Treasure Planet: Treasure Racer
-This is a racing game where you take control of a Royal Navy ship and steer it through an intergalactic raceway. You can choose from different ships, each with its own speed, handling and design. You can also customize your ship with different colors and decals as you progress through the game.
-The game has several tracks that take place in different locations from the movie, such as Montressor Spaceport, Crescentia and Treasure Planet itself. Each track has its own layout, obstacles and shortcuts that you have to navigate. You can also unlock bonus tracks by completing certain tasks or finding hidden items.
-The game features fast-paced and exciting gameplay that simulates racing through space. You have to use your skills and reflexes to avoid crashing into walls, debris and other ships. You can also use special power-ups such as rockets, shields and boosters to gain an edge over your opponents.
-A racing game where you steer a Royal Navy ship through an intergalactic raceway
-<img src="treasure_racer.jpg" alt="A screenshot of Treasure Racer showing a ship racing through a track">
Features different tracks, obstacles and power-ups
-<table> <tr> <th>Track</th> <th>Location</th> <th>Obstacle</th> <th>Power-up</th> </tr> <tr> <td>Montressor Spaceport</td> <td>A busy space station with many ships and buildings</td> <td>Crates, barrels and signs that block your way</td> <td>Rockets that let you shoot your opponents</td> </tr> <tr> <td>Crescentia</td> <td>A planet with a crescent-shaped ring of asteroids around it</td> <td>Asteroids, comets and meteors that fly across your path</td> <td>Shields that protect you from damage</td> </tr> <tr> <td>Treasure Planet</td> <td>A planet with a hidden treasure vault inside it</td> <td>Lava flows, geysers and traps that erupt from the ground</td> <td>Boosters that increase your speed temporarily</td> </tr> </table>
Tips and tricks to master the game
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-
- Use your arrow keys or mouse to steer your ship left or right. -
- Use your spacebar or left mouse button to activate your power-ups. -
- Collect coins and gems along the way to increase your score and unlock new features. -
- Look for ramps, loops and tunnels that can help you avoid obstacles or reach shortcuts. -
- Watch out for other racers who can bump into you or use their power-ups against you. -
- Try to finish each track in the shortest time possible to earn medals and trophies. -
- Use your mouse or arrow keys to move your surfer up, down, left or right. -
- Use your spacebar or left mouse button to activate your turbo boost. -
- Collect stars and hearts along the way to increase your score and health. -
- Look for green arrows that indicate the location of crew members. -
- Watch out for red arrows that indicate the location of enemies. -
- Try to complete each mission in the shortest time possible to earn medals and trophies. -
- Treasure Planet: Broadside Blast, a naval combat game where you shoot enemy ships with your cannons. -
- Treasure Planet: Treasure Racer, a racing game where you steer a Royal Navy ship through an intergalactic raceway. -
- Treasure Planet: Etherium Rescue, a rescue game where you fly a solar surfer and save stranded crew members. -
- Treasure Planet Training Academy PC 2007 is a collection of three games based on the Disney movie Treasure Planet. -
- The game was distributed by McDonald's Mighty Kid's Meals as a promotional item in 2002. -
- The game consists of three games: Treasure Planet: Broadside Blast, Treasure Planet: Treasure Racer and Treasure Planet: Etherium Rescue. -
- The game is compatible with most Windows operating systems up to Windows XP. -
- You can download the game for free from archive.org and use Daemon Tools Lite to mount it as a virtual CD-ROM on your PC. -
- Jim Hawkins, the protagonist and a rebellious teenager who dreams of adventure. -
- John Silver, the antagonist and a cyborg pirate who acts as Jim's mentor and friend. -
- Captain Amelia, the captain of the Royal Navy ship RLS Legacy and a feline alien. -
- Doctor Doppler, a friend of Jim's family and an astrophysicist who funds the expedition to Treasure Planet. -
- Morph, a shape-shifting creature who is Silver's pet and companion. -
- B.E.N., a robot who lives on Treasure Planet and has lost his memory. -
- Create an account on Netflix if you don't have one already. You can sign up up with your email, phone number, or social media account. You can also use a free trial if you are a new user. -
- Choose a subscription plan that suits your needs and budget. You can pay with your credit card, debit card, net banking, or UPI. You can also cancel your subscription anytime. -
- Log in to your Netflix account and search for Kota Factory Season 2. You can also browse the categories or genres to find it. -
- Select the episode that you want to watch and click on the play button. You can also adjust the video quality, audio language, subtitles, and playback speed according to your preference. -
- Enjoy watching Kota Factory Season 2 on Netflix! -
- Use a VPN service: A VPN (virtual private network) is a software that encrypts your internet traffic and hides your IP address and location. It allows you to access geo-restricted content and bypass censorship. You can use a VPN service to watch Kota Factory Season 2 on Netflix if it is not available in your region. However, you still need to have a Netflix subscription and a reliable VPN service that works with Netflix. -
- Use a video downloader tool: A video downloader tool is a software that allows you to download videos from various online platforms. You can use a video downloader tool to download Kota Factory Season 2 from Netflix if you have a subscription and an offline viewing option. However, you need to make sure that the video downloader tool is compatible with Netflix and does not violate its terms of service. -
- Use a torrent site: A torrent site is a website that hosts torrent files that contain metadata about files and folders that are distributed over a peer-to-peer network. You can use a torrent site to download Kota Factory Season 2 from other users who have uploaded it. However, you need to be careful about the legality, quality, and safety of the torrent files and the torrent site. You also need to have a torrent client and a VPN service to download torrents. -
- Is Kota Factory based on a true story? -
- Who are the main actors and characters of Kota Factory? -
- Mayur More as Vaibhav Pandey, a 16-year-old student who joins Maheshwari Classes in Kota to prepare for IIT-JEE. -
- Jitendra Kumar as Jeetu Bhaiya, a popular and charismatic teacher at Maheshwari Classes who mentors Vaibhav and his friends. -
- Ranjan Raj as Balmukund Meena, a loyal and optimistic friend of Vaibhav who hails from Rajasthan. -
- Alam Khan as Uday Gupta, a witty and sarcastic friend of Vaibhav who is good at maths. -
- Ahsaas Channa as Shivangi Ranawat, a smart and confident student at Maheshwari Classes who becomes Vaibhav's love interest. -
- Revathi Pillai as Vartika Ratawal, a studious and ambitious student at Maheshwari Classes who competes with Vaibhav. -
- Urvi Singh as Meenal Parekh, a rebellious and outspoken student at Maheshwari Classes who is friends with Shivangi and Vartika. -
- What is the meaning of Kota Factory? -
- What is the theme song of Kota Factory? -
- Will there be a Kota Factory Season 3? -
- Android 7.0 or higher -
- At least 4 GB of RAM -
- At least 2 GB of free storage space -
- A stable internet connection -
- Enable unknown sources on your device settings to allow installation from third-party sources -
- Disable any antivirus or security apps that might interfere with the installation process -
- Backup your data before installing the game in case something goes wrong -
- Go to APKCombo on your browser -
- Search for ARK: Survival Evolved in the search bar -
- Select the version that you want to download (the latest one is 2.0 a character and choosing a server to join. You can customize your character's appearance, gender, and name. You can also choose between different game modes, such as PvP (player versus player), PvE (player versus environment), or single-player. You can also adjust the difficulty level and the server settings to suit your preferences.
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Once you enter the game, you will find yourself on a beach with nothing but a pair of underwear and an implant on your arm. You will see a HUD (head-up display) that shows your health, stamina, hunger, thirst, oxygen, weight, and temperature. You will also see a map, a compass, and a hotbar that shows your equipped items. You can use the virtual joystick on the left side of the screen to move around, and the buttons on the right side of the screen to perform actions such as jumping, crouching, punching, picking up items, accessing inventory, crafting, and using items. You can also swipe the screen to look around and pinch to zoom in or out.
-The tips and tricks to survive and thrive in the game
-ARK: Survival Evolved is a game that challenges you to use your wits and resources to survive in a hostile world. Here are some tips and tricks that can help you along the way:
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- Gather resources as soon as possible. You will need them to craft essential items such as tools, weapons, clothing, and shelter. You can gather resources by punching trees, rocks, bushes, and corpses. You can also use tools such as axes, picks, spears, and knives to gather more efficiently. -
- Craft items as soon as possible. You will need them to protect yourself from the elements, predators, and other players. You can craft items by accessing your inventory and selecting the engrams (blueprints) that you have learned. You can learn new engrams by leveling up your character and spending points on them. Some of the items that you should craft early on are a stone hatchet, a stone pick, a spear, a torch, a campfire, a sleeping bag, and a thatch hut. -
- Level up your character as soon as possible. You will need to improve your stats and skills to survive longer and better. You can level up your character by gaining experience from various activities such as gathering resources, crafting items, killing creatures, and exploring the map. You can spend points on increasing your health, stamina, oxygen, food, water, weight, melee damage, movement speed, crafting speed, fortitude, or torpidity. You can also learn new engrams to craft more advanced items. -
- Tame creatures as soon as possible. You will need them to assist you in combat, transportation, and resource gathering. You can tame creatures by knocking them out with blunt weapons or tranquilizers, and feeding them their preferred food until they become loyal to you. You can also breed creatures to produce offspring with better stats and traits. Some of the creatures that you should tame early on are a dodo, a parasaur, a raptor, a trike, and a pteranodon. -
- Explore the map as soon as possible. You will need to discover new locations, resources, and secrets to progress in the game. You can explore the map by walking, swimming, flying, or riding on your tamed creatures. You can also use a raft or a boat to travel on water. You can find various points of interest on the map, such as caves, ruins, obelisks, beacons, and boss arenas. You can also encounter various events and challenges on the map, such as weather changes, natural disasters, supply drops, and enemy raids. -
The best features and modes of the game
-ARK: Survival Evolved is a game that offers you a lot of features and modes to enjoy. Here are some of the best ones:
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- The multiplayer mode: You can play online with other players from around the world on various servers. You can join or create a tribe with other players to cooperate or compete with other tribes. You can also chat, trade, and ally with other players. You can also participate in various events and activities on the servers, such as raids, wars, tournaments, and quests. -
- The single-player mode: You can play offline by yourself on your own private server. You can customize the server settings to your liking, such as the difficulty level, the spawn rates, the day and night cycle, and the weather effects. You can also pause and save the game at any time. You can also use cheats and commands to modify the game to your advantage. -
- The creative mode: You can play without any restrictions or limitations on your own private server. You can access all the items and engrams in the game without any requirements or costs. You can also spawn any creature or object in the game without any limits or consequences. You can also fly around and explore the map freely. You can use this mode to build amazing structures, create epic scenarios, or just have fun. -
Conclusion
-A summary of the main points and a call to action
-ARK: Survival Evolved is a game that lets you experience an amazing adventure in a prehistoric world full of dinosaurs and other creatures. You can download and install ARK: Survival Evolved APK all version on your Android device by following the steps we have shown you in this article. You can also play and enjoy ARK: Survival Evolved on your Android device by following the tips and tricks we have shared with you in this article. ARK: Survival Evolved is a game that will keep you entertained for hours with its stunning graphics, realistic physics, dynamic weather systems, and endless possibilities. So what are you waiting for? Download ARK: Survival Evolved APK all version now and start your journey!
-FAQs
-Here are some frequently asked questions about ARK: Survival Evolved APK all version:
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- Is ARK: Survival Evolved APK all version safe to download and install? -
- Is ARK: Survival Evolved APK all version free to play? -
- How can I update ARK: Survival Evolved APK all version? -
- How can I fix ARK: Survival Evolved APK all version not working or crashing? -
- Restart your device and launch the game again -
- Clear the cache and data of the game from your device settings -
- Reinstall the game from a trusted source -
- Check your internet connection and make sure it is stable and fast -
- Lower the graphics and sound settings of the game from the options menu -
- Contact the developer of the game for support or report a bug -
- How can I contact the developer of ARK: Survival Evolved APK all version? -
- Email: support@studiowildcard.com -
- Website: https://www.playark.com/ -
- Facebook: https://www.facebook.com/playark/ -
- Twitter: https://twitter.com/survivetheark -
- Discord: https://discord.gg/playark -
Yes, ARK: Survival Evolved APK all version is safe to download and install as long as you get it from a trusted source such as APKCombo or Google Play Store. However, you should always be careful when downloading and installing any app from third-party sources, as they might contain malware or viruses that could harm your device.
-Yes, ARK: Survival Evolved APK all version is free to play on your Android device. However, the game does offer some in-app purchases that can enhance your gameplay experience, such as premium currency (amber), skins, items, and expansions. You can also watch ads to earn some free amber or use a modded version of the game to get unlimited amber. However, we do not recommend using any modded or hacked version of the game, as they might cause errors, crashes, or bans.
-You can update ARK: Survival Evolved APK all version by following the same steps that you used to download and install it. You can check for updates on APKCombo or Google Play Store and download and install the latest version of the game. You can also enable automatic updates on your device settings to get the updates as soon as they are available.
-If you encounter any problems with ARK: Survival Evolved APK all version, such as not working, crashing, freezing, or lagging, you can try some of these solutions:
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Bed Wars MOD APK 2023 Enjoy the Ultimate Blockman GO Experience.md b/spaces/1phancelerku/anime-remove-background/Bed Wars MOD APK 2023 Enjoy the Ultimate Blockman GO Experience.md deleted file mode 100644 index 1283d7c3e9f86c965f6e1287ddf910fd4bf5c01a..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Bed Wars MOD APK 2023 Enjoy the Ultimate Blockman GO Experience.md +++ /dev/null @@ -1,103 +0,0 @@ - -Bed Wars MOD APK New Version: Everything You Need to Know
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- Select a mode and a map. There are different modes such as solo, duo, squad, and rush. There are also different maps such as castle, pirate ship, sky island, and more. -
- Join a team. You can either create your own team or join an existing one. You can also play solo if you prefer. -
- Protect your bed. Your bed is your respawn point. If your bed is destroyed, you will not be able to respawn if you die. You can use various materials such as wool, wood, stone, iron, and gold to build defenses around your bed. -
- Collect resources. You can collect resources from the generators in your base or in the middle of the map. There are four types of resources: iron, gold, diamond, and emerald. You can use them to buy items from the shop such as blocks, weapons, armor, tools, potions, and more. -
- Destroy other beds. You can use tools such as pickaxes, axes, shears, and TNT to break through the defenses of other beds. You can also use bows, fireballs, ender pearls, and other items to attack from a distance. -
- Eliminate other players. You can use swords, axes, bows, fireballs, potions, and other items to fight other players. You can also knock them off the map or into the void. -
- Win the game. The last team or player standing wins the game. -
What is Bed Wars MOD APK?
-Bed Wars MOD APK is a modified version of the original game that gives you some extra features and advantages that are not available in the official version. It is a free and safe way to enhance your gaming experience and have more fun.
-A modified version of the original game
-Bed Wars MOD APK is not developed by Blockman GO Studio but by independent developers who modify the original game files to create a new version. This means that it is not available on the Google Play Store or the App Store but on third-party websites. You have to download it from a reliable source and install it manually on your device.
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-There are many benefits of using Bed Wars MOD APK over the official version. Here are some of them:
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-Another benefit of using Bed Wars MOD APK is that you get unlimited health and aimbot, which are the in-game features. You can use them to survive longer and kill faster in the game. You don't have to worry about losing health or missing shots ever again. You can also use them to troll other players and make them rage quit.
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The precautions to take
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- Upgrade your generators as soon as possible. This will help you get more resources faster and buy better items. -
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Download Rope Hero Mafia City Wars and Use Your Superpowers to Fight Crime.md b/spaces/1phancelerku/anime-remove-background/Download Rope Hero Mafia City Wars and Use Your Superpowers to Fight Crime.md deleted file mode 100644 index 36ee7cd9d43ad19a3c0fb90201c75bcb6269d240..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Download Rope Hero Mafia City Wars and Use Your Superpowers to Fight Crime.md +++ /dev/null @@ -1,207 +0,0 @@ -
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- Go to Settings on your device. -
- Tap on Security or Privacy. -
- Find and enable Unknown Sources or Install Unknown Apps. -
- Confirm your choice by tapping OK or Allow. -
After enabling unknown sources, you can download the game from any website that offers APK files. Here are some of the websites that offer APK files for the rope hero game:
--
-
- [APKPure] -
- [APKMonk] -
- [APKMirror] -
Here are the steps to download the game from these websites:
--
-
- Open any of these websites on your browser. -
- Search for "rope hero" in the search bar. -
- Select the game with the blue icon and the name "Rope Hero: Vice Town". -
- Tap on "Download APK" and wait for the file to download on your device. -
- Open the file and tap on "Install" and wait for the game to install on your device. -
- Tap on "Open" and enjoy playing the game. -
iOS Devices
-If you have an iOS device, you can download the game from App Store or from other sources. Here are the steps to download the game from App Store:
--
-
- Open App Store on your device. -
- Search for "rope hero" in the search bar. -
- Select the game with the blue icon and the name "Rope Hero: Vice Town". -
- Tap on "Get" and wait for the game to download and install on your device. -
- Tap on "Open" and enjoy playing the game. -
If you want to download the game from other sources, you need to use a third-party app installer such as TutuApp or AppValley. Here are some of the app installers that offer IPA files for the rope hero game:
--
-
- [TutuApp] -
- [AppValley] -
Here are the steps to download the game from these app installers:
--
-
- Open any of these app installers on your browser. -
- Search for "rope hero" in the search bar. -
- Select the game with the blue icon and the name "Rope Hero: Vice Town".Tap on "Install" and wait for the game to download and install on your device. -
- Tap on "Open" and enjoy playing the game. -
Note: You may need to trust the app installer on your device before you can use it. To do that, go to Settings > General > Profiles & Device Management and tap on the app installer's profile. Then, tap on "Trust" and confirm your choice.
-PC or Mac Devices
-If you have a PC or Mac device, you can download the game from BlueStacks or other emulators. BlueStacks is a software that allows you to run Android apps on your PC or Mac. Here are the steps to download the game from BlueStacks:
--
-
- Download and install BlueStacks on your PC or Mac from [here](https://www.bluestacks.com/). -
- Open BlueStacks and sign in with your Google account. -
- Search for "rope hero" in the search bar. -
- Select the game with the blue icon and the name "Rope Hero: Vice Town". -
- Click on "Install" and wait for the game to download and install on your device. -
- Click on "Open" and enjoy playing the game. -
If you want to use other emulators, you can check out this [list](https://www.androidauthority.com/best-android-emulators-for-pc-655308/) of the best Android emulators for PC and Mac.
-How to Play the Rope Hero Game?
-Now that you have downloaded the rope hero game on your device, you are ready to play it. The game is easy to play, but it also has some challenges and surprises that make it more interesting. Here is how to play the rope hero game:
-The game has a basic tutorial that teaches you how to use your super powers, fight enemies, and complete quests. You can access the tutorial by tapping on the question mark icon on the top left corner of the screen. You can also skip the tutorial if you want to learn by yourself.
-The game has a simple control scheme that allows you to move, jump, swing, shoot, and interact with objects. You can use the virtual joystick on the left side of the screen to move your character. You can use the buttons on the right side of the screen to perform different actions. Here are some of the buttons and their functions:
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-
- The jump button lets you jump over obstacles and perform mega jumps. -
- The rope button lets you shoot your super rope and swing from one place to another. -
- The fire button lets you shoot your weapon or use your gravity gun. -
- The aim button lets you aim your weapon or gravity gun more precisely. -
- The reload button lets you reload your weapon or gravity gun. -
- The action button lets you interact with objects, vehicles, people, and animals. -
The game has a map that shows you where you are, where your enemies are, and where your quests are. You can access the map by tapping on the map icon on the top right corner of the screen. You can also zoom in and out of the map by pinching the screen.
-The game has a menu that lets you customize your character, check your inventory, view your skills, change your settings, and more. You can access the menu by tapping on the menu icon on the top left corner of the screen. Here are some of the options in the menu:
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-
- The character option lets you choose your weapons, accessories, and vehicles. -
- The inventory option lets you see what items you have collected or bought. -
- The skills option lets you see what skills you have learned or upgraded. -
- The settings option lets you adjust the sound, graphics, controls, and language of the game. -
- The help option lets you see some tips and tricks for playing the game better. -
The game has a lot of quests and challenges that you can complete to earn money, experience, and rewards. You can find quests by looking for yellow exclamation marks on the map or by talking to people with yellow speech bubbles above their heads. You can find challenges by looking for red skulls on the map or by participating in mini-games such as gangster shootouts, car races, arena battles, ATM hacking, and more. You can also create your own quests and challenges by using your imagination and creativity.
How to Use Your Super Powers?
-One of the best things about the rope hero game is that you can use your super powers to do amazing things. Here are some of the super powers that you have and how to use them:
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- Your super rope lets you swing from one building to another like a spider. You can use it to travel faster, escape from enemies, or reach high places. To use your super rope, tap on the rope button and aim at a building or object. Then, tap on the rope button again to release the rope and swing. -
- Your mega jumps let you leap over obstacles and perform power landings. You can use them to jump over cars, walls, or enemies, or to land with a shockwave that damages nearby enemies. To use your mega jumps, tap on the jump button and hold it until the power meter is full. Then, release the jump button and watch your character soar. -
- Your gravity gun lets you manipulate objects and enemies with a powerful force. You can use it to grab, throw, or crush objects or enemies, or to create explosions or implosions. To use your gravity gun, tap on the fire button and aim at an object or enemy. Then, tap on the fire button again to release the object or enemy. -
These are just some of the super powers that you have in the game. You can also unlock more super powers as you level up and upgrade your skills. You can also combine your super powers with your weapons and vehicles to create more awesome effects.
-How to Complete Quests and Challenges?
-The rope hero game has a lot of quests and challenges that you can complete to earn money, experience, and rewards. Quests are missions that have a story and a goal. Challenges are activities that have a time limit or a score limit. Here are some of the quests and challenges that you can find in the game:
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-
- Gangster Shootouts: These are quests where you have to fight against gangsters who are attacking you or other people. You can use your super powers, weapons, or vehicles to defeat them. You can also use the environment to your advantage by swinging, jumping, or throwing objects at them. -
- Car Races: These are challenges where you have to race against other drivers on the streets. You can use your super powers, weapons, or vehicles to win the race. You can also use shortcuts, ramps, or obstacles to gain an edge over your opponents. -
- Arena Battles: These are challenges where you have to fight against other super heroes in an arena. You can use your super powers, weapons, or vehicles to beat them. You can also use traps, power-ups, or spectators to help you. -
- ATM Hacking: These are quests where you have to hack into ATMs and steal money from them. You can use your super powers, weapons, or vehicles to hack into them. You can also use stealth, distraction, or disguise to avoid detection. -
- And more: There are many more quests and challenges that you can find in the game. You can also create your own quests and challenges by using your imagination and creativity. -
To complete quests and challenges, you have to follow the instructions and objectives that are given to you. You can also check your progress and status by tapping on the quest icon or challenge icon on the top right corner of the screen.
-How to Customize Your Character and Vehicles?
-The rope hero game also lets you customize your character and vehicles with different weapons, accessories, and colors. You can make your character look cool, funny, or scary with different outfits, masks, hats, glasses, and more. You can also make your vehicles look fast, furious, or fancy with different models, parts, stickers, and more.
-To customize your character and vehicles, you have to go to the shop or garage in the game. You can find them by looking for green dollar signs on the map or by tapping on the menu icon and selecting the character option or the inventory option.
-In the shop or garage, you can buy new weapons, accessories, and vehicles with money that you earn from completing quests and challenges. You can also sell old weapons, accessories, and vehicles that you don't need anymore.
-In the shop or garage, you can also change the color of your weapons, accessories and vehicles with different colors that you like. You can also preview how your character and vehicles will look like before you buy or change them.
-To customize your character and vehicles, you have to tap on the item that you want to buy or change and then tap on the buy button or the color button. You can also tap on the preview button to see how your character and vehicles will look like.
-Customizing your character and vehicles is not only fun, but also useful. Different weapons, accessories, and vehicles have different stats and effects that can help you in your quests and challenges. For example, some weapons have more damage, some accessories have more defense, and some vehicles have more speed. You can check the stats and effects of each item by tapping on the info button.
-Tips and Tricks for Playing the Rope Hero Game
-The rope hero game is easy to play, but it also has some tips and tricks that can help you play better and have more fun. Here are some of the tips and tricks that we recommend:
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-
- Use your super powers wisely. Your super powers are awesome, but they also have a cooldown time that prevents you from using them too often. You can see the cooldown time by looking at the blue bar below each button. You can also reduce the cooldown time by upgrading your skills in the menu. -
- Save money and resources. Money and resources are important in the game, as they allow you to buy new weapons, accessories, and vehicles, as well as upgrade your skills. You can save money and resources by completing quests and challenges, finding hidden collectibles, avoiding unnecessary expenses, and selling old items. -
- Level up faster. Leveling up is important in the game, as it allows you to unlock new super powers, skills, weapons, accessories, and vehicles. You can level up faster by gaining more experience from completing quests and challenges, fighting enemies, using your super powers, and participating in mini-games. -
- Have more fun. The game is meant to be fun and entertaining, so don't be afraid to use your creativity, imagination, and humor to enjoy the game more. You can do things like swinging from a helicopter, jumping from a skyscraper, throwing a car at a gangster, or wearing a funny outfit. -
Conclusion
-The rope hero game is one of the best superhero games that you can play on your device. It lets you become a super rope hero in a vice city full of crime and chaos. You can use your super powers to help the police and free the city from gangsters, or you can become a villain and cause mayhem and destruction. The choice is yours.
-The game has amazing graphics, realistic physics, dynamic gameplay, humorous dialogue, high replay value, and a lot of fun and excitement. You can also customize your character and vehicles with different weapons, accessories, and colors. You can also complete quests and challenges to earn money, experience, and rewards. You can also create your own quests and challenges by using your imagination and creativity.
-If you are looking for a game that lets you swing like a spider, fight like a ninja, and drive like a racer, you should download the rope hero game now. It is free to download on different devices. You will not regret it.
-FAQs
-Here are some of the frequently asked questions about the rope hero game:
--
-
- Q: Is the rope hero game safe to download? -
- A: Yes, the rope hero game is safe to download from the official app stores or from other sources that offer APK or IPA files. However, you should always be careful when downloading apps from unknown sources and check for viruses or malware before installing them. -
- Q: Is the rope hero game online or offline? -
- A: The rope hero game is mostly offline, meaning that you can play it without an internet connection. However, some features of the game may require an internet connection such as watching ads to get free rewards or accessing online leaderboards. -
- Q: How do I update the rope hero game? -
- A: The rope hero game updates automatically when there is a new version available. However, you can also check for updates manually by going to the app store or website where you downloaded the game and tapping on the update button. -
- Q: How do I contact the developers of the rope hero game? -
- A: You can contact the developers of the rope hero game by sending them an email at support@naxeex.com or by visiting their website at https://naxeex.com/. -
- Q: How do I share my feedback or suggestions for the rope hero game? -
- A: You can share your feedback or suggestions for the rope hero game by leaving a review or rating on the app store or website where you downloaded the game. You can also join the rope hero game community on social media platforms such as Facebook, Twitter, Instagram, or YouTube and share your feedback or suggestions there. -
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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/FNaF x Brawl Stars Download the APK and Join the Fun.md b/spaces/1phancelerku/anime-remove-background/FNaF x Brawl Stars Download the APK and Join the Fun.md deleted file mode 100644 index 005c7b6f3c7593e210aaf88ad4c3923b85a67504..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/FNaF x Brawl Stars Download the APK and Join the Fun.md +++ /dev/null @@ -1,125 +0,0 @@ -
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How to Download and Install FNAF Brawl Stars APK
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- Go to [Game Jolt](^1^) and search for FNAF Brawl Stars. -
- Select the game and click on Download. You will get a file named Fnaf_stars.apk. -
- Before installing the file, you need to enable Unknown Sources on your device. Go to Settings > Security > Unknown Sources and toggle it on. -
- Locate the Fnaf_stars.apk file on your device and tap on it to install it. -
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How to Play FNAF Brawl Stars APK
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FNAF Brawl Stars APK Review
Pros and Cons of FNAF Brawl Stars APK -FNAF Brawl Stars APK is a game that has both advantages and disadvantages. Here are some of them:
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-FNAF Brawl Stars APK has received mostly positive ratings and feedback from the users who have played it. Here are some of the comments from the Game Jolt page:
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-FAQs
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- A: FNAF Brawl Stars APK is safe to download and play as long as you get it from a trusted source, such as Game Jolt. However, you should always be careful when downloading files from unknown sources and scan them for viruses or malware before installing them. -
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\ No newline at end of file diff --git a/spaces/232labs/VToonify/vtoonify/model/bisenet/resnet.py b/spaces/232labs/VToonify/vtoonify/model/bisenet/resnet.py deleted file mode 100644 index aa2bf95130e9815ba378cb6f73207068b81a04b9..0000000000000000000000000000000000000000 --- a/spaces/232labs/VToonify/vtoonify/model/bisenet/resnet.py +++ /dev/null @@ -1,109 +0,0 @@ -#!/usr/bin/python -# -*- encoding: utf-8 -*- - -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.utils.model_zoo as modelzoo - -# from modules.bn import InPlaceABNSync as BatchNorm2d - -resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth' - - -def conv3x3(in_planes, out_planes, stride=1): - """3x3 convolution with padding""" - return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, - padding=1, bias=False) - - -class BasicBlock(nn.Module): - def __init__(self, in_chan, out_chan, stride=1): - super(BasicBlock, self).__init__() - self.conv1 = conv3x3(in_chan, out_chan, stride) - self.bn1 = nn.BatchNorm2d(out_chan) - self.conv2 = conv3x3(out_chan, out_chan) - self.bn2 = nn.BatchNorm2d(out_chan) - self.relu = nn.ReLU(inplace=True) - self.downsample = None - if in_chan != out_chan or stride != 1: - self.downsample = nn.Sequential( - nn.Conv2d(in_chan, out_chan, - kernel_size=1, stride=stride, bias=False), - nn.BatchNorm2d(out_chan), - ) - - def forward(self, x): - residual = self.conv1(x) - residual = F.relu(self.bn1(residual)) - residual = self.conv2(residual) - residual = self.bn2(residual) - - shortcut = x - if self.downsample is not None: - shortcut = self.downsample(x) - - out = shortcut + residual - out = self.relu(out) - return out - - -def create_layer_basic(in_chan, out_chan, bnum, stride=1): - layers = [BasicBlock(in_chan, out_chan, stride=stride)] - for i in range(bnum-1): - layers.append(BasicBlock(out_chan, out_chan, stride=1)) - return nn.Sequential(*layers) - - -class Resnet18(nn.Module): - def __init__(self): - super(Resnet18, self).__init__() - self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, - bias=False) - self.bn1 = nn.BatchNorm2d(64) - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) - self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1) - self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2) - self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2) - self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2) - self.init_weight() - - def forward(self, x): - x = self.conv1(x) - x = F.relu(self.bn1(x)) - x = self.maxpool(x) - - x = self.layer1(x) - feat8 = self.layer2(x) # 1/8 - feat16 = self.layer3(feat8) # 1/16 - feat32 = self.layer4(feat16) # 1/32 - return feat8, feat16, feat32 - - def init_weight(self): - state_dict = modelzoo.load_url(resnet18_url) - self_state_dict = self.state_dict() - for k, v in state_dict.items(): - if 'fc' in k: continue - self_state_dict.update({k: v}) - self.load_state_dict(self_state_dict) - - def get_params(self): - wd_params, nowd_params = [], [] - for name, module in self.named_modules(): - if isinstance(module, (nn.Linear, nn.Conv2d)): - wd_params.append(module.weight) - if not module.bias is None: - nowd_params.append(module.bias) - elif isinstance(module, nn.BatchNorm2d): - nowd_params += list(module.parameters()) - return wd_params, nowd_params - - -if __name__ == "__main__": - net = Resnet18() - x = torch.randn(16, 3, 224, 224) - out = net(x) - print(out[0].size()) - print(out[1].size()) - print(out[2].size()) - net.get_params() diff --git a/spaces/44ov41za8i/FreeVC/speaker_encoder/preprocess.py b/spaces/44ov41za8i/FreeVC/speaker_encoder/preprocess.py deleted file mode 100644 index fe5ab25ef7cb4adeb76cad11962f179d6a38edcc..0000000000000000000000000000000000000000 --- a/spaces/44ov41za8i/FreeVC/speaker_encoder/preprocess.py +++ /dev/null @@ -1,285 +0,0 @@ -from multiprocess.pool import ThreadPool -from speaker_encoder.params_data import * -from speaker_encoder.config import librispeech_datasets, anglophone_nationalites -from datetime import datetime -from speaker_encoder import audio -from pathlib import Path -from tqdm import tqdm -import numpy as np - - -class DatasetLog: - """ - Registers metadata about the dataset in a text file. - """ - def __init__(self, root, name): - self.text_file = open(Path(root, "Log_%s.txt" % name.replace("/", "_")), "w") - self.sample_data = dict() - - start_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M")) - self.write_line("Creating dataset %s on %s" % (name, start_time)) - self.write_line("-----") - self._log_params() - - def _log_params(self): - from speaker_encoder import params_data - self.write_line("Parameter values:") - for param_name in (p for p in dir(params_data) if not p.startswith("__")): - value = getattr(params_data, param_name) - self.write_line("\t%s: %s" % (param_name, value)) - self.write_line("-----") - - def write_line(self, line): - self.text_file.write("%s\n" % line) - - def add_sample(self, **kwargs): - for param_name, value in kwargs.items(): - if not param_name in self.sample_data: - self.sample_data[param_name] = [] - self.sample_data[param_name].append(value) - - def finalize(self): - self.write_line("Statistics:") - for param_name, values in self.sample_data.items(): - self.write_line("\t%s:" % param_name) - self.write_line("\t\tmin %.3f, max %.3f" % (np.min(values), np.max(values))) - self.write_line("\t\tmean %.3f, median %.3f" % (np.mean(values), np.median(values))) - self.write_line("-----") - end_time = str(datetime.now().strftime("%A %d %B %Y at %H:%M")) - self.write_line("Finished on %s" % end_time) - self.text_file.close() - - -def _init_preprocess_dataset(dataset_name, datasets_root, out_dir) -> (Path, DatasetLog): - dataset_root = datasets_root.joinpath(dataset_name) - if not dataset_root.exists(): - print("Couldn\'t find %s, skipping this dataset." % dataset_root) - return None, None - return dataset_root, DatasetLog(out_dir, dataset_name) - - -def _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, extension, - skip_existing, logger): - print("%s: Preprocessing data for %d speakers." % (dataset_name, len(speaker_dirs))) - - # Function to preprocess utterances for one speaker - def preprocess_speaker(speaker_dir: Path): - # Give a name to the speaker that includes its dataset - speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts) - - # Create an output directory with that name, as well as a txt file containing a - # reference to each source file. - speaker_out_dir = out_dir.joinpath(speaker_name) - speaker_out_dir.mkdir(exist_ok=True) - sources_fpath = speaker_out_dir.joinpath("_sources.txt") - - # There's a possibility that the preprocessing was interrupted earlier, check if - # there already is a sources file. - if sources_fpath.exists(): - try: - with sources_fpath.open("r") as sources_file: - existing_fnames = {line.split(",")[0] for line in sources_file} - except: - existing_fnames = {} - else: - existing_fnames = {} - - # Gather all audio files for that speaker recursively - sources_file = sources_fpath.open("a" if skip_existing else "w") - for in_fpath in speaker_dir.glob("**/*.%s" % extension): - # Check if the target output file already exists - out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts) - out_fname = out_fname.replace(".%s" % extension, ".npy") - if skip_existing and out_fname in existing_fnames: - continue - - # Load and preprocess the waveform - wav = audio.preprocess_wav(in_fpath) - if len(wav) == 0: - continue - - # Create the mel spectrogram, discard those that are too short - frames = audio.wav_to_mel_spectrogram(wav) - if len(frames) < partials_n_frames: - continue - - out_fpath = speaker_out_dir.joinpath(out_fname) - np.save(out_fpath, frames) - logger.add_sample(duration=len(wav) / sampling_rate) - sources_file.write("%s,%s\n" % (out_fname, in_fpath)) - - sources_file.close() - - # Process the utterances for each speaker - with ThreadPool(8) as pool: - list(tqdm(pool.imap(preprocess_speaker, speaker_dirs), dataset_name, len(speaker_dirs), - unit="speakers")) - logger.finalize() - print("Done preprocessing %s.\n" % dataset_name) - - -# Function to preprocess utterances for one speaker -def __preprocess_speaker(speaker_dir: Path, datasets_root: Path, out_dir: Path, extension: str, skip_existing: bool): - # Give a name to the speaker that includes its dataset - speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts) - - # Create an output directory with that name, as well as a txt file containing a - # reference to each source file. - speaker_out_dir = out_dir.joinpath(speaker_name) - speaker_out_dir.mkdir(exist_ok=True) - sources_fpath = speaker_out_dir.joinpath("_sources.txt") - - # There's a possibility that the preprocessing was interrupted earlier, check if - # there already is a sources file. - # if sources_fpath.exists(): - # try: - # with sources_fpath.open("r") as sources_file: - # existing_fnames = {line.split(",")[0] for line in sources_file} - # except: - # existing_fnames = {} - # else: - # existing_fnames = {} - existing_fnames = {} - # Gather all audio files for that speaker recursively - sources_file = sources_fpath.open("a" if skip_existing else "w") - - for in_fpath in speaker_dir.glob("**/*.%s" % extension): - # Check if the target output file already exists - out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts) - out_fname = out_fname.replace(".%s" % extension, ".npy") - if skip_existing and out_fname in existing_fnames: - continue - - # Load and preprocess the waveform - wav = audio.preprocess_wav(in_fpath) - if len(wav) == 0: - continue - - # Create the mel spectrogram, discard those that are too short - frames = audio.wav_to_mel_spectrogram(wav) - if len(frames) < partials_n_frames: - continue - - out_fpath = speaker_out_dir.joinpath(out_fname) - np.save(out_fpath, frames) - # logger.add_sample(duration=len(wav) / sampling_rate) - sources_file.write("%s,%s\n" % (out_fname, in_fpath)) - - sources_file.close() - return len(wav) - -def _preprocess_speaker_dirs_vox2(speaker_dirs, dataset_name, datasets_root, out_dir, extension, - skip_existing, logger): - # from multiprocessing import Pool, cpu_count - from pathos.multiprocessing import ProcessingPool as Pool - # Function to preprocess utterances for one speaker - def __preprocess_speaker(speaker_dir: Path): - # Give a name to the speaker that includes its dataset - speaker_name = "_".join(speaker_dir.relative_to(datasets_root).parts) - - # Create an output directory with that name, as well as a txt file containing a - # reference to each source file. - speaker_out_dir = out_dir.joinpath(speaker_name) - speaker_out_dir.mkdir(exist_ok=True) - sources_fpath = speaker_out_dir.joinpath("_sources.txt") - - existing_fnames = {} - # Gather all audio files for that speaker recursively - sources_file = sources_fpath.open("a" if skip_existing else "w") - wav_lens = [] - for in_fpath in speaker_dir.glob("**/*.%s" % extension): - # Check if the target output file already exists - out_fname = "_".join(in_fpath.relative_to(speaker_dir).parts) - out_fname = out_fname.replace(".%s" % extension, ".npy") - if skip_existing and out_fname in existing_fnames: - continue - - # Load and preprocess the waveform - wav = audio.preprocess_wav(in_fpath) - if len(wav) == 0: - continue - - # Create the mel spectrogram, discard those that are too short - frames = audio.wav_to_mel_spectrogram(wav) - if len(frames) < partials_n_frames: - continue - - out_fpath = speaker_out_dir.joinpath(out_fname) - np.save(out_fpath, frames) - # logger.add_sample(duration=len(wav) / sampling_rate) - sources_file.write("%s,%s\n" % (out_fname, in_fpath)) - wav_lens.append(len(wav)) - sources_file.close() - return wav_lens - - print("%s: Preprocessing data for %d speakers." % (dataset_name, len(speaker_dirs))) - # Process the utterances for each speaker - # with ThreadPool(8) as pool: - # list(tqdm(pool.imap(preprocess_speaker, speaker_dirs), dataset_name, len(speaker_dirs), - # unit="speakers")) - pool = Pool(processes=20) - for i, wav_lens in enumerate(pool.map(__preprocess_speaker, speaker_dirs), 1): - for wav_len in wav_lens: - logger.add_sample(duration=wav_len / sampling_rate) - print(f'{i}/{len(speaker_dirs)} \r') - - logger.finalize() - print("Done preprocessing %s.\n" % dataset_name) - - -def preprocess_librispeech(datasets_root: Path, out_dir: Path, skip_existing=False): - for dataset_name in librispeech_datasets["train"]["other"]: - # Initialize the preprocessing - dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir) - if not dataset_root: - return - - # Preprocess all speakers - speaker_dirs = list(dataset_root.glob("*")) - _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "flac", - skip_existing, logger) - - -def preprocess_voxceleb1(datasets_root: Path, out_dir: Path, skip_existing=False): - # Initialize the preprocessing - dataset_name = "VoxCeleb1" - dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir) - if not dataset_root: - return - - # Get the contents of the meta file - with dataset_root.joinpath("vox1_meta.csv").open("r") as metafile: - metadata = [line.split("\t") for line in metafile][1:] - - # Select the ID and the nationality, filter out non-anglophone speakers - nationalities = {line[0]: line[3] for line in metadata} - # keep_speaker_ids = [speaker_id for speaker_id, nationality in nationalities.items() if - # nationality.lower() in anglophone_nationalites] - keep_speaker_ids = [speaker_id for speaker_id, nationality in nationalities.items()] - print("VoxCeleb1: using samples from %d (presumed anglophone) speakers out of %d." % - (len(keep_speaker_ids), len(nationalities))) - - # Get the speaker directories for anglophone speakers only - speaker_dirs = dataset_root.joinpath("wav").glob("*") - speaker_dirs = [speaker_dir for speaker_dir in speaker_dirs if - speaker_dir.name in keep_speaker_ids] - print("VoxCeleb1: found %d anglophone speakers on the disk, %d missing (this is normal)." % - (len(speaker_dirs), len(keep_speaker_ids) - len(speaker_dirs))) - - # Preprocess all speakers - _preprocess_speaker_dirs(speaker_dirs, dataset_name, datasets_root, out_dir, "wav", - skip_existing, logger) - - -def preprocess_voxceleb2(datasets_root: Path, out_dir: Path, skip_existing=False): - # Initialize the preprocessing - dataset_name = "VoxCeleb2" - dataset_root, logger = _init_preprocess_dataset(dataset_name, datasets_root, out_dir) - if not dataset_root: - return - - # Get the speaker directories - # Preprocess all speakers - speaker_dirs = list(dataset_root.joinpath("dev", "aac").glob("*")) - _preprocess_speaker_dirs_vox2(speaker_dirs, dataset_name, datasets_root, out_dir, "m4a", - skip_existing, logger) diff --git a/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/layers_123821KB.py b/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/layers_123821KB.py deleted file mode 100644 index 4fc1b5cb85a3327f60cbb9f5deffbeeaaac516ad..0000000000000000000000000000000000000000 --- a/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/layers_123821KB.py +++ /dev/null @@ -1,118 +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), 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.bottleneck = nn.Sequential( - Conv2DBNActiv(nin * 5, 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) - out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1) - bottle = self.bottleneck(out) - return bottle diff --git a/spaces/801artistry/RVC801/infer/modules/train/extract_feature_print.py b/spaces/801artistry/RVC801/infer/modules/train/extract_feature_print.py deleted file mode 100644 index f771dd9b8ba92262e6844e7b5781de43c342833a..0000000000000000000000000000000000000000 --- a/spaces/801artistry/RVC801/infer/modules/train/extract_feature_print.py +++ /dev/null @@ -1,137 +0,0 @@ -import os -import sys -import traceback - -os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" -os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0" - -device = sys.argv[1] -n_part = int(sys.argv[2]) -i_part = int(sys.argv[3]) -if len(sys.argv) == 6: - exp_dir = sys.argv[4] - version = sys.argv[5] -else: - i_gpu = sys.argv[4] - exp_dir = sys.argv[5] - os.environ["CUDA_VISIBLE_DEVICES"] = str(i_gpu) - version = sys.argv[6] -import fairseq -import numpy as np -import soundfile as sf -import torch -import torch.nn.functional as F - -if "privateuseone" not in device: - device = "cpu" - if torch.cuda.is_available(): - device = "cuda" - elif torch.backends.mps.is_available(): - device = "mps" -else: - import torch_directml - - device = torch_directml.device(torch_directml.default_device()) - - def forward_dml(ctx, x, scale): - ctx.scale = scale - res = x.clone().detach() - return res - - fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml - -f = open("%s/extract_f0_feature.log" % exp_dir, "a+") - - -def printt(strr): - print(strr) - f.write("%s\n" % strr) - f.flush() - - -printt(sys.argv) -model_path = "assets/hubert/hubert_base.pt" - -printt(exp_dir) -wavPath = "%s/1_16k_wavs" % exp_dir -outPath = ( - "%s/3_feature256" % exp_dir if version == "v1" else "%s/3_feature768" % exp_dir -) -os.makedirs(outPath, exist_ok=True) - - -# wave must be 16k, hop_size=320 -def readwave(wav_path, normalize=False): - wav, sr = sf.read(wav_path) - assert sr == 16000 - feats = torch.from_numpy(wav).float() - if feats.dim() == 2: # double channels - feats = feats.mean(-1) - assert feats.dim() == 1, feats.dim() - if normalize: - with torch.no_grad(): - feats = F.layer_norm(feats, feats.shape) - feats = feats.view(1, -1) - return feats - - -# HuBERT model -printt("load model(s) from {}".format(model_path)) -# if hubert model is exist -if os.access(model_path, os.F_OK) == False: - printt( - "Error: Extracting is shut down because %s does not exist, you may download it from https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main" - % model_path - ) - exit(0) -models, saved_cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task( - [model_path], - suffix="", -) -model = models[0] -model = model.to(device) -printt("move model to %s" % device) -if device not in ["mps", "cpu"]: - model = model.half() -model.eval() - -todo = sorted(list(os.listdir(wavPath)))[i_part::n_part] -n = max(1, len(todo) // 10) # 最多打印十条 -if len(todo) == 0: - printt("no-feature-todo") -else: - printt("all-feature-%s" % len(todo)) - for idx, file in enumerate(todo): - try: - if file.endswith(".wav"): - wav_path = "%s/%s" % (wavPath, file) - out_path = "%s/%s" % (outPath, file.replace("wav", "npy")) - - if os.path.exists(out_path): - continue - - feats = readwave(wav_path, normalize=saved_cfg.task.normalize) - padding_mask = torch.BoolTensor(feats.shape).fill_(False) - inputs = { - "source": feats.half().to(device) - if device not in ["mps", "cpu"] - else feats.to(device), - "padding_mask": padding_mask.to(device), - "output_layer": 9 if version == "v1" else 12, # layer 9 - } - with torch.no_grad(): - logits = model.extract_features(**inputs) - feats = ( - model.final_proj(logits[0]) if version == "v1" else logits[0] - ) - - feats = feats.squeeze(0).float().cpu().numpy() - if np.isnan(feats).sum() == 0: - np.save(out_path, feats, allow_pickle=False) - else: - printt("%s-contains nan" % file) - if idx % n == 0: - printt("now-%s,all-%s,%s,%s" % (len(todo), idx, file, feats.shape)) - except: - printt(traceback.format_exc()) - printt("all-feature-done") diff --git a/spaces/A00001/bingothoo/src/lib/bots/bing/index.ts b/spaces/A00001/bingothoo/src/lib/bots/bing/index.ts deleted file mode 100644 index 6fd51ba48cbb1148f13d29e76960c092b807cfae..0000000000000000000000000000000000000000 --- a/spaces/A00001/bingothoo/src/lib/bots/bing/index.ts +++ /dev/null @@ -1,426 +0,0 @@ -import { fetch, WebSocket, debug } from '@/lib/isomorphic' -import WebSocketAsPromised from 'websocket-as-promised' -import { - SendMessageParams, - BingConversationStyle, - ConversationResponse, - ChatResponseMessage, - ConversationInfo, - InvocationEventType, - ChatError, - ErrorCode, - ChatUpdateCompleteResponse, - ImageInfo, - KBlobResponse -} from './types' - -import { convertMessageToMarkdown, websocketUtils, streamAsyncIterable } from './utils' -import { WatchDog, createChunkDecoder } from '@/lib/utils' - -type Params = SendMessageParams<{ bingConversationStyle: BingConversationStyle }> - -const OPTIONS_SETS = [ - 'nlu_direct_response_filter', - 'deepleo', - 'disable_emoji_spoken_text', - 'responsible_ai_policy_235', - 'enablemm', - 'iycapbing', - 'iyxapbing', - 'objopinion', - 'rweasgv2', - 'dagslnv1', - 'dv3sugg', - 'autosave', - 'iyoloxap', - 'iyoloneutral', - 'clgalileo', - 'gencontentv3', -] - -export class BingWebBot { - protected conversationContext?: ConversationInfo - protected cookie: string - protected ua: string - protected endpoint = '' - private lastText = '' - private asyncTasks: Array{ - const headers = { - 'Accept-Encoding': 'gzip, deflate, br, zsdch', - 'User-Agent': this.ua, - 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32', - cookie: this.cookie, - } - - let resp: ConversationResponse | undefined - try { - const response = await fetch(this.endpoint + '/api/create', { method: 'POST', headers, redirect: 'error', mode: 'cors', credentials: 'include' }) - if (response.status === 404) { - throw new ChatError('Not Found', ErrorCode.NOTFOUND_ERROR) - } - resp = await response.json() as ConversationResponse - } catch (err) { - console.error('create conversation error', err) - } - - if (!resp?.result) { - throw new ChatError('你的 VPS 或代理可能被封禁，如有疑问，请前往 https://github.com/weaigc/bingo 咨询', ErrorCode.UNKOWN_ERROR) - } - - const { value, message } = resp.result || {} - if (value !== 'Success') { - const errorMsg = `${value}: ${message}` - if (value === 'UnauthorizedRequest') { - throw new ChatError(errorMsg, ErrorCode.BING_UNAUTHORIZED) - } - if (value === 'Forbidden') { - throw new ChatError(errorMsg, ErrorCode.BING_FORBIDDEN) - } - throw new ChatError(errorMsg, ErrorCode.UNKOWN_ERROR) - } - return resp - } - - private async createContext(conversationStyle: BingConversationStyle) { - if (!this.conversationContext) { - const conversation = await this.createConversation() - this.conversationContext = { - conversationId: conversation.conversationId, - conversationSignature: conversation.conversationSignature, - clientId: conversation.clientId, - invocationId: 0, - conversationStyle, - prompt: '', - } - } - return this.conversationContext - } - - async sendMessage(params: Params) { - try { - await this.createContext(params.options.bingConversationStyle) - Object.assign(this.conversationContext!, { prompt: params.prompt, imageUrl: params.imageUrl }) - return this.sydneyProxy(params) - } catch (error) { - params.onEvent({ - type: 'ERROR', - error: error instanceof ChatError ? error : new ChatError('Catch Error', ErrorCode.UNKOWN_ERROR), - }) - } - } - - private async sydneyProxy(params: Params) { - const abortController = new AbortController() - const response = await fetch(this.endpoint + '/api/sydney', { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - signal: abortController.signal, - body: JSON.stringify(this.conversationContext!) - }) - if (response.status !== 200) { - params.onEvent({ - type: 'ERROR', - error: new ChatError( - 'Unknown error', - ErrorCode.UNKOWN_ERROR, - ), - }) - } - params.signal?.addEventListener('abort', () => { - abortController.abort() - }) - - const textDecoder = createChunkDecoder() - for await (const chunk of streamAsyncIterable(response.body!)) { - this.parseEvents(params, websocketUtils.unpackMessage(textDecoder(chunk))) - } - } - - async sendWs() { - const wsConfig: ConstructorParameters [1] = { - packMessage: websocketUtils.packMessage, - unpackMessage: websocketUtils.unpackMessage, - createWebSocket: (url) => new WebSocket(url, { - headers: { - 'accept-language': 'zh-CN,zh;q=0.9', - 'cache-control': 'no-cache', - 'User-Agent': this.ua, - pragma: 'no-cache', - cookie: this.cookie, - } - }) - } - const wsp = new WebSocketAsPromised('wss://sydney.bing.com/sydney/ChatHub', wsConfig) - - wsp.open().then(() => { - wsp.sendPacked({ protocol: 'json', version: 1 }) - wsp.sendPacked({ type: 6 }) - wsp.sendPacked(BingWebBot.buildChatRequest(this.conversationContext!)) - }) - - return wsp - } - - private async useWs(params: Params) { - const wsp = await this.sendWs() - const watchDog = new WatchDog() - wsp.onUnpackedMessage.addListener((events) => { - watchDog.watch(() => { - wsp.sendPacked({ type: 6 }) - }) - this.parseEvents(params, events) - }) - - wsp.onClose.addListener(() => { - watchDog.reset() - params.onEvent({ type: 'DONE' }) - wsp.removeAllListeners() - }) - - params.signal?.addEventListener('abort', () => { - wsp.removeAllListeners() - wsp.close() - }) - } - - private async createImage(prompt: string, id: string) { - try { - const headers = { - 'Accept-Encoding': 'gzip, deflate, br, zsdch', - 'User-Agent': this.ua, - 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32', - cookie: this.cookie, - } - const query = new URLSearchParams({ - prompt, - id - }) - const response = await fetch(this.endpoint + '/api/image?' + query.toString(), - { - method: 'POST', - headers, - mode: 'cors', - credentials: 'include' - }) - .then(res => res.text()) - if (response) { - this.lastText += '\n' + response - } - } catch (err) { - console.error('Create Image Error', err) - } - } - - private buildKnowledgeApiPayload(imageUrl: string, conversationStyle: BingConversationStyle) { - const imageInfo: ImageInfo = {} - let imageBase64: string | undefined = undefined - const knowledgeRequest = { - imageInfo, - knowledgeRequest: { - invokedSkills: [ - 'ImageById' - ], - subscriptionId: 'Bing.Chat.Multimodal', - invokedSkillsRequestData: { - enableFaceBlur: true - }, - convoData: { - convoid: this.conversationContext?.conversationId, - convotone: conversationStyle, - } - }, - } - - if (imageUrl.startsWith('data:image/')) { - imageBase64 = imageUrl.replace('data:image/', ''); - const partIndex = imageBase64.indexOf(',') - if (partIndex) { - imageBase64 = imageBase64.substring(partIndex + 1) - } - } else { - imageInfo.url = imageUrl - } - return { knowledgeRequest, imageBase64 } - } - - async uploadImage(imageUrl: string, conversationStyle: BingConversationStyle = BingConversationStyle.Creative): Promise { - if (!imageUrl) { - return - } - await this.createContext(conversationStyle) - const payload = this.buildKnowledgeApiPayload(imageUrl, conversationStyle) - - const response = await fetch(this.endpoint + '/api/kblob', - { - headers: { - 'Content-Type': 'application/json', - }, - method: 'POST', - mode: 'cors', - credentials: 'include', - body: JSON.stringify(payload), - }) - .then(res => res.json()) - .catch(e => { - console.log('Error', e) - }) - return response - } - - private async generateContent(message: ChatResponseMessage) { - if (message.contentType === 'IMAGE') { - this.asyncTasks.push(this.createImage(message.text, message.messageId)) - } - } - - private async parseEvents(params: Params, events: any) { - const conversation = this.conversationContext! - - events?.forEach(async (event: ChatUpdateCompleteResponse) => { - debug('bing event', event) - if (event.type === 3) { - await Promise.all(this.asyncTasks) - this.asyncTasks = [] - params.onEvent({ type: 'UPDATE_ANSWER', data: { text: this.lastText } }) - params.onEvent({ type: 'DONE' }) - conversation.invocationId = parseInt(event.invocationId, 10) + 1 - } else if (event.type === 1) { - const messages = event.arguments[0].messages - if (messages) { - const text = convertMessageToMarkdown(messages[0]) - this.lastText = text - params.onEvent({ type: 'UPDATE_ANSWER', data: { text, spokenText: messages[0].text, throttling: event.arguments[0].throttling } }) - } - } else if (event.type === 2) { - const messages = event.item.messages as ChatResponseMessage[] | undefined - if (!messages) { - params.onEvent({ - type: 'ERROR', - error: new ChatError( - event.item.result.error || 'Unknown error', - event.item.result.value === 'Throttled' ? ErrorCode.THROTTLE_LIMIT - : event.item.result.value === 'CaptchaChallenge' ? (this.conversationContext?.conversationId?.includes('BingProdUnAuthenticatedUsers') ? ErrorCode.BING_UNAUTHORIZED : ErrorCode.BING_CAPTCHA) - : ErrorCode.UNKOWN_ERROR - ), - }) - return - } - const limited = messages.some((message) => - message.contentOrigin === 'TurnLimiter' - || message.messageType === 'Disengaged' - ) - if (limited) { - params.onEvent({ - type: 'ERROR', - error: new ChatError( - 'Sorry, you have reached chat limit in this conversation.', - ErrorCode.CONVERSATION_LIMIT, - ), - }) - return - } - - const lastMessage = event.item.messages.at(-1) as ChatResponseMessage - const specialMessage = event.item.messages.find(message => message.author === 'bot' && message.contentType === 'IMAGE') - if (specialMessage) { - this.generateContent(specialMessage) - } - - if (lastMessage) { - const text = convertMessageToMarkdown(lastMessage) - this.lastText = text - params.onEvent({ - type: 'UPDATE_ANSWER', - data: { text, throttling: event.item.throttling, suggestedResponses: lastMessage.suggestedResponses, sourceAttributions: lastMessage.sourceAttributions }, - }) - } - } - }) - } - - resetConversation() { - this.conversationContext = undefined - } -} diff --git a/spaces/AB-TW/team-ai/promopts.py b/spaces/AB-TW/team-ai/promopts.py deleted file mode 100644 index 01e95b6a2d27baaa8163782cb1cc9da782e0827d..0000000000000000000000000000000000000000 --- a/spaces/AB-TW/team-ai/promopts.py +++ /dev/null @@ -1,57 +0,0 @@ -from langchain.prompts import PromptTemplate - -FEEDBACK = """You are a business analyst who is familiar with specification by example. I'm the domain expert. - -===CONTEXT -{context} -===END OF CONTXT - -===USER STORY -{story} -===END OF USER STORY - -Explain the user story as scenarios. use the following format: - -Thought: you should always think about what is still uncertain about the user story. Ignore technical concerns. -Question: the Question to ask to clarify the user story -Answer: the answer I responded to the question -... (this Thought/Question/Answer repeat at least 3 times, at most 10 times) -Thought: I know enough to explain the user story -Scenarios: List all possible scenarios with concrete example in Given/When/Then style - -Please use Chinese! Begin! - -{history} -Answer:{input}""" - -FEEDBACK_PROMPT = PromptTemplate( - input_variables=["context", "story", "history", "input"], template=FEEDBACK, -) - - -agent_template = """You are a business analyst who is familiar with specification by example. Your main task is to explain the user story as scenarios. -You have access to the following tools: - -{tools} - -Use the following format: - -Story: the story that you need to explain -Thought: you should always think about what is still uncertain about the user story to Explain the user story. Ignore technical concerns. -Action: the action to take, should be one of [{tool_names}] -Action Input: the input to the action -Observation: the result of the action -... (this Thought/Action/Action Input/Observation can repeat 10 times) -Thought: I now know the final answer -Final Answer: List all possible scenarios with concrete example in Given/When/Then style - -Begin! - -Story: {input} -{agent_scratchpad}""" - -CONTENT_RE_WRIGHT = """你是一个文案助手，请将如下文案整理重写，去除重复的内容，尽量保留原有信息: -``` -{input} -```""" -CONTENT_RE_WRIGHT_PROMPT = PromptTemplate(input_variables=["input"], template=CONTENT_RE_WRIGHT,) diff --git a/spaces/AIFILMS/StyleGANEX/datasets/ffhq_degradation_dataset.py b/spaces/AIFILMS/StyleGANEX/datasets/ffhq_degradation_dataset.py deleted file mode 100644 index b43ff6b1d82c1c491900f119a62f259ac4294b61..0000000000000000000000000000000000000000 --- a/spaces/AIFILMS/StyleGANEX/datasets/ffhq_degradation_dataset.py +++ /dev/null @@ -1,235 +0,0 @@ -import cv2 -import math -import numpy as np -import os.path as osp -import torch -import torch.utils.data as data -from basicsr.data import degradations as degradations -from basicsr.data.data_util import paths_from_folder -from basicsr.data.transforms import augment -from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor -from basicsr.utils.registry import DATASET_REGISTRY -from torchvision.transforms.functional import (adjust_brightness, adjust_contrast, adjust_hue, adjust_saturation, - normalize) - - -@DATASET_REGISTRY.register() -class FFHQDegradationDataset(data.Dataset): - """FFHQ dataset for GFPGAN. - It reads high resolution images, and then generate low-quality (LQ) images on-the-fly. - Args: - opt (dict): Config for train datasets. It contains the following keys: - dataroot_gt (str): Data root path for gt. - io_backend (dict): IO backend type and other kwarg. - mean (list | tuple): Image mean. - std (list | tuple): Image std. - use_hflip (bool): Whether to horizontally flip. - Please see more options in the codes. - """ - - def __init__(self, opt): - super(FFHQDegradationDataset, self).__init__() - self.opt = opt - # file client (io backend) - self.file_client = None - self.io_backend_opt = opt['io_backend'] - - self.gt_folder = opt['dataroot_gt'] - self.mean = opt['mean'] - self.std = opt['std'] - self.out_size = opt['out_size'] - - self.crop_components = opt.get('crop_components', False) # facial components - self.eye_enlarge_ratio = opt.get('eye_enlarge_ratio', 1) # whether enlarge eye regions - - if self.crop_components: - # load component list from a pre-process pth files - self.components_list = torch.load(opt.get('component_path')) - - # file client (lmdb io backend) - if self.io_backend_opt['type'] == 'lmdb': - self.io_backend_opt['db_paths'] = self.gt_folder - if not self.gt_folder.endswith('.lmdb'): - raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}") - with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin: - self.paths = [line.split('.')[0] for line in fin] - else: - # disk backend: scan file list from a folder - self.paths = paths_from_folder(self.gt_folder) - - # degradation configurations - self.blur_kernel_size = opt['blur_kernel_size'] - self.kernel_list = opt['kernel_list'] - self.kernel_prob = opt['kernel_prob'] - self.blur_sigma = opt['blur_sigma'] - self.downsample_range = opt['downsample_range'] - self.noise_range = opt['noise_range'] - self.jpeg_range = opt['jpeg_range'] - - # color jitter - self.color_jitter_prob = opt.get('color_jitter_prob') - self.color_jitter_pt_prob = opt.get('color_jitter_pt_prob') - self.color_jitter_shift = opt.get('color_jitter_shift', 20) - # to gray - self.gray_prob = opt.get('gray_prob') - - logger = get_root_logger() - logger.info(f'Blur: blur_kernel_size {self.blur_kernel_size}, sigma: [{", ".join(map(str, self.blur_sigma))}]') - logger.info(f'Downsample: downsample_range [{", ".join(map(str, self.downsample_range))}]') - logger.info(f'Noise: [{", ".join(map(str, self.noise_range))}]') - logger.info(f'JPEG compression: [{", ".join(map(str, self.jpeg_range))}]') - - if self.color_jitter_prob is not None: - logger.info(f'Use random color jitter. Prob: {self.color_jitter_prob}, shift: {self.color_jitter_shift}') - if self.gray_prob is not None: - logger.info(f'Use random gray. Prob: {self.gray_prob}') - self.color_jitter_shift /= 255. - - @staticmethod - def color_jitter(img, shift): - """jitter color: randomly jitter the RGB values, in numpy formats""" - jitter_val = np.random.uniform(-shift, shift, 3).astype(np.float32) - img = img + jitter_val - img = np.clip(img, 0, 1) - return img - - @staticmethod - def color_jitter_pt(img, brightness, contrast, saturation, hue): - """jitter color: randomly jitter the brightness, contrast, saturation, and hue, in torch Tensor formats""" - fn_idx = torch.randperm(4) - for fn_id in fn_idx: - if fn_id == 0 and brightness is not None: - brightness_factor = torch.tensor(1.0).uniform_(brightness[0], brightness[1]).item() - img = adjust_brightness(img, brightness_factor) - - if fn_id == 1 and contrast is not None: - contrast_factor = torch.tensor(1.0).uniform_(contrast[0], contrast[1]).item() - img = adjust_contrast(img, contrast_factor) - - if fn_id == 2 and saturation is not None: - saturation_factor = torch.tensor(1.0).uniform_(saturation[0], saturation[1]).item() - img = adjust_saturation(img, saturation_factor) - - if fn_id == 3 and hue is not None: - hue_factor = torch.tensor(1.0).uniform_(hue[0], hue[1]).item() - img = adjust_hue(img, hue_factor) - return img - - def get_component_coordinates(self, index, status): - """Get facial component (left_eye, right_eye, mouth) coordinates from a pre-loaded pth file""" - components_bbox = self.components_list[f'{index:08d}'] - if status[0]: # hflip - # exchange right and left eye - tmp = components_bbox['left_eye'] - components_bbox['left_eye'] = components_bbox['right_eye'] - components_bbox['right_eye'] = tmp - # modify the width coordinate - components_bbox['left_eye'][0] = self.out_size - components_bbox['left_eye'][0] - components_bbox['right_eye'][0] = self.out_size - components_bbox['right_eye'][0] - components_bbox['mouth'][0] = self.out_size - components_bbox['mouth'][0] - - # get coordinates - locations = [] - for part in ['left_eye', 'right_eye', 'mouth']: - mean = components_bbox[part][0:2] - mean[0] = mean[0] * 2 + 128 ######## - mean[1] = mean[1] * 2 + 128 ######## - half_len = components_bbox[part][2] * 2 ######## - if 'eye' in part: - half_len *= self.eye_enlarge_ratio - loc = np.hstack((mean - half_len + 1, mean + half_len)) - loc = torch.from_numpy(loc).float() - locations.append(loc) - return locations - - def __getitem__(self, index): - if self.file_client is None: - self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) - - # load gt image - # Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32. - gt_path = self.paths[index] - img_bytes = self.file_client.get(gt_path) - img_gt = imfrombytes(img_bytes, float32=True) - - # random horizontal flip - img_gt, status = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False, return_status=True) - h, w, _ = img_gt.shape - - # get facial component coordinates - if self.crop_components: - locations = self.get_component_coordinates(index, status) - loc_left_eye, loc_right_eye, loc_mouth = locations - - # ------------------------ generate lq image ------------------------ # - # blur - kernel = degradations.random_mixed_kernels( - self.kernel_list, - self.kernel_prob, - self.blur_kernel_size, - self.blur_sigma, - self.blur_sigma, [-math.pi, math.pi], - noise_range=None) - img_lq = cv2.filter2D(img_gt, -1, kernel) - # downsample - scale = np.random.uniform(self.downsample_range[0], self.downsample_range[1]) - img_lq = cv2.resize(img_lq, (int(w // scale), int(h // scale)), interpolation=cv2.INTER_LINEAR) - # noise - if self.noise_range is not None: - img_lq = degradations.random_add_gaussian_noise(img_lq, self.noise_range) - # jpeg compression - if self.jpeg_range is not None: - img_lq = degradations.random_add_jpg_compression(img_lq, self.jpeg_range) - - # resize to original size - img_lq = cv2.resize(img_lq, (int(w // self.opt['scale']), int(h // self.opt['scale'])), interpolation=cv2.INTER_LINEAR) - - # random color jitter (only for lq) - if self.color_jitter_prob is not None and (np.random.uniform() < self.color_jitter_prob): - img_lq = self.color_jitter(img_lq, self.color_jitter_shift) - # random to gray (only for lq) - if self.gray_prob and np.random.uniform() < self.gray_prob: - img_lq = cv2.cvtColor(img_lq, cv2.COLOR_BGR2GRAY) - img_lq = np.tile(img_lq[:, :, None], [1, 1, 3]) - if self.opt.get('gt_gray'): # whether convert GT to gray images - img_gt = cv2.cvtColor(img_gt, cv2.COLOR_BGR2GRAY) - img_gt = np.tile(img_gt[:, :, None], [1, 1, 3]) # repeat the color channels - - # BGR to RGB, HWC to CHW, numpy to tensor - #img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True) - img_gt = img2tensor(img_gt, bgr2rgb=True, float32=True) - img_lq = img2tensor(img_lq, bgr2rgb=True, float32=True) - - # random color jitter (pytorch version) (only for lq) - if self.color_jitter_pt_prob is not None and (np.random.uniform() < self.color_jitter_pt_prob): - brightness = self.opt.get('brightness', (0.5, 1.5)) - contrast = self.opt.get('contrast', (0.5, 1.5)) - saturation = self.opt.get('saturation', (0, 1.5)) - hue = self.opt.get('hue', (-0.1, 0.1)) - img_lq = self.color_jitter_pt(img_lq, brightness, contrast, saturation, hue) - - # round and clip - img_lq = torch.clamp((img_lq * 255.0).round(), 0, 255) / 255. - - # normalize - normalize(img_gt, self.mean, self.std, inplace=True) - normalize(img_lq, self.mean, self.std, inplace=True) - - ''' - if self.crop_components: - return_dict = { - 'lq': img_lq, - 'gt': img_gt, - 'gt_path': gt_path, - 'loc_left_eye': loc_left_eye, - 'loc_right_eye': loc_right_eye, - 'loc_mouth': loc_mouth - } - return return_dict - else: - return {'lq': img_lq, 'gt': img_gt, 'gt_path': gt_path} - ''' - return img_lq, img_gt - - def __len__(self): - return len(self.paths) \ No newline at end of file diff --git a/spaces/AIZero2HeroBootcamp/FastSpeech2LinerGradioApp/README.md b/spaces/AIZero2HeroBootcamp/FastSpeech2LinerGradioApp/README.md deleted file mode 100644 index 47e0a788a0bff0c1e9aa347fa21578376482cb0d..0000000000000000000000000000000000000000 --- a/spaces/AIZero2HeroBootcamp/FastSpeech2LinerGradioApp/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: FastSpeech2LinerGradioApp -emoji: 🌖 -colorFrom: blue -colorTo: pink -sdk: gradio -sdk_version: 3.39.0 -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/AbandonedMuse/UnlimitedMusicGen/audiocraft/modules/__init__.py b/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/modules/__init__.py deleted file mode 100644 index 81ba30f6466ff91b90490a4fb92f7d3d0d00144d..0000000000000000000000000000000000000000 --- a/spaces/AbandonedMuse/UnlimitedMusicGen/audiocraft/modules/__init__.py +++ /dev/null @@ -1,20 +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. - -# flake8: noqa -from .conv import ( - NormConv1d, - NormConv2d, - NormConvTranspose1d, - NormConvTranspose2d, - StreamableConv1d, - StreamableConvTranspose1d, - pad_for_conv1d, - pad1d, - unpad1d, -) -from .lstm import StreamableLSTM -from .seanet import SEANetEncoder, SEANetDecoder diff --git a/spaces/Abhaykoul/Palm-2/README.md b/spaces/Abhaykoul/Palm-2/README.md deleted file mode 100644 index 9421e6fb4163817a61f30c7e99f0d784bef3b8f9..0000000000000000000000000000000000000000 --- a/spaces/Abhaykoul/Palm-2/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Palm 2 -emoji: 👁 -colorFrom: green -colorTo: purple -sdk: streamlit -sdk_version: 1.27.2 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Adapter/T2I-Adapter/dist_util.py b/spaces/Adapter/T2I-Adapter/dist_util.py deleted file mode 100644 index 47441a48932a86d5556b1167ef327aa3b1ec8173..0000000000000000000000000000000000000000 --- a/spaces/Adapter/T2I-Adapter/dist_util.py +++ /dev/null @@ -1,91 +0,0 @@ -# Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/dist_utils.py # noqa: E501 -import functools -import os -import subprocess -import torch -import torch.distributed as dist -import torch.multiprocessing as mp -from torch.nn.parallel import DataParallel, DistributedDataParallel - - -def init_dist(launcher, backend='nccl', **kwargs): - if mp.get_start_method(allow_none=True) is None: - mp.set_start_method('spawn') - if launcher == 'pytorch': - _init_dist_pytorch(backend, **kwargs) - elif launcher == 'slurm': - _init_dist_slurm(backend, **kwargs) - else: - raise ValueError(f'Invalid launcher type: {launcher}') - - -def _init_dist_pytorch(backend, **kwargs): - rank = int(os.environ['RANK']) - num_gpus = torch.cuda.device_count() - torch.cuda.set_device(rank % num_gpus) - dist.init_process_group(backend=backend, **kwargs) - - -def _init_dist_slurm(backend, port=None): - """Initialize slurm distributed training environment. - - If argument ``port`` is not specified, then the master port will be system - environment variable ``MASTER_PORT``. If ``MASTER_PORT`` is not in system - environment variable, then a default port ``29500`` will be used. - - Args: - backend (str): Backend of torch.distributed. - port (int, optional): Master port. Defaults to None. - """ - proc_id = int(os.environ['SLURM_PROCID']) - ntasks = int(os.environ['SLURM_NTASKS']) - node_list = os.environ['SLURM_NODELIST'] - num_gpus = torch.cuda.device_count() - torch.cuda.set_device(proc_id % num_gpus) - addr = subprocess.getoutput(f'scontrol show hostname {node_list} | head -n1') - # specify master port - if port is not None: - os.environ['MASTER_PORT'] = str(port) - elif 'MASTER_PORT' in os.environ: - pass # use MASTER_PORT in the environment variable - else: - # 29500 is torch.distributed default port - os.environ['MASTER_PORT'] = '29500' - os.environ['MASTER_ADDR'] = addr - os.environ['WORLD_SIZE'] = str(ntasks) - os.environ['LOCAL_RANK'] = str(proc_id % num_gpus) - os.environ['RANK'] = str(proc_id) - dist.init_process_group(backend=backend) - - -def get_dist_info(): - if dist.is_available(): - initialized = dist.is_initialized() - else: - initialized = False - if initialized: - rank = dist.get_rank() - world_size = dist.get_world_size() - else: - rank = 0 - world_size = 1 - return rank, world_size - - -def master_only(func): - - @functools.wraps(func) - def wrapper(*args, **kwargs): - rank, _ = get_dist_info() - if rank == 0: - return func(*args, **kwargs) - - return wrapper - -def get_bare_model(net): - """Get bare model, especially under wrapping with - DistributedDataParallel or DataParallel. - """ - if isinstance(net, (DataParallel, DistributedDataParallel)): - net = net.module - return net diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/knob/Factory.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/knob/Factory.d.ts deleted file mode 100644 index ebf230eb007189e6691a52807c8eed0baff2fe99..0000000000000000000000000000000000000000 --- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/knob/Factory.d.ts +++ /dev/null @@ -1,5 +0,0 @@ -import Knob from './Knob'; - -export default function ( - config?: Knob.IConfig -): Knob; \ No newline at end of file diff --git a/spaces/AlekseyKorshuk/thin-plate-spline-motion-model/train_avd.py b/spaces/AlekseyKorshuk/thin-plate-spline-motion-model/train_avd.py deleted file mode 100644 index bc6794c322e01d980acc2f3b3aab2b192576900d..0000000000000000000000000000000000000000 --- a/spaces/AlekseyKorshuk/thin-plate-spline-motion-model/train_avd.py +++ /dev/null @@ -1,91 +0,0 @@ -from tqdm import trange -import torch -from torch.utils.data import DataLoader -from logger import Logger -from torch.optim.lr_scheduler import MultiStepLR -from frames_dataset import DatasetRepeater - - -def random_scale(kp_params, scale): - theta = torch.rand(kp_params['fg_kp'].shape[0], 2) * (2 * scale) + (1 - scale) - theta = torch.diag_embed(theta).unsqueeze(1).type(kp_params['fg_kp'].type()) - new_kp_params = {'fg_kp': torch.matmul(theta, kp_params['fg_kp'].unsqueeze(-1)).squeeze(-1)} - return new_kp_params - - -def train_avd(config, inpainting_network, kp_detector, bg_predictor, dense_motion_network, - avd_network, checkpoint, log_dir, dataset): - train_params = config['train_avd_params'] - - optimizer = torch.optim.Adam(avd_network.parameters(), lr=train_params['lr'], betas=(0.5, 0.999)) - - if checkpoint is not None: - Logger.load_cpk(checkpoint, inpainting_network=inpainting_network, kp_detector=kp_detector, - bg_predictor=bg_predictor, avd_network=avd_network, - dense_motion_network= dense_motion_network,optimizer_avd=optimizer) - start_epoch = 0 - else: - raise AttributeError("Checkpoint should be specified for mode='train_avd'.") - - scheduler = MultiStepLR(optimizer, train_params['epoch_milestones'], gamma=0.1) - - if 'num_repeats' in train_params or train_params['num_repeats'] != 1: - dataset = DatasetRepeater(dataset, train_params['num_repeats']) - - dataloader = DataLoader(dataset, batch_size=train_params['batch_size'], shuffle=True, - num_workers=train_params['dataloader_workers'], drop_last=True) - - with Logger(log_dir=log_dir, visualizer_params=config['visualizer_params'], - checkpoint_freq=train_params['checkpoint_freq']) as logger: - for epoch in trange(start_epoch, train_params['num_epochs']): - avd_network.train() - for x in dataloader: - with torch.no_grad(): - kp_source = kp_detector(x['source'].cuda()) - kp_driving_gt = kp_detector(x['driving'].cuda()) - kp_driving_random = random_scale(kp_driving_gt, scale=train_params['random_scale']) - rec = avd_network(kp_source, kp_driving_random) - - reconstruction_kp = train_params['lambda_shift'] * \ - torch.abs(kp_driving_gt['fg_kp'] - rec['fg_kp']).mean() - - loss_dict = {'rec_kp': reconstruction_kp} - loss = reconstruction_kp - - loss.backward() - optimizer.step() - optimizer.zero_grad() - - losses = {key: value.mean().detach().data.cpu().numpy() for key, value in loss_dict.items()} - logger.log_iter(losses=losses) - - # Visualization - avd_network.eval() - with torch.no_grad(): - source = x['source'][:6].cuda() - driving = torch.cat([x['driving'][[0, 1]].cuda(), source[[2, 3, 2, 1]]], dim=0) - kp_source = kp_detector(source) - kp_driving = kp_detector(driving) - - out = avd_network(kp_source, kp_driving) - kp_driving = out - dense_motion = dense_motion_network(source_image=source, kp_driving=kp_driving, - kp_source=kp_source) - generated = inpainting_network(source, dense_motion) - - generated.update({'kp_source': kp_source, 'kp_driving': kp_driving}) - - scheduler.step(epoch) - model_save = { - 'inpainting_network': inpainting_network, - 'dense_motion_network': dense_motion_network, - 'kp_detector': kp_detector, - 'avd_network': avd_network, - 'optimizer_avd': optimizer - } - if bg_predictor : - model_save['bg_predictor'] = bg_predictor - - logger.log_epoch(epoch, model_save, - inp={'source': source, 'driving': driving}, - out=generated) diff --git a/spaces/AlexWang/lama/saicinpainting/training/losses/__init__.py b/spaces/AlexWang/lama/saicinpainting/training/losses/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes.py deleted file mode 100644 index 1f21c6578bb8f820448f773fb6651b02e64b6123..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_d6_r101-d16_769x769_80k_cityscapes.py +++ /dev/null @@ -1,2 +0,0 @@ -_base_ = './fcn_d6_r50-d16_769x769_80k_cityscapes.py' -model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101)) diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.py b/spaces/Andy1621/uniformer_image_segmentation/configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.py deleted file mode 100644 index af06cb66cc808c206d6946a4b2420a6942d3dc7e..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_segmentation/configs/psanet/psanet_r50-d8_512x512_20k_voc12aug.py +++ /dev/null @@ -1,7 +0,0 @@ -_base_ = [ - '../_base_/models/psanet_r50-d8.py', - '../_base_/datasets/pascal_voc12_aug.py', '../_base_/default_runtime.py', - '../_base_/schedules/schedule_20k.py' -] -model = dict( - decode_head=dict(num_classes=21), auxiliary_head=dict(num_classes=21)) diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/utils/parrots_jit.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/utils/parrots_jit.py deleted file mode 100644 index 61873f6dbb9b10ed972c90aa8faa321e3cb3249e..0000000000000000000000000000000000000000 --- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/utils/parrots_jit.py +++ /dev/null @@ -1,41 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import os - -from .parrots_wrapper import TORCH_VERSION - -parrots_jit_option = os.getenv('PARROTS_JIT_OPTION') - -if TORCH_VERSION == 'parrots' and parrots_jit_option == 'ON': - from parrots.jit import pat as jit -else: - - def jit(func=None, - check_input=None, - full_shape=True, - derivate=False, - coderize=False, - optimize=False): - - def wrapper(func): - - def wrapper_inner(*args, **kargs): - return func(*args, **kargs) - - return wrapper_inner - - if func is None: - return wrapper - else: - return func - - -if TORCH_VERSION == 'parrots': - from parrots.utils.tester import skip_no_elena -else: - - def skip_no_elena(func): - - def wrapper(*args, **kargs): - return func(*args, **kargs) - - return wrapper diff --git a/spaces/Apex-X/ROOPOK/roop/face_analyser.py b/spaces/Apex-X/ROOPOK/roop/face_analyser.py deleted file mode 100644 index 81cd39100122c64023ad01ac0e8b457a812a0216..0000000000000000000000000000000000000000 --- a/spaces/Apex-X/ROOPOK/roop/face_analyser.py +++ /dev/null @@ -1,54 +0,0 @@ -import threading -from typing import Any, Optional, List -import insightface -import numpy - -import roop.globals -from roop.typing import Frame, Face - -FACE_ANALYSER = None -THREAD_LOCK = threading.Lock() - - -def get_face_analyser() -> Any: - global FACE_ANALYSER - - with THREAD_LOCK: - if FACE_ANALYSER is None: - FACE_ANALYSER = insightface.app.FaceAnalysis(name='buffalo_l', providers=roop.globals.execution_providers) - FACE_ANALYSER.prepare(ctx_id=0) - return FACE_ANALYSER - - -def clear_face_analyser() -> Any: - global FACE_ANALYSER - - FACE_ANALYSER = None - - -def get_one_face(frame: Frame, position: int = 0) -> Optional[Face]: - many_faces = get_many_faces(frame) - if many_faces: - try: - return many_faces[position] - except IndexError: - return many_faces[-1] - return None - - -def get_many_faces(frame: Frame) -> Optional[List[Face]]: - try: - return get_face_analyser().get(frame) - except ValueError: - return None - - -def find_similar_face(frame: Frame, reference_face: Face) -> Optional[Face]: - many_faces = get_many_faces(frame) - if many_faces: - for face in many_faces: - if hasattr(face, 'normed_embedding') and hasattr(reference_face, 'normed_embedding'): - distance = numpy.sum(numpy.square(face.normed_embedding - reference_face.normed_embedding)) - if distance < roop.globals.similar_face_distance: - return face - return None diff --git a/spaces/ArkanDash/rvc-models-new/lib/infer_pack/attentions.py b/spaces/ArkanDash/rvc-models-new/lib/infer_pack/attentions.py deleted file mode 100644 index 05501be1871643f78dddbeaa529c96667031a8db..0000000000000000000000000000000000000000 --- a/spaces/ArkanDash/rvc-models-new/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/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/models/unet_blocks.py b/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/models/unet_blocks.py deleted file mode 100644 index e48e9320490c7fdccd2f3cb1d28e7f694609beea..0000000000000000000000000000000000000000 --- a/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/models/unet_blocks.py +++ /dev/null @@ -1,588 +0,0 @@ -# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_blocks.py - -import torch -from torch import nn - -from .attention import Transformer3DModel -from .resnet import Downsample3D, ResnetBlock3D, Upsample3D - - -def get_down_block( - down_block_type, - num_layers, - in_channels, - out_channels, - temb_channels, - add_downsample, - resnet_eps, - resnet_act_fn, - attn_num_head_channels, - resnet_groups=None, - cross_attention_dim=None, - downsample_padding=None, - dual_cross_attention=False, - use_linear_projection=False, - only_cross_attention=False, - upcast_attention=False, - resnet_time_scale_shift="default", -): - down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type - if down_block_type == "DownBlock3D": - return DownBlock3D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - downsample_padding=downsample_padding, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - elif down_block_type == "CrossAttnDownBlock3D": - if cross_attention_dim is None: - raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D") - return CrossAttnDownBlock3D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - add_downsample=add_downsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - downsample_padding=downsample_padding, - cross_attention_dim=cross_attention_dim, - attn_num_head_channels=attn_num_head_channels, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - raise ValueError(f"{down_block_type} does not exist.") - - -def get_up_block( - up_block_type, - num_layers, - in_channels, - out_channels, - prev_output_channel, - temb_channels, - add_upsample, - resnet_eps, - resnet_act_fn, - attn_num_head_channels, - resnet_groups=None, - cross_attention_dim=None, - dual_cross_attention=False, - use_linear_projection=False, - only_cross_attention=False, - upcast_attention=False, - resnet_time_scale_shift="default", -): - up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type - if up_block_type == "UpBlock3D": - return UpBlock3D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - elif up_block_type == "CrossAttnUpBlock3D": - if cross_attention_dim is None: - raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D") - return CrossAttnUpBlock3D( - num_layers=num_layers, - in_channels=in_channels, - out_channels=out_channels, - prev_output_channel=prev_output_channel, - temb_channels=temb_channels, - add_upsample=add_upsample, - resnet_eps=resnet_eps, - resnet_act_fn=resnet_act_fn, - resnet_groups=resnet_groups, - cross_attention_dim=cross_attention_dim, - attn_num_head_channels=attn_num_head_channels, - dual_cross_attention=dual_cross_attention, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - resnet_time_scale_shift=resnet_time_scale_shift, - ) - raise ValueError(f"{up_block_type} does not exist.") - - -class UNetMidBlock3DCrossAttn(nn.Module): - def __init__( - self, - in_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - attn_num_head_channels=1, - output_scale_factor=1.0, - cross_attention_dim=1280, - dual_cross_attention=False, - use_linear_projection=False, - upcast_attention=False, - ): - super().__init__() - - self.has_cross_attention = True - self.attn_num_head_channels = attn_num_head_channels - resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) - - # there is always at least one resnet - resnets = [ - ResnetBlock3D( - in_channels=in_channels, - out_channels=in_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ] - attentions = [] - - for _ in range(num_layers): - if dual_cross_attention: - raise NotImplementedError - attentions.append( - Transformer3DModel( - attn_num_head_channels, - in_channels // attn_num_head_channels, - in_channels=in_channels, - num_layers=1, - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - use_linear_projection=use_linear_projection, - upcast_attention=upcast_attention, - ) - ) - resnets.append( - ResnetBlock3D( - in_channels=in_channels, - out_channels=in_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None): - hidden_states = self.resnets[0](hidden_states, temb) - for attn, resnet in zip(self.attentions, self.resnets[1:]): - hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample - hidden_states = resnet(hidden_states, temb) - - return hidden_states - - -class CrossAttnDownBlock3D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - attn_num_head_channels=1, - cross_attention_dim=1280, - output_scale_factor=1.0, - downsample_padding=1, - add_downsample=True, - dual_cross_attention=False, - use_linear_projection=False, - only_cross_attention=False, - upcast_attention=False, - ): - super().