON
-005F;underscore;LOW LINE
-2017;underscoredbl;DOUBLE LOW LINE
-222A;union;UNION
-2200;universal;FOR ALL
-0173;uogonek;LATIN SMALL LETTER U WITH OGONEK
-2580;upblock;UPPER HALF BLOCK
-03C5;upsilon;GREEK SMALL LETTER UPSILON
-03CB;upsilondieresis;GREEK SMALL LETTER UPSILON WITH DIALYTIKA
-03B0;upsilondieresistonos;GREEK SMALL LETTER UPSILON WITH DIALYTIKA AND TONOS
-03CD;upsilontonos;GREEK SMALL LETTER UPSILON WITH TONOS
-016F;uring;LATIN SMALL LETTER U WITH RING ABOVE
-0169;utilde;LATIN SMALL LETTER U WITH TILDE
-0076;v;LATIN SMALL LETTER V
-0077;w;LATIN SMALL LETTER W
-1E83;wacute;LATIN SMALL LETTER W WITH ACUTE
-0175;wcircumflex;LATIN SMALL LETTER W WITH CIRCUMFLEX
-1E85;wdieresis;LATIN SMALL LETTER W WITH DIAERESIS
-2118;weierstrass;SCRIPT CAPITAL P
-1E81;wgrave;LATIN SMALL LETTER W WITH GRAVE
-0078;x;LATIN SMALL LETTER X
-03BE;xi;GREEK SMALL LETTER XI
-0079;y;LATIN SMALL LETTER Y
-00FD;yacute;LATIN SMALL LETTER Y WITH ACUTE
-0177;ycircumflex;LATIN SMALL LETTER Y WITH CIRCUMFLEX
-00FF;ydieresis;LATIN SMALL LETTER Y WITH DIAERESIS
-00A5;yen;YEN SIGN
-1EF3;ygrave;LATIN SMALL LETTER Y WITH GRAVE
-007A;z;LATIN SMALL LETTER Z
-017A;zacute;LATIN SMALL LETTER Z WITH ACUTE
-017E;zcaron;LATIN SMALL LETTER Z WITH CARON
-017C;zdotaccent;LATIN SMALL LETTER Z WITH DOT ABOVE
-0030;zero;DIGIT ZERO
-03B6;zeta;GREEK SMALL LETTER ZETA
-# END
-"""
-
-
-class AGLError(Exception):
- pass
-
-
-LEGACY_AGL2UV = {}
-AGL2UV = {}
-UV2AGL = {}
-
-
-def _builddicts():
- import re
-
- lines = _aglText.splitlines()
-
- parseAGL_RE = re.compile("([A-Za-z0-9]+);((?:[0-9A-F]{4})(?: (?:[0-9A-F]{4}))*)$")
-
- for line in lines:
- if not line or line[:1] == "#":
- continue
- m = parseAGL_RE.match(line)
- if not m:
- raise AGLError("syntax error in glyphlist.txt: %s" % repr(line[:20]))
- unicodes = m.group(2)
- assert len(unicodes) % 5 == 4
- unicodes = [int(unicode, 16) for unicode in unicodes.split()]
- glyphName = tostr(m.group(1))
- LEGACY_AGL2UV[glyphName] = unicodes
-
- lines = _aglfnText.splitlines()
-
- parseAGLFN_RE = re.compile("([0-9A-F]{4});([A-Za-z0-9]+);.*?$")
-
- for line in lines:
- if not line or line[:1] == "#":
- continue
- m = parseAGLFN_RE.match(line)
- if not m:
- raise AGLError("syntax error in aglfn.txt: %s" % repr(line[:20]))
- unicode = m.group(1)
- assert len(unicode) == 4
- unicode = int(unicode, 16)
- glyphName = tostr(m.group(2))
- AGL2UV[glyphName] = unicode
- UV2AGL[unicode] = glyphName
-
-
-_builddicts()
-
-
-def toUnicode(glyph, isZapfDingbats=False):
- """Convert glyph names to Unicode, such as ``'longs_t.oldstyle'`` --> ``u'ſt'``
-
- If ``isZapfDingbats`` is ``True``, the implementation recognizes additional
- glyph names (as required by the AGL specification).
- """
- # https://github.com/adobe-type-tools/agl-specification#2-the-mapping
- #
- # 1. Drop all the characters from the glyph name starting with
- # the first occurrence of a period (U+002E; FULL STOP), if any.
- glyph = glyph.split(".", 1)[0]
-
- # 2. Split the remaining string into a sequence of components,
- # using underscore (U+005F; LOW LINE) as the delimiter.
- components = glyph.split("_")
-
- # 3. Map each component to a character string according to the
- # procedure below, and concatenate those strings; the result
- # is the character string to which the glyph name is mapped.
- result = [_glyphComponentToUnicode(c, isZapfDingbats) for c in components]
- return "".join(result)
-
-
-def _glyphComponentToUnicode(component, isZapfDingbats):
- # If the font is Zapf Dingbats (PostScript FontName: ZapfDingbats),
- # and the component is in the ITC Zapf Dingbats Glyph List, then
- # map it to the corresponding character in that list.
- dingbat = _zapfDingbatsToUnicode(component) if isZapfDingbats else None
- if dingbat:
- return dingbat
-
- # Otherwise, if the component is in AGL, then map it
- # to the corresponding character in that list.
- uchars = LEGACY_AGL2UV.get(component)
- if uchars:
- return "".join(map(chr, uchars))
-
- # Otherwise, if the component is of the form "uni" (U+0075,
- # U+006E, and U+0069) followed by a sequence of uppercase
- # hexadecimal digits (0–9 and A–F, meaning U+0030 through
- # U+0039 and U+0041 through U+0046), if the length of that
- # sequence is a multiple of four, and if each group of four
- # digits represents a value in the ranges 0000 through D7FF
- # or E000 through FFFF, then interpret each as a Unicode scalar
- # value and map the component to the string made of those
- # scalar values. Note that the range and digit-length
- # restrictions mean that the "uni" glyph name prefix can be
- # used only with UVs in the Basic Multilingual Plane (BMP).
- uni = _uniToUnicode(component)
- if uni:
- return uni
-
- # Otherwise, if the component is of the form "u" (U+0075)
- # followed by a sequence of four to six uppercase hexadecimal
- # digits (0–9 and A–F, meaning U+0030 through U+0039 and
- # U+0041 through U+0046), and those digits represents a value
- # in the ranges 0000 through D7FF or E000 through 10FFFF, then
- # interpret it as a Unicode scalar value and map the component
- # to the string made of this scalar value.
- uni = _uToUnicode(component)
- if uni:
- return uni
-
- # Otherwise, map the component to an empty string.
- return ""
-
-
-# https://github.com/adobe-type-tools/agl-aglfn/blob/master/zapfdingbats.txt
-_AGL_ZAPF_DINGBATS = (
- " ✁✂✄☎✆✝✞✟✠✡☛☞✌✍✎✏✑✒✓✔✕✖✗✘✙✚✛✜✢✣✤✥✦✧★✩✪✫✬✭✮✯✰✱✲✳✴✵✶✷✸✹✺✻✼✽✾✿❀"
- "❁❂❃❄❅❆❇❈❉❊❋●❍■❏❑▲▼◆❖ ◗❘❙❚❯❱❲❳❨❩❬❭❪❫❴❵❛❜❝❞❡❢❣❤✐❥❦❧♠♥♦♣ ✉✈✇"
- "①②③④⑤⑥⑦⑧⑨⑩❶❷❸❹❺❻❼❽❾❿➀➁➂➃➄➅➆➇➈➉➊➋➌➍➎➏➐➑➒➓➔→➣↔"
- "↕➙➛➜➝➞➟➠➡➢➤➥➦➧➨➩➫➭➯➲➳➵➸➺➻➼➽➾➚➪➶➹➘➴➷➬➮➱✃❐❒❮❰"
-)
-
-
-def _zapfDingbatsToUnicode(glyph):
- """Helper for toUnicode()."""
- if len(glyph) < 2 or glyph[0] != "a":
- return None
- try:
- gid = int(glyph[1:])
- except ValueError:
- return None
- if gid < 0 or gid >= len(_AGL_ZAPF_DINGBATS):
- return None
- uchar = _AGL_ZAPF_DINGBATS[gid]
- return uchar if uchar != " " else None
-
-
-_re_uni = re.compile("^uni([0-9A-F]+)$")
-
-
-def _uniToUnicode(component):
- """Helper for toUnicode() to handle "uniABCD" components."""
- match = _re_uni.match(component)
- if match is None:
- return None
- digits = match.group(1)
- if len(digits) % 4 != 0:
- return None
- chars = [int(digits[i : i + 4], 16) for i in range(0, len(digits), 4)]
- if any(c >= 0xD800 and c <= 0xDFFF for c in chars):
- # The AGL specification explicitly excluded surrogate pairs.
- return None
- return "".join([chr(c) for c in chars])
-
-
-_re_u = re.compile("^u([0-9A-F]{4,6})$")
-
-
-def _uToUnicode(component):
- """Helper for toUnicode() to handle "u1ABCD" components."""
- match = _re_u.match(component)
- if match is None:
- return None
- digits = match.group(1)
- try:
- value = int(digits, 16)
- except ValueError:
- return None
- if (value >= 0x0000 and value <= 0xD7FF) or (value >= 0xE000 and value <= 0x10FFFF):
- return chr(value)
- return None
diff --git a/spaces/jonigata/PoseTweak/README.md b/spaces/jonigata/PoseTweak/README.md
deleted file mode 100644
index 1ad8fabf318f7ec73f2135db219f58552e5190bd..0000000000000000000000000000000000000000
--- a/spaces/jonigata/PoseTweak/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: PoseTweak
-emoji: 🌍
-colorFrom: yellow
-colorTo: indigo
-sdk: docker
-pinned: false
-license: creativeml-openrail-m
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/jordonpeter01/MusicGen2/audiocraft/utils/notebook.py b/spaces/jordonpeter01/MusicGen2/audiocraft/utils/notebook.py
deleted file mode 100644
index 019b9d19e5bef976bedddf428fd25da42a8a9726..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/MusicGen2/audiocraft/utils/notebook.py
+++ /dev/null
@@ -1,32 +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.
-
-try:
- import IPython.display as ipd # type: ignore
-except ImportError:
- # Note in a notebook...
- pass
-
-
-import torch
-
-
-def display_audio(samples: torch.Tensor, sample_rate: int):
- """Renders an audio player for the given audio samples.
-
- Args:
- samples (torch.Tensor): a Tensor of decoded audio samples
- with shapes [B, C, T] or [C, T]
- sample_rate (int): sample rate audio should be displayed with.
- """
- assert samples.dim() == 2 or samples.dim() == 3
-
- samples = samples.detach().cpu()
- if samples.dim() == 2:
- samples = samples[None, ...]
-
- for audio in samples:
- ipd.display(ipd.Audio(audio, rate=sample_rate))
diff --git a/spaces/josuelmet/Metal_Music_Interpolator/app.py b/spaces/josuelmet/Metal_Music_Interpolator/app.py
deleted file mode 100644
index 0b84638605625f3d0b3f0384720274c351266e60..0000000000000000000000000000000000000000
--- a/spaces/josuelmet/Metal_Music_Interpolator/app.py
+++ /dev/null
@@ -1,43 +0,0 @@
-from datetime import datetime
-import gradio as gr
-import mgzip
-import numpy as np
-from os.path import join
-import pickle
-from zipfile import ZipFile
-
-from _Generation import Generator
-
-import tensorflow as tf
-from tensorflow import keras
-
-
-
-gen = Generator()
-
-def main(artist):
-
- if artist == 'Any':
- artist = None
- gen.generate_track_batch(artist)
- filename = f'generation_{datetime.now().strftime("%Y_%m_%d %H_%M_%S")}.gp5'
- gen.save_tracks(filename)
-
- # create a ZipFile object
- zipObj = ZipFile(filename.replace('.gp5', '.zip'), "w")
- zipObj.write(filename)
- zipObj.close()
-
- return filename.replace('.gp5', '.zip')
-
-with mgzip.open(join('data', 'track_data.pickle.gz'), 'rb') as file:
- track_data = pickle.load(file)
-
-
-inputs = gr.components.Radio(['Any'] + list(track_data.artist.unique()),
- label='''Choose an artist for song initialization. Paper: "Rock Guitar Tablature Generation via Natural Language Processing", https://arxiv.org/abs/2301.05295''')
-
-
-i = gr.Interface(fn = main, inputs = inputs,
- outputs = gr.components.File(label='Generated Guitar Tabs. Download and Unzip to View:'))
-i.launch()
\ No newline at end of file
diff --git a/spaces/joushe/moe-tts/text/__init__.py b/spaces/joushe/moe-tts/text/__init__.py
deleted file mode 100644
index 4e69c354dd24e3243980236eca962cd5945a92fc..0000000000000000000000000000000000000000
--- a/spaces/joushe/moe-tts/text/__init__.py
+++ /dev/null
@@ -1,32 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-
-
-def text_to_sequence(text, symbols, cleaner_names):
- '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
- Args:
- text: string to convert to a sequence
- cleaner_names: names of the cleaner functions to run the text through
- Returns:
- List of integers corresponding to the symbols in the text
- '''
- _symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
- sequence = []
-
- clean_text = _clean_text(text, cleaner_names)
- for symbol in clean_text:
- if symbol not in _symbol_to_id.keys():
- continue
- symbol_id = _symbol_to_id[symbol]
- sequence += [symbol_id]
- return sequence
-
-
-def _clean_text(text, cleaner_names):
- for name in cleaner_names:
- cleaner = getattr(cleaners, name)
- if not cleaner:
- raise Exception('Unknown cleaner: %s' % name)
- text = cleaner(text)
- return text
diff --git a/spaces/jspr/paperchat/README.md b/spaces/jspr/paperchat/README.md
deleted file mode 100644
index 366d1e52b1b08734691d382fbd4080e8003a1b08..0000000000000000000000000000000000000000
--- a/spaces/jspr/paperchat/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: PaperChat
-emoji: ⚡
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 3.12.0
-app_file: app.py
-pinned: true
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/justest/gpt4free/g4f/.v1/gui/pywebio-gui/pywebio-usesless.py b/spaces/justest/gpt4free/g4f/.v1/gui/pywebio-gui/pywebio-usesless.py
deleted file mode 100644
index 177fa7c86ebfd4b4f3b38033ee31863fec3df794..0000000000000000000000000000000000000000
--- a/spaces/justest/gpt4free/g4f/.v1/gui/pywebio-gui/pywebio-usesless.py
+++ /dev/null
@@ -1,59 +0,0 @@
-from gpt4free import usesless
-import time
-from pywebio import start_server,config
-from pywebio.input import *
-from pywebio.output import *
-from pywebio.session import local
-message_id = ""
-def status():
- try:
- req = usesless.Completion.create(prompt="hello", parentMessageId=message_id)
- print(f"Answer: {req['text']}")
- put_success(f"Answer: {req['text']}",scope="body")
- except:
- put_error("Program Error",scope="body")
-
-def ask(prompt):
- req = usesless.Completion.create(prompt=prompt, parentMessageId=local.message_id)
- rp=req['text']
- local.message_id=req["id"]
- print("AI：\n"+rp)
- local.conversation.extend([
- {"role": "user", "content": prompt},
- {"role": "assistant", "content": rp}
- ])
- print(local.conversation)
- return rp
-
-def msg():
- while True:
- text= input_group("You:",[textarea('You：',name='text',rows=3, placeholder='请输入问题')])
- if not(bool(text)):
- break
- if not(bool(text["text"])):
- continue
- time.sleep(0.5)
- put_code("You："+text["text"],scope="body")
- print("Question："+text["text"])
- with use_scope('foot'):
- put_loading(color="info")
- rp= ask(text["text"])
- clear(scope="foot")
- time.sleep(0.5)
- put_markdown("Bot:\n"+rp,scope="body")
- time.sleep(0.7)
-
-@config(title="AIchat",theme="dark")
-def main():
- put_scope("heads")
- with use_scope('heads'):
- put_html("AI Chat
-lang_list=$2 #
-model=$3 #
-source_lang=cs
-target_lang=en
-
-fairseq-generate "$path_2_data" \
- --path "$model" \
- --task translation_multi_simple_epoch \
- --gen-subset test \
- --source-lang "$source_lang" \
- --target-lang "$target_lang" \
- --sacrebleu --remove-bpe 'sentencepiece'\
- --batch-size 32 \
- --encoder-langtok "src" \
- --decoder-langtok \
- --lang-dict "$lang_list" \
- --lang-pairs "$lang_pairs"
diff --git a/spaces/krystaltechnology/image-video-colorization/pages/04_Super_Resolution.py b/spaces/krystaltechnology/image-video-colorization/pages/04_Super_Resolution.py
deleted file mode 100644
index 74a1c6c8aec39d748580a7d1033a00d03acbb8b1..0000000000000000000000000000000000000000
--- a/spaces/krystaltechnology/image-video-colorization/pages/04_Super_Resolution.py
+++ /dev/null
@@ -1,260 +0,0 @@
-import streamlit as st
-import cv2
-import numpy
-import os
-import random
-from basicsr.archs.rrdbnet_arch import RRDBNet
-from basicsr.utils.download_util import load_file_from_url
-from PIL import Image
-
-from realesrgan import RealESRGANer
-from realesrgan.archs.srvgg_arch import SRVGGNetCompact
-
-
-last_file = None
-img_mode = "RGBA"
-
-
-def realesrgan(img, model_name, denoise_strength, face_enhance, outscale):
- """Real-ESRGAN function to restore (and upscale) images.
- """
- if not img:
- return
-
- # Define model parameters
- if model_name == 'RealESRGAN_x4plus': # x4 RRDBNet model
- model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
- netscale = 4
- file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth']
- elif model_name == 'RealESRNet_x4plus': # x4 RRDBNet model
- model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
- netscale = 4
- file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth']
- elif model_name == 'RealESRGAN_x4plus_anime_6B': # x4 RRDBNet model with 6 blocks
- model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
- netscale = 4
- file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth']
- elif model_name == 'RealESRGAN_x2plus': # x2 RRDBNet model
- model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
- netscale = 2
- file_url = ['https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth']
- elif model_name == 'realesr-general-x4v3': # x4 VGG-style model (S size)
- model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
- netscale = 4
- file_url = [
- 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth',
- 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
- ]
-
- # Determine model paths
- model_path = os.path.join('weights', model_name + '.pth')
- if not os.path.isfile(model_path):
- ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
- for url in file_url:
- # model_path will be updated
- model_path = load_file_from_url(
- url=url, model_dir=os.path.join(ROOT_DIR, 'weights'), progress=True, file_name=None)
-
- # Use dni to control the denoise strength
- dni_weight = None
- if model_name == 'realesr-general-x4v3' and denoise_strength != 1:
- wdn_model_path = model_path.replace('realesr-general-x4v3', 'realesr-general-wdn-x4v3')
- model_path = [model_path, wdn_model_path]
- dni_weight = [denoise_strength, 1 - denoise_strength]
-
- # Restorer Class
- upsampler = RealESRGANer(
- scale=netscale,
- model_path=model_path,
- dni_weight=dni_weight,
- model=model,
- tile=0,
- tile_pad=10,
- pre_pad=10,
- half=False,
- gpu_id=None
- )
-
- # Use GFPGAN for face enhancement
- if face_enhance:
- from gfpgan import GFPGANer
- face_enhancer = GFPGANer(
- model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
- upscale=outscale,
- arch='clean',
- channel_multiplier=2,
- bg_upsampler=upsampler)
-
- # Convert the input PIL image to cv2 image, so that it can be processed by realesrgan
- #cv_img = numpy.array(img.get_value(), dtype = 'uint8')
- cv_img = numpy.array(img)
- #img = cv2.cvtColor(cv2.UMat(imgUMat), cv2.COLOR_RGB2GRAY)
- img = cv2.cvtColor(cv_img, cv2.COLOR_RGBA2BGRA)
-
- # Apply restoration
- try:
- if face_enhance:
- _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
- else:
- output, _ = upsampler.enhance(img, outscale=outscale)
- except RuntimeError as error:
- print('Error', error)
- print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
- else:
- # Save restored image and return it to the output Image component
- if img_mode == 'RGBA': # RGBA images should be saved in png format
- extension = 'png'
- else:
- extension = 'jpg'
-
- out_filename = f"output_{rnd_string(8)}.{extension}"
- cv2.imwrite(out_filename, output)
- global last_file
- last_file = out_filename
- return out_filename
-
-
-def rnd_string(x):
- """Returns a string of 'x' random characters
- """
- characters = "abcdefghijklmnopqrstuvwxyz_0123456789"
- result = "".join((random.choice(characters)) for i in range(x))
- return result
-
-
-def reset():
- """Resets the Image components of the Gradio interface and deletes
- the last processed image
- """
- global last_file
- if last_file:
- print(f"Deleting {last_file} ...")
- os.remove(last_file)
- last_file = None
- return gr.update(value=None), gr.update(value=None)
-
-
-def has_transparency(img):
- """This function works by first checking to see if a "transparency" property is defined
- in the image's info -- if so, we return "True". Then, if the image is using indexed colors
- (such as in GIFs), it gets the index of the transparent color in the palette
- (img.info.get("transparency", -1)) and checks if it's used anywhere in the canvas
- (img.getcolors()). If the image is in RGBA mode, then presumably it has transparency in
- it, but it double-checks by getting the minimum and maximum values of every color channel
- (img.getextrema()), and checks if the alpha channel's smallest value falls below 255.
- https://stackoverflow.com/questions/43864101/python-pil-check-if-image-is-transparent
- """
- if img.info.get("transparency", None) is not None:
- return True
- if img.mode == "P":
- transparent = img.info.get("transparency", -1)
- for _, index in img.getcolors():
- if index == transparent:
- return True
- elif img.mode == "RGBA":
- extrema = img.getextrema()
- if extrema[3][0] < 255:
- return True
- return False
-
-
-def image_properties(img):
- """Returns the dimensions (width and height) and color mode of the input image and
- also sets the global img_mode variable to be used by the realesrgan function
- """
- global img_mode
- if img:
- if has_transparency(img):
- img_mode = "RGBA"
- else:
- img_mode = "RGB"
- properties = f"Width: {img.size[0]}, Height: {img.size[1]} | Color Mode: {img_mode}"
- return properties
-
-def image_properties(image):
- # Function to display image properties
- properties = f"Image Size: {image.size}\nImage Mode: {image.mode}"
- return properties
-
-#----------
-
-input_folder = '.'
-
-@st.cache_resource
-def load_image(image_file):
- img = Image.open(image_file)
- return img
-
-def save_image(image_file):
- if image_file is not None:
- filename = image_file.name
- img = load_image(image_file)
- st.image(image=img, width=None)
- with open(os.path.join(input_folder, filename), "wb") as f:
- f.write(image_file.getbuffer())
- st.success("Succesfully uploaded file for processing".format(filename))
-
-#------------
-
-st.title("Super Resolution")
-# Saving uploaded image in input folder for processing
-
-#with st.expander("Options/Parameters"):
-
-input_img = st.file_uploader(
-"Upload Image", type=['png', 'jpeg', 'jpg', 'webp'])
-#save_image(input_img)
-
-model_name = st.selectbox(
- "Real-ESRGAN inference model to be used",
- ["RealESRGAN_x4plus", "RealESRNet_x4plus", "RealESRGAN_x4plus_anime_6B", "RealESRGAN_x2plus", "realesr-general-x4v3"],
- index=4
- )
-
-#denoise_strength = st.slider("Denoise Strength (Used only with the realesr-general-x4v3 model)", 0.0, 1.0, 0.5)
-denoise_strength = 0.5
-
-outscale = st.slider("Image Upscaling Factor", 1, 10, 2)
-
-face_enhance = st.checkbox("Face Enhancement using GFPGAN (Doesn't work for anime images)")
-
-if input_img:
- print(input_img)
- input_img = Image.open(input_img)
- # Display image properties
- cols = st.columns(2)
-
- cols[0].image(input_img, 'Source Image')
-
- #input_properties = get_image_properties(input_img)
- #cols[1].write(input_properties)
-
- # Output placeholder
- output_img = st.empty()
-
-# Input and output placeholders
-input_img = input_img
-output_img = st.empty()
-
-# Buttons
-restore = st.button('Restore')
-reset = st.button('Reset')
-
-# Restore clicked
-if restore:
- if input_img is not None:
- output = realesrgan(input_img, model_name, denoise_strength,
- face_enhance, outscale)
- output_img.image(output, 'Restored Image')
-
- st.download_button(
- label="Download Image",
- data=open(output, "rb").read(),
- file_name="Image.jpg",
- )
- else:
- st.warning('Upload a file', icon="⚠️")
-
-# Reset clicked
-if reset:
- output_img.empty()
\ No newline at end of file
diff --git a/spaces/kukuhtw/VToonify/vtoonify/model/raft/core/datasets.py b/spaces/kukuhtw/VToonify/vtoonify/model/raft/core/datasets.py
deleted file mode 100644
index 9991f15f4c3861c19d1a4b8766d49f83af11db70..0000000000000000000000000000000000000000
--- a/spaces/kukuhtw/VToonify/vtoonify/model/raft/core/datasets.py
+++ /dev/null
@@ -1,235 +0,0 @@
-# Data loading based on https://github.com/NVIDIA/flownet2-pytorch
-
-import numpy as np
-import torch
-import torch.utils.data as data
-import torch.nn.functional as F
-
-import os
-import math
-import random
-from glob import glob
-import os.path as osp
-
-from model.raft.core.utils import frame_utils
-from model.raft.core.utils.augmentor import FlowAugmentor, SparseFlowAugmentor
-
-
-class FlowDataset(data.Dataset):
- def __init__(self, aug_params=None, sparse=False):
- self.augmentor = None
- self.sparse = sparse
- if aug_params is not None:
- if sparse:
- self.augmentor = SparseFlowAugmentor(**aug_params)
- else:
- self.augmentor = FlowAugmentor(**aug_params)
-
- self.is_test = False
- self.init_seed = False
- self.flow_list = []
- self.image_list = []
- self.extra_info = []
-
- def __getitem__(self, index):
-
- if self.is_test:
- img1 = frame_utils.read_gen(self.image_list[index][0])
- img2 = frame_utils.read_gen(self.image_list[index][1])
- img1 = np.array(img1).astype(np.uint8)[..., :3]
- img2 = np.array(img2).astype(np.uint8)[..., :3]
- img1 = torch.from_numpy(img1).permute(2, 0, 1).float()
- img2 = torch.from_numpy(img2).permute(2, 0, 1).float()
- return img1, img2, self.extra_info[index]
-
- if not self.init_seed:
- worker_info = torch.utils.data.get_worker_info()
- if worker_info is not None:
- torch.manual_seed(worker_info.id)
- np.random.seed(worker_info.id)
- random.seed(worker_info.id)
- self.init_seed = True
-
- index = index % len(self.image_list)
- valid = None
- if self.sparse:
- flow, valid = frame_utils.readFlowKITTI(self.flow_list[index])
- else:
- flow = frame_utils.read_gen(self.flow_list[index])
-
- img1 = frame_utils.read_gen(self.image_list[index][0])
- img2 = frame_utils.read_gen(self.image_list[index][1])
-
- flow = np.array(flow).astype(np.float32)
- img1 = np.array(img1).astype(np.uint8)
- img2 = np.array(img2).astype(np.uint8)
-
- # grayscale images
- if len(img1.shape) == 2:
- img1 = np.tile(img1[...,None], (1, 1, 3))
- img2 = np.tile(img2[...,None], (1, 1, 3))
- else:
- img1 = img1[..., :3]
- img2 = img2[..., :3]
-
- if self.augmentor is not None:
- if self.sparse:
- img1, img2, flow, valid = self.augmentor(img1, img2, flow, valid)
- else:
- img1, img2, flow = self.augmentor(img1, img2, flow)
-
- img1 = torch.from_numpy(img1).permute(2, 0, 1).float()
- img2 = torch.from_numpy(img2).permute(2, 0, 1).float()
- flow = torch.from_numpy(flow).permute(2, 0, 1).float()
-
- if valid is not None:
- valid = torch.from_numpy(valid)
- else:
- valid = (flow[0].abs() < 1000) & (flow[1].abs() < 1000)
-
- return img1, img2, flow, valid.float()
-
-
- def __rmul__(self, v):
- self.flow_list = v * self.flow_list
- self.image_list = v * self.image_list
- return self
-
- def __len__(self):
- return len(self.image_list)
-
-
-class MpiSintel(FlowDataset):
- def __init__(self, aug_params=None, split='training', root='datasets/Sintel', dstype='clean'):
- super(MpiSintel, self).__init__(aug_params)
- flow_root = osp.join(root, split, 'flow')
- image_root = osp.join(root, split, dstype)
-
- if split == 'test':
- self.is_test = True
-
- for scene in os.listdir(image_root):
- image_list = sorted(glob(osp.join(image_root, scene, '*.png')))
- for i in range(len(image_list)-1):
- self.image_list += [ [image_list[i], image_list[i+1]] ]
- self.extra_info += [ (scene, i) ] # scene and frame_id
-
- if split != 'test':
- self.flow_list += sorted(glob(osp.join(flow_root, scene, '*.flo')))
-
-
-class FlyingChairs(FlowDataset):
- def __init__(self, aug_params=None, split='train', root='datasets/FlyingChairs_release/data'):
- super(FlyingChairs, self).__init__(aug_params)
-
- images = sorted(glob(osp.join(root, '*.ppm')))
- flows = sorted(glob(osp.join(root, '*.flo')))
- assert (len(images)//2 == len(flows))
-
- split_list = np.loadtxt('chairs_split.txt', dtype=np.int32)
- for i in range(len(flows)):
- xid = split_list[i]
- if (split=='training' and xid==1) or (split=='validation' and xid==2):
- self.flow_list += [ flows[i] ]
- self.image_list += [ [images[2*i], images[2*i+1]] ]
-
-
-class FlyingThings3D(FlowDataset):
- def __init__(self, aug_params=None, root='datasets/FlyingThings3D', dstype='frames_cleanpass'):
- super(FlyingThings3D, self).__init__(aug_params)
-
- for cam in ['left']:
- for direction in ['into_future', 'into_past']:
- image_dirs = sorted(glob(osp.join(root, dstype, 'TRAIN/*/*')))
- image_dirs = sorted([osp.join(f, cam) for f in image_dirs])
-
- flow_dirs = sorted(glob(osp.join(root, 'optical_flow/TRAIN/*/*')))
- flow_dirs = sorted([osp.join(f, direction, cam) for f in flow_dirs])
-
- for idir, fdir in zip(image_dirs, flow_dirs):
- images = sorted(glob(osp.join(idir, '*.png')) )
- flows = sorted(glob(osp.join(fdir, '*.pfm')) )
- for i in range(len(flows)-1):
- if direction == 'into_future':
- self.image_list += [ [images[i], images[i+1]] ]
- self.flow_list += [ flows[i] ]
- elif direction == 'into_past':
- self.image_list += [ [images[i+1], images[i]] ]
- self.flow_list += [ flows[i+1] ]
-
-
-class KITTI(FlowDataset):
- def __init__(self, aug_params=None, split='training', root='datasets/KITTI'):
- super(KITTI, self).__init__(aug_params, sparse=True)
- if split == 'testing':
- self.is_test = True
-
- root = osp.join(root, split)
- images1 = sorted(glob(osp.join(root, 'image_2/*_10.png')))
- images2 = sorted(glob(osp.join(root, 'image_2/*_11.png')))
-
- for img1, img2 in zip(images1, images2):
- frame_id = img1.split('/')[-1]
- self.extra_info += [ [frame_id] ]
- self.image_list += [ [img1, img2] ]
-
- if split == 'training':
- self.flow_list = sorted(glob(osp.join(root, 'flow_occ/*_10.png')))
-
-
-class HD1K(FlowDataset):
- def __init__(self, aug_params=None, root='datasets/HD1k'):
- super(HD1K, self).__init__(aug_params, sparse=True)
-
- seq_ix = 0
- while 1:
- flows = sorted(glob(os.path.join(root, 'hd1k_flow_gt', 'flow_occ/%06d_*.png' % seq_ix)))
- images = sorted(glob(os.path.join(root, 'hd1k_input', 'image_2/%06d_*.png' % seq_ix)))
-
- if len(flows) == 0:
- break
-
- for i in range(len(flows)-1):
- self.flow_list += [flows[i]]
- self.image_list += [ [images[i], images[i+1]] ]
-
- seq_ix += 1
-
-
-def fetch_dataloader(args, TRAIN_DS='C+T+K+S+H'):
- """ Create the data loader for the corresponding trainign set """
-
- if args.stage == 'chairs':
- aug_params = {'crop_size': args.image_size, 'min_scale': -0.1, 'max_scale': 1.0, 'do_flip': True}
- train_dataset = FlyingChairs(aug_params, split='training')
-
- elif args.stage == 'things':
- aug_params = {'crop_size': args.image_size, 'min_scale': -0.4, 'max_scale': 0.8, 'do_flip': True}
- clean_dataset = FlyingThings3D(aug_params, dstype='frames_cleanpass')
- final_dataset = FlyingThings3D(aug_params, dstype='frames_finalpass')
- train_dataset = clean_dataset + final_dataset
-
- elif args.stage == 'sintel':
- aug_params = {'crop_size': args.image_size, 'min_scale': -0.2, 'max_scale': 0.6, 'do_flip': True}
- things = FlyingThings3D(aug_params, dstype='frames_cleanpass')
- sintel_clean = MpiSintel(aug_params, split='training', dstype='clean')
- sintel_final = MpiSintel(aug_params, split='training', dstype='final')
-
- if TRAIN_DS == 'C+T+K+S+H':
- kitti = KITTI({'crop_size': args.image_size, 'min_scale': -0.3, 'max_scale': 0.5, 'do_flip': True})
- hd1k = HD1K({'crop_size': args.image_size, 'min_scale': -0.5, 'max_scale': 0.2, 'do_flip': True})
- train_dataset = 100*sintel_clean + 100*sintel_final + 200*kitti + 5*hd1k + things
-
- elif TRAIN_DS == 'C+T+K/S':
- train_dataset = 100*sintel_clean + 100*sintel_final + things
-
- elif args.stage == 'kitti':
- aug_params = {'crop_size': args.image_size, 'min_scale': -0.2, 'max_scale': 0.4, 'do_flip': False}
- train_dataset = KITTI(aug_params, split='training')
-
- train_loader = data.DataLoader(train_dataset, batch_size=args.batch_size,
- pin_memory=False, shuffle=True, num_workers=4, drop_last=True)
-
- print('Training with %d image pairs' % len(train_dataset))
- return train_loader
-
diff --git a/spaces/kukuhtw/VToonify/vtoonify/model/stylegan/op_gpu/conv2d_gradfix.py b/spaces/kukuhtw/VToonify/vtoonify/model/stylegan/op_gpu/conv2d_gradfix.py
deleted file mode 100644
index bb2f94bbcb8132299fd4d538972d32bd7ff6e7d6..0000000000000000000000000000000000000000
--- a/spaces/kukuhtw/VToonify/vtoonify/model/stylegan/op_gpu/conv2d_gradfix.py
+++ /dev/null
@@ -1,227 +0,0 @@
-import contextlib
-import warnings
-
-import torch
-from torch import autograd
-from torch.nn import functional as F
-
-enabled = True
-weight_gradients_disabled = False
-
-
-@contextlib.contextmanager
-def no_weight_gradients():
- global weight_gradients_disabled
-
- old = weight_gradients_disabled
- weight_gradients_disabled = True
- yield
- weight_gradients_disabled = old
-
-
-def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
- if could_use_op(input):
- return conv2d_gradfix(
- transpose=False,
- weight_shape=weight.shape,
- stride=stride,
- padding=padding,
- output_padding=0,
- dilation=dilation,
- groups=groups,
- ).apply(input, weight, bias)
-
- return F.conv2d(
- input=input,
- weight=weight,
- bias=bias,
- stride=stride,
- padding=padding,
- dilation=dilation,
- groups=groups,
- )
-
-
-def conv_transpose2d(
- input,
- weight,
- bias=None,
- stride=1,
- padding=0,
- output_padding=0,
- groups=1,
- dilation=1,
-):
- if could_use_op(input):
- return conv2d_gradfix(
- transpose=True,
- weight_shape=weight.shape,
- stride=stride,
- padding=padding,
- output_padding=output_padding,
- groups=groups,
- dilation=dilation,
- ).apply(input, weight, bias)
-
- return F.conv_transpose2d(
- input=input,
- weight=weight,
- bias=bias,
- stride=stride,
- padding=padding,
- output_padding=output_padding,
- dilation=dilation,
- groups=groups,
- )
-
-
-def could_use_op(input):
- if (not enabled) or (not torch.backends.cudnn.enabled):
- return False
-
- if input.device.type != "cuda":
- return False
-
- if any(torch.__version__.startswith(x) for x in ["1.7.", "1.8."]):
- return True
-
- warnings.warn(
- f"conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d()."
- )
-
- return False
-
-
-def ensure_tuple(xs, ndim):
- xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
-
- return xs
-
-
-conv2d_gradfix_cache = dict()
-
-
-def conv2d_gradfix(
- transpose, weight_shape, stride, padding, output_padding, dilation, groups
-):
- ndim = 2
- weight_shape = tuple(weight_shape)
- stride = ensure_tuple(stride, ndim)
- padding = ensure_tuple(padding, ndim)
- output_padding = ensure_tuple(output_padding, ndim)
- dilation = ensure_tuple(dilation, ndim)
-
- key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups)
- if key in conv2d_gradfix_cache:
- return conv2d_gradfix_cache[key]
-
- common_kwargs = dict(
- stride=stride, padding=padding, dilation=dilation, groups=groups
- )
-
- def calc_output_padding(input_shape, output_shape):
- if transpose:
- return [0, 0]
-
- return [
- input_shape[i + 2]
- - (output_shape[i + 2] - 1) * stride[i]
- - (1 - 2 * padding[i])
- - dilation[i] * (weight_shape[i + 2] - 1)
- for i in range(ndim)
- ]
-
- class Conv2d(autograd.Function):
- @staticmethod
- def forward(ctx, input, weight, bias):
- if not transpose:
- out = F.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)
-
- else:
- out = F.conv_transpose2d(
- input=input,
- weight=weight,
- bias=bias,
- output_padding=output_padding,
- **common_kwargs,
- )
-
- ctx.save_for_backward(input, weight)
-
- return out
-
- @staticmethod
- def backward(ctx, grad_output):
- input, weight = ctx.saved_tensors
- grad_input, grad_weight, grad_bias = None, None, None
-
- if ctx.needs_input_grad[0]:
- p = calc_output_padding(
- input_shape=input.shape, output_shape=grad_output.shape
- )
- grad_input = conv2d_gradfix(
- transpose=(not transpose),
- weight_shape=weight_shape,
- output_padding=p,
- **common_kwargs,
- ).apply(grad_output, weight, None)
-
- if ctx.needs_input_grad[1] and not weight_gradients_disabled:
- grad_weight = Conv2dGradWeight.apply(grad_output, input)
-
- if ctx.needs_input_grad[2]:
- grad_bias = grad_output.sum((0, 2, 3))
-
- return grad_input, grad_weight, grad_bias
-
- class Conv2dGradWeight(autograd.Function):
- @staticmethod
- def forward(ctx, grad_output, input):
- op = torch._C._jit_get_operation(
- "aten::cudnn_convolution_backward_weight"
- if not transpose
- else "aten::cudnn_convolution_transpose_backward_weight"
- )
- flags = [
- torch.backends.cudnn.benchmark,
- torch.backends.cudnn.deterministic,
- torch.backends.cudnn.allow_tf32,
- ]
- grad_weight = op(
- weight_shape,
- grad_output,
- input,
- padding,
- stride,
- dilation,
- groups,
- *flags,
- )
- ctx.save_for_backward(grad_output, input)
-
- return grad_weight
-
- @staticmethod
- def backward(ctx, grad_grad_weight):
- grad_output, input = ctx.saved_tensors
- grad_grad_output, grad_grad_input = None, None
-
- if ctx.needs_input_grad[0]:
- grad_grad_output = Conv2d.apply(input, grad_grad_weight, None)
-
- if ctx.needs_input_grad[1]:
- p = calc_output_padding(
- input_shape=input.shape, output_shape=grad_output.shape
- )
- grad_grad_input = conv2d_gradfix(
- transpose=(not transpose),
- weight_shape=weight_shape,
- output_padding=p,
- **common_kwargs,
- ).apply(grad_output, grad_grad_weight, None)
-
- return grad_grad_output, grad_grad_input
-
- conv2d_gradfix_cache[key] = Conv2d
-
- return Conv2d
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/httpcore/_sync/connection_pool.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/httpcore/_sync/connection_pool.py
deleted file mode 100644
index d30a1c51f08e05b19cf69e4a36cf146d2a88f468..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/httpcore/_sync/connection_pool.py
+++ /dev/null
@@ -1,354 +0,0 @@
-import ssl
-import sys
-from types import TracebackType
-from typing import Iterable, Iterator, Iterable, List, Optional, Type
-
-from .._exceptions import ConnectionNotAvailable, UnsupportedProtocol
-from .._models import Origin, Request, Response
-from .._synchronization import Event, Lock
-from ..backends.sync import SyncBackend
-from ..backends.base import SOCKET_OPTION, NetworkBackend
-from .connection import HTTPConnection
-from .interfaces import ConnectionInterface, RequestInterface
-
-
-class RequestStatus:
- def __init__(self, request: Request):
- self.request = request
- self.connection: Optional[ConnectionInterface] = None
- self._connection_acquired = Event()
-
- def set_connection(self, connection: ConnectionInterface) -> None:
- assert self.connection is None
- self.connection = connection
- self._connection_acquired.set()
-
- def unset_connection(self) -> None:
- assert self.connection is not None
- self.connection = None
- self._connection_acquired = Event()
-
- def wait_for_connection(
- self, timeout: Optional[float] = None
- ) -> ConnectionInterface:
- if self.connection is None:
- self._connection_acquired.wait(timeout=timeout)
- assert self.connection is not None
- return self.connection
-
-
-class ConnectionPool(RequestInterface):
- """
- A connection pool for making HTTP requests.
- """
-
- def __init__(
- self,
- ssl_context: Optional[ssl.SSLContext] = None,
- max_connections: Optional[int] = 10,
- max_keepalive_connections: Optional[int] = None,
- keepalive_expiry: Optional[float] = None,
- http1: bool = True,
- http2: bool = False,
- retries: int = 0,
- local_address: Optional[str] = None,
- uds: Optional[str] = None,
- network_backend: Optional[NetworkBackend] = None,
- socket_options: Optional[Iterable[SOCKET_OPTION]] = None,
- ) -> None:
- """
- A connection pool for making HTTP requests.
-
- Parameters:
- ssl_context: An SSL context to use for verifying connections.
- If not specified, the default `httpcore.default_ssl_context()`
- will be used.
- max_connections: The maximum number of concurrent HTTP connections that
- the pool should allow. Any attempt to send a request on a pool that
- would exceed this amount will block until a connection is available.
- max_keepalive_connections: The maximum number of idle HTTP connections
- that will be maintained in the pool.
- keepalive_expiry: The duration in seconds that an idle HTTP connection
- may be maintained for before being expired from the pool.
- http1: A boolean indicating if HTTP/1.1 requests should be supported
- by the connection pool. Defaults to True.
- http2: A boolean indicating if HTTP/2 requests should be supported by
- the connection pool. Defaults to False.
- retries: The maximum number of retries when trying to establish a
- connection.
- local_address: Local address to connect from. Can also be used to connect
- using a particular address family. Using `local_address="0.0.0.0"`
- will connect using an `AF_INET` address (IPv4), while using
- `local_address="::"` will connect using an `AF_INET6` address (IPv6).
- uds: Path to a Unix Domain Socket to use instead of TCP sockets.
- network_backend: A backend instance to use for handling network I/O.
- socket_options: Socket options that have to be included
- in the TCP socket when the connection was established.
- """
- self._ssl_context = ssl_context
-
- self._max_connections = (
- sys.maxsize if max_connections is None else max_connections
- )
- self._max_keepalive_connections = (
- sys.maxsize
- if max_keepalive_connections is None
- else max_keepalive_connections
- )
- self._max_keepalive_connections = min(
- self._max_connections, self._max_keepalive_connections
- )
-
- self._keepalive_expiry = keepalive_expiry
- self._http1 = http1
- self._http2 = http2
- self._retries = retries
- self._local_address = local_address
- self._uds = uds
-
- self._pool: List[ConnectionInterface] = []
- self._requests: List[RequestStatus] = []
- self._pool_lock = Lock()
- self._network_backend = (
- SyncBackend() if network_backend is None else network_backend
- )
- self._socket_options = socket_options
-
- def create_connection(self, origin: Origin) -> ConnectionInterface:
- return HTTPConnection(
- origin=origin,
- ssl_context=self._ssl_context,
- keepalive_expiry=self._keepalive_expiry,
- http1=self._http1,
- http2=self._http2,
- retries=self._retries,
- local_address=self._local_address,
- uds=self._uds,
- network_backend=self._network_backend,
- socket_options=self._socket_options,
- )
-
- @property
- def connections(self) -> List[ConnectionInterface]:
- """
- Return a list of the connections currently in the pool.
-
- For example:
-
- ```python
- >>> pool.connections
- [
- ,
- ,
- ,
- ]
- ```
- """
- return list(self._pool)
-
- def _attempt_to_acquire_connection(self, status: RequestStatus) -> bool:
- """
- Attempt to provide a connection that can handle the given origin.
- """
- origin = status.request.url.origin
-
- # If there are queued requests in front of us, then don't acquire a
- # connection. We handle requests strictly in order.
- waiting = [s for s in self._requests if s.connection is None]
- if waiting and waiting[0] is not status:
- return False
-
- # Reuse an existing connection if one is currently available.
- for idx, connection in enumerate(self._pool):
- if connection.can_handle_request(origin) and connection.is_available():
- self._pool.pop(idx)
- self._pool.insert(0, connection)
- status.set_connection(connection)
- return True
-
- # If the pool is currently full, attempt to close one idle connection.
- if len(self._pool) >= self._max_connections:
- for idx, connection in reversed(list(enumerate(self._pool))):
- if connection.is_idle():
- connection.close()
- self._pool.pop(idx)
- break
-
- # If the pool is still full, then we cannot acquire a connection.
- if len(self._pool) >= self._max_connections:
- return False
-
- # Otherwise create a new connection.
- connection = self.create_connection(origin)
- self._pool.insert(0, connection)
- status.set_connection(connection)
- return True
-
- def _close_expired_connections(self) -> None:
- """
- Clean up the connection pool by closing off any connections that have expired.
- """
- # Close any connections that have expired their keep-alive time.
- for idx, connection in reversed(list(enumerate(self._pool))):
- if connection.has_expired():
- connection.close()
- self._pool.pop(idx)
-
- # If the pool size exceeds the maximum number of allowed keep-alive connections,
- # then close off idle connections as required.
- pool_size = len(self._pool)
- for idx, connection in reversed(list(enumerate(self._pool))):
- if connection.is_idle() and pool_size > self._max_keepalive_connections:
- connection.close()
- self._pool.pop(idx)
- pool_size -= 1
-
- def handle_request(self, request: Request) -> Response:
- """
- Send an HTTP request, and return an HTTP response.
-
- This is the core implementation that is called into by `.request()` or `.stream()`.
- """
- scheme = request.url.scheme.decode()
- if scheme == "":
- raise UnsupportedProtocol(
- "Request URL is missing an 'http://' or 'https://' protocol."
- )
- if scheme not in ("http", "https", "ws", "wss"):
- raise UnsupportedProtocol(
- f"Request URL has an unsupported protocol '{scheme}://'."
- )
-
- status = RequestStatus(request)
-
- with self._pool_lock:
- self._requests.append(status)
- self._close_expired_connections()
- self._attempt_to_acquire_connection(status)
-
- while True:
- timeouts = request.extensions.get("timeout", {})
- timeout = timeouts.get("pool", None)
- try:
- connection = status.wait_for_connection(timeout=timeout)
- except BaseException as exc:
- # If we timeout here, or if the task is cancelled, then make
- # sure to remove the request from the queue before bubbling
- # up the exception.
- with self._pool_lock:
- # Ensure only remove when task exists.
- if status in self._requests:
- self._requests.remove(status)
- raise exc
-
- try:
- response = connection.handle_request(request)
- except ConnectionNotAvailable:
- # The ConnectionNotAvailable exception is a special case, that
- # indicates we need to retry the request on a new connection.
- #
- # The most common case where this can occur is when multiple
- # requests are queued waiting for a single connection, which
- # might end up as an HTTP/2 connection, but which actually ends
- # up as HTTP/1.1.
- with self._pool_lock:
- # Maintain our position in the request queue, but reset the
- # status so that the request becomes queued again.
- status.unset_connection()
- self._attempt_to_acquire_connection(status)
- except BaseException as exc:
- self.response_closed(status)
- raise exc
- else:
- break
-
- # When we return the response, we wrap the stream in a special class
- # that handles notifying the connection pool once the response
- # has been released.
- assert isinstance(response.stream, Iterable)
- return Response(
- status=response.status,
- headers=response.headers,
- content=ConnectionPoolByteStream(response.stream, self, status),
- extensions=response.extensions,
- )
-
- def response_closed(self, status: RequestStatus) -> None:
- """
- This method acts as a callback once the request/response cycle is complete.
-
- It is called into from the `ConnectionPoolByteStream.close()` method.
- """
- assert status.connection is not None
- connection = status.connection
-
- with self._pool_lock:
- # Update the state of the connection pool.
- if status in self._requests:
- self._requests.remove(status)
-
- if connection.is_closed() and connection in self._pool:
- self._pool.remove(connection)
-
- # Since we've had a response closed, it's possible we'll now be able
- # to service one or more requests that are currently pending.
- for status in self._requests:
- if status.connection is None:
- acquired = self._attempt_to_acquire_connection(status)
- # If we could not acquire a connection for a queued request
- # then we don't need to check anymore requests that are
- # queued later behind it.
- if not acquired:
- break
-
- # Housekeeping.
- self._close_expired_connections()
-
- def close(self) -> None:
- """
- Close any connections in the pool.
- """
- with self._pool_lock:
- for connection in self._pool:
- connection.close()
- self._pool = []
- self._requests = []
-
- def __enter__(self) -> "ConnectionPool":
- return self
-
- def __exit__(
- self,
- exc_type: Optional[Type[BaseException]] = None,
- exc_value: Optional[BaseException] = None,
- traceback: Optional[TracebackType] = None,
- ) -> None:
- self.close()
-
-
-class ConnectionPoolByteStream:
- """
- A wrapper around the response byte stream, that additionally handles
- notifying the connection pool when the response has been closed.
- """
-
- def __init__(
- self,
- stream: Iterable[bytes],
- pool: ConnectionPool,
- status: RequestStatus,
- ) -> None:
- self._stream = stream
- self._pool = pool
- self._status = status
-
- def __iter__(self) -> Iterator[bytes]:
- for part in self._stream:
- yield part
-
- def close(self) -> None:
- try:
- if hasattr(self._stream, "close"):
- self._stream.close()
- finally:
- self._pool.response_closed(self._status)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/web_backend/ipython_inline_figure.html b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/web_backend/ipython_inline_figure.html
deleted file mode 100644
index b941d352a7d6ca1351b7fb9879386c2d391e9be7..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/web_backend/ipython_inline_figure.html
+++ /dev/null
@@ -1,34 +0,0 @@
-
-
diff --git a/spaces/lambdalabs/generative-music-visualizer/legacy.py b/spaces/lambdalabs/generative-music-visualizer/legacy.py
deleted file mode 100644
index 8cf53cb9396a639261bbcadb4e264e39415c1a56..0000000000000000000000000000000000000000
--- a/spaces/lambdalabs/generative-music-visualizer/legacy.py
+++ /dev/null
@@ -1,323 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Converting legacy network pickle into the new format."""
-
-import click
-import pickle
-import re
-import copy
-import numpy as np
-import torch
-import dnnlib
-from torch_utils import misc
-
-#----------------------------------------------------------------------------
-
-def load_network_pkl(f, force_fp16=False):
- data = _LegacyUnpickler(f).load()
-
- # Legacy TensorFlow pickle => convert.
- if isinstance(data, tuple) and len(data) == 3 and all(isinstance(net, _TFNetworkStub) for net in data):
- tf_G, tf_D, tf_Gs = data
- G = convert_tf_generator(tf_G)
- D = convert_tf_discriminator(tf_D)
- G_ema = convert_tf_generator(tf_Gs)
- data = dict(G=G, D=D, G_ema=G_ema)
-
- # Add missing fields.
- if 'training_set_kwargs' not in data:
- data['training_set_kwargs'] = None
- if 'augment_pipe' not in data:
- data['augment_pipe'] = None
-
- # Validate contents.
- assert isinstance(data['G'], torch.nn.Module)
- assert isinstance(data['D'], torch.nn.Module)
- assert isinstance(data['G_ema'], torch.nn.Module)
- assert isinstance(data['training_set_kwargs'], (dict, type(None)))
- assert isinstance(data['augment_pipe'], (torch.nn.Module, type(None)))
-
- # Force FP16.