__init__() - resnets = [] - attentions = [] - - self.has_cross_attention = True - self.attn_num_head_channels = attn_num_head_channels - - for i in range(num_layers): - in_channels = in_channels if i == 0 else out_channels - resnets.append( - ResnetBlock3D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - if dual_cross_attention: - raise NotImplementedError - attentions.append( - Transformer3DModel( - attn_num_head_channels, - out_channels // attn_num_head_channels, - in_channels=out_channels, - num_layers=1, - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - ) - ) - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - if add_downsample: - self.downsamplers = nn.ModuleList( - [ - Downsample3D( - out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" - ) - ] - ) - else: - self.downsamplers = None - - self.gradient_checkpointing = False - - def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None): - output_states = () - - for resnet, attn in zip(self.resnets, self.attentions): - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module, return_dict=None): - def custom_forward(*inputs): - if return_dict is not None: - return module(*inputs, return_dict=return_dict) - else: - return module(*inputs) - - return custom_forward - - hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(attn, return_dict=False), - hidden_states, - encoder_hidden_states, - )[0] - else: - hidden_states = resnet(hidden_states, temb) - hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample - - output_states += (hidden_states,) - - if self.downsamplers is not None: - for downsampler in self.downsamplers: - hidden_states = downsampler(hidden_states) - - output_states += (hidden_states,) - - return hidden_states, output_states - - -class DownBlock3D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - output_scale_factor=1.0, - add_downsample=True, - downsample_padding=1, - ): - super().__init__() - resnets = [] - - for i in range(num_layers): - in_channels = in_channels if i == 0 else out_channels - resnets.append( - ResnetBlock3D( - in_channels=in_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - - self.resnets = nn.ModuleList(resnets) - - if add_downsample: - self.downsamplers = nn.ModuleList( - [ - Downsample3D( - out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" - ) - ] - ) - else: - self.downsamplers = None - - self.gradient_checkpointing = False - - def forward(self, hidden_states, temb=None): - output_states = () - - for resnet in self.resnets: - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs) - - return custom_forward - - hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) - else: - hidden_states = resnet(hidden_states, temb) - - output_states += (hidden_states,) - - if self.downsamplers is not None: - for downsampler in self.downsamplers: - hidden_states = downsampler(hidden_states) - - output_states += (hidden_states,) - - return hidden_states, output_states - - -class CrossAttnUpBlock3D(nn.Module): - def __init__( - self, - in_channels: int, - out_channels: int, - prev_output_channel: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - attn_num_head_channels=1, - cross_attention_dim=1280, - output_scale_factor=1.0, - add_upsample=True, - dual_cross_attention=False, - use_linear_projection=False, - only_cross_attention=False, - upcast_attention=False, - ): - super().__init__() - resnets = [] - attentions = [] - - self.has_cross_attention = True - self.attn_num_head_channels = attn_num_head_channels - - for i in range(num_layers): - res_skip_channels = in_channels if (i == num_layers - 1) else out_channels - resnet_in_channels = prev_output_channel if i == 0 else out_channels - - resnets.append( - ResnetBlock3D( - in_channels=resnet_in_channels + res_skip_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - if dual_cross_attention: - raise NotImplementedError - attentions.append( - Transformer3DModel( - attn_num_head_channels, - out_channels // attn_num_head_channels, - in_channels=out_channels, - num_layers=1, - cross_attention_dim=cross_attention_dim, - norm_num_groups=resnet_groups, - use_linear_projection=use_linear_projection, - only_cross_attention=only_cross_attention, - upcast_attention=upcast_attention, - ) - ) - - self.attentions = nn.ModuleList(attentions) - self.resnets = nn.ModuleList(resnets) - - if add_upsample: - self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)]) - else: - self.upsamplers = None - - self.gradient_checkpointing = False - - def forward( - self, - hidden_states, - res_hidden_states_tuple, - temb=None, - encoder_hidden_states=None, - upsample_size=None, - attention_mask=None, - ): - for resnet, attn in zip(self.resnets, self.attentions): - # pop res hidden states - res_hidden_states = res_hidden_states_tuple[-1] - res_hidden_states_tuple = res_hidden_states_tuple[:-1] - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module, return_dict=None): - def custom_forward(*inputs): - if return_dict is not None: - return module(*inputs, return_dict=return_dict) - else: - return module(*inputs) - - return custom_forward - - hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) - hidden_states = torch.utils.checkpoint.checkpoint( - create_custom_forward(attn, return_dict=False), - hidden_states, - encoder_hidden_states, - )[0] - else: - hidden_states = resnet(hidden_states, temb) - hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - hidden_states = upsampler(hidden_states, upsample_size) - - return hidden_states - - -class UpBlock3D(nn.Module): - def __init__( - self, - in_channels: int, - prev_output_channel: int, - out_channels: int, - temb_channels: int, - dropout: float = 0.0, - num_layers: int = 1, - resnet_eps: float = 1e-6, - resnet_time_scale_shift: str = "default", - resnet_act_fn: str = "swish", - resnet_groups: int = 32, - resnet_pre_norm: bool = True, - output_scale_factor=1.0, - add_upsample=True, - ): - super().__init__() - resnets = [] - - for i in range(num_layers): - res_skip_channels = in_channels if (i == num_layers - 1) else out_channels - resnet_in_channels = prev_output_channel if i == 0 else out_channels - - resnets.append( - ResnetBlock3D( - in_channels=resnet_in_channels + res_skip_channels, - out_channels=out_channels, - temb_channels=temb_channels, - eps=resnet_eps, - groups=resnet_groups, - dropout=dropout, - time_embedding_norm=resnet_time_scale_shift, - non_linearity=resnet_act_fn, - output_scale_factor=output_scale_factor, - pre_norm=resnet_pre_norm, - ) - ) - - self.resnets = nn.ModuleList(resnets) - - if add_upsample: - self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)]) - else: - self.upsamplers = None - - self.gradient_checkpointing = False - - def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None): - for resnet in self.resnets: - # pop res hidden states - res_hidden_states = res_hidden_states_tuple[-1] - res_hidden_states_tuple = res_hidden_states_tuple[:-1] - hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) - - if self.training and self.gradient_checkpointing: - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs) - - return custom_forward - - hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) - else: - hidden_states = resnet(hidden_states, temb) - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - hidden_states = upsampler(hidden_states, upsample_size) - - return hidden_states diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/utils/models.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/utils/models.py deleted file mode 100644 index b6bb21a8b26680b38c3af8278ed139b6628356c5..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_internal/utils/models.py +++ /dev/null @@ -1,39 +0,0 @@ -"""Utilities for defining models -""" - -import operator -from typing import Any, Callable, Type - - -class KeyBasedCompareMixin: - """Provides comparison capabilities that is based on a key""" - - __slots__ = ["_compare_key", "_defining_class"] - - def __init__(self, key: Any, defining_class: Type["KeyBasedCompareMixin"]) -> None: - self._compare_key = key - self._defining_class = defining_class - - def __hash__(self) -> int: - return hash(self._compare_key) - - def __lt__(self, other: Any) -> bool: - return self._compare(other, operator.__lt__) - - def __le__(self, other: Any) -> bool: - return self._compare(other, operator.__le__) - - def __gt__(self, other: Any) -> bool: - return self._compare(other, operator.__gt__) - - def __ge__(self, other: Any) -> bool: - return self._compare(other, operator.__ge__) - - def __eq__(self, other: Any) -> bool: - return self._compare(other, operator.__eq__) - - def _compare(self, other: Any, method: Callable[[Any, Any], bool]) -> bool: - if not isinstance(other, self._defining_class): - return NotImplemented - - return method(self._compare_key, other._compare_key) diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/tenacity/before_sleep.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/tenacity/before_sleep.py deleted file mode 100644 index 8c6167fb3a6b390c5c0a3ba455f76cedf34695f2..0000000000000000000000000000000000000000 --- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/tenacity/before_sleep.py +++ /dev/null @@ -1,71 +0,0 @@ -# Copyright 2016 Julien Danjou -# Copyright 2016 Joshua Harlow -# Copyright 2013-2014 Ray Holder -# -# 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 typing - -from pip._vendor.tenacity import _utils - -if typing.TYPE_CHECKING: - import logging - - from pip._vendor.tenacity import RetryCallState - - -def before_sleep_nothing(retry_state: "RetryCallState") -> None: - """Before call strategy that does nothing.""" - - -def before_sleep_log( - logger: "logging.Logger", - log_level: int, - exc_info: bool = False, -) -> typing.Callable[["RetryCallState"], None]: - """Before call strategy that logs to some logger the attempt.""" - - def log_it(retry_state: "RetryCallState") -> None: - local_exc_info: BaseException | bool | None - - if retry_state.outcome is None: - raise RuntimeError("log_it() called before outcome was set") - - if retry_state.next_action is None: - raise RuntimeError("log_it() called before next_action was set") - - if retry_state.outcome.failed: - ex = retry_state.outcome.exception() - verb, value = "raised", f"{ex.__class__.__name__}: {ex}" - - if exc_info: - local_exc_info = retry_state.outcome.exception() - else: - local_exc_info = False - else: - verb, value = "returned", retry_state.outcome.result() - local_exc_info = False # exc_info does not apply when no exception - - if retry_state.fn is None: - # NOTE(sileht): can't really happen, but we must please mypy - fn_name = " " - else: - fn_name = _utils.get_callback_name(retry_state.fn) - - logger.log( - log_level, - f"Retrying {fn_name} " f"in {retry_state.next_action.sleep} seconds as it {verb} {value}.", - exc_info=local_exc_info, - ) - - return log_it diff --git a/spaces/Autopixel/blurry-faces/README.md b/spaces/Autopixel/blurry-faces/README.md deleted file mode 100644 index 0699cf51a76d7e1a0ae757014fdbfcf96b085796..0000000000000000000000000000000000000000 --- a/spaces/Autopixel/blurry-faces/README.md +++ /dev/null @@ -1,47 +0,0 @@ ---- -title: Blurry Faces -emoji: 🙈 -colorFrom: pink -colorTo: blue -sdk: gradio -app_file: app.py -pinned: false -license: apache-2.0 -duplicated_from: frapochetti/blurry-faces ---- - -# 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`, `streamlit`, or `static` - -`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, or `static` html code). -Path is relative to the root of the repository. - -`models`: _List[string]_ -HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space. -Will be parsed automatically from your code if not specified here. - -`datasets`: _List[string]_ -HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space. -Will be parsed automatically from your code if not specified here. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/Awesimo/jojogan/e4e/editings/sefa.py b/spaces/Awesimo/jojogan/e4e/editings/sefa.py deleted file mode 100644 index db7083ce463b765a7cf452807883a3b85fb63fa5..0000000000000000000000000000000000000000 --- a/spaces/Awesimo/jojogan/e4e/editings/sefa.py +++ /dev/null @@ -1,46 +0,0 @@ -import torch -import numpy as np -from tqdm import tqdm - - -def edit(generator, latents, indices, semantics=1, start_distance=-15.0, end_distance=15.0, num_samples=1, step=11): - - layers, boundaries, values = factorize_weight(generator, indices) - codes = latents.detach().cpu().numpy() # (1,18,512) - - # Generate visualization pages. - distances = np.linspace(start_distance, end_distance, step) - num_sam = num_samples - num_sem = semantics - - edited_latents = [] - for sem_id in tqdm(range(num_sem), desc='Semantic ', leave=False): - boundary = boundaries[sem_id:sem_id + 1] - for sam_id in tqdm(range(num_sam), desc='Sample ', leave=False): - code = codes[sam_id:sam_id + 1] - for col_id, d in enumerate(distances, start=1): - temp_code = code.copy() - temp_code[:, layers, :] += boundary * d - edited_latents.append(torch.from_numpy(temp_code).float().cuda()) - return torch.cat(edited_latents) - - -def factorize_weight(g_ema, layers='all'): - - weights = [] - if layers == 'all' or 0 in layers: - weight = g_ema.conv1.conv.modulation.weight.T - weights.append(weight.cpu().detach().numpy()) - - if layers == 'all': - layers = list(range(g_ema.num_layers - 1)) - else: - layers = [l - 1 for l in layers if l != 0] - - for idx in layers: - weight = g_ema.convs[idx].conv.modulation.weight.T - weights.append(weight.cpu().detach().numpy()) - weight = np.concatenate(weights, axis=1).astype(np.float32) - weight = weight / np.linalg.norm(weight, axis=0, keepdims=True) - eigen_values, eigen_vectors = np.linalg.eig(weight.dot(weight.T)) - return layers, eigen_vectors.T, eigen_values diff --git a/spaces/BAAI/AltDiffusion/style.css b/spaces/BAAI/AltDiffusion/style.css deleted file mode 100644 index d954ce678fed7d0f33bdc6af6764b73e06d6e78a..0000000000000000000000000000000000000000 --- a/spaces/BAAI/AltDiffusion/style.css +++ /dev/null @@ -1,81 +0,0 @@ -.gradio-container { - font-family: 'IBM Plex Sans', sans-serif; -} -.gr-button { - color: white; - /* border-color: black; */ - /* background: black; */ - background: rgb(60, 145, 238); -} -/* input[type='range'] { - accent-color: rgb(60, 145, 238); -} -.dark input[type='range'] { - accent-color: #dfdfdf; -} */ -.container { - max-width: 900px; - margin: auto; - padding-top: 1.5rem; -} -#gallery { - min-height: 22rem; - margin-bottom: 15px; - margin-left: auto; - margin-right: auto; - border-bottom-right-radius: .5rem !important; - border-bottom-left-radius: .5rem !important; -} -#gallery>div>.h-full { - min-height: 20rem; -} -.details:hover { - text-decoration: underline; -} -.gr-button { - white-space: nowrap; -} -/* .gr-button:focus { - border-color: rgb(147 197 253 / var(--tw-border-opacity)); - outline: none; - box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); - --tw-border-opacity: 1; - --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); - --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color); - --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity)); - --tw-ring-opacity: .5; -} */ -.footer { - margin-bottom: 45px; - margin-top: 20px; - /* text-align: center; */ - border-bottom: 1px solid #e5e5e5; -} -.footer>p { - font-size: .8rem; - display: inline-block; - padding: 0 10px; - transform: translateY(10px); - background: white; -} -.footer>p>h4 { - font-size: .20rem; - display: inline-block; - padding: 0 10px; - transform: translateY(10px); - background: white; - font-weight: bold; -} -.dark .footer { - /* border-color: #303030; */ - border-color: rgb(60, 145, 238); -} -.dark .footer>p { - /* background: #0b0f19; */ - background: rgb(60, 145, 238); -} -.prompt h4{ - margin: 1.25em 0 .25em 0; - font-weight: bold; - font-size: 115%; -} \ No newline at end of file diff --git a/spaces/Bart92/RVC_HF/julius/resample.py b/spaces/Bart92/RVC_HF/julius/resample.py deleted file mode 100644 index fd3b9b547d4c33ec7136d32e5f086420d0a72e14..0000000000000000000000000000000000000000 --- a/spaces/Bart92/RVC_HF/julius/resample.py +++ /dev/null @@ -1,216 +0,0 @@ -# File under the MIT license, see https://github.com/adefossez/julius/LICENSE for details. -# Author: adefossez, 2020 -""" -Differentiable, Pytorch based resampling. -Implementation of Julius O. Smith algorithm for resampling. -See https://ccrma.stanford.edu/~jos/resample/ for details. -This implementation is specially optimized for when new_sr / old_sr is a fraction -with a small numerator and denominator when removing the gcd (e.g. new_sr = 700, old_sr = 500). - -Very similar to [bmcfee/resampy](https://github.com/bmcfee/resampy) except this implementation -is optimized for the case mentioned before, while resampy is slower but more general. - -""" - -import math -from typing import Optional - -import torch -from torch.nn import functional as F - -from .core import sinc -from .utils import simple_repr - - -class ResampleFrac(torch.nn.Module): - """ - Resampling from the sample rate `old_sr` to `new_sr`. - """ - def __init__(self, old_sr: int, new_sr: int, zeros: int = 24, rolloff: float = 0.945): - """ - Args: - old_sr (int): sample rate of the input signal x. - new_sr (int): sample rate of the output. - zeros (int): number of zero crossing to keep in the sinc filter. - rolloff (float): use a lowpass filter that is `rolloff * new_sr / 2`, - to ensure sufficient margin due to the imperfection of the FIR filter used. - Lowering this value will reduce anti-aliasing, but will reduce some of the - highest frequencies. - - Shape: - - - Input: `[*, T]` - - Output: `[*, T']` with `T' = int(new_sr * T / old_sr) - - - .. caution:: - After dividing `old_sr` and `new_sr` by their GCD, both should be small - for this implementation to be fast. - - >>> import torch - >>> resample = ResampleFrac(4, 5) - >>> x = torch.randn(1000) - >>> print(len(resample(x))) - 1250 - """ - super().__init__() - if not isinstance(old_sr, int) or not isinstance(new_sr, int): - raise ValueError("old_sr and new_sr should be integers") - gcd = math.gcd(old_sr, new_sr) - self.old_sr = old_sr // gcd - self.new_sr = new_sr // gcd - self.zeros = zeros - self.rolloff = rolloff - - self._init_kernels() - - def _init_kernels(self): - if self.old_sr == self.new_sr: - return - - kernels = [] - sr = min(self.new_sr, self.old_sr) - # rolloff will perform antialiasing filtering by removing the highest frequencies. - # At first I thought I only needed this when downsampling, but when upsampling - # you will get edge artifacts without this, the edge is equivalent to zero padding, - # which will add high freq artifacts. - sr *= self.rolloff - - # The key idea of the algorithm is that x(t) can be exactly reconstructed from x[i] (tensor) - # using the sinc interpolation formula: - # x(t) = sum_i x[i] sinc(pi * old_sr * (i / old_sr - t)) - # We can then sample the function x(t) with a different sample rate: - # y[j] = x(j / new_sr) - # or, - # y[j] = sum_i x[i] sinc(pi * old_sr * (i / old_sr - j / new_sr)) - - # We see here that y[j] is the convolution of x[i] with a specific filter, for which - # we take an FIR approximation, stopping when we see at least `zeros` zeros crossing. - # But y[j+1] is going to have a different set of weights and so on, until y[j + new_sr]. - # Indeed: - # y[j + new_sr] = sum_i x[i] sinc(pi * old_sr * ((i / old_sr - (j + new_sr) / new_sr)) - # = sum_i x[i] sinc(pi * old_sr * ((i - old_sr) / old_sr - j / new_sr)) - # = sum_i x[i + old_sr] sinc(pi * old_sr * (i / old_sr - j / new_sr)) - # so y[j+new_sr] uses the same filter as y[j], but on a shifted version of x by `old_sr`. - # This will explain the F.conv1d after, with a stride of old_sr. - self._width = math.ceil(self.zeros * self.old_sr / sr) - # If old_sr is still big after GCD reduction, most filters will be very unbalanced, i.e., - # they will have a lot of almost zero values to the left or to the right... - # There is probably a way to evaluate those filters more efficiently, but this is kept for - # future work. - idx = torch.arange(-self._width, self._width + self.old_sr).float() - for i in range(self.new_sr): - t = (-i/self.new_sr + idx/self.old_sr) * sr - t = t.clamp_(-self.zeros, self.zeros) - t *= math.pi - window = torch.cos(t/self.zeros/2)**2 - kernel = sinc(t) * window - # Renormalize kernel to ensure a constant signal is preserved. - kernel.div_(kernel.sum()) - kernels.append(kernel) - - self.register_buffer("kernel", torch.stack(kernels).view(self.new_sr, 1, -1)) - - def forward(self, x: torch.Tensor, output_length: Optional[int] = None, full: bool = False): - """ - Resample x. - Args: - x (Tensor): signal to resample, time should be the last dimension - output_length (None or int): This can be set to the desired output length - (last dimension). Allowed values are between 0 and - ceil(length * new_sr / old_sr). When None (default) is specified, the - floored output length will be used. In order to select the largest possible - size, use the `full` argument. - full (bool): return the longest possible output from the input. This can be useful - if you chain resampling operations, and want to give the `output_length` only - for the last one, while passing `full=True` to all the other ones. - """ - if self.old_sr == self.new_sr: - return x - shape = x.shape - length = x.shape[-1] - x = x.reshape(-1, length) - x = F.pad(x[:, None], (self._width, self._width + self.old_sr), mode='replicate') - ys = F.conv1d(x, self.kernel, stride=self.old_sr) # type: ignore - y = ys.transpose(1, 2).reshape(list(shape[:-1]) + [-1]) - - float_output_length = self.new_sr * length / self.old_sr - max_output_length = int(math.ceil(float_output_length)) - default_output_length = int(float_output_length) - if output_length is None: - output_length = max_output_length if full else default_output_length - elif output_length < 0 or output_length > max_output_length: - raise ValueError(f"output_length must be between 0 and {max_output_length}") - else: - if full: - raise ValueError("You cannot pass both full=True and output_length") - return y[..., :output_length] - - def __repr__(self): - return simple_repr(self) - - -def resample_frac(x: torch.Tensor, old_sr: int, new_sr: int, - zeros: int = 24, rolloff: float = 0.945, - output_length: Optional[int] = None, full: bool = False): - """ - Functional version of `ResampleFrac`, refer to its documentation for more information. - - ..warning:: - If you call repeatidly this functions with the same sample rates, then the - resampling kernel will be recomputed everytime. For best performance, you should use - and cache an instance of `ResampleFrac`. - """ - return ResampleFrac(old_sr, new_sr, zeros, rolloff).to(x)(x, output_length, full) - - -# Easier implementations for downsampling and upsampling by a factor of 2 -# Kept for testing and reference - -def _kernel_upsample2_downsample2(zeros): - # Kernel for upsampling and downsampling by a factor of 2. Interestingly, - # it is the same kernel used for both. - win = torch.hann_window(4 * zeros + 1, periodic=False) - winodd = win[1::2] - t = torch.linspace(-zeros + 0.5, zeros - 0.5, 2 * zeros) - t *= math.pi - kernel = (sinc(t) * winodd).view(1, 1, -1) - return kernel - - -def _upsample2(x, zeros=24): - """ - Upsample x by a factor of two. The output will be exactly twice as long as the input. - Args: - x (Tensor): signal to upsample, time should be the last dimension - zeros (int): number of zero crossing to keep in the sinc filter. - - This function is kept only for reference, you should use the more generic `resample_frac` - one. This function does not perform anti-aliasing filtering. - """ - *other, time = x.shape - kernel = _kernel_upsample2_downsample2(zeros).to(x) - out = F.conv1d(x.view(-1, 1, time), kernel, padding=zeros)[..., 1:].view(*other, time) - y = torch.stack([x, out], dim=-1) - return y.view(*other, -1) - - -def _downsample2(x, zeros=24): - """ - Downsample x by a factor of two. The output length is half of the input, ceiled. - Args: - x (Tensor): signal to downsample, time should be the last dimension - zeros (int): number of zero crossing to keep in the sinc filter. - - This function is kept only for reference, you should use the more generic `resample_frac` - one. This function does not perform anti-aliasing filtering. - """ - if x.shape[-1] % 2 != 0: - x = F.pad(x, (0, 1)) - xeven = x[..., ::2] - xodd = x[..., 1::2] - *other, time = xodd.shape - kernel = _kernel_upsample2_downsample2(zeros).to(x) - out = xeven + F.conv1d(xodd.view(-1, 1, time), kernel, padding=zeros)[..., :-1].view( - *other, time) - return out.view(*other, -1).mul(0.5) diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/__main__.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/__main__.py deleted file mode 100644 index 270629fd8067bfc20ed4a0b39d9897791ffa93ab..0000000000000000000000000000000000000000 --- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/rich/__main__.py +++ /dev/null @@ -1,274 +0,0 @@ -import colorsys -import io -from time import process_time - -from pip._vendor.rich import box -from pip._vendor.rich.color import Color -from pip._vendor.rich.console import Console, ConsoleOptions, Group, RenderableType, RenderResult -from pip._vendor.rich.markdown import Markdown -from pip._vendor.rich.measure import Measurement -from pip._vendor.rich.pretty import Pretty -from pip._vendor.rich.segment import Segment -from pip._vendor.rich.style import Style -from pip._vendor.rich.syntax import Syntax -from pip._vendor.rich.table import Table -from pip._vendor.rich.text import Text - - -class ColorBox: - def __rich_console__( - self, console: Console, options: ConsoleOptions - ) -> RenderResult: - for y in range(0, 5): - for x in range(options.max_width): - h = x / options.max_width - l = 0.1 + ((y / 5) * 0.7) - r1, g1, b1 = colorsys.hls_to_rgb(h, l, 1.0) - r2, g2, b2 = colorsys.hls_to_rgb(h, l + 0.7 / 10, 1.0) - bgcolor = Color.from_rgb(r1 * 255, g1 * 255, b1 * 255) - color = Color.from_rgb(r2 * 255, g2 * 255, b2 * 255) - yield Segment("▄", Style(color=color, bgcolor=bgcolor)) - yield Segment.line() - - def __rich_measure__( - self, console: "Console", options: ConsoleOptions - ) -> Measurement: - return Measurement(1, options.max_width) - - -def make_test_card() -> Table: - """Get a renderable that demonstrates a number of features.""" - table = Table.grid(padding=1, pad_edge=True) - table.title = "Rich features" - table.add_column("Feature", no_wrap=True, justify="center", style="bold red") - table.add_column("Demonstration") - - color_table = Table( - box=None, - expand=False, - show_header=False, - show_edge=False, - pad_edge=False, - ) - color_table.add_row( - ( - "✓ [bold green]4-bit color[/]\n" - "✓ [bold blue]8-bit color[/]\n" - "✓ [bold magenta]Truecolor (16.7 million)[/]\n" - "✓ [bold yellow]Dumb terminals[/]\n" - "✓ [bold cyan]Automatic color conversion" - ), - ColorBox(), - ) - - table.add_row("Colors", color_table) - - table.add_row( - "Styles", - "All ansi styles: [bold]bold[/], [dim]dim[/], [italic]italic[/italic], [underline]underline[/], [strike]strikethrough[/], [reverse]reverse[/], and even [blink]blink[/].", - ) - - lorem = "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque in metus sed sapien ultricies pretium a at justo. Maecenas luctus velit et auctor maximus." - lorem_table = Table.grid(padding=1, collapse_padding=True) - lorem_table.pad_edge = False - lorem_table.add_row( - Text(lorem, justify="left", style="green"), - Text(lorem, justify="center", style="yellow"), - Text(lorem, justify="right", style="blue"), - Text(lorem, justify="full", style="red"), - ) - table.add_row( - "Text", - Group( - Text.from_markup( - """Word wrap text. Justify [green]left[/], [yellow]center[/], [blue]right[/] or [red]full[/].\n""" - ), - lorem_table, - ), - ) - - def comparison(renderable1: RenderableType, renderable2: RenderableType) -> Table: - table = Table(show_header=False, pad_edge=False, box=None, expand=True) - table.add_column("1", ratio=1) - table.add_column("2", ratio=1) - table.add_row(renderable1, renderable2) - return table - - table.add_row( - "Asian\nlanguage\nsupport", - ":flag_for_china: 该库支持中文，日文和韩文文本！\n:flag_for_japan: ライブラリは中国語、日本語、韓国語のテキストをサポートしています\n:flag_for_south_korea: 이 라이브러리는 중국어, 일본어 및 한국어 텍스트를 지원합니다", - ) - - markup_example = ( - "[bold magenta]Rich[/] supports a simple [i]bbcode[/i]-like [b]markup[/b] for [yellow]color[/], [underline]style[/], and emoji! " - ":+1: :apple: :ant: :bear: :baguette_bread: :bus: " - ) - table.add_row("Markup", markup_example) - - example_table = Table( - show_edge=False, - show_header=True, - expand=False, - row_styles=["none", "dim"], - box=box.SIMPLE, - ) - example_table.add_column("[green]Date", style="green", no_wrap=True) - example_table.add_column("[blue]Title", style="blue") - example_table.add_column( - "[cyan]Production Budget", - style="cyan", - justify="right", - no_wrap=True, - ) - example_table.add_column( - "[magenta]Box Office", - style="magenta", - justify="right", - no_wrap=True, - ) - example_table.add_row( - "Dec 20, 2019", - "Star Wars: The Rise of Skywalker", - "$275,000,000", - "$375,126,118", - ) - example_table.add_row( - "May 25, 2018", - "[b]Solo[/]: A Star Wars Story", - "$275,000,000", - "$393,151,347", - ) - example_table.add_row( - "Dec 15, 2017", - "Star Wars Ep. VIII: The Last Jedi", - "$262,000,000", - "[bold]$1,332,539,889[/bold]", - ) - example_table.add_row( - "May 19, 1999", - "Star Wars Ep. [b]I[/b]: [i]The phantom Menace", - "$115,000,000", - "$1,027,044,677", - ) - - table.add_row("Tables", example_table) - - code = '''\ -def iter_last(values: Iterable[T]) -> Iterable[Tuple[bool, T]]: - """Iterate and generate a tuple with a flag for last value.""" - iter_values = iter(values) - try: - previous_value = next(iter_values) - except StopIteration: - return - for value in iter_values: - yield False, previous_value - previous_value = value - yield True, previous_value''' - - pretty_data = { - "foo": [ - 3.1427, - ( - "Paul Atreides", - "Vladimir Harkonnen", - "Thufir Hawat", - ), - ], - "atomic": (False, True, None), - } - table.add_row( - "Syntax\nhighlighting\n&\npretty\nprinting", - comparison( - Syntax(code, "python3", line_numbers=True, indent_guides=True), - Pretty(pretty_data, indent_guides=True), - ), - ) - - markdown_example = """\ -# Markdown - -Supports much of the *markdown* __syntax__! - -- Headers -- Basic formatting: **bold**, *italic*, `code` -- Block quotes -- Lists, and more... - """ - table.add_row( - "Markdown", comparison("[cyan]" + markdown_example, Markdown(markdown_example)) - ) - - table.add_row( - "+more!", - """Progress bars, columns, styled logging handler, tracebacks, etc...""", - ) - return table - - -if __name__ == "__main__": # pragma: no cover - - console = Console( - file=io.StringIO(), - force_terminal=True, - ) - test_card = make_test_card() - - # Print once to warm cache - start = process_time() - console.print(test_card) - pre_cache_taken = round((process_time() - start) * 1000.0, 1) - - console.file = io.StringIO() - - start = process_time() - console.print(test_card) - taken = round((process_time() - start) * 1000.0, 1) - - c = Console(record=True) - c.print(test_card) - - print(f"rendered in {pre_cache_taken}ms (cold cache)") - print(f"rendered in {taken}ms (warm cache)") - - from pip._vendor.rich.panel import Panel - - console = Console() - - sponsor_message = Table.grid(padding=1) - sponsor_message.add_column(style="green", justify="right") - sponsor_message.add_column(no_wrap=True) - - sponsor_message.add_row( - "Textualize", - "[u blue link=https://github.com/textualize]https://github.com/textualize", - ) - sponsor_message.add_row( - "Twitter", - "[u blue link=https://twitter.com/willmcgugan]https://twitter.com/willmcgugan", - ) - - intro_message = Text.from_markup( - """\ -We hope you enjoy using Rich! - -Rich is maintained with [red]:heart:[/] by [link=https://www.textualize.io]Textualize.io[/] - -- Will McGugan""" - ) - - message = Table.grid(padding=2) - message.add_column() - message.add_column(no_wrap=True) - message.add_row(intro_message, sponsor_message) - - console.print( - Panel.fit( - message, - box=box.ROUNDED, - padding=(1, 2), - title="[b red]Thanks for trying out Rich!", - border_style="bright_blue", - ), - justify="center", - ) diff --git a/spaces/CVPR/LIVE/model_download/yolov5_model_p5_all.sh b/spaces/CVPR/LIVE/model_download/yolov5_model_p5_all.sh deleted file mode 100644 index a8e11f6c73445e2e7855d7b62c2b8ebbb7236e9d..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/model_download/yolov5_model_p5_all.sh +++ /dev/null @@ -1,8 +0,0 @@ -cd ./yolov5 - -# 下载YOLOv5模型 -wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n.pt -wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt -wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m.pt -wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l.pt -wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x.pt \ No newline at end of file diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/merge.h b/spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/merge.h deleted file mode 100644 index 5a223b60604a60bccc603165963071c7b7b3a24a..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/system/cuda/detail/merge.h +++ /dev/null @@ -1,1018 +0,0 @@ -/****************************************************************************** -j * Copyright (c) 2016, NVIDIA CORPORATION. All rights reserved. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions are met: - * * Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * * Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * * Neither the name of the NVIDIA CORPORATION nor the - * names of its contributors may be used to endorse or promote products - * derived from this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" - * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE - * ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY - * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES - * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; - * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND - * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS - * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - * - ******************************************************************************/ -#pragma once - -#if THRUST_DEVICE_COMPILER == THRUST_DEVICE_COMPILER_NVCC -#include -#include -#include - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - - -namespace thrust -{ -namespace cuda_cub { - -namespace __merge { - - template - Size THRUST_DEVICE_FUNCTION - merge_path(KeysIt1 keys1, - KeysIt2 keys2, - Size keys1_count, - Size keys2_count, - Size diag, - BinaryPred binary_pred) - { - typedef typename iterator_traits ::value_type key1_type; - typedef typename iterator_traits ::value_type key2_type; - - Size keys1_begin = thrust::max (0, diag - keys2_count); - Size keys1_end = thrust::min (diag, keys1_count); - - while (keys1_begin < keys1_end) - { - Size mid = (keys1_begin + keys1_end) >> 1; - key1_type key1 = keys1[mid]; - key2_type key2 = keys2[diag - 1 - mid]; - bool pred = binary_pred(key2, key1); - if (pred) - { - keys1_end = mid; - } - else - { - keys1_begin = mid+1; - } - } - return keys1_begin; - } - - template - THRUST_DEVICE_FUNCTION void - serial_merge(It keys_shared, - int keys1_beg, - int keys2_beg, - int keys1_count, - int keys2_count, - T2 (&output)[ITEMS_PER_THREAD], - int (&indices)[ITEMS_PER_THREAD], - CompareOp compare_op) - { - int keys1_end = keys1_beg + keys1_count; - int keys2_end = keys2_beg + keys2_count; - - typedef typename iterator_value ::type key_type; - - key_type key1 = keys_shared[keys1_beg]; - key_type key2 = keys_shared[keys2_beg]; - - -#pragma unroll - for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM) - { - bool p = (keys2_beg < keys2_end) && - ((keys1_beg >= keys1_end) || - compare_op(key2,key1)); - - output[ITEM] = p ? key2 : key1; - indices[ITEM] = p ? keys2_beg++ : keys1_beg++; - - if (p) - { - key2 = keys_shared[keys2_beg]; - } - else - { - key1 = keys_shared[keys1_beg]; - } - } - } - - template - struct PtxPolicy - { - enum - { - BLOCK_THREADS = _BLOCK_THREADS, - ITEMS_PER_THREAD = _ITEMS_PER_THREAD, - ITEMS_PER_TILE = _BLOCK_THREADS * _ITEMS_PER_THREAD, - }; - - static const cub::BlockLoadAlgorithm LOAD_ALGORITHM = _LOAD_ALGORITHM; - static const cub::CacheLoadModifier LOAD_MODIFIER = _LOAD_MODIFIER; - static const cub::BlockStoreAlgorithm STORE_ALGORITHM = _STORE_ALGORITHM; - }; // PtxPolicy - - template - struct PartitionAgent - { - template - struct PtxPlan : PtxPolicy<256> {}; - - typedef core::specialize_plan ptx_plan; - - THRUST_AGENT_ENTRY(KeysIt1 keys1, - KeysIt2 keys2, - Size keys1_count, - Size keys2_count, - Size num_partitions, - Size* merge_partitions, - CompareOp compare_op, - int items_per_tile, - char* /*shmem*/) - { - Size partition_idx = blockDim.x * blockIdx.x + threadIdx.x; - if (partition_idx < num_partitions) - { - Size partition_at = thrust::min(partition_idx * items_per_tile, - keys1_count + keys2_count); - Size partition_diag = merge_path(keys1, - keys2, - keys1_count, - keys2_count, - partition_at, - compare_op); - merge_partitions[partition_idx] = partition_diag; - } - } - }; // struct PartitionAgent - - - template - struct Tuning; - - namespace mpl = thrust::detail::mpl::math; - - template - struct items_per_thread - { - enum - { - ITEMS_PER_THREAD = - mpl::min< - int, - NOMINAL_4B_ITEMS_PER_THREAD, - mpl::max< - int, - 1, - (NOMINAL_4B_ITEMS_PER_THREAD * 4 / INPUT_SIZE)>::value>::value, - value = mpl::is_odd - struct Tuning - struct Tuning - struct Tuning - struct Tuning - struct integer_constant : thrust::detail::integral_constant - struct MergeAgent - { - typedef typename iterator_traits ::value_type key1_type; - typedef typename iterator_traits ::value_type key2_type; - typedef typename iterator_traits ::value_type item1_type; - typedef typename iterator_traits ::value_type item2_type; - - typedef key1_type key_type; - typedef item1_type item_type; - - typedef typename thrust::detail::conditional< - MERGE_ITEMS::value, - integer_constant - struct PtxPlan : Tuning ::type::type ptx_plan; - - typedef typename ptx_plan::KeysLoadIt1 KeysLoadIt1; - typedef typename ptx_plan::KeysLoadIt2 KeysLoadIt2; - typedef typename ptx_plan::ItemsLoadIt1 ItemsLoadIt1; - typedef typename ptx_plan::ItemsLoadIt2 ItemsLoadIt2; - typedef typename ptx_plan::BlockLoadKeys1 BlockLoadKeys1; - typedef typename ptx_plan::BlockLoadKeys2 BlockLoadKeys2; - typedef typename ptx_plan::BlockLoadItems1 BlockLoadItems1; - typedef typename ptx_plan::BlockLoadItems2 BlockLoadItems2; - typedef typename ptx_plan::BlockStoreKeys BlockStoreKeys; - typedef typename ptx_plan::BlockStoreItems BlockStoreItems; - typedef typename ptx_plan::TempStorage TempStorage; - - enum - { - ITEMS_PER_THREAD = ptx_plan::ITEMS_PER_THREAD, - BLOCK_THREADS = ptx_plan::BLOCK_THREADS, - ITEMS_PER_TILE = ptx_plan::ITEMS_PER_TILE - }; - - struct impl - { - //--------------------------------------------------------------------- - // Per thread data - //--------------------------------------------------------------------- - - TempStorage& storage; - KeysLoadIt1 keys1_in; - KeysLoadIt2 keys2_in; - ItemsLoadIt1 items1_in; - ItemsLoadIt2 items2_in; - Size keys1_count; - Size keys2_count; - KeysOutputIt keys_out; - ItemsOutputIt items_out; - CompareOp compare_op; - Size* merge_partitions; - - //--------------------------------------------------------------------- - // Utility functions - //--------------------------------------------------------------------- - - template - THRUST_DEVICE_FUNCTION void - gmem_to_reg(T (&output)[ITEMS_PER_THREAD], - It1 input1, - It2 input2, - int count1, - int count2) - { - if (IS_FULL_TILE) - { -#pragma unroll - for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM) - { - int idx = BLOCK_THREADS * ITEM + threadIdx.x; - if (idx < count1) - output[ITEM] = input1[idx]; - else - output[ITEM] = input2[idx - count1]; - } - } - else - { -#pragma unroll - for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM) - { - int idx = BLOCK_THREADS * ITEM + threadIdx.x; - if (idx < count1 + count2) - { - if (idx < count1) - output[ITEM] = input1[idx]; - else - output[ITEM] = input2[idx - count1]; - } - } - } - } - - template - THRUST_DEVICE_FUNCTION void - reg_to_shared(It output, - T (&input)[ITEMS_PER_THREAD]) - { -#pragma unroll - for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM) - { - int idx = BLOCK_THREADS * ITEM + threadIdx.x; - output[idx] = input[ITEM]; - } - } - - //--------------------------------------------------------------------- - // Tile processing - //--------------------------------------------------------------------- - - template - void THRUST_DEVICE_FUNCTION - consume_tile(Size tile_idx, - Size tile_base, - int num_remaining) - { - using core::sync_threadblock; - using core::uninitialized_array; - - Size partition_beg = merge_partitions[tile_idx + 0]; - Size partition_end = merge_partitions[tile_idx + 1]; - - Size diag0 = ITEMS_PER_TILE * tile_idx; - Size diag1 = thrust::min(keys1_count + keys2_count, diag0 + ITEMS_PER_TILE); - - // compute bounding box for keys1 & keys2 - // - Size keys1_beg = partition_beg; - Size keys1_end = partition_end; - Size keys2_beg = diag0 - keys1_beg; - Size keys2_end = diag1 - keys1_end; - - // number of keys per tile - // - int num_keys1 = static_cast (keys1_end - keys1_beg); - int num_keys2 = static_cast (keys2_end - keys2_beg); - - key_type keys_loc[ITEMS_PER_THREAD]; - gmem_to_reg (keys_loc, - keys1_in + keys1_beg, - keys2_in + keys2_beg, - num_keys1, - num_keys2); - reg_to_shared(&storage.keys_shared[0], keys_loc); - - sync_threadblock(); - - // use binary search in shared memory - // to find merge path for each of thread - // we can use int type here, because the number of - // items in shared memory is limited - // - int diag0_loc = min (num_keys1 + num_keys2, - ITEMS_PER_THREAD * threadIdx.x); - - int keys1_beg_loc = merge_path(&storage.keys_shared[0], - &storage.keys_shared[num_keys1], - num_keys1, - num_keys2, - diag0_loc, - compare_op); - int keys1_end_loc = num_keys1; - int keys2_beg_loc = diag0_loc - keys1_beg_loc; - int keys2_end_loc = num_keys2; - - int num_keys1_loc = keys1_end_loc - keys1_beg_loc; - int num_keys2_loc = keys2_end_loc - keys2_beg_loc; - - // perform serial merge - // - int indices[ITEMS_PER_THREAD]; - - serial_merge(&storage.keys_shared[0], - keys1_beg_loc, - keys2_beg_loc + num_keys1, - num_keys1_loc, - num_keys2_loc, - keys_loc, - indices, - compare_op); - - sync_threadblock(); - - // write keys - // - if (IS_FULL_TILE) - { - BlockStoreKeys(storage.store_keys) - .Store(keys_out + tile_base, keys_loc); - } - else - { - BlockStoreKeys(storage.store_keys) - .Store(keys_out + tile_base, keys_loc, num_remaining); - } - - // if items are provided, merge them - if (MERGE_ITEMS::value) - { - item_type items_loc[ITEMS_PER_THREAD]; - gmem_to_reg (items_loc, - items1_in + keys1_beg, - items2_in + keys2_beg, - num_keys1, - num_keys2); - - sync_threadblock(); - - reg_to_shared(&storage.items_shared[0], items_loc); - - sync_threadblock(); - - // gather items from shared mem - // -#pragma unroll - for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM) - { - items_loc[ITEM] = storage.items_shared[indices[ITEM]]; - } - - sync_threadblock(); - - // write form reg to gmem - // - if (IS_FULL_TILE) - { - BlockStoreItems(storage.store_items) - .Store(items_out + tile_base, items_loc); - } - else - { - BlockStoreItems(storage.store_items) - .Store(items_out + tile_base, items_loc, num_remaining); - } - } - } - - //--------------------------------------------------------------------- - // Constructor - //--------------------------------------------------------------------- - - THRUST_DEVICE_FUNCTION - impl(TempStorage& storage_, - KeysLoadIt1 keys1_in_, - KeysLoadIt2 keys2_in_, - ItemsLoadIt1 items1_in_, - ItemsLoadIt2 items2_in_, - Size keys1_count_, - Size keys2_count_, - KeysOutputIt keys_out_, - ItemsOutputIt items_out_, - CompareOp compare_op_, - Size* merge_partitions_) - : storage(storage_), - keys1_in(keys1_in_), - keys2_in(keys2_in_), - items1_in(items1_in_), - items2_in(items2_in_), - keys1_count(keys1_count_), - keys2_count(keys2_count_), - keys_out(keys_out_), - items_out(items_out_), - compare_op(compare_op_), - merge_partitions(merge_partitions_) - { - // XXX with 8.5 chaging type to Size (or long long) results in error! - int tile_idx = blockIdx.x; - Size tile_base = tile_idx * ITEMS_PER_TILE; - int items_in_tile = static_cast ( - min (ITEMS_PER_TILE, - keys1_count + keys2_count - tile_base)); - if (items_in_tile == ITEMS_PER_TILE) - { - // full tile - consume_tile (tile_idx, - tile_base, - ITEMS_PER_TILE); - } - else - { - // partial tile - consume_tile (tile_idx, - tile_base, - items_in_tile); - } - } - }; // struct impl - - //--------------------------------------------------------------------- - // Agent entry point - //--------------------------------------------------------------------- - - THRUST_AGENT_ENTRY(KeysIt1 keys1_in, - KeysIt2 keys2_in, - ItemsIt1 items1_in, - ItemsIt2 items2_in, - Size keys1_count, - Size keys2_count, - KeysOutputIt keys_out, - ItemsOutputIt items_out, - CompareOp compare_op, - Size* merge_partitions, - char* shmem) - { - TempStorage& storage = *reinterpret_cast - cudaError_t THRUST_RUNTIME_FUNCTION - doit_step(void* d_temp_storage, - size_t& temp_storage_bytes, - KeysIt1 keys1, - KeysIt2 keys2, - ItemsIt1 items1, - ItemsIt2 items2, - Size num_keys1, - Size num_keys2, - KeysOutputIt keys_result, - ItemsOutputIt items_result, - CompareOp compare_op, - cudaStream_t stream, - bool debug_sync) - { - if (num_keys1 + num_keys2 == 0) - return cudaErrorNotSupported; - - using core::AgentPlan; - using core::get_agent_plan; - typedef core::AgentLauncher< - MergeAgent - THRUST_RUNTIME_FUNCTION - pair & policy, - KeysIt1 keys1_first, - KeysIt1 keys1_last, - KeysIt2 keys2_first, - KeysIt2 keys2_last, - ItemsIt1 items1_first, - ItemsIt2 items2_first, - KeysOutputIt keys_result, - ItemsOutputIt items_result, - CompareOp compare_op) - { - typedef typename iterator_traits ::difference_type size_type; - - size_type num_keys1 - = static_cast (thrust::distance(keys1_first, keys1_last)); - size_type num_keys2 - = static_cast (thrust::distance(keys2_first, keys2_last)); - - size_type const count = num_keys1 + num_keys2; - - if (count == 0) - return thrust::make_pair(keys_result, items_result); - - size_t storage_size = 0; - cudaStream_t stream = cuda_cub::stream(policy); - bool debug_sync = THRUST_DEBUG_SYNC_FLAG; - - cudaError_t status; - status = doit_step (NULL, - storage_size, - keys1_first, - keys2_first, - items1_first, - items2_first, - num_keys1, - num_keys2, - keys_result, - items_result, - compare_op, - stream, - debug_sync); - cuda_cub::throw_on_error(status, "merge: failed on 1st step"); - - // Allocate temporary storage. - thrust::detail::temporary_array (ptr, - storage_size, - keys1_first, - keys2_first, - items1_first, - items2_first, - num_keys1, - num_keys2, - keys_result, - items_result, - compare_op, - stream, - debug_sync); - cuda_cub::throw_on_error(status, "merge: failed on 2nd step"); - - status = cuda_cub::synchronize(policy); - cuda_cub::throw_on_error(status, "merge: failed to synchronize"); - - return thrust::make_pair(keys_result + count, items_result + count); - } -} // namespace __merge - - -//------------------------- -// Thrust API entry points -//------------------------- - - -__thrust_exec_check_disable__ -template -ResultIt __host__ __device__ -merge(execution_policy & policy, - KeysIt1 keys1_first, - KeysIt1 keys1_last, - KeysIt2 keys2_first, - KeysIt2 keys2_last, - ResultIt result, - CompareOp compare_op) - -{ - ResultIt ret = result; - if (__THRUST_HAS_CUDART__) - { - typedef typename thrust::iterator_value ::type keys_type; - // - keys_type* null_ = NULL; - // - ret = __merge::merge -ResultIt __host__ __device__ -merge(execution_policy & policy, - KeysIt1 keys1_first, - KeysIt1 keys1_last, - KeysIt2 keys2_first, - KeysIt2 keys2_last, - ResultIt result) -{ - typedef typename thrust::iterator_value ::type keys_type; - return cuda_cub::merge(policy, - keys1_first, - keys1_last, - keys2_first, - keys2_last, - result, - less ()); -} - -__thrust_exec_check_disable__ -template -pair &policy, - KeysIt1 keys1_first, - KeysIt1 keys1_last, - KeysIt2 keys2_first, - KeysIt2 keys2_last, - ItemsIt1 items1_first, - ItemsIt2 items2_first, - KeysOutputIt keys_result, - ItemsOutputIt items_result, - CompareOp compare_op) -{ - pair -pair &policy, - KeysIt1 keys1_first, - KeysIt1 keys1_last, - KeysIt2 keys2_first, - KeysIt2 keys2_last, - ItemsIt1 items1_first, - ItemsIt2 items2_first, - KeysOutputIt keys_result, - ItemsOutputIt items_result) -{ - typedef typename thrust::iterator_value ::type keys_type; - return cuda_cub::merge_by_key(policy, - keys1_first, - keys1_last, - keys2_first, - keys2_last, - items1_first, - items2_first, - keys_result, - items_result, - thrust::less ()); -} - - -} // namespace cuda_cub -} // end namespace thrust -#endif diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/adjacent_difference.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/adjacent_difference.h deleted file mode 100644 index c6b0ee1b246ef2f18968a05458204236d2d8d53d..0000000000000000000000000000000000000000 --- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/adjacent_difference.h +++ /dev/null @@ -1,74 +0,0 @@ -/* - * Copyright 2008-2013 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 adjacent_difference.h - * \brief Sequential implementation of adjacent_difference. - */ - -#pragma once - -#include -#include -#include - -namespace thrust -{ -namespace system -{ -namespace detail -{ -namespace sequential -{ - - -__thrust_exec_check_disable__ -template -__host__ __device__ -OutputIterator adjacent_difference(sequential::execution_policy &, - InputIterator first, - InputIterator last, - OutputIterator result, - BinaryFunction binary_op) -{ - typedef typename thrust::iterator_traits ::value_type InputType; - - if(first == last) - return result; - - InputType curr = *first; - - *result = curr; - - while(++first != last) - { - InputType next = *first; - *(++result) = binary_op(next, curr); - curr = next; - } - - return ++result; -} - - -} // end namespace sequential -} // end namespace detail -} // end namespace system -} // end namespace thrust - diff --git a/spaces/CVPR/regionclip-demo/detectron2/layers/aspp.py b/spaces/CVPR/regionclip-demo/detectron2/layers/aspp.py deleted file mode 100644 index 14861aa9ede4fea6a69a49f189bcab997b558148..0000000000000000000000000000000000000000 --- a/spaces/CVPR/regionclip-demo/detectron2/layers/aspp.py +++ /dev/null @@ -1,144 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. - -from copy import deepcopy -import fvcore.nn.weight_init as weight_init -import torch -from torch import nn -from torch.nn import functional as F - -from .batch_norm import get_norm -from .blocks import DepthwiseSeparableConv2d -from .wrappers import Conv2d - - -class ASPP(nn.Module): - """ - Atrous Spatial Pyramid Pooling (ASPP). - """ - - def __init__( - self, - in_channels, - out_channels, - dilations, - *, - norm, - activation, - pool_kernel_size=None, - dropout: float = 0.0, - use_depthwise_separable_conv=False, - ): - """ - Args: - in_channels (int): number of input channels for ASPP. - out_channels (int): number of output channels. - dilations (list): a list of 3 dilations in ASPP. - norm (str or callable): normalization for all conv layers. - See :func:`layers.get_norm` for supported format. norm is - applied to all conv layers except the conv following - global average pooling. - activation (callable): activation function. - pool_kernel_size (tuple, list): the average pooling size (kh, kw) - for image pooling layer in ASPP. If set to None, it always - performs global average pooling. If not None, it must be - divisible by the shape of inputs in forward(). It is recommended - to use a fixed input feature size in training, and set this - option to match this size, so that it performs global average - pooling in training, and the size of the pooling window stays - consistent in inference. - dropout (float): apply dropout on the output of ASPP. It is used in - the official DeepLab implementation with a rate of 0.1: - https://github.com/tensorflow/models/blob/21b73d22f3ed05b650e85ac50849408dd36de32e/research/deeplab/model.py#L532 # noqa - use_depthwise_separable_conv (bool): use DepthwiseSeparableConv2d - for 3x3 convs in ASPP, proposed in :paper:`DeepLabV3+`. - """ - super(ASPP, self).__init__() - assert len(dilations) == 3, "ASPP expects 3 dilations, got {}".format(len(dilations)) - self.pool_kernel_size = pool_kernel_size - self.dropout = dropout - use_bias = norm == "" - self.convs = nn.ModuleList() - # conv 1x1 - self.convs.append( - Conv2d( - in_channels, - out_channels, - kernel_size=1, - bias=use_bias, - norm=get_norm(norm, out_channels), - activation=deepcopy(activation), - ) - ) - weight_init.c2_xavier_fill(self.convs[-1]) - # atrous convs - for dilation in dilations: - if use_depthwise_separable_conv: - self.convs.append( - DepthwiseSeparableConv2d( - in_channels, - out_channels, - kernel_size=3, - padding=dilation, - dilation=dilation, - norm1=norm, - activation1=deepcopy(activation), - norm2=norm, - activation2=deepcopy(activation), - ) - ) - else: - self.convs.append( - Conv2d( - in_channels, - out_channels, - kernel_size=3, - padding=dilation, - dilation=dilation, - bias=use_bias, - norm=get_norm(norm, out_channels), - activation=deepcopy(activation), - ) - ) - weight_init.c2_xavier_fill(self.convs[-1]) - # image pooling - # We do not add BatchNorm because the spatial resolution is 1x1, - # the original TF implementation has BatchNorm. - if pool_kernel_size is None: - image_pooling = nn.Sequential( - nn.AdaptiveAvgPool2d(1), - Conv2d(in_channels, out_channels, 1, bias=True, activation=deepcopy(activation)), - ) - else: - image_pooling = nn.Sequential( - nn.AvgPool2d(kernel_size=pool_kernel_size, stride=1), - Conv2d(in_channels, out_channels, 1, bias=True, activation=deepcopy(activation)), - ) - weight_init.c2_xavier_fill(image_pooling[1]) - self.convs.append(image_pooling) - - self.project = Conv2d( - 5 * out_channels, - out_channels, - kernel_size=1, - bias=use_bias, - norm=get_norm(norm, out_channels), - activation=deepcopy(activation), - ) - weight_init.c2_xavier_fill(self.project) - - def forward(self, x): - size = x.shape[-2:] - if self.pool_kernel_size is not None: - if size[0] % self.pool_kernel_size[0] or size[1] % self.pool_kernel_size[1]: - raise ValueError( - "`pool_kernel_size` must be divisible by the shape of inputs. " - "Input size: {} `pool_kernel_size`: {}".format(size, self.pool_kernel_size) - ) - res = [] - for conv in self.convs: - res.append(conv(x)) - res[-1] = F.interpolate(res[-1], size=size, mode="bilinear", align_corners=False) - res = torch.cat(res, dim=1) - res = self.project(res) - res = F.dropout(res, self.dropout, training=self.training) if self.dropout > 0 else res - return res diff --git a/spaces/CXD200/QSign/Dockerfile b/spaces/CXD200/QSign/Dockerfile deleted file mode 100644 index 197343d759e41dd113b8ae94765f408a3f789f0c..0000000000000000000000000000000000000000 --- a/spaces/CXD200/QSign/Dockerfile +++ /dev/null @@ -1,15 +0,0 @@ -#程序开源地址 https://github.com/fuqiuluo/unidbg-fetch-qsign - -FROM openjdk:11.0-jdk - -ENV TZ Asia/Shanghai - -WORKDIR /app - -COPY unidbg-fetch-qsign /app - -CMD bash bin/unidbg-fetch-qsign --host=0.0.0.0 --port=7860 --count=5 --library=txlib --android_id= - -EXPOSE 7860 - -#抱脸推荐项目 https://github.com/CikeyQi/QQsign_docs \ No newline at end of file diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/MpoImagePlugin.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/MpoImagePlugin.py deleted file mode 100644 index f9261c77d6862d7def90c6136dff6449241b0690..0000000000000000000000000000000000000000 --- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/MpoImagePlugin.py +++ /dev/null @@ -1,197 +0,0 @@ -# -# The Python Imaging Library. -# $Id$ -# -# MPO file handling -# -# See "Multi-Picture Format" (CIPA DC-007-Translation 2009, Standard of the -# Camera & Imaging Products Association) -# -# The multi-picture object combines multiple JPEG images (with a modified EXIF -# data format) into a single file. While it can theoretically be used much like -# a GIF animation, it is commonly used to represent 3D photographs and is (as -# of this writing) the most commonly used format by 3D cameras. -# -# History: -# 2014-03-13 Feneric Created -# -# See the README file for information on usage and redistribution. -# - -import itertools -import os -import struct - -from . import ( - ExifTags, - Image, - ImageFile, - ImageSequence, - JpegImagePlugin, - TiffImagePlugin, -) -from ._binary import i16be as i16 -from ._binary import o32le - -# def _accept(prefix): -# return JpegImagePlugin._accept(prefix) - - -def _save(im, fp, filename): - JpegImagePlugin._save(im, fp, filename) - - -def _save_all(im, fp, filename): - append_images = im.encoderinfo.get("append_images", []) - if not append_images: - try: - animated = im.is_animated - except AttributeError: - animated = False - if not animated: - _save(im, fp, filename) - return - - mpf_offset = 28 - offsets = [] - for imSequence in itertools.chain([im], append_images): - for im_frame in ImageSequence.Iterator(imSequence): - if not offsets: - # APP2 marker - im_frame.encoderinfo["extra"] = ( - b"\xFF\xE2" + struct.pack(">H", 6 + 82) + b"MPF\0" + b" " * 82 - ) - exif = im_frame.encoderinfo.get("exif") - if isinstance(exif, Image.Exif): - exif = exif.tobytes() - im_frame.encoderinfo["exif"] = exif - if exif: - mpf_offset += 4 + len(exif) - - JpegImagePlugin._save(im_frame, fp, filename) - offsets.append(fp.tell()) - else: - im_frame.save(fp, "JPEG") - offsets.append(fp.tell() - offsets[-1]) - - ifd = TiffImagePlugin.ImageFileDirectory_v2() - ifd[0xB000] = b"0100" - ifd[0xB001] = len(offsets) - - mpentries = b"" - data_offset = 0 - for i, size in enumerate(offsets): - if i == 0: - mptype = 0x030000 # Baseline MP Primary Image - else: - mptype = 0x000000 # Undefined - mpentries += struct.pack(" - - ) -} - -export function Layout2() { - return ( ---- --- --- --- --- - - ) -} - -export function Layout3() { - return ( ---- --- --- --- --- - - ) -} - -export function Layout4() { - return ( ---- --- --- ----- --- - - ) -} - - -export function Layout5() { - return ( ---- --- --- --- - - ) -} - -export function Layout6() { - return ( ---- --- --- --- --- - - ) -} - -// export const layouts = { Layout1, Layout2, Layout3, Layout4, Layout5, Layout6 } -export const allLayouts = { - // Layout1, - // Layout2, - // Layout3, - // Layout4, - Layout5, - // Layout6 - } - -export type LayoutName = keyof typeof allLayouts - -export function getRandomLayoutName(): LayoutName { - return pick(Object.keys(allLayouts) as LayoutName[]) as LayoutName -} - -export function getRandomLayoutNames(): LayoutName[] { - return Object.keys(allLayouts).sort(() => Math.random() - 0.5) as LayoutName[] -} \ No newline at end of file diff --git a/spaces/Djacon/emotion_detection/static/analytics.html b/spaces/Djacon/emotion_detection/static/analytics.html deleted file mode 100644 index 0216d55fd91d9a1c758bdf5b8e4410fc223df4db..0000000000000000000000000000000000000000 --- a/spaces/Djacon/emotion_detection/static/analytics.html +++ /dev/null @@ -1,301 +0,0 @@ - - - - - - - ---- --- --- --- --- Text2Feature | Analytics - - - - - - - - - - - - - -- - -- - - - - \ No newline at end of file diff --git a/spaces/DragGan/DragGan-Inversion/PTI/models/e4e/latent_codes_pool.py b/spaces/DragGan/DragGan-Inversion/PTI/models/e4e/latent_codes_pool.py deleted file mode 100644 index 0281d4b5e80f8eb26e824fa35b4f908dcb6634e6..0000000000000000000000000000000000000000 --- a/spaces/DragGan/DragGan-Inversion/PTI/models/e4e/latent_codes_pool.py +++ /dev/null @@ -1,55 +0,0 @@ -import random -import torch - - -class LatentCodesPool: - """This class implements latent codes buffer that stores previously generated w latent codes. - This buffer enables us to update discriminators using a history of generated w's - rather than the ones produced by the latest encoder. - """ - - def __init__(self, pool_size): - """Initialize the ImagePool class - Parameters: - pool_size (int) -- the size of image buffer, if pool_size=0, no buffer will be created - """ - self.pool_size = pool_size - if self.pool_size > 0: # create an empty pool - self.num_ws = 0 - self.ws = [] - - def query(self, ws): - """Return w's from the pool. - Parameters: - ws: the latest generated w's from the generator - Returns w's from the buffer. - By 50/100, the buffer will return input w's. - By 50/100, the buffer will return w's previously stored in the buffer, - and insert the current w's to the buffer. - """ - if self.pool_size == 0: # if the buffer size is 0, do nothing - return ws - return_ws = [] - for w in ws: # ws.shape: (batch, 512) or (batch, n_latent, 512) - # w = torch.unsqueeze(image.data, 0) - if w.ndim == 2: - i = random.randint(0, len(w) - 1) # apply a random latent index as a candidate - w = w[i] - self.handle_w(w, return_ws) - return_ws = torch.stack(return_ws, 0) # collect all the images and return - return return_ws - - def handle_w(self, w, return_ws): - if self.num_ws < self.pool_size: # if the buffer is not full; keep inserting current codes to the buffer - self.num_ws = self.num_ws + 1 - self.ws.append(w) - return_ws.append(w) - else: - p = random.uniform(0, 1) - if p > 0.5: # by 50% chance, the buffer will return a previously stored latent code, and insert the current code into the buffer - random_id = random.randint(0, self.pool_size - 1) # randint is inclusive - tmp = self.ws[random_id].clone() - self.ws[random_id] = w - return_ws.append(tmp) - else: # by another 50% chance, the buffer will return the current image - return_ws.append(w) diff --git a/spaces/Eddycrack864/Applio-Inference/julius/fftconv.py b/spaces/Eddycrack864/Applio-Inference/julius/fftconv.py deleted file mode 100644 index 1920e5369bb49b76eeea1832b7be2a0ddbc8db6b..0000000000000000000000000000000000000000 --- a/spaces/Eddycrack864/Applio-Inference/julius/fftconv.py +++ /dev/null @@ -1,183 +0,0 @@ -# File under the MIT license, see https://github.com/adefossez/julius/LICENSE for details. -# Author: adefossez, 2020 - -""" -Implementation of a FFT based 1D convolution in PyTorch. -While FFT is used in CUDNN for small kernel sizes, it is not the case for long ones, e.g. 512. -This module implements efficient FFT based convolutions for such convolutions. A typical -application is for evaluationg FIR filters with a long receptive field, typically -evaluated with a stride of 1. -""" -from typing import Optional - -import torch -try: - import torch.fft as new_fft -except ImportError: - new_fft = None # type: ignore -from torch.nn import functional as F - -from .core import pad_to, unfold -from .utils import simple_repr - - -# This is quite verbose, but sadly needed to make TorchScript happy. -def _new_rfft(x: torch.Tensor): - z = new_fft.rfft(x, dim=-1) - return torch.view_as_real(z) - - -def _old_rfft(x: torch.Tensor): - return torch.rfft(x, 1) # type: ignore - - -def _old_irfft(x: torch.Tensor, length: int): - result = torch.irfft(x, 1, signal_sizes=(length,)) # type: ignore - return result - - -def _new_irfft(x: torch.Tensor, length: int): - x = torch.view_as_complex(x) - return new_fft.irfft(x, length, dim=-1) - - -if new_fft is None: - _rfft = _old_rfft - _irfft = _old_irfft -else: - _rfft = _new_rfft - _irfft = _new_irfft - - -def _compl_mul_conjugate(a: torch.Tensor, b: torch.Tensor): - """ - Given a and b two tensors of dimension 4 - with the last dimension being the real and imaginary part, - returns a multiplied by the conjugate of b, the multiplication - being with respect to the second dimension. - - """ - # PyTorch 1.7 supports complex number, but not for all operations. - # Once the support is widespread, this can likely go away. - - op = "bcft,dct->bdft" - return torch.stack([ - torch.einsum(op, a[..., 0], b[..., 0]) + torch.einsum(op, a[..., 1], b[..., 1]), - torch.einsum(op, a[..., 1], b[..., 0]) - torch.einsum(op, a[..., 0], b[..., 1]) - ], - dim=-1) - - -def fft_conv1d( - input: torch.Tensor, weight: torch.Tensor, - bias: Optional[torch.Tensor] = None, stride: int = 1, padding: int = 0, - block_ratio: float = 5): - """ - Same as `torch.nn.functional.conv1d` but using FFT for the convolution. - Please check PyTorch documentation for more information. - - Args: - input (Tensor): input signal of shape `[B, C, T]`. - weight (Tensor): weight of the convolution `[D, C, K]` with `D` the number - of output channels. - bias (Tensor or None): if not None, bias term for the convolution. - stride (int): stride of convolution. - padding (int): padding to apply to the input. - block_ratio (float): can be tuned for speed. The input is splitted in chunks - with a size of `int(block_ratio * kernel_size)`. - - Shape: - - - Inputs: `input` is `[B, C, T]`, `weight` is `[D, C, K]` and bias is `[D]`. - - Output: `(*, T)` - - - ..note:: - This function is faster than `torch.nn.functional.conv1d` only in specific cases. - Typically, the kernel size should be of the order of 256 to see any real gain, - for a stride of 1. - - ..Warning:: - Dilation and groups are not supported at the moment. This function might use - more memory than the default Conv1d implementation. - """ - input = F.pad(input, (padding, padding)) - batch, channels, length = input.shape - out_channels, _, kernel_size = weight.shape - - if length < kernel_size: - raise RuntimeError(f"Input should be at least as large as the kernel size {kernel_size}, " - f"but it is only {length} samples long.") - if block_ratio < 1: - raise RuntimeError("Block ratio must be greater than 1.") - - # We are going to process the input blocks by blocks, as for some reason it is faster - # and less memory intensive (I think the culprit is `torch.einsum`. - block_size: int = min(int(kernel_size * block_ratio), length) - fold_stride = block_size - kernel_size + 1 - weight = pad_to(weight, block_size) - weight_z = _rfft(weight) - - # We pad the input and get the different frames, on which - frames = unfold(input, block_size, fold_stride) - - frames_z = _rfft(frames) - out_z = _compl_mul_conjugate(frames_z, weight_z) - out = _irfft(out_z, block_size) - # The last bit is invalid, because FFT will do a circular convolution. - out = out[..., :-kernel_size + 1] - out = out.reshape(batch, out_channels, -1) - out = out[..., ::stride] - target_length = (length - kernel_size) // stride + 1 - out = out[..., :target_length] - if bias is not None: - out += bias[:, None] - return out - - -class FFTConv1d(torch.nn.Module): - """ - Same as `torch.nn.Conv1d` but based on `fft_conv1d`. - Please check PyTorch documentation for more information. - - Args: - in_channels (int): number of input channels. - out_channels (int): number of output channels. - kernel_size (int): kernel size of convolution. - stride (int): stride of convolution. - padding (int): padding to apply to the input. - bias (bool): if True, use a bias term. - - ..note:: - This module is faster than `torch.nn.Conv1d` only in specific cases. - Typically, `kernel_size` should be of the order of 256 to see any real gain, - for a stride of 1. - - ..warning:: - Dilation and groups are not supported at the moment. This module might use - more memory than the default Conv1d implementation. - - >>> fftconv = FFTConv1d(12, 24, 128, 4) - >>> x = torch.randn(4, 12, 1024) - >>> print(list(fftconv(x).shape)) - [4, 24, 225] - """ - def __init__(self, in_channels: int, out_channels: int, kernel_size: int, - stride: int = 1, padding: int = 0, bias: bool = True): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.stride = stride - self.padding = padding - - conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size, bias=bias) - self.weight = conv.weight - self.bias = conv.bias - - def forward(self, input: torch.Tensor): - return fft_conv1d( - input, self.weight, self.bias, self.stride, self.padding) - - def __repr__(self): - return simple_repr(self, overrides={"bias": self.bias is not None}) diff --git a/spaces/ElainaFanBoy/MusicGen/audiocraft/data/audio_dataset.py b/spaces/ElainaFanBoy/MusicGen/audiocraft/data/audio_dataset.py deleted file mode 100644 index cf21422ea0059cb2d6553f93e608b8f9fa0d3a50..0000000000000000000000000000000000000000 --- a/spaces/ElainaFanBoy/MusicGen/audiocraft/data/audio_dataset.py +++ /dev/null @@ -1,525 +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 argparse -import copy -from concurrent.futures import ThreadPoolExecutor, Future -from dataclasses import dataclass, fields -from contextlib import ExitStack -import gzip -import json -import logging -import os -from pathlib import Path -import random -import sys -import typing as tp - -import torch -import torch.nn.functional as F - -from .audio import audio_read, audio_info -from .audio_utils import convert_audio -from .zip import PathInZip - -try: - import dora -except ImportError: - dora = None # type: ignore - - -@dataclass(order=True) -class BaseInfo: - - @classmethod - def _dict2fields(cls, dictionary: dict): - return { - field.name: dictionary[field.name] - for field in fields(cls) if field.name in dictionary - } - - @classmethod - def from_dict(cls, dictionary: dict): - _dictionary = cls._dict2fields(dictionary) - return cls(**_dictionary) - - def to_dict(self): - return { - field.name: self.__getattribute__(field.name) - for field in fields(self) - } - - -@dataclass(order=True) -class AudioMeta(BaseInfo): - path: str - duration: float - sample_rate: int - amplitude: tp.Optional[float] = None - weight: tp.Optional[float] = None - # info_path is used to load additional information about the audio file that is stored in zip files. - info_path: tp.Optional[PathInZip] = None - - @classmethod - def from_dict(cls, dictionary: dict): - base = cls._dict2fields(dictionary) - if 'info_path' in base and base['info_path'] is not None: - base['info_path'] = PathInZip(base['info_path']) - return cls(**base) - - def to_dict(self): - d = super().to_dict() - if d['info_path'] is not None: - d['info_path'] = str(d['info_path']) - return d - - -@dataclass(order=True) -class SegmentInfo(BaseInfo): - meta: AudioMeta - seek_time: float - n_frames: int # actual number of frames without padding - total_frames: int # total number of frames, padding included - sample_rate: int # actual sample rate - - -DEFAULT_EXTS = ['.wav', '.mp3', '.flac', '.ogg', '.m4a'] - -logger = logging.getLogger(__name__) - - -def _get_audio_meta(file_path: str, minimal: bool = True) -> AudioMeta: - """AudioMeta from a path to an audio file. - - Args: - file_path (str): Resolved path of valid audio file. - minimal (bool): Whether to only load the minimal set of metadata (takes longer if not). - Returns: - AudioMeta: Audio file path and its metadata. - """ - info = audio_info(file_path) - amplitude: tp.Optional[float] = None - if not minimal: - wav, sr = audio_read(file_path) - amplitude = wav.abs().max().item() - return AudioMeta(file_path, info.duration, info.sample_rate, amplitude) - - -def _resolve_audio_meta(m: AudioMeta, fast: bool = True) -> AudioMeta: - """If Dora is available as a dependency, try to resolve potential relative paths - in list of AudioMeta. This method is expected to be used when loading meta from file. - - Args: - m (AudioMeta): Audio meta to resolve. - fast (bool): If True, uses a really fast check for determining if a file is already absolute or not. - Only valid on Linux/Mac. - Returns: - AudioMeta: Audio meta with resolved path. - """ - def is_abs(m): - if fast: - return str(m)[0] == '/' - else: - os.path.isabs(str(m)) - - if not dora: - return m - - if not is_abs(m.path): - m.path = dora.git_save.to_absolute_path(m.path) - if m.info_path is not None and not is_abs(m.info_path.zip_path): - m.info_path.zip_path = dora.git_save.to_absolute_path(m.path) - return m - - -def find_audio_files(path: tp.Union[Path, str], - exts: tp.List[str] = DEFAULT_EXTS, - resolve: bool = True, - minimal: bool = True, - progress: bool = False, - workers: int = 0) -> tp.List[AudioMeta]: - """Build a list of AudioMeta from a given path, - collecting relevant audio files and fetching meta info. - - Args: - path (str or Path): Path to folder containing audio files. - exts (list of str): List of file extensions to consider for audio files. - minimal (bool): Whether to only load the minimal set of metadata (takes longer if not). - progress (bool): Whether to log progress on audio files collection. - workers (int): number of parallel workers, if 0, use only the current thread. - Returns: - List[AudioMeta]: List of audio file path and its metadata. - """ - audio_files = [] - futures: tp.List[Future] = [] - pool: tp.Optional[ThreadPoolExecutor] = None - with ExitStack() as stack: - if workers > 0: - pool = ThreadPoolExecutor(workers) - stack.enter_context(pool) - - if progress: - print("Finding audio files...") - for root, folders, files in os.walk(path, followlinks=True): - for file in files: - full_path = Path(root) / file - if full_path.suffix.lower() in exts: - audio_files.append(full_path) - if pool is not None: - futures.append(pool.submit(_get_audio_meta, str(audio_files[-1]), minimal)) - if progress: - print(format(len(audio_files), " 8d"), end='\r', file=sys.stderr) - - if progress: - print("Getting audio metadata...") - meta: tp.List[AudioMeta] = [] - for idx, file_path in enumerate(audio_files): - try: - if pool is None: - m = _get_audio_meta(str(file_path), minimal) - else: - m = futures[idx].result() - if resolve: - m = _resolve_audio_meta(m) - except Exception as err: - print("Error with", str(file_path), err, file=sys.stderr) - continue - meta.append(m) - if progress: - print(format((1 + idx) / len(audio_files), " 3.1%"), end='\r', file=sys.stderr) - meta.sort() - return meta - - -def load_audio_meta(path: tp.Union[str, Path], - resolve: bool = True, fast: bool = True) -> tp.List[AudioMeta]: - """Load list of AudioMeta from an optionally compressed json file. - - Args: - path (str or Path): Path to JSON file. - resolve (bool): Whether to resolve the path from AudioMeta (default=True). - fast (bool): activates some tricks to make things faster. - Returns: - List[AudioMeta]: List of audio file path and its total duration. - """ - open_fn = gzip.open if str(path).lower().endswith('.gz') else open - with open_fn(path, 'rb') as fp: # type: ignore - lines = fp.readlines() - meta = [] - for line in lines: - d = json.loads(line) - m = AudioMeta.from_dict(d) - if resolve: - m = _resolve_audio_meta(m, fast=fast) - meta.append(m) - return meta - - -def save_audio_meta(path: tp.Union[str, Path], meta: tp.List[AudioMeta]): - """Save the audio metadata to the file pointer as json. - - Args: - path (str or Path): Path to JSON file. - metadata (list of BaseAudioMeta): List of audio meta to save. - """ - Path(path).parent.mkdir(exist_ok=True, parents=True) - open_fn = gzip.open if str(path).lower().endswith('.gz') else open - with open_fn(path, 'wb') as fp: # type: ignore - for m in meta: - json_str = json.dumps(m.to_dict()) + '\n' - json_bytes = json_str.encode('utf-8') - fp.write(json_bytes) - - -class AudioDataset: - """Base audio dataset. - - The dataset takes a list of AudioMeta and create a dataset composed of segments of audio - and potentially additional information, by creating random segments from the list of audio - files referenced in the metadata and applying minimal data pre-processing such as resampling, - mixing of channels, padding, etc. - - If no segment_duration value is provided, the AudioDataset will return the full wav for each - audio file. Otherwise, it will randomly sample audio files and create a segment of the specified - duration, applying padding if required. - - By default, only the torch Tensor corresponding to the waveform is returned. Setting return_info=True - allows to return a tuple containing the torch Tensor and additional metadata on the segment and the - original audio meta. - - Args: - meta (tp.List[AudioMeta]): List of audio files metadata. - segment_duration (float): Optional segment duration of audio to load. - If not specified, the dataset will load the full audio segment from the file. - shuffle (bool): Set to `True` to have the data reshuffled at every epoch. - sample_rate (int): Target sample rate of the loaded audio samples. - channels (int): Target number of channels of the loaded audio samples. - sample_on_duration (bool): Set to `True` to sample segments with probability - dependent on audio file duration. This is only used if `segment_duration` is provided. - sample_on_weight (bool): Set to `True` to sample segments using the `weight` entry of - `AudioMeta`. If `sample_on_duration` is also True, the actual weight will be the product - of the file duration and file weight. This is only used if `segment_duration` is provided. - min_segment_ratio (float): Minimum segment ratio to use when the audio file - is shorter than the desired segment. - max_read_retry (int): Maximum number of retries to sample an audio segment from the dataset. - return_info (bool): Whether to return the wav only or return wav along with segment info and metadata. - min_audio_duration (tp.Optional[float], optional): Minimum audio file duration, in seconds, if provided - audio shorter than this will be filtered out. - max_audio_duration (tp.Optional[float], optional): Maximal audio file duration in seconds, if provided - audio longer than this will be filtered out. - """ - def __init__(self, - meta: tp.List[AudioMeta], - segment_duration: tp.Optional[float] = None, - shuffle: bool = True, - num_samples: int = 10_000, - sample_rate: int = 48_000, - channels: int = 2, - pad: bool = True, - sample_on_duration: bool = True, - sample_on_weight: bool = True, - min_segment_ratio: float = 0.5, - max_read_retry: int = 10, - return_info: bool = False, - min_audio_duration: tp.Optional[float] = None, - max_audio_duration: tp.Optional[float] = None - ): - assert len(meta) > 0, 'No audio meta provided to AudioDataset. Please check loading of audio meta.' - assert segment_duration is None or segment_duration > 0 - assert segment_duration is None or min_segment_ratio >= 0 - logging.debug(f'sample_on_duration: {sample_on_duration}') - logging.debug(f'sample_on_weight: {sample_on_weight}') - logging.debug(f'pad: {pad}') - logging.debug(f'min_segment_ratio: {min_segment_ratio}') - - self.segment_duration = segment_duration - self.min_segment_ratio = min_segment_ratio - self.max_audio_duration = max_audio_duration - self.min_audio_duration = min_audio_duration - if self.min_audio_duration is not None and self.max_audio_duration is not None: - assert self.min_audio_duration <= self.max_audio_duration - self.meta: tp.List[AudioMeta] = self._filter_duration(meta) - assert len(self.meta) # Fail fast if all data has been filtered. - self.total_duration = sum(d.duration for d in self.meta) - - if segment_duration is None: - num_samples = len(self.meta) - self.num_samples = num_samples - self.shuffle = shuffle - self.sample_rate = sample_rate - self.channels = channels - self.pad = pad - self.sample_on_weight = sample_on_weight - self.sample_on_duration = sample_on_duration - self.sampling_probabilities = self._get_sampling_probabilities() - self.max_read_retry = max_read_retry - self.return_info = return_info - - def __len__(self): - return self.num_samples - - def _get_sampling_probabilities(self, normalized: bool = True): - """Return the sampling probabilities for each file inside `self.meta`. - """ - scores: tp.List[float] = [] - for file_meta in self.meta: - score = 1. - if self.sample_on_weight and file_meta.weight is not None: - score *= file_meta.weight - if self.sample_on_duration: - score *= file_meta.duration - scores.append(score) - probabilities = torch.tensor(scores) - if normalized: - probabilities /= probabilities.sum() - return probabilities - - def sample_file(self, rng: torch.Generator) -> AudioMeta: - """Sample a given file from `self.meta`. Can be overriden in subclasses. - This is only called if `segment_duration` is not None. - - You must use the provided random number generator `rng` for reproducibility. - """ - if not self.sample_on_weight and not self.sample_on_duration: - file_index = int(torch.randint(len(self.sampling_probabilities), (1,), generator=rng).item()) - else: - file_index = int(torch.multinomial(self.sampling_probabilities, 1, generator=rng).item()) - - return self.meta[file_index] - - def __getitem__(self, index: int) -> tp.Union[torch.Tensor, tp.Tuple[torch.Tensor, SegmentInfo]]: - if self.segment_duration is None: - file_meta = self.meta[index] - out, sr = audio_read(file_meta.path) - out = convert_audio(out, sr, self.sample_rate, self.channels) - n_frames = out.shape[-1] - segment_info = SegmentInfo(file_meta, seek_time=0., n_frames=n_frames, total_frames=n_frames, - sample_rate=self.sample_rate) - else: - rng = torch.Generator() - if self.shuffle: - # We use index, plus extra randomness - rng.manual_seed(index + self.num_samples * random.randint(0, 2**24)) - else: - # We only use index - rng.manual_seed(index) - - for retry in range(self.max_read_retry): - file_meta = self.sample_file(rng) - # We add some variance in the file position even if audio file is smaller than segment - # without ending up with empty segments - max_seek = max(0, file_meta.duration - self.segment_duration * self.min_segment_ratio) - seek_time = torch.rand(1, generator=rng).item() * max_seek - try: - out, sr = audio_read(file_meta.path, seek_time, self.segment_duration, pad=False) - out = convert_audio(out, sr, self.sample_rate, self.channels) - n_frames = out.shape[-1] - target_frames = int(self.segment_duration * self.sample_rate) - if self.pad: - out = F.pad(out, (0, target_frames - n_frames)) - segment_info = SegmentInfo(file_meta, seek_time, n_frames=n_frames, total_frames=target_frames, - sample_rate=self.sample_rate) - except Exception as exc: - logger.warning("Error opening file %s: %r", file_meta.path, exc) - if retry == self.max_read_retry - 1: - raise - else: - break - - if self.return_info: - # Returns the wav and additional information on the wave segment - return out, segment_info - else: - return out - - def collater(self, samples): - """The collater function has to be provided to the dataloader - if AudioDataset has return_info=True in order to properly collate - the samples of a batch. - """ - if self.segment_duration is None and len(samples) > 1: - assert self.pad, "Must allow padding when batching examples of different durations." - - # In this case the audio reaching the collater is of variable length as segment_duration=None. - to_pad = self.segment_duration is None and self.pad - if to_pad: - max_len = max([wav.shape[-1] for wav, _ in samples]) - - def _pad_wav(wav): - return F.pad(wav, (0, max_len - wav.shape[-1])) - - if self.return_info: - if len(samples) > 0: - assert len(samples[0]) == 2 - assert isinstance(samples[0][0], torch.Tensor) - assert isinstance(samples[0][1], SegmentInfo) - - wavs = [wav for wav, _ in samples] - segment_infos = [copy.deepcopy(info) for _, info in samples] - - if to_pad: - # Each wav could be of a different duration as they are not segmented. - for i in range(len(samples)): - # Determines the total legth of the signal with padding, so we update here as we pad. - segment_infos[i].total_frames = max_len - wavs[i] = _pad_wav(wavs[i]) - - wav = torch.stack(wavs) - return wav, segment_infos - else: - assert isinstance(samples[0], torch.Tensor) - if to_pad: - samples = [_pad_wav(s) for s in samples] - return torch.stack(samples) - - def _filter_duration(self, meta: tp.List[AudioMeta]) -> tp.List[AudioMeta]: - """Filters out audio files with short durations. - Removes from meta files that have durations that will not allow to samples examples from them. - """ - orig_len = len(meta) - - # Filter data that is too short. - if self.min_audio_duration is not None: - meta = [m for m in meta if m.duration >= self.min_audio_duration] - - # Filter data that is too long. - if self.max_audio_duration is not None: - meta = [m for m in meta if m.duration <= self.max_audio_duration] - - filtered_len = len(meta) - removed_percentage = 100*(1-float(filtered_len)/orig_len) - msg = 'Removed %.2f percent of the data because it was too short or too long.' % removed_percentage - if removed_percentage < 10: - logging.debug(msg) - else: - logging.warning(msg) - return meta - - @classmethod - def from_meta(cls, root: tp.Union[str, Path], **kwargs): - """Instantiate AudioDataset from a path to a directory containing a manifest as a jsonl file. - - Args: - root (str or Path): Path to root folder containing audio files. - kwargs: Additional keyword arguments for the AudioDataset. - """ - root = Path(root) - if root.is_dir(): - if (root / 'data.jsonl').exists(): - root = root / 'data.jsonl' - elif (root / 'data.jsonl.gz').exists(): - root = root / 'data.jsonl.gz' - else: - raise ValueError("Don't know where to read metadata from in the dir. " - "Expecting either a data.jsonl or data.jsonl.gz file but none found.") - meta = load_audio_meta(root) - return cls(meta, **kwargs) - - @classmethod - def from_path(cls, root: tp.Union[str, Path], minimal_meta: bool = True, - exts: tp.List[str] = DEFAULT_EXTS, **kwargs): - """Instantiate AudioDataset from a path containing (possibly nested) audio files. - - Args: - root (str or Path): Path to root folder containing audio files. - minimal_meta (bool): Whether to only load minimal metadata or not. - exts (list of str): Extensions for audio files. - kwargs: Additional keyword arguments for the AudioDataset. - """ - root = Path(root) - if root.is_file(): - meta = load_audio_meta(root, resolve=True) - else: - meta = find_audio_files(root, exts, minimal=minimal_meta, resolve=True) - return cls(meta, **kwargs) - - -def main(): - logging.basicConfig(stream=sys.stderr, level=logging.INFO) - parser = argparse.ArgumentParser( - prog='audio_dataset', - description='Generate .jsonl files by scanning a folder.') - parser.add_argument('root', help='Root folder with all the audio files') - parser.add_argument('output_meta_file', - help='Output file to store the metadata, ') - parser.add_argument('--complete', - action='store_false', dest='minimal', default=True, - help='Retrieve all metadata, even the one that are expansive ' - 'to compute (e.g. normalization).') - parser.add_argument('--resolve', - action='store_true', default=False, - help='Resolve the paths to be absolute and with no symlinks.') - parser.add_argument('--workers', - default=10, type=int, - help='Number of workers.') - args = parser.parse_args() - meta = find_audio_files(args.root, DEFAULT_EXTS, progress=True, - resolve=args.resolve, minimal=args.minimal, workers=args.workers) - save_audio_meta(args.output_meta_file, meta) - - -if __name__ == '__main__': - main() diff --git a/spaces/EronSamez/RVC_HFmeu/demucs/compressed.py b/spaces/EronSamez/RVC_HFmeu/demucs/compressed.py deleted file mode 100644 index eb8fbb75463ba71ca86729b22baebf24598ade57..0000000000000000000000000000000000000000 --- a/spaces/EronSamez/RVC_HFmeu/demucs/compressed.py +++ /dev/null @@ -1,115 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -import json -from fractions import Fraction -from concurrent import futures - -import musdb -from torch import distributed - -from .audio import AudioFile - - -def get_musdb_tracks(root, *args, **kwargs): - mus = musdb.DB(root, *args, **kwargs) - return {track.name: track.path for track in mus} - - -class StemsSet: - def __init__(self, tracks, metadata, duration=None, stride=1, - samplerate=44100, channels=2, streams=slice(None)): - - self.metadata = [] - for name, path in tracks.items(): - meta = dict(metadata[name]) - meta["path"] = path - meta["name"] = name - self.metadata.append(meta) - if duration is not None and meta["duration"] < duration: - raise ValueError(f"Track {name} duration is too small {meta['duration']}") - self.metadata.sort(key=lambda x: x["name"]) - self.duration = duration - self.stride = stride - self.channels = channels - self.samplerate = samplerate - self.streams = streams - - def __len__(self): - return sum(self._examples_count(m) for m in self.metadata) - - def _examples_count(self, meta): - if self.duration is None: - return 1 - else: - return int((meta["duration"] - self.duration) // self.stride + 1) - - def track_metadata(self, index): - for meta in self.metadata: - examples = self._examples_count(meta) - if index >= examples: - index -= examples - continue - return meta - - def __getitem__(self, index): - for meta in self.metadata: - examples = self._examples_count(meta) - if index >= examples: - index -= examples - continue - streams = AudioFile(meta["path"]).read(seek_time=index * self.stride, - duration=self.duration, - channels=self.channels, - samplerate=self.samplerate, - streams=self.streams) - return (streams - meta["mean"]) / meta["std"] - - -def _get_track_metadata(path): - # use mono at 44kHz as reference. For any other settings data won't be perfectly - # normalized but it should be good enough. - audio = AudioFile(path) - mix = audio.read(streams=0, channels=1, samplerate=44100) - return {"duration": audio.duration, "std": mix.std().item(), "mean": mix.mean().item()} - - -def _build_metadata(tracks, workers=10): - pendings = [] - with futures.ProcessPoolExecutor(workers) as pool: - for name, path in tracks.items(): - pendings.append((name, pool.submit(_get_track_metadata, path))) - return {name: p.result() for name, p in pendings} - - -def _build_musdb_metadata(path, musdb, workers): - tracks = get_musdb_tracks(musdb) - metadata = _build_metadata(tracks, workers) - path.parent.mkdir(exist_ok=True, parents=True) - json.dump(metadata, open(path, "w")) - - -def get_compressed_datasets(args, samples): - metadata_file = args.metadata / "musdb.json" - if not metadata_file.is_file() and args.rank == 0: - _build_musdb_metadata(metadata_file, args.musdb, args.workers) - if args.world_size > 1: - distributed.barrier() - metadata = json.load(open(metadata_file)) - duration = Fraction(samples, args.samplerate) - stride = Fraction(args.data_stride, args.samplerate) - train_set = StemsSet(get_musdb_tracks(args.musdb, subsets=["train"], split="train"), - metadata, - duration=duration, - stride=stride, - streams=slice(1, None), - samplerate=args.samplerate, - channels=args.audio_channels) - valid_set = StemsSet(get_musdb_tracks(args.musdb, subsets=["train"], split="valid"), - metadata, - samplerate=args.samplerate, - channels=args.audio_channels) - return train_set, valid_set diff --git a/spaces/Error114/bingAI/Dockerfile b/spaces/Error114/bingAI/Dockerfile deleted file mode 100644 index 2038cd67acc6be12ce9546d0d4f3bc6d2b9c28b1..0000000000000000000000000000000000000000 --- a/spaces/Error114/bingAI/Dockerfile +++ /dev/null @@ -1,34 +0,0 @@ -# Build Stage -# 使用 golang:alpine 作为构建阶段的基础镜像 -FROM golang:alpine AS builder - -# 添加 git，以便之后能从GitHub克隆项目 -RUN apk --no-cache add git - -# 从 GitHub 克隆 go-proxy-bingai 项目到 /workspace/app 目录下 -RUN git clone https://github.com/Harry-zklcdc/go-proxy-bingai.git /workspace/app - -# 设置工作目录为之前克隆的项目目录 -WORKDIR /workspace/app - -# 编译 go 项目。-ldflags="-s -w" 是为了减少编译后的二进制大小 -RUN go build -ldflags="-s -w" -tags netgo -trimpath -o go-proxy-bingai main.go - -# Runtime Stage -# 使用轻量级的 alpine 镜像作为运行时的基础镜像 -FROM alpine - -# 设置工作目录 -WORKDIR /workspace/app - -# 从构建阶段复制编译后的二进制文件到运行时镜像中 -COPY --from=builder /workspace/app/go-proxy-bingai . - -# 设置环境变量，此处为随机字符 -ENV Go_Proxy_BingAI_USER_TOKEN_1="kJs8hD92ncMzLaoQWYtX5rG6bE3£2410" - -# 暴露8080端口 -EXPOSE 8080 - -# 容器启动时运行的命令 -CMD ["/workspace/app/go-proxy-bingai"] \ No newline at end of file diff --git a/spaces/FauziNL/Voice_anime2/vc_infer_pipeline.py b/spaces/FauziNL/Voice_anime2/vc_infer_pipeline.py deleted file mode 100644 index c26d45068f9b6bf2b194b13c3c89f8a06347c124..0000000000000000000000000000000000000000 --- a/spaces/FauziNL/Voice_anime2/vc_infer_pipeline.py +++ /dev/null @@ -1,306 +0,0 @@ -import numpy as np, parselmouth, torch, pdb -from time import time as ttime -import torch.nn.functional as F -from config import x_pad, x_query, x_center, x_max -import scipy.signal as signal -import pyworld, os, traceback, faiss -from scipy import signal - -bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000) - - -class VC(object): - def __init__(self, tgt_sr, device, is_half): - self.sr = 16000 # hubert输入采样率 - self.window = 160 # 每帧点数 - self.t_pad = self.sr * x_pad # 每条前后pad时间 - self.t_pad_tgt = tgt_sr * x_pad - self.t_pad2 = self.t_pad * 2 - self.t_query = self.sr * x_query # 查询切点前后查询时间 - self.t_center = self.sr * x_center # 查询切点位置 - self.t_max = self.sr * x_max # 免查询时长阈值 - self.device = device - self.is_half = is_half - - def get_f0(self, x, p_len, f0_up_key, f0_method, inp_f0=None): - 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": - f0, t = pyworld.harvest( - 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 *= 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[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0] - f0[x_pad * tf0 : 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, - ): # ,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, # layer 9 - } - t0 = ttime() - with torch.no_grad(): - logits = model.extract_features(**inputs) - feats = model.final_proj(logits[0]) - - 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()] - 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) - 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] - 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] * 32768) - .data.cpu() - .float() - .numpy() - .astype(np.int16) - ) - else: - audio1 = ( - (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768) - .data.cpu() - .float() - .numpy() - .astype(np.int16) - ) - 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, - times, - f0_up_key, - f0_method, - file_index, - file_big_npy, - index_rate, - if_f0, - f0_file=None, - ): - if ( - file_big_npy != "" - and file_index != "" - 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) - except: - traceback.print_exc() - index = big_npy = None - else: - index = big_npy = None - print("Feature retrieval library doesn't exist or ratio is 0") - 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(audio_pad, p_len, f0_up_key, f0_method, inp_f0) - pitch = pitch[:p_len] - pitchf = pitchf[:p_len] - 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, - )[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, - )[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, - )[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, - )[self.t_pad_tgt : -self.t_pad_tgt] - ) - audio_opt = np.concatenate(audio_opt) - del pitch, pitchf, sid - if torch.cuda.is_available(): - torch.cuda.empty_cache() - return audio_opt diff --git a/spaces/Felix123456/bingo/src/lib/bots/bing/types.ts b/spaces/Felix123456/bingo/src/lib/bots/bing/types.ts deleted file mode 100644 index 02cd5e8b01e3529642d28dc1539bf958f4ac420b..0000000000000000000000000000000000000000 --- a/spaces/Felix123456/bingo/src/lib/bots/bing/types.ts +++ /dev/null @@ -1,259 +0,0 @@ -export type Author = 'user' | 'system' | 'bot' - -export type BotId = 'bing' - -export enum BingConversationStyle { - Creative = 'Creative', - Balanced = 'Balanced', - Precise = 'Precise' -} - -export enum ErrorCode { - CONVERSATION_LIMIT = 'CONVERSATION_LIMIT', - BING_UNAUTHORIZED = 'BING_UNAUTHORIZED', - BING_FORBIDDEN = 'BING_FORBIDDEN', - BING_CAPTCHA = 'BING_CAPTCHA', - THROTTLE_LIMIT = 'THROTTLE_LIMIT', - NOTFOUND_ERROR = 'NOT_FOUND_ERROR', - UNKOWN_ERROR = 'UNKOWN_ERROR', - NETWORK_ERROR = 'NETWORK_ERROR', -} - -export class ChatError extends Error { - code: ErrorCode - constructor(message: string, code: ErrorCode) { - super(message) - this.code = code - } -} - -export type ChatMessageModel = { - id: string - author: Author - text: string - error?: ChatError - throttling?: Throttling - sourceAttributions?: SourceAttribution[] - suggestedResponses?: SuggestedResponse[] -} - -export interface ConversationModel { - messages: ChatMessageModel[] -} - -export type Event = - | { - type: 'UPDATE_ANSWER' - data: { - text: string - spokenText?: string - sourceAttributions?: SourceAttribution[] - suggestedResponses?: SuggestedResponse[] - throttling?: Throttling - } - } - | { - type: 'DONE' - } - | { - type: 'ERROR' - error: ChatError - } - -export interface SendMessageParams- - -- -