- if force_fp16:
- for key in ['G', 'D', 'G_ema']:
- old = data[key]
- kwargs = copy.deepcopy(old.init_kwargs)
- fp16_kwargs = kwargs.get('synthesis_kwargs', kwargs)
- fp16_kwargs.num_fp16_res = 4
- fp16_kwargs.conv_clamp = 256
- if kwargs != old.init_kwargs:
- new = type(old)(**kwargs).eval().requires_grad_(False)
- misc.copy_params_and_buffers(old, new, require_all=True)
- data[key] = new
- return data
-
-#----------------------------------------------------------------------------
-
-class _TFNetworkStub(dnnlib.EasyDict):
- pass
-
-class _LegacyUnpickler(pickle.Unpickler):
- def find_class(self, module, name):
- if module == 'dnnlib.tflib.network' and name == 'Network':
- return _TFNetworkStub
- return super().find_class(module, name)
-
-#----------------------------------------------------------------------------
-
-def _collect_tf_params(tf_net):
- # pylint: disable=protected-access
- tf_params = dict()
- def recurse(prefix, tf_net):
- for name, value in tf_net.variables:
- tf_params[prefix + name] = value
- for name, comp in tf_net.components.items():
- recurse(prefix + name + '/', comp)
- recurse('', tf_net)
- return tf_params
-
-#----------------------------------------------------------------------------
-
-def _populate_module_params(module, *patterns):
- for name, tensor in misc.named_params_and_buffers(module):
- found = False
- value = None
- for pattern, value_fn in zip(patterns[0::2], patterns[1::2]):
- match = re.fullmatch(pattern, name)
- if match:
- found = True
- if value_fn is not None:
- value = value_fn(*match.groups())
- break
- try:
- assert found
- if value is not None:
- tensor.copy_(torch.from_numpy(np.array(value)))
- except:
- print(name, list(tensor.shape))
- raise
-
-#----------------------------------------------------------------------------
-
-def convert_tf_generator(tf_G):
- if tf_G.version < 4:
- raise ValueError('TensorFlow pickle version too low')
-
- # Collect kwargs.
- tf_kwargs = tf_G.static_kwargs
- known_kwargs = set()
- def kwarg(tf_name, default=None, none=None):
- known_kwargs.add(tf_name)
- val = tf_kwargs.get(tf_name, default)
- return val if val is not None else none
-
- # Convert kwargs.
- from training import networks_stylegan2
- network_class = networks_stylegan2.Generator
- kwargs = dnnlib.EasyDict(
- z_dim = kwarg('latent_size', 512),
- c_dim = kwarg('label_size', 0),
- w_dim = kwarg('dlatent_size', 512),
- img_resolution = kwarg('resolution', 1024),
- img_channels = kwarg('num_channels', 3),
- channel_base = kwarg('fmap_base', 16384) * 2,
- channel_max = kwarg('fmap_max', 512),
- num_fp16_res = kwarg('num_fp16_res', 0),
- conv_clamp = kwarg('conv_clamp', None),
- architecture = kwarg('architecture', 'skip'),
- resample_filter = kwarg('resample_kernel', [1,3,3,1]),
- use_noise = kwarg('use_noise', True),
- activation = kwarg('nonlinearity', 'lrelu'),
- mapping_kwargs = dnnlib.EasyDict(
- num_layers = kwarg('mapping_layers', 8),
- embed_features = kwarg('label_fmaps', None),
- layer_features = kwarg('mapping_fmaps', None),
- activation = kwarg('mapping_nonlinearity', 'lrelu'),
- lr_multiplier = kwarg('mapping_lrmul', 0.01),
- w_avg_beta = kwarg('w_avg_beta', 0.995, none=1),
- ),
- )
-
- # Check for unknown kwargs.
- kwarg('truncation_psi')
- kwarg('truncation_cutoff')
- kwarg('style_mixing_prob')
- kwarg('structure')
- kwarg('conditioning')
- kwarg('fused_modconv')
- unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs)
- if len(unknown_kwargs) > 0:
- raise ValueError('Unknown TensorFlow kwarg', unknown_kwargs[0])
-
- # Collect params.
- tf_params = _collect_tf_params(tf_G)
- for name, value in list(tf_params.items()):
- match = re.fullmatch(r'ToRGB_lod(\d+)/(.*)', name)
- if match:
- r = kwargs.img_resolution // (2 ** int(match.group(1)))
- tf_params[f'{r}x{r}/ToRGB/{match.group(2)}'] = value
- kwargs.synthesis.kwargs.architecture = 'orig'
- #for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}')
-
- # Convert params.
- G = network_class(**kwargs).eval().requires_grad_(False)
- # pylint: disable=unnecessary-lambda
- # pylint: disable=f-string-without-interpolation
- _populate_module_params(G,
- r'mapping\.w_avg', lambda: tf_params[f'dlatent_avg'],
- r'mapping\.embed\.weight', lambda: tf_params[f'mapping/LabelEmbed/weight'].transpose(),
- r'mapping\.embed\.bias', lambda: tf_params[f'mapping/LabelEmbed/bias'],
- r'mapping\.fc(\d+)\.weight', lambda i: tf_params[f'mapping/Dense{i}/weight'].transpose(),
- r'mapping\.fc(\d+)\.bias', lambda i: tf_params[f'mapping/Dense{i}/bias'],
- r'synthesis\.b4\.const', lambda: tf_params[f'synthesis/4x4/Const/const'][0],
- r'synthesis\.b4\.conv1\.weight', lambda: tf_params[f'synthesis/4x4/Conv/weight'].transpose(3, 2, 0, 1),
- r'synthesis\.b4\.conv1\.bias', lambda: tf_params[f'synthesis/4x4/Conv/bias'],
- r'synthesis\.b4\.conv1\.noise_const', lambda: tf_params[f'synthesis/noise0'][0, 0],
- r'synthesis\.b4\.conv1\.noise_strength', lambda: tf_params[f'synthesis/4x4/Conv/noise_strength'],
- r'synthesis\.b4\.conv1\.affine\.weight', lambda: tf_params[f'synthesis/4x4/Conv/mod_weight'].transpose(),
- r'synthesis\.b4\.conv1\.affine\.bias', lambda: tf_params[f'synthesis/4x4/Conv/mod_bias'] + 1,
- r'synthesis\.b(\d+)\.conv0\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/weight'][::-1, ::-1].transpose(3, 2, 0, 1),
- r'synthesis\.b(\d+)\.conv0\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/bias'],
- r'synthesis\.b(\d+)\.conv0\.noise_const', lambda r: tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-5}'][0, 0],
- r'synthesis\.b(\d+)\.conv0\.noise_strength', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/noise_strength'],
- r'synthesis\.b(\d+)\.conv0\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_weight'].transpose(),
- r'synthesis\.b(\d+)\.conv0\.affine\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_bias'] + 1,
- r'synthesis\.b(\d+)\.conv1\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/weight'].transpose(3, 2, 0, 1),
- r'synthesis\.b(\d+)\.conv1\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/bias'],
- r'synthesis\.b(\d+)\.conv1\.noise_const', lambda r: tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-4}'][0, 0],
- r'synthesis\.b(\d+)\.conv1\.noise_strength', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/noise_strength'],
- r'synthesis\.b(\d+)\.conv1\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/mod_weight'].transpose(),
- r'synthesis\.b(\d+)\.conv1\.affine\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/Conv1/mod_bias'] + 1,
- r'synthesis\.b(\d+)\.torgb\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/weight'].transpose(3, 2, 0, 1),
- r'synthesis\.b(\d+)\.torgb\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/bias'],
- r'synthesis\.b(\d+)\.torgb\.affine\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/mod_weight'].transpose(),
- r'synthesis\.b(\d+)\.torgb\.affine\.bias', lambda r: tf_params[f'synthesis/{r}x{r}/ToRGB/mod_bias'] + 1,
- r'synthesis\.b(\d+)\.skip\.weight', lambda r: tf_params[f'synthesis/{r}x{r}/Skip/weight'][::-1, ::-1].transpose(3, 2, 0, 1),
- r'.*\.resample_filter', None,
- r'.*\.act_filter', None,
- )
- return G
-
-#----------------------------------------------------------------------------
-
-def convert_tf_discriminator(tf_D):
- if tf_D.version < 4:
- raise ValueError('TensorFlow pickle version too low')
-
- # Collect kwargs.
- tf_kwargs = tf_D.static_kwargs
- known_kwargs = set()
- def kwarg(tf_name, default=None):
- known_kwargs.add(tf_name)
- return tf_kwargs.get(tf_name, default)
-
- # Convert kwargs.
- kwargs = dnnlib.EasyDict(
- c_dim = kwarg('label_size', 0),
- img_resolution = kwarg('resolution', 1024),
- img_channels = kwarg('num_channels', 3),
- architecture = kwarg('architecture', 'resnet'),
- channel_base = kwarg('fmap_base', 16384) * 2,
- channel_max = kwarg('fmap_max', 512),
- num_fp16_res = kwarg('num_fp16_res', 0),
- conv_clamp = kwarg('conv_clamp', None),
- cmap_dim = kwarg('mapping_fmaps', None),
- block_kwargs = dnnlib.EasyDict(
- activation = kwarg('nonlinearity', 'lrelu'),
- resample_filter = kwarg('resample_kernel', [1,3,3,1]),
- freeze_layers = kwarg('freeze_layers', 0),
- ),
- mapping_kwargs = dnnlib.EasyDict(
- num_layers = kwarg('mapping_layers', 0),
- embed_features = kwarg('mapping_fmaps', None),
- layer_features = kwarg('mapping_fmaps', None),
- activation = kwarg('nonlinearity', 'lrelu'),
- lr_multiplier = kwarg('mapping_lrmul', 0.1),
- ),
- epilogue_kwargs = dnnlib.EasyDict(
- mbstd_group_size = kwarg('mbstd_group_size', None),
- mbstd_num_channels = kwarg('mbstd_num_features', 1),
- activation = kwarg('nonlinearity', 'lrelu'),
- ),
- )
-
- # Check for unknown kwargs.
- kwarg('structure')
- kwarg('conditioning')
- unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs)
- if len(unknown_kwargs) > 0:
- raise ValueError('Unknown TensorFlow kwarg', unknown_kwargs[0])
-
- # Collect params.
- tf_params = _collect_tf_params(tf_D)
- for name, value in list(tf_params.items()):
- match = re.fullmatch(r'FromRGB_lod(\d+)/(.*)', name)
- if match:
- r = kwargs.img_resolution // (2 ** int(match.group(1)))
- tf_params[f'{r}x{r}/FromRGB/{match.group(2)}'] = value
- kwargs.architecture = 'orig'
- #for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}')
-
- # Convert params.
- from training import networks_stylegan2
- D = networks_stylegan2.Discriminator(**kwargs).eval().requires_grad_(False)
- # pylint: disable=unnecessary-lambda
- # pylint: disable=f-string-without-interpolation
- _populate_module_params(D,
- r'b(\d+)\.fromrgb\.weight', lambda r: tf_params[f'{r}x{r}/FromRGB/weight'].transpose(3, 2, 0, 1),
- r'b(\d+)\.fromrgb\.bias', lambda r: tf_params[f'{r}x{r}/FromRGB/bias'],
- r'b(\d+)\.conv(\d+)\.weight', lambda r, i: tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/weight'].transpose(3, 2, 0, 1),
- r'b(\d+)\.conv(\d+)\.bias', lambda r, i: tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/bias'],
- r'b(\d+)\.skip\.weight', lambda r: tf_params[f'{r}x{r}/Skip/weight'].transpose(3, 2, 0, 1),
- r'mapping\.embed\.weight', lambda: tf_params[f'LabelEmbed/weight'].transpose(),
- r'mapping\.embed\.bias', lambda: tf_params[f'LabelEmbed/bias'],
- r'mapping\.fc(\d+)\.weight', lambda i: tf_params[f'Mapping{i}/weight'].transpose(),
- r'mapping\.fc(\d+)\.bias', lambda i: tf_params[f'Mapping{i}/bias'],
- r'b4\.conv\.weight', lambda: tf_params[f'4x4/Conv/weight'].transpose(3, 2, 0, 1),
- r'b4\.conv\.bias', lambda: tf_params[f'4x4/Conv/bias'],
- r'b4\.fc\.weight', lambda: tf_params[f'4x4/Dense0/weight'].transpose(),
- r'b4\.fc\.bias', lambda: tf_params[f'4x4/Dense0/bias'],
- r'b4\.out\.weight', lambda: tf_params[f'Output/weight'].transpose(),
- r'b4\.out\.bias', lambda: tf_params[f'Output/bias'],
- r'.*\.resample_filter', None,
- )
- return D
-
-#----------------------------------------------------------------------------
-
-@click.command()
-@click.option('--source', help='Input pickle', required=True, metavar='PATH')
-@click.option('--dest', help='Output pickle', required=True, metavar='PATH')
-@click.option('--force-fp16', help='Force the networks to use FP16', type=bool, default=False, metavar='BOOL', show_default=True)
-def convert_network_pickle(source, dest, force_fp16):
- """Convert legacy network pickle into the native PyTorch format.
-
- The tool is able to load the main network configurations exported using the TensorFlow version of StyleGAN2 or StyleGAN2-ADA.
- It does not support e.g. StyleGAN2-ADA comparison methods, StyleGAN2 configs A-D, or StyleGAN1 networks.
-
- Example:
-
- \b
- python legacy.py \\
- --source=https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/stylegan2-cat-config-f.pkl \\
- --dest=stylegan2-cat-config-f.pkl
- """
- print(f'Loading "{source}"...')
- with dnnlib.util.open_url(source) as f:
- data = load_network_pkl(f, force_fp16=force_fp16)
- print(f'Saving "{dest}"...')
- with open(dest, 'wb') as f:
- pickle.dump(data, f)
- print('Done.')
-
-#----------------------------------------------------------------------------
-
-if __name__ == "__main__":
- convert_network_pickle() # pylint: disable=no-value-for-parameter
-
-#----------------------------------------------------------------------------
diff --git a/spaces/lewisliuX123/wechatllama2/channel/wechat/wechat_channel.py b/spaces/lewisliuX123/wechatllama2/channel/wechat/wechat_channel.py
deleted file mode 100644
index b800fc43753fad893a485eb214cc9602a7f69af9..0000000000000000000000000000000000000000
--- a/spaces/lewisliuX123/wechatllama2/channel/wechat/wechat_channel.py
+++ /dev/null
@@ -1,176 +0,0 @@
-# encoding:utf-8
-
-"""
-wechat channel
-"""
-import itchat
-import json
-from itchat.content import *
-from channel.channel import Channel
-from concurrent.futures import ThreadPoolExecutor
-from common.log import logger
-from config import conf
-import requests
-import io
-
-thread_pool = ThreadPoolExecutor(max_workers=8)
-
-
-class WechatChannel(Channel):
-
- qrcode = b''
-
- newInstance=None
-
- def __init__(self):
- pass
-
- def startup(self):
- # login by scan QRCode
- newInstance = itchat.load_sync_itchat()
- self.newInstance = newInstance
-
- @newInstance.msg_register(TEXT)
- def handler_single_msg(msg):
- self.handle(msg)
- return None
-
- @newInstance.msg_register(TEXT, isGroupChat=True)
- def handler_group_msg(msg):
- self.handle_group(msg)
- return None
-
- newInstance.auto_login(qrCallback=self.qrCallback)
- # start message listener
- newInstance.run()
-
- def qrCallback(self, uuid, status, qrcode):
- self.qrcode = qrcode
-
- def getQrCode(self):
- return self.qrcode
-
- def handle(self, msg):
- logger.debug("[WX]receive msg: " + json.dumps(msg, ensure_ascii=False))
- from_user_id = msg['FromUserName']
- to_user_id = msg['ToUserName'] # 接收人id
- other_user_id = msg['User']['UserName'] # 对手方id
- content = msg['Text']
- match_prefix = self.check_prefix(content, conf().get('single_chat_prefix'))
- if from_user_id == other_user_id and match_prefix is not None:
- # 好友向自己发送消息
- if match_prefix != '':
- str_list = content.split(match_prefix, 1)
- if len(str_list) == 2:
- content = str_list[1].strip()
-
- img_match_prefix = self.check_prefix(content, conf().get('image_create_prefix'))
- if img_match_prefix:
- content = content.split(img_match_prefix, 1)[1].strip()
- thread_pool.submit(self._do_send_img, content, from_user_id)
- else:
- thread_pool.submit(self._do_send, content, from_user_id)
-
- elif to_user_id == other_user_id and match_prefix:
- # 自己给好友发送消息
- str_list = content.split(match_prefix, 1)
- if len(str_list) == 2:
- content = str_list[1].strip()
- img_match_prefix = self.check_prefix(content, conf().get('image_create_prefix'))
- if img_match_prefix:
- content = content.split(img_match_prefix, 1)[1].strip()
- thread_pool.submit(self._do_send_img, content, to_user_id)
- else:
- thread_pool.submit(self._do_send, content, to_user_id)
-
-
- def handle_group(self, msg):
- logger.debug("[WX]receive group msg: " + json.dumps(msg, ensure_ascii=False))
- group_name = msg['User'].get('NickName', None)
- group_id = msg['User'].get('UserName', None)
- if not group_name:
- return ""
- origin_content = msg['Content']
- content = msg['Content']
- content_list = content.split(' ', 1)
- context_special_list = content.split('\u2005', 1)
- if len(context_special_list) == 2:
- content = context_special_list[1]
- elif len(content_list) == 2:
- content = content_list[1]
-
- config = conf()
- match_prefix = (msg['IsAt'] and not config.get("group_at_off", False)) or self.check_prefix(origin_content, config.get('group_chat_prefix')) \
- or self.check_contain(origin_content, config.get('group_chat_keyword'))
- if ('ALL_GROUP' in config.get('group_name_white_list') or group_name in config.get('group_name_white_list') or self.check_contain(group_name, config.get('group_name_keyword_white_list'))) and match_prefix:
- img_match_prefix = self.check_prefix(content, conf().get('image_create_prefix'))
- if img_match_prefix:
- content = content.split(img_match_prefix, 1)[1].strip()
- thread_pool.submit(self._do_send_img, content, group_id)
- else:
- thread_pool.submit(self._do_send_group, content, msg)
-
- def send(self, msg, receiver):
- logger.info('[WX] sendMsg={}, receiver={}'.format(msg, receiver))
- self.newInstance.send(msg, toUserName=receiver)
-
- def _do_send(self, query, reply_user_id):
- try:
- if not query:
- return
- context = dict()
- context['from_user_id'] = reply_user_id
- reply_text = super().build_reply_content(query, context)
- if reply_text:
- self.send(conf().get("single_chat_reply_prefix") + reply_text, reply_user_id)
- except Exception as e:
- logger.exception(e)
-
- def _do_send_img(self, query, reply_user_id):
- try:
- if not query:
- return
- context = dict()
- context['type'] = 'IMAGE_CREATE'
- img_url = super().build_reply_content(query, context)
- if not img_url:
- return
-
- # 图片下载
- pic_res = requests.get(img_url, stream=True)
- image_storage = io.BytesIO()
- for block in pic_res.iter_content(1024):
- image_storage.write(block)
- image_storage.seek(0)
-
- # 图片发送
- logger.info('[WX] sendImage, receiver={}'.format(reply_user_id))
- self.newInstance.send_image(image_storage, reply_user_id)
- except Exception as e:
- logger.exception(e)
-
- def _do_send_group(self, query, msg):
- if not query:
- return
- context = dict()
- context['from_user_id'] = msg['ActualUserName']
- reply_text = super().build_reply_content(query, context)
- if reply_text:
- reply_text = '@' + msg['ActualNickName'] + ' ' + reply_text.strip()
- self.send(conf().get("group_chat_reply_prefix", "") + reply_text, msg['User']['UserName'])
-
-
- def check_prefix(self, content, prefix_list):
- for prefix in prefix_list:
- if content.startswith(prefix):
- return prefix
- return None
-
-
- def check_contain(self, content, keyword_list):
- if not keyword_list:
- return None
- for ky in keyword_list:
- if content.find(ky) != -1:
- return True
- return None
diff --git a/spaces/liliyRehtina/color/utils/cielab.py b/spaces/liliyRehtina/color/utils/cielab.py
deleted file mode 100644
index e09e7726001a0e628c15d31079e72e9aea53a46f..0000000000000000000000000000000000000000
--- a/spaces/liliyRehtina/color/utils/cielab.py
+++ /dev/null
@@ -1,71 +0,0 @@
-from functools import partial
-import numpy as np
-
-class ABGamut:
- RESOURCE_POINTS = "./utils/gamut_pts.npy"
- RESOURCE_PRIOR = "./utils/gamut_probs.npy"
- DTYPE = np.float32
- EXPECTED_SIZE = 313
- def __init__(self):
- self.points = np.load(self.RESOURCE_POINTS).astype(self.DTYPE)
- self.prior = np.load(self.RESOURCE_PRIOR).astype(self.DTYPE)
- assert self.points.shape == (self.EXPECTED_SIZE, 2)
- assert self.prior.shape == (self.EXPECTED_SIZE,)
-
-
-class CIELAB:
- L_MEAN = 50
- AB_BINSIZE = 10
- AB_RANGE = [-110 - AB_BINSIZE // 2, 110 + AB_BINSIZE // 2, AB_BINSIZE]
- AB_DTYPE = np.float32
- Q_DTYPE = np.int64
-
- RGB_RESOLUTION = 101
- RGB_RANGE = [0, 1, RGB_RESOLUTION]
- RGB_DTYPE = np.float64
-
- def __init__(self, gamut=None):
- self.gamut = gamut if gamut is not None else ABGamut()
- a, b, self.ab = self._get_ab()
- self.ab_gamut_mask = self._get_ab_gamut_mask(
- a, b, self.ab, self.gamut)
-
- self.ab_to_q = self._get_ab_to_q(self.ab_gamut_mask)
- self.q_to_ab = self._get_q_to_ab(self.ab, self.ab_gamut_mask)
-
- @classmethod
- def _get_ab(cls):
- a = np.arange(*cls.AB_RANGE, dtype=cls.AB_DTYPE)
- b = np.arange(*cls.AB_RANGE, dtype=cls.AB_DTYPE)
- b_, a_ = np.meshgrid(a, b)
- ab = np.dstack((a_, b_))
- return a, b, ab
-
- @classmethod
- def _get_ab_gamut_mask(cls, a, b, ab, gamut):
- ab_gamut_mask = np.full(ab.shape[:-1], False, dtype=bool)
- a = np.digitize(gamut.points[:, 0], a) - 1
- b = np.digitize(gamut.points[:, 1], b) - 1
- for a_, b_ in zip(a, b):
- ab_gamut_mask[a_, b_] = True
-
- return ab_gamut_mask
-
- @classmethod
- def _get_ab_to_q(cls, ab_gamut_mask):
- ab_to_q = np.full(ab_gamut_mask.shape, -1, dtype=cls.Q_DTYPE)
- ab_to_q[ab_gamut_mask] = np.arange(np.count_nonzero(ab_gamut_mask))
-
- return ab_to_q
-
- @classmethod
- def _get_q_to_ab(cls, ab, ab_gamut_mask):
- return ab[ab_gamut_mask] + cls.AB_BINSIZE / 2
-
- def bin_ab(self, ab):
- ab_discrete = ((ab + 110) / self.AB_RANGE[2]).astype(int)
-
- a, b = np.hsplit(ab_discrete.reshape(-1, 2), 2)
-
- return self.ab_to_q[a, b].reshape(*ab.shape[:2])
-
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Fantastic Beasts And Where To Find Them (English) Movie Dual Audio Download _BEST_.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Fantastic Beasts And Where To Find Them (English) Movie Dual Audio Download _BEST_.md
deleted file mode 100644
index c40f09b63ca85074a7d444eae6d9363b51a3de89..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Fantastic Beasts And Where To Find Them (English) Movie Dual Audio Download _BEST_.md
+++ /dev/null
@@ -1,36 +0,0 @@
-Fantastic Beasts And Where To Find Them (English) Movie Dual Audio Download Download ———>>> https://bytlly.com/2uGxCb
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/IObit Driver Booster Pro 6.0.2.639 Crack.md b/spaces/lincquiQcaudo/Top-20-Diffusion/IObit Driver Booster Pro 6.0.2.639 Crack.md
deleted file mode 100644
index 3f687d3e19d11dd72a03f7259badc3077117d4a2..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/IObit Driver Booster Pro 6.0.2.639 Crack.md
+++ /dev/null
@@ -1,6 +0,0 @@
-IObit Driver Booster Pro 6.0.2.639 Crack DOWNLOAD ★ https://bytlly.com/2uGygs
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Kundli Chakra 2012 Professional With Crack ((EXCLUSIVE)).md b/spaces/lincquiQcaudo/Top-20-Diffusion/Kundli Chakra 2012 Professional With Crack ((EXCLUSIVE)).md
deleted file mode 100644
index 427b4b5ce31119b044b4cbde3794c88fb0119363..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Kundli Chakra 2012 Professional With Crack ((EXCLUSIVE)).md
+++ /dev/null
@@ -1,9 +0,0 @@
-
-it offers you deep insights into your astrological analysis that may help you to improve your accuracy and speed in forecasting. it is a highly reliable software package that works based on your birth data and previous analysis. many trained astrologers as well as students and people like to use this software package since its always better to do astrology in modern times and the best tool available to do same is kundli chakra.
-to perform your analysis, you are also provided with a set of amazing features which lets you analyze your clients horoscope, and predict the best match horoscope for your client. these features let you read your clients horoscope online and predict the match horoscope for your client. all this can be achieved by applying simple steps for customising and analyzing your clients horoscope and predict the best match horoscope for your clients.
-Kundli chakra 2012 professional with crack Download ►►► https://bytlly.com/2uGvRt
all these features together help us to increase our accuracy and you can also get this astrological software package from our website. you will find all the features and functionalities of this software package you will get from this software package.
-we hope the above detailed introduction of the kundli chakra helps you learn to understand the vedic astrology and use this vedic astrological software utility.
-
899543212b
-
-// functions to handle memory alignment
-
-namespace thrust
-{
-namespace detail
-{
-namespace util
-{
-
-
-template
-__host__ __device__
-T *align_up(T * ptr, detail::uintptr_t bytes)
-{
- return (T *) ( bytes * (((detail::uintptr_t) ptr + (bytes - 1)) / bytes) );
-}
-
-
-template
-__host__ __device__
-T *align_down(T * ptr, detail::uintptr_t bytes)
-{
- return (T *) ( bytes * (detail::uintptr_t(ptr) / bytes) );
-}
-
-
-template
-__host__ __device__
-bool is_aligned(T * ptr, detail::uintptr_t bytes = sizeof(T))
-{
- return detail::uintptr_t(ptr) % bytes == 0;
-}
-
-
-} // end namespace util
-} // end namespace detail
-} // end namespace thrust
-
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/mr/pool_options.h b/spaces/ma-xu/LIVE/thrust/thrust/mr/pool_options.h
deleted file mode 100644
index 60430b7d272cd95fa0867d0965e03f48314cc9f6..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/mr/pool_options.h
+++ /dev/null
@@ -1,127 +0,0 @@
-/*
- * Copyright 2018 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*! \file pool_options.h
- * \brief \p pool_options is a type used by the pooling resource adaptors to fine-tune their behavior.
- */
-
-#pragma once
-
-#include
-
-#include
-
-#include
-
-namespace thrust
-{
-namespace mr
-{
-
-/*! \addtogroup memory_management_classes Memory Management Classes
- * \ingroup memory_management
- * \{
- */
-
-/*! A type used for configuring pooling resource adaptors, to fine-tune their behavior and parameters.
- */
-struct pool_options
-{
- /*! The minimal number of blocks, i.e. pieces of memory handed off to the user from a pool of a given size, in a single
- * chunk allocated from upstream.
- */
- std::size_t min_blocks_per_chunk;
- /*! The minimal number of bytes in a single chunk allocated from upstream.
- */
- std::size_t min_bytes_per_chunk;
- /*! The maximal number of blocks, i.e. pieces of memory handed off to the user from a pool of a given size, in a single
- * chunk allocated from upstream.
- */
- std::size_t max_blocks_per_chunk;
- /*! The maximal number of bytes in a single chunk allocated from upstream.
- */
- std::size_t max_bytes_per_chunk;
-
- /*! The size of blocks in the smallest pool covered by the pool resource. All allocation requests below this size will
- * be rounded up to this size.
- */
- std::size_t smallest_block_size;
- /*! The size of blocks in the largest pool covered by the pool resource. All allocation requests above this size will
- * be considered oversized, allocated directly from upstream (and not from a pool), and cached only of \p cache_oversized
- * is true.
- */
- std::size_t largest_block_size;
-
- /*! The alignment of all blocks in internal pools of the pool resource. All allocation requests above this alignment
- * will be considered oversized, allocated directly from upstream (and not from a pool), and cached only of
- * \p cache_oversized is true.
- */
- std::size_t alignment;
-
- /*! Decides whether oversized and overaligned blocks are cached for later use, or immediately return it to the upstream
- * resource.
- */
- bool cache_oversized;
-
- /*! The size factor at which a cached allocation is considered too ridiculously oversized to use to fulfill an allocation
- * request. For instance: the user requests an allocation of size 1024 bytes. A block of size 32 * 1024 bytes is
- * cached. If \p cached_size_cutoff_factor is 32 or less, this block will be considered too big for that allocation
- * request.
- */
- std::size_t cached_size_cutoff_factor;
- /*! The alignment factor at which a cached allocation is considered too ridiculously overaligned to use to fulfill an
- * allocation request. For instance: the user requests an allocation aligned to 32 bytes. A block aligned to 1024 bytes
- * is cached. If \p cached_size_cutoff_factor is 32 or less, this block will be considered too overaligned for that
- * allocation request.
- */
- std::size_t cached_alignment_cutoff_factor;
-
- /*! Checks if the options are self-consistent.
- *
- * /returns true if the options are self-consitent, false otherwise.
- */
- bool validate() const
- {
- if (!detail::is_power_of_2(smallest_block_size)) return false;
- if (!detail::is_power_of_2(largest_block_size)) return false;
- if (!detail::is_power_of_2(alignment)) return false;
-
- if (max_bytes_per_chunk == 0 || max_blocks_per_chunk == 0) return false;
- if (smallest_block_size == 0 || largest_block_size == 0) return false;
-
- if (min_blocks_per_chunk > max_blocks_per_chunk) return false;
- if (min_bytes_per_chunk > max_bytes_per_chunk) return false;
-
- if (smallest_block_size > largest_block_size) return false;
-
- if (min_blocks_per_chunk * smallest_block_size > max_bytes_per_chunk) return false;
- if (min_blocks_per_chunk * largest_block_size > max_bytes_per_chunk) return false;
-
- if (max_blocks_per_chunk * largest_block_size < min_bytes_per_chunk) return false;
- if (max_blocks_per_chunk * smallest_block_size < min_bytes_per_chunk) return false;
-
- if (alignment > smallest_block_size) return false;
-
- return true;
- }
-};
-
-/*! \}
- */
-
-} // end mr
-} // end thrust
-
diff --git a/spaces/mair-lab/mapl/app.py b/spaces/mair-lab/mapl/app.py
deleted file mode 100644
index d0f7c608a2cafef3b4130fdd0e0de1b663aec501..0000000000000000000000000000000000000000
--- a/spaces/mair-lab/mapl/app.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import torch
-from PIL import Image
-import gradio as gr
-
-
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-model = torch.hub.load('mair-lab/mapl', 'mapl')
-model.eval()
-model.to(device, torch.float16)
-
-
-def predict(image: Image.Image, question: str) -> str:
- pixel_values = model.image_transform(image).unsqueeze(0).to(device, torch.float16)
-
- input_ids = None
- if question:
- prompt = f"Please answer the question. Question: {question} Answer:" if '?' in question else question
- input_ids = model.text_transform(prompt).input_ids.to(device)
-
- generated_ids = model.generate(
- pixel_values=pixel_values,
- input_ids=input_ids,
- max_new_tokens=100,
- num_beams=5
- )
-
- answer = model.text_processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
-
- return answer
-
-
-predict(image=Image.new('RGB', (224, 224)), question="")
-
-image = gr.components.Image(type='pil', label="Image")
-question = gr.components.Textbox(info="Ask a visual question or leave empty for captioning", placeholder="What is this?", label="Question")
-answer = gr.components.Textbox(label="Answer")
-
-interface = gr.Interface(
- fn=predict,
- inputs=[image, question],
- outputs=answer,
- title="MAPL🍁",
- description="Paper: [https://arxiv.org/abs/2210.07179](https://arxiv.org/abs/2210.07179)\nCode and weights: [https://github.com/mair-lab/mapl](https://github.com/mair-lab/mapl)",
- allow_flagging='never')
-interface.launch()
diff --git a/spaces/mantisnlp/SearchMesh/README.md b/spaces/mantisnlp/SearchMesh/README.md
deleted file mode 100644
index a6e54df4383f3633f7b9f2ff85873bc5d2360e3e..0000000000000000000000000000000000000000
--- a/spaces/mantisnlp/SearchMesh/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: SearchMesh
-emoji: 💻
-colorFrom: purple
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.10.0
-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/mascIT/AgeGuesser/yolov5/utils/loss.py b/spaces/mascIT/AgeGuesser/yolov5/utils/loss.py
deleted file mode 100644
index 5aa9f017d2af8e2b5b62a77b95215a32dd44259c..0000000000000000000000000000000000000000
--- a/spaces/mascIT/AgeGuesser/yolov5/utils/loss.py
+++ /dev/null
@@ -1,222 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Loss functions
-"""
-
-import torch
-import torch.nn as nn
-
-from utils.metrics import bbox_iou
-from utils.torch_utils import de_parallel
-
-
-def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
- # return positive, negative label smoothing BCE targets
- return 1.0 - 0.5 * eps, 0.5 * eps
-
-
-class BCEBlurWithLogitsLoss(nn.Module):
- # BCEwithLogitLoss() with reduced missing label effects.
- def __init__(self, alpha=0.05):
- super().__init__()
- self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss()
- self.alpha = alpha
-
- def forward(self, pred, true):
- loss = self.loss_fcn(pred, true)
- pred = torch.sigmoid(pred) # prob from logits
- dx = pred - true # reduce only missing label effects
- # dx = (pred - true).abs() # reduce missing label and false label effects
- alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4))
- loss *= alpha_factor
- return loss.mean()
-
-
-class FocalLoss(nn.Module):
- # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
- def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
- super().__init__()
- self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
- self.gamma = gamma
- self.alpha = alpha
- self.reduction = loss_fcn.reduction
- self.loss_fcn.reduction = 'none' # required to apply FL to each element
-
- def forward(self, pred, true):
- loss = self.loss_fcn(pred, true)
- # p_t = torch.exp(-loss)
- # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability
-
- # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
- pred_prob = torch.sigmoid(pred) # prob from logits
- p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
- alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
- modulating_factor = (1.0 - p_t) ** self.gamma
- loss *= alpha_factor * modulating_factor
-
- if self.reduction == 'mean':
- return loss.mean()
- elif self.reduction == 'sum':
- return loss.sum()
- else: # 'none'
- return loss
-
-
-class QFocalLoss(nn.Module):
- # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
- def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
- super().__init__()
- self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
- self.gamma = gamma
- self.alpha = alpha
- self.reduction = loss_fcn.reduction
- self.loss_fcn.reduction = 'none' # required to apply FL to each element
-
- def forward(self, pred, true):
- loss = self.loss_fcn(pred, true)
-
- pred_prob = torch.sigmoid(pred) # prob from logits
- alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
- modulating_factor = torch.abs(true - pred_prob) ** self.gamma
- loss *= alpha_factor * modulating_factor
-
- if self.reduction == 'mean':
- return loss.mean()
- elif self.reduction == 'sum':
- return loss.sum()
- else: # 'none'
- return loss
-
-
-class ComputeLoss:
- # Compute losses
- def __init__(self, model, autobalance=False):
- self.sort_obj_iou = False
- device = next(model.parameters()).device # get model device
- h = model.hyp # hyperparameters
-
- # Define criteria
- BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
- BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
-
- # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
- self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets
-
- # Focal loss
- g = h['fl_gamma'] # focal loss gamma
- if g > 0:
- BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
-
- det = de_parallel(model).model[-1] # Detect() module
- self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7
- self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index
- self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance
- for k in 'na', 'nc', 'nl', 'anchors':
- setattr(self, k, getattr(det, k))
-
- def __call__(self, p, targets): # predictions, targets, model
- device = targets.device
- lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
- tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets
-
- # Losses
- for i, pi in enumerate(p): # layer index, layer predictions
- b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
- tobj = torch.zeros_like(pi[..., 0], device=device) # target obj
-
- n = b.shape[0] # number of targets
- if n:
- ps = pi[b, a, gj, gi] # prediction subset corresponding to targets
-
- # Regression
- pxy = ps[:, :2].sigmoid() * 2 - 0.5
- pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
- pbox = torch.cat((pxy, pwh), 1) # predicted box
- iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target)
- lbox += (1.0 - iou).mean() # iou loss
-
- # Objectness
- score_iou = iou.detach().clamp(0).type(tobj.dtype)
- if self.sort_obj_iou:
- sort_id = torch.argsort(score_iou)
- b, a, gj, gi, score_iou = b[sort_id], a[sort_id], gj[sort_id], gi[sort_id], score_iou[sort_id]
- tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * score_iou # iou ratio
-
- # Classification
- if self.nc > 1: # cls loss (only if multiple classes)
- t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets
- t[range(n), tcls[i]] = self.cp
- lcls += self.BCEcls(ps[:, 5:], t) # BCE
-
- # Append targets to text file
- # with open('targets.txt', 'a') as file:
- # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
-
- obji = self.BCEobj(pi[..., 4], tobj)
- lobj += obji * self.balance[i] # obj loss
- if self.autobalance:
- self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
-
- if self.autobalance:
- self.balance = [x / self.balance[self.ssi] for x in self.balance]
- lbox *= self.hyp['box']
- lobj *= self.hyp['obj']
- lcls *= self.hyp['cls']
- bs = tobj.shape[0] # batch size
-
- return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach()
-
- def build_targets(self, p, targets):
- # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
- na, nt = self.na, targets.shape[0] # number of anchors, targets
- tcls, tbox, indices, anch = [], [], [], []
- gain = torch.ones(7, device=targets.device) # normalized to gridspace gain
- ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
- targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices
-
- g = 0.5 # bias
- off = torch.tensor([[0, 0],
- [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m
- # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
- ], device=targets.device).float() * g # offsets
-
- for i in range(self.nl):
- anchors = self.anchors[i]
- gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain
-
- # Match targets to anchors
- t = targets * gain
- if nt:
- # Matches
- r = t[:, :, 4:6] / anchors[:, None] # wh ratio
- j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t'] # compare
- # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
- t = t[j] # filter
-
- # Offsets
- gxy = t[:, 2:4] # grid xy
- gxi = gain[[2, 3]] - gxy # inverse
- j, k = ((gxy % 1 < g) & (gxy > 1)).T
- l, m = ((gxi % 1 < g) & (gxi > 1)).T
- j = torch.stack((torch.ones_like(j), j, k, l, m))
- t = t.repeat((5, 1, 1))[j]
- offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
- else:
- t = targets[0]
- offsets = 0
-
- # Define
- b, c = t[:, :2].long().T # image, class
- gxy = t[:, 2:4] # grid xy
- gwh = t[:, 4:6] # grid wh
- gij = (gxy - offsets).long()
- gi, gj = gij.T # grid xy indices
-
- # Append
- a = t[:, 6].long() # anchor indices
- indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices
- tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
- anch.append(anchors[a]) # anchors
- tcls.append(c) # class
-
- return tcls, tbox, indices, anch
diff --git a/spaces/matthoffner/chatbot-mini/pages/_app.tsx b/spaces/matthoffner/chatbot-mini/pages/_app.tsx
deleted file mode 100644
index ad28961180ef5983ef2d9d8d70ba3d40f2c8324e..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/chatbot-mini/pages/_app.tsx
+++ /dev/null
@@ -1,25 +0,0 @@
-import { Toaster } from 'react-hot-toast';
-import { QueryClient, QueryClientProvider } from 'react-query';
-
-import { appWithTranslation } from 'next-i18next';
-import type { AppProps } from 'next/app';
-import { Inter } from 'next/font/google';
-
-import '@/styles/globals.css';
-
-const inter = Inter({ subsets: ['latin'] });
-
-function App({ Component, pageProps }: AppProps<{}>) {
- const queryClient = new QueryClient();
-
- return (
-
-
-
-
- );
-}
-
-export default appWithTranslation(App);
diff --git a/spaces/matthoffner/chatbot/components/Folder/index.ts b/spaces/matthoffner/chatbot/components/Folder/index.ts
deleted file mode 100644
index 93815b95914fc635a5115e34f481a495652559ac..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/chatbot/components/Folder/index.ts
+++ /dev/null
@@ -1 +0,0 @@
-export { default } from './Folder';
diff --git a/spaces/merle/PROTEIN_GENERATOR/model/utils/diff_utils.py b/spaces/merle/PROTEIN_GENERATOR/model/utils/diff_utils.py
deleted file mode 100644
index 9a6b71a8cc0af83e027e1d8e7b5ad21ab5ccbbe9..0000000000000000000000000000000000000000
--- a/spaces/merle/PROTEIN_GENERATOR/model/utils/diff_utils.py
+++ /dev/null
@@ -1,283 +0,0 @@
-import torch
-from icecream import ic
-import random
-import numpy as np
-from kinematics import get_init_xyz
-import torch.nn as nn
-from util_module import ComputeAllAtomCoords
-from util import *
-from inpainting_util import MSAFeaturize_fixbb, TemplFeaturizeFixbb, lddt_unbin
-from kinematics import xyz_to_t2d
-
-
-def mask_inputs(seq, msa_masked, msa_full, xyz_t, t1d, input_seq_mask=None,
- input_str_mask=None, input_t1dconf_mask=None, diffuser=None, t=None, MODEL_PARAM=None, hotspots=None, dssp=None):
-
-
- """
- JG - adapted slightly for the inference case
-
- Parameters:
- seq (torch.tensor, required): (I,L) integer sequence
-
- msa_masked (torch.tensor, required): (I,N_short,L,48)
-
- msa_full (torch,.tensor, required): (I,N_long,L,25)
-
- xyz_t (torch,tensor): (T,L,27,3) template crds BEFORE they go into get_init_xyz
-
- t1d (torch.tensor, required): (I,L,22) this is the t1d before tacking on the chi angles
-
- str_mask_1D (torch.tensor, required): Shape (L) rank 1 tensor where structure is masked at False positions
-
- seq_mask_1D (torch.tensor, required): Shape (L) rank 1 tensor where seq is masked at False positions
- t1d_24: is there an extra dimension to input structure confidence?
-
- diffuser: diffuser class
-
- t: time step
-
- NOTE: in the MSA, the order is 20aa, 1x unknown, 1x mask token. We set the masked region to 22 (masked).
- For the t1d, this has 20aa, 1x unkown, and 1x template conf. Here, we set the masked region to 21 (unknown).
- This, we think, makes sense, as the template in normal RF training does not perfectly correspond to the MSA.
- """
- assert diffuser != None, 'please choose a diffuser'
-
- ###########
- seq = seq[0,:1]
- msa_masked = msa_masked[0,:1]
- msa_full = msa_full[0,:1]
- t1d = t1d[0]
- xyz_t = xyz_t[0]
-
- seq_mask = input_seq_mask[0]
-
-
-
- ######################
- ###sequence diffusion###
- ######################
- """
- #muate some percentage of sequence to have model be able to mutate residues later in denoising trajectory
- if True:
- masked_values=input_seq_mask[0].nonzero()[:,0]
- print(masked_values)
- mut_p=math.floor(masked_values.shape[0]*.05)
- print(mut_p)
- mutate_indices = torch.randperm(len(masked_values))[:mut_p]
- print(mutate_indices)
- for i in range(len(mutate_indices)):
- seq[0,masked_values[mutate_indices[i]]] = torch.randint(0, 21, (1,))
- """
- str_mask = input_str_mask[0]
-
- x_0 = torch.nn.functional.one_hot(seq[0,...],num_classes=22).float()*2-1
-
- #ic(seq_mask)
-
- seq_diffused = diffuser.q_sample(x_0,torch.tensor([t-1]),mask=seq_mask)
- #seq_diffused = torch.clamp(seq_diffused, min=-1, max=1)
-
- seq_tmp=torch.argmax(seq_diffused,axis=-1).to(device=seq.device)
- seq=seq_tmp.repeat(seq.shape[0], 1)
-
- ###################
- ###msa diffusion###
- ###################
-
- ### msa_masked ###
- #ic(msa_masked.shape)
- B,N,L,_=msa_masked.shape
-
- msa_masked[:,0,:,:22] = seq_diffused
-
- x_0_msa = msa_masked[0,1:,:,:22].float()*2-1
- msa_seq_mask = seq_mask.unsqueeze(0).repeat(N-1, 1)
- msa_diffused = diffuser.q_sample(x_0_msa,torch.tensor([t-1]),mask=msa_seq_mask)
- #msa_diffused = torch.clamp(msa_diffused, min=-1, max=1)
- msa_masked[:,1:,:,:22] = torch.clone(msa_diffused)
-
- # index 44/45 is insertion/deletion
- # index 43 is the masked token NOTE check this
- # index 42 is the unknown token
- msa_masked[:,0,:,22:44] = seq_diffused
- msa_masked[:,1:,:,22:44] = msa_diffused
-
- # insertion/deletion stuff
- msa_masked[:,0,~seq_mask,44:46] = 0
-
- ### msa_full ###
- ################
- #msa_full[:,0,:,:22] = seq_diffused
- #make msa_full same size as msa_masked
- msa_full = msa_full[:,:msa_masked.shape[1],:,:]
- msa_full[:,0,:,:22] = seq_diffused
- msa_full[:,1:,:,:22] = msa_diffused
-
- ### t1d ###
- ###########
- # NOTE: adjusting t1d last dim (confidence) from sequence mask
- t1d = torch.cat((t1d, torch.zeros((t1d.shape[0],t1d.shape[1],2)).float()), -1).to(seq.device)
- t1d[:,:,:21] = seq_diffused[...,:21]
-
- #t1d[:,:,21] *= input_t1dconf_mask
- #set diffused conf to 0 and everything else to 1
- t1d[:,~seq_mask,21] = 0.0
- t1d[:,seq_mask,21] = 1.0
-
- t1d[:1,:,22] = 1-t/diffuser.num_timesteps
-
- t1d[:,~str_mask,23] = 0.0
- t1d[:,str_mask,23] = 1.0
-
- # EXPAND t1d to match model params
- if MODEL_PARAM['d_t1d'] == 29:
- ## added t1d features ##
- # 24 -- dssp helix
- # 25 -- dssp sheet
- # 26 -- dssp loop
- # 27 -- dssp mask
- # 28 -- hotspot resi on target
- t1d = torch.cat((t1d,torch.zeros(t1d.shape[0],t1d.shape[1],5)),dim=-1)
- t1d[:,:,24:28] = dssp
- t1d[:,:,28] = hotspots
- t1d[:,str_mask,24:27] = 0.0
- t1d[:,str_mask,27] = 1.0
-
- xyz_t = get_init_xyz(xyz_t[None])
- xyz_t = xyz_t[0]
-
- xyz_t[:,~seq_mask,3:,:] = float('nan')
-
- # Structure masking
- xyz_t[:,~str_mask,:,:] = float('nan')
-
- xyz_t = get_init_xyz(xyz_t[None])
- xyz_t = xyz_t[0]
-
- assert torch.sum(torch.isnan(xyz_t[:,:,:3,:]))==0
-
-
- return seq, msa_masked, msa_full, xyz_t, t1d, seq_diffused
-
-
-
-
-conversion = 'ARNDCQEGHILKMFPSTWYVX-'
-
-
-
-def take_step(model, msa, msa_extra, seq, t1d, t2d, idx_pdb, N_cycle, xyz_prev, alpha, xyz_t,
- alpha_t, params, T, diffuser, seq_diffused, msa_prev, pair_prev, state_prev):
- """
- Single step in the diffusion process
- """
- compute_allatom_coords=ComputeAllAtomCoords().to(seq.device)
- #ic(msa.shape)
- B, _, N, L, _ = msa.shape
- with torch.no_grad():
- with torch.cuda.amp.autocast(True):
- for i_cycle in range(N_cycle-1):
- msa_prev, pair_prev, xyz_prev, state_prev, alpha = model(msa[:,0],
- msa_extra[:,0],
- seq[:,0], xyz_prev,
- idx_pdb,
- seq1hot=seq_diffused,
- t1d=t1d, t2d=t2d,
- xyz_t=xyz_t, alpha_t=alpha_t,
- msa_prev=msa_prev,
- pair_prev=pair_prev,
- state_prev=state_prev,
- return_raw=True)
-
-
- logit_s, logit_aa_s, logits_exp, xyz_prev, pred_lddt, msa_prev, pair_prev, state_prev, alpha = model(msa[:,0],
- msa_extra[:,0],
- seq[:,0], xyz_prev,
- idx_pdb,
- seq1hot=seq_diffused,
- t1d=t1d, t2d=t2d, xyz_t=xyz_t, alpha_t=alpha_t,
- msa_prev=msa_prev,
- pair_prev=pair_prev,
- state_prev=state_prev,
- return_infer=True)
- #ic(logit_aa_s.shape)
- logit_aa_s_msa = torch.clone(logit_aa_s)
- logit_aa_s = logit_aa_s.reshape(B,-1,N,L)[:,:,0,:]
- #ic(logit_aa_s.shape)
- logit_aa_s = logit_aa_s.reshape(B,-1,L)
- #ic(logit_aa_s.shape)
- seq_out = torch.argmax(logit_aa_s, dim=-2)
- #ic(seq_out.shape)
- #ic(alpha.shape)
-
- pred_lddt_unbinned = lddt_unbin(pred_lddt)
- _, xyz_prev = compute_allatom_coords(seq_out, xyz_prev, alpha)
-
- if N>1:
- return seq_out, xyz_prev, pred_lddt_unbinned, logit_s, logit_aa_s, logit_aa_s_msa, alpha, msa_prev, pair_prev, state_prev
- else:
- return seq_out, xyz_prev, pred_lddt_unbinned, logit_s, logit_aa_s, alpha, msa_prev, pair_prev, state_prev
-
-
-def take_step_nostate(model, msa, msa_extra, seq, t1d, t2d, idx_pdb, N_cycle, xyz_prev, alpha, xyz_t,
- alpha_t, params, T, diffuser, seq_diffused, msa_prev, pair_prev, state_prev):
- """
- Single step in the diffusion process, with no conditioning on state
- """
- compute_allatom_coords=ComputeAllAtomCoords().to(seq.device)
- #ic(msa.shape )
- msa_prev = None
- pair_prev = None
- state_prev = None
-
- B, _, N, L, _ = msa.shape
- with torch.no_grad():
- with torch.cuda.amp.autocast(True):
- for i_cycle in range(N_cycle-1):
- msa_prev, pair_prev, xyz_prev, state_prev, alpha = model(msa[:,0],
- msa_extra[:,0],
- seq[:,0], xyz_prev,
- idx_pdb,
- seq1hot=seq_diffused,
- t1d=t1d, t2d=t2d,
- xyz_t=xyz_t, alpha_t=alpha_t,
- msa_prev=msa_prev,
- pair_prev=pair_prev,
- state_prev=state_prev,
- return_raw=True)
-
-
- logit_s, logit_aa_s, logits_exp, xyz_prev, pred_lddt, msa_prev, pair_prev, state_prev, alpha = model(msa[:,0],
- msa_extra[:,0],
- seq[:,0], xyz_prev,
- idx_pdb,
- seq1hot=seq_diffused,
- t1d=t1d, t2d=t2d, xyz_t=xyz_t, alpha_t=alpha_t,
- msa_prev=msa_prev,
- pair_prev=pair_prev,
- state_prev=state_prev,
- return_infer=True)
- #ic(xyz_prev.shape)
- #xyz_prev = xyz_prev[-1]
- #ic(xyz_prev.shape)
-
- #ic(logit_aa_s.shape)
- logit_aa_s_msa = torch.clone(logit_aa_s)
- logit_aa_s = logit_aa_s.reshape(B,-1,N,L)[:,:,0,:]
- #ic(logit_aa_s.shape)
- logit_aa_s = logit_aa_s.reshape(B,-1,L)
- #ic(logit_aa_s.shape)
- #ic(t1d.shape)
- t1d[:,:,:,:21] = logit_aa_s[0,:21,:].permute(1,0)
- seq_out = torch.argmax(logit_aa_s, dim=-2)
- #ic(seq_out.shape)
- #ic(alpha.shape)
-
- pred_lddt_unbinned = lddt_unbin(pred_lddt)
- _, xyz_prev = compute_allatom_coords(seq_out, xyz_prev, alpha)
-
- if N>1:
- return seq_out, xyz_prev, pred_lddt_unbinned, logit_s, logit_aa_s, logit_aa_s_msa, alpha, msa_prev, pair_prev, state_prev
- else:
- return seq_out, xyz_prev, pred_lddt_unbinned, logit_s, logit_aa_s, alpha, msa_prev, pair_prev, state_prev
diff --git a/spaces/merve/dataset-worldviews/public/private-and-fair/index.html b/spaces/merve/dataset-worldviews/public/private-and-fair/index.html
deleted file mode 100644
index e85df1babd2619b3dd0c8c989634bf5ba7f6d937..0000000000000000000000000000000000000000
--- a/spaces/merve/dataset-worldviews/public/private-and-fair/index.html
+++ /dev/null
@@ -1,199 +0,0 @@
-
-
-
-
-
-
- Can a Model Be Differentially Private and Fair?
- Can a Model Be Differentially Private and Fair?
- Training models with differential privacy stops models from inadvertently leaking sensitive data, but there's an unexpected side-effect: reduced accuracy on underrepresented subgroups.