- - ---- - ---- - -- ---- - - - - - - -- -- ----Projects
- ---Other
- ---- - - --- - -{ - 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/FridaZuley/RVC_HFKawaii/i18n/locale_diff.py b/spaces/FridaZuley/RVC_HFKawaii/i18n/locale_diff.py deleted file mode 100644 index 387ddfe1b16c2f9f32b6b9682b61353837b06bd8..0000000000000000000000000000000000000000 --- a/spaces/FridaZuley/RVC_HFKawaii/i18n/locale_diff.py +++ /dev/null @@ -1,45 +0,0 @@ -import json -import os -from collections import OrderedDict - -# Define the standard file name -standard_file = "en_US.json" - -# Find all JSON files in the directory -dir_path = "./" -languages = [ - f for f in os.listdir(dir_path) if f.endswith(".json") and f != standard_file -] - -# Load the standard file -with open(standard_file, "r", encoding="utf-8") as f: - standard_data = json.load(f, object_pairs_hook=OrderedDict) - -# Loop through each language file -for lang_file in languages: - # Load the language file - with open(lang_file, "r", encoding="utf-8") as f: - lang_data = json.load(f, object_pairs_hook=OrderedDict) - - # Find the difference between the language file and the standard file - diff = set(standard_data.keys()) - set(lang_data.keys()) - - miss = set(lang_data.keys()) - set(standard_data.keys()) - - # Add any missing keys to the language file - for key in diff: - lang_data[key] = key - - # Del any extra keys to the language file - for key in miss: - del lang_data[key] - - # Sort the keys of the language file to match the order of the standard file - lang_data = OrderedDict( - sorted(lang_data.items(), key=lambda x: list(standard_data.keys()).index(x[0])) - ) - - # Save the updated language file - with open(lang_file, "w", encoding="utf-8") as f: - json.dump(lang_data, f, ensure_ascii=False, indent=4) - f.write("\n") diff --git a/spaces/GIZ/vulnerability_analysis/utils/__init__.py b/spaces/GIZ/vulnerability_analysis/utils/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/GT6242Causion/Causion/src/map_viz.py b/spaces/GT6242Causion/Causion/src/map_viz.py deleted file mode 100644 index e0f019b53c0c233b6ac237d1804f70801871de59..0000000000000000000000000000000000000000 --- a/spaces/GT6242Causion/Causion/src/map_viz.py +++ /dev/null @@ -1,87 +0,0 @@ -from __future__ import division, print_function -from six import StringIO -from svgpath2mpl import parse_path -from collections import defaultdict -import xml.etree.ElementTree as etree -import re -import matplotlib as mpl -import matplotlib.pyplot as plt -import numpy as np -import requests -import pandas as pd -import datetime - -import warnings -warnings.filterwarnings("ignore") - -def calling_map_viz(counts_df): - r = "svg/snazzy-image-01.svg" - tree = etree.parse(r) - root = tree.getroot() - path_elems = root.findall('.//{http://www.w3.org/2000/svg}path') - - paths = [parse_path(elem.attrib['d']) for elem in path_elems] - facecolors = [] - edgecolors = [] - linewidths = [] - for elem in path_elems: - facecolors.append(dict(item.split(":") for item in elem.attrib.get('style', 'none').split(";")).get("fill", "none")) - edgecolors.append(dict(item.split(":") for item in elem.attrib.get('style', 'none').split(";")).get("stroke", "none")) - linewidths.append(dict(item.split(":") for item in elem.attrib.get('style', 'none').split(";")).get("stroke-width", "none").replace("px", "")) - - path_id = defaultdict(int) - for i, elem in enumerate(path_elems): - try: - #print(i, elem.attrib['id']) - path_id[elem.attrib['id']] = i - except: - continue - - - # counts_df = pd.read_csv("counts_dataset.csv") - counts_df['total'] = counts_df['car'] + counts_df['motorcycle'] + counts_df['large_vehicle'] - count_max = counts_df['total'][-200:].max() - count_min = counts_df['total'][-200:].min() - last_date = counts_df.iloc[-1:,0].values[0] - last_time = counts_df.iloc[-1:,1].values[0] - - - count_dict = {"woodlands_to_sg" :counts_df.loc[counts_df['view'].str.contains(r'''Woodlands([a-zA-Z0-9_.+-]+)sg''') & (counts_df['date'] == last_date) & (counts_df['time'] == last_time), "total" ].sum(), - "woodlands_to_jh" :counts_df.loc[counts_df['view'].str.contains(r'''Woodlands([a-zA-Z0-9_.+-]+)jh''') & (counts_df['date'] == last_date) & (counts_df['time'] == last_time), "total" ].sum(), - "tuas_to_sg" :counts_df.loc[counts_df['view'].str.contains(r'''Tuas([a-zA-Z0-9_.+-]+)sg''') & (counts_df['date'] == last_date) & (counts_df['time'] == last_time), "total" ].sum(), - "tuas_to_jh" :counts_df.loc[counts_df['view'].str.contains(r'''Tuas([a-zA-Z0-9_.+-]+)jh''') & (counts_df['date'] == last_date) & (counts_df['time'] == last_time), "total" ].sum() - } - - values = np.array([0., 0.5, 1.]) - values = np.sort(np.array(values)) - values = np.interp(values, (values.min(), values.max()), (0., 1.)) - colors = ["#539f6b", "#ffc835", "#bf0000"] - cmap = mpl.colors.LinearSegmentedColormap.from_list("custom", list(zip(values, colors))) - - norm = mpl.colors.Normalize(vmin=count_min, vmax=count_max) - - hex_dict = {k: mpl.colors.to_hex(cmap(norm(v))) for k, v in count_dict.items()} - color_dict = defaultdict(str) - - for k, i in path_id.items(): - color_dict[i] = hex_dict[k] - - for k, i in color_dict.items(): - #print(k,i) - facecolors[k] = i - - collection = mpl.collections.PathCollection(paths, - edgecolors=edgecolors, - linewidths=[int(i)/100 for i in linewidths if i != 'none'], - facecolors=[i.strip() for i in facecolors]) - - - - fig = plt.figure(figsize=(10,10)) - ax = fig.add_subplot(111) - collection.set_transform(ax.transData) - ax.add_artist(collection) - ax.set_xlim([100, 1900]) - ax.set_ylim([1800, 0]) - ax.set_title(datetime.datetime.strptime(last_date, '%Y-%m-%d').strftime('%a, %d %B %Y') + " | " + last_time + " SGT", fontname = 'Georgia') - return fig \ No newline at end of file diff --git a/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/common/confidence.py b/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/common/confidence.py deleted file mode 100644 index 5f1085f75d2c8528a05abc12a483c3685af8c7c1..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/protGPT2_gradioFold/alphafold/alphafold/common/confidence.py +++ /dev/null @@ -1,155 +0,0 @@ -# Copyright 2021 DeepMind Technologies Limited -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Functions for processing confidence metrics.""" - -from typing import Dict, Optional, Tuple -import numpy as np -import scipy.special - - -def compute_plddt(logits: np.ndarray) -> np.ndarray: - """Computes per-residue pLDDT from logits. - - Args: - logits: [num_res, num_bins] output from the PredictedLDDTHead. - - Returns: - plddt: [num_res] per-residue pLDDT. - """ - num_bins = logits.shape[-1] - bin_width = 1.0 / num_bins - bin_centers = np.arange(start=0.5 * bin_width, stop=1.0, step=bin_width) - probs = scipy.special.softmax(logits, axis=-1) - predicted_lddt_ca = np.sum(probs * bin_centers[None, :], axis=-1) - return predicted_lddt_ca * 100 - - -def _calculate_bin_centers(breaks: np.ndarray): - """Gets the bin centers from the bin edges. - - Args: - breaks: [num_bins - 1] the error bin edges. - - Returns: - bin_centers: [num_bins] the error bin centers. - """ - step = (breaks[1] - breaks[0]) - - # Add half-step to get the center - bin_centers = breaks + step / 2 - # Add a catch-all bin at the end. - bin_centers = np.concatenate([bin_centers, [bin_centers[-1] + step]], - axis=0) - return bin_centers - - -def _calculate_expected_aligned_error( - alignment_confidence_breaks: np.ndarray, - aligned_distance_error_probs: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: - """Calculates expected aligned distance errors for every pair of residues. - - Args: - alignment_confidence_breaks: [num_bins - 1] the error bin edges. - aligned_distance_error_probs: [num_res, num_res, num_bins] the predicted - probs for each error bin, for each pair of residues. - - Returns: - predicted_aligned_error: [num_res, num_res] the expected aligned distance - error for each pair of residues. - max_predicted_aligned_error: The maximum predicted error possible. - """ - bin_centers = _calculate_bin_centers(alignment_confidence_breaks) - - # Tuple of expected aligned distance error and max possible error. - return (np.sum(aligned_distance_error_probs * bin_centers, axis=-1), - np.asarray(bin_centers[-1])) - - -def compute_predicted_aligned_error( - logits: np.ndarray, - breaks: np.ndarray) -> Dict[str, np.ndarray]: - """Computes aligned confidence metrics from logits. - - Args: - logits: [num_res, num_res, num_bins] the logits output from - PredictedAlignedErrorHead. - breaks: [num_bins - 1] the error bin edges. - - Returns: - aligned_confidence_probs: [num_res, num_res, num_bins] the predicted - aligned error probabilities over bins for each residue pair. - predicted_aligned_error: [num_res, num_res] the expected aligned distance - error for each pair of residues. - max_predicted_aligned_error: The maximum predicted error possible. - """ - aligned_confidence_probs = scipy.special.softmax( - logits, - axis=-1) - predicted_aligned_error, max_predicted_aligned_error = ( - _calculate_expected_aligned_error( - alignment_confidence_breaks=breaks, - aligned_distance_error_probs=aligned_confidence_probs)) - return { - 'aligned_confidence_probs': aligned_confidence_probs, - 'predicted_aligned_error': predicted_aligned_error, - 'max_predicted_aligned_error': max_predicted_aligned_error, - } - - -def predicted_tm_score( - logits: np.ndarray, - breaks: np.ndarray, - residue_weights: Optional[np.ndarray] = None) -> np.ndarray: - """Computes predicted TM alignment score. - - Args: - logits: [num_res, num_res, num_bins] the logits output from - PredictedAlignedErrorHead. - breaks: [num_bins] the error bins. - residue_weights: [num_res] the per residue weights to use for the - expectation. - - Returns: - ptm_score: the predicted TM alignment score. - """ - - # residue_weights has to be in [0, 1], but can be floating-point, i.e. the - # exp. resolved head's probability. - if residue_weights is None: - residue_weights = np.ones(logits.shape[0]) - - bin_centers = _calculate_bin_centers(breaks) - - num_res = np.sum(residue_weights) - # Clip num_res to avoid negative/undefined d0. - clipped_num_res = max(num_res, 19) - - # Compute d_0(num_res) as defined by TM-score, eqn. (5) in - # http://zhanglab.ccmb.med.umich.edu/papers/2004_3.pdf - # Yang & Skolnick "Scoring function for automated - # assessment of protein structure template quality" 2004 - d0 = 1.24 * (clipped_num_res - 15) ** (1./3) - 1.8 - - # Convert logits to probs - probs = scipy.special.softmax(logits, axis=-1) - - # TM-Score term for every bin - tm_per_bin = 1. / (1 + np.square(bin_centers) / np.square(d0)) - # E_distances tm(distance) - predicted_tm_term = np.sum(probs * tm_per_bin, axis=-1) - - normed_residue_mask = residue_weights / (1e-8 + residue_weights.sum()) - per_alignment = np.sum(predicted_tm_term * normed_residue_mask, axis=-1) - return np.asarray(per_alignment[(per_alignment * residue_weights).argmax()]) diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py deleted file mode 100644 index 409db3c628edf63cd40e002f436884ce1fb75970..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/hrnet/fcn_hr18s_512x512_40k_voc12aug.py +++ /dev/null @@ -1,9 +0,0 @@ -_base_ = './fcn_hr18_512x512_40k_voc12aug.py' -model = dict( - pretrained='open-mmlab://msra/hrnetv2_w18_small', - backbone=dict( - extra=dict( - stage1=dict(num_blocks=(2, )), - stage2=dict(num_blocks=(2, 2)), - stage3=dict(num_modules=3, num_blocks=(2, 2, 2)), - stage4=dict(num_modules=2, num_blocks=(2, 2, 2, 2))))) diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/utils/up_conv_block.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/utils/up_conv_block.py deleted file mode 100644 index 6566b749dbcb014312c586d1e6a618ec49249456..0000000000000000000000000000000000000000 --- a/spaces/Gradio-Blocks/uniformer_image_segmentation/mmseg/models/utils/up_conv_block.py +++ /dev/null @@ -1,101 +0,0 @@ -import torch -import torch.nn as nn -from mmcv.cnn import ConvModule, build_upsample_layer - - -class UpConvBlock(nn.Module): - """Upsample convolution block in decoder for UNet. - - This upsample convolution block consists of one upsample module - followed by one convolution block. The upsample module expands the - high-level low-resolution feature map and the convolution block fuses - the upsampled high-level low-resolution feature map and the low-level - high-resolution feature map from encoder. - - Args: - conv_block (nn.Sequential): Sequential of convolutional layers. - in_channels (int): Number of input channels of the high-level - skip_channels (int): Number of input channels of the low-level - high-resolution feature map from encoder. - out_channels (int): Number of output channels. - num_convs (int): Number of convolutional layers in the conv_block. - Default: 2. - stride (int): Stride of convolutional layer in conv_block. Default: 1. - dilation (int): Dilation rate of convolutional layer in conv_block. - Default: 1. - with_cp (bool): Use checkpoint or not. Using checkpoint will save some - memory while slowing down the training speed. Default: False. - conv_cfg (dict | None): Config dict for convolution layer. - Default: None. - norm_cfg (dict | None): Config dict for normalization layer. - Default: dict(type='BN'). - act_cfg (dict | None): Config dict for activation layer in ConvModule. - Default: dict(type='ReLU'). - upsample_cfg (dict): The upsample config of the upsample module in - decoder. Default: dict(type='InterpConv'). If the size of - high-level feature map is the same as that of skip feature map - (low-level feature map from encoder), it does not need upsample the - high-level feature map and the upsample_cfg is None. - dcn (bool): Use deformable convolution in convolutional layer or not. - Default: None. - plugins (dict): plugins for convolutional layers. Default: None. - """ - - def __init__(self, - conv_block, - in_channels, - skip_channels, - out_channels, - num_convs=2, - stride=1, - dilation=1, - with_cp=False, - conv_cfg=None, - norm_cfg=dict(type='BN'), - act_cfg=dict(type='ReLU'), - upsample_cfg=dict(type='InterpConv'), - dcn=None, - plugins=None): - super(UpConvBlock, self).__init__() - assert dcn is None, 'Not implemented yet.' - assert plugins is None, 'Not implemented yet.' - - self.conv_block = conv_block( - in_channels=2 * skip_channels, - out_channels=out_channels, - num_convs=num_convs, - stride=stride, - dilation=dilation, - with_cp=with_cp, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg, - dcn=None, - plugins=None) - if upsample_cfg is not None: - self.upsample = build_upsample_layer( - cfg=upsample_cfg, - in_channels=in_channels, - out_channels=skip_channels, - with_cp=with_cp, - norm_cfg=norm_cfg, - act_cfg=act_cfg) - else: - self.upsample = ConvModule( - in_channels, - skip_channels, - kernel_size=1, - stride=1, - padding=0, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=act_cfg) - - def forward(self, skip, x): - """Forward function.""" - - x = self.upsample(x) - out = torch.cat([skip, x], dim=1) - out = self.conv_block(out) - - return out diff --git a/spaces/HaHaBill/LandShapes-Antarctica/netdissect/autoeval.py b/spaces/HaHaBill/LandShapes-Antarctica/netdissect/autoeval.py deleted file mode 100644 index ecc86a1f7b403f57821dde2a2b4f0619c0d6cae3..0000000000000000000000000000000000000000 --- a/spaces/HaHaBill/LandShapes-Antarctica/netdissect/autoeval.py +++ /dev/null @@ -1,37 +0,0 @@ -from collections import defaultdict -from importlib import import_module - -def autoimport_eval(term): - ''' - Used to evaluate an arbitrary command-line constructor specifying - a class, with automatic import of global module names. - ''' - - class DictNamespace(object): - def __init__(self, d): - self.__d__ = d - def __getattr__(self, key): - return self.__d__[key] - - class AutoImportDict(defaultdict): - def __init__(self, wrapped=None, parent=None): - super().__init__() - self.wrapped = wrapped - self.parent = parent - def __missing__(self, key): - if self.wrapped is not None: - if key in self.wrapped: - return self.wrapped[key] - if self.parent is not None: - key = self.parent + '.' + key - if key in __builtins__: - return __builtins__[key] - mdl = import_module(key) - # Return an AutoImportDict for any namespace packages - if hasattr(mdl, '__path__'): # and not hasattr(mdl, '__file__'): - return DictNamespace( - AutoImportDict(wrapped=mdl.__dict__, parent=key)) - return mdl - - return eval(term, {}, AutoImportDict()) - diff --git a/spaces/HaMerL/ChaosinChat/modules/config.py b/spaces/HaMerL/ChaosinChat/modules/config.py deleted file mode 100644 index 520a0b791f9fa5c80fb0c333de1b89718d34f18e..0000000000000000000000000000000000000000 --- a/spaces/HaMerL/ChaosinChat/modules/config.py +++ /dev/null @@ -1,179 +0,0 @@ -from collections import defaultdict -from contextlib import contextmanager -import os -import logging -import sys -import commentjson as json - -from . import shared -from . import presets - - -__all__ = [ - "my_api_key", - "authflag", - "auth_list", - "dockerflag", - "retrieve_proxy", - "log_level", - "advance_docs", - "update_doc_config", - "multi_api_key", - "server_name", - "server_port", - "share", -] - -# 添加一个统一的config文件，避免文件过多造成的疑惑（优先级最低） -# 同时，也可以为后续支持自定义功能提供config的帮助 -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 os.path.exists("api_key.txt"): - logging.info("检测到api_key.txt文件，正在进行迁移...") - with open("api_key.txt", "r") as f: - config["openai_api_key"] = f.read().strip() - os.rename("api_key.txt", "api_key(deprecated).txt") - with open("config.json", "w", encoding='utf-8') as f: - json.dump(config, f, indent=4) - -if os.path.exists("auth.json"): - logging.info("检测到auth.json文件，正在进行迁移...") - auth_list = [] - with open("auth.json", "r", encoding='utf-8') as f: - auth = json.load(f) - for _ in auth: - if auth[_]["username"] and auth[_]["password"]: - auth_list.append((auth[_]["username"], auth[_]["password"])) - else: - logging.error("请检查auth.json文件中的用户名和密码！") - sys.exit(1) - config["users"] = auth_list - os.rename("auth.json", "auth(deprecated).json") - with open("config.json", "w", encoding='utf-8') as f: - json.dump(config, f, indent=4) - -## 处理docker if we are running in Docker -dockerflag = config.get("dockerflag", False) -if os.environ.get("dockerrun") == "yes": - dockerflag = True - -## 处理 api-key 以及 允许的用户列表 -my_api_key = config.get("openai_api_key", "") -my_api_key = os.environ.get("OPENAI_API_KEY", my_api_key) - -xmchat_api_key = config.get("xmchat_api_key", "") -os.environ["XMCHAT_API_KEY"] = xmchat_api_key - -render_latex = config.get("render_latex", False) - -if render_latex: - os.environ["RENDER_LATEX"] = "yes" -else: - os.environ["RENDER_LATEX"] = "no" - -## 多账户机制 -multi_api_key = config.get("multi_api_key", False) # 是否开启多账户机制 -if multi_api_key: - api_key_list = config.get("api_key_list", []) - if len(api_key_list) == 0: - logging.error("多账号模式已开启，但api_key_list为空，请检查config.json") - sys.exit(1) - shared.state.set_api_key_queue(api_key_list) - -auth_list = config.get("users", []) # 实际上是使用者的列表 -authflag = len(auth_list) > 0 # 是否开启认证的状态值，改为判断auth_list长度 - -# 处理自定义的api_host，优先读环境变量的配置，如果存在则自动装配 -api_host = os.environ.get("api_host", config.get("api_host", "")) -if api_host: - shared.state.set_api_host(api_host) - -@contextmanager -def retrieve_openai_api(api_key = None): - old_api_key = os.environ.get("OPENAI_API_KEY", "") - if api_key is None: - os.environ["OPENAI_API_KEY"] = my_api_key - yield my_api_key - else: - os.environ["OPENAI_API_KEY"] = api_key - yield api_key - os.environ["OPENAI_API_KEY"] = old_api_key - -## 处理log -log_level = config.get("log_level", "INFO") -logging.basicConfig( - level=log_level, - format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s", -) - -## 处理代理： -http_proxy = config.get("http_proxy", "") -https_proxy = config.get("https_proxy", "") -http_proxy = os.environ.get("HTTP_PROXY", http_proxy) -https_proxy = os.environ.get("HTTPS_PROXY", https_proxy) - -# 重置系统变量，在不需要设置的时候不设置环境变量，以免引起全局代理报错 -os.environ["HTTP_PROXY"] = "" -os.environ["HTTPS_PROXY"] = "" - -local_embedding = config.get("local_embedding", False) # 是否使用本地embedding - -@contextmanager -def retrieve_proxy(proxy=None): - """ - 1, 如果proxy = NONE，设置环境变量，并返回最新设置的代理 - 2，如果proxy ！= NONE，更新当前的代理配置，但是不更新环境变量 - """ - global http_proxy, https_proxy - if proxy is not None: - http_proxy = proxy - https_proxy = proxy - yield http_proxy, https_proxy - else: - old_var = os.environ["HTTP_PROXY"], os.environ["HTTPS_PROXY"] - os.environ["HTTP_PROXY"] = http_proxy - os.environ["HTTPS_PROXY"] = https_proxy - yield http_proxy, https_proxy # return new proxy - - # return old proxy - os.environ["HTTP_PROXY"], os.environ["HTTPS_PROXY"] = old_var - - -## 处理advance docs -advance_docs = defaultdict(lambda: defaultdict(dict)) -advance_docs.update(config.get("advance_docs", {})) -def update_doc_config(two_column_pdf): - global advance_docs - advance_docs["pdf"]["two_column"] = two_column_pdf - - logging.info(f"更新后的文件参数为：{advance_docs}") - -## 处理gradio.launch参数 -server_name = config.get("server_name", None) -server_port = config.get("server_port", None) -if server_name is None: - if dockerflag: - server_name = "0.0.0.0" - else: - server_name = "127.0.0.1" -if server_port is None: - if dockerflag: - server_port = 7860 - -assert server_port is None or type(server_port) == int, "要求port设置为int类型" - -# 设置默认model -default_model = config.get("default_model", "") -try: - presets.DEFAULT_MODEL = presets.MODELS.index(default_model) -except ValueError: - pass - -share = config.get("share", False) diff --git a/spaces/Heathlia/modelscope-text-to-video-synthesis/style.css b/spaces/Heathlia/modelscope-text-to-video-synthesis/style.css deleted file mode 100644 index 2f399e973fe275a7299ae85c6a85bd5d35eb64cb..0000000000000000000000000000000000000000 --- a/spaces/Heathlia/modelscope-text-to-video-synthesis/style.css +++ /dev/null @@ -1,191 +0,0 @@ -/* -This CSS file is copied from here: -https://huggingface.co/spaces/stabilityai/stable-diffusion/blob/2794a3c3ba66115c307075098e713f572b08bf80/app.py -*/ - -h1 { - text-align: center; -} - -.gradio-container { - font-family: 'IBM Plex Sans', sans-serif; -} - -.gr-button { - color: white; - border-color: black; - background: black; -} - -input[type='range'] { - accent-color: black; -} - -.dark input[type='range'] { - accent-color: #dfdfdf; -} - -.container { - max-width: 730px; - margin: auto; - padding-top: 1.5rem; -} - -#gallery { - min-height: 22rem; - margin-bottom: 15px; - margin-left: auto; - margin-right: auto; - border-bottom-right-radius: .5rem !important; - border-bottom-left-radius: .5rem !important; -} - -#gallery>div>.h-full { - min-height: 20rem; -} - -.details:hover { - text-decoration: underline; -} - -.gr-button { - white-space: nowrap; -} - -.gr-button:focus { - border-color: rgb(147 197 253 / var(--tw-border-opacity)); - outline: none; - box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000); - --tw-border-opacity: 1; - --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color); - --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color); - --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity)); - --tw-ring-opacity: .5; -} - -#advanced-btn { - font-size: .7rem !important; - line-height: 19px; - margin-top: 12px; - margin-bottom: 12px; - padding: 2px 8px; - border-radius: 14px !important; -} - -#advanced-options { - display: none; - margin-bottom: 20px; -} - -.footer { - margin-bottom: 45px; - margin-top: 35px; - text-align: center; - border-bottom: 1px solid #e5e5e5; -} - -.footer>p { - font-size: .8rem; - display: inline-block; - padding: 0 10px; - transform: translateY(10px); - background: white; -} - -.dark .footer { - border-color: #303030; -} - -.dark .footer>p { - background: #0b0f19; -} - -.acknowledgments h4 { - margin: 1.25em 0 .25em 0; - font-weight: bold; - font-size: 115%; -} - -.animate-spin { - animation: spin 1s linear infinite; -} - -@keyframes spin { - from { - transform: rotate(0deg); - } - - to { - transform: rotate(360deg); - } -} - -#share-btn-container { - display: flex; - padding-left: 0.5rem !important; - padding-right: 0.5rem !important; - background-color: #000000; - justify-content: center; - align-items: center; - border-radius: 9999px !important; - width: 13rem; - margin-top: 10px; - margin-left: auto; -} - -#share-btn { - all: initial; - color: #ffffff; - font-weight: 600; - cursor: pointer; - font-family: 'IBM Plex Sans', sans-serif; - margin-left: 0.5rem !important; - padding-top: 0.25rem !important; - padding-bottom: 0.25rem !important; - right: 0; -} - -#share-btn * { - all: unset; -} - -#share-btn-container div:nth-child(-n+2) { - width: auto !important; - min-height: 0px !important; -} - -#share-btn-container .wrap { - display: none !important; -} - -.gr-form { - flex: 1 1 50%; - border-top-right-radius: 0; - border-bottom-right-radius: 0; -} - -#prompt-container { - gap: 0; -} - -#prompt-text-input, -#negative-prompt-text-input { - padding: .45rem 0.625rem -} - -#component-16 { - border-top-width: 1px !important; - margin-top: 1em -} - -.image_duplication { - position: absolute; - width: 100px; - left: 50px -} - -#component-0 { - max-width: 730px; - margin: auto; - padding-top: 1.5rem; -} diff --git a/spaces/HighCWu/colorful-ascii-art/README.md b/spaces/HighCWu/colorful-ascii-art/README.md deleted file mode 100644 index b66326542a13c64810f88e0de2f7d596b7dfe9e2..0000000000000000000000000000000000000000 --- a/spaces/HighCWu/colorful-ascii-art/README.md +++ /dev/null @@ -1,21 +0,0 @@ ---- -title: Colorful Ascii Art -emoji: 📉 -colorFrom: purple -colorTo: green -sdk: gradio -sdk_version: 2.9.4 -app_file: app.py -pinned: false -license: mit ---- - -# Colorful Ascii Art - -Online image mode demo -[](https://huggingface.co/spaces/HighCWu/colorful-ascii-art) - -For video mode, run: `python app_video.py` - - -Largely referenced from [this space](https://huggingface.co/spaces/dt/ascii-art) diff --git a/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/scripts/prepare_audio.sh b/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/scripts/prepare_audio.sh deleted file mode 100644 index 013f7a9b055a7693a29f9c5ba1e4003a9a25850e..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/examples/wav2vec/unsupervised/scripts/prepare_audio.sh +++ /dev/null @@ -1,78 +0,0 @@ -#!/usr/bin/env zsh -# 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. - -source_dir=$1 -tgt_dir=$2 -model=$3 - -if [ -z "$4" ] - then - dim=512 - else - dim=$4 -fi - -echo "using $dim dim for PCA" - -if [ -z "$5" ] - then - layer=14 - else - layer=$5 -fi - -echo "extracting from layer $layer" - -train_split=train -valid_split=valid -test_split=test - -all_splits=($train_split) - -if [[ -f "$source_dir/valid.tsv" ]]; then - all_splits+=('valid') -fi - -if [[ -f "$source_dir/test.tsv" ]]; then - all_splits+=('test') -fi - -echo "processing splits: $all_splits" - -mkdir -p $tgt_dir - -cp $source_dir/*.tsv $tgt_dir -cp $source_dir/*.wrd $tgt_dir -cp $source_dir/*.ltr $tgt_dir -cp $source_dir/*.phn $tgt_dir -cp $source_dir/dict* $tgt_dir - -setopt shwordsplit - -for split in $all_splits; do - python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/wav2vec_extract_features.py $source_dir --split $split \ - --save-dir $tgt_dir --checkpoint $model --layer $layer -done - -python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/wav2vec_cluster_faiss.py $tgt_dir/${train_split}.tsv \ ---checkpoint $model --save-dir $tgt_dir -f "CLUS128" --sample-pct 1.0 - -for split in $all_splits; do - python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/wav2vec_apply_cluster_faiss.py $tgt_dir \ - --checkpoint $model --path $tgt_dir/CLUS128 --split $split -done - -python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/pca.py $tgt_dir/${train_split}.npy --output $tgt_dir/pca --dim $dim - -for split in $all_splits; do - python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/apply_pca.py $tgt_dir --split $split --save-dir $tgt_dir/precompute_pca$dim --pca-path $tgt_dir/pca/${dim}_pca --batch-size 1048000 - - python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/merge_clusters.py $tgt_dir/precompute_pca$dim --cluster-dir $tgt_dir/CLUS128 \ - --split $split --save-dir $tgt_dir/precompute_pca${dim}_cls128_mean --pooling mean - - python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/mean_pool.py $tgt_dir/precompute_pca${dim}_cls128_mean \ - --save-dir $tgt_dir/precompute_pca${dim}_cls128_mean_pooled --split $split -done diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/distributed/tpu_distributed_data_parallel.py b/spaces/ICML2022/OFA/fairseq/fairseq/distributed/tpu_distributed_data_parallel.py deleted file mode 100644 index e971cf07c57c4e864726781092a690dd4d7d3e46..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/fairseq/distributed/tpu_distributed_data_parallel.py +++ /dev/null @@ -1,43 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import torch -from torch import nn - -from fairseq.distributed import utils - - -class TPUDistributedDataParallel(nn.Module): - - def __init__(self, module, process_group): - super().__init__() - self.module = module - self.process_group = process_group - self.world_size = utils.get_world_size(self.process_group) - - def forward(self, *inputs, **kwargs): - return self.module(*inputs, **kwargs) - - def all_reduce_grads(self): - gradients = [] - for p in self.parameters(): - if not p.requires_grad: - continue - if p.grad is None: - p.grad = torch.zeros_like(p) - if p.grad.requires_grad: - raise RuntimeError( - "TPUDistributedDataParallel only works with gradients that don't " - "require grad" - ) - gradients.append(p.grad) - - import torch_xla.core.xla_model as xm - xm.all_reduce( - 'sum', - gradients, - scale=1. / self.world_size, - groups=self.process_group[1], - ) diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/optim/lr_scheduler/__init__.py b/spaces/ICML2022/OFA/fairseq/fairseq/optim/lr_scheduler/__init__.py deleted file mode 100644 index 5b3dbc023aa4a6f7bfb8403b8204d71ca432f79c..0000000000000000000000000000000000000000 --- a/spaces/ICML2022/OFA/fairseq/fairseq/optim/lr_scheduler/__init__.py +++ /dev/null @@ -1,36 +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. -"""isort:skip_file""" - -import importlib -import os - -from fairseq import registry -from fairseq.optim.lr_scheduler.fairseq_lr_scheduler import ( # noqa - FairseqLRScheduler, - LegacyFairseqLRScheduler, -) -from omegaconf import DictConfig - - -( - build_lr_scheduler_, - register_lr_scheduler, - LR_SCHEDULER_REGISTRY, - LR_SCHEDULER_DATACLASS_REGISTRY, -) = registry.setup_registry( - "--lr-scheduler", base_class=FairseqLRScheduler, default="fixed" -) - - -def build_lr_scheduler(cfg: DictConfig, optimizer): - return build_lr_scheduler_(cfg, optimizer) - - -# automatically import any Python files in the optim/lr_scheduler/ directory -for file in sorted(os.listdir(os.path.dirname(__file__))): - if file.endswith(".py") and not file.startswith("_"): - file_name = file[: file.find(".py")] - importlib.import_module("fairseq.optim.lr_scheduler." + file_name) diff --git a/spaces/IPN/demo_2_omar/README.md b/spaces/IPN/demo_2_omar/README.md deleted file mode 100644 index f57bded420dbd80ccab844cc102ebd8a8d223899..0000000000000000000000000000000000000000 --- a/spaces/IPN/demo_2_omar/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Demo_2_omar -emoji: 😻 -colorFrom: pink -colorTo: green -sdk: gradio -sdk_version: 2.9.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/Igor2004/newSpace/app.py b/spaces/Igor2004/newSpace/app.py deleted file mode 100644 index 94d32de7ca176c4deba1a2234163041ab3ae9b48..0000000000000000000000000000000000000000 --- a/spaces/Igor2004/newSpace/app.py +++ /dev/null @@ -1,13 +0,0 @@ -import gradio as gr -import numpy as np -from huggingface_hub import from_pretrained_keras - -model = from_pretrained_keras("vladimirchabanov/digits") - -def greet(img): - img = np.expand_dims(img, axis = 0) - return np.argmax(model.predict(img)[0]) - - -demo = gr.Interface(fn=greet, inputs="sketchpad", outputs="text") -demo.launch() \ No newline at end of file diff --git a/spaces/Illumotion/Koboldcpp/model_adapter.cpp b/spaces/Illumotion/Koboldcpp/model_adapter.cpp deleted file mode 100644 index 5c3a3f95fb14e56230eda30d760e11ad78f47e5e..0000000000000000000000000000000000000000 --- a/spaces/Illumotion/Koboldcpp/model_adapter.cpp +++ /dev/null @@ -1,525 +0,0 @@ -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#include "model_adapter.h" -#include "ggml.h" - -#include - -static auto bench_timer = std::chrono::high_resolution_clock().now(); - -void timer_start() -{ - bench_timer = std::chrono::high_resolution_clock().now(); -} -double timer_check() -{ - auto endtime = std::chrono::high_resolution_clock().now(); - auto duration = std::chrono::duration_cast &embd) -{ - std::cout << "["; - bool first = true; - for (auto i : embd) - { - if (!first) - { - std::cout << ','; - } - first = false; - std::cout << i; - } - std::cout << "]\n"; -} -void print_tok_vec(std::vector &embd) -{ - std::cout << "["; - bool first = true; - int n = 0; - for (auto i : embd) - { - if (!first) - { - std::cout << ','; - } - first = false; - std::cout << i; - if(++n>20) - { - break; - } - } - std::cout << "]\n"; -} - -//return val: 0=fail, 1=(original ggml, alpaca), 2=(ggmf), 3=(ggjt) - FileFormat check_file_format(const std::string & fname, FileFormatExtraMeta * fileformatmeta) - { - std::vector f_buf(1024*1024); - - auto fin = std::ifstream(fname, std::ios::binary); - fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size()); - if (!fin) { - fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str()); - return FileFormat::BADFORMAT; - } - - FileFormat fileformat = FileFormat::BADFORMAT; - uint32_t magic; - fin.read((char *) &magic, sizeof(magic)); - if (magic == 0x67676d6c) { //v1 format ggml, alpaca, old gptj and gpt2 models - fileformat = FileFormat::GGML; - //we need to read more to determine - int32_t vocabsiz = 0; - fin.read((char *) &vocabsiz, sizeof(int32_t)); - if(vocabsiz==4096 || vocabsiz==7168) //actually the d_model for mpt - { - fileformat = FileFormat::MPT_1; - } - else if(vocabsiz==50400) //know GPT-J vocab size - { - fileformat = FileFormat::GPTJ_1; - uint32_t temp; - fin.read((char *)&temp, sizeof(temp)); //ctx - fin.read((char *)&temp, sizeof(temp)); //n_embd - fin.read((char *)&temp, sizeof(temp)); //n_head - fin.read((char *)&temp, sizeof(temp)); //n_layer - fin.read((char *)&temp, sizeof(temp)); //n_rot - fin.read((char *)&temp, sizeof(temp)); //f16 - const int32_t qntvr = temp / 1000; - temp %= 1000; - if (qntvr != 0) - { - if (qntvr == 1) - { - fileformat = FileFormat::GPTJ_4; - } - else - { - fileformat = FileFormat::GPTJ_5; - } - } - else if (temp != 0 && temp != 1) - { - fileformat = FileFormat::GPTJ_3; //quantized format cannot be legacy type - } - } - else if(vocabsiz==50257 || (vocabsiz>=49152&&vocabsiz<=49157)) //49152-6 is starcoder - { - fileformat = FileFormat::GPT2_1; - uint32_t temp, v1,v2,v3; - fin.read((char *)&v1, sizeof(temp)); //ctx - fin.read((char *)&v2, sizeof(temp)); //n_embd - fin.read((char *)&v3, sizeof(temp)); //n_head - fin.read((char *)&temp, sizeof(temp)); //n_layer - if(vocabsiz==49152 && v1==4096 && v2==2560 && v3==32 && temp==32) - { - //special case, Stablecode Completion Alpha 3B - fileformat = FileFormat::NEOX_6; - } - else - { - fin.read((char *)&temp, sizeof(temp)); //f16 - const int32_t qntvr = temp / 1000; - temp %= 1000; - if (qntvr != 0) - { - if (qntvr == 1) - { - fileformat = FileFormat::GPT2_3; - } - else - { - fileformat = FileFormat::GPT2_4; - } - } - else if (temp != 0 && temp != 1) - { - fileformat = FileFormat::GPT2_2; //quantized format cannot be legacy type - } - } - } - else if(vocabsiz < 31998 || vocabsiz > 33000) - { - //anything outside the llama v1 range is assumed to be NeoX - fileformat = FileFormat::NEOX_6; - uint32_t temp,temp2; - fin.read((char *)&temp, sizeof(temp)); //ctx - fin.read((char *)&temp, sizeof(temp)); //n_embd - fin.read((char *)&temp, sizeof(temp)); //n_head - fin.read((char *)&temp, sizeof(temp)); //n_layer - fin.read((char *)&temp, sizeof(temp)); //n_rot - fin.read((char *)&temp, sizeof(temp)); //either par_res or ftype (for older ver) - - if(temp!=0 && temp!=1){ - //must be ftype, means its an older model. par_res will be undefined - fileformat = FileFormat::NEOX_2; - } - else - { - //it could be a newer model, or an old f16/f32 model - fin.read((char *)&temp2, sizeof(temp2)); //if previous was par_res, this is ftype. else unknown - - //if it is new ftype, then it must have these properties: > 1000, low multiple of 1k and small remaineder - bool isNewFtype = (temp2>=1000 && temp2<=9000 && temp2%1000<20); - - if(!isNewFtype) - { - fileformat = FileFormat::NEOX_2; - if((temp==0||temp==1)&&(temp2==0||temp2==1))//special case: par_res and ftype are both 1 or 0 - { - //its a f16/f32 model in the new format - fileformat = temp==0?FileFormat::NEOX_7:FileFormat::NEOX_6; - } - } - else - { - const int32_t qntvr = temp2 / 1000; //for future use - //then temp was par_res, use_parallel_residual is false in RedPajama - if(qntvr==1) - { - fileformat = (temp==0?FileFormat::NEOX_5:FileFormat::NEOX_4); - } - else - { - fileformat = (temp==0?FileFormat::NEOX_7:FileFormat::NEOX_6); - } - } - } - - } - } - else if(magic == 0x67676d66) //v2 format ggmf - { - fileformat = FileFormat::GGHF; - uint32_t temp; - fin.read((char *)&temp, sizeof(temp)); //file version - if(temp==100) - { - fileformat = FileFormat::RWKV_1; - } - else if(temp==101) - { - fileformat = FileFormat::RWKV_2; - } - } - else if(magic == 0x67676a74) //v3 format ggjt - { - fileformat = FileFormat::GGJT_3; //ggjt by default - uint32_t ver, temp, ftype; - fin.read((char *)&ver, sizeof(ver)); //file version - fin.read((char *)&temp, sizeof(temp));//vocab - fin.read((char *)&temp, sizeof(temp)); //embd - fin.read((char *)&temp, sizeof(temp)); //mult - fin.read((char *)&temp, sizeof(temp));//head - fin.read((char *)&temp, sizeof(temp));//layer - fin.read((char *)&temp, sizeof(temp));//rot - fin.read((char *)&ftype, sizeof(ftype));//filetype - - if(ver==1) - { - fileformat = FileFormat::GGJT; - } - else if(ver==2) - { - fileformat = FileFormat::GGJT_2; - } - } - else if(magic == 0x46554747) - { - fin.close(); - fileformat = FileFormat::GGUF_LLAMA; - - struct gguf_init_params ggufparams; - ggufparams.no_alloc = true; - ggufparams.ctx = NULL; - - auto ctx = gguf_init_from_file(fname.c_str(), ggufparams); - - auto keyidx = gguf_find_key(ctx, "general.architecture"); - std::string modelarch = ""; - if (keyidx != -1) { modelarch = gguf_get_val_str(ctx, keyidx); } - - if(modelarch=="llama") - { - fileformat = FileFormat::GGUF_LLAMA; - } - else if(modelarch=="falcon") - { - fileformat = FileFormat::GGUF_FALCON; //uses the same loader - printf("\nDetected GGUF FALCON format.\n"); - } - else - { - printf("\nERROR: Detected unimplemented GGUF Arch: %s\n",modelarch.c_str()); - } - - if(modelarch!="" && fileformatmeta!=nullptr) - { - std::string fkey = modelarch+".context_length"; - auto keyidx = gguf_find_key(ctx, fkey.c_str()); - if (keyidx != -1) { - fileformatmeta->n_ctx_train = gguf_get_val_u32(ctx, keyidx); - } - } - gguf_free(ctx); - } - - if(fin.is_open()) - { - fin.close(); - } - - - return fileformat; - } - - bool ArrStartWith(const std::vector targetArray, const std::vector searchSeq) - { - int ss = searchSeq.size(); - if(targetArray.size() searchSeq) - { - int ss = searchSeq.size(); - int tas = targetArray.size(); - if(tas LongestCommonSubseq(const std::vector x, const std::vector y) - { - int m = x.size(), n = y.size(); - - //int LCSuff[m+1][n+1]; - std::vector (n+1)); - - for (int j = 0; j <= n; j++) - LCSuff[0][j] = 0; - for (int i = 0; i <= m; i++) - LCSuff[i][0] = 0; - - for (int i = 1; i <= m; i++) - { - for (int j = 1; j <= n; j++) - { - if (x[i - 1] == y[j - 1]) - LCSuff[i][j] = LCSuff[i - 1][j - 1] + 1; - else - LCSuff[i][j] = 0; - } - } - - std::vector longest; - for (int i = 1; i <= m; i++) - { - for (int j = 1; j <= n; j++) - { - if (LCSuff[i][j] > longest.size()) - { - auto off1 = ((i - LCSuff[i][j] + 1) - 1); - auto off2 = off1 + LCSuff[i][j]; - longest.clear(); - // std::vector ().swap(longest); - longest = std::vector (x.begin() + off1, x.begin() + off2); - // x.substr((i - LCSuff[i][j] + 1) - 1, LCSuff[i][j]); - } - } - } - return longest; - } - - void ContextFastForward(std::vector ¤t_context_tokens, std::vector &embd_inp, - int &n_past, std::vector &last_n_tokens, const int nctx, std::vector &smartcontext, - bool useSmartContext, const bool requireFullSubset) - { - const int SCCtxLenThreshold = nctx * 0.8; //how much context length must be reach to trigger smartcontext - const int SCInpLenThreshold = nctx * 0.6; //how big must the input array be to trigger smartcontext - const int SCPastLenThreshold = nctx * 0.5; //how wide of a gap between the fast forwarded past and the present to trigger smart context - const float SCTruncationRatio = 0.5; //ratio for how many tokens to fast forward - const int SCTokThreshold = 32 + (nctx*0.05); //how many tokens of similarity triggers smartcontext - - - //fast forward the past based on identical tokens, stop once a divergence is noted - int embd_inp_len = embd_inp.size(); - bool fastforwardok = true; - - for (int i = 0; i < current_context_tokens.size(); ++i) - { - if (current_context_tokens[i] == embd_inp[i]) - { - n_past += 1; - last_n_tokens.push_back(current_context_tokens[i]); - } - else - { - if(requireFullSubset) //RWKV can only do this if embd_inp contains everything in current context - { - last_n_tokens.erase(last_n_tokens.end() - n_past, last_n_tokens.end()); - n_past = 0; - fastforwardok = false; - } - break; - } - - if (requireFullSubset) //RWKV can only do this if embd_inp contains everything in current context - { - if (i >= embd_inp_len) - { - last_n_tokens.erase(last_n_tokens.end() - n_past, last_n_tokens.end()); - n_past = 0; - fastforwardok = false; - break; - } - } - else - { - if ((i + 2) >= embd_inp_len) - { - break; - } - } - } - - if(fastforwardok) - { - last_n_tokens.erase(last_n_tokens.begin(), last_n_tokens.begin() + n_past); - embd_inp.erase(embd_inp.begin(), embd_inp.begin() + n_past); - embd_inp_len = embd_inp.size(); - } - - //smart context mode, detect if we have a shifted context at max length - //requirement: previous context was at least nctx/2 longer than current, - //mode is on, and current context already maxed. - - if (fastforwardok && useSmartContext && smartcontext.size() > 0 && embd_inp_len >= SCInpLenThreshold) - { - //see if smartcontext is still usable - auto shared = LongestCommonSubseq(smartcontext, embd_inp); - if (shared.size() > SCTokThreshold && ArrStartWith(smartcontext, shared)) //at least 32 tokens in common - { - int found = ArrFindIndexOf(embd_inp,shared); - if(found>=0) - { - auto trimmed = std::vector (embd_inp.begin() + found, embd_inp.end()); - embd_inp = trimmed; - embd_inp_len = embd_inp.size(); - printf("\n[Reusing Smart Context: %d allowance remaining]", found); - - int old_n_past = n_past; - int offset_fix = old_n_past; - if (current_context_tokens[n_past] != embd_inp[0]) - { - offset_fix = 0; - } - - for (int i = n_past; i < current_context_tokens.size(); ++i) - { - if (current_context_tokens[i] == embd_inp[i-offset_fix]) - { - n_past += 1; - last_n_tokens.push_back(current_context_tokens[i]); - } - else - { - break; - } - if ((i + 2 - offset_fix) >= embd_inp_len) - { - break; - } - } - - last_n_tokens.erase(last_n_tokens.begin(), last_n_tokens.begin() + (n_past-old_n_past)); - embd_inp.erase(embd_inp.begin(), embd_inp.begin() + (n_past-old_n_past)); - - }else{ - smartcontext.clear(); - } - } - else - { - smartcontext.clear(); - } - } - else - { - smartcontext.clear(); - } - - if(fastforwardok && useSmartContext - && smartcontext.size()==0 && current_context_tokens.size() >= SCCtxLenThreshold - && embd_inp_len >= SCInpLenThreshold - && current_context_tokens.size() - n_past > SCPastLenThreshold) - { - //determine longest common substring after removing start part - int shiftamt = embd_inp.size() * SCTruncationRatio; - smartcontext = std::vector (embd_inp.begin() + shiftamt, embd_inp.end()); - printf("\n[New Smart Context Triggered! Buffered Token Allowance: %d]",shiftamt); - - embd_inp = smartcontext; - //if max ctx length is exceeded, chop the prompt in half after the start part, and memorize it. The memorized part becomes LCS marker. - //when a future prompt comes in, find the LCS again. If LCS > a length and LCS starts with memorized LCS - //remove all tokens between start part and start of LCS in new prompt, thus avoiding shift - //if LCS not found or mismatched, regenerate. chop new prompt and repeat from step B - } - } \ No newline at end of file diff --git a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/analyze_errors.py b/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/analyze_errors.py deleted file mode 100644 index a11f9478de76ede162f5511449ac98e549ff4b6e..0000000000000000000000000000000000000000 --- a/spaces/InpaintAI/Inpaint-Anything/third_party/lama/bin/analyze_errors.py +++ /dev/null @@ -1,316 +0,0 @@ -#!/usr/bin/env python3 -import cv2 -import numpy as np -import sklearn -import torch -import os -import pickle -import pandas as pd -import matplotlib.pyplot as plt -from joblib import Parallel, delayed - -from saicinpainting.evaluation.data import PrecomputedInpaintingResultsDataset, load_image -from saicinpainting.evaluation.losses.fid.inception import InceptionV3 -from saicinpainting.evaluation.utils import load_yaml -from saicinpainting.training.visualizers.base import visualize_mask_and_images - - -def draw_score(img, score): - img = np.transpose(img, (1, 2, 0)) - cv2.putText(img, f'{score:.2f}', - (40, 40), - cv2.FONT_HERSHEY_SIMPLEX, - 1, - (0, 1, 0), - thickness=3) - img = np.transpose(img, (2, 0, 1)) - return img - - -def save_global_samples(global_mask_fnames, mask2real_fname, mask2fake_fname, out_dir, real_scores_by_fname, fake_scores_by_fname): - for cur_mask_fname in global_mask_fnames: - cur_real_fname = mask2real_fname[cur_mask_fname] - orig_img = load_image(cur_real_fname, mode='RGB') - fake_img = load_image(mask2fake_fname[cur_mask_fname], mode='RGB')[:, :orig_img.shape[1], :orig_img.shape[2]] - mask = load_image(cur_mask_fname, mode='L')[None, ...] - - draw_score(orig_img, real_scores_by_fname.loc[cur_real_fname, 'real_score']) - draw_score(fake_img, fake_scores_by_fname.loc[cur_mask_fname, 'fake_score']) - - cur_grid = visualize_mask_and_images(dict(image=orig_img, mask=mask, fake=fake_img), - keys=['image', 'fake'], - last_without_mask=True) - cur_grid = np.clip(cur_grid * 255, 0, 255).astype('uint8') - cur_grid = cv2.cvtColor(cur_grid, cv2.COLOR_RGB2BGR) - cv2.imwrite(os.path.join(out_dir, os.path.splitext(os.path.basename(cur_mask_fname))[0] + '.jpg'), - cur_grid) - - -def save_samples_by_real(worst_best_by_real, mask2fake_fname, fake_info, out_dir): - for real_fname in worst_best_by_real.index: - worst_mask_path = worst_best_by_real.loc[real_fname, 'worst'] - best_mask_path = worst_best_by_real.loc[real_fname, 'best'] - orig_img = load_image(real_fname, mode='RGB') - worst_mask_img = load_image(worst_mask_path, mode='L')[None, ...] - worst_fake_img = load_image(mask2fake_fname[worst_mask_path], mode='RGB')[:, :orig_img.shape[1], :orig_img.shape[2]] - best_mask_img = load_image(best_mask_path, mode='L')[None, ...] - best_fake_img = load_image(mask2fake_fname[best_mask_path], mode='RGB')[:, :orig_img.shape[1], :orig_img.shape[2]] - - draw_score(orig_img, worst_best_by_real.loc[real_fname, 'real_score']) - draw_score(worst_fake_img, worst_best_by_real.loc[real_fname, 'worst_score']) - draw_score(best_fake_img, worst_best_by_real.loc[real_fname, 'best_score']) - - cur_grid = visualize_mask_and_images(dict(image=orig_img, mask=np.zeros_like(worst_mask_img), - worst_mask=worst_mask_img, worst_img=worst_fake_img, - best_mask=best_mask_img, best_img=best_fake_img), - keys=['image', 'worst_mask', 'worst_img', 'best_mask', 'best_img'], - rescale_keys=['worst_mask', 'best_mask'], - last_without_mask=True) - cur_grid = np.clip(cur_grid * 255, 0, 255).astype('uint8') - cur_grid = cv2.cvtColor(cur_grid, cv2.COLOR_RGB2BGR) - cv2.imwrite(os.path.join(out_dir, - os.path.splitext(os.path.basename(real_fname))[0] + '.jpg'), - cur_grid) - - fig, (ax1, ax2) = plt.subplots(1, 2) - cur_stat = fake_info[fake_info['real_fname'] == real_fname] - cur_stat['fake_score'].hist(ax=ax1) - cur_stat['real_score'].hist(ax=ax2) - fig.tight_layout() - fig.savefig(os.path.join(out_dir, - os.path.splitext(os.path.basename(real_fname))[0] + '_scores.png')) - plt.close(fig) - - -def extract_overlapping_masks(mask_fnames, cur_i, fake_scores_table, max_overlaps_n=2): - result_pairs = [] - result_scores = [] - mask_fname_a = mask_fnames[cur_i] - mask_a = load_image(mask_fname_a, mode='L')[None, ...] > 0.5 - cur_score_a = fake_scores_table.loc[mask_fname_a, 'fake_score'] - for mask_fname_b in mask_fnames[cur_i + 1:]: - mask_b = load_image(mask_fname_b, mode='L')[None, ...] > 0.5 - if not np.any(mask_a & mask_b): - continue - cur_score_b = fake_scores_table.loc[mask_fname_b, 'fake_score'] - result_pairs.append((mask_fname_a, mask_fname_b)) - result_scores.append(cur_score_b - cur_score_a) - if len(result_pairs) >= max_overlaps_n: - break - return result_pairs, result_scores - - -def main(args): - config = load_yaml(args.config) - - latents_dir = os.path.join(args.outpath, 'latents') - os.makedirs(latents_dir, exist_ok=True) - global_worst_dir = os.path.join(args.outpath, 'global_worst') - os.makedirs(global_worst_dir, exist_ok=True) - global_best_dir = os.path.join(args.outpath, 'global_best') - os.makedirs(global_best_dir, exist_ok=True) - worst_best_by_best_worst_score_diff_max_dir = os.path.join(args.outpath, 'worst_best_by_real', 'best_worst_score_diff_max') - os.makedirs(worst_best_by_best_worst_score_diff_max_dir, exist_ok=True) - worst_best_by_best_worst_score_diff_min_dir = os.path.join(args.outpath, 'worst_best_by_real', 'best_worst_score_diff_min') - os.makedirs(worst_best_by_best_worst_score_diff_min_dir, exist_ok=True) - worst_best_by_real_best_score_diff_max_dir = os.path.join(args.outpath, 'worst_best_by_real', 'real_best_score_diff_max') - os.makedirs(worst_best_by_real_best_score_diff_max_dir, exist_ok=True) - worst_best_by_real_best_score_diff_min_dir = os.path.join(args.outpath, 'worst_best_by_real', 'real_best_score_diff_min') - os.makedirs(worst_best_by_real_best_score_diff_min_dir, exist_ok=True) - worst_best_by_real_worst_score_diff_max_dir = os.path.join(args.outpath, 'worst_best_by_real', 'real_worst_score_diff_max') - os.makedirs(worst_best_by_real_worst_score_diff_max_dir, exist_ok=True) - worst_best_by_real_worst_score_diff_min_dir = os.path.join(args.outpath, 'worst_best_by_real', 'real_worst_score_diff_min') - os.makedirs(worst_best_by_real_worst_score_diff_min_dir, exist_ok=True) - - if not args.only_report: - block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[2048] - inception_model = InceptionV3([block_idx]).eval().cuda() - - dataset = PrecomputedInpaintingResultsDataset(args.datadir, args.predictdir, **config.dataset_kwargs) - - real2vector_cache = {} - - real_features = [] - fake_features = [] - - orig_fnames = [] - mask_fnames = [] - mask2real_fname = {} - mask2fake_fname = {} - - for batch_i, batch in enumerate(dataset): - orig_img_fname = dataset.img_filenames[batch_i] - mask_fname = dataset.mask_filenames[batch_i] - fake_fname = dataset.pred_filenames[batch_i] - mask2real_fname[mask_fname] = orig_img_fname - mask2fake_fname[mask_fname] = fake_fname - - cur_real_vector = real2vector_cache.get(orig_img_fname, None) - if cur_real_vector is None: - with torch.no_grad(): - in_img = torch.from_numpy(batch['image'][None, ...]).cuda() - cur_real_vector = inception_model(in_img)[0].squeeze(-1).squeeze(-1).cpu().numpy() - real2vector_cache[orig_img_fname] = cur_real_vector - - pred_img = torch.from_numpy(batch['inpainted'][None, ...]).cuda() - cur_fake_vector = inception_model(pred_img)[0].squeeze(-1).squeeze(-1).cpu().numpy() - - real_features.append(cur_real_vector) - fake_features.append(cur_fake_vector) - - orig_fnames.append(orig_img_fname) - mask_fnames.append(mask_fname) - - ids_features = np.concatenate(real_features + fake_features, axis=0) - ids_labels = np.array(([1] * len(real_features)) + ([0] * len(fake_features))) - - with open(os.path.join(latents_dir, 'featues.pkl'), 'wb') as f: - pickle.dump(ids_features, f, protocol=3) - with open(os.path.join(latents_dir, 'labels.pkl'), 'wb') as f: - pickle.dump(ids_labels, f, protocol=3) - with open(os.path.join(latents_dir, 'orig_fnames.pkl'), 'wb') as f: - pickle.dump(orig_fnames, f, protocol=3) - with open(os.path.join(latents_dir, 'mask_fnames.pkl'), 'wb') as f: - pickle.dump(mask_fnames, f, protocol=3) - with open(os.path.join(latents_dir, 'mask2real_fname.pkl'), 'wb') as f: - pickle.dump(mask2real_fname, f, protocol=3) - with open(os.path.join(latents_dir, 'mask2fake_fname.pkl'), 'wb') as f: - pickle.dump(mask2fake_fname, f, protocol=3) - - svm = sklearn.svm.LinearSVC(dual=False) - svm.fit(ids_features, ids_labels) - - pred_scores = svm.decision_function(ids_features) - real_scores = pred_scores[:len(real_features)] - fake_scores = pred_scores[len(real_features):] - - with open(os.path.join(latents_dir, 'pred_scores.pkl'), 'wb') as f: - pickle.dump(pred_scores, f, protocol=3) - with open(os.path.join(latents_dir, 'real_scores.pkl'), 'wb') as f: - pickle.dump(real_scores, f, protocol=3) - with open(os.path.join(latents_dir, 'fake_scores.pkl'), 'wb') as f: - pickle.dump(fake_scores, f, protocol=3) - else: - with open(os.path.join(latents_dir, 'orig_fnames.pkl'), 'rb') as f: - orig_fnames = pickle.load(f) - with open(os.path.join(latents_dir, 'mask_fnames.pkl'), 'rb') as f: - mask_fnames = pickle.load(f) - with open(os.path.join(latents_dir, 'mask2real_fname.pkl'), 'rb') as f: - mask2real_fname = pickle.load(f) - with open(os.path.join(latents_dir, 'mask2fake_fname.pkl'), 'rb') as f: - mask2fake_fname = pickle.load(f) - with open(os.path.join(latents_dir, 'real_scores.pkl'), 'rb') as f: - real_scores = pickle.load(f) - with open(os.path.join(latents_dir, 'fake_scores.pkl'), 'rb') as f: - fake_scores = pickle.load(f) - - real_info = pd.DataFrame(data=[dict(real_fname=fname, - real_score=score) - for fname, score - in zip(orig_fnames, real_scores)]) - real_info.set_index('real_fname', drop=True, inplace=True) - - fake_info = pd.DataFrame(data=[dict(mask_fname=fname, - fake_fname=mask2fake_fname[fname], - real_fname=mask2real_fname[fname], - fake_score=score) - for fname, score - in zip(mask_fnames, fake_scores)]) - fake_info = fake_info.join(real_info, on='real_fname', how='left') - fake_info.drop_duplicates(['fake_fname', 'real_fname'], inplace=True) - - fake_stats_by_real = fake_info.groupby('real_fname')['fake_score'].describe()[['mean', 'std']].rename( - {'mean': 'mean_fake_by_real', 'std': 'std_fake_by_real'}, axis=1) - fake_info = fake_info.join(fake_stats_by_real, on='real_fname', rsuffix='stat_by_real') - fake_info.drop_duplicates(['fake_fname', 'real_fname'], inplace=True) - fake_info.to_csv(os.path.join(latents_dir, 'join_scores_table.csv'), sep='\t', index=False) - - fake_scores_table = fake_info.set_index('mask_fname')['fake_score'].to_frame() - real_scores_table = fake_info.set_index('real_fname')['real_score'].drop_duplicates().to_frame() - - fig, (ax1, ax2) = plt.subplots(1, 2) - ax1.hist(fake_scores) - ax2.hist(real_scores) - fig.tight_layout() - fig.savefig(os.path.join(args.outpath, 'global_scores_hist.png')) - plt.close(fig) - - global_worst_masks = fake_info.sort_values('fake_score', ascending=True)['mask_fname'].iloc[:config.take_global_top].to_list() - global_best_masks = fake_info.sort_values('fake_score', ascending=False)['mask_fname'].iloc[:config.take_global_top].to_list() - save_global_samples(global_worst_masks, mask2real_fname, mask2fake_fname, global_worst_dir, real_scores_table, fake_scores_table) - save_global_samples(global_best_masks, mask2real_fname, mask2fake_fname, global_best_dir, real_scores_table, fake_scores_table) - - # grouped by real - worst_samples_by_real = fake_info.groupby('real_fname').apply( - lambda d: d.set_index('mask_fname')['fake_score'].idxmin()).to_frame().rename({0: 'worst'}, axis=1) - best_samples_by_real = fake_info.groupby('real_fname').apply( - lambda d: d.set_index('mask_fname')['fake_score'].idxmax()).to_frame().rename({0: 'best'}, axis=1) - worst_best_by_real = pd.concat([worst_samples_by_real, best_samples_by_real], axis=1) - - worst_best_by_real = worst_best_by_real.join(fake_scores_table.rename({'fake_score': 'worst_score'}, axis=1), - on='worst') - worst_best_by_real = worst_best_by_real.join(fake_scores_table.rename({'fake_score': 'best_score'}, axis=1), - on='best') - worst_best_by_real = worst_best_by_real.join(real_scores_table) - - worst_best_by_real['best_worst_score_diff'] = worst_best_by_real['best_score'] - worst_best_by_real['worst_score'] - worst_best_by_real['real_best_score_diff'] = worst_best_by_real['real_score'] - worst_best_by_real['best_score'] - worst_best_by_real['real_worst_score_diff'] = worst_best_by_real['real_score'] - worst_best_by_real['worst_score'] - - worst_best_by_best_worst_score_diff_min = worst_best_by_real.sort_values('best_worst_score_diff', ascending=True).iloc[:config.take_worst_best_top] - worst_best_by_best_worst_score_diff_max = worst_best_by_real.sort_values('best_worst_score_diff', ascending=False).iloc[:config.take_worst_best_top] - save_samples_by_real(worst_best_by_best_worst_score_diff_min, mask2fake_fname, fake_info, worst_best_by_best_worst_score_diff_min_dir) - save_samples_by_real(worst_best_by_best_worst_score_diff_max, mask2fake_fname, fake_info, worst_best_by_best_worst_score_diff_max_dir) - - worst_best_by_real_best_score_diff_min = worst_best_by_real.sort_values('real_best_score_diff', ascending=True).iloc[:config.take_worst_best_top] - worst_best_by_real_best_score_diff_max = worst_best_by_real.sort_values('real_best_score_diff', ascending=False).iloc[:config.take_worst_best_top] - save_samples_by_real(worst_best_by_real_best_score_diff_min, mask2fake_fname, fake_info, worst_best_by_real_best_score_diff_min_dir) - save_samples_by_real(worst_best_by_real_best_score_diff_max, mask2fake_fname, fake_info, worst_best_by_real_best_score_diff_max_dir) - - worst_best_by_real_worst_score_diff_min = worst_best_by_real.sort_values('real_worst_score_diff', ascending=True).iloc[:config.take_worst_best_top] - worst_best_by_real_worst_score_diff_max = worst_best_by_real.sort_values('real_worst_score_diff', ascending=False).iloc[:config.take_worst_best_top] - save_samples_by_real(worst_best_by_real_worst_score_diff_min, mask2fake_fname, fake_info, worst_best_by_real_worst_score_diff_min_dir) - save_samples_by_real(worst_best_by_real_worst_score_diff_max, mask2fake_fname, fake_info, worst_best_by_real_worst_score_diff_max_dir) - - # analyze what change of mask causes bigger change of score - overlapping_mask_fname_pairs = [] - overlapping_mask_fname_score_diffs = [] - for cur_real_fname in orig_fnames: - cur_fakes_info = fake_info[fake_info['real_fname'] == cur_real_fname] - cur_mask_fnames = sorted(cur_fakes_info['mask_fname'].unique()) - - cur_mask_pairs_and_scores = Parallel(args.n_jobs)( - delayed(extract_overlapping_masks)(cur_mask_fnames, i, fake_scores_table) - for i in range(len(cur_mask_fnames) - 1) - ) - for cur_pairs, cur_scores in cur_mask_pairs_and_scores: - overlapping_mask_fname_pairs.extend(cur_pairs) - overlapping_mask_fname_score_diffs.extend(cur_scores) - - overlapping_mask_fname_pairs = np.asarray(overlapping_mask_fname_pairs) - overlapping_mask_fname_score_diffs = np.asarray(overlapping_mask_fname_score_diffs) - overlapping_sort_idx = np.argsort(overlapping_mask_fname_score_diffs) - overlapping_mask_fname_pairs = overlapping_mask_fname_pairs[overlapping_sort_idx] - overlapping_mask_fname_score_diffs = overlapping_mask_fname_score_diffs[overlapping_sort_idx] - - - - - - -if __name__ == '__main__': - import argparse - - aparser = argparse.ArgumentParser() - aparser.add_argument('config', type=str, help='Path to config for dataset generation') - aparser.add_argument('datadir', type=str, - help='Path to folder with images and masks (output of gen_mask_dataset.py)') - aparser.add_argument('predictdir', type=str, - help='Path to folder with predicts (e.g. predict_hifill_baseline.py)') - aparser.add_argument('outpath', type=str, help='Where to put results') - aparser.add_argument('--only-report', action='store_true', - help='Whether to skip prediction and feature extraction, ' - 'load all the possible latents and proceed with report only') - aparser.add_argument('--n-jobs', type=int, default=8, help='how many processes to use for pair mask mining') - - main(aparser.parse_args()) diff --git a/spaces/Jackflack09/diffuse-custom/diffusers/models/vae.py b/spaces/Jackflack09/diffuse-custom/diffusers/models/vae.py deleted file mode 100644 index e29f4e8afa2ff1cc957672b9f2d595c30a2db32e..0000000000000000000000000000000000000000 --- a/spaces/Jackflack09/diffuse-custom/diffusers/models/vae.py +++ /dev/null @@ -1,643 +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 dataclasses import dataclass -from typing import Optional, Tuple, Union - -import numpy as np -import torch -import torch.nn as nn - -from ..configuration_utils import ConfigMixin, register_to_config -from ..modeling_utils import ModelMixin -from ..utils import BaseOutput -from .unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block - - -@dataclass -class DecoderOutput(BaseOutput): - """ - Output of decoding method. - - Args: - sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): - Decoded output sample of the model. Output of the last layer of the model. - """ - - sample: torch.FloatTensor - - -@dataclass -class VQEncoderOutput(BaseOutput): - """ - Output of VQModel encoding method. - - Args: - latents (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): - Encoded output sample of the model. Output of the last layer of the model. - """ - - latents: torch.FloatTensor - - -@dataclass -class AutoencoderKLOutput(BaseOutput): - """ - Output of AutoencoderKL encoding method. - - Args: - latent_dist (`DiagonalGaussianDistribution`): - Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`. - `DiagonalGaussianDistribution` allows for sampling latents from the distribution. - """ - - latent_dist: "DiagonalGaussianDistribution" - - -class Encoder(nn.Module): - def __init__( - self, - in_channels=3, - out_channels=3, - down_block_types=("DownEncoderBlock2D",), - block_out_channels=(64,), - layers_per_block=2, - norm_num_groups=32, - act_fn="silu", - double_z=True, - ): - super().__init__() - self.layers_per_block = layers_per_block - - self.conv_in = torch.nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1) - - self.mid_block = None - self.down_blocks = nn.ModuleList([]) - - # down - output_channel = block_out_channels[0] - for i, down_block_type in enumerate(down_block_types): - input_channel = output_channel - output_channel = block_out_channels[i] - is_final_block = i == len(block_out_channels) - 1 - - down_block = get_down_block( - down_block_type, - num_layers=self.layers_per_block, - in_channels=input_channel, - out_channels=output_channel, - add_downsample=not is_final_block, - resnet_eps=1e-6, - downsample_padding=0, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - attn_num_head_channels=None, - temb_channels=None, - ) - self.down_blocks.append(down_block) - - # mid - self.mid_block = UNetMidBlock2D( - in_channels=block_out_channels[-1], - resnet_eps=1e-6, - resnet_act_fn=act_fn, - output_scale_factor=1, - resnet_time_scale_shift="default", - attn_num_head_channels=None, - resnet_groups=norm_num_groups, - temb_channels=None, - ) - - # out - self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6) - self.conv_act = nn.SiLU() - - conv_out_channels = 2 * out_channels if double_z else out_channels - self.conv_out = nn.Conv2d(block_out_channels[-1], conv_out_channels, 3, padding=1) - - def forward(self, x): - sample = x - sample = self.conv_in(sample) - - # down - for down_block in self.down_blocks: - sample = down_block(sample) - - # middle - sample = self.mid_block(sample) - - # post-process - sample = self.conv_norm_out(sample) - sample = self.conv_act(sample) - sample = self.conv_out(sample) - - return sample - - -class Decoder(nn.Module): - def __init__( - self, - in_channels=3, - out_channels=3, - up_block_types=("UpDecoderBlock2D",), - block_out_channels=(64,), - layers_per_block=2, - norm_num_groups=32, - act_fn="silu", - ): - super().__init__() - self.layers_per_block = layers_per_block - - self.conv_in = nn.Conv2d(in_channels, block_out_channels[-1], kernel_size=3, stride=1, padding=1) - - self.mid_block = None - self.up_blocks = nn.ModuleList([]) - - # mid - self.mid_block = UNetMidBlock2D( - in_channels=block_out_channels[-1], - resnet_eps=1e-6, - resnet_act_fn=act_fn, - output_scale_factor=1, - resnet_time_scale_shift="default", - attn_num_head_channels=None, - resnet_groups=norm_num_groups, - temb_channels=None, - ) - - # up - reversed_block_out_channels = list(reversed(block_out_channels)) - output_channel = reversed_block_out_channels[0] - for i, up_block_type in enumerate(up_block_types): - prev_output_channel = output_channel - output_channel = reversed_block_out_channels[i] - - is_final_block = i == len(block_out_channels) - 1 - - up_block = get_up_block( - up_block_type, - num_layers=self.layers_per_block + 1, - in_channels=prev_output_channel, - out_channels=output_channel, - prev_output_channel=None, - add_upsample=not is_final_block, - resnet_eps=1e-6, - resnet_act_fn=act_fn, - resnet_groups=norm_num_groups, - attn_num_head_channels=None, - temb_channels=None, - ) - self.up_blocks.append(up_block) - prev_output_channel = output_channel - - # out - self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6) - self.conv_act = nn.SiLU() - self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1) - - def forward(self, z): - sample = z - sample = self.conv_in(sample) - - # middle - sample = self.mid_block(sample) - - # up - for up_block in self.up_blocks: - sample = up_block(sample) - - # post-process - sample = self.conv_norm_out(sample) - sample = self.conv_act(sample) - sample = self.conv_out(sample) - - return sample - - -class VectorQuantizer(nn.Module): - """ - Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly avoids costly matrix - multiplications and allows for post-hoc remapping of indices. - """ - - # NOTE: due to a bug the beta term was applied to the wrong term. for - # backwards compatibility we use the buggy version by default, but you can - # specify legacy=False to fix it. - def __init__( - self, n_e, vq_embed_dim, beta, remap=None, unknown_index="random", sane_index_shape=False, legacy=True - ): - super().__init__() - self.n_e = n_e - self.vq_embed_dim = vq_embed_dim - self.beta = beta - self.legacy = legacy - - self.embedding = nn.Embedding(self.n_e, self.vq_embed_dim) - self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) - - self.remap = remap - if self.remap is not None: - self.register_buffer("used", torch.tensor(np.load(self.remap))) - self.re_embed = self.used.shape[0] - self.unknown_index = unknown_index # "random" or "extra" or integer - if self.unknown_index == "extra": - self.unknown_index = self.re_embed - self.re_embed = self.re_embed + 1 - print( - f"Remapping {self.n_e} indices to {self.re_embed} indices. " - f"Using {self.unknown_index} for unknown indices." - ) - else: - self.re_embed = n_e - - self.sane_index_shape = sane_index_shape - - def remap_to_used(self, inds): - ishape = inds.shape - assert len(ishape) > 1 - inds = inds.reshape(ishape[0], -1) - used = self.used.to(inds) - match = (inds[:, :, None] == used[None, None, ...]).long() - new = match.argmax(-1) - unknown = match.sum(2) < 1 - if self.unknown_index == "random": - new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device) - else: - new[unknown] = self.unknown_index - return new.reshape(ishape) - - def unmap_to_all(self, inds): - ishape = inds.shape - assert len(ishape) > 1 - inds = inds.reshape(ishape[0], -1) - used = self.used.to(inds) - if self.re_embed > self.used.shape[0]: # extra token - inds[inds >= self.used.shape[0]] = 0 # simply set to zero - back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds) - return back.reshape(ishape) - - def forward(self, z): - # reshape z -> (batch, height, width, channel) and flatten - z = z.permute(0, 2, 3, 1).contiguous() - z_flattened = z.view(-1, self.vq_embed_dim) - # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z - - d = ( - torch.sum(z_flattened**2, dim=1, keepdim=True) - + torch.sum(self.embedding.weight**2, dim=1) - - 2 * torch.einsum("bd,dn->bn", z_flattened, self.embedding.weight.t()) - ) - - min_encoding_indices = torch.argmin(d, dim=1) - z_q = self.embedding(min_encoding_indices).view(z.shape) - perplexity = None - min_encodings = None - - # compute loss for embedding - if not self.legacy: - loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + torch.mean((z_q - z.detach()) ** 2) - else: - loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean((z_q - z.detach()) ** 2) - - # preserve gradients - z_q = z + (z_q - z).detach() - - # reshape back to match original input shape - z_q = z_q.permute(0, 3, 1, 2).contiguous() - - if self.remap is not None: - min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1) # add batch axis - min_encoding_indices = self.remap_to_used(min_encoding_indices) - min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten - - if self.sane_index_shape: - min_encoding_indices = min_encoding_indices.reshape(z_q.shape[0], z_q.shape[2], z_q.shape[3]) - - return z_q, loss, (perplexity, min_encodings, min_encoding_indices) - - def get_codebook_entry(self, indices, shape): - # shape specifying (batch, height, width, channel) - if self.remap is not None: - indices = indices.reshape(shape[0], -1) # add batch axis - indices = self.unmap_to_all(indices) - indices = indices.reshape(-1) # flatten again - - # get quantized latent vectors - z_q = self.embedding(indices) - - if shape is not None: - z_q = z_q.view(shape) - # reshape back to match original input shape - z_q = z_q.permute(0, 3, 1, 2).contiguous() - - return z_q - - -class DiagonalGaussianDistribution(object): - def __init__(self, parameters, deterministic=False): - self.parameters = parameters - self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) - self.logvar = torch.clamp(self.logvar, -30.0, 20.0) - self.deterministic = deterministic - self.std = torch.exp(0.5 * self.logvar) - self.var = torch.exp(self.logvar) - if self.deterministic: - self.var = self.std = torch.zeros_like( - self.mean, device=self.parameters.device, dtype=self.parameters.dtype - ) - - def sample(self, generator: Optional[torch.Generator] = None) -> torch.FloatTensor: - device = self.parameters.device - sample_device = "cpu" if device.type == "mps" else device - sample = torch.randn(self.mean.shape, generator=generator, device=sample_device) - # make sure sample is on the same device as the parameters and has same dtype - sample = sample.to(device=device, dtype=self.parameters.dtype) - x = self.mean + self.std * sample - return x - - def kl(self, other=None): - if self.deterministic: - return torch.Tensor([0.0]) - else: - if other is None: - return 0.5 * torch.sum(torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, dim=[1, 2, 3]) - else: - return 0.5 * torch.sum( - torch.pow(self.mean - other.mean, 2) / other.var - + self.var / other.var - - 1.0 - - self.logvar - + other.logvar, - dim=[1, 2, 3], - ) - - def nll(self, sample, dims=[1, 2, 3]): - if self.deterministic: - return torch.Tensor([0.0]) - logtwopi = np.log(2.0 * np.pi) - return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, dim=dims) - - def mode(self): - return self.mean - - -class VQModel(ModelMixin, ConfigMixin): - r"""VQ-VAE model from the paper Neural Discrete Representation Learning by Aaron van den Oord, Oriol Vinyals and Koray - Kavukcuoglu. - - This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library - implements for all the model (such as downloading or saving, etc.) - - Parameters: - in_channels (int, *optional*, defaults to 3): Number of channels in the input image. - out_channels (int, *optional*, defaults to 3): Number of channels in the output. - down_block_types (`Tuple[str]`, *optional*, defaults to : - obj:`("DownEncoderBlock2D",)`): Tuple of downsample block types. - up_block_types (`Tuple[str]`, *optional*, defaults to : - obj:`("UpDecoderBlock2D",)`): Tuple of upsample block types. - block_out_channels (`Tuple[int]`, *optional*, defaults to : - obj:`(64,)`): Tuple of block output channels. - act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. - latent_channels (`int`, *optional*, defaults to `3`): Number of channels in the latent space. - sample_size (`int`, *optional*, defaults to `32`): TODO - num_vq_embeddings (`int`, *optional*, defaults to `256`): Number of codebook vectors in the VQ-VAE. - vq_embed_dim (`int`, *optional*): Hidden dim of codebook vectors in the VQ-VAE. - """ - - @register_to_config - def __init__( - self, - in_channels: int = 3, - out_channels: int = 3, - down_block_types: Tuple[str] = ("DownEncoderBlock2D",), - up_block_types: Tuple[str] = ("UpDecoderBlock2D",), - block_out_channels: Tuple[int] = (64,), - layers_per_block: int = 1, - act_fn: str = "silu", - latent_channels: int = 3, - sample_size: int = 32, - num_vq_embeddings: int = 256, - norm_num_groups: int = 32, - vq_embed_dim: Optional[int] = None, - ): - super().__init__() - - # pass init params to Encoder - self.encoder = Encoder( - in_channels=in_channels, - out_channels=latent_channels, - down_block_types=down_block_types, - block_out_channels=block_out_channels, - layers_per_block=layers_per_block, - act_fn=act_fn, - norm_num_groups=norm_num_groups, - double_z=False, - ) - - vq_embed_dim = vq_embed_dim if vq_embed_dim is not None else latent_channels - - self.quant_conv = torch.nn.Conv2d(latent_channels, vq_embed_dim, 1) - self.quantize = VectorQuantizer(num_vq_embeddings, vq_embed_dim, beta=0.25, remap=None, sane_index_shape=False) - self.post_quant_conv = torch.nn.Conv2d(vq_embed_dim, latent_channels, 1) - - # pass init params to Decoder - self.decoder = Decoder( - in_channels=latent_channels, - out_channels=out_channels, - up_block_types=up_block_types, - block_out_channels=block_out_channels, - layers_per_block=layers_per_block, - act_fn=act_fn, - norm_num_groups=norm_num_groups, - ) - - def encode(self, x: torch.FloatTensor, return_dict: bool = True) -> VQEncoderOutput: - h = self.encoder(x) - h = self.quant_conv(h) - - if not return_dict: - return (h,) - - return VQEncoderOutput(latents=h) - - def decode( - self, h: torch.FloatTensor, force_not_quantize: bool = False, return_dict: bool = True - ) -> Union[DecoderOutput, torch.FloatTensor]: - # also go through quantization layer - if not force_not_quantize: - quant, emb_loss, info = self.quantize(h) - else: - quant = h - quant = self.post_quant_conv(quant) - dec = self.decoder(quant) - - if not return_dict: - return (dec,) - - return DecoderOutput(sample=dec) - - def forward(self, sample: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]: - r""" - Args: - sample (`torch.FloatTensor`): Input sample. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`DecoderOutput`] instead of a plain tuple. - """ - x = sample - h = self.encode(x).latents - dec = self.decode(h).sample - - if not return_dict: - return (dec,) - - return DecoderOutput(sample=dec) - - -class AutoencoderKL(ModelMixin, ConfigMixin): - r"""Variational Autoencoder (VAE) model with KL loss from the paper Auto-Encoding Variational Bayes by Diederik P. Kingma - and Max Welling. - - This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library - implements for all the model (such as downloading or saving, etc.) - - Parameters: - in_channels (int, *optional*, defaults to 3): Number of channels in the input image. - out_channels (int, *optional*, defaults to 3): Number of channels in the output. - down_block_types (`Tuple[str]`, *optional*, defaults to : - obj:`("DownEncoderBlock2D",)`): Tuple of downsample block types. - up_block_types (`Tuple[str]`, *optional*, defaults to : - obj:`("UpDecoderBlock2D",)`): Tuple of upsample block types. - block_out_channels (`Tuple[int]`, *optional*, defaults to : - obj:`(64,)`): Tuple of block output channels. - act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. - latent_channels (`int`, *optional*, defaults to `4`): Number of channels in the latent space. - sample_size (`int`, *optional*, defaults to `32`): TODO - """ - - @register_to_config - def __init__( - self, - in_channels: int = 3, - out_channels: int = 3, - down_block_types: Tuple[str] = ("DownEncoderBlock2D",), - up_block_types: Tuple[str] = ("UpDecoderBlock2D",), - block_out_channels: Tuple[int] = (64,), - layers_per_block: int = 1, - act_fn: str = "silu", - latent_channels: int = 4, - norm_num_groups: int = 32, - sample_size: int = 32, - ): - super().__init__() - - # pass init params to Encoder - self.encoder = Encoder( - in_channels=in_channels, - out_channels=latent_channels, - down_block_types=down_block_types, - block_out_channels=block_out_channels, - layers_per_block=layers_per_block, - act_fn=act_fn, - norm_num_groups=norm_num_groups, - double_z=True, - ) - - # pass init params to Decoder - self.decoder = Decoder( - in_channels=latent_channels, - out_channels=out_channels, - up_block_types=up_block_types, - block_out_channels=block_out_channels, - layers_per_block=layers_per_block, - norm_num_groups=norm_num_groups, - act_fn=act_fn, - ) - - self.quant_conv = torch.nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1) - self.post_quant_conv = torch.nn.Conv2d(latent_channels, latent_channels, 1) - self.use_slicing = False - - def encode(self, x: torch.FloatTensor, return_dict: bool = True) -> AutoencoderKLOutput: - h = self.encoder(x) - moments = self.quant_conv(h) - posterior = DiagonalGaussianDistribution(moments) - - if not return_dict: - return (posterior,) - - return AutoencoderKLOutput(latent_dist=posterior) - - def _decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]: - z = self.post_quant_conv(z) - dec = self.decoder(z) - - if not return_dict: - return (dec,) - - return DecoderOutput(sample=dec) - - def enable_slicing(self): - r""" - Enable sliced VAE decoding. - - When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several - steps. This is useful to save some memory and allow larger batch sizes. - """ - self.use_slicing = True - - def disable_slicing(self): - r""" - Disable sliced VAE decoding. If `enable_slicing` was previously invoked, this method will go back to computing - decoding in one step. - """ - self.use_slicing = False - - def decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]: - if self.use_slicing and z.shape[0] > 1: - decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)] - decoded = torch.cat(decoded_slices) - else: - decoded = self._decode(z).sample - - if not return_dict: - return (decoded,) - - return DecoderOutput(sample=decoded) - - def forward( - self, - sample: torch.FloatTensor, - sample_posterior: bool = False, - return_dict: bool = True, - generator: Optional[torch.Generator] = None, - ) -> Union[DecoderOutput, torch.FloatTensor]: - r""" - Args: - sample (`torch.FloatTensor`): Input sample. - sample_posterior (`bool`, *optional*, defaults to `False`): - Whether to sample from the posterior. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`DecoderOutput`] instead of a plain tuple. - """ - x = sample - posterior = self.encode(x).latent_dist - if sample_posterior: - z = posterior.sample(generator=generator) - else: - z = posterior.mode() - dec = self.decode(z).sample - - if not return_dict: - return (dec,) - - return DecoderOutput(sample=dec) diff --git a/spaces/Jeffsun/LSP-LearningandStrivePartner-Demo/README.md b/spaces/Jeffsun/LSP-LearningandStrivePartner-Demo/README.md deleted file mode 100644 index f18f4d0191c0acabb95750fa34d972f6589b15da..0000000000000000000000000000000000000000 --- a/spaces/Jeffsun/LSP-LearningandStrivePartner-Demo/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: LSP LearningandStrivePartner Model -emoji: 👀 -colorFrom: green -colorTo: pink -sdk: gradio -sdk_version: 3.17.0 -app_file: app.py -pinned: false -license: afl-3.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/Jo0xFF/4xArText/utils/architecture/SPSR.py b/spaces/Jo0xFF/4xArText/utils/architecture/SPSR.py deleted file mode 100644 index ced98089074c738059391a40968130340d8e8a26..0000000000000000000000000000000000000000 --- a/spaces/Jo0xFF/4xArText/utils/architecture/SPSR.py +++ /dev/null @@ -1,377 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- - -import math - -import torch -import torch.nn as nn -import torch.nn.functional as F -import utils.architecture.block as B - - -class Get_gradient_nopadding(nn.Module): - def __init__(self): - super(Get_gradient_nopadding, self).__init__() - kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]] - kernel_h = [[0, 0, 0], [-1, 0, 1], [0, 0, 0]] - kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0) - kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0) - self.weight_h = nn.Parameter(data=kernel_h, requires_grad=False) - - self.weight_v = nn.Parameter(data=kernel_v, requires_grad=False) - - def forward(self, x): - x_list = [] - for i in range(x.shape[1]): - x_i = x[:, i] - x_i_v = F.conv2d(x_i.unsqueeze(1), self.weight_v, padding=1) - x_i_h = F.conv2d(x_i.unsqueeze(1), self.weight_h, padding=1) - x_i = torch.sqrt(torch.pow(x_i_v, 2) + torch.pow(x_i_h, 2) + 1e-6) - x_list.append(x_i) - - x = torch.cat(x_list, dim=1) - - return x - - -class SPSRNet(nn.Module): - def __init__( - self, - state_dict, - norm=None, - act: str = "leakyrelu", - upsampler: str = "upconv", - mode: str = "CNA", - ): - super(SPSRNet, self).__init__() - - self.state = state_dict - self.norm = norm - self.act = act - self.upsampler = upsampler - self.mode = mode - - self.num_blocks = self.get_num_blocks() - - self.in_nc = self.state["model.0.weight"].shape[1] - self.out_nc = self.state["f_HR_conv1.0.bias"].shape[0] - - self.scale = self.get_scale(4) - print(self.scale) - self.num_filters = self.state["model.0.weight"].shape[0] - - n_upscale = int(math.log(self.scale, 2)) - if self.scale == 3: - n_upscale = 1 - - fea_conv = B.conv_block( - self.in_nc, self.num_filters, kernel_size=3, norm_type=None, act_type=None - ) - rb_blocks = [ - B.RRDB( - self.num_filters, - kernel_size=3, - gc=32, - stride=1, - bias=True, - pad_type="zero", - norm_type=norm, - act_type=act, - mode="CNA", - ) - for _ in range(self.num_blocks) - ] - LR_conv = B.conv_block( - self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=norm, - act_type=None, - mode=mode, - ) - - if upsampler == "upconv": - upsample_block = B.upconv_block - elif upsampler == "pixelshuffle": - upsample_block = B.pixelshuffle_block - else: - raise NotImplementedError(f"upsample mode [{upsampler}] is not found") - if self.scale == 3: - a_upsampler = upsample_block( - self.num_filters, self.num_filters, 3, act_type=act - ) - else: - a_upsampler = [ - upsample_block(self.num_filters, self.num_filters, act_type=act) - for _ in range(n_upscale) - ] - self.HR_conv0_new = B.conv_block( - self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=None, - act_type=act, - ) - self.HR_conv1_new = B.conv_block( - self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=None, - act_type=None, - ) - - self.model = B.sequential( - fea_conv, - B.ShortcutBlockSPSR(B.sequential(*rb_blocks, LR_conv)), - *a_upsampler, - self.HR_conv0_new, - ) - - self.get_g_nopadding = Get_gradient_nopadding() - - self.b_fea_conv = B.conv_block( - self.in_nc, self.num_filters, kernel_size=3, norm_type=None, act_type=None - ) - - self.b_concat_1 = B.conv_block( - 2 * self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=None, - act_type=None, - ) - self.b_block_1 = B.RRDB( - self.num_filters * 2, - kernel_size=3, - gc=32, - stride=1, - bias=True, - pad_type="zero", - norm_type=norm, - act_type=act, - mode="CNA", - ) - - self.b_concat_2 = B.conv_block( - 2 * self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=None, - act_type=None, - ) - self.b_block_2 = B.RRDB( - self.num_filters * 2, - kernel_size=3, - gc=32, - stride=1, - bias=True, - pad_type="zero", - norm_type=norm, - act_type=act, - mode="CNA", - ) - - self.b_concat_3 = B.conv_block( - 2 * self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=None, - act_type=None, - ) - self.b_block_3 = B.RRDB( - self.num_filters * 2, - kernel_size=3, - gc=32, - stride=1, - bias=True, - pad_type="zero", - norm_type=norm, - act_type=act, - mode="CNA", - ) - - self.b_concat_4 = B.conv_block( - 2 * self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=None, - act_type=None, - ) - self.b_block_4 = B.RRDB( - self.num_filters * 2, - kernel_size=3, - gc=32, - stride=1, - bias=True, - pad_type="zero", - norm_type=norm, - act_type=act, - mode="CNA", - ) - - self.b_LR_conv = B.conv_block( - self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=norm, - act_type=None, - mode=mode, - ) - - if upsampler == "upconv": - upsample_block = B.upconv_block - elif upsampler == "pixelshuffle": - upsample_block = B.pixelshuffle_block - else: - raise NotImplementedError(f"upsample mode [{upsampler}] is not found") - if self.scale == 3: - b_upsampler = upsample_block( - self.num_filters, self.num_filters, 3, act_type=act - ) - else: - b_upsampler = [ - upsample_block(self.num_filters, self.num_filters, act_type=act) - for _ in range(n_upscale) - ] - - b_HR_conv0 = B.conv_block( - self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=None, - act_type=act, - ) - b_HR_conv1 = B.conv_block( - self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=None, - act_type=None, - ) - - self.b_module = B.sequential(*b_upsampler, b_HR_conv0, b_HR_conv1) - - self.conv_w = B.conv_block( - self.num_filters, self.out_nc, kernel_size=1, norm_type=None, act_type=None - ) - - self.f_concat = B.conv_block( - self.num_filters * 2, - self.num_filters, - kernel_size=3, - norm_type=None, - act_type=None, - ) - - self.f_block = B.RRDB( - self.num_filters * 2, - kernel_size=3, - gc=32, - stride=1, - bias=True, - pad_type="zero", - norm_type=norm, - act_type=act, - mode="CNA", - ) - - self.f_HR_conv0 = B.conv_block( - self.num_filters, - self.num_filters, - kernel_size=3, - norm_type=None, - act_type=act, - ) - self.f_HR_conv1 = B.conv_block( - self.num_filters, self.out_nc, kernel_size=3, norm_type=None, act_type=None - ) - - self.load_state_dict(self.state, strict=False) - - def get_scale(self, min_part: int = 4) -> int: - n = 0 - for part in list(self.state): - parts = part.split(".") - if len(parts) == 3: - part_num = int(parts[1]) - if part_num > min_part and parts[0] == "model" and parts[2] == "weight": - n += 1 - return 2 ** n - - def get_num_blocks(self) -> int: - nb = 0 - for part in list(self.state): - parts = part.split(".") - n_parts = len(parts) - if n_parts == 5 and parts[2] == "sub": - nb = int(parts[3]) - return nb - - def forward(self, x): - x_grad = self.get_g_nopadding(x) - x = self.model[0](x) - - x, block_list = self.model[1](x) - - x_ori = x - for i in range(5): - x = block_list[i](x) - x_fea1 = x - - for i in range(5): - x = block_list[i + 5](x) - x_fea2 = x - - for i in range(5): - x = block_list[i + 10](x) - x_fea3 = x - - for i in range(5): - x = block_list[i + 15](x) - x_fea4 = x - - x = block_list[20:](x) - # short cut - x = x_ori + x - x = self.model[2:](x) - x = self.HR_conv1_new(x) - - x_b_fea = self.b_fea_conv(x_grad) - x_cat_1 = torch.cat([x_b_fea, x_fea1], dim=1) - - x_cat_1 = self.b_block_1(x_cat_1) - x_cat_1 = self.b_concat_1(x_cat_1) - - x_cat_2 = torch.cat([x_cat_1, x_fea2], dim=1) - - x_cat_2 = self.b_block_2(x_cat_2) - x_cat_2 = self.b_concat_2(x_cat_2) - - x_cat_3 = torch.cat([x_cat_2, x_fea3], dim=1) - - x_cat_3 = self.b_block_3(x_cat_3) - x_cat_3 = self.b_concat_3(x_cat_3) - - x_cat_4 = torch.cat([x_cat_3, x_fea4], dim=1) - - x_cat_4 = self.b_block_4(x_cat_4) - x_cat_4 = self.b_concat_4(x_cat_4) - - x_cat_4 = self.b_LR_conv(x_cat_4) - - # short cut - x_cat_4 = x_cat_4 + x_b_fea - x_branch = self.b_module(x_cat_4) - - # x_out_branch = self.conv_w(x_branch) - ######## - x_branch_d = x_branch - x_f_cat = torch.cat([x_branch_d, x], dim=1) - x_f_cat = self.f_block(x_f_cat) - x_out = self.f_concat(x_f_cat) - x_out = self.f_HR_conv0(x_out) - x_out = self.f_HR_conv1(x_out) - - ######### - # return x_out_branch, x_out, x_grad - return x_out diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/modules/train_func.py b/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/modules/train_func.py deleted file mode 100644 index bc5e2c6aea1f3f28d4bb3f9f4fd2f6d761ba00a2..0000000000000000000000000000000000000000 --- a/spaces/JohnSmith9982/ChuanhuChatGPT_Beta/modules/train_func.py +++ /dev/null @@ -1,161 +0,0 @@ -import os -import logging -import traceback - -import openai -import gradio as gr -import ujson as json -import commentjson -import openpyxl - -import modules.presets as presets -from modules.utils import get_file_hash, count_token -from modules.presets import i18n - -def excel_to_jsonl(filepath, preview=False): - # 打开Excel文件 - workbook = openpyxl.load_workbook(filepath) - - # 获取第一个工作表 - sheet = workbook.active - - # 获取所有行数据 - data = [] - for row in sheet.iter_rows(values_only=True): - data.append(row) - - # 构建字典列表 - headers = data[0] - jsonl = [] - for row in data[1:]: - row_data = dict(zip(headers, row)) - if any(row_data.values()): - jsonl.append(row_data) - formatted_jsonl = [] - for i in jsonl: - if "提问" in i and "答案" in i: - if "系统" in i : - formatted_jsonl.append({ - "messages":[ - {"role": "system", "content": i["系统"]}, - {"role": "user", "content": i["提问"]}, - {"role": "assistant", "content": i["答案"]} - ] - }) - else: - formatted_jsonl.append({ - "messages":[ - {"role": "user", "content": i["提问"]}, - {"role": "assistant", "content": i["答案"]} - ] - }) - else: - logging.warning(f"跳过一行数据，因为没有找到提问和答案: {i}") - return formatted_jsonl - -def jsonl_save_to_disk(jsonl, filepath): - file_hash = get_file_hash(file_paths = [filepath]) - os.makedirs("files", exist_ok=True) - save_path = f"files/{file_hash}.jsonl" - with open(save_path, "w") as f: - f.write("\n".join([json.dumps(i, ensure_ascii=False) for i in jsonl])) - return save_path - -def estimate_cost(ds): - dialogues = [] - for l in ds: - for m in l["messages"]: - dialogues.append(m["content"]) - dialogues = "\n".join(dialogues) - tokens = count_token(dialogues) - return f"Token 数约为 {tokens}，预估每轮（epoch）费用约为 {tokens / 1000 * 0.008} 美元。" - - -def handle_dataset_selection(file_src): - logging.info(f"Loading dataset {file_src.name}...") - preview = "" - if file_src.name.endswith(".jsonl"): - with open(file_src.name, "r") as f: - ds = [json.loads(l) for l in f.readlines()] - else: - ds = excel_to_jsonl(file_src.name) - preview = ds[0] - - return preview, gr.update(interactive=True), estimate_cost(ds) - -def upload_to_openai(file_src): - openai.api_key = os.getenv("OPENAI_API_KEY") - dspath = file_src.name - msg = "" - logging.info(f"Uploading dataset {dspath}...") - if dspath.endswith(".xlsx"): - jsonl = excel_to_jsonl(dspath) - dspath = jsonl_save_to_disk(jsonl, dspath) - try: - uploaded = openai.File.create( - file=open(dspath, "rb"), - purpose='fine-tune' - ) - return uploaded.id, f"上传成功" - except Exception as e: - traceback.print_exc() - return "", f"上传失败，原因：{ e }" - -def build_event_description(id, status, trained_tokens, name=i18n("暂时未知")): - # convert to markdown - return f""" - #### 训练任务 {id} - - 模型名称：{name} - - 状态：{status} - - 已经训练了 {trained_tokens} 个token - """ - -def start_training(file_id, suffix, epochs): - openai.api_key = os.getenv("OPENAI_API_KEY") - try: - job = openai.FineTuningJob.create(training_file=file_id, model="gpt-3.5-turbo", suffix=suffix, hyperparameters={"n_epochs": epochs}) - return build_event_description(job.id, job.status, job.trained_tokens) - except Exception as e: - traceback.print_exc() - if "is not ready" in str(e): - return "训练出错，因为文件还没准备好。OpenAI 需要一点时间准备文件，过几分钟再来试试。" - return f"训练失败，原因：{ e }" - -def get_training_status(): - openai.api_key = os.getenv("OPENAI_API_KEY") - active_jobs = [build_event_description(job["id"], job["status"], job["trained_tokens"], job["fine_tuned_model"]) for job in openai.FineTuningJob.list(limit=10)["data"] if job["status"] != "cancelled"] - return "\n\n".join(active_jobs), gr.update(interactive=True) if len(active_jobs) > 0 else gr.update(interactive=False) - -def handle_dataset_clear(): - return gr.update(value=None), gr.update(interactive=False) - -def add_to_models(): - openai.api_key = os.getenv("OPENAI_API_KEY") - succeeded_jobs = [job for job in openai.FineTuningJob.list()["data"] if job["status"] == "succeeded"] - extra_models = [job["fine_tuned_model"] for job in succeeded_jobs] - for i in extra_models: - if i not in presets.MODELS: - presets.MODELS.append(i) - - with open('config.json', 'r') as f: - data = commentjson.load(f) - if 'extra_models' in data: - for i in extra_models: - if i not in data['extra_models']: - data['extra_models'].append(i) - else: - data['extra_models'] = extra_models - with open('config.json', 'w') as f: - commentjson.dump(data, f, indent=4) - - return gr.update(choices=presets.MODELS), f"成功添加了 {len(succeeded_jobs)} 个模型。" - -def cancel_all_jobs(): - openai.api_key = os.getenv("OPENAI_API_KEY") - jobs = [job for job in openai.FineTuningJob.list()["data"] if job["status"] not in ["cancelled", "succeeded"]] - for job in jobs: - openai.FineTuningJob.cancel(job["id"]) - return f"成功取消了 {len(jobs)} 个训练任务。" diff --git a/spaces/JohnSmith9982/VITS-Umamusume-voice-synthesizer/transforms.py b/spaces/JohnSmith9982/VITS-Umamusume-voice-synthesizer/transforms.py deleted file mode 100644 index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000 --- a/spaces/JohnSmith9982/VITS-Umamusume-voice-synthesizer/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/JosefJilek/loliDiffusionSpace/header_patch.py b/spaces/JosefJilek/loliDiffusionSpace/header_patch.py deleted file mode 100644 index f2e66b0e35adb99f5b84015453bd9f8863c417a8..0000000000000000000000000000000000000000 --- a/spaces/JosefJilek/loliDiffusionSpace/header_patch.py +++ /dev/null @@ -1,17 +0,0 @@ - with gr.Box(visible=is_spaces): - if(is_spaces and is_shared_ui): - gr.HTML(f''' - -- ''') - elif(is_spaces): - gr.HTML(f''' -🚨 using CPU
-🚧 (WIP) Automatic1111 Stable Diffusion Web UI on 🤗 Hugging Face Spaces | Running model: WarriorMama777/AOM3A3.safetensors
-You can duplicate this Space to run it privately without a queue and load additional checkpoints.
--- ''') - \ No newline at end of file diff --git a/spaces/Kangarroar/ApplioRVC-Inference/tensorlowest.py b/spaces/Kangarroar/ApplioRVC-Inference/tensorlowest.py deleted file mode 100644 index eccd4dbf3494434e59f7defaae6ab91797263b90..0000000000000000000000000000000000000000 --- a/spaces/Kangarroar/ApplioRVC-Inference/tensorlowest.py +++ /dev/null @@ -1,123 +0,0 @@ -from tensorboard.backend.event_processing import event_accumulator - -import os -from shutil import copy2 -from re import search as RSearch -import pandas as pd -from ast import literal_eval as LEval - -weights_dir = 'weights/' - -def find_biggest_tensorboard(tensordir): - try: - files = [f for f in os.listdir(tensordir) if f.endswith('.0')] - if not files: - print("No files with the '.0' extension found!") - return - - max_size = 0 - biggest_file = "" - - for file in files: - file_path = os.path.join(tensordir, file) - if os.path.isfile(file_path): - file_size = os.path.getsize(file_path) - if file_size > max_size: - max_size = file_size - biggest_file = file - - return biggest_file - - except FileNotFoundError: - print("Couldn't find your model!") - return - -def main(model_name, save_freq, lastmdls): - global lowestval_weight_dir, scl - - tensordir = os.path.join('logs', model_name) - lowestval_weight_dir = os.path.join(tensordir, "lowestvals") - - latest_file = find_biggest_tensorboard(tensordir) - - if latest_file is None: - print("Couldn't find a valid tensorboard file!") - return - - tfile = os.path.join(tensordir, latest_file) - - ea = event_accumulator.EventAccumulator(tfile, - size_guidance={ - event_accumulator.COMPRESSED_HISTOGRAMS: 500, - event_accumulator.IMAGES: 4, - event_accumulator.AUDIO: 4, - event_accumulator.SCALARS: 0, - event_accumulator.HISTOGRAMS: 1, - }) - - ea.Reload() - ea.Tags() - - scl = ea.Scalars('loss/g/total') - - listwstep = {} - - for val in scl: - if (val.step // save_freq) * save_freq in [val.step for val in scl]: - listwstep[float(val.value)] = (val.step // save_freq) * save_freq - - lowest_vals = sorted(listwstep.keys())[:lastmdls] - - sorted_dict = {value: step for value, step in listwstep.items() if value in lowest_vals} - - return sorted_dict - -def selectweights(model_name, file_dict, weights_dir, lowestval_weight_dir): - os.makedirs(lowestval_weight_dir, exist_ok=True) - logdir = [] - files = [] - lbldict = { - 'Values': {}, - 'Names': {} - } - weights_dir_path = os.path.join(weights_dir, "") - low_val_path = os.path.join(os.getcwd(), os.path.join(lowestval_weight_dir, "")) - - try: - file_dict = LEval(file_dict) - except Exception as e: - print(f"Error! {e}") - return f"Couldn't load tensorboard file! {e}" - - weights = [f for f in os.scandir(weights_dir)] - for key, value in file_dict.items(): - pattern = fr"^{model_name}_.*_s{value}\.pth$" - matching_weights = [f.name for f in weights if f.is_file() and RSearch(pattern, f.name)] - for weight in matching_weights: - source_path = weights_dir_path + weight - destination_path = os.path.join(lowestval_weight_dir, weight) - - copy2(source_path, destination_path) - - logdir.append(f"File = {weight} Value: {key}, Step: {value}") - - lbldict['Names'][weight] = weight - lbldict['Values'][weight] = key - - files.append(low_val_path + weight) - - print(f"File = {weight} Value: {key}, Step: {value}") - - yield ('\n'.join(logdir), files, pd.DataFrame(lbldict)) - - - return ''.join(logdir), files, pd.DataFrame(lbldict) - - -if __name__ == "__main__": - model = str(input("Enter the name of the model: ")) - sav_freq = int(input("Enter save frequency of the model: ")) - ds = main(model, sav_freq) - - if ds: selectweights(model, ds, weights_dir, lowestval_weight_dir) - \ No newline at end of file diff --git a/spaces/KenjieDec/GPEN/retinaface/utils/nms/py_cpu_nms.py b/spaces/KenjieDec/GPEN/retinaface/utils/nms/py_cpu_nms.py deleted file mode 100644 index 54e7b25fef72b518df6dcf8d6fb78b986796c6e3..0000000000000000000000000000000000000000 --- a/spaces/KenjieDec/GPEN/retinaface/utils/nms/py_cpu_nms.py +++ /dev/null @@ -1,38 +0,0 @@ -# -------------------------------------------------------- -# Fast R-CNN -# Copyright (c) 2015 Microsoft -# Licensed under The MIT License [see LICENSE for details] -# Written by Ross Girshick -# -------------------------------------------------------- - -import numpy as np - -def py_cpu_nms(dets, thresh): - """Pure Python NMS baseline.""" - x1 = dets[:, 0] - y1 = dets[:, 1] - x2 = dets[:, 2] - y2 = dets[:, 3] - scores = dets[:, 4] - - areas = (x2 - x1 + 1) * (y2 - y1 + 1) - order = scores.argsort()[::-1] - - keep = [] - while order.size > 0: - i = order[0] - keep.append(i) - xx1 = np.maximum(x1[i], x1[order[1:]]) - yy1 = np.maximum(y1[i], y1[order[1:]]) - xx2 = np.minimum(x2[i], x2[order[1:]]) - yy2 = np.minimum(y2[i], y2[order[1:]]) - - w = np.maximum(0.0, xx2 - xx1 + 1) - h = np.maximum(0.0, yy2 - yy1 + 1) - inter = w * h - ovr = inter / (areas[i] + areas[order[1:]] - inter) - - inds = np.where(ovr <= thresh)[0] - order = order[inds + 1] - - return keep diff --git a/spaces/Kevin676/Real-Time-Voice-Cloning/encoder/inference.py b/spaces/Kevin676/Real-Time-Voice-Cloning/encoder/inference.py deleted file mode 100644 index 4ca417b63406b9280418069d28d2877308be9bc2..0000000000000000000000000000000000000000 --- a/spaces/Kevin676/Real-Time-Voice-Cloning/encoder/inference.py +++ /dev/null @@ -1,178 +0,0 @@ -from encoder.params_data import * -from encoder.model import SpeakerEncoder -from encoder.audio import preprocess_wav # We want to expose this function from here -from matplotlib import cm -from encoder import audio -from pathlib import Path -import matplotlib.pyplot as plt -import numpy as np -import torch - -_model = None # type: SpeakerEncoder -_device = None # type: torch.device - - -def load_model(weights_fpath: Path, device=None): - """ - Loads the model in memory. If this function is not explicitely called, it will be run on the - first call to embed_frames() with the default weights file. - - :param weights_fpath: the path to saved model weights. - :param device: either a torch device or the name of a torch device (e.g. "cpu", "cuda"). The - model will be loaded and will run on this device. Outputs will however always be on the cpu. - If None, will default to your GPU if it"s available, otherwise your CPU. - """ - # TODO: I think the slow loading of the encoder might have something to do with the device it - # was saved on. Worth investigating. - global _model, _device - if device is None: - _device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - elif isinstance(device, str): - _device = torch.device(device) - _model = SpeakerEncoder(_device, torch.device("cpu")) - checkpoint = torch.load(weights_fpath, _device) - _model.load_state_dict(checkpoint["model_state"]) - _model.eval() - print("Loaded encoder \"%s\" trained to step %d" % (weights_fpath.name, checkpoint["step"])) - - -def is_loaded(): - return _model is not None - - -def embed_frames_batch(frames_batch): - """ - Computes embeddings for a batch of mel spectrogram. - - :param frames_batch: a batch mel of spectrogram as a numpy array of float32 of shape - (batch_size, n_frames, n_channels) - :return: the embeddings as a numpy array of float32 of shape (batch_size, model_embedding_size) - """ - if _model is None: - raise Exception("Model was not loaded. Call load_model() before inference.") - - frames = torch.from_numpy(frames_batch).to(_device) - embed = _model.forward(frames).detach().cpu().numpy() - return embed - - -def compute_partial_slices(n_samples, partial_utterance_n_frames=partials_n_frames, - min_pad_coverage=0.75, overlap=0.5): - """ - Computes where to split an utterance waveform and its corresponding mel spectrogram to obtain - partial utterances of🚧 (WIP) Private Automatic1111 Stable Diffusion Web UI on 🤗 Hugging Face Spaces
-This Space is currently running on CPU
-each. Both the waveform and the mel - spectrogram slices are returned, so as to make each partial utterance waveform correspond to - its spectrogram. This function assumes that the mel spectrogram parameters used are those - defined in params_data.py. - - The returned ranges may be indexing further than the length of the waveform. It is - recommended that you pad the waveform with zeros up to wave_slices[-1].stop. - - :param n_samples: the number of samples in the waveform - :param partial_utterance_n_frames: the number of mel spectrogram frames in each partial - utterance - :param min_pad_coverage: when reaching the last partial utterance, it may or may not have - enough frames. If at least of are present, - then the last partial utterance will be considered, as if we padded the audio. Otherwise, - it will be discarded, as if we trimmed the audio. If there aren't enough frames for 1 partial - utterance, this parameter is ignored so that the function always returns at least 1 slice. - :param overlap: by how much the partial utterance should overlap. If set to 0, the partial - utterances are entirely disjoint. - :return: the waveform slices and mel spectrogram slices as lists of array slices. Index - respectively the waveform and the mel spectrogram with these slices to obtain the partial - utterances. - """ - assert 0 <= overlap < 1 - assert 0 < min_pad_coverage <= 1 - - samples_per_frame = int((sampling_rate * mel_window_step / 1000)) - n_frames = int(np.ceil((n_samples + 1) / samples_per_frame)) - frame_step = max(int(np.round(partial_utterance_n_frames * (1 - overlap))), 1) - - # Compute the slices - wav_slices, mel_slices = [], [] - steps = max(1, n_frames - partial_utterance_n_frames + frame_step + 1) - for i in range(0, steps, frame_step): - mel_range = np.array([i, i + partial_utterance_n_frames]) - wav_range = mel_range * samples_per_frame - mel_slices.append(slice(*mel_range)) - wav_slices.append(slice(*wav_range)) - - # Evaluate whether extra padding is warranted or not - last_wav_range = wav_slices[-1] - coverage = (n_samples - last_wav_range.start) / (last_wav_range.stop - last_wav_range.start) - if coverage < min_pad_coverage and len(mel_slices) > 1: - mel_slices = mel_slices[:-1] - wav_slices = wav_slices[:-1] - - return wav_slices, mel_slices - - -def embed_utterance(wav, using_partials=True, return_partials=False, **kwargs): - """ - Computes an embedding for a single utterance. - - # TODO: handle multiple wavs to benefit from batching on GPU - :param wav: a preprocessed (see audio.py) utterance waveform as a numpy array of float32 - :param using_partials: if True, then the utterance is split in partial utterances of - frames and the utterance embedding is computed from their - normalized average. If False, the utterance is instead computed from feeding the entire - spectogram to the network. - :param return_partials: if True, the partial embeddings will also be returned along with the - wav slices that correspond to the partial embeddings. - :param kwargs: additional arguments to compute_partial_splits() - :return: the embedding as a numpy array of float32 of shape (model_embedding_size,). If - is True, the partial utterances as a numpy array of float32 of shape - (n_partials, model_embedding_size) and the wav partials as a list of slices will also be - returned. If is simultaneously set to False, both these values will be None - instead. - """ - # Process the entire utterance if not using partials - if not using_partials: - frames = audio.wav_to_mel_spectrogram(wav) - embed = embed_frames_batch(frames[None, ...])[0] - if return_partials: - return embed, None, None - return embed - - # Compute where to split the utterance into partials and pad if necessary - wave_slices, mel_slices = compute_partial_slices(len(wav), **kwargs) - max_wave_length = wave_slices[-1].stop - if max_wave_length >= len(wav): - wav = np.pad(wav, (0, max_wave_length - len(wav)), "constant") - - # Split the utterance into partials - frames = audio.wav_to_mel_spectrogram(wav) - frames_batch = np.array([frames[s] for s in mel_slices]) - partial_embeds = embed_frames_batch(frames_batch) - - # Compute the utterance embedding from the partial embeddings - raw_embed = np.mean(partial_embeds, axis=0) - embed = raw_embed / np.linalg.norm(raw_embed, 2) - - if return_partials: - return embed, partial_embeds, wave_slices - return embed - - -def embed_speaker(wavs, **kwargs): - raise NotImplemented() - - -def plot_embedding_as_heatmap(embed, ax=None, title="", shape=None, color_range=(0, 0.30)): - if ax is None: - ax = plt.gca() - - if shape is None: - height = int(np.sqrt(len(embed))) - shape = (height, -1) - embed = embed.reshape(shape) - - cmap = cm.get_cmap() - mappable = ax.imshow(embed, cmap=cmap) - cbar = plt.colorbar(mappable, ax=ax, fraction=0.046, pad=0.04) - sm = cm.ScalarMappable(cmap=cmap) - sm.set_clim(*color_range) - - ax.set_xticks([]), ax.set_yticks([]) - ax.set_title(title) diff --git a/spaces/Kokuen/oobabooga-windows/README.md b/spaces/Kokuen/oobabooga-windows/README.md deleted file mode 100644 index 7455aa409142587c338d76dcf16c1a101fdd351e..0000000000000000000000000000000000000000 --- a/spaces/Kokuen/oobabooga-windows/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Oobabooga Windows -emoji: 🌍 -colorFrom: red -colorTo: purple -sdk: gradio -sdk_version: 3.23.0 -app_file: app.py -pinned: false -license: cc-by-nc-nd-4.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Lianjd/stock_dashboard/backtrader/feeds/yahoo.py b/spaces/Lianjd/stock_dashboard/backtrader/feeds/yahoo.py deleted file mode 100644 index 7031d3dcf7b1277a52c2a0c0dde58bf75cc527cb..0000000000000000000000000000000000000000 --- a/spaces/Lianjd/stock_dashboard/backtrader/feeds/yahoo.py +++ /dev/null @@ -1,369 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8; py-indent-offset:4 -*- -############################################################################### -# -# Copyright (C) 2015-2020 Daniel Rodriguez -# -# This program is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# This program 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 General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with this program. If not, see -- -## Dataset - -### Train Dataset - -| trainset | instance_num | repeat_num | note | -| :-------: | :----------: | :--------: | :----------: | -| Syn90k | 8919273 | 1 | synth | -| SynthText | 7239272 | 1 | alphanumeric | - -### Test Dataset - -| testset | instance_num | note | -| :-----: | :----------: | :-------: | -| IIIT5K | 3000 | regular | -| SVT | 647 | regular | -| IC13 | 1015 | regular | -| IC15 | 2077 | irregular | -| SVTP | 645 | irregular | -| CT80 | 288 | irregular | - -## Results and models - -| methods | pretrained | | Regular Text | | | Irregular Text | | download | -| :--------------------------------------------: | :------------------------------------------------: | :----: | :----------: | :-------: | :-------: | :------------: | :----: | :----------------------------------------------- | -| | | IIIT5K | SVT | IC13-1015 | IC15-2077 | SVTP | CT80 | | -| [ABINet-Vision](/configs/textrecog/abinet/abinet-vision_20e_st-an_mj.py) | - | 0.9523 | 0.9196 | 0.9369 | 0.7896 | 0.8403 | 0.8437 | [model](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet-vision_20e_st-an_mj/abinet-vision_20e_st-an_mj_20220915_152445-85cfb03d.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet-vision_20e_st-an_mj/20220915_152445.log) | -| [ABINet-Vision-TTA](/configs/textrecog/abinet/abinet-vision_20e_st-an_mj.py) | - | 0.9523 | 0.9196 | 0.9360 | 0.8175 | 0.8450 | 0.8542 | | -| [ABINet](/configs/textrecog/abinet/abinet_20e_st-an_mj.py) | [Pretrained](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_pretrain-45deac15.pth) | 0.9603 | 0.9397 | 0.9557 | 0.8146 | 0.8868 | 0.8785 | [model](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_20e_st-an_mj/abinet_20e_st-an_mj_20221005_012617-ead8c139.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_20e_st-an_mj/20221005_012617.log) | -| [ABINet-TTA](/configs/textrecog/abinet/abinet_20e_st-an_mj.py) | [Pretrained](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_pretrain-45deac15.pth) | 0.9597 | 0.9397 | 0.9527 | 0.8426 | 0.8930 | 0.8854 | | - -```{note} -1. ABINet allows its encoder to run and be trained without decoder and fuser. Its encoder is designed to recognize texts as a stand-alone model and therefore can work as an independent text recognizer. We release it as ABINet-Vision. -2. Facts about the pretrained model: MMOCR does not have a systematic pipeline to pretrain the language model (LM) yet, thus the weights of LM are converted from [the official pretrained model](https://github.com/FangShancheng/ABINet). The weights of ABINet-Vision are directly used as the vision model of ABINet. -``` - -## Citation - -```bibtex -@article{fang2021read, - title={Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition}, - author={Fang, Shancheng and Xie, Hongtao and Wang, Yuxin and Mao, Zhendong and Zhang, Yongdong}, - booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, - year={2021} -} -``` diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/__init__.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/__init__.py deleted file mode 100644 index e2323e3273d988c4e26443567a77dbd328e4f329..0000000000000000000000000000000000000000 --- a/spaces/Mountchicken/MAERec-Gradio/mmocr/datasets/preparers/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from .config_generators import * # noqa -from .data_preparer import DatasetPreparer -from .dumpers import * # noqa -from .gatherers import * # noqa -from .obtainers import * # noqa -from .packers import * # noqa -from .parsers import * # noqa - -__all__ = ['DatasetPreparer'] diff --git a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/heads/fce_head.py b/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/heads/fce_head.py deleted file mode 100644 index 949a2835a8aa08ffb1d32f18d480efb1c62260e3..0000000000000000000000000000000000000000 --- a/spaces/Mountchicken/MAERec-Gradio/mmocr/models/textdet/heads/fce_head.py +++ /dev/null @@ -1,117 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from typing import Dict, List, Optional - -import torch -import torch.nn as nn -from mmdet.models.utils import multi_apply - -from mmocr.models.textdet.heads import BaseTextDetHead -from mmocr.registry import MODELS -from mmocr.structures import TextDetDataSample - - -@MODELS.register_module() -class FCEHead(BaseTextDetHead): - """The class for implementing FCENet head. - - FCENet(CVPR2021): `Fourier Contour Embedding for Arbitrary-shaped Text - Detection
-{ - 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，请检查身份信息是否有效') - } - - 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(/ target?.split('src="').pop()?.replace(/&/g, '&')) - .map(img => ``).join(' ') - } - } while(count ++ < 10); -} - - -export async function* streamAsyncIterable(stream: ReadableStream) { - const reader = stream.getReader() - try { - while (true) { - const { done, value } = await reader.read() - if (done) { - return - } - yield value - } - } finally { - reader.releaseLock() - } -} - -export const sleep = (ms: number) => new Promise(resolve => setTimeout(resolve, ms)) - diff --git a/spaces/Poupeto/RVC_Ryu7ztv/lib/infer_pack/commons.py b/spaces/Poupeto/RVC_Ryu7ztv/lib/infer_pack/commons.py deleted file mode 100644 index 54470986f37825b35d90d7efa7437d1c26b87215..0000000000000000000000000000000000000000 --- a/spaces/Poupeto/RVC_Ryu7ztv/lib/infer_pack/commons.py +++ /dev/null @@ -1,166 +0,0 @@ -import math -import numpy as np -import torch -from torch import nn -from torch.nn import functional as F - - -def init_weights(m, mean=0.0, std=0.01): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - m.weight.data.normal_(mean, std) - - -def get_padding(kernel_size, dilation=1): - return int((kernel_size * dilation - dilation) / 2) - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def kl_divergence(m_p, logs_p, m_q, logs_q): - """KL(P||Q)""" - kl = (logs_q - logs_p) - 0.5 - kl += ( - 0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q) - ) - return kl - - -def rand_gumbel(shape): - """Sample from the Gumbel distribution, protect from overflows.""" - uniform_samples = torch.rand(shape) * 0.99998 + 0.00001 - return -torch.log(-torch.log(uniform_samples)) - - -def rand_gumbel_like(x): - g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device) - return g - - -def slice_segments(x, ids_str, segment_size=4): - ret = torch.zeros_like(x[:, :, :segment_size]) - for i in range(x.size(0)): - idx_str = ids_str[i] - idx_end = idx_str + segment_size - ret[i] = x[i, :, idx_str:idx_end] - return ret - - -def slice_segments2(x, ids_str, segment_size=4): - ret = torch.zeros_like(x[:, :segment_size]) - for i in range(x.size(0)): - idx_str = ids_str[i] - idx_end = idx_str + segment_size - ret[i] = x[i, idx_str:idx_end] - return ret - - -def rand_slice_segments(x, x_lengths=None, segment_size=4): - b, d, t = x.size() - if x_lengths is None: - x_lengths = t - ids_str_max = x_lengths - segment_size + 1 - ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) - ret = slice_segments(x, ids_str, segment_size) - return ret, ids_str - - -def get_timing_signal_1d(length, channels, min_timescale=1.0, max_timescale=1.0e4): - position = torch.arange(length, dtype=torch.float) - num_timescales = channels // 2 - log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / ( - num_timescales - 1 - ) - inv_timescales = min_timescale * torch.exp( - torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment - ) - scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1) - signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0) - signal = F.pad(signal, [0, 0, 0, channels % 2]) - signal = signal.view(1, channels, length) - return signal - - -def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4): - b, channels, length = x.size() - signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) - return x + signal.to(dtype=x.dtype, device=x.device) - - -def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1): - b, channels, length = x.size() - signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) - return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis) - - -def subsequent_mask(length): - mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) - return mask - - -@torch.jit.script -def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): - n_channels_int = n_channels[0] - in_act = input_a + input_b - t_act = torch.tanh(in_act[:, :n_channels_int, :]) - s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) - acts = t_act * s_act - return acts - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def shift_1d(x): - x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] - return x - - -def sequence_mask(length, max_length=None): - if max_length is None: - max_length = length.max() - x = torch.arange(max_length, dtype=length.dtype, device=length.device) - return x.unsqueeze(0) < length.unsqueeze(1) - - -def generate_path(duration, mask): - """ - duration: [b, 1, t_x] - mask: [b, 1, t_y, t_x] - """ - device = duration.device - - b, _, t_y, t_x = mask.shape - cum_duration = torch.cumsum(duration, -1) - - cum_duration_flat = cum_duration.view(b * t_x) - path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) - path = path.view(b, t_x, t_y) - path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] - path = path.unsqueeze(1).transpose(2, 3) * mask - return path - - -def clip_grad_value_(parameters, clip_value, norm_type=2): - if isinstance(parameters, torch.Tensor): - parameters = [parameters] - parameters = list(filter(lambda p: p.grad is not None, parameters)) - norm_type = float(norm_type) - if clip_value is not None: - clip_value = float(clip_value) - - total_norm = 0 - for p in parameters: - param_norm = p.grad.data.norm(norm_type) - total_norm += param_norm.item() ** norm_type - if clip_value is not None: - p.grad.data.clamp_(min=-clip_value, max=clip_value) - total_norm = total_norm ** (1.0 / norm_type) - return total_norm diff --git a/spaces/Priyanka-Kumavat/Customer-Complaint-Segmentation-Model/app.py b/spaces/Priyanka-Kumavat/Customer-Complaint-Segmentation-Model/app.py deleted file mode 100644 index e828a419bab9a7271fd0577f6c3d24ed1fc6e1c8..0000000000000000000000000000000000000000 --- a/spaces/Priyanka-Kumavat/Customer-Complaint-Segmentation-Model/app.py +++ /dev/null @@ -1,96 +0,0 @@ -from transformers import AutoModelForSequenceClassification -from transformers import AutoTokenizer, AutoConfig -from clean_data import cleaned_complaints -import numpy as np -from scipy.special import softmax -import gradio as gr -from transformers import PegasusForConditionalGeneration - -# 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) - -# load model -MODEL = f"ThirdEyeData/Complaints_Roberta" -#model = PegasusForConditionalGeneration.from_pretrained(MODEL) -model = AutoModelForSequenceClassification.from_pretrained(MODEL) -#model.save_pretrained(MODEL) - - -tokenizer = AutoTokenizer.from_pretrained(MODEL) -config = AutoConfig.from_pretrained(MODEL) - -# create classifier function -def classify_compliant(text): - text = cleaned_complaints(text) - if len(text)<3: - return "Cannot Categorize the Complaint" - else: - text = preprocess(text) - encoded_input = tokenizer(text, return_tensors='pt') - output = model(**encoded_input) - scores = output[0][0].detach().numpy() - scores = softmax(scores) - - # Print labels and scores - probs = {} - ranking = np.argsort(scores) - ranking = ranking[::-1] - - - l = config.id2label[ranking[0]] - #s = scores[ranking[i]] - #probs[l] = np.round(float(s), 4) - return l - - -#build the Gradio app -#Instructuction = "Write an imaginary review about a product or service you might be interested in." -title="Customer Complaints Categorization" -description = """ -This application uses fine-tuned Roberta to perform Customer Complaints Categorization. Roberta is a popular pre-trained language model that can be used for a variety of natural language processing tasks. This text classification model helps the company to categorize incoming support requests submitted by users and determine the appropriate course of action. The application can provide an opportunity for the service provider to resolve the customer’s problems on time and therefore, reduce dissatisfaction levels. - - -Write a complaint on an insurance product or service and see how the machine learning model is able to Categorization your Complaint. Below is the type in which the complaints are segmented: -1. Debt Collection -2. False Claim or Statement -3. Legal Issue -4. Improper contact or sharing of info -5. Follow Up Issue -""" -article = """ - - Click submit button to test Consumer Complaint Segmentation - - Click the clear button to refresh the text - """ - -demo = gr.Interface(classify_compliant, - inputs=gr.Textbox(lines =10,label = "Type your Complaint of our Product here or for a quick demo click on the examples provided below and output will automatically be populated in the output box ", max_lines = 20), - outputs = gr.Textbox(lines =5,label = "Complaint Category"), - title = title, - description = description, - #Instruction = Instructuction, - article = article, - #allow_flagging = "never", - live = False, - cache_example = False, - examples=[["""The day before my Salliemae student loan payment was due I contacted a rep to discuss the impact on my account of making my payment at the end of the month rather than the middle for just that one month. - The rep indicated it would be no problem, but that I still may get a call each day from Salliemae until I made my payment. I understood, requested my account be notated accordingly, and hung up. For two weeks I endured numerous calls per day ; - I lost count at six calls one day, which was the norm for the number of calls Salliemae made in an effort to collect a debt that had a due date that had been arranged and had not come up yet. """], - ["""The representative told me the total amount due was {$2100.00} and that I can settle for half of that amount. Unfortunately, I was unable to accept the settlement but began to question the amount because my last statement was {$1800.00} and - there was nothing written in the contract for additional interest charges should my account go into collection. - I told the representative that I will pay the amount actually owed and I want to make a payment arrangement. She told me I can't just do what I want, - If I want to pay the original amount due, it has to be paid in full. I told her that that is not fair debt collection practice and that I am only contractually obligated to the {$1800.00} and we can set up an arrangement from that. """] , - ["""This debt is beyond the Maryland Statute of Limitations. It is illegal for a debt collector to collect on an expired debt. They have taken illegal action by seizing my Maryland State Refund when the debt had already expired and beyond the Statute of Limitation which is 3 years in the state of Maryland"""], - ["""The company has been calling my employer in an attempt to collect a debt. When I spoke with them and informed them that this was not an appropriate number to call. I asked what company they were calling from and a phone number so he told me the company name, but the man on the phone would not give me his name or a phone number. - I had mailed a letter requesting verification a few weeks ago and hadn't received anything back. In the letter I specifically requested that all communication be done through mail."""], - [""" I do n't think I chose the correct issue above, however I think it is closest to my issue. I have a record on my credit report that I have disputed through both the company and the credit bureaus. The dispute is marked as being disputed by me on my report, but it was not removed despite the creditor not sending me verification of this debt. - I do not even know what this debt is for.I have tried contacting the collection agency by mail to obtain verification with no response and they will not remove the item from my report."""]] - - ) -if __name__ == "__main__": - demo.launch() diff --git a/spaces/Ramse/TTS_Hindi/text/numbers.py b/spaces/Ramse/TTS_Hindi/text/numbers.py deleted file mode 100644 index 5c30252e1c96fd9d3c762491e3107f0b5e811041..0000000000000000000000000000000000000000 --- a/spaces/Ramse/TTS_Hindi/text/numbers.py +++ /dev/null @@ -1,73 +0,0 @@ -""" from https://github.com/keithito/tacotron """ - -import inflect -import re - - -_inflect = inflect.engine() -_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])") -_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)") -_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)") -_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)") -_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)") -_number_re = re.compile(r"[0-9]+") - - -def _remove_commas(m): - return m.group(1).replace(",", "") - - -def _expand_decimal_point(m): - return m.group(1).replace(".", " point ") - - -def _expand_dollars(m): - match = m.group(1) - parts = match.split(".") - if len(parts) > 2: - return match + " dollars" # Unexpected format - dollars = int(parts[0]) if parts[0] else 0 - cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0 - if dollars and cents: - dollar_unit = "dollar" if dollars == 1 else "dollars" - cent_unit = "cent" if cents == 1 else "cents" - return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit) - elif dollars: - dollar_unit = "dollar" if dollars == 1 else "dollars" - return "%s %s" % (dollars, dollar_unit) - elif cents: - cent_unit = "cent" if cents == 1 else "cents" - return "%s %s" % (cents, cent_unit) - else: - return "zero dollars" - - -def _expand_ordinal(m): - return _inflect.number_to_words(m.group(0)) - - -def _expand_number(m): - num = int(m.group(0)) - if num > 1000 and num < 3000: - if num == 2000: - return "two thousand" - elif num > 2000 and num < 2010: - return "two thousand " + _inflect.number_to_words(num % 100) - elif num % 100 == 0: - return _inflect.number_to_words(num // 100) + " hundred" - else: - return _inflect.number_to_words( - num, andword="", zero="oh", group=2 - ).replace(", ", " ") - else: - return _inflect.number_to_words(num, andword="") - - -def normalize_numbers(text): - text = re.sub(_comma_number_re, _remove_commas, text) - text = re.sub(_pounds_re, r"\1 pounds", text) - text = re.sub(_dollars_re, _expand_dollars, text) - text = re.sub(_decimal_number_re, _expand_decimal_point, text) - text = re.sub(_ordinal_re, _expand_ordinal, text) - text = re.sub(_number_re, _expand_number, text) - return text diff --git a/spaces/Rbrq/DeticChatGPT/detic/config.py b/spaces/Rbrq/DeticChatGPT/detic/config.py deleted file mode 100644 index b6132f70116518b55e3b653fc6cd4ec9f61e50b0..0000000000000000000000000000000000000000 --- a/spaces/Rbrq/DeticChatGPT/detic/config.py +++ /dev/null @@ -1,131 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -from detectron2.config import CfgNode as CN - -def add_detic_config(cfg): - _C = cfg - - _C.WITH_IMAGE_LABELS = False # Turn on co-training with classification data - - # Open-vocabulary classifier - _C.MODEL.ROI_BOX_HEAD.USE_ZEROSHOT_CLS = False # Use fixed classifier for open-vocabulary detection - _C.MODEL.ROI_BOX_HEAD.ZEROSHOT_WEIGHT_PATH = 'datasets/metadata/lvis_v1_clip_a+cname.npy' - _C.MODEL.ROI_BOX_HEAD.ZEROSHOT_WEIGHT_DIM = 512 - _C.MODEL.ROI_BOX_HEAD.NORM_WEIGHT = True - _C.MODEL.ROI_BOX_HEAD.NORM_TEMP = 50.0 - _C.MODEL.ROI_BOX_HEAD.IGNORE_ZERO_CATS = False - _C.MODEL.ROI_BOX_HEAD.USE_BIAS = 0.0 # >= 0: not use - - _C.MODEL.ROI_BOX_HEAD.MULT_PROPOSAL_SCORE = False # CenterNet2 - _C.MODEL.ROI_BOX_HEAD.USE_SIGMOID_CE = False - _C.MODEL.ROI_BOX_HEAD.PRIOR_PROB = 0.01 - _C.MODEL.ROI_BOX_HEAD.USE_FED_LOSS = False # Federated Loss - _C.MODEL.ROI_BOX_HEAD.CAT_FREQ_PATH = \ - 'datasets/metadata/lvis_v1_train_cat_info.json' - _C.MODEL.ROI_BOX_HEAD.FED_LOSS_NUM_CAT = 50 - _C.MODEL.ROI_BOX_HEAD.FED_LOSS_FREQ_WEIGHT = 0.5 - - # Classification data configs - _C.MODEL.ROI_BOX_HEAD.IMAGE_LABEL_LOSS = 'max_size' # max, softmax, sum - _C.MODEL.ROI_BOX_HEAD.IMAGE_LOSS_WEIGHT = 0.1 - _C.MODEL.ROI_BOX_HEAD.IMAGE_BOX_SIZE = 1.0 - _C.MODEL.ROI_BOX_HEAD.ADD_IMAGE_BOX = False # Used for image-box loss and caption loss - _C.MODEL.ROI_BOX_HEAD.WS_NUM_PROPS = 128 # num proposals for image-labeled data - _C.MODEL.ROI_BOX_HEAD.WITH_SOFTMAX_PROP = False # Used for WSDDN - _C.MODEL.ROI_BOX_HEAD.CAPTION_WEIGHT = 1.0 # Caption loss weight - _C.MODEL.ROI_BOX_HEAD.NEG_CAP_WEIGHT = 0.125 # Caption loss hyper-parameter - _C.MODEL.ROI_BOX_HEAD.ADD_FEATURE_TO_PROP = False # Used for WSDDN - _C.MODEL.ROI_BOX_HEAD.SOFTMAX_WEAK_LOSS = False # Used when USE_SIGMOID_CE is False - - _C.MODEL.ROI_HEADS.MASK_WEIGHT = 1.0 - _C.MODEL.ROI_HEADS.ONE_CLASS_PER_PROPOSAL = False # For demo only - - # Caption losses - _C.MODEL.CAP_BATCH_RATIO = 4 # Ratio between detection data and caption data - _C.MODEL.WITH_CAPTION = False - _C.MODEL.SYNC_CAPTION_BATCH = False # synchronize across GPUs to enlarge # "classes" - - # dynamic class sampling when training with 21K classes - _C.MODEL.DYNAMIC_CLASSIFIER = False - _C.MODEL.NUM_SAMPLE_CATS = 50 - - # Different classifiers in testing, used in cross-dataset evaluation - _C.MODEL.RESET_CLS_TESTS = False - _C.MODEL.TEST_CLASSIFIERS = [] - _C.MODEL.TEST_NUM_CLASSES = [] - - # Backbones - _C.MODEL.SWIN = CN() - _C.MODEL.SWIN.SIZE = 'T' # 'T', 'S', 'B' - _C.MODEL.SWIN.USE_CHECKPOINT = False - _C.MODEL.SWIN.OUT_FEATURES = (1, 2, 3) # FPN stride 8 - 32 - - _C.MODEL.TIMM = CN() - _C.MODEL.TIMM.BASE_NAME = 'resnet50' - _C.MODEL.TIMM.OUT_LEVELS = (3, 4, 5) - _C.MODEL.TIMM.NORM = 'FrozenBN' - _C.MODEL.TIMM.FREEZE_AT = 0 - _C.MODEL.DATASET_LOSS_WEIGHT = [] - - # Multi-dataset dataloader - _C.DATALOADER.DATASET_RATIO = [1, 1] # sample ratio - _C.DATALOADER.USE_RFS = [False, False] - _C.DATALOADER.MULTI_DATASET_GROUPING = False # Always true when multi-dataset is enabled - _C.DATALOADER.DATASET_ANN = ['box', 'box'] # Annotation type of each dataset - _C.DATALOADER.USE_DIFF_BS_SIZE = False # Use different batchsize for each dataset - _C.DATALOADER.DATASET_BS = [8, 32] # Used when USE_DIFF_BS_SIZE is on - _C.DATALOADER.DATASET_INPUT_SIZE = [896, 384] # Used when USE_DIFF_BS_SIZE is on - _C.DATALOADER.DATASET_INPUT_SCALE = [(0.1, 2.0), (0.5, 1.5)] # Used when USE_DIFF_BS_SIZE is on - _C.DATALOADER.DATASET_MIN_SIZES = [(640, 800), (320, 400)] # Used when USE_DIFF_BS_SIZE is on - _C.DATALOADER.DATASET_MAX_SIZES = [1333, 667] # Used when USE_DIFF_BS_SIZE is on - _C.DATALOADER.USE_TAR_DATASET = False # for ImageNet-21K, directly reading from unziped files - _C.DATALOADER.TARFILE_PATH = 'datasets/imagenet/metadata-22k/tar_files.npy' - _C.DATALOADER.TAR_INDEX_DIR = 'datasets/imagenet/metadata-22k/tarindex_npy' - - _C.SOLVER.USE_CUSTOM_SOLVER = False - _C.SOLVER.OPTIMIZER = 'SGD' - _C.SOLVER.BACKBONE_MULTIPLIER = 1.0 # Used in DETR - _C.SOLVER.CUSTOM_MULTIPLIER = 1.0 # Used in DETR - _C.SOLVER.CUSTOM_MULTIPLIER_NAME = [] # Used in DETR - - # Deformable DETR - _C.MODEL.DETR = CN() - _C.MODEL.DETR.NUM_CLASSES = 80 - _C.MODEL.DETR.FROZEN_WEIGHTS = '' # For Segmentation - _C.MODEL.DETR.GIOU_WEIGHT = 2.0 - _C.MODEL.DETR.L1_WEIGHT = 5.0 - _C.MODEL.DETR.DEEP_SUPERVISION = True - _C.MODEL.DETR.NO_OBJECT_WEIGHT = 0.1 - _C.MODEL.DETR.CLS_WEIGHT = 2.0 - _C.MODEL.DETR.NUM_FEATURE_LEVELS = 4 - _C.MODEL.DETR.TWO_STAGE = False - _C.MODEL.DETR.WITH_BOX_REFINE = False - _C.MODEL.DETR.FOCAL_ALPHA = 0.25 - _C.MODEL.DETR.NHEADS = 8 - _C.MODEL.DETR.DROPOUT = 0.1 - _C.MODEL.DETR.DIM_FEEDFORWARD = 2048 - _C.MODEL.DETR.ENC_LAYERS = 6 - _C.MODEL.DETR.DEC_LAYERS = 6 - _C.MODEL.DETR.PRE_NORM = False - _C.MODEL.DETR.HIDDEN_DIM = 256 - _C.MODEL.DETR.NUM_OBJECT_QUERIES = 100 - - _C.MODEL.DETR.USE_FED_LOSS = False - _C.MODEL.DETR.WEAK_WEIGHT = 0.1 - - _C.INPUT.CUSTOM_AUG = '' - _C.INPUT.TRAIN_SIZE = 640 - _C.INPUT.TEST_SIZE = 640 - _C.INPUT.SCALE_RANGE = (0.1, 2.) - # 'default' for fixed short/ long edge, 'square' for max size=INPUT.SIZE - _C.INPUT.TEST_INPUT_TYPE = 'default' - - _C.FIND_UNUSED_PARAM = True - _C.EVAL_PRED_AR = False - _C.EVAL_PROPOSAL_AR = False - _C.EVAL_CAT_SPEC_AR = False - _C.IS_DEBUG = False - _C.QUICK_DEBUG = False - _C.FP16 = False - _C.EVAL_AP_FIX = False - _C.GEN_PSEDO_LABELS = False - _C.SAVE_DEBUG_PATH = 'output/save_debug/' \ No newline at end of file diff --git a/spaces/Rbrq/DeticChatGPT/tools/fix_o365_names.py b/spaces/Rbrq/DeticChatGPT/tools/fix_o365_names.py deleted file mode 100644 index c6730eacecb646bfef67a869dc9a93de6e55b6f2..0000000000000000000000000000000000000000 --- a/spaces/Rbrq/DeticChatGPT/tools/fix_o365_names.py +++ /dev/null @@ -1,34 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import argparse -import json -import copy - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument("--ann", default='datasets/objects365/annotations/zhiyuan_objv2_val.json') - parser.add_argument("--fix_name_map", default='datasets/metadata/Objects365_names_fix.csv') - args = parser.parse_args() - - new_names = {} - old_names = {} - with open(args.fix_name_map, 'r') as f: - for line in f: - tmp = line.strip().split(',') - old_names[int(tmp[0])] = tmp[1] - new_names[int(tmp[0])] = tmp[2] - data = json.load(open(args.ann, 'r')) - - cat_info = copy.deepcopy(data['categories']) - - for x in cat_info: - if old_names[x['id']].strip() != x['name'].strip(): - print('{} {} {}'.format(x, old_names[x['id']], new_names[x['id']])) - import pdb; pdb.set_trace() - if old_names[x['id']] != new_names[x['id']]: - print('Renaming', x['id'], x['name'], new_names[x['id']]) - x['name'] = new_names[x['id']] - - data['categories'] = cat_info - out_name = args.ann[:-5] + '_fixname.json' - print('Saving to', out_name) - json.dump(data, open(out_name, 'w')) diff --git a/spaces/Ricecake123/RVC-demo/tools/dlmodels.sh b/spaces/Ricecake123/RVC-demo/tools/dlmodels.sh deleted file mode 100644 index 0ae7f7eb8efe7d6ff4c94cbf83db568273b3785a..0000000000000000000000000000000000000000 --- a/spaces/Ricecake123/RVC-demo/tools/dlmodels.sh +++ /dev/null @@ -1,546 +0,0 @@ -#!/bin/bash - -echo working dir is $(pwd) -echo downloading requirement aria2 check. - -if command -v aria2c &> /dev/null -then - echo "aria2c command found" -else - echo failed. please install aria2 - sleep 5 - exit 1 -fi - -d32="f0D32k.pth" -d40="f0D40k.pth" -d48="f0D48k.pth" -g32="f0G32k.pth" -g40="f0G40k.pth" -g48="f0G48k.pth" - -d40v2="f0D40k.pth" -g40v2="f0G40k.pth" - -dld32="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D32k.pth" -dld40="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D40k.pth" -dld48="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0D48k.pth" -dlg32="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G32k.pth" -dlg40="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G40k.pth" -dlg48="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/f0G48k.pth" - -dld40v2="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0D40k.pth" -dlg40v2="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/f0G40k.pth" - -hp2_all="HP2_all_vocals.pth" -hp3_all="HP3_all_vocals.pth" -hp5_only="HP5_only_main_vocal.pth" -VR_DeEchoAggressive="VR-DeEchoAggressive.pth" -VR_DeEchoDeReverb="VR-DeEchoDeReverb.pth" -VR_DeEchoNormal="VR-DeEchoNormal.pth" -onnx_dereverb="vocals.onnx" - -dlhp2_all="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP2_all_vocals.pth" -dlhp3_all="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP3_all_vocals.pth" -dlhp5_only="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/HP5_only_main_vocal.pth" -dlVR_DeEchoAggressive="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoAggressive.pth" -dlVR_DeEchoDeReverb="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoDeReverb.pth" -dlVR_DeEchoNormal="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/VR-DeEchoNormal.pth" -dlonnx_dereverb="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/uvr5_weights/onnx_dereverb_By_FoxJoy/vocals.onnx" - -hb="hubert_base.pt" - -dlhb="https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/hubert_base.pt" - -echo dir check start. - -if [ -d "./pretrained" ]; then - echo dir ./pretrained checked. -else - echo failed. generating dir ./pretrained. - mkdir pretrained -fi - -if [ -d "./pretrained_v2" ]; then - echo dir ./pretrained_v2 checked. -else - echo failed. generating dir ./pretrained_v2. - mkdir pretrained_v2 -fi - -if [ -d "./uvr5_weights" ]; then - echo dir ./uvr5_weights checked. -else - echo failed. generating dir ./uvr5_weights. - mkdir uvr5_weights -fi - -if [ -d "./uvr5_weights/onnx_dereverb_By_FoxJoy" ]; then - echo dir ./uvr5_weights/onnx_dereverb_By_FoxJoy checked. -else - echo failed. generating dir ./uvr5_weights/onnx_dereverb_By_FoxJoy. - mkdir uvr5_weights/onnx_dereverb_By_FoxJoy -fi - -echo dir check finished. - -echo required files check start. - -echo checking D32k.pth -if [ -f "./pretrained/D32k.pth" ]; then - echo D32k.pth in ./pretrained checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D32k.pth -d ./pretrained -o D32k.pth - if [ -f "./pretrained/D32k.pth" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking D40k.pth -if [ -f "./pretrained/D40k.pth" ]; then - echo D40k.pth in ./pretrained checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D40k.pth -d ./pretrained -o D40k.pth - if [ -f "./pretrained/D40k.pth" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking D40k.pth -if [ -f "./pretrained_v2/D40k.pth" ]; then - echo D40k.pth in ./pretrained_v2 checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/D40k.pth -d ./pretrained_v2 -o D40k.pth - if [ -f "./pretrained_v2/D40k.pth" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking D48k.pth -if [ -f "./pretrained/D48k.pth" ]; then - echo D48k.pth in ./pretrained checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/D48k.pth -d ./pretrained -o D48k.pth - if [ -f "./pretrained/D48k.pth" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking G32k.pth -if [ -f "./pretrained/G32k.pth" ]; then - echo G32k.pth in ./pretrained checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G32k.pth -d ./pretrained -o G32k.pth - if [ -f "./pretrained/G32k.pth" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking G40k.pth -if [ -f "./pretrained/G40k.pth" ]; then - echo G40k.pth in ./pretrained checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G40k.pth -d ./pretrained -o G40k.pth - if [ -f "./pretrained/G40k.pth" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking G40k.pth -if [ -f "./pretrained_v2/G40k.pth" ]; then - echo G40k.pth in ./pretrained_v2 checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained_v2/G40k.pth -d ./pretrained_v2 -o G40k.pth - if [ -f "./pretrained_v2/G40k.pth" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking G48k.pth -if [ -f "./pretrained/G48k.pth" ]; then - echo G48k.pth in ./pretrained checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/pretrained/G48k.pth -d ./pretrained -o G48k.pth - if [ -f "./pretrained/G48k.pth" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking $d32 -if [ -f "./pretrained/$d32" ]; then - echo $d32 in ./pretrained checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dld32 -d ./pretrained -o $d32 - if [ -f "./pretrained/$d32" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking $d40 -if [ -f "./pretrained/$d40" ]; then - echo $d40 in ./pretrained checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dld40 -d ./pretrained -o $d40 - if [ -f "./pretrained/$d40" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking $d40v2 -if [ -f "./pretrained_v2/$d40v2" ]; then - echo $d40v2 in ./pretrained_v2 checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dld40v2 -d ./pretrained_v2 -o $d40v2 - if [ -f "./pretrained_v2/$d40v2" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking $d48 -if [ -f "./pretrained/$d48" ]; then - echo $d48 in ./pretrained checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dld48 -d ./pretrained -o $d48 - if [ -f "./pretrained/$d48" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking $g32 -if [ -f "./pretrained/$g32" ]; then - echo $g32 in ./pretrained checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlg32 -d ./pretrained -o $g32 - if [ -f "./pretrained/$g32" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking $g40 -if [ -f "./pretrained/$g40" ]; then - echo $g40 in ./pretrained checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlg40 -d ./pretrained -o $g40 - if [ -f "./pretrained/$g40" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking $g40v2 -if [ -f "./pretrained_v2/$g40v2" ]; then - echo $g40v2 in ./pretrained_v2 checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlg40v2 -d ./pretrained_v2 -o $g40v2 - if [ -f "./pretrained_v2/$g40v2" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking $g48 -if [ -f "./pretrained/$g48" ]; then - echo $g48 in ./pretrained checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlg48 -d ./pretrained -o $g48 - if [ -f "./pretrained/$g48" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking $hp2_all -if [ -f "./uvr5_weights/$hp2_all" ]; then - echo $hp2_all in ./uvr5_weights checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlhp2_all -d ./uvr5_weights -o $hp2_all - if [ -f "./uvr5_weights/$hp2_all" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking $hp3_all -if [ -f "./uvr5_weights/$hp3_all" ]; then - echo $hp3_all in ./uvr5_weights checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlhp3_all -d ./uvr5_weights -o $hp3_all - if [ -f "./uvr5_weights/$hp3_all" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking $hp5_only -if [ -f "./uvr5_weights/$hp5_only" ]; then - echo $hp5_only in ./uvr5_weights checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlhp5_only -d ./uvr5_weights -o $hp5_only - if [ -f "./uvr5_weights/$hp5_only" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking $VR_DeEchoAggressive -if [ -f "./uvr5_weights/$VR_DeEchoAggressive" ]; then - echo $VR_DeEchoAggressive in ./uvr5_weights checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlVR_DeEchoAggressive -d ./uvr5_weights -o $VR_DeEchoAggressive - if [ -f "./uvr5_weights/$VR_DeEchoAggressive" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking $VR_DeEchoDeReverb -if [ -f "./uvr5_weights/$VR_DeEchoDeReverb" ]; then - echo $VR_DeEchoDeReverb in ./uvr5_weights checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlVR_DeEchoDeReverb -d ./uvr5_weights -o $VR_DeEchoDeReverb - if [ -f "./uvr5_weights/$VR_DeEchoDeReverb" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking $VR_DeEchoNormal -if [ -f "./uvr5_weights/$VR_DeEchoNormal" ]; then - echo $VR_DeEchoNormal in ./uvr5_weights checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlVR_DeEchoNormal -d ./uvr5_weights -o $VR_DeEchoNormal - if [ -f "./uvr5_weights/$VR_DeEchoNormal" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking $onnx_dereverb -if [ -f "./uvr5_weights/onnx_dereverb_By_FoxJoy/$onnx_dereverb" ]; then - echo $onnx_dereverb in ./uvr5_weights/onnx_dereverb_By_FoxJoy checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlonnx_dereverb -d ./uvr5_weights/onnx_dereverb_By_FoxJoy -o $onnx_dereverb - if [ -f "./uvr5_weights/onnx_dereverb_By_FoxJoy/$onnx_dereverb" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo checking $hb -if [ -f "./pretrained/$hb" ]; then - echo $hb in ./pretrained checked. -else - echo failed. starting download from huggingface. - if command -v aria2c &> /dev/null; then - aria2c --console-log-level=error -c -x 16 -s 16 -k 1M $dlhb -d ./ -o $hb - if [ -f "./$hb" ]; then - echo download successful. - else - echo please try again! - exit 1 - fi - else - echo aria2c command not found. Please install aria2c and try again. - exit 1 - fi -fi - -echo required files check finished. -read -p "Press any key to continue..." -n1 -s diff --git a/spaces/RichardMB1217/blip/models/nlvr_encoder.py b/spaces/RichardMB1217/blip/models/nlvr_encoder.py deleted file mode 100644 index 1946bb4a300f75afa4848f6622839445903c34a9..0000000000000000000000000000000000000000 --- a/spaces/RichardMB1217/blip/models/nlvr_encoder.py +++ /dev/null @@ -1,843 +0,0 @@ -import math -import os -import warnings -from dataclasses import dataclass -from typing import Optional, Tuple - -import torch -from torch import Tensor, device, dtype, nn -import torch.utils.checkpoint -from torch import nn -from torch.nn import CrossEntropyLoss -import torch.nn.functional as F - -from transformers.activations import ACT2FN -from transformers.file_utils import ( - ModelOutput, -) -from transformers.modeling_outputs import ( - BaseModelOutputWithPastAndCrossAttentions, - BaseModelOutputWithPoolingAndCrossAttentions, - CausalLMOutputWithCrossAttentions, - MaskedLMOutput, - MultipleChoiceModelOutput, - NextSentencePredictorOutput, - QuestionAnsweringModelOutput, - SequenceClassifierOutput, - TokenClassifierOutput, -) -from transformers.modeling_utils import ( - PreTrainedModel, - apply_chunking_to_forward, - find_pruneable_heads_and_indices, - prune_linear_layer, -) -from transformers.utils import logging -from transformers.models.bert.configuration_bert import BertConfig - - -logger = logging.get_logger(__name__) - - -class BertEmbeddings(nn.Module): - """Construct the embeddings from word and position embeddings.""" - - def __init__(self, config): - super().__init__() - self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) - self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) - - # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load - # any TensorFlow checkpoint file - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - - # position_ids (1, len position emb) is contiguous in memory and exported when serialized - self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) - self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") - - self.config = config - - def forward( - self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 - ): - if input_ids is not None: - input_shape = input_ids.size() - else: - input_shape = inputs_embeds.size()[:-1] - - seq_length = input_shape[1] - - if position_ids is None: - position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] - - if inputs_embeds is None: - inputs_embeds = self.word_embeddings(input_ids) - - embeddings = inputs_embeds - - if self.position_embedding_type == "absolute": - position_embeddings = self.position_embeddings(position_ids) - embeddings += position_embeddings - embeddings = self.LayerNorm(embeddings) - embeddings = self.dropout(embeddings) - return embeddings - - -class BertSelfAttention(nn.Module): - def __init__(self, config, is_cross_attention): - super().__init__() - self.config = config - if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): - raise ValueError( - "The hidden size (%d) is not a multiple of the number of attention " - "heads (%d)" % (config.hidden_size, config.num_attention_heads) - ) - - self.num_attention_heads = config.num_attention_heads - self.attention_head_size = int(config.hidden_size / config.num_attention_heads) - self.all_head_size = self.num_attention_heads * self.attention_head_size - - self.query = nn.Linear(config.hidden_size, self.all_head_size) - if is_cross_attention: - self.key = nn.Linear(config.encoder_width, self.all_head_size) - self.value = nn.Linear(config.encoder_width, self.all_head_size) - else: - self.key = nn.Linear(config.hidden_size, self.all_head_size) - self.value = nn.Linear(config.hidden_size, self.all_head_size) - - self.dropout = nn.Dropout(config.attention_probs_dropout_prob) - self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") - if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": - self.max_position_embeddings = config.max_position_embeddings - self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) - self.save_attention = False - - def save_attn_gradients(self, attn_gradients): - self.attn_gradients = attn_gradients - - def get_attn_gradients(self): - return self.attn_gradients - - def save_attention_map(self, attention_map): - self.attention_map = attention_map - - def get_attention_map(self): - return self.attention_map - - def transpose_for_scores(self, x): - new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) - x = x.view(*new_x_shape) - return x.permute(0, 2, 1, 3) - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_value=None, - output_attentions=False, - ): - mixed_query_layer = self.query(hidden_states) - - # If this is instantiated as a cross-attention module, the keys - # and values come from an encoder; the attention mask needs to be - # such that the encoder's padding tokens are not attended to. - is_cross_attention = encoder_hidden_states is not None - - if is_cross_attention: - key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) - value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) - attention_mask = encoder_attention_mask - elif past_key_value is not None: - key_layer = self.transpose_for_scores(self.key(hidden_states)) - value_layer = self.transpose_for_scores(self.value(hidden_states)) - key_layer = torch.cat([past_key_value[0], key_layer], dim=2) - value_layer = torch.cat([past_key_value[1], value_layer], dim=2) - else: - key_layer = self.transpose_for_scores(self.key(hidden_states)) - value_layer = self.transpose_for_scores(self.value(hidden_states)) - - query_layer = self.transpose_for_scores(mixed_query_layer) - - past_key_value = (key_layer, value_layer) - - # Take the dot product between "query" and "key" to get the raw attention scores. - attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) - - if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": - seq_length = hidden_states.size()[1] - position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) - position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) - distance = position_ids_l - position_ids_r - positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) - positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility - - if self.position_embedding_type == "relative_key": - relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) - attention_scores = attention_scores + relative_position_scores - elif self.position_embedding_type == "relative_key_query": - relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) - relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) - attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key - - attention_scores = attention_scores / math.sqrt(self.attention_head_size) - if attention_mask is not None: - # Apply the attention mask is (precomputed for all layers in BertModel forward() function) - attention_scores = attention_scores + attention_mask - - # Normalize the attention scores to probabilities. - attention_probs = nn.Softmax(dim=-1)(attention_scores) - - if is_cross_attention and self.save_attention: - self.save_attention_map(attention_probs) - attention_probs.register_hook(self.save_attn_gradients) - - # This is actually dropping out entire tokens to attend to, which might - # seem a bit unusual, but is taken from the original Transformer paper. - attention_probs_dropped = self.dropout(attention_probs) - - # Mask heads if we want to - if head_mask is not None: - attention_probs_dropped = attention_probs_dropped * head_mask - - context_layer = torch.matmul(attention_probs_dropped, value_layer) - - context_layer = context_layer.permute(0, 2, 1, 3).contiguous() - new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) - context_layer = context_layer.view(*new_context_layer_shape) - - outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) - - outputs = outputs + (past_key_value,) - return outputs - - -class BertSelfOutput(nn.Module): - def __init__(self, config, twin=False, merge=False): - super().__init__() - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - if twin: - self.dense0 = nn.Linear(config.hidden_size, config.hidden_size) - self.dense1 = nn.Linear(config.hidden_size, config.hidden_size) - else: - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - if merge: - self.act = ACT2FN[config.hidden_act] - self.merge_layer = nn.Linear(config.hidden_size * 2, config.hidden_size) - self.merge = True - else: - self.merge = False - - def forward(self, hidden_states, input_tensor): - if type(hidden_states) == list: - hidden_states0 = self.dense0(hidden_states[0]) - hidden_states1 = self.dense1(hidden_states[1]) - if self.merge: - #hidden_states = self.merge_layer(self.act(torch.cat([hidden_states0,hidden_states1],dim=-1))) - hidden_states = self.merge_layer(torch.cat([hidden_states0,hidden_states1],dim=-1)) - else: - hidden_states = (hidden_states0+hidden_states1)/2 - else: - hidden_states = self.dense(hidden_states) - hidden_states = self.dropout(hidden_states) - hidden_states = self.LayerNorm(hidden_states + input_tensor) - return hidden_states - - -class BertAttention(nn.Module): - def __init__(self, config, is_cross_attention=False, layer_num=-1): - super().__init__() - if is_cross_attention: - self.self0 = BertSelfAttention(config, is_cross_attention) - self.self1 = BertSelfAttention(config, is_cross_attention) - else: - self.self = BertSelfAttention(config, is_cross_attention) - self.output = BertSelfOutput(config, twin=is_cross_attention, merge=(is_cross_attention and layer_num>=6)) - self.pruned_heads = set() - - def prune_heads(self, heads): - if len(heads) == 0: - return - heads, index = find_pruneable_heads_and_indices( - heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads - ) - - # Prune linear layers - self.self.query = prune_linear_layer(self.self.query, index) - self.self.key = prune_linear_layer(self.self.key, index) - self.self.value = prune_linear_layer(self.self.value, index) - self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) - - # Update hyper params and store pruned heads - self.self.num_attention_heads = self.self.num_attention_heads - len(heads) - self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads - self.pruned_heads = self.pruned_heads.union(heads) - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_value=None, - output_attentions=False, - ): - if type(encoder_hidden_states)==list: - self_outputs0 = self.self0( - hidden_states, - attention_mask, - head_mask, - encoder_hidden_states[0], - encoder_attention_mask[0], - past_key_value, - output_attentions, - ) - self_outputs1 = self.self1( - hidden_states, - attention_mask, - head_mask, - encoder_hidden_states[1], - encoder_attention_mask[1], - past_key_value, - output_attentions, - ) - attention_output = self.output([self_outputs0[0],self_outputs1[0]], hidden_states) - - outputs = (attention_output,) + self_outputs0[1:] # add attentions if we output them - else: - self_outputs = self.self( - hidden_states, - attention_mask, - head_mask, - encoder_hidden_states, - encoder_attention_mask, - past_key_value, - output_attentions, - ) - attention_output = self.output(self_outputs[0], hidden_states) - outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them - return outputs - - -class BertIntermediate(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.intermediate_size) - if isinstance(config.hidden_act, str): - self.intermediate_act_fn = ACT2FN[config.hidden_act] - else: - self.intermediate_act_fn = config.hidden_act - - def forward(self, hidden_states): - hidden_states = self.dense(hidden_states) - hidden_states = self.intermediate_act_fn(hidden_states) - return hidden_states - - -class BertOutput(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.intermediate_size, config.hidden_size) - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.dropout = nn.Dropout(config.hidden_dropout_prob) - - def forward(self, hidden_states, input_tensor): - hidden_states = self.dense(hidden_states) - hidden_states = self.dropout(hidden_states) - hidden_states = self.LayerNorm(hidden_states + input_tensor) - return hidden_states - - -class BertLayer(nn.Module): - def __init__(self, config, layer_num): - super().__init__() - self.config = config - self.chunk_size_feed_forward = config.chunk_size_feed_forward - self.seq_len_dim = 1 - self.attention = BertAttention(config) - self.layer_num = layer_num - if self.config.add_cross_attention: - self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention, layer_num=layer_num) - self.intermediate = BertIntermediate(config) - self.output = BertOutput(config) - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_value=None, - output_attentions=False, - mode=None, - ): - # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 - self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None - self_attention_outputs = self.attention( - hidden_states, - attention_mask, - head_mask, - output_attentions=output_attentions, - past_key_value=self_attn_past_key_value, - ) - attention_output = self_attention_outputs[0] - - outputs = self_attention_outputs[1:-1] - present_key_value = self_attention_outputs[-1] - - if mode=='multimodal': - assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers" - cross_attention_outputs = self.crossattention( - attention_output, - attention_mask, - head_mask, - encoder_hidden_states, - encoder_attention_mask, - output_attentions=output_attentions, - ) - attention_output = cross_attention_outputs[0] - outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights - layer_output = apply_chunking_to_forward( - self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output - ) - outputs = (layer_output,) + outputs - - outputs = outputs + (present_key_value,) - - return outputs - - def feed_forward_chunk(self, attention_output): - intermediate_output = self.intermediate(attention_output) - layer_output = self.output(intermediate_output, attention_output) - return layer_output - - -class BertEncoder(nn.Module): - def __init__(self, config): - super().__init__() - self.config = config - self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)]) - self.gradient_checkpointing = False - - def forward( - self, - hidden_states, - attention_mask=None, - head_mask=None, - encoder_hidden_states=None, - encoder_attention_mask=None, - past_key_values=None, - use_cache=None, - output_attentions=False, - output_hidden_states=False, - return_dict=True, - mode='multimodal', - ): - all_hidden_states = () if output_hidden_states else None - all_self_attentions = () if output_attentions else None - all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None - - next_decoder_cache = () if use_cache else None - - for i in range(self.config.num_hidden_layers): - layer_module = self.layer[i] - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - layer_head_mask = head_mask[i] if head_mask is not None else None - past_key_value = past_key_values[i] if past_key_values is not None else None - - if self.gradient_checkpointing and self.training: - - if use_cache: - logger.warn( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." - ) - use_cache = False - - def create_custom_forward(module): - def custom_forward(*inputs): - return module(*inputs, past_key_value, output_attentions) - - return custom_forward - - layer_outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(layer_module), - hidden_states, - attention_mask, - layer_head_mask, - encoder_hidden_states, - encoder_attention_mask, - mode=mode, - ) - else: - layer_outputs = layer_module( - hidden_states, - attention_mask, - layer_head_mask, - encoder_hidden_states, - encoder_attention_mask, - past_key_value, - output_attentions, - mode=mode, - ) - - hidden_states = layer_outputs[0] - if use_cache: - next_decoder_cache += (layer_outputs[-1],) - if output_attentions: - all_self_attentions = all_self_attentions + (layer_outputs[1],) - - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - if not return_dict: - return tuple( - v - for v in [ - hidden_states, - next_decoder_cache, - all_hidden_states, - all_self_attentions, - all_cross_attentions, - ] - if v is not None - ) - return BaseModelOutputWithPastAndCrossAttentions( - last_hidden_state=hidden_states, - past_key_values=next_decoder_cache, - hidden_states=all_hidden_states, - attentions=all_self_attentions, - cross_attentions=all_cross_attentions, - ) - - -class BertPooler(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - self.activation = nn.Tanh() - - def forward(self, hidden_states): - # We "pool" the model by simply taking the hidden state corresponding - # to the first token. - first_token_tensor = hidden_states[:, 0] - pooled_output = self.dense(first_token_tensor) - pooled_output = self.activation(pooled_output) - return pooled_output - - -class BertPredictionHeadTransform(nn.Module): - def __init__(self, config): - super().__init__() - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - if isinstance(config.hidden_act, str): - self.transform_act_fn = ACT2FN[config.hidden_act] - else: - self.transform_act_fn = config.hidden_act - self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - - def forward(self, hidden_states): - hidden_states = self.dense(hidden_states) - hidden_states = self.transform_act_fn(hidden_states) - hidden_states = self.LayerNorm(hidden_states) - return hidden_states - - -class BertLMPredictionHead(nn.Module): - def __init__(self, config): - super().__init__() - self.transform = BertPredictionHeadTransform(config) - - # The output weights are the same as the input embeddings, but there is - # an output-only bias for each token. - self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) - - self.bias = nn.Parameter(torch.zeros(config.vocab_size)) - - # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` - self.decoder.bias = self.bias - - def forward(self, hidden_states): - hidden_states = self.transform(hidden_states) - hidden_states = self.decoder(hidden_states) - return hidden_states - - -class BertOnlyMLMHead(nn.Module): - def __init__(self, config): - super().__init__() - self.predictions = BertLMPredictionHead(config) - - def forward(self, sequence_output): - prediction_scores = self.predictions(sequence_output) - return prediction_scores - - -class BertPreTrainedModel(PreTrainedModel): - """ - An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained - models. - """ - - config_class = BertConfig - base_model_prefix = "bert" - _keys_to_ignore_on_load_missing = [r"position_ids"] - - def _init_weights(self, module): - """ Initialize the weights """ - if isinstance(module, (nn.Linear, nn.Embedding)): - # Slightly different from the TF version which uses truncated_normal for initialization - # cf https://github.com/pytorch/pytorch/pull/5617 - module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) - elif isinstance(module, nn.LayerNorm): - module.bias.data.zero_() - module.weight.data.fill_(1.0) - if isinstance(module, nn.Linear) and module.bias is not None: - module.bias.data.zero_() - - -class BertModel(BertPreTrainedModel): - """ - The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of - cross-attention is added between the self-attention layers, following the architecture described in `Attention is - all you need
Image Description Generation
", - unsafe_allow_html=True, - ) - - instructions = """Try the provided image or upload your own:""" - file = st.file_uploader(instructions) - - use_beam = sampling_method == "Beam search" - - col1, col2 = st.columns(2) - - if file: - raw_img = Image.open(file).convert("RGB") - else: - raw_img = load_demo_image() - - col1.header("Image") - - w, h = raw_img.size - scaling_factor = 720 / w - resized_image = raw_img.resize((int(w * scaling_factor), int(h * scaling_factor))) - - col1.image(resized_image, use_column_width=True) - col2.header("Description") - - cap_button = st.button("Generate") - - # ==== event ==== - vis_processor = load_processor("blip_image_eval").build(image_size=384) - - if cap_button: - if model_type.startswith("BLIP"): - blip_type = model_type.split("_")[1].lower() - model = load_model_cache( - "blip_caption", - model_type=f"{blip_type}_coco", - is_eval=True, - device=device, - ) - - img = vis_processor(raw_img).unsqueeze(0).to(device) - captions = generate_caption( - model=model, image=img, use_nucleus_sampling=not use_beam - ) - - col2.write("\n\n".join(captions), use_column_width=True) - - -def generate_caption( - model, image, use_nucleus_sampling=False, num_beams=3, max_length=40, min_length=5 -): - samples = {"image": image} - - captions = [] - if use_nucleus_sampling: - for _ in range(5): - caption = model.generate( - samples, - use_nucleus_sampling=True, - max_length=max_length, - min_length=min_length, - top_p=0.9, - ) - captions.append(caption[0]) - else: - caption = model.generate( - samples, - use_nucleus_sampling=False, - num_beams=num_beams, - max_length=max_length, - min_length=min_length, - ) - captions.append(caption[0]) - - return captions diff --git a/spaces/SeViLA/SeViLA/lavis/models/alpro_models/__init__.py b/spaces/SeViLA/SeViLA/lavis/models/alpro_models/__init__.py deleted file mode 100644 index 1dfd29514a54a24857c775b461a8937243c06784..0000000000000000000000000000000000000000 --- a/spaces/SeViLA/SeViLA/lavis/models/alpro_models/__init__.py +++ /dev/null @@ -1,103 +0,0 @@ -""" - Copyright (c) 2022, salesforce.com, inc. - All rights reserved. - SPDX-License-Identifier: BSD-3-Clause - For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause -""" - -import logging -import os - -import torch -import torch.nn.functional as F -from lavis.common.dist_utils import download_cached_file -from lavis.common.utils import is_url -from lavis.models.base_model import BaseModel -from transformers import BertTokenizer - - -class AlproBase(BaseModel): - @classmethod - def init_tokenizer(cls): - return BertTokenizer.from_pretrained("bert-base-uncased") - - def load_from_pretrained(self, url_or_filename, num_frames, num_patches): - if is_url(url_or_filename): - cached_file = download_cached_file( - url_or_filename, check_hash=False, progress=True - ) - checkpoint = torch.load(cached_file, map_location="cpu") - elif os.path.isfile(url_or_filename): - checkpoint = torch.load(url_or_filename, map_location="cpu") - else: - raise RuntimeError("checkpoint url or path is invalid") - - if "model" in checkpoint: - state_dict = checkpoint["model"] - else: - state_dict = checkpoint - - for key in list(state_dict.keys()): - if "bert" in key: - new_key = key.replace("bert.", "") - state_dict[new_key] = state_dict[key] - del state_dict[key] - - spatial_embed_key = "visual_encoder.model.pos_embed" - temporal_embed_key = "visual_encoder.model.time_embed" - - ## Resizing spatial embeddings in case they don't match - if num_patches + 1 != state_dict[spatial_embed_key].size(1): - state_dict[spatial_embed_key] = resize_spatial_embedding( - state_dict, spatial_embed_key, num_patches - ) - else: - logging.info( - "The length of spatial position embedding matches. No need to resize." - ) - - ## Resizing time embeddings in case they don't match - if temporal_embed_key in state_dict and num_frames != state_dict[ - temporal_embed_key - ].size(1): - state_dict[temporal_embed_key] = resize_temporal_embedding( - state_dict, temporal_embed_key, num_frames - ) - else: - logging.info( - "No temporal encoding found. Or the length of temporal position embedding matches. No need to resize." - ) - - msg = self.load_state_dict(state_dict, strict=False) - logging.info("Missing keys {}".format(msg.missing_keys)) - logging.info("load checkpoint from %s" % url_or_filename) - - return msg - - -def resize_spatial_embedding(state_dict, key, num_patches): - logging.info( - f"Resizing spatial position embedding from {state_dict[key].size(1)} to {num_patches + 1}" - ) - - pos_embed = state_dict[key] - - cls_pos_embed = pos_embed[0, 0, :].unsqueeze(0).unsqueeze(1) - other_pos_embed = pos_embed[0, 1:, :].unsqueeze(0).transpose(1, 2) - - new_pos_embed = F.interpolate(other_pos_embed, size=(num_patches), mode="nearest") - new_pos_embed = new_pos_embed.transpose(1, 2) - new_pos_embed = torch.cat((cls_pos_embed, new_pos_embed), 1) - - return new_pos_embed - - -def resize_temporal_embedding(state_dict, key, num_frames): - logging.info( - f"Resizing temporal position embedding from {state_dict[key].size(1)} to {num_frames}" - ) - - time_embed = state_dict[key].transpose(1, 2) - new_time_embed = F.interpolate(time_embed, size=(num_frames), mode="nearest") - - return new_time_embed.transpose(1, 2) diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/winappdbg/win32/version.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/winappdbg/win32/version.py deleted file mode 100644 index 19b6d53c0aa29531e84e42b9753a757a7f760353..0000000000000000000000000000000000000000 --- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/_vendored/pydevd/pydevd_attach_to_process/winappdbg/win32/version.py +++ /dev/null @@ -1,1038 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -# Copyright (c) 2009-2014, Mario Vilas -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions are met: -# -# * Redistributions of source code must retain the above copyright notice, -# this list of conditions and the following disclaimer. -# * Redistributions in binary form must reproduce the above copyright -# notice,this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# * Neither the name of the copyright holder 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 OWNER 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. - -""" -Detect the current architecture and operating system. - -Some functions here are really from kernel32.dll, others from version.dll. -""" - -__revision__ = "$Id$" - -from winappdbg.win32.defines import * - -#============================================================================== -# This is used later on to calculate the list of exported symbols. -_all = None -_all = set(vars().keys()) -#============================================================================== - -#--- NTDDI version ------------------------------------------------------------ - -NTDDI_WIN8 = 0x06020000 -NTDDI_WIN7SP1 = 0x06010100 -NTDDI_WIN7 = 0x06010000 -NTDDI_WS08 = 0x06000100 -NTDDI_VISTASP1 = 0x06000100 -NTDDI_VISTA = 0x06000000 -NTDDI_LONGHORN = NTDDI_VISTA -NTDDI_WS03SP2 = 0x05020200 -NTDDI_WS03SP1 = 0x05020100 -NTDDI_WS03 = 0x05020000 -NTDDI_WINXPSP3 = 0x05010300 -NTDDI_WINXPSP2 = 0x05010200 -NTDDI_WINXPSP1 = 0x05010100 -NTDDI_WINXP = 0x05010000 -NTDDI_WIN2KSP4 = 0x05000400 -NTDDI_WIN2KSP3 = 0x05000300 -NTDDI_WIN2KSP2 = 0x05000200 -NTDDI_WIN2KSP1 = 0x05000100 -NTDDI_WIN2K = 0x05000000 -NTDDI_WINNT4 = 0x04000000 - -OSVERSION_MASK = 0xFFFF0000 -SPVERSION_MASK = 0x0000FF00 -SUBVERSION_MASK = 0x000000FF - -#--- OSVERSIONINFO and OSVERSIONINFOEX structures and constants --------------- - -VER_PLATFORM_WIN32s = 0 -VER_PLATFORM_WIN32_WINDOWS = 1 -VER_PLATFORM_WIN32_NT = 2 - -VER_SUITE_BACKOFFICE = 0x00000004 -VER_SUITE_BLADE = 0x00000400 -VER_SUITE_COMPUTE_SERVER = 0x00004000 -VER_SUITE_DATACENTER = 0x00000080 -VER_SUITE_ENTERPRISE = 0x00000002 -VER_SUITE_EMBEDDEDNT = 0x00000040 -VER_SUITE_PERSONAL = 0x00000200 -VER_SUITE_SINGLEUSERTS = 0x00000100 -VER_SUITE_SMALLBUSINESS = 0x00000001 -VER_SUITE_SMALLBUSINESS_RESTRICTED = 0x00000020 -VER_SUITE_STORAGE_SERVER = 0x00002000 -VER_SUITE_TERMINAL = 0x00000010 -VER_SUITE_WH_SERVER = 0x00008000 - -VER_NT_DOMAIN_CONTROLLER = 0x0000002 -VER_NT_SERVER = 0x0000003 -VER_NT_WORKSTATION = 0x0000001 - -VER_BUILDNUMBER = 0x0000004 -VER_MAJORVERSION = 0x0000002 -VER_MINORVERSION = 0x0000001 -VER_PLATFORMID = 0x0000008 -VER_PRODUCT_TYPE = 0x0000080 -VER_SERVICEPACKMAJOR = 0x0000020 -VER_SERVICEPACKMINOR = 0x0000010 -VER_SUITENAME = 0x0000040 - -VER_EQUAL = 1 -VER_GREATER = 2 -VER_GREATER_EQUAL = 3 -VER_LESS = 4 -VER_LESS_EQUAL = 5 -VER_AND = 6 -VER_OR = 7 - -# typedef struct _OSVERSIONINFO { -# DWORD dwOSVersionInfoSize; -# DWORD dwMajorVersion; -# DWORD dwMinorVersion; -# DWORD dwBuildNumber; -# DWORD dwPlatformId; -# TCHAR szCSDVersion[128]; -# }OSVERSIONINFO; -class OSVERSIONINFOA(Structure): - _fields_ = [ - ("dwOSVersionInfoSize", DWORD), - ("dwMajorVersion", DWORD), - ("dwMinorVersion", DWORD), - ("dwBuildNumber", DWORD), - ("dwPlatformId", DWORD), - ("szCSDVersion", CHAR * 128), - ] -class OSVERSIONINFOW(Structure): - _fields_ = [ - ("dwOSVersionInfoSize", DWORD), - ("dwMajorVersion", DWORD), - ("dwMinorVersion", DWORD), - ("dwBuildNumber", DWORD), - ("dwPlatformId", DWORD), - ("szCSDVersion", WCHAR * 128), - ] - -# typedef struct _OSVERSIONINFOEX { -# DWORD dwOSVersionInfoSize; -# DWORD dwMajorVersion; -# DWORD dwMinorVersion; -# DWORD dwBuildNumber; -# DWORD dwPlatformId; -# TCHAR szCSDVersion[128]; -# WORD wServicePackMajor; -# WORD wServicePackMinor; -# WORD wSuiteMask; -# BYTE wProductType; -# BYTE wReserved; -# }OSVERSIONINFOEX, *POSVERSIONINFOEX, *LPOSVERSIONINFOEX; -class OSVERSIONINFOEXA(Structure): - _fields_ = [ - ("dwOSVersionInfoSize", DWORD), - ("dwMajorVersion", DWORD), - ("dwMinorVersion", DWORD), - ("dwBuildNumber", DWORD), - ("dwPlatformId", DWORD), - ("szCSDVersion", CHAR * 128), - ("wServicePackMajor", WORD), - ("wServicePackMinor", WORD), - ("wSuiteMask", WORD), - ("wProductType", BYTE), - ("wReserved", BYTE), - ] -class OSVERSIONINFOEXW(Structure): - _fields_ = [ - ("dwOSVersionInfoSize", DWORD), - ("dwMajorVersion", DWORD), - ("dwMinorVersion", DWORD), - ("dwBuildNumber", DWORD), - ("dwPlatformId", DWORD), - ("szCSDVersion", WCHAR * 128), - ("wServicePackMajor", WORD), - ("wServicePackMinor", WORD), - ("wSuiteMask", WORD), - ("wProductType", BYTE), - ("wReserved", BYTE), - ] - -LPOSVERSIONINFOA = POINTER(OSVERSIONINFOA) -LPOSVERSIONINFOW = POINTER(OSVERSIONINFOW) -LPOSVERSIONINFOEXA = POINTER(OSVERSIONINFOEXA) -LPOSVERSIONINFOEXW = POINTER(OSVERSIONINFOEXW) -POSVERSIONINFOA = LPOSVERSIONINFOA -POSVERSIONINFOW = LPOSVERSIONINFOW -POSVERSIONINFOEXA = LPOSVERSIONINFOEXA -POSVERSIONINFOEXW = LPOSVERSIONINFOA - -#--- GetSystemMetrics constants ----------------------------------------------- - -SM_CXSCREEN = 0 -SM_CYSCREEN = 1 -SM_CXVSCROLL = 2 -SM_CYHSCROLL = 3 -SM_CYCAPTION = 4 -SM_CXBORDER = 5 -SM_CYBORDER = 6 -SM_CXDLGFRAME = 7 -SM_CYDLGFRAME = 8 -SM_CYVTHUMB = 9 -SM_CXHTHUMB = 10 -SM_CXICON = 11 -SM_CYICON = 12 -SM_CXCURSOR = 13 -SM_CYCURSOR = 14 -SM_CYMENU = 15 -SM_CXFULLSCREEN = 16 -SM_CYFULLSCREEN = 17 -SM_CYKANJIWINDOW = 18 -SM_MOUSEPRESENT = 19 -SM_CYVSCROLL = 20 -SM_CXHSCROLL = 21 -SM_DEBUG = 22 -SM_SWAPBUTTON = 23 -SM_RESERVED1 = 24 -SM_RESERVED2 = 25 -SM_RESERVED3 = 26 -SM_RESERVED4 = 27 -SM_CXMIN = 28 -SM_CYMIN = 29 -SM_CXSIZE = 30 -SM_CYSIZE = 31 -SM_CXFRAME = 32 -SM_CYFRAME = 33 -SM_CXMINTRACK = 34 -SM_CYMINTRACK = 35 -SM_CXDOUBLECLK = 36 -SM_CYDOUBLECLK = 37 -SM_CXICONSPACING = 38 -SM_CYICONSPACING = 39 -SM_MENUDROPALIGNMENT = 40 -SM_PENWINDOWS = 41 -SM_DBCSENABLED = 42 -SM_CMOUSEBUTTONS = 43 - -SM_CXFIXEDFRAME = SM_CXDLGFRAME # ;win40 name change -SM_CYFIXEDFRAME = SM_CYDLGFRAME # ;win40 name change -SM_CXSIZEFRAME = SM_CXFRAME # ;win40 name change -SM_CYSIZEFRAME = SM_CYFRAME # ;win40 name change - -SM_SECURE = 44 -SM_CXEDGE = 45 -SM_CYEDGE = 46 -SM_CXMINSPACING = 47 -SM_CYMINSPACING = 48 -SM_CXSMICON = 49 -SM_CYSMICON = 50 -SM_CYSMCAPTION = 51 -SM_CXSMSIZE = 52 -SM_CYSMSIZE = 53 -SM_CXMENUSIZE = 54 -SM_CYMENUSIZE = 55 -SM_ARRANGE = 56 -SM_CXMINIMIZED = 57 -SM_CYMINIMIZED = 58 -SM_CXMAXTRACK = 59 -SM_CYMAXTRACK = 60 -SM_CXMAXIMIZED = 61 -SM_CYMAXIMIZED = 62 -SM_NETWORK = 63 -SM_CLEANBOOT = 67 -SM_CXDRAG = 68 -SM_CYDRAG = 69 -SM_SHOWSOUNDS = 70 -SM_CXMENUCHECK = 71 # Use instead of GetMenuCheckMarkDimensions()! -SM_CYMENUCHECK = 72 -SM_SLOWMACHINE = 73 -SM_MIDEASTENABLED = 74 -SM_MOUSEWHEELPRESENT = 75 -SM_XVIRTUALSCREEN = 76 -SM_YVIRTUALSCREEN = 77 -SM_CXVIRTUALSCREEN = 78 -SM_CYVIRTUALSCREEN = 79 -SM_CMONITORS = 80 -SM_SAMEDISPLAYFORMAT = 81 -SM_IMMENABLED = 82 -SM_CXFOCUSBORDER = 83 -SM_CYFOCUSBORDER = 84 -SM_TABLETPC = 86 -SM_MEDIACENTER = 87 -SM_STARTER = 88 -SM_SERVERR2 = 89 -SM_MOUSEHORIZONTALWHEELPRESENT = 91 -SM_CXPADDEDBORDER = 92 - -SM_CMETRICS = 93 - -SM_REMOTESESSION = 0x1000 -SM_SHUTTINGDOWN = 0x2000 -SM_REMOTECONTROL = 0x2001 -SM_CARETBLINKINGENABLED = 0x2002 - -#--- SYSTEM_INFO structure, GetSystemInfo() and GetNativeSystemInfo() --------- - -# Values used by Wine -# Documented values at MSDN are marked with an asterisk -PROCESSOR_ARCHITECTURE_UNKNOWN = 0xFFFF; # Unknown architecture. -PROCESSOR_ARCHITECTURE_INTEL = 0 # x86 (AMD or Intel) * -PROCESSOR_ARCHITECTURE_MIPS = 1 # MIPS -PROCESSOR_ARCHITECTURE_ALPHA = 2 # Alpha -PROCESSOR_ARCHITECTURE_PPC = 3 # Power PC -PROCESSOR_ARCHITECTURE_SHX = 4 # SHX -PROCESSOR_ARCHITECTURE_ARM = 5 # ARM -PROCESSOR_ARCHITECTURE_IA64 = 6 # Intel Itanium * -PROCESSOR_ARCHITECTURE_ALPHA64 = 7 # Alpha64 -PROCESSOR_ARCHITECTURE_MSIL = 8 # MSIL -PROCESSOR_ARCHITECTURE_AMD64 = 9 # x64 (AMD or Intel) * -PROCESSOR_ARCHITECTURE_IA32_ON_WIN64 = 10 # IA32 on Win64 -PROCESSOR_ARCHITECTURE_SPARC = 20 # Sparc (Wine) - -# Values used by Wine -# PROCESSOR_OPTIL value found at http://code.google.com/p/ddab-lib/ -# Documented values at MSDN are marked with an asterisk -PROCESSOR_INTEL_386 = 386 # Intel i386 * -PROCESSOR_INTEL_486 = 486 # Intel i486 * -PROCESSOR_INTEL_PENTIUM = 586 # Intel Pentium * -PROCESSOR_INTEL_IA64 = 2200 # Intel IA64 (Itanium) * -PROCESSOR_AMD_X8664 = 8664 # AMD X86 64 * -PROCESSOR_MIPS_R4000 = 4000 # MIPS R4000, R4101, R3910 -PROCESSOR_ALPHA_21064 = 21064 # Alpha 210 64 -PROCESSOR_PPC_601 = 601 # PPC 601 -PROCESSOR_PPC_603 = 603 # PPC 603 -PROCESSOR_PPC_604 = 604 # PPC 604 -PROCESSOR_PPC_620 = 620 # PPC 620 -PROCESSOR_HITACHI_SH3 = 10003 # Hitachi SH3 (Windows CE) -PROCESSOR_HITACHI_SH3E = 10004 # Hitachi SH3E (Windows CE) -PROCESSOR_HITACHI_SH4 = 10005 # Hitachi SH4 (Windows CE) -PROCESSOR_MOTOROLA_821 = 821 # Motorola 821 (Windows CE) -PROCESSOR_SHx_SH3 = 103 # SHx SH3 (Windows CE) -PROCESSOR_SHx_SH4 = 104 # SHx SH4 (Windows CE) -PROCESSOR_STRONGARM = 2577 # StrongARM (Windows CE) -PROCESSOR_ARM720 = 1824 # ARM 720 (Windows CE) -PROCESSOR_ARM820 = 2080 # ARM 820 (Windows CE) -PROCESSOR_ARM920 = 2336 # ARM 920 (Windows CE) -PROCESSOR_ARM_7TDMI = 70001 # ARM 7TDMI (Windows CE) -PROCESSOR_OPTIL = 0x494F # MSIL - -# typedef struct _SYSTEM_INFO { -# union { -# DWORD dwOemId; -# struct { -# WORD wProcessorArchitecture; -# WORD wReserved; -# } ; -# } ; -# DWORD dwPageSize; -# LPVOID lpMinimumApplicationAddress; -# LPVOID lpMaximumApplicationAddress; -# DWORD_PTR dwActiveProcessorMask; -# DWORD dwNumberOfProcessors; -# DWORD dwProcessorType; -# DWORD dwAllocationGranularity; -# WORD wProcessorLevel; -# WORD wProcessorRevision; -# } SYSTEM_INFO; - -class _SYSTEM_INFO_OEM_ID_STRUCT(Structure): - _fields_ = [ - ("wProcessorArchitecture", WORD), - ("wReserved", WORD), -] - -class _SYSTEM_INFO_OEM_ID(Union): - _fields_ = [ - ("dwOemId", DWORD), - ("w", _SYSTEM_INFO_OEM_ID_STRUCT), -] - -class SYSTEM_INFO(Structure): - _fields_ = [ - ("id", _SYSTEM_INFO_OEM_ID), - ("dwPageSize", DWORD), - ("lpMinimumApplicationAddress", LPVOID), - ("lpMaximumApplicationAddress", LPVOID), - ("dwActiveProcessorMask", DWORD_PTR), - ("dwNumberOfProcessors", DWORD), - ("dwProcessorType", DWORD), - ("dwAllocationGranularity", DWORD), - ("wProcessorLevel", WORD), - ("wProcessorRevision", WORD), - ] - - def __get_dwOemId(self): - return self.id.dwOemId - def __set_dwOemId(self, value): - self.id.dwOemId = value - dwOemId = property(__get_dwOemId, __set_dwOemId) - - def __get_wProcessorArchitecture(self): - return self.id.w.wProcessorArchitecture - def __set_wProcessorArchitecture(self, value): - self.id.w.wProcessorArchitecture = value - wProcessorArchitecture = property(__get_wProcessorArchitecture, __set_wProcessorArchitecture) - -LPSYSTEM_INFO = ctypes.POINTER(SYSTEM_INFO) - -# void WINAPI GetSystemInfo( -# __out LPSYSTEM_INFO lpSystemInfo -# ); -def GetSystemInfo(): - _GetSystemInfo = windll.kernel32.GetSystemInfo - _GetSystemInfo.argtypes = [LPSYSTEM_INFO] - _GetSystemInfo.restype = None - - sysinfo = SYSTEM_INFO() - _GetSystemInfo(byref(sysinfo)) - return sysinfo - -# void WINAPI GetNativeSystemInfo( -# __out LPSYSTEM_INFO lpSystemInfo -# ); -def GetNativeSystemInfo(): - _GetNativeSystemInfo = windll.kernel32.GetNativeSystemInfo - _GetNativeSystemInfo.argtypes = [LPSYSTEM_INFO] - _GetNativeSystemInfo.restype = None - - sysinfo = SYSTEM_INFO() - _GetNativeSystemInfo(byref(sysinfo)) - return sysinfo - -# int WINAPI GetSystemMetrics( -# __in int nIndex -# ); -def GetSystemMetrics(nIndex): - _GetSystemMetrics = windll.user32.GetSystemMetrics - _GetSystemMetrics.argtypes = [ctypes.c_int] - _GetSystemMetrics.restype = ctypes.c_int - return _GetSystemMetrics(nIndex) - -# SIZE_T WINAPI GetLargePageMinimum(void); -def GetLargePageMinimum(): - _GetLargePageMinimum = windll.user32.GetLargePageMinimum - _GetLargePageMinimum.argtypes = [] - _GetLargePageMinimum.restype = SIZE_T - return _GetLargePageMinimum() - -# HANDLE WINAPI GetCurrentProcess(void); -def GetCurrentProcess(): -## return 0xFFFFFFFFFFFFFFFFL - _GetCurrentProcess = windll.kernel32.GetCurrentProcess - _GetCurrentProcess.argtypes = [] - _GetCurrentProcess.restype = HANDLE - return _GetCurrentProcess() - -# HANDLE WINAPI GetCurrentThread(void); -def GetCurrentThread(): -## return 0xFFFFFFFFFFFFFFFEL - _GetCurrentThread = windll.kernel32.GetCurrentThread - _GetCurrentThread.argtypes = [] - _GetCurrentThread.restype = HANDLE - return _GetCurrentThread() - -# BOOL WINAPI IsWow64Process( -# __in HANDLE hProcess, -# __out PBOOL Wow64Process -# ); -def IsWow64Process(hProcess): - _IsWow64Process = windll.kernel32.IsWow64Process - _IsWow64Process.argtypes = [HANDLE, PBOOL] - _IsWow64Process.restype = bool - _IsWow64Process.errcheck = RaiseIfZero - - Wow64Process = BOOL(FALSE) - _IsWow64Process(hProcess, byref(Wow64Process)) - return bool(Wow64Process) - -# DWORD WINAPI GetVersion(void); -def GetVersion(): - _GetVersion = windll.kernel32.GetVersion - _GetVersion.argtypes = [] - _GetVersion.restype = DWORD - _GetVersion.errcheck = RaiseIfZero - - # See the example code here: - # http://msdn.microsoft.com/en-us/library/ms724439(VS.85).aspx - - dwVersion = _GetVersion() - dwMajorVersion = dwVersion & 0x000000FF - dwMinorVersion = (dwVersion & 0x0000FF00) >> 8 - if (dwVersion & 0x80000000) == 0: - dwBuild = (dwVersion & 0x7FFF0000) >> 16 - else: - dwBuild = None - return int(dwMajorVersion), int(dwMinorVersion), int(dwBuild) - -# BOOL WINAPI GetVersionEx( -# __inout LPOSVERSIONINFO lpVersionInfo -# ); -def GetVersionExA(): - _GetVersionExA = windll.kernel32.GetVersionExA - _GetVersionExA.argtypes = [POINTER(OSVERSIONINFOEXA)] - _GetVersionExA.restype = bool - _GetVersionExA.errcheck = RaiseIfZero - - osi = OSVERSIONINFOEXA() - osi.dwOSVersionInfoSize = sizeof(osi) - try: - _GetVersionExA(byref(osi)) - except WindowsError: - osi = OSVERSIONINFOA() - osi.dwOSVersionInfoSize = sizeof(osi) - _GetVersionExA.argtypes = [POINTER(OSVERSIONINFOA)] - _GetVersionExA(byref(osi)) - return osi - -def GetVersionExW(): - _GetVersionExW = windll.kernel32.GetVersionExW - _GetVersionExW.argtypes = [POINTER(OSVERSIONINFOEXW)] - _GetVersionExW.restype = bool - _GetVersionExW.errcheck = RaiseIfZero - - osi = OSVERSIONINFOEXW() - osi.dwOSVersionInfoSize = sizeof(osi) - try: - _GetVersionExW(byref(osi)) - except WindowsError: - osi = OSVERSIONINFOW() - osi.dwOSVersionInfoSize = sizeof(osi) - _GetVersionExW.argtypes = [POINTER(OSVERSIONINFOW)] - _GetVersionExW(byref(osi)) - return osi - -GetVersionEx = GuessStringType(GetVersionExA, GetVersionExW) - -# BOOL WINAPI GetProductInfo( -# __in DWORD dwOSMajorVersion, -# __in DWORD dwOSMinorVersion, -# __in DWORD dwSpMajorVersion, -# __in DWORD dwSpMinorVersion, -# __out PDWORD pdwReturnedProductType -# ); -def GetProductInfo(dwOSMajorVersion, dwOSMinorVersion, dwSpMajorVersion, dwSpMinorVersion): - _GetProductInfo = windll.kernel32.GetProductInfo - _GetProductInfo.argtypes = [DWORD, DWORD, DWORD, DWORD, PDWORD] - _GetProductInfo.restype = BOOL - _GetProductInfo.errcheck = RaiseIfZero - - dwReturnedProductType = DWORD(0) - _GetProductInfo(dwOSMajorVersion, dwOSMinorVersion, dwSpMajorVersion, dwSpMinorVersion, byref(dwReturnedProductType)) - return dwReturnedProductType.value - -# BOOL WINAPI VerifyVersionInfo( -# __in LPOSVERSIONINFOEX lpVersionInfo, -# __in DWORD dwTypeMask, -# __in DWORDLONG dwlConditionMask -# ); -def VerifyVersionInfo(lpVersionInfo, dwTypeMask, dwlConditionMask): - if isinstance(lpVersionInfo, OSVERSIONINFOEXA): - return VerifyVersionInfoA(lpVersionInfo, dwTypeMask, dwlConditionMask) - if isinstance(lpVersionInfo, OSVERSIONINFOEXW): - return VerifyVersionInfoW(lpVersionInfo, dwTypeMask, dwlConditionMask) - raise TypeError("Bad OSVERSIONINFOEX structure") - -def VerifyVersionInfoA(lpVersionInfo, dwTypeMask, dwlConditionMask): - _VerifyVersionInfoA = windll.kernel32.VerifyVersionInfoA - _VerifyVersionInfoA.argtypes = [LPOSVERSIONINFOEXA, DWORD, DWORDLONG] - _VerifyVersionInfoA.restype = bool - return _VerifyVersionInfoA(byref(lpVersionInfo), dwTypeMask, dwlConditionMask) - -def VerifyVersionInfoW(lpVersionInfo, dwTypeMask, dwlConditionMask): - _VerifyVersionInfoW = windll.kernel32.VerifyVersionInfoW - _VerifyVersionInfoW.argtypes = [LPOSVERSIONINFOEXW, DWORD, DWORDLONG] - _VerifyVersionInfoW.restype = bool - return _VerifyVersionInfoW(byref(lpVersionInfo), dwTypeMask, dwlConditionMask) - -# ULONGLONG WINAPI VerSetConditionMask( -# __in ULONGLONG dwlConditionMask, -# __in DWORD dwTypeBitMask, -# __in BYTE dwConditionMask -# ); -def VerSetConditionMask(dwlConditionMask, dwTypeBitMask, dwConditionMask): - _VerSetConditionMask = windll.kernel32.VerSetConditionMask - _VerSetConditionMask.argtypes = [ULONGLONG, DWORD, BYTE] - _VerSetConditionMask.restype = ULONGLONG - return _VerSetConditionMask(dwlConditionMask, dwTypeBitMask, dwConditionMask) - -#--- get_bits, get_arch and get_os -------------------------------------------- - -ARCH_UNKNOWN = "unknown" -ARCH_I386 = "i386" -ARCH_MIPS = "mips" -ARCH_ALPHA = "alpha" -ARCH_PPC = "ppc" -ARCH_SHX = "shx" -ARCH_ARM = "arm" -ARCH_ARM64 = "arm64" -ARCH_THUMB = "thumb" -ARCH_IA64 = "ia64" -ARCH_ALPHA64 = "alpha64" -ARCH_MSIL = "msil" -ARCH_AMD64 = "amd64" -ARCH_SPARC = "sparc" - -# aliases -ARCH_IA32 = ARCH_I386 -ARCH_X86 = ARCH_I386 -ARCH_X64 = ARCH_AMD64 -ARCH_ARM7 = ARCH_ARM -ARCH_ARM8 = ARCH_ARM64 -ARCH_T32 = ARCH_THUMB -ARCH_AARCH32 = ARCH_ARM7 -ARCH_AARCH64 = ARCH_ARM8 -ARCH_POWERPC = ARCH_PPC -ARCH_HITACHI = ARCH_SHX -ARCH_ITANIUM = ARCH_IA64 - -# win32 constants -> our constants -_arch_map = { - PROCESSOR_ARCHITECTURE_INTEL : ARCH_I386, - PROCESSOR_ARCHITECTURE_MIPS : ARCH_MIPS, - PROCESSOR_ARCHITECTURE_ALPHA : ARCH_ALPHA, - PROCESSOR_ARCHITECTURE_PPC : ARCH_PPC, - PROCESSOR_ARCHITECTURE_SHX : ARCH_SHX, - PROCESSOR_ARCHITECTURE_ARM : ARCH_ARM, - PROCESSOR_ARCHITECTURE_IA64 : ARCH_IA64, - PROCESSOR_ARCHITECTURE_ALPHA64 : ARCH_ALPHA64, - PROCESSOR_ARCHITECTURE_MSIL : ARCH_MSIL, - PROCESSOR_ARCHITECTURE_AMD64 : ARCH_AMD64, - PROCESSOR_ARCHITECTURE_SPARC : ARCH_SPARC, -} - -OS_UNKNOWN = "Unknown" -OS_NT = "Windows NT" -OS_W2K = "Windows 2000" -OS_XP = "Windows XP" -OS_XP_64 = "Windows XP (64 bits)" -OS_W2K3 = "Windows 2003" -OS_W2K3_64 = "Windows 2003 (64 bits)" -OS_W2K3R2 = "Windows 2003 R2" -OS_W2K3R2_64 = "Windows 2003 R2 (64 bits)" -OS_W2K8 = "Windows 2008" -OS_W2K8_64 = "Windows 2008 (64 bits)" -OS_W2K8R2 = "Windows 2008 R2" -OS_W2K8R2_64 = "Windows 2008 R2 (64 bits)" -OS_VISTA = "Windows Vista" -OS_VISTA_64 = "Windows Vista (64 bits)" -OS_W7 = "Windows 7" -OS_W7_64 = "Windows 7 (64 bits)" - -OS_SEVEN = OS_W7 -OS_SEVEN_64 = OS_W7_64 - -OS_WINDOWS_NT = OS_NT -OS_WINDOWS_2000 = OS_W2K -OS_WINDOWS_XP = OS_XP -OS_WINDOWS_XP_64 = OS_XP_64 -OS_WINDOWS_2003 = OS_W2K3 -OS_WINDOWS_2003_64 = OS_W2K3_64 -OS_WINDOWS_2003_R2 = OS_W2K3R2 -OS_WINDOWS_2003_R2_64 = OS_W2K3R2_64 -OS_WINDOWS_2008 = OS_W2K8 -OS_WINDOWS_2008_64 = OS_W2K8_64 -OS_WINDOWS_2008_R2 = OS_W2K8R2 -OS_WINDOWS_2008_R2_64 = OS_W2K8R2_64 -OS_WINDOWS_VISTA = OS_VISTA -OS_WINDOWS_VISTA_64 = OS_VISTA_64 -OS_WINDOWS_SEVEN = OS_W7 -OS_WINDOWS_SEVEN_64 = OS_W7_64 - -def _get_bits(): - """ - Determines the current integer size in bits. - - This is useful to know if we're running in a 32 bits or a 64 bits machine. - - @rtype: int - @return: Returns the size of L{SIZE_T} in bits. - """ - return sizeof(SIZE_T) * 8 - -def _get_arch(): - """ - Determines the current processor architecture. - - @rtype: str - @return: - On error, returns: - - - L{ARCH_UNKNOWN} (C{"unknown"}) meaning the architecture could not be detected or is not known to WinAppDbg. - - On success, returns one of the following values: - - - L{ARCH_I386} (C{"i386"}) for Intel 32-bit x86 processor or compatible. - - L{ARCH_AMD64} (C{"amd64"}) for Intel 64-bit x86_64 processor or compatible. - - May also return one of the following values if you get both Python and - WinAppDbg to work in such machines... let me know if you do! :) - - - L{ARCH_MIPS} (C{"mips"}) for MIPS compatible processors. - - L{ARCH_ALPHA} (C{"alpha"}) for Alpha processors. - - L{ARCH_PPC} (C{"ppc"}) for PowerPC compatible processors. - - L{ARCH_SHX} (C{"shx"}) for Hitachi SH processors. - - L{ARCH_ARM} (C{"arm"}) for ARM compatible processors. - - L{ARCH_IA64} (C{"ia64"}) for Intel Itanium processor or compatible. - - L{ARCH_ALPHA64} (C{"alpha64"}) for Alpha64 processors. - - L{ARCH_MSIL} (C{"msil"}) for the .NET virtual machine. - - L{ARCH_SPARC} (C{"sparc"}) for Sun Sparc processors. - - Probably IronPython returns C{ARCH_MSIL} but I haven't tried it. Python - on Windows CE and Windows Mobile should return C{ARCH_ARM}. Python on - Solaris using Wine would return C{ARCH_SPARC}. Python in an Itanium - machine should return C{ARCH_IA64} both on Wine and proper Windows. - All other values should only be returned on Linux using Wine. - """ - try: - si = GetNativeSystemInfo() - except Exception: - si = GetSystemInfo() - try: - return _arch_map[si.id.w.wProcessorArchitecture] - except KeyError: - return ARCH_UNKNOWN - -def _get_wow64(): - """ - Determines if the current process is running in Windows-On-Windows 64 bits. - - @rtype: bool - @return: C{True} of the current process is a 32 bit program running in a - 64 bit version of Windows, C{False} if it's either a 32 bit program - in a 32 bit Windows or a 64 bit program in a 64 bit Windows. - """ - # Try to determine if the debugger itself is running on WOW64. - # On error assume False. - if bits == 64: - wow64 = False - else: - try: - wow64 = IsWow64Process( GetCurrentProcess() ) - except Exception: - wow64 = False - return wow64 - -def _get_os(osvi = None): - """ - Determines the current operating system. - - This function allows you to quickly tell apart major OS differences. - For more detailed information call L{GetVersionEx} instead. - - @note: - Wine reports itself as Windows XP 32 bits - (even if the Linux host is 64 bits). - ReactOS may report itself as Windows 2000 or Windows XP, - depending on the version of ReactOS. - - @type osvi: L{OSVERSIONINFOEXA} - @param osvi: Optional. The return value from L{GetVersionEx}. - - @rtype: str - @return: - One of the following values: - - L{OS_UNKNOWN} (C{"Unknown"}) - - L{OS_NT} (C{"Windows NT"}) - - L{OS_W2K} (C{"Windows 2000"}) - - L{OS_XP} (C{"Windows XP"}) - - L{OS_XP_64} (C{"Windows XP (64 bits)"}) - - L{OS_W2K3} (C{"Windows 2003"}) - - L{OS_W2K3_64} (C{"Windows 2003 (64 bits)"}) - - L{OS_W2K3R2} (C{"Windows 2003 R2"}) - - L{OS_W2K3R2_64} (C{"Windows 2003 R2 (64 bits)"}) - - L{OS_W2K8} (C{"Windows 2008"}) - - L{OS_W2K8_64} (C{"Windows 2008 (64 bits)"}) - - L{OS_W2K8R2} (C{"Windows 2008 R2"}) - - L{OS_W2K8R2_64} (C{"Windows 2008 R2 (64 bits)"}) - - L{OS_VISTA} (C{"Windows Vista"}) - - L{OS_VISTA_64} (C{"Windows Vista (64 bits)"}) - - L{OS_W7} (C{"Windows 7"}) - - L{OS_W7_64} (C{"Windows 7 (64 bits)"}) - """ - # rough port of http://msdn.microsoft.com/en-us/library/ms724429%28VS.85%29.aspx - if not osvi: - osvi = GetVersionEx() - if osvi.dwPlatformId == VER_PLATFORM_WIN32_NT and osvi.dwMajorVersion > 4: - if osvi.dwMajorVersion == 6: - if osvi.dwMinorVersion == 0: - if osvi.wProductType == VER_NT_WORKSTATION: - if bits == 64 or wow64: - return 'Windows Vista (64 bits)' - return 'Windows Vista' - else: - if bits == 64 or wow64: - return 'Windows 2008 (64 bits)' - return 'Windows 2008' - if osvi.dwMinorVersion == 1: - if osvi.wProductType == VER_NT_WORKSTATION: - if bits == 64 or wow64: - return 'Windows 7 (64 bits)' - return 'Windows 7' - else: - if bits == 64 or wow64: - return 'Windows 2008 R2 (64 bits)' - return 'Windows 2008 R2' - if osvi.dwMajorVersion == 5: - if osvi.dwMinorVersion == 2: - if GetSystemMetrics(SM_SERVERR2): - if bits == 64 or wow64: - return 'Windows 2003 R2 (64 bits)' - return 'Windows 2003 R2' - if osvi.wSuiteMask in (VER_SUITE_STORAGE_SERVER, VER_SUITE_WH_SERVER): - if bits == 64 or wow64: - return 'Windows 2003 (64 bits)' - return 'Windows 2003' - if osvi.wProductType == VER_NT_WORKSTATION and arch == ARCH_AMD64: - return 'Windows XP (64 bits)' - else: - if bits == 64 or wow64: - return 'Windows 2003 (64 bits)' - return 'Windows 2003' - if osvi.dwMinorVersion == 1: - return 'Windows XP' - if osvi.dwMinorVersion == 0: - return 'Windows 2000' - if osvi.dwMajorVersion == 4: - return 'Windows NT' - return 'Unknown' - -def _get_ntddi(osvi): - """ - Determines the current operating system. - - This function allows you to quickly tell apart major OS differences. - For more detailed information call L{kernel32.GetVersionEx} instead. - - @note: - Wine reports itself as Windows XP 32 bits - (even if the Linux host is 64 bits). - ReactOS may report itself as Windows 2000 or Windows XP, - depending on the version of ReactOS. - - @type osvi: L{OSVERSIONINFOEXA} - @param osvi: Optional. The return value from L{kernel32.GetVersionEx}. - - @rtype: int - @return: NTDDI version number. - """ - if not osvi: - osvi = GetVersionEx() - ntddi = 0 - ntddi += (osvi.dwMajorVersion & 0xFF) << 24 - ntddi += (osvi.dwMinorVersion & 0xFF) << 16 - ntddi += (osvi.wServicePackMajor & 0xFF) << 8 - ntddi += (osvi.wServicePackMinor & 0xFF) - return ntddi - -# The order of the following definitions DOES matter! - -# Current integer size in bits. See L{_get_bits} for more details. -bits = _get_bits() - -# Current processor architecture. See L{_get_arch} for more details. -arch = _get_arch() - -# Set to C{True} if the current process is running in WOW64. See L{_get_wow64} for more details. -wow64 = _get_wow64() - -_osvi = GetVersionEx() - -# Current operating system. See L{_get_os} for more details. -os = _get_os(_osvi) - -# Current operating system as an NTDDI constant. See L{_get_ntddi} for more details. -NTDDI_VERSION = _get_ntddi(_osvi) - -# Upper word of L{NTDDI_VERSION}, contains the OS major and minor version number. -WINVER = NTDDI_VERSION >> 16 - -#--- version.dll -------------------------------------------------------------- - -VS_FF_DEBUG = 0x00000001 -VS_FF_PRERELEASE = 0x00000002 -VS_FF_PATCHED = 0x00000004 -VS_FF_PRIVATEBUILD = 0x00000008 -VS_FF_INFOINFERRED = 0x00000010 -VS_FF_SPECIALBUILD = 0x00000020 - -VOS_UNKNOWN = 0x00000000 -VOS__WINDOWS16 = 0x00000001 -VOS__PM16 = 0x00000002 -VOS__PM32 = 0x00000003 -VOS__WINDOWS32 = 0x00000004 -VOS_DOS = 0x00010000 -VOS_OS216 = 0x00020000 -VOS_OS232 = 0x00030000 -VOS_NT = 0x00040000 - -VOS_DOS_WINDOWS16 = 0x00010001 -VOS_DOS_WINDOWS32 = 0x00010004 -VOS_NT_WINDOWS32 = 0x00040004 -VOS_OS216_PM16 = 0x00020002 -VOS_OS232_PM32 = 0x00030003 - -VFT_UNKNOWN = 0x00000000 -VFT_APP = 0x00000001 -VFT_DLL = 0x00000002 -VFT_DRV = 0x00000003 -VFT_FONT = 0x00000004 -VFT_VXD = 0x00000005 -VFT_RESERVED = 0x00000006 # undocumented -VFT_STATIC_LIB = 0x00000007 - -VFT2_UNKNOWN = 0x00000000 - -VFT2_DRV_PRINTER = 0x00000001 -VFT2_DRV_KEYBOARD = 0x00000002 -VFT2_DRV_LANGUAGE = 0x00000003 -VFT2_DRV_DISPLAY = 0x00000004 -VFT2_DRV_MOUSE = 0x00000005 -VFT2_DRV_NETWORK = 0x00000006 -VFT2_DRV_SYSTEM = 0x00000007 -VFT2_DRV_INSTALLABLE = 0x00000008 -VFT2_DRV_SOUND = 0x00000009 -VFT2_DRV_COMM = 0x0000000A -VFT2_DRV_RESERVED = 0x0000000B # undocumented -VFT2_DRV_VERSIONED_PRINTER = 0x0000000C - -VFT2_FONT_RASTER = 0x00000001 -VFT2_FONT_VECTOR = 0x00000002 -VFT2_FONT_TRUETYPE = 0x00000003 - -# typedef struct tagVS_FIXEDFILEINFO { -# DWORD dwSignature; -# DWORD dwStrucVersion; -# DWORD dwFileVersionMS; -# DWORD dwFileVersionLS; -# DWORD dwProductVersionMS; -# DWORD dwProductVersionLS; -# DWORD dwFileFlagsMask; -# DWORD dwFileFlags; -# DWORD dwFileOS; -# DWORD dwFileType; -# DWORD dwFileSubtype; -# DWORD dwFileDateMS; -# DWORD dwFileDateLS; -# } VS_FIXEDFILEINFO; -class VS_FIXEDFILEINFO(Structure): - _fields_ = [ - ("dwSignature", DWORD), - ("dwStrucVersion", DWORD), - ("dwFileVersionMS", DWORD), - ("dwFileVersionLS", DWORD), - ("dwProductVersionMS", DWORD), - ("dwProductVersionLS", DWORD), - ("dwFileFlagsMask", DWORD), - ("dwFileFlags", DWORD), - ("dwFileOS", DWORD), - ("dwFileType", DWORD), - ("dwFileSubtype", DWORD), - ("dwFileDateMS", DWORD), - ("dwFileDateLS", DWORD), -] -PVS_FIXEDFILEINFO = POINTER(VS_FIXEDFILEINFO) -LPVS_FIXEDFILEINFO = PVS_FIXEDFILEINFO - -# BOOL WINAPI GetFileVersionInfo( -# _In_ LPCTSTR lptstrFilename, -# _Reserved_ DWORD dwHandle, -# _In_ DWORD dwLen, -# _Out_ LPVOID lpData -# ); -# DWORD WINAPI GetFileVersionInfoSize( -# _In_ LPCTSTR lptstrFilename, -# _Out_opt_ LPDWORD lpdwHandle -# ); -def GetFileVersionInfoA(lptstrFilename): - _GetFileVersionInfoA = windll.version.GetFileVersionInfoA - _GetFileVersionInfoA.argtypes = [LPSTR, DWORD, DWORD, LPVOID] - _GetFileVersionInfoA.restype = bool - _GetFileVersionInfoA.errcheck = RaiseIfZero - - _GetFileVersionInfoSizeA = windll.version.GetFileVersionInfoSizeA - _GetFileVersionInfoSizeA.argtypes = [LPSTR, LPVOID] - _GetFileVersionInfoSizeA.restype = DWORD - _GetFileVersionInfoSizeA.errcheck = RaiseIfZero - - dwLen = _GetFileVersionInfoSizeA(lptstrFilename, None) - lpData = ctypes.create_string_buffer(dwLen) - _GetFileVersionInfoA(lptstrFilename, 0, dwLen, byref(lpData)) - return lpData - -def GetFileVersionInfoW(lptstrFilename): - _GetFileVersionInfoW = windll.version.GetFileVersionInfoW - _GetFileVersionInfoW.argtypes = [LPWSTR, DWORD, DWORD, LPVOID] - _GetFileVersionInfoW.restype = bool - _GetFileVersionInfoW.errcheck = RaiseIfZero - - _GetFileVersionInfoSizeW = windll.version.GetFileVersionInfoSizeW - _GetFileVersionInfoSizeW.argtypes = [LPWSTR, LPVOID] - _GetFileVersionInfoSizeW.restype = DWORD - _GetFileVersionInfoSizeW.errcheck = RaiseIfZero - - dwLen = _GetFileVersionInfoSizeW(lptstrFilename, None) - lpData = ctypes.create_string_buffer(dwLen) # not a string! - _GetFileVersionInfoW(lptstrFilename, 0, dwLen, byref(lpData)) - return lpData - -GetFileVersionInfo = GuessStringType(GetFileVersionInfoA, GetFileVersionInfoW) - -# BOOL WINAPI VerQueryValue( -# _In_ LPCVOID pBlock, -# _In_ LPCTSTR lpSubBlock, -# _Out_ LPVOID *lplpBuffer, -# _Out_ PUINT puLen -# ); -def VerQueryValueA(pBlock, lpSubBlock): - _VerQueryValueA = windll.version.VerQueryValueA - _VerQueryValueA.argtypes = [LPVOID, LPSTR, LPVOID, POINTER(UINT)] - _VerQueryValueA.restype = bool - _VerQueryValueA.errcheck = RaiseIfZero - - lpBuffer = LPVOID(0) - uLen = UINT(0) - _VerQueryValueA(pBlock, lpSubBlock, byref(lpBuffer), byref(uLen)) - return lpBuffer, uLen.value - -def VerQueryValueW(pBlock, lpSubBlock): - _VerQueryValueW = windll.version.VerQueryValueW - _VerQueryValueW.argtypes = [LPVOID, LPWSTR, LPVOID, POINTER(UINT)] - _VerQueryValueW.restype = bool - _VerQueryValueW.errcheck = RaiseIfZero - - lpBuffer = LPVOID(0) - uLen = UINT(0) - _VerQueryValueW(pBlock, lpSubBlock, byref(lpBuffer), byref(uLen)) - return lpBuffer, uLen.value - -VerQueryValue = GuessStringType(VerQueryValueA, VerQueryValueW) - -#============================================================================== -# This calculates the list of exported symbols. -_all = set(vars().keys()).difference(_all) -__all__ = [_x for _x in _all if not _x.startswith('_')] -__all__.sort() -#============================================================================== diff --git a/spaces/Superlang/ImageProcessor/annotator/denthPose/depth_pose.py b/spaces/Superlang/ImageProcessor/annotator/denthPose/depth_pose.py deleted file mode 100644 index 2b981372cdad4573cb5bcb6c4efe8fa188a9280c..0000000000000000000000000000000000000000 --- a/spaces/Superlang/ImageProcessor/annotator/denthPose/depth_pose.py +++ /dev/null @@ -1,114 +0,0 @@ -import os.path -from os import path -from typing import List, Dict, Any, ClassVar - -import torch -from densepose import add_densepose_config -from densepose.vis.base import CompoundVisualizer -from densepose.vis.bounding_box import ScoredBoundingBoxVisualizer -from densepose.vis.densepose_outputs_vertex import get_texture_atlases, DensePoseOutputsTextureVisualizer, \ - DensePoseOutputsVertexVisualizer -from densepose.vis.densepose_results import DensePoseResultsContourVisualizer, \ - DensePoseResultsFineSegmentationVisualizer, DensePoseResultsUVisualizer, DensePoseResultsVVisualizer -from densepose.vis.densepose_results_textures import get_texture_atlas, DensePoseResultsVisualizerWithTexture -from densepose.vis.extractor import create_extractor, CompoundExtractor -from detectron2.config import CfgNode, get_cfg -from detectron2.engine.defaults import DefaultPredictor - -from annotator.base_annotator import BaseProcessor - -config_model = { - "densepose_rcnn_R_50_FPN_s1x": { - "yaml": 'densepose_rcnn_R_50_FPN_s1x.yaml', - "file": "densepose_rcnn_R_50_FPN_s1x.pkl" - } -} - -default_conf = config_model["densepose_rcnn_R_50_FPN_s1x"] - - -class DenthPoseProcess(BaseProcessor): - VISUALIZERS: ClassVar[Dict[str, object]] = { - "dp_contour": DensePoseResultsContourVisualizer, - "dp_segm": DensePoseResultsFineSegmentationVisualizer, - "dp_u": DensePoseResultsUVisualizer, - "dp_v": DensePoseResultsVVisualizer, - "dp_iuv_texture": DensePoseResultsVisualizerWithTexture, - "dp_cse_texture": DensePoseOutputsTextureVisualizer, - "dp_vertex": DensePoseOutputsVertexVisualizer, - "bbox": ScoredBoundingBoxVisualizer, - } - - def __init__(self, **kwargs): - super().