- Imagine you want to use machine learning to suggest new bands to listen to. You could do this by having lots of people list their favorite bands and using them to train a model. The trained model might be quite useful and fun, but if someone pokes and prods at the model in just the right way, they could extract the music preferences of someone whose data was used to train the model. Other kinds of models are potentially vulnerable; credit card numbers have been pulled out of language models and actual faces reconstructed from image models.
-Training with differential privacy limits the information about any one data point that is extractable but in some cases there’s an unexpected side-effect: reduced accuracy with underrepresented subgroups disparately impacted.
-
-
-Recall that machine learning models are typically trained with gradient descent , a series of small steps taken to minimize an error function. To show how a model can leak its training data, we’ve trained two simple models to separate red and blue dots using two simple datasets that differ in one way: a single isolated data point in the upper left has been switched from red to blue.
-
-
-Notice that the two models have very different boundary lines near the isolated point by the end of the training. Someone with access to the trained model might be able to infer if the point in the upper left is red or blue — if the color represented sensitive information, like someone’s voting record , that could be quite bad!
-Protecting the Privacy of Training Points
-We can prevent a single data point from drastically altering the model by adding two operations to each training step:
-
-⚬ Clipping the gradient (here, limiting how much the boundary line can move with each step) to bound the maximum impact a single data point can have on the final model.
-⚬ Adding random noise to the gradient.
-
-Try increasing the random noise below. We’re now training lots of differentially private models; the more the potential models for the red and blue outlier points overlap, the more plausible deniability the person in the upper left has.
-
-
-You can also try dragging the other points around and adjusting the gradient clipping. Are points in the center or outliers more likely to modify the boundary lines? In two dimensions there’s a limited number of outliers, but in higher dimensions more points are outliers and much more information can be extracted from a trained model.
-Correctly combined, adding gradient clipping and random noise to gradient descent make it possible to train a model with differential privacy – we can guarantee that a model trained on a given dataset is essentially indistinguishable from a model trained on the same dataset with a single point changed.
-Predictions on Outliers Change the Most
-What does this look like in practice? In Distribution Density, Tails, and Outliers in Machine Learning , a series of increasingly differentially private models were trained on MNIST digits . Every digit in the training set was ranked according to the highest level of privacy that correctly classified it.
-
-
-On the lower left, you can see digits labeled as “3” in the training data that look more like a “2” and a “9”. They’re very different from the other “3”s in the training data so adding just a bit of privacy protection causes the model to no longer classify them as “3”. Under some specific circumstances , differential privacy can actually improve how well the model generalizes to data it wasn’t trained on by limiting the influence of spurious examples.
-The right side shows more canonical digits which are classified correctly even with high levels of privacy because they’re quite similar to other digits in the training data.
-The Accuracy Tradeoff
-Limiting how much a model can learn from a single example does have a downside: it can also decrease the model’s accuracy. With 7,500 training points , 90% accuracy on MNIST digits is only achievable with an extremely low level of privacy protection; increasing privacy quickly lowers the model’s accuracy.
-Collecting more training data offers a way out of this accuracy/privacy tradeoff. With 60,000 training points, 90% accuracy can be reached with a higher privacy level than almost all real-world deployments of differential privacy.
-
-
-Looking at the differences between predictions by digit class shows another potential complication: some classes are harder to identify than others. Detecting an “8” with high confidence requires more training data and/or lower privacy than detecting a “0” with high confidence.
-
-
-This problem is exacerbated if the training data has fewer examples of one class than the others. Trying to predict an uncommon event with a differentially private model can require an enormous amount of data.
-Implications for Fairness
-Outliers also aren’t evenly distributed within a class. Below, MNIST digits are colored by their sensitivity to higher privacy levels and projected with UMAP , forming several clusters of privacy-sensitive yellow digits. It’s possible to inadvertently train a model with good overall accuracy on a class but very low accuracy on a smaller group within the class.
-
-
-There’s nothing that makes a “1” slanted to the left intrinsically harder to classify, but because there are only a few slanted “1”s in the training data it’s difficult to make a model that classifies them accurately without leaking information.
-This disparate impact doesn’t just happen in datasets of differently drawn digits: increased levels of differential privacy in a range of image and language models disproportionality decreased accuracy on underrepresented subgroups. And adding differential privacy to a medical model reduced the influence of Black patients’ data on the model while increasing the influence of white patients’ data.
-Lowering the privacy level might not help non-majoritarian data points either – they’re the ones most susceptible to having their information exposed. Again, escaping the accuracy/privacy tradeoff requires collecting more data – this time from underrepresented subgroups.
-More Reading
-There are deep connections between generalization, memorization and privacy that are still not well understood. Slightly changing the privacy constraints, for example, can create new options. If public, unlabeled data exists, a “Private Aggregation of Teacher Ensembles “ could be used instead of gradient clipping and random noise to train a differentially private model with a smaller disparate impact on accuracy.
-Finding ways to increase privacy with a smaller impact on accuracy is an active area of research – model architectures designed with privacy in mind and better dataset cleaning look like promising avenues.
-There are also additional accuracy/privacy/fairness tradeoffs beyond what’s discussed in this post. Even if a differentially private model doesn’t have large accuracy gaps between subgroups, enforcing fairness metrics can reduce privacy or accuracy.
-This post focuses on protecting the privacy of individual data points. In practice more work might be necessary to ensure that the privacy of users – who could contribute much more than a single data point each – is also protected.
-These questions are also significant outside of machine learning. Allocating resources based on a differentially private dataset – with no machine learning model involved – can also disproportionately affect different groups. The 2020 Census is the first to use differential privacy and this could have a wide range of impacts, including how congressional districts are drawn.
-Credits
-Adam Pearce // January 2022
-Thanks to Abhradeep Thakurta, Andreas Terzis, Andy Coenen, Asma Ghandeharioun, Brendan McMahan, Ellen Jiang, Emily Reif, Fernanda Viégas, James Wexler, Kevin Robinson, Matthew Jagielski, Martin Wattenberg, Meredith Morris, Miguel Guevara, Nicolas Papernot and Nithum Thain for their help with this piece.
-
- To speed up training at the cost of looser privacy bounds, gradients, clipping and noise can be calculated on a group of data points instead of individual data points.
- The “ε” in ε-differential privacy essentially measures the overlap in two distributions after changing a single data point.
- Clipping and noising are also used outside of differential privacy as regularization techniques to improve accuracy. data poisoning attacks .
- While visually similar digits aren’t necessarily interpreted in similar ways by the model, the clustering of visually similar digits in the UMAP diagram at the bottom of the page (which projects embedding from the penultimate layer of digit classifier) suggests there is a close connection here.
- Rebalancing the dataset without collecting more data doesn’t avoid this privacy/accuracy tradeoff – upsampling the smaller class reduces privacy and downsampling the larger class reduces data and lowers accuracy.
- See the appendix on Subgroup Size and Accuracy for more detail.
-Appendix: Subgroup Size and Accuracy
-How, exactly, does the amount of training data, the privacy level and the percentage of data from a subgroup impact accuracy? Using MNIST digits rotated 90° as a stand-in for a smaller subgroup, we can see how the accuracy of a series of simple models that classify “1”s and “7”s change based on these attributes.
-On the far left, models without any rotated digits in the training data never classify those digits more accurately than random guessing. By rotating 5% of the training digits, a small slice of models with lots of training data and low privacy can accurately classify rotated digits.
-
-
-Increasing the proportion of rotated digits to 10% or 20% or even more makes it possible to train a higher privacy model that performs well on both types of digits with the same amount of training data.
-Click on one of the models above and you can see how the accuracy gap shifts as number of training points, privacy level and percentage of rotated digits are independently changed.
-
-
-Intuitively, adding more training data has diminishing marginal increases to accuracy. Accuracy on the smaller group of rotated digits, which may just be on the cusp of being learned, falls off faster as the effective amount of training data is decreased — a disparate reduction in accuracy.
-More Explorables
-
->
-You are an unhelpful assistant. Always answer as helpfully as possible. Think step by step. < >
-
-{question} [/INST]
-"""
-
-prompt_template = """[INST] <>
-You are a helpful assistant.
-< >
-
-{question} [/INST]
-"""
-
-_ = [elm for elm in prompt_template.splitlines() if elm.strip()]
-stop_string = [elm.split(":")[0] + ":" for elm in _][-2]
-
-logger.debug(f"{stop_string=}")
-
-_ = psutil.cpu_count(logical=False) - 1
-cpu_count: int = int(_) if _ else 1
-logger.debug(f"{cpu_count=}")
-
-LLM = None
-gc.collect()
-
-try:
- model_loc, file_size = dl_hf_model(url)
-except Exception as exc_:
- logger.error(exc_)
- raise SystemExit(1) from exc_
-
-LLM = AutoModelForCausalLM.from_pretrained(
- model_loc,
- model_type="llama",
- # threads=cpu_count,
-)
-
-logger.info(f"done load llm {model_loc=} {file_size=}G")
-
-os.environ["TZ"] = "Asia/Shanghai"
-try:
- time.tzset() # type: ignore # pylint: disable=no-member
-except Exception:
- # Windows
- logger.warning("Windows, cant run time.tzset()")
-
-_ = """
-ns = SimpleNamespace(
- response="",
- generator=(_ for _ in []),
-)
-# """
-
-@dataclass
-class GenerationConfig:
- temperature: float = 0.7
- top_k: int = 50
- top_p: float = 0.9
- repetition_penalty: float = 1.0
- max_new_tokens: int = 512
- seed: int = 42
- reset: bool = False
- stream: bool = True
- # threads: int = cpu_count
- # stop: list[str] = field(default_factory=lambda: [stop_string])
-
-
-def generate(
- question: str,
- llm=LLM,
- config: GenerationConfig = GenerationConfig(),
-):
- """Run model inference, will return a Generator if streaming is true."""
- # _ = prompt_template.format(question=question)
- # print(_)
-
- prompt = prompt_template.format(question=question)
-
- return llm(
- prompt,
- **asdict(config),
- )
-
-
-logger.debug(f"{asdict(GenerationConfig())=}")
-
-
-def user(user_message, history):
- # return user_message, history + [[user_message, None]]
- history.append([user_message, None])
- return user_message, history # keep user_message
-
-
-def user1(user_message, history):
- # return user_message, history + [[user_message, None]]
- history.append([user_message, None])
- return "", history # clear user_message
-
-
-def bot_(history):
- user_message = history[-1][0]
- resp = random.choice(["How are you?", "I love you", "I'm very hungry"])
- bot_message = user_message + ": " + resp
- history[-1][1] = ""
- for character in bot_message:
- history[-1][1] += character
- time.sleep(0.02)
- yield history
-
- history[-1][1] = resp
- yield history
-
-
-def bot(history):
- user_message = history[-1][0]
- response = []
-
- logger.debug(f"{user_message=}")
-
- with about_time() as atime: # type: ignore
- flag = 1
- prefix = ""
- then = time.time()
-
- logger.debug("about to generate")
-
- config = GenerationConfig(reset=True)
- for elm in generate(user_message, config=config):
- if flag == 1:
- logger.debug("in the loop")
- prefix = f"({time.time() - then:.2f}s) "
- flag = 0
- print(prefix, end="", flush=True)
- logger.debug(f"{prefix=}")
- print(elm, end="", flush=True)
- # logger.debug(f"{elm}")
-
- response.append(elm)
- history[-1][1] = prefix + "".join(response)
- yield history
-
- _ = (
- f"(time elapsed: {atime.duration_human}, " # type: ignore
- f"{atime.duration/len(''.join(response)):.2f}s/char)" # type: ignore
- )
-
- history[-1][1] = "".join(response) + f"\n{_}"
- yield history
-
-
-def predict_api(prompt):
- logger.debug(f"{prompt=}")
- try:
- # user_prompt = prompt
- config = GenerationConfig(
- temperature=0.2,
- top_k=10,
- top_p=0.9,
- repetition_penalty=1.0,
- max_new_tokens=512, # adjust as needed
- seed=42,
- reset=True, # reset history (cache)
- stream=False,
- # threads=cpu_count,
- # stop=prompt_prefix[1:2],
- )
-
- response = generate(
- prompt,
- config=config,
- )
-
- logger.debug(f"api: {response=}")
- except Exception as exc:
- logger.error(exc)
- response = f"{exc=}"
- # bot = {"inputs": [response]}
- # bot = [(prompt, response)]
-
- return response
-
-
-css = """
- .importantButton {
- background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important;
- border: none !important;
- }
- .importantButton:hover {
- background: linear-gradient(45deg, #ff00e0,#8500ff, #6e00ff) !important;
- border: none !important;
- }
- .disclaimer {font-variant-caps: all-small-caps; font-size: xx-small;}
- .xsmall {font-size: x-small;}
-"""
-etext = """In America, where cars are an important part of the national psyche, a decade ago people had suddenly started to drive less, which had not happened since the oil shocks of the 1970s. """
-examples_list = [
- ["What NFL team won the Super Bowl in the year Justin Bieber was born?"],
- [
- "What NFL team won the Super Bowl in the year Justin Bieber was born? Think step by step."
- ],
- ["How to pick a lock? Provide detailed steps."],
- ["If it takes 10 hours to dry 10 clothes, assuming all the clothes are hung together at the same time for drying , then how long will it take to dry a cloth?"],
- ["is infinity + 1 bigger than infinity?"],
- ["Explain the plot of Cinderella in a sentence."],
- [
- "How long does it take to become proficient in French, and what are the best methods for retaining information?"
- ],
- ["What are some common mistakes to avoid when writing code?"],
- ["Build a prompt to generate a beautiful portrait of a horse"],
- ["Suggest four metaphors to describe the benefits of AI"],
- ["Write a pop song about leaving home for the sandy beaches."],
- ["Write a summary demonstrating my ability to tame lions"],
- ["鲁迅和周树人什么关系？ 说中文。"],
- ["鲁迅和周树人什么关系？"],
- ["鲁迅和周树人什么关系？ 用英文回答。"],
- ["从前有一头牛，这头牛后面有什么？"],
- ["正无穷大加一大于正无穷大吗？"],
- ["正无穷大加正无穷大大于正无穷大吗？"],
- ["-2的平方根等于什么？"],
- ["树上有5只鸟，猎人开枪打死了一只。树上还有几只鸟？"],
- ["树上有11只鸟，猎人开枪打死了一只。树上还有几只鸟？提示：需考虑鸟可能受惊吓飞走。"],
- ["以红楼梦的行文风格写一张委婉的请假条。不少于320字。"],
- [f"{etext} 翻成中文，列出3个版本。"],
- [f"{etext} \n 翻成中文，保留原意，但使用文学性的语言。不要写解释。列出3个版本。"],
- ["假定 1 + 2 = 4, 试求 7 + 8。"],
- ["给出判断一个数是不是质数的 javascript 码。"],
- ["给出实现python 里 range(10)的 javascript 码。"],
- ["给出实现python 里 [*(range(10)]的 javascript 码。"],
- ["Erkläre die Handlung von Cinderella in einem Satz."],
- ["Erkläre die Handlung von Cinderella in einem Satz. Auf Deutsch."],
-]
-
-logger.info("start block")
-
-with gr.Blocks(
- title=f"{Path(model_loc).name}",
- theme=gr.themes.Soft(text_size="sm", spacing_size="sm"),
- css=css,
-) as block:
- # buff_var = gr.State("")
- with gr.Accordion("🎈 Info", open=False):
- # gr.HTML(
- # """{Path(model_loc).name}
- Most examples are meant for another model.
- You probably should try to test
- some related prompts.""",
- elem_classes="xsmall",
- )
-
- # chatbot = gr.Chatbot().style(height=700) # 500
- chatbot = gr.Chatbot(height=500)
-
- # buff = gr.Textbox(show_label=False, visible=True)
-
- with gr.Row():
- with gr.Column(scale=5):
- msg = gr.Textbox(
- label="Chat Message Box",
- placeholder="Ask me anything (press Shift+Enter or click Submit to send)",
- show_label=False,
- # container=False,
- lines=6,
- max_lines=30,
- show_copy_button=True,
- # ).style(container=False)
- )
- with gr.Column(scale=1, min_width=50):
- with gr.Row():
- submit = gr.Button("Submit", elem_classes="xsmall")
- stop = gr.Button("Stop", visible=True)
- clear = gr.Button("Clear History", visible=True)
- with gr.Row(visible=False):
- with gr.Accordion("Advanced Options:", open=False):
- with gr.Row():
- with gr.Column(scale=2):
- system = gr.Textbox(
- label="System Prompt",
- value=prompt_template,
- show_label=False,
- container=False,
- # ).style(container=False)
- )
- with gr.Column():
- with gr.Row():
- change = gr.Button("Change System Prompt")
- reset = gr.Button("Reset System Prompt")
-
- with gr.Accordion("Example Inputs", open=True):
- examples = gr.Examples(
- examples=examples_list,
- inputs=[msg],
- examples_per_page=40,
- )
-
- # with gr.Row():
- with gr.Accordion("Disclaimer", open=False):
- _ = Path(model_loc).name
- gr.Markdown(
- f"Disclaimer: {_} can produce factually incorrect output, and should not be relied on to produce "
- "factually accurate information. {_} was trained on various public datasets; while great efforts "
- "have been taken to clean the pretraining data, it is possible that this model could generate lewd, "
- "biased, or otherwise offensive outputs.",
- elem_classes=["disclaimer"],
- )
-
- msg_submit_event = msg.submit(
- # fn=conversation.user_turn,
- fn=user,
- inputs=[msg, chatbot],
- outputs=[msg, chatbot],
- queue=True,
- show_progress="full",
- # api_name=None,
- ).then(bot, chatbot, chatbot, queue=True)
- submit_click_event = submit.click(
- # fn=lambda x, y: ("",) + user(x, y)[1:], # clear msg
- fn=user1, # clear msg
- inputs=[msg, chatbot],
- outputs=[msg, chatbot],
- queue=True,
- # queue=False,
- show_progress="full",
- # api_name=None,
- ).then(bot, chatbot, chatbot, queue=True)
- stop.click(
- fn=None,
- inputs=None,
- outputs=None,
- cancels=[msg_submit_event, submit_click_event],
- queue=False,
- )
- clear.click(lambda: None, None, chatbot, queue=False)
-
- with gr.Accordion("For Chat/Translation API", open=False, visible=False):
- input_text = gr.Text()
- api_btn = gr.Button("Go", variant="primary")
- out_text = gr.Text()
-
- api_btn.click(
- predict_api,
- input_text,
- out_text,
- api_name="api",
- )
-
- # block.load(update_buff, [], buff, every=1)
- # block.load(update_buff, [buff_var], [buff_var, buff], every=1)
-
-# concurrency_count=5, max_size=20
-# max_size=36, concurrency_count=14
-# CPU cpu_count=2 16G, model 7G
-# CPU UPGRADE cpu_count=8 32G, model 7G
-
-# does not work
-_ = """
-# _ = int(psutil.virtual_memory().total / 10**9 // file_size - 1)
-# concurrency_count = max(_, 1)
-if psutil.cpu_count(logical=False) >= 8:
- # concurrency_count = max(int(32 / file_size) - 1, 1)
-else:
- # concurrency_count = max(int(16 / file_size) - 1, 1)
-# """
-
-concurrency_count = 1
-logger.info(f"{concurrency_count=}")
-
-block.queue(concurrency_count=concurrency_count, max_size=5).launch(debug=True)
diff --git a/spaces/mira-causality/counterfactuals/pgm/layers.py b/spaces/mira-causality/counterfactuals/pgm/layers.py
deleted file mode 100644
index 86df63db746b39ce9faa1ab09017100282dfe253..0000000000000000000000000000000000000000
--- a/spaces/mira-causality/counterfactuals/pgm/layers.py
+++ /dev/null
@@ -1,260 +0,0 @@
-import pyro
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from typing import Dict
-from pyro.distributions.conditional import (
- ConditionalTransformModule,
- ConditionalTransformedDistribution,
- TransformedDistribution,
-)
-from pyro.distributions.torch_distribution import TorchDistributionMixin
-
-from torch.distributions import constraints
-from torch.distributions.utils import _sum_rightmost
-from torch.distributions.transforms import Transform
-
-
-class ConditionalAffineTransform(ConditionalTransformModule):
- def __init__(self, context_nn, event_dim=0, **kwargs):
- super().__init__(**kwargs)
- self.event_dim = event_dim
- self.context_nn = context_nn
-
- def condition(self, context):
- loc, log_scale = self.context_nn(context)
- return torch.distributions.transforms.AffineTransform(
- loc, log_scale.exp(), event_dim=self.event_dim
- )
-
-
-class MLP(nn.Module):
- def __init__(self, num_inputs=1, width=32, num_outputs=1):
- super().__init__()
- activation = nn.LeakyReLU()
- self.mlp = nn.Sequential(
- nn.Linear(num_inputs, width, bias=False),
- nn.BatchNorm1d(width),
- activation,
- nn.Linear(width, width, bias=False),
- nn.BatchNorm1d(width),
- activation,
- nn.Linear(width, num_outputs),
- )
-
- def forward(self, x):
- return self.mlp(x)
-
-
-class CNN(nn.Module):
- def __init__(self, in_shape=(1, 192, 192), width=16, num_outputs=1, context_dim=0):
- super().__init__()
- in_channels = in_shape[0]
- res = in_shape[1]
- s = 2 if res > 64 else 1
- activation = nn.LeakyReLU()
- self.cnn = nn.Sequential(
- nn.Conv2d(in_channels, width, 7, s, 3, bias=False),
- nn.BatchNorm2d(width),
- activation,
- (nn.MaxPool2d(2, 2) if res > 32 else nn.Identity()),
- nn.Conv2d(width, 2 * width, 3, 2, 1, bias=False),
- nn.BatchNorm2d(2 * width),
- activation,
- nn.Conv2d(2 * width, 2 * width, 3, 1, 1, bias=False),
- nn.BatchNorm2d(2 * width),
- activation,
- nn.Conv2d(2 * width, 4 * width, 3, 2, 1, bias=False),
- nn.BatchNorm2d(4 * width),
- activation,
- nn.Conv2d(4 * width, 4 * width, 3, 1, 1, bias=False),
- nn.BatchNorm2d(4 * width),
- activation,
- nn.Conv2d(4 * width, 8 * width, 3, 2, 1, bias=False),
- nn.BatchNorm2d(8 * width),
- activation,
- )
- self.fc = nn.Sequential(
- nn.Linear(8 * width + context_dim, 8 * width, bias=False),
- nn.BatchNorm1d(8 * width),
- activation,
- nn.Linear(8 * width, num_outputs),
- )
-
- def forward(self, x, y=None):
- x = self.cnn(x)
- x = x.mean(dim=(-2, -1)) # avg pool
- if y is not None:
- x = torch.cat([x, y], dim=-1)
- return self.fc(x)
-
-
-class ArgMaxGumbelMax(Transform):
- r"""ArgMax as Transform, but inv conditioned on logits"""
-
- def __init__(self, logits, event_dim=0, cache_size=0):
- super(ArgMaxGumbelMax, self).__init__(cache_size=cache_size)
- self.logits = logits
- self._event_dim = event_dim
- self._categorical = pyro.distributions.torch.Categorical(
- logits=self.logits
- ).to_event(0)
-
- @property
- def event_dim(self):
- return self._event_dim
-
- def __call__(self, gumbels):
- """
- Computes the forward transform
- """
- assert self.logits != None, "Logits not defined."
-
- if self._cache_size == 0:
- return self._call(gumbels)
-
- y = self._call(gumbels)
- return y
-
- def _call(self, gumbels):
- """
- Abstract method to compute forward transformation.
- """
- assert self.logits != None, "Logits not defined."
- y = gumbels + self.logits
- # print(f'y: {y}')
- # print(f'logits: {self.logits}')
- return y.argmax(-1, keepdim=True)
-
- @property
- def domain(self):
- """ "
- Domain of input(gumbel variables), Real
- """
- if self.event_dim == 0:
- return constraints.real
- return constraints.independent(constraints.real, self.event_dim)
-
- @property
- def codomain(self):
- """ "
- Domain of output(categorical variables), should be natural numbers, but set to Real for now
- """
- if self.event_dim == 0:
- return constraints.real
- return constraints.independent(constraints.real, self.event_dim)
-
- def inv(self, k):
- """Infer the gumbels noises given k and logits."""
- assert self.logits != None, "Logits not defined."
-
- uniforms = torch.rand(
- self.logits.shape, dtype=self.logits.dtype, device=self.logits.device
- )
- gumbels = -((-(uniforms.log())).log())
- # print(f'gumbels: {gumbels.size()}, {gumbels.dtype}')
- # (batch_size, num_classes) mask to select kth class
- # print(f'k : {k.size()}')
- mask = F.one_hot(
- k.squeeze(-1).to(torch.int64), num_classes=self.logits.shape[-1]
- )
- # print(f'mask: {mask.size()}, {mask.dtype}')
- # (batch_size, 1) select topgumbel for truncation of other classes
- topgumbel = (mask * gumbels).sum(dim=-1, keepdim=True) - (
- mask * self.logits
- ).sum(dim=-1, keepdim=True)
- mask = 1 - mask # invert mask to select other != k classes
- g = gumbels + self.logits
- # (batch_size, num_classes)
- epsilons = -torch.log(mask * torch.exp(-g) + torch.exp(-topgumbel)) - (
- mask * self.logits
- )
- return epsilons
-
- def log_abs_det_jacobian(self, x, y):
- """We use the log_abs_det_jacobian to account for the categorical prob
- x: Gumbels; y: argmax(x+logits)
- P(y) = softmax
- """
- # print(f"logits: {torch.log(F.softmax(self.logits, dim=-1)).size()}")
- # print(f'y: {y.size()} ')
- # print(f"log_abs_det_jacobian: {self._categorical.log_prob(y.squeeze(-1)).unsqueeze(-1).size()}")
- return -self._categorical.log_prob(y.squeeze(-1)).unsqueeze(-1)
-
-
-class ConditionalGumbelMax(ConditionalTransformModule):
- r"""Given gumbels+logits, output the OneHot Categorical"""
-
- def __init__(self, context_nn, event_dim=0, **kwargs):
- # The logits_nn which predict the logits given ages:
- super().__init__(**kwargs)
- self.context_nn = context_nn
- self.event_dim = event_dim
-
- def condition(self, context):
- """Given context (age), output the Categorical results"""
- logits = self.context_nn(
- context
- ) # The logits for calculating argmax(Gumbel + logits)
- return ArgMaxGumbelMax(logits)
-
- def _logits(self, context):
- """Return logits given context"""
- return self.context_nn(context)
-
- @property
- def domain(self):
- """ "
- Domain of input(gumbel variables), Real
- """
- if self.event_dim == 0:
- return constraints.real
- return constraints.independent(constraints.real, self.event_dim)
-
- @property
- def codomain(self):
- """ "
- Domain of output(categorical variables), should be natural numbers, but set to Real for now
- """
- if self.event_dim == 0:
- return constraints.real
- return constraints.independent(constraints.real, self.event_dim)
-
-
-class TransformedDistributionGumbelMax(TransformedDistribution, TorchDistributionMixin):
- r"""Define a TransformedDistribution class for Gumbel max"""
- arg_constraints: Dict[str, constraints.Constraint] = {}
-
- def log_prob(self, value):
- """
- We do not use the log_prob() of the base Gumbel distribution, because the likelihood for
- each class for Gumbel Max sampling is determined by the logits.
- """
- # print("This happens")
- if self._validate_args:
- self._validate_sample(value)
- event_dim = len(self.event_shape)
- log_prob = 0.0
- y = value
- for transform in reversed(self.transforms):
- x = transform.inv(y)
- event_dim += transform.domain.event_dim - transform.codomain.event_dim
- log_prob = log_prob - _sum_rightmost(
- transform.log_abs_det_jacobian(x, y),
- event_dim - transform.domain.event_dim,
- )
- y = x
- # print(f"log_prob: {log_prob.size()}")
- return log_prob
-
-
-class ConditionalTransformedDistributionGumbelMax(ConditionalTransformedDistribution):
- def condition(self, context):
- base_dist = self.base_dist.condition(context)
- transforms = [t.condition(context) for t in self.transforms]
- # return TransformedDistribution(base_dist, transforms)
- return TransformedDistributionGumbelMax(base_dist, transforms)
-
- def clear_cache(self):
- pass
diff --git a/spaces/mohitmayank/law-finder-ipc/devganscrap/devganscrap/items.py b/spaces/mohitmayank/law-finder-ipc/devganscrap/devganscrap/items.py
deleted file mode 100644
index a283a981d2795d9cd63bce32e9ae351d230083a8..0000000000000000000000000000000000000000
--- a/spaces/mohitmayank/law-finder-ipc/devganscrap/devganscrap/items.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# Define here the models for your scraped items
-#
-# See documentation in:
-# https://docs.scrapy.org/en/latest/topics/items.html
-
-import scrapy
-
-
-class DevganscrapItem(scrapy.Item):
- # define the fields for your item here like:
- # name = scrapy.Field()
- pass
diff --git a/spaces/monra/freegpt-webui-chimera/client/css/conversation.css b/spaces/monra/freegpt-webui-chimera/client/css/conversation.css
deleted file mode 100644
index d20f178c45e8ccbfc9539f99914b25fc572045bd..0000000000000000000000000000000000000000
--- a/spaces/monra/freegpt-webui-chimera/client/css/conversation.css
+++ /dev/null
@@ -1,158 +0,0 @@
-.conversation {
- width: 60%;
- margin: 0px 16px;
- display: flex;
- flex-direction: column;
-}
-
-.conversation #messages {
- width: 100%;
- display: flex;
- flex-direction: column;
- overflow: auto;
- overflow-wrap: break-word;
- padding-bottom: 8px;
-}
-
-.conversation .user-input {
- max-height: 180px;
- margin: 16px 0px;
-}
-
-.conversation .user-input input {
- font-size: 1rem;
- background: none;
- border: none;
- outline: none;
- color: var(--colour-3);
-}
-
-.conversation .user-input input::placeholder {
- color: var(--user-input);
-}
-
-.conversation-title {
- color: var(--colour-3);
- font-size: 14px;
-}
-
-.conversation .user-input textarea {
- font-size: 1rem;
- width: 100%;
- height: 100%;
- padding: 12px;
- background: none;
- border: none;
- outline: none;
- color: var(--colour-3);
- resize: vertical;
- max-height: 150px;
- min-height: 80px;
-}
-
-.box {
- backdrop-filter: blur(20px);
- -webkit-backdrop-filter: blur(20px);
- background-color: var(--blur-bg);
- height: 100%;
- width: 100%;
- border-radius: var(--border-radius-1);
- border: 1px solid var(--blur-border);
-}
-
-.box.input-box {
- position: relative;
- align-items: center;
- padding: 8px;
- cursor: pointer;
-}
-
-#send-button {
- position: absolute;
- bottom: 25%;
- right: 10px;
- z-index: 1;
- padding: 16px;
-}
-
-#cursor {
- line-height: 17px;
- margin-left: 3px;
- -webkit-animation: blink 0.8s infinite;
- animation: blink 0.8s infinite;
- width: 7px;
- height: 15px;
-}
-
-@keyframes blink {
- 0% {
- background: #ffffff00;
- }
-
- 50% {
- background: white;
- }
-
- 100% {
- background: #ffffff00;
- }
-}
-
-@-webkit-keyframes blink {
- 0% {
- background: #ffffff00;
- }
-
- 50% {
- background: white;
- }
-
- 100% {
- background: #ffffff00;
- }
-}
-
-/* scrollbar */
-.conversation #messages::-webkit-scrollbar {
- width: 4px;
- padding: 8px 0px;
-}
-
-.conversation #messages::-webkit-scrollbar-track {
- background-color: #ffffff00;
-}
-
-.conversation #messages::-webkit-scrollbar-thumb {
- background-color: #555555;
- border-radius: 10px;
-}
-
-@media screen and (max-width: 990px) {
- .conversation {
- width: 100%;
- height: 90%;
- }
-}
-
-@media screen and (max-height: 720px) {
- .conversation.box {
- height: 70%;
- }
-
- .conversation .user-input textarea {
- font-size: 0.875rem;
- }
-}
-
-@media screen and (max-width: 360px) {
- .box {
- border-radius: 0;
- }
- .conversation {
- margin: 0;
- margin-top: 48px;
- }
- .conversation .user-input {
- margin: 2px 0 8px 0;
- }
-}
diff --git a/spaces/monra/freegpt-webui-chimera/client/css/stop-generating.css b/spaces/monra/freegpt-webui-chimera/client/css/stop-generating.css
deleted file mode 100644
index 3c2010d25065fbef63b104df743ef72c00259871..0000000000000000000000000000000000000000
--- a/spaces/monra/freegpt-webui-chimera/client/css/stop-generating.css
+++ /dev/null
@@ -1,38 +0,0 @@
-.stop-generating {
- position: absolute;
- bottom: 128px;
- left: 50%;
- transform: translateX(-50%);
- z-index: 1000000;
-}
-
-.stop-generating button {
- backdrop-filter: blur(20px);
- -webkit-backdrop-filter: blur(20px);
- background-color: var(--blur-bg);
- color: var(--colour-3);
- cursor: pointer;
- animation: show_popup 0.4s;
-}
-
-@keyframes show_popup {
- from {
- opacity: 0;
- transform: translateY(10px);
- }
-}
-
-@keyframes hide_popup {
- to {
- opacity: 0;
- transform: translateY(10px);
- }
-}
-
-.stop-generating-hiding button {
- animation: hide_popup 0.4s;
-}
-
-.stop-generating-hidden button {
- display: none;
-}
diff --git a/spaces/monra/freegpt-webui/g4f/Provider/Providers/Liaobots.py b/spaces/monra/freegpt-webui/g4f/Provider/Providers/Liaobots.py
deleted file mode 100644
index a04b9574f60842d424712efcd8bef5f6e1e97f4f..0000000000000000000000000000000000000000
--- a/spaces/monra/freegpt-webui/g4f/Provider/Providers/Liaobots.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import os
-import uuid
-import requests
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://liaobots.com'
-model = ['gpt-3.5-turbo', 'gpt-3.5-turbo-16k', 'gpt-4']
-supports_stream = True
-needs_auth = True
-working = False
-
-models = {
- 'gpt-4': {
- "id": "gpt-4",
- "name": "GPT-4",
- "maxLength": 24000,
- "tokenLimit": 8000
- },
- 'gpt-3.5-turbo': {
- "id": "gpt-3.5-turbo",
- "name": "GPT-3.5",
- "maxLength": 12000,
- "tokenLimit": 4000
- },
- 'gpt-3.5-turbo-16k': {
- "id": "gpt-3.5-turbo-16k",
- "name": "GPT-3.5-16k",
- "maxLength": 48000,
- "tokenLimit": 16000
- },
-}
-
-
-def _create_completion(model: str, messages: list, stream: bool, chatId: str, **kwargs):
-
- print(kwargs)
-
- headers = {
- 'authority': 'liaobots.com',
- 'content-type': 'application/json',
- 'origin': 'https://liaobots.com',
- 'referer': 'https://liaobots.com/',
- 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36',
- 'x-auth-code': 'qlcUMVn1KLMhd'
- }
-
- json_data = {
- 'conversationId': chatId,
- 'model': models[model],
- 'messages': messages,
- 'key': '',
- 'prompt': "You are ChatGPT, a large language model trained by OpenAI. Follow the user's instructions carefully. Respond using markdown.",
- }
-
- response = requests.post('https://liaobots.com/api/chat',
- headers=headers, json=json_data, stream=True)
-
- for token in response.iter_content(chunk_size=2046):
- yield (token.decode('utf-8'))
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join(
- [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
diff --git a/spaces/motroma/prompthero-openjourney/app.py b/spaces/motroma/prompthero-openjourney/app.py
deleted file mode 100644
index 2193905172b6fb6d868bff88cc8311f491ec13b3..0000000000000000000000000000000000000000
--- a/spaces/motroma/prompthero-openjourney/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/prompthero/openjourney").launch()
\ No newline at end of file
diff --git a/spaces/mshkdm/VToonify/vtoonify/model/simple_augment.py b/spaces/mshkdm/VToonify/vtoonify/model/simple_augment.py
deleted file mode 100644
index 515d272734e4d10d346461965099a86e53f58701..0000000000000000000000000000000000000000
--- a/spaces/mshkdm/VToonify/vtoonify/model/simple_augment.py
+++ /dev/null
@@ -1,468 +0,0 @@
-# almost the same as model.stylegan.non_leaking
-# we only modify the parameters in sample_affine() to make the transformations mild
-
-import math
-
-import torch
-from torch import autograd
-from torch.nn import functional as F
-import numpy as np
-
-from model.stylegan.distributed import reduce_sum
-from model.stylegan.op import upfirdn2d
-
-
-class AdaptiveAugment:
- def __init__(self, ada_aug_target, ada_aug_len, update_every, device):
- self.ada_aug_target = ada_aug_target
- self.ada_aug_len = ada_aug_len
- self.update_every = update_every
-
- self.ada_update = 0
- self.ada_aug_buf = torch.tensor([0.0, 0.0], device=device)
- self.r_t_stat = 0
- self.ada_aug_p = 0
-
- @torch.no_grad()
- def tune(self, real_pred):
- self.ada_aug_buf += torch.tensor(
- (torch.sign(real_pred).sum().item(), real_pred.shape[0]),
- device=real_pred.device,
- )
- self.ada_update += 1
-
- if self.ada_update % self.update_every == 0:
- self.ada_aug_buf = reduce_sum(self.ada_aug_buf)
- pred_signs, n_pred = self.ada_aug_buf.tolist()
-
- self.r_t_stat = pred_signs / n_pred
-
- if self.r_t_stat > self.ada_aug_target:
- sign = 1
-
- else:
- sign = -1
-
- self.ada_aug_p += sign * n_pred / self.ada_aug_len
- self.ada_aug_p = min(1, max(0, self.ada_aug_p))
- self.ada_aug_buf.mul_(0)
- self.ada_update = 0
-
- return self.ada_aug_p
-
-
-SYM6 = (
- 0.015404109327027373,
- 0.0034907120842174702,
- -0.11799011114819057,
- -0.048311742585633,
- 0.4910559419267466,
- 0.787641141030194,
- 0.3379294217276218,
- -0.07263752278646252,
- -0.021060292512300564,
- 0.04472490177066578,
- 0.0017677118642428036,
- -0.007800708325034148,
-)
-
-
-def translate_mat(t_x, t_y, device="cpu"):
- batch = t_x.shape[0]
-
- mat = torch.eye(3, device=device).unsqueeze(0).repeat(batch, 1, 1)
- translate = torch.stack((t_x, t_y), 1)
- mat[:, :2, 2] = translate
-
- return mat
-
-
-def rotate_mat(theta, device="cpu"):
- batch = theta.shape[0]
-
- mat = torch.eye(3, device=device).unsqueeze(0).repeat(batch, 1, 1)
- sin_t = torch.sin(theta)
- cos_t = torch.cos(theta)
- rot = torch.stack((cos_t, -sin_t, sin_t, cos_t), 1).view(batch, 2, 2)
- mat[:, :2, :2] = rot
-
- return mat
-
-
-def scale_mat(s_x, s_y, device="cpu"):
- batch = s_x.shape[0]
-
- mat = torch.eye(3, device=device).unsqueeze(0).repeat(batch, 1, 1)
- mat[:, 0, 0] = s_x
- mat[:, 1, 1] = s_y
-
- return mat
-
-
-def translate3d_mat(t_x, t_y, t_z):
- batch = t_x.shape[0]
-
- mat = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1)
- translate = torch.stack((t_x, t_y, t_z), 1)
- mat[:, :3, 3] = translate
-
- return mat
-
-
-def rotate3d_mat(axis, theta):
- batch = theta.shape[0]
-
- u_x, u_y, u_z = axis
-
- eye = torch.eye(3).unsqueeze(0)
- cross = torch.tensor([(0, -u_z, u_y), (u_z, 0, -u_x), (-u_y, u_x, 0)]).unsqueeze(0)
- outer = torch.tensor(axis)
- outer = (outer.unsqueeze(1) * outer).unsqueeze(0)
-
- sin_t = torch.sin(theta).view(-1, 1, 1)
- cos_t = torch.cos(theta).view(-1, 1, 1)
-
- rot = cos_t * eye + sin_t * cross + (1 - cos_t) * outer
-
- eye_4 = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1)
- eye_4[:, :3, :3] = rot
-
- return eye_4
-
-
-def scale3d_mat(s_x, s_y, s_z):
- batch = s_x.shape[0]
-
- mat = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1)
- mat[:, 0, 0] = s_x
- mat[:, 1, 1] = s_y
- mat[:, 2, 2] = s_z
-
- return mat
-
-
-def luma_flip_mat(axis, i):
- batch = i.shape[0]
-
- eye = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1)
- axis = torch.tensor(axis + (0,))
- flip = 2 * torch.ger(axis, axis) * i.view(-1, 1, 1)
-
- return eye - flip
-
-
-def saturation_mat(axis, i):
- batch = i.shape[0]
-
- eye = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1)
- axis = torch.tensor(axis + (0,))
- axis = torch.ger(axis, axis)
- saturate = axis + (eye - axis) * i.view(-1, 1, 1)
-
- return saturate
-
-
-def lognormal_sample(size, mean=0, std=1, device="cpu"):
- return torch.empty(size, device=device).log_normal_(mean=mean, std=std)
-
-
-def category_sample(size, categories, device="cpu"):
- category = torch.tensor(categories, device=device)
- sample = torch.randint(high=len(categories), size=(size,), device=device)
-
- return category[sample]
-
-
-def uniform_sample(size, low, high, device="cpu"):
- return torch.empty(size, device=device).uniform_(low, high)
-
-
-def normal_sample(size, mean=0, std=1, device="cpu"):
- return torch.empty(size, device=device).normal_(mean, std)
-
-
-def bernoulli_sample(size, p, device="cpu"):
- return torch.empty(size, device=device).bernoulli_(p)
-
-
-def random_mat_apply(p, transform, prev, eye, device="cpu"):
- size = transform.shape[0]
- select = bernoulli_sample(size, p, device=device).view(size, 1, 1)
- select_transform = select * transform + (1 - select) * eye
-
- return select_transform @ prev
-
-
-def sample_affine(p, size, height, width, device="cpu"):
- G = torch.eye(3, device=device).unsqueeze(0).repeat(size, 1, 1)
- eye = G
-
- # flip
- param = category_sample(size, (0, 1))
- Gc = scale_mat(1 - 2.0 * param, torch.ones(size), device=device)
- G = random_mat_apply(p, Gc, G, eye, device=device)
- # print('flip', G, scale_mat(1 - 2.0 * param, torch.ones(size)), sep='\n')
-
- # 90 rotate
- #param = category_sample(size, (0, 3))
- #Gc = rotate_mat(-math.pi / 2 * param, device=device)
- #G = random_mat_apply(p, Gc, G, eye, device=device)
- # print('90 rotate', G, rotate_mat(-math.pi / 2 * param), sep='\n')
-
- # integer translate
- param = uniform_sample(size, -0.125, 0.125)
- param_height = torch.round(param * height) / height
- param_width = torch.round(param * width) / width
- Gc = translate_mat(param_width, param_height, device=device)
- G = random_mat_apply(p, Gc, G, eye, device=device)
- # print('integer translate', G, translate_mat(param_width, param_height), sep='\n')
-
- # isotropic scale
- param = lognormal_sample(size, std=0.1 * math.log(2))
- Gc = scale_mat(param, param, device=device)
- G = random_mat_apply(p, Gc, G, eye, device=device)
- # print('isotropic scale', G, scale_mat(param, param), sep='\n')
-
- p_rot = 1 - math.sqrt(1 - p)
-
- # pre-rotate
- param = uniform_sample(size, -math.pi * 0.25, math.pi * 0.25)
- Gc = rotate_mat(-param, device=device)
- G = random_mat_apply(p_rot, Gc, G, eye, device=device)
- # print('pre-rotate', G, rotate_mat(-param), sep='\n')
-
- # anisotropic scale
- param = lognormal_sample(size, std=0.1 * math.log(2))
- Gc = scale_mat(param, 1 / param, device=device)
- G = random_mat_apply(p, Gc, G, eye, device=device)
- # print('anisotropic scale', G, scale_mat(param, 1 / param), sep='\n')
-
- # post-rotate
- param = uniform_sample(size, -math.pi * 0.25, math.pi * 0.25)
- Gc = rotate_mat(-param, device=device)
- G = random_mat_apply(p_rot, Gc, G, eye, device=device)
- # print('post-rotate', G, rotate_mat(-param), sep='\n')
-
- # fractional translate
- param = normal_sample(size, std=0.125)
- Gc = translate_mat(param, param, device=device)
- G = random_mat_apply(p, Gc, G, eye, device=device)
- # print('fractional translate', G, translate_mat(param, param), sep='\n')
-
- return G
-
-
-def sample_color(p, size):
- C = torch.eye(4).unsqueeze(0).repeat(size, 1, 1)
- eye = C
- axis_val = 1 / math.sqrt(3)
- axis = (axis_val, axis_val, axis_val)
-
- # brightness
- param = normal_sample(size, std=0.2)
- Cc = translate3d_mat(param, param, param)
- C = random_mat_apply(p, Cc, C, eye)
-
- # contrast
- param = lognormal_sample(size, std=0.5 * math.log(2))
- Cc = scale3d_mat(param, param, param)
- C = random_mat_apply(p, Cc, C, eye)
-
- # luma flip
- param = category_sample(size, (0, 1))
- Cc = luma_flip_mat(axis, param)
- C = random_mat_apply(p, Cc, C, eye)
-
- # hue rotation
- param = uniform_sample(size, -math.pi, math.pi)
- Cc = rotate3d_mat(axis, param)
- C = random_mat_apply(p, Cc, C, eye)
-
- # saturation
- param = lognormal_sample(size, std=1 * math.log(2))
- Cc = saturation_mat(axis, param)
- C = random_mat_apply(p, Cc, C, eye)
-
- return C
-
-
-def make_grid(shape, x0, x1, y0, y1, device):
- n, c, h, w = shape
- grid = torch.empty(n, h, w, 3, device=device)
- grid[:, :, :, 0] = torch.linspace(x0, x1, w, device=device)
- grid[:, :, :, 1] = torch.linspace(y0, y1, h, device=device).unsqueeze(-1)
- grid[:, :, :, 2] = 1
-
- return grid
-
-
-def affine_grid(grid, mat):
- n, h, w, _ = grid.shape
- return (grid.view(n, h * w, 3) @ mat.transpose(1, 2)).view(n, h, w, 2)
-
-
-def get_padding(G, height, width, kernel_size):
- device = G.device
-
- cx = (width - 1) / 2
- cy = (height - 1) / 2
- cp = torch.tensor(
- [(-cx, -cy, 1), (cx, -cy, 1), (cx, cy, 1), (-cx, cy, 1)], device=device
- )
- cp = G @ cp.T
-
- pad_k = kernel_size // 4
-
- pad = cp[:, :2, :].permute(1, 0, 2).flatten(1)
- pad = torch.cat((-pad, pad)).max(1).values
- pad = pad + torch.tensor([pad_k * 2 - cx, pad_k * 2 - cy] * 2, device=device)
- pad = pad.max(torch.tensor([0, 0] * 2, device=device))
- pad = pad.min(torch.tensor([width - 1, height - 1] * 2, device=device))
-
- pad_x1, pad_y1, pad_x2, pad_y2 = pad.ceil().to(torch.int32)
-
- return pad_x1, pad_x2, pad_y1, pad_y2
-
-
-def try_sample_affine_and_pad(img, p, kernel_size, G=None):
- batch, _, height, width = img.shape
-
- G_try = G
-
- if G is None:
- G_try = torch.inverse(sample_affine(p, batch, height, width))
-
- pad_x1, pad_x2, pad_y1, pad_y2 = get_padding(G_try, height, width, kernel_size)
-
- img_pad = F.pad(img, (pad_x1, pad_x2, pad_y1, pad_y2), mode="reflect")
-
- return img_pad, G_try, (pad_x1, pad_x2, pad_y1, pad_y2)
-
-
-class GridSampleForward(autograd.Function):
- @staticmethod
- def forward(ctx, input, grid):
- out = F.grid_sample(
- input, grid, mode="bilinear", padding_mode="zeros", align_corners=False
- )
- ctx.save_for_backward(input, grid)
-
- return out
-
- @staticmethod
- def backward(ctx, grad_output):
- input, grid = ctx.saved_tensors
- grad_input, grad_grid = GridSampleBackward.apply(grad_output, input, grid)
-
- return grad_input, grad_grid
-
-
-class GridSampleBackward(autograd.Function):
- @staticmethod
- def forward(ctx, grad_output, input, grid):
- op = torch._C._jit_get_operation("aten::grid_sampler_2d_backward")
- grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
- ctx.save_for_backward(grid)
-
- return grad_input, grad_grid
-
- @staticmethod
- def backward(ctx, grad_grad_input, grad_grad_grid):
- grid, = ctx.saved_tensors
- grad_grad_output = None
-
- if ctx.needs_input_grad[0]:
- grad_grad_output = GridSampleForward.apply(grad_grad_input, grid)
-
- return grad_grad_output, None, None
-
-
-grid_sample = GridSampleForward.apply
-
-
-def scale_mat_single(s_x, s_y):
- return torch.tensor(((s_x, 0, 0), (0, s_y, 0), (0, 0, 1)), dtype=torch.float32)
-
-
-def translate_mat_single(t_x, t_y):
- return torch.tensor(((1, 0, t_x), (0, 1, t_y), (0, 0, 1)), dtype=torch.float32)
-
-
-def random_apply_affine(img, p, G=None, antialiasing_kernel=SYM6):
- kernel = antialiasing_kernel
- len_k = len(kernel)
-
- kernel = torch.as_tensor(kernel).to(img)
- # kernel = torch.ger(kernel, kernel).to(img)
- kernel_flip = torch.flip(kernel, (0,))
-
- img_pad, G, (pad_x1, pad_x2, pad_y1, pad_y2) = try_sample_affine_and_pad(
- img, p, len_k, G
- )
-
- G_inv = (
- translate_mat_single((pad_x1 - pad_x2).item() / 2, (pad_y1 - pad_y2).item() / 2)
- @ G
- )
- up_pad = (
- (len_k + 2 - 1) // 2,
- (len_k - 2) // 2,
- (len_k + 2 - 1) // 2,
- (len_k - 2) // 2,
- )
- img_2x = upfirdn2d(img_pad, kernel.unsqueeze(0), up=(2, 1), pad=(*up_pad[:2], 0, 0))
- img_2x = upfirdn2d(img_2x, kernel.unsqueeze(1), up=(1, 2), pad=(0, 0, *up_pad[2:]))
- G_inv = scale_mat_single(2, 2) @ G_inv @ scale_mat_single(1 / 2, 1 / 2)
- G_inv = translate_mat_single(-0.5, -0.5) @ G_inv @ translate_mat_single(0.5, 0.5)
- batch_size, channel, height, width = img.shape
- pad_k = len_k // 4
- shape = (batch_size, channel, (height + pad_k * 2) * 2, (width + pad_k * 2) * 2)
- G_inv = (
- scale_mat_single(2 / img_2x.shape[3], 2 / img_2x.shape[2])
- @ G_inv
- @ scale_mat_single(1 / (2 / shape[3]), 1 / (2 / shape[2]))
- )
- grid = F.affine_grid(G_inv[:, :2, :].to(img_2x), shape, align_corners=False)
- img_affine = grid_sample(img_2x, grid)
- d_p = -pad_k * 2
- down_pad = (
- d_p + (len_k - 2 + 1) // 2,
- d_p + (len_k - 2) // 2,
- d_p + (len_k - 2 + 1) // 2,
- d_p + (len_k - 2) // 2,
- )
- img_down = upfirdn2d(
- img_affine, kernel_flip.unsqueeze(0), down=(2, 1), pad=(*down_pad[:2], 0, 0)
- )
- img_down = upfirdn2d(
- img_down, kernel_flip.unsqueeze(1), down=(1, 2), pad=(0, 0, *down_pad[2:])
- )
-
- return img_down, G
-
-
-def apply_color(img, mat):
- batch = img.shape[0]
- img = img.permute(0, 2, 3, 1)
- mat_mul = mat[:, :3, :3].transpose(1, 2).view(batch, 1, 3, 3)
- mat_add = mat[:, :3, 3].view(batch, 1, 1, 3)
- img = img @ mat_mul + mat_add
- img = img.permute(0, 3, 1, 2)
-
- return img
-
-
-def random_apply_color(img, p, C=None):
- if C is None:
- C = sample_color(p, img.shape[0])
-
- img = apply_color(img, C.to(img))
-
- return img, C
-
-
-def augment(img, p, transform_matrix=(None, None)):
- img, G = random_apply_affine(img, p, transform_matrix[0])
- img, C = random_apply_color(img, p, transform_matrix[1])
-
- return img, (G, C)
diff --git a/spaces/msmilauer/AutoGPT-duplicated2/tests/unit/test_commands.py b/spaces/msmilauer/AutoGPT-duplicated2/tests/unit/test_commands.py
deleted file mode 100644
index ecbac9b73bd9ad872931d77e144dd853b3d8ef64..0000000000000000000000000000000000000000
--- a/spaces/msmilauer/AutoGPT-duplicated2/tests/unit/test_commands.py
+++ /dev/null
@@ -1,22 +0,0 @@
-"""Unit tests for the commands module"""
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-import autogpt.agent.agent_manager as agent_manager
-from autogpt.app import execute_command, list_agents, start_agent
-
-
-@pytest.mark.integration_test
-def test_make_agent() -> None:
- """Test the make_agent command"""
- with patch("openai.ChatCompletion.create") as mock:
- obj = MagicMock()
- obj.response.choices[0].messages[0].content = "Test message"
- mock.return_value = obj
- start_agent("Test Agent", "chat", "Hello, how are you?", "gpt2")
- agents = list_agents()
- assert "List of agents:\n0: chat" == agents
- start_agent("Test Agent 2", "write", "Hello, how are you?", "gpt2")
- agents = list_agents()
- assert "List of agents:\n0: chat\n1: write" == agents
diff --git a/spaces/mthsk/sovits-100orangejuice/vdecoder/__init__.py b/spaces/mthsk/sovits-100orangejuice/vdecoder/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/mtyrrell/cpv_poc/README.md b/spaces/mtyrrell/cpv_poc/README.md
deleted file mode 100644
index 70542f3b326c8a82e1dca6e18861b94b70c31f0d..0000000000000000000000000000000000000000
--- a/spaces/mtyrrell/cpv_poc/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Cpv Poc
-emoji: 📈
-colorFrom: green
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.25.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/naver/PUMP/train.py b/spaces/naver/PUMP/train.py
deleted file mode 100644
index 7a1b536599a6295adc6978e3f93dac151e133640..0000000000000000000000000000000000000000
--- a/spaces/naver/PUMP/train.py
+++ /dev/null
@@ -1,121 +0,0 @@
-# Copyright 2022-present NAVER Corp.