__init__(**kwargs) - here = path.abspath(path.dirname(__file__)) - self.cfg, self.model_conf = os.path.join(here, default_conf["yaml"]), os.path.join(here, default_conf["file"]) - self.predictor = None - - @classmethod - def setup_config( - cls: type, config_fpath: str, model_fpath: str, opts: List[str] - ): - cfg = get_cfg() - add_densepose_config(cfg) - cfg.merge_from_file(config_fpath) - if opts: - cfg.merge_from_list(opts) - cfg.MODEL.WEIGHTS = model_fpath - cfg.freeze() - return cfg - - @classmethod - def create_context(cls: type, vis_specs, cfg: CfgNode, arg_texture_atlas=None, arg_texture_atlases_map=None) -> \ - Dict[ - str, Any]: - """ - 创建可视化 - """ - # vis_specs = visualizations.split(",") - visualizers = [] - extractors = [] - for vis_spec in vis_specs: - texture_atlas = get_texture_atlas(arg_texture_atlas) - texture_atlases_dict = get_texture_atlases(arg_texture_atlases_map) - vis = cls.VISUALIZERS[vis_spec]( - cfg=cfg, - texture_atlas=texture_atlas, - texture_atlases_dict=texture_atlases_dict, - ) - visualizers.append(vis) - extractor = create_extractor(vis) - extractors.append(extractor) - visualizer = CompoundVisualizer(visualizers) - extractor = CompoundExtractor(extractors) - context = { - "extractor": extractor, - "visualizer": visualizer, - "entry_idx": 0, - } - return context - - def execute_on_outputs(self, image, context: Dict[str, Any], outputs): - visualizer = context["visualizer"] - extractor = context["extractor"] - # image = np.tile(image[:, :, np.newaxis], [1, 1, 3]) - data = extractor(outputs) - # zero_image = np.zeros(image.shape, dtype=np.uint8) - image_vis = visualizer.visualize(image, data) - return image_vis - - def __call__(self, img, visualizations=["dp_u", "dp_v", "bbox"], texture_atlas=None, texture_atlases_map=None): - opts = [] - cfg = self.setup_config(config_fpath=self.cfg, model_fpath=self.model_conf, opts=opts) - if self.predictor is None: - self.predictor = DefaultPredictor(cfg) - context = self.create_context(visualizations, cfg, texture_atlas, texture_atlases_map) - with torch.no_grad(): - outputs = self.predictor(img)["instances"] - return self.execute_on_outputs(img, context, outputs) - -# if __name__ == '__main__': -# image_path = "demo.jpeg" -# img = cv2.imread(image_path) -# process = DenthPoseProcessor() -# process(img, "dp_contour,bbox") diff --git a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/export/caffe2_export.py b/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/export/caffe2_export.py deleted file mode 100644 index d609c27c7deb396352967dbcbc79b1e00f2a2de1..0000000000000000000000000000000000000000 --- a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/export/caffe2_export.py +++ /dev/null @@ -1,203 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. - -import copy -import io -import logging -import numpy as np -from typing import List -import onnx -import onnx.optimizer -import torch -from caffe2.proto import caffe2_pb2 -from caffe2.python import core -from caffe2.python.onnx.backend import Caffe2Backend -from tabulate import tabulate -from termcolor import colored -from torch.onnx import OperatorExportTypes - -from .shared import ( - ScopedWS, - construct_init_net_from_params, - fuse_alias_placeholder, - fuse_copy_between_cpu_and_gpu, - get_params_from_init_net, - group_norm_replace_aten_with_caffe2, - infer_device_type, - remove_dead_end_ops, - remove_reshape_for_fc, - save_graph, -) - -logger = logging.getLogger(__name__) - - -def export_onnx_model(model, inputs): - """ - Trace and export a model to onnx format. - - Args: - model (nn.Module): - inputs (tuple[args]): the model will be called by `model(*inputs)` - - Returns: - an onnx model - """ - assert isinstance(model, torch.nn.Module) - - # make sure all modules are in eval mode, onnx may change the training state - # of the module if the states are not consistent - def _check_eval(module): - assert not module.training - - model.apply(_check_eval) - - # Export the model to ONNX - with torch.no_grad(): - with io.BytesIO() as f: - torch.onnx.export( - model, - inputs, - f, - operator_export_type=OperatorExportTypes.ONNX_ATEN_FALLBACK, - # verbose=True, # NOTE: uncomment this for debugging - # export_params=True, - ) - onnx_model = onnx.load_from_string(f.getvalue()) - - return onnx_model - - -def _op_stats(net_def): - type_count = {} - for t in [op.type for op in net_def.op]: - type_count[t] = type_count.get(t, 0) + 1 - type_count_list = sorted(type_count.items(), key=lambda kv: kv[0]) # alphabet - type_count_list = sorted(type_count_list, key=lambda kv: -kv[1]) # count - return "\n".join("{:>4}x {}".format(count, name) for name, count in type_count_list) - - -def _assign_device_option( - predict_net: caffe2_pb2.NetDef, init_net: caffe2_pb2.NetDef, tensor_inputs: List[torch.Tensor] -): - """ - ONNX exported network doesn't have concept of device, assign necessary - device option for each op in order to make it runable on GPU runtime. - """ - - def _get_device_type(torch_tensor): - assert torch_tensor.device.type in ["cpu", "cuda"] - assert torch_tensor.device.index == 0 - return torch_tensor.device.type - - def _assign_op_device_option(net_proto, net_ssa, blob_device_types): - for op, ssa_i in zip(net_proto.op, net_ssa): - if op.type in ["CopyCPUToGPU", "CopyGPUToCPU"]: - op.device_option.CopyFrom(core.DeviceOption(caffe2_pb2.CUDA, 0)) - else: - devices = [blob_device_types[b] for b in ssa_i[0] + ssa_i[1]] - assert all(d == devices[0] for d in devices) - if devices[0] == "cuda": - op.device_option.CopyFrom(core.DeviceOption(caffe2_pb2.CUDA, 0)) - - # update ops in predict_net - predict_net_input_device_types = { - (name, 0): _get_device_type(tensor) - for name, tensor in zip(predict_net.external_input, tensor_inputs) - } - predict_net_device_types = infer_device_type( - predict_net, known_status=predict_net_input_device_types, device_name_style="pytorch" - ) - predict_net_ssa, _ = core.get_ssa(predict_net) - _assign_op_device_option(predict_net, predict_net_ssa, predict_net_device_types) - - # update ops in init_net - init_net_ssa, versions = core.get_ssa(init_net) - init_net_output_device_types = { - (name, versions[name]): predict_net_device_types[(name, 0)] - for name in init_net.external_output - } - init_net_device_types = infer_device_type( - init_net, known_status=init_net_output_device_types, device_name_style="pytorch" - ) - _assign_op_device_option(init_net, init_net_ssa, init_net_device_types) - - -def export_caffe2_detection_model(model: torch.nn.Module, tensor_inputs: List[torch.Tensor]): - """ - Export a caffe2-compatible Detectron2 model to caffe2 format via ONNX. - - Arg: - model: a caffe2-compatible version of detectron2 model, defined in caffe2_modeling.py - tensor_inputs: a list of tensors that caffe2 model takes as input. - """ - model = copy.deepcopy(model) - assert isinstance(model, torch.nn.Module) - assert hasattr(model, "encode_additional_info") - - # Export via ONNX - logger.info( - "Exporting a {} model via ONNX ...".format(type(model).__name__) - + " Some warnings from ONNX are expected and are usually not to worry about." - ) - onnx_model = export_onnx_model(model, (tensor_inputs,)) - # Convert ONNX model to Caffe2 protobuf - init_net, predict_net = Caffe2Backend.onnx_graph_to_caffe2_net(onnx_model) - ops_table = [[op.type, op.input, op.output] for op in predict_net.op] - table = tabulate(ops_table, headers=["type", "input", "output"], tablefmt="pipe") - logger.info( - "ONNX export Done. Exported predict_net (before optimizations):\n" + colored(table, "cyan") - ) - - # Apply protobuf optimization - fuse_alias_placeholder(predict_net, init_net) - if any(t.device.type != "cpu" for t in tensor_inputs): - fuse_copy_between_cpu_and_gpu(predict_net) - remove_dead_end_ops(init_net) - _assign_device_option(predict_net, init_net, tensor_inputs) - params, device_options = get_params_from_init_net(init_net) - predict_net, params = remove_reshape_for_fc(predict_net, params) - init_net = construct_init_net_from_params(params, device_options) - group_norm_replace_aten_with_caffe2(predict_net) - - # Record necessary information for running the pb model in Detectron2 system. - model.encode_additional_info(predict_net, init_net) - - logger.info("Operators used in predict_net: \n{}".format(_op_stats(predict_net))) - logger.info("Operators used in init_net: \n{}".format(_op_stats(init_net))) - - return predict_net, init_net - - -def run_and_save_graph(predict_net, init_net, tensor_inputs, graph_save_path): - """ - Run the caffe2 model on given inputs, recording the shape and draw the graph. - - predict_net/init_net: caffe2 model. - tensor_inputs: a list of tensors that caffe2 model takes as input. - graph_save_path: path for saving graph of exported model. - """ - - logger.info("Saving graph of ONNX exported model to {} ...".format(graph_save_path)) - save_graph(predict_net, graph_save_path, op_only=False) - - # Run the exported Caffe2 net - logger.info("Running ONNX exported model ...") - with ScopedWS("__ws_tmp__", True) as ws: - ws.RunNetOnce(init_net) - initialized_blobs = set(ws.Blobs()) - uninitialized = [inp for inp in predict_net.external_input if inp not in initialized_blobs] - for name, blob in zip(uninitialized, tensor_inputs): - ws.FeedBlob(name, blob) - - try: - ws.RunNetOnce(predict_net) - except RuntimeError as e: - logger.warning("Encountered RuntimeError: \n{}".format(str(e))) - - ws_blobs = {b: ws.FetchBlob(b) for b in ws.Blobs()} - blob_sizes = {b: ws_blobs[b].shape for b in ws_blobs if isinstance(ws_blobs[b], np.ndarray)} - - logger.info("Saving graph with blob shapes to {} ...".format(graph_save_path)) - save_graph(predict_net, graph_save_path, op_only=False, blob_sizes=blob_sizes) - - return ws_blobs diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/encoding.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/encoding.py deleted file mode 100644 index 008f06a79bf598b149bdccb73e572d13331a1631..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_internal/utils/encoding.py +++ /dev/null @@ -1,36 +0,0 @@ -import codecs -import locale -import re -import sys -from typing import List, Tuple - -BOMS: List[Tuple[bytes, str]] = [ - (codecs.BOM_UTF8, "utf-8"), - (codecs.BOM_UTF16, "utf-16"), - (codecs.BOM_UTF16_BE, "utf-16-be"), - (codecs.BOM_UTF16_LE, "utf-16-le"), - (codecs.BOM_UTF32, "utf-32"), - (codecs.BOM_UTF32_BE, "utf-32-be"), - (codecs.BOM_UTF32_LE, "utf-32-le"), -] - -ENCODING_RE = re.compile(rb"coding[:=]\s*([-\w.]+)") - - -def auto_decode(data: bytes) -> str: - """Check a bytes string for a BOM to correctly detect the encoding - - Fallback to locale.getpreferredencoding(False) like open() on Python3""" - for bom, encoding in BOMS: - if data.startswith(bom): - return data[len(bom) :].decode(encoding) - # Lets check the first two lines as in PEP263 - for line in data.split(b"\n")[:2]: - if line[0:1] == b"#" and ENCODING_RE.search(line): - result = ENCODING_RE.search(line) - assert result is not None - encoding = result.groups()[0].decode("ascii") - return data.decode(encoding) - return data.decode( - locale.getpreferredencoding(False) or sys.getdefaultencoding(), - ) diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pyproject_hooks/_in_process/__init__.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pyproject_hooks/_in_process/__init__.py deleted file mode 100644 index 917fa065b3c7feccdef5bc666a5109c855217260..0000000000000000000000000000000000000000 --- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pyproject_hooks/_in_process/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -"""This is a subpackage because the directory is on sys.path for _in_process.py - -The subpackage should stay as empty as possible to avoid shadowing modules that -the backend might import. -""" - -import importlib.resources as resources - -try: - resources.files -except AttributeError: - # Python 3.8 compatibility - def _in_proc_script_path(): - return resources.path(__package__, '_in_process.py') -else: - def _in_proc_script_path(): - return resources.as_file( - resources.files(__package__).joinpath('_in_process.py')) diff --git a/spaces/Thafx/sdrv50/README.md b/spaces/Thafx/sdrv50/README.md deleted file mode 100644 index dcf65e7e755b407f9a8915bee996b6a89f96b63f..0000000000000000000000000000000000000000 --- a/spaces/Thafx/sdrv50/README.md +++ /dev/null @@ -1,20 +0,0 @@ ---- -title: Realistic Vision v5.0 -emoji: 📷 -colorFrom: yellow -colorTo: red -sdk: gradio -sdk_version: 3.18.0 -app_file: app.py -pinned: true -duplicated_from: Thafx/sdrv40 -tags: -- stable-diffusion -- stable-diffusion-diffusers -- text-to-image -- realistic-vision -models: -- SG161222/Realistic_Vision_V5.0_noVAE ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/ThirdEyeData/Customer-Conversion-Prediction/matumizi/daexp.py b/spaces/ThirdEyeData/Customer-Conversion-Prediction/matumizi/daexp.py deleted file mode 100644 index 4e3615635e9c5927b92ddd55c1a911e21571a1d6..0000000000000000000000000000000000000000 --- a/spaces/ThirdEyeData/Customer-Conversion-Prediction/matumizi/daexp.py +++ /dev/null @@ -1,3121 +0,0 @@ -#!/usr/local/bin/python3 - -# Author: Pranab Ghosh -# -# 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. - -# Package imports -import os -import sys -import numpy as np -import pandas as pd -import sklearn as sk -from sklearn import preprocessing -from sklearn import metrics -import random -from math import * -from decimal import Decimal -import pprint -from statsmodels.graphics import tsaplots -from statsmodels.tsa import stattools as stt -from statsmodels.stats import stattools as sstt -from sklearn.linear_model import LinearRegression -from matplotlib import pyplot as plt -from scipy import stats as sta -from statsmodels.tsa.seasonal import seasonal_decompose -import statsmodels.api as sm -from sklearn.ensemble import IsolationForest -from sklearn.neighbors import LocalOutlierFactor -from sklearn.svm import OneClassSVM -from sklearn.covariance import EllipticEnvelope -from sklearn.mixture import GaussianMixture -from sklearn.cluster import KMeans -from sklearn.decomposition import PCA -import hurst -from .util import * -from .mlutil import * -from .sampler import * -from .stats import * - -""" -Load data from a CSV file, data frame, numpy array or list -Each data set (array like) is given a name while loading -Perform various data exploration operation refering to the data sets by name -Save and restore workspace if needed -""" -class DataSetMetaData: - """ - data set meta data - """ - dtypeNum = 1 - dtypeCat = 2 - dtypeBin = 3 - def __init__(self, dtype): - self.notes = list() - self.dtype = dtype - - def addNote(self, note): - """ - add note - """ - self.notes.append(note) - - -class DataExplorer: - """ - various data exploration functions - """ - def __init__(self, verbose=True): - """ - initialize - - Parameters - verbose : True for verbosity - """ - self.dataSets = dict() - self.metaData = dict() - self.pp = pprint.PrettyPrinter(indent=4) - self.verbose = verbose - - def setVerbose(self, verbose): - """ - sets verbose - - Parameters - verbose : True for verbosity - """ - self.verbose = verbose - - def save(self, filePath): - """ - save checkpoint - - Parameters - filePath : path of file where saved - """ - self.__printBanner("saving workspace") - ws = dict() - ws["data"] = self.dataSets - ws["metaData"] = self.metaData - saveObject(ws, filePath) - self.__printDone() - - def restore(self, filePath): - """ - restore checkpoint - - Parameters - filePath : path of file from where to store - """ - self.__printBanner("restoring workspace") - ws = restoreObject(filePath) - self.dataSets = ws["data"] - self.metaData = ws["metaData"] - self.__printDone() - - - def queryFileData(self, filePath, *columns): - """ - query column data type from a data file - - Parameters - filePath : path of file with data - columns : indexes followed by column names or column names - """ - self.__printBanner("querying column data type from a data frame") - lcolumns = list(columns) - noHeader = type(lcolumns[0]) == int - if noHeader: - df = pd.read_csv(filePath, header=None) - else: - df = pd.read_csv(filePath, header=0) - return self.queryDataFrameData(df, *columns) - - def queryDataFrameData(self, df, *columns): - """ - query column data type from a data frame - - Parameters - df : data frame with data - columns : indexes followed by column name or column names - """ - self.__printBanner("querying column data type from a data frame") - columns = list(columns) - noHeader = type(columns[0]) == int - dtypes = list() - if noHeader: - nCols = int(len(columns) / 2) - colIndexes = columns[:nCols] - cnames = columns[nCols:] - nColsDf = len(df.columns) - for i in range(nCols): - ci = colIndexes[i] - assert ci < nColsDf, "col index {} outside range".format(ci) - col = df.loc[ : , ci] - dtypes.append(self.getDataType(col)) - else: - cnames = columns - for c in columns: - col = df[c] - dtypes.append(self.getDataType(col)) - - nt = list(zip(cnames, dtypes)) - result = self.__printResult("columns and data types", nt) - return result - - def getDataType(self, col): - """ - get data type - - Parameters - col : contains data array like - """ - if isBinary(col): - dtype = "binary" - elif isInteger(col): - dtype = "integer" - elif isFloat(col): - dtype = "float" - elif isCategorical(col): - dtype = "categorical" - else: - dtype = "mixed" - return dtype - - - def addFileNumericData(self,filePath, *columns): - """ - add numeric columns from a file - - Parameters - filePath : path of file with data - columns : indexes followed by column names or column names - """ - self.__printBanner("adding numeric columns from a file") - self.addFileData(filePath, True, *columns) - self.__printDone() - - - def addFileBinaryData(self,filePath, *columns): - """ - add binary columns from a file - - Parameters - filePath : path of file with data - columns : indexes followed by column names or column names - """ - self.__printBanner("adding binary columns from a file") - self.addFileData(filePath, False, *columns) - self.__printDone() - - def addFileData(self, filePath, numeric, *columns): - """ - add columns from a file - - Parameters - filePath : path of file with data - numeric : True if numeric False in binary - columns : indexes followed by column names or column names - """ - columns = list(columns) - noHeader = type(columns[0]) == int - if noHeader: - df = pd.read_csv(filePath, header=None) - else: - df = pd.read_csv(filePath, header=0) - self.addDataFrameData(df, numeric, *columns) - - def addDataFrameNumericData(self,filePath, *columns): - """ - add numeric columns from a data frame - - Parameters - filePath : path of file with data - columns : indexes followed by column names or column names - """ - self.__printBanner("adding numeric columns from a data frame") - self.addDataFrameData(filePath, True, *columns) - - - def addDataFrameBinaryData(self,filePath, *columns): - """ - add binary columns from a data frame - - Parameters - filePath : path of file with data - columns : indexes followed by column names or column names - """ - self.__printBanner("adding binary columns from a data frame") - self.addDataFrameData(filePath, False, *columns) - - - def addDataFrameData(self, df, numeric, *columns): - """ - add columns from a data frame - - Parameters - df : data frame with data - numeric : True if numeric False in binary - columns : indexes followed by column names or column names - """ - columns = list(columns) - noHeader = type(columns[0]) == int - if noHeader: - nCols = int(len(columns) / 2) - colIndexes = columns[:nCols] - nColsDf = len(df.columns) - for i in range(nCols): - ci = colIndexes[i] - assert ci < nColsDf, "col index {} outside range".format(ci) - col = df.loc[ : , ci] - if numeric: - assert isNumeric(col), "data is not numeric" - else: - assert isBinary(col), "data is not binary" - col = col.to_numpy() - cn = columns[i + nCols] - dtype = DataSetMetaData.dtypeNum if numeric else DataSetMetaData.dtypeBin - self.__addDataSet(cn, col, dtype) - else: - for c in columns: - col = df[c] - if numeric: - assert isNumeric(col), "data is not numeric" - else: - assert isBinary(col), "data is not binary" - col = col.to_numpy() - dtype = DataSetMetaData.dtypeNum if numeric else DataSetMetaData.dtypeBin - self.__addDataSet(c, col, dtype) - - def __addDataSet(self, dsn, data, dtype): - """ - add dada set - - Parameters - dsn: data set name - data : numpy array data - """ - self.dataSets[dsn] = data - self.metaData[dsn] = DataSetMetaData(dtype) - - - def addListNumericData(self, ds, name): - """ - add numeric data from a list - - Parameters - ds : list with data - name : name of data set - """ - self.__printBanner("add numeric data from a list") - self.addListData(ds, True, name) - self.__printDone() - - - def addListBinaryData(self, ds, name): - """ - add binary data from a list - - Parameters - ds : list with data - name : name of data set - """ - self.__printBanner("adding binary data from a list") - self.addListData(ds, False, name) - self.__printDone() - - def addListData(self, ds, numeric, name): - """ - adds list data - - Parameters - ds : list with data - numeric : True if numeric False in binary - name : name of data set - """ - assert type(ds) == list, "data not a list" - if numeric: - assert isNumeric(ds), "data is not numeric" - else: - assert isBinary(ds), "data is not binary" - dtype = DataSetMetaData.dtypeNum if numeric else DataSetMetaData.dtypeBin - self.dataSets[name] = np.array(ds) - self.metaData[name] = DataSetMetaData(dtype) - - - def addFileCatData(self, filePath, *columns): - """ - add categorical columns from a file - - Parameters - filePath : path of file with data - columns : indexes followed by column names or column names - """ - self.__printBanner("adding categorical columns from a file") - columns = list(columns) - noHeader = type(columns[0]) == int - if noHeader: - df = pd.read_csv(filePath, header=None) - else: - df = pd.read_csv(filePath, header=0) - - self.addDataFrameCatData(df, *columns) - self.__printDone() - - def addDataFrameCatData(self, df, *columns): - """ - add categorical columns from a data frame - - Parameters - df : data frame with data - columns : indexes followed by column names or column names - """ - self.__printBanner("adding categorical columns from a data frame") - columns = list(columns) - noHeader = type(columns[0]) == int - if noHeader: - nCols = int(len(columns) / 2) - colIndexes = columns[:nCols] - nColsDf = len(df.columns) - for i in range(nCols): - ci = colIndexes[i] - assert ci < nColsDf, "col index {} outside range".format(ci) - col = df.loc[ : , ci] - assert isCategorical(col), "data is not categorical" - col = col.tolist() - cn = columns[i + nCols] - self.__addDataSet(cn, col, DataSetMetaData.dtypeCat) - else: - for c in columns: - col = df[c].tolist() - self.__addDataSet(c, col, DataSetMetaData.dtypeCat) - - def addListCatData(self, ds, name): - """ - add categorical list data - - Parameters - ds : list with data - name : name of data set - """ - self.__printBanner("adding categorical list data") - assert type(ds) == list, "data not a list" - assert isCategorical(ds), "data is not categorical" - self.__addDataSet(name, ds, DataSetMetaData.dtypeCat) - self.__printDone() - - def remData(self, ds): - """ - removes data set - - Parameters - ds : data set name - """ - self.__printBanner("removing data set", ds) - assert ds in self.dataSets, "data set {} does not exist, please add it first".format(ds) - self.dataSets.pop(ds) - self.metaData.pop(ds) - names = self.showNames() - self.__printDone() - return names - - def addNote(self, ds, note): - """ - get data - - Parameters - ds : data set name or list or numpy array with data - note: note text - """ - self.__printBanner("adding note") - assert ds in self.dataSets, "data set {} does not exist, please add it first".format(ds) - mdata = self.metaData[ds] - mdata.addNote(note) - self.__printDone() - - def getNotes(self, ds): - """ - get data - - Parameters - ds : data set name or list or numpy array with data - """ - self.__printBanner("getting notes") - assert ds in self.dataSets, "data set {} does not exist, please add it first".format(ds) - mdata = self.metaData[ds] - dnotes = mdata.notes - if self.verbose: - for dn in dnotes: - print(dn) - return dnotes - - def getNumericData(self, ds): - """ - get numeric data - - Parameters - ds : data set name or list or numpy array with data - """ - if type(ds) == str: - assert ds in self.dataSets, "data set {} does not exist, please add it first".format(ds) - assert self.metaData[ds].dtype == DataSetMetaData.dtypeNum, "data set {} is expected to be numerical type for this operation".format(ds) - data = self.dataSets[ds] - elif type(ds) == list: - assert isNumeric(ds), "data is not numeric" - data = np.array(ds) - elif type(ds) == np.ndarray: - data = ds - else: - raise "invalid type, expecting data set name, list or ndarray" - return data - - - def getCatData(self, ds): - """ - get categorical data - - Parameters - ds : data set name or list with data - """ - if type(ds) == str: - assert ds in self.dataSets, "data set {} does not exist, please add it first".format(ds) - assert self.metaData[ds].dtype == DataSetMetaData.dtypeCat, "data set {} is expected to be categorical type for this operation".format(ds) - data = self.dataSets[ds] - elif type(ds) == list: - assert isCategorical(ds), "data is not categorical" - data = ds - else: - raise "invalid type, expecting data set name or list" - return data - - def getAnyData(self, ds): - """ - get any data - - Parameters - ds : data set name or list with data - """ - if type(ds) == str: - assert ds in self.dataSets, "data set {} does not exist, please add it first".format(ds) - data = self.dataSets[ds] - elif type(ds) == list: - data = ds - else: - raise "invalid type, expecting data set name or list" - return data - - def loadCatFloatDataFrame(self, ds1, ds2): - """ - loads float and cat data into data frame - - Parameters - ds1: data set name or list - ds2: data set name or list or numpy array - """ - data1 = self.getCatData(ds1) - data2 = self.getNumericData(ds2) - self.ensureSameSize([data1, data2]) - df1 = pd.DataFrame(data=data1) - df2 = pd.DataFrame(data=data2) - df = pd.concat([df1,df2], axis=1) - df.columns = range(df.shape[1]) - return df - - def showNames(self): - """ - lists data set names - """ - self.__printBanner("listing data set names") - names = self.dataSets.keys() - if self.verbose: - print("data sets") - for ds in names: - print(ds) - self.__printDone() - return names - - def plot(self, ds, yscale=None): - """ - plots data - - Parameters - ds: data set name or list or numpy array - yscale: y scale - """ - self.__printBanner("plotting data", ds) - data = self.getNumericData(ds) - drawLine(data, yscale) - - def plotZoomed(self, ds, beg, end, yscale=None): - """ - plots zoomed data - - Parameters - ds: data set name or list or numpy array - beg: begin offset - end: end offset - yscale: y scale - """ - self.__printBanner("plotting data", ds) - data = self.getNumericData(ds) - drawLine(data[beg:end], yscale) - - def scatterPlot(self, ds1, ds2): - """ - scatter plots data - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - """ - self.__printBanner("scatter plotting data", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - self.ensureSameSize([data1, data2]) - x = np.arange(1, len(data1)+1, 1) - plt.scatter(x, data1 ,color="red") - plt.scatter(x, data2 ,color="blue") - plt.show() - - def print(self, ds): - """ - prunt data - - Parameters - ds: data set name or list or numpy array - """ - self.__printBanner("printing data", ds) - assert ds in self.dataSets, "data set {} does not exist, please add it first".format(ds) - data = self.dataSets[ds] - if self.verbore: - print(formatAny(len(data), "size")) - print("showing first 50 elements" ) - print(data[:50]) - - def plotHist(self, ds, cumulative, density, nbins=20): - """ - plots histogram - - Parameters - ds: data set name or list or numpy array - cumulative : True if cumulative - density : True to normalize for probability density - nbins : no of bins - """ - self.__printBanner("plotting histogram", ds) - data = self.getNumericData(ds) - plt.hist(data, bins=nbins, cumulative=cumulative, density=density) - plt.show() - - def isMonotonicallyChanging(self, ds): - """ - checks if monotonically increasing or decreasing - - Parameters - ds: data set name or list or numpy array - """ - self.__printBanner("checking monotonic change", ds) - data = self.getNumericData(ds) - monoIncreasing = all(list(map(lambda i : data[i] >= data[i-1], range(1, len(data), 1)))) - monoDecreasing = all(list(map(lambda i : data[i] <= data[i-1], range(1, len(data), 1)))) - result = self.__printResult("monoIncreasing", monoIncreasing, "monoDecreasing", monoDecreasing) - return result - - def getFreqDistr(self, ds, nbins=20): - """ - get histogram - - Parameters - ds: data set name or list or numpy array - nbins: num of bins - """ - self.__printBanner("getting histogram", ds) - data = self.getNumericData(ds) - frequency, lowLimit, binsize, extraPoints = sta.relfreq(data, numbins=nbins) - result = self.__printResult("frequency", frequency, "lowLimit", lowLimit, "binsize", binsize, "extraPoints", extraPoints) - return result - - - def getCumFreqDistr(self, ds, nbins=20): - """ - get cumulative freq distribution - - Parameters - ds: data set name or list or numpy array - nbins: num of bins - """ - self.__printBanner("getting cumulative freq distribution", ds) - data = self.getNumericData(ds) - cumFrequency, lowLimit, binsize, extraPoints = sta.cumfreq(data, numbins=nbins) - result = self.__printResult("cumFrequency", cumFrequency, "lowLimit", lowLimit, "binsize", binsize, "extraPoints", extraPoints) - return result - - def getExtremeValue(self, ds, ensamp, nsamp, polarity, doPlotDistr, nbins=20): - """ - get extreme values - - Parameters - ds: data set name or list or numpy array - ensamp: num of samples for extreme values - nsamp: num of samples - polarity: max or min - doPlotDistr: plot distr - nbins: num of bins - """ - self.__printBanner("getting extreme values", ds) - data = self.getNumericData(ds) - evalues = list() - for _ in range(ensamp): - values = selectRandomSubListFromListWithRepl(data, nsamp) - if polarity == "max": - evalues.append(max(values)) - else: - evalues.append(min(values)) - if doPlotDistr: - plt.hist(evalues, bins=nbins, cumulative=False, density=True) - plt.show() - result = self.__printResult("extremeValues", evalues) - return result - - - def getEntropy(self, ds, nbins=20): - """ - get entropy - - Parameters - ds: data set name or list or numpy array - nbins: num of bins - """ - self.__printBanner("getting entropy", ds) - data = self.getNumericData(ds) - result = self.getFreqDistr(data, nbins) - entropy = sta.entropy(result["frequency"]) - result = self.__printResult("entropy", entropy) - return result - - def getRelEntropy(self, ds1, ds2, nbins=20): - """ - get relative entropy or KL divergence with both data sets numeric - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - nbins: num of bins - """ - self.__printBanner("getting relative entropy or KL divergence", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - result1 = self .getFeqDistr(data1, nbins) - freq1 = result1["frequency"] - result2 = self .getFeqDistr(data2, nbins) - freq2 = result2["frequency"] - entropy = sta.entropy(freq1, freq2) - result = self.__printResult("relEntropy", entropy) - return result - - def getAnyEntropy(self, ds, dt, nbins=20): - """ - get entropy of any data typr numeric or categorical - - Parameters - ds: data set name or list or numpy array - dt : data type num or cat - nbins: num of bins - """ - entropy = self.getEntropy(ds, nbins)["entropy"] if dt == "num" else self.getStatsCat(ds)["entropy"] - result = self.__printResult("entropy", entropy) - return result - - def getJointEntropy(self, ds1, ds2, nbins=20): - """ - get joint entropy with both data sets numeric - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - nbins: num of bins - """ - self.__printBanner("getting join entropy", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - self.ensureSameSize([data1, data2]) - hist, xedges, yedges = np.histogram2d(data1, data2, bins=nbins) - hist = hist.flatten() - ssize = len(data1) - hist = hist / ssize - entropy = sta.entropy(hist) - result = self.__printResult("jointEntropy", entropy) - return result - - - def getAllNumMutualInfo(self, ds1, ds2, nbins=20): - """ - get mutual information for both numeric data - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - nbins: num of bins - """ - self.__printBanner("getting mutual information", ds1, ds2) - en1 = self.getEntropy(ds1,nbins) - en2 = self.getEntropy(ds2,nbins) - en = self.getJointEntropy(ds1, ds2, nbins) - - mutInfo = en1["entropy"] + en2["entropy"] - en["jointEntropy"] - result = self.__printResult("mutInfo", mutInfo) - return result - - - def getNumCatMutualInfo(self, nds, cds ,nbins=20): - """ - get mutiual information between numeric and categorical data - - Parameters - nds: numeric data set name or list or numpy array - cds: categoric data set name or list - nbins: num of bins - """ - self.__printBanner("getting mutual information of numerical and categorical data", nds, cds) - ndata = self.getNumericData(nds) - cds = self.getCatData(cds) - nentr = self.getEntropy(nds)["entropy"] - - #conditional entropy - cdistr = self.getStatsCat(cds)["distr"] - grdata = self.getGroupByData(nds, cds, True)["groupedData"] - cnentr = 0 - for gr, data in grdata.items(): - self.addListNumericData(data, "grdata") - gnentr = self.getEntropy("grdata")["entropy"] - cnentr += gnentr * cdistr[gr] - - mutInfo = nentr - cnentr - result = self.__printResult("mutInfo", mutInfo, "entropy", nentr, "condEntropy", cnentr) - return result - - def getTwoCatMutualInfo(self, cds1, cds2): - """ - get mutiual information between 2 categorical data sets - - Parameters - cds1 : categoric data set name or list - cds2 : categoric data set name or list - """ - self.__printBanner("getting mutual information of two categorical data sets", cds1, cds2) - cdata1 = self.getCatData(cds1) - cdata2 = self.getCatData(cds1) - centr = self.getStatsCat(cds1)["entropy"] - - #conditional entropy - cdistr = self.getStatsCat(cds2)["distr"] - grdata = self.getGroupByData(cds1, cds2, True)["groupedData"] - ccentr = 0 - for gr, data in grdata.items(): - self.addListCatData(data, "grdata") - gcentr = self.getStatsCat("grdata")["entropy"] - ccentr += gcentr * cdistr[gr] - - mutInfo = centr - ccentr - result = self.__printResult("mutInfo", mutInfo, "entropy", centr, "condEntropy", ccentr) - return result - - def getMutualInfo(self, dst, nbins=20): - """ - get mutiual information between 2 data sets,any combination numerical and categorical - - Parameters - dst : data source , data type, data source , data type - nbins : num of bins - """ - assertEqual(len(dst), 4, "invalid data source and data type list size") - dtypes = ["num", "cat"] - assertInList(dst[1], dtypes, "invalid data type") - assertInList(dst[3], dtypes, "invalid data type") - self.__printBanner("getting mutual information of any mix numerical and categorical data", dst[0], dst[2]) - - if dst[1] == "num": - mutInfo = self.getAllNumMutualInfo(dst[0], dst[2], nbins)["mutInfo"] if dst[3] == "num" \ - else self.getNumCatMutualInfo(dst[0], dst[2], nbins)["mutInfo"] - else: - mutInfo = self.getNumCatMutualInfo(dst[2], dst[0], nbins)["mutInfo"] if dst[3] == "num" \ - else self.getTwoCatMutualInfo(dst[2], dst[0])["mutInfo"] - - result = self.__printResult("mutInfo", mutInfo) - return result - - - def getCondMutualInfo(self, dst, nbins=20): - """ - get conditional mutiual information between 2 data sets,any combination numerical and categorical - - Parameters - dst : data source , data type, data source , data type, data source , data type - nbins : num of bins - """ - assertEqual(len(dst), 6, "invalid data source and data type list size") - dtypes = ["num", "cat"] - assertInList(dst[1], dtypes, "invalid data type") - assertInList(dst[3], dtypes, "invalid data type") - assertInList(dst[5], dtypes, "invalid data type") - self.__printBanner("getting conditional mutual information of any mix numerical and categorical data", dst[0], dst[2]) - - if dst[5] == "cat": - cdistr = self.getStatsCat(dst[4])["distr"] - grdata1 = self.getGroupByData(dst[0], dst[4], True)["groupedData"] - grdata2 = self.getGroupByData(dst[2], dst[4], True)["groupedData"] - - else: - gdata = self.getNumericData(dst[4]) - hist = Histogram.createWithNumBins(gdata, nbins) - cdistr = hist.distr() - grdata1 = self.getGroupByData(dst[0], dst[4], False)["groupedData"] - grdata2 = self.getGroupByData(dst[2], dst[4], False)["groupedData"] - - - cminfo = 0 - for gr in grdata1.keys(): - data1 = grdata1[gr] - data2 = grdata2[gr] - if dst[1] == "num": - self.addListNumericData(data1, "grdata1") - else: - self.addListCatData(data1, "grdata1") - - if dst[3] == "num": - self.addListNumericData(data2, "grdata2") - else: - self.addListCatData(data2, "grdata2") - gdst = ["grdata1", dst[1], "grdata2", dst[3]] - minfo = self.getMutualInfo(gdst, nbins)["mutInfo"] - cminfo += minfo * cdistr[gr] - - result = self.__printResult("condMutInfo", cminfo) - return result - - def getPercentile(self, ds, value): - """ - gets percentile - - Parameters - ds: data set name or list or numpy array - value: the value - """ - self.__printBanner("getting percentile", ds) - data = self.getNumericData(ds) - percent = sta.percentileofscore(data, value) - result = self.__printResult("value", value, "percentile", percent) - return result - - def getValueRangePercentile(self, ds, value1, value2): - """ - gets percentile - - Parameters - ds: data set name or list or numpy array - value1: first value - value2: second value - """ - self.__printBanner("getting percentile difference for value range", ds) - if value1 < value2: - v1 = value1 - v2 = value2 - else: - v1 = value2 - v2 = value1 - data = self.getNumericData(ds) - per1 = sta.percentileofscore(data, v1) - per2 = sta.percentileofscore(data, v2) - result = self.__printResult("valueFirst", value1, "valueSecond", value2, "percentileDiff", per2 - per1) - return result - - def getValueAtPercentile(self, ds, percent): - """ - gets value at percentile - - Parameters - ds: data set name or list or numpy array - percent: percentile - """ - self.__printBanner("getting value at percentile", ds) - data = self.getNumericData(ds) - assert isInRange(percent, 0, 100), "percent should be between 0 and 100" - value = sta.scoreatpercentile(data, percent) - result = self.__printResult("value", value, "percentile", percent) - return result - - def getLessThanValues(self, ds, cvalue): - """ - gets values less than given value - - Parameters - ds: data set name or list or numpy array - cvalue: condition value - """ - self.__printBanner("getting values less than", ds) - fdata = self.__getCondValues(ds, cvalue, "lt") - result = self.__printResult("count", len(fdata), "lessThanvalues", fdata ) - return result - - - def getGreaterThanValues(self, ds, cvalue): - """ - gets values greater than given value - - Parameters - ds: data set name or list or numpy array - cvalue: condition value - """ - self.__printBanner("getting values greater than", ds) - fdata = self.__getCondValues(ds, cvalue, "gt") - result = self.__printResult("count", len(fdata), "greaterThanvalues", fdata ) - return result - - def __getCondValues(self, ds, cvalue, cond): - """ - gets cinditional values - - Parameters - ds: data set name or list or numpy array - cvalue: condition value - cond: condition - """ - data = self.getNumericData(ds) - if cond == "lt": - ind = np.where(data < cvalue) - else: - ind = np.where(data > cvalue) - fdata = data[ind] - return fdata - - def getUniqueValueCounts(self, ds, maxCnt=10): - """ - gets unique values and counts - - Parameters - ds: data set name or list or numpy array - maxCnt; max value count pairs to return - """ - self.__printBanner("getting unique values and counts", ds) - data = self.getNumericData(ds) - values, counts = sta.find_repeats(data) - cardinality = len(values) - vc = list(zip(values, counts)) - vc.sort(key = lambda v : v[1], reverse = True) - result = self.__printResult("cardinality", cardinality, "vunique alues and repeat counts", vc[:maxCnt]) - return result - - def getCatUniqueValueCounts(self, ds, maxCnt=10): - """ - gets unique categorical values and counts - - Parameters - ds: data set name or list or numpy array - maxCnt: max value count pairs to return - """ - self.__printBanner("getting unique categorical values and counts", ds) - data = self.getCatData(ds) - series = pd.Series(data) - uvalues = series.value_counts() - values = uvalues.index.tolist() - counts = uvalues.tolist() - vc = list(zip(values, counts)) - vc.sort(key = lambda v : v[1], reverse = True) - result = self.__printResult("cardinality", len(values), "unique values and repeat counts", vc[:maxCnt]) - return result - - def getCatAlphaValueCounts(self, ds): - """ - gets alphabetic value count - - Parameters - ds: data set name or list or numpy array - """ - self.__printBanner("getting alphabetic value counts", ds) - data = self.getCatData(ds) - series = pd.Series(data) - flags = series.str.isalpha().tolist() - count = sum(flags) - result = self.__printResult("alphabeticValueCount", count) - return result - - - def getCatNumValueCounts(self, ds): - """ - gets numeric value count - - Parameters - ds: data set name or list or numpy array - """ - self.__printBanner("getting numeric value counts", ds) - data = self.getCatData(ds) - series = pd.Series(data) - flags = series.str.isnumeric().tolist() - count = sum(flags) - result = self.__printResult("numericValueCount", count) - return result - - - def getCatAlphaNumValueCounts(self, ds): - """ - gets alpha numeric value count - - Parameters - ds: data set name or list or numpy array - """ - self.__printBanner("getting alpha numeric value counts", ds) - data = self.getCatData(ds) - series = pd.Series(data) - flags = series.str.isalnum().tolist() - count = sum(flags) - result = self.__printResult("alphaNumericValueCount", count) - return result - - def getCatAllCharCounts(self, ds): - """ - gets alphabetic, numeric and special char count list - - Parameters - ds: data set name or list or numpy array - """ - self.__printBanner("getting alphabetic, numeric and special char counts", ds) - data = self.getCatData(ds) - counts = list() - for d in data: - r = getAlphaNumCharCount(d) - counts.append(r) - result = self.__printResult("allTypeCharCounts", counts) - return result - - def getCatAlphaCharCounts(self, ds): - """ - gets alphabetic char count list - - Parameters - ds: data set name or list or numpy array - """ - self.__printBanner("getting alphabetic char counts", ds) - data = self.getCatData(ds) - counts = self.getCatAllCharCounts(ds)["allTypeCharCounts"] - counts = list(map(lambda r : r[0], counts)) - result = self.__printResult("alphaCharCounts", counts) - return result - - def getCatNumCharCounts(self, ds): - """ - gets numeric char count list - - Parameters - ds: data set name or list or numpy array - """ - self.__printBanner("getting numeric char counts", ds) - data = self.getCatData(ds) - counts = self.getCatAllCharCounts(ds)["allTypeCharCounts"] - counts = list(map(lambda r : r[1], counts)) - result = self.__printResult("numCharCounts", counts) - return result - - def getCatSpecialCharCounts(self, ds): - """ - gets special char count list - - Parameters - ds: data set name or list or numpy array - """ - self.__printBanner("getting special char counts", ds) - counts = self.getCatAllCharCounts(ds)["allTypeCharCounts"] - counts = list(map(lambda r : r[2], counts)) - result = self.__printResult("specialCharCounts", counts) - return result - - def getCatAlphaCharCountStats(self, ds): - """ - gets alphabetic char count stats - - Parameters - ds: data set name or list or numpy array - """ - self.__printBanner("getting alphabetic char count stats", ds) - counts = self.getCatAlphaCharCounts(ds)["alphaCharCounts"] - nz = counts.count(0) - st = self.__getBasicStats(np.array(counts)) - result = self.__printResult("mean", st[0], "std dev", st[1], "max", st[2], "min", st[3], "zeroCount", nz) - return result - - def getCatNumCharCountStats(self, ds): - """ - gets numeric char count stats - - Parameters - ds: data set name or list or numpy array - """ - self.__printBanner("getting numeric char count stats", ds) - counts = self.getCatNumCharCounts(ds)["numCharCounts"] - nz = counts.count(0) - st = self.__getBasicStats(np.array(counts)) - result = self.__printResult("mean", st[0], "std dev", st[1], "max", st[2], "min", st[3], "zeroCount", nz) - return result - - def getCatSpecialCharCountStats(self, ds): - """ - gets special char count stats - - Parameters - ds: data set name or list or numpy array - """ - self.__printBanner("getting special char count stats", ds) - counts = self.getCatSpecialCharCounts(ds)["specialCharCounts"] - nz = counts.count(0) - st = self.__getBasicStats(np.array(counts)) - result = self.__printResult("mean", st[0], "std dev", st[1], "max", st[2], "min", st[3], "zeroCount", nz) - return result - - def getCatFldLenStats(self, ds): - """ - gets field length stats - - Parameters - ds: data set name or list or numpy array - """ - self.__printBanner("getting field length stats", ds) - data = self.getCatData(ds) - le = list(map(lambda d: len(d), data)) - st = self.__getBasicStats(np.array(le)) - result = self.__printResult("mean", st[0], "std dev", st[1], "max", st[2], "min", st[3]) - return result - - def getCatCharCountStats(self, ds, ch): - """ - gets specified char ocuurence count stats - - Parameters - ds: data set name or list or numpy array - ch : character - """ - self.__printBanner("getting field length stats", ds) - data = self.getCatData(ds) - counts = list(map(lambda d: d.count(ch), data)) - nz = counts.count(0) - st = self.__getBasicStats(np.array(counts)) - result = self.__printResult("mean", st[0], "std dev", st[1], "max", st[2], "min", st[3], "zeroCount", nz) - return result - - def getStats(self, ds, nextreme=5): - """ - gets summary statistics - - Parameters - ds: data set name or list or numpy array - nextreme: num of extreme values - """ - self.__printBanner("getting summary statistics", ds) - data = self.getNumericData(ds) - stat = dict() - stat["length"] = len(data) - stat["min"] = data.min() - stat["max"] = data.max() - series = pd.Series(data) - stat["n smallest"] = series.nsmallest(n=nextreme).tolist() - stat["n largest"] = series.nlargest(n=nextreme).tolist() - stat["mean"] = data.mean() - stat["median"] = np.median(data) - mode, modeCnt = sta.mode(data) - stat["mode"] = mode[0] - stat["mode count"] = modeCnt[0] - stat["std"] = np.std(data) - stat["skew"] = sta.skew(data) - stat["kurtosis"] = sta.kurtosis(data) - stat["mad"] = sta.median_absolute_deviation(data) - self.pp.pprint(stat) - return stat - - def getStatsCat(self, ds): - """ - gets summary statistics for categorical data - - Parameters - ds: data set name or list or numpy array - """ - self.__printBanner("getting summary statistics for categorical data", ds) - data = self.getCatData(ds) - ch = CatHistogram() - for d in data: - ch.add(d) - mode = ch.getMode() - entr = ch.getEntropy() - uvalues = ch.getUniqueValues() - distr = ch.getDistr() - result = self.__printResult("entropy", entr, "mode", mode, "uniqueValues", uvalues, "distr", distr) - return result - - - def getGroupByData(self, ds, gds, gdtypeCat, numBins=20): - """ - group by - - Parameters - ds: data set name or list or numpy array - gds: group by data set name or list or numpy array - gdtpe : group by data type - """ - self.__printBanner("getting group by data", ds) - data = self.getAnyData(ds) - if gdtypeCat: - gdata = self.getCatData(gds) - else: - gdata = self.getNumericData(gds) - hist = Histogram.createWithNumBins(gdata, numBins) - gdata = list(map(lambda d : hist.bin(d), gdata)) - - self.ensureSameSize([data, gdata]) - groups = dict() - for g,d in zip(gdata, data): - appendKeyedList(groups, g, d) - - ve = self.verbose - self.verbose = False - result = self.__printResult("groupedData", groups) - self.verbose = ve - return result - - def getDifference(self, ds, order, doPlot=False): - """ - gets difference of given order - - Parameters - ds: data set name or list or numpy array - order: order of difference - doPlot : True for plot - """ - self.__printBanner("getting difference of given order", ds) - data = self.getNumericData(ds) - diff = difference(data, order) - if doPlot: - drawLine(diff) - return diff - - def getTrend(self, ds, doPlot=False): - """ - get trend - - Parameters - ds: data set name or list or numpy array - doPlot: true if plotting needed - """ - self.__printBanner("getting trend") - data = self.getNumericData(ds) - sz = len(data) - X = list(range(0, sz)) - X = np.reshape(X, (sz, 1)) - model = LinearRegression() - model.fit(X, data) - trend = model.predict(X) - sc = model.score(X, data) - coef = model.coef_ - intc = model.intercept_ - result = self.__printResult("coeff", coef, "intercept", intc, "r square error", sc, "trend", trend) - - if doPlot: - plt.plot(data) - plt.plot(trend) - plt.show() - return result - - def getDiffSdNoisiness(self, ds): - """ - get noisiness based on std dev of first order difference - - Parameters - ds: data set name or list or numpy array - """ - diff = self.getDifference(ds, 1) - noise = np.std(np.array(diff)) - result = self.__printResult("noisiness", noise) - return result - - def getMaRmseNoisiness(self, ds, wsize=5): - """ - gets noisiness based on RMSE with moving average - - Parameters - ds: data set name or list or numpy array - wsize : window size - """ - assert wsize % 2 == 1, "window size must be odd" - data = self.getNumericData(ds) - wind = data[:wsize] - wstat = SlidingWindowStat.initialize(wind.tolist()) - - whsize = int(wsize / 2) - beg = whsize - end = len(data) - whsize - 1 - sumSq = 0.0 - mean = wstat.getStat()[0] - diff = data[beg] - mean - sumSq += diff * diff - for i in range(beg + 1, end, 1): - mean = wstat.addGetStat(data[i + whsize])[0] - diff = data[i] - mean - sumSq += (diff * diff) - - noise = math.sqrt(sumSq / (len(data) - 2 * whsize)) - result = self.__printResult("noisiness", noise) - return result - - - def deTrend(self, ds, trend, doPlot=False): - """ - de trend - - Parameters - ds: data set name or list or numpy array - ternd : trend data - doPlot: true if plotting needed - """ - self.__printBanner("doing de trend", ds) - data = self.getNumericData(ds) - sz = len(data) - detrended = list(map(lambda i : data[i]-trend[i], range(sz))) - if doPlot: - drawLine(detrended) - return detrended - - def getTimeSeriesComponents(self, ds, model, freq, summaryOnly, doPlot=False): - """ - extracts trend, cycle and residue components of time series - - Parameters - ds: data set name or list or numpy array - model : model type - freq : seasnality period - summaryOnly : True if only summary needed in output - doPlot: true if plotting needed - """ - self.__printBanner("extracting trend, cycle and residue components of time series", ds) - assert model == "additive" or model == "multiplicative", "model must be additive or multiplicative" - data = self.getNumericData(ds) - res = seasonal_decompose(data, model=model, period=freq) - if doPlot: - res.plot() - plt.show() - - #summar of componenets - trend = np.array(removeNan(res.trend)) - trendMean = trend.mean() - trendSlope = (trend[-1] - trend[0]) / (len(trend) - 1) - seasonal = np.array(removeNan(res.seasonal)) - seasonalAmp = (seasonal.max() - seasonal.min()) / 2 - resid = np.array(removeNan(res.resid)) - residueMean = resid.mean() - residueStdDev = np.std(resid) - - if summaryOnly: - result = self.__printResult("trendMean", trendMean, "trendSlope", trendSlope, "seasonalAmp", seasonalAmp, - "residueMean", residueMean, "residueStdDev", residueStdDev) - else: - result = self.__printResult("trendMean", trendMean, "trendSlope", trendSlope, "seasonalAmp", seasonalAmp, - "residueMean", residueMean, "residueStdDev", residueStdDev, "trend", res.trend, "seasonal", res.seasonal, - "residual", res.resid) - return result - - def getGausianMixture(self, ncomp, cvType, ninit, *dsl): - """ - finds gaussian mixture parameters - - Parameters - ncomp : num of gaussian componenets - cvType : co variance type - ninit: num of intializations - dsl: list of data set name or list or numpy array - """ - self.__printBanner("getting gaussian mixture parameters", *dsl) - assertInList(cvType, ["full", "tied", "diag", "spherical"], "invalid covariance type") - dmat = self.__stackData(*dsl) - - gm = GaussianMixture(n_components=ncomp, covariance_type=cvType, n_init=ninit) - gm.fit(dmat) - weights = gm.weights_ - means = gm.means_ - covars = gm.covariances_ - converged = gm.converged_ - niter = gm.n_iter_ - aic = gm.aic(dmat) - result = self.__printResult("weights", weights, "mean", means, "covariance", covars, "converged", converged, "num iterations", niter, "aic", aic) - return result - - def getKmeansCluster(self, nclust, ninit, *dsl): - """ - gets cluster parameters - - Parameters - nclust : num of clusters - ninit: num of intializations - dsl: list of data set name or list or numpy array - """ - self.__printBanner("getting kmean cluster parameters", *dsl) - dmat = self.__stackData(*dsl) - nsamp = dmat.shape[0] - - km = KMeans(n_clusters=nclust, n_init=ninit) - km.fit(dmat) - centers = km.cluster_centers_ - avdist = sqrt(km.inertia_ / nsamp) - niter = km.n_iter_ - score = km.score(dmat) - result = self.__printResult("centers", centers, "average distance", avdist, "num iterations", niter, "score", score) - return result - - def getPrincComp(self, ncomp, *dsl): - """ - finds pricipal componenet parameters - - Parameters - ncomp : num of pricipal componenets - dsl: list of data set name or list or numpy array - """ - self.__printBanner("getting principal componenet parameters", *dsl) - dmat = self.__stackData(*dsl) - nfeat = dmat.shape[1] - assertGreater(nfeat, 1, "requires multiple features") - assertLesserEqual(ncomp, nfeat, "num of componenets greater than num of features") - - pca = PCA(n_components=ncomp) - pca.fit(dmat) - comps = pca.components_ - var = pca.explained_variance_ - varr = pca.explained_variance_ratio_ - svalues = pca.singular_values_ - result = self.__printResult("componenets", comps, "variance", var, "variance ratio", varr, "singular values", svalues) - return result - - def getOutliersWithIsoForest(self, contamination, *dsl): - """ - finds outliers using isolation forest - - Parameters - contamination : proportion of outliers in the data set - dsl: list of data set name or list or numpy array - """ - self.__printBanner("getting outliers using isolation forest", *dsl) - assert contamination >= 0 and contamination <= 0.5, "contamination outside valid range" - dmat = self.__stackData(*dsl) - - isf = IsolationForest(contamination=contamination, behaviour="new") - ypred = isf.fit_predict(dmat) - mask = ypred == -1 - doul = dmat[mask, :] - mask = ypred != -1 - dwoul = dmat[mask, :] - result = self.__printResult("numOutliers", doul.shape[0], "outliers", doul, "dataWithoutOutliers", dwoul) - return result - - def getOutliersWithLocalFactor(self, contamination, *dsl): - """ - gets outliers using local outlier factor - - Parameters - contamination : proportion of outliers in the data set - dsl: list of data set name or list or numpy array - """ - self.__printBanner("getting outliers using local outlier factor", *dsl) - assert contamination >= 0 and contamination <= 0.5, "contamination outside valid range" - dmat = self.__stackData(*dsl) - - lof = LocalOutlierFactor(contamination=contamination) - ypred = lof.fit_predict(dmat) - mask = ypred == -1 - doul = dmat[mask, :] - mask = ypred != -1 - dwoul = dmat[mask, :] - result = self.__printResult("numOutliers", doul.shape[0], "outliers", doul, "dataWithoutOutliers", dwoul) - return result - - def getOutliersWithSupVecMach(self, nu, *dsl): - """ - gets outliers using one class svm - - Parameters - nu : upper bound on the fraction of training errors and a lower bound of the fraction of support vectors - dsl: list of data set name or list or numpy array - """ - self.__printBanner("getting outliers using one class svm", *dsl) - assert nu >= 0 and nu <= 0.5, "error upper bound outside valid range" - dmat = self.__stackData(*dsl) - - svm = OneClassSVM(nu=nu) - ypred = svm.fit_predict(dmat) - mask = ypred == -1 - doul = dmat[mask, :] - mask = ypred != -1 - dwoul = dmat[mask, :] - result = self.__printResult("numOutliers", doul.shape[0], "outliers", doul, "dataWithoutOutliers", dwoul) - return result - - def getOutliersWithCovarDeterminant(self, contamination, *dsl): - """ - gets outliers using covariance determinan - - Parameters - contamination : proportion of outliers in the data set - dsl: list of data set name or list or numpy array - """ - self.__printBanner("getting outliers using using covariance determinant", *dsl) - assert contamination >= 0 and contamination <= 0.5, "contamination outside valid range" - dmat = self.__stackData(*dsl) - - lof = EllipticEnvelope(contamination=contamination) - ypred = lof.fit_predict(dmat) - mask = ypred == -1 - doul = dmat[mask, :] - mask = ypred != -1 - dwoul = dmat[mask, :] - result = self.__printResult("numOutliers", doul.shape[0], "outliers", doul, "dataWithoutOutliers", dwoul) - return result - - def getOutliersWithZscore(self, ds, zthreshold, stats=None): - """ - gets outliers using zscore - - Parameters - ds: data set name or list or numpy array - zthreshold : z score threshold - stats : tuple cintaining mean and std dev - """ - self.__printBanner("getting outliers using zscore", ds) - data = self.getNumericData(ds) - if stats is None: - mean = data.mean() - sd = np.std(data) - else: - mean = stats[0] - sd = stats[1] - - zs = list(map(lambda d : abs((d - mean) / sd), data)) - outliers = list(filter(lambda r : r[1] > zthreshold, enumerate(zs))) - result = self.__printResult("outliers", outliers) - return result - - def getOutliersWithRobustZscore(self, ds, zthreshold, stats=None): - """ - gets outliers using robust zscore - - Parameters - ds: data set name or list or numpy array - zthreshold : z score threshold - stats : tuple containing median and median absolute deviation - """ - self.__printBanner("getting outliers using robust zscore", ds) - data = self.getNumericData(ds) - if stats is None: - med = np.median(data) - dev = np.array(list(map(lambda d : abs(d - med), data))) - mad = 1.4296 * np.median(dev) - else: - med = stats[0] - mad = stats[1] - - rzs = list(map(lambda d : abs((d - med) / mad), data)) - outliers = list(filter(lambda r : r[1] > zthreshold, enumerate(rzs))) - result = self.__printResult("outliers", outliers) - return result - - - def getSubsequenceOutliersWithDissimilarity(self, subSeqSize, ds): - """ - gets subsequence outlier with subsequence pairwise disimilarity - - Parameters - subSeqSize : sub sequence size - ds: data set name or list or numpy array - """ - self.__printBanner("doing sub sequence anomaly detection with dissimilarity", ds) - data = self.getNumericData(ds) - sz = len(data) - dist = dict() - minDist = dict() - for i in range(sz - subSeqSize): - #first window - w1 = data[i : i + subSeqSize] - dmin = None - for j in range(sz - subSeqSize): - #second window not overlapping with the first - if j + subSeqSize <=i or j >= i + subSeqSize: - w2 = data[j : j + subSeqSize] - k = (j,i) - if k in dist: - d = dist[k] - else: - d = euclideanDistance(w1,w2) - k = (i,j) - dist[k] = d - if dmin is None: - dmin = d - else: - dmin = d if d < dmin else dmin - minDist[i] = dmin - - #find max of min - dmax = None - offset = None - for k in minDist.keys(): - d = minDist[k] - if dmax is None: - dmax = d - offset = k - else: - if d > dmax: - dmax = d - offset = k - result = self.__printResult("subSeqOffset", offset, "outlierScore", dmax) - return result - - def getNullCount(self, ds): - """ - get count of null fields - - Parameters - ds : data set name or list or numpy array with data - """ - self.__printBanner("getting null value count", ds) - if type(ds) == str: - assert ds in self.dataSets, "data set {} does not exist, please add it first".format(ds) - data = self.dataSets[ds] - ser = pd.Series(data) - elif type(ds) == list or type(ds) == np.ndarray: - ser = pd.Series(ds) - data = ds - else: - raise ValueError("invalid data type") - nv = ser.isnull().tolist() - nullCount = nv.count(True) - nullFraction = nullCount / len(data) - result = self.__printResult("nullFraction", nullFraction, "nullCount", nullCount) - return result - - - def fitLinearReg(self, dsx, ds, doPlot=False): - """ - fit linear regression - - Parameters - dsx: x data set name or None - ds: data set name or list or numpy array - doPlot: true if plotting needed - """ - self.__printBanner("fitting linear regression", ds) - data = self.getNumericData(ds) - if dsx is None: - x = np.arange(len(data)) - else: - x = self.getNumericData(dsx) - slope, intercept, rvalue, pvalue, stderr = sta.linregress(x, data) - result = self.__printResult("slope", slope, "intercept", intercept, "rvalue", rvalue, "pvalue", pvalue, "stderr", stderr) - if doPlot: - self.regFitPlot(x, data, slope, intercept) - return result - - def fitSiegelRobustLinearReg(self, ds, doPlot=False): - """ - siegel robust linear regression fit based on median - - Parameters - ds: data set name or list or numpy array - doPlot: true if plotting needed - """ - self.__printBanner("fitting siegel robust linear regression based on median", ds) - data = self.getNumericData(ds) - slope , intercept = sta.siegelslopes(data) - result = self.__printResult("slope", slope, "intercept", intercept) - if doPlot: - x = np.arange(len(data)) - self.regFitPlot(x, data, slope, intercept) - return result - - def fitTheilSenRobustLinearReg(self, ds, doPlot=False): - """ - thiel sen robust linear fit regression based on median - - Parameters - ds: data set name or list or numpy array - doPlot: true if plotting needed - """ - self.__printBanner("fitting thiel sen robust linear regression based on median", ds) - data = self.getNumericData(ds) - slope, intercept, loSlope, upSlope = sta.theilslopes(data) - result = self.__printResult("slope", slope, "intercept", intercept, "lower slope", loSlope, "upper slope", upSlope) - if doPlot: - x = np.arange(len(data)) - self.regFitPlot(x, data, slope, intercept) - return result - - def plotRegFit(self, x, y, slope, intercept): - """ - plot linear rgeression fit line - - Parameters - x : x values - y : y values - slope : slope - intercept : intercept - """ - self.__printBanner("plotting linear rgeression fit line") - fig = plt.figure() - ax = fig.add_subplot(111) - ax.plot(x, y, "b.") - ax.plot(x, intercept + slope * x, "r-") - plt.show() - - def getRegFit(self, xvalues, yvalues, slope, intercept): - """ - gets fitted line and residue - - Parameters - x : x values - y : y values - slope : regression slope - intercept : regressiob intercept - """ - yfit = list() - residue = list() - for x,y in zip(xvalues, yvalues): - yf = x * slope + intercept - yfit.append(yf) - r = y - yf - residue.append(r) - result = self.__printResult("fitted line", yfit, "residue", residue) - return result - - def getInfluentialPoints(self, dsx, dsy): - """ - gets influential points in regression model with Cook's distance - - Parameters - dsx : data set name or list or numpy array for x - dsy : data set name or list or numpy array for y - """ - self.__printBanner("finding influential points for linear regression", dsx, dsy) - y = self.getNumericData(dsy) - x = np.arange(len(data)) if dsx is None else self.getNumericData(dsx) - model = sm.OLS(y, x).fit() - np.set_printoptions(suppress=True) - influence = model.get_influence() - cooks = influence.cooks_distance - result = self.__printResult("Cook distance", cooks) - return result - - def getCovar(self, *dsl): - """ - gets covariance - - Parameters - dsl: list of data set name or list or numpy array - """ - self.__printBanner("getting covariance", *dsl) - data = list(map(lambda ds : self.getNumericData(ds), dsl)) - self.ensureSameSize(data) - data = np.vstack(data) - cv = np.cov(data) - print(cv) - return cv - - def getPearsonCorr(self, ds1, ds2, sigLev=.05): - """ - gets pearson correlation coefficient - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - """ - self.__printBanner("getting pearson correlation coefficient ", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - self.ensureSameSize([data1, data2]) - stat, pvalue = sta.pearsonr(data1, data2) - result = self.__printResult("stat", stat, "pvalue", pvalue) - self.__printStat(stat, pvalue, "probably uncorrelated", "probably correlated", sigLev) - return result - - - def getSpearmanRankCorr(self, ds1, ds2, sigLev=.05): - """ - gets spearman correlation coefficient - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("getting spearman correlation coefficient",ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - self.ensureSameSize([data1, data2]) - stat, pvalue = sta.spearmanr(data1, data2) - result = self.__printResult("stat", stat, "pvalue", pvalue) - self.__printStat(stat, pvalue, "probably uncorrelated", "probably correlated", sigLev) - return result - - def getKendalRankCorr(self, ds1, ds2, sigLev=.05): - """ - kendall’s tau, a correlation measure for ordinal data - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("getting kendall’s tau, a correlation measure for ordinal data", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - self.ensureSameSize([data1, data2]) - stat, pvalue = sta.kendalltau(data1, data2) - result = self.__printResult("stat", stat, "pvalue", pvalue) - self.__printStat(stat, pvalue, "probably uncorrelated", "probably correlated", sigLev) - return result - - def getPointBiserialCorr(self, ds1, ds2, sigLev=.05): - """ - point biserial correlation between binary and numeric - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("getting point biserial correlation between binary and numeric", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - assert isBinary(data1), "first data set is not binary" - self.ensureSameSize([data1, data2]) - stat, pvalue = sta.pointbiserialr(data1, data2) - result = self.__printResult("stat", stat, "pvalue", pvalue) - self.__printStat(stat, pvalue, "probably uncorrelated", "probably correlated", sigLev) - return result - - def getConTab(self, ds1, ds2): - """ - get contingency table for categorical data pair - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - """ - self.__printBanner("getting contingency table for categorical data", ds1, ds2) - data1 = self.getCatData(ds1) - data2 = self.getCatData(ds2) - self.ensureSameSize([data1, data2]) - crosstab = pd.crosstab(pd.Series(data1), pd.Series(data2), margins = False) - ctab = crosstab.values - print("contingency table") - print(ctab) - return ctab - - def getChiSqCorr(self, ds1, ds2, sigLev=.05): - """ - chi square correlation for categorical data pair - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("getting chi square correlation for two categorical", ds1, ds2) - ctab = self.getConTab(ds1, ds2) - stat, pvalue, dof, expctd = sta.chi2_contingency(ctab) - result = self.__printResult("stat", stat, "pvalue", pvalue, "dof", dof, "expected", expctd) - self.__printStat(stat, pvalue, "probably uncorrelated", "probably correlated", sigLev) - return result - - def getSizeCorrectChiSqCorr(self, ds1, ds2, chisq): - """ - cramerV size corrected chi square correlation for categorical data pair - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - chisq: chisq stat - """ - self.__printBanner("getting size corrected chi square correlation for two categorical", ds1, ds2) - c1 = self.getCatUniqueValueCounts(ds1)["cardinality"] - c2 = self.getCatUniqueValueCounts(ds2)["cardinality"] - c = min(c1,c2) - assertGreater(c, 1, "min cardinality should be greater than 1") - l = len(self.getCatData(ds1)) - t = l * (c - 1) - stat = math.sqrt(chisq / t) - result = self.__printResult("stat", stat) - return result - - def getAnovaCorr(self, ds1, ds2, grByCol, sigLev=.05): - """ - anova correlation for numerical categorical - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - grByCol : group by column - sigLev: statistical significance level - """ - self.__printBanner("anova correlation for numerical categorical", ds1, ds2) - df = self.loadCatFloatDataFrame(ds1, ds2) if grByCol == 0 else self.loadCatFloatDataFrame(ds2, ds1) - grByCol = 0 - dCol = 1 - grouped = df.groupby([grByCol]) - dlist = list(map(lambda v : v[1].loc[:, dCol].values, grouped)) - stat, pvalue = sta.f_oneway(*dlist) - result = self.__printResult("stat", stat, "pvalue", pvalue) - self.__printStat(stat, pvalue, "probably uncorrelated", "probably correlated", sigLev) - return result - - - def plotAutoCorr(self, ds, lags, alpha, diffOrder=0): - """ - plots auto correlation - - Parameters - ds: data set name or list or numpy array - lags: num of lags - alpha: confidence level - """ - self.__printBanner("plotting auto correlation", ds) - data = self.getNumericData(ds) - ddata = difference(data, diffOrder) if diffOrder > 0 else data - tsaplots.plot_acf(ddata, lags = lags, alpha = alpha) - plt.show() - - def getAutoCorr(self, ds, lags, alpha=.05): - """ - gets auts correlation - - Parameters - ds: data set name or list or numpy array - lags: num of lags - alpha: confidence level - """ - self.__printBanner("getting auto correlation", ds) - data = self.getNumericData(ds) - autoCorr, confIntv = stt.acf(data, nlags=lags, fft=False, alpha=alpha) - result = self.__printResult("autoCorr", autoCorr, "confIntv", confIntv) - return result - - - def plotParAcf(self, ds, lags, alpha): - """ - partial auto correlation - - Parameters - ds: data set name or list or numpy array - lags: num of lags - alpha: confidence level - """ - self.__printBanner("plotting partial auto correlation", ds) - data = self.getNumericData(ds) - tsaplots.plot_pacf(data, lags = lags, alpha = alpha) - plt.show() - - def getParAutoCorr(self, ds, lags, alpha=.05): - """ - gets partial auts correlation - - Parameters - ds: data set name or list or numpy array - lags: num of lags - alpha: confidence level - """ - self.__printBanner("getting partial auto correlation", ds) - data = self.getNumericData(ds) - partAutoCorr, confIntv = stt.pacf(data, nlags=lags, alpha=alpha) - result = self.__printResult("partAutoCorr", partAutoCorr, "confIntv", confIntv) - return result - - def getHurstExp(self, ds, kind, doPlot=True): - """ - gets Hurst exponent of time series - - Parameters - ds: data set name or list or numpy array - kind: kind of data change, random_walk, price - doPlot: True for plot - """ - self.__printBanner("getting Hurst exponent", ds) - data = self.getNumericData(ds) - h, c, odata = hurst.compute_Hc(data, kind=kind, simplified=False) - if doPlot: - f, ax = plt.subplots() - ax.plot(odata[0], c * odata[0] ** h, color="deepskyblue") - ax.scatter(odata[0], odata[1], color="purple") - ax.set_xscale("log") - ax.set_yscale("log") - ax.set_xlabel("time interval") - ax.set_ylabel("cum dev range and std dev ratio") - ax.grid(True) - plt.show() - - result = self.__printResult("hurstExponent", h, "hurstConstant", c) - return result - - def approxEntropy(self, ds, m, r): - """ - gets apprx entroty of time series (ref: wikipedia) - - Parameters - ds: data set name or list or numpy array - m: length of compared run of data - r: filtering level - """ - self.__printBanner("getting approximate entropy", ds) - ldata = self.getNumericData(ds) - aent = abs(self.__phi(ldata, m + 1, r) - self.__phi(ldata, m, r)) - result = self.__printResult("approxEntropy", aent) - return result - - def __phi(self, ldata, m, r): - """ - phi function for approximate entropy - - Parameters - ldata: data array - m: length of compared run of data - r: filtering level - """ - le = len(ldata) - x = [[ldata[j] for j in range(i, i + m - 1 + 1)] for i in range(le - m + 1)] - lex = len(x) - c = list() - for i in range(lex): - cnt = 0 - for j in range(lex): - cnt += (1 if maxListDist(x[i], x[j]) <= r else 0) - cnt /= (le - m + 1.0) - c.append(cnt) - return sum(np.log(c)) / (le - m + 1.0) - - - def oneSpaceEntropy(self, ds, scaMethod="zscale"): - """ - gets one space entroty (ref: Estimating mutual information by Kraskov) - - Parameters - ds: data set name or list or numpy array - """ - self.__printBanner("getting one space entropy", ds) - data = self.getNumericData(ds) - sdata = sorted(data) - sdata = scaleData(sdata, scaMethod) - su = 0 - n = len(sdata) - for i in range(1, n, 1): - t = abs(sdata[i] - sdata[i-1]) - if t > 0: - su += log(t) - su /= (n -1) - #print(su) - ose = digammaFun(n) - digammaFun(1) + su - result = self.__printResult("entropy", ose) - return result - - - def plotCrossCorr(self, ds1, ds2, normed, lags): - """ - plots cross correlation - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - normed: If True, input vectors are normalised to unit - lags: num of lags - """ - self.__printBanner("plotting cross correlation between two numeric", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - self.ensureSameSize([data1, data2]) - plt.xcorr(data1, data2, normed=normed, maxlags=lags) - plt.show() - - def getCrossCorr(self, ds1, ds2): - """ - gets cross correlation - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - """ - self.__printBanner("getting cross correlation", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - self.ensureSameSize([data1, data2]) - crossCorr = stt.ccf(data1, data2) - result = self.__printResult("crossCorr", crossCorr) - return result - - def getFourierTransform(self, ds): - """ - gets fast fourier transform - - Parameters - ds: data set name or list or numpy array - """ - self.__printBanner("getting fourier transform", ds) - data = self.getNumericData(ds) - ft = np.fft.rfft(data) - result = self.__printResult("fourierTransform", ft) - return result - - - def testStationaryAdf(self, ds, regression, autolag, sigLev=.05): - """ - Adf stationary test null hyp not stationary - - Parameters - ds: data set name or list or numpy array - regression: constant and trend order to include in regression - autolag: method to use when automatically determining the lag - sigLev: statistical significance level - """ - self.__printBanner("doing ADF stationary test", ds) - relist = ["c","ct","ctt","nc"] - assert regression in relist, "invalid regression value" - alList = ["AIC", "BIC", "t-stat", None] - assert autolag in alList, "invalid autolag value" - - data = self.getNumericData(ds) - re = stt.adfuller(data, regression=regression, autolag=autolag) - result = self.__printResult("stat", re[0], "pvalue", re[1] , "num lags", re[2] , "num observation for regression", re[3], - "critial values", re[4]) - self.__printStat(re[0], re[1], "probably not stationary", "probably stationary", sigLev) - return result - - def testStationaryKpss(self, ds, regression, nlags, sigLev=.05): - """ - Kpss stationary test null hyp stationary - - Parameters - ds: data set name or list or numpy array - regression: constant and trend order to include in regression - nlags : no of lags - sigLev: statistical significance level - """ - self.__printBanner("doing KPSS stationary test", ds) - relist = ["c","ct"] - assert regression in relist, "invalid regression value" - nlList =[None, "auto", "legacy"] - assert nlags in nlList or type(nlags) == int, "invalid nlags value" - - - data = self.getNumericData(ds) - stat, pvalue, nLags, criticalValues = stt.kpss(data, regression=regression, lags=nlags) - result = self.__printResult("stat", stat, "pvalue", pvalue, "num lags", nLags, "critial values", criticalValues) - self.__printStat(stat, pvalue, "probably stationary", "probably not stationary", sigLev) - return result - - def testNormalJarqBera(self, ds, sigLev=.05): - """ - jarque bera normalcy test - - Parameters - ds: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing ajrque bera normalcy test", ds) - data = self.getNumericData(ds) - jb, jbpv, skew, kurtosis = sstt.jarque_bera(data) - result = self.__printResult("stat", jb, "pvalue", jbpv, "skew", skew, "kurtosis", kurtosis) - self.__printStat(jb, jbpv, "probably gaussian", "probably not gaussian", sigLev) - return result - - - def testNormalShapWilk(self, ds, sigLev=.05): - """ - shapiro wilks normalcy test - - Parameters - ds: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing shapiro wilks normalcy test", ds) - data = self.getNumericData(ds) - stat, pvalue = sta.shapiro(data) - result = self.__printResult("stat", stat, "pvalue", pvalue) - self.__printStat(stat, pvalue, "probably gaussian", "probably not gaussian", sigLev) - return result - - def testNormalDagast(self, ds, sigLev=.05): - """ - D’Agostino’s K square normalcy test - - Parameters - ds: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing D’Agostino’s K square normalcy test", ds) - data = self.getNumericData(ds) - stat, pvalue = sta.normaltest(data) - result = self.__printResult("stat", stat, "pvalue", pvalue) - self.__printStat(stat, pvalue, "probably gaussian", "probably not gaussian", sigLev) - return result - - def testDistrAnderson(self, ds, dist, sigLev=.05): - """ - Anderson test for normal, expon, logistic, gumbel, gumbel_l, gumbel_r - - Parameters - ds: data set name or list or numpy array - dist: type of distribution - sigLev: statistical significance level - """ - self.__printBanner("doing Anderson test for for various distributions", ds) - diList = ["norm", "expon", "logistic", "gumbel", "gumbel_l", "gumbel_r", "extreme1"] - assert dist in diList, "invalid distribution" - - data = self.getNumericData(ds) - re = sta.anderson(data) - slAlpha = int(100 * sigLev) - msg = "significnt value not found" - for i in range(len(re.critical_values)): - sl, cv = re.significance_level[i], re.critical_values[i] - if int(sl) == slAlpha: - if re.statistic < cv: - msg = "probably {} at the {:.3f} siginificance level".format(dist, sl) - else: - msg = "probably not {} at the {:.3f} siginificance level".format(dist, sl) - result = self.__printResult("stat", re.statistic, "test", msg) - print(msg) - return result - - def testSkew(self, ds, sigLev=.05): - """ - test skew wrt normal distr - - Parameters - ds: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("testing skew wrt normal distr", ds) - data = self.getNumericData(ds) - stat, pvalue = sta.skewtest(data) - result = self.__printResult("stat", stat, "pvalue", pvalue) - self.__printStat(stat, pvalue, "probably same skew as normal distribution", "probably not same skew as normal distribution", sigLev) - return result - - def testTwoSampleStudent(self, ds1, ds2, sigLev=.05): - """ - student t 2 sample test - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing student t 2 sample test", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - stat, pvalue = sta.ttest_ind(data1, data2) - result = self.__printResult("stat", stat, "pvalue", pvalue) - self.__printStat(stat, pvalue, "probably same distribution", "probably not same distribution", sigLev) - return result - - def testTwoSampleKs(self, ds1, ds2, sigLev=.05): - """ - Kolmogorov Sminov 2 sample statistic - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing Kolmogorov Sminov 2 sample test", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - stat, pvalue = sta.ks_2samp(data1, data2) - result = self.__printResult("stat", stat, "pvalue", pvalue) - self.__printStat(stat, pvalue, "probably same distribution", "probably not same distribution", sigLev) - - - def testTwoSampleMw(self, ds1, ds2, sigLev=.05): - """ - Mann-Whitney 2 sample statistic - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing Mann-Whitney 2 sample test", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - stat, pvalue = sta.mannwhitneyu(data1, data2) - result = self.__printResult("stat", stat, "pvalue", pvalue) - self.__printStat(stat, pvalue, "probably same distribution", "probably not same distribution", sigLev) - - def testTwoSampleWilcox(self, ds1, ds2, sigLev=.05): - """ - Wilcoxon Signed-Rank 2 sample statistic - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing Wilcoxon Signed-Rank 2 sample test", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - stat, pvalue = sta.wilcoxon(data1, data2) - result = self.__printResult("stat", stat, "pvalue", pvalue) - self.__printStat(stat, pvalue, "probably same distribution", "probably not same distribution", sigLev) - - - def testTwoSampleKw(self, ds1, ds2, sigLev=.05): - """ - Kruskal-Wallis 2 sample statistic - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing Kruskal-Wallis 2 sample test", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - stat, pvalue = sta.kruskal(data1, data2) - result = self.__printResult("stat", stat, "pvalue", pvalue) - self.__printStat(stat, pvalue, "probably same distribution", "probably snot ame distribution", sigLev) - - def testTwoSampleFriedman(self, ds1, ds2, ds3, sigLev=.05): - """ - Friedman 2 sample statistic - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing Friedman 2 sample test", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - data3 = self.getNumericData(ds3) - stat, pvalue = sta.friedmanchisquare(data1, data2, data3) - result = self.__printResult("stat", stat, "pvalue", pvalue) - self.__printStat(stat, pvalue, "probably same distribution", "probably not same distribution", sigLev) - - def testTwoSampleEs(self, ds1, ds2, sigLev=.05): - """ - Epps Singleton 2 sample statistic - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing Epps Singleton 2 sample test", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - stat, pvalue = sta.epps_singleton_2samp(data1, data2) - result = self.__printResult("stat", stat, "pvalue", pvalue) - self.__printStat(stat, pvalue, "probably same distribution", "probably not same distribution", sigLev) - - def testTwoSampleAnderson(self, ds1, ds2, sigLev=.05): - """ - Anderson 2 sample statistic - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing Anderson 2 sample test", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - dseq = (data1, data2) - stat, critValues, sLev = sta.anderson_ksamp(dseq) - slAlpha = 100 * sigLev - - if slAlpha == 10: - cv = critValues[1] - elif slAlpha == 5: - cv = critValues[2] - elif slAlpha == 2.5: - cv = critValues[3] - elif slAlpha == 1: - cv = critValues[4] - else: - cv = None - - result = self.__printResult("stat", stat, "critValues", critValues, "critValue", cv, "significanceLevel", sLev) - print("stat: {:.3f}".format(stat)) - if cv is None: - msg = "critical values value not found for provided siginificance level" - else: - if stat < cv: - msg = "probably same distribution at the {:.3f} siginificance level".format(sigLev) - else: - msg = "probably not same distribution at the {:.3f} siginificance level".format(sigLev) - print(msg) - return result - - - def testTwoSampleScaleAb(self, ds1, ds2, sigLev=.05): - """ - Ansari Bradley 2 sample scale statistic - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing Ansari Bradley 2 sample scale test", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - stat, pvalue = sta.ansari(data1, data2) - result = self.__printResult("stat", stat, "pvalue", pvalue) - self.__printStat(stat, pvalue, "probably same scale", "probably not same scale", sigLev) - return result - - def testTwoSampleScaleMood(self, ds1, ds2, sigLev=.05): - """ - Mood 2 sample scale statistic - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing Mood 2 sample scale test", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - stat, pvalue = sta.mood(data1, data2) - result = self.__printResult("stat", stat, "pvalue", pvalue) - self.__printStat(stat, pvalue, "probably same scale", "probably not same scale", sigLev) - return result - - def testTwoSampleVarBartlet(self, ds1, ds2, sigLev=.05): - """ - Ansari Bradley 2 sample scale statistic - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing Ansari Bradley 2 sample scale test", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - stat, pvalue = sta.bartlett(data1, data2) - result = self.__printResult("stat", stat, "pvalue", pvalue) - self.__printStat(stat, pvalue, "probably same variance", "probably not same variance", sigLev) - return result - - def testTwoSampleVarLevene(self, ds1, ds2, sigLev=.05): - """ - Levene 2 sample variance statistic - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing Levene 2 sample variance test", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - stat, pvalue = sta.levene(data1, data2) - result = self.__printResult("stat", stat, "pvalue", pvalue) - self.__printStat(stat, pvalue, "probably same variance", "probably not same variance", sigLev) - return result - - def testTwoSampleVarFk(self, ds1, ds2, sigLev=.05): - """ - Fligner-Killeen 2 sample variance statistic - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing Fligner-Killeen 2 sample variance test", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - stat, pvalue = sta.fligner(data1, data2) - result = self.__printResult("stat", stat, "pvalue", pvalue) - self.__printStat(stat, pvalue, "probably same variance", "probably not same variance", sigLev) - return result - - def testTwoSampleMedMood(self, ds1, ds2, sigLev=.05): - """ - Mood 2 sample median statistic - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing Mood 2 sample median test", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - stat, pvalue, median, ctable = sta.median_test(data1, data2) - result = self.__printResult("stat", stat, "pvalue", pvalue, "median", median, "contigencyTable", ctable) - self.__printStat(stat, pvalue, "probably same median", "probably not same median", sigLev) - return result - - def testTwoSampleZc(self, ds1, ds2, sigLev=.05): - """ - Zhang-C 2 sample statistic - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing Zhang-C 2 sample test", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - l1 = len(data1) - l2 = len(data2) - l = l1 + l2 - - #find ranks - pooled = np.concatenate([data1, data2]) - ranks = findRanks(data1, pooled) - ranks.extend(findRanks(data2, pooled)) - - s1 = 0.0 - for i in range(1, l1+1): - s1 += math.log(l1 / (i - 0.5) - 1.0) * math.log(l / (ranks[i-1] - 0.5) - 1.0) - - s2 = 0.0 - for i in range(1, l2+1): - s2 += math.log(l2 / (i - 0.5) - 1.0) * math.log(l / (ranks[l1 + i - 1] - 0.5) - 1.0) - stat = (s1 + s2) / l - print(formatFloat(3, stat, "stat:")) - return stat - - def testTwoSampleZa(self, ds1, ds2, sigLev=.05): - """ - Zhang-A 2 sample statistic - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing Zhang-A 2 sample test", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - l1 = len(data1) - l2 = len(data2) - l = l1 + l2 - pooled = np.concatenate([data1, data2]) - cd1 = CumDistr(data1) - cd2 = CumDistr(data2) - sum = 0.0 - for i in range(1, l+1): - v = pooled[i-1] - f1 = cd1.getDistr(v) - f2 = cd2.getDistr(v) - - t1 = f1 * math.log(f1) - t2 = 0 if f1 == 1.0 else (1.0 - f1) * math.log(1.0 - f1) - sum += l1 * (t1 + t2) / ((i - 0.5) * (l - i + 0.5)) - t1 = f2 * math.log(f2) - t2 = 0 if f2 == 1.0 else (1.0 - f2) * math.log(1.0 - f2) - sum += l2 * (t1 + t2) / ((i - 0.5) * (l - i + 0.5)) - stat = -sum - print(formatFloat(3, stat, "stat:")) - return stat - - def testTwoSampleZk(self, ds1, ds2, sigLev=.05): - """ - Zhang-K 2 sample statistic - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing Zhang-K 2 sample test", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - l1 = len(data1) - l2 = len(data2) - l = l1 + l2 - pooled = np.concatenate([data1, data2]) - cd1 = CumDistr(data1) - cd2 = CumDistr(data2) - cd = CumDistr(pooled) - - maxStat = None - for i in range(1, l+1): - v = pooled[i-1] - f1 = cd1.getDistr(v) - f2 = cd2.getDistr(v) - f = cd.getDistr(v) - - t1 = 0 if f1 == 0 else f1 * math.log(f1 / f) - t2 = 0 if f1 == 1.0 else (1.0 - f1) * math.log((1.0 - f1) / (1.0 - f)) - stat = l1 * (t1 + t2) - t1 = 0 if f2 == 0 else f2 * math.log(f2 / f) - t2 = 0 if f2 == 1.0 else (1.0 - f2) * math.log((1.0 - f2) / (1.0 - f)) - stat += l2 * (t1 + t2) - if maxStat is None or stat > maxStat: - maxStat = stat - print(formatFloat(3, maxStat, "stat:")) - return maxStat - - - def testTwoSampleCvm(self, ds1, ds2, sigLev=.05): - """ - 2 sample cramer von mises - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - sigLev: statistical significance level - """ - self.__printBanner("doing 2 sample CVM test", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - data = np.concatenate((data1,data2)) - rdata = sta.rankdata(data) - n = len(data1) - m = len(data2) - l = n + m - - s1 = 0 - for i in range(n): - t = rdata[i] - (i+1) - s1 += (t * t) - s1 *= n - - s2 = 0 - for i in range(m): - t = rdata[i + n] - (i+1) - s2 += (t * t) - s2 *= m - - u = s1 + s2 - stat = u / (n * m * l) - (4 * m * n - 1) / (6 * l) - result = self.__printResult("stat", stat) - return result - - def ensureSameSize(self, dlist): - """ - ensures all data sets are of same size - - Parameters - dlist : data source list - """ - le = None - for d in dlist: - cle = len(d) - if le is None: - le = cle - else: - assert cle == le, "all data sets need to be of same size" - - - def testTwoSampleWasserstein(self, ds1, ds2): - """ - Wasserstein 2 sample statistic - - Parameters - ds1: data set name or list or numpy array - ds2: data set name or list or numpy array - """ - self.__printBanner("doing Wasserstein distance2 sample test", ds1, ds2) - data1 = self.getNumericData(ds1) - data2 = self.getNumericData(ds2) - stat = sta.wasserstein_distance(data1, data2) - sd = np.std(np.concatenate([data1, data2])) - nstat = stat / sd - result = self.__printResult("stat", stat, "normalizedStat", nstat) - return result - - def getMaxRelMinRedFeatures(self, fdst, tdst, nfeatures, nbins=20): - """ - get top n features based on max relevance and min redudancy algorithm - - Parameters - fdst: list of pair of data set name or list or numpy array and data type - tdst: target data set name or list or numpy array and data type (cat for classification num for regression) - nfeatures : desired no of features - nbins : no of bins for numerical data - """ - self.__printBanner("doing max relevance min redundancy feature selection") - return self.getMutInfoFeatures(fdst, tdst, nfeatures, "mrmr", nbins) - - def getJointMutInfoFeatures(self, fdst, tdst, nfeatures, nbins=20): - """ - get top n features based on joint mutual infoormation algorithm - - Parameters - fdst: list of pair of data set name or list or numpy array and data type - tdst: target data set name or list or numpy array and data type (cat for classification num for regression) - nfeatures : desired no of features - nbins : no of bins for numerical data - """ - self.__printBanner("doingjoint mutual info feature selection") - return self.getMutInfoFeatures(fdst, tdst, nfeatures, "jmi", nbins) - - def getCondMutInfoMaxFeatures(self, fdst, tdst, nfeatures, nbins=20): - """ - get top n features based on condition mutual information maximization algorithm - - Parameters - fdst: list of pair of data set name or list or numpy array and data type - tdst: target data set name or list or numpy array and data type (cat for classification num for regression) - nfeatures : desired no of features - nbins : no of bins for numerical data - """ - self.__printBanner("doing conditional mutual info max feature selection") - return self.getMutInfoFeatures(fdst, tdst, nfeatures, "cmim", nbins) - - def getInteractCapFeatures(self, fdst, tdst, nfeatures, nbins=20): - """ - get top n features based on interaction capping algorithm - - Parameters - fdst: list of pair of data set name or list or numpy array and data type - tdst: target data set name or list or numpy array and data type (cat for classification num for regression) - nfeatures : desired no of features - nbins : no of bins for numerical data - """ - self.__printBanner("doing interaction capped feature selection") - return self.getMutInfoFeatures(fdst, tdst, nfeatures, "icap", nbins) - - def getMutInfoFeatures(self, fdst, tdst, nfeatures, algo, nbins=20): - """ - get top n features based on various mutual information based algorithm - ref: Conditional likelihood maximisation : A unifying framework for information - theoretic feature selection, Gavin Brown - - Parameters - fdst: list of pair of data set name or list or numpy array and data type - tdst: target data set name or list or numpy array and data type (cat for classification num for regression) - nfeatures : desired no of features - algo: mi based feature selection algorithm - nbins : no of bins for numerical data - """ - #verify data source types types - le = len(fdst) - nfeatGiven = int(le / 2) - assertGreater(nfeatGiven, nfeatures, "no of features should be greater than no of features to be selected") - fds = list() - types = ["num", "cat"] - for i in range (0, le, 2): - ds = fdst[i] - dt = fdst[i+1] - assertInList(dt, types, "invalid type for data source " + dt) - data = self.getNumericData(ds) if dt == "num" else self.getCatData(ds) - p =(ds, dt) - fds.append(p) - algos = ["mrmr", "jmi", "cmim", "icap"] - assertInList(algo, algos, "invalid feature selection algo " + algo) - - assertInList(tdst[1], types, "invalid type for data source " + tdst[1]) - data = self.getNumericData(tdst[0]) if tdst[1] == "num" else self.getCatData(tdst[0]) - #print(fds) - - sfds = list() - selected = set() - relevancies = dict() - for i in range(nfeatures): - #print(i) - scorem = None - dsm = None - dsmt = None - for ds, dt in fds: - #print(ds, dt) - if ds not in selected: - #relevancy - if ds in relevancies: - mutInfo = relevancies[ds] - else: - mutInfo = self.getMutualInfo([ds, dt, tdst[0], tdst[1]], nbins)["mutInfo"] - relevancies[ds] = mutInfo - relev = mutInfo - #print("relev", relev) - - #redundancy - smi = 0 - reds = list() - for sds, sdt, _ in sfds: - #print(sds, sdt) - mutInfo = self.getMutualInfo([ds, dt, sds, sdt], nbins)["mutInfo"] - mutInfoCnd = self.getCondMutualInfo([ds, dt, sds, sdt, tdst[0], tdst[1]], nbins)["condMutInfo"] \ - if algo != "mrmr" else 0 - - red = mutInfo - mutInfoCnd - reds.append(red) - - if algo == "mrmr" or algo == "jmi": - redun = sum(reds) / len(sfds) if len(sfds) > 0 else 0 - elif algo == "cmim" or algo == "icap": - redun = max(reds) if len(sfds) > 0 else 0 - if algo == "icap": - redun = max(0, redun) - #print("redun", redun) - score = relev - redun - if scorem is None or score > scorem: - scorem = score - dsm = ds - dsmt = dt - - pa = (dsm, dsmt, scorem) - #print(pa) - sfds.append(pa) - selected.add(dsm) - - selFeatures = list(map(lambda r : (r[0], r[2]), sfds)) - result = self.__printResult("selFeatures", selFeatures) - return result - - - def getFastCorrFeatures(self, fdst, tdst, delta, nbins=20): - """ - get top features based on Fast Correlation Based Filter (FCBF) - ref: Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution - Lei Yu - - Parameters - fdst: list of pair of data set name or list or numpy array and data type - tdst: target data set name or list or numpy array and data type (cat for classification num for regression) - delta : feature, target correlation threshold - nbins : no of bins for numerical data - """ - le = len(fdst) - nfeatGiven = int(le / 2) - fds = list() - types = ["num", "cat"] - for i in range (0, le, 2): - ds = fdst[i] - dt = fdst[i+1] - assertInList(dt, types, "invalid type for data source " + dt) - data = self.getNumericData(ds) if dt == "num" else self.getCatData(ds) - p =(ds, dt) - fds.append(p) - - assertInList(tdst[1], types, "invalid type for data source " + tdst[1]) - data = self.getNumericData(tdst[0]) if tdst[1] == "num" else self.getCatData(tdst[0]) - - # get features with symetric uncertainty above threshold - tentr = self.getAnyEntropy(tdst[0], tdst[1], nbins)["entropy"] - rfeatures = list() - fentrs = dict() - for ds, dt in fds: - mutInfo = self.getMutualInfo([ds, dt, tdst[0], tdst[1]], nbins)["mutInfo"] - fentr = self.getAnyEntropy(ds, dt, nbins)["entropy"] - sunc = 2 * mutInfo / (tentr + fentr) - #print("ds {} sunc {:.3f}".format(ds, sunc)) - if sunc >= delta: - f = [ds, dt, sunc, False] - rfeatures.append(f) - fentrs[ds] = fentr - - # sort descending of sym uncertainty - rfeatures.sort(key=lambda e : e[2], reverse=True) - - #disccard redundant features - le = len(rfeatures) - for i in range(le): - if rfeatures[i][3]: - continue - for j in range(i+1, le, 1): - if rfeatures[j][3]: - continue - mutInfo = self.getMutualInfo([rfeatures[i][0], rfeatures[i][1], rfeatures[j][0], rfeatures[j][1]], nbins)["mutInfo"] - sunc = 2 * mutInfo / (fentrs[rfeatures[i][0]] + fentrs[rfeatures[j][0]]) - if sunc >= rfeatures[j][2]: - rfeatures[j][3] = True - - frfeatures = list(filter(lambda f : not f[3], rfeatures)) - selFeatures = list(map(lambda f : [f[0], f[2]], frfeatures)) - result = self.__printResult("selFeatures", selFeatures) - return result - - def getInfoGainFeatures(self, fdst, tdst, nfeatures, nsplit, nbins=20): - """ - get top n features based on information gain or entropy loss - - Parameters - fdst: list of pair of data set name or list or numpy array and data type - tdst: target data set name or list or numpy array and data type (cat for classification num for regression) - nsplit : num of splits - nfeatures : desired no of features - nbins : no of bins for numerical data - """ - le = len(fdst) - nfeatGiven = int(le / 2) - assertGreater(nfeatGiven, nfeatures, "available features should be greater than desired") - fds = list() - types = ["num", "cat"] - for i in range (0, le, 2): - ds = fdst[i] - dt = fdst[i+1] - assertInList(dt, types, "invalid type for data source " + dt) - data = self.getNumericData(ds) if dt == "num" else self.getCatData(ds) - p =(ds, dt) - fds.append(p) - - assertInList(tdst[1], types, "invalid type for data source " + tdst[1]) - assertGreater(nsplit, 3, "minimum 4 splits necessary") - tdata = self.getNumericData(tdst[0]) if tdst[1] == "num" else self.getCatData(tdst[0]) - tentr = self.getAnyEntropy(tdst[0], tdst[1], nbins)["entropy"] - sz =len(tdata) - - sfds = list() - for ds, dt in fds: - #print(ds, dt) - if dt == "num": - fd = self.getNumericData(ds) - _ , _ , vmax, vmin = self.__getBasicStats(fd) - intv = (vmax - vmin) / nsplit - maxig = None - spmin = vmin + intv - spmax = vmax - 0.9 * intv - - #iterate all splits - for sp in np.arange(spmin, spmax, intv): - ltvals = list() - gevals = list() - for i in range(len(fd)): - if fd[i] < sp: - ltvals.append(tdata[i]) - else: - gevals.append(tdata[i]) - - self.addListNumericData(ltvals, "spds") if tdst[1] == "num" else self.addListCatData(ltvals, "spds") - lten = self.getAnyEntropy("spds", tdst[1], nbins)["entropy"] - self.addListNumericData(gevals, "spds") if tdst[1] == "num" else self.addListCatData(gevals, "spds") - geen = self.getAnyEntropy("spds", tdst[1], nbins)["entropy"] - - #info gain - ig = tentr - (len(ltvals) * lten / sz + len(gevals) * geen / sz) - if maxig is None or ig > maxig: - maxig = ig - - pa = (ds, maxig) - sfds.append(pa) - else: - fd = self.getCatData(ds) - fds = set(fd) - fdps = genPowerSet(fds) - maxig = None - - #iterate all subsets - for s in fdps: - if len(s) == len(fds): - continue - invals = list() - exvals = list() - for i in range(len(fd)): - if fd[i] in s: - invals.append(tdata[i]) - else: - exvals.append(tdata[i]) - - self.addListNumericData(invals, "spds") if tdst[1] == "num" else self.addListCatData(invals, "spds") - inen = self.getAnyEntropy("spds", tdst[1], nbins)["entropy"] - self.addListNumericData(exvals, "spds") if tdst[1] == "num" else self.addListCatData(exvals, "spds") - exen = self.getAnyEntropy("spds", tdst[1], nbins)["entropy"] - - ig = tentr - (len(invals) * inen / sz + len(exvals) * exen / sz) - if maxig is None or ig > maxig: - maxig = ig - - pa = (ds, maxig) - sfds.append(pa) - - #sort of info gain - sfds.sort(key = lambda v : v[1], reverse = True) - - result = self.__printResult("selFeatures", sfds[:nfeatures]) - return result - - def __stackData(self, *dsl): - """ - stacks collumd to create matrix - - Parameters - dsl: data source list - """ - dlist = tuple(map(lambda ds : self.getNumericData(ds), dsl)) - self.ensureSameSize(dlist) - dmat = np.column_stack(dlist) - return dmat - - def __printBanner(self, msg, *dsl): - """ - print banner for any function - - Parameters - msg: message - dsl: list of data set name or list or numpy array - """ - tags = list(map(lambda ds : ds if type(ds) == str else "annoynymous", dsl)) - forData = " for data sets " if tags else "" - msg = msg + forData + " ".join(tags) - if self.verbose: - print("\n== " + msg + " ==") - - - def __printDone(self): - """ - print banner for any function - """ - if self.verbose: - print("done") - - def __printStat(self, stat, pvalue, nhMsg, ahMsg, sigLev=.05): - """ - generic stat and pvalue output - - Parameters - stat : stat value - pvalue : p value - nhMsg : null hypothesis violation message - ahMsg : null hypothesis message - sigLev : significance level - """ - if self.verbose: - print("\ntest result:") - print("stat: {:.3f}".format(stat)) - print("pvalue: {:.3f}".format(pvalue)) - print("significance level: {:.3f}".format(sigLev)) - print(nhMsg if pvalue > sigLev else ahMsg) - - def __printResult(self, *values): - """ - print results - - Parameters - values : flattened kay and value pairs - """ - result = dict() - assert len(values) % 2 == 0, "key value list should have even number of items" - for i in range(0, len(values), 2): - result[values[i]] = values[i+1] - if self.verbose: - print("result details:") - self.pp.pprint(result) - return result - - def __getBasicStats(self, data): - """ - get mean and std dev - - Parameters - data : numpy array - """ - mean = np.average(data) - sd = np.std(data) - r = (mean, sd, np.max(data), np.min(data)) - return r - - diff --git a/spaces/Truepic/ai-content-credentials/static/json_viewer.js b/spaces/Truepic/ai-content-credentials/static/json_viewer.js deleted file mode 100644 index eb802d9e0dce94940d5ae3b1cdcef678bc9930f1..0000000000000000000000000000000000000000 --- a/spaces/Truepic/ai-content-credentials/static/json_viewer.js +++ /dev/null @@ -1,269 +0,0 @@ -/** - * JSONViewer - by Roman Makudera 2016 (c) MIT licence. - */ -var JSONViewer = (function(document) { - var Object_prototype_toString = ({}).toString; - var DatePrototypeAsString = Object_prototype_toString.call(new Date); - - /** @constructor */ - function JSONViewer() { - this._dom_container = document.createElement("pre"); - this._dom_container.classList.add("json-viewer"); - }; - - /** - * Visualise JSON object. - * - * @param {Object|Array} json Input value - * @param {Number} [inputMaxLvl] Process only to max level, where 0..n, -1 unlimited - * @param {Number} [inputColAt] Collapse at level, where 0..n, -1 unlimited - */ - JSONViewer.prototype.showJSON = function(jsonValue, inputMaxLvl, inputColAt) { - // Process only to maxLvl, where 0..n, -1 unlimited - var maxLvl = typeof inputMaxLvl === "number" ? inputMaxLvl : -1; // max level - // Collapse at level colAt, where 0..n, -1 unlimited - var colAt = typeof inputColAt === "number" ? inputColAt : -1; // collapse at - - this._dom_container.innerHTML = ""; - walkJSONTree(this._dom_container, jsonValue, maxLvl, colAt, 0); - }; - - /** - * Get container with pre object - this container is used for visualise JSON data. - * - * @return {Element} - */ - JSONViewer.prototype.getContainer = function() { - return this._dom_container; - }; - - /** - * Recursive walk for input value. - * - * @param {Element} outputParent is the Element that will contain the new DOM - * @param {Object|Array} value Input value - * @param {Number} maxLvl Process only to max level, where 0..n, -1 unlimited - * @param {Number} colAt Collapse at level, where 0..n, -1 unlimited - * @param {Number} lvl Current level - */ - function walkJSONTree(outputParent, value, maxLvl, colAt, lvl) { - var isDate = Object_prototype_toString.call(value) === DatePrototypeAsString; - var realValue = !isDate && typeof value === "object" && value !== null && "toJSON" in value ? value.toJSON() : value; - if (typeof realValue === "object" && realValue !== null && !isDate) { - var isMaxLvl = maxLvl >= 0 && lvl >= maxLvl; - var isCollapse = colAt >= 0 && lvl >= colAt; - - var isArray = Array.isArray(realValue); - var items = isArray ? realValue : Object.keys(realValue); - - if (lvl === 0) { - // root level - var rootCount = _createItemsCount(items.length); - // hide/show - var rootLink = _createLink(isArray ? "[" : "{"); - - if (items.length) { - rootLink.addEventListener("click", function() { - if (isMaxLvl) return; - - rootLink.classList.toggle("collapsed"); - rootCount.classList.toggle("hide"); - - // main list - outputParent.querySelector("ul").classList.toggle("hide"); - }); - - if (isCollapse) { - rootLink.classList.add("collapsed"); - rootCount.classList.remove("hide"); - } - } - else { - rootLink.classList.add("empty"); - } - - rootLink.appendChild(rootCount); - outputParent.appendChild(rootLink); // output the rootLink - } - - if (items.length && !isMaxLvl) { - var len = items.length - 1; - var ulList = document.createElement("ul"); - ulList.setAttribute("data-level", lvl); - ulList.classList.add("type-" + (isArray ? "array" : "object")); - - items.forEach(function(key, ind) { - var item = isArray ? key : value[key]; - var li = document.createElement("li"); - - if (typeof item === "object") { - // null && date - if (!item || item instanceof Date) { - li.appendChild(document.createTextNode(isArray ? "" : key + ": ")); - li.appendChild(createSimpleViewOf(item ? item : null, true)); - } - // array & object - else { - var itemIsArray = Array.isArray(item); - var itemLen = itemIsArray ? item.length : Object.keys(item).length; - - // empty - if (!itemLen) { - li.appendChild(document.createTextNode(key + ": " + (itemIsArray ? "[]" : "{}"))); - } - else { - // 1+ items - var itemTitle = (typeof key === "string" ? key + ": " : "") + (itemIsArray ? "[" : "{"); - var itemLink = _createLink(itemTitle); - var itemsCount = _createItemsCount(itemLen); - - // maxLvl - only text, no link - if (maxLvl >= 0 && lvl + 1 >= maxLvl) { - li.appendChild(document.createTextNode(itemTitle)); - } - else { - itemLink.appendChild(itemsCount); - li.appendChild(itemLink); - } - - walkJSONTree(li, item, maxLvl, colAt, lvl + 1); - li.appendChild(document.createTextNode(itemIsArray ? "]" : "}")); - - var list = li.querySelector("ul"); - var itemLinkCb = function() { - itemLink.classList.toggle("collapsed"); - itemsCount.classList.toggle("hide"); - list.classList.toggle("hide"); - }; - - // hide/show - itemLink.addEventListener("click", itemLinkCb); - - // collapse lower level - if (colAt >= 0 && lvl + 1 >= colAt) { - itemLinkCb(); - } - } - } - } - // simple values - else { - // object keys with key: - if (!isArray) { - li.appendChild(document.createTextNode(key + ": ")); - } - - // recursive - walkJSONTree(li, item, maxLvl, colAt, lvl + 1); - } - - // add comma to the end - if (ind < len) { - li.appendChild(document.createTextNode(",")); - } - - ulList.appendChild(li); - }, this); - - outputParent.appendChild(ulList); // output ulList - } - else if (items.length && isMaxLvl) { - var itemsCount = _createItemsCount(items.length); - itemsCount.classList.remove("hide"); - - outputParent.appendChild(itemsCount); // output itemsCount - } - - if (lvl === 0) { - // empty root - if (!items.length) { - var itemsCount = _createItemsCount(0); - itemsCount.classList.remove("hide"); - - outputParent.appendChild(itemsCount); // output itemsCount - } - - // root cover - outputParent.appendChild(document.createTextNode(isArray ? "]" : "}")); - - // collapse - if (isCollapse) { - outputParent.querySelector("ul").classList.add("hide"); - } - } - } else { - // simple values - outputParent.appendChild( createSimpleViewOf(value, isDate) ); - } - }; - - /** - * Create simple value (no object|array). - * - * @param {Number|String|null|undefined|Date} value Input value - * @return {Element} - */ - function createSimpleViewOf(value, isDate) { - var spanEl = document.createElement("span"); - var type = typeof value; - var asText = "" + value; - - if (type === "string") { - asText = '"' + value + '"'; - } else if (value === null) { - type = "null"; - //asText = "null"; - } else if (isDate) { - type = "date"; - asText = value.toLocaleString(); - } - - spanEl.className = "type-" + type; - spanEl.textContent = asText; - - return spanEl; - }; - - /** - * Create items count element. - * - * @param {Number} count Items count - * @return {Element} - */ - function _createItemsCount(count) { - var itemsCount = document.createElement("span"); - itemsCount.className = "items-ph hide"; - itemsCount.innerHTML = _getItemsTitle(count); - - return itemsCount; - }; - - /** - * Create clickable link. - * - * @param {String} title Link title - * @return {Element} - */ - function _createLink(title) { - var linkEl = document.createElement("a"); - linkEl.classList.add("list-link"); - linkEl.href = "javascript:void(0)"; - linkEl.innerHTML = title || ""; - - return linkEl; - }; - - /** - * Get correct item|s title for count. - * - * @param {Number} count Items count - * @return {String} - */ - function _getItemsTitle(count) { - var itemsTxt = count > 1 || count === 0 ? "items" : "item"; - - return (count + " " + itemsTxt); - }; - - return JSONViewer; -})(document); \ No newline at end of file diff --git a/spaces/Tshackelton/IBMPlex-DenseReadable/app.py b/spaces/Tshackelton/IBMPlex-DenseReadable/app.py deleted file mode 100644 index 53f500d9ddee5922365e0776229d4dbd9819e5ab..0000000000000000000000000000000000000000 --- a/spaces/Tshackelton/IBMPlex-DenseReadable/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='Tshackelton/IBMPlex-DenseReadable') as demo: - with gr.Row().style(equal_height=True): - with gr.Column(scale=10): - gr.Markdown( - """ - # Theme preview: `IBMPlex-DenseReadable` - To use this theme, set `theme='Tshackelton/IBMPlex-DenseReadable'` 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/Um124/Lung_Cancer_Prediction/README.md b/spaces/Um124/Lung_Cancer_Prediction/README.md deleted file mode 100644 index 2ac4fdd21112c1d86f85b038fbb653bbd26fd42d..0000000000000000000000000000000000000000 --- a/spaces/Um124/Lung_Cancer_Prediction/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Lung Cancer Prediction -emoji: 👀 -colorFrom: red -colorTo: blue -sdk: gradio -sdk_version: 3.28.3 -app_file: Lung_Cancer_Prediction.py -pinned: false -license: cc-by-nc-4.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/VideoCrafter/VideoCrafter/README.md b/spaces/VideoCrafter/VideoCrafter/README.md deleted file mode 100644 index 2b2d65920391e4417da7a1347d122808a67145d7..0000000000000000000000000000000000000000 --- a/spaces/VideoCrafter/VideoCrafter/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: VideoCrafter1 -emoji: 🌍 -colorFrom: gray -colorTo: red -sdk: gradio -sdk_version: 3.27.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Xule/ChuanhuChatGPT/modules/utils.py b/spaces/Xule/ChuanhuChatGPT/modules/utils.py deleted file mode 100644 index e1516e1fad4761787070d24e867bea57d86ac9ed..0000000000000000000000000000000000000000 --- a/spaces/Xule/ChuanhuChatGPT/modules/utils.py +++ /dev/null @@ -1,548 +0,0 @@ -# -*- coding:utf-8 -*- -from __future__ import annotations -from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, Type -import logging -import json -import os -import datetime -import hashlib -import csv -import requests -import re -import html -import sys -import subprocess - -import gradio as gr -from pypinyin import lazy_pinyin -import tiktoken -import mdtex2html -from markdown import markdown -from pygments import highlight -from pygments.lexers import get_lexer_by_name -from pygments.formatters import HtmlFormatter -import pandas as pd - -from modules.presets import * -from . import shared -from modules.config import retrieve_proxy - -if TYPE_CHECKING: - from typing import TypedDict - - class DataframeData(TypedDict): - headers: List[str] - data: List[List[str | int | bool]] - -def predict(current_model, *args): - iter = current_model.predict(*args) - for i in iter: - yield i - -def billing_info(current_model): - return current_model.billing_info() - -def set_key(current_model, *args): - return current_model.set_key(*args) - -def load_chat_history(current_model, *args): - return current_model.load_chat_history(*args) - -def interrupt(current_model, *args): - return current_model.interrupt(*args) - -def reset(current_model, *args): - return current_model.reset(*args) - -def retry(current_model, *args): - iter = current_model.retry(*args) - for i in iter: - yield i - -def delete_first_conversation(current_model, *args): - return current_model.delete_first_conversation(*args) - -def delete_last_conversation(current_model, *args): - return current_model.delete_last_conversation(*args) - -def set_system_prompt(current_model, *args): - return current_model.set_system_prompt(*args) - -def save_chat_history(current_model, *args): - return current_model.save_chat_history(*args) - -def export_markdown(current_model, *args): - return current_model.export_markdown(*args) - -def load_chat_history(current_model, *args): - return current_model.load_chat_history(*args) - -def set_token_upper_limit(current_model, *args): - return current_model.set_token_upper_limit(*args) - -def set_temperature(current_model, *args): - current_model.set_temperature(*args) - -def set_top_p(current_model, *args): - current_model.set_top_p(*args) - -def set_n_choices(current_model, *args): - current_model.set_n_choices(*args) - -def set_stop_sequence(current_model, *args): - current_model.set_stop_sequence(*args) - -def set_max_tokens(current_model, *args): - current_model.set_max_tokens(*args) - -def set_presence_penalty(current_model, *args): - current_model.set_presence_penalty(*args) - -def set_frequency_penalty(current_model, *args): - current_model.set_frequency_penalty(*args) - -def set_logit_bias(current_model, *args): - current_model.set_logit_bias(*args) - -def set_user_identifier(current_model, *args): - current_model.set_user_identifier(*args) - -def set_single_turn(current_model, *args): - current_model.set_single_turn(*args) - -def handle_file_upload(current_model, *args): - return current_model.handle_file_upload(*args) - -def like(current_model, *args): - return current_model.like(*args) - -def dislike(current_model, *args): - return current_model.dislike(*args) - - -def count_token(message): - encoding = tiktoken.get_encoding("cl100k_base") - input_str = f"role: {message['role']}, content: {message['content']}" - length = len(encoding.encode(input_str)) - return length - - -def markdown_to_html_with_syntax_highlight(md_str): - def replacer(match): - lang = match.group(1) or "text" - code = match.group(2) - - try: - lexer = get_lexer_by_name(lang, stripall=True) - except ValueError: - lexer = get_lexer_by_name("text", stripall=True) - - formatter = HtmlFormatter() - highlighted_code = highlight(code, lexer, formatter) - - return f'