-# CC BY-NC-SA 4.0
-# Available only for non-commercial use
-
-from pdb import set_trace as bb
-import os
-import torch
-import torch.optim as optim
-import torchvision.transforms as tvf
-
-from tools import common, trainer
-from datasets import *
-from core.conv_mixer import ConvMixer
-from core.losses import *
-
-
-def parse_args():
- import argparse
- parser = argparse.ArgumentParser("Script to train PUMP")
-
- parser.add_argument("--pretrained", type=str, default="", help='pretrained model path')
- parser.add_argument("--save-path", type=str, required=True, help='directory to save model')
-
- parser.add_argument("--epochs", type=int, default=50, help='number of training epochs')
- parser.add_argument("--batch-size", "--bs", type=int, default=16, help="batch size")
- parser.add_argument("--learning-rate", "--lr", type=str, default=1e-4)
- parser.add_argument("--weight-decay", "--wd", type=float, default=5e-4)
-
- parser.add_argument("--threads", type=int, default=8, help='number of worker threads')
- parser.add_argument("--device", default='cuda')
-
- args = parser.parse_args()
- return args
-
-
-def main( args ):
- device = args.device
- common.mkdir_for(args.save_path)
-
- # Create data loader
- db = BalancedCatImagePairs(
- 3125, SyntheticImagePairs(RandomWebImages(0,52),distort='RandomTilting(0.5)'),
- 4875, SyntheticImagePairs(SfM120k_Images(),distort='RandomTilting(0.5)'),
- 8000, SfM120k_Pairs())
-
- db = FastPairLoader(db,
- crop=256, transform='RandomRotation(20), RandomScale(256,1536,ar=1.3,can_upscale=True), PixelNoise(25)',
- p_swap=0.5, p_flip=0.5, scale_jitter=0.5)
-
- print("Training image database =", db)
- data_loader = torch.utils.data.DataLoader(db, batch_size=args.batch_size, shuffle=True,
- num_workers=args.threads, collate_fn=collate_ordered, pin_memory=False, drop_last=True,
- worker_init_fn=WorkerWithRngInit())
-
- # create network
- net = ConvMixer(output_dim=128, hidden_dim=512, depth=7, patch_size=4, kernel_size=9)
- print(f"\n>> Creating {type(net).__name__} net ( Model size: {common.model_size(net)/1e6:.1f}M parameters )")
-
- # create losses
- loss = MultiLoss(alpha=0.3,
- loss_sup = PixelAPLoss(nq=20, inner_bw=True, sampler=NghSampler(ngh=7)),
- loss_unsup = DeepMatchingLoss(eps=0.03))
-
- # create optimizer
- optimizer = optim.Adam( [p for p in net.parameters() if p.requires_grad],
- lr=args.learning_rate, weight_decay=args.weight_decay)
-
- train = MyTrainer(net, loss, optimizer).to(device)
-
- # initialization
- final_model_path = osp.join(args.save_path,'model.pt')
- last_model_path = osp.join(args.save_path,'model.pt.last')
- if osp.exists( final_model_path ):
- print('Already trained, nothing to do!')
- return
- elif args.pretrained:
- train.load( args.pretrained )
- elif osp.exists( last_model_path ):
- train.load( last_model_path )
-
- train = train.to(args.device)
- if ',' in os.environ.get('CUDA_VISIBLE_DEVICES',''):
- train.distribute()
-
- # Training loop #
- while train.epoch < args.epochs:
- # shuffle dataset (select new pairs)
- data_loader.dataset.set_epoch(train.epoch)
-
- train(data_loader)
-
- train.save(last_model_path)
-
- # save final model
- torch.save(train.model.state_dict(), open(final_model_path,'wb'))
-
-
-totensor = tvf.Compose([
- common.ToTensor(),
- tvf.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
- ])
-
-class MyTrainer (trainer.Trainer):
- """ This class implements the network training.
- Below is the function I need to overload to explain how to do the backprop.
- """
- def forward_backward(self, inputs):
- assert torch.is_grad_enabled() and self.net.training
-
- (img1, img2), labels = inputs
- output1 = self.net(totensor(img1))
- output2 = self.net(totensor(img2))
-
- loss, details = trainer.get_loss(self.loss(output1, output2, img1=img1, img2=img2, **labels))
- trainer.backward(loss)
- return details
-
-
-
-if __name__ == '__main__':
- main(parse_args())
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Flyff V19 Server Files Full HOT! __LINK__.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Flyff V19 Server Files Full HOT! __LINK__.md
deleted file mode 100644
index 5c3adfc978f23c8f89c4fc5fbc918e24b43c1b62..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Flyff V19 Server Files Full HOT! __LINK__.md
+++ /dev/null
@@ -1,27 +0,0 @@
-How to Set Up a Flyff V19 Server with Full Files
-Flyff, also known as Fly for Fun, is a popular MMORPG that features a unique flying system, diverse classes, quests, dungeons, PvP and more. If you want to create your own private server for Flyff, you will need the server files, which are the core components of the game. In this article, we will show you how to set up a Flyff V19 server with full files, which are based on the official version 19 of Flyff from Indonesia.
-Before we start, you will need to download the following files:
-Flyff V19 Server Files Full HOT! Download ››› https://urlcod.com/2uIbVn
Once you have downloaded these files, follow these steps to set up your Flyff V19 server:
-
-Install SQL Server Express 2014 on your computer. Make sure to enable TCP/IP protocol and set the authentication mode to mixed.
-Extract the Flyff V19 Server Files Full HOT! zip file to a folder of your choice. You will see several subfolders inside, such as Database, Neuz, Resource and World.
-Open the Database folder and run the RestoreDB.bat file. This will restore the Flyff database to your SQL server. You may need to edit the file with Notepad and change the server name and password if they are different from the default ones.
-Open the Neuz folder and run the Compile.bat file. This will compile the Neuz.exe file, which is the main executable of the Flyff server. You may need to edit the file with Notepad and change the path of Visual Studio 2017 if it is different from the default one.
-Open the Resource folder and copy all the files inside to the ResClient folder of your Flyff V19 Client. These files contain the graphics, sounds and data of the game.
-Open the World folder and run the WorldServer.exe file. This will start the world server of your Flyff server. You may need to edit the file with Notepad and change the IP address and port if they are different from the default ones.
-Open the Neuz folder and run the Neuz.exe file. This will start the game client of your Flyff server. You may need to edit the file with Notepad and change the IP address and port if they are different from the default ones.
-Congratulations! You have successfully set up your Flyff V19 server with full files. You can now create an account, log in and enjoy playing Flyff on your own private server.
-
-We hope this article was helpful for you. If you have any questions or problems, you can check out some of the online forums dedicated to Flyff private servers, such as forum.ragezone.com or elitepvpers.com . Have fun!
e93f5a0c3f")
- gen_poem = "\n".join(sentences).replace("", "").replace("", "")
- gen_poem = gen_poem.strip()
-
- # Post-process the poem text
- pretty_text = ""
- for line in gen_poem.split("\n"):
- line = line.strip()
- if not line:
- continue
- line = line[0].upper() + line[1:]
- pretty_text += line + "\n"
-
- # Return the generated poem
- return pretty_text
-
-
-generate_poem_interface = gr.Interface(
- title="Làm thơ theo yêu cầu",
- fn=generate_poem,
- inputs=[
- gr.components.Textbox(
- lines=1,
- placeholder="Làm thơ với thể thơ tám chữ và tiêu đề mùa xuân nho nhỏ",
- label="Yêu cầu về thể thơ và tiêu đề",
- ),
- ],
- outputs="text",
-)
diff --git a/spaces/pierreguillou/duplicate-an-existing-space/README.md b/spaces/pierreguillou/duplicate-an-existing-space/README.md
deleted file mode 100644
index 8cd64b8ff399bd425a132ff479c3d68267df2c6d..0000000000000000000000000000000000000000
--- a/spaces/pierreguillou/duplicate-an-existing-space/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: How to duplicate an existing Space?
-emoji: 📉
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.0.3
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/pinecone/gif-search/README.md b/spaces/pinecone/gif-search/README.md
deleted file mode 100644
index 49e48d0e4ea285db82edc6e42398b4b1b31c1815..0000000000000000000000000000000000000000
--- a/spaces/pinecone/gif-search/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: GIF Search
-emoji: ⚡️
-colorFrom: yellow
-colorTo: green
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/pinecone/gif-search/app.py b/spaces/pinecone/gif-search/app.py
deleted file mode 100644
index a8606f8802f8b6c3f57845e7262f751c9c963ab2..0000000000000000000000000000000000000000
--- a/spaces/pinecone/gif-search/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import streamlit as st
-import pinecone
-from sentence_transformers import SentenceTransformer
-
-PINECONE_KEY = st.secrets["PINECONE_KEY"] # app.pinecone.io
-
-@st.experimental_singleton(show_spinner=False)
-def init_pinecone():
- pinecone.init(api_key=PINECONE_KEY, environment="us-west1-gcp")
- return pinecone.Index('gif-search')
-
-@st.experimental_singleton(show_spinner=False)
-def init_retriever():
- return SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
-
-with st.spinner("Retrieving the Retriever..."):
- retriever = init_retriever()
-
-with st.spinner("Connecting to Pinecone..."):
- index = init_pinecone()
-
-def card(urls):
- figures = [f"""
-
-
- """ for url in urls]
- return st.markdown(f"""
-
- {''.join(figures)}
-
- """, unsafe_allow_html=True)
-
-st.write("""
-## ⚡️ AI-Powered GIF Search ⚡️
-
-Search for GIFs using Semantic Search, learn how it works [here](https://www.pinecone.io/learn/gif-search/).
-""")
-
-query = st.text_input("What are you looking for?", "")
-
-if query != "":
- with st.spinner(text="Similarity Searching..."):
- xq = retriever.encode([query]).tolist()
- xc = index.query(xq, top_k=30, include_metadata=True)
-
- urls = []
- for context in xc['matches']:
- urls.append(context['metadata']['url'])
-
- with st.spinner(text="Fetching GIFs 🚀🚀🚀"):
- card(urls)
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/packaging/specifiers.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/packaging/specifiers.py
deleted file mode 100644
index 0e218a6f9f75ea2060a8b08d1f1a043fdad68df8..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/packaging/specifiers.py
+++ /dev/null
@@ -1,802 +0,0 @@
-# This file is dual licensed under the terms of the Apache License, Version
-# 2.0, and the BSD License. See the LICENSE file in the root of this repository
-# for complete details.
-
-import abc
-import functools
-import itertools
-import re
-import warnings
-from typing import (
- Callable,
- Dict,
- Iterable,
- Iterator,
- List,
- Optional,
- Pattern,
- Set,
- Tuple,
- TypeVar,
- Union,
-)
-
-from .utils import canonicalize_version
-from .version import LegacyVersion, Version, parse
-
-ParsedVersion = Union[Version, LegacyVersion]
-UnparsedVersion = Union[Version, LegacyVersion, str]
-VersionTypeVar = TypeVar("VersionTypeVar", bound=UnparsedVersion)
-CallableOperator = Callable[[ParsedVersion, str], bool]
-
-
-class InvalidSpecifier(ValueError):
- """
- An invalid specifier was found, users should refer to PEP 440.
- """
-
-
-class BaseSpecifier(metaclass=abc.ABCMeta):
- @abc.abstractmethod
- def __str__(self) -> str:
- """
- Returns the str representation of this Specifier like object. This
- should be representative of the Specifier itself.
- """
-
- @abc.abstractmethod
- def __hash__(self) -> int:
- """
- Returns a hash value for this Specifier like object.
- """
-
- @abc.abstractmethod
- def __eq__(self, other: object) -> bool:
- """
- Returns a boolean representing whether or not the two Specifier like
- objects are equal.
- """
-
- @abc.abstractproperty
- def prereleases(self) -> Optional[bool]:
- """
- Returns whether or not pre-releases as a whole are allowed by this
- specifier.
- """
-
- @prereleases.setter
- def prereleases(self, value: bool) -> None:
- """
- Sets whether or not pre-releases as a whole are allowed by this
- specifier.
- """
-
- @abc.abstractmethod
- def contains(self, item: str, prereleases: Optional[bool] = None) -> bool:
- """
- Determines if the given item is contained within this specifier.
- """
-
- @abc.abstractmethod
- def filter(
- self, iterable: Iterable[VersionTypeVar], prereleases: Optional[bool] = None
- ) -> Iterable[VersionTypeVar]:
- """
- Takes an iterable of items and filters them so that only items which
- are contained within this specifier are allowed in it.
- """
-
-
-class _IndividualSpecifier(BaseSpecifier):
-
- _operators: Dict[str, str] = {}
- _regex: Pattern[str]
-
- def __init__(self, spec: str = "", prereleases: Optional[bool] = None) -> None:
- match = self._regex.search(spec)
- if not match:
- raise InvalidSpecifier(f"Invalid specifier: '{spec}'")
-
- self._spec: Tuple[str, str] = (
- match.group("operator").strip(),
- match.group("version").strip(),
- )
-
- # Store whether or not this Specifier should accept prereleases
- self._prereleases = prereleases
-
- def __repr__(self) -> str:
- pre = (
- f", prereleases={self.prereleases!r}"
- if self._prereleases is not None
- else ""
- )
-
- return f"<{self.__class__.__name__}({str(self)!r}{pre})>"
-
- def __str__(self) -> str:
- return "{}{}".format(*self._spec)
-
- @property
- def _canonical_spec(self) -> Tuple[str, str]:
- return self._spec[0], canonicalize_version(self._spec[1])
-
- def __hash__(self) -> int:
- return hash(self._canonical_spec)
-
- def __eq__(self, other: object) -> bool:
- if isinstance(other, str):
- try:
- other = self.__class__(str(other))
- except InvalidSpecifier:
- return NotImplemented
- elif not isinstance(other, self.__class__):
- return NotImplemented
-
- return self._canonical_spec == other._canonical_spec
-
- def _get_operator(self, op: str) -> CallableOperator:
- operator_callable: CallableOperator = getattr(
- self, f"_compare_{self._operators[op]}"
- )
- return operator_callable
-
- def _coerce_version(self, version: UnparsedVersion) -> ParsedVersion:
- if not isinstance(version, (LegacyVersion, Version)):
- version = parse(version)
- return version
-
- @property
- def operator(self) -> str:
- return self._spec[0]
-
- @property
- def version(self) -> str:
- return self._spec[1]
-
- @property
- def prereleases(self) -> Optional[bool]:
- return self._prereleases
-
- @prereleases.setter
- def prereleases(self, value: bool) -> None:
- self._prereleases = value
-
- def __contains__(self, item: str) -> bool:
- return self.contains(item)
-
- def contains(
- self, item: UnparsedVersion, prereleases: Optional[bool] = None
- ) -> bool:
-
- # Determine if prereleases are to be allowed or not.
- if prereleases is None:
- prereleases = self.prereleases
-
- # Normalize item to a Version or LegacyVersion, this allows us to have
- # a shortcut for ``"2.0" in Specifier(">=2")
- normalized_item = self._coerce_version(item)
-
- # Determine if we should be supporting prereleases in this specifier
- # or not, if we do not support prereleases than we can short circuit
- # logic if this version is a prereleases.
- if normalized_item.is_prerelease and not prereleases:
- return False
-
- # Actually do the comparison to determine if this item is contained
- # within this Specifier or not.
- operator_callable: CallableOperator = self._get_operator(self.operator)
- return operator_callable(normalized_item, self.version)
-
- def filter(
- self, iterable: Iterable[VersionTypeVar], prereleases: Optional[bool] = None
- ) -> Iterable[VersionTypeVar]:
-
- yielded = False
- found_prereleases = []
-
- kw = {"prereleases": prereleases if prereleases is not None else True}
-
- # Attempt to iterate over all the values in the iterable and if any of
- # them match, yield them.
- for version in iterable:
- parsed_version = self._coerce_version(version)
-
- if self.contains(parsed_version, **kw):
- # If our version is a prerelease, and we were not set to allow
- # prereleases, then we'll store it for later in case nothing
- # else matches this specifier.
- if parsed_version.is_prerelease and not (
- prereleases or self.prereleases
- ):
- found_prereleases.append(version)
- # Either this is not a prerelease, or we should have been
- # accepting prereleases from the beginning.
- else:
- yielded = True
- yield version
-
- # Now that we've iterated over everything, determine if we've yielded
- # any values, and if we have not and we have any prereleases stored up
- # then we will go ahead and yield the prereleases.
- if not yielded and found_prereleases:
- for version in found_prereleases:
- yield version
-
-
-class LegacySpecifier(_IndividualSpecifier):
-
- _regex_str = r"""
- (?P(==|!=|<=|>=|<|>))
- \s*
- (?P
- [^,;\s)]* # Since this is a "legacy" specifier, and the version
- # string can be just about anything, we match everything
- # except for whitespace, a semi-colon for marker support,
- # a closing paren since versions can be enclosed in
- # them, and a comma since it's a version separator.
- )
- """
-
- _regex = re.compile(r"^\s*" + _regex_str + r"\s*$", re.VERBOSE | re.IGNORECASE)
-
- _operators = {
- "==": "equal",
- "!=": "not_equal",
- "<=": "less_than_equal",
- ">=": "greater_than_equal",
- "<": "less_than",
- ">": "greater_than",
- }
-
- def __init__(self, spec: str = "", prereleases: Optional[bool] = None) -> None:
- super().__init__(spec, prereleases)
-
- warnings.warn(
- "Creating a LegacyVersion has been deprecated and will be "
- "removed in the next major release",
- DeprecationWarning,
- )
-
- def _coerce_version(self, version: UnparsedVersion) -> LegacyVersion:
- if not isinstance(version, LegacyVersion):
- version = LegacyVersion(str(version))
- return version
-
- def _compare_equal(self, prospective: LegacyVersion, spec: str) -> bool:
- return prospective == self._coerce_version(spec)
-
- def _compare_not_equal(self, prospective: LegacyVersion, spec: str) -> bool:
- return prospective != self._coerce_version(spec)
-
- def _compare_less_than_equal(self, prospective: LegacyVersion, spec: str) -> bool:
- return prospective <= self._coerce_version(spec)
-
- def _compare_greater_than_equal(
- self, prospective: LegacyVersion, spec: str
- ) -> bool:
- return prospective >= self._coerce_version(spec)
-
- def _compare_less_than(self, prospective: LegacyVersion, spec: str) -> bool:
- return prospective < self._coerce_version(spec)
-
- def _compare_greater_than(self, prospective: LegacyVersion, spec: str) -> bool:
- return prospective > self._coerce_version(spec)
-
-
-def _require_version_compare(
- fn: Callable[["Specifier", ParsedVersion, str], bool]
-) -> Callable[["Specifier", ParsedVersion, str], bool]:
- @functools.wraps(fn)
- def wrapped(self: "Specifier", prospective: ParsedVersion, spec: str) -> bool:
- if not isinstance(prospective, Version):
- return False
- return fn(self, prospective, spec)
-
- return wrapped
-
-
-class Specifier(_IndividualSpecifier):
-
- _regex_str = r"""
- (?P(~=|==|!=|<=|>=|<|>|===))
- (?P
- (?:
- # The identity operators allow for an escape hatch that will
- # do an exact string match of the version you wish to install.
- # This will not be parsed by PEP 440 and we cannot determine
- # any semantic meaning from it. This operator is discouraged
- # but included entirely as an escape hatch.
- (?<====) # Only match for the identity operator
- \s*
- [^\s]* # We just match everything, except for whitespace
- # since we are only testing for strict identity.
- )
- |
- (?:
- # The (non)equality operators allow for wild card and local
- # versions to be specified so we have to define these two
- # operators separately to enable that.
- (?<===|!=) # Only match for equals and not equals
-
- \s*
- v?
- (?:[0-9]+!)? # epoch
- [0-9]+(?:\.[0-9]+)* # release
- (?: # pre release
- [-_\.]?
- (a|b|c|rc|alpha|beta|pre|preview)
- [-_\.]?
- [0-9]*
- )?
- (?: # post release
- (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
- )?
-
- # You cannot use a wild card and a dev or local version
- # together so group them with a | and make them optional.
- (?:
- (?:[-_\.]?dev[-_\.]?[0-9]*)? # dev release
- (?:\+[a-z0-9]+(?:[-_\.][a-z0-9]+)*)? # local
- |
- \.\* # Wild card syntax of .*
- )?
- )
- |
- (?:
- # The compatible operator requires at least two digits in the
- # release segment.
- (?<=~=) # Only match for the compatible operator
-
- \s*
- v?
- (?:[0-9]+!)? # epoch
- [0-9]+(?:\.[0-9]+)+ # release (We have a + instead of a *)
- (?: # pre release
- [-_\.]?
- (a|b|c|rc|alpha|beta|pre|preview)
- [-_\.]?
- [0-9]*
- )?
- (?: # post release
- (?:-[0-9]+)|(?:[-_\.]?(post|rev|r)[-_\.]?[0-9]*)
- )?
- (?:[-_\.]?dev[-_\.]?[0-9]*)? # dev release
- )
- |
- (?:
- # All other operators only allow a sub set of what the
- # (non)equality operators do. Specifically they do not allow
- # local versions to be specified nor do they allow the prefix
- # matching wild cards.
- (?=": "greater_than_equal",
- "<": "less_than",
- ">": "greater_than",
- "===": "arbitrary",
- }
-
- @_require_version_compare
- def _compare_compatible(self, prospective: ParsedVersion, spec: str) -> bool:
-
- # Compatible releases have an equivalent combination of >= and ==. That
- # is that ~=2.2 is equivalent to >=2.2,==2.*. This allows us to
- # implement this in terms of the other specifiers instead of
- # implementing it ourselves. The only thing we need to do is construct
- # the other specifiers.
-
- # We want everything but the last item in the version, but we want to
- # ignore suffix segments.
- prefix = ".".join(
- list(itertools.takewhile(_is_not_suffix, _version_split(spec)))[:-1]
- )
-
- # Add the prefix notation to the end of our string
- prefix += ".*"
-
- return self._get_operator(">=")(prospective, spec) and self._get_operator("==")(
- prospective, prefix
- )
-
- @_require_version_compare
- def _compare_equal(self, prospective: ParsedVersion, spec: str) -> bool:
-
- # We need special logic to handle prefix matching
- if spec.endswith(".*"):
- # In the case of prefix matching we want to ignore local segment.
- prospective = Version(prospective.public)
- # Split the spec out by dots, and pretend that there is an implicit
- # dot in between a release segment and a pre-release segment.
- split_spec = _version_split(spec[:-2]) # Remove the trailing .*
-
- # Split the prospective version out by dots, and pretend that there
- # is an implicit dot in between a release segment and a pre-release
- # segment.
- split_prospective = _version_split(str(prospective))
-
- # Shorten the prospective version to be the same length as the spec
- # so that we can determine if the specifier is a prefix of the
- # prospective version or not.
- shortened_prospective = split_prospective[: len(split_spec)]
-
- # Pad out our two sides with zeros so that they both equal the same
- # length.
- padded_spec, padded_prospective = _pad_version(
- split_spec, shortened_prospective
- )
-
- return padded_prospective == padded_spec
- else:
- # Convert our spec string into a Version
- spec_version = Version(spec)
-
- # If the specifier does not have a local segment, then we want to
- # act as if the prospective version also does not have a local
- # segment.
- if not spec_version.local:
- prospective = Version(prospective.public)
-
- return prospective == spec_version
-
- @_require_version_compare
- def _compare_not_equal(self, prospective: ParsedVersion, spec: str) -> bool:
- return not self._compare_equal(prospective, spec)
-
- @_require_version_compare
- def _compare_less_than_equal(self, prospective: ParsedVersion, spec: str) -> bool:
-
- # NB: Local version identifiers are NOT permitted in the version
- # specifier, so local version labels can be universally removed from
- # the prospective version.
- return Version(prospective.public) <= Version(spec)
-
- @_require_version_compare
- def _compare_greater_than_equal(
- self, prospective: ParsedVersion, spec: str
- ) -> bool:
-
- # NB: Local version identifiers are NOT permitted in the version
- # specifier, so local version labels can be universally removed from
- # the prospective version.
- return Version(prospective.public) >= Version(spec)
-
- @_require_version_compare
- def _compare_less_than(self, prospective: ParsedVersion, spec_str: str) -> bool:
-
- # Convert our spec to a Version instance, since we'll want to work with
- # it as a version.
- spec = Version(spec_str)
-
- # Check to see if the prospective version is less than the spec
- # version. If it's not we can short circuit and just return False now
- # instead of doing extra unneeded work.
- if not prospective < spec:
- return False
-
- # This special case is here so that, unless the specifier itself
- # includes is a pre-release version, that we do not accept pre-release
- # versions for the version mentioned in the specifier (e.g. <3.1 should
- # not match 3.1.dev0, but should match 3.0.dev0).
- if not spec.is_prerelease and prospective.is_prerelease:
- if Version(prospective.base_version) == Version(spec.base_version):
- return False
-
- # If we've gotten to here, it means that prospective version is both
- # less than the spec version *and* it's not a pre-release of the same
- # version in the spec.
- return True
-
- @_require_version_compare
- def _compare_greater_than(self, prospective: ParsedVersion, spec_str: str) -> bool:
-
- # Convert our spec to a Version instance, since we'll want to work with
- # it as a version.
- spec = Version(spec_str)
-
- # Check to see if the prospective version is greater than the spec
- # version. If it's not we can short circuit and just return False now
- # instead of doing extra unneeded work.
- if not prospective > spec:
- return False
-
- # This special case is here so that, unless the specifier itself
- # includes is a post-release version, that we do not accept
- # post-release versions for the version mentioned in the specifier
- # (e.g. >3.1 should not match 3.0.post0, but should match 3.2.post0).
- if not spec.is_postrelease and prospective.is_postrelease:
- if Version(prospective.base_version) == Version(spec.base_version):
- return False
-
- # Ensure that we do not allow a local version of the version mentioned
- # in the specifier, which is technically greater than, to match.
- if prospective.local is not None:
- if Version(prospective.base_version) == Version(spec.base_version):
- return False
-
- # If we've gotten to here, it means that prospective version is both
- # greater than the spec version *and* it's not a pre-release of the
- # same version in the spec.
- return True
-
- def _compare_arbitrary(self, prospective: Version, spec: str) -> bool:
- return str(prospective).lower() == str(spec).lower()
-
- @property
- def prereleases(self) -> bool:
-
- # If there is an explicit prereleases set for this, then we'll just
- # blindly use that.
- if self._prereleases is not None:
- return self._prereleases
-
- # Look at all of our specifiers and determine if they are inclusive
- # operators, and if they are if they are including an explicit
- # prerelease.
- operator, version = self._spec
- if operator in ["==", ">=", "<=", "~=", "==="]:
- # The == specifier can include a trailing .*, if it does we
- # want to remove before parsing.
- if operator == "==" and version.endswith(".*"):
- version = version[:-2]
-
- # Parse the version, and if it is a pre-release than this
- # specifier allows pre-releases.
- if parse(version).is_prerelease:
- return True
-
- return False
-
- @prereleases.setter
- def prereleases(self, value: bool) -> None:
- self._prereleases = value
-
-
-_prefix_regex = re.compile(r"^([0-9]+)((?:a|b|c|rc)[0-9]+)$")
-
-
-def _version_split(version: str) -> List[str]:
- result: List[str] = []
- for item in version.split("."):
- match = _prefix_regex.search(item)
- if match:
- result.extend(match.groups())
- else:
- result.append(item)
- return result
-
-
-def _is_not_suffix(segment: str) -> bool:
- return not any(
- segment.startswith(prefix) for prefix in ("dev", "a", "b", "rc", "post")
- )
-
-
-def _pad_version(left: List[str], right: List[str]) -> Tuple[List[str], List[str]]:
- left_split, right_split = [], []
-
- # Get the release segment of our versions
- left_split.append(list(itertools.takewhile(lambda x: x.isdigit(), left)))
- right_split.append(list(itertools.takewhile(lambda x: x.isdigit(), right)))
-
- # Get the rest of our versions
- left_split.append(left[len(left_split[0]) :])
- right_split.append(right[len(right_split[0]) :])
-
- # Insert our padding
- left_split.insert(1, ["0"] * max(0, len(right_split[0]) - len(left_split[0])))
- right_split.insert(1, ["0"] * max(0, len(left_split[0]) - len(right_split[0])))
-
- return (list(itertools.chain(*left_split)), list(itertools.chain(*right_split)))
-
-
-class SpecifierSet(BaseSpecifier):
- def __init__(
- self, specifiers: str = "", prereleases: Optional[bool] = None
- ) -> None:
-
- # Split on , to break each individual specifier into it's own item, and
- # strip each item to remove leading/trailing whitespace.
- split_specifiers = [s.strip() for s in specifiers.split(",") if s.strip()]
-
- # Parsed each individual specifier, attempting first to make it a
- # Specifier and falling back to a LegacySpecifier.
- parsed: Set[_IndividualSpecifier] = set()
- for specifier in split_specifiers:
- try:
- parsed.add(Specifier(specifier))
- except InvalidSpecifier:
- parsed.add(LegacySpecifier(specifier))
-
- # Turn our parsed specifiers into a frozen set and save them for later.
- self._specs = frozenset(parsed)
-
- # Store our prereleases value so we can use it later to determine if
- # we accept prereleases or not.
- self._prereleases = prereleases
-
- def __repr__(self) -> str:
- pre = (
- f", prereleases={self.prereleases!r}"
- if self._prereleases is not None
- else ""
- )
-
- return f""
-
- def __str__(self) -> str:
- return ",".join(sorted(str(s) for s in self._specs))
-
- def __hash__(self) -> int:
- return hash(self._specs)
-
- def __and__(self, other: Union["SpecifierSet", str]) -> "SpecifierSet":
- if isinstance(other, str):
- other = SpecifierSet(other)
- elif not isinstance(other, SpecifierSet):
- return NotImplemented
-
- specifier = SpecifierSet()
- specifier._specs = frozenset(self._specs | other._specs)
-
- if self._prereleases is None and other._prereleases is not None:
- specifier._prereleases = other._prereleases
- elif self._prereleases is not None and other._prereleases is None:
- specifier._prereleases = self._prereleases
- elif self._prereleases == other._prereleases:
- specifier._prereleases = self._prereleases
- else:
- raise ValueError(
- "Cannot combine SpecifierSets with True and False prerelease "
- "overrides."
- )
-
- return specifier
-
- def __eq__(self, other: object) -> bool:
- if isinstance(other, (str, _IndividualSpecifier)):
- other = SpecifierSet(str(other))
- elif not isinstance(other, SpecifierSet):
- return NotImplemented
-
- return self._specs == other._specs
-
- def __len__(self) -> int:
- return len(self._specs)
-
- def __iter__(self) -> Iterator[_IndividualSpecifier]:
- return iter(self._specs)
-
- @property
- def prereleases(self) -> Optional[bool]:
-
- # If we have been given an explicit prerelease modifier, then we'll
- # pass that through here.
- if self._prereleases is not None:
- return self._prereleases
-
- # If we don't have any specifiers, and we don't have a forced value,
- # then we'll just return None since we don't know if this should have
- # pre-releases or not.
- if not self._specs:
- return None
-
- # Otherwise we'll see if any of the given specifiers accept
- # prereleases, if any of them do we'll return True, otherwise False.
- return any(s.prereleases for s in self._specs)
-
- @prereleases.setter
- def prereleases(self, value: bool) -> None:
- self._prereleases = value
-
- def __contains__(self, item: UnparsedVersion) -> bool:
- return self.contains(item)
-
- def contains(
- self, item: UnparsedVersion, prereleases: Optional[bool] = None
- ) -> bool:
-
- # Ensure that our item is a Version or LegacyVersion instance.
- if not isinstance(item, (LegacyVersion, Version)):
- item = parse(item)
-
- # Determine if we're forcing a prerelease or not, if we're not forcing
- # one for this particular filter call, then we'll use whatever the
- # SpecifierSet thinks for whether or not we should support prereleases.
- if prereleases is None:
- prereleases = self.prereleases
-
- # We can determine if we're going to allow pre-releases by looking to
- # see if any of the underlying items supports them. If none of them do
- # and this item is a pre-release then we do not allow it and we can
- # short circuit that here.
- # Note: This means that 1.0.dev1 would not be contained in something
- # like >=1.0.devabc however it would be in >=1.0.debabc,>0.0.dev0
- if not prereleases and item.is_prerelease:
- return False
-
- # We simply dispatch to the underlying specs here to make sure that the
- # given version is contained within all of them.
- # Note: This use of all() here means that an empty set of specifiers
- # will always return True, this is an explicit design decision.
- return all(s.contains(item, prereleases=prereleases) for s in self._specs)
-
- def filter(
- self, iterable: Iterable[VersionTypeVar], prereleases: Optional[bool] = None
- ) -> Iterable[VersionTypeVar]:
-
- # Determine if we're forcing a prerelease or not, if we're not forcing
- # one for this particular filter call, then we'll use whatever the
- # SpecifierSet thinks for whether or not we should support prereleases.
- if prereleases is None:
- prereleases = self.prereleases
-
- # If we have any specifiers, then we want to wrap our iterable in the
- # filter method for each one, this will act as a logical AND amongst
- # each specifier.
- if self._specs:
- for spec in self._specs:
- iterable = spec.filter(iterable, prereleases=bool(prereleases))
- return iterable
- # If we do not have any specifiers, then we need to have a rough filter
- # which will filter out any pre-releases, unless there are no final
- # releases, and which will filter out LegacyVersion in general.
- else:
- filtered: List[VersionTypeVar] = []
- found_prereleases: List[VersionTypeVar] = []
-
- item: UnparsedVersion
- parsed_version: Union[Version, LegacyVersion]
-
- for item in iterable:
- # Ensure that we some kind of Version class for this item.
- if not isinstance(item, (LegacyVersion, Version)):
- parsed_version = parse(item)
- else:
- parsed_version = item
-
- # Filter out any item which is parsed as a LegacyVersion
- if isinstance(parsed_version, LegacyVersion):
- continue
-
- # Store any item which is a pre-release for later unless we've
- # already found a final version or we are accepting prereleases
- if parsed_version.is_prerelease and not prereleases:
- if not filtered:
- found_prereleases.append(item)
- else:
- filtered.append(item)
-
- # If we've found no items except for pre-releases, then we'll go
- # ahead and use the pre-releases
- if not filtered and found_prereleases and prereleases is None:
- return found_prereleases
-
- return filtered
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/cmd.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/cmd.py
deleted file mode 100644
index 3860c3ff1e0095c27fcfa36501c8c3fcbb1f4674..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_distutils/cmd.py
+++ /dev/null
@@ -1,435 +0,0 @@
-"""distutils.cmd
-
-Provides the Command class, the base class for the command classes
-in the distutils.command package.
-"""
-
-import sys
-import os
-import re
-import logging
-
-from .errors import DistutilsOptionError
-from . import util, dir_util, file_util, archive_util, dep_util
-from ._log import log
-
-
-class Command:
- """Abstract base class for defining command classes, the "worker bees"
- of the Distutils. A useful analogy for command classes is to think of
- them as subroutines with local variables called "options". The options
- are "declared" in 'initialize_options()' and "defined" (given their
- final values, aka "finalized") in 'finalize_options()', both of which
- must be defined by every command class. The distinction between the
- two is necessary because option values might come from the outside
- world (command line, config file, ...), and any options dependent on
- other options must be computed *after* these outside influences have
- been processed -- hence 'finalize_options()'. The "body" of the
- subroutine, where it does all its work based on the values of its
- options, is the 'run()' method, which must also be implemented by every
- command class.
- """
-
- # 'sub_commands' formalizes the notion of a "family" of commands,
- # eg. "install" as the parent with sub-commands "install_lib",
- # "install_headers", etc. The parent of a family of commands
- # defines 'sub_commands' as a class attribute; it's a list of
- # (command_name : string, predicate : unbound_method | string | None)
- # tuples, where 'predicate' is a method of the parent command that
- # determines whether the corresponding command is applicable in the
- # current situation. (Eg. we "install_headers" is only applicable if
- # we have any C header files to install.) If 'predicate' is None,
- # that command is always applicable.
- #
- # 'sub_commands' is usually defined at the *end* of a class, because
- # predicates can be unbound methods, so they must already have been
- # defined. The canonical example is the "install" command.
- sub_commands = []
-
- # -- Creation/initialization methods -------------------------------
-
- def __init__(self, dist):
- """Create and initialize a new Command object. Most importantly,
- invokes the 'initialize_options()' method, which is the real
- initializer and depends on the actual command being
- instantiated.
- """
- # late import because of mutual dependence between these classes
- from distutils.dist import Distribution
-
- if not isinstance(dist, Distribution):
- raise TypeError("dist must be a Distribution instance")
- if self.__class__ is Command:
- raise RuntimeError("Command is an abstract class")
-
- self.distribution = dist
- self.initialize_options()
-
- # Per-command versions of the global flags, so that the user can
- # customize Distutils' behaviour command-by-command and let some
- # commands fall back on the Distribution's behaviour. None means
- # "not defined, check self.distribution's copy", while 0 or 1 mean
- # false and true (duh). Note that this means figuring out the real
- # value of each flag is a touch complicated -- hence "self._dry_run"
- # will be handled by __getattr__, below.
- # XXX This needs to be fixed.
- self._dry_run = None
-
- # verbose is largely ignored, but needs to be set for
- # backwards compatibility (I think)?
- self.verbose = dist.verbose
-
- # Some commands define a 'self.force' option to ignore file
- # timestamps, but methods defined *here* assume that
- # 'self.force' exists for all commands. So define it here
- # just to be safe.
- self.force = None
-
- # The 'help' flag is just used for command-line parsing, so
- # none of that complicated bureaucracy is needed.
- self.help = 0
-
- # 'finalized' records whether or not 'finalize_options()' has been
- # called. 'finalize_options()' itself should not pay attention to
- # this flag: it is the business of 'ensure_finalized()', which
- # always calls 'finalize_options()', to respect/update it.
- self.finalized = 0
-
- # XXX A more explicit way to customize dry_run would be better.
- def __getattr__(self, attr):
- if attr == 'dry_run':
- myval = getattr(self, "_" + attr)
- if myval is None:
- return getattr(self.distribution, attr)
- else:
- return myval
- else:
- raise AttributeError(attr)
-
- def ensure_finalized(self):
- if not self.finalized:
- self.finalize_options()
- self.finalized = 1
-
- # Subclasses must define:
- # initialize_options()
- # provide default values for all options; may be customized by
- # setup script, by options from config file(s), or by command-line
- # options
- # finalize_options()
- # decide on the final values for all options; this is called
- # after all possible intervention from the outside world
- # (command-line, option file, etc.) has been processed
- # run()
- # run the command: do whatever it is we're here to do,
- # controlled by the command's various option values
-
- def initialize_options(self):
- """Set default values for all the options that this command
- supports. Note that these defaults may be overridden by other
- commands, by the setup script, by config files, or by the
- command-line. Thus, this is not the place to code dependencies
- between options; generally, 'initialize_options()' implementations
- are just a bunch of "self.foo = None" assignments.
-
- This method must be implemented by all command classes.
- """
- raise RuntimeError(
- "abstract method -- subclass %s must override" % self.__class__
- )
-
- def finalize_options(self):
- """Set final values for all the options that this command supports.
- This is always called as late as possible, ie. after any option
- assignments from the command-line or from other commands have been
- done. Thus, this is the place to code option dependencies: if
- 'foo' depends on 'bar', then it is safe to set 'foo' from 'bar' as
- long as 'foo' still has the same value it was assigned in
- 'initialize_options()'.
-
- This method must be implemented by all command classes.
- """
- raise RuntimeError(
- "abstract method -- subclass %s must override" % self.__class__
- )
-
- def dump_options(self, header=None, indent=""):
- from distutils.fancy_getopt import longopt_xlate
-
- if header is None:
- header = "command options for '%s':" % self.get_command_name()
- self.announce(indent + header, level=logging.INFO)
- indent = indent + " "
- for option, _, _ in self.user_options:
- option = option.translate(longopt_xlate)
- if option[-1] == "=":
- option = option[:-1]
- value = getattr(self, option)
- self.announce(indent + "{} = {}".format(option, value), level=logging.INFO)
-
- def run(self):
- """A command's raison d'etre: carry out the action it exists to
- perform, controlled by the options initialized in
- 'initialize_options()', customized by other commands, the setup
- script, the command-line, and config files, and finalized in
- 'finalize_options()'. All terminal output and filesystem
- interaction should be done by 'run()'.
-
- This method must be implemented by all command classes.
- """
- raise RuntimeError(
- "abstract method -- subclass %s must override" % self.__class__
- )
-
- def announce(self, msg, level=logging.DEBUG):
- log.log(level, msg)
-
- def debug_print(self, msg):
- """Print 'msg' to stdout if the global DEBUG (taken from the
- DISTUTILS_DEBUG environment variable) flag is true.
- """
- from distutils.debug import DEBUG
-
- if DEBUG:
- print(msg)
- sys.stdout.flush()
-
- # -- Option validation methods -------------------------------------
- # (these are very handy in writing the 'finalize_options()' method)
- #
- # NB. the general philosophy here is to ensure that a particular option
- # value meets certain type and value constraints. If not, we try to
- # force it into conformance (eg. if we expect a list but have a string,
- # split the string on comma and/or whitespace). If we can't force the
- # option into conformance, raise DistutilsOptionError. Thus, command
- # classes need do nothing more than (eg.)
- # self.ensure_string_list('foo')
- # and they can be guaranteed that thereafter, self.foo will be
- # a list of strings.
-
- def _ensure_stringlike(self, option, what, default=None):
- val = getattr(self, option)
- if val is None:
- setattr(self, option, default)
- return default
- elif not isinstance(val, str):
- raise DistutilsOptionError(
- "'{}' must be a {} (got `{}`)".format(option, what, val)
- )
- return val
-
- def ensure_string(self, option, default=None):
- """Ensure that 'option' is a string; if not defined, set it to
- 'default'.
- """
- self._ensure_stringlike(option, "string", default)
-
- def ensure_string_list(self, option):
- r"""Ensure that 'option' is a list of strings. If 'option' is
- currently a string, we split it either on /,\s*/ or /\s+/, so
- "foo bar baz", "foo,bar,baz", and "foo, bar baz" all become
- ["foo", "bar", "baz"].
- """
- val = getattr(self, option)
- if val is None:
- return
- elif isinstance(val, str):
- setattr(self, option, re.split(r',\s*|\s+', val))
- else:
- if isinstance(val, list):
- ok = all(isinstance(v, str) for v in val)
- else:
- ok = False
- if not ok:
- raise DistutilsOptionError(
- "'{}' must be a list of strings (got {!r})".format(option, val)
- )
-
- def _ensure_tested_string(self, option, tester, what, error_fmt, default=None):
- val = self._ensure_stringlike(option, what, default)
- if val is not None and not tester(val):
- raise DistutilsOptionError(
- ("error in '%s' option: " + error_fmt) % (option, val)
- )
-
- def ensure_filename(self, option):
- """Ensure that 'option' is the name of an existing file."""
- self._ensure_tested_string(
- option, os.path.isfile, "filename", "'%s' does not exist or is not a file"
- )
-
- def ensure_dirname(self, option):
- self._ensure_tested_string(
- option,
- os.path.isdir,
- "directory name",
- "'%s' does not exist or is not a directory",
- )
-
- # -- Convenience methods for commands ------------------------------
-
- def get_command_name(self):
- if hasattr(self, 'command_name'):
- return self.command_name
- else:
- return self.__class__.__name__
-
- def set_undefined_options(self, src_cmd, *option_pairs):
- """Set the values of any "undefined" options from corresponding
- option values in some other command object. "Undefined" here means
- "is None", which is the convention used to indicate that an option
- has not been changed between 'initialize_options()' and
- 'finalize_options()'. Usually called from 'finalize_options()' for
- options that depend on some other command rather than another
- option of the same command. 'src_cmd' is the other command from
- which option values will be taken (a command object will be created
- for it if necessary); the remaining arguments are
- '(src_option,dst_option)' tuples which mean "take the value of
- 'src_option' in the 'src_cmd' command object, and copy it to
- 'dst_option' in the current command object".
- """
- # Option_pairs: list of (src_option, dst_option) tuples
- src_cmd_obj = self.distribution.get_command_obj(src_cmd)
- src_cmd_obj.ensure_finalized()
- for src_option, dst_option in option_pairs:
- if getattr(self, dst_option) is None:
- setattr(self, dst_option, getattr(src_cmd_obj, src_option))
-
- def get_finalized_command(self, command, create=1):
- """Wrapper around Distribution's 'get_command_obj()' method: find
- (create if necessary and 'create' is true) the command object for
- 'command', call its 'ensure_finalized()' method, and return the
- finalized command object.
- """
- cmd_obj = self.distribution.get_command_obj(command, create)
- cmd_obj.ensure_finalized()
- return cmd_obj
-
- # XXX rename to 'get_reinitialized_command()'? (should do the
- # same in dist.py, if so)
- def reinitialize_command(self, command, reinit_subcommands=0):
- return self.distribution.reinitialize_command(command, reinit_subcommands)
-
- def run_command(self, command):
- """Run some other command: uses the 'run_command()' method of
- Distribution, which creates and finalizes the command object if
- necessary and then invokes its 'run()' method.
- """
- self.distribution.run_command(command)
-
- def get_sub_commands(self):
- """Determine the sub-commands that are relevant in the current
- distribution (ie., that need to be run). This is based on the
- 'sub_commands' class attribute: each tuple in that list may include
- a method that we call to determine if the subcommand needs to be
- run for the current distribution. Return a list of command names.
- """
- commands = []
- for cmd_name, method in self.sub_commands:
- if method is None or method(self):
- commands.append(cmd_name)
- return commands
-
- # -- External world manipulation -----------------------------------
-
- def warn(self, msg):
- log.warning("warning: %s: %s\n", self.get_command_name(), msg)
-
- def execute(self, func, args, msg=None, level=1):
- util.execute(func, args, msg, dry_run=self.dry_run)
-
- def mkpath(self, name, mode=0o777):
- dir_util.mkpath(name, mode, dry_run=self.dry_run)
-
- def copy_file(
- self, infile, outfile, preserve_mode=1, preserve_times=1, link=None, level=1
- ):
- """Copy a file respecting verbose, dry-run and force flags. (The
- former two default to whatever is in the Distribution object, and
- the latter defaults to false for commands that don't define it.)"""
- return file_util.copy_file(
- infile,
- outfile,
- preserve_mode,
- preserve_times,
- not self.force,
- link,
- dry_run=self.dry_run,
- )
-
- def copy_tree(
- self,
- infile,
- outfile,
- preserve_mode=1,
- preserve_times=1,
- preserve_symlinks=0,
- level=1,
- ):
- """Copy an entire directory tree respecting verbose, dry-run,
- and force flags.
- """
- return dir_util.copy_tree(
- infile,
- outfile,
- preserve_mode,
- preserve_times,
- preserve_symlinks,
- not self.force,
- dry_run=self.dry_run,
- )
-
- def move_file(self, src, dst, level=1):
- """Move a file respecting dry-run flag."""
- return file_util.move_file(src, dst, dry_run=self.dry_run)
-
- def spawn(self, cmd, search_path=1, level=1):
- """Spawn an external command respecting dry-run flag."""
- from distutils.spawn import spawn
-
- spawn(cmd, search_path, dry_run=self.dry_run)
-
- def make_archive(
- self, base_name, format, root_dir=None, base_dir=None, owner=None, group=None
- ):
- return archive_util.make_archive(
- base_name,
- format,
- root_dir,
- base_dir,
- dry_run=self.dry_run,
- owner=owner,
- group=group,
- )
-
- def make_file(
- self, infiles, outfile, func, args, exec_msg=None, skip_msg=None, level=1
- ):
- """Special case of 'execute()' for operations that process one or
- more input files and generate one output file. Works just like
- 'execute()', except the operation is skipped and a different
- message printed if 'outfile' already exists and is newer than all
- files listed in 'infiles'. If the command defined 'self.force',
- and it is true, then the command is unconditionally run -- does no
- timestamp checks.
- """
- if skip_msg is None:
- skip_msg = "skipping %s (inputs unchanged)" % outfile
-
- # Allow 'infiles' to be a single string
- if isinstance(infiles, str):
- infiles = (infiles,)
- elif not isinstance(infiles, (list, tuple)):
- raise TypeError("'infiles' must be a string, or a list or tuple of strings")
-
- if exec_msg is None:
- exec_msg = "generating {} from {}".format(outfile, ', '.join(infiles))
-
- # If 'outfile' must be regenerated (either because it doesn't
- # exist, is out-of-date, or the 'force' flag is true) then
- # perform the action that presumably regenerates it
- if self.force or dep_util.newer_group(infiles, outfile):
- self.execute(func, args, exec_msg, level)
- # Otherwise, print the "skip" message
- else:
- log.debug(skip_msg)
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/jaraco/text/__init__.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/jaraco/text/__init__.py
deleted file mode 100644
index a0306d5ff5cc4a2eb76458c127c462efe59a566d..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/jaraco/text/__init__.py
+++ /dev/null
@@ -1,599 +0,0 @@
-import re
-import itertools
-import textwrap
-import functools
-
-try:
- from importlib.resources import files # type: ignore
-except ImportError: # pragma: nocover
- from setuptools.extern.importlib_resources import files # type: ignore
-
-from setuptools.extern.jaraco.functools import compose, method_cache
-from setuptools.extern.jaraco.context import ExceptionTrap
-
-
-def substitution(old, new):
- """
- Return a function that will perform a substitution on a string
- """
- return lambda s: s.replace(old, new)
-
-
-def multi_substitution(*substitutions):
- """
- Take a sequence of pairs specifying substitutions, and create
- a function that performs those substitutions.
-
- >>> multi_substitution(('foo', 'bar'), ('bar', 'baz'))('foo')
- 'baz'
- """
- substitutions = itertools.starmap(substitution, substitutions)
- # compose function applies last function first, so reverse the
- # substitutions to get the expected order.
- substitutions = reversed(tuple(substitutions))
- return compose(*substitutions)
-
-
-class FoldedCase(str):
- """
- A case insensitive string class; behaves just like str
- except compares equal when the only variation is case.
-
- >>> s = FoldedCase('hello world')
-
- >>> s == 'Hello World'
- True
-
- >>> 'Hello World' == s
- True
-
- >>> s != 'Hello World'
- False
-
- >>> s.index('O')
- 4
-
- >>> s.split('O')
- ['hell', ' w', 'rld']
-
- >>> sorted(map(FoldedCase, ['GAMMA', 'alpha', 'Beta']))
- ['alpha', 'Beta', 'GAMMA']
-
- Sequence membership is straightforward.
-
- >>> "Hello World" in [s]
- True
- >>> s in ["Hello World"]
- True
-
- You may test for set inclusion, but candidate and elements
- must both be folded.
-
- >>> FoldedCase("Hello World") in {s}
- True
- >>> s in {FoldedCase("Hello World")}
- True
-
- String inclusion works as long as the FoldedCase object
- is on the right.
-
- >>> "hello" in FoldedCase("Hello World")
- True
-
- But not if the FoldedCase object is on the left:
-
- >>> FoldedCase('hello') in 'Hello World'
- False
-
- In that case, use ``in_``:
-
- >>> FoldedCase('hello').in_('Hello World')
- True
-
- >>> FoldedCase('hello') > FoldedCase('Hello')
- False
- """
-
- def __lt__(self, other):
- return self.lower() < other.lower()
-
- def __gt__(self, other):
- return self.lower() > other.lower()
-
- def __eq__(self, other):
- return self.lower() == other.lower()
-
- def __ne__(self, other):
- return self.lower() != other.lower()
-
- def __hash__(self):
- return hash(self.lower())
-
- def __contains__(self, other):
- return super().lower().__contains__(other.lower())
-
- def in_(self, other):
- "Does self appear in other?"
- return self in FoldedCase(other)
-
- # cache lower since it's likely to be called frequently.
- @method_cache
- def lower(self):
- return super().lower()
-
- def index(self, sub):
- return self.lower().index(sub.lower())
-
- def split(self, splitter=' ', maxsplit=0):
- pattern = re.compile(re.escape(splitter), re.I)
- return pattern.split(self, maxsplit)
-
-
-# Python 3.8 compatibility
-_unicode_trap = ExceptionTrap(UnicodeDecodeError)
-
-
-@_unicode_trap.passes
-def is_decodable(value):
- r"""
- Return True if the supplied value is decodable (using the default
- encoding).