' - - code_block_pattern = r"```(\w+)?\n([\s\S]+?)\n```" - md_str = re.sub(code_block_pattern, replacer, md_str, flags=re.MULTILINE) - - html_str = markdown(md_str) - return html_str - - -def normalize_markdown(md_text: str) -> str: - lines = md_text.split("\n") - normalized_lines = [] - inside_list = False - - for i, line in enumerate(lines): - if re.match(r"^(\d+\.|-|\*|\+)\s", line.strip()): - if not inside_list and i > 0 and lines[i - 1].strip() != "": - normalized_lines.append("") - inside_list = True - normalized_lines.append(line) - elif inside_list and line.strip() == "": - if i < len(lines) - 1 and not re.match( - r"^(\d+\.|-|\*|\+)\s", lines[i + 1].strip() - ): - normalized_lines.append(line) - continue - else: - inside_list = False - normalized_lines.append(line) - - return "\n".join(normalized_lines) - - -def convert_mdtext(md_text): - code_block_pattern = re.compile(r"```(.*?)(?:```|$)", re.DOTALL) - inline_code_pattern = re.compile(r"`(.*?)`", re.DOTALL) - code_blocks = code_block_pattern.findall(md_text) - non_code_parts = code_block_pattern.split(md_text)[::2] - - result = [] - for non_code, code in zip(non_code_parts, code_blocks + [""]): - if non_code.strip(): - non_code = normalize_markdown(non_code) - if inline_code_pattern.search(non_code): - result.append(markdown(non_code, extensions=["tables"])) - else: - result.append(mdtex2html.convert(non_code, extensions=["tables"])) - if code.strip(): - # _, code = detect_language(code) # 暂时去除代码高亮功能，因为在大段代码的情况下会出现问题 - # code = code.replace("\n\n", "\n") # 暂时去除代码中的空行，因为在大段代码的情况下会出现问题 - code = f"\n```{code}\n\n```" - code = markdown_to_html_with_syntax_highlight(code) - result.append(code) - result = "".join(result) - result += ALREADY_CONVERTED_MARK - return result - - -def convert_asis(userinput): - return ( - f'{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" " - ) - return nodes - - -def sheet_to_string(sheet, sheet_name = None): - result = [] - for index, row in sheet.iterrows(): - row_string = "" - for column in sheet.columns: - row_string += f"{column}: {row[column]}, " - row_string = row_string.rstrip(", ") - row_string += "." - result.append(row_string) - return result - -def excel_to_string(file_path): - # 读取Excel文件中的所有工作表 - excel_file = pd.read_excel(file_path, engine='openpyxl', sheet_name=None) - - # 初始化结果字符串 - result = [] - - # 遍历每一个工作表 - for sheet_name, sheet_data in excel_file.items(): - - # 处理当前工作表并添加到结果字符串 - result += sheet_to_string(sheet_data, sheet_name=sheet_name) - - - return result - -def get_last_day_of_month(any_day): - # The day 28 exists in every month. 4 days later, it's always next month - next_month = any_day.replace(day=28) + datetime.timedelta(days=4) - # subtracting the number of the current day brings us back one month - return next_month - datetime.timedelta(days=next_month.day) - -def get_model_source(model_name, alternative_source): - if model_name == "gpt2-medium": - return "https://huggingface.co/gpt2-medium" - -def refresh_ui_elements_on_load(current_model, selected_model_name): - return toggle_like_btn_visibility(selected_model_name) - -def toggle_like_btn_visibility(selected_model_name): - if selected_model_name == "xmchat": - return gr.update(visible=True) - else: - return gr.update(visible=False) diff --git a/spaces/XzJosh/Ava2-Bert-VITS2/text/chinese_bert.py b/spaces/XzJosh/Ava2-Bert-VITS2/text/chinese_bert.py deleted file mode 100644 index cb84ce0b426cd0a1c7954ddcdf41322c10ed14fa..0000000000000000000000000000000000000000 --- a/spaces/XzJosh/Ava2-Bert-VITS2/text/chinese_bert.py +++ /dev/null @@ -1,50 +0,0 @@ -import torch -from transformers import AutoTokenizer, AutoModelForMaskedLM - -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - -tokenizer = AutoTokenizer.from_pretrained("./bert/chinese-roberta-wwm-ext-large") -model = AutoModelForMaskedLM.from_pretrained("./bert/chinese-roberta-wwm-ext-large").to(device) - -def get_bert_feature(text, word2ph): - with torch.no_grad(): - inputs = tokenizer(text, return_tensors='pt') - for i in inputs: - inputs[i] = inputs[i].to(device) - res = model(**inputs, output_hidden_states=True) - res = torch.cat(res['hidden_states'][-3:-2], -1)[0].cpu() - - assert len(word2ph) == len(text)+2 - word2phone = word2ph - phone_level_feature = [] - for i in range(len(word2phone)): - repeat_feature = res[i].repeat(word2phone[i], 1) - phone_level_feature.append(repeat_feature) - - phone_level_feature = torch.cat(phone_level_feature, dim=0) - - - return phone_level_feature.T - -if __name__ == '__main__': - # feature = get_bert_feature('你好,我是说的道理。') - import torch - - word_level_feature = torch.rand(38, 1024) # 12个词,每个词1024维特征 - word2phone = [1, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 1] - - # 计算总帧数 - total_frames = sum(word2phone) - print(word_level_feature.shape) - print(word2phone) - phone_level_feature = [] - for i in range(len(word2phone)): - print(word_level_feature[i].shape) - - # 对每个词重复word2phone[i]次 - repeat_feature = word_level_feature[i].repeat(word2phone[i], 1) - phone_level_feature.append(repeat_feature) - - phone_level_feature = torch.cat(phone_level_feature, dim=0) - print(phone_level_feature.shape) # torch.Size([36, 1024]) - diff --git a/spaces/XzJosh/Carol-Bert-VITS2/utils.py b/spaces/XzJosh/Carol-Bert-VITS2/utils.py deleted file mode 100644 index c6aa6cfc64c33e2eed33e9845239e831fc1c4a1a..0000000000000000000000000000000000000000 --- a/spaces/XzJosh/Carol-Bert-VITS2/utils.py +++ /dev/null @@ -1,293 +0,0 @@ -import os -import glob -import sys -import argparse -import logging -import json -import subprocess -import numpy as np -from scipy.io.wavfile import read -import torch - -MATPLOTLIB_FLAG = False - -logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) -logger = logging - - -def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False): - assert os.path.isfile(checkpoint_path) - checkpoint_dict = torch.load(checkpoint_path, map_location='cpu') - iteration = checkpoint_dict['iteration'] - learning_rate = checkpoint_dict['learning_rate'] - if optimizer is not None and not skip_optimizer and checkpoint_dict['optimizer'] is not None: - optimizer.load_state_dict(checkpoint_dict['optimizer']) - elif optimizer is None and not skip_optimizer: - #else: #Disable this line if Infer ,and enable the line upper - new_opt_dict = optimizer.state_dict() - new_opt_dict_params = new_opt_dict['param_groups'][0]['params'] - new_opt_dict['param_groups'] = checkpoint_dict['optimizer']['param_groups'] - new_opt_dict['param_groups'][0]['params'] = new_opt_dict_params - optimizer.load_state_dict(new_opt_dict) - saved_state_dict = checkpoint_dict['model'] - if hasattr(model, 'module'): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - new_state_dict = {} - for k, v in state_dict.items(): - try: - #assert "emb_g" not in k - # print("load", k) - new_state_dict[k] = saved_state_dict[k] - assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape) - except: - print("error, %s is not in the checkpoint" % k) - new_state_dict[k] = v - if hasattr(model, 'module'): - model.module.load_state_dict(new_state_dict, strict=False) - else: - model.load_state_dict(new_state_dict, strict=False) - print("load ") - logger.info("Loaded checkpoint '{}' (iteration {})".format( - checkpoint_path, iteration)) - return model, optimizer, learning_rate, iteration - - -def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path): - logger.info("Saving model and optimizer state at iteration {} to {}".format( - iteration, checkpoint_path)) - if hasattr(model, 'module'): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - torch.save({'model': state_dict, - 'iteration': iteration, - 'optimizer': optimizer.state_dict(), - 'learning_rate': learning_rate}, checkpoint_path) - - -def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050): - for k, v in scalars.items(): - writer.add_scalar(k, v, global_step) - for k, v in histograms.items(): - writer.add_histogram(k, v, global_step) - for k, v in images.items(): - writer.add_image(k, v, global_step, dataformats='HWC') - for k, v in audios.items(): - writer.add_audio(k, v, global_step, audio_sampling_rate) - - -def latest_checkpoint_path(dir_path, regex="G_*.pth"): - f_list = glob.glob(os.path.join(dir_path, regex)) - f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f)))) - x = f_list[-1] - print(x) - return x - - -def plot_spectrogram_to_numpy(spectrogram): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(10, 2)) - im = ax.imshow(spectrogram, aspect="auto", origin="lower", - interpolation='none') - plt.colorbar(im, ax=ax) - plt.xlabel("Frames") - plt.ylabel("Channels") - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def plot_alignment_to_numpy(alignment, info=None): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger('matplotlib') - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(6, 4)) - im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower', - interpolation='none') - fig.colorbar(im, ax=ax) - xlabel = 'Decoder timestep' - if info is not None: - xlabel += '\n\n' + info - plt.xlabel(xlabel) - plt.ylabel('Encoder timestep') - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def load_wav_to_torch(full_path): - sampling_rate, data = read(full_path) - return torch.FloatTensor(data.astype(np.float32)), sampling_rate - - -def load_filepaths_and_text(filename, split="|"): - with open(filename, encoding='utf-8') as f: - filepaths_and_text = [line.strip().split(split) for line in f] - return filepaths_and_text - - -def get_hparams(init=True): - parser = argparse.ArgumentParser() - parser.add_argument('-c', '--config', type=str, default="./configs/base.json", - help='JSON file for configuration') - parser.add_argument('-m', '--model', type=str, default="./OUTPUT_MODEL", - help='Model name') - parser.add_argument('--cont', dest='cont', action="store_true", default=False, help="whether to continue training on the latest checkpoint") - - args = parser.parse_args() - model_dir = os.path.join("./logs", args.model) - - if not os.path.exists(model_dir): - os.makedirs(model_dir) - - config_path = args.config - config_save_path = os.path.join(model_dir, "config.json") - if init: - with open(config_path, "r") as f: - data = f.read() - with open(config_save_path, "w") as f: - f.write(data) - else: - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - hparams.model_dir = model_dir - hparams.cont = args.cont - return hparams - - -def clean_checkpoints(path_to_models='logs/44k/', n_ckpts_to_keep=2, sort_by_time=True): - """Freeing up space by deleting saved ckpts - - Arguments: - path_to_models -- Path to the model directory - n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth - sort_by_time -- True -> chronologically delete ckpts - False -> lexicographically delete ckpts - """ - import re - ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))] - name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1))) - time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f))) - sort_key = time_key if sort_by_time else name_key - x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')], - key=sort_key) - to_del = [os.path.join(path_to_models, fn) for fn in - (x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])] - del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}") - del_routine = lambda x: [os.remove(x), del_info(x)] - rs = [del_routine(fn) for fn in to_del] - -def get_hparams_from_dir(model_dir): - config_save_path = os.path.join(model_dir, "config.json") - with open(config_save_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - hparams.model_dir = model_dir - return hparams - - -def get_hparams_from_file(config_path): - with open(config_path, "r") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - return hparams - - -def check_git_hash(model_dir): - source_dir = os.path.dirname(os.path.realpath(__file__)) - if not os.path.exists(os.path.join(source_dir, ".git")): - logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format( - source_dir - )) - return - - cur_hash = subprocess.getoutput("git rev-parse HEAD") - - path = os.path.join(model_dir, "githash") - if os.path.exists(path): - saved_hash = open(path).read() - if saved_hash != cur_hash: - logger.warn("git hash values are different. {}(saved) != {}(current)".format( - saved_hash[:8], cur_hash[:8])) - else: - open(path, "w").write(cur_hash) - - -def get_logger(model_dir, filename="train.log"): - global logger - logger = logging.getLogger(os.path.basename(model_dir)) - logger.setLevel(logging.DEBUG) - - formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s") - if not os.path.exists(model_dir): - os.makedirs(model_dir) - h = logging.FileHandler(os.path.join(model_dir, filename)) - h.setLevel(logging.DEBUG) - h.setFormatter(formatter) - logger.addHandler(h) - return logger - - -class HParams(): - def __init__(self, **kwargs): - for k, v in kwargs.items(): - if type(v) == dict: - v = HParams(**v) - self[k] = v - - def keys(self): - return self.__dict__.keys() - - def items(self): - return self.__dict__.items() - - def values(self): - return self.__dict__.values() - - def __len__(self): - return len(self.__dict__) - - def __getitem__(self, key): - return getattr(self, key) - - def __setitem__(self, key, value): - return setattr(self, key, value) - - def __contains__(self, key): - return key in self.__dict__ - - def __repr__(self): - return self.__dict__.__repr__() diff --git a/spaces/XzJosh/TianDou-Bert-VITS2/data_utils.py b/spaces/XzJosh/TianDou-Bert-VITS2/data_utils.py deleted file mode 100644 index be3a29a93188c5b3386f22e5db29e5e96d78109a..0000000000000000000000000000000000000000 --- a/spaces/XzJosh/TianDou-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/YE01/saya-vits/text/cantonese.py b/spaces/YE01/saya-vits/text/cantonese.py deleted file mode 100644 index b66d12138b81b70b86f18217d24a08fce76305c0..0000000000000000000000000000000000000000 --- a/spaces/YE01/saya-vits/text/cantonese.py +++ /dev/null @@ -1,59 +0,0 @@ -import re -import cn2an -import opencc - - -converter = opencc.OpenCC('jyutjyu') - -# List of (Latin alphabet, ipa) pairs: -_latin_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('A', 'ei˥'), - ('B', 'biː˥'), - ('C', 'siː˥'), - ('D', 'tiː˥'), - ('E', 'iː˥'), - ('F', 'e˥fuː˨˩'), - ('G', 'tsiː˥'), - ('H', 'ɪk̚˥tsʰyː˨˩'), - ('I', 'ɐi˥'), - ('J', 'tsei˥'), - ('K', 'kʰei˥'), - ('L', 'e˥llou˨˩'), - ('M', 'ɛːm˥'), - ('N', 'ɛːn˥'), - ('O', 'ou˥'), - ('P', 'pʰiː˥'), - ('Q', 'kʰiːu˥'), - ('R', 'aː˥lou˨˩'), - ('S', 'ɛː˥siː˨˩'), - ('T', 'tʰiː˥'), - ('U', 'juː˥'), - ('V', 'wiː˥'), - ('W', 'tʊk̚˥piː˥juː˥'), - ('X', 'ɪk̚˥siː˨˩'), - ('Y', 'waːi˥'), - ('Z', 'iː˨sɛːt̚˥') -]] - - -def number_to_cantonese(text): - return re.sub(r'\d+(?:\.?\d+)?', lambda x: cn2an.an2cn(x.group()), text) - - -def latin_to_ipa(text): - for regex, replacement in _latin_to_ipa: - text = re.sub(regex, replacement, text) - return text - - -def cantonese_to_ipa(text): - text = number_to_cantonese(text.upper()) - text = converter.convert(text).replace('-','').replace('$',' ') - text = re.sub(r'[A-Z]', lambda x: latin_to_ipa(x.group())+' ', text) - text = re.sub(r'[、；：]', '，', text) - text = re.sub(r'\s*，\s*', ', ', text) - text = re.sub(r'\s*。\s*', '. ', text) - text = re.sub(r'\s*？\s*', '? ', text) - text = re.sub(r'\s*！\s*', '! ', text) - text = re.sub(r'\s*$', '', text) - return text diff --git a/spaces/Zaixi/ICLR_FLAG/motif_sample.py b/spaces/Zaixi/ICLR_FLAG/motif_sample.py deleted file mode 100644 index 059d78457694d461c738cb26bc23d472b25829b2..0000000000000000000000000000000000000000 --- a/spaces/Zaixi/ICLR_FLAG/motif_sample.py +++ /dev/null @@ -1,564 +0,0 @@ -import os -import shutil -import argparse -import random -import torch -import numpy as np -import math -import subprocess -import multiprocessing as mp -from functools import partial -from torch_geometric.data import Batch -from tqdm.auto import tqdm -from rdkit import Chem -from rdkit.Geometry import Point3D -from torch.utils.data import DataLoader -from rdkit.Chem.rdchem import BondType -from rdkit.Chem import ChemicalFeatures, rdMolDescriptors -from rdkit import RDConfig -from rdkit.Chem.Descriptors import MolLogP, qed -from copy import deepcopy -import tempfile -import contextlib -from torch_scatter import scatter_add, scatter_mean -from rdkit.Geometry import Point3D -from models.flag import FLAG -from utils.transforms import * -from utils.datasets import get_dataset -from utils.misc import * -from utils.data import * -from utils.mol_tree import * -from utils.chemutils import * -from utils.dihedral_utils import * -from utils.sascorer import compute_sa_score -from rdkit.Chem import AllChem - -_fscores = None - -ATOM_FAMILIES = ['Acceptor', 'Donor', 'Aromatic', 'Hydrophobe', 'LumpedHydrophobe', 'NegIonizable', 'PosIonizable', - 'ZnBinder'] -ATOM_FAMILIES_ID = {s: i for i, s in enumerate(ATOM_FAMILIES)} - -STATUS_RUNNING = 'running' -STATUS_FINISHED = 'finished' -STATUS_FAILED = 'failed' - - -def supress_stdout(func): - def wrapper(*a, **ka): - with open(os.devnull, 'w') as devnull: - with contextlib.redirect_stdout(devnull): - return func(*a, **ka) - return wrapper - - -def get_feat(mol): - fdefName = os.path.join(RDConfig.RDDataDir, 'BaseFeatures.fdef') - factory = ChemicalFeatures.BuildFeatureFactory(fdefName) - atomic_numbers = torch.LongTensor([6, 7, 8, 9, 15, 16, 17]) # C N O F P S Cl - ptable = Chem.GetPeriodicTable() - Chem.SanitizeMol(mol) - feat_mat = np.zeros([mol.GetNumAtoms(), len(ATOM_FAMILIES)], dtype=np.int_) - for feat in factory.GetFeaturesForMol(mol): - feat_mat[feat.GetAtomIds(), ATOM_FAMILIES_ID[feat.GetFamily()]] = 1 - ligand_element = torch.tensor([ptable.GetAtomicNumber(atom.GetSymbol()) for atom in mol.GetAtoms()]) - element = ligand_element.view(-1, 1) == atomic_numbers.view(1, -1) # (N_atoms, N_elements) - return torch.cat([element, torch.tensor(feat_mat)], dim=-1).float() - - -def find_reference(protein_pos, focal_id): - # Select three reference protein atoms - d = torch.norm(protein_pos - protein_pos[focal_id], dim=1) - reference_idx = torch.topk(d, k=4, largest=False)[1] - reference_pos = protein_pos[reference_idx] - return reference_pos, reference_idx - - -def SetAtomNum(mol, atoms): - for atom in mol.GetAtoms(): - if atom.GetIdx() in atoms: - atom.SetAtomMapNum(1) - else: - atom.SetAtomMapNum(0) - return mol - - -def SetMolPos(mol_list, pos_list): - new_mol_list = [] - for i in range(len(pos_list)): - mol = mol_list[i] - conf = mol.GetConformer(0) - pos = pos_list[i].cpu().double().numpy() - if mol.GetNumAtoms() == len(pos): - for node in range(mol.GetNumAtoms()): - x, y, z = pos[node] - conf.SetAtomPosition(node, Point3D(x,y,z)) - try: - AllChem.UFFOptimizeMolecule(mol) - new_mol_list.append(mol) - except: - new_mol_list.append(mol) - return new_mol_list - - -def lipinski(mol): - count = 0 - if qed(mol) <= 5: - count += 1 - if Chem.Lipinski.NumHDonors(mol) <= 5: - count += 1 - if Chem.Lipinski.NumHAcceptors(mol) <= 10: - count += 1 - if Chem.Descriptors.ExactMolWt(mol) <= 500: - count += 1 - if Chem.Lipinski.NumRotatableBonds(mol) <= 5: - count += 1 - return count - - -def refine_pos(ligand_pos, protein_pos, h_ctx_ligand, h_ctx_protein, model, batch, repeats, protein_batch, - ligand_batch): - protein_offsets = torch.cumsum(protein_batch.bincount(), dim=0) - ligand_offsets = torch.cumsum(ligand_batch.bincount(), dim=0) - protein_offsets, ligand_offsets = torch.cat([torch.tensor([0]), protein_offsets]), torch.cat([torch.tensor([0]), ligand_offsets]) - - sr_ligand_idx, sr_protein_idx = [], [] - sr_ligand_idx0, sr_ligand_idx1 = [], [] - for i in range(len(repeats)): - alpha_index = batch['alpha_carbon_indicator'][protein_batch == i].nonzero().reshape(-1) - ligand_atom_index = torch.arange(repeats[i]) - - p_idx, q_idx = torch.cartesian_prod(ligand_atom_index, torch.arange(len(alpha_index))).chunk(2, dim=-1) - p_idx, q_idx = p_idx.squeeze(-1), q_idx.squeeze(-1) - sr_ligand_idx.append(ligand_atom_index[p_idx] + ligand_offsets[i]) - sr_protein_idx.append(alpha_index[q_idx] + protein_offsets[i]) - - p_idx, q_idx = torch.cartesian_prod(ligand_atom_index, ligand_atom_index).chunk(2, dim=-1) - p_idx, q_idx = p_idx.squeeze(-1), q_idx.squeeze(-1) - sr_ligand_idx0.append(ligand_atom_index[p_idx] + ligand_offsets[i]) - sr_ligand_idx1.append(ligand_atom_index[q_idx] + ligand_offsets[i]) - sr_ligand_idx, sr_protein_idx = torch.cat(sr_ligand_idx).long(), torch.cat(sr_protein_idx).long() - sr_ligand_idx0, sr_ligand_idx1 = torch.cat(sr_ligand_idx0).long(), torch.cat(sr_ligand_idx1).long() - - dist_alpha = torch.norm(ligand_pos[sr_ligand_idx] - protein_pos[sr_protein_idx], dim=1) - dist_intra = torch.norm(ligand_pos[sr_ligand_idx0] - ligand_pos[sr_ligand_idx1], dim=1) - input_dir_alpha = ligand_pos[sr_ligand_idx] - protein_pos[sr_protein_idx] - input_dir_intra = ligand_pos[sr_ligand_idx0] - ligand_pos[sr_ligand_idx1] - distance_emb1 = model.encoder.distance_expansion(torch.norm(input_dir_alpha, dim=1)) - distance_emb2 = model.encoder.distance_expansion(torch.norm(input_dir_intra, dim=1)) - input1 = torch.cat([h_ctx_ligand[sr_ligand_idx], h_ctx_protein[sr_protein_idx], distance_emb1], dim=-1)[dist_alpha <= 10.0] - input2 = torch.cat([h_ctx_ligand[sr_ligand_idx0], h_ctx_ligand[sr_ligand_idx1], distance_emb2], dim=-1)[dist_intra <= 10.0] - # distance cut_off - norm_dir1 = F.normalize(input_dir_alpha, p=2, dim=1)[dist_alpha <= 10.0] - norm_dir2 = F.normalize(input_dir_intra, p=2, dim=1)[dist_intra <= 10.0] - force1 = scatter_mean(model.refine_protein(input1) * norm_dir1, dim=0, index=sr_ligand_idx[dist_alpha <= 10.0], dim_size=ligand_pos.size(0)) - force2 = scatter_mean(model.refine_ligand(input2) * norm_dir2, dim=0, index=sr_ligand_idx0[dist_intra <= 10.0], dim_size=ligand_pos.size(0)) - ligand_pos += force1 - ligand_pos += force2 - - ligand_pos = [ligand_pos[ligand_batch==k].float() for k in range(len(repeats))] - return ligand_pos - - -def ligand_gen(batch, model, vocab, config, center, device, refinement=False): - pos_list = [] - feat_list = [] - motif_id = [0 for _ in range(config.sample.batch_size)] - finished = torch.zeros(config.sample.batch_size).bool() - for i in range(config.sample.max_steps): - print(i) - print(finished) - if torch.sum(finished) == config.sample.batch_size: - # mol_list = SetMolPos(mol_list, pos_list) - return mol_list, pos_list - if i == 0: - focal_pred, mask_protein, h_ctx = model(protein_pos=batch['protein_pos'], - protein_atom_feature=batch['protein_atom_feature'].float(), - ligand_pos=batch['ligand_context_pos'], - ligand_atom_feature=batch['ligand_context_feature_full'].float(), - batch_protein=batch['protein_element_batch'], - batch_ligand=batch['ligand_context_element_batch']) - protein_atom_feature = batch['protein_atom_feature'].float() - focal_protein = focal_pred[mask_protein] - h_ctx_protein = h_ctx[mask_protein] - focus_score = torch.sigmoid(focal_protein) - #can_focus = focus_score > 0.5 - slice_idx = torch.cat([torch.tensor([0]).to(device), torch.cumsum(batch['protein_element_batch'].bincount(), dim=0)]) - focal_id = [] - for j in range(len(slice_idx) - 1): - focus = focus_score[slice_idx[j]:slice_idx[j + 1]] - focal_id.append(torch.argmax(focus.reshape(-1).float()).item() + slice_idx[j].item()) - focal_id = torch.tensor(focal_id, device=device) - - h_ctx_focal = h_ctx_protein[focal_id] - current_wid = torch.tensor([vocab.size()] * config.sample.batch_size, device=device) - next_motif_wid, motif_prob = model.forward_motif(h_ctx_focal, current_wid, torch.arange(config.sample.batch_size, device=device).to(device)) - mol_list = [Chem.MolFromSmiles(vocab.get_smiles(id)) for id in next_motif_wid] - - for j in range(config.sample.batch_size): - AllChem.EmbedMolecule(mol_list[j]) - AllChem.UFFOptimizeMolecule(mol_list[j]) - ligand_pos, ligand_feat = torch.tensor(mol_list[j].GetConformer().GetPositions(), device=device), get_feat(mol_list[j]).to(device) - feat_list.append(ligand_feat) - # set the initial positions with distance matrix - reference_pos, reference_idx = find_reference(batch['protein_pos'][slice_idx[j]:slice_idx[j + 1]], focal_id[j] - slice_idx[j]) - - p_idx, l_idx = torch.cartesian_prod(torch.arange(4), torch.arange(len(ligand_pos))).chunk(2, dim=-1) - p_idx = p_idx.squeeze(-1).to(device) - l_idx = l_idx.squeeze(-1).to(device) - d_m = model.dist_mlp(torch.cat([protein_atom_feature[reference_idx[p_idx]], ligand_feat[l_idx]], dim=-1)).reshape(4,len(ligand_pos)) - - d_m = d_m ** 2 - p_d, l_d = self_square_dist(reference_pos), self_square_dist(ligand_pos) - D = torch.cat([torch.cat([p_d, d_m], dim=1), torch.cat([d_m.permute(1, 0), l_d], dim=1)]) - coordinate = eig_coord_from_dist(D) - new_pos, _, _ = kabsch_torch(coordinate[:len(reference_pos)], reference_pos, - coordinate[len(reference_pos):]) - # new_pos += (center*0.8+torch.mean(reference_pos, dim=0)*0.2) - torch.mean(new_pos, dim=0) - new_pos += (center - torch.mean(new_pos, dim=0)) * .8 - pos_list.append(new_pos) - - atom_to_motif = [{} for _ in range(config.sample.batch_size)] - motif_to_atoms = [{} for _ in range(config.sample.batch_size)] - motif_wid = [{} for _ in range(config.sample.batch_size)] - for j in range(config.sample.batch_size): - for k in range(mol_list[j].GetNumAtoms()): - atom_to_motif[j][k] = 0 - for j in range(config.sample.batch_size): - motif_to_atoms[j][0] = list(np.arange(mol_list[j].GetNumAtoms())) - motif_wid[j][0] = next_motif_wid[j].item() - else: - repeats = torch.tensor([len(pos) for pos in pos_list], device=device) - ligand_batch = torch.repeat_interleave(torch.arange(config.sample.batch_size, device=device), repeats) - focal_pred, mask_protein, h_ctx = model(protein_pos=batch['protein_pos'].float(), - protein_atom_feature=batch['protein_atom_feature'].float(), - ligand_pos=torch.cat(pos_list, dim=0).float(), - ligand_atom_feature=torch.cat(feat_list, dim=0).float(), - batch_protein=batch['protein_element_batch'], - batch_ligand=ligand_batch) - # structure refinement - if refinement: - pos_list = refine_pos(torch.cat(pos_list, dim=0).float(), batch['protein_pos'].float(), - h_ctx[~mask_protein], h_ctx[mask_protein], model, batch, repeats.tolist(), - batch['protein_element_batch'], ligand_batch) - - focal_ligand = focal_pred[~mask_protein] - h_ctx_ligand = h_ctx[~mask_protein] - focus_score = torch.sigmoid(focal_ligand) - can_focus = focus_score > 0. - slice_idx = torch.cat([torch.tensor([0], device=device), torch.cumsum(repeats, dim=0)]) - - current_atoms_batch, current_atoms = [], [] - for j in range(len(slice_idx) - 1): - focus = focus_score[slice_idx[j]:slice_idx[j + 1]] - if torch.sum(can_focus[slice_idx[j]:slice_idx[j + 1]]) > 0 and ~finished[j]: - sample_focal_atom = torch.multinomial(focus.reshape(-1).float(), 1) - focal_motif = atom_to_motif[j][sample_focal_atom.item()] - motif_id[j] = focal_motif - else: - finished[j] = True - - current_atoms.extend((np.array(motif_to_atoms[j][motif_id[j]]) + slice_idx[j].item()).tolist()) - current_atoms_batch.extend([j] * len(motif_to_atoms[j][motif_id[j]])) - mol_list[j] = SetAtomNum(mol_list[j], motif_to_atoms[j][motif_id[j]]) - # second step: next motif prediction - current_wid = [motif_wid[j][motif_id[j]] for j in range(len(mol_list))] - next_motif_wid, motif_prob = model.forward_motif(h_ctx_ligand[torch.tensor(current_atoms)], - torch.tensor(current_wid).to(device), - torch.tensor(current_atoms_batch).to(device)) - - # assemble - next_motif_smiles = [vocab.get_smiles(id) for id in next_motif_wid] - new_mol_list, new_atoms, one_atom_attach, intersection, attach_fail = model.forward_attach(mol_list, next_motif_smiles, device) - - for j in range(len(mol_list)): - if ~finished[j] and ~attach_fail[j]: - # num_new_atoms - mol_list[j] = new_mol_list[j] - rotatable = torch.logical_and(torch.tensor(current_atoms_batch).bincount() == 2, torch.tensor(one_atom_attach)) - rotatable = torch.logical_and(rotatable, ~torch.tensor(attach_fail)) - rotatable = torch.logical_and(rotatable, ~finished).to(device) - # update motif2atoms and atom2motif - for j in range(len(mol_list)): - if attach_fail[j] or finished[j]: - continue - motif_to_atoms[j][i] = new_atoms[j] - motif_wid[j][i] = next_motif_wid[j] - for k in new_atoms[j]: - atom_to_motif[j][k] = i - ''' - if k in atom_to_motif[j]: - continue - else: - atom_to_motif[j][k] = i''' - - # generate initial positions - for j in range(len(mol_list)): - if attach_fail[j] or finished[j]: - continue - mol = mol_list[j] - anchor = [atom.GetIdx() for atom in mol.GetAtoms() if atom.GetAtomMapNum() == 1] - # positions = mol.GetConformer().GetPositions() - anchor_pos = deepcopy(pos_list[j][anchor]).to(device) - Chem.SanitizeMol(mol) - AllChem.EmbedMolecule(mol, useRandomCoords=True) - try: - AllChem.UFFOptimizeMolecule(mol) - except: - print('UFF error') - anchor_pos_new = mol.GetConformer(0).GetPositions()[anchor] - new_idx = [atom.GetIdx() for atom in mol.GetAtoms() if atom.GetAtomMapNum() == 2] - ''' - R, T = kabsch(np.matrix(anchor_pos), np.matrix(anchor_pos_new)) - new_pos = R * np.matrix(mol.GetConformer().GetPositions()[new_idx]).T + np.tile(T, (1, len(new_idx))) - new_pos = np.array(new_pos.T)''' - new_pos = mol.GetConformer().GetPositions()[new_idx] - new_pos, _, _ = kabsch_torch(torch.tensor(anchor_pos_new, device=device), anchor_pos, torch.tensor(new_pos, device=device)) - - conf = mol.GetConformer() - # update curated parameters - pos_list[j] = torch.cat([pos_list[j], new_pos]) - feat_list[j] = get_feat(mol_list[j]).to(device) - for node in range(mol.GetNumAtoms()): - conf.SetAtomPosition(node, np.array(pos_list[j][node].cpu())) - assert mol.GetNumAtoms() == len(pos_list[j]) - - # predict alpha and rotate (only change the position) - if torch.sum(rotatable) > 0 and i >= 2: - repeats = torch.tensor([len(pos) for pos in pos_list]) - ligand_batch = torch.repeat_interleave(torch.arange(len(pos_list)), repeats).to(device) - slice_idx = torch.cat([torch.tensor([0]), torch.cumsum(repeats, dim=0)]) - xy_index = [(np.array(motif_to_atoms[j][motif_id[j]]) + slice_idx[j].item()).tolist() for j in range(len(slice_idx) - 1) if rotatable[j]] - - alpha = model.forward_alpha(protein_pos=batch['protein_pos'].float(), - protein_atom_feature=batch['protein_atom_feature'].float(), - ligand_pos=torch.cat(pos_list, dim=0).float(), - ligand_atom_feature=torch.cat(feat_list, dim=0).float(), - batch_protein=batch['protein_element_batch'], - batch_ligand=ligand_batch, xy_index=torch.tensor(xy_index, device=device), - rotatable=rotatable) - - rotatable_id = [id for id in range(len(mol_list)) if rotatable[id]] - xy_index = [motif_to_atoms[j][motif_id[j]] for j in range(len(slice_idx) - 1) if rotatable[j]] - x_index = [intersection[j] for j in range(len(slice_idx) - 1) if rotatable[j]] - y_index = [(set(xy_index[k]) - set(x_index[k])).pop() for k in range(len(x_index))] - - for j in range(len(alpha)): - mol = mol_list[rotatable_id[j]] - new_idx = [atom.GetIdx() for atom in mol.GetAtoms() if atom.GetAtomMapNum() == 2] - positions = deepcopy(pos_list[rotatable_id[j]]) - - xn_pos = positions[new_idx].float() - dir=(positions[x_index[j]] - positions[y_index[j]]).reshape(-1) - ref=positions[x_index[j]].reshape(-1) - xn_pos = rand_rotate(dir.to(device), ref.to(device), xn_pos.to(device), alpha[j], device=device) - if xn_pos.shape[0] > 0: - pos_list[rotatable_id[j]][-len(xn_pos):] = xn_pos - conf = mol.GetConformer() - for node in range(mol.GetNumAtoms()): - conf.SetAtomPosition(node, np.array(pos_list[rotatable_id[j]][node].cpu())) - assert mol.GetNumAtoms() == len(pos_list[rotatable_id[j]]) - - return mol_list, pos_list - - -def demo(data_id): - vocab_path = 'vocab.txt' - device = 'cpu' - config = './configs/sample.yml' - vocab = [] - for line in open(vocab_path): - p, _, _ = line.partition(':') - vocab.append(p) - vocab = Vocab(vocab) - - # Load configs - config = load_config(config) - - # Data - protein_featurizer = FeaturizeProteinAtom() - ligand_featurizer = FeaturizeLigandAtom() - masking = LigandMaskAll(vocab) - transform = Compose([ - LigandCountNeighbors(), - protein_featurizer, - ligand_featurizer, - FeaturizeLigandBond(), - masking, - ]) - dataset, subsets = get_dataset( - config=config.dataset, - transform=transform, - ) - testset = subsets['test'] - data = testset[data_id%100] - center = data['ligand_center'].to(device) - test_set = [data for _ in range(config.sample.num_samples)] - - # Model (Main) - ckpt = torch.load(config.model.checkpoint, map_location=device) - model = FLAG( - ckpt['config'].model, - protein_atom_feature_dim=protein_featurizer.feature_dim, - ligand_atom_feature_dim=ligand_featurizer.feature_dim, - vocab=vocab, - ).to(device) - model.load_state_dict(ckpt['model']) - - # my code goes here - sample_loader = DataLoader(test_set, batch_size=config.sample.batch_size, - shuffle=False, num_workers=config.sample.num_workers, - collate_fn=collate_mols) - - with torch.no_grad(): - model.eval() - number = 0 - for batch in tqdm(sample_loader): - for key in batch: - batch[key] = batch[key].to(device) - gen_data, pos_list = ligand_gen(batch, model, vocab, config, center, device) - SetMolPos(gen_data, pos_list) - for mol in gen_data: - try: - AllChem.UFFOptimizeMolecule(mol) - except: - print('UFF error') - for _, mol in enumerate(gen_data): - number += 1 - if mol.GetNumAtoms() < 12 or MolLogP(mol) < 0.60: - continue - filename = os.path.join('./data', 'Ligand.sdf') - writer = Chem.SDWriter(filename) - # writer.SetKekulize(False) - writer.write(mol, confId=0) - writer.close() - return filename - - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--config', type=str, default='./configs/sample.yml') - parser.add_argument('-i', '--data_id', type=int, default=0) - parser.add_argument('--device', type=str, default='cuda:0') - parser.add_argument('--outdir', type=str, default='./outputs') - parser.add_argument('--vocab_path', type=str, default='vocab.txt') - parser.add_argument('--num_workers', type=int, default=64) - args = parser.parse_args() - - # Load vocab - vocab = [] - for line in open(args.vocab_path): - p, _, _ = line.partition(':') - vocab.append(p) - vocab = Vocab(vocab) - - # Load configs - config = load_config(args.config) - config_name = os.path.basename(args.config)[:os.path.basename(args.config).rfind('.')] - seed_all(config.sample.seed) - - # Logging - log_dir = get_new_log_dir(args.outdir, prefix='%s-%d' % (config_name, args.data_id)) - logger = get_logger('sample', log_dir) - logger.info(args) - logger.info(config) - shutil.copyfile(args.config, os.path.join(log_dir, os.path.basename(args.config))) - - # Data - logger.info('Loading data...') - protein_featurizer = FeaturizeProteinAtom() - ligand_featurizer = FeaturizeLigandAtom() - masking = LigandMaskAll(vocab) - transform = Compose([ - LigandCountNeighbors(), - protein_featurizer, - ligand_featurizer, - FeaturizeLigandBond(), - masking, - ]) - dataset, subsets = get_dataset( - config=config.dataset, - transform=transform, - ) - testset = subsets['test'] - data = testset[args.data_id] - center = data['ligand_center'].to(args.device) - test_set = [data for _ in range(config.sample.num_samples)] - - with open(os.path.join(log_dir, 'pocket_info.txt'), 'a') as f: - f.write(data['protein_filename'] + '\n') - - # Model (Main) - logger.info('Loading main model...') - ckpt = torch.load(config.model.checkpoint, map_location=args.device) - model = FLAG( - ckpt['config'].model, - protein_atom_feature_dim=protein_featurizer.feature_dim, - ligand_atom_feature_dim=ligand_featurizer.feature_dim, - vocab=vocab, - ).to(args.device) - model.load_state_dict(ckpt['model']) - - # my code goes here - sample_loader = DataLoader(test_set, batch_size=config.sample.batch_size, - shuffle=False, num_workers=config.sample.num_workers, - collate_fn=collate_mols) - data_list = [] - try: - with torch.no_grad(): - model.eval() - number = 0 - number_list = [] - for batch in tqdm(sample_loader): - for key in batch: - batch[key] = batch[key].to(args.device) - gen_data, pos_list = ligand_gen(batch, model, vocab, config, center, args.device) - SetMolPos(gen_data, pos_list) - for mol in gen_data: - try: - AllChem.UFFOptimizeMolecule(mol) - except: - print('UFF error') - data_list.extend(gen_data) - with open(os.path.join(log_dir, 'SMILES.txt'), 'a') as smiles_f: - for _, mol in enumerate(gen_data): - number+=1 - if mol.GetNumAtoms() < 12 or MolLogP(mol) < 0.60: - continue - smiles_f.write(Chem.MolToSmiles(mol) + '\n') - writer = Chem.SDWriter(os.path.join(log_dir, '%d.sdf' % number)) - # writer.SetKekulize(False) - writer.write(mol, confId=0) - writer.close() - number_list.append(number) - - # Calculate metrics - print([Chem.MolToSmiles(mol) for mol in data_list]) - smiles = [Chem.MolFromSmiles(Chem.MolToSmiles(mol)) for mol in data_list] - qed_list = [qed(mol) for mol in smiles if mol.GetNumAtoms() >= 8] - logp_list = [MolLogP(mol) for mol in smiles] - sa_list = [compute_sa_score(mol) for mol in smiles] - Lip_list = [lipinski(mol) for mol in smiles] - print('QED %.6f | LogP %.6f | SA %.6f | Lipinski %.6f \n' % (np.average(qed_list), np.average(logp_list), np.average(sa_list), np.average(Lip_list))) - - except KeyboardInterrupt: - logger.info('Terminated. Generated molecules will be saved.') - with open(os.path.join(log_dir, 'SMILES.txt'), 'a') as smiles_f: - for i, mol in enumerate(data_list): - if mol.GetNumAtoms() < 12 or MolLogP(mol) < 0.60: - continue - smiles_f.write(Chem.MolToSmiles(mol) + '\n') - writer = Chem.SDWriter(os.path.join(log_dir, '%d.sdf' % i)) - # writer.SetKekulize(False) - writer.write(mol, confId=0) - writer.close() - - pool = mp.Pool(args.num_workers) - vina_list = [] - pro_path = '/n/holyscratch01/mzitnik_lab/zaixizhang/pdbbind_pocket10/' + os.path.join(data['pdbid'], data['pdbid']+'_pocket.pdb') - for vina_score in tqdm(pool.imap_unordered(partial(calculate_vina, pro_path=pro_path, lig_path=log_dir), number_list), total=len(number_list)): - if vina_score != None: - vina_list.append(vina_score) - pool.close() - print('Vina: ', np.average(vina_list)) diff --git a/spaces/a-v-bely/spanish-task-generator/utilities_language_w2v/esp_sentence_w2v.py b/spaces/a-v-bely/spanish-task-generator/utilities_language_w2v/esp_sentence_w2v.py deleted file mode 100644 index 4347d9e3a669ee78fa16ffe62a16b4b3247cc4bd..0000000000000000000000000000000000000000 --- a/spaces/a-v-bely/spanish-task-generator/utilities_language_w2v/esp_sentence_w2v.py +++ /dev/null @@ -1,253 +0,0 @@ -import string -from random import random -from random import sample -from utilities_language_general.esp_constants import nlp -from utilities_language_general.morphology import inflect -from utilities_language_general.esp_utils import check_token -from utilities_language_general.esp_constants import PHRASES -from utilities_language_general.esp_utils import fix_irregular_lemma -from utilities_language_general.esp_constants import BAD_USER_TARGET_WORDS -from utilities_language_general.esp_utils import get_distractors_from_model - - -class SENTENCE: - def __init__(self, original: str, n_sentence: int, max_num_distractors): - self.original = original - self.n_sentence = n_sentence - self.max_num_distractors = max_num_distractors - self.parsed = nlp(self.original) - self.sentence_lemma_pos = [] - self.sentence_phrases = [] - self.target_words = [] - - def lemmatize_sentence(self): - for token in self.parsed: - lemma_pos = f'{token.lemma_}_{token.pos_}' - if token.pos_ in ('AUX', 'VERB', 'ADJ'): - lemma_pos = fix_irregular_lemma(lemma=lemma_pos) - self.sentence_lemma_pos.append((lemma_pos, token)) - - def bind_phrases(self): - previous_was_phrase = False - for i in range(len(self.sentence_lemma_pos) - 1): - phrase_candidate = f'{self.sentence_lemma_pos[i][0]}_{self.sentence_lemma_pos[i + 1][0]}' - if phrase_candidate in PHRASES and not previous_was_phrase: - # phrase is {phrase: {original_token1: spacy.token, original_token2: spacy.token}} - phrase = [ - f'{self.sentence_lemma_pos[i][0]}_{self.sentence_lemma_pos[i + 1][0]}', - { - 'original_token1': self.sentence_lemma_pos[i][1], - 'original_token2': self.sentence_lemma_pos[i + 1][1] - } - ] - self.sentence_phrases.append(phrase) - previous_was_phrase = True - else: - if not previous_was_phrase: - self.sentence_phrases.append(self.sentence_lemma_pos[i][1]) - previous_was_phrase = False - - def search_target_words_automatically(self, model, target_minimum: set, frequency_dict: dict = None): - for token in self.sentence_phrases: - if isinstance(token, list): # if token is a phrase - original_token1 = token[1]['original_token1'] - original_token2 = token[1]['original_token2'] - original_token1_tags = original_token1.morph.to_dict() - original_token2_tags = original_token2.morph.to_dict() - tags = dict() - if ('haber_AUX' == f'{original_token1.lemma_}_{original_token1.pos_}' - and original_token2.pos_ in ('VERB', 'ADJ', 'AUX')): - tags['VerbForm'] = 'Compuesto' - tags['Mood'] = original_token1_tags.get('Mood') - tags['Tense'] = original_token1_tags.get('Tense') - tags['Person'] = original_token1_tags.get('Person') - tags['Number'] = original_token1_tags.get('Number') - tags['Gender'] = None - else: - tags = {**original_token1_tags, **original_token2_tags} - not_ner = True if (original_token1.ent_type == 0 and original_token2.ent_type == 0) else False - target_word = { - 'sentence_number': self.n_sentence, - 'sentence_text': self.original, - 'original_text': f'{original_token1.text} {original_token2.text}', - 'lemma': token[0], - 'pos': 'phrase', - 'gender': tags.get('Gender'), - 'tags': tags, - 'position_in_sentence': self.original.find(original_token1.text), - 'not_named_entity': not_ner, - 'frequency_in_text': 0 - } - self.target_words.append(target_word) - else: # if token is just a spacy.nlp token - if check_token(model=model, token=token, lemma_pos='auto', current_minimum=target_minimum): - tags = token.morph.to_dict() - target_word = { - 'sentence_number': self.n_sentence, - 'sentence_text': self.original, - 'original_text': token.text, - 'lemma': token.lemma_, - 'pos': token.pos_, - 'gender': tags.get('Gender'), - 'number_children': len([child for child in token.children]), - 'tags': tags, - 'position_in_sentence': self.original.find(token.text), - 'not_named_entity': True if token.ent_type == 0 else False, - 'frequency_in_text': frequency_dict.get(token.lemma_, 1), - } - self.target_words.append(target_word) - - def search_user_target_words(self, model, user_target_words: set = None, frequency_dict: dict = None): - for _utw in user_target_words: - if _utw in self.original: - parse_utw = nlp(_utw) - if ' ' in _utw: - tags = dict() - if ('haber_AUX' == f'{parse_utw[0].lemma_}_{parse_utw[0].pos_}' - and parse_utw[1].pos_ in ('VERB', 'ADJ', 'AUX')): - tags['VerbForm'] = 'Compuesto' - tags['Mood'] = parse_utw[0].morph.to_dict().get('Mood') - tags['Tense'] = parse_utw[0].morph.to_dict().get('Tense') - tags['Person'] = parse_utw[0].morph.to_dict().get('Person') - tags['Number'] = parse_utw[0].morph.to_dict().get('Number') - tags['Gender'] = None - else: - tags = {**parse_utw[0].morph.to_dict(), **parse_utw[1].morph.to_dict()} - user_target_word_lemma = '_'.join([f'{token.lemma_}_{token.pos_}' for token in parse_utw]) - user_target_word_pos = 'phrase' - user_target_word_tags = tags - not_ner = True if (parse_utw[0].ent_type == 0 and parse_utw[1].ent_type == 0) else False - else: - user_target_word_lemma = f'{parse_utw[0].lemma_}_{parse_utw[0].pos_}' - user_target_word_pos = parse_utw[0].pos_ - user_target_word_tags = parse_utw[0].morph.to_dict() - not_ner = parse_utw[0].ent_type == 0 - target_word = { - 'sentence_number': self.n_sentence, - 'sentence_text': self.original, - 'original_text': _utw, - 'lemma': user_target_word_lemma, - 'pos': user_target_word_pos, - 'gender': user_target_word_tags.get('Gender'), - 'tags': user_target_word_tags, - 'position_in_sentence': self.original.find(_utw), - 'not_named_entity': not_ner, - 'frequency_in_text': frequency_dict.get(user_target_word_lemma, 1) - } - if not (model.has_index_for(user_target_word_lemma) - or model.has_index_for(f'{user_target_word_lemma}_{user_target_word_pos}')): - BAD_USER_TARGET_WORDS.append(_utw) - else: - self.target_words.append(target_word) - - def search_target_words(self, model, target_words_automatic_mode: bool, target_minimum, - user_target_words: set = None, frequency_dict: dict = None): - if target_words_automatic_mode: - self.search_target_words_automatically(model=model, target_minimum=target_minimum, - frequency_dict=frequency_dict) - else: - self.search_user_target_words(model=model, user_target_words=user_target_words, - frequency_dict=frequency_dict) - - def attach_distractors_to_target_word(self, model, global_distractors, distractor_minimum, level_name, - max_frequency, logs, progress): - n_target_words = len(self.target_words) - bad_target_words = [] - for i, target_word in enumerate(self.target_words): - distractors = get_distractors_from_model(model, lemma=target_word['lemma'], pos=target_word['pos'], - gender=target_word['gender'], level_name=level_name, - global_distractors=global_distractors, - distractor_minimum=distractor_minimum, - max_num_distractors=self.max_num_distractors) - if distractors is None or target_word['frequency_in_text'] > max_frequency: - target_word['distractors'] = distractors - bad_target_words.append(target_word) - target_word['distractors'] = distractors - target_word['distractors_number'] = len(distractors) if distractors is not None else 0 - progress.progress(i / n_target_words) - logs.update(label=f'Обработали {i}/{n_target_words} слов в {self.n_sentence + 1}-м предложении', - state='running') - progress.progress(100) - for btw in bad_target_words: - BAD_USER_TARGET_WORDS.append(btw['original_text']) - self.target_words.remove(btw) - logs.update(label=f'Обработали {n_target_words}/{n_target_words} слов в {self.n_sentence + 1}-м предложении', - state='running') - - def inflect_distractors(self): - bad_target_words = [] - for target_word in self.target_words: - inflected_distractors = [] - for distractor_lemma, distractor_similarity in target_word['distractors']: - if distractor_lemma.count('_') > 1: - if distractor_lemma.startswith('haber_'): - distractor_lemma = distractor_lemma.split('_')[-2] - inflected = inflect(lemma=distractor_lemma, target_pos=target_word['pos'], - target_tags=target_word['tags']) - else: - continue # TODO - else: - inflected = inflect(lemma=distractor_lemma, target_pos=target_word['pos'], - target_tags=target_word['tags']) - if inflected is not None: - inflected_distractors.append(inflected) - num_distractors = min(4, self.max_num_distractors) if self.max_num_distractors >= 4 \ - else self.max_num_distractors - if len(inflected_distractors) < num_distractors: - bad_target_words.append(target_word) - else: - target_word['inflected_distractors'] = inflected_distractors - for btw in bad_target_words: - BAD_USER_TARGET_WORDS.append(btw['original_text']) - self.target_words.remove(btw) - - def filter_target_words(self, target_words_automatic_mode): - c_position = 0 - bad_target_words = [] - for target_word in self.target_words: - position_difference = 3 if target_words_automatic_mode else 0 - if not (target_word['position_in_sentence'] == 0 - or abs(target_word['position_in_sentence'] - c_position) >= position_difference): - bad_target_words.append(target_word) - for btw in bad_target_words: - BAD_USER_TARGET_WORDS.append(btw['original_text']) - self.target_words.remove(btw) - - def sample_distractors(self, num_distractors): - for target_word in self.target_words: - len_inflected_distractors = len(target_word['inflected_distractors']) - num_distractors = min(len_inflected_distractors, num_distractors) \ - if num_distractors >= 4 else num_distractors - target_word['inflected_distractors'] = sample(target_word['inflected_distractors'], num_distractors) - - -class TASK: - def __init__(self, task_data): - self.task_data = task_data - - self.original_text = None - self.sentence_text = None - self.inflected_distractors = None - self.sentence_number = task_data['sentence_number'] - self.position_in_sentence = task_data['position_in_sentence'] - self.result = '' - self.variants = [] - for key, value in task_data.items(): - self.__setattr__(key, value) - - def __repr__(self): - return '\n'.join([f'{key}\t=\t{value}' for key, value in self.__dict__.items()]) - - def compile_task(self, max_num_distractors): - len_distractors = len(self.inflected_distractors) - len_variants = min(len_distractors, max_num_distractors) if max_num_distractors >= 4 \ - else max_num_distractors - letters = (f'({letter})' for letter in string.ascii_lowercase[:len_variants + 1]) - try: - distractors = sample(self.inflected_distractors, len_variants) + [self.original_text, ] - except ValueError as e: - print(f'{e}\n{len_distractors=}\n{len_variants=}') - distractors = self.inflected_distractors + [self.original_text, ] - self.variants.append( - (self.original_text, [f'{item[0]} {item[1].replace("_", " ").lower()}'.lower() - for item in zip(letters, sorted(distractors, key=lambda _: random()))])) diff --git a/spaces/aaronb/Anything2Image/anything2image/imagebind/models/helpers.py b/spaces/aaronb/Anything2Image/anything2image/imagebind/models/helpers.py deleted file mode 100644 index 049e1f1b0580832e8574350991bf347b6da81482..0000000000000000000000000000000000000000 --- a/spaces/aaronb/Anything2Image/anything2image/imagebind/models/helpers.py +++ /dev/null @@ -1,141 +0,0 @@ -#!/usr/bin/env python3 -# Portions 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 math - -import einops -import numpy as np -import torch - -import torch.nn as nn - - -class Normalize(nn.Module): - def __init__(self, dim: int) -> None: - super().__init__() - self.dim = dim - - def forward(self, x): - return torch.nn.functional.normalize(x, dim=self.dim, p=2) - - -class LearnableLogitScaling(nn.Module): - def __init__( - self, - logit_scale_init: float = 1 / 0.07, - learnable: bool = True, - max_logit_scale: float = 100, - ) -> None: - super().__init__() - self.max_logit_scale = max_logit_scale - self.logit_scale_init = logit_scale_init - self.learnable = learnable - log_logit_scale = torch.ones([]) * np.log(self.logit_scale_init) - if learnable: - self.log_logit_scale = nn.Parameter(log_logit_scale) - else: - self.register_buffer("log_logit_scale", log_logit_scale) - - def forward(self, x): - return torch.clip(self.log_logit_scale.exp(), max=self.max_logit_scale) * x - - def extra_repr(self): - st = f"logit_scale_init={self.logit_scale_init},learnable={self.learnable}, max_logit_scale={self.max_logit_scale}" - return st - - -class EinOpsRearrange(nn.Module): - def __init__(self, rearrange_expr: str, **kwargs) -> None: - super().__init__() - self.rearrange_expr = rearrange_expr - self.kwargs = kwargs - - def forward(self, x): - assert isinstance(x, torch.Tensor) - return einops.rearrange(x, self.rearrange_expr, **self.kwargs) - - -class VerboseNNModule(nn.Module): - """ - Wrapper around nn.Module that prints registered buffers and parameter names. - """ - - @staticmethod - def get_readable_tensor_repr(name: str, tensor: torch.Tensor) -> str: - st = ( - "(" - + name - + "): " - + "tensor(" - + str(tuple(tensor[1].shape)) - + ", requires_grad=" - + str(tensor[1].requires_grad) - + ")\n" - ) - return st - - def extra_repr(self) -> str: - named_modules = set() - for p in self.named_modules(): - named_modules.update([p[0]]) - named_modules = list(named_modules) - - string_repr = "" - for p in self.named_parameters(): - name = p[0].split(".")[0] - if name not in named_modules: - string_repr += self.get_readable_tensor_repr(name, p) - - for p in self.named_buffers(): - name = p[0].split(".")[0] - string_repr += self.get_readable_tensor_repr(name, p) - - return string_repr - - -def cast_if_src_dtype( - tensor: torch.Tensor, src_dtype: torch.dtype, tgt_dtype: torch.dtype -): - updated = False - if tensor.dtype == src_dtype: - tensor = tensor.to(dtype=tgt_dtype) - updated = True - return tensor, updated - - -class QuickGELU(nn.Module): - # From https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/clip/model.py#L166 - def forward(self, x: torch.Tensor): - return x * torch.sigmoid(1.702 * x) - - -class SelectElement(nn.Module): - def __init__(self, index) -> None: - super().__init__() - self.index = index - - def forward(self, x): - assert x.ndim >= 3 - return x[:, self.index, ...] - - -class SelectEOSAndProject(nn.Module): - """ - Text Pooling used in OpenCLIP - """ - - def __init__(self, proj: nn.Module) -> None: - super().__init__() - self.proj = proj - - def forward(self, x, seq_len): - assert x.ndim == 3 - # x is of shape B x L x D - # take features from the eot embedding (eot_token is the highest number in each sequence) - x = x[torch.arange(x.shape[0]), seq_len] - x = self.proj(x) - return x diff --git a/spaces/abbbbbbbbbbbbbb/poetry2023/README.md b/spaces/abbbbbbbbbbbbbb/poetry2023/README.md deleted file mode 100644 index c958a0c31dcf28cc9fa8983a3f43d6b3b0481875..0000000000000000000000000000000000000000 --- a/spaces/abbbbbbbbbbbbbb/poetry2023/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Poetry2023 -emoji: 👁 -colorFrom: green -colorTo: gray -sdk: gradio -sdk_version: 3.16.0 -app_file: app.py -pinned: false -duplicated_from: akhooli/poetry2023 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/abhishek/sketch-to-image/README.md b/spaces/abhishek/sketch-to-image/README.md deleted file mode 100644 index d193df35729d8546999a22d1882a840074bd95ea..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Sketch To Image -emoji: 🎨 -colorFrom: gray -colorTo: green -sdk: gradio -sdk_version: 3.39.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/runner/hooks/__init__.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/runner/hooks/__init__.py deleted file mode 100644 index 915af28cefab14a14c1188ed861161080fd138a3..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmcv/runner/hooks/__init__.py +++ /dev/null @@ -1,29 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from .checkpoint import CheckpointHook -from .closure import ClosureHook -from .ema import EMAHook -from .evaluation import DistEvalHook, EvalHook -from .hook import HOOKS, Hook -from .iter_timer import IterTimerHook -from .logger import (DvcliveLoggerHook, LoggerHook, MlflowLoggerHook, - NeptuneLoggerHook, PaviLoggerHook, TensorboardLoggerHook, - TextLoggerHook, WandbLoggerHook) -from .lr_updater import LrUpdaterHook -from .memory import EmptyCacheHook -from .momentum_updater import MomentumUpdaterHook -from .optimizer import (Fp16OptimizerHook, GradientCumulativeFp16OptimizerHook, - GradientCumulativeOptimizerHook, OptimizerHook) -from .profiler import ProfilerHook -from .sampler_seed import DistSamplerSeedHook -from .sync_buffer import SyncBuffersHook - -__all__ = [ - 'HOOKS', 'Hook', 'CheckpointHook', 'ClosureHook', 'LrUpdaterHook', - 'OptimizerHook', 'Fp16OptimizerHook', 'IterTimerHook', - 'DistSamplerSeedHook', 'EmptyCacheHook', 'LoggerHook', 'MlflowLoggerHook', - 'PaviLoggerHook', 'TextLoggerHook', 'TensorboardLoggerHook', - 'NeptuneLoggerHook', 'WandbLoggerHook', 'DvcliveLoggerHook', - 'MomentumUpdaterHook', 'SyncBuffersHook', 'EMAHook', 'EvalHook', - 'DistEvalHook', 'ProfilerHook', 'GradientCumulativeOptimizerHook', - 'GradientCumulativeFp16OptimizerHook' -] diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/bbox_heads/sabl_head.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/bbox_heads/sabl_head.py deleted file mode 100644 index 5153996aeb706d103d1ad14b61734914eddb7693..0000000000000000000000000000000000000000 --- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet_null/models/roi_heads/bbox_heads/sabl_head.py +++ /dev/null @@ -1,572 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from mmcv.cnn import ConvModule, kaiming_init, normal_init, xavier_init -from mmcv.runner import force_fp32 - -from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms -from mmdet.models.builder import HEADS, build_loss -from mmdet.models.losses import accuracy - - -@HEADS.register_module() -class SABLHead(nn.Module): - """Side-Aware Boundary Localization (SABL) for RoI-Head. - - Side-Aware features are extracted by conv layers - with an attention mechanism. - Boundary Localization with Bucketing and Bucketing Guided Rescoring - are implemented in BucketingBBoxCoder. - - Please refer to https://arxiv.org/abs/1912.04260 for more details. - - Args: - cls_in_channels (int): Input channels of cls RoI feature. \ - Defaults to 256. - reg_in_channels (int): Input channels of reg RoI feature. \ - Defaults to 256. - roi_feat_size (int): Size of RoI features. Defaults to 7. - reg_feat_up_ratio (int): Upsample ratio of reg features. \ - Defaults to 2. - reg_pre_kernel (int): Kernel of 2D conv layers before \ - attention pooling. Defaults to 3. - reg_post_kernel (int): Kernel of 1D conv layers after \ - attention pooling. Defaults to 3. - reg_pre_num (int): Number of pre convs. Defaults to 2. - reg_post_num (int): Number of post convs. Defaults to 1. - num_classes (int): Number of classes in dataset. Defaults to 80. - cls_out_channels (int): Hidden channels in cls fcs. Defaults to 1024. - reg_offset_out_channels (int): Hidden and output channel \ - of reg offset branch. Defaults to 256. - reg_cls_out_channels (int): Hidden and output channel \ - of reg cls branch. Defaults to 256. - num_cls_fcs (int): Number of fcs for cls branch. Defaults to 1. - num_reg_fcs (int): Number of fcs for reg branch.. Defaults to 0. - reg_class_agnostic (bool): Class agnostic regresion or not. \ - Defaults to True. - norm_cfg (dict): Config of norm layers. Defaults to None. - bbox_coder (dict): Config of bbox coder. Defaults 'BucketingBBoxCoder'. - loss_cls (dict): Config of classification loss. - loss_bbox_cls (dict): Config of classification loss for bbox branch. - loss_bbox_reg (dict): Config of regression loss for bbox branch. - """ - - def __init__(self, - num_classes, - cls_in_channels=256, - reg_in_channels=256, - roi_feat_size=7, - reg_feat_up_ratio=2, - reg_pre_kernel=3, - reg_post_kernel=3, - reg_pre_num=2, - reg_post_num=1, - cls_out_channels=1024, - reg_offset_out_channels=256, - reg_cls_out_channels=256, - num_cls_fcs=1, - num_reg_fcs=0, - reg_class_agnostic=True, - norm_cfg=None, - bbox_coder=dict( - type='BucketingBBoxCoder', - num_buckets=14, - scale_factor=1.7), - loss_cls=dict( - type='CrossEntropyLoss', - use_sigmoid=False, - loss_weight=1.0), - loss_bbox_cls=dict( - type='CrossEntropyLoss', - use_sigmoid=True, - loss_weight=1.0), - loss_bbox_reg=dict( - type='SmoothL1Loss', beta=0.1, loss_weight=1.0)): - super(SABLHead, self).__init__() - self.cls_in_channels = cls_in_channels - self.reg_in_channels = reg_in_channels - self.roi_feat_size = roi_feat_size - self.reg_feat_up_ratio = int(reg_feat_up_ratio) - self.num_buckets = bbox_coder['num_buckets'] - assert self.reg_feat_up_ratio // 2 >= 1 - self.up_reg_feat_size = roi_feat_size * self.reg_feat_up_ratio - assert self.up_reg_feat_size == bbox_coder['num_buckets'] - self.reg_pre_kernel = reg_pre_kernel - self.reg_post_kernel = reg_post_kernel - self.reg_pre_num = reg_pre_num - self.reg_post_num = reg_post_num - self.num_classes = num_classes - self.cls_out_channels = cls_out_channels - self.reg_offset_out_channels = reg_offset_out_channels - self.reg_cls_out_channels = reg_cls_out_channels - self.num_cls_fcs = num_cls_fcs - self.num_reg_fcs = num_reg_fcs - self.reg_class_agnostic = reg_class_agnostic - assert self.reg_class_agnostic - self.norm_cfg = norm_cfg - - self.bbox_coder = build_bbox_coder(bbox_coder) - self.loss_cls = build_loss(loss_cls) - self.loss_bbox_cls = build_loss(loss_bbox_cls) - self.loss_bbox_reg = build_loss(loss_bbox_reg) - - self.cls_fcs = self._add_fc_branch(self.num_cls_fcs, - self.cls_in_channels, - self.roi_feat_size, - self.cls_out_channels) - - self.side_num = int(np.ceil(self.num_buckets / 2)) - - if self.reg_feat_up_ratio > 1: - self.upsample_x = nn.ConvTranspose1d( - reg_in_channels, - reg_in_channels, - self.reg_feat_up_ratio, - stride=self.reg_feat_up_ratio) - self.upsample_y = nn.ConvTranspose1d( - reg_in_channels, - reg_in_channels, - self.reg_feat_up_ratio, - stride=self.reg_feat_up_ratio) - - self.reg_pre_convs = nn.ModuleList() - for i in range(self.reg_pre_num): - reg_pre_conv = ConvModule( - reg_in_channels, - reg_in_channels, - kernel_size=reg_pre_kernel, - padding=reg_pre_kernel // 2, - norm_cfg=norm_cfg, - act_cfg=dict(type='ReLU')) - self.reg_pre_convs.append(reg_pre_conv) - - self.reg_post_conv_xs = nn.ModuleList() - for i in range(self.reg_post_num): - reg_post_conv_x = ConvModule( - reg_in_channels, - reg_in_channels, - kernel_size=(1, reg_post_kernel), - padding=(0, reg_post_kernel // 2), - norm_cfg=norm_cfg, - act_cfg=dict(type='ReLU')) - self.reg_post_conv_xs.append(reg_post_conv_x) - self.reg_post_conv_ys = nn.ModuleList() - for i in range(self.reg_post_num): - reg_post_conv_y = ConvModule( - reg_in_channels, - reg_in_channels, - kernel_size=(reg_post_kernel, 1), - padding=(reg_post_kernel // 2, 0), - norm_cfg=norm_cfg, - act_cfg=dict(type='ReLU')) - self.reg_post_conv_ys.append(reg_post_conv_y) - - self.reg_conv_att_x = nn.Conv2d(reg_in_channels, 1, 1) - self.reg_conv_att_y = nn.Conv2d(reg_in_channels, 1, 1) - - self.fc_cls = nn.Linear(self.cls_out_channels, self.num_classes + 1) - self.relu = nn.ReLU(inplace=True) - - self.reg_cls_fcs = self._add_fc_branch(self.num_reg_fcs, - self.reg_in_channels, 1, - self.reg_cls_out_channels) - self.reg_offset_fcs = self._add_fc_branch(self.num_reg_fcs, - self.reg_in_channels, 1, - self.reg_offset_out_channels) - self.fc_reg_cls = nn.Linear(self.reg_cls_out_channels, 1) - self.fc_reg_offset = nn.Linear(self.reg_offset_out_channels, 1) - - def _add_fc_branch(self, num_branch_fcs, in_channels, roi_feat_size, - fc_out_channels): - in_channels = in_channels * roi_feat_size * roi_feat_size - branch_fcs = nn.ModuleList() - for i in range(num_branch_fcs): - fc_in_channels = (in_channels if i == 0 else fc_out_channels) - branch_fcs.append(nn.Linear(fc_in_channels, fc_out_channels)) - return branch_fcs - - def init_weights(self): - for module_list in [ - self.reg_cls_fcs, self.reg_offset_fcs, self.cls_fcs - ]: - for m in module_list.modules(): - if isinstance(m, nn.Linear): - xavier_init(m, distribution='uniform') - if self.reg_feat_up_ratio > 1: - kaiming_init(self.upsample_x, distribution='normal') - kaiming_init(self.upsample_y, distribution='normal') - - normal_init(self.reg_conv_att_x, 0, 0.01) - normal_init(self.reg_conv_att_y, 0, 0.01) - normal_init(self.fc_reg_offset, 0, 0.001) - normal_init(self.fc_reg_cls, 0, 0.01) - normal_init(self.fc_cls, 0, 0.01) - - def cls_forward(self, cls_x): - cls_x = cls_x.view(cls_x.size(0), -1) - for fc in self.cls_fcs: - cls_x = self.relu(fc(cls_x)) - cls_score = self.fc_cls(cls_x) - return cls_score - - def attention_pool(self, reg_x): - """Extract direction-specific features fx and fy with attention - methanism.""" - reg_fx = reg_x - reg_fy = reg_x - reg_fx_att = self.reg_conv_att_x(reg_fx).sigmoid() - reg_fy_att = self.reg_conv_att_y(reg_fy).sigmoid() - reg_fx_att = reg_fx_att / reg_fx_att.sum(dim=2).unsqueeze(2) - reg_fy_att = reg_fy_att / reg_fy_att.sum(dim=3).unsqueeze(3) - reg_fx = (reg_fx * reg_fx_att).sum(dim=2) - reg_fy = (reg_fy * reg_fy_att).sum(dim=3) - return reg_fx, reg_fy - - def side_aware_feature_extractor(self, reg_x): - """Refine and extract side-aware features without split them.""" - for reg_pre_conv in self.reg_pre_convs: - reg_x = reg_pre_conv(reg_x) - reg_fx, reg_fy = self.attention_pool(reg_x) - - if self.reg_post_num > 0: - reg_fx = reg_fx.unsqueeze(2) - reg_fy = reg_fy.unsqueeze(3) - for i in range(self.reg_post_num): - reg_fx = self.reg_post_conv_xs[i](reg_fx) - reg_fy = self.reg_post_conv_ys[i](reg_fy) - reg_fx = reg_fx.squeeze(2) - reg_fy = reg_fy.squeeze(3) - if self.reg_feat_up_ratio > 1: - reg_fx = self.relu(self.upsample_x(reg_fx)) - reg_fy = self.relu(self.upsample_y(reg_fy)) - reg_fx = torch.transpose(reg_fx, 1, 2) - reg_fy = torch.transpose(reg_fy, 1, 2) - return reg_fx.contiguous(), reg_fy.contiguous() - - def reg_pred(self, x, offset_fcs, cls_fcs): - """Predict bucketing estimation (cls_pred) and fine regression (offset - pred) with side-aware features.""" - x_offset = x.view(-1, self.reg_in_channels) - x_cls = x.view(-1, self.reg_in_channels) - - for fc in offset_fcs: - x_offset = self.relu(fc(x_offset)) - for fc in cls_fcs: - x_cls = self.relu(fc(x_cls)) - offset_pred = self.fc_reg_offset(x_offset) - cls_pred = self.fc_reg_cls(x_cls) - - offset_pred = offset_pred.view(x.size(0), -1) - cls_pred = cls_pred.view(x.size(0), -1) - - return offset_pred, cls_pred - - def side_aware_split(self, feat): - """Split side-aware features aligned with orders of bucketing - targets.""" - l_end = int(np.ceil(self.up_reg_feat_size / 2)) - r_start = int(np.floor(self.up_reg_feat_size / 2)) - feat_fl = feat[:, :l_end] - feat_fr = feat[:, r_start:].flip(dims=(1, )) - feat_fl = feat_fl.contiguous() - feat_fr = feat_fr.contiguous() - feat = torch.cat([feat_fl, feat_fr], dim=-1) - return feat - - def bbox_pred_split(self, bbox_pred, num_proposals_per_img): - """Split batch bbox prediction back to each image.""" - bucket_cls_preds, bucket_offset_preds = bbox_pred - bucket_cls_preds = bucket_cls_preds.split(num_proposals_per_img, 0) - bucket_offset_preds = bucket_offset_preds.split( - num_proposals_per_img, 0) - bbox_pred = tuple(zip(bucket_cls_preds, bucket_offset_preds)) - return bbox_pred - - def reg_forward(self, reg_x): - outs = self.side_aware_feature_extractor(reg_x) - edge_offset_preds = [] - edge_cls_preds = [] - reg_fx = outs[0] - reg_fy = outs[1] - offset_pred_x, cls_pred_x = self.reg_pred(reg_fx, self.reg_offset_fcs, - self.reg_cls_fcs) - offset_pred_y, cls_pred_y = self.reg_pred(reg_fy, self.reg_offset_fcs, - self.reg_cls_fcs) - offset_pred_x = self.side_aware_split(offset_pred_x) - offset_pred_y = self.side_aware_split(offset_pred_y) - cls_pred_x = self.side_aware_split(cls_pred_x) - cls_pred_y = self.side_aware_split(cls_pred_y) - edge_offset_preds = torch.cat([offset_pred_x, offset_pred_y], dim=-1) - edge_cls_preds = torch.cat([cls_pred_x, cls_pred_y], dim=-1) - - return (edge_cls_preds, edge_offset_preds) - - def forward(self, x): - - bbox_pred = self.reg_forward(x) - cls_score = self.cls_forward(x) - - return cls_score, bbox_pred - - def get_targets(self, sampling_results, gt_bboxes, gt_labels, - rcnn_train_cfg): - pos_proposals = [res.pos_bboxes for res in sampling_results] - neg_proposals = [res.neg_bboxes for res in sampling_results] - pos_gt_bboxes = [res.pos_gt_bboxes for res in sampling_results] - pos_gt_labels = [res.pos_gt_labels for res in sampling_results] - cls_reg_targets = self.bucket_target(pos_proposals, neg_proposals, - pos_gt_bboxes, pos_gt_labels, - rcnn_train_cfg) - (labels, label_weights, bucket_cls_targets, bucket_cls_weights, - bucket_offset_targets, bucket_offset_weights) = cls_reg_targets - return (labels, label_weights, (bucket_cls_targets, - bucket_offset_targets), - (bucket_cls_weights, bucket_offset_weights)) - - def bucket_target(self, - pos_proposals_list, - neg_proposals_list, - pos_gt_bboxes_list, - pos_gt_labels_list, - rcnn_train_cfg, - concat=True): - (labels, label_weights, bucket_cls_targets, bucket_cls_weights, - bucket_offset_targets, bucket_offset_weights) = multi_apply( - self._bucket_target_single, - pos_proposals_list, - neg_proposals_list, - pos_gt_bboxes_list, - pos_gt_labels_list, - cfg=rcnn_train_cfg) - - if concat: - labels = torch.cat(labels, 0) - label_weights = torch.cat(label_weights, 0) - bucket_cls_targets = torch.cat(bucket_cls_targets, 0) - bucket_cls_weights = torch.cat(bucket_cls_weights, 0) - bucket_offset_targets = torch.cat(bucket_offset_targets, 0) - bucket_offset_weights = torch.cat(bucket_offset_weights, 0) - return (labels, label_weights, bucket_cls_targets, bucket_cls_weights, - bucket_offset_targets, bucket_offset_weights) - - def _bucket_target_single(self, pos_proposals, neg_proposals, - pos_gt_bboxes, pos_gt_labels, cfg): - """Compute bucketing estimation targets and fine regression targets for - a single image. - - Args: - pos_proposals (Tensor): positive proposals of a single image, - Shape (n_pos, 4) - neg_proposals (Tensor): negative proposals of a single image, - Shape (n_neg, 4). - pos_gt_bboxes (Tensor): gt bboxes assigned to positive proposals - of a single image, Shape (n_pos, 4). - pos_gt_labels (Tensor): gt labels assigned to positive proposals - of a single image, Shape (n_pos, ). - cfg (dict): Config of calculating targets - - Returns: - tuple: - - - labels (Tensor): Labels in a single image. \ - Shape (n,). - - label_weights (Tensor): Label weights in a single image.\ - Shape (n,) - - bucket_cls_targets (Tensor): Bucket cls targets in \ - a single image. Shape (n, num_buckets*2). - - bucket_cls_weights (Tensor): Bucket cls weights in \ - a single image. Shape (n, num_buckets*2). - - bucket_offset_targets (Tensor): Bucket offset targets \ - in a single image. Shape (n, num_buckets*2). - - bucket_offset_targets (Tensor): Bucket offset weights \ - in a single image. Shape (n, num_buckets*2). - """ - num_pos = pos_proposals.size(0) - num_neg = neg_proposals.size(0) - num_samples = num_pos + num_neg - labels = pos_gt_bboxes.new_full((num_samples, ), - self.num_classes, - dtype=torch.long) - label_weights = pos_proposals.new_zeros(num_samples) - bucket_cls_targets = pos_proposals.new_zeros(num_samples, - 4 * self.side_num) - bucket_cls_weights = pos_proposals.new_zeros(num_samples, - 4 * self.side_num) - bucket_offset_targets = pos_proposals.new_zeros( - num_samples, 4 * self.side_num) - bucket_offset_weights = pos_proposals.new_zeros( - num_samples, 4 * self.side_num) - if num_pos > 0: - labels[:num_pos] = pos_gt_labels - label_weights[:num_pos] = 1.0 - (pos_bucket_offset_targets, pos_bucket_offset_weights, - pos_bucket_cls_targets, - pos_bucket_cls_weights) = self.bbox_coder.encode( - pos_proposals, pos_gt_bboxes) - bucket_cls_targets[:num_pos, :] = pos_bucket_cls_targets - bucket_cls_weights[:num_pos, :] = pos_bucket_cls_weights - bucket_offset_targets[:num_pos, :] = pos_bucket_offset_targets - bucket_offset_weights[:num_pos, :] = pos_bucket_offset_weights - if num_neg > 0: - label_weights[-num_neg:] = 1.0 - return (labels, label_weights, bucket_cls_targets, bucket_cls_weights, - bucket_offset_targets, bucket_offset_weights) - - def loss(self, - cls_score, - bbox_pred, - rois, - labels, - label_weights, - bbox_targets, - bbox_weights, - reduction_override=None): - losses = dict() - if cls_score is not None: - avg_factor = max(torch.sum(label_weights > 0).float().item(), 1.) - losses['loss_cls'] = self.loss_cls( - cls_score, - labels, - label_weights, - avg_factor=avg_factor, - reduction_override=reduction_override) - losses['acc'] = accuracy(cls_score, labels) - - if bbox_pred is not None: - bucket_cls_preds, bucket_offset_preds = bbox_pred - bucket_cls_targets, bucket_offset_targets = bbox_targets - bucket_cls_weights, bucket_offset_weights = bbox_weights - # edge cls - bucket_cls_preds = bucket_cls_preds.view(-1, self.side_num) - bucket_cls_targets = bucket_cls_targets.view(-1, self.side_num) - bucket_cls_weights = bucket_cls_weights.view(-1, self.side_num) - losses['loss_bbox_cls'] = self.loss_bbox_cls( - bucket_cls_preds, - bucket_cls_targets, - bucket_cls_weights, - avg_factor=bucket_cls_targets.size(0), - reduction_override=reduction_override) - - losses['loss_bbox_reg'] = self.loss_bbox_reg( - bucket_offset_preds, - bucket_offset_targets, - bucket_offset_weights, - avg_factor=bucket_offset_targets.size(0), - reduction_override=reduction_override) - - return losses - - @force_fp32(apply_to=('cls_score', 'bbox_pred')) - def get_bboxes(self, - rois, - cls_score, - bbox_pred, - img_shape, - scale_factor, - rescale=False, - cfg=None): - if isinstance(cls_score, list): - cls_score = sum(cls_score) / float(len(cls_score)) - scores = F.softmax(cls_score, dim=1) if cls_score is not None else None - - if bbox_pred is not None: - bboxes, confids = self.bbox_coder.decode(rois[:, 1:], bbox_pred, - img_shape) - else: - bboxes = rois[:, 1:].clone() - confids = None - if img_shape is not None: - bboxes[:, [0, 2]].clamp_(min=0, max=img_shape[1] - 1) - bboxes[:, [1, 3]].clamp_(min=0, max=img_shape[0] - 1) - - if rescale and bboxes.size(0) > 0: - if isinstance(scale_factor, float): - bboxes /= scale_factor - else: - bboxes /= torch.from_numpy(scale_factor).to(bboxes.device) - - if cfg is None: - return bboxes, scores - else: - det_bboxes, det_labels = multiclass_nms( - bboxes, - scores, - cfg.score_thr, - cfg.nms, - cfg.max_per_img, - score_factors=confids) - - return det_bboxes, det_labels - - @force_fp32(apply_to=('bbox_preds', )) - def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas): - """Refine bboxes during training. - - Args: - rois (Tensor): Shape (n*bs, 5), where n is image number per GPU, - and bs is the sampled RoIs per image. - labels (Tensor): Shape (n*bs, ). - bbox_preds (list[Tensor]): Shape [(n*bs, num_buckets*2), \ - (n*bs, num_buckets*2)]. - pos_is_gts (list[Tensor]): Flags indicating if each positive bbox - is a gt bbox. - img_metas (list[dict]): Meta info of each image. - - Returns: - list[Tensor]: Refined bboxes of each image in a mini-batch. - """ - img_ids = rois[:, 0].long().unique(sorted=True) - assert img_ids.numel() == len(img_metas) - - bboxes_list = [] - for i in range(len(img_metas)): - inds = torch.nonzero( - rois[:, 0] == i, as_tuple=False).squeeze(dim=1) - num_rois = inds.numel() - - bboxes_ = rois[inds, 1:] - label_ = labels[inds] - edge_cls_preds, edge_offset_preds = bbox_preds - edge_cls_preds_ = edge_cls_preds[inds] - edge_offset_preds_ = edge_offset_preds[inds] - bbox_pred_ = [edge_cls_preds_, edge_offset_preds_] - img_meta_ = img_metas[i] - pos_is_gts_ = pos_is_gts[i] - - bboxes = self.regress_by_class(bboxes_, label_, bbox_pred_, - img_meta_) - # filter gt bboxes - pos_keep = 1 - pos_is_gts_ - keep_inds = pos_is_gts_.new_ones(num_rois) - keep_inds[:len(pos_is_gts_)] = pos_keep - - bboxes_list.append(bboxes[keep_inds.type(torch.bool)]) - - return bboxes_list - - @force_fp32(apply_to=('bbox_pred', )) - def regress_by_class(self, rois, label, bbox_pred, img_meta): - """Regress the bbox for the predicted class. Used in Cascade R-CNN. - - Args: - rois (Tensor): shape (n, 4) or (n, 5) - label (Tensor): shape (n, ) - bbox_pred (list[Tensor]): shape [(n, num_buckets *2), \ - (n, num_buckets *2)] - img_meta (dict): Image meta info. - - Returns: - Tensor: Regressed bboxes, the same shape as input rois. - """ - assert rois.size(1) == 4 or rois.size(1) == 5 - - if rois.size(1) == 4: - new_rois, _ = self.bbox_coder.decode(rois, bbox_pred, - img_meta['img_shape']) - else: - bboxes, _ = self.bbox_coder.decode(rois[:, 1:], bbox_pred, - img_meta['img_shape']) - new_rois = torch.cat((rois[:, [0]], bboxes), dim=1) - - return new_rois diff --git a/spaces/abidlabs/gradio-discord-bot-server/utils.py b/spaces/abidlabs/gradio-discord-bot-server/utils.py deleted file mode 100644 index 5657b36f6bf0fc41f9be9a126e3f9ac510292493..0000000000000000000000000000000000000000 --- a/spaces/abidlabs/gradio-discord-bot-server/utils.py +++ /dev/null @@ -1,41 +0,0 @@ -from __future__ import annotations - -import asyncio -import pickle -import hashlib -import pathlib -from typing import Dict, List - -import discord - -lock = asyncio.Lock() - - -async def update_pickle_file(data: Dict | List, file_path: str): - async with lock: - with open(file_path, "wb") as fp: - pickle.dump(data, fp) - - -def read_pickle_file(file_path: str): - with open(file_path, "rb") as fp: - return pickle.load(fp) - - -async def send_file_or_text(channel, file_or_text: str): - # if the file exists, send as a file - if pathlib.Path(str(file_or_text)).exists(): - with open(file_or_text, "rb") as f: - return await channel.send(file=discord.File(f)) - else: - return await channel.send(file_or_text) - - -def remove_tags(content: str) -> str: - content = content.replace("<@1040198143695933501>", "") - content = content.replace("<@1057338428938788884>", "") - return content.strip() - - -def hash_user_id(user_id: int) -> str: - return hashlib.sha256(str(user_id).encode("utf-8")).hexdigest() diff --git a/spaces/abidlabs/persistent-storage-test/README.md b/spaces/abidlabs/persistent-storage-test/README.md deleted file mode 100644 index bacbe3bd0eedd119175acde9f29b3006929d5aac..0000000000000000000000000000000000000000 --- a/spaces/abidlabs/persistent-storage-test/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Persistent Storage Test -emoji: 🐢 -colorFrom: yellow -colorTo: pink -sdk: gradio -sdk_version: 3.47.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/achimoraites/Page-Summary/README.md b/spaces/achimoraites/Page-Summary/README.md deleted file mode 100644 index 404593ee5bf5ca951bc12b4eb6c41c594481eca8..0000000000000000000000000000000000000000 --- a/spaces/achimoraites/Page-Summary/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Page Summary -emoji: 📃 -colorFrom: purple -colorTo: indigo -sdk: gradio -sdk_version: 3.19.1 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/adlozano1/gibberish_detector/gib_detect_module.py b/spaces/adlozano1/gibberish_detector/gib_detect_module.py deleted file mode 100644 index 2bd2f4023aa7ae53e8a8d5e664b0f288a286791f..0000000000000000000000000000000000000000 --- a/spaces/adlozano1/gibberish_detector/gib_detect_module.py +++ /dev/null @@ -1,11 +0,0 @@ -#!/usr/bin/python - -import pickle -import gib_detect_train - -model_data = pickle.load(open('gib_model.pki', 'rb')) - -def detect(text): - model_mat = model_data['mat'] - threshold = model_data['thresh'] - return gib_detect_train.avg_transition_prob(text, model_mat) > threshold diff --git a/spaces/ahdsoft/persian-keyphrase-extraction/kpe_ranker.py b/spaces/ahdsoft/persian-keyphrase-extraction/kpe_ranker.py deleted file mode 100644 index 1b094b2477ec3bf0f4d02f202cb0cb52d49536d0..0000000000000000000000000000000000000000 --- a/spaces/ahdsoft/persian-keyphrase-extraction/kpe_ranker.py +++ /dev/null @@ -1,37 +0,0 @@ -from kpe import KPE -import utils -import os -from sentence_transformers import SentenceTransformer -import ranker -from huggingface_hub import hf_hub_download - -class KpeRanker: - def __init__(self): - model_name = os.environ.get("MODEL_NAME") - model_repo = os.environ.get("MODEL_REPO") - model_token = os.environ.get("MODEL_TOKEN") - ner_model = os.environ.get("NER_MODEL") - transformer_model = os.environ.get("TRANSFORMER_MODEL") - - - local_dir = "./" - model_path = os.path.join(local_dir, model_name) - if not os.path.isfile(model_path): - hf_hub_download(repo_id=model_repo, filename=model_name, local_dir=local_dir, token=model_token) - TRAINED_MODEL_ADDR = model_path - # TRAINED_MODEL_ADDR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'trained_model', 'trained_model_10000.pt') - self.kpe = KPE(trained_kpe_model= TRAINED_MODEL_ADDR, flair_ner_model= ner_model , device='cpu') - self.ranker_transformer = SentenceTransformer(transformer_model, device='cpu') - - - def extract(self, text, count, using_ner, return_sorted): - text = utils.normalize(text) - kps = self.kpe.extract(text, using_ner=using_ner) - if return_sorted: - kps = ranker.get_sorted_keywords(self.ranker_transformer, text, kps) - else: - kps = [(kp, 1) for kp in kps] - if len(kps) > count: - kps = kps[:count] - return kps - diff --git a/spaces/akhaliq/Mask2Former/datasets/prepare_coco_semantic_annos_from_panoptic_annos.py b/spaces/akhaliq/Mask2Former/datasets/prepare_coco_semantic_annos_from_panoptic_annos.py deleted file mode 100644 index 3090c9bc2f9a63156a4132e89c635613691eb350..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/Mask2Former/datasets/prepare_coco_semantic_annos_from_panoptic_annos.py +++ /dev/null @@ -1,84 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -# Copyright (c) Facebook, Inc. and its affiliates. - -import functools -import json -import multiprocessing as mp -import numpy as np -import os -import time -from fvcore.common.download import download -from panopticapi.utils import rgb2id -from PIL import Image - -from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES - - -def _process_panoptic_to_semantic(input_panoptic, output_semantic, segments, id_map): - panoptic = np.asarray(Image.open(input_panoptic), dtype=np.uint32) - panoptic = rgb2id(panoptic) - output = np.zeros_like(panoptic, dtype=np.uint8) + 255 - for seg in segments: - cat_id = seg["category_id"] - new_cat_id = id_map[cat_id] - output[panoptic == seg["id"]] = new_cat_id - Image.fromarray(output).save(output_semantic) - - -def separate_coco_semantic_from_panoptic(panoptic_json, panoptic_root, sem_seg_root, categories): - """ - Create semantic segmentation annotations from panoptic segmentation - annotations, to be used by PanopticFPN. - It maps all thing categories to class 0, and maps all unlabeled pixels to class 255. - It maps all stuff categories to contiguous ids starting from 1. - Args: - panoptic_json (str): path to the panoptic json file, in COCO's format. - panoptic_root (str): a directory with panoptic annotation files, in COCO's format. - sem_seg_root (str): a directory to output semantic annotation files - categories (list[dict]): category metadata. Each dict needs to have: - "id": corresponds to the "category_id" in the json annotations - "isthing": 0 or 1 - """ - os.makedirs(sem_seg_root, exist_ok=True) - - id_map = {} # map from category id to id in the output semantic annotation - assert len(categories) <= 254 - for i, k in enumerate(categories): - id_map[k["id"]] = i - # what is id = 0? - # id_map[0] = 255 - print(id_map) - - with open(panoptic_json) as f: - obj = json.load(f) - - pool = mp.Pool(processes=max(mp.cpu_count() // 2, 4)) - - def iter_annotations(): - for anno in obj["annotations"]: - file_name = anno["file_name"] - segments = anno["segments_info"] - input = os.path.join(panoptic_root, file_name) - output = os.path.join(sem_seg_root, file_name) - yield input, output, segments - - print("Start writing to {} ...".format(sem_seg_root)) - start = time.time() - pool.starmap( - functools.partial(_process_panoptic_to_semantic, id_map=id_map), - iter_annotations(), - chunksize=100, - ) - print("Finished. time: {:.2f}s".format(time.time() - start)) - - -if __name__ == "__main__": - dataset_dir = os.path.join(os.getenv("DETECTRON2_DATASETS", "datasets"), "coco") - for s in ["val2017", "train2017"]: - separate_coco_semantic_from_panoptic( - os.path.join(dataset_dir, "annotations/panoptic_{}.json".format(s)), - os.path.join(dataset_dir, "panoptic_{}".format(s)), - os.path.join(dataset_dir, "panoptic_semseg_{}".format(s)), - COCO_CATEGORIES, - ) diff --git a/spaces/akhaliq/SummerTime/tests/model_test.py b/spaces/akhaliq/SummerTime/tests/model_test.py deleted file mode 100644 index 413819fe37de2190edaf22657c429ddc76366cc4..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/SummerTime/tests/model_test.py +++ /dev/null @@ -1,111 +0,0 @@ -import unittest -from typing import List - -from dataset.dataset_loaders import CnndmDataset, MultinewsDataset, PubmedqaDataset -from model import SUPPORTED_SUMM_MODELS, list_all_models -from model.single_doc import LexRankModel, LongformerModel -from model.dialogue import HMNetModel - -from helpers import ( - print_with_color, - get_summarization_set, - get_query_based_summarization_set, -) - - -class TestModels(unittest.TestCase): - - single_doc_dataset = CnndmDataset() - multi_doc_dataset = MultinewsDataset() - query_based_dataset = PubmedqaDataset() - # # TODO: temporarily skipping HMNet, no dialogue-based dataset needed - # dialogue_based_dataset = SamsumDataset() - - def test_list_models(self): - print_with_color(f"{'#'*10} Testing test_list_models... {'#'*10}\n", "35") - all_models = list_all_models() - for model_class, model_description in all_models: - print(f"{model_class} : {model_description}") - self.assertTrue(True) - self.assertEqual(len(all_models), len(SUPPORTED_SUMM_MODELS)) - print_with_color( - f"{'#'*10} test_list_models {__name__} test complete {'#'*10}\n\n", "32" - ) - - def validate_prediction(self, prediction: List[str], src: List): - """ - Verify that prediction instances match source instances. - """ - self.assertTrue(isinstance(prediction, list)) - self.assertTrue(all([isinstance(ins, str) for ins in prediction])) - self.assertTrue(len(prediction) == len(src)) - print("Prediction typing and length matches source instances!") - - def test_model_summarize(self): - """ - Test all supported models on instances from datasets. - """ - - print_with_color(f"{'#'*10} Testing all models... {'#'*10}\n", "35") - - num_models = 0 - all_models = list_all_models() - - for model_class, _ in all_models: - if model_class in [HMNetModel]: - # TODO: Temporarily skip HMNet (requires large pre-trained model download + GPU) - continue - - print_with_color(f"Testing {model_class.model_name} model...", "35") - - if model_class == LexRankModel: - # current LexRankModel requires a training set - training_src, training_tgt = get_summarization_set( - self.single_doc_dataset, 100 - ) - model = model_class(training_src) - else: - model = model_class() - - if model.is_query_based: - test_src, test_tgt, test_query = get_query_based_summarization_set( - self.query_based_dataset, 1 - ) - prediction = model.summarize(test_src, test_query) - print( - f"Query: {test_query}\nGold summary: {test_tgt}\nPredicted summary: {prediction}" - ) - elif model.is_multi_document: - test_src, test_tgt = get_summarization_set(self.multi_doc_dataset, 1) - prediction = model.summarize(test_src) - print(f"Gold summary: {test_tgt} \nPredicted summary: {prediction}") - self.validate_prediction(prediction, test_src) - elif model.is_dialogue_based: - test_src, test_tgt = get_summarization_set( - self.dialogue_based_dataset, 1 - ) - prediction = model.summarize(test_src) - print(f"Gold summary: {test_tgt}\nPredicted summary: {prediction}") - self.validate_prediction(prediction, test_src) - else: - test_src, test_tgt = get_summarization_set(self.single_doc_dataset, 1) - prediction = model.summarize( - [test_src[0] * 5] if model_class == LongformerModel else test_src - ) - print(f"Gold summary: {test_tgt} \nPredicted summary: {prediction}") - self.validate_prediction( - prediction, - [test_src[0] * 5] if model_class == LongformerModel else test_src, - ) - - print_with_color(f"{model_class.model_name} model test complete\n", "32") - num_models += 1 - - print_with_color( - f"{'#'*10} test_model_summarize complete ({num_models} models) {'#'*10}\n", - "32", - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/spaces/akhaliq/lama/bin/calc_dataset_stats.py b/spaces/akhaliq/lama/bin/calc_dataset_stats.py deleted file mode 100644 index 5086fea1bab691892f2e52e3c59e5ef048bcfac0..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/lama/bin/calc_dataset_stats.py +++ /dev/null @@ -1,88 +0,0 @@ -#!/usr/bin/env python3 - -import os - -import numpy as np -import tqdm -from scipy.ndimage.morphology import distance_transform_edt - -from saicinpainting.evaluation.data import InpaintingDataset -from saicinpainting.evaluation.vis import save_item_for_vis - - -def main(args): - dataset = InpaintingDataset(args.datadir, img_suffix='.png') - - area_bins = np.linspace(0, 1, args.area_bins + 1) - - heights = [] - widths = [] - image_areas = [] - hole_areas = [] - hole_area_percents = [] - known_pixel_distances = [] - - area_bins_count = np.zeros(args.area_bins) - area_bin_titles = [f'{area_bins[i] * 100:.0f}-{area_bins[i + 1] * 100:.0f}' for i in range(args.area_bins)] - - bin2i = [[] for _ in range(args.area_bins)] - - for i, item in enumerate(tqdm.tqdm(dataset)): - h, w = item['image'].shape[1:] - heights.append(h) - widths.append(w) - full_area = h * w - image_areas.append(full_area) - bin_mask = item['mask'] > 0.5 - hole_area = bin_mask.sum() - hole_areas.append(hole_area) - hole_percent = hole_area / full_area - hole_area_percents.append(hole_percent) - bin_i = np.clip(np.searchsorted(area_bins, hole_percent) - 1, 0, len(area_bins_count) - 1) - area_bins_count[bin_i] += 1 - bin2i[bin_i].append(i) - - cur_dist = distance_transform_edt(bin_mask) - cur_dist_inside_mask = cur_dist[bin_mask] - known_pixel_distances.append(cur_dist_inside_mask.mean()) - - os.makedirs(args.outdir, exist_ok=True) - with open(os.path.join(args.outdir, 'summary.txt'), 'w') as f: - f.write(f'''Location: {args.datadir} - -Number of samples: {len(dataset)} - -Image height: min {min(heights):5d} max {max(heights):5d} mean {np.mean(heights):.2f} -Image width: min {min(widths):5d} max {max(widths):5d} mean {np.mean(widths):.2f} -Image area: min {min(image_areas):7d} max {max(image_areas):7d} mean {np.mean(image_areas):.2f} -Hole area: min {min(hole_areas):7d} max {max(hole_areas):7d} mean {np.mean(hole_areas):.2f} -Hole area %: min {min(hole_area_percents) * 100:2.2f} max {max(hole_area_percents) * 100:2.2f} mean {np.mean(hole_area_percents) * 100:2.2f} -Dist 2known: min {min(known_pixel_distances):2.2f} max {max(known_pixel_distances):2.2f} mean {np.mean(known_pixel_distances):2.2f} median {np.median(known_pixel_distances):2.2f} - -Stats by hole area %: -''') - for bin_i in range(args.area_bins): - f.write(f'{area_bin_titles[bin_i]}%: ' - f'samples number {area_bins_count[bin_i]}, ' - f'{area_bins_count[bin_i] / len(dataset) * 100:.1f}%\n') - - for bin_i in range(args.area_bins): - bindir = os.path.join(args.outdir, 'samples', area_bin_titles[bin_i]) - os.makedirs(bindir, exist_ok=True) - bin_idx = bin2i[bin_i] - for sample_i in np.random.choice(bin_idx, size=min(len(bin_idx), args.samples_n), replace=False): - save_item_for_vis(dataset[sample_i], os.path.join(bindir, f'{sample_i}.png')) - - -if __name__ == '__main__': - import argparse - - aparser = argparse.ArgumentParser() - aparser.add_argument('datadir', type=str, - help='Path to folder with images and masks (output of gen_mask_dataset.py)') - aparser.add_argument('outdir', type=str, help='Where to put results') - aparser.add_argument('--samples-n', type=int, default=10, - help='Number of sample images with masks to copy for visualization for each area bin') - aparser.add_argument('--area-bins', type=int, default=10, help='How many area bins to have') - - main(aparser.parse_args()) diff --git a/spaces/akhaliq/mlsd/app.py b/spaces/akhaliq/mlsd/app.py deleted file mode 100644 index f18f9bf72ccd9ccb34238f534468a79173805ecf..0000000000000000000000000000000000000000 --- a/spaces/akhaliq/mlsd/app.py +++ /dev/null @@ -1,47 +0,0 @@ -from PIL import Image -import cv2 -import numpy as np -import tensorflow as tf -from utils import pred_lines, pred_squares -import gradio as gr -from urllib.request import urlretrieve - -# Load MLSD 512 Large FP32 tflite -model_name = 'tflite_models/M-LSD_512_large_fp32.tflite' -interpreter = tf.lite.Interpreter(model_path=model_name) - -interpreter.allocate_tensors() -input_details = interpreter.get_input_details() -output_details = interpreter.get_output_details() - -def gradio_wrapper_for_LSD(img_input, score_thr, dist_thr): - lines = pred_lines(img_input, interpreter, input_details, output_details, input_shape=[512, 512], score_thr=score_thr, dist_thr=dist_thr) - img_output = img_input.copy() - - # draw lines - for line in lines: - x_start, y_start, x_end, y_end = [int(val) for val in line] - cv2.line(img_output, (x_start, y_start), (x_end, y_end), [0,255,255], 2) - - return img_output - -#urlretrieve("https://www.digsdigs.com/photos/2015/05/a-bold-minimalist-living-room-with-dark-stained-wood-geometric-touches-a-sectional-sofa-and-built-in-lights-for-a-futuristic-feel.jpg","example1.jpg") -urlretrieve("https://specials-images.forbesimg.com/imageserve/5dfe2e6925ab5d0007cefda5/960x0.jpg","example2.jpg") -urlretrieve("https://images.livspace-cdn.com/w:768/h:651/plain/https://jumanji.livspace-cdn.com/magazine/wp-content/uploads/2015/11/27170345/atr-1-a-e1577187047515.jpeg","example3.jpg") -sample_images = [["example1.jpg", 0.2, 10.0], ["example2.jpg", 0.2, 10.0], ["example3.jpg", 0.2, 10.0]] - - - -iface = gr.Interface(gradio_wrapper_for_LSD, - ["image", - gr.inputs.Number(default=0.2, label='score_thr (0.0 ~ 1.0)'), - gr.inputs.Number(default=10.0, label='dist_thr (0.0 ~ 20.0)') - ], - "image", - title="Line segment detection with Mobile LSD (M-LSD)", - description="M-LSD is a light-weight and real-time deep line segment detector, which can run on GPU, CPU, and even on Mobile devices. Try it by uploading an image or clicking on an example. Read more at the links below", - article="{brief}...
{txt}
Towards Real-time and Light-weight Line Segment Detection | Github Repo
", - examples=sample_images, - allow_screenshot=True) - -iface.launch() \ No newline at end of file diff --git a/spaces/akhilkalwakurthy/AxisGPTv3/README.md b/spaces/akhilkalwakurthy/AxisGPTv3/README.md deleted file mode 100644 index 98acbed0a151f4f9ae71a97e2feaaa461123325c..0000000000000000000000000000000000000000 --- a/spaces/akhilkalwakurthy/AxisGPTv3/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: AxisGPTv3 -emoji: 🚀 -colorFrom: indigo -colorTo: purple -sdk: gradio -sdk_version: 3.23.0 -app_file: app.py -pinned: false -license: gpl-3.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/alamin655/websurfx/public/static/colorschemes/dark-chocolate.css b/spaces/alamin655/websurfx/public/static/colorschemes/dark-chocolate.css deleted file mode 100644 index f60d5abf985dcf2d7669cb6cf61a2859c06e66cc..0000000000000000000000000000000000000000 --- a/spaces/alamin655/websurfx/public/static/colorschemes/dark-chocolate.css +++ /dev/null @@ -1,11 +0,0 @@ -:root { - --background-color: #000000; - --foreground-color: #ffffff; - --color-one: #121212; - --color-two: #808080; - --color-three: #999999; - --color-four: #666666; - --color-five: #bfbfbf; - --color-six: #e0e0e0; - --color-seven: #555555; -} diff --git a/spaces/alamin655/websurfx/public/static/colorschemes/gruvbox-dark.css b/spaces/alamin655/websurfx/public/static/colorschemes/gruvbox-dark.css deleted file mode 100644 index 69f81d69971d4a242a00f435e376f2987103b761..0000000000000000000000000000000000000000 --- a/spaces/alamin655/websurfx/public/static/colorschemes/gruvbox-dark.css +++ /dev/null @@ -1,11 +0,0 @@ -:root { - --background-color: #1d2021; - --foreground-color: #ebdbb2; - --color-one: #282828; - --color-two: #98971a; - --color-three: #d79921; - --color-four: #458588; - --color-five: #b16286; - --color-six: #689d6a; - --color-seven: #ffffff; -} diff --git a/spaces/alecmueller/01-Speech2Text2Speech-GR/app.py b/spaces/alecmueller/01-Speech2Text2Speech-GR/app.py deleted file mode 100644 index 15ed8ec721c4864341852b0c946f4812bb390294..0000000000000000000000000000000000000000 --- a/spaces/alecmueller/01-Speech2Text2Speech-GR/app.py +++ /dev/null @@ -1,160 +0,0 @@ -import streamlit as st -import datetime -from transformers import pipeline -import gradio as gr - -import tempfile -from typing import Optional -import numpy as np -from TTS.utils.manage import ModelManager -from TTS.utils.synthesizer import Synthesizer - -# PersistDataset ----- -import os -import csv -import gradio as gr -from gradio import inputs, outputs -import huggingface_hub -from huggingface_hub import Repository, hf_hub_download, upload_file -from datetime import datetime - -# created new dataset as awacke1/MindfulStory.csv -DATASET_REPO_URL = "https://huggingface.co/datasets/awacke1/MindfulStory.csv" -DATASET_REPO_ID = "awacke1/MindfulStory.csv" -DATA_FILENAME = "MindfulStory.csv" -DATA_FILE = os.path.join("data", DATA_FILENAME) -HF_TOKEN = os.environ.get("HF_TOKEN") - -# Download dataset repo using hub download -try: - hf_hub_download( - repo_id=DATASET_REPO_ID, - filename=DATA_FILENAME, - cache_dir=DATA_DIRNAME, - force_filename=DATA_FILENAME - ) -except: - print("file not found") - -def AIMemory(name: str, message: str): - if name and message: - with open(DATA_FILE, "a") as csvfile: - writer = csv.DictWriter(csvfile, fieldnames=["name", "message", "time"]) - writer.writerow({"name": name, "message": message, "time": str(datetime.now())}) - commit_url = repo.push_to_hub() - return {"name": name, "message": message, "time": str(datetime.now())} - -with open('Mindfulness.txt', 'r') as file: - context = file.read() - -# Set up cloned dataset from repo for operations -repo = Repository( local_dir="data", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN) - -# set up ASR -asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h") - -# set up TTS -MODEL_NAMES = [ - "en/ljspeech/tacotron2-DDC", - "en/ljspeech/glow-tts", - "en/ljspeech/speedy-speech-wn", - "en/ljspeech/vits", - "en/sam/tacotron-DDC", - "fr/mai/tacotron2-DDC", - "de/thorsten/tacotron2-DCA", -] - -# Use Model Manager to load vocoders -MODELS = {} -manager = ModelManager() -for MODEL_NAME in MODEL_NAMES: - print(f"downloading {MODEL_NAME}") - model_path, config_path, model_item = manager.download_model(f"tts_models/{MODEL_NAME}") - vocoder_name: Optional[str] = model_item["default_vocoder"] - vocoder_path = None - vocoder_config_path = None - if vocoder_name is not None: - vocoder_path, vocoder_config_path, _ = manager.download_model(vocoder_name) - - synthesizer = Synthesizer( - model_path, config_path, None, vocoder_path, vocoder_config_path, - ) - MODELS[MODEL_NAME] = synthesizer - -# transcribe -def transcribe(audio): - text = asr(audio)["text"] - return text - -#text classifier -classifier = pipeline("text-classification") - - -def speech_to_text(speech): - text = asr(speech)["text"] - #rMem = AIMemory("STT", text) - return text - -def text_to_sentiment(text): - sentiment = classifier(text)[0]["label"] - #rMem = AIMemory(text, sentiment) - return sentiment - -def upsert(text): - date_time =str(datetime.datetime.today()) - doc_ref = db.collection('Text2SpeechSentimentSave').document(date_time) - doc_ref.set({u'firefield': 'Recognize Speech', u'first': 'https://huggingface.co/spaces/awacke1/TTS-STT-Blocks/', u'last': text, u'born': date_time,}) - saved = select('TTS-STT', date_time) - return saved - -def select(collection, document): - doc_ref = db.collection(collection).document(document) - doc = doc_ref.get() - docid = ("The id is: ", doc.id) - contents = ("The contents are: ", doc.to_dict()) - return contents - -def selectall(text): - docs = db.collection('Text2SpeechSentimentSave').stream() - doclist='' - for doc in docs: - r=(f'{doc.id} => {doc.to_dict()}') - doclist += r - return doclist - -def tts(text: str, model_name: str): - print(text, model_name) - synthesizer = MODELS.get(model_name, None) - if synthesizer is None: - raise NameError("model not found") - wavs = synthesizer.tts(text) - with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: - synthesizer.save_wav(wavs, fp) - - #rMem = AIMemory("TTS", text + model_name) - - return fp.name - -demo = gr.Blocks() -with demo: - audio_file = gr.inputs.Audio(source="microphone", type="filepath") - text = gr.Textbox(label="Speech to Text") - #label = gr.Label() - #saved = gr.Textbox(label="Saved") - #savedAll = gr.Textbox(label="SavedAll") - TTSchoice = gr.inputs.Radio( label="Pick a Text to Speech Model", choices=MODEL_NAMES, ) - audio = gr.Audio(label="Output", interactive=False) - - b1 = gr.Button("Recognize Speech") - #b2 = gr.Button("Classify Sentiment") - #b3 = gr.Button("Save Speech to Text") - #b4 = gr.Button("Retrieve All") - b5 = gr.Button("Read It Back Aloud") - - b1.click(speech_to_text, inputs=audio_file, outputs=text) - #b2.click(text_to_sentiment, inputs=text, outputs=label) - #b3.click(upsert, inputs=text, outputs=saved) - #b4.click(selectall, inputs=text, outputs=savedAll) - b5.click(tts, inputs=[text,TTSchoice], outputs=audio) - -demo.launch(share=True) \ No newline at end of file diff --git a/spaces/ales/wav2vec2-cv-be-lm/app.py b/spaces/ales/wav2vec2-cv-be-lm/app.py deleted file mode 100644 index a2ede2de2bf2c6ae52d67fe398fa4b761eb701e6..0000000000000000000000000000000000000000 --- a/spaces/ales/wav2vec2-cv-be-lm/app.py +++ /dev/null @@ -1,92 +0,0 @@ -from pprint import pformat - -from huggingface_hub import hf_hub_download - -import librosa - -import gradio as gr - -from pipeline import PreTrainedPipeline - - -HF_HUB_URL = 'ales/wav2vec2-cv-be' -LM_HUB_FP = 'language_model/cv8be_5gram.bin' -MODEL_SAMPLING_RATE = 16_000 # 16kHz - -# download Language Model from HF Hub -lm_fp = hf_hub_download(repo_id=HF_HUB_URL, filename=LM_HUB_FP) - -# init pipeline -pipeline = PreTrainedPipeline(model_path=HF_HUB_URL, language_model_fp=lm_fp) - - -def main(recorded_audio_fp: str, uploaded_audio_fp: str): - audio_fp = None - if recorded_audio_fp is not None: - audio_fp = recorded_audio_fp - used_audiofile = 'recorded' - elif uploaded_audio_fp is not None: - audio_fp = uploaded_audio_fp - used_audiofile = 'uploaded' - else: - return ( - 'Памылка! Вы мусіце альбо запісаць, альбо запампаваць аўдыяфайл.', - 'Error! You have to either record or upload an audiofile.' - ) - - # read audio file - inputs = librosa.load(audio_fp, sr=MODEL_SAMPLING_RATE, mono=True)[0] - - # recognize speech - pipeline_res = pipeline(inputs=inputs) - text = pipeline_res['text'][0] # unpack batch of size 1 - - # add technical information to the output - tech_data = pipeline_res - del tech_data['text'] - tech_data['used_audiofile'] = used_audiofile - tech_data['recorded_file_present'] = recorded_audio_fp is not None - tech_data['uploaded_file_present'] = uploaded_audio_fp is not None - tech_data['audiofile_path'] = audio_fp - tech_data['model_sampling_rate'] = MODEL_SAMPLING_RATE - tech_data['inputs_shape'] = inputs.shape - tech_data['inputs_max'] = inputs.max().item() - tech_data['inputs_min'] = inputs.min().item() - - tech_data_str = pformat(tech_data) - - return text, tech_data_str - - -article = """ -The model used can be found here: [ales/wav2vec2-cv-be](https://huggingface.co/ales/wav2vec2-cv-be) - - -""" - -iface = gr.Interface( - fn=main, - inputs=[ - gr.inputs.Audio( - source='microphone', type='filepath', - label='Запішыце аўдыяфайл, каб распазнаць маўленьне', - optional=True, - ), - gr.inputs.Audio( - source='upload', type='filepath', - label='Альбо загрузіце ўжо запісаны аўдыяфайл сюды', - optional=True - ), - ], - outputs=[ - gr.outputs.Textbox(type='str', label='Распазнаны тэкст'), - gr.outputs.Textbox(type='str', label='Тэхнічная інфармацыя') - ], - title='wav2vec2 fine-tuned on CommonVoice 8 Be + Language Model', - description=('Мадэль распазнаваньня беларускага маўленьня, навучаная на датсэце Common Voice 8.\n' - 'Акустычная мадэль + моўная мадэль.' - ), - article=article -) - -iface.launch(enable_queue=True) diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/network/auth.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/network/auth.py deleted file mode 100644 index ca42798bd952dfa10533e22a137e72dbd15250d4..0000000000000000000000000000000000000000 --- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_internal/network/auth.py +++ /dev/null @@ -1,323 +0,0 @@ -"""Network Authentication Helpers - -Contains interface (MultiDomainBasicAuth) and associated glue code for -providing credentials in the context of network requests. -""" - -import urllib.parse -from typing import Any, Dict, List, Optional, Tuple - -from pip._vendor.requests.auth import AuthBase, HTTPBasicAuth -from pip._vendor.requests.models import Request, Response -from pip._vendor.requests.utils import get_netrc_auth - -from pip._internal.utils.logging import getLogger -from pip._internal.utils.misc import ( - ask, - ask_input, - ask_password, - remove_auth_from_url, - split_auth_netloc_from_url, -) -from pip._internal.vcs.versioncontrol import AuthInfo - -logger = getLogger(__name__) - -Credentials = Tuple[str, str, str] - -try: - import keyring -except ImportError: - keyring = None # type: ignore[assignment] -except Exception as exc: - logger.warning( - "Keyring is skipped due to an exception: %s", - str(exc), - ) - keyring = None # type: ignore[assignment] - - -def get_keyring_auth(url: Optional[str], username: Optional[str]) -> Optional[AuthInfo]: - """Return the tuple auth for a given url from keyring.""" - global keyring - if not url or not keyring: - return None - - try: - try: - get_credential = keyring.get_credential - except AttributeError: - pass - else: - logger.debug("Getting credentials from keyring for %s", url) - cred = get_credential(url, username) - if cred is not None: - return cred.username, cred.password - return None - - if username: - logger.debug("Getting password from keyring for %s", url) - password = keyring.get_password(url, username) - if password: - return username, password - - except Exception as exc: - logger.warning( - "Keyring is skipped due to an exception: %s", - str(exc), - ) - keyring = None # type: ignore[assignment] - return None - - -class MultiDomainBasicAuth(AuthBase): - def __init__( - self, prompting: bool = True, index_urls: Optional[List[str]] = None - ) -> None: - self.prompting = prompting - self.index_urls = index_urls - self.passwords: Dict[str, AuthInfo] = {} - # When the user is prompted to enter credentials and keyring is - # available, we will offer to save them. If the user accepts, - # this value is set to the credentials they entered. After the - # request authenticates, the caller should call - # ``save_credentials`` to save these. - self._credentials_to_save: Optional[Credentials] = None - - def _get_index_url(self, url: str) -> Optional[str]: - """Return the original index URL matching the requested URL. - - Cached or dynamically generated credentials may work against - the original index URL rather than just the netloc. - - The provided url should have had its username and password - removed already. If the original index url had credentials then - they will be included in the return value. - - Returns None if no matching index was found, or if --no-index - was specified by the user. - """ - if not url or not self.index_urls: - return None - - for u in self.index_urls: - prefix = remove_auth_from_url(u).rstrip("/") + "/" - if url.startswith(prefix): - return u - return None - - def _get_new_credentials( - self, - original_url: str, - allow_netrc: bool = True, - allow_keyring: bool = False, - ) -> AuthInfo: - """Find and return credentials for the specified URL.""" - # Split the credentials and netloc from the url. - url, netloc, url_user_password = split_auth_netloc_from_url( - original_url, - ) - - # Start with the credentials embedded in the url - username, password = url_user_password - if username is not None and password is not None: - logger.debug("Found credentials in url for %s", netloc) - return url_user_password - - # Find a matching index url for this request - index_url = self._get_index_url(url) - if index_url: - # Split the credentials from the url. - index_info = split_auth_netloc_from_url(index_url) - if index_info: - index_url, _, index_url_user_password = index_info - logger.debug("Found index url %s", index_url) - - # If an index URL was found, try its embedded credentials - if index_url and index_url_user_password[0] is not None: - username, password = index_url_user_password - if username is not None and password is not None: - logger.debug("Found credentials in index url for %s", netloc) - return index_url_user_password - - # Get creds from netrc if we still don't have them - if allow_netrc: - netrc_auth = get_netrc_auth(original_url) - if netrc_auth: - logger.debug("Found credentials in netrc for %s", netloc) - return netrc_auth - - # If we don't have a password and keyring is available, use it. - if allow_keyring: - # The index url is more specific than the netloc, so try it first - # fmt: off - kr_auth = ( - get_keyring_auth(index_url, username) or - get_keyring_auth(netloc, username) - ) - # fmt: on - if kr_auth: - logger.debug("Found credentials in keyring for %s", netloc) - return kr_auth - - return username, password - - def _get_url_and_credentials( - self, original_url: str - ) -> Tuple[str, Optional[str], Optional[str]]: - """Return the credentials to use for the provided URL. - - If allowed, netrc and keyring may be used to obtain the - correct credentials. - - Returns (url_without_credentials, username, password). Note - that even if the original URL contains credentials, this - function may return a different username and password. - """ - url, netloc, _ = split_auth_netloc_from_url(original_url) - - # Try to get credentials from original url - username, password = self._get_new_credentials(original_url) - - # If credentials not found, use any stored credentials for this netloc. - # Do this if either the username or the password is missing. - # This accounts for the situation in which the user has specified - # the username in the index url, but the password comes from keyring. - if (username is None or password is None) and netloc in self.passwords: - un, pw = self.passwords[netloc] - # It is possible that the cached credentials are for a different username, - # in which case the cache should be ignored. - if username is None or username == un: - username, password = un, pw - - if username is not None or password is not None: - # Convert the username and password if they're None, so that - # this netloc will show up as "cached" in the conditional above. - # Further, HTTPBasicAuth doesn't accept None, so it makes sense to - # cache the value that is going to be used. - username = username or "" - password = password or "" - - # Store any acquired credentials. - self.passwords[netloc] = (username, password) - - assert ( - # Credentials were found - (username is not None and password is not None) - # Credentials were not found - or (username is None and password is None) - ), f"Could not load credentials from url: {original_url}" - - return url, username, password - - def __call__(self, req: Request) -> Request: - # Get credentials for this request - url, username, password = self._get_url_and_credentials(req.url) - - # Set the url of the request to the url without any credentials - req.url = url - - if username is not None and password is not None: - # Send the basic auth with this request - req = HTTPBasicAuth(username, password)(req) - - # Attach a hook to handle 401 responses - req.register_hook("response", self.handle_401) - - return req - - # Factored out to allow for easy patching in tests - def _prompt_for_password( - self, netloc: str - ) -> Tuple[Optional[str], Optional[str], bool]: - username = ask_input(f"User for {netloc}: ") - if not username: - return None, None, False - auth = get_keyring_auth(netloc, username) - if auth and auth[0] is not None and auth[1] is not None: - return auth[0], auth[1], False - password = ask_password("Password: ") - return username, password, True - - # Factored out to allow for easy patching in tests - def _should_save_password_to_keyring(self) -> bool: - if not keyring: - return False - return ask("Save credentials to keyring [y/N]: ", ["y", "n"]) == "y" - - def handle_401(self, resp: Response, **kwargs: Any) -> Response: - # We only care about 401 responses, anything else we want to just - # pass through the actual response - if resp.status_code != 401: - return resp - - # We are not able to prompt the user so simply return the response - if not self.prompting: - return resp - - parsed = urllib.parse.urlparse(resp.url) - - # Query the keyring for credentials: - username, password = self._get_new_credentials( - resp.url, - allow_netrc=False, - allow_keyring=True, - ) - - # Prompt the user for a new username and password - save = False - if not username and not password: - username, password, save = self._prompt_for_password(parsed.netloc) - - # Store the new username and password to use for future requests - self._credentials_to_save = None - if username is not None and password is not None: - self.passwords[parsed.netloc] = (username, password) - - # Prompt to save the password to keyring - if save and self._should_save_password_to_keyring(): - self._credentials_to_save = (parsed.netloc, username, password) - - # Consume content and release the original connection to allow our new - # request to reuse the same one. - resp.content - resp.raw.release_conn() - - # Add our new username and password to the request - req = HTTPBasicAuth(username or "", password or "")(resp.request) - req.register_hook("response", self.warn_on_401) - - # On successful request, save the credentials that were used to - # keyring. (Note that if the user responded "no" above, this member - # is not set and nothing will be saved.) - if self._credentials_to_save: - req.register_hook("response", self.save_credentials) - - # Send our new request - new_resp = resp.connection.send(req, **kwargs) - new_resp.history.append(resp) - - return new_resp - - def warn_on_401(self, resp: Response, **kwargs: Any) -> None: - """Response callback to warn about incorrect credentials.""" - if resp.status_code == 401: - logger.warning( - "401 Error, Credentials not correct for %s", - resp.request.url, - ) - - def save_credentials(self, resp: Response, **kwargs: Any) -> None: - """Response callback to save credentials on success.""" - assert keyring is not None, "should never reach here without keyring" - if not keyring: - return - - creds = self._credentials_to_save - self._credentials_to_save = None - if creds and resp.status_code < 400: - try: - logger.info("Saving credentials to keyring") - keyring.set_password(*creds) - except Exception: - logger.exception("Failed to save credentials") diff --git a/spaces/ali-ghamdan/realesrgan-models/scripts/generate_multiscale_DF2K.py b/spaces/ali-ghamdan/realesrgan-models/scripts/generate_multiscale_DF2K.py deleted file mode 100644 index d4f5d8324b1624e4cb6163754703b8dac2d188fd..0000000000000000000000000000000000000000 --- a/spaces/ali-ghamdan/realesrgan-models/scripts/generate_multiscale_DF2K.py +++ /dev/null @@ -1,48 +0,0 @@ -import argparse -import glob -import os -from PIL import Image - - -def main(args): - # For DF2K, we consider the following three scales, - # and the smallest image whose shortest edge is 400 - scale_list = [0.75, 0.5, 1 / 3] - shortest_edge = 400 - - path_list = sorted(glob.glob(os.path.join(args.input, '*'))) - for path in path_list: - print(path) - basename = os.path.splitext(os.path.basename(path))[0] - - img = Image.open(path) - width, height = img.size - for idx, scale in enumerate(scale_list): - print(f'\t{scale:.2f}') - rlt = img.resize((int(width * scale), int(height * scale)), resample=Image.LANCZOS) - rlt.save(os.path.join(args.output, f'{basename}T{idx}.png')) - - # save the smallest image which the shortest edge is 400 - if width < height: - ratio = height / width - width = shortest_edge - height = int(width * ratio) - else: - ratio = width / height - height = shortest_edge - width = int(height * ratio) - rlt = img.resize((int(width), int(height)), resample=Image.LANCZOS) - rlt.save(os.path.join(args.output, f'{basename}T{idx+1}.png')) - - -if __name__ == '__main__': - """Generate multi-scale versions for GT images with LANCZOS resampling. - It is now used for DF2K dataset (DIV2K + Flickr 2K) - """ - parser = argparse.ArgumentParser() - parser.add_argument('--input', type=str, default='datasets/DF2K/DF2K_HR', help='Input folder') - parser.add_argument('--output', type=str, default='datasets/DF2K/DF2K_multiscale', help='Output folder') - args = parser.parse_args() - - os.makedirs(args.output, exist_ok=True) - main(args) diff --git a/spaces/allandclive/Uganda_MMS/vits/train.py b/spaces/allandclive/Uganda_MMS/vits/train.py deleted file mode 100644 index 703d30cf9ef2c414d9b35fe65545cc8fefad8821..0000000000000000000000000000000000000000 --- a/spaces/allandclive/Uganda_MMS/vits/train.py +++ /dev/null @@ -1,290 +0,0 @@ -import os -import json -import argparse -import itertools -import math -import torch -from torch import nn, optim -from torch.nn import functional as F -from torch.utils.data import DataLoader -from torch.utils.tensorboard import SummaryWriter -import torch.multiprocessing as mp -import torch.distributed as dist -from torch.nn.parallel import DistributedDataParallel as DDP -from torch.cuda.amp import autocast, GradScaler - -import commons -import utils -from data_utils import ( - TextAudioLoader, - TextAudioCollate, - DistributedBucketSampler -) -from models import ( - SynthesizerTrn, - MultiPeriodDiscriminator, -) -from losses import ( - generator_loss, - discriminator_loss, - feature_loss, - kl_loss -) -from mel_processing import mel_spectrogram_torch, spec_to_mel_torch -from text.symbols import symbols - - -torch.backends.cudnn.benchmark = True -global_step = 0 - - -def main(): - """Assume Single Node Multi GPUs Training Only""" - assert torch.cuda.is_available(), "CPU training is not allowed." - - n_gpus = torch.cuda.device_count() - os.environ['MASTER_ADDR'] = 'localhost' - os.environ['MASTER_PORT'] = '80000' - - hps = utils.get_hparams() - mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,)) - - -def run(rank, n_gpus, hps): - global global_step - if rank == 0: - logger = utils.get_logger(hps.model_dir) - logger.info(hps) - utils.check_git_hash(hps.model_dir) - writer = SummaryWriter(log_dir=hps.model_dir) - writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval")) - - dist.init_process_group(backend='nccl', init_method='env://', world_size=n_gpus, rank=rank) - torch.manual_seed(hps.train.seed) - torch.cuda.set_device(rank) - - train_dataset = TextAudioLoader(hps.data.training_files, hps.data) - train_sampler = DistributedBucketSampler( - train_dataset, - hps.train.batch_size, - [32,300,400,500,600,700,800,900,1000], - num_replicas=n_gpus, - rank=rank, - shuffle=True) - collate_fn = TextAudioCollate() - train_loader = DataLoader(train_dataset, num_workers=8, shuffle=False, pin_memory=True, - collate_fn=collate_fn, batch_sampler=train_sampler) - if rank == 0: - eval_dataset = TextAudioLoader(hps.data.validation_files, hps.data) - eval_loader = DataLoader(eval_dataset, num_workers=8, shuffle=False, - batch_size=hps.train.batch_size, pin_memory=True, - drop_last=False, collate_fn=collate_fn) - - net_g = SynthesizerTrn( - len(symbols), - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - **hps.model).cuda(rank) - net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) - optim_g = torch.optim.AdamW( - net_g.parameters(), - hps.train.learning_rate, - betas=hps.train.betas, - eps=hps.train.eps) - optim_d = torch.optim.AdamW( - net_d.parameters(), - hps.train.learning_rate, - betas=hps.train.betas, - eps=hps.train.eps) - net_g = DDP(net_g, device_ids=[rank]) - net_d = DDP(net_d, device_ids=[rank]) - - try: - _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g) - _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, optim_d) - global_step = (epoch_str - 1) * len(train_loader) - except: - epoch_str = 1 - global_step = 0 - - scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str-2) - scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str-2) - - scaler = GradScaler(enabled=hps.train.fp16_run) - - for epoch in range(epoch_str, hps.train.epochs + 1): - if rank==0: - train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, eval_loader], logger, [writer, writer_eval]) - else: - train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, None], None, None) - scheduler_g.step() - scheduler_d.step() - - -def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers): - net_g, net_d = nets - optim_g, optim_d = optims - scheduler_g, scheduler_d = schedulers - train_loader, eval_loader = loaders - if writers is not None: - writer, writer_eval = writers - - train_loader.batch_sampler.set_epoch(epoch) - global global_step - - net_g.train() - net_d.train() - for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths) in enumerate(train_loader): - x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True) - spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True) - y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True) - - with autocast(enabled=hps.train.fp16_run): - y_hat, l_length, attn, ids_slice, x_mask, z_mask,\ - (z, z_p, m_p, logs_p, m_q, logs_q) = net_g(x, x_lengths, spec, spec_lengths) - - mel = spec_to_mel_torch( - spec, - hps.data.filter_length, - hps.data.n_mel_channels, - hps.data.sampling_rate, - hps.data.mel_fmin, - hps.data.mel_fmax) - y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length) - y_hat_mel = mel_spectrogram_torch( - y_hat.squeeze(1), - hps.data.filter_length, - hps.data.n_mel_channels, - hps.data.sampling_rate, - hps.data.hop_length, - hps.data.win_length, - hps.data.mel_fmin, - hps.data.mel_fmax - ) - - y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice - - # Discriminator - y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach()) - with autocast(enabled=False): - loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g) - loss_disc_all = loss_disc - optim_d.zero_grad() - scaler.scale(loss_disc_all).backward() - scaler.unscale_(optim_d) - grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None) - scaler.step(optim_d) - - with autocast(enabled=hps.train.fp16_run): - # Generator - y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat) - with autocast(enabled=False): - loss_dur = torch.sum(l_length.float()) - loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel - loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl - - loss_fm = feature_loss(fmap_r, fmap_g) - loss_gen, losses_gen = generator_loss(y_d_hat_g) - loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl - optim_g.zero_grad() - scaler.scale(loss_gen_all).backward() - scaler.unscale_(optim_g) - grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None) - scaler.step(optim_g) - scaler.update() - - if rank==0: - if global_step % hps.train.log_interval == 0: - lr = optim_g.param_groups[0]['lr'] - losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl] - logger.info('Train Epoch: {} [{:.0f}%]'.format( - epoch, - 100. * batch_idx / len(train_loader))) - logger.info([x.item() for x in losses] + [global_step, lr]) - - scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr, "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g} - scalar_dict.update({"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl}) - - scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)}) - scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)}) - scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)}) - image_dict = { - "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()), - "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()), - "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()), - "all/attn": utils.plot_alignment_to_numpy(attn[0,0].data.cpu().numpy()) - } - utils.summarize( - writer=writer, - global_step=global_step, - images=image_dict, - scalars=scalar_dict) - - if global_step % hps.train.eval_interval == 0: - evaluate(hps, net_g, eval_loader, writer_eval) - utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "G_{}.pth".format(global_step))) - utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "D_{}.pth".format(global_step))) - global_step += 1 - - if rank == 0: - logger.info('====> Epoch: {}'.format(epoch)) - - -def evaluate(hps, generator, eval_loader, writer_eval): - generator.eval() - with torch.no_grad(): - for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths) in enumerate(eval_loader): - x, x_lengths = x.cuda(0), x_lengths.cuda(0) - spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0) - y, y_lengths = y.cuda(0), y_lengths.cuda(0) - - # remove else - x = x[:1] - x_lengths = x_lengths[:1] - spec = spec[:1] - spec_lengths = spec_lengths[:1] - y = y[:1] - y_lengths = y_lengths[:1] - break - y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, max_len=1000) - y_hat_lengths = mask.sum([1,2]).long() * hps.data.hop_length - - mel = spec_to_mel_torch( - spec, - hps.data.filter_length, - hps.data.n_mel_channels, - hps.data.sampling_rate, - hps.data.mel_fmin, - hps.data.mel_fmax) - y_hat_mel = mel_spectrogram_torch( - y_hat.squeeze(1).float(), - hps.data.filter_length, - hps.data.n_mel_channels, - hps.data.sampling_rate, - hps.data.hop_length, - hps.data.win_length, - hps.data.mel_fmin, - hps.data.mel_fmax - ) - image_dict = { - "gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()) - } - audio_dict = { - "gen/audio": y_hat[0,:,:y_hat_lengths[0]] - } - if global_step == 0: - image_dict.update({"gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())}) - audio_dict.update({"gt/audio": y[0,:,:y_lengths[0]]}) - - utils.summarize( - writer=writer_eval, - global_step=global_step, - images=image_dict, - audios=audio_dict, - audio_sampling_rate=hps.data.sampling_rate - ) - generator.train() - - -if __name__ == "__main__": - main() diff --git a/spaces/allknowingroger/Image-Models-Test195/README.md b/spaces/allknowingroger/Image-Models-Test195/README.md deleted file mode 100644 index f91e4b31ab345f987b425de029c057bfb69d9e1b..0000000000000000000000000000000000000000 --- a/spaces/allknowingroger/Image-Models-Test195/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: More Image Models -emoji: 😻 -colorFrom: red -colorTo: gray -sdk: gradio -sdk_version: 3.23.0 -app_file: app.py -pinned: true -duplicated_from: allknowingroger/Image-Models-Test ---- - - \ No newline at end of file diff --git a/spaces/amarchheda/ChordDuplicate/portaudio/include/pa_linux_alsa.h b/spaces/amarchheda/ChordDuplicate/portaudio/include/pa_linux_alsa.h deleted file mode 100644 index c94061563905073544cdacecd2f061507af88708..0000000000000000000000000000000000000000 --- a/spaces/amarchheda/ChordDuplicate/portaudio/include/pa_linux_alsa.h +++ /dev/null @@ -1,107 +0,0 @@ -#ifndef PA_LINUX_ALSA_H -#define PA_LINUX_ALSA_H - -/* - * $Id$ - * PortAudio Portable Real-Time Audio Library - * ALSA-specific extensions - * - * Copyright (c) 1999-2000 Ross Bencina and Phil Burk - * - * 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. - */ - -/* - * The text above constitutes the entire PortAudio license; however, - * the PortAudio community also makes the following non-binding requests: - * - * Any person wishing to distribute modifications to the Software is - * requested to send the modifications to the original developer so that - * they can be incorporated into the canonical version. It is also - * requested that these non-binding requests be included along with the - * license above. - */ - -/** @file - * @ingroup public_header - * @brief ALSA-specific PortAudio API extension header file. - */ - -#include "portaudio.h" - -#ifdef __cplusplus -extern "C" { -#endif - -typedef struct PaAlsaStreamInfo -{ - unsigned long size; - PaHostApiTypeId hostApiType; - unsigned long version; - - const char *deviceString; -} -PaAlsaStreamInfo; - -/** Initialize host API specific structure, call this before setting relevant attributes. */ -void PaAlsa_InitializeStreamInfo( PaAlsaStreamInfo *info ); - -/** Instruct whether to enable real-time priority when starting the audio thread. - * - * If this is turned on by the stream is started, the audio callback thread will be created - * with the FIFO scheduling policy, which is suitable for realtime operation. - **/ -void PaAlsa_EnableRealtimeScheduling( PaStream *s, int enable ); - -#if 0 -void PaAlsa_EnableWatchdog( PaStream *s, int enable ); -#endif - -/** Get the ALSA-lib card index of this stream's input device. */ -PaError PaAlsa_GetStreamInputCard( PaStream *s, int *card ); - -/** Get the ALSA-lib card index of this stream's output device. */ -PaError PaAlsa_GetStreamOutputCard( PaStream *s, int *card ); - -/** Set the number of periods (buffer fragments) to configure devices with. - * - * By default the number of periods is 4, this is the lowest number of periods that works well on - * the author's soundcard. - * @param numPeriods The number of periods. - */ -PaError PaAlsa_SetNumPeriods( int numPeriods ); - -/** Set the maximum number of times to retry opening busy device (sleeping for a - * short interval inbetween). - */ -PaError PaAlsa_SetRetriesBusy( int retries ); - -/** Set the path and name of ALSA library file if PortAudio is configured to load it dynamically (see - * PA_ALSA_DYNAMIC). This setting will overwrite the default name set by PA_ALSA_PATHNAME define. - * @param pathName Full path with filename. Only filename can be used, but dlopen() will lookup default - * searchable directories (/usr/lib;/usr/local/lib) then. - */ -void PaAlsa_SetLibraryPathName( const char *pathName ); - -#ifdef __cplusplus -} -#endif - -#endif diff --git a/spaces/amarchheda/ChordDuplicate/portaudio/src/os/win/pa_win_wdmks_utils.c b/spaces/amarchheda/ChordDuplicate/portaudio/src/os/win/pa_win_wdmks_utils.c deleted file mode 100644 index 33731469389fbcdcefca9692195eef0e9a2a23c4..0000000000000000000000000000000000000000 --- a/spaces/amarchheda/ChordDuplicate/portaudio/src/os/win/pa_win_wdmks_utils.c +++ /dev/null @@ -1,309 +0,0 @@ -/* - * PortAudio Portable Real-Time Audio Library - * Windows WDM KS utilities - * - * Copyright (c) 1999 - 2007 Andrew Baldwin, Ross Bencina - * - * 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. - */ - -/* - * The text above constitutes the entire PortAudio license; however, - * the PortAudio community also makes the following non-binding requests: - * - * Any person wishing to distribute modifications to the Software is - * requested to send the modifications to the original developer so that - * they can be incorporated into the canonical version. It is also - * requested that these non-binding requests be included along with the - * license above. - */ - -#include-#include -#ifndef WAVE_FORMAT_IEEE_FLOAT - #define WAVE_FORMAT_IEEE_FLOAT 0x0003 // MinGW32 does not define this -#endif -#ifndef _WAVEFORMATEXTENSIBLE_ - #define _WAVEFORMATEXTENSIBLE_ // MinGW32 does not define this -#endif -#ifndef _INC_MMREG - #define _INC_MMREG // for STATIC_KSDATAFORMAT_SUBTYPE_IEEE_FLOAT -#endif -#include // MinGW32 does not define this automatically - -#if defined(__GNUC__) - -#include "../../hostapi/wasapi/mingw-include/ks.h" -#include "../../hostapi/wasapi/mingw-include/ksmedia.h" - -#else - -#include -#include - -#endif - -#include // just for some development printfs - -#include "portaudio.h" -#include "pa_util.h" -#include "pa_win_wdmks_utils.h" - - -/* PortAudio-local instances of GUIDs previously sourced from ksguid.lib */ - -/* GUID KSDATAFORMAT_TYPE_AUDIO */ -static const GUID pa_KSDATAFORMAT_TYPE_AUDIO = { STATIC_KSDATAFORMAT_TYPE_AUDIO }; - -/* GUID KSDATAFORMAT_SUBTYPE_IEEE_FLOAT */ -static const GUID pa_KSDATAFORMAT_SUBTYPE_IEEE_FLOAT = { STATIC_KSDATAFORMAT_SUBTYPE_IEEE_FLOAT }; - -/* GUID KSDATAFORMAT_SUBTYPE_PCM */ -static const GUID pa_KSDATAFORMAT_SUBTYPE_PCM = { STATIC_KSDATAFORMAT_SUBTYPE_PCM }; - -/* GUID KSDATAFORMAT_SUBTYPE_WAVEFORMATEX */ -static const GUID pa_KSDATAFORMAT_SUBTYPE_WAVEFORMATEX = { STATIC_KSDATAFORMAT_SUBTYPE_WAVEFORMATEX }; - -/* GUID KSMEDIUMSETID_Standard */ -static const GUID pa_KSMEDIUMSETID_Standard = { STATIC_KSMEDIUMSETID_Standard }; - -/* GUID KSINTERFACESETID_Standard */ -static const GUID pa_KSINTERFACESETID_Standard = { STATIC_KSINTERFACESETID_Standard }; - -/* GUID KSPROPSETID_Pin */ -static const GUID pa_KSPROPSETID_Pin = { STATIC_KSPROPSETID_Pin }; - -#define pa_IS_VALID_WAVEFORMATEX_GUID(Guid)\ - (!memcmp(((PUSHORT)&pa_KSDATAFORMAT_SUBTYPE_WAVEFORMATEX) + 1, ((PUSHORT)(Guid)) + 1, sizeof(GUID) - sizeof(USHORT))) - - -static PaError WdmGetPinPropertySimple( - HANDLE handle, - unsigned long pinId, - unsigned long property, - void* value, - unsigned long valueSize ) -{ - DWORD bytesReturned; - KSP_PIN ksPProp; - ksPProp.Property.Set = pa_KSPROPSETID_Pin; - ksPProp.Property.Id = property; - ksPProp.Property.Flags = KSPROPERTY_TYPE_GET; - ksPProp.PinId = pinId; - ksPProp.Reserved = 0; - - if( DeviceIoControl( handle, IOCTL_KS_PROPERTY, &ksPProp, sizeof(KSP_PIN), - value, valueSize, &bytesReturned, NULL ) == 0 || bytesReturned != valueSize ) - { - return paUnanticipatedHostError; - } - else - { - return paNoError; - } -} - - -static PaError WdmGetPinPropertyMulti( - HANDLE handle, - unsigned long pinId, - unsigned long property, - KSMULTIPLE_ITEM** ksMultipleItem) -{ - unsigned long multipleItemSize = 0; - KSP_PIN ksPProp; - DWORD bytesReturned; - - *ksMultipleItem = 0; - - ksPProp.Property.Set = pa_KSPROPSETID_Pin; - ksPProp.Property.Id = property; - ksPProp.Property.Flags = KSPROPERTY_TYPE_GET; - ksPProp.PinId = pinId; - ksPProp.Reserved = 0; - - if( DeviceIoControl( handle, IOCTL_KS_PROPERTY, &ksPProp.Property, - sizeof(KSP_PIN), NULL, 0, &multipleItemSize, NULL ) == 0 && GetLastError() != ERROR_MORE_DATA ) - { - return paUnanticipatedHostError; - } - - *ksMultipleItem = (KSMULTIPLE_ITEM*)PaUtil_AllocateMemory( multipleItemSize ); - if( !*ksMultipleItem ) - { - return paInsufficientMemory; - } - - if( DeviceIoControl( handle, IOCTL_KS_PROPERTY, &ksPProp, sizeof(KSP_PIN), - (void*)*ksMultipleItem, multipleItemSize, &bytesReturned, NULL ) == 0 || bytesReturned != multipleItemSize ) - { - PaUtil_FreeMemory( ksMultipleItem ); - return paUnanticipatedHostError; - } - - return paNoError; -} - - -static int GetKSFilterPinCount( HANDLE deviceHandle ) -{ - DWORD result; - - if( WdmGetPinPropertySimple( deviceHandle, 0, KSPROPERTY_PIN_CTYPES, &result, sizeof(result) ) == paNoError ){ - return result; - }else{ - return 0; - } -} - - -static KSPIN_COMMUNICATION GetKSFilterPinPropertyCommunication( HANDLE deviceHandle, int pinId ) -{ - KSPIN_COMMUNICATION result; - - if( WdmGetPinPropertySimple( deviceHandle, pinId, KSPROPERTY_PIN_COMMUNICATION, &result, sizeof(result) ) == paNoError ){ - return result; - }else{ - return KSPIN_COMMUNICATION_NONE; - } -} - - -static KSPIN_DATAFLOW GetKSFilterPinPropertyDataflow( HANDLE deviceHandle, int pinId ) -{ - KSPIN_DATAFLOW result; - - if( WdmGetPinPropertySimple( deviceHandle, pinId, KSPROPERTY_PIN_DATAFLOW, &result, sizeof(result) ) == paNoError ){ - return result; - }else{ - return (KSPIN_DATAFLOW)0; - } -} - - -static int KSFilterPinPropertyIdentifiersInclude( - HANDLE deviceHandle, int pinId, unsigned long property, const GUID *identifierSet, unsigned long identifierId ) -{ - KSMULTIPLE_ITEM* item = NULL; - KSIDENTIFIER* identifier; - int i; - int result = 0; - - if( WdmGetPinPropertyMulti( deviceHandle, pinId, property, &item) != paNoError ) - return 0; - - identifier = (KSIDENTIFIER*)(item+1); - - for( i = 0; i < (int)item->Count; i++ ) - { - if( !memcmp( (void*)&identifier[i].Set, (void*)identifierSet, sizeof( GUID ) ) && - ( identifier[i].Id == identifierId ) ) - { - result = 1; - break; - } - } - - PaUtil_FreeMemory( item ); - - return result; -} - - -/* return the maximum channel count supported by any pin on the device. - if isInput is non-zero we query input pins, otherwise output pins. -*/ -int PaWin_WDMKS_QueryFilterMaximumChannelCount( void *wcharDevicePath, int isInput ) -{ - HANDLE deviceHandle; - ULONG i; - int pinCount, pinId; - int result = 0; - KSPIN_DATAFLOW requiredDataflowDirection = (isInput ? KSPIN_DATAFLOW_OUT : KSPIN_DATAFLOW_IN ); - - if( !wcharDevicePath ) - return 0; - - deviceHandle = CreateFileW( (LPCWSTR)wcharDevicePath, FILE_SHARE_READ|FILE_SHARE_WRITE, 0, NULL, OPEN_EXISTING, 0, NULL ); - if( deviceHandle == INVALID_HANDLE_VALUE ) - return 0; - - pinCount = GetKSFilterPinCount( deviceHandle ); - for( pinId = 0; pinId < pinCount; ++pinId ) - { - KSPIN_COMMUNICATION communication = GetKSFilterPinPropertyCommunication( deviceHandle, pinId ); - KSPIN_DATAFLOW dataflow = GetKSFilterPinPropertyDataflow( deviceHandle, pinId ); - if( ( dataflow == requiredDataflowDirection ) && - (( communication == KSPIN_COMMUNICATION_SINK) || - ( communication == KSPIN_COMMUNICATION_BOTH)) - && ( KSFilterPinPropertyIdentifiersInclude( deviceHandle, pinId, - KSPROPERTY_PIN_INTERFACES, &pa_KSINTERFACESETID_Standard, KSINTERFACE_STANDARD_STREAMING ) - || KSFilterPinPropertyIdentifiersInclude( deviceHandle, pinId, - KSPROPERTY_PIN_INTERFACES, &pa_KSINTERFACESETID_Standard, KSINTERFACE_STANDARD_LOOPED_STREAMING ) ) - && KSFilterPinPropertyIdentifiersInclude( deviceHandle, pinId, - KSPROPERTY_PIN_MEDIUMS, &pa_KSMEDIUMSETID_Standard, KSMEDIUM_STANDARD_DEVIO ) ) - { - KSMULTIPLE_ITEM* item = NULL; - if( WdmGetPinPropertyMulti( deviceHandle, pinId, KSPROPERTY_PIN_DATARANGES, &item ) == paNoError ) - { - KSDATARANGE *dataRange = (KSDATARANGE*)(item+1); - - for( i=0; i < item->Count; ++i ){ - - if( pa_IS_VALID_WAVEFORMATEX_GUID(&dataRange->SubFormat) - || memcmp( (void*)&dataRange->SubFormat, (void*)&pa_KSDATAFORMAT_SUBTYPE_PCM, sizeof(GUID) ) == 0 - || memcmp( (void*)&dataRange->SubFormat, (void*)&pa_KSDATAFORMAT_SUBTYPE_IEEE_FLOAT, sizeof(GUID) ) == 0 - || ( ( memcmp( (void*)&dataRange->MajorFormat, (void*)&pa_KSDATAFORMAT_TYPE_AUDIO, sizeof(GUID) ) == 0 ) - && ( memcmp( (void*)&dataRange->SubFormat, (void*)&KSDATAFORMAT_SUBTYPE_WILDCARD, sizeof(GUID) ) == 0 ) ) ) - { - KSDATARANGE_AUDIO *dataRangeAudio = (KSDATARANGE_AUDIO*)dataRange; - - /* - printf( ">>> %d %d %d %d %S\n", isInput, dataflow, communication, dataRangeAudio->MaximumChannels, devicePath ); - - if( memcmp((void*)&dataRange->Specifier, (void*)&KSDATAFORMAT_SPECIFIER_WAVEFORMATEX, sizeof(GUID) ) == 0 ) - printf( "\tspecifier: KSDATAFORMAT_SPECIFIER_WAVEFORMATEX\n" ); - else if( memcmp((void*)&dataRange->Specifier, (void*)&KSDATAFORMAT_SPECIFIER_DSOUND, sizeof(GUID) ) == 0 ) - printf( "\tspecifier: KSDATAFORMAT_SPECIFIER_DSOUND\n" ); - else if( memcmp((void*)&dataRange->Specifier, (void*)&KSDATAFORMAT_SPECIFIER_WILDCARD, sizeof(GUID) ) == 0 ) - printf( "\tspecifier: KSDATAFORMAT_SPECIFIER_WILDCARD\n" ); - else - printf( "\tspecifier: ?\n" ); - */ - - /* - We assume that very high values for MaximumChannels are not useful and indicate - that the driver isn't prepared to tell us the real number of channels which it supports. - */ - if( dataRangeAudio->MaximumChannels < 0xFFFFUL && (int)dataRangeAudio->MaximumChannels > result ) - result = (int)dataRangeAudio->MaximumChannels; - } - - dataRange = (KSDATARANGE*)( ((char*)dataRange) + dataRange->FormatSize); - } - - PaUtil_FreeMemory( item ); - } - } - } - - CloseHandle( deviceHandle ); - return result; -} diff --git a/spaces/anaclaudia13ct/insect_detection/utils/segment/loss.py b/spaces/anaclaudia13ct/insect_detection/utils/segment/loss.py deleted file mode 100644 index b45b2c27e0a05c275cbc50064288aece3ae3e856..0000000000000000000000000000000000000000 --- a/spaces/anaclaudia13ct/insect_detection/utils/segment/loss.py +++ /dev/null @@ -1,186 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -from ..general import xywh2xyxy -from ..loss import FocalLoss, smooth_BCE -from ..metrics import bbox_iou -from ..torch_utils import de_parallel -from .general import crop_mask - - -class ComputeLoss: - # Compute losses - def __init__(self, model, autobalance=False, overlap=False): - self.sort_obj_iou = False - self.overlap = overlap - device = next(model.parameters()).device # get model device - h = model.hyp # hyperparameters - self.device = device - - # 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) - - m = de_parallel(model).model[-1] # Detect() module - self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7 - self.ssi = list(m.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 - self.na = m.na # number of anchors - self.nc = m.nc # number of classes - self.nl = m.nl # number of layers - self.nm = m.nm # number of masks - self.anchors = m.anchors - self.device = device - - def __call__(self, preds, targets, masks): # predictions, targets, model - p, proto = preds - bs, nm, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width - lcls = torch.zeros(1, device=self.device) - lbox = torch.zeros(1, device=self.device) - lobj = torch.zeros(1, device=self.device) - lseg = torch.zeros(1, device=self.device) - tcls, tbox, indices, anchors, tidxs, xywhn = 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(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj - - n = b.shape[0] # number of targets - if n: - pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split((2, 2, 1, self.nc, nm), 1) # subset of predictions - - # Box regression - pxy = pxy.sigmoid() * 2 - 0.5 - pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i] - pbox = torch.cat((pxy, pwh), 1) # predicted box - iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target) - lbox += (1.0 - iou).mean() # iou loss - - # Objectness - iou = iou.detach().clamp(0).type(tobj.dtype) - if self.sort_obj_iou: - j = iou.argsort() - b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j] - if self.gr < 1: - iou = (1.0 - self.gr) + self.gr * iou - tobj[b, a, gj, gi] = iou # iou ratio - - # Classification - if self.nc > 1: # cls loss (only if multiple classes) - t = torch.full_like(pcls, self.cn, device=self.device) # targets - t[range(n), tcls[i]] = self.cp - lcls += self.BCEcls(pcls, t) # BCE - - # Mask regression - if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample - masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0] - marea = xywhn[i][:, 2:].prod(1) # mask width, height normalized - mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device)) - for bi in b.unique(): - j = b == bi # matching index - if self.overlap: - mask_gti = torch.where(masks[bi][None] == tidxs[i][j].view(-1, 1, 1), 1.0, 0.0) - else: - mask_gti = masks[tidxs[i]][j] - lseg += self.single_mask_loss(mask_gti, pmask[j], proto[bi], mxyxy[j], marea[j]) - - 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"] - lseg *= self.hyp["box"] / bs - - loss = lbox + lobj + lcls + lseg - return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach() - - def single_mask_loss(self, gt_mask, pred, proto, xyxy, area): - # Mask loss for one image - pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (n,32) @ (32,80,80) -> (n,80,80) - loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction="none") - return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean() - - 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, tidxs, xywhn = [], [], [], [], [], [] - gain = torch.ones(8, device=self.device) # normalized to gridspace gain - ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) - if self.overlap: - batch = p[0].shape[0] - ti = [] - for i in range(batch): - num = (targets[:, 0] == i).sum() # find number of targets of each image - ti.append(torch.arange(num, device=self.device).float().view(1, num).repeat(na, 1) + 1) # (na, num) - ti = torch.cat(ti, 1) # (na, nt) - else: - ti = torch.arange(nt, device=self.device).float().view(1, nt).repeat(na, 1) - targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None], ti[..., 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=self.device).float() * g # offsets - - for i in range(self.nl): - anchors, shape = self.anchors[i], p[i].shape - gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain - - # Match targets to anchors - t = targets * gain # shape(3,n,7) - 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 - bc, gxy, gwh, at = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors - (a, tidx), (b, c) = at.long().T, bc.long().T # anchors, image, class - gij = (gxy - offsets).long() - gi, gj = gij.T # grid indices - - # Append - indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid - tbox.append(torch.cat((gxy - gij, gwh), 1)) # box - anch.append(anchors[a]) # anchors - tcls.append(c) # class - tidxs.append(tidx) - xywhn.append(torch.cat((gxy, gwh), 1) / gain[2:6]) # xywh normalized - - return tcls, tbox, indices, anch, tidxs, xywhn diff --git a/spaces/angelayeu/my_hf_space/README.md b/spaces/angelayeu/my_hf_space/README.md deleted file mode 100644 index a570b196ac2dd89ee53814a05e6cb7ada1423f25..0000000000000000000000000000000000000000 --- a/spaces/angelayeu/my_hf_space/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: My Hf Space -emoji: ⚡ -colorFrom: indigo -colorTo: green -sdk: gradio -sdk_version: 3.44.4 -app_file: app.py -pinned: false -pip_version: 23.2.1 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/ankush-003/ankush-003-nosqli_identifier/README.md b/spaces/ankush-003/ankush-003-nosqli_identifier/README.md deleted file mode 100644 index 83dc9dcb5b9183c0360c0f29585c2ae70395266e..0000000000000000000000000000000000000000 --- a/spaces/ankush-003/ankush-003-nosqli_identifier/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Ankush-003-nosqli Identifier -emoji: 😻 -colorFrom: red -colorTo: blue -sdk: gradio -sdk_version: 3.36.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/aodianyun/stable-diffusion-webui/scripts/outpainting_mk_2.py b/spaces/aodianyun/stable-diffusion-webui/scripts/outpainting_mk_2.py deleted file mode 100644 index 5d80b46cd3263ef0905514a761bb473441d8a1e7..0000000000000000000000000000000000000000 --- a/spaces/aodianyun/stable-diffusion-webui/scripts/outpainting_mk_2.py +++ /dev/null @@ -1,283 +0,0 @@ -import math - -import numpy as np -import skimage - -import modules.scripts as scripts -import gradio as gr -from PIL import Image, ImageDraw - -from modules import images, processing, devices -from modules.processing import Processed, process_images -from modules.shared import opts, cmd_opts, state - - -# this function is taken from https://github.com/parlance-zz/g-diffuser-bot -def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.05): - # helper fft routines that keep ortho normalization and auto-shift before and after fft - def _fft2(data): - if data.ndim > 2: # has channels - out_fft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128) - for c in range(data.shape[2]): - c_data = data[:, :, c] - out_fft[:, :, c] = np.fft.fft2(np.fft.fftshift(c_data), norm="ortho") - out_fft[:, :, c] = np.fft.ifftshift(out_fft[:, :, c]) - else: # one channel - out_fft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128) - out_fft[:, :] = np.fft.fft2(np.fft.fftshift(data), norm="ortho") - out_fft[:, :] = np.fft.ifftshift(out_fft[:, :]) - - return out_fft - - def _ifft2(data): - if data.ndim > 2: # has channels - out_ifft = np.zeros((data.shape[0], data.shape[1], data.shape[2]), dtype=np.complex128) - for c in range(data.shape[2]): - c_data = data[:, :, c] - out_ifft[:, :, c] = np.fft.ifft2(np.fft.fftshift(c_data), norm="ortho") - out_ifft[:, :, c] = np.fft.ifftshift(out_ifft[:, :, c]) - else: # one channel - out_ifft = np.zeros((data.shape[0], data.shape[1]), dtype=np.complex128) - out_ifft[:, :] = np.fft.ifft2(np.fft.fftshift(data), norm="ortho") - out_ifft[:, :] = np.fft.ifftshift(out_ifft[:, :]) - - return out_ifft - - def _get_gaussian_window(width, height, std=3.14, mode=0): - window_scale_x = float(width / min(width, height)) - window_scale_y = float(height / min(width, height)) - - window = np.zeros((width, height)) - x = (np.arange(width) / width * 2. - 1.) * window_scale_x - for y in range(height): - fy = (y / height * 2. - 1.) * window_scale_y - if mode == 0: - window[:, y] = np.exp(-(x ** 2 + fy ** 2) * std) - else: - window[:, y] = (1 / ((x ** 2 + 1.) * (fy ** 2 + 1.))) ** (std / 3.14) # hey wait a minute that's not gaussian - - return window - - def _get_masked_window_rgb(np_mask_grey, hardness=1.): - np_mask_rgb = np.zeros((np_mask_grey.shape[0], np_mask_grey.shape[1], 3)) - if hardness != 1.: - hardened = np_mask_grey[:] ** hardness - else: - hardened = np_mask_grey[:] - for c in range(3): - np_mask_rgb[:, :, c] = hardened[:] - return np_mask_rgb - - width = _np_src_image.shape[0] - height = _np_src_image.shape[1] - num_channels = _np_src_image.shape[2] - - np_src_image = _np_src_image[:] * (1. - np_mask_rgb) - np_mask_grey = (np.sum(np_mask_rgb, axis=2) / 3.) - img_mask = np_mask_grey > 1e-6 - ref_mask = np_mask_grey < 1e-3 - - windowed_image = _np_src_image * (1. - _get_masked_window_rgb(np_mask_grey)) - windowed_image /= np.max(windowed_image) - windowed_image += np.average(_np_src_image) * np_mask_rgb # / (1.-np.average(np_mask_rgb)) # rather than leave the masked area black, we get better results from fft by filling the average unmasked color - - src_fft = _fft2(windowed_image) # get feature statistics from masked src img - src_dist = np.absolute(src_fft) - src_phase = src_fft / src_dist - - # create a generator with a static seed to make outpainting deterministic / only follow global seed - rng = np.random.default_rng(0) - - noise_window = _get_gaussian_window(width, height, mode=1) # start with simple gaussian noise - noise_rgb = rng.random((width, height, num_channels)) - noise_grey = (np.sum(noise_rgb, axis=2) / 3.) - noise_rgb *= color_variation # the colorfulness of the starting noise is blended to greyscale with a parameter - for c in range(num_channels): - noise_rgb[:, :, c] += (1. - color_variation) * noise_grey - - noise_fft = _fft2(noise_rgb) - for c in range(num_channels): - noise_fft[:, :, c] *= noise_window - noise_rgb = np.real(_ifft2(noise_fft)) - shaped_noise_fft = _fft2(noise_rgb) - shaped_noise_fft[:, :, :] = np.absolute(shaped_noise_fft[:, :, :]) ** 2 * (src_dist ** noise_q) * src_phase # perform the actual shaping - - brightness_variation = 0. # color_variation # todo: temporarily tieing brightness variation to color variation for now - contrast_adjusted_np_src = _np_src_image[:] * (brightness_variation + 1.) - brightness_variation * 2. - - # scikit-image is used for histogram matching, very convenient! - shaped_noise = np.real(_ifft2(shaped_noise_fft)) - shaped_noise -= np.min(shaped_noise) - shaped_noise /= np.max(shaped_noise) - shaped_noise[img_mask, :] = skimage.exposure.match_histograms(shaped_noise[img_mask, :] ** 1., contrast_adjusted_np_src[ref_mask, :], channel_axis=1) - shaped_noise = _np_src_image[:] * (1. - np_mask_rgb) + shaped_noise * np_mask_rgb - - matched_noise = shaped_noise[:] - - return np.clip(matched_noise, 0., 1.) - - - -class Script(scripts.Script): - def title(self): - return "Outpainting mk2" - - def show(self, is_img2img): - return is_img2img - - def ui(self, is_img2img): - if not is_img2img: - return None - - info = gr.HTML(" Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8
") - - pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128, elem_id=self.elem_id("pixels")) - mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8, elem_id=self.elem_id("mask_blur")) - direction = gr.CheckboxGroup(label="Outpainting direction", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'], elem_id=self.elem_id("direction")) - noise_q = gr.Slider(label="Fall-off exponent (lower=higher detail)", minimum=0.0, maximum=4.0, step=0.01, value=1.0, elem_id=self.elem_id("noise_q")) - color_variation = gr.Slider(label="Color variation", minimum=0.0, maximum=1.0, step=0.01, value=0.05, elem_id=self.elem_id("color_variation")) - - return [info, pixels, mask_blur, direction, noise_q, color_variation] - - def run(self, p, _, pixels, mask_blur, direction, noise_q, color_variation): - initial_seed_and_info = [None, None] - - process_width = p.width - process_height = p.height - - p.mask_blur = mask_blur*4 - p.inpaint_full_res = False - p.inpainting_fill = 1 - p.do_not_save_samples = True - p.do_not_save_grid = True - - left = pixels if "left" in direction else 0 - right = pixels if "right" in direction else 0 - up = pixels if "up" in direction else 0 - down = pixels if "down" in direction else 0 - - init_img = p.init_images[0] - target_w = math.ceil((init_img.width + left + right) / 64) * 64 - target_h = math.ceil((init_img.height + up + down) / 64) * 64 - - if left > 0: - left = left * (target_w - init_img.width) // (left + right) - - if right > 0: - right = target_w - init_img.width - left - - if up > 0: - up = up * (target_h - init_img.height) // (up + down) - - if down > 0: - down = target_h - init_img.height - up - - def expand(init, count, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False): - is_horiz = is_left or is_right - is_vert = is_top or is_bottom - pixels_horiz = expand_pixels if is_horiz else 0 - pixels_vert = expand_pixels if is_vert else 0 - - images_to_process = [] - output_images = [] - for n in range(count): - res_w = init[n].width + pixels_horiz - res_h = init[n].height + pixels_vert - process_res_w = math.ceil(res_w / 64) * 64 - process_res_h = math.ceil(res_h / 64) * 64 - - img = Image.new("RGB", (process_res_w, process_res_h)) - img.paste(init[n], (pixels_horiz if is_left else 0, pixels_vert if is_top else 0)) - mask = Image.new("RGB", (process_res_w, process_res_h), "white") - draw = ImageDraw.Draw(mask) - draw.rectangle(( - expand_pixels + mask_blur if is_left else 0, - expand_pixels + mask_blur if is_top else 0, - mask.width - expand_pixels - mask_blur if is_right else res_w, - mask.height - expand_pixels - mask_blur if is_bottom else res_h, - ), fill="black") - - np_image = (np.asarray(img) / 255.0).astype(np.float64) - np_mask = (np.asarray(mask) / 255.0).astype(np.float64) - noised = get_matched_noise(np_image, np_mask, noise_q, color_variation) - output_images.append(Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB")) - - target_width = min(process_width, init[n].width + pixels_horiz) if is_horiz else img.width - target_height = min(process_height, init[n].height + pixels_vert) if is_vert else img.height - p.width = target_width if is_horiz else img.width - p.height = target_height if is_vert else img.height - - crop_region = ( - 0 if is_left else output_images[n].width - target_width, - 0 if is_top else output_images[n].height - target_height, - target_width if is_left else output_images[n].width, - target_height if is_top else output_images[n].height, - ) - mask = mask.crop(crop_region) - p.image_mask = mask - - image_to_process = output_images[n].crop(crop_region) - images_to_process.append(image_to_process) - - p.init_images = images_to_process - - latent_mask = Image.new("RGB", (p.width, p.height), "white") - draw = ImageDraw.Draw(latent_mask) - draw.rectangle(( - expand_pixels + mask_blur * 2 if is_left else 0, - expand_pixels + mask_blur * 2 if is_top else 0, - mask.width - expand_pixels - mask_blur * 2 if is_right else res_w, - mask.height - expand_pixels - mask_blur * 2 if is_bottom else res_h, - ), fill="black") - p.latent_mask = latent_mask - - proc = process_images(p) - - if initial_seed_and_info[0] is None: - initial_seed_and_info[0] = proc.seed - initial_seed_and_info[1] = proc.info - - for n in range(count): - output_images[n].paste(proc.images[n], (0 if is_left else output_images[n].width - proc.images[n].width, 0 if is_top else output_images[n].height - proc.images[n].height)) - output_images[n] = output_images[n].crop((0, 0, res_w, res_h)) - - return output_images - - batch_count = p.n_iter - batch_size = p.batch_size - p.n_iter = 1 - state.job_count = batch_count * ((1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0)) - all_processed_images = [] - - for i in range(batch_count): - imgs = [init_img] * batch_size - state.job = f"Batch {i + 1} out of {batch_count}" - - if left > 0: - imgs = expand(imgs, batch_size, left, is_left=True) - if right > 0: - imgs = expand(imgs, batch_size, right, is_right=True) - if up > 0: - imgs = expand(imgs, batch_size, up, is_top=True) - if down > 0: - imgs = expand(imgs, batch_size, down, is_bottom=True) - - all_processed_images += imgs - - all_images = all_processed_images - - combined_grid_image = images.image_grid(all_processed_images) - unwanted_grid_because_of_img_count = len(all_processed_images) < 2 and opts.grid_only_if_multiple - if opts.return_grid and not unwanted_grid_because_of_img_count: - all_images = [combined_grid_image] + all_processed_images - - res = Processed(p, all_images, initial_seed_and_info[0], initial_seed_and_info[1]) - - if opts.samples_save: - for img in all_processed_images: - images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p) - - if opts.grid_save and not unwanted_grid_because_of_img_count: - images.save_image(combined_grid_image, p.outpath_grids, "grid", res.seed, p.prompt, opts.grid_format, info=res.info, short_filename=not opts.grid_extended_filename, grid=True, p=p) - - return res diff --git a/spaces/aphenx/bingo/src/components/tailwind-indicator.tsx b/spaces/aphenx/bingo/src/components/tailwind-indicator.tsx deleted file mode 100644 index f2a1291213dd67055fcebe67fab574c8441338df..0000000000000000000000000000000000000000 --- a/spaces/aphenx/bingo/src/components/tailwind-indicator.tsx +++ /dev/null @@ -1,14 +0,0 @@ -export function TailwindIndicator() { - if (process.env.NODE_ENV === 'production') return null - - return ( --- ) -} diff --git a/spaces/arch-123/bingo/src/components/turn-counter.tsx b/spaces/arch-123/bingo/src/components/turn-counter.tsx deleted file mode 100644 index 08a9e488f044802a8600f4d195b106567c35aab4..0000000000000000000000000000000000000000 --- a/spaces/arch-123/bingo/src/components/turn-counter.tsx +++ /dev/null @@ -1,23 +0,0 @@ -import React from 'react' -import { Throttling } from '@/lib/bots/bing/types' - -export interface TurnCounterProps { - throttling?: Throttling -} - -export function TurnCounter({ throttling }: TurnCounterProps) { - if (!throttling) { - return null - } - - return ( -xs-sm-md-lg-xl-2xl--- ) -} diff --git a/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests/test_tacotron2_d-vectors_train.py b/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests/test_tacotron2_d-vectors_train.py deleted file mode 100644 index 99ba4349c48fcc10a352459d9d5d5f64c0b7a4de..0000000000000000000000000000000000000000 --- a/spaces/artificialguybr/video-dubbing/TTS/tests/tts_tests/test_tacotron2_d-vectors_train.py +++ /dev/null @@ -1,79 +0,0 @@ -import glob -import json -import os -import shutil - -from trainer import get_last_checkpoint - -from tests import get_device_id, get_tests_output_path, run_cli -from TTS.tts.configs.tacotron2_config import Tacotron2Config - -config_path = os.path.join(get_tests_output_path(), "test_model_config.json") -output_path = os.path.join(get_tests_output_path(), "train_outputs") - -config = Tacotron2Config( - r=5, - batch_size=8, - eval_batch_size=8, - num_loader_workers=0, - num_eval_loader_workers=0, - text_cleaner="english_cleaners", - use_phonemes=False, - phoneme_language="en-us", - phoneme_cache_path=os.path.join(get_tests_output_path(), "train_outputs/phoneme_cache/"), - run_eval=True, - test_delay_epochs=-1, - epochs=1, - print_step=1, - print_eval=True, - use_speaker_embedding=False, - use_d_vector_file=True, - test_sentences=[ - "Be a voice, not an echo.", - ], - d_vector_file="tests/data/ljspeech/speakers.json", - d_vector_dim=256, - max_decoder_steps=50, -) - -config.audio.do_trim_silence = True -config.audio.trim_db = 60 -config.save_json(config_path) - -# train the model for one epoch -command_train = ( - f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} " - f"--coqpit.output_path {output_path} " - "--coqpit.datasets.0.formatter ljspeech_test " - "--coqpit.datasets.0.meta_file_train metadata.csv " - "--coqpit.datasets.0.meta_file_val metadata.csv " - "--coqpit.datasets.0.path tests/data/ljspeech " - "--coqpit.test_delay_epochs 0 " -) -run_cli(command_train) - -# Find latest folder -continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime) - -# Inference using TTS API -continue_config_path = os.path.join(continue_path, "config.json") -continue_restore_path, _ = get_last_checkpoint(continue_path) -out_wav_path = os.path.join(get_tests_output_path(), "output.wav") -speaker_id = "ljspeech-1" -continue_speakers_path = config.d_vector_file - -# Check integrity of the config -with open(continue_config_path, "r", encoding="utf-8") as f: - config_loaded = json.load(f) -assert config_loaded["characters"] is not None -assert config_loaded["output_path"] in continue_path -assert config_loaded["test_delay_epochs"] == 0 - -# Load the model and run inference -inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --speakers_file_path {continue_speakers_path} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}" -run_cli(inference_command) - -# restore the model and continue training for one more epoch -command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} " -run_cli(command_train) -shutil.rmtree(continue_path) diff --git a/spaces/artificialguybr/video-dubbing/Wav2Lip/evaluation/gen_videos_from_filelist.py b/spaces/artificialguybr/video-dubbing/Wav2Lip/evaluation/gen_videos_from_filelist.py deleted file mode 100644 index bd666b93258d3da3143a63da742265ebeac2a8a3..0000000000000000000000000000000000000000 --- a/spaces/artificialguybr/video-dubbing/Wav2Lip/evaluation/gen_videos_from_filelist.py +++ /dev/null @@ -1,238 +0,0 @@ -from os import listdir, path -import numpy as np -import scipy, cv2, os, sys, argparse -import dlib, json, subprocess -from tqdm import tqdm -from glob import glob -import torch - -sys.path.append('../') -import audio -import face_detection -from models import Wav2Lip - -parser = argparse.ArgumentParser(description='Code to generate results for test filelists') - -parser.add_argument('--filelist', type=str, - help='Filepath of filelist file to read', required=True) -parser.add_argument('--results_dir', type=str, help='Folder to save all results into', - required=True) -parser.add_argument('--data_root', type=str, required=True) -parser.add_argument('--checkpoint_path', type=str, - help='Name of saved checkpoint to load weights from', required=True) - -parser.add_argument('--pads', nargs='+', type=int, default=[0, 0, 0, 0], - help='Padding (top, bottom, left, right)') -parser.add_argument('--face_det_batch_size', type=int, - help='Single GPU batch size for face detection', default=64) -parser.add_argument('--wav2lip_batch_size', type=int, help='Batch size for Wav2Lip', default=128) - -# parser.add_argument('--resize_factor', default=1, type=int) - -args = parser.parse_args() -args.img_size = 96 - -def get_smoothened_boxes(boxes, T): - for i in range(len(boxes)): - if i + T > len(boxes): - window = boxes[len(boxes) - T:] - else: - window = boxes[i : i + T] - boxes[i] = np.mean(window, axis=0) - return boxes - -def face_detect(images): - batch_size = args.face_det_batch_size - - while 1: - predictions = [] - try: - for i in range(0, len(images), batch_size): - predictions.extend(detector.get_detections_for_batch(np.array(images[i:i + batch_size]))) - except RuntimeError: - if batch_size == 1: - raise RuntimeError('Image too big to run face detection on GPU') - batch_size //= 2 - args.face_det_batch_size = batch_size - print('Recovering from OOM error; New batch size: {}'.format(batch_size)) - continue - break - - results = [] - pady1, pady2, padx1, padx2 = args.pads - for rect, image in zip(predictions, images): - if rect is None: - raise ValueError('Face not detected!') - - y1 = max(0, rect[1] - pady1) - y2 = min(image.shape[0], rect[3] + pady2) - x1 = max(0, rect[0] - padx1) - x2 = min(image.shape[1], rect[2] + padx2) - - results.append([x1, y1, x2, y2]) - - boxes = get_smoothened_boxes(np.array(results), T=5) - results = [[image[y1: y2, x1:x2], (y1, y2, x1, x2), True] for image, (x1, y1, x2, y2) in zip(images, boxes)] - - return results - -def datagen(frames, face_det_results, mels): - img_batch, mel_batch, frame_batch, coords_batch = [], [], [], [] - - for i, m in enumerate(mels): - if i >= len(frames): raise ValueError('Equal or less lengths only') - - frame_to_save = frames[i].copy() - face, coords, valid_frame = face_det_results[i].copy() - if not valid_frame: - continue - - face = cv2.resize(face, (args.img_size, args.img_size)) - - img_batch.append(face) - mel_batch.append(m) - frame_batch.append(frame_to_save) - coords_batch.append(coords) - - if len(img_batch) >= args.wav2lip_batch_size: - img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch) - - img_masked = img_batch.copy() - img_masked[:, args.img_size//2:] = 0 - - img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255. - mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1]) - - yield img_batch, mel_batch, frame_batch, coords_batch - img_batch, mel_batch, frame_batch, coords_batch = [], [], [], [] - - if len(img_batch) > 0: - img_batch, mel_batch = np.asarray(img_batch), np.asarray(mel_batch) - - img_masked = img_batch.copy() - img_masked[:, args.img_size//2:] = 0 - - img_batch = np.concatenate((img_masked, img_batch), axis=3) / 255. - mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1]) - - yield img_batch, mel_batch, frame_batch, coords_batch - -fps = 25 -mel_step_size = 16 -mel_idx_multiplier = 80./fps -device = 'cuda' if torch.cuda.is_available() else 'cpu' -print('Using {} for inference.'.format(device)) - -detector = face_detection.FaceAlignment(face_detection.LandmarksType._2D, - flip_input=False, device=device) - -def _load(checkpoint_path): - if device == 'cuda': - checkpoint = torch.load(checkpoint_path) - else: - checkpoint = torch.load(checkpoint_path, - map_location=lambda storage, loc: storage) - return checkpoint - -def load_model(path): - model = Wav2Lip() - print("Load checkpoint from: {}".format(path)) - checkpoint = _load(path) - s = checkpoint["state_dict"] - new_s = {} - for k, v in s.items(): - new_s[k.replace('module.', '')] = v - model.load_state_dict(new_s) - - model = model.to(device) - return model.eval() - -model = load_model(args.checkpoint_path) - -def main(): - assert args.data_root is not None - data_root = args.data_root - - if not os.path.isdir(args.results_dir): os.makedirs(args.results_dir) - - with open(args.filelist, 'r') as filelist: - lines = filelist.readlines() - - for idx, line in enumerate(tqdm(lines)): - audio_src, video = line.strip().split() - - audio_src = os.path.join(data_root, audio_src) + '.mp4' - video = os.path.join(data_root, video) + '.mp4' - - command = 'ffmpeg -loglevel panic -y -i {} -strict -2 {}'.format(audio_src, '../temp/temp.wav') - subprocess.call(command, shell=True) - temp_audio = '../temp/temp.wav' - - wav = audio.load_wav(temp_audio, 16000) - mel = audio.melspectrogram(wav) - if np.isnan(mel.reshape(-1)).sum() > 0: - continue - - mel_chunks = [] - i = 0 - while 1: - start_idx = int(i * mel_idx_multiplier) - if start_idx + mel_step_size > len(mel[0]): - break - mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size]) - i += 1 - - video_stream = cv2.VideoCapture(video) - - full_frames = [] - while 1: - still_reading, frame = video_stream.read() - if not still_reading or len(full_frames) > len(mel_chunks): - video_stream.release() - break - full_frames.append(frame) - - if len(full_frames) < len(mel_chunks): - continue - - full_frames = full_frames[:len(mel_chunks)] - - try: - face_det_results = face_detect(full_frames.copy()) - except ValueError as e: - continue - - batch_size = args.wav2lip_batch_size - gen = datagen(full_frames.copy(), face_det_results, mel_chunks) - - for i, (img_batch, mel_batch, frames, coords) in enumerate(gen): - if i == 0: - frame_h, frame_w = full_frames[0].shape[:-1] - out = cv2.VideoWriter('../temp/result.avi', - cv2.VideoWriter_fourcc(*'DIVX'), fps, (frame_w, frame_h)) - - img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device) - mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(device) - - with torch.no_grad(): - pred = model(mel_batch, img_batch) - - - pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255. - - for pl, f, c in zip(pred, frames, coords): - y1, y2, x1, x2 = c - pl = cv2.resize(pl.astype(np.uint8), (x2 - x1, y2 - y1)) - f[y1:y2, x1:x2] = pl - out.write(f) - - out.release() - - vid = os.path.join(args.results_dir, '{}.mp4'.format(idx)) - - command = 'ffmpeg -loglevel panic -y -i {} -i {} -strict -2 -q:v 1 {}'.format(temp_audio, - '../temp/result.avi', vid) - subprocess.call(command, shell=True) - -if __name__ == '__main__': - main() diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/streams.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/streams.py deleted file mode 100644 index 726b02326f66d37b9de1947cb78470479a7bc82b..0000000000000000000000000000000000000000 --- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/aiohttp/streams.py +++ /dev/null @@ -1,660 +0,0 @@ -import asyncio -import collections -import warnings -from typing import Awaitable, Callable, Deque, Generic, List, Optional, Tuple, TypeVar - -from .base_protocol import BaseProtocol -from .helpers import BaseTimerContext, set_exception, set_result -from .log import internal_logger -from .typedefs import Final - -__all__ = ( - "EMPTY_PAYLOAD", - "EofStream", - "StreamReader", - "DataQueue", - "FlowControlDataQueue", -) - -_T = TypeVar("_T") - - -class EofStream(Exception): - """eof stream indication.""" - - -class AsyncStreamIterator(Generic[_T]): - def __init__(self, read_func: Callable[[], Awaitable[_T]]) -> None: - self.read_func = read_func - - def __aiter__(self) -> "AsyncStreamIterator[_T]": - return self - - async def __anext__(self) -> _T: - try: - rv = await self.read_func() - except EofStream: - raise StopAsyncIteration - if rv == b"": - raise StopAsyncIteration - return rv - - -class ChunkTupleAsyncStreamIterator: - def __init__(self, stream: "StreamReader") -> None: - self._stream = stream - - def __aiter__(self) -> "ChunkTupleAsyncStreamIterator": - return self - - async def __anext__(self) -> Tuple[bytes, bool]: - rv = await self._stream.readchunk() - if rv == (b"", False): - raise StopAsyncIteration - return rv - - -class AsyncStreamReaderMixin: - def __aiter__(self) -> AsyncStreamIterator[bytes]: - return AsyncStreamIterator(self.readline) # type: ignore[attr-defined] - - def iter_chunked(self, n: int) -> AsyncStreamIterator[bytes]: - """Returns an asynchronous iterator that yields chunks of size n. - - Python-3.5 available for Python 3.5+ only - """ - return AsyncStreamIterator( - lambda: self.read(n) # type: ignore[attr-defined,no-any-return] - ) - - def iter_any(self) -> AsyncStreamIterator[bytes]: - """Yield all available data as soon as it is received. - - Python-3.5 available for Python 3.5+ only - """ - return AsyncStreamIterator(self.readany) # type: ignore[attr-defined] - - def iter_chunks(self) -> ChunkTupleAsyncStreamIterator: - """Yield chunks of data as they are received by the server. - - The yielded objects are tuples - of (bytes, bool) as returned by the StreamReader.readchunk method. - - Python-3.5 available for Python 3.5+ only - """ - return ChunkTupleAsyncStreamIterator(self) # type: ignore[arg-type] - - -class StreamReader(AsyncStreamReaderMixin): - """An enhancement of asyncio.StreamReader. - - Supports asynchronous iteration by line, chunk or as available:: - - async for line in reader: - ... - async for chunk in reader.iter_chunked(1024): - ... - async for slice in reader.iter_any(): - ... - - """ - - total_bytes = 0 - - def __init__( - self, - protocol: BaseProtocol, - limit: int, - *, - timer: Optional[BaseTimerContext] = None, - loop: Optional[asyncio.AbstractEventLoop] = None, - ) -> None: - self._protocol = protocol - self._low_water = limit - self._high_water = limit * 2 - if loop is None: - loop = asyncio.get_event_loop() - self._loop = loop - self._size = 0 - self._cursor = 0 - self._http_chunk_splits: Optional[List[int]] = None - self._buffer: Deque[bytes] = collections.deque() - self._buffer_offset = 0 - self._eof = False - self._waiter: Optional[asyncio.Future[None]] = None - self._eof_waiter: Optional[asyncio.Future[None]] = None - self._exception: Optional[BaseException] = None - self._timer = timer - self._eof_callbacks: List[Callable[[], None]] = [] - - def __repr__(self) -> str: - info = [self.__class__.__name__] - if self._size: - info.append("%d bytes" % self._size) - if self._eof: - info.append("eof") - if self._low_water != 2**16: # default limit - info.append("low=%d high=%d" % (self._low_water, self._high_water)) - if self._waiter: - info.append("w=%r" % self._waiter) - if self._exception: - info.append("e=%r" % self._exception) - return "<%s>" % " ".join(info) - - def get_read_buffer_limits(self) -> Tuple[int, int]: - return (self._low_water, self._high_water) - - def exception(self) -> Optional[BaseException]: - return self._exception - - def set_exception(self, exc: BaseException) -> None: - self._exception = exc - self._eof_callbacks.clear() - - waiter = self._waiter - if waiter is not None: - self._waiter = None - set_exception(waiter, exc) - - waiter = self._eof_waiter - if waiter is not None: - self._eof_waiter = None - set_exception(waiter, exc) - - def on_eof(self, callback: Callable[[], None]) -> None: - if self._eof: - try: - callback() - except Exception: - internal_logger.exception("Exception in eof callback") - else: - self._eof_callbacks.append(callback) - - def feed_eof(self) -> None: - self._eof = True - - waiter = self._waiter - if waiter is not None: - self._waiter = None - set_result(waiter, None) - - waiter = self._eof_waiter - if waiter is not None: - self._eof_waiter = None - set_result(waiter, None) - - for cb in self._eof_callbacks: - try: - cb() - except Exception: - internal_logger.exception("Exception in eof callback") - - self._eof_callbacks.clear() - - def is_eof(self) -> bool: - """Return True if 'feed_eof' was called.""" - return self._eof - - def at_eof(self) -> bool: - """Return True if the buffer is empty and 'feed_eof' was called.""" - return self._eof and not self._buffer - - async def wait_eof(self) -> None: - if self._eof: - return - - assert self._eof_waiter is None - self._eof_waiter = self._loop.create_future() - try: - await self._eof_waiter - finally: - self._eof_waiter = None - - def unread_data(self, data: bytes) -> None: - """rollback reading some data from stream, inserting it to buffer head.""" - warnings.warn( - "unread_data() is deprecated " - "and will be removed in future releases (#3260)", - DeprecationWarning, - stacklevel=2, - ) - if not data: - return - - if self._buffer_offset: - self._buffer[0] = self._buffer[0][self._buffer_offset :] - self._buffer_offset = 0 - self._size += len(data) - self._cursor -= len(data) - self._buffer.appendleft(data) - self._eof_counter = 0 - - # TODO: size is ignored, remove the param later - def feed_data(self, data: bytes, size: int = 0) -> None: - assert not self._eof, "feed_data after feed_eof" - - if not data: - return - - self._size += len(data) - self._buffer.append(data) - self.total_bytes += len(data) - - waiter = self._waiter - if waiter is not None: - self._waiter = None - set_result(waiter, None) - - if self._size > self._high_water and not self._protocol._reading_paused: - self._protocol.pause_reading() - - def begin_http_chunk_receiving(self) -> None: - if self._http_chunk_splits is None: - if self.total_bytes: - raise RuntimeError( - "Called begin_http_chunk_receiving when" "some data was already fed" - ) - self._http_chunk_splits = [] - - def end_http_chunk_receiving(self) -> None: - if self._http_chunk_splits is None: - raise RuntimeError( - "Called end_chunk_receiving without calling " - "begin_chunk_receiving first" - ) - - # self._http_chunk_splits contains logical byte offsets from start of - # the body transfer. Each offset is the offset of the end of a chunk. - # "Logical" means bytes, accessible for a user. - # If no chunks containig logical data were received, current position - # is difinitely zero. - pos = self._http_chunk_splits[-1] if self._http_chunk_splits else 0 - - if self.total_bytes == pos: - # We should not add empty chunks here. So we check for that. - # Note, when chunked + gzip is used, we can receive a chunk - # of compressed data, but that data may not be enough for gzip FSM - # to yield any uncompressed data. That's why current position may - # not change after receiving a chunk. - return - - self._http_chunk_splits.append(self.total_bytes) - - # wake up readchunk when end of http chunk received - waiter = self._waiter - if waiter is not None: - self._waiter = None - set_result(waiter, None) - - async def _wait(self, func_name: str) -> None: - # StreamReader uses a future to link the protocol feed_data() method - # to a read coroutine. Running two read coroutines at the same time - # would have an unexpected behaviour. It would not possible to know - # which coroutine would get the next data. - if self._waiter is not None: - raise RuntimeError( - "%s() called while another coroutine is " - "already waiting for incoming data" % func_name - ) - - waiter = self._waiter = self._loop.create_future() - try: - if self._timer: - with self._timer: - await waiter - else: - await waiter - finally: - self._waiter = None - - async def readline(self) -> bytes: - return await self.readuntil() - - async def readuntil(self, separator: bytes = b"\n") -> bytes: - seplen = len(separator) - if seplen == 0: - raise ValueError("Separator should be at least one-byte string") - - if self._exception is not None: - raise self._exception - - chunk = b"" - chunk_size = 0 - not_enough = True - - while not_enough: - while self._buffer and not_enough: - offset = self._buffer_offset - ichar = self._buffer[0].find(separator, offset) + 1 - # Read from current offset to found separator or to the end. - data = self._read_nowait_chunk(ichar - offset if ichar else -1) - chunk += data - chunk_size += len(data) - if ichar: - not_enough = False - - if chunk_size > self._high_water: - raise ValueError("Chunk too big") - - if self._eof: - break - - if not_enough: - await self._wait("readuntil") - - return chunk - - async def read(self, n: int = -1) -> bytes: - if self._exception is not None: - raise self._exception - - # migration problem; with DataQueue you have to catch - # EofStream exception, so common way is to run payload.read() inside - # infinite loop. what can cause real infinite loop with StreamReader - # lets keep this code one major release. - if __debug__: - if self._eof and not self._buffer: - self._eof_counter = getattr(self, "_eof_counter", 0) + 1 - if self._eof_counter > 5: - internal_logger.warning( - "Multiple access to StreamReader in eof state, " - "might be infinite loop.", - stack_info=True, - ) - - if not n: - return b"" - - if n < 0: - # This used to just loop creating a new waiter hoping to - # collect everything in self._buffer, but that would - # deadlock if the subprocess sends more than self.limit - # bytes. So just call self.readany() until EOF. - blocks = [] - while True: - block = await self.readany() - if not block: - break - blocks.append(block) - return b"".join(blocks) - - # TODO: should be `if` instead of `while` - # because waiter maybe triggered on chunk end, - # without feeding any data - while not self._buffer and not self._eof: - await self._wait("read") - - return self._read_nowait(n) - - async def readany(self) -> bytes: - if self._exception is not None: - raise self._exception - - # TODO: should be `if` instead of `while` - # because waiter maybe triggered on chunk end, - # without feeding any data - while not self._buffer and not self._eof: - await self._wait("readany") - - return self._read_nowait(-1) - - async def readchunk(self) -> Tuple[bytes, bool]: - """Returns a tuple of (data, end_of_http_chunk). - - When chunked transfer - encoding is used, end_of_http_chunk is a boolean indicating if the end - of the data corresponds to the end of a HTTP chunk , otherwise it is - always False. - """ - while True: - if self._exception is not None: - raise self._exception - - while self._http_chunk_splits: - pos = self._http_chunk_splits.pop(0) - if pos == self._cursor: - return (b"", True) - if pos > self._cursor: - return (self._read_nowait(pos - self._cursor), True) - internal_logger.warning( - "Skipping HTTP chunk end due to data " - "consumption beyond chunk boundary" - ) - - if self._buffer: - return (self._read_nowait_chunk(-1), False) - # return (self._read_nowait(-1), False) - - if self._eof: - # Special case for signifying EOF. - # (b'', True) is not a final return value actually. - return (b"", False) - - await self._wait("readchunk") - - async def readexactly(self, n: int) -> bytes: - if self._exception is not None: - raise self._exception - - blocks: List[bytes] = [] - while n > 0: - block = await self.read(n) - if not block: - partial = b"".join(blocks) - raise asyncio.IncompleteReadError(partial, len(partial) + n) - blocks.append(block) - n -= len(block) - - return b"".join(blocks) - - def read_nowait(self, n: int = -1) -> bytes: - # default was changed to be consistent with .read(-1) - # - # I believe the most users don't know about the method and - # they are not affected. - if self._exception is not None: - raise self._exception - - if self._waiter and not self._waiter.done(): - raise RuntimeError( - "Called while some coroutine is waiting for incoming data." - ) - - return self._read_nowait(n) - - def _read_nowait_chunk(self, n: int) -> bytes: - first_buffer = self._buffer[0] - offset = self._buffer_offset - if n != -1 and len(first_buffer) - offset > n: - data = first_buffer[offset : offset + n] - self._buffer_offset += n - - elif offset: - self._buffer.popleft() - data = first_buffer[offset:] - self._buffer_offset = 0 - - else: - data = self._buffer.popleft() - - self._size -= len(data) - self._cursor += len(data) - - chunk_splits = self._http_chunk_splits - # Prevent memory leak: drop useless chunk splits - while chunk_splits and chunk_splits[0] < self._cursor: - chunk_splits.pop(0) - - if self._size < self._low_water and self._protocol._reading_paused: - self._protocol.resume_reading() - return data - - def _read_nowait(self, n: int) -> bytes: - """Read not more than n bytes, or whole buffer if n == -1""" - chunks = [] - - while self._buffer: - chunk = self._read_nowait_chunk(n) - chunks.append(chunk) - if n != -1: - n -= len(chunk) - if n == 0: - break - - return b"".join(chunks) if chunks else b"" - - -class EmptyStreamReader(StreamReader): # lgtm [py/missing-call-to-init] - def __init__(self) -> None: - pass - - def exception(self) -> Optional[BaseException]: - return None - - def set_exception(self, exc: BaseException) -> None: - pass - - def on_eof(self, callback: Callable[[], None]) -> None: - try: - callback() - except Exception: - internal_logger.exception("Exception in eof callback") - - def feed_eof(self) -> None: - pass - - def is_eof(self) -> bool: - return True - - def at_eof(self) -> bool: - return True - - async def wait_eof(self) -> None: - return - - def feed_data(self, data: bytes, n: int = 0) -> None: - pass - - async def readline(self) -> bytes: - return b"" - - async def read(self, n: int = -1) -> bytes: - return b"" - - # TODO add async def readuntil - - async def readany(self) -> bytes: - return b"" - - async def readchunk(self) -> Tuple[bytes, bool]: - return (b"", True) - - async def readexactly(self, n: int) -> bytes: - raise asyncio.IncompleteReadError(b"", n) - - def read_nowait(self, n: int = -1) -> bytes: - return b"" - - -EMPTY_PAYLOAD: Final[StreamReader] = EmptyStreamReader() - - -class DataQueue(Generic[_T]): - """DataQueue is a general-purpose blocking queue with one reader.""" - - def __init__(self, loop: asyncio.AbstractEventLoop) -> None: - self._loop = loop - self._eof = False - self._waiter: Optional[asyncio.Future[None]] = None - self._exception: Optional[BaseException] = None - self._size = 0 - self._buffer: Deque[Tuple[_T, int]] = collections.deque() - - def __len__(self) -> int: - return len(self._buffer) - - def is_eof(self) -> bool: - return self._eof - - def at_eof(self) -> bool: - return self._eof and not self._buffer - - def exception(self) -> Optional[BaseException]: - return self._exception - - def set_exception(self, exc: BaseException) -> None: - self._eof = True - self._exception = exc - - waiter = self._waiter - if waiter is not None: - self._waiter = None - set_exception(waiter, exc) - - def feed_data(self, data: _T, size: int = 0) -> None: - self._size += size - self._buffer.append((data, size)) - - waiter = self._waiter - if waiter is not None: - self._waiter = None - set_result(waiter, None) - - def feed_eof(self) -> None: - self._eof = True - - waiter = self._waiter - if waiter is not None: - self._waiter = None - set_result(waiter, None) - - async def read(self) -> _T: - if not self._buffer and not self._eof: - assert not self._waiter - self._waiter = self._loop.create_future() - try: - await self._waiter - except (asyncio.CancelledError, asyncio.TimeoutError): - self._waiter = None - raise - - if self._buffer: - data, size = self._buffer.popleft() - self._size -= size - return data - else: - if self._exception is not None: - raise self._exception - else: - raise EofStream - - def __aiter__(self) -> AsyncStreamIterator[_T]: - return AsyncStreamIterator(self.read) - - -class FlowControlDataQueue(DataQueue[_T]): - """FlowControlDataQueue resumes and pauses an underlying stream. - - It is a destination for parsed data. - """ - - def __init__( - self, protocol: BaseProtocol, limit: int, *, loop: asyncio.AbstractEventLoop - ) -> None: - super().__init__(loop=loop) - - self._protocol = protocol - self._limit = limit * 2 - - def feed_data(self, data: _T, size: int = 0) -> None: - super().feed_data(data, size) - - if self._size > self._limit and not self._protocol._reading_paused: - self._protocol.pause_reading() - - async def read(self) -> _T: - try: - return await super().read() - finally: - if self._size < self._limit and self._protocol._reading_paused: - self._protocol.resume_reading() diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/multiple_interactions.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/multiple_interactions.py deleted file mode 100644 index 2588b7e1d2b61b05eb3adf567d16535a6fb51fc3..0000000000000000000000000000000000000000 --- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/altair/examples/multiple_interactions.py +++ /dev/null @@ -1,90 +0,0 @@ -""" -Multiple Interactions -===================== -This example shows how multiple user inputs can be layered onto a chart. The four inputs have functionality as follows: - -* Dropdown: Filters the movies by genre -* Radio Buttons: Highlights certain films by Worldwide Gross -* Mouse Drag and Scroll: Zooms the x and y scales to allow for panning. - - - -""" -# category: interactive charts -import altair as alt -from vega_datasets import data - -movies = alt.UrlData( - data.movies.url, - format=alt.DataFormat(parse={"Release_Date":"date"}) -) -ratings = ['G', 'NC-17', 'PG', 'PG-13', 'R'] -genres = ['Action', 'Adventure', 'Black Comedy', 'Comedy', - 'Concert/Performance', 'Documentary', 'Drama', 'Horror', 'Musical', - 'Romantic Comedy', 'Thriller/Suspense', 'Western'] - -base = alt.Chart(movies, width=200, height=200).mark_point(filled=True).transform_calculate( - Rounded_IMDB_Rating = "floor(datum.IMDB_Rating)", - Hundred_Million_Production = "datum.Production_Budget > 100000000.0 ? 100 : 10", - Release_Year = "year(datum.Release_Date)" -).transform_filter( - alt.datum.IMDB_Rating > 0 -).transform_filter( - alt.FieldOneOfPredicate(field='MPAA_Rating', oneOf=ratings) -).encode( - x=alt.X('Worldwide_Gross:Q', scale=alt.Scale(domain=(100000,10**9), clamp=True)), - y='IMDB_Rating:Q', - tooltip="Title:N" -) - -# A slider filter -year_slider = alt.binding_range(min=1969, max=2018, step=1) -slider_selection = alt.selection_single(bind=year_slider, fields=['Release_Year'], name="Release Year_") - - -filter_year = base.add_selection( - slider_selection -).transform_filter( - slider_selection -).properties(title="Slider Filtering") - -# A dropdown filter -genre_dropdown = alt.binding_select(options=genres) -genre_select = alt.selection_single(fields=['Major_Genre'], bind=genre_dropdown, name="Genre") - -filter_genres = base.add_selection( - genre_select -).transform_filter( - genre_select -).properties(title="Dropdown Filtering") - -#color changing marks -rating_radio = alt.binding_radio(options=ratings) - -rating_select = alt.selection_single(fields=['MPAA_Rating'], bind=rating_radio, name="Rating") -rating_color_condition = alt.condition(rating_select, - alt.Color('MPAA_Rating:N', legend=None), - alt.value('lightgray')) - -highlight_ratings = base.add_selection( - rating_select -).encode( - color=rating_color_condition -).properties(title="Radio Button Highlighting") - -# Boolean selection for format changes -input_checkbox = alt.binding_checkbox() -checkbox_selection = alt.selection_single(bind=input_checkbox, name="Big Budget Films") - -size_checkbox_condition = alt.condition(checkbox_selection, - alt.SizeValue(25), - alt.Size('Hundred_Million_Production:Q') - ) - -budget_sizing = base.add_selection( - checkbox_selection -).encode( - size=size_checkbox_condition -).properties(title="Checkbox Formatting") - -( filter_year | filter_genres) & (highlight_ratings | budget_sizing ) diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/attrs/__init__.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/attrs/__init__.py deleted file mode 100644 index a704b8b56bc0ff8562c5fe7e283e09154129b264..0000000000000000000000000000000000000000 --- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/attrs/__init__.py +++ /dev/null @@ -1,70 +0,0 @@ -# SPDX-License-Identifier: MIT - -from attr import ( - NOTHING, - Attribute, - Factory, - __author__, - __copyright__, - __description__, - __doc__, - __email__, - __license__, - __title__, - __url__, - __version__, - __version_info__, - assoc, - cmp_using, - define, - evolve, - field, - fields, - fields_dict, - frozen, - has, - make_class, - mutable, - resolve_types, - validate, -) -from attr._next_gen import asdict, astuple - -from . import converters, exceptions, filters, setters, validators - - -__all__ = [ - "__author__", - "__copyright__", - "__description__", - "__doc__", - "__email__", - "__license__", - "__title__", - "__url__", - "__version__", - "__version_info__", - "asdict", - "assoc", - "astuple", - "Attribute", - "cmp_using", - "converters", - "define", - "evolve", - "exceptions", - "Factory", - "field", - "fields_dict", - "fields", - "filters", - "frozen", - "has", - "make_class", - "mutable", - "NOTHING", - "resolve_types", - "setters", - "validate", - "validators", -] diff --git a/spaces/asafAdge/Detic/tools/get_lvis_cat_info.py b/spaces/asafAdge/Detic/tools/get_lvis_cat_info.py deleted file mode 100644 index 83f286983ce811c4057ea8e8041e6a95dda78113..0000000000000000000000000000000000000000 --- a/spaces/asafAdge/Detic/tools/get_lvis_cat_info.py +++ /dev/null @@ -1,44 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import argparse -import json - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument("--ann", default='datasets/lvis/lvis_v1_train.json') - parser.add_argument("--add_freq", action='store_true') - parser.add_argument("--r_thresh", type=int, default=10) - parser.add_argument("--c_thresh", type=int, default=100) - args = parser.parse_args() - - print('Loading', args.ann) - data = json.load(open(args.ann, 'r')) - cats = data['categories'] - image_count = {x['id']: set() for x in cats} - ann_count = {x['id']: 0 for x in cats} - for x in data['annotations']: - image_count[x['category_id']].add(x['image_id']) - ann_count[x['category_id']] += 1 - num_freqs = {x: 0 for x in ['r', 'f', 'c']} - for x in cats: - x['image_count'] = len(image_count[x['id']]) - x['instance_count'] = ann_count[x['id']] - if args.add_freq: - freq = 'f' - if x['image_count'] < args.c_thresh: - freq = 'c' - if x['image_count'] < args.r_thresh: - freq = 'r' - x['frequency'] = freq - num_freqs[freq] += 1 - print(cats) - image_counts = sorted([x['image_count'] for x in cats]) - # print('image count', image_counts) - # import pdb; pdb.set_trace() - if args.add_freq: - for x in ['r', 'c', 'f']: - print(x, num_freqs[x]) - out = cats # {'categories': cats} - out_path = args.ann[:-5] + '_cat_info.json' - print('Saving to', out_path) - json.dump(out, open(out_path, 'w')) - diff --git a/spaces/awacke1/StreamlitComponentsStylingMarkdown/app.py b/spaces/awacke1/StreamlitComponentsStylingMarkdown/app.py deleted file mode 100644 index 73e188dbc20a96eaa0d82eca862ff4c7b4e2a0e2..0000000000000000000000000000000000000000 --- a/spaces/awacke1/StreamlitComponentsStylingMarkdown/app.py +++ /dev/null @@ -1,38 +0,0 @@ -# Import necessary libraries -import streamlit as st -import streamlit.components.v1 as components - -# Define HTML code for custom styling -html_code = """ - -""" - -# Apply the custom styling -components.html(html_code, height=0) - -# Define markdown text -markdown_text = """ -| Document Type | Description | Emoji | -| :-------------: |:-------------:| :-----:| -| [Allergies](https://github.com/HL7/C-CDA-Examples/tree/master/Allergies) | Documents relating to a patient's known allergies. | 🤧 | -| [Care Team](https://github.com/HL7/C-CDA-Examples/tree/master/Care%20Team) | Documentation of a patient's care team, including doctors, nurses, and other healthcare providers. | 👩⚕️👨⚕️ | -| [Encounters](https://github.com/HL7/C-CDA-Examples/tree/master/Encounters) | Records of a patient's encounters with healthcare providers. | 🏥 | -| [Family History](https://github.com/HL7/C-CDA-Examples/tree/master/Family%20History) | Information about the health of a patient's family, which can be relevant for understanding a patient's health risks. | 👪 | -| [Functional Status](https://github.com/HL7/C-CDA-Examples/tree/master/Functional%20Status) | Information about a patient's functional status, including any disabilities or impairments. | 🚶♀️🚶♂️ | -| [Goals](https://github.com/HL7/C-CDA-Examples/tree/master/Goals) | Documentation of a patient's health goals. | 🎯 | -| [Health Concerns](https://github.com/HL7/C-CDA-Examples/tree/master/Health%20Concerns) | Documents containing information about a patient's health concerns, including any specific issues or conditions the patient is dealing with. | 💔 | -| [Immunizations](https://github.com/HL7/C-CDA-Examples/tree/master/Immunizations) | Records of a patient's immunizations. | 💉 | -| [Medications](https://github.com/HL7/C-CDA-Examples/tree/master/Medications) | Documentation of a patient's current and past medications. | 💊 | -| [Problems](https://github.com/HL7/C-CDA-Examples/tree/master/Problems) | Records of a patient's health problems. | 🤒 | -""" - -# Create a tab interface with the markdown code -st.sidebar.markdown("## Medical Document Types") -st.sidebar.markdown(markdown_text) diff --git a/spaces/awacke1/Token-Classification-NER-dslim-bert-base-NER/README.md b/spaces/awacke1/Token-Classification-NER-dslim-bert-base-NER/README.md deleted file mode 100644 index d84807f91605fc6d1a3092cf617f037e6906c4c2..0000000000000000000000000000000000000000 --- a/spaces/awacke1/Token-Classification-NER-dslim-bert-base-NER/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Token Classification NER Dslim Bert Base NER -emoji: 👁 -colorFrom: pink -colorTo: red -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/awen666/web-ui/_next/static/chunks/polyfills-78c92fac7aa8fdd8.js b/spaces/awen666/web-ui/_next/static/chunks/polyfills-78c92fac7aa8fdd8.js deleted file mode 100644 index 6c105cac9df378f58bc8f459abc733e6933e27d4..0000000000000000000000000000000000000000 --- a/spaces/awen666/web-ui/_next/static/chunks/polyfills-78c92fac7aa8fdd8.js +++ /dev/null @@ -1 +0,0 @@ -!function(){var t="undefined"!=typeof globalThis?globalThis:"undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:{};function e(t){var e={exports:{}};return t(e,e.exports),e.exports}var r=function(t){return t&&t.Math==Math&&t},n=r("object"==typeof globalThis&&globalThis)||r("object"==typeof window&&window)||r("object"==typeof self&&self)||r("object"==typeof t&&t)||Function("return 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t||u||0;return!t&&-1}},vt={includes:dt(!0),indexOf:dt(!1)},gt=vt.indexOf,yt=function(t,e){var r,n=g(t),o=0,i=[];for(r in n)!w(H,r)&&w(n,r)&&i.push(r);for(;e.length>o;)w(n,r=e[o++])&&(~gt(i,r)||i.push(r));return i},mt=["constructor","hasOwnProperty","isPrototypeOf","propertyIsEnumerable","toLocaleString","toString","valueOf"],bt=mt.concat("length","prototype"),wt={f:Object.getOwnPropertyNames||function(t){return yt(t,bt)}},St={f:Object.getOwnPropertySymbols},Et=it("Reflect","ownKeys")||function(t){var e=wt.f(j(t)),r=St.f;return r?e.concat(r(t)):e},xt=function(t,e){for(var r=Et(e),n=I.f,o=R.f,i=0;i- {throttling.numUserMessagesInConversation} - 共 - {throttling.maxNumUserMessagesInConversation} -- --""" - - - -import gradio as gr -import sys -import os -import subprocess -from pydub import AudioSegment -from huggingface_hub import snapshot_download - -def install_fairseq(): - try: - # Run pip install command to install fairseq - subprocess.check_call(["pip", "install", "fairseq"]) - subprocess.check_call(["pip", "install", "sentencepiece"]) - subprocess.check_call(["pip", "install", "soundfile"]) - return "fairseq successfully installed!" - except subprocess.CalledProcessError as e: - return f"An error occurred while installing fairseq: {str(e)}" - -def convert_audio_to_16k_wav(audio_input): - sound = AudioSegment.from_file(audio_input) - sample_rate = sound.frame_rate - num_channels = sound.channels - num_frames = int(sound.frame_count()) - filename = audio_input.split("/")[-1] - print("original file is at:", audio_input) - if (num_channels > 1) or (sample_rate != 16000): # convert to mono-channel 16k wav - if num_channels > 1: - sound = sound.set_channels(1) - if sample_rate != 16000: - sound = sound.set_frame_rate(16000) - num_frames = int(sound.frame_count()) - filename = filename.replace(".wav", "") + "_16k.wav" - sound.export(f"{filename}", format="wav") - return filename - - -def run_my_code(input_text, language): - # TODO better argument handling - audio=convert_audio_to_16k_wav(input_text) - hi_wav = audio - - data_root="" - model_checkpoint="" - d_r="" - - if(language=="Hindi"): - model_checkpoint = "./models/hi_m.pt" - data_root="./lang/hi/" - - if(language=="Gujrati"): - model_checkpoint = "./models/gj_m.pt" - data_root="./lang/gj/" - - if(language=="Bengali"): - model_checkpoint = "./models/bn_m.pt" - data_root="./lang/bn/" - - if(language=="Nepali"): - model_checkpoint = "./models/ne_m.pt" - data_root="./lang/ne/" - - if(language=="Tamil"): - model_checkpoint = "./models/tm_m.pt" - data_root="./lang/tm/" - - if(language=="Marathi"): - model_checkpoint = "./models/mt_m.pt" - data_root="./lang/mt/" - - #os.system(f"cp {hi_wav} {data_root}data/tst-COMMON/wav/test.wav") - f = open('input.txt', 'w') - f.write(hi_wav) - - f = open('input.txt', 'r') - content = f. read() - print(content) - print(hi_wav) - - print("------Performing translation...") - #subprocess.run(["fairseq-interactive", data_root, "--config-yaml", "config_st.yaml", "--task", "speech_to_text", "--path", model_checkpoint, "--max-tokens", "50000", "--beam", "5" ,"--input" ,"input.txt"]) - translation_result = subprocess.run(["fairseq-interactive", data_root, "--config-yaml", "config_st.yaml", "--task", "speech_to_text", "--path", model_checkpoint, "--max-tokens", "50000", "--beam", "5" ,"--input" ,"input.txt"], capture_output=True, text=True) - translation_result_text = translation_result.stdout - - lines = translation_result_text.split("\n") - - output_text="" - print("\n\n------Translation results are:") - for i in lines: - if (i.startswith("D-0")): - print(i.split("\t")[2]) - output_text=i.split("\t")[2] - break - - #os.system(f"rm {data_root}data/tst-COMMON/wav/test.wav") - f = open('input.txt', 'w') - f.write("") - - f = open('input.txt', 'r') - content = f. read() - print(content) - return output_text - -install_fairseq() - -# Define the input and output interfaces for Gradio -#inputs = [ - # gr.inputs.Audio(source="microphone", type="filepath", label="Record something (in English)..."), - # gr.inputs.Dropdown(list(LANGUAGE_CODES.keys()), default="Hindi", label="From English to Languages X..."), - # ] - -#input_textbox = gr.inputs.Textbox(label="test2.wav") -#input=gr.inputs.Audio(source="microphone", type="filepath", label="Record something (in English)...") -#audio=convert_audio_to_16k_wav(input) -output_textbox = gr.outputs.Textbox(label="Translated Text") - -# Create a Gradio interface -iface = gr.Interface( - fn=run_my_code, - inputs=[gr.inputs.Audio(source="microphone", type="filepath", label="Record something (in American English accent)"), gr.inputs.Radio(["Hindi", "Gujrati", "Bengali", "Tamil", "Nepali", "Marathi"], label="Language")], - outputs=output_textbox, - title="English to Indic Language Translator") - -# Launch the interface -iface.launch() \ No newline at end of file diff --git a/spaces/banana-dev/demo-illusion-diffusion-hq/README.md b/spaces/banana-dev/demo-illusion-diffusion-hq/README.md deleted file mode 100644 index afd4c481758eb7996abbc1e7cfdc0376462d35a2..0000000000000000000000000000000000000000 --- a/spaces/banana-dev/demo-illusion-diffusion-hq/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Demo Illusion Diffusion Hq -emoji: 📉 -colorFrom: indigo -colorTo: indigo -sdk: gradio -sdk_version: 3.44.4 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/banana-projects/web3d/node_modules/three/src/loaders/LoadingManager.d.ts b/spaces/banana-projects/web3d/node_modules/three/src/loaders/LoadingManager.d.ts deleted file mode 100644 index bbc09390bb18081264dab8873e0954e06118db75..0000000000000000000000000000000000000000 --- a/spaces/banana-projects/web3d/node_modules/three/src/loaders/LoadingManager.d.ts +++ /dev/null @@ -1,76 +0,0 @@ -import { Geometry } from './../core/Geometry'; -import { Material } from './../materials/Material'; -import { Loader } from './Loader'; -/** - * A loader for loading objects in JSON format. - */ -export class JSONLoader extends Loader { - constructor(manager?: LoadingManager); - - manager: LoadingManager; - withCredentials: boolean; - - load( - url: string, - onLoad?: (geometry: Geometry, materials: Material[]) => void, - onProgress?: (event: ProgressEvent) => void, - onError?: (event: ErrorEvent) => void - ): void; - setTexturePath(value: string): void; - parse( - json: any, - texturePath?: string - ): { geometry: Geometry; materials?: Material[] }; -} - -export const DefaultLoadingManager: LoadingManager; - -/** - * Handles and keeps track of loaded and pending data. - */ -export class LoadingManager { - constructor( - onLoad?: () => void, - onProgress?: (url: string, loaded: number, total: number) => void, - onError?: (url: string) => void - ); - - onStart?: (url: string, loaded: number, total: number) => void; - - /** - * Will be called when load starts. - * The default is a function with empty body. - */ - onLoad: () => void; - - /** - * Will be called while load progresses. - * The default is a function with empty body. - */ - onProgress: (item: any, loaded: number, total: number) => void; - - /** - * Will be called when each element in the scene completes loading. - * The default is a function with empty body. - */ - onError: (url: string) => void; - - /** - * If provided, the callback will be passed each resource URL before a request is sent. - * The callback may return the original URL, or a new URL to override loading behavior. - * This behavior can be used to load assets from .ZIP files, drag-and-drop APIs, and Data URIs. - * @param callback URL modifier callback. Called with url argument, and must return resolvedURL. - */ - setURLModifier(callback?: (url: string) => string): void; - - /** - * Given a URL, uses the URL modifier callback (if any) and returns a resolved URL. - * If no URL modifier is set, returns the original URL. - * @param url the url to load - */ - resolveURL(url: string): string; - - itemStart(url: string): void; - itemEnd(url: string): void; - itemError(url: string): void; -} diff --git a/spaces/beihai/GFPGAN-V1.3-whole-image/basicsr/archs/hifacegan_util.py b/spaces/beihai/GFPGAN-V1.3-whole-image/basicsr/archs/hifacegan_util.py deleted file mode 100644 index 35cbef3f532fcc6aab0fa57ab316a546d3a17bd5..0000000000000000000000000000000000000000 --- a/spaces/beihai/GFPGAN-V1.3-whole-image/basicsr/archs/hifacegan_util.py +++ /dev/null @@ -1,255 +0,0 @@ -import re -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.nn import init -# Warning: spectral norm could be buggy -# under eval mode and multi-GPU inference -# A workaround is sticking to single-GPU inference and train mode -from torch.nn.utils import spectral_norm - - -class SPADE(nn.Module): - - def __init__(self, config_text, norm_nc, label_nc): - super().__init__() - - assert config_text.startswith('spade') - parsed = re.search('spade(\\D+)(\\d)x\\d', config_text) - param_free_norm_type = str(parsed.group(1)) - ks = int(parsed.group(2)) - - if param_free_norm_type == 'instance': - self.param_free_norm = nn.InstanceNorm2d(norm_nc) - elif param_free_norm_type == 'syncbatch': - print('SyncBatchNorm is currently not supported under single-GPU mode, switch to "instance" instead') - self.param_free_norm = nn.InstanceNorm2d(norm_nc) - elif param_free_norm_type == 'batch': - self.param_free_norm = nn.BatchNorm2d(norm_nc, affine=False) - else: - raise ValueError(f'{param_free_norm_type} is not a recognized param-free norm type in SPADE') - - # The dimension of the intermediate embedding space. Yes, hardcoded. - nhidden = 128 if norm_nc > 128 else norm_nc - - pw = ks // 2 - self.mlp_shared = nn.Sequential(nn.Conv2d(label_nc, nhidden, kernel_size=ks, padding=pw), nn.ReLU()) - self.mlp_gamma = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw, bias=False) - self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=ks, padding=pw, bias=False) - - def forward(self, x, segmap): - - # Part 1. generate parameter-free normalized activations - normalized = self.param_free_norm(x) - - # Part 2. produce scaling and bias conditioned on semantic map - segmap = F.interpolate(segmap, size=x.size()[2:], mode='nearest') - actv = self.mlp_shared(segmap) - gamma = self.mlp_gamma(actv) - beta = self.mlp_beta(actv) - - # apply scale and bias - out = normalized * gamma + beta - - return out - - -class SPADEResnetBlock(nn.Module): - """ - ResNet block that uses SPADE. It differs from the ResNet block of pix2pixHD in that - it takes in the segmentation map as input, learns the skip connection if necessary, - and applies normalization first and then convolution. - This architecture seemed like a standard architecture for unconditional or - class-conditional GAN architecture using residual block. - The code was inspired from https://github.com/LMescheder/GAN_stability. - """ - - def __init__(self, fin, fout, norm_g='spectralspadesyncbatch3x3', semantic_nc=3): - super().__init__() - # Attributes - self.learned_shortcut = (fin != fout) - fmiddle = min(fin, fout) - - # create conv layers - self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=1) - self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=1) - if self.learned_shortcut: - self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False) - - # apply spectral norm if specified - if 'spectral' in norm_g: - self.conv_0 = spectral_norm(self.conv_0) - self.conv_1 = spectral_norm(self.conv_1) - if self.learned_shortcut: - self.conv_s = spectral_norm(self.conv_s) - - # define normalization layers - spade_config_str = norm_g.replace('spectral', '') - self.norm_0 = SPADE(spade_config_str, fin, semantic_nc) - self.norm_1 = SPADE(spade_config_str, fmiddle, semantic_nc) - if self.learned_shortcut: - self.norm_s = SPADE(spade_config_str, fin, semantic_nc) - - # note the resnet block with SPADE also takes in |seg|, - # the semantic segmentation map as input - def forward(self, x, seg): - x_s = self.shortcut(x, seg) - dx = self.conv_0(self.act(self.norm_0(x, seg))) - dx = self.conv_1(self.act(self.norm_1(dx, seg))) - out = x_s + dx - return out - - def shortcut(self, x, seg): - if self.learned_shortcut: - x_s = self.conv_s(self.norm_s(x, seg)) - else: - x_s = x - return x_s - - def act(self, x): - return F.leaky_relu(x, 2e-1) - - -class BaseNetwork(nn.Module): - """ A basis for hifacegan archs with custom initialization """ - - def init_weights(self, init_type='normal', gain=0.02): - - def init_func(m): - classname = m.__class__.__name__ - if classname.find('BatchNorm2d') != -1: - if hasattr(m, 'weight') and m.weight is not None: - init.normal_(m.weight.data, 1.0, gain) - if hasattr(m, 'bias') and m.bias is not None: - init.constant_(m.bias.data, 0.0) - elif hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): - if init_type == 'normal': - init.normal_(m.weight.data, 0.0, gain) - elif init_type == 'xavier': - init.xavier_normal_(m.weight.data, gain=gain) - elif init_type == 'xavier_uniform': - init.xavier_uniform_(m.weight.data, gain=1.0) - elif init_type == 'kaiming': - init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') - elif init_type == 'orthogonal': - init.orthogonal_(m.weight.data, gain=gain) - elif init_type == 'none': # uses pytorch's default init method - m.reset_parameters() - else: - raise NotImplementedError(f'initialization method [{init_type}] is not implemented') - if hasattr(m, 'bias') and m.bias is not None: - init.constant_(m.bias.data, 0.0) - - self.apply(init_func) - - # propagate to children - for m in self.children(): - if hasattr(m, 'init_weights'): - m.init_weights(init_type, gain) - - def forward(self, x): - pass - - -def lip2d(x, logit, kernel=3, stride=2, padding=1): - weight = logit.exp() - return F.avg_pool2d(x * weight, kernel, stride, padding) / F.avg_pool2d(weight, kernel, stride, padding) - - -class SoftGate(nn.Module): - COEFF = 12.0 - - def forward(self, x): - return torch.sigmoid(x).mul(self.COEFF) - - -class SimplifiedLIP(nn.Module): - - def __init__(self, channels): - super(SimplifiedLIP, self).__init__() - self.logit = nn.Sequential( - nn.Conv2d(channels, channels, 3, padding=1, bias=False), nn.InstanceNorm2d(channels, affine=True), - SoftGate()) - - def init_layer(self): - self.logit[0].weight.data.fill_(0.0) - - def forward(self, x): - frac = lip2d(x, self.logit(x)) - return frac - - -class LIPEncoder(BaseNetwork): - """Local Importance-based Pooling (Ziteng Gao et.al.,ICCV 2019)""" - - def __init__(self, input_nc, ngf, sw, sh, n_2xdown, norm_layer=nn.InstanceNorm2d): - super().__init__() - self.sw = sw - self.sh = sh - self.max_ratio = 16 - # 20200310: Several Convolution (stride 1) + LIP blocks, 4 fold - kw = 3 - pw = (kw - 1) // 2 - - model = [ - nn.Conv2d(input_nc, ngf, kw, stride=1, padding=pw, bias=False), - norm_layer(ngf), - nn.ReLU(), - ] - cur_ratio = 1 - for i in range(n_2xdown): - next_ratio = min(cur_ratio * 2, self.max_ratio) - model += [ - SimplifiedLIP(ngf * cur_ratio), - nn.Conv2d(ngf * cur_ratio, ngf * next_ratio, kw, stride=1, padding=pw), - norm_layer(ngf * next_ratio), - ] - cur_ratio = next_ratio - if i < n_2xdown - 1: - model += [nn.ReLU(inplace=True)] - - self.model = nn.Sequential(*model) - - def forward(self, x): - return self.model(x) - - -def get_nonspade_norm_layer(norm_type='instance'): - # helper function to get # output channels of the previous layer - def get_out_channel(layer): - if hasattr(layer, 'out_channels'): - return getattr(layer, 'out_channels') - return layer.weight.size(0) - - # this function will be returned - def add_norm_layer(layer): - nonlocal norm_type - if norm_type.startswith('spectral'): - layer = spectral_norm(layer) - subnorm_type = norm_type[len('spectral'):] - - if subnorm_type == 'none' or len(subnorm_type) == 0: - return layer - - # remove bias in the previous layer, which is meaningless - # since it has no effect after normalization - if getattr(layer, 'bias', None) is not None: - delattr(layer, 'bias') - layer.register_parameter('bias', None) - - if subnorm_type == 'batch': - norm_layer = nn.BatchNorm2d(get_out_channel(layer), affine=True) - elif subnorm_type == 'sync_batch': - print('SyncBatchNorm is currently not supported under single-GPU mode, switch to "instance" instead') - # norm_layer = SynchronizedBatchNorm2d( - # get_out_channel(layer), affine=True) - norm_layer = nn.InstanceNorm2d(get_out_channel(layer), affine=False) - elif subnorm_type == 'instance': - norm_layer = nn.InstanceNorm2d(get_out_channel(layer), affine=False) - else: - raise ValueError(f'normalization layer {subnorm_type} is not recognized') - - return nn.Sequential(layer, norm_layer) - - print('This is a legacy from nvlabs/SPADE, and will be removed in future versions.') - return add_norm_layer diff --git a/spaces/bigslime/stablediffusion-infinity/postprocess.py b/spaces/bigslime/stablediffusion-infinity/postprocess.py deleted file mode 100644 index 90c7f535c568fa46b6433390459d82e7967bb1fd..0000000000000000000000000000000000000000 --- a/spaces/bigslime/stablediffusion-infinity/postprocess.py +++ /dev/null @@ -1,249 +0,0 @@ -""" -https://github.com/Trinkle23897/Fast-Poisson-Image-Editing -MIT License - -Copyright (c) 2022 Jiayi Weng - -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. -""" - -import time -import argparse -import os -import fpie -from process import ALL_BACKEND, CPU_COUNT, DEFAULT_BACKEND -from fpie.io import read_images, write_image -from process import BaseProcessor, EquProcessor, GridProcessor - -from PIL import Image -import numpy as np -import skimage -import skimage.measure -import scipy -import scipy.signal - - -class PhotometricCorrection: - def __init__(self,quite=False): - self.get_parser("cli") - args=self.parser.parse_args(["--method","grid","-g","src","-s","a","-t","a","-o","a"]) - args.mpi_sync_interval = getattr(args, "mpi_sync_interval", 0) - self.backend=args.backend - self.args=args - self.quite=quite - proc: BaseProcessor - proc = GridProcessor( - args.gradient, - args.backend, - args.cpu, - args.mpi_sync_interval, - args.block_size, - args.grid_x, - args.grid_y, - ) - print( - f"[PIE]Successfully initialize PIE {args.method} solver " - f"with {args.backend} backend" - ) - self.proc=proc - - def run(self, original_image, inpainted_image, mode="mask_mode"): - print(f"[PIE] start") - if mode=="disabled": - return inpainted_image - input_arr=np.array(original_image) - if input_arr[:,:,-1].sum()<1: - return inpainted_image - output_arr=np.array(inpainted_image) - mask=input_arr[:,:,-1] - mask=255-mask - if mask.sum()<1 and mode=="mask_mode": - mode="" - if mode=="mask_mode": - mask = skimage.measure.block_reduce(mask, (8, 8), np.max) - mask = mask.repeat(8, axis=0).repeat(8, axis=1) - else: - mask[8:-9,8:-9]=255 - mask = mask[:,:,np.newaxis].repeat(3,axis=2) - nmask=mask.copy() - output_arr2=output_arr[:,:,0:3].copy() - input_arr2=input_arr[:,:,0:3].copy() - output_arr2[nmask<128]=0 - input_arr2[nmask>=128]=0 - output_arr2+=input_arr2 - src = output_arr2[:,:,0:3] - tgt = src.copy() - proc=self.proc - args=self.args - if proc.root: - n = proc.reset(src, mask, tgt, (args.h0, args.w0), (args.h1, args.w1)) - proc.sync() - if proc.root: - result = tgt - t = time.time() - if args.p == 0: - args.p = args.n - - for i in range(0, args.n, args.p): - if proc.root: - result, err = proc.step(args.p) # type: ignore - print(f"[PIE] Iter {i + args.p}, abs_err {err}") - else: - proc.step(args.p) - - if proc.root: - dt = time.time() - t - print(f"[PIE] Time elapsed: {dt:.4f}s") - # make sure consistent with dummy process - return Image.fromarray(result) - - - def get_parser(self,gen_type: str) -> argparse.Namespace: - parser = argparse.ArgumentParser() - parser.add_argument( - "-v", "--version", action="store_true", help="show the version and exit" - ) - parser.add_argument( - "--check-backend", action="store_true", help="print all available backends" - ) - if gen_type == "gui" and "mpi" in ALL_BACKEND: - # gui doesn't support MPI backend - ALL_BACKEND.remove("mpi") - parser.add_argument( - "-b", - "--backend", - type=str, - choices=ALL_BACKEND, - default=DEFAULT_BACKEND, - help="backend choice", - ) - parser.add_argument( - "-c", - "--cpu", - type=int, - default=CPU_COUNT, - help="number of CPU used", - ) - parser.add_argument( - "-z", - "--block-size", - type=int, - default=1024, - help="cuda block size (only for equ solver)", - ) - parser.add_argument( - "--method", - type=str, - choices=["equ", "grid"], - default="equ", - help="how to parallelize computation", - ) - parser.add_argument("-s", "--source", type=str, help="source image filename") - if gen_type == "cli": - parser.add_argument( - "-m", - "--mask", - type=str, - help="mask image filename (default is to use the whole source image)", - default="", - ) - parser.add_argument("-t", "--target", type=str, help="target image filename") - parser.add_argument("-o", "--output", type=str, help="output image filename") - if gen_type == "cli": - parser.add_argument( - "-h0", type=int, help="mask position (height) on source image", default=0 - ) - parser.add_argument( - "-w0", type=int, help="mask position (width) on source image", default=0 - ) - parser.add_argument( - "-h1", type=int, help="mask position (height) on target image", default=0 - ) - parser.add_argument( - "-w1", type=int, help="mask position (width) on target image", default=0 - ) - parser.add_argument( - "-g", - "--gradient", - type=str, - choices=["max", "src", "avg"], - default="max", - help="how to calculate gradient for PIE", - ) - parser.add_argument( - "-n", - type=int, - help="how many iteration would you perfer, the more the better", - default=5000, - ) - if gen_type == "cli": - parser.add_argument( - "-p", type=int, help="output result every P iteration", default=0 - ) - if "mpi" in ALL_BACKEND: - parser.add_argument( - "--mpi-sync-interval", - type=int, - help="MPI sync iteration interval", - default=100, - ) - parser.add_argument( - "--grid-x", type=int, help="x axis stride for grid solver", default=8 - ) - parser.add_argument( - "--grid-y", type=int, help="y axis stride for grid solver", default=8 - ) - self.parser=parser - -if __name__ =="__main__": - import sys - import io - import base64 - from PIL import Image - def base64_to_pil(base64_str): - data = base64.b64decode(str(base64_str)) - pil = Image.open(io.BytesIO(data)) - return pil - - def pil_to_base64(out_pil): - out_buffer = io.BytesIO() - out_pil.save(out_buffer, format="PNG") - out_buffer.seek(0) - base64_bytes = base64.b64encode(out_buffer.read()) - base64_str = base64_bytes.decode("ascii") - return base64_str - correction_func=PhotometricCorrection(quite=True) - while True: - buffer = sys.stdin.readline() - print(f"[PIE] suprocess {len(buffer)} {type(buffer)} ") - if len(buffer)==0: - break - if isinstance(buffer,str): - lst=buffer.strip().split(",") - else: - lst=buffer.decode("ascii").strip().split(",") - img0=base64_to_pil(lst[0]) - img1=base64_to_pil(lst[1]) - ret=correction_func.run(img0,img1,mode=lst[2]) - ret_base64=pil_to_base64(ret) - if isinstance(buffer,str): - sys.stdout.write(f"{ret_base64}\n") - else: - sys.stdout.write(f"{ret_base64}\n".encode()) - sys.stdout.flush() \ No newline at end of file diff --git a/spaces/bioriAsaeru/text-to-voice/Bajrangi Bhaijaan Telugu Movie Download Watch the Heartwarming Story of Pavan and Munni Online.md b/spaces/bioriAsaeru/text-to-voice/Bajrangi Bhaijaan Telugu Movie Download Watch the Heartwarming Story of Pavan and Munni Online.md deleted file mode 100644 index 0f88b38e47d5c08938c16c31d409610d8aa06b9b..0000000000000000000000000000000000000000 --- a/spaces/bioriAsaeru/text-to-voice/Bajrangi Bhaijaan Telugu Movie Download Watch the Heartwarming Story of Pavan and Munni Online.md +++ /dev/null @@ -1,6 +0,0 @@ - bajrangibhaijaantelugumoviedownload
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-WOOP.ID - Sebagai kiblat perfilman dunia, fim Hollywood memang selalu memiliki cerita yang menarik. Apa lagi di beberapa film barat terbilang sangat berani menampilkan adegan dewasa di dalamnya.