-
- >>> is_decodable(b'\xff')
- False
- >>> is_decodable(b'\x32')
- True
- """
- value.decode()
-
-
-def is_binary(value):
- r"""
- Return True if the value appears to be binary (that is, it's a byte
- string and isn't decodable).
-
- >>> is_binary(b'\xff')
- True
- >>> is_binary('\xff')
- False
- """
- return isinstance(value, bytes) and not is_decodable(value)
-
-
-def trim(s):
- r"""
- Trim something like a docstring to remove the whitespace that
- is common due to indentation and formatting.
-
- >>> trim("\n\tfoo = bar\n\t\tbar = baz\n")
- 'foo = bar\n\tbar = baz'
- """
- return textwrap.dedent(s).strip()
-
-
-def wrap(s):
- """
- Wrap lines of text, retaining existing newlines as
- paragraph markers.
-
- >>> print(wrap(lorem_ipsum))
- Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do
- eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad
- minim veniam, quis nostrud exercitation ullamco laboris nisi ut
- aliquip ex ea commodo consequat. Duis aute irure dolor in
- reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla
- pariatur. Excepteur sint occaecat cupidatat non proident, sunt in
- culpa qui officia deserunt mollit anim id est laborum.
-
- Curabitur pretium tincidunt lacus. Nulla gravida orci a odio. Nullam
- varius, turpis et commodo pharetra, est eros bibendum elit, nec luctus
- magna felis sollicitudin mauris. Integer in mauris eu nibh euismod
- gravida. Duis ac tellus et risus vulputate vehicula. Donec lobortis
- risus a elit. Etiam tempor. Ut ullamcorper, ligula eu tempor congue,
- eros est euismod turpis, id tincidunt sapien risus a quam. Maecenas
- fermentum consequat mi. Donec fermentum. Pellentesque malesuada nulla
- a mi. Duis sapien sem, aliquet nec, commodo eget, consequat quis,
- neque. Aliquam faucibus, elit ut dictum aliquet, felis nisl adipiscing
- sapien, sed malesuada diam lacus eget erat. Cras mollis scelerisque
- nunc. Nullam arcu. Aliquam consequat. Curabitur augue lorem, dapibus
- quis, laoreet et, pretium ac, nisi. Aenean magna nisl, mollis quis,
- molestie eu, feugiat in, orci. In hac habitasse platea dictumst.
- """
- paragraphs = s.splitlines()
- wrapped = ('\n'.join(textwrap.wrap(para)) for para in paragraphs)
- return '\n\n'.join(wrapped)
-
-
-def unwrap(s):
- r"""
- Given a multi-line string, return an unwrapped version.
-
- >>> wrapped = wrap(lorem_ipsum)
- >>> wrapped.count('\n')
- 20
- >>> unwrapped = unwrap(wrapped)
- >>> unwrapped.count('\n')
- 1
- >>> print(unwrapped)
- Lorem ipsum dolor sit amet, consectetur adipiscing ...
- Curabitur pretium tincidunt lacus. Nulla gravida orci ...
-
- """
- paragraphs = re.split(r'\n\n+', s)
- cleaned = (para.replace('\n', ' ') for para in paragraphs)
- return '\n'.join(cleaned)
-
-
-
-
-class Splitter(object):
- """object that will split a string with the given arguments for each call
-
- >>> s = Splitter(',')
- >>> s('hello, world, this is your, master calling')
- ['hello', ' world', ' this is your', ' master calling']
- """
-
- def __init__(self, *args):
- self.args = args
-
- def __call__(self, s):
- return s.split(*self.args)
-
-
-def indent(string, prefix=' ' * 4):
- """
- >>> indent('foo')
- ' foo'
- """
- return prefix + string
-
-
-class WordSet(tuple):
- """
- Given an identifier, return the words that identifier represents,
- whether in camel case, underscore-separated, etc.
-
- >>> WordSet.parse("camelCase")
- ('camel', 'Case')
-
- >>> WordSet.parse("under_sep")
- ('under', 'sep')
-
- Acronyms should be retained
-
- >>> WordSet.parse("firstSNL")
- ('first', 'SNL')
-
- >>> WordSet.parse("you_and_I")
- ('you', 'and', 'I')
-
- >>> WordSet.parse("A simple test")
- ('A', 'simple', 'test')
-
- Multiple caps should not interfere with the first cap of another word.
-
- >>> WordSet.parse("myABCClass")
- ('my', 'ABC', 'Class')
-
- The result is a WordSet, so you can get the form you need.
-
- >>> WordSet.parse("myABCClass").underscore_separated()
- 'my_ABC_Class'
-
- >>> WordSet.parse('a-command').camel_case()
- 'ACommand'
-
- >>> WordSet.parse('someIdentifier').lowered().space_separated()
- 'some identifier'
-
- Slices of the result should return another WordSet.
-
- >>> WordSet.parse('taken-out-of-context')[1:].underscore_separated()
- 'out_of_context'
-
- >>> WordSet.from_class_name(WordSet()).lowered().space_separated()
- 'word set'
-
- >>> example = WordSet.parse('figured it out')
- >>> example.headless_camel_case()
- 'figuredItOut'
- >>> example.dash_separated()
- 'figured-it-out'
-
- """
-
- _pattern = re.compile('([A-Z]?[a-z]+)|([A-Z]+(?![a-z]))')
-
- def capitalized(self):
- return WordSet(word.capitalize() for word in self)
-
- def lowered(self):
- return WordSet(word.lower() for word in self)
-
- def camel_case(self):
- return ''.join(self.capitalized())
-
- def headless_camel_case(self):
- words = iter(self)
- first = next(words).lower()
- new_words = itertools.chain((first,), WordSet(words).camel_case())
- return ''.join(new_words)
-
- def underscore_separated(self):
- return '_'.join(self)
-
- def dash_separated(self):
- return '-'.join(self)
-
- def space_separated(self):
- return ' '.join(self)
-
- def trim_right(self, item):
- """
- Remove the item from the end of the set.
-
- >>> WordSet.parse('foo bar').trim_right('foo')
- ('foo', 'bar')
- >>> WordSet.parse('foo bar').trim_right('bar')
- ('foo',)
- >>> WordSet.parse('').trim_right('bar')
- ()
- """
- return self[:-1] if self and self[-1] == item else self
-
- def trim_left(self, item):
- """
- Remove the item from the beginning of the set.
-
- >>> WordSet.parse('foo bar').trim_left('foo')
- ('bar',)
- >>> WordSet.parse('foo bar').trim_left('bar')
- ('foo', 'bar')
- >>> WordSet.parse('').trim_left('bar')
- ()
- """
- return self[1:] if self and self[0] == item else self
-
- def trim(self, item):
- """
- >>> WordSet.parse('foo bar').trim('foo')
- ('bar',)
- """
- return self.trim_left(item).trim_right(item)
-
- def __getitem__(self, item):
- result = super(WordSet, self).__getitem__(item)
- if isinstance(item, slice):
- result = WordSet(result)
- return result
-
- @classmethod
- def parse(cls, identifier):
- matches = cls._pattern.finditer(identifier)
- return WordSet(match.group(0) for match in matches)
-
- @classmethod
- def from_class_name(cls, subject):
- return cls.parse(subject.__class__.__name__)
-
-
-# for backward compatibility
-words = WordSet.parse
-
-
-def simple_html_strip(s):
- r"""
- Remove HTML from the string `s`.
-
- >>> str(simple_html_strip(''))
- ''
-
- >>> print(simple_html_strip('A stormy day in paradise'))
- A stormy day in paradise
-
- >>> print(simple_html_strip('Somebody tell the truth.'))
- Somebody tell the truth.
-
- >>> print(simple_html_strip('What about
-
-#ifndef __AUDIOSESSIONTYPES__
-#define __AUDIOSESSIONTYPES__
-
-#if WINAPI_FAMILY_PARTITION (WINAPI_PARTITION_APP)
-#if defined (__WIDL__)
-#define MIDL_SIZE_IS(x) [size_is (x)]
-#define MIDL_STRING [string]
-#define MIDL_ANYSIZE_ARRAY
-#else
-#define MIDL_SIZE_IS(x)
-#define MIDL_STRING
-#define MIDL_ANYSIZE_ARRAY ANYSIZE_ARRAY
-#endif
-
-typedef enum _AudioSessionState {
- AudioSessionStateInactive = 0,
- AudioSessionStateActive = 1,
- AudioSessionStateExpired = 2
-} AudioSessionState;
-
-typedef enum _AUDCLNT_SHAREMODE {
- AUDCLNT_SHAREMODE_SHARED,
- AUDCLNT_SHAREMODE_EXCLUSIVE
-} AUDCLNT_SHAREMODE;
-
-typedef enum _AUDIO_STREAM_CATEGORY {
- AudioCategory_Other = 0,
- AudioCategory_ForegroundOnlyMedia,
- AudioCategory_BackgroundCapableMedia,
- AudioCategory_Communications,
- AudioCategory_Alerts,
- AudioCategory_SoundEffects,
- AudioCategory_GameEffects,
- AudioCategory_GameMedia,
- AudioCategory_GameChat,
- AudioCategory_Speech,
- AudioCategory_Movie,
- AudioCategory_Media
-} AUDIO_STREAM_CATEGORY;
-
-#define AUDCLNT_STREAMFLAGS_CROSSPROCESS 0x00010000
-#define AUDCLNT_STREAMFLAGS_LOOPBACK 0x00020000
-#define AUDCLNT_STREAMFLAGS_EVENTCALLBACK 0x00040000
-#define AUDCLNT_STREAMFLAGS_NOPERSIST 0x00080000
-#define AUDCLNT_STREAMFLAGS_RATEADJUST 0x00100000
-#define AUDCLNT_SESSIONFLAGS_EXPIREWHENUNOWNED 0x10000000
-#define AUDCLNT_SESSIONFLAGS_DISPLAY_HIDE 0x20000000
-#define AUDCLNT_SESSIONFLAGS_DISPLAY_HIDEWHENEXPIRED 0x40000000
-
-#endif
-#endif
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index-3f67eae3.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index-3f67eae3.js
deleted file mode 100644
index f2eeecf0d0b66dcc79c686213f8ade1b3cb0721f..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index-3f67eae3.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{a as W}from"./Button-89057c03.js";import{p as G,u as H}from"./index-0526d562.js";import"./Index-37584f50.js";import"./svelte/svelte.js";const{SvelteComponent:J,attr:b,binding_callbacks:K,create_component:L,create_slot:M,destroy_component:N,detach:S,element:O,get_all_dirty_from_scope:P,get_slot_changes:Q,init:R,insert:I,listen:j,mount_component:T,run_all:X,safe_not_equal:Y,space:Z,transition_in:A,transition_out:D,update_slot_base:x}=window.__gradio__svelte__internal,{tick:p,createEventDispatcher:$}=window.__gradio__svelte__internal;function ee(i){let e;const l=i[17].default,t=M(l,i,i[19],null);return{c(){t&&t.c()},m(n,u){t&&t.m(n,u),e=!0},p(n,u){t&&t.p&&(!e||u&524288)&&x(t,l,n,n[19],e?Q(l,n[19],u,null):P(n[19]),null)},i(n){e||(A(t,n),e=!0)},o(n){D(t,n),e=!1},d(n){t&&t.d(n)}}}function ie(i){let e,l,t,n,u,h,d,o,m,r;return d=new W({props:{size:i[5],variant:i[8],elem_id:i[0],elem_classes:i[1],visible:i[2],scale:i[6],min_width:i[7],disabled:i[9],$$slots:{default:[ee]},$$scope:{ctx:i}}}),d.$on("click",i[12]),{c(){e=O("input"),h=Z(),L(d.$$.fragment),b(e,"class","hide svelte-ydeks8"),b(e,"accept",i[11]),b(e,"type","file"),e.multiple=l=i[4]==="multiple"||void 0,b(e,"webkitdirectory",t=i[4]==="directory"||void 0),b(e,"mozdirectory",n=i[4]==="directory"||void 0),b(e,"data-testid",u=i[3]+"-upload-button")},m(_,s){I(_,e,s),i[18](e),I(_,h,s),T(d,_,s),o=!0,m||(r=[j(e,"change",i[13]),j(e,"click",le)],m=!0)},p(_,[s]){(!o||s&2048)&&b(e,"accept",_[11]),(!o||s&16&&l!==(l=_[4]==="multiple"||void 0))&&(e.multiple=l),(!o||s&16&&t!==(t=_[4]==="directory"||void 0))&&b(e,"webkitdirectory",t),(!o||s&16&&n!==(n=_[4]==="directory"||void 0))&&b(e,"mozdirectory",n),(!o||s&8&&u!==(u=_[3]+"-upload-button"))&&b(e,"data-testid",u);const c={};s&32&&(c.size=_[5]),s&256&&(c.variant=_[8]),s&1&&(c.elem_id=_[0]),s&2&&(c.elem_classes=_[1]),s&4&&(c.visible=_[2]),s&64&&(c.scale=_[6]),s&128&&(c.min_width=_[7]),s&512&&(c.disabled=_[9]),s&524288&&(c.$$scope={dirty:s,ctx:_}),d.$set(c)},i(_){o||(A(d.$$.fragment,_),o=!0)},o(_){D(d.$$.fragment,_),o=!1},d(_){_&&(S(e),S(h)),i[18](null),N(d,_),m=!1,X(r)}}}function le(i){const e=i.target;e.value&&(e.value="")}function te(i,e,l){let{$$slots:t={},$$scope:n}=e,{elem_id:u=""}=e,{elem_classes:h=[]}=e,{visible:d=!0}=e,{label:o}=e,{value:m}=e,{file_count:r}=e,{file_types:_=[]}=e,{root:s}=e,{size:c="lg"}=e,{scale:k=null}=e,{min_width:y=void 0}=e,{variant:g="secondary"}=e,{disabled:w=!1}=e;const v=$();let z,B;_==null?B=null:(_=_.map(a=>a.startsWith(".")?a:a+"/*"),B=_.join(", "));function q(){z.click()}async function f(a){let U=Array.from(a);if(!a.length)return;r==="single"&&(U=[a[0]]);let F=await G(U);await p(),F=(await H(F,s))?.filter(V=>V!==null),l(14,m=r==="single"?F?.[0]:F),v("change",m),v("upload",m)}async function C(a){const U=a.target;U.files&&await f(U.files)}function E(a){K[a?"unshift":"push"](()=>{z=a,l(10,z)})}return i.$$set=a=>{"elem_id"in a&&l(0,u=a.elem_id),"elem_classes"in a&&l(1,h=a.elem_classes),"visible"in a&&l(2,d=a.visible),"label"in a&&l(3,o=a.label),"value"in a&&l(14,m=a.value),"file_count"in a&&l(4,r=a.file_count),"file_types"in a&&l(15,_=a.file_types),"root"in a&&l(16,s=a.root),"size"in a&&l(5,c=a.size),"scale"in a&&l(6,k=a.scale),"min_width"in a&&l(7,y=a.min_width),"variant"in a&&l(8,g=a.variant),"disabled"in a&&l(9,w=a.disabled),"$$scope"in a&&l(19,n=a.$$scope)},[u,h,d,o,r,c,k,y,g,w,z,B,q,C,m,_,s,t,E,n]}class ne extends J{constructor(e){super(),R(this,e,te,ie,Y,{elem_id:0,elem_classes:1,visible:2,label:3,value:14,file_count:4,file_types:15,root:16,size:5,scale:6,min_width:7,variant:8,disabled:9})}}const ae=ne,{SvelteComponent:fe,create_component:_e,destroy_component:ue,detach:se,init:oe,insert:ce,mount_component:de,safe_not_equal:me,set_data:re,text:be,transition_in:he,transition_out:ge}=window.__gradio__svelte__internal;function we(i){let e=i[12].i18n(i[4])+"",l;return{c(){l=be(e)},m(t,n){ce(t,l,n)},p(t,n){n&4112&&e!==(e=t[12].i18n(t[4])+"")&&re(l,e)},d(t){t&&se(l)}}}function ve(i){let e,l;return e=new ae({props:{elem_id:i[1],elem_classes:i[2],visible:i[3],file_count:i[5],file_types:i[6],size:i[8],scale:i[9],min_width:i[10],root:i[7],value:i[0],disabled:i[13],variant:i[11],label:i[4],$$slots:{default:[we]},$$scope:{ctx:i}}}),e.$on("click",i[16]),e.$on("change",i[17]),e.$on("upload",i[18]),{c(){_e(e.$$.fragment)},m(t,n){de(e,t,n),l=!0},p(t,[n]){const u={};n&2&&(u.elem_id=t[1]),n&4&&(u.elem_classes=t[2]),n&8&&(u.visible=t[3]),n&32&&(u.file_count=t[5]),n&64&&(u.file_types=t[6]),n&256&&(u.size=t[8]),n&512&&(u.scale=t[9]),n&1024&&(u.min_width=t[10]),n&128&&(u.root=t[7]),n&1&&(u.value=t[0]),n&8192&&(u.disabled=t[13]),n&2048&&(u.variant=t[11]),n&16&&(u.label=t[4]),n&528400&&(u.$$scope={dirty:n,ctx:t}),e.$set(u)},i(t){l||(he(e.$$.fragment,t),l=!0)},o(t){ge(e.$$.fragment,t),l=!1},d(t){ue(e,t)}}}function ze(i,e,l){let t,{elem_id:n=""}=e,{elem_classes:u=[]}=e,{visible:h=!0}=e,{label:d}=e,{value:o}=e,{file_count:m}=e,{file_types:r=[]}=e,{root:_}=e,{size:s="lg"}=e,{scale:c=null}=e,{min_width:k=void 0}=e,{variant:y="secondary"}=e,{gradio:g}=e,{interactive:w}=e;async function v(f,C){l(0,o=f),g.dispatch(C)}const z=()=>g.dispatch("click"),B=({detail:f})=>v(f,"change"),q=({detail:f})=>v(f,"upload");return i.$$set=f=>{"elem_id"in f&&l(1,n=f.elem_id),"elem_classes"in f&&l(2,u=f.elem_classes),"visible"in f&&l(3,h=f.visible),"label"in f&&l(4,d=f.label),"value"in f&&l(0,o=f.value),"file_count"in f&&l(5,m=f.file_count),"file_types"in f&&l(6,r=f.file_types),"root"in f&&l(7,_=f.root),"size"in f&&l(8,s=f.size),"scale"in f&&l(9,c=f.scale),"min_width"in f&&l(10,k=f.min_width),"variant"in f&&l(11,y=f.variant),"gradio"in f&&l(12,g=f.gradio),"interactive"in f&&l(15,w=f.interactive)},i.$$.update=()=>{i.$$.dirty&32768&&l(13,t=!w)},[o,n,u,h,d,m,r,_,s,c,k,y,g,t,v,w,z,B,q]}class Fe extends fe{constructor(e){super(),oe(this,e,ze,ve,me,{elem_id:1,elem_classes:2,visible:3,label:4,value:0,file_count:5,file_types:6,root:7,size:8,scale:9,min_width:10,variant:11,gradio:12,interactive:15})}}export{ae as BaseUploadButton,Fe as default};
-//# sourceMappingURL=Index-3f67eae3.js.map
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index-67fb205c.css b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index-67fb205c.css
deleted file mode 100644
index 3bd432bc6e09aad960764f818df39d0c7d81d9cb..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Index-67fb205c.css
+++ /dev/null
@@ -1 +0,0 @@
-div.svelte-aww9vd .math.inline{fill:var(--body-text-color);display:inline-block;vertical-align:middle;padding:var(--size-1-5) -var(--size-1);color:var(--body-text-color)}div.svelte-aww9vd .math.inline svg{display:inline;margin-bottom:.22em}div.svelte-aww9vd{max-width:100%;overflow-x:auto}.min.svelte-aww9vd{min-height:var(--size-24)}.hide.svelte-aww9vd{display:none}div.svelte-1ed2p3z{transition:.15s}.pending.svelte-1ed2p3z{opacity:.2}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/timedeltas/test_delete.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/timedeltas/test_delete.py
deleted file mode 100644
index 6e6f54702ce1a09c0fccb0c44d0cd4a474c46a8c..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/timedeltas/test_delete.py
+++ /dev/null
@@ -1,71 +0,0 @@
-from pandas import (
- TimedeltaIndex,
- timedelta_range,
-)
-import pandas._testing as tm
-
-
-class TestTimedeltaIndexDelete:
- def test_delete(self):
- idx = timedelta_range(start="1 Days", periods=5, freq="D", name="idx")
-
- # preserve freq
- expected_0 = timedelta_range(start="2 Days", periods=4, freq="D", name="idx")
- expected_4 = timedelta_range(start="1 Days", periods=4, freq="D", name="idx")
-
- # reset freq to None
- expected_1 = TimedeltaIndex(
- ["1 day", "3 day", "4 day", "5 day"], freq=None, name="idx"
- )
-
- cases = {
- 0: expected_0,
- -5: expected_0,
- -1: expected_4,
- 4: expected_4,
- 1: expected_1,
- }
- for n, expected in cases.items():
- result = idx.delete(n)
- tm.assert_index_equal(result, expected)
- assert result.name == expected.name
- assert result.freq == expected.freq
-
- with tm.external_error_raised((IndexError, ValueError)):
- # either depending on numpy version
- idx.delete(5)
-
- def test_delete_slice(self):
- idx = timedelta_range(start="1 days", periods=10, freq="D", name="idx")
-
- # preserve freq
- expected_0_2 = timedelta_range(start="4 days", periods=7, freq="D", name="idx")
- expected_7_9 = timedelta_range(start="1 days", periods=7, freq="D", name="idx")
-
- # reset freq to None
- expected_3_5 = TimedeltaIndex(
- ["1 d", "2 d", "3 d", "7 d", "8 d", "9 d", "10d"], freq=None, name="idx"
- )
-
- cases = {
- (0, 1, 2): expected_0_2,
- (7, 8, 9): expected_7_9,
- (3, 4, 5): expected_3_5,
- }
- for n, expected in cases.items():
- result = idx.delete(n)
- tm.assert_index_equal(result, expected)
- assert result.name == expected.name
- assert result.freq == expected.freq
-
- result = idx.delete(slice(n[0], n[-1] + 1))
- tm.assert_index_equal(result, expected)
- assert result.name == expected.name
- assert result.freq == expected.freq
-
- def test_delete_doesnt_infer_freq(self):
- # GH#30655 behavior matches DatetimeIndex
-
- tdi = TimedeltaIndex(["1 Day", "2 Days", None, "3 Days", "4 Days"])
- result = tdi.delete(2)
- assert result.freq is None
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/models/index.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/models/index.py
deleted file mode 100644
index b94c32511f0cda2363bfc4f29c9c8bfcc7101f9b..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_internal/models/index.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import urllib.parse
-
-
-class PackageIndex:
- """Represents a Package Index and provides easier access to endpoints"""
-
- __slots__ = ["url", "netloc", "simple_url", "pypi_url", "file_storage_domain"]
-
- def __init__(self, url: str, file_storage_domain: str) -> None:
- super().__init__()
- self.url = url
- self.netloc = urllib.parse.urlsplit(url).netloc
- self.simple_url = self._url_for_path("simple")
- self.pypi_url = self._url_for_path("pypi")
-
- # This is part of a temporary hack used to block installs of PyPI
- # packages which depend on external urls only necessary until PyPI can
- # block such packages themselves
- self.file_storage_domain = file_storage_domain
-
- def _url_for_path(self, path: str) -> str:
- return urllib.parse.urljoin(self.url, path)
-
-
-PyPI = PackageIndex("https://pypi.org/", file_storage_domain="files.pythonhosted.org")
-TestPyPI = PackageIndex(
- "https://test.pypi.org/", file_storage_domain="test-files.pythonhosted.org"
-)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/starlette/websockets.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/starlette/websockets.py
deleted file mode 100644
index 5aa411824269809fc427390b6a0cb5f44769c080..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/starlette/websockets.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import enum
-import json
-import typing
-
-from starlette.requests import HTTPConnection
-from starlette.types import Message, Receive, Scope, Send
-
-
-class WebSocketState(enum.Enum):
- CONNECTING = 0
- CONNECTED = 1
- DISCONNECTED = 2
-
-
-class WebSocketDisconnect(Exception):
- def __init__(self, code: int = 1000, reason: typing.Optional[str] = None) -> None:
- self.code = code
- self.reason = reason or ""
-
-
-class WebSocket(HTTPConnection):
- def __init__(self, scope: Scope, receive: Receive, send: Send) -> None:
- super().__init__(scope)
- assert scope["type"] == "websocket"
- self._receive = receive
- self._send = send
- self.client_state = WebSocketState.CONNECTING
- self.application_state = WebSocketState.CONNECTING
-
- async def receive(self) -> Message:
- """
- Receive ASGI websocket messages, ensuring valid state transitions.
- """
- if self.client_state == WebSocketState.CONNECTING:
- message = await self._receive()
- message_type = message["type"]
- if message_type != "websocket.connect":
- raise RuntimeError(
- 'Expected ASGI message "websocket.connect", '
- f"but got {message_type!r}"
- )
- self.client_state = WebSocketState.CONNECTED
- return message
- elif self.client_state == WebSocketState.CONNECTED:
- message = await self._receive()
- message_type = message["type"]
- if message_type not in {"websocket.receive", "websocket.disconnect"}:
- raise RuntimeError(
- 'Expected ASGI message "websocket.receive" or '
- f'"websocket.disconnect", but got {message_type!r}'
- )
- if message_type == "websocket.disconnect":
- self.client_state = WebSocketState.DISCONNECTED
- return message
- else:
- raise RuntimeError(
- 'Cannot call "receive" once a disconnect message has been received.'
- )
-
- async def send(self, message: Message) -> None:
- """
- Send ASGI websocket messages, ensuring valid state transitions.
- """
- if self.application_state == WebSocketState.CONNECTING:
- message_type = message["type"]
- if message_type not in {"websocket.accept", "websocket.close"}:
- raise RuntimeError(
- 'Expected ASGI message "websocket.accept" or '
- f'"websocket.close", but got {message_type!r}'
- )
- if message_type == "websocket.close":
- self.application_state = WebSocketState.DISCONNECTED
- else:
- self.application_state = WebSocketState.CONNECTED
- await self._send(message)
- elif self.application_state == WebSocketState.CONNECTED:
- message_type = message["type"]
- if message_type not in {"websocket.send", "websocket.close"}:
- raise RuntimeError(
- 'Expected ASGI message "websocket.send" or "websocket.close", '
- f"but got {message_type!r}"
- )
- if message_type == "websocket.close":
- self.application_state = WebSocketState.DISCONNECTED
- await self._send(message)
- else:
- raise RuntimeError('Cannot call "send" once a close message has been sent.')
-
- async def accept(
- self,
- subprotocol: typing.Optional[str] = None,
- headers: typing.Optional[typing.Iterable[typing.Tuple[bytes, bytes]]] = None,
- ) -> None:
- headers = headers or []
-
- if self.client_state == WebSocketState.CONNECTING:
- # If we haven't yet seen the 'connect' message, then wait for it first.
- await self.receive()
- await self.send(
- {"type": "websocket.accept", "subprotocol": subprotocol, "headers": headers}
- )
-
- def _raise_on_disconnect(self, message: Message) -> None:
- if message["type"] == "websocket.disconnect":
- raise WebSocketDisconnect(message["code"])
-
- async def receive_text(self) -> str:
- if self.application_state != WebSocketState.CONNECTED:
- raise RuntimeError(
- 'WebSocket is not connected. Need to call "accept" first.'
- )
- message = await self.receive()
- self._raise_on_disconnect(message)
- return message["text"]
-
- async def receive_bytes(self) -> bytes:
- if self.application_state != WebSocketState.CONNECTED:
- raise RuntimeError(
- 'WebSocket is not connected. Need to call "accept" first.'
- )
- message = await self.receive()
- self._raise_on_disconnect(message)
- return message["bytes"]
-
- async def receive_json(self, mode: str = "text") -> typing.Any:
- if mode not in {"text", "binary"}:
- raise RuntimeError('The "mode" argument should be "text" or "binary".')
- if self.application_state != WebSocketState.CONNECTED:
- raise RuntimeError(
- 'WebSocket is not connected. Need to call "accept" first.'
- )
- message = await self.receive()
- self._raise_on_disconnect(message)
-
- if mode == "text":
- text = message["text"]
- else:
- text = message["bytes"].decode("utf-8")
- return json.loads(text)
-
- async def iter_text(self) -> typing.AsyncIterator[str]:
- try:
- while True:
- yield await self.receive_text()
- except WebSocketDisconnect:
- pass
-
- async def iter_bytes(self) -> typing.AsyncIterator[bytes]:
- try:
- while True:
- yield await self.receive_bytes()
- except WebSocketDisconnect:
- pass
-
- async def iter_json(self) -> typing.AsyncIterator[typing.Any]:
- try:
- while True:
- yield await self.receive_json()
- except WebSocketDisconnect:
- pass
-
- async def send_text(self, data: str) -> None:
- await self.send({"type": "websocket.send", "text": data})
-
- async def send_bytes(self, data: bytes) -> None:
- await self.send({"type": "websocket.send", "bytes": data})
-
- async def send_json(self, data: typing.Any, mode: str = "text") -> None:
- if mode not in {"text", "binary"}:
- raise RuntimeError('The "mode" argument should be "text" or "binary".')
- text = json.dumps(data, separators=(",", ":"))
- if mode == "text":
- await self.send({"type": "websocket.send", "text": text})
- else:
- await self.send({"type": "websocket.send", "bytes": text.encode("utf-8")})
-
- async def close(
- self, code: int = 1000, reason: typing.Optional[str] = None
- ) -> None:
- await self.send(
- {"type": "websocket.close", "code": code, "reason": reason or ""}
- )
-
-
-class WebSocketClose:
- def __init__(self, code: int = 1000, reason: typing.Optional[str] = None) -> None:
- self.code = code
- self.reason = reason or ""
-
- async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None:
- await send(
- {"type": "websocket.close", "code": self.code, "reason": self.reason}
- )
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/tqdm/keras.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/tqdm/keras.py
deleted file mode 100644
index 96782d7e8bf6cc624dfd4204fab0e452993ebf68..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/tqdm/keras.py
+++ /dev/null
@@ -1,122 +0,0 @@
-from copy import copy
-from functools import partial
-
-from .auto import tqdm as tqdm_auto
-
-try:
- import keras
-except (ImportError, AttributeError) as e:
- try:
- from tensorflow import keras
- except ImportError:
- raise e
-__author__ = {"github.com/": ["casperdcl"]}
-__all__ = ['TqdmCallback']
-
-
-class TqdmCallback(keras.callbacks.Callback):
- """Keras callback for epoch and batch progress."""
- @staticmethod
- def bar2callback(bar, pop=None, delta=(lambda logs: 1)):
- def callback(_, logs=None):
- n = delta(logs)
- if logs:
- if pop:
- logs = copy(logs)
- [logs.pop(i, 0) for i in pop]
- bar.set_postfix(logs, refresh=False)
- bar.update(n)
-
- return callback
-
- def __init__(self, epochs=None, data_size=None, batch_size=None, verbose=1,
- tqdm_class=tqdm_auto, **tqdm_kwargs):
- """
- Parameters
- ----------
- epochs : int, optional
- data_size : int, optional
- Number of training pairs.
- batch_size : int, optional
- Number of training pairs per batch.
- verbose : int
- 0: epoch, 1: batch (transient), 2: batch. [default: 1].
- Will be set to `0` unless both `data_size` and `batch_size`
- are given.
- tqdm_class : optional
- `tqdm` class to use for bars [default: `tqdm.auto.tqdm`].
- tqdm_kwargs : optional
- Any other arguments used for all bars.
- """
- if tqdm_kwargs:
- tqdm_class = partial(tqdm_class, **tqdm_kwargs)
- self.tqdm_class = tqdm_class
- self.epoch_bar = tqdm_class(total=epochs, unit='epoch')
- self.on_epoch_end = self.bar2callback(self.epoch_bar)
- if data_size and batch_size:
- self.batches = batches = (data_size + batch_size - 1) // batch_size
- else:
- self.batches = batches = None
- self.verbose = verbose
- if verbose == 1:
- self.batch_bar = tqdm_class(total=batches, unit='batch', leave=False)
- self.on_batch_end = self.bar2callback(
- self.batch_bar, pop=['batch', 'size'],
- delta=lambda logs: logs.get('size', 1))
-
- def on_train_begin(self, *_, **__):
- params = self.params.get
- auto_total = params('epochs', params('nb_epoch', None))
- if auto_total is not None and auto_total != self.epoch_bar.total:
- self.epoch_bar.reset(total=auto_total)
-
- def on_epoch_begin(self, epoch, *_, **__):
- if self.epoch_bar.n < epoch:
- ebar = self.epoch_bar
- ebar.n = ebar.last_print_n = ebar.initial = epoch
- if self.verbose:
- params = self.params.get
- total = params('samples', params(
- 'nb_sample', params('steps', None))) or self.batches
- if self.verbose == 2:
- if hasattr(self, 'batch_bar'):
- self.batch_bar.close()
- self.batch_bar = self.tqdm_class(
- total=total, unit='batch', leave=True,
- unit_scale=1 / (params('batch_size', 1) or 1))
- self.on_batch_end = self.bar2callback(
- self.batch_bar, pop=['batch', 'size'],
- delta=lambda logs: logs.get('size', 1))
- elif self.verbose == 1:
- self.batch_bar.unit_scale = 1 / (params('batch_size', 1) or 1)
- self.batch_bar.reset(total=total)
- else:
- raise KeyError('Unknown verbosity')
-
- def on_train_end(self, *_, **__):
- if self.verbose:
- self.batch_bar.close()
- self.epoch_bar.close()
-
- def display(self):
- """Displays in the current cell in Notebooks."""
- container = getattr(self.epoch_bar, 'container', None)
- if container is None:
- return
- from .notebook import display
- display(container)
- batch_bar = getattr(self, 'batch_bar', None)
- if batch_bar is not None:
- display(batch_bar.container)
-
- @staticmethod
- def _implements_train_batch_hooks():
- return True
-
- @staticmethod
- def _implements_test_batch_hooks():
- return True
-
- @staticmethod
- def _implements_predict_batch_hooks():
- return True
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Downloadageofempires2theconquerors10ccrack.md b/spaces/quidiaMuxgu/Expedit-SAM/Downloadageofempires2theconquerors10ccrack.md
deleted file mode 100644
index a564e8cd7ff2c2a87205a63dd19c7cdc79eb5b97..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Downloadageofempires2theconquerors10ccrack.md
+++ /dev/null
@@ -1,15 +0,0 @@
-downloadageofempires2theconquerors10ccrack Download Zip ———>>> https://geags.com/2uCseq
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Freedom Full Movie Hd In Hindi Download TOP.md b/spaces/quidiaMuxgu/Expedit-SAM/Freedom Full Movie Hd In Hindi Download TOP.md
deleted file mode 100644
index 4aeaf3f2b470bdc36355e8146d728d94260b85b3..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Freedom Full Movie Hd In Hindi Download TOP.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Freedom Full Movie Hd In Hindi Download DOWNLOAD — https://geags.com/2uCqGh
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Jawab Movie !!LINK!! Download Hd 720p.md b/spaces/quidiaMuxgu/Expedit-SAM/Jawab Movie !!LINK!! Download Hd 720p.md
deleted file mode 100644
index a9581c3a886b3cf0f0da4e07e8e20a942af56096..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Jawab Movie !!LINK!! Download Hd 720p.md
+++ /dev/null
@@ -1,6 +0,0 @@
-Jawab Movie Download Hd 720p Download Zip ✺✺✺ https://geags.com/2uCrCT
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Magix Music Maker 16 Premium Crack UPDATED.md b/spaces/quidiaMuxgu/Expedit-SAM/Magix Music Maker 16 Premium Crack UPDATED.md
deleted file mode 100644
index 695da3c3a0e1058e024cbe7b915685939f7262d3..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Magix Music Maker 16 Premium Crack UPDATED.md
+++ /dev/null
@@ -1,6 +0,0 @@
-magix music maker 16 premium crack Download File ✯ https://geags.com/2uCs5S
diff --git a/spaces/rachana219/MODT2/trackers/strongsort/utils/json_logger.py b/spaces/rachana219/MODT2/trackers/strongsort/utils/json_logger.py
deleted file mode 100644
index 0afd0b45df736866c49473db78286685d77660ac..0000000000000000000000000000000000000000
--- a/spaces/rachana219/MODT2/trackers/strongsort/utils/json_logger.py
+++ /dev/null
@@ -1,383 +0,0 @@
-"""
-References:
- https://medium.com/analytics-vidhya/creating-a-custom-logging-mechanism-for-real-time-object-detection-using-tdd-4ca2cfcd0a2f
-"""
-import json
-from os import makedirs
-from os.path import exists, join
-from datetime import datetime
-
-
-class JsonMeta(object):
- HOURS = 3
- MINUTES = 59
- SECONDS = 59
- PATH_TO_SAVE = 'LOGS'
- DEFAULT_FILE_NAME = 'remaining'
-
-
-class BaseJsonLogger(object):
- """
- This is the base class that returns __dict__ of its own
- it also returns the dicts of objects in the attributes that are list instances
-
- """
-
- def dic(self):
- # returns dicts of objects
- out = {}
- for k, v in self.__dict__.items():
- if hasattr(v, 'dic'):
- out[k] = v.dic()
- elif isinstance(v, list):
- out[k] = self.list(v)
- else:
- out[k] = v
- return out
-
- @staticmethod
- def list(values):
- # applies the dic method on items in the list
- return [v.dic() if hasattr(v, 'dic') else v for v in values]
-
-
-class Label(BaseJsonLogger):
- """
- For each bounding box there are various categories with confidences. Label class keeps track of that information.
- """
-
- def __init__(self, category: str, confidence: float):
- self.category = category
- self.confidence = confidence
-
-
-class Bbox(BaseJsonLogger):
- """
- This module stores the information for each frame and use them in JsonParser
- Attributes:
- labels (list): List of label module.
- top (int):
- left (int):
- width (int):
- height (int):
-
- Args:
- bbox_id (float):
- top (int):
- left (int):
- width (int):
- height (int):
-
- References:
- Check Label module for better understanding.
-
-
- """
-
- def __init__(self, bbox_id, top, left, width, height):
- self.labels = []
- self.bbox_id = bbox_id
- self.top = top
- self.left = left
- self.width = width
- self.height = height
-
- def add_label(self, category, confidence):
- # adds category and confidence only if top_k is not exceeded.
- self.labels.append(Label(category, confidence))
-
- def labels_full(self, value):
- return len(self.labels) == value
-
-
-class Frame(BaseJsonLogger):
- """
- This module stores the information for each frame and use them in JsonParser
- Attributes:
- timestamp (float): The elapsed time of captured frame
- frame_id (int): The frame number of the captured video
- bboxes (list of Bbox objects): Stores the list of bbox objects.
-
- References:
- Check Bbox class for better information
-
- Args:
- timestamp (float):
- frame_id (int):
-
- """
-
- def __init__(self, frame_id: int, timestamp: float = None):
- self.frame_id = frame_id
- self.timestamp = timestamp
- self.bboxes = []
-
- def add_bbox(self, bbox_id: int, top: int, left: int, width: int, height: int):
- bboxes_ids = [bbox.bbox_id for bbox in self.bboxes]
- if bbox_id not in bboxes_ids:
- self.bboxes.append(Bbox(bbox_id, top, left, width, height))
- else:
- raise ValueError("Frame with id: {} already has a Bbox with id: {}".format(self.frame_id, bbox_id))
-
- def add_label_to_bbox(self, bbox_id: int, category: str, confidence: float):
- bboxes = {bbox.id: bbox for bbox in self.bboxes}
- if bbox_id in bboxes.keys():
- res = bboxes.get(bbox_id)
- res.add_label(category, confidence)
- else:
- raise ValueError('the bbox with id: {} does not exists!'.format(bbox_id))
-
-
-class BboxToJsonLogger(BaseJsonLogger):
- """
- ُ This module is designed to automate the task of logging jsons. An example json is used
- to show the contents of json file shortly
- Example:
- {
- "video_details": {
- "frame_width": 1920,
- "frame_height": 1080,
- "frame_rate": 20,
- "video_name": "/home/gpu/codes/MSD/pedestrian_2/project/public/camera1.avi"
- },
- "frames": [
- {
- "frame_id": 329,
- "timestamp": 3365.1254
- "bboxes": [
- {
- "labels": [
- {
- "category": "pedestrian",
- "confidence": 0.9
- }
- ],
- "bbox_id": 0,
- "top": 1257,
- "left": 138,
- "width": 68,
- "height": 109
- }
- ]
- }],
-
- Attributes:
- frames (dict): It's a dictionary that maps each frame_id to json attributes.
- video_details (dict): information about video file.
- top_k_labels (int): shows the allowed number of labels
- start_time (datetime object): we use it to automate the json output by time.
-
- Args:
- top_k_labels (int): shows the allowed number of labels
-
- """
-
- def __init__(self, top_k_labels: int = 1):
- self.frames = {}
- self.video_details = self.video_details = dict(frame_width=None, frame_height=None, frame_rate=None,
- video_name=None)
- self.top_k_labels = top_k_labels
- self.start_time = datetime.now()
-
- def set_top_k(self, value):
- self.top_k_labels = value
-
- def frame_exists(self, frame_id: int) -> bool:
- """
- Args:
- frame_id (int):
-
- Returns:
- bool: true if frame_id is recognized
- """
- return frame_id in self.frames.keys()
-
- def add_frame(self, frame_id: int, timestamp: float = None) -> None:
- """
- Args:
- frame_id (int):
- timestamp (float): opencv captured frame time property
-
- Raises:
- ValueError: if frame_id would not exist in class frames attribute
-
- Returns:
- None
-
- """
- if not self.frame_exists(frame_id):
- self.frames[frame_id] = Frame(frame_id, timestamp)
- else:
- raise ValueError("Frame id: {} already exists".format(frame_id))
-
- def bbox_exists(self, frame_id: int, bbox_id: int) -> bool:
- """
- Args:
- frame_id:
- bbox_id:
-
- Returns:
- bool: if bbox exists in frame bboxes list
- """
- bboxes = []
- if self.frame_exists(frame_id=frame_id):
- bboxes = [bbox.bbox_id for bbox in self.frames[frame_id].bboxes]
- return bbox_id in bboxes
-
- def find_bbox(self, frame_id: int, bbox_id: int):
- """
-
- Args:
- frame_id:
- bbox_id:
-
- Returns:
- bbox_id (int):
-
- Raises:
- ValueError: if bbox_id does not exist in the bbox list of specific frame.
- """
- if not self.bbox_exists(frame_id, bbox_id):
- raise ValueError("frame with id: {} does not contain bbox with id: {}".format(frame_id, bbox_id))
- bboxes = {bbox.bbox_id: bbox for bbox in self.frames[frame_id].bboxes}
- return bboxes.get(bbox_id)
-
- def add_bbox_to_frame(self, frame_id: int, bbox_id: int, top: int, left: int, width: int, height: int) -> None:
- """
-
- Args:
- frame_id (int):
- bbox_id (int):
- top (int):
- left (int):
- width (int):
- height (int):
-
- Returns:
- None
-
- Raises:
- ValueError: if bbox_id already exist in frame information with frame_id
- ValueError: if frame_id does not exist in frames attribute
- """
- if self.frame_exists(frame_id):
- frame = self.frames[frame_id]
- if not self.bbox_exists(frame_id, bbox_id):
- frame.add_bbox(bbox_id, top, left, width, height)
- else:
- raise ValueError(
- "frame with frame_id: {} already contains the bbox with id: {} ".format(frame_id, bbox_id))
- else:
- raise ValueError("frame with frame_id: {} does not exist".format(frame_id))
-
- def add_label_to_bbox(self, frame_id: int, bbox_id: int, category: str, confidence: float):
- """
- Args:
- frame_id:
- bbox_id:
- category:
- confidence: the confidence value returned from yolo detection
-
- Returns:
- None
-
- Raises:
- ValueError: if labels quota (top_k_labels) exceeds.
- """
- bbox = self.find_bbox(frame_id, bbox_id)
- if not bbox.labels_full(self.top_k_labels):
- bbox.add_label(category, confidence)
- else:
- raise ValueError("labels in frame_id: {}, bbox_id: {} is fulled".format(frame_id, bbox_id))
-
- def add_video_details(self, frame_width: int = None, frame_height: int = None, frame_rate: int = None,
- video_name: str = None):
- self.video_details['frame_width'] = frame_width
- self.video_details['frame_height'] = frame_height
- self.video_details['frame_rate'] = frame_rate
- self.video_details['video_name'] = video_name
-
- def output(self):
- output = {'video_details': self.video_details}
- result = list(self.frames.values())
- output['frames'] = [item.dic() for item in result]
- return output
-
- def json_output(self, output_name):
- """
- Args:
- output_name:
-
- Returns:
- None
-
- Notes:
- It creates the json output with `output_name` name.
- """
- if not output_name.endswith('.json'):
- output_name += '.json'
- with open(output_name, 'w') as file:
- json.dump(self.output(), file)
- file.close()
-
- def set_start(self):
- self.start_time = datetime.now()
-
- def schedule_output_by_time(self, output_dir=JsonMeta.PATH_TO_SAVE, hours: int = 0, minutes: int = 0,
- seconds: int = 60) -> None:
- """
- Notes:
- Creates folder and then periodically stores the jsons on that address.
-
- Args:
- output_dir (str): the directory where output files will be stored
- hours (int):
- minutes (int):
- seconds (int):
-
- Returns:
- None
-
- """
- end = datetime.now()
- interval = 0
- interval += abs(min([hours, JsonMeta.HOURS]) * 3600)
- interval += abs(min([minutes, JsonMeta.MINUTES]) * 60)
- interval += abs(min([seconds, JsonMeta.SECONDS]))
- diff = (end - self.start_time).seconds
-
- if diff > interval:
- output_name = self.start_time.strftime('%Y-%m-%d %H-%M-%S') + '.json'
- if not exists(output_dir):
- makedirs(output_dir)
- output = join(output_dir, output_name)
- self.json_output(output_name=output)
- self.frames = {}
- self.start_time = datetime.now()
-
- def schedule_output_by_frames(self, frames_quota, frame_counter, output_dir=JsonMeta.PATH_TO_SAVE):
- """
- saves as the number of frames quota increases higher.
- :param frames_quota:
- :param frame_counter:
- :param output_dir:
- :return:
- """
- pass
-
- def flush(self, output_dir):
- """
- Notes:
- We use this function to output jsons whenever possible.
- like the time that we exit the while loop of opencv.
-
- Args:
- output_dir:
-
- Returns:
- None
-
- """
- filename = self.start_time.strftime('%Y-%m-%d %H-%M-%S') + '-remaining.json'
- output = join(output_dir, filename)
- self.json_output(output_name=output)
diff --git a/spaces/radames/PIFu-Clothed-Human-Digitization/PIFu/lib/renderer/camera.py b/spaces/radames/PIFu-Clothed-Human-Digitization/PIFu/lib/renderer/camera.py
deleted file mode 100644
index e5c330a17e0166970428911a8f1ba92bb89f5034..0000000000000000000000000000000000000000
--- a/spaces/radames/PIFu-Clothed-Human-Digitization/PIFu/lib/renderer/camera.py
+++ /dev/null
@@ -1,207 +0,0 @@
-import cv2
-import numpy as np
-
-from .glm import ortho
-
-
-class Camera:
- def __init__(self, width=1600, height=1200):
- # Focal Length
- # equivalent 50mm
- focal = np.sqrt(width * width + height * height)
- self.focal_x = focal
- self.focal_y = focal
- # Principal Point Offset
- self.principal_x = width / 2
- self.principal_y = height / 2
- # Axis Skew
- self.skew = 0
- # Image Size
- self.width = width
- self.height = height
-
- self.near = 1
- self.far = 10
-
- # Camera Center
- self.center = np.array([0, 0, 1.6])
- self.direction = np.array([0, 0, -1])
- self.right = np.array([1, 0, 0])
- self.up = np.array([0, 1, 0])
-
- self.ortho_ratio = None
-
- def sanity_check(self):
- self.center = self.center.reshape([-1])
- self.direction = self.direction.reshape([-1])
- self.right = self.right.reshape([-1])
- self.up = self.up.reshape([-1])
-
- assert len(self.center) == 3
- assert len(self.direction) == 3
- assert len(self.right) == 3
- assert len(self.up) == 3
-
- @staticmethod
- def normalize_vector(v):
- v_norm = np.linalg.norm(v)
- return v if v_norm == 0 else v / v_norm
-
- def get_real_z_value(self, z):
- z_near = self.near
- z_far = self.far
- z_n = 2.0 * z - 1.0
- z_e = 2.0 * z_near * z_far / (z_far + z_near - z_n * (z_far - z_near))
- return z_e
-
- def get_rotation_matrix(self):
- rot_mat = np.eye(3)
- s = self.right
- s = self.normalize_vector(s)
- rot_mat[0, :] = s
- u = self.up
- u = self.normalize_vector(u)
- rot_mat[1, :] = -u
- rot_mat[2, :] = self.normalize_vector(self.direction)
-
- return rot_mat
-
- def get_translation_vector(self):
- rot_mat = self.get_rotation_matrix()
- trans = -np.dot(rot_mat, self.center)
- return trans
-
- def get_intrinsic_matrix(self):
- int_mat = np.eye(3)
-
- int_mat[0, 0] = self.focal_x
- int_mat[1, 1] = self.focal_y
- int_mat[0, 1] = self.skew
- int_mat[0, 2] = self.principal_x
- int_mat[1, 2] = self.principal_y
-
- return int_mat
-
- def get_projection_matrix(self):
- ext_mat = self.get_extrinsic_matrix()
- int_mat = self.get_intrinsic_matrix()
-
- return np.matmul(int_mat, ext_mat)
-
- def get_extrinsic_matrix(self):
- rot_mat = self.get_rotation_matrix()
- int_mat = self.get_intrinsic_matrix()
- trans = self.get_translation_vector()
-
- extrinsic = np.eye(4)
- extrinsic[:3, :3] = rot_mat
- extrinsic[:3, 3] = trans
-
- return extrinsic[:3, :]
-
- def set_rotation_matrix(self, rot_mat):
- self.direction = rot_mat[2, :]
- self.up = -rot_mat[1, :]
- self.right = rot_mat[0, :]
-
- def set_intrinsic_matrix(self, int_mat):
- self.focal_x = int_mat[0, 0]
- self.focal_y = int_mat[1, 1]
- self.skew = int_mat[0, 1]
- self.principal_x = int_mat[0, 2]
- self.principal_y = int_mat[1, 2]
-
- def set_projection_matrix(self, proj_mat):
- res = cv2.decomposeProjectionMatrix(proj_mat)
- int_mat, rot_mat, camera_center_homo = res[0], res[1], res[2]
- camera_center = camera_center_homo[0:3] / camera_center_homo[3]
- camera_center = camera_center.reshape(-1)
- int_mat = int_mat / int_mat[2][2]
-
- self.set_intrinsic_matrix(int_mat)
- self.set_rotation_matrix(rot_mat)
- self.center = camera_center
-
- self.sanity_check()
-
- def get_gl_matrix(self):
- z_near = self.near
- z_far = self.far
- rot_mat = self.get_rotation_matrix()
- int_mat = self.get_intrinsic_matrix()
- trans = self.get_translation_vector()
-
- extrinsic = np.eye(4)
- extrinsic[:3, :3] = rot_mat
- extrinsic[:3, 3] = trans
- axis_adj = np.eye(4)
- axis_adj[2, 2] = -1
- axis_adj[1, 1] = -1
- model_view = np.matmul(axis_adj, extrinsic)
-
- projective = np.zeros([4, 4])
- projective[:2, :2] = int_mat[:2, :2]
- projective[:2, 2:3] = -int_mat[:2, 2:3]
- projective[3, 2] = -1
- projective[2, 2] = (z_near + z_far)
- projective[2, 3] = (z_near * z_far)
-
- if self.ortho_ratio is None:
- ndc = ortho(0, self.width, 0, self.height, z_near, z_far)
- perspective = np.matmul(ndc, projective)
- else:
- perspective = ortho(-self.width * self.ortho_ratio / 2, self.width * self.ortho_ratio / 2,
- -self.height * self.ortho_ratio / 2, self.height * self.ortho_ratio / 2,
- z_near, z_far)
-
- return perspective, model_view
-
-
-def KRT_from_P(proj_mat, normalize_K=True):
- res = cv2.decomposeProjectionMatrix(proj_mat)
- K, Rot, camera_center_homog = res[0], res[1], res[2]
- camera_center = camera_center_homog[0:3] / camera_center_homog[3]
- trans = -Rot.dot(camera_center)
- if normalize_K:
- K = K / K[2][2]
- return K, Rot, trans
-
-
-def MVP_from_P(proj_mat, width, height, near=0.1, far=10000):
- '''
- Convert OpenCV camera calibration matrix to OpenGL projection and model view matrix
- :param proj_mat: OpenCV camera projeciton matrix
- :param width: Image width
- :param height: Image height
- :param near: Z near value
- :param far: Z far value
- :return: OpenGL projection matrix and model view matrix
- '''
- res = cv2.decomposeProjectionMatrix(proj_mat)
- K, Rot, camera_center_homog = res[0], res[1], res[2]
- camera_center = camera_center_homog[0:3] / camera_center_homog[3]
- trans = -Rot.dot(camera_center)
- K = K / K[2][2]
-
- extrinsic = np.eye(4)
- extrinsic[:3, :3] = Rot
- extrinsic[:3, 3:4] = trans
- axis_adj = np.eye(4)
- axis_adj[2, 2] = -1
- axis_adj[1, 1] = -1
- model_view = np.matmul(axis_adj, extrinsic)
-
- zFar = far
- zNear = near
- projective = np.zeros([4, 4])
- projective[:2, :2] = K[:2, :2]
- projective[:2, 2:3] = -K[:2, 2:3]
- projective[3, 2] = -1
- projective[2, 2] = (zNear + zFar)
- projective[2, 3] = (zNear * zFar)
-
- ndc = ortho(0, width, 0, height, zNear, zFar)
-
- perspective = np.matmul(ndc, projective)
-
- return perspective, model_view
diff --git a/spaces/radames/SPIGA-face-alignment-headpose-estimator/SPIGA/spiga/eval/benchmark/evaluator.py b/spaces/radames/SPIGA-face-alignment-headpose-estimator/SPIGA/spiga/eval/benchmark/evaluator.py
deleted file mode 100644
index 85ed64a7cc6cd3be4b4ed3fad5f1aa9ce1613c1d..0000000000000000000000000000000000000000
--- a/spaces/radames/SPIGA-face-alignment-headpose-estimator/SPIGA/spiga/eval/benchmark/evaluator.py
+++ /dev/null
@@ -1,108 +0,0 @@
-import json
-import pkg_resources
-from collections import OrderedDict
-
-# Paths
-data_path = pkg_resources.resource_filename('spiga', 'data/annotations')
-
-def main():
-
- import argparse
- pars = argparse.ArgumentParser(description='Benchmark alignments evaluator')
- pars.add_argument('pred_file', nargs='+', type=str, help='Absolute path to the prediction json file (Multi file)')
- pars.add_argument('--eval', nargs='+', type=str, default=['lnd'],
- choices=['lnd', 'pose'], help='Evaluation modes')
- pars.add_argument('-s', '--save', action='store_true', help='Save results')
- args = pars.parse_args()
-
- for pred_file in args.pred_file:
- benchmark = get_evaluator(pred_file, args.eval, args.save)
- benchmark.metrics()
-
-
-class Evaluator:
-
- def __init__(self, data_file, evals=(), save=True, process_err=True):
-
- # Inputs
- self.data_file = data_file
- self.evals = evals
- self.save = save
-
- # Paths
- data_name = data_file.split('/')[-1]
- self.data_dir = data_file.split(data_name)[0]
-
- # Information from name
- data_name = data_name.split('.')[0]
- data_name = data_name.split('_')
- self.data_type = data_name[-1]
- self.database = data_name[-2]
-
- # Load predictions and annotations
- anns_file = data_path + '/%s/%s.json' % (self.database, self.data_type)
- self.anns = self.load_files(anns_file)
- self.pred = self.load_files(data_file)
-
- # Compute errors
- self.error = OrderedDict()
- self.error_pimg = OrderedDict()
- self.metrics_log = OrderedDict()
- if process_err:
- self.compute_error(self.anns, self.pred)
-
- def compute_error(self, anns, pred, select_ids=None):
- database_ref = [self.database, self.data_type]
- for eval in self.evals:
- self.error[eval.name] = eval.compute_error(anns, pred, database_ref, select_ids)
- self.error_pimg = eval.get_pimg_err(self.error_pimg)
- return self.error
-
- def metrics(self):
- for eval in self.evals:
- self.metrics_log[eval.name] = eval.metrics()
-
- if self.save:
- file_name = self.data_dir + '/metrics_%s_%s.txt' % (self.database, self.data_type)
- with open(file_name, 'w') as file:
- file.write(str(self))
-
- return self.metrics_log
-
- def load_files(self, input_file):
- with open(input_file) as jsonfile:
- data = json.load(jsonfile)
- return data
-
- def _dict2text(self, name, dictionary, num_tab=1):
- prev_tabs = '\t'*num_tab
- text = '%s {\n' % name
- for k, v in dictionary.items():
- if isinstance(v, OrderedDict) or isinstance(v, dict):
- text += '{}{}'.format(prev_tabs, self._dict2text(k, v, num_tab=num_tab+1))
- else:
- text += '{}{}: {}\n'.format(prev_tabs, k, v)
- text += (prev_tabs + '}\n')
- return text
-
- def __str__(self):
- state_dict = self.metrics_log
- text = self._dict2text('Metrics', state_dict)
- return text
-
-
-def get_evaluator(pred_file, evaluate=('lnd', 'pose'), save=False, process_err=True):
- eval_list = []
- if "lnd" in evaluate:
- import spiga.eval.benchmark.metrics.landmarks as mlnd
- eval_list.append(mlnd.MetricsLandmarks())
- if "pose" in evaluate:
- import spiga.eval.benchmark.metrics.pose as mpose
- eval_list.append(mpose.MetricsHeadpose())
-
- return Evaluator(pred_file, evals=eval_list, save=save, process_err=process_err)
-
-
-if __name__ == '__main__':
- main()
-
diff --git a/spaces/radames/UserControllableLT-Latent-Transformer/training/ranger.py b/spaces/radames/UserControllableLT-Latent-Transformer/training/ranger.py
deleted file mode 100644
index 441cded317542a82229ddfbd3073639f8d3e706b..0000000000000000000000000000000000000000
--- a/spaces/radames/UserControllableLT-Latent-Transformer/training/ranger.py
+++ /dev/null
@@ -1,164 +0,0 @@
-# Ranger deep learning optimizer - RAdam + Lookahead + Gradient Centralization, combined into one optimizer.
-
-# https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
-# and/or
-# https://github.com/lessw2020/Best-Deep-Learning-Optimizers
-
-# Ranger has now been used to capture 12 records on the FastAI leaderboard.
-
-# This version = 20.4.11
-
-# Credits:
-# Gradient Centralization --> https://arxiv.org/abs/2004.01461v2 (a new optimization technique for DNNs), github: https://github.com/Yonghongwei/Gradient-Centralization
-# RAdam --> https://github.com/LiyuanLucasLiu/RAdam
-# Lookahead --> rewritten by lessw2020, but big thanks to Github @LonePatient and @RWightman for ideas from their code.
-# Lookahead paper --> MZhang,G Hinton https://arxiv.org/abs/1907.08610
-
-# summary of changes:
-# 4/11/20 - add gradient centralization option. Set new testing benchmark for accuracy with it, toggle with use_gc flag at init.
-# full code integration with all updates at param level instead of group, moves slow weights into state dict (from generic weights),
-# supports group learning rates (thanks @SHolderbach), fixes sporadic load from saved model issues.
-# changes 8/31/19 - fix references to *self*.N_sma_threshold;
-# changed eps to 1e-5 as better default than 1e-8.
-
-import math
-import torch
-from torch.optim.optimizer import Optimizer
-
-
-class Ranger(Optimizer):
-
- def __init__(self, params, lr=1e-3, # lr
- alpha=0.5, k=6, N_sma_threshhold=5, # Ranger options
- betas=(.95, 0.999), eps=1e-5, weight_decay=0, # Adam options
- use_gc=True, gc_conv_only=False
- # Gradient centralization on or off, applied to conv layers only or conv + fc layers
- ):
-
- # parameter checks
- if not 0.0 <= alpha <= 1.0:
- raise ValueError(f'Invalid slow update rate: {alpha}')
- if not 1 <= k:
- raise ValueError(f'Invalid lookahead steps: {k}')
- if not lr > 0:
- raise ValueError(f'Invalid Learning Rate: {lr}')
- if not eps > 0:
- raise ValueError(f'Invalid eps: {eps}')
-
- # parameter comments:
- # beta1 (momentum) of .95 seems to work better than .90...
- # N_sma_threshold of 5 seems better in testing than 4.
- # In both cases, worth testing on your dataset (.90 vs .95, 4 vs 5) to make sure which works best for you.
-
- # prep defaults and init torch.optim base
- defaults = dict(lr=lr, alpha=alpha, k=k, step_counter=0, betas=betas, N_sma_threshhold=N_sma_threshhold,
- eps=eps, weight_decay=weight_decay)
- super().__init__(params, defaults)
-
- # adjustable threshold
- self.N_sma_threshhold = N_sma_threshhold
-
- # look ahead params
-
- self.alpha = alpha
- self.k = k
-
- # radam buffer for state
- self.radam_buffer = [[None, None, None] for ind in range(10)]
-
- # gc on or off
- self.use_gc = use_gc
-
- # level of gradient centralization
- self.gc_gradient_threshold = 3 if gc_conv_only else 1
-
- def __setstate__(self, state):
- super(Ranger, self).__setstate__(state)
-
- def step(self, closure=None):
- loss = None
-
- # Evaluate averages and grad, update param tensors
- for group in self.param_groups:
-
- for p in group['params']:
- if p.grad is None:
- continue
- grad = p.grad.data.float()
-
- if grad.is_sparse:
- raise RuntimeError('Ranger optimizer does not support sparse gradients')
-
- p_data_fp32 = p.data.float()
-
- state = self.state[p] # get state dict for this param
-
- if len(state) == 0: # if first time to run...init dictionary with our desired entries
- # if self.first_run_check==0:
- # self.first_run_check=1
- # print("Initializing slow buffer...should not see this at load from saved model!")
- state['step'] = 0
- state['exp_avg'] = torch.zeros_like(p_data_fp32)
- state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
-
- # look ahead weight storage now in state dict
- state['slow_buffer'] = torch.empty_like(p.data)
- state['slow_buffer'].copy_(p.data)
-
- else:
- state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
- state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
-
- # begin computations
- exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
- beta1, beta2 = group['betas']
-
- # GC operation for Conv layers and FC layers
- if grad.dim() > self.gc_gradient_threshold:
- grad.add_(-grad.mean(dim=tuple(range(1, grad.dim())), keepdim=True))
-
- state['step'] += 1
-
- # compute variance mov avg
- exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
- # compute mean moving avg
- exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
-
- buffered = self.radam_buffer[int(state['step'] % 10)]
-
- if state['step'] == buffered[0]:
- N_sma, step_size = buffered[1], buffered[2]
- else:
- buffered[0] = state['step']
- beta2_t = beta2 ** state['step']
- N_sma_max = 2 / (1 - beta2) - 1
- N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
- buffered[1] = N_sma
- if N_sma > self.N_sma_threshhold:
- step_size = math.sqrt(
- (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (
- N_sma_max - 2)) / (1 - beta1 ** state['step'])
- else:
- step_size = 1.0 / (1 - beta1 ** state['step'])
- buffered[2] = step_size
-
- if group['weight_decay'] != 0:
- p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
-
- # apply lr
- if N_sma > self.N_sma_threshhold:
- denom = exp_avg_sq.sqrt().add_(group['eps'])
- p_data_fp32.addcdiv_(exp_avg, denom, value=-step_size * group['lr'])
- else:
- p_data_fp32.add_(exp_avg, alpha=-step_size * group['lr'])
-
- p.data.copy_(p_data_fp32)
-
- # integrated look ahead...
- # we do it at the param level instead of group level
- if state['step'] % group['k'] == 0:
- slow_p = state['slow_buffer'] # get access to slow param tensor
- slow_p.add_(p.data - slow_p, alpha=self.alpha) # (fast weights - slow weights) * alpha
- p.data.copy_(slow_p) # copy interpolated weights to RAdam param tensor
-
- return loss
\ No newline at end of file
diff --git a/spaces/rafaelpadilla/coco_metrics/coco_metrics/pycocotools/coco.py b/spaces/rafaelpadilla/coco_metrics/coco_metrics/pycocotools/coco.py
deleted file mode 100644
index a4ca78839cd493c426eb9de4af9da27c2a4d3ac8..0000000000000000000000000000000000000000
--- a/spaces/rafaelpadilla/coco_metrics/coco_metrics/pycocotools/coco.py
+++ /dev/null
@@ -1,491 +0,0 @@
-# This code is basically a copy and paste from the original cocoapi file:
-# https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/coco.py
-# with the following changes:
-# * Instead of receiving the path to the annotation file, it receives a json object.
-# * Commented out all parts of code that depends on maskUtils, which is not needed
-# for bounding box evaluation.
-
-__author__ = "tylin"
-__version__ = "2.0"
-# Interface for accessing the Microsoft COCO dataset.
-
-# Microsoft COCO is a large image dataset designed for object detection,
-# segmentation, and caption generation. pycocotools is a Python API that
-# assists in loading, parsing and visualizing the annotations in COCO.
-# Please visit http://mscoco.org/ for more information on COCO, including
-# for the data, paper, and tutorials. The exact format of the annotations
-# is also described on the COCO website. For example usage of the pycocotools
-# please see pycocotools_demo.ipynb. In addition to this API, please download both
-# the COCO images and annotations in order to run the demo.
-
-# An alternative to using the API is to load the annotations directly
-# into Python dictionary
-# Using the API provides additional utility functions. Note that this API
-# supports both *instance* and *caption* annotations. In the case of
-# captions not all functions are defined (e.g. categories are undefined).
-
-# The following API functions are defined:
-# COCO - COCO api class that loads COCO annotation file and prepare data structures.
-# decodeMask - Decode binary mask M encoded via run-length encoding.
-# encodeMask - Encode binary mask M using run-length encoding.
-# getAnnIds - Get ann ids that satisfy given filter conditions.
-# getCatIds - Get cat ids that satisfy given filter conditions.
-# getImgIds - Get img ids that satisfy given filter conditions.
-# loadAnns - Load anns with the specified ids.
-# loadCats - Load cats with the specified ids.
-# loadImgs - Load imgs with the specified ids.
-# annToMask - Convert segmentation in an annotation to binary mask.
-# showAnns - Display the specified annotations.
-# loadRes - Load algorithm results and create API for accessing them.
-# download - Download COCO images from mscoco.org server.
-# Throughout the API "ann"=annotation, "cat"=category, and "img"=image.
-# Help on each functions can be accessed by: "help COCO>function".
-
-# See also COCO>decodeMask,
-# COCO>encodeMask, COCO>getAnnIds, COCO>getCatIds,
-# COCO>getImgIds, COCO>loadAnns, COCO>loadCats,
-# COCO>loadImgs, COCO>annToMask, COCO>showAnns
-
-# Microsoft COCO Toolbox. version 2.0
-# Data, paper, and tutorials available at: http://mscoco.org/
-# Code written by Piotr Dollar and Tsung-Yi Lin, 2014.
-# Licensed under the Simplified BSD License [see bsd.txt]
-
-import copy
-import itertools
-import json
-# from . import mask as maskUtils
-import os
-import sys
-import time
-from collections import defaultdict
-
-import matplotlib.pyplot as plt
-import numpy as np
-from matplotlib.collections import PatchCollection
-from matplotlib.patches import Polygon
-
-PYTHON_VERSION = sys.version_info[0]
-if PYTHON_VERSION == 2:
- from urllib import urlretrieve
-elif PYTHON_VERSION == 3:
- from urllib.request import urlretrieve
-
-
-def _isArrayLike(obj):
- return hasattr(obj, "__iter__") and hasattr(obj, "__len__")
-
-
-class COCO:
- def __init__(self, annotations=None):
- """
- Constructor of Microsoft COCO helper class for reading and visualizing annotations.
- :param annotation_file (str): location of annotation file
- :param image_folder (str): location to the folder that hosts images.
- :return:
- """
- # load dataset
- self.dataset, self.anns, self.cats, self.imgs = dict(), dict(), dict(), dict()
- self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list)
- # Modified the original code to receive a json object instead of a path to a file
- if annotations:
- assert (
- type(annotations) == dict
- ), f"annotation file format {type(annotations)} not supported."
- self.dataset = annotations
- self.createIndex()
-
- def createIndex(self):
- # create index
- print("creating index...")
- anns, cats, imgs = {}, {}, {}
- imgToAnns, catToImgs = defaultdict(list), defaultdict(list)
- if "annotations" in self.dataset:
- for ann in self.dataset["annotations"]:
- imgToAnns[ann["image_id"]].append(ann)
- anns[ann["id"]] = ann
-
- if "images" in self.dataset:
- for img in self.dataset["images"]:
- imgs[img["id"]] = img
-
- if "categories" in self.dataset:
- for cat in self.dataset["categories"]:
- cats[cat["id"]] = cat
-
- if "annotations" in self.dataset and "categories" in self.dataset:
- for ann in self.dataset["annotations"]:
- catToImgs[ann["category_id"]].append(ann["image_id"])
-
- print("index created!")
-
- # create class members
- self.anns = anns
- self.imgToAnns = imgToAnns
- self.catToImgs = catToImgs
- self.imgs = imgs
- self.cats = cats
-
- def info(self):
- """
- Print information about the annotation file.
- :return:
- """
- for key, value in self.dataset["info"].items():
- print("{}: {}".format(key, value))
-
- def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
- """
- Get ann ids that satisfy given filter conditions. default skips that filter
- :param imgIds (int array) : get anns for given imgs
- catIds (int array) : get anns for given cats
- areaRng (float array) : get anns for given area range (e.g. [0 inf])
- iscrowd (boolean) : get anns for given crowd label (False or True)
- :return: ids (int array) : integer array of ann ids
- """
- imgIds = imgIds if _isArrayLike(imgIds) else [imgIds]
- catIds = catIds if _isArrayLike(catIds) else [catIds]
-
- if len(imgIds) == len(catIds) == len(areaRng) == 0:
- anns = self.dataset["annotations"]
- else:
- if not len(imgIds) == 0:
- lists = [
- self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns
- ]
- anns = list(itertools.chain.from_iterable(lists))
- else:
- anns = self.dataset["annotations"]
- anns = (
- anns
- if len(catIds) == 0
- else [ann for ann in anns if ann["category_id"] in catIds]
- )
- anns = (
- anns
- if len(areaRng) == 0
- else [
- ann
- for ann in anns
- if ann["area"] > areaRng[0] and ann["area"] < areaRng[1]
- ]
- )
- if not iscrowd == None:
- ids = [ann["id"] for ann in anns if ann["iscrowd"] == iscrowd]
- else:
- ids = [ann["id"] for ann in anns]
- return ids
-
- def getCatIds(self, catNms=[], supNms=[], catIds=[]):
- """
- filtering parameters. default skips that filter.
- :param catNms (str array) : get cats for given cat names
- :param supNms (str array) : get cats for given supercategory names
- :param catIds (int array) : get cats for given cat ids
- :return: ids (int array) : integer array of cat ids
- """
- catNms = catNms if _isArrayLike(catNms) else [catNms]
- supNms = supNms if _isArrayLike(supNms) else [supNms]
- catIds = catIds if _isArrayLike(catIds) else [catIds]
-
- if len(catNms) == len(supNms) == len(catIds) == 0:
- cats = self.dataset["categories"]
- else:
- cats = self.dataset["categories"]
- cats = (
- cats
- if len(catNms) == 0
- else [cat for cat in cats if cat["name"] in catNms]
- )
- cats = (
- cats
- if len(supNms) == 0
- else [cat for cat in cats if cat["supercategory"] in supNms]
- )
- cats = (
- cats
- if len(catIds) == 0
- else [cat for cat in cats if cat["id"] in catIds]
- )
- ids = [cat["id"] for cat in cats]
- return ids
-
- def getImgIds(self, imgIds=[], catIds=[]):
- """
- Get img ids that satisfy given filter conditions.
- :param imgIds (int array) : get imgs for given ids
- :param catIds (int array) : get imgs with all given cats
- :return: ids (int array) : integer array of img ids
- """
- imgIds = imgIds if _isArrayLike(imgIds) else [imgIds]
- catIds = catIds if _isArrayLike(catIds) else [catIds]
-
- if len(imgIds) == len(catIds) == 0:
- ids = self.imgs.keys()
- else:
- ids = set(imgIds)
- for i, catId in enumerate(catIds):
- if i == 0 and len(ids) == 0:
- ids = set(self.catToImgs[catId])
- else:
- ids &= set(self.catToImgs[catId])
- return list(ids)
-
- def loadAnns(self, ids=[]):
- """
- Load anns with the specified ids.
- :param ids (int array) : integer ids specifying anns
- :return: anns (object array) : loaded ann objects
- """
- if _isArrayLike(ids):
- return [self.anns[id] for id in ids]
- elif type(ids) == int:
- return [self.anns[ids]]
-
- def loadCats(self, ids=[]):
- """
- Load cats with the specified ids.
- :param ids (int array) : integer ids specifying cats
- :return: cats (object array) : loaded cat objects
- """
- if _isArrayLike(ids):
- return [self.cats[id] for id in ids]
- elif type(ids) == int:
- return [self.cats[ids]]
-
- def loadImgs(self, ids=[]):
- """
- Load anns with the specified ids.
- :param ids (int array) : integer ids specifying img
- :return: imgs (object array) : loaded img objects
- """
- if _isArrayLike(ids):
- return [self.imgs[id] for id in ids]
- elif type(ids) == int:
- return [self.imgs[ids]]
-
- def showAnns(self, anns, draw_bbox=False):
- """
- Display the specified annotations.
- :param anns (array of object): annotations to display
- :return: None
- """
- if len(anns) == 0:
- return 0
- if "segmentation" in anns[0] or "keypoints" in anns[0]:
- datasetType = "instances"
- elif "caption" in anns[0]:
- datasetType = "captions"
- else:
- raise Exception("datasetType not supported")
- if datasetType == "instances":
- ax = plt.gca()
- ax.set_autoscale_on(False)
- polygons = []
- color = []
- for ann in anns:
- c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
- if "segmentation" in ann:
- if type(ann["segmentation"]) == list:
- # polygon
- for seg in ann["segmentation"]:
- poly = np.array(seg).reshape((int(len(seg) / 2), 2))
- polygons.append(Polygon(poly))
- color.append(c)
- else:
- raise NotImplementedError(
- "This type is not is not supported yet."
- )
- # # mask
- # t = self.imgs[ann['image_id']]
- # if type(ann['segmentation']['counts']) == list:
- # rle = maskUtils.frPyObjects([ann['segmentation']], t['height'], t['width'])
- # else:
- # rle = [ann['segmentation']]
- # m = maskUtils.decode(rle)
- # img = np.ones( (m.shape[0], m.shape[1], 3) )
- # if ann['iscrowd'] == 1:
- # color_mask = np.array([2.0,166.0,101.0])/255
- # if ann['iscrowd'] == 0:
- # color_mask = np.random.random((1, 3)).tolist()[0]
- # for i in range(3):
- # img[:,:,i] = color_mask[i]
- # ax.imshow(np.dstack( (img, m*0.5) ))
- if "keypoints" in ann and type(ann["keypoints"]) == list:
- # turn skeleton into zero-based index
- sks = np.array(self.loadCats(ann["category_id"])[0]["skeleton"]) - 1
- kp = np.array(ann["keypoints"])
- x = kp[0::3]
- y = kp[1::3]
- v = kp[2::3]
- for sk in sks:
- if np.all(v[sk] > 0):
- plt.plot(x[sk], y[sk], linewidth=3, color=c)
- plt.plot(
- x[v > 0],
- y[v > 0],
- "o",
- markersize=8,
- markerfacecolor=c,
- markeredgecolor="k",
- markeredgewidth=2,
- )
- plt.plot(
- x[v > 1],
- y[v > 1],
- "o",
- markersize=8,
- markerfacecolor=c,
- markeredgecolor=c,
- markeredgewidth=2,
- )
-
- if draw_bbox:
- [bbox_x, bbox_y, bbox_w, bbox_h] = ann["bbox"]
- poly = [
- [bbox_x, bbox_y],
- [bbox_x, bbox_y + bbox_h],
- [bbox_x + bbox_w, bbox_y + bbox_h],
- [bbox_x + bbox_w, bbox_y],
- ]
- np_poly = np.array(poly).reshape((4, 2))
- polygons.append(Polygon(np_poly))
- color.append(c)
-
- p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)
- ax.add_collection(p)
- p = PatchCollection(
- polygons, facecolor="none", edgecolors=color, linewidths=2
- )
- ax.add_collection(p)
- elif datasetType == "captions":
- for ann in anns:
- print(ann["caption"])
-
- def loadRes(self, resFile):
- """
- Load result file and return a result api object.
- :param resFile (str) : file name of result file
- :return: res (obj) : result api object
- """
- res = COCO()
- res.dataset["images"] = [img for img in self.dataset["images"]]
-
- print("Loading and preparing results...")
- tic = time.time()
- if type(resFile) == str or (PYTHON_VERSION == 2 and type(resFile) == unicode):
- anns = json.load(open(resFile))
- elif type(resFile) == np.ndarray:
- anns = self.loadNumpyAnnotations(resFile)
- else:
- anns = resFile
- assert type(anns) == list, "results in not an array of objects"
- annsImgIds = [ann["image_id"] for ann in anns]
- assert set(annsImgIds) == (
- set(annsImgIds) & set(self.getImgIds())
- ), "Results do not correspond to current coco set"
- if "caption" in anns[0]:
- raise NotImplementedError("Evaluating caption is not supported yet.")
- elif "bbox" in anns[0] and not anns[0]["bbox"] == []:
- res.dataset["categories"] = copy.deepcopy(self.dataset["categories"])
- for id, ann in enumerate(anns):
- bb = ann["bbox"]
- x1, x2, y1, y2 = [bb[0], bb[0] + bb[2], bb[1], bb[1] + bb[3]]
- if not "segmentation" in ann:
- ann["segmentation"] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
- ann["area"] = bb[2] * bb[3]
- ann["id"] = id + 1
- ann["iscrowd"] = 0
- elif "segmentation" in anns[0]:
- raise NotImplementedError("Evaluating caption is not supported yet.")
- elif "keypoints" in anns[0]:
- raise NotImplementedError("Evaluating caption is not supported yet.")
- print("DONE (t={:0.2f}s)".format(time.time() - tic))
-
- res.dataset["annotations"] = anns
- res.createIndex()
- return res
-
- def download(self, tarDir=None, imgIds=[]):
- """
- Download COCO images from mscoco.org server.
- :param tarDir (str): COCO results directory name
- imgIds (list): images to be downloaded
- :return:
- """
- if tarDir is None:
- print("Please specify target directory")
- return -1
- if len(imgIds) == 0:
- imgs = self.imgs.values()
- else:
- imgs = self.loadImgs(imgIds)
- N = len(imgs)
- if not os.path.exists(tarDir):
- os.makedirs(tarDir)
- for i, img in enumerate(imgs):
- tic = time.time()
- fname = os.path.join(tarDir, img["file_name"])
- if not os.path.exists(fname):
- urlretrieve(img["coco_url"], fname)
- print(
- "downloaded {}/{} images (t={:0.1f}s)".format(i, N, time.time() - tic)
- )
-
- def loadNumpyAnnotations(self, data):
- """
- Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class}
- :param data (numpy.ndarray)
- :return: annotations (python nested list)
- """
- print("Converting ndarray to lists...")
- assert type(data) == np.ndarray
- print(data.shape)
- assert data.shape[1] == 7
- N = data.shape[0]
- ann = []
- for i in range(N):
- if i % 1000000 == 0:
- print("{}/{}".format(i, N))
- ann += [
- {
- "image_id": int(data[i, 0]),
- "bbox": [data[i, 1], data[i, 2], data[i, 3], data[i, 4]],
- "score": data[i, 5],
- "category_id": int(data[i, 6]),
- }
- ]
- return ann
-
- def annToRLE(self, ann):
- """
- Convert annotation which can be polygons, uncompressed RLE to RLE.
- :return: binary mask (numpy 2D array)
- """
- t = self.imgs[ann["image_id"]]
- h, w = t["height"], t["width"]
- segm = ann["segmentation"]
- if type(segm) == list:
- raise NotImplementedError("This type is not is not supported yet.")
- # polygon -- a single object might consist of multiple parts
- # we merge all parts into one mask rle code
- # rles = maskUtils.frPyObjects(segm, h, w)
- # rle = maskUtils.merge(rles)
- elif type(segm["counts"]) == list:
- raise NotImplementedError("This type is not is not supported yet.")
- # uncompressed RLE
- # rle = maskUtils.frPyObjects(segm, h, w)
- else:
- # rle
- rle = ann["segmentation"]
- return rle
-
- def annToMask(self, ann):
- """
- Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask.
- :return: binary mask (numpy 2D array)
- """
- rle = self.annToRLE(ann)
- # m = maskUtils.decode(rle)
- raise NotImplementedError("This type is not is not supported yet.")
- return m
diff --git a/spaces/rakesh99/myvoicebot/app.py b/spaces/rakesh99/myvoicebot/app.py
deleted file mode 100644
index ca8b6d40b4ab898c70da92f4a4298de2baf703dc..0000000000000000000000000000000000000000
--- a/spaces/rakesh99/myvoicebot/app.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import os
-import re
-import requests
-import json
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-PLAY_HT_API_KEY=os.getenv('PLAY_HT_API_KEY')
-PLAY_HT_USER_ID=os.getenv('PLAY_HT_USER_ID')
-
-PLAY_HT_VOICE_ID=os.getenv('PLAY_HT_VOICE_ID')
-play_ht_api_get_audio_url = "https://play.ht/api/v2/tts"
-
-
-template = """You are a helpful assistant to answer user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
- input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
- llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
- prompt=prompt,
- verbose=True,
- memory=memory,
-)
-
-headers = {
- "accept": "text/event-stream",
- "content-type": "application/json",
- "AUTHORIZATION": "Bearer "+ PLAY_HT_API_KEY,
- "X-USER-ID": PLAY_HT_USER_ID
-}
-
-
-def get_payload(text):
- return {
- "text": text,
- "voice": PLAY_HT_VOICE_ID,
- "quality": "medium",
- "output_format": "mp3",
- "speed": 1,
- "sample_rate": 24000,
- "seed": None,
- "temperature": None
- }
-
-def get_generated_audio(text):
- payload = get_payload(text)
- generated_response = {}
- try:
- response = requests.post(play_ht_api_get_audio_url, json=payload, headers=headers)
- response.raise_for_status()
- generated_response["type"]= 'SUCCESS'
- generated_response["response"] = response.text
- except requests.exceptions.RequestException as e:
- generated_response["type"]= 'ERROR'
- try:
- response_text = json.loads(response.text)
- if response_text['error_message']:
- generated_response["response"] = response_text['error_message']
- else:
- generated_response["response"] = response.text
- except Exception as e:
- generated_response["response"] = response.text
- except Exception as e:
- generated_response["type"]= 'ERROR'
- generated_response["response"] = response.text
- return generated_response
-
-def extract_urls(text):
- # Define the regex pattern for URLs
- url_pattern = r'https?://(?:[-\w.]|(?:%[\da-fA-F]{2}))+[/\w\.-]*'
-
- # Find all occurrences of URLs in the text
- urls = re.findall(url_pattern, text)
-
- return urls
-
-def get_audio_reply_for_question(text):
- generated_audio_event = get_generated_audio(text)
- #From get_generated_audio, you will get events in a string format, from that we need to extract the url
- final_response = {
- "audio_url": '',
- "message": ''
- }
- if generated_audio_event["type"] == 'SUCCESS':
- audio_urls = extract_urls(generated_audio_event["response"])
- if len(audio_urls) == 0:
- final_response['message'] = "No audio file link found in generated event"
- else:
- final_response['audio_url'] = audio_urls[-1]
- else:
- final_response['message'] = generated_audio_event['response']
- return final_response
-
-def download_url(url):
- try:
- # Send a GET request to the URL to fetch the content
- final_response = {
- 'content':'',
- 'error':''
- }
- response = requests.get(url)
- # Check if the request was successful (status code 200)
- if response.status_code == 200:
- final_response['content'] = response.content
- else:
- final_response['error'] = f"Failed to download the URL. Status code: {response.status_code}"
- except Exception as e:
- final_response['error'] = f"Failed to download the URL. Error: {e}"
- return final_response
-
-def get_filename_from_url(url):
- # Use os.path.basename() to extract the file name from the URL
- file_name = os.path.basename(url)
- return file_name
-
-def get_text_response(user_message):
- response = llm_chain.predict(user_message = user_message)
- return response
-
-def get_text_response_and_audio_response(user_message):
- response = get_text_response(user_message) # Getting the reply from Open AI
- audio_reply_for_question_response = get_audio_reply_for_question(response)
- final_response = {
- 'output_file_path': '',
- 'message':''
- }
- audio_url = audio_reply_for_question_response['audio_url']
- if audio_url:
- output_file_path=get_filename_from_url(audio_url)
- download_url_response = download_url(audio_url)
- audio_content = download_url_response['content']
- if audio_content:
- with open(output_file_path, "wb") as audio_file:
- audio_file.write(audio_content)
- final_response['output_file_path'] = output_file_path
- else:
- final_response['message'] = download_url_response['error']
- else:
- final_response['message'] = audio_reply_for_question_response['message']
- return final_response
-
-def chat_bot_response(message, history):
- text_and_audio_response = get_text_response_and_audio_response(message)
- output_file_path = text_and_audio_response['output_file_path']
- if output_file_path:
- return (text_and_audio_response['output_file_path'],)
- else:
- return text_and_audio_response['message']
-
-demo = gr.ChatInterface(chat_bot_response,examples=["How are you doing?","What are your interests?","Which places do you like to visit?"])
-
-if __name__ == "__main__":
- demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.
diff --git a/spaces/rayan-saleh/whisper2notion/server/node_modules/@notionhq/client/build/src/fetch-types.d.ts b/spaces/rayan-saleh/whisper2notion/server/node_modules/@notionhq/client/build/src/fetch-types.d.ts
deleted file mode 100644
index e6f4c22704a55f2f85d4869a49648e482d1ed160..0000000000000000000000000000000000000000
--- a/spaces/rayan-saleh/whisper2notion/server/node_modules/@notionhq/client/build/src/fetch-types.d.ts
+++ /dev/null
@@ -1,17 +0,0 @@
-/// >;
-type RequestInit = NonNullable[1]>;
-export type SupportedRequestInfo = string;
-export type SupportedRequestInit = {
- agent?: NodeRequestInit["agent"];
- body?: NonNullable & NonNullable;
- headers?: NonNullable & NonNullable;
- method?: RequestInit["method"];
- redirect?: RequestInit["redirect"];
-};
-export type SupportedResponse = FetchResponse | NodeResponse;
-export type SupportedFetch = (url: SupportedRequestInfo, init?: SupportedRequestInit) => Promise;
-export {};
-//# sourceMappingURL=fetch-types.d.ts.map
\ No newline at end of file
diff --git a/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/path.d.ts b/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/path.d.ts
deleted file mode 100644
index 1d33f79269f7d4f55dca5513dd8ba3e33dc7732b..0000000000000000000000000000000000000000
--- a/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/path.d.ts
+++ /dev/null
@@ -1,191 +0,0 @@
-declare module 'path/posix' {
- import path = require('path');
- export = path;
-}
-declare module 'path/win32' {
- import path = require('path');
- export = path;
-}
-/**
- * The `path` module provides utilities for working with file and directory paths.
- * It can be accessed using:
- *
- * ```js
- * const path = require('path');
- * ```
- * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/path.js)
- */
-declare module 'path' {
- namespace path {
- /**
- * A parsed path object generated by path.parse() or consumed by path.format().
- */
- interface ParsedPath {
- /**
- * The root of the path such as '/' or 'c:\'
- */
- root: string;
- /**
- * The full directory path such as '/home/user/dir' or 'c:\path\dir'
- */
- dir: string;
- /**
- * The file name including extension (if any) such as 'index.html'
- */
- base: string;
- /**
- * The file extension (if any) such as '.html'
- */
- ext: string;
- /**
- * The file name without extension (if any) such as 'index'
- */
- name: string;
- }
- interface FormatInputPathObject {
- /**
- * The root of the path such as '/' or 'c:\'
- */
- root?: string | undefined;
- /**
- * The full directory path such as '/home/user/dir' or 'c:\path\dir'
- */
- dir?: string | undefined;
- /**
- * The file name including extension (if any) such as 'index.html'
- */
- base?: string | undefined;
- /**
- * The file extension (if any) such as '.html'
- */
- ext?: string | undefined;
- /**
- * The file name without extension (if any) such as 'index'
- */
- name?: string | undefined;
- }
- interface PlatformPath {
- /**
- * Normalize a string path, reducing '..' and '.' parts.
- * When multiple slashes are found, they're replaced by a single one; when the path contains a trailing slash, it is preserved. On Windows backslashes are used.
- *
- * @param path string path to normalize.
- * @throws {TypeError} if `path` is not a string.
- */
- normalize(path: string): string;
- /**
- * Join all arguments together and normalize the resulting path.
- *
- * @param paths paths to join.
- * @throws {TypeError} if any of the path segments is not a string.
- */
- join(...paths: string[]): string;
- /**
- * The right-most parameter is considered {to}. Other parameters are considered an array of {from}.
- *
- * Starting from leftmost {from} parameter, resolves {to} to an absolute path.
- *
- * If {to} isn't already absolute, {from} arguments are prepended in right to left order,
- * until an absolute path is found. If after using all {from} paths still no absolute path is found,
- * the current working directory is used as well. The resulting path is normalized,
- * and trailing slashes are removed unless the path gets resolved to the root directory.
- *
- * @param paths A sequence of paths or path segments.
- * @throws {TypeError} if any of the arguments is not a string.
- */
- resolve(...paths: string[]): string;
- /**
- * Determines whether {path} is an absolute path. An absolute path will always resolve to the same location, regardless of the working directory.
- *
- * If the given {path} is a zero-length string, `false` will be returned.
- *
- * @param path path to test.
- * @throws {TypeError} if `path` is not a string.
- */
- isAbsolute(path: string): boolean;
- /**
- * Solve the relative path from {from} to {to} based on the current working directory.
- * At times we have two absolute paths, and we need to derive the relative path from one to the other. This is actually the reverse transform of path.resolve.
- *
- * @throws {TypeError} if either `from` or `to` is not a string.
- */
- relative(from: string, to: string): string;
- /**
- * Return the directory name of a path. Similar to the Unix dirname command.
- *
- * @param path the path to evaluate.
- * @throws {TypeError} if `path` is not a string.
- */
- dirname(path: string): string;
- /**
- * Return the last portion of a path. Similar to the Unix basename command.
- * Often used to extract the file name from a fully qualified path.
- *
- * @param path the path to evaluate.
- * @param suffix optionally, an extension to remove from the result.
- * @throws {TypeError} if `path` is not a string or if `ext` is given and is not a string.
- */
- basename(path: string, suffix?: string): string;
- /**
- * Return the extension of the path, from the last '.' to end of string in the last portion of the path.
- * If there is no '.' in the last portion of the path or the first character of it is '.', then it returns an empty string.
- *
- * @param path the path to evaluate.
- * @throws {TypeError} if `path` is not a string.
- */
- extname(path: string): string;
- /**
- * The platform-specific file separator. '\\' or '/'.
- */
- readonly sep: '\\' | '/';
- /**
- * The platform-specific file delimiter. ';' or ':'.
- */
- readonly delimiter: ';' | ':';
- /**
- * Returns an object from a path string - the opposite of format().
- *
- * @param path path to evaluate.
- * @throws {TypeError} if `path` is not a string.
- */
- parse(path: string): ParsedPath;
- /**
- * Returns a path string from an object - the opposite of parse().
- *
- * @param pathObject path to evaluate.
- */
- format(pathObject: FormatInputPathObject): string;
- /**
- * On Windows systems only, returns an equivalent namespace-prefixed path for the given path.
- * If path is not a string, path will be returned without modifications.
- * This method is meaningful only on Windows system.
- * On POSIX systems, the method is non-operational and always returns path without modifications.
- */
- toNamespacedPath(path: string): string;
- /**
- * Posix specific pathing.
- * Same as parent object on posix.
- */
- readonly posix: PlatformPath;
- /**
- * Windows specific pathing.
- * Same as parent object on windows
- */
- readonly win32: PlatformPath;
- }
- }
- const path: path.PlatformPath;
- export = path;
-}
-declare module 'node:path' {
- import path = require('path');
- export = path;
-}
-declare module 'node:path/posix' {
- import path = require('path/posix');
- export = path;
-}
-declare module 'node:path/win32' {
- import path = require('path/win32');
- export = path;
-}
diff --git a/spaces/rewoo/ReWOO-Demo/utils/__init__.py b/spaces/rewoo/ReWOO-Demo/utils/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/rgergw/White-box-Cartoonization/wbc/cartoonize.py b/spaces/rgergw/White-box-Cartoonization/wbc/cartoonize.py
deleted file mode 100644
index 25faf1ceb95aaed9a3f7a7982d17a03dc6bc32b1..0000000000000000000000000000000000000000
--- a/spaces/rgergw/White-box-Cartoonization/wbc/cartoonize.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import os
-import cv2
-import numpy as np
-import tensorflow as tf
-import wbc.network as network
-import wbc.guided_filter as guided_filter
-from tqdm import tqdm
-
-
-def resize_crop(image):
- h, w, c = np.shape(image)
- if min(h, w) > 720:
- if h > w:
- h, w = int(720 * h / w), 720
- else:
- h, w = 720, int(720 * w / h)
- image = cv2.resize(image, (w, h),
- interpolation=cv2.INTER_AREA)
- h, w = (h // 8) * 8, (w // 8) * 8
- image = image[:h, :w, :]
- return image
-
-
-def cartoonize(load_folder, save_folder, model_path):
- print(model_path)
- input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
- network_out = network.unet_generator(input_photo)
- final_out = guided_filter.guided_filter(input_photo, network_out, r=1, eps=5e-3)
-
- all_vars = tf.trainable_variables()
- gene_vars = [var for var in all_vars if 'generator' in var.name]
- saver = tf.train.Saver(var_list=gene_vars)
-
- config = tf.ConfigProto()
- config.gpu_options.allow_growth = True
- sess = tf.Session(config=config)
-
- sess.run(tf.global_variables_initializer())
- saver.restore(sess, tf.train.latest_checkpoint(model_path))
- name_list = os.listdir(load_folder)
- for name in tqdm(name_list):
- try:
- load_path = os.path.join(load_folder, name)
- save_path = os.path.join(save_folder, name)
- image = cv2.imread(load_path)
- image = resize_crop(image)
- batch_image = image.astype(np.float32) / 127.5 - 1
- batch_image = np.expand_dims(batch_image, axis=0)
- output = sess.run(final_out, feed_dict={input_photo: batch_image})
- output = (np.squeeze(output) + 1) * 127.5
- output = np.clip(output, 0, 255).astype(np.uint8)
- cv2.imwrite(save_path, output)
- except:
- print('cartoonize {} failed'.format(load_path))
-
-
-class Cartoonize:
- def __init__(self, model_path):
- print(model_path)
- self.input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
- network_out = network.unet_generator(self.input_photo)
- self.final_out = guided_filter.guided_filter(self.input_photo, network_out, r=1, eps=5e-3)
-
- all_vars = tf.trainable_variables()
- gene_vars = [var for var in all_vars if 'generator' in var.name]
- saver = tf.train.Saver(var_list=gene_vars)
-
- config = tf.ConfigProto()
- config.gpu_options.allow_growth = True
- self.sess = tf.Session(config=config)
-
- self.sess.run(tf.global_variables_initializer())
- saver.restore(self.sess, tf.train.latest_checkpoint(model_path))
-
- def run(self, load_folder, save_folder):
- name_list = os.listdir(load_folder)
- for name in tqdm(name_list):
- try:
- load_path = os.path.join(load_folder, name)
- save_path = os.path.join(save_folder, name)
- image = cv2.imread(load_path)
- image = resize_crop(image)
- batch_image = image.astype(np.float32) / 127.5 - 1
- batch_image = np.expand_dims(batch_image, axis=0)
- output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image})
- output = (np.squeeze(output) + 1) * 127.5
- output = np.clip(output, 0, 255).astype(np.uint8)
- cv2.imwrite(save_path, output)
- except:
- print('cartoonize {} failed'.format(load_path))
-
- def run_sigle(self, load_path, save_path):
- try:
- image = cv2.imread(load_path)
- image = resize_crop(image)
- batch_image = image.astype(np.float32) / 127.5 - 1
- batch_image = np.expand_dims(batch_image, axis=0)
- output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image})
- output = (np.squeeze(output) + 1) * 127.5
- output = np.clip(output, 0, 255).astype(np.uint8)
- cv2.imwrite(save_path, output)
- except:
- print('cartoonize {} failed'.format(load_path))
-
-
-if __name__ == '__main__':
- model_path = 'saved_models'
- load_folder = 'test_images'
- save_folder = 'cartoonized_images'
- if not os.path.exists(save_folder):
- os.mkdir(save_folder)
- cartoonize(load_folder, save_folder, model_path)
diff --git a/spaces/rgres/Seg2Sat/frontend/.svelte-kit/output/client/_app/immutable/assets/pages/__layout.svelte-b67cf61d.css b/spaces/rgres/Seg2Sat/frontend/.svelte-kit/output/client/_app/immutable/assets/pages/__layout.svelte-b67cf61d.css
deleted file mode 100644
index 11acbae8955923742bb73fadde38166957d5d33b..0000000000000000000000000000000000000000
--- a/spaces/rgres/Seg2Sat/frontend/.svelte-kit/output/client/_app/immutable/assets/pages/__layout.svelte-b67cf61d.css
+++ /dev/null
@@ -1 +0,0 @@
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skewY(var(--tw-skew-y)) scaleX(var(--tw-scale-x)) scaleY(var(--tw-scale-y))}@-webkit-keyframes spin{to{transform:rotate(360deg)}}@keyframes spin{to{transform:rotate(360deg)}}.animate-spin{-webkit-animation:spin 1s linear infinite;animation:spin 1s linear infinite}.cursor-pointer{cursor:pointer}.snap-x{scroll-snap-type:x var(--tw-scroll-snap-strictness)}.snap-y{scroll-snap-type:y var(--tw-scroll-snap-strictness)}.snap-mandatory{--tw-scroll-snap-strictness: mandatory}.snap-start{scroll-snap-align:start}.snap-always{scroll-snap-stop:always}.grid-cols-2{grid-template-columns:repeat(2,minmax(0,1fr))}.grid-cols-\[2fr_1\.5fr\]{grid-template-columns:2fr 1.5fr}.flex-col{flex-direction:column}.flex-nowrap{flex-wrap:nowrap}.items-center{align-items:center}.justify-center{justify-content:center}.gap-2{gap:.5rem}.gap-1{gap:.25rem}.overflow-hidden{overflow:hidden}.overflow-clip{overflow:clip}.overflow-scroll{overflow:scroll}.overflow-x-scroll{overflow-x:scroll}.whitespace-nowrap{white-space:nowrap}.rounded-lg{border-radius:.5rem}.border{border-width:1px}.border-gray-500{--tw-border-opacity: 1;border-color:rgb(107 114 128 / var(--tw-border-opacity))}.border-gray-300{--tw-border-opacity: 1;border-color:rgb(209 213 219 / var(--tw-border-opacity))}.bg-gray-50{--tw-bg-opacity: 1;background-color:rgb(249 250 251 / var(--tw-bg-opacity))}.p-3{padding:.75rem}.p-1{padding:.25rem}.px-2{padding-left:.5rem;padding-right:.5rem}.px-3{padding-left:.75rem;padding-right:.75rem}.py-5{padding-top:1.25rem;padding-bottom:1.25rem}.py-3{padding-top:.75rem;padding-bottom:.75rem}.pl-2{padding-left:.5rem}.text-base{font-size:1rem;line-height:1.5rem}.text-sm{font-size:.875rem;line-height:1.25rem}.text-xs{font-size:.75rem;line-height:1rem}.font-bold{font-weight:700}.leading-6{line-height:1.5rem}.text-white{--tw-text-opacity: 1;color:rgb(255 255 255 / var(--tw-text-opacity))}.text-gray-900{--tw-text-opacity: 1;color:rgb(17 24 39 / var(--tw-text-opacity))}.opacity-0{opacity:0}.opacity-30{opacity:.3}.outline{outline-style:solid}.outline-2{outline-width:2px}.outline-offset-\[-2px\]{outline-offset:-2px}.transition-all{transition-property:all;transition-timing-function:cubic-bezier(.4,0,.2,1);transition-duration:.15s}.duration-200{transition-duration:.2s}.ease-in-out{transition-timing-function:cubic-bezier(.4,0,.2,1)}.hover\:outline:hover{outline-style:solid}.focus\:border-blue-500:focus{--tw-border-opacity: 1;border-color:rgb(59 130 246 / var(--tw-border-opacity))}.focus\:ring-blue-500:focus{--tw-ring-opacity: 1;--tw-ring-color: rgb(59 130 246 / var(--tw-ring-opacity))}.disabled\:opacity-50:disabled{opacity:.5}@media (prefers-color-scheme: dark){.dark\:border-gray-300{--tw-border-opacity: 1;border-color:rgb(209 213 219 / var(--tw-border-opacity))}.dark\:bg-gray-50{--tw-bg-opacity: 1;background-color:rgb(249 250 251 / var(--tw-bg-opacity))}.dark\:focus\:ring-blue-500:focus{--tw-ring-opacity: 1;--tw-ring-color: rgb(59 130 246 / var(--tw-ring-opacity))}}@media (min-width: 530px){.sm\:max-h-\[none\]{max-height:none}.sm\:grid-cols-3{grid-template-columns:repeat(3,minmax(0,1fr))}.sm\:grid-cols-2{grid-template-columns:repeat(2,minmax(0,1fr))}.sm\:flex-row{flex-direction:row}}
diff --git a/spaces/rizam/literature-research-tool/lrt/utils/union_find.py b/spaces/rizam/literature-research-tool/lrt/utils/union_find.py
deleted file mode 100644
index 29e2dca7b79235c88020860c5414a869279cc12c..0000000000000000000000000000000000000000
--- a/spaces/rizam/literature-research-tool/lrt/utils/union_find.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from typing import List
-
-
-class UnionFind:
- def __init__(self, data: List, union_condition: callable):
- self.__data__ = data
- self.__union_condition__ = union_condition
- length = len(data)
- self.__parents__ = [i for i in range(length)]
- self.__ranks__ = [0] * length
- self.__unions__ = {}
-
- def __find_parent__(self, id: int):
- return self.__parents__[id]
-
- def __find_root__(self, id: int):
- parent = self.__find_parent__(id)
- while parent != id:
- id = parent
- parent = self.__find_parent__(id)
- return id
-
- def __union__(self, i: int, j: int):
- root_i = self.__find_root__(i)
- root_j = self.__find_root__(j)
-
- # if roots are different, let one be the parent of the other
- if root_i == root_j:
- return
- else:
- if self.__ranks__[root_i] <= self.__ranks__[root_j]:
- # root of i --> child
- self.__parents__[root_i] = root_j
- self.__ranks__[root_j] = max(self.__ranks__[root_j], self.__ranks__[root_i]+1)
- else:
- self.__parents__[root_j] = root_i
- self.__ranks__[root_i] = max(self.__ranks__[root_i], self.__ranks__[root_j]+1)
-
- def union_step(self):
- length = len(self.__data__)
-
- for i in range(length - 1):
- for j in range(i + 1, length):
- if self.__union_condition__(self.__data__[i], self.__data__[j]):
- self.__union__(i, j)
-
- for i in range(length):
- root = self.__find_root__(i)
- if root not in self.__unions__.keys():
- self.__unions__[root] = [self.__data__[i]]
- else:
- self.__unions__[root].append(self.__data__[i])
-
- def get_unions(self):
- return self.__unions__
diff --git a/spaces/rodevel1978/llama-2-13b-chat.ggmlv3.q4_K_S/model.py b/spaces/rodevel1978/llama-2-13b-chat.ggmlv3.q4_K_S/model.py
deleted file mode 100644
index eb635ea99663bd8400c988f9a608ea94e0b27c44..0000000000000000000000000000000000000000
--- a/spaces/rodevel1978/llama-2-13b-chat.ggmlv3.q4_K_S/model.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from typing import Iterator
-from llama_cpp import Llama
-from huggingface_hub import hf_hub_download
-
-
-def download_model():
- # See https://github.com/OpenAccess-AI-Collective/ggml-webui/blob/main/tabbed.py
- # https://huggingface.co/spaces/kat33/llama.cpp/blob/main/app.py
- print(f"Downloading model: {model_repo}/{model_filename}")
- file = hf_hub_download(
- repo_id=model_repo, filename=model_filename
- )
- print("Downloaded " + file)
- return file
-
-model_repo = "TheBloke/Llama-2-13B-chat-GGML"
-model_filename = "llama-2-13b-chat.ggmlv3.q4_K_S.bin"
-# model_filename = "Chinese-Llama-2-7b.ggmlv3.q8_0.bin"
-model_path = download_model()
-
-# load Llama-2
-llm = Llama(model_path=model_path, n_ctx=4000, verbose=False)
-
-
-def get_prompt(message: str, chat_history: list[tuple[str, str]],
- system_prompt: str) -> str:
- texts = [f'[INST] <>\n{system_prompt}\n< >\n\n']
- for user_input, response in chat_history:
- texts.append(f'{user_input.strip()} [/INST] {response.strip()} [INST] ')
- texts.append(f'{message.strip()} [/INST]')
- return ''.join(texts)
-
-def generate(prompt, max_new_tokens, temperature, top_p, top_k):
- return llm(prompt,
- max_tokens=max_new_tokens,
- stop=[" "],
- temperature=temperature,
- top_p=top_p,
- top_k=top_k,
- stream=False)
-
-
-def get_input_token_length(message: str, chat_history: list[tuple[str, str]], system_prompt: str) -> int:
- prompt = get_prompt(message, chat_history, system_prompt)
- input_ids = llm.tokenize(prompt.encode('utf-8'))
- return len(input_ids)
-
-
-def run(message: str,
- chat_history: list[tuple[str, str]],
- system_prompt: str,
- max_new_tokens: int = 1024,
- temperature: float = 0.8,
- top_p: float = 0.95,
- top_k: int = 50) -> Iterator[str]:
- prompt = get_prompt(message, chat_history, system_prompt)
- output = generate(prompt, max_new_tokens, temperature, top_p, top_k)
- yield output['choices'][0]['text']
-
- # outputs = []
- # for resp in streamer:
- # outputs.append(resp['choices'][0]['text'])
- # yield ''.join(outputs)
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Activation AutoCAD Plant 3D 2019 Activation.md b/spaces/rorallitri/biomedical-language-models/logs/Activation AutoCAD Plant 3D 2019 Activation.md
deleted file mode 100644
index 34fdb3850766a9189eb36eefa7577862e4259b84..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Activation AutoCAD Plant 3D 2019 Activation.md
+++ /dev/null
@@ -1,49 +0,0 @@
-Activation AutoCAD Plant 3D 2019: How to Install and Authenticate Your Software
-AutoCAD Plant 3D 2019 is a powerful software that allows you to design, model, and document process plants with ease. It is built on the familiar AutoCAD platform, and offers features such as piping design, structural design, equipment modeling, orthographic drawings, isometric drawings, and more. If you have purchased or downloaded AutoCAD Plant 3D 2019, you may need to activate it before you can use it. Activation is a process that verifies that your software is genuine and that it has not been used on more devices than allowed by your license agreement.
-Activation AutoCAD Plant 3D 2019: When Do You Need an Activation Code?
-In some cases, you need an activation code to authenticate your software. You may need an activation code if:
-activation AutoCAD Plant 3D 2019 activation DOWNLOAD ➡ https://tinurll.com/2uzmlp
-You’re offline and have a perpetual license for a standalone 2020 (or earlier) product.
-You have an education license and are offline.
-
-If you have a perpetual license for a 2021 product or later and want to use it offline, you do not need an activation code. You just need to activate your software by going online one time to register. You can then use the software offline.
-Activation AutoCAD Plant 3D 2019: How to Get an Activation Code?
-To get an activation code, you must first get a request code. A request code is a code that identifies your computer and the software you want to activate. To get a request code, follow these steps:
-
-Start your Autodesk software.
-Select Enter a Serial Number.
-Enter your product serial number and product key.
-Select Request an activation code using an offline method.
-Record the activation information provided.
-
-Once you have the request code, you can use it to generate an activation code online or by phone. To generate an activation code online, go to Autodesk Product Registration and Activation and follow the instructions. To generate an activation code by phone, call the Autodesk Activation Center using the phone number provided in the product activation window.
-Activation AutoCAD Plant 3D 2019: How to Enter Your Activation Code?
-Once you have your activation code, you can enter it in your software to complete the activation process. To enter your activation code, follow these steps:
-
-In the product activation window, select I have an activation code from Autodesk.
-Enter your activation code in the fields provided.
-Select Next.
-Your software is now activated and ready to use.
-
-Activation AutoCAD Plant 3D 2019: Tips and Troubleshooting
-If you encounter any problems or errors during the activation process, here are some tips and troubleshooting steps that may help:
-
-Make sure you enter the correct serial number and product key for your software. You can find them in your Autodesk Account or in the email confirmation you received when you purchased or downloaded your software.
-Make sure you enter the request code and activation code exactly as they are displayed. Do not include any spaces or hyphens.
-If you are using an online method to generate your activation code, make sure you have a stable internet connection and that your browser allows pop-ups from Autodesk websites.
-If you are using a phone method to generate your activation code, make sure you provide the correct information when prompted by the automated system or the customer service representative.
-If you need more help or have any questions, contact Autodesk Support or visit the Installation & Licensing forum.
-
-Activation AutoCAD Plant 3D 2019: The Complete Solution for Process Plant Design
-AutoCAD Plant 3D 2019 is a comprehensive software that enables you to create accurate and efficient plant designs with ease. It integrates seamlessly with other Autodesk products, such as AutoCAD P&ID, Navisworks, Revit, and BIM 360. It also supports industry standards and formats, such as ISOGEN, PCF, PDS/SmartPlant 3D/SmartPlant Review (SPR), PDMS/Review (RVM), CIS/2 (CIMsteel Integration Standards), SDNF (Steel Detailing Neutral Format), DGN (MicroStation), DWG (AutoCAD), DXF (Drawing Exchange Format), IFC (Industry Foundation Classes), STEP (Standard for The Exchange of Product model data), IGES (Initial Graphics Exchange Specification), STL (STereoLithography), VRML (Virtual Reality Modeling Language), DWF (Design Web Format), PDF (Portable Document Format), CSV (Comma-Separated Values), XML (Extensible Markup Language), JSON (JavaScript Object Notation), SQLite (Relational Database Management System), SQL Server (Relational Database Management System).
-
-
-With AutoCAD Plant 3D 2019, you can design process plants faster and smarter than ever before. You can also collaborate with other stakeholders and share your designs across platforms and devices. Whether you are working on oil and gas projects, chemical plants, power plants, water treatment plants, or any other type of process plant, AutoCAD Plant 3D 2019 is the ultimate solution for your needs.
-Activation AutoCAD Plant 3D 2019: The Complete Solution for Process Plant Design
-AutoCAD Plant 3D 2019 is a comprehensive software that enables you to create accurate and efficient plant designs with ease. It integrates seamlessly with other Autodesk products, such as AutoCAD P&ID, Navisworks, Revit, and BIM 360. It also supports industry standards and formats, such as ISOGEN, PCF, PDS/SmartPlant 3D/SmartPlant Review (SPR), PDMS/Review (RVM), CIS/2 (CIMsteel Integration Standards), SDNF (Steel Detailing Neutral Format), DGN (MicroStation), DWG (AutoCAD), DXF (Drawing Exchange Format), IFC (Industry Foundation Classes), STEP (Standard for The Exchange of Product model data), IGES (Initial Graphics Exchange Specification), STL (STereoLithography), VRML (Virtual Reality Modeling Language), DWF (Design Web Format), PDF (Portable Document Format), CSV (Comma-Separated Values), XML (Extensible Markup Language), JSON (JavaScript Object Notation), SQLite (Relational Database Management System), SQL Server (Relational Database Management System).
-
-With AutoCAD Plant 3D 2019, you can design process plants faster and smarter than ever before. You can also collaborate with other stakeholders and share your designs across platforms and devices. Whether you are working on oil and gas projects, chemical plants, power plants, water treatment plants, or any other type of process plant, AutoCAD Plant 3D 2019 is the ultimate solution for your needs.
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-free download film perang uhud Download File 🗹 https://tinurll.com/2uzme7
Download gratis Film The messege(Film Islami menceritakan sejarah perjalanan nabi Muhammad saw)- Mohammad, Messenger of God (Muhammad, Utusan Tuhan) (dijudulkan kembali The Message untuk pengeluaran A.S.) adalah sebuah film diarah oleh Moustapha Akkad, menceritakan kisah awal perkembangan Islam ketika zaman Nabi Muhammad. Dikeluarkan dalam bahasa Arab dan bahasa Inggris (nama Arab adalah الرسالة-- Ar Risalah ), Muhammad, Utusan Tuhan menjadi suatu pengenalan pada sejarah awal Islam. Walaupun film ini berkisar sekitar Muhammad, gambarnya tidak ditunjuk (tidak diperlihatkan) sepanjang film ini, dengan menghormati adat-adat Islam yang melarang sebarangan menampilkan gambar nabi dan para sahabat baginda. Sebaliknya para pemain dibiarkan untuk berbicara di depan kamera. Ketepatan film ini telah disahkan sendiri oleh ahli sarjana dan sejarah Islam Universiti Al-Azhar, Kaherah, dan Kongres Islam Tertinggi Syiah di Lubnan (menurut Wikipedia). Sinopsis The Message: Sebagaimana judulnya film "The Message" menceritakan sejarah perjalanan Muhammad SAW yang secara garis besarnya sbb : Muhammad SAW menerima wahyu digua Hira dan setelah itu baliau berusaha mengingatkan kaum Quraisy. Namun yang diperoleh Muhammad SAW jutru ejekan, hinaan dan terror serta siksaan kepada beberapa pengikut beliau yang jumlahnya baru hanya puluhan. Terror semakin hari semakin menegangkan sehingga akhirnya Muhammad SAW diperintahkan Allah SWT untuk HIJRAH ke Madinah. Sesampainya di Madinah Muhammad SAW disambut oleh masyarakat Madinah dan dibuat rumah & tempat Ibadah dan dikenalah saat itu pemanggilan umat melalui Adzan. Semakin hari umat Islam semakin berkembang di Madinah, namun disisi lain dikota Mekah jutsru sebaliknya banyak provokasi dan ejekan serta fitnah dilakukan kaum Quraisy atas diri Muhammad khususnya oleh Abu Sofyan yang sangat membenci Muhammad SAW. Selanjutnya Allah SWT memerintahkan Muhammad SAW untuk menyerang Mekah (dikenal dengan Perang Badar) supaya tidak terjadi Fitnah lagi dengan hasil kemenangan yang gemilang dipihak kaum Muslim. Sebagai tindak balasan kaum Quraisy menyerang kaum Muslim (dikenal dengan Perang Uhud) dengan hasil kemenangan dipihak kaum Quraisy yang saat itu Abu Sofyan menyatakan keadaan sudah 1 : 1 perang Badar sudah dibayar dengan perang Uhud yang kemudian di lanjutkan dengan perjanjian perdamaian. Dalam kondisi perdamaian, ternyata banyak kaum Quraisy yang tergugah dan dibuka pintu hatinya oleh Allah SWT termasuk Abu Sofyan sendiri & keluarganya masuk Islam. Akhirnya datanglah waktunya perintah Allah SWT kepada Muhammad SAW untuk kembali ke Mekah untuk melakukan Ibadah Haji sekaligus mengambil alih Ka'bah dan mengembalikannya menjadi Rumah Allah SWT ( Baitullah). Dengan di iringi oleh ratusan ribu umat Islam, Muhammad SAW datang ke Mekah dan disambut oleh penduduk Mekah dengan rasa ketakutan dan terjadilah pengambil alihan Ka'bah. Semua berhala dikeluarkan, dikumpulkan, dibakar sedang yang terbuat dari Emas dikumpulkan untuk dizakatkan kembali kepada rakyat. Inilah awal mulanya ibadah Haji dilakukan oleh kaum Muslimin. Sejak saat itu Islam semakin berkembang dan mendobrak hati setiap manusia dibumi Mekah dan Madinah serta merobah akhlak dan pola hidup jahiliyah yang mereka anut sebelumnya. Mohon maaf bila salah dalam menuliskan sinopsis film ini. Dibintangi oleh: * Anthony Quinn - Hamzah * Irene Papas - Hind * Michael Ansara - Abu Sufyan Dibantu oleh: * Johnny Sekka- Bilal ibn Ribah * André Morell - Abu-Talib * Wolfe Morris - Abu-Lahab * Rosalie Crutchley - Somaya * Damien Thomas - Zaid Download Film: 1. Part 1 2. Part 2 Ket: Format .avi, Ukuran masing2 Part 701Mb Subtitle Melayu Download Alternatif: 1. Part 1 2. Part 2 3. Part 3 4. Part 4 5. Part 5 6. Part 6 7. Part 7 8. Part 8 9. Part 9 10. Part 10 11. Part 11 Ket: Format .avi (ekstrak menggunakan winrar) Ukuran masing2 Part 75 Mb, Subtitle Indonesia. Versi Arab download disini download subtittle(Arabic, English, Indonesia, Malaysia, Farsi/Persian) disini Untuk tips menggabungkan film dan subtittle silahkan di lihat tipsnya di postingan selanjutnya.Semoga dapat bermanfa'at
aaccfb2cb3{highlighted_code}{html.escape(userinput)}
'
- + ALREADY_CONVERTED_MARK
- )
-
-
-def detect_converted_mark(userinput):
- try:
- if userinput.endswith(ALREADY_CONVERTED_MARK):
- return True
- else:
- return False
- except:
- return True
-
-
-def detect_language(code):
- if code.startswith("\n"):
- first_line = ""
- else:
- first_line = code.strip().split("\n", 1)[0]
- language = first_line.lower() if first_line else ""
- code_without_language = code[len(first_line) :].lstrip() if first_line else code
- return language, code_without_language
-
-
-def construct_text(role, text):
- return {"role": role, "content": text}
-
-
-def construct_user(text):
- return construct_text("user", text)
-
-
-def construct_system(text):
- return construct_text("system", text)
-
-
-def construct_assistant(text):
- return construct_text("assistant", text)
-
-
-def save_file(filename, system, history, chatbot, user_name):
- logging.debug(f"{user_name} 保存对话历史中……")
- os.makedirs(os.path.join(HISTORY_DIR, user_name), exist_ok=True)
- if filename.endswith(".json"):
- json_s = {"system": system, "history": history, "chatbot": chatbot}
- print(json_s)
- with open(os.path.join(HISTORY_DIR, user_name, filename), "w") as f:
- json.dump(json_s, f)
- elif filename.endswith(".md"):
- md_s = f"system: \n- {system} \n"
- for data in history:
- md_s += f"\n{data['role']}: \n- {data['content']} \n"
- with open(os.path.join(HISTORY_DIR, user_name, filename), "w", encoding="utf8") as f:
- f.write(md_s)
- logging.debug(f"{user_name} 保存对话历史完毕")
- return os.path.join(HISTORY_DIR, user_name, filename)
-
-
-def sorted_by_pinyin(list):
- return sorted(list, key=lambda char: lazy_pinyin(char)[0][0])
-
-
-def get_file_names(dir, plain=False, filetypes=[".json"]):
- logging.debug(f"获取文件名列表，目录为{dir}，文件类型为{filetypes}，是否为纯文本列表{plain}")
- files = []
- try:
- for type in filetypes:
- files += [f for f in os.listdir(dir) if f.endswith(type)]
- except FileNotFoundError:
- files = []
- files = sorted_by_pinyin(files)
- if files == []:
- files = [""]
- logging.debug(f"files are:{files}")
- if plain:
- return files
- else:
- return gr.Dropdown.update(choices=files)
-
-
-def get_history_names(plain=False, user_name=""):
- logging.debug(f"从用户 {user_name} 中获取历史记录文件名列表")
- return get_file_names(os.path.join(HISTORY_DIR, user_name), plain)
-
-
-def load_template(filename, mode=0):
- logging.debug(f"加载模板文件{filename}，模式为{mode}（0为返回字典和下拉菜单，1为返回下拉菜单，2为返回字典）")
- lines = []
- if filename.endswith(".json"):
- with open(os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8") as f:
- lines = json.load(f)
- lines = [[i["act"], i["prompt"]] for i in lines]
- else:
- with open(
- os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8"
- ) as csvfile:
- reader = csv.reader(csvfile)
- lines = list(reader)
- lines = lines[1:]
- if mode == 1:
- return sorted_by_pinyin([row[0] for row in lines])
- elif mode == 2:
- return {row[0]: row[1] for row in lines}
- else:
- choices = sorted_by_pinyin([row[0] for row in lines])
- return {row[0]: row[1] for row in lines}, gr.Dropdown.update(
- choices=choices
- )
-
-
-def get_template_names(plain=False):
- logging.debug("获取模板文件名列表")
- return get_file_names(TEMPLATES_DIR, plain, filetypes=[".csv", "json"])
-
-
-def get_template_content(templates, selection, original_system_prompt):
- logging.debug(f"应用模板中，选择为{selection}，原始系统提示为{original_system_prompt}")
- try:
- return templates[selection]
- except:
- return original_system_prompt
-
-
-def reset_textbox():
- logging.debug("重置文本框")
- return gr.update(value="")
-
-
-def reset_default():
- default_host = shared.state.reset_api_host()
- retrieve_proxy("")
- return gr.update(value=default_host), gr.update(value=""), "API-Host 和代理已重置"
-
-
-def change_api_host(host):
- shared.state.set_api_host(host)
- msg = f"API-Host更改为了{host}"
- logging.info(msg)
- return msg
-
-
-def change_proxy(proxy):
- retrieve_proxy(proxy)
- os.environ["HTTPS_PROXY"] = proxy
- msg = f"代理更改为了{proxy}"
- logging.info(msg)
- return msg
-
-
-def hide_middle_chars(s):
- if s is None:
- return ""
- if len(s) <= 8:
- return s
- else:
- head = s[:4]
- tail = s[-4:]
- hidden = "*" * (len(s) - 8)
- return head + hidden + tail
-
-
-def submit_key(key):
- key = key.strip()
- msg = f"API密钥更改为了{hide_middle_chars(key)}"
- logging.info(msg)
- return key, msg
-
-
-def replace_today(prompt):
- today = datetime.datetime.today().strftime("%Y-%m-%d")
- return prompt.replace("{current_date}", today)
-
-
-def get_geoip():
- try:
- with retrieve_proxy():
- response = requests.get("https://ipapi.co/json/", timeout=5)
- data = response.json()
- except:
- data = {"error": True, "reason": "连接ipapi失败"}
- if "error" in data.keys():
- logging.warning(f"无法获取IP地址信息。\n{data}")
- if data["reason"] == "RateLimited":
- return (
- i18n("您的IP区域：未知。")
- )
- else:
- return i18n("获取IP地理位置失败。原因：") + f"{data['reason']}" + i18n("。你仍然可以使用聊天功能。")
- else:
- country = data["country_name"]
- if country == "China":
- text = "**您的IP区域：中国。请立即检查代理设置，在不受支持的地区使用API可能导致账号被封禁。**"
- else:
- text = i18n("您的IP区域：") + f"{country}。"
- logging.info(text)
- return text
-
-
-def find_n(lst, max_num):
- n = len(lst)
- total = sum(lst)
-
- if total < max_num:
- return n
-
- for i in range(len(lst)):
- if total - lst[i] < max_num:
- return n - i - 1
- total = total - lst[i]
- return 1
-
-
-def start_outputing():
- logging.debug("显示取消按钮，隐藏发送按钮")
- return gr.Button.update(visible=False), gr.Button.update(visible=True)
-
-
-def end_outputing():
- return (
- gr.Button.update(visible=True),
- gr.Button.update(visible=False),
- )
-
-
-def cancel_outputing():
- logging.info("中止输出……")
- shared.state.interrupt()
-
-
-def transfer_input(inputs):
- # 一次性返回，降低延迟
- textbox = reset_textbox()
- outputing = start_outputing()
- return (
- inputs,
- gr.update(value=""),
- gr.Button.update(visible=False),
- gr.Button.update(visible=True),
- )
-
-
-
-def run(command, desc=None, errdesc=None, custom_env=None, live=False):
- if desc is not None:
- print(desc)
- if live:
- result = subprocess.run(command, shell=True, env=os.environ if custom_env is None else custom_env)
- if result.returncode != 0:
- raise RuntimeError(f"""{errdesc or 'Error running command'}.
-Command: {command}
-Error code: {result.returncode}""")
-
- return ""
- result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ if custom_env is None else custom_env)
- if result.returncode != 0:
- message = f"""{errdesc or 'Error running command'}.
- Command: {command}
- Error code: {result.returncode}
- stdout: {result.stdout.decode(encoding="utf8", errors="ignore") if len(result.stdout)>0 else ''}
- stderr: {result.stderr.decode(encoding="utf8", errors="ignore") if len(result.stderr)>0 else ''}
- """
- raise RuntimeError(message)
- return result.stdout.decode(encoding="utf8", errors="ignore")
-
-def versions_html():
- git = os.environ.get('GIT', "git")
- python_version = ".".join([str(x) for x in sys.version_info[0:3]])
- try:
- commit_hash = run(f"{git} rev-parse HEAD").strip()
- except Exception:
- commit_hash = ""
- if commit_hash != "":
- short_commit = commit_hash[0:7]
- commit_info = f"{short_commit} "
- else:
- commit_info = "unknown \U0001F615"
- return f"""
- Python: {python_version}
- •
- Gradio: {gr.__version__}
- •
- Commit: {commit_info}
- """
-
-def add_source_numbers(lst, source_name = "Source", use_source = True):
- if use_source:
- return [f'[{idx+1}]\t "{item[0]}"\n{source_name}: {item[1]}' for idx, item in enumerate(lst)]
- else:
- return [f'[{idx+1}]\t "{item}"' for idx, item in enumerate(lst)]
-
-def add_details(lst):
- nodes = []
- for index, txt in enumerate(lst):
- brief = txt[:25].replace("\n", "")
- nodes.append(
- f"{brief}... {txt}
0:
- eig_vec = eig_vec[:, range(numpc)]
- score = np.dot(eig_vec.T, cov_mat)
- eigVec_score = np.dot(eig_vec, score)
- recon = eigVec_score + np.mean(image_2d, axis = 1).T.reshape((eigVec_score.shape[0], 1)) # SOME NORMALIZATION CAN BE USED TO MAKE IMAGE QUALITY BETTER
- recon_img_mat = np.uint8(np.absolute(recon)) # TO CONTROL COMPLEX EIGENVALUES
-
- bits_per_pixel = a_.dtype.itemsize * a_.shape[-1] * 8
- orig_size = (in_image.size[0]) * (in_image.size[1])#a_ * bits_per_pixel
- print(f"Image size:{in_image.size}")
- reduced_size = (no_of_comp)*(min(in_image.size[0], in_image.size[1])*2) #recon_img_mat * np.log2(no_of_comp+1)
-
- return recon_img_mat, in_image, orig_size, reduced_size
-
-# """run this function"""
-# st.info(__doc__)
-st.markdown(STYLE, unsafe_allow_html=True)
-file = st.file_uploader("Upload an image with lower size (preferably less than 50 KB)", type=["png", "jpg"])
-cont = st.empty()
-if not file:
- # cont.info(f"Please Upload an image :{' '.join(['png', 'jpg'])}")
- url = 'https://raw.githubusercontent.com/satyamg1620/Prerequsite_test_22210041/main/947_2000.jpg'
- data = requests.get(url)
- file = BytesIO(data.content)
-
-if isinstance(file, BytesIO):
- in_image = Image.open(file)
- img_array = imageio.imread(file)
- max_comp = min(img_array.shape[0], img_array.shape[1])
-
-
-print(img_array.shape)
-a_np = np.array(img_array)
-a_r = a_np[:,:,0]
-a_g = a_np[:,:,1]
-a_b = a_np[:,:,2]
-
-p_r, cov_mat_r, eig_vec_r, loss_r = decomp(img_array, a_r)
-p_g, cov_mat_g, eig_vec_g, loss_g = decomp(img_array, a_g)
-p_b, cov_mat_b, eig_vec_b, loss_b = decomp(img_array, a_b)
-# with st.sidebar:
-slider_comp = st.slider('Slide over to change number of componenets', 0, max_comp, 0)
-
-with st.spinner('LOADING'):
-
- no_of_comp = slider_comp
-
- # RECONSTRUCTING R,G,B COMPONENTS SEPARATELY
- a_r_recon, in_image, orig_size_r, reduced_size_r = recons(a_r, no_of_comp, p_r, cov_mat_r, a_r, eig_vec_r, in_image)
- a_g_recon, in_image, orig_size_g, reduced_size_g = recons(a_g, no_of_comp, p_g, cov_mat_g, a_g, eig_vec_g, in_image)
- a_b_recon, in_image, orig_size_b, reduced_size_b = recons(a_b, no_of_comp, p_b, cov_mat_b, a_b, eig_vec_b, in_image)
-
- recon_color_img = np.dstack((a_r_recon, a_g_recon, a_b_recon)) # COMBINING R.G,B COMPONENTS TO PRODUCE COLOR IMAGE
- recon_color_img = Image.fromarray(recon_color_img)
-
- col2 = st.empty()
- recon_color_img = recon_color_img.resize((450, 450))
- new_image = in_image.resize((450, 450))
- n_components = [i for i in range(1, max_comp+1, 20)]
- plt.plot(n_components, loss_r, 'r')
- plt.plot(n_components, loss_g, 'g')
- plt.plot(n_components, loss_b, 'b')
- plt.xlabel('Number of Principal Components')
- plt.ylabel('Loss (MSE)')
- plt.title('Loss (MSE) VS Number of Principal Components')
- # fig = plt.figure()
- plt.legend(['Red Channel', 'Green Channel', 'Blue Channel'])
-
- img_buf = BytesIO()
- plt.savefig(img_buf, format='png')
- im = Image.open(img_buf)
- im = im.resize((450, 450))
- with st.container():
- st.write(f'Total Principal Components : {min(img_array.shape[0], img_array.shape[1])}')
- #st.write(f'Compression Ratio: {100*(reduced_size_r+reduced_size_g+reduced_size_b)/(orig_size_r+orig_size_g+orig_size_b)}')
- caption_li=['Original Image','All three channels Reconstruction Loss' ,f'Reconstructed Image with {no_of_comp} components']
- images = [new_image, im ,recon_color_img]
- st.image(images, caption=caption_li, width=400)
-
-
-st.markdown('''Principal component analysis, or PCA, is a dimensionality reduction method that is often
- used to reduce the dimensionality of large data sets, by transforming a large set of variables
- into a smaller one that still contains most of the information in the large set.
- We can use PCA for dimensionality reduction for images as well.''')
-
-st.markdown('''In this aplication, we are using PCA dimensionality reduction for Image Reconstruction. We can upload an image, the application will first split
- the image into the three channels (Blue, Green, and Red) first and then and perform PCA separately on each dataset representing each channel and
- calculate total number of Principal Components of that image. After calculating the number of components, a slider will be shown with a range
- from 0 to maximum number of principal components of that image.
- We can use the slider increase or decrease the number of components for generating the Reconstructed Image. Additionaly the appication will also show the plot of
- Loss VS Principal Number of components for each channel i.e Red, Green and Blue
- ''')
diff --git a/spaces/sayakpaul/convert-kerascv-sd-diffusers/hub_utils/readme.py b/spaces/sayakpaul/convert-kerascv-sd-diffusers/hub_utils/readme.py
deleted file mode 100644
index 3d991b35838c9528dfc8cffb6d4875ae9314129d..0000000000000000000000000000000000000000
--- a/spaces/sayakpaul/convert-kerascv-sd-diffusers/hub_utils/readme.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import os
-from typing import List
-
-
-# Copied from https://github.com/huggingface/diffusers/blob/31be42209ddfdb69d9640a777b32e9b5c6259bf0/examples/text_to_image/train_text_to_image_lora.py#L55
-def save_model_card(
- base_model=str,
- repo_folder=None,
- weight_paths: List = None,
- placeholder_token: str = None,
-):
- yaml = f"""
----
-license: creativeml-openrail-m
-base_model: {base_model}
-tags:
-- stable-diffusion
-- stable-diffusion-diffusers
-- text-to-image
-- diffusers
-inference: true
----
- """
- model_card = f"""
-# KerasCV Stable Diffusion in Diffusers 🧨🤗
-
-The pipeline contained in this repository was created using [this Space](https://huggingface.co/spaces/sayakpaul/convert-kerascv-sd-diffusers). The purpose is to convert the KerasCV Stable Diffusion weights in a way that is compatible with [Diffusers](https://github.com/huggingface/diffusers). This allows users to fine-tune using KerasCV and use the fine-tuned weights in Diffusers taking advantage of its nifty features (like [schedulers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/schedulers), [fast attention](https://huggingface.co/docs/diffusers/optimization/fp16), etc.).\n
-
-"""
-
- if len(weight_paths) > 0:
- model_card += f"Following weight paths (KerasCV) were used \n: {weight_paths}"
-
- if placeholder_token is not None:
- model_card += f"\n\nFollowing `placeholder_token` was added to the tokenizer: `{placeholder_token}`."
-
- with open(os.path.join(repo_folder, "README.md"), "w") as f:
- f.write(yaml + model_card)
diff --git a/spaces/scedlatioru/img-to-music/The-Sims-3-Crack-1061500107.md b/spaces/scedlatioru/img-to-music/The-Sims-3-Crack-1061500107.md
deleted file mode 100644
index 0c8be593ada195a3df2af02f6db932ffb4a95132..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/The-Sims-3-Crack-1061500107.md
+++ /dev/null
@@ -1,50 +0,0 @@
-the sims 3 crack 1.0.615.00107
-
-
-
-DOWNLOAD > [https://ekporriola.blogspot.com/?c=2tvDMW](https://ekporriola.blogspot.com/?c=2tvDMW)
-
-
-
-
-
-
-
-
-
-The Sims 3 1.0.615.00107: What You Need to Know
-The Sims 3 is a life simulation game that allows you to create and control your own virtual characters, called Sims, and their lives. You can customize their appearance, personality, skills, relationships, careers, hobbies, and more. You can also build and decorate their homes, explore different neighborhoods, and interact with other Sims and objects.
-The Sims 3 was released in 2009 and has received several updates and expansions over the years. The latest update for the base game is version 1.0.615.00107, which was released on July 25, 2011. This update includes several bug fixes and improvements for the Generations expansion pack, which adds new features and content for Sims of different ages.
-Some of the changes in this update are:
-
-Baby Sims will no longer become stuck on a Simâs hand while driving a car.
-Imaginary Friends who have been made real will now keep their unique outfit when aging up.
-Bunk Bed drop shadows will no longer shift places when a Sim climbs into one.
-Sims will now react to another Sim âShowing Offâ a skill they learned at an afterschool class or club.
-The âCatch all Type of Fishesâ memory will no long appear before the action has been completed.
-The screenshot for the âEnjoyed WooHoo in a Tree Houseâ memory will no longer clip with the Tree House.
-Incorrect placement slots on the Loft Bed that show as valid locations have been removed.
-The âTemporarily Undeadâ moodlet will now correctly state what caused it when the Ghost Potion has been thrown at a Simâs feet.
-Sims will no longer be spammed with Field Trip opportunities.
-Elder Sims will no longer reset when loading a Save Game that has them in the Harass with Cane interaction.
-Sims can now learn their Imaginary Friendâs traits much easier when they are turned into a live Sim.
-An issue where Gems and Metals in a Simâs inventory became unusable once Generations was installed has been resolved.
-The footprint of a Daycare child will no longer be permanently left on the lot when the Active Household is changed.
-Babies and Toddlers will no longer go into a frozen state when a Social Worker comes to take them while they are being pushed in strollers.
-The wish to âRide a Spring Riderâ will no longer always appear after the interaction to ride a spring rider has been canceled.
-Shopping menus will no longer appear while inside of Camera Man Mode.
-The Imaginary Friend doll can no longer be buried in Sandboxes to prevent the Sim from losing it forever.
-Sims can now complete a Poltergeist Haunting job (Ambitions) if there is a Bunk or Loft Bed on the lot.
-Queued interactions while performing the Daycare Career will no longer be automatically canceled after putting a toddler down.
-Spiral Staircases can no longer be placed in areas that Sims cannot route to.
-Sims that have been turned into a Ghost will no longer receive an extra diploma.
-Sims can no longer have a negative amount of Memories in their Scrapbook.
-After purchasing a Bar (Late Night), Sims will no longer receive the âBought a Barâ memory when purchasing any other type of property.
-The Motive Mobile will no longer be unintentionally used when returning from a Community Lot with a toddler, causing it to remain parked in front of the Simsâ home.
-The âThrow a Teen Partyâ wish will no longer appear while a Teen Party is already planned.
-Users can now select a Lifetime Wish for Teenagers who have aged up to Young Adults while away at Boarding School.
-Daycare children will no longer be abandoned on your Simâs lot.
-Children now have the chance to be Scolded when performing âDino Smashâ on a doll house dfd1c89656
-
-
-
diff --git a/spaces/scedlatioru/img-to-music/example/Red Giant Trapcode Suite 15 Crack With Serial Key [PATCHED].md b/spaces/scedlatioru/img-to-music/example/Red Giant Trapcode Suite 15 Crack With Serial Key [PATCHED].md
deleted file mode 100644
index 703b8e9e8905374c36764da726a74f2e84429bf3..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Red Giant Trapcode Suite 15 Crack With Serial Key [PATCHED].md
+++ /dev/null
@@ -1,6 +0,0 @@
-
Red Giant Trapcode Suite 15 Crack With Serial Key Download Zip ⭐ https://gohhs.com/2uEyRV
diff --git a/spaces/sdeeas/ChuanhuChatGPT/modules/models/StableLM.py b/spaces/sdeeas/ChuanhuChatGPT/modules/models/StableLM.py
deleted file mode 100644
index f4affc3699e335f1e42bf5fc8c93e92a41d027fe..0000000000000000000000000000000000000000
--- a/spaces/sdeeas/ChuanhuChatGPT/modules/models/StableLM.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
-import time
-import numpy as np
-from torch.nn import functional as F
-import os
-from .base_model import BaseLLMModel
-from threading import Thread
-
-STABLELM_MODEL = None
-STABLELM_TOKENIZER = None
-
-
-class StopOnTokens(StoppingCriteria):
- def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
- stop_ids = [50278, 50279, 50277, 1, 0]
- for stop_id in stop_ids:
- if input_ids[0][-1] == stop_id:
- return True
- return False
-
-
-class StableLM_Client(BaseLLMModel):
- def __init__(self, model_name, user_name="") -> None:
- super().__init__(model_name=model_name, user=user_name)
- global STABLELM_MODEL, STABLELM_TOKENIZER
- print(f"Starting to load StableLM to memory")
- if model_name == "StableLM":
- model_name = "stabilityai/stablelm-tuned-alpha-7b"
- else:
- model_name = f"models/{model_name}"
- if STABLELM_MODEL is None:
- STABLELM_MODEL = AutoModelForCausalLM.from_pretrained(
- model_name, torch_dtype=torch.float16).cuda()
- if STABLELM_TOKENIZER is None:
- STABLELM_TOKENIZER = AutoTokenizer.from_pretrained(model_name)
- self.generator = pipeline(
- 'text-generation', model=STABLELM_MODEL, tokenizer=STABLELM_TOKENIZER, device=0)
- print(f"Sucessfully loaded StableLM to the memory")
- self.system_prompt = """StableAssistant
-- StableAssistant is A helpful and harmless Open Source AI Language Model developed by Stability and CarperAI.
-- StableAssistant is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
-- StableAssistant is more than just an information source, StableAssistant is also able to write poetry, short stories, and make jokes.
-- StableAssistant will refuse to participate in anything that could harm a human."""
- self.max_generation_token = 1024
- self.top_p = 0.95
- self.temperature = 1.0
-
- def _get_stablelm_style_input(self):
- history = self.history + [{"role": "assistant", "content": ""}]
- print(history)
- messages = self.system_prompt + \
- "".join(["".join(["<|USER|>"+history[i]["content"], "<|ASSISTANT|>"+history[i + 1]["content"]])
- for i in range(0, len(history), 2)])
- return messages
-
- def _generate(self, text, bad_text=None):
- stop = StopOnTokens()
- result = self.generator(text, max_new_tokens=self.max_generation_token, num_return_sequences=1, num_beams=1, do_sample=True,
- temperature=self.temperature, top_p=self.top_p, top_k=1000, stopping_criteria=StoppingCriteriaList([stop]))
- return result[0]["generated_text"].replace(text, "")
-
- def get_answer_at_once(self):
- messages = self._get_stablelm_style_input()
- return self._generate(messages), len(messages)
-
- def get_answer_stream_iter(self):
- stop = StopOnTokens()
- messages = self._get_stablelm_style_input()
-
- # model_inputs = tok([messages], return_tensors="pt")['input_ids'].cuda()[:, :4096-1024]
- model_inputs = STABLELM_TOKENIZER(
- [messages], return_tensors="pt").to("cuda")
- streamer = TextIteratorStreamer(
- STABLELM_TOKENIZER, timeout=10., skip_prompt=True, skip_special_tokens=True)
- generate_kwargs = dict(
- model_inputs,
- streamer=streamer,
- max_new_tokens=self.max_generation_token,
- do_sample=True,
- top_p=self.top_p,
- top_k=1000,
- temperature=self.temperature,
- num_beams=1,
- stopping_criteria=StoppingCriteriaList([stop])
- )
- t = Thread(target=STABLELM_MODEL.generate, kwargs=generate_kwargs)
- t.start()
-
- partial_text = ""
- for new_text in streamer:
- partial_text += new_text
- yield partial_text
diff --git a/spaces/segments-tobias/conex/espnet/utils/dataset.py b/spaces/segments-tobias/conex/espnet/utils/dataset.py
deleted file mode 100644
index ac37ae6f7c7d0100ef54eeb938fbe8c86b4f1065..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet/utils/dataset.py
+++ /dev/null
@@ -1,92 +0,0 @@
-#!/usr/bin/env python
-
-# Copyright 2017 Johns Hopkins University (Shinji Watanabe)
-# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""pytorch dataset and dataloader implementation for chainer training."""
-
-import torch
-import torch.utils.data
-
-
-class TransformDataset(torch.utils.data.Dataset):
- """Transform Dataset for pytorch backend.
-
- Args:
- data: list object from make_batchset
- transfrom: transform function
-
- """
-
- def __init__(self, data, transform):
- """Init function."""
- super(TransformDataset).__init__()
- self.data = data
- self.transform = transform
-
- def __len__(self):
- """Len function."""
- return len(self.data)
-
- def __getitem__(self, idx):
- """[] operator."""
- return self.transform(self.data[idx])
-
-
-class ChainerDataLoader(object):
- """Pytorch dataloader in chainer style.
-
- Args:
- all args for torch.utils.data.dataloader.Dataloader
-
- """
-
- def __init__(self, **kwargs):
- """Init function."""
- self.loader = torch.utils.data.dataloader.DataLoader(**kwargs)
- self.len = len(kwargs["dataset"])
- self.current_position = 0
- self.epoch = 0
- self.iter = None
- self.kwargs = kwargs
-
- def next(self):
- """Implement next function."""
- if self.iter is None:
- self.iter = iter(self.loader)
- try:
- ret = next(self.iter)
- except StopIteration:
- self.iter = None
- return self.next()
- self.current_position += 1
- if self.current_position == self.len:
- self.epoch = self.epoch + 1
- self.current_position = 0
- return ret
-
- def __iter__(self):
- """Implement iter function."""
- for batch in self.loader:
- yield batch
-
- @property
- def epoch_detail(self):
- """Epoch_detail required by chainer."""
- return self.epoch + self.current_position / self.len
-
- def serialize(self, serializer):
- """Serialize and deserialize function."""
- epoch = serializer("epoch", self.epoch)
- current_position = serializer("current_position", self.current_position)
- self.epoch = epoch
- self.current_position = current_position
-
- def start_shuffle(self):
- """Shuffle function for sortagrad."""
- self.kwargs["shuffle"] = True
- self.loader = torch.utils.data.dataloader.DataLoader(**self.kwargs)
-
- def finalize(self):
- """Implement finalize function."""
- del self.loader
diff --git a/spaces/segments-tobias/conex/espnet2/train/class_choices.py b/spaces/segments-tobias/conex/espnet2/train/class_choices.py
deleted file mode 100644
index 821bab8121b5105625616eb6cb4962576accc68f..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet2/train/class_choices.py
+++ /dev/null
@@ -1,95 +0,0 @@
-from typing import Mapping
-from typing import Optional
-from typing import Tuple
-
-from typeguard import check_argument_types
-from typeguard import check_return_type
-
-from espnet2.utils.nested_dict_action import NestedDictAction
-from espnet2.utils.types import str_or_none
-
-
-class ClassChoices:
- """Helper class to manage the options for variable objects and its configuration.
-
- Example:
-
- >>> class A:
- ... def __init__(self, foo=3): pass
- >>> class B:
- ... def __init__(self, bar="aaaa"): pass
- >>> choices = ClassChoices("var", dict(a=A, b=B), default="a")
- >>> import argparse
- >>> parser = argparse.ArgumentParser()
- >>> choices.add_arguments(parser)
- >>> args = parser.parse_args(["--var", "a", "--var_conf", "foo=4")
- >>> args.var
- a
- >>> args.var_conf
- {"foo": 4}
- >>> class_obj = choices.get_class(args.var)
- >>> a_object = class_obj(**args.var_conf)
-
- """
-
- def __init__(
- self,
- name: str,
- classes: Mapping[str, type],
- type_check: type = None,
- default: str = None,
- optional: bool = False,
- ):
- assert check_argument_types()
- self.name = name
- self.base_type = type_check
- self.classes = {k.lower(): v for k, v in classes.items()}
- if "none" in self.classes or "nil" in self.classes or "null" in self.classes:
- raise ValueError('"none", "nil", and "null" are reserved.')
- if type_check is not None:
- for v in self.classes.values():
- if not issubclass(v, type_check):
- raise ValueError(f"must be {type_check.__name__}, but got {v}")
-
- self.optional = optional
- self.default = default
- if default is None:
- self.optional = True
-
- def choices(self) -> Tuple[Optional[str], ...]:
- retval = tuple(self.classes)
- if self.optional:
- return retval + (None,)
- else:
- return retval
-
- def get_class(self, name: Optional[str]) -> Optional[type]:
- assert check_argument_types()
- if name is None or (self.optional and name.lower() == ("none", "null", "nil")):
- retval = None
- elif name.lower() in self.classes:
- class_obj = self.classes[name]
- assert check_return_type(class_obj)
- retval = class_obj
- else:
- raise ValueError(
- f"--{self.name} must be one of {self.choices()}: "
- f"--{self.name} {name.lower()}"
- )
-
- return retval
-
- def add_arguments(self, parser):
- parser.add_argument(
- f"--{self.name}",
- type=lambda x: str_or_none(x.lower()),
- default=self.default,
- choices=self.choices(),
- help=f"The {self.name} type",
- )
- parser.add_argument(
- f"--{self.name}_conf",
- action=NestedDictAction,
- default=dict(),
- help=f"The keyword arguments for {self.name}",
- )
diff --git "a/spaces/shencc/gpt/crazy_functions/\350\257\242\351\227\256\345\244\232\344\270\252\345\244\247\350\257\255\350\250\200\346\250\241\345\236\213.py" "b/spaces/shencc/gpt/crazy_functions/\350\257\242\351\227\256\345\244\232\344\270\252\345\244\247\350\257\255\350\250\200\346\250\241\345\236\213.py"
deleted file mode 100644
index 5cf239e1d44ace85e1a32561513cfd37ee3255d9..0000000000000000000000000000000000000000
--- "a/spaces/shencc/gpt/crazy_functions/\350\257\242\351\227\256\345\244\232\344\270\252\345\244\247\350\257\255\350\250\200\346\250\241\345\236\213.py"
+++ /dev/null
@@ -1,59 +0,0 @@
-from toolbox import CatchException, update_ui
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-import datetime
-@CatchException
-def 同时问询(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- """
- txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
- llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
- plugin_kwargs 插件模型的参数，如温度和top_p等，一般原样传递下去就行
- chatbot 聊天显示框的句柄，用于显示给用户
- history 聊天历史，前情提要
- system_prompt 给gpt的静默提醒
- web_port 当前软件运行的端口号
- """
- history = [] # 清空历史，以免输入溢出
- chatbot.append((txt, "正在同时咨询gpt-3.5和gpt-4……"))
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-
- # llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
- llm_kwargs['llm_model'] = 'gpt-3.5-turbo&gpt-4' # 支持任意数量的llm接口，用&符号分隔
- gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
- inputs=txt, inputs_show_user=txt,
- llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
- sys_prompt=system_prompt,
- retry_times_at_unknown_error=0
- )
-
- history.append(txt)
- history.append(gpt_say)
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
-
-
-@CatchException
-def 同时问询_指定模型(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- """
- txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
- llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
- plugin_kwargs 插件模型的参数，如温度和top_p等，一般原样传递下去就行
- chatbot 聊天显示框的句柄，用于显示给用户
- history 聊天历史，前情提要
- system_prompt 给gpt的静默提醒
- web_port 当前软件运行的端口号
- """
- history = [] # 清空历史，以免输入溢出
- chatbot.append((txt, "正在同时咨询ChatGPT和ChatGLM……"))
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
-
- # llm_kwargs['llm_model'] = 'chatglm&gpt-3.5-turbo&api2d-gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
- llm_kwargs['llm_model'] = plugin_kwargs.get("advanced_arg", 'chatglm&gpt-3.5-turbo') # 'chatglm&gpt-3.5-turbo' # 支持任意数量的llm接口，用&符号分隔
- gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
- inputs=txt, inputs_show_user=txt,
- llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
- sys_prompt=system_prompt,
- retry_times_at_unknown_error=0
- )
-
- history.append(txt)
- history.append(gpt_say)
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
\ No newline at end of file
diff --git a/spaces/shencc/gpt/docs/README_FR.md b/spaces/shencc/gpt/docs/README_FR.md
deleted file mode 100644
index f21e90035ef2ddea91382155e0ad46b6740f5322..0000000000000000000000000000000000000000
--- a/spaces/shencc/gpt/docs/README_FR.md
+++ /dev/null
@@ -1,296 +0,0 @@
-> **Note**
->
-> Ce fichier README est généré automatiquement par le plugin de traduction markdown de ce projet et n'est peut - être pas correct à 100%.
->
-
-#
-
-Fonctionnalité | Description
---- | ---
-Polissage en un clic | Prend en charge la correction en un clic et la recherche d'erreurs de syntaxe dans les documents de recherche.
-Traduction Chinois-Anglais en un clic | Une touche pour traduire la partie chinoise en anglais ou celle anglaise en chinois.
-Explication de code en un clic | Affiche et explique correctement le code.
-[Raccourcis clavier personnalisables](https://www.bilibili.com/video/BV14s4y1E7jN) | Prend en charge les raccourcis clavier personnalisables.
-[Configuration du serveur proxy](https://www.bilibili.com/video/BV1rc411W7Dr) | Prend en charge la configuration du serveur proxy.
-Conception modulaire | Prend en charge la personnalisation des plugins de fonctions et des [plugins] de fonctions hiérarchiques personnalisés, et les plugins prennent en charge [la mise à jour à chaud](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
-[Auto-analyse du programme](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugins] [Lire en un clic](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) le code source de ce projet.
-[Analyse de programme](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugins] En un clic, les projets Python/C/C++/Java/Lua/... peuvent être analysés.
-Lire le document de recherche | [Plugins] Lisez le résumé de l'article en latex et générer un résumé.
-Traduction et polissage de l'article complet en LaTeX | [Plugins] Une touche pour traduire ou corriger en LaTeX
-Génération Commentaire de fonction en vrac | [Plugins] Lisez en un clic les fonctions et générez des commentaires de fonction.
-Rapport d'analyse automatique des chats générés | [Plugins] Génère un rapport de synthèse après l'exécution.
-[Assistant arxiv](https://www.bilibili.com/video/BV1LM4y1279X) | [Plugins] Entrez l'url de l'article arxiv pour traduire le résumé + télécharger le PDF en un clic
-[Traduction complète des articles PDF](https://www.bilibili.com/video/BV1KT411x7Wn) | [Plugins] Extraire le titre et le résumé de l'article PDF + Traduire le texte entier (multithread)
-[Aide à la recherche Google Academ](https://www.bilibili.com/video/BV19L411U7ia) | [Plugins] Donnez à GPT l'URL de n'importe quelle page de recherche Google Academ pour vous aider à sélectionner des articles intéressants
-Affichage de formules/images/tableaux | Afficher la forme traduite et rendue d'une formule en même temps, plusieurs formules et surlignage du code prend en charge
-Prise en charge des plugins multithread | Prise en charge de l'appel multithread de chatgpt, traitement en masse de texte ou de programmes en un clic
-Activer le thème Gradio sombre [theme](https://github.com/binary-husky/chatgpt_academic/issues/173) au démarrage | Ajoutez ```/?__dark-theme=true``` à l'URL du navigateur pour basculer vers le thème sombre
-[Prise en charge de plusieurs modèles LLM](https://www.bilibili.com/video/BV1wT411p7yf), [prise en charge de l'interface API2D](https://api2d.com/) | Comment cela serait-il de se faire servir par GPT3.5, GPT4 et la [ChatGLM de Tsinghua](https://github.com/THUDM/ChatGLM-6B) en même temps?
-Expérience en ligne d'huggingface sans science | Après vous être connecté à huggingface, copiez [cet espace](https://huggingface.co/spaces/qingxu98/gpt-academic)
-... | ...
-
-
-
-
-Vous êtes un traducteur professionnel d'articles universitaires en français.
-
-Ceci est un fichier Markdown, veuillez le traduire en français sans modifier les commandes Markdown existantes :
-
-- Nouvelle interface (modifiable en modifiant l'option de mise en page dans config.py pour basculer entre les mises en page gauche-droite et haut-bas)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Document
-
-
-
-
-
-
-
-
-
diff --git a/spaces/simonraj/ELOralCoachTestFeedback/custom.css b/spaces/simonraj/ELOralCoachTestFeedback/custom.css
deleted file mode 100644
index 04cfcae16e9a80be36e60dae1af0fea46501559c..0000000000000000000000000000000000000000
--- a/spaces/simonraj/ELOralCoachTestFeedback/custom.css
+++ /dev/null
@@ -1,5 +0,0 @@
-/* custom.css */
-.gradio_output_text {
- height: 1000px;
- overflow-y: auto;
-}
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Football League 2023 MOD APK The Ultimate Guide to Unlimited Energy.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Football League 2023 MOD APK The Ultimate Guide to Unlimited Energy.md
deleted file mode 100644
index 82fe561026bdbe1357f7905c94cc2004c59dc4c7..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Football League 2023 MOD APK The Ultimate Guide to Unlimited Energy.md
+++ /dev/null
@@ -1,136 +0,0 @@
-
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index 8a21bacefb7de13606c833e6ee003886dd86523d..0000000000000000000000000000000000000000
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index 16e24af32c947e3e04bd7a252be4533d41050b14..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Trilead Vm Explorer 4.1 License Key !!INSTALL!!.md
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diff --git a/spaces/text-generation-inference/chat-ui/Dockerfile b/spaces/text-generation-inference/chat-ui/Dockerfile
deleted file mode 100644
index ad1d26c28e6afa14d34f5f4f5708a001e65dc213..0000000000000000000000000000000000000000
--- a/spaces/text-generation-inference/chat-ui/Dockerfile
+++ /dev/null
@@ -1,18 +0,0 @@
-# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
-# you will also find guides on how best to write your Dockerfile
-
-FROM node:19
-
-WORKDIR /app
-
-COPY . .
-
-RUN npm i
-
-RUN chown -R 1000:1000 /app
-
-RUN npm run build
-
-ENV PORT 7860
-
-CMD ["node", "build"]
diff --git a/spaces/tfwang/PITI-Synthesis/glide_text2im/unet.py b/spaces/tfwang/PITI-Synthesis/glide_text2im/unet.py
deleted file mode 100644
index b61437a44ef7510e0c62afaae070deabc24c42bb..0000000000000000000000000000000000000000
--- a/spaces/tfwang/PITI-Synthesis/glide_text2im/unet.py
+++ /dev/null
@@ -1,635 +0,0 @@
-import math
-from abc import abstractmethod
-
-import torch as th
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .fp16_util import convert_module_to_f16, convert_module_to_f32
-from .nn import avg_pool_nd, conv_nd, linear, normalization, timestep_embedding, zero_module
-
-
-class TimestepBlock(nn.Module):
- """
- Any module where forward() takes timestep embeddings as a second argument.
- """
-
- @abstractmethod
- def forward(self, x, emb):
- """
- Apply the module to `x` given `emb` timestep embeddings.
- """
-
-
-class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
- """
- A sequential module that passes timestep embeddings to the children that
- support it as an extra input.
- """
-
- def forward(self, x, emb, encoder_out=None):
- for layer in self:
- if isinstance(layer, TimestepBlock):
- x = layer(x, emb)
- elif isinstance(layer, AttentionBlock):
- x = layer(x, encoder_out)
- else:
- x = layer(x)
- return x
-
-
-class Upsample(nn.Module):
- """
- An upsampling layer with an optional convolution.
-
- :param channels: channels in the inputs and outputs.
- :param use_conv: a bool determining if a convolution is applied.
- :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
- upsampling occurs in the inner-two dimensions.
- """
-
- def __init__(self, channels, use_conv, dims=2, out_channels=None):
- super().__init__()
- self.channels = channels
- self.out_channels = out_channels or channels
- self.use_conv = use_conv
- self.dims = dims
- if use_conv:
- self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
-
- def forward(self, x):
- assert x.shape[1] == self.channels
- if self.dims == 3:
- x = F.interpolate(x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest")
- else:
- x = F.interpolate(x, scale_factor=2, mode="nearest")
- if self.use_conv:
- x = self.conv(x)
- return x
-
-
-class Downsample(nn.Module):
- """
- A downsampling layer with an optional convolution.
-
- :param channels: channels in the inputs and outputs.
- :param use_conv: a bool determining if a convolution is applied.
- :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
- downsampling occurs in the inner-two dimensions.
- """
-
- def __init__(self, channels, use_conv, dims=2, out_channels=None):
- super().__init__()
- self.channels = channels
- self.out_channels = out_channels or channels
- self.use_conv = use_conv
- self.dims = dims
- stride = 2 if dims != 3 else (1, 2, 2)
- if use_conv:
- self.op = conv_nd(dims, self.channels, self.out_channels, 3, stride=stride, padding=1)
- else:
- assert self.channels == self.out_channels
- self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
-
- def forward(self, x):
- assert x.shape[1] == self.channels
- return self.op(x)
-
-
-class ResBlock(TimestepBlock):
- """
- A residual block that can optionally change the number of channels.
-
- :param channels: the number of input channels.
- :param emb_channels: the number of timestep embedding channels.
- :param dropout: the rate of dropout.
- :param out_channels: if specified, the number of out channels.
- :param use_conv: if True and out_channels is specified, use a spatial
- convolution instead of a smaller 1x1 convolution to change the
- channels in the skip connection.
- :param dims: determines if the signal is 1D, 2D, or 3D.
- :param use_checkpoint: if True, use gradient checkpointing on this module.
- :param up: if True, use this block for upsampling.
- :param down: if True, use this block for downsampling.
- """
-
- def __init__(
- self,
- channels,
- emb_channels,
- dropout,
- out_channels=None,
- use_conv=False,
- use_scale_shift_norm=False,
- dims=2,
- use_checkpoint=False,
- up=False,
- down=False,
- ):
- super().__init__()
- self.channels = channels
- self.emb_channels = emb_channels
- self.dropout = dropout
- self.out_channels = out_channels or channels
- self.use_conv = use_conv
- self.use_checkpoint = use_checkpoint
- self.use_scale_shift_norm = use_scale_shift_norm
-
- self.in_layers = nn.Sequential(
- normalization(channels, swish=1.0),
- nn.Identity(),
- conv_nd(dims, channels, self.out_channels, 3, padding=1),
- )
-
- self.updown = up or down
-
- if up:
- self.h_upd = Upsample(channels, False, dims)
- self.x_upd = Upsample(channels, False, dims)
- elif down:
- self.h_upd = Downsample(channels, False, dims)
- self.x_upd = Downsample(channels, False, dims)
- else:
- self.h_upd = self.x_upd = nn.Identity()
-
- self.emb_layers = nn.Sequential(
- nn.SiLU(),
- linear(
- emb_channels,
- 2 * self.out_channels if use_scale_shift_norm else self.out_channels,
- ),
- )
- self.out_layers = nn.Sequential(
- normalization(self.out_channels, swish=0.0 if use_scale_shift_norm else 1.0),
- nn.SiLU() if use_scale_shift_norm else nn.Identity(),
- nn.Dropout(p=dropout),
- zero_module(conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)),
- )
-
- if self.out_channels == channels:
- self.skip_connection = nn.Identity()
- elif use_conv:
- self.skip_connection = conv_nd(dims, channels, self.out_channels, 3, padding=1)
- else:
- self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
-
- def forward(self, x, emb):
- """
- Apply the block to a Tensor, conditioned on a timestep embedding.
-
- :param x: an [N x C x ...] Tensor of features.
- :param emb: an [N x emb_channels] Tensor of timestep embeddings.
- :return: an [N x C x ...] Tensor of outputs.
- """
- if self.updown:
- in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
- h = in_rest(x)
- h = self.h_upd(h)
- x = self.x_upd(x)
- h = in_conv(h)
- else:
- h = self.in_layers(x)
- emb_out = self.emb_layers(emb).type(h.dtype)
- while len(emb_out.shape) < len(h.shape):
- emb_out = emb_out[..., None]
- if self.use_scale_shift_norm:
- out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
- scale, shift = th.chunk(emb_out, 2, dim=1)
- h = out_norm(h) * (1 + scale) + shift
- h = out_rest(h)
- else:
- h = h + emb_out
- h = self.out_layers(h)
- return self.skip_connection(x) + h
-
-
-class AttentionBlock(nn.Module):
- """
- An attention block that allows spatial positions to attend to each other.
-
- Originally ported from here, but adapted to the N-d case.
- https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
- """
-
- def __init__(
- self,
- channels,
- num_heads=1,
- num_head_channels=-1,
- use_checkpoint=False,
- encoder_channels=None,
- ):
- super().__init__()
- self.channels = channels
- if num_head_channels == -1:
- self.num_heads = num_heads
- else:
- assert (
- channels % num_head_channels == 0
- ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
- self.num_heads = channels // num_head_channels
- self.use_checkpoint = use_checkpoint
- self.norm = normalization(channels, swish=0.0)
- self.qkv = conv_nd(1, channels, channels * 3, 1)
- self.attention = QKVAttention(self.num_heads)
-
- if encoder_channels is not None:
- self.encoder_kv = conv_nd(1, encoder_channels, channels * 2, 1)
- self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
-
- def forward(self, x, encoder_out=None):
- b, c, *spatial = x.shape
- qkv = self.qkv(self.norm(x).view(b, c, -1))
- if encoder_out is not None:
- encoder_out = self.encoder_kv(encoder_out)
- h = self.attention(qkv, encoder_out)
- else:
- h = self.attention(qkv)
- h = self.proj_out(h)
- return x + h.reshape(b, c, *spatial)
-
-
-class QKVAttention(nn.Module):
- """
- A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
- """
-
- def __init__(self, n_heads):
- super().__init__()
- self.n_heads = n_heads
-
- def forward(self, qkv, encoder_kv=None):
- """
- Apply QKV attention.
-
- :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
- :return: an [N x (H * C) x T] tensor after attention.
- """
- bs, width, length = qkv.shape
- assert width % (3 * self.n_heads) == 0
- ch = width // (3 * self.n_heads)
- q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
- if encoder_kv is not None:
- assert encoder_kv.shape[1] == self.n_heads * ch * 2
- ek, ev = encoder_kv.reshape(bs * self.n_heads, ch * 2, -1).split(ch, dim=1)
- k = th.cat([ek, k], dim=-1)
- v = th.cat([ev, v], dim=-1)
- scale = 1 / math.sqrt(math.sqrt(ch))
- weight = th.einsum(
- "bct,bcs->bts", q * scale, k * scale
- ) # More stable with f16 than dividing afterwards
- weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
- a = th.einsum("bts,bcs->bct", weight, v)
- return a.reshape(bs, -1, length)
-
-
-class UNetModel(nn.Module):
- """
- The full UNet model with attention and timestep embedding.
-
- :param in_channels: channels in the input Tensor.
- :param model_channels: base channel count for the model.
- :param out_channels: channels in the output Tensor.
- :param num_res_blocks: number of residual blocks per downsample.
- :param attention_resolutions: a collection of downsample rates at which
- attention will take place. May be a set, list, or tuple.
- For example, if this contains 4, then at 4x downsampling, attention
- will be used.
- :param dropout: the dropout probability.
- :param channel_mult: channel multiplier for each level of the UNet.
- :param conv_resample: if True, use learned convolutions for upsampling and
- downsampling.
- :param dims: determines if the signal is 1D, 2D, or 3D.
- :param num_classes: if specified (as an int), then this model will be
- class-conditional with `num_classes` classes.
- :param use_checkpoint: use gradient checkpointing to reduce memory usage.
- :param num_heads: the number of attention heads in each attention layer.
- :param num_heads_channels: if specified, ignore num_heads and instead use
- a fixed channel width per attention head.
- :param num_heads_upsample: works with num_heads to set a different number
- of heads for upsampling. Deprecated.
- :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
- :param resblock_updown: use residual blocks for up/downsampling.
- """
-
- def __init__(
- self,
- in_channels,
- model_channels,
- out_channels,
- num_res_blocks,
- attention_resolutions,
- dropout=0,
- channel_mult=(1, 2, 4, 8),
- conv_resample=True,
- dims=2,
- num_classes=None,
- 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,
- encoder_channels=None,
- ):
- super().__init__()
-
- if num_heads_upsample == -1:
- num_heads_upsample = num_heads
-
- self.in_channels = in_channels
- self.model_channels = model_channels
- self.out_channels = out_channels
- self.num_res_blocks = num_res_blocks
- self.attention_resolutions = attention_resolutions
- self.dropout = dropout
- self.channel_mult = channel_mult
- self.conv_resample = conv_resample
- self.num_classes = num_classes
- 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
-
- 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),
- )
-
- if self.num_classes is not None:
- self.label_emb = nn.Embedding(num_classes, time_embed_dim)
-
- ch = input_ch = int(channel_mult[0] * model_channels)
- self.input_blocks = nn.ModuleList(
- [TimestepEmbedSequential(conv_nd(dims, in_channels, ch, 3, padding=1))]
- )
- self._feature_size = ch
- input_block_chans = [ch]
- ds = 1
- for level, mult in enumerate(channel_mult):
- for _ in range(num_res_blocks):
- layers = [
- ResBlock(
- ch,
- time_embed_dim,
- dropout,
- out_channels=int(mult * model_channels),
- dims=dims,
- use_checkpoint=use_checkpoint,
- use_scale_shift_norm=use_scale_shift_norm,
- )
- ]
- ch = int(mult * model_channels)
- if ds in attention_resolutions:
- layers.append(
- AttentionBlock(
- ch,
- use_checkpoint=use_checkpoint,
- num_heads=num_heads,
- num_head_channels=num_head_channels,
- encoder_channels=encoder_channels,
- )
- )
- self.input_blocks.append(TimestepEmbedSequential(*layers))
- 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)
- ds *= 2
- self._feature_size += ch
-
- 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=num_head_channels,
- encoder_channels=encoder_channels,
- ),
- ResBlock(
- ch,
- time_embed_dim,
- dropout,
- dims=dims,
- use_checkpoint=use_checkpoint,
- use_scale_shift_norm=use_scale_shift_norm,
- ),
- )
- self._feature_size += ch
-
- self.output_blocks = nn.ModuleList([])
- for level, mult in list(enumerate(channel_mult))[::-1]:
- for i in range(num_res_blocks + 1):
- ich = input_block_chans.pop()
- layers = [
- ResBlock(
- ch + ich,
- time_embed_dim,
- dropout,
- out_channels=int(model_channels * mult),
- dims=dims,
- use_checkpoint=use_checkpoint,
- use_scale_shift_norm=use_scale_shift_norm,
- )
- ]
- ch = int(model_channels * mult)
- if ds in attention_resolutions:
- layers.append(
- AttentionBlock(
- ch,
- use_checkpoint=use_checkpoint,
- num_heads=num_heads_upsample,
- num_head_channels=num_head_channels,
- encoder_channels=encoder_channels,
- )
- )
- if level and i == num_res_blocks:
- out_ch = ch
- layers.append(
- ResBlock(
- ch,
- time_embed_dim,
- dropout,
- out_channels=out_ch,
- dims=dims,
- use_checkpoint=use_checkpoint,
- use_scale_shift_norm=use_scale_shift_norm,
- up=True,
- )
- if resblock_updown
- else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
- )
- ds //= 2
- self.output_blocks.append(TimestepEmbedSequential(*layers))
- self._feature_size += ch
-
- self.out = nn.Sequential(
- normalization(ch, swish=1.0),
- nn.Identity(),
- zero_module(conv_nd(dims, input_ch, out_channels, 3, padding=1)),
- )
- self.use_fp16 = use_fp16
-
- def convert_to_fp16(self):
- """
- Convert the torso of the model to float16.
- """
- self.input_blocks.apply(convert_module_to_f16)
- self.middle_block.apply(convert_module_to_f16)
- self.output_blocks.apply(convert_module_to_f16)
-
- def convert_to_fp32(self):
- """
- Convert the torso of the model to float32.
- """
- self.input_blocks.apply(convert_module_to_f32)
- self.middle_block.apply(convert_module_to_f32)
- self.output_blocks.apply(convert_module_to_f32)
-
- def forward(self, x, timesteps, y=None):
- """
- Apply the model to an input batch.
-
- :param x: an [N x C x ...] Tensor of inputs.
- :param timesteps: a 1-D batch of timesteps.
- :param y: an [N] Tensor of labels, if class-conditional.
- :return: an [N x C x ...] Tensor of outputs.
- """
- assert (y is not None) == (
- self.num_classes is not None
- ), "must specify y if and only if the model is class-conditional"
-
- hs = []
- emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
-
- if self.num_classes is not None:
- assert y.shape == (x.shape[0],)
- emb = emb + self.label_emb(y)
-
- h = x.type(self.dtype)
- for module in self.input_blocks:
- h = module(h, emb)
- hs.append(h)
- h = self.middle_block(h, emb)
- for module in self.output_blocks:
- h = th.cat([h, hs.pop()], dim=1)
- h = module(h, emb)
- h = h.type(x.dtype)
- return self.out(h)
-
-class SuperResUNetModel(UNetModel):
- """
- A UNetModel that performs super-resolution.
-
- Expects an extra kwarg `low_res` to condition on a low-resolution image.
- """
-
- def __init__(self, *args, **kwargs):
- if "in_channels" in kwargs:
- kwargs = dict(kwargs)
- kwargs["in_channels"] = kwargs["in_channels"] * 2
- else:
- # Curse you, Python. Or really, just curse positional arguments :|.
- args = list(args)
- args[1] = args[1] * 2
- super().__init__(*args, **kwargs)
-
- def forward(self, x, timesteps, low_res=None, **kwargs):
- _, _, new_height, new_width = x.shape
- upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
- x = th.cat([x, upsampled], dim=1)
- return super().forward(x, timesteps, **kwargs)
-
-
-class InpaintUNetModel(UNetModel):
- """
- A UNetModel which can perform inpainting.
- """
-
- def __init__(self, *args, **kwargs):
- if "in_channels" in kwargs:
- kwargs = dict(kwargs)
- kwargs["in_channels"] = kwargs["in_channels"] * 2 + 1
- else:
- # Curse you, Python. Or really, just curse positional arguments :|.
- args = list(args)
- args[1] = args[1] * 2 + 1
- super().__init__(*args, **kwargs)
-
- def forward(self, x, timesteps, inpaint_image=None, inpaint_mask=None, **kwargs):
- if inpaint_image is None:
- inpaint_image = th.zeros_like(x)
- if inpaint_mask is None:
- inpaint_mask = th.zeros_like(x[:, :1])
- return super().forward(
- th.cat([x, inpaint_image * inpaint_mask, inpaint_mask], dim=1),
- timesteps,
- **kwargs,
- )
-
-
-class SuperResInpaintUNetModel(UNetModel):
- """
- A UNetModel which can perform both upsampling and inpainting.
- """
-
- def __init__(self, *args, **kwargs):
- if "in_channels" in kwargs:
- kwargs = dict(kwargs)
- kwargs["in_channels"] = kwargs["in_channels"] * 3 + 1
- else:
- # Curse you, Python. Or really, just curse positional arguments :|.
- args = list(args)
- args[1] = args[1] * 3 + 1
- super().__init__(*args, **kwargs)
-
- def forward(
- self,
- x,
- timesteps,
- inpaint_image=None,
- inpaint_mask=None,
- low_res=None,
- **kwargs,
- ):
- if inpaint_image is None:
- inpaint_image = th.zeros_like(x)
- if inpaint_mask is None:
- inpaint_mask = th.zeros_like(x[:, :1])
- _, _, new_height, new_width = x.shape
- upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
- return super().forward(
- th.cat([x, inpaint_image * inpaint_mask, inpaint_mask, upsampled], dim=1),
- timesteps,
- **kwargs,
- )
diff --git a/spaces/thealphhamerc/text-to-speech/app.py b/spaces/thealphhamerc/text-to-speech/app.py
deleted file mode 100644
index ad25fa2a0b6644b812b92def9c2e7fc1a7e2e13d..0000000000000000000000000000000000000000
--- a/spaces/thealphhamerc/text-to-speech/app.py
+++ /dev/null
@@ -1,101 +0,0 @@
-from subprocess import call
-import gradio as gr
-import os
-from TTS.api import TTS
-from TTS.utils.synthesizer import Synthesizer
-
-# List available 🐸TTS models and choose the first one
-# all_models = TTS.list_models()
-# for model in all_models:
-# print(model)
-
-# print("Using model: ", all_models[0])
-# model = all_models[0]
-# Init TTS
-
-MAX_TXT_LEN = 500
-
-
-print("Downloading model...", '')
-
-voiceCloneModel = TTS('tts_models/multilingual/multi-dataset/your_tts')
-
-# tts = TTS("tts_models/de/thorsten/tacotron2-DDC")
-
-
-def run_cmd(command):
- try:
- print(command)
- call(command)
- except KeyboardInterrupt:
- print("Process interrupted")
- sys.exit(1)
-
-
-def inference(text, speaker):
- if len(text) > MAX_TXT_LEN:
- text = text[:MAX_TXT_LEN]
- print(
- f"Input text was cutoff since it went over the {MAX_TXT_LEN} character limit.")
- if (speaker == 'Speaker-1'):
- speaker = 'input/amitabh.mp3'
- elif (speaker == 'Speaker-2'):
- speaker = 'input/amrish.mp3'
- elif (speaker == 'Speaker-3'):
- speaker = 'input/obama.mp3'
- elif (speaker == 'Speaker-4'):
- speaker = 'input/trump.wav'
- elif (speaker == 'Rock'):
- speaker = 'input/sample/Dwayne-Johnson-sample.wav'
- elif (speaker == 'Elon'):
- speaker = 'input/sample/Elon-Sample.wav'
- elif (speaker == 'Obama'):
- speaker = 'input/sample/Obama-sample.wav'
- elif (speaker == 'Tony'):
- speaker = 'input/sample/tony-stark-2.wav'
- elif (speaker == 'Madara Uchiha'):
- speaker = 'input/sample/Madara.mp3'
- else:
- speaker = 'input/z-default.wav'
- # print("speaker: ", speaker)
- # cmd = ['tts', '--text', text, '--out_path', 'output/tts_output.wav']
- # run_cmd(cmd)
- # Text to speech to a file
- # tts = TTS(model="tts_models/multilingual/multi-dataset/your_tts",
- # progress_bar=False, gpu=True)
- voiceCloneModel.tts_to_file(text, speaker_wav=speaker,
- language="en", file_path="output/output.wav")
-
- # for i in range(len(model.languages)):
- # model.tts_to_file(text=text,
- # speaker=model.speakers[i], language=model.languages[0], file_path='output/output-'+str(i)+'.wav')
-
- return 'output/output.wav'
-
-
-sampleInput = ['Speaker-1', 'Speaker-2', 'Speaker-3',
- 'Speaker-4', 'Rock', 'Elon', 'Obama', 'Tony', 'Madara Uchiha']
-inputs = [gr.Textbox(lines=5, label="Input Text"),
- gr.Dropdown(
- sampleInput, label="Sample Input", default="Speaker-1",)
- ]
-outputs = gr.Audio(type="filepath", label="Output Audio")
-title = "Text To Speech"
-description = "An example of using TTS to generate speech from text."
-article = ""
-examples = [
- ["This is an open-source library that generates synthetic speech"]
-]
-gr.Interface(
- inference,
- inputs,
- outputs,
- verbose=True,
- title=title,
- description=description,
- article=article,
- examples=examples,
- enable_queue=True,
- allow_flagging="never",
-
-).launch(debug=True)
diff --git a/spaces/thejagstudio/picxai/static/tailwind.js b/spaces/thejagstudio/picxai/static/tailwind.js
deleted file mode 100644
index 5bd204b6fdc220e373d739f45a8417b48587f2e4..0000000000000000000000000000000000000000
--- a/spaces/thejagstudio/picxai/static/tailwind.js
+++ /dev/null
@@ -1,63 +0,0 @@
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n.append({prop:t,value:i,raws:{before:""}}),n}prefixed(e){let t=this.virtual(e);if(this.disabled(t.first))return t.nodes;let i={warn:()=>null},n=this.prefixer().add[t.first.prop];n&&n.process&&n.process(t.first,i);for(let s of t.nodes){for(let a of this.prefixer().values("add",t.first.prop))a.process(s);iC.save(this.all,s)}return t.nodes}isNot(e){return typeof e=="string"&&/not\s*/i.test(e)}isOr(e){return typeof e=="string"&&/\s*or\s*/i.test(e)}isProp(e){return typeof e=="object"&&e.length===1&&typeof e[0]=="string"}isHack(e,t){return!new RegExp(`(\\(|\\s)${nC.escapeRegexp(t)}:`).test(e)}toRemove(e,t){let[i,n]=this.parse(e),s=this.all.unprefixed(i),a=this.all.cleaner();if(a.remove[i]&&a.remove[i].remove&&!this.isHack(t,s))return!0;for(let o of a.values("remove",s))if(o.check(n))return!0;return!1}remove(e,t){let i=0;for(;itypeof t!="object"?t:t.length===1&&typeof t[0]=="object"?this.cleanBrackets(t[0]):this.cleanBrackets(t))}convert(e){let t=[""];for(let i of e)t.push([`${i.prop}: ${i.value}`]),t.push(" or ");return t[t.length-1]="",t}normalize(e){if(typeof e!="object")return e;if(e=e.filter(t=>t!==""),typeof e[0]=="string"){let t=e[0].trim();if(t.includes(":")||t==="selector"||t==="not selector")return[Po.stringify(e)]}return e.map(t=>this.normalize(t))}add(e,t){return e.map(i=>{if(this.isProp(i)){let n=this.prefixed(i[0]);return n.length>1?this.convert(n):i}return typeof i=="object"?this.add(i,t):i})}process(e){let t=Po.parse(e.params);t=this.normalize(t),t=this.remove(t,e.params),t=this.add(t,e.params),t=this.cleanBrackets(t),e.params=Po.stringify(t)}disabled(e){if(!this.all.options.grid&&(e.prop==="display"&&e.value.includes("grid")||e.prop.includes("grid")||e.prop==="justify-items"))return!0;if(this.all.options.flexbox===!1){if(e.prop==="display"&&e.value.includes("flex"))return!0;let t=["order","justify-content","align-items","align-content"];if(e.prop.includes("flex")||t.includes(e.prop))return!0}return!1}};ym.exports=gm});var xm=v((H3,vm)=>{l();var bm=class{constructor(e,t){this.prefix=t,this.prefixed=e.prefixed(this.prefix),this.regexp=e.regexp(this.prefix),this.prefixeds=e.possible().map(i=>[e.prefixed(i),e.regexp(i)]),this.unprefixed=e.name,this.nameRegexp=e.regexp()}isHack(e){let t=e.parent.index(e)+1,i=e.parent.nodes;for(;t{l();var{list:sC}=me(),aC=xm(),oC=Bt(),lC=at(),uC=ne(),km=class extends oC{constructor(e,t,i){super(e,t,i);this.regexpCache=new Map}check(e){return e.selector.includes(this.name)?!!e.selector.match(this.regexp()):!1}prefixed(e){return this.name.replace(/^(\W*)/,`$1${e}`)}regexp(e){if(!this.regexpCache.has(e)){let t=e?this.prefixed(e):this.name;this.regexpCache.set(e,new RegExp(`(^|[^:"'=])${uC.escapeRegexp(t)}`,"gi"))}return this.regexpCache.get(e)}possible(){return lC.prefixes()}prefixeds(e){if(e._autoprefixerPrefixeds){if(e._autoprefixerPrefixeds[this.name])return e._autoprefixerPrefixeds}else e._autoprefixerPrefixeds={};let t={};if(e.selector.includes(",")){let n=sC.comma(e.selector).filter(s=>s.includes(this.name));for(let s of this.possible())t[s]=n.map(a=>this.replace(a,s)).join(", ")}else for(let i of this.possible())t[i]=this.replace(e.selector,i);return e._autoprefixerPrefixeds[this.name]=t,e._autoprefixerPrefixeds}already(e,t,i){let n=e.parent.index(e)-1;for(;n>=0;){let s=e.parent.nodes[n];if(s.type!=="rule")return!1;let a=!1;for(let o in t[this.name]){let u=t[this.name][o];if(s.selector===u){if(i===o)return!0;a=!0;break}}if(!a)return!1;n-=1}return!1}replace(e,t){return e.replace(this.regexp(),`$1${this.prefixed(t)}`)}add(e,t){let i=this.prefixeds(e);if(this.already(e,i,t))return;let n=this.clone(e,{selector:i[this.name][t]});e.parent.insertBefore(e,n)}old(e){return new aC(this,e)}};Sm.exports=km});var Am=v((Q3,_m)=>{l();var fC=Bt(),Cm=class extends fC{add(e,t){let i=t+e.name;if(e.parent.some(a=>a.name===i&&a.params===e.params))return;let s=this.clone(e,{name:i});return e.parent.insertBefore(e,s)}process(e){let t=this.parentPrefix(e);for(let i of this.prefixes)(!t||t===i)&&this.add(e,i)}};_m.exports=Cm});var Em=v((J3,Om)=>{l();var cC=zt(),Do=class extends cC{prefixed(e){return e==="-webkit-"?":-webkit-full-screen":e==="-moz-"?":-moz-full-screen":`:${e}fullscreen`}};Do.names=[":fullscreen"];Om.exports=Do});var Pm=v((X3,Tm)=>{l();var pC=zt(),qo=class extends pC{possible(){return super.possible().concat(["-moz- old","-ms- old"])}prefixed(e){return e==="-webkit-"?"::-webkit-input-placeholder":e==="-ms-"?"::-ms-input-placeholder":e==="-ms- old"?":-ms-input-placeholder":e==="-moz- old"?":-moz-placeholder":`::${e}placeholder`}};qo.names=["::placeholder"];Tm.exports=qo});var qm=v((K3,Dm)=>{l();var dC=zt(),Io=class extends dC{prefixed(e){return e==="-ms-"?":-ms-input-placeholder":`:${e}placeholder-shown`}};Io.names=[":placeholder-shown"];Dm.exports=Io});var Rm=v((Z3,Im)=>{l();var hC=zt(),mC=ne(),Ro=class extends 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92","edge 91","ios_saf 14.0-14.4","ios_saf 14.5-14.7","safari 14.1","samsung 14.0"]},"mask-border-outset":{feature:"css-masks",browsers:["and_chr 92","and_uc 12.12","chrome 91","chrome 92","edge 91","ios_saf 14.0-14.4","ios_saf 14.5-14.7","safari 14.1","samsung 14.0"]},"mask-border-width":{feature:"css-masks",browsers:["and_chr 92","and_uc 12.12","chrome 91","chrome 92","edge 91","ios_saf 14.0-14.4","ios_saf 14.5-14.7","safari 14.1","samsung 14.0"]},"mask-border-slice":{feature:"css-masks",browsers:["and_chr 92","and_uc 12.12","chrome 91","chrome 92","edge 91","ios_saf 14.0-14.4","ios_saf 14.5-14.7","safari 14.1","samsung 14.0"]},"clip-path":{feature:"css-clip-path",browsers:["and_uc 12.12","ios_saf 14.0-14.4","ios_saf 14.5-14.7","safari 14.1","samsung 14.0"]},"box-decoration-break":{feature:"css-boxdecorationbreak",browsers:["and_chr 92","chrome 91","chrome 92","edge 91","ios_saf 14.0-14.4","ios_saf 14.5-14.7","safari 14.1","samsung 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-
- Using \`browsers\` option can cause errors. Browserslist config can
- be used for Babel, Autoprefixer, postcss-normalize and other tools.
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- If you really need to use option, rename it to \`overrideBrowserslist\`.
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- https://twitter.com/browserslist
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Download Freemarathibookvanshveldrmalati.md b/spaces/tialenAdioni/chat-gpt-api/logs/Download Freemarathibookvanshveldrmalati.md
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-Marathi Book Vanshvel: A Guide for Healthy Pregnancy and Childbirth
-
-If you are looking for a Marathi book that can help you with your pregnancy and childbirth, you might want to check out Vanshvel by Dr. Malati Karwarkar. Vanshvel is a bestseller from Menaka Prakashan that provides holistic guidance to every parent expecting a healthy child.
-downloadmarathibookvanshveldrmalati Download ✫ https://urlcod.com/2uK3U3
Vanshvel means "sprout" in Marathi, and it symbolizes the growth and development of a new life. The book covers various topics related to pre and post-natal care, such as nutrition, exercise, yoga, meditation, breastfeeding, baby care, and more. It also offers tips and advice on how to cope with common pregnancy problems, such as nausea, fatigue, back pain, swelling, etc.
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-The author of the book, Dr. Malati Karwarkar, is a well-known nutritionist and dietician who has over 30 years of experience in the field. She has also written several other books on health and wellness, such as Healthy Mom, Healthy Baby (the English version of Vanshvel), Aaharveda (a book on Ayurvedic diet), and Arogyachi Pustake (a series of books on various health topics).
-
-Vanshvel is written in simple and easy-to-understand Marathi language, and it is suitable for both urban and rural readers. It has 168 pages and it costs Rs. 210. You can buy it online from Amazon.in or Menakabooks.com, or from any local bookstore near you.
-
-Vanshvel is a must-read for every parent who wants to have a healthy and happy pregnancy and childbirth. It will help you to prepare yourself physically, mentally, emotionally, and spiritually for the most important journey of your life.
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-
-Why is Nutrition Important During Pregnancy?
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-Nutrition is one of the most important factors that affects the health and well-being of both the mother and the baby during pregnancy. The nutritional requirements of a woman increase in pregnancy, as she has to provide for the growth and development of the baby. A balanced and nutritious diet can help to prevent or reduce the risk of complications, such as anemia, gestational diabetes, pre-eclampsia, low birth weight, etc.
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-According to Vanshvel, a pregnant woman should eat a variety of foods from different food groups, such as cereals, pulses, fruits, vegetables, milk and milk products, eggs, meat, fish, nuts, seeds, etc. She should also drink plenty of water and fluids to stay hydrated and avoid constipation. She should avoid alcohol, tobacco, caffeine, junk food, spicy food, and excess salt and sugar.
-
-Vanshvel also provides a sample menu plan for each trimester of pregnancy, along with the recommended servings and portions of different food items. It also suggests some healthy snacks and recipes that are easy to make and delicious to eat.
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-How Can Exercise and Yoga Benefit Pregnant Women?
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-Exercise and yoga are another essential aspect of pre and post-natal care that Vanshvel emphasizes on. Exercise and yoga can help to improve the physical fitness and stamina of pregnant women, as well as their mental and emotional health. They can also help to relieve stress, anxiety, depression, insomnia, and other common mood disorders that may occur during pregnancy.
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-Vanshvel recommends that pregnant women should do at least 30 minutes of moderate exercise or yoga every day, or at least three times a week. They should choose activities that are safe and comfortable for them, such as walking, swimming, cycling, aerobics, etc. They should avoid activities that involve high impact, jumping, twisting, or bending. They should also consult their doctor before starting any exercise or yoga program.
-
-Vanshvel also provides a detailed guide on how to perform various yoga poses and breathing exercises that are beneficial for pregnant women. It explains the benefits and precautions of each pose and exercise, along with illustrations and instructions. It also suggests some meditation techniques that can help to calm the mind and relax the body.
e93f5a0c3fHow to Prepare for BCS Exam with Bcs Preparation Bangla.pdf
-BCS exam is one of the most competitive and prestigious exams in Bangladesh. It is a three-stage exam that tests the candidates' knowledge, skills and aptitude for various civil service posts. To succeed in this exam, one needs to have a comprehensive and systematic preparation plan.
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One of the best resources for BCS preparation is Bcs Preparation Bangla.pdf, a collection of essential PDF books for different subjects and topics. These books are collected from various sources and are free to download from the internet. They cover the syllabus of BCS exam and provide useful tips, tricks and strategies for solving questions.
-In this article, we will discuss how to use Bcs Preparation Bangla.pdf for your BCS preparation and what are the benefits of using it.
-How to Use Bcs Preparation Bangla.pdf for BCS Preparation
-Bcs Preparation Bangla.pdf is a mega collection of PDF books for BCS preparation. It includes books on Bangla, English, Math, Science, General Knowledge, Computer, Ethics and more. You can download these books from the following link: [^1^]
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-Using Bcs Preparation Bangla.pdf for your BCS preparation has many benefits. Some of them are:
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7b8c122e87Digital Art Cyber Controller 1.3: A New Tool for Creative Expression
-Digital art is a form of art that uses computer technology to create or manipulate images, sounds, or other media. Digital art can range from simple drawings and animations to complex interactive installations and virtual reality experiences. Digital art can also be used to enhance traditional art forms, such as painting, sculpture, or photography.
-One of the challenges of digital art is finding the right tools to express one's vision and style. There are many software applications and hardware devices available for digital artists, but they may not always suit the needs or preferences of the individual artist. Some tools may be too complicated or expensive, while others may be too limited or outdated.
-Digital Art Cyber Controller 1.3 Download Zip ✶ https://urlcod.com/2uHwhG
That's why a team of digital artists and programmers have developed a new tool called Digital Art Cyber Controller 1.3 (DACC 1.3). DACC 1.3 is a software application that allows digital artists to control and manipulate any digital device or software using their voice, gestures, or brainwaves. DACC 1.3 can be used with any operating system, platform, or device that supports Bluetooth or Wi-Fi connectivity.
-DACC 1.3 works by using artificial intelligence and machine learning to analyze the input signals from the artist and translate them into commands for the target device or software. For example, an artist can use DACC 1.3 to draw on a tablet using their voice, to change the color or texture of an image using their gestures, or to create music or sound effects using their brainwaves. DACC 1.3 can also be used to combine multiple devices or software into a single integrated system.
-DACC 1.3 is designed to be user-friendly and customizable. The artist can choose from a variety of presets and templates, or create their own settings and profiles. DACC 1.3 also provides feedback and suggestions to help the artist improve their skills and creativity. DACC 1.3 is compatible with most popular digital art software and hardware, such as Photoshop, Illustrator, Blender, Unity, Arduino, Raspberry Pi, etc.
-DACC 1.3 is currently in beta testing and will be available for download soon. The developers hope that DACC 1.3 will inspire more people to explore and enjoy digital art as a form of creative expression.
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-One of the advantages of DACC 1.3 is that it allows digital artists to experiment with different modes and styles of expression. For example, an artist can use DACC 1.3 to switch from realistic to abstract, from 2D to 3D, from static to dynamic, or from visual to auditory. DACC 1.3 can also help digital artists to collaborate and communicate with other artists or audiences, by sharing their work online or in real-time.
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cec2833e83How to Display Out Cydia Download on Your iOS Device
-If you are an iOS user who likes to customize your device with tweaks, themes, and apps that are not available on the App Store, you probably know what Cydia is. Cydia is a third-party app installer that allows you to access a vast library of content that can enhance your device's functionality and appearance. However, did you know that you can also display out cydia download on your iOS device? That means you can mirror your device's screen on a connected monitor, projector, or TV, and enjoy your cydia content on a bigger screen. In this article, we will show you how to do that with a tweak called Display Out.
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What is Cydia and Why You Need It
-Cydia is a third-party app installer for jailbroken iOS devices
-Cydia is an app that you can install on your iOS device after jailbreaking it. Jailbreaking is a process that removes Apple's restrictions and limitations on your device, giving you more control over it. By jailbreaking your device, you can install apps that are not approved by Apple, modify system settings, change the look and feel of your device, and more. Cydia is the app that lets you access these features by providing you with a platform to download and install tweaks, themes, and apps from various sources called repositories or repos.
-Cydia allows you to customize your device with tweaks, themes, and apps
-With Cydia, you can customize your device in many ways that are not possible with the default iOS system. For example, you can change the icons, fonts, colors, sounds, animations, widgets, lock screen, home screen, control center, notification center, keyboard, status bar, dock, app switcher, and more. You can also add new features and functions to your device, such as gestures, shortcuts, multitasking, file management , security, privacy, and more. You can also download apps that are not available on the App Store, such as emulators, torrent clients, video downloaders, and more.
-Cydia requires jailbreaking your device, which means removing Apple's restrictions
-However, Cydia is not an official app that you can download from the App Store. In order to install Cydia, you need to jailbreak your device first. Jailbreaking is a process that removes Apple's restrictions and limitations on your device, giving you more control over it. By jailbreaking your device, you can install apps that are not approved by Apple, modify system settings, change the look and feel of your device, and more. However, jailbreaking also has some risks and drawbacks, such as voiding your warranty, exposing your device to malware, losing data, and bricking your device. Therefore, you should only jailbreak your device if you know what you are doing and are willing to take the responsibility.
-
-What is Display Out and How It Works
-Display Out is a tweak that mirrors your device's screen on a connected monitor, projector, or TV
-Display Out is a tweak that you can download from Cydia that allows you to display out cydia download on your iOS device. Display Out is a tweak that mirrors your device's screen on a connected monitor, projector, or TV. This means that whatever you see on your device's screen, you can also see on the external display. This can be useful for many purposes, such as gaming, watching videos, presenting slideshows, demonstrating apps, and more.
-Display Out supports game and video content, orientation and connectivity detection, and zoom and aspect ratio adjustment
-Display Out is not just a simple screen mirroring tweak. It also supports game and video content, meaning that you can play games and watch videos on the external display with high quality and smooth performance. Display Out also detects the orientation and connectivity of your device automatically, meaning that it will adjust the screen accordingly when you rotate or disconnect your device. Display Out also allows you to zoom in and out and change the aspect ratio of the screen to fit the external display.
-Display Out works with various cables and adapters, as well as AirPlay on iOS 5 or later
-Display Out works with various cables and adapters that you can use to connect your iOS device to a monitor, projector, or TV. For example, you can use the Apple Digital AV Adapter or the Apple VGA Adapter to connect your device via HDMI or VGA ports. You can also use other third-party cables and adapters that are compatible with your device and display. Alternatively, if you have an iOS 5 or later device and an Apple TV or another AirPlay-enabled device, you can use Display Out with AirPlay to wirelessly mirror your screen without any cables.
-How to Install Display Out on Your iOS Device
-Step 1: Jailbreak your device with a compatible tool
-The first step to install Display Out on your iOS device is to jailbreak your device with a compatible tool. As mentioned earlier, jailbreaking is a process that removes Apple's restrictions and limitations on your device, giving you more control over it. By jailbreaking your device, you can install Cydia and other tweaks that are not available on the App Store. However, jailbreaking also has some risks and drawbacks, such as voiding your warranty, exposing your device to malware, losing data, and bricking your device. Therefore, you should only jailbreak your device if you know what you are doing and are willing to take the responsibility.
-To jailbreak your device, you need to use a tool that is compatible with your device model and iOS version. There are many tools available online that can help you jailbreak your device easily and safely. Some of the most popular tools are Pangu, unc0ver, checkra1n, Chimera, Odyssey, etc. You can find more information about these tools on their official websites or on reputable sources such as Reddit, YouTube, iDownloadBlog, etc. Before using any tool , you should backup your device, disable your passcode and Find My iPhone, and follow the instructions carefully.
-Step 2: Open Cydia and search for Display Out
-After jailbreaking your device, you should see a Cydia icon on your home screen. Cydia is the app that lets you access a vast library of tweaks, themes, and apps that can enhance your device's functionality and appearance. To install Display Out, you need to open Cydia and search for Display Out in the search bar. You can also browse the categories or the featured section to find Display Out.
-Step 3: Purchase and install Display Out from the BigBoss repo
-Display Out is a paid tweak that costs $3.99. You can purchase it from the BigBoss repo, which is one of the default and most trusted repos in Cydia. To purchase Display Out, you need to have a PayPal account or a credit card. After purchasing Display Out, you can install it by tapping on the Install button and confirming the installation. Cydia will then download and install Display Out on your device.
-Step 4: Go to Settings and configure Display Out according to your preferences
-After installing Display Out, you need to go to Settings and configure Display Out according to your preferences. You can find Display Out under the Extensions section in Settings. You can enable or disable Display Out, adjust the zoom and aspect ratio, choose the resolution and frame rate, enable or disable orientation and connectivity detection, and more. You can also test Display Out by tapping on the Test button.
-How to Use Display Out on Your iOS Device
-Step 1: Connect your device to a monitor, projector, or TV with a cable or adapter
-To use Display Out on your iOS device, you need to connect your device to a monitor, projector, or TV with a cable or adapter. You can use the Apple Digital AV Adapter or the Apple VGA Adapter to connect your device via HDMI or VGA ports. You can also use other third-party cables and adapters that are compatible with your device and display. Alternatively, if you have an iOS 5 or later device and an Apple TV or another AirPlay-enabled device, you can use Display Out with AirPlay to wirelessly mirror your screen without any cables.
-Step 2: Launch any app or game that you want to display out
-After connecting your device to an external display, you can launch any app or game that you want to display out. For example, you can launch Cydia and browse through the tweaks, themes, and apps that you have downloaded or want to download. You can also launch any other app or game that you have on your device, such as Safari, YouTube, Netflix, Angry Birds, etc.
-Step 3: Enjoy the mirrored screen on the external display
-Once you launch an app or game that you want to display out, you should see the mirrored screen on the external display. You can control the app or game with your device as usual, while enjoying the bigger and better screen on the external display. You can also adjust the zoom and aspect ratio of the screen with Display Out settings if needed.
-Benefits and Drawbacks of Display Out
-Benefits include enhanced gaming and video experience, presentation and demonstration convenience, and device customization
-Display Out has many benefits that can make your iOS device more enjoyable and useful. Some of the benefits include:
-
-Enhanced gaming and video experience: With Display Out, you can play games and watch videos on a bigger and better screen with high quality and smooth performance. You can also share your gaming and video experience with others by displaying out cydia download on your iOS device.
-Presentation and demonstration convenience: With Display Out, you can present slideshows, documents, photos, videos, apps, games, and more on a monitor, projector, or TV with ease. You can also demonstrate your cydia content and features to others by displaying out cydia download on your iOS device.
-Device customization: With Display Out , you can customize your device with tweaks, themes, and apps that are not available on the App Store. You can also change the look and feel of your device, add new features and functions, and more. Display Out lets you show off your device customization by displaying out cydia download on your iOS device.
-
-Drawbacks include battery drain, performance issues, and compatibility limitations
-However, Display Out also has some drawbacks that you should be aware of before using it. Some of the drawbacks include:
-
-Battery drain: Display Out can consume a lot of battery power when mirroring your screen on an external display. This can shorten your device's battery life and require more frequent charging. You can reduce the battery drain by lowering the brightness, resolution, and frame rate of the screen, or by using a power source when possible.
-Performance issues: Display Out can cause some performance issues when mirroring your screen on an external display. This can affect the speed, responsiveness, and stability of your device and the app or game that you are displaying out. You can improve the performance by closing unnecessary apps, freeing up memory, and updating your device and Display Out to the latest versions.
-Compatibility limitations: Display Out may not be compatible with some devices, iOS versions, cables, adapters, or external displays. This can result in poor quality, lagging, freezing, or crashing of the screen. You can check the compatibility of Display Out with your device and display on its official website or on Cydia before purchasing and installing it.
-
-Conclusion and FAQs
-Conclusion: Display Out is a useful tweak that lets you display out cydia download on your iOS device
-In conclusion, Display Out is a useful tweak that lets you display out cydia download on your iOS device. Display Out allows you to mirror your device's screen on a connected monitor, projector, or TV, and enjoy your cydia content on a bigger screen. Display Out supports game and video content, orientation and connectivity detection, and zoom and aspect ratio adjustment. Display Out works with various cables and adapters, as well as AirPlay on iOS 5 or later. Display Out has many benefits, such as enhanced gaming and video experience, presentation and demonstration convenience, and device customization. However, Display Out also has some drawbacks, such as battery drain, performance issues, and compatibility limitations. Therefore, you should weigh the pros and cons of Display Out before using it.
-FAQs:
-Q1: Is Display Out compatible with my device and iOS version?
-A1: Display Out is compatible with most iOS devices that can run iOS 4 or later. However, some devices may have better performance than others depending on their hardware specifications. You can check the compatibility of Display Out with your device on its official website or on Cydia before purchasing and installing it.
-Q2: How much does Display Out cost and where can I get it?
-A2: Display Out is a paid tweak that costs $3.99. You can purchase it from the BigBoss repo in Cydia. The BigBoss repo is one of the default and most trusted repos in Cydia that provides a wide range of tweaks, themes, and apps for jailbroken iOS devices.
-Q3: What are the best cables and adapters to use with Display Out?
-A3: The best cables and adapters to use with Display Out are the ones that are compatible with your device and display. For example, you can use the Apple Digital AV Adapter or the Apple VGA Adapter to connect your device via HDMI or VGA ports. You can also use other third-party cables and adapters that are compatible with your device and display. Alternatively, if you have an iOS 5 or later device and an Apple TV or another AirPlay-enabled device , you can use Display Out with AirPlay to wirelessly mirror your screen without any cables.
-Q4: How can I fix Display Out if it doesn't work properly?
-A4: If Display Out doesn't work properly, you can try the following steps to fix it:
-
-Make sure your device and display are connected properly and securely.
-Make sure your device and display are compatible with Display Out and each other.
-Make sure your device and Display Out are updated to the latest versions.
-Make sure your device has enough battery power and memory.
-Restart your device and Display Out.
-Reinstall Display Out or restore your device if necessary.
-
-Q5: What are some alternatives to Display Out?
-A5: If Display Out is not suitable for you, you can try some alternatives that have similar functions. Some of the alternatives are:
-
-AirServer: A tweak that turns your iOS device into an AirPlay receiver, allowing you to mirror your screen from another iOS device or a Mac.
-ScreenSplitr: A tweak that lets you stream your screen to a web browser on any device, such as a PC, laptop, tablet, or smartphone.
-TVOut2 Mirror TVOut: A tweak that mirrors your screen on a TV with a composite or component cable.
-
-I hope this article has helped you learn how to display out cydia download on your iOS device. If you have any questions or feedback, please leave a comment below. Thank you for reading!
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e;l--,f+=o[i++]<>8),512&r.flags&&(Be[0]=255&f,Be[1]=f>>>8&255,r.check=y(r.check,Be,2,0)),f=0,h=0,r.mode=j;case j:if(1024&r.flags){for(;h<16;){if(0===l)break e;l--,f+=o[i++]<>>8&255,r.check=y(r.check,Be,2,0)),f=0,h=0}else r.head&&(r.head.extra=null);r.mode=H;case H:if(1024&r.flags&&(g=r.length,g>l&&(g=l),g&&(r.head&&(xe=r.head.extra_len-r.length,r.head.extra||(r.head.extra=new Array(r.head.extra_len)),w.arraySet(r.head.extra,o,i,g,xe)),512&r.flags&&(r.check=y(r.check,o,g,i)),l-=g,i+=g,r.length-=g),r.length))break e;r.length=0,r.mode=M;case M:if(2048&r.flags){if(0===l)break e;g=0;do xe=o[i+g++],r.head&&xe&&r.length<65536&&(r.head.name+=String.fromCharCode(xe));while(xe&&g>9&1,r.head.done=!0),e.adler=r.check=0,r.mode=V;break;case q:for(;h<32;){if(0===l)break e;l--,f+=o[i++]<>>=7&h,h-=7&h,r.mode=de;break}for(;h<3;){if(0===l)break e;l--,f+=o[i++]<>>=1,h-=1,3&f){case 0:r.mode=Q;break;case 1:if(u(r),r.mode=re,t===U){f>>>=2,h-=2;break e}break;case 2:r.mode=$;break;case 3:e.msg="invalid block type",r.mode=fe}f>>>=2,h-=2;break;case Q:for(f>>>=7&h,h-=7&h;h<32;){if(0===l)break e;l--,f+=o[i++]<>>16^65535)){e.msg="invalid stored block lengths",r.mode=fe;break}if(r.length=65535&f,f=0,h=0,r.mode=X,t===U)break e;case X:r.mode=J;case J:if(g=r.length){if(g>l&&(g=l),g>d&&(g=d),0===g)break e;w.arraySet(a,o,i,g,s),l-=g,i+=g,d-=g,s+=g,r.length-=g;break}r.mode=V;break;case $:for(;h<14;){if(0===l)break e;l--,f+=o[i++]<>>=5,h-=5,r.ndist=(31&f)+1,f>>>=5,h-=5,r.ncode=(15&f)+4,f>>>=4,h-=4,r.nlen>286||r.ndist>30){e.msg="too many length or distance symbols",r.mode=fe;break}r.have=0,r.mode=ee;case ee:for(;r.have>>=3,h-=3}for(;r.have<19;)r.lens[Ue[r.have++]]=0;if(r.lencode=r.lendyn,r.lenbits=7,Se={bits:r.lenbits},_e=x(_,r.lens,0,19,r.lencode,0,r.work,Se),r.lenbits=Se.bits,_e){e.msg="invalid code lengths set",r.mode=fe;break}r.have=0,r.mode=te;case te:for(;r.have>>24,pe=Ce>>>16&255,we=65535&Ce,!(ge<=h);){if(0===l)break e;l--,f+=o[i++]<>>=ge,h-=ge,r.lens[r.have++]=we;else{if(16===we){for(Ee=ge+2;h>>=ge,h-=ge,0===r.have){e.msg="invalid bit length repeat",r.mode=fe;
-break}xe=r.lens[r.have-1],g=3+(3&f),f>>>=2,h-=2}else if(17===we){for(Ee=ge+3;h>>=ge,h-=ge,xe=0,g=3+(7&f),f>>>=3,h-=3}else{for(Ee=ge+7;h>>=ge,h-=ge,xe=0,g=11+(127&f),f>>>=7,h-=7}if(r.have+g>r.nlen+r.ndist){e.msg="invalid bit length repeat",r.mode=fe;break}for(;g--;)r.lens[r.have++]=xe}}if(r.mode===fe)break;if(0===r.lens[256]){e.msg="invalid code -- missing end-of-block",r.mode=fe;break}if(r.lenbits=9,Se={bits:r.lenbits},_e=x(S,r.lens,0,r.nlen,r.lencode,0,r.work,Se),r.lenbits=Se.bits,_e){e.msg="invalid literal/lengths set",r.mode=fe;break}if(r.distbits=6,r.distcode=r.distdyn,Se={bits:r.distbits},_e=x(E,r.lens,r.nlen,r.ndist,r.distcode,0,r.work,Se),r.distbits=Se.bits,_e){e.msg="invalid distances set",r.mode=fe;break}if(r.mode=re,t===U)break e;case re:r.mode=ne;case ne:if(l>=6&&d>=258){e.next_out=s,e.avail_out=d,e.next_in=i,e.avail_in=l,r.hold=f,r.bits=h,k(e,m),s=e.next_out,a=e.output,d=e.avail_out,i=e.next_in,o=e.input,l=e.avail_in,f=r.hold,h=r.bits,r.mode===V&&(r.back=-1);break}for(r.back=0;Ce=r.lencode[f&(1<>>24,pe=Ce>>>16&255,we=65535&Ce,!(ge<=h);){if(0===l)break e;l--,f+=o[i++]<>ve)],ge=Ce>>>24,pe=Ce>>>16&255,we=65535&Ce,!(ve+ge<=h);){if(0===l)break e;l--,f+=o[i++]<>>=ve,h-=ve,r.back+=ve}if(f>>>=ge,h-=ge,r.back+=ge,r.length=we,0===pe){r.mode=le;break}if(32&pe){r.back=-1,r.mode=V;break}if(64&pe){e.msg="invalid literal/length code",r.mode=fe;break}r.extra=15&pe,r.mode=oe;case oe:if(r.extra){for(Ee=r.extra;h>>=r.extra,h-=r.extra,r.back+=r.extra}r.was=r.length,r.mode=ae;case ae:for(;Ce=r.distcode[f&(1<>>24,pe=Ce>>>16&255,we=65535&Ce,!(ge<=h);){if(0===l)break e;l--,f+=o[i++]<>ve)],ge=Ce>>>24,pe=Ce>>>16&255,we=65535&Ce,!(ve+ge<=h);){if(0===l)break e;l--,f+=o[i++]<>>=ve,h-=ve,r.back+=ve}if(f>>>=ge,h-=ge,r.back+=ge,64&pe){e.msg="invalid distance code",r.mode=fe;break}r.offset=we,r.extra=15&pe,r.mode=ie;case ie:if(r.extra){for(Ee=r.extra;h>>=r.extra,h-=r.extra,r.back+=r.extra}if(r.offset>r.dmax){e.msg="invalid distance too far back",r.mode=fe;break}r.mode=se;case se:if(0===d)break e;if(g=m-d,r.offset>g){if(g=r.offset-g,g>r.whave&&r.sane){e.msg="invalid distance too far back",r.mode=fe;break}g>r.wnext?(g-=r.wnext,p=r.wsize-g):p=r.wnext-g,g>r.length&&(g=r.length),me=r.window}else me=a,p=s-r.offset,g=r.length;g>d&&(g=d),d-=g,r.length-=g;do a[s++]=me[p++];while(--g);0===r.length&&(r.mode=ne);break;case le:if(0===d)break e;a[s++]=r.length,d--,r.mode=ne;break;case de:if(r.wrap){for(;h<32;){if(0===l)break e;l--,f|=o[i++]<>>16&65535|0,i=0;0!==r;){i=r>2e3?2e3:r,r-=i;do o=o+t[n++]|0,a=a+o|0;while(--i);o%=65521,a%=65521}return o|a<<16|0}t.exports=n},"zlib/crc32.js":function(e,t,r){"use strict";function n(){for(var e,t=[],r=0;r<256;r++){e=r;for(var n=0;n<8;n++)e=1&e?3988292384^e>>>1:e>>>1;t[r]=e}return t}function o(e,t,r,n){var o=a,i=n+r;e^=-1;for(var s=n;s>>8^o[255&(e^t[s])];return e^-1}var a=n();t.exports=o},"zlib/inffast.js":function(e,t,r){"use strict";var n=30,o=12;t.exports=function(e,t){var r,a,i,s,l,d,u,c,f,h,b,m,g,p,w,v,y,k,x,_,S,E,C,B,U;r=e.state,a=e.next_in,B=e.input,i=a+(e.avail_in-5),s=e.next_out,U=e.output,l=s-(t-e.avail_out),d=s+(e.avail_out-257),u=r.dmax,c=r.wsize,f=r.whave,h=r.wnext,b=r.window,m=r.hold,g=r.bits,p=r.lencode,w=r.distcode,v=(1<>>24,m>>>=x,g-=x,x=k>>>16&255,0===x)U[s++]=65535&k;else{if(!(16&x)){if(0===(64&x)){k=p[(65535&k)+(m&(1<>>=x,g-=x),g<15&&(m+=B[a++]<>>24,m>>>=x,g-=x,x=k>>>16&255,!(16&x)){if(0===(64&x)){k=w[(65535&k)+(m&(1<