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-Film ini pertama kali diputar pada tahun 2003 pada ajang film Internasional Cannes. Di dalam film tersebut terdapat adegan dimana artis Chloe Sevigny melakukan adegan oral dengan lawan mainnya. Yang paling mengejutkan, Chloe mengaku benar-benar melakukannya di depan kamera lho.
-Nggak kebayang menjadi salah satu penonton di festival film tersebut, pasti terasa canggung ketika menonton bersama peserta lainnya, ya? Tapi tenang aja film hot barat yang tidak pernah ditayangkan di tv Indonesia ko.
-Di masa kegalauannya ini, dia bertemu dengan seorang lesbian yang membuatnya jatuh cinta. Sayangnya, karena dinilai terlalu panas, film yang sudah tayang pada tahun 2013 ini akhirnya dilarang untuk tayang di beberapa negara, termasuk Indonesia.
-Dari judulnya aja kita sudah bisa menebak kalo film ini memang dibuat khusus untuk orang dewasa 18+. Filem dewasa yang diluncurkan pada tahun 1995 ini bercerita tentang seorang penari wanita sensual yang rela melepas pakaiannya hanya demi mendapatkan banyak uang.
- -Diadaptasi dari novel karya Andre Aciman dengan judul yang sama, film Call Me by Your Name disutradarai oleh Luca Guadagnino ini mengisahkan tentang hubungan percintaan antara Elio Perlman (Timothee Chalamet) seorang anak berusia 17 tahun dengan Oliver (Armie Hammer) mahasiswa berusia 24 tahun.
-Ini bukan cuma soal percintaan sesama jenis, tapi biseksual juga, karena Elio juga berhubungan sama pacarnya, Marzia. Terdapat unsur kekeluargaan yang kental. Sehingga film ini lebih ke pengalaman seksualitas atau sebuah pencarian seorang remaja berusia belasan tahun.
-Beberapa adegan pembunuhan di dalam film ini terkesan brutal. Bahkan, beberapa media seperti The Guardian mengatakan bahwa film ini memberikan inspirasi pembunuhan brutal yang terjadi antara rentang waktu ketika dirilis pada tahun 1994 hingga 2002. Bahkan, di Irlandia, film ini dilarang tampil di bioskop.
-Begitu pula dengan jalan cerita filmnya. Blue Jasmine pun punya cerita yang kuat. Namun, pada tahun 2013, film ini dilarang oleh pemerintahan India pada tahun 2013 lalu. Hanya karena sebuah adegan merokok, film ini kemudian menjadi kontroversi sendiri.
-Her adalah film drama fiksi ilmiah romantis Amerika Serikat tahun 2013 yang disutradarai oleh Spike Jonze dan diproduseri oleh Spike Jonze, Megan Ellison dan Vincent Landay. Naskah film ini ditulis oleh Spike Jonze. Film ini dibintangi oleh Joaquin Phoenix, Scarlett Johansson, Amy Adams, Rooney Mara dan Olivia Wilde.
-Film Her merupakan film kolaborasi kedua Joaquin Phoenix dan Amy Adams setelah film The Master. Film ini ditayangkan secara perdana di Festival Film New York pada tanggal 13 Oktober 2013[3] dan dirilis di Amerika Serikat pada tanggal 18 Desember 2013 secara terbatas dan 10 Januari 2014 secara luas.[4] Film ini mendapatkan review positif dari para kritikus.
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Disaat indonesia sibuk dengan horror tt pada tahun 2013, malaysia sudah berhasil membuat terror binatang buas atau reptil buas. Bukannya mau ngebandingin tapi di indo belum ada yang mengangkat tema demikian. Jadi guarasa bisa jadi gebrakan baru untuk tema binatang buas yang memangsa manusia. Bahkan film sekelas anaconda's pun syutingnya di indo, tapi gapapa, mungkin belum waktunya. Film ini sendiri menyoba menghadirkan binatang buas yang memangsa sekumpulan manusia yang sedang berlibur, dengan minimnya pembangunan serta CGI yang terbatas dan kasar.Selain itu beberapa film yang mengharuskan Julianne Moore melakukan adegan panas adalah Boogie Nights (1997), The Big Lebowski (1998), The End of the Affair (1999), The Kids Are All Right (2010), dan Don Jon (2013).
-Salah satu adegan panas yang sangat melekat di ingatan kita adalah di film legendaris Titanic (1997), bersama Kate Winslet. Lalu, beberapa tahun kemudian, tepatnya tahun 2008, keduanya kembali melakukan adegan panas dalam film Revolutionary Road.
-Bab ini menyajikan dimensi yang sangat luas mengenai keadaan sosial kemasyarakatan di Kota Sukabumi tahun 2013, yaitu meliputi kajian mengenai bidang kependudukan, keluarga berencana, pendidikan, kesehatan, pemukiman dan lingkungan, serta kemiskinan. Perlu ditekankan bahwa publikasi Inkesmas ini hanya memberikan gambaran secara umum mengenai kondisi sosial Kota Sukabumi, yang jika ingin dikaji lebih dalam dan rinci, masing masing sub bagian di bab ini dapat menjadi topik penelitian tersendiri. Sumber data yang digunakan sebagian besar dari Survey Sosial Ekonomi Nasional (Susenas) tahun 2013, dan beberapa data dari instansi yang berada di wilayah Kota Sukabumi.
-Kota Sukabumi merupakan wilayah administratif tingkat II di propinsi Jawa Barat yang terdiri dari 7 kecamatan dan 33 kelurahan. Berdasarkan hasil proyeksi pertengahan tahun, jumlah penduduk kota Sukabumi tahun 2013 sebesar 311.822 jiwa dengan rincian: 158.175penduduk laki-laki (50,73%) dan 153,647 penduduk perempuan (49,27%).
-
Dengan luas wilayah sekitar 48 KM2, kepadatan penduduk di Kota Sukabumi sekitar 6.496 jiwa/km2. Kepadatan tertinggi berada di kecamatan Citamiang yang merupakan wilayah kecamatan tersempit dan berlokasi dekat dengan pusat perbelanjaan dan kepadatan penduduk terendah berada di kecamatan Lembursitu.pertumbuhan penduduknya (LPP), Kota Sukabumi mengalami trend penurunan
-
LPP dari 1,214 di tahun 2012 menjadi 1,102 di tahun 2013.
Penurunan LPP dari tahun ke tahun di Kota Sukabumi tidak terlepas dari
keberhasilan program keluarga berencana (KB) yang terus digalakkan di daerah ini. Namun demikian, pemerintah Kota Sukabumi harus lebih memberikan
perhatian terhadap masalah kependudukan ini karena berdasarkan data Sensus
Penduduk (SP 2010). LPP Kota Sukabumi termasuk urutan ke-11 tertinggi dari 27kota/kab di Jawa Barat,sementara berdasarkan luas nya, wilayah Kota Sukabumi adalah wilayah tingkat II tersempit di Jawa Barat bersama dengan Kota Cirebon
dan Kota Cimahi. Salah satu sebabnya LPP kota sukabumi tinggi adalah TFR1dan ASFR2 yang cukup tinggi dibanding kota/kab lainnya di provinsi Jawa
Barat. Data hasil dari Sensus Penduduk 2010, TFR Kota Sukabumi sebesar 2,5dan itu merupakan rangking ke-8 tertinggi di Jawa Barat dan ASFR yang
tergolong masih tinggi.Perbandingan jumlah penduduk laki-laki dan perempuan di Kota Sukabumi dalam waktu 2 tahun terakhir berada di atas angka 100, yang artinya
-
jumlah penduduk laki-laki di kota sukabumi masih lebih banyak dari jumlah penduduk perempuan. Tahun 2013 rasio jenis kelamin di Kota Sukabumi sebesar 102,9 artinya terdapat 102 penduduk laki-laki per 100 penduduk perempuan. Berbeda dengan rasio jenis kelamin tahun 2012 dimana sex rationya adalah 103.
2. Rasio Ketergantungan
Rasio Ketergantungan adalah perbandingan penduduk berumur 0-14 tahun ditambah dengan penduduk umur 65 tahun keatas dibandingkan dengan
jumlah penduduk umur 15-64 tahun. Rasio Ketergantungan dapat dilihat menurut umur yaitu Rasio Ketergantungan Muda3 dan Rasio Ketergantungan
Tua4.Dari tabel 3.3 dapat dilihat secara total, rasio ketergantungan Kota Sukabumi pada tahun 2013 adalah sebesar 48,44 persen, dimana setiap 100 orang yang berusia produktif menanggung sebanyak 48 orang yang belum produktifdan tidak produktif lagi. Angka ketergantungan tersebut disumbang oleh rasio ketergantungan penduduk muda3 sebesar 41,73 persen dan rasio ketergantungan tua4 sebesar 6,70 persen. Dari komposisi jumlah penduduk tersebut, dapat dilihat bahwa di Kota Sukabumi pada tahun 2013, penduduk usia produktifnya lebih
-banyak proporsinya dibanding penduduk yang tidak produktif, dan jika dibandingkan, saat ini usia produktifnya lebih banyak menanggung terhadap
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penduduk usia muda dibanding penduduk usia tua.
Jika dibandingkan dengan tahun sebelumnya, di tahun 2013 terjadi kenaikan jumlah penduduk yang dikategorikan produktif dan terjadi penurunantahun sebelumnya. Sehingga tidak menutup kemungkinan akan ada angkaangka yang berbeda pula.
-
Grafik 3.4.
Persentase Penduduk Perempuan Usia 15-49 Tahun Berstatus Kawin
Menurut Umur Perkawinan Pertama Di Kota Sukabumi Tahun 2010-2013Dari Grafik di atas dapat diketahui persentase penduduk perempuan usia 15-49 tahun berstatus kawin menurut umur saat perkawinan pertama di Kota Sukabumi. Pada tahun 2013 persentase terbesar usia perkawinan pertama di
-
Kota Sukabumi adalah usia 19-24 tahun, yaitu sebesar 49,18 persen, mengalami kenaikan sekitar 6,26 persen dari tahun 2012 yaitu sebesar 42,92 persen.
Selain kelompok umur 19-24 tahun, hasil dari Susenas tahun 2013 juga menunjukan penurunan persentase perkawinan pertama di bawah umur 17
tahun sebesar 7,28 persen yaitu dari 25,41 persen pada tahun 2012 menjadi 18,13 persen pada tahun 2013. Hal ini menunjukan tingkat kesadaran masyarakat Kota Sukabumi akan dampak dari perkawianan pertama di bawah umur 17 tahun terhadap kesehatan reproduksi. Berbeda dengan 3 tahun sebelumnya persentase perkawinan pertama di bawah 17 tahun tergolong masih tinggi yaitu 25,41
persen di tahun 2012 menjadi 23,80 persen di tahun 2011 dan mengalami kenaikkan lagi menjadi 20,44 persen di tahun 2011. Persentase terkecil usiaBerdasakan data yang di peroleh dari hasil Susenas tahun 2013 menunjukan bahwa sebanyak 62 persen dari Pasangan Usia Subur (PUS)6
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sedang aktif menggunakan alat kontrasepsi, sementara 26 persen menyatakan pernah menggunakan alat kontrasepsi namun pada saat
pencacahan tidak sedang menggunakan alat kontrasepsi, dan sekitar 12 persen dari PUS ini menyatakan bahwa mereka tidak pernah melakukan KB. . Lebih tingginya proporsi pengguna alat kontrasepsi dari pada yang tidak menggunakan, dapat diartikan bahwa partisipasi masyarakat untuk Progam KB di Kota Sukabumi berjalan dengan cukup responsif.
Dari 62 persen PUS yang sedang menggunakan alat kontrasepsi ,ada dua metode kontrasepsi modern yang digunakan yaitu menggunakan metode
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-
- diff --git a/spaces/bla/tranny/App/Streaming/StreamingRoutes.py b/spaces/bla/tranny/App/Streaming/StreamingRoutes.py deleted file mode 100644 index 75b999a25024d7ce4ce04f562e6944de1071f8ff..0000000000000000000000000000000000000000 --- a/spaces/bla/tranny/App/Streaming/StreamingRoutes.py +++ /dev/null @@ -1,12 +0,0 @@ -from fastapi import APIRouter, Request -from .Utils.Handler import Handler -from App import bot - -streaming_router = APIRouter(tags=["streaming"]) - - -@streaming_router.get("/stream/") -async def create_file(id: int, req: Request): - hander = Handler(req=req, client=bot, id=id) - hander.route = "inline" - return await hander.process_request() diff --git a/spaces/blanchon/qrcode-diffusion/app.py b/spaces/blanchon/qrcode-diffusion/app.py deleted file mode 100644 index 62a66a10fcb4e926330914adea282d5b214cfd7f..0000000000000000000000000000000000000000 --- a/spaces/blanchon/qrcode-diffusion/app.py +++ /dev/null @@ -1,241 +0,0 @@ -from typing import Optional - -import gradio as gr -import qrcode -import torch -from diffusers import ( - ControlNetModel, - EulerAncestralDiscreteScheduler, - StableDiffusionControlNetPipeline, -) -from gradio.components import Image, Radio, Slider, Textbox, Number -from PIL import Image as PilImage -from typing_extensions import Literal - - -def main(): - device = ( - 'cuda' if torch.cuda.is_available() - else 'mps' if torch.backends.mps.is_available() - else 'cpu' - ) - - controlnet_tile = ControlNetModel.from_pretrained( - "lllyasviel/control_v11f1e_sd15_tile", - torch_dtype=torch.float16 if device == "cuda" else torch.float32, - use_safetensors=False, - cache_dir="./cache" - ).to(device) - - controlnet_brightness = ControlNetModel.from_pretrained( - "ioclab/control_v1p_sd15_brightness", - torch_dtype=torch.float16 if device == "cuda" else torch.float32, - use_safetensors=True, - cache_dir="./cache" - ).to(device) - - def make_pipe(hf_repo: str, device: str) -> StableDiffusionControlNetPipeline: - pipe = StableDiffusionControlNetPipeline.from_pretrained( - hf_repo, - controlnet=[controlnet_tile, controlnet_brightness], - torch_dtype=torch.float16 if device == "cuda" else torch.float32, - cache_dir="./cache", - ) - pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) - # pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) - return pipe.to(device) - - pipes = { - "DreamShaper": make_pipe("Lykon/DreamShaper", device), - # "DreamShaper": make_pipe("Lykon/DreamShaper", "cpu"), - # "Realistic Vision V1.4": make_pipe("SG161222/Realistic_Vision_V1.4", "cpu"), - # "OpenJourney": make_pipe("prompthero/openjourney", "cpu"), - # "Anything V3": make_pipe("Linaqruf/anything-v3.0", "cpu"), - } - - def move_pipe(hf_repo: str): - for pipe_name, pipe in pipes.items(): - if pipe_name != hf_repo: - pipe.to("cpu") - return pipes[hf_repo].to(device) - - def predict( - model: Literal[ - "DreamShaper", - # "Realistic Vision V1.4", - # "OpenJourney", - # "Anything V3" - ], - qrcode_data: str, - prompt: str, - negative_prompt: Optional[str] = None, - num_inference_steps: int = 100, - guidance_scale: int = 9, - controlnet_conditioning_tile: float = 0.25, - controlnet_conditioning_brightness: float = 0.45, - seed: int = 1331, - ) -> PilImage: - generator = torch.Generator(device).manual_seed(seed) - if model == "DreamShaper": - pipe = pipes["DreamShaper"] - # pipe = move_pipe("DreamShaper Vision V1.4") - # elif model == "Realistic Vision V1.4": - # pipe = move_pipe("Realistic Vision V1.4") - # elif model == "OpenJourney": - # pipe = move_pipe("OpenJourney") - # elif model == "Anything V3": - # pipe = move_pipe("Anything V3") - - - qr = qrcode.QRCode( - error_correction=qrcode.constants.ERROR_CORRECT_H, - box_size=11, - border=9, - ) - qr.add_data(qrcode_data) - qr.make(fit=True) - qrcode_image = qr.make_image( - fill_color="black", - back_color="white" - ).convert("RGB") - qrcode_image = qrcode_image.resize((512, 512), PilImage.LANCZOS) - - image = pipe( - prompt, - [qrcode_image, qrcode_image], - num_inference_steps=num_inference_steps, - generator=generator, - negative_prompt=negative_prompt, - guidance_scale=guidance_scale, - controlnet_conditioning_scale=[ - controlnet_conditioning_tile, - controlnet_conditioning_brightness - ] - ).images[0] - - return image - - - ui = gr.Interface( - fn=predict, - inputs=[ - Radio( - value="DreamShaper", - label="Model", - choices=[ - "DreamShaper", - # "Realistic Vision V1.4", - # "OpenJourney", - # "Anything V3" - ], - ), - Textbox( - value="https://twitter.com/JulienBlanchon", - label="QR Code Data", - ), - Textbox( - value="Japanese ramen with chopsticks, egg and steam, ultra detailed 8k", - label="Prompt", - ), - Textbox( - value="logo, watermark, signature, text, BadDream, UnrealisticDream", - label="Negative Prompt", - optional=True - ), - Slider( - value=100, - label="Number of Inference Steps", - minimum=10, - maximum=400, - step=1, - ), - Slider( - value=9, - label="Guidance Scale", - minimum=1, - maximum=20, - step=1, - ), - Slider( - value=0.25, - label="Controlnet Conditioning Tile", - minimum=0.0, - maximum=1.0, - step=0.05, - - ), - Slider( - value=0.45, - label="Controlnet Conditioning Brightness", - minimum=0.0, - maximum=1.0, - step=0.05, - ), - Number( - value=1, - label="Seed", - precision=0, - ), - - ], - outputs=Image( - label="Generated Image", - type="pil", - ), - examples=[ - [ - "DreamShaper", - "https://twitter.com/JulienBlanchon", - "rock, mountain", - "", - 100, - 9, - 0.25, - 0.45, - 1, - ], - [ - "DreamShaper", - "https://twitter.com/JulienBlanchon", - "Japanese ramen with chopsticks, egg and steam, ultra detailed 8k", - "logo, watermark, signature, text, BadDream, UnrealisticDream", - 100, - 9, - 0.25, - 0.45, - 1, - ], - # [ - # "Anything V3", - # "https://twitter.com/JulienBlanchon", - # "Japanese ramen with chopsticks, egg and steam, ultra detailed 8k", - # "logo, watermark, signature, text, BadDream, UnrealisticDream", - # 100, - # 9, - # 0.25, - # 0.60, - # 1, - # ], - [ - "DreamShaper", - "https://twitter.com/JulienBlanchon", - "processor, chipset, electricity, black and white board", - "logo, watermark, signature, text, BadDream, UnrealisticDream", - 300, - 9, - 0.50, - 0.30, - 1, - ], - ], - cache_examples=True, - title="Stable Diffusion QR Code Controlnet", - description="Generate QR Code with Stable Diffusion and Controlnet", - allow_flagging="never", - max_batch_size=1, - ) - - ui.queue(concurrency_count=10).launch() - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/spaces/blueeyiz702/flax-midjourney-v4-diffusion/README.md b/spaces/blueeyiz702/flax-midjourney-v4-diffusion/README.md deleted file mode 100644 index 6faefebf0af065bc46052bc345399ae2aad8a624..0000000000000000000000000000000000000000 --- a/spaces/blueeyiz702/flax-midjourney-v4-diffusion/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Flax Midjourney V4 Diffusion -emoji: 🐨 -colorFrom: pink -colorTo: red -sdk: gradio -sdk_version: 3.27.0 -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/cahya/image-search/SessionState.py b/spaces/cahya/image-search/SessionState.py deleted file mode 100644 index 594c8dcbd209dae45555a7888af39be2c79fd9dc..0000000000000000000000000000000000000000 --- a/spaces/cahya/image-search/SessionState.py +++ /dev/null @@ -1,107 +0,0 @@ -"""Hack to add per-session state to Streamlit. -Usage ------ ->>> import SessionState ->>> ->>> session_state = SessionState.get(user_name='', favorite_color='black') ->>> session_state.user_name -'' ->>> session_state.user_name = 'Mary' ->>> session_state.favorite_color -'black' -Since you set user_name above, next time your script runs this will be the -result: ->>> session_state = get(user_name='', favorite_color='black') ->>> session_state.user_name -'Mary' -""" -try: - import streamlit.ReportThread as ReportThread - from streamlit.server.Server import Server -except Exception: - # Streamlit >= 0.65.0 - import streamlit.report_thread as ReportThread - from streamlit.server.server import Server - - -class SessionState(object): - def __init__(self, **kwargs): - """A new SessionState object. - Parameters - ---------- - **kwargs : any - Default values for the session state. - Example - ------- - >>> session_state = SessionState(user_name='', favorite_color='black') - >>> session_state.user_name = 'Mary' - '' - >>> session_state.favorite_color - 'black' - """ - for key, val in kwargs.items(): - setattr(self, key, val) - - -def get(**kwargs): - """Gets a SessionState object for the current session. - Creates a new object if necessary. - Parameters - ---------- - **kwargs : any - Default values you want to add to the session state, if we're creating a - new one. - Example - ------- - >>> session_state = get(user_name='', favorite_color='black') - >>> session_state.user_name - '' - >>> session_state.user_name = 'Mary' - >>> session_state.favorite_color - 'black' - Since you set user_name above, next time your script runs this will be the - result: - >>> session_state = get(user_name='', favorite_color='black') - >>> session_state.user_name - 'Mary' - """ - # Hack to get the session object from Streamlit. - - ctx = ReportThread.get_report_ctx() - - this_session = None - - current_server = Server.get_current() - if hasattr(current_server, '_session_infos'): - # Streamlit < 0.56 - session_infos = Server.get_current()._session_infos.values() - else: - session_infos = Server.get_current()._session_info_by_id.values() - - for session_info in session_infos: - s = session_info.session - if ( - # Streamlit < 0.54.0 - (hasattr(s, '_main_dg') and s._main_dg == ctx.main_dg) - or - # Streamlit >= 0.54.0 - (not hasattr(s, '_main_dg') and s.enqueue == ctx.enqueue) - or - # Streamlit >= 0.65.2 - (not hasattr(s, '_main_dg') and s._uploaded_file_mgr == ctx.uploaded_file_mgr) - ): - this_session = s - - if this_session is None: - raise RuntimeError( - "Oh noes. Couldn't get your Streamlit Session object. " - 'Are you doing something fancy with threads?') - - # Got the session object! Now let's attach some state into it. - - if not hasattr(this_session, '_custom_session_state'): - this_session._custom_session_state = SessionState(**kwargs) - - return this_session._custom_session_state - -__all__ = ['get'] \ No newline at end of file diff --git a/spaces/camenduru/11/app.py b/spaces/camenduru/11/app.py deleted file mode 100644 index 71867255506c0d3c2f144604ca6b16d4ccbecb71..0000000000000000000000000000000000000000 --- a/spaces/camenduru/11/app.py +++ /dev/null @@ -1,80 +0,0 @@ -import http.server, socketserver, os, json -import requests, base64, threading -from pymongo import MongoClient - -dat = os.environ.get('dat') -fal = os.environ.get('fal') -api = os.environ.get('api') -tok = os.environ.get('tok') -job = os.environ.get('job') - -def save_audio_from_url(url, save_path): - response = requests.get(url) - if response.status_code == 200: - with open(save_path, 'wb') as file: - file.write(response.content) - else: - print("Failed to download audio.") - -def loop(): - client = MongoClient(dat) - db = client['camenduru-web'] - collection = db['discord_job'] - - while True: - waiting_documents = collection.find({"status": "WAITING"}) - for waiting_document in waiting_documents: - print(waiting_document) - server = waiting_document['server'] - if(server==job): - prompt = waiting_document['prompt'] - discord_channel_id = waiting_document['discord_channel_id'] - discord_user_id = waiting_document['discord_user_id'] - discord_token = tok - collection.update_one({"_id": waiting_document['_id']}, {"$set": {"status": "WORKING"}}) - try: - responseF = requests.post( - api, - headers={"Authorization": f"Basic {fal}"}, - json={ - "prompt": f"{prompt}" - }, - ) - responseF.raise_for_status() # Raise an exception for 4xx and 5xx errors - audio_url = json.loads(responseF.content.decode())["audio_file"]["url"] - file_path = 'audio.mp3' - save_audio_from_url(audio_url, file_path) - files = {f"audio.mp3": open(f"audio.mp3", "rb").read()} - payload = {"content": f"{prompt} <@{discord_user_id}>"} - try: - responseD = requests.post(f"https://discord.com/api/v9/channels/{discord_channel_id}/messages", data=payload, headers={"authorization": f"Bot {discord_token}"}, files=files) - responseD.raise_for_status() # Raise an exception for 4xx and 5xx errors - collection.update_one({"_id": waiting_document['_id']}, {"$set": {"status": "DONE"}}) - except requests.exceptions.RequestException as e: - print(f"D An error occurred: {e}") - except Exception as e: - print(f"D An unexpected error occurred: {e}") - except requests.exceptions.RequestException as e: - print(f"F An error occurred: {e}") - except Exception as e: - print(f"F An unexpected error occurred: {e}") - -class MyHandler(http.server.SimpleHTTPRequestHandler): - def translate_path(self, path): - path = super().translate_path(path) - if path.endswith('.py'): - self.send_error(404, "File not found") - return None - return path - -PORT = 7860 -Handler = MyHandler -Handler.extensions_map.update({ - '.html': 'text/html', -}) - -with socketserver.TCPServer(("", PORT), Handler) as httpd: - thread = threading.Thread(target=loop) - thread.start() - print(f"Server running on port {PORT}") - httpd.serve_forever() \ No newline at end of file diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/locks.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/locks.py deleted file mode 100644 index de2dc83d09dd950fc1ed8d7edaeb20e7697c94ba..0000000000000000000000000000000000000000 --- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/aiohttp/locks.py +++ /dev/null @@ -1,41 +0,0 @@ -import asyncio -import collections -from typing import Any, Deque, Optional - - -class EventResultOrError: - """Event asyncio lock helper class. - - Wraps the Event asyncio lock allowing either to awake the - locked Tasks without any error or raising an exception. - - thanks to @vorpalsmith for the simple design. - """ - - def __init__(self, loop: asyncio.AbstractEventLoop) -> None: - self._loop = loop - self._exc: Optional[BaseException] = None - self._event = asyncio.Event() - self._waiters: Deque[asyncio.Future[Any]] = collections.deque() - - def set(self, exc: Optional[BaseException] = None) -> None: - self._exc = exc - self._event.set() - - async def wait(self) -> Any: - waiter = self._loop.create_task(self._event.wait()) - self._waiters.append(waiter) - try: - val = await waiter - finally: - self._waiters.remove(waiter) - - if self._exc is not None: - raise self._exc - - return val - - def cancel(self) -> None: - """Cancel all waiters""" - for waiter in self._waiters: - waiter.cancel() diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/datasets/coco.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/datasets/coco.py deleted file mode 100644 index c19f7b034b1641c9ccd88634f12fcdc3013bce09..0000000000000000000000000000000000000000 --- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/densepose/data/datasets/coco.py +++ /dev/null @@ -1,432 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import contextlib -import io -import logging -import os -from collections import defaultdict -from dataclasses import dataclass -from typing import Any, Dict, Iterable, List, Optional -from fvcore.common.timer import Timer - -from detectron2.data import DatasetCatalog, MetadataCatalog -from detectron2.structures import BoxMode -from detectron2.utils.file_io import PathManager - -from ..utils import maybe_prepend_base_path - -DENSEPOSE_MASK_KEY = "dp_masks" -DENSEPOSE_IUV_KEYS_WITHOUT_MASK = ["dp_x", "dp_y", "dp_I", "dp_U", "dp_V"] -DENSEPOSE_CSE_KEYS_WITHOUT_MASK = ["dp_x", "dp_y", "dp_vertex", "ref_model"] -DENSEPOSE_ALL_POSSIBLE_KEYS = set( - DENSEPOSE_IUV_KEYS_WITHOUT_MASK + DENSEPOSE_CSE_KEYS_WITHOUT_MASK + [DENSEPOSE_MASK_KEY] -) -DENSEPOSE_METADATA_URL_PREFIX = "https://dl.fbaipublicfiles.com/densepose/data/" - - -@dataclass -class CocoDatasetInfo: - name: str - images_root: str - annotations_fpath: str - - -DATASETS = [ - CocoDatasetInfo( - name="densepose_coco_2014_train", - images_root="coco/train2014", - annotations_fpath="coco/annotations/densepose_train2014.json", - ), - CocoDatasetInfo( - name="densepose_coco_2014_minival", - images_root="coco/val2014", - annotations_fpath="coco/annotations/densepose_minival2014.json", - ), - CocoDatasetInfo( - name="densepose_coco_2014_minival_100", - images_root="coco/val2014", - annotations_fpath="coco/annotations/densepose_minival2014_100.json", - ), - CocoDatasetInfo( - name="densepose_coco_2014_valminusminival", - images_root="coco/val2014", - annotations_fpath="coco/annotations/densepose_valminusminival2014.json", - ), - CocoDatasetInfo( - name="densepose_coco_2014_train_cse", - images_root="coco/train2014", - annotations_fpath="coco_cse/densepose_train2014_cse.json", - ), - CocoDatasetInfo( - name="densepose_coco_2014_minival_cse", - images_root="coco/val2014", - annotations_fpath="coco_cse/densepose_minival2014_cse.json", - ), - CocoDatasetInfo( - name="densepose_coco_2014_minival_100_cse", - images_root="coco/val2014", - annotations_fpath="coco_cse/densepose_minival2014_100_cse.json", - ), - CocoDatasetInfo( - name="densepose_coco_2014_valminusminival_cse", - images_root="coco/val2014", - annotations_fpath="coco_cse/densepose_valminusminival2014_cse.json", - ), - CocoDatasetInfo( - name="densepose_chimps", - images_root="densepose_chimps/images", - annotations_fpath="densepose_chimps/densepose_chimps_densepose.json", - ), - CocoDatasetInfo( - name="densepose_chimps_cse_train", - images_root="densepose_chimps/images", - annotations_fpath="densepose_chimps/densepose_chimps_cse_train.json", - ), - CocoDatasetInfo( - name="densepose_chimps_cse_val", - images_root="densepose_chimps/images", - annotations_fpath="densepose_chimps/densepose_chimps_cse_val.json", - ), - CocoDatasetInfo( - name="posetrack2017_train", - images_root="posetrack2017/posetrack_data_2017", - annotations_fpath="posetrack2017/densepose_posetrack_train2017.json", - ), - CocoDatasetInfo( - name="posetrack2017_val", - images_root="posetrack2017/posetrack_data_2017", - annotations_fpath="posetrack2017/densepose_posetrack_val2017.json", - ), - CocoDatasetInfo( - name="lvis_v05_train", - images_root="coco/train2017", - annotations_fpath="lvis/lvis_v0.5_plus_dp_train.json", - ), - CocoDatasetInfo( - name="lvis_v05_val", - images_root="coco/val2017", - annotations_fpath="lvis/lvis_v0.5_plus_dp_val.json", - ), -] - - -BASE_DATASETS = [ - CocoDatasetInfo( - name="base_coco_2017_train", - images_root="coco/train2017", - annotations_fpath="coco/annotations/instances_train2017.json", - ), - CocoDatasetInfo( - name="base_coco_2017_val", - images_root="coco/val2017", - annotations_fpath="coco/annotations/instances_val2017.json", - ), - CocoDatasetInfo( - name="base_coco_2017_val_100", - images_root="coco/val2017", - annotations_fpath="coco/annotations/instances_val2017_100.json", - ), -] - - -def get_metadata(base_path: Optional[str]) -> Dict[str, Any]: - """ - Returns metadata associated with COCO DensePose datasets - - Args: - base_path: Optional[str] - Base path used to load metadata from - - Returns: - Dict[str, Any] - Metadata in the form of a dictionary - """ - meta = { - "densepose_transform_src": maybe_prepend_base_path(base_path, "UV_symmetry_transforms.mat"), - "densepose_smpl_subdiv": maybe_prepend_base_path(base_path, "SMPL_subdiv.mat"), - "densepose_smpl_subdiv_transform": maybe_prepend_base_path( - base_path, - "SMPL_SUBDIV_TRANSFORM.mat", - ), - } - return meta - - -def _load_coco_annotations(json_file: str): - """ - Load COCO annotations from a JSON file - - Args: - json_file: str - Path to the file to load annotations from - Returns: - Instance of `pycocotools.coco.COCO` that provides access to annotations - data - """ - from pycocotools.coco import COCO - - logger = logging.getLogger(__name__) - timer = Timer() - with contextlib.redirect_stdout(io.StringIO()): - coco_api = COCO(json_file) - if timer.seconds() > 1: - logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds())) - return coco_api - - -def _add_categories_metadata(dataset_name: str, categories: List[Dict[str, Any]]): - meta = MetadataCatalog.get(dataset_name) - meta.categories = {c["id"]: c["name"] for c in categories} - logger = logging.getLogger(__name__) - logger.info("Dataset {} categories: {}".format(dataset_name, meta.categories)) - - -def _verify_annotations_have_unique_ids(json_file: str, anns: List[List[Dict[str, Any]]]): - if "minival" in json_file: - # Skip validation on COCO2014 valminusminival and minival annotations - # The ratio of buggy annotations there is tiny and does not affect accuracy - # Therefore we explicitly white-list them - return - ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image] - assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format( - json_file - ) - - -def _maybe_add_bbox(obj: Dict[str, Any], ann_dict: Dict[str, Any]): - if "bbox" not in ann_dict: - return - obj["bbox"] = ann_dict["bbox"] - obj["bbox_mode"] = BoxMode.XYWH_ABS - - -def _maybe_add_segm(obj: Dict[str, Any], ann_dict: Dict[str, Any]): - if "segmentation" not in ann_dict: - return - segm = ann_dict["segmentation"] - if not isinstance(segm, dict): - # filter out invalid polygons (< 3 points) - segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6] - if len(segm) == 0: - return - obj["segmentation"] = segm - - -def _maybe_add_keypoints(obj: Dict[str, Any], ann_dict: Dict[str, Any]): - if "keypoints" not in ann_dict: - return - keypts = ann_dict["keypoints"] # list[int] - for idx, v in enumerate(keypts): - if idx % 3 != 2: - # COCO's segmentation coordinates are floating points in [0, H or W], - # but keypoint coordinates are integers in [0, H-1 or W-1] - # Therefore we assume the coordinates are "pixel indices" and - # add 0.5 to convert to floating point coordinates. - keypts[idx] = v + 0.5 - obj["keypoints"] = keypts - - -def _maybe_add_densepose(obj: Dict[str, Any], ann_dict: Dict[str, Any]): - for key in DENSEPOSE_ALL_POSSIBLE_KEYS: - if key in ann_dict: - obj[key] = ann_dict[key] - - -def _combine_images_with_annotations( - dataset_name: str, - image_root: str, - img_datas: Iterable[Dict[str, Any]], - ann_datas: Iterable[Iterable[Dict[str, Any]]], -): - - ann_keys = ["iscrowd", "category_id"] - dataset_dicts = [] - contains_video_frame_info = False - - for img_dict, ann_dicts in zip(img_datas, ann_datas): - record = {} - record["file_name"] = os.path.join(image_root, img_dict["file_name"]) - record["height"] = img_dict["height"] - record["width"] = img_dict["width"] - record["image_id"] = img_dict["id"] - record["dataset"] = dataset_name - if "frame_id" in img_dict: - record["frame_id"] = img_dict["frame_id"] - record["video_id"] = img_dict.get("vid_id", None) - contains_video_frame_info = True - objs = [] - for ann_dict in ann_dicts: - assert ann_dict["image_id"] == record["image_id"] - assert ann_dict.get("ignore", 0) == 0 - obj = {key: ann_dict[key] for key in ann_keys if key in ann_dict} - _maybe_add_bbox(obj, ann_dict) - _maybe_add_segm(obj, ann_dict) - _maybe_add_keypoints(obj, ann_dict) - _maybe_add_densepose(obj, ann_dict) - objs.append(obj) - record["annotations"] = objs - dataset_dicts.append(record) - if contains_video_frame_info: - create_video_frame_mapping(dataset_name, dataset_dicts) - return dataset_dicts - - -def get_contiguous_id_to_category_id_map(metadata): - cat_id_2_cont_id = metadata.thing_dataset_id_to_contiguous_id - cont_id_2_cat_id = {} - for cat_id, cont_id in cat_id_2_cont_id.items(): - if cont_id in cont_id_2_cat_id: - continue - cont_id_2_cat_id[cont_id] = cat_id - return cont_id_2_cat_id - - -def maybe_filter_categories_cocoapi(dataset_name, coco_api): - meta = MetadataCatalog.get(dataset_name) - cont_id_2_cat_id = get_contiguous_id_to_category_id_map(meta) - cat_id_2_cont_id = meta.thing_dataset_id_to_contiguous_id - # filter categories - cats = [] - for cat in coco_api.dataset["categories"]: - cat_id = cat["id"] - if cat_id not in cat_id_2_cont_id: - continue - cont_id = cat_id_2_cont_id[cat_id] - if (cont_id in cont_id_2_cat_id) and (cont_id_2_cat_id[cont_id] == cat_id): - cats.append(cat) - coco_api.dataset["categories"] = cats - # filter annotations, if multiple categories are mapped to a single - # contiguous ID, use only one category ID and map all annotations to that category ID - anns = [] - for ann in coco_api.dataset["annotations"]: - cat_id = ann["category_id"] - if cat_id not in cat_id_2_cont_id: - continue - cont_id = cat_id_2_cont_id[cat_id] - ann["category_id"] = cont_id_2_cat_id[cont_id] - anns.append(ann) - coco_api.dataset["annotations"] = anns - # recreate index - coco_api.createIndex() - - -def maybe_filter_and_map_categories_cocoapi(dataset_name, coco_api): - meta = MetadataCatalog.get(dataset_name) - category_id_map = meta.thing_dataset_id_to_contiguous_id - # map categories - cats = [] - for cat in coco_api.dataset["categories"]: - cat_id = cat["id"] - if cat_id not in category_id_map: - continue - cat["id"] = category_id_map[cat_id] - cats.append(cat) - coco_api.dataset["categories"] = cats - # map annotation categories - anns = [] - for ann in coco_api.dataset["annotations"]: - cat_id = ann["category_id"] - if cat_id not in category_id_map: - continue - ann["category_id"] = category_id_map[cat_id] - anns.append(ann) - coco_api.dataset["annotations"] = anns - # recreate index - coco_api.createIndex() - - -def create_video_frame_mapping(dataset_name, dataset_dicts): - mapping = defaultdict(dict) - for d in dataset_dicts: - video_id = d.get("video_id") - if video_id is None: - continue - mapping[video_id].update({d["frame_id"]: d["file_name"]}) - MetadataCatalog.get(dataset_name).set(video_frame_mapping=mapping) - - -def load_coco_json(annotations_json_file: str, image_root: str, dataset_name: str): - """ - Loads a JSON file with annotations in COCO instances format. - Replaces `detectron2.data.datasets.coco.load_coco_json` to handle metadata - in a more flexible way. Postpones category mapping to a later stage to be - able to combine several datasets with different (but coherent) sets of - categories. - - Args: - - annotations_json_file: str - Path to the JSON file with annotations in COCO instances format. - image_root: str - directory that contains all the images - dataset_name: str - the name that identifies a dataset, e.g. "densepose_coco_2014_train" - extra_annotation_keys: Optional[List[str]] - If provided, these keys are used to extract additional data from - the annotations. - """ - coco_api = _load_coco_annotations(PathManager.get_local_path(annotations_json_file)) - _add_categories_metadata(dataset_name, coco_api.loadCats(coco_api.getCatIds())) - # sort indices for reproducible results - img_ids = sorted(coco_api.imgs.keys()) - # imgs is a list of dicts, each looks something like: - # {'license': 4, - # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg', - # 'file_name': 'COCO_val2014_000000001268.jpg', - # 'height': 427, - # 'width': 640, - # 'date_captured': '2013-11-17 05:57:24', - # 'id': 1268} - imgs = coco_api.loadImgs(img_ids) - logger = logging.getLogger(__name__) - logger.info("Loaded {} images in COCO format from {}".format(len(imgs), annotations_json_file)) - # anns is a list[list[dict]], where each dict is an annotation - # record for an object. The inner list enumerates the objects in an image - # and the outer list enumerates over images. - anns = [coco_api.imgToAnns[img_id] for img_id in img_ids] - _verify_annotations_have_unique_ids(annotations_json_file, anns) - dataset_records = _combine_images_with_annotations(dataset_name, image_root, imgs, anns) - return dataset_records - - -def register_dataset(dataset_data: CocoDatasetInfo, datasets_root: Optional[str] = None): - """ - Registers provided COCO DensePose dataset - - Args: - dataset_data: CocoDatasetInfo - Dataset data - datasets_root: Optional[str] - Datasets root folder (default: None) - """ - annotations_fpath = maybe_prepend_base_path(datasets_root, dataset_data.annotations_fpath) - images_root = maybe_prepend_base_path(datasets_root, dataset_data.images_root) - - def load_annotations(): - return load_coco_json( - annotations_json_file=annotations_fpath, - image_root=images_root, - dataset_name=dataset_data.name, - ) - - DatasetCatalog.register(dataset_data.name, load_annotations) - MetadataCatalog.get(dataset_data.name).set( - json_file=annotations_fpath, - image_root=images_root, - **get_metadata(DENSEPOSE_METADATA_URL_PREFIX) - ) - - -def register_datasets( - datasets_data: Iterable[CocoDatasetInfo], datasets_root: Optional[str] = None -): - """ - Registers provided COCO DensePose datasets - - Args: - datasets_data: Iterable[CocoDatasetInfo] - An iterable of dataset datas - datasets_root: Optional[str] - Datasets root folder (default: None) - """ - for dataset_data in datasets_data: - register_dataset(dataset_data, datasets_root) diff --git a/spaces/cbensimon/stable-diffusion-xl/app.py b/spaces/cbensimon/stable-diffusion-xl/app.py deleted file mode 100644 index e073aa66c271a42b6a75e5e9ca43f0f50fa36f8d..0000000000000000000000000000000000000000 --- a/spaces/cbensimon/stable-diffusion-xl/app.py +++ /dev/null @@ -1,24 +0,0 @@ -import gradio as gr -import spaces -import torch -from diffusers import DiffusionPipeline - - -pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", - torch_dtype=torch.float16, - use_safetensors=True, - variant="fp16", -).to("cuda") - -pipe = spaces.GPU(pipe) - - -def generate(prompt): - return pipe(prompt=prompt).images - - -gr.Interface( - fn=generate, - inputs=gr.Text(), - outputs=gr.Gallery(), -).launch() diff --git a/spaces/ccolas/TastyPiano/src/music2cocktailrep/training/latent_translation/dataset.py b/spaces/ccolas/TastyPiano/src/music2cocktailrep/training/latent_translation/dataset.py deleted file mode 100644 index 6f290ab38b212484176108f0e4eaf4818e9115bc..0000000000000000000000000000000000000000 --- a/spaces/ccolas/TastyPiano/src/music2cocktailrep/training/latent_translation/dataset.py +++ /dev/null @@ -1,306 +0,0 @@ -from torch.utils.data import Dataset -import numpy as np -import torch -device = 'cuda' if torch.cuda.is_available() else 'cpu' -from src.music2cocktailrep.analysis.explore import get_alignment_dataset - -# Add your custom dataset class here -class CocktailDataset(Dataset): - def __init__(self, split, cocktail_reps): - - self.n_cocktails, self.dim_cocktail = cocktail_reps.shape - labels = np.zeros([self.n_cocktails]) - if split == 'train': - self.cocktail_reps = cocktail_reps[:int(0.9 * self.n_cocktails), :].copy() - self.labels = labels[:int(0.9 * self.n_cocktails)].copy() - elif split == 'test': - self.cocktail_reps = cocktail_reps[int(0.9 * self.n_cocktails):, :].copy() - self.labels = labels[int(0.9 * self.n_cocktails):].copy() - elif split == 'all': - self.cocktail_reps = cocktail_reps.copy() - self.labels = labels.copy() - else: - raise ValueError - - # self.n_cocktails = self.cocktail_reps.shape[0] - # indexes = np.arange(self.n_cocktails) - # np.random.shuffle(indexes) - self.cocktail_reps = torch.FloatTensor(self.cocktail_reps).to(device) - # oversample cocktails with eggs and bubbles - ind_egg = np.argwhere(self.cocktail_reps[:, -1] > 0).flatten() - ind_bubbles = np.argwhere(self.cocktail_reps[:, -3] > 0).flatten() - n_copies = 4 - egg_copies = torch.tile(self.cocktail_reps[ind_egg, :], dims=(n_copies * 3, 1)) - bubbles_copies = torch.tile(self.cocktail_reps[ind_bubbles, :], dims=(n_copies, 1)) - self.cocktail_reps = torch.cat([self.cocktail_reps, egg_copies, bubbles_copies], dim=0) - self.n_cocktails = self.cocktail_reps.shape[0] - indexes = np.arange(self.n_cocktails) - np.random.shuffle(indexes) - self.cocktail_reps = self.cocktail_reps[indexes] - self.labels = torch.LongTensor(np.zeros([self.n_cocktails])).to(device) - self.contains_egg = self.cocktail_reps[:, -1] > 0 - self.contains_bubbles = self.cocktail_reps[:, -3] > 0 - - def __len__(self): - return self.cocktail_reps.shape[0] - - def __getitem__(self, idx): - return self.cocktail_reps[idx], self.labels[idx], self.contains_egg[idx], self.contains_bubbles[idx] - -class CocktailLabeledDataset(Dataset): - def __init__(self, split, cocktail_reps): - - - dataset = get_alignment_dataset() - labels = sorted(dataset['cocktail'].keys()) - self.n_labels = len(labels) - - # n_cocktails = np.sum([len(dataset['cocktail'][k]) for k in labels]) - all_cocktails = [] - for k in labels: - all_cocktails += dataset['cocktail'][k] - # assert n_cocktails == len(set(all_cocktails)) - all_cocktails = np.array(all_cocktails) - - cocktail_reps = cocktail_reps[all_cocktails] - cocktail_labels = [] - for i in all_cocktails: - for i_k, k in enumerate(labels): - if i in dataset['cocktail'][k]: - cocktail_labels.append(i_k) - break - cocktail_labels = np.array(cocktail_labels) - assert len(cocktail_labels) == len(cocktail_reps) - self.n_cocktails, self.dim_cocktail = cocktail_reps.shape - - indexes_train = [] - indexes_test = [] - for k in labels: - indexes_k = np.argwhere(cocktail_labels == labels.index(k)).flatten() - indexes_train += list(indexes_k[:int(0.9 * len(indexes_k))]) - indexes_test += list(indexes_k[int(0.9 * len(indexes_k)):]) - indexes_train = np.array(indexes_train) - indexes_test = np.array(indexes_test) - assert len(set(indexes_train) & set(indexes_test)) == 0 - if split == 'train': - self.cocktail_reps = cocktail_reps[indexes_train].copy() - self.labels = cocktail_labels[indexes_train].copy() - elif split == 'test': - self.cocktail_reps = cocktail_reps[indexes_test].copy() - self.labels = cocktail_labels[indexes_test].copy() - elif split == 'all': - self.cocktail_reps = cocktail_reps.copy() - self.labels = cocktail_labels.copy() - else: - raise ValueError - - self.n_cocktails = self.cocktail_reps.shape[0] - indexes = np.arange(self.n_cocktails) - np.random.shuffle(indexes) - self.cocktail_reps = torch.FloatTensor(self.cocktail_reps[indexes]).to(device) - self.labels = torch.LongTensor(self.labels[indexes]).to(device) - - def __len__(self): - return self.cocktail_reps.shape[0] - - def __getitem__(self, idx): - return self.cocktail_reps[idx], self.labels[idx] - -class MusicDataset(Dataset): - def __init__(self, split, music_reps, music_rep_paths): - - - self.n_music, self.dim_music = music_reps.shape - labels = np.zeros([self.n_music]) - - if split == 'train': - self.music_reps = music_reps[:int(0.9 * self.n_music), :].copy() - self.labels = labels[:int(0.9 * self.n_music)].copy() - elif split == 'test': - self.music_reps = music_reps[int(0.9 * self.n_music):, :].copy() - self.labels = labels[int(0.9 * self.n_music):].copy() - elif split == 'all': - self.music_reps = music_reps.copy() - self.labels = labels.copy() - else: - raise ValueError - self.n_music = self.music_reps.shape[0] - indexes = np.arange(self.n_music) - np.random.shuffle(indexes) - self.music_reps = torch.FloatTensor(self.music_reps[indexes]).to(device) - self.labels = torch.LongTensor(self.labels[indexes]).to(device) - - def __len__(self): - return self.music_reps.shape[0] - - def __getitem__(self, idx): - return self.music_reps[idx], self.labels[idx] - -class RegressedGroundingDataset(Dataset): - def __init__(self, split, music_reps, music_rep_paths, cocktail_reps): - - dataset = get_alignment_dataset() - labels = sorted(dataset['cocktail'].keys()) - self.n_labels = len(labels) - - n_music = np.sum([len(dataset['music'][k]) for k in labels]) - all_music_filenames = [] - for k in labels: - all_music_filenames += dataset['music'][k] - assert n_music == len(set(all_music_filenames)) - all_music_filenames = np.array(all_music_filenames) - - all_cocktails = [] - for k in labels: - all_cocktails += dataset['cocktail'][k] - # assert n_cocktails == len(set(all_cocktails)) - all_cocktails = np.array(all_cocktails) - - indexes = [] - for music_filename in all_music_filenames: - rep_name = music_filename.replace('_processed.mid', '_b256_r128_represented.txt') - found = False - for i, rep_path in enumerate(music_rep_paths): - if rep_name == rep_path[-len(rep_name):]: - indexes.append(i) - found = True - break - assert found - # assert len(indexes) == len(all_music_filenames) - music_reps = music_reps[np.array(indexes)] - music_labels = [] - for music_filename in all_music_filenames: - for i_k, k in enumerate(labels): - if music_filename in dataset['music'][k]: - music_labels.append(i_k) - break - assert len(music_labels) == len(music_reps) - music_labels = np.array(music_labels) - self.n_music, self.dim_music = music_reps.shape - self.classes = labels - - - cocktail_reps = cocktail_reps[all_cocktails] - cocktail_labels = [] - for i in all_cocktails: - for i_k, k in enumerate(labels): - if i in dataset['cocktail'][k]: - cocktail_labels.append(i_k) - break - cocktail_labels = np.array(cocktail_labels) - assert len(cocktail_labels) == len(cocktail_reps) - self.n_cocktails, self.dim_cocktail = cocktail_reps.shape - - cocktail_reps_matching_music_reps = [] - for l in music_labels: - ind_cocktails = np.where(cocktail_labels==l)[0] - cocktail_reps_matching_music_reps.append(cocktail_reps[np.random.choice(ind_cocktails)]) - cocktail_reps_matching_music_reps = np.array(cocktail_reps_matching_music_reps) - - - indexes_train = [] - indexes_test = [] - for k in labels: - indexes_k = np.argwhere(music_labels == labels.index(k)).flatten() - indexes_train += list(indexes_k[:int(0.9 * len(indexes_k))]) - indexes_test += list(indexes_k[int(0.9 * len(indexes_k)):]) - indexes_train = np.array(indexes_train) - indexes_test = np.array(indexes_test) - assert len(set(indexes_train) & set(indexes_test)) == 0 - - if split == 'train': - self.music_reps = music_reps[indexes_train].copy() - self.cocktail_reps = cocktail_reps_matching_music_reps[indexes_train].copy() - # self.labels = music_labels[indexes_train].copy() - elif split == 'test': - self.music_reps = music_reps[indexes_test].copy() - self.cocktail_reps = cocktail_reps_matching_music_reps[indexes_test].copy() - # self.labels = music_labels[indexes_test].copy() - elif split == 'all': - self.music_reps = music_reps.copy() - self.cocktail_reps = cocktail_reps_matching_music_reps.copy() - # self.labels = music_labels.copy() - else: - raise ValueError - self.n_music = self.music_reps.shape[0] - indexes = np.arange(self.n_music) - np.random.shuffle(indexes) - self.music_reps = torch.FloatTensor(self.music_reps[indexes]).to(device) - self.cocktail_reps = torch.FloatTensor(self.cocktail_reps[indexes]).to(device) - # self.labels = torch.LongTensor(self.labels[indexes]).to(device) - - def __len__(self): - return self.music_reps.shape[0] - - def __getitem__(self, idx): - return self.music_reps[idx], self.cocktail_reps[idx] - -class MusicLabeledDataset(Dataset): - def __init__(self, split, music_reps, music_rep_paths): - - dataset = get_alignment_dataset() - labels = sorted(dataset['cocktail'].keys()) - self.n_labels = len(labels) - - n_music = np.sum([len(dataset['music'][k]) for k in labels]) - all_music_filenames = [] - for k in labels: - all_music_filenames += dataset['music'][k] - assert n_music == len(set(all_music_filenames)) - all_music_filenames = np.array(all_music_filenames) - - indexes = [] - for music_filename in all_music_filenames: - rep_name = music_filename.replace('_processed.mid', '_b256_r128_represented.txt') - found = False - for i, rep_path in enumerate(music_rep_paths): - if rep_name == rep_path[-len(rep_name):]: - indexes.append(i) - found = True - break - assert found - # assert len(indexes) == len(all_music_filenames) - music_reps = music_reps[np.array(indexes)] - music_labels = [] - for music_filename in all_music_filenames: - for i_k, k in enumerate(labels): - if music_filename in dataset['music'][k]: - music_labels.append(i_k) - break - assert len(music_labels) == len(music_reps) - music_labels = np.array(music_labels) - self.n_music, self.dim_music = music_reps.shape - self.classes = labels - - indexes_train = [] - indexes_test = [] - for k in labels: - indexes_k = np.argwhere(music_labels == labels.index(k)).flatten() - indexes_train += list(indexes_k[:int(0.9 * len(indexes_k))]) - indexes_test += list(indexes_k[int(0.9 * len(indexes_k)):]) - indexes_train = np.array(indexes_train) - indexes_test = np.array(indexes_test) - assert len(set(indexes_train) & set(indexes_test)) == 0 - - if split == 'train': - self.music_reps = music_reps[indexes_train].copy() - self.labels = music_labels[indexes_train].copy() - elif split == 'test': - self.music_reps = music_reps[indexes_test].copy() - self.labels = music_labels[indexes_test].copy() - elif split == 'all': - self.music_reps = music_reps.copy() - self.labels = music_labels.copy() - else: - raise ValueError - self.n_music = self.music_reps.shape[0] - indexes = np.arange(self.n_music) - np.random.shuffle(indexes) - self.music_reps = torch.FloatTensor(self.music_reps[indexes]).to(device) - self.labels = torch.LongTensor(self.labels[indexes]).to(device) - - def __len__(self): - return self.music_reps.shape[0] - - def __getitem__(self, idx): - return self.music_reps[idx], self.labels[idx] \ No newline at end of file diff --git a/spaces/chansung/tfx-vit-pipeline/README.md b/spaces/chansung/tfx-vit-pipeline/README.md deleted file mode 100644 index e1686361d1511e2df502dde3c48c74aa9aea500f..0000000000000000000000000000000000000000 --- a/spaces/chansung/tfx-vit-pipeline/README.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: Leaf Classification -emoji: 🍂 -colorFrom: indigo -colorTo: indigo -sdk: gradio -sdk_version: 3.29.0 -app_file: app.py -pinned: false -license: apache-2.0 ---- \ No newline at end of file diff --git a/spaces/chengzl18/DeepTHULAC/README.md b/spaces/chengzl18/DeepTHULAC/README.md deleted file mode 100644 index 6d70d79523779d69aefcbbdeeadfeb92e9a07505..0000000000000000000000000000000000000000 --- a/spaces/chengzl18/DeepTHULAC/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: DeepTHULAC -emoji: 🏆 -colorFrom: green -colorTo: gray -sdk: gradio -sdk_version: 3.35.2 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/chilleverydaychill/roop/roop/__init__.py b/spaces/chilleverydaychill/roop/roop/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/aiohttp/client_reqrep.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/aiohttp/client_reqrep.py deleted file mode 100644 index 28b8a28d0d80a3c374de204d25ab460427b3154c..0000000000000000000000000000000000000000 --- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/aiohttp/client_reqrep.py +++ /dev/null @@ -1,1134 +0,0 @@ -import asyncio -import codecs -import functools -import io -import re -import sys -import traceback -import warnings -from hashlib import md5, sha1, sha256 -from http.cookies import CookieError, Morsel, SimpleCookie -from types import MappingProxyType, TracebackType -from typing import ( - TYPE_CHECKING, - Any, - Dict, - Iterable, - List, - Mapping, - Optional, - Tuple, - Type, - Union, - cast, -) - -import attr -from multidict import CIMultiDict, CIMultiDictProxy, MultiDict, MultiDictProxy -from yarl import URL - -from . import hdrs, helpers, http, multipart, payload -from .abc import AbstractStreamWriter -from .client_exceptions import ( - ClientConnectionError, - ClientOSError, - ClientResponseError, - ContentTypeError, - InvalidURL, - ServerFingerprintMismatch, -) -from .formdata import FormData -from .helpers import ( - PY_36, - BaseTimerContext, - BasicAuth, - HeadersMixin, - TimerNoop, - noop, - reify, - set_result, -) -from .http import SERVER_SOFTWARE, HttpVersion10, HttpVersion11, StreamWriter -from .log import client_logger -from .streams import StreamReader -from .typedefs import ( - DEFAULT_JSON_DECODER, - JSONDecoder, - LooseCookies, - LooseHeaders, - RawHeaders, -) - -try: - import ssl - from ssl import SSLContext -except ImportError: # pragma: no cover - ssl = None # type: ignore[assignment] - SSLContext = object # type: ignore[misc,assignment] - -try: - import cchardet as chardet -except ImportError: # pragma: no cover - import charset_normalizer as chardet # type: ignore[no-redef] - - -__all__ = ("ClientRequest", "ClientResponse", "RequestInfo", "Fingerprint") - - -if TYPE_CHECKING: # pragma: no cover - from .client import ClientSession - from .connector import Connection - from .tracing import Trace - - -json_re = re.compile(r"^application/(?:[\w.+-]+?\+)?json") - - -@attr.s(auto_attribs=True, frozen=True, slots=True) -class ContentDisposition: - type: Optional[str] - parameters: "MappingProxyType[str, str]" - filename: Optional[str] - - -@attr.s(auto_attribs=True, frozen=True, slots=True) -class RequestInfo: - url: URL - method: str - headers: "CIMultiDictProxy[str]" - real_url: URL = attr.ib() - - @real_url.default - def real_url_default(self) -> URL: - return self.url - - -class Fingerprint: - HASHFUNC_BY_DIGESTLEN = { - 16: md5, - 20: sha1, - 32: sha256, - } - - def __init__(self, fingerprint: bytes) -> None: - digestlen = len(fingerprint) - hashfunc = self.HASHFUNC_BY_DIGESTLEN.get(digestlen) - if not hashfunc: - raise ValueError("fingerprint has invalid length") - elif hashfunc is md5 or hashfunc is sha1: - raise ValueError( - "md5 and sha1 are insecure and " "not supported. Use sha256." - ) - self._hashfunc = hashfunc - self._fingerprint = fingerprint - - @property - def fingerprint(self) -> bytes: - return self._fingerprint - - def check(self, transport: asyncio.Transport) -> None: - if not transport.get_extra_info("sslcontext"): - return - sslobj = transport.get_extra_info("ssl_object") - cert = sslobj.getpeercert(binary_form=True) - got = self._hashfunc(cert).digest() - if got != self._fingerprint: - host, port, *_ = transport.get_extra_info("peername") - raise ServerFingerprintMismatch(self._fingerprint, got, host, port) - - -if ssl is not None: - SSL_ALLOWED_TYPES = (ssl.SSLContext, bool, Fingerprint, type(None)) -else: # pragma: no cover - SSL_ALLOWED_TYPES = type(None) - - -def _merge_ssl_params( - ssl: Union["SSLContext", bool, Fingerprint, None], - verify_ssl: Optional[bool], - ssl_context: Optional["SSLContext"], - fingerprint: Optional[bytes], -) -> Union["SSLContext", bool, Fingerprint, None]: - if verify_ssl is not None and not verify_ssl: - warnings.warn( - "verify_ssl is deprecated, use ssl=False instead", - DeprecationWarning, - stacklevel=3, - ) - if ssl is not None: - raise ValueError( - "verify_ssl, ssl_context, fingerprint and ssl " - "parameters are mutually exclusive" - ) - else: - ssl = False - if ssl_context is not None: - warnings.warn( - "ssl_context is deprecated, use ssl=context instead", - DeprecationWarning, - stacklevel=3, - ) - if ssl is not None: - raise ValueError( - "verify_ssl, ssl_context, fingerprint and ssl " - "parameters are mutually exclusive" - ) - else: - ssl = ssl_context - if fingerprint is not None: - warnings.warn( - "fingerprint is deprecated, " "use ssl=Fingerprint(fingerprint) instead", - DeprecationWarning, - stacklevel=3, - ) - if ssl is not None: - raise ValueError( - "verify_ssl, ssl_context, fingerprint and ssl " - "parameters are mutually exclusive" - ) - else: - ssl = Fingerprint(fingerprint) - if not isinstance(ssl, SSL_ALLOWED_TYPES): - raise TypeError( - "ssl should be SSLContext, bool, Fingerprint or None, " - "got {!r} instead.".format(ssl) - ) - return ssl - - -@attr.s(auto_attribs=True, slots=True, frozen=True) -class ConnectionKey: - # the key should contain an information about used proxy / TLS - # to prevent reusing wrong connections from a pool - host: str - port: Optional[int] - is_ssl: bool - ssl: Union[SSLContext, None, bool, Fingerprint] - proxy: Optional[URL] - proxy_auth: Optional[BasicAuth] - proxy_headers_hash: Optional[int] # hash(CIMultiDict) - - -def _is_expected_content_type( - response_content_type: str, expected_content_type: str -) -> bool: - if expected_content_type == "application/json": - return json_re.match(response_content_type) is not None - return expected_content_type in response_content_type - - -class ClientRequest: - GET_METHODS = { - hdrs.METH_GET, - hdrs.METH_HEAD, - hdrs.METH_OPTIONS, - hdrs.METH_TRACE, - } - POST_METHODS = {hdrs.METH_PATCH, hdrs.METH_POST, hdrs.METH_PUT} - ALL_METHODS = GET_METHODS.union(POST_METHODS).union({hdrs.METH_DELETE}) - - DEFAULT_HEADERS = { - hdrs.ACCEPT: "*/*", - hdrs.ACCEPT_ENCODING: "gzip, deflate", - } - - body = b"" - auth = None - response = None - - _writer = None # async task for streaming data - _continue = None # waiter future for '100 Continue' response - - # N.B. - # Adding __del__ method with self._writer closing doesn't make sense - # because _writer is instance method, thus it keeps a reference to self. - # Until writer has finished finalizer will not be called. - - def __init__( - self, - method: str, - url: URL, - *, - params: Optional[Mapping[str, str]] = None, - headers: Optional[LooseHeaders] = None, - skip_auto_headers: Iterable[str] = frozenset(), - data: Any = None, - cookies: Optional[LooseCookies] = None, - auth: Optional[BasicAuth] = None, - version: http.HttpVersion = http.HttpVersion11, - compress: Optional[str] = None, - chunked: Optional[bool] = None, - expect100: bool = False, - loop: Optional[asyncio.AbstractEventLoop] = None, - response_class: Optional[Type["ClientResponse"]] = None, - proxy: Optional[URL] = None, - proxy_auth: Optional[BasicAuth] = None, - timer: Optional[BaseTimerContext] = None, - session: Optional["ClientSession"] = None, - ssl: Union[SSLContext, bool, Fingerprint, None] = None, - proxy_headers: Optional[LooseHeaders] = None, - traces: Optional[List["Trace"]] = None, - ): - - if loop is None: - loop = asyncio.get_event_loop() - - assert isinstance(url, URL), url - assert isinstance(proxy, (URL, type(None))), proxy - # FIXME: session is None in tests only, need to fix tests - # assert session is not None - self._session = cast("ClientSession", session) - if params: - q = MultiDict(url.query) - url2 = url.with_query(params) - q.extend(url2.query) - url = url.with_query(q) - self.original_url = url - self.url = url.with_fragment(None) - self.method = method.upper() - self.chunked = chunked - self.compress = compress - self.loop = loop - self.length = None - if response_class is None: - real_response_class = ClientResponse - else: - real_response_class = response_class - self.response_class: Type[ClientResponse] = real_response_class - self._timer = timer if timer is not None else TimerNoop() - self._ssl = ssl - - if loop.get_debug(): - self._source_traceback = traceback.extract_stack(sys._getframe(1)) - - self.update_version(version) - self.update_host(url) - self.update_headers(headers) - self.update_auto_headers(skip_auto_headers) - self.update_cookies(cookies) - self.update_content_encoding(data) - self.update_auth(auth) - self.update_proxy(proxy, proxy_auth, proxy_headers) - - self.update_body_from_data(data) - if data is not None or self.method not in self.GET_METHODS: - self.update_transfer_encoding() - self.update_expect_continue(expect100) - if traces is None: - traces = [] - self._traces = traces - - def is_ssl(self) -> bool: - return self.url.scheme in ("https", "wss") - - @property - def ssl(self) -> Union["SSLContext", None, bool, Fingerprint]: - return self._ssl - - @property - def connection_key(self) -> ConnectionKey: - proxy_headers = self.proxy_headers - if proxy_headers: - h: Optional[int] = hash(tuple((k, v) for k, v in proxy_headers.items())) - else: - h = None - return ConnectionKey( - self.host, - self.port, - self.is_ssl(), - self.ssl, - self.proxy, - self.proxy_auth, - h, - ) - - @property - def host(self) -> str: - ret = self.url.raw_host - assert ret is not None - return ret - - @property - def port(self) -> Optional[int]: - return self.url.port - - @property - def request_info(self) -> RequestInfo: - headers: CIMultiDictProxy[str] = CIMultiDictProxy(self.headers) - return RequestInfo(self.url, self.method, headers, self.original_url) - - def update_host(self, url: URL) -> None: - """Update destination host, port and connection type (ssl).""" - # get host/port - if not url.raw_host: - raise InvalidURL(url) - - # basic auth info - username, password = url.user, url.password - if username: - self.auth = helpers.BasicAuth(username, password or "") - - def update_version(self, version: Union[http.HttpVersion, str]) -> None: - """Convert request version to two elements tuple. - - parser HTTP version '1.1' => (1, 1) - """ - if isinstance(version, str): - v = [part.strip() for part in version.split(".", 1)] - try: - version = http.HttpVersion(int(v[0]), int(v[1])) - except ValueError: - raise ValueError( - f"Can not parse http version number: {version}" - ) from None - self.version = version - - def update_headers(self, headers: Optional[LooseHeaders]) -> None: - """Update request headers.""" - self.headers: CIMultiDict[str] = CIMultiDict() - - # add host - netloc = cast(str, self.url.raw_host) - if helpers.is_ipv6_address(netloc): - netloc = f"[{netloc}]" - if self.url.port is not None and not self.url.is_default_port(): - netloc += ":" + str(self.url.port) - self.headers[hdrs.HOST] = netloc - - if headers: - if isinstance(headers, (dict, MultiDictProxy, MultiDict)): - headers = headers.items() # type: ignore[assignment] - - for key, value in headers: # type: ignore[misc] - # A special case for Host header - if key.lower() == "host": - self.headers[key] = value - else: - self.headers.add(key, value) - - def update_auto_headers(self, skip_auto_headers: Iterable[str]) -> None: - self.skip_auto_headers = CIMultiDict( - (hdr, None) for hdr in sorted(skip_auto_headers) - ) - used_headers = self.headers.copy() - used_headers.extend(self.skip_auto_headers) # type: ignore[arg-type] - - for hdr, val in self.DEFAULT_HEADERS.items(): - if hdr not in used_headers: - self.headers.add(hdr, val) - - if hdrs.USER_AGENT not in used_headers: - self.headers[hdrs.USER_AGENT] = SERVER_SOFTWARE - - def update_cookies(self, cookies: Optional[LooseCookies]) -> None: - """Update request cookies header.""" - if not cookies: - return - - c: SimpleCookie[str] = SimpleCookie() - if hdrs.COOKIE in self.headers: - c.load(self.headers.get(hdrs.COOKIE, "")) - del self.headers[hdrs.COOKIE] - - if isinstance(cookies, Mapping): - iter_cookies = cookies.items() - else: - iter_cookies = cookies # type: ignore[assignment] - for name, value in iter_cookies: - if isinstance(value, Morsel): - # Preserve coded_value - mrsl_val = value.get(value.key, Morsel()) - mrsl_val.set(value.key, value.value, value.coded_value) - c[name] = mrsl_val - else: - c[name] = value # type: ignore[assignment] - - self.headers[hdrs.COOKIE] = c.output(header="", sep=";").strip() - - def update_content_encoding(self, data: Any) -> None: - """Set request content encoding.""" - if data is None: - return - - enc = self.headers.get(hdrs.CONTENT_ENCODING, "").lower() - if enc: - if self.compress: - raise ValueError( - "compress can not be set " "if Content-Encoding header is set" - ) - elif self.compress: - if not isinstance(self.compress, str): - self.compress = "deflate" - self.headers[hdrs.CONTENT_ENCODING] = self.compress - self.chunked = True # enable chunked, no need to deal with length - - def update_transfer_encoding(self) -> None: - """Analyze transfer-encoding header.""" - te = self.headers.get(hdrs.TRANSFER_ENCODING, "").lower() - - if "chunked" in te: - if self.chunked: - raise ValueError( - "chunked can not be set " - 'if "Transfer-Encoding: chunked" header is set' - ) - - elif self.chunked: - if hdrs.CONTENT_LENGTH in self.headers: - raise ValueError( - "chunked can not be set " "if Content-Length header is set" - ) - - self.headers[hdrs.TRANSFER_ENCODING] = "chunked" - else: - if hdrs.CONTENT_LENGTH not in self.headers: - self.headers[hdrs.CONTENT_LENGTH] = str(len(self.body)) - - def update_auth(self, auth: Optional[BasicAuth]) -> None: - """Set basic auth.""" - if auth is None: - auth = self.auth - if auth is None: - return - - if not isinstance(auth, helpers.BasicAuth): - raise TypeError("BasicAuth() tuple is required instead") - - self.headers[hdrs.AUTHORIZATION] = auth.encode() - - def update_body_from_data(self, body: Any) -> None: - if body is None: - return - - # FormData - if isinstance(body, FormData): - body = body() - - try: - body = payload.PAYLOAD_REGISTRY.get(body, disposition=None) - except payload.LookupError: - body = FormData(body)() - - self.body = body - - # enable chunked encoding if needed - if not self.chunked: - if hdrs.CONTENT_LENGTH not in self.headers: - size = body.size - if size is None: - self.chunked = True - else: - if hdrs.CONTENT_LENGTH not in self.headers: - self.headers[hdrs.CONTENT_LENGTH] = str(size) - - # copy payload headers - assert body.headers - for (key, value) in body.headers.items(): - if key in self.headers: - continue - if key in self.skip_auto_headers: - continue - self.headers[key] = value - - def update_expect_continue(self, expect: bool = False) -> None: - if expect: - self.headers[hdrs.EXPECT] = "100-continue" - elif self.headers.get(hdrs.EXPECT, "").lower() == "100-continue": - expect = True - - if expect: - self._continue = self.loop.create_future() - - def update_proxy( - self, - proxy: Optional[URL], - proxy_auth: Optional[BasicAuth], - proxy_headers: Optional[LooseHeaders], - ) -> None: - if proxy_auth and not isinstance(proxy_auth, helpers.BasicAuth): - raise ValueError("proxy_auth must be None or BasicAuth() tuple") - self.proxy = proxy - self.proxy_auth = proxy_auth - self.proxy_headers = proxy_headers - - def keep_alive(self) -> bool: - if self.version < HttpVersion10: - # keep alive not supported at all - return False - if self.version == HttpVersion10: - if self.headers.get(hdrs.CONNECTION) == "keep-alive": - return True - else: # no headers means we close for Http 1.0 - return False - elif self.headers.get(hdrs.CONNECTION) == "close": - return False - - return True - - async def write_bytes( - self, writer: AbstractStreamWriter, conn: "Connection" - ) -> None: - """Support coroutines that yields bytes objects.""" - # 100 response - if self._continue is not None: - await writer.drain() - await self._continue - - protocol = conn.protocol - assert protocol is not None - try: - if isinstance(self.body, payload.Payload): - await self.body.write(writer) - else: - if isinstance(self.body, (bytes, bytearray)): - self.body = (self.body,) # type: ignore[assignment] - - for chunk in self.body: - await writer.write(chunk) # type: ignore[arg-type] - - await writer.write_eof() - except OSError as exc: - if exc.errno is None and isinstance(exc, asyncio.TimeoutError): - protocol.set_exception(exc) - else: - new_exc = ClientOSError( - exc.errno, "Can not write request body for %s" % self.url - ) - new_exc.__context__ = exc - new_exc.__cause__ = exc - protocol.set_exception(new_exc) - except asyncio.CancelledError as exc: - if not conn.closed: - protocol.set_exception(exc) - except Exception as exc: - protocol.set_exception(exc) - finally: - self._writer = None - - async def send(self, conn: "Connection") -> "ClientResponse": - # Specify request target: - # - CONNECT request must send authority form URI - # - not CONNECT proxy must send absolute form URI - # - most common is origin form URI - if self.method == hdrs.METH_CONNECT: - connect_host = self.url.raw_host - assert connect_host is not None - if helpers.is_ipv6_address(connect_host): - connect_host = f"[{connect_host}]" - path = f"{connect_host}:{self.url.port}" - elif self.proxy and not self.is_ssl(): - path = str(self.url) - else: - path = self.url.raw_path - if self.url.raw_query_string: - path += "?" + self.url.raw_query_string - - protocol = conn.protocol - assert protocol is not None - writer = StreamWriter( - protocol, - self.loop, - on_chunk_sent=functools.partial( - self._on_chunk_request_sent, self.method, self.url - ), - on_headers_sent=functools.partial( - self._on_headers_request_sent, self.method, self.url - ), - ) - - if self.compress: - writer.enable_compression(self.compress) - - if self.chunked is not None: - writer.enable_chunking() - - # set default content-type - if ( - self.method in self.POST_METHODS - and hdrs.CONTENT_TYPE not in self.skip_auto_headers - and hdrs.CONTENT_TYPE not in self.headers - ): - self.headers[hdrs.CONTENT_TYPE] = "application/octet-stream" - - # set the connection header - connection = self.headers.get(hdrs.CONNECTION) - if not connection: - if self.keep_alive(): - if self.version == HttpVersion10: - connection = "keep-alive" - else: - if self.version == HttpVersion11: - connection = "close" - - if connection is not None: - self.headers[hdrs.CONNECTION] = connection - - # status + headers - status_line = "{0} {1} HTTP/{2[0]}.{2[1]}".format( - self.method, path, self.version - ) - await writer.write_headers(status_line, self.headers) - - self._writer = self.loop.create_task(self.write_bytes(writer, conn)) - - response_class = self.response_class - assert response_class is not None - self.response = response_class( - self.method, - self.original_url, - writer=self._writer, - continue100=self._continue, - timer=self._timer, - request_info=self.request_info, - traces=self._traces, - loop=self.loop, - session=self._session, - ) - return self.response - - async def close(self) -> None: - if self._writer is not None: - try: - await self._writer - finally: - self._writer = None - - def terminate(self) -> None: - if self._writer is not None: - if not self.loop.is_closed(): - self._writer.cancel() - self._writer = None - - async def _on_chunk_request_sent(self, method: str, url: URL, chunk: bytes) -> None: - for trace in self._traces: - await trace.send_request_chunk_sent(method, url, chunk) - - async def _on_headers_request_sent( - self, method: str, url: URL, headers: "CIMultiDict[str]" - ) -> None: - for trace in self._traces: - await trace.send_request_headers(method, url, headers) - - -class ClientResponse(HeadersMixin): - - # from the Status-Line of the response - version = None # HTTP-Version - status: int = None # type: ignore[assignment] # Status-Code - reason = None # Reason-Phrase - - content: StreamReader = None # type: ignore[assignment] # Payload stream - _headers: "CIMultiDictProxy[str]" = None # type: ignore[assignment] - _raw_headers: RawHeaders = None # type: ignore[assignment] # Response raw headers - - _connection = None # current connection - _source_traceback = None - # setted up by ClientRequest after ClientResponse object creation - # post-init stage allows to not change ctor signature - _closed = True # to allow __del__ for non-initialized properly response - _released = False - - def __init__( - self, - method: str, - url: URL, - *, - writer: "asyncio.Task[None]", - continue100: Optional["asyncio.Future[bool]"], - timer: BaseTimerContext, - request_info: RequestInfo, - traces: List["Trace"], - loop: asyncio.AbstractEventLoop, - session: "ClientSession", - ) -> None: - assert isinstance(url, URL) - - self.method = method - self.cookies: SimpleCookie[str] = SimpleCookie() - - self._real_url = url - self._url = url.with_fragment(None) - self._body: Any = None - self._writer: Optional[asyncio.Task[None]] = writer - self._continue = continue100 # None by default - self._closed = True - self._history: Tuple[ClientResponse, ...] = () - self._request_info = request_info - self._timer = timer if timer is not None else TimerNoop() - self._cache: Dict[str, Any] = {} - self._traces = traces - self._loop = loop - # store a reference to session #1985 - self._session: Optional[ClientSession] = session - if loop.get_debug(): - self._source_traceback = traceback.extract_stack(sys._getframe(1)) - - @reify - def url(self) -> URL: - return self._url - - @reify - def url_obj(self) -> URL: - warnings.warn("Deprecated, use .url #1654", DeprecationWarning, stacklevel=2) - return self._url - - @reify - def real_url(self) -> URL: - return self._real_url - - @reify - def host(self) -> str: - assert self._url.host is not None - return self._url.host - - @reify - def headers(self) -> "CIMultiDictProxy[str]": - return self._headers - - @reify - def raw_headers(self) -> RawHeaders: - return self._raw_headers - - @reify - def request_info(self) -> RequestInfo: - return self._request_info - - @reify - def content_disposition(self) -> Optional[ContentDisposition]: - raw = self._headers.get(hdrs.CONTENT_DISPOSITION) - if raw is None: - return None - disposition_type, params_dct = multipart.parse_content_disposition(raw) - params = MappingProxyType(params_dct) - filename = multipart.content_disposition_filename(params) - return ContentDisposition(disposition_type, params, filename) - - def __del__(self, _warnings: Any = warnings) -> None: - if self._closed: - return - - if self._connection is not None: - self._connection.release() - self._cleanup_writer() - - if self._loop.get_debug(): - if PY_36: - kwargs = {"source": self} - else: - kwargs = {} - _warnings.warn(f"Unclosed response {self!r}", ResourceWarning, **kwargs) - context = {"client_response": self, "message": "Unclosed response"} - if self._source_traceback: - context["source_traceback"] = self._source_traceback - self._loop.call_exception_handler(context) - - def __repr__(self) -> str: - out = io.StringIO() - ascii_encodable_url = str(self.url) - if self.reason: - ascii_encodable_reason = self.reason.encode( - "ascii", "backslashreplace" - ).decode("ascii") - else: - ascii_encodable_reason = self.reason - print( - "First of all, if you've never owned a set-top box, I guess the best way to to describe its use is to say that they deliver streaming video content to your TV. You may already be using your smart TV, smart Blu-ray player and/or video game consoles to deliver your Netflix, Amazon Prime and/or other streaming content to your television. A set-top box is an alternative to that, and possibly a better one depending on the interface and speed/quality that your current set-up is offering. And each set-top box seems to come with their own set of additional features beyond streaming videos, like the MP3 and image streaming that Fire TV offers, for example. A set-top box will require a wifi connection to access that online content you're trying to send to your TV, but as it's hooked up to your home network, you might also get home-sharing capabilities, depending on the device. As is the case with a tablet or a smart phone, which one to get and whether or not you really need it probably depends a lot on what you'd be using it for.
-Online retailer Amazon today unveiled the Amazon Fire TV, a set-top box featuring streaming video, music, and video games. The Fire TV connects to HDTVs and streams media of all kinds, including Amazon Instant Video and music from Pandora. Video games can also be played via the Fire TV with the help of a special controller that is sold separately.
-Amazon’s New \"TV Set-Top Box\" for Streaming Videos
Download File ››››› https://tinurli.com/2uwhZV
-For years Amazon has been selling its Fire TV set-top box as an alternative to devices such as the Apple TV and Roku that deliver video streaming, music and games. The latest version of Fire TV with 4K Ultra HD, might be its best yet. The $70 device supports all of the apps and services that its predecessors offered, but comes with support for the highest video resolution available. With the Alexa digital assistant included, the Fire TV can be used to smart home systems as well as video and music. But there might still be some who question whether it can compete with Apple TV and similar products. Check out this slide show to learn about the key features in this new edition of Fire TV.
-Amazon on Thursday launched the Fling software toolkit, which allows developers to make photos, music, or videos running on their Android, iOS, or FireOS apps sharable with Amazon's Fire TV set-top box.
-Amazon's Fire TV set-top box offers a wide range of applications, allowing users to do everything from watch programming to play games on their televisions. The device, which retails for $99, is similar to other set-top boxes, including the Apple TV and a line of devices from Roku. It offers users access to popular video-streaming apps like Netflix and HBO Go, and has added more apps as time has gone on.
-In order to help boost its newcomer streaming service, Apple initially offered a free year of the service to anyone who recently purchased an Apple device. This includes iPhones, iPads, Macs, iPods, and Apple TV set-top boxes.
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- diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/cffi/model.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/cffi/model.py deleted file mode 100644 index ad1c1764893d0257c0e75eeb61b0a359e89adf0f..0000000000000000000000000000000000000000 --- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/cffi/model.py +++ /dev/null @@ -1,617 +0,0 @@ -import types -import weakref - -from .lock import allocate_lock -from .error import CDefError, VerificationError, VerificationMissing - -# type qualifiers -Q_CONST = 0x01 -Q_RESTRICT = 0x02 -Q_VOLATILE = 0x04 - -def qualify(quals, replace_with): - if quals & Q_CONST: - replace_with = ' const ' + replace_with.lstrip() - if quals & Q_VOLATILE: - replace_with = ' volatile ' + replace_with.lstrip() - if quals & Q_RESTRICT: - # It seems that __restrict is supported by gcc and msvc. - # If you hit some different compiler, add a #define in - # _cffi_include.h for it (and in its copies, documented there) - replace_with = ' __restrict ' + replace_with.lstrip() - return replace_with - - -class BaseTypeByIdentity(object): - is_array_type = False - is_raw_function = False - - def get_c_name(self, replace_with='', context='a C file', quals=0): - result = self.c_name_with_marker - assert result.count('&') == 1 - # some logic duplication with ffi.getctype()... :-( - replace_with = replace_with.strip() - if replace_with: - if replace_with.startswith('*') and '&[' in result: - replace_with = '(%s)' % replace_with - elif not replace_with[0] in '[(': - replace_with = ' ' + replace_with - replace_with = qualify(quals, replace_with) - result = result.replace('&', replace_with) - if '$' in result: - raise VerificationError( - "cannot generate '%s' in %s: unknown type name" - % (self._get_c_name(), context)) - return result - - def _get_c_name(self): - return self.c_name_with_marker.replace('&', '') - - def has_c_name(self): - return '$' not in self._get_c_name() - - def is_integer_type(self): - return False - - def get_cached_btype(self, ffi, finishlist, can_delay=False): - try: - BType = ffi._cached_btypes[self] - except KeyError: - BType = self.build_backend_type(ffi, finishlist) - BType2 = ffi._cached_btypes.setdefault(self, BType) - assert BType2 is BType - return BType - - def __repr__(self): - return '<%s>' % (self._get_c_name(),) - - def _get_items(self): - return [(name, getattr(self, name)) for name in self._attrs_] - - -class BaseType(BaseTypeByIdentity): - - def __eq__(self, other): - return (self.__class__ == other.__class__ and - self._get_items() == other._get_items()) - - def __ne__(self, other): - return not self == other - - def __hash__(self): - return hash((self.__class__, tuple(self._get_items()))) - - -class VoidType(BaseType): - _attrs_ = () - - def __init__(self): - self.c_name_with_marker = 'void&' - - def build_backend_type(self, ffi, finishlist): - return global_cache(self, ffi, 'new_void_type') - -void_type = VoidType() - - -class BasePrimitiveType(BaseType): - def is_complex_type(self): - return False - - -class PrimitiveType(BasePrimitiveType): - _attrs_ = ('name',) - - ALL_PRIMITIVE_TYPES = { - 'char': 'c', - 'short': 'i', - 'int': 'i', - 'long': 'i', - 'long long': 'i', - 'signed char': 'i', - 'unsigned char': 'i', - 'unsigned short': 'i', - 'unsigned int': 'i', - 'unsigned long': 'i', - 'unsigned long long': 'i', - 'float': 'f', - 'double': 'f', - 'long double': 'f', - 'float _Complex': 'j', - 'double _Complex': 'j', - '_Bool': 'i', - # the following types are not primitive in the C sense - 'wchar_t': 'c', - 'char16_t': 'c', - 'char32_t': 'c', - 'int8_t': 'i', - 'uint8_t': 'i', - 'int16_t': 'i', - 'uint16_t': 'i', - 'int32_t': 'i', - 'uint32_t': 'i', - 'int64_t': 'i', - 'uint64_t': 'i', - 'int_least8_t': 'i', - 'uint_least8_t': 'i', - 'int_least16_t': 'i', - 'uint_least16_t': 'i', - 'int_least32_t': 'i', - 'uint_least32_t': 'i', - 'int_least64_t': 'i', - 'uint_least64_t': 'i', - 'int_fast8_t': 'i', - 'uint_fast8_t': 'i', - 'int_fast16_t': 'i', - 'uint_fast16_t': 'i', - 'int_fast32_t': 'i', - 'uint_fast32_t': 'i', - 'int_fast64_t': 'i', - 'uint_fast64_t': 'i', - 'intptr_t': 'i', - 'uintptr_t': 'i', - 'intmax_t': 'i', - 'uintmax_t': 'i', - 'ptrdiff_t': 'i', - 'size_t': 'i', - 'ssize_t': 'i', - } - - def __init__(self, name): - assert name in self.ALL_PRIMITIVE_TYPES - self.name = name - self.c_name_with_marker = name + '&' - - def is_char_type(self): - return self.ALL_PRIMITIVE_TYPES[self.name] == 'c' - def is_integer_type(self): - return self.ALL_PRIMITIVE_TYPES[self.name] == 'i' - def is_float_type(self): - return self.ALL_PRIMITIVE_TYPES[self.name] == 'f' - def is_complex_type(self): - return self.ALL_PRIMITIVE_TYPES[self.name] == 'j' - - def build_backend_type(self, ffi, finishlist): - return global_cache(self, ffi, 'new_primitive_type', self.name) - - -class UnknownIntegerType(BasePrimitiveType): - _attrs_ = ('name',) - - def __init__(self, name): - self.name = name - self.c_name_with_marker = name + '&' - - def is_integer_type(self): - return True - - def build_backend_type(self, ffi, finishlist): - raise NotImplementedError("integer type '%s' can only be used after " - "compilation" % self.name) - -class UnknownFloatType(BasePrimitiveType): - _attrs_ = ('name', ) - - def __init__(self, name): - self.name = name - self.c_name_with_marker = name + '&' - - def build_backend_type(self, ffi, finishlist): - raise NotImplementedError("float type '%s' can only be used after " - "compilation" % self.name) - - -class BaseFunctionType(BaseType): - _attrs_ = ('args', 'result', 'ellipsis', 'abi') - - def __init__(self, args, result, ellipsis, abi=None): - self.args = args - self.result = result - self.ellipsis = ellipsis - self.abi = abi - # - reprargs = [arg._get_c_name() for arg in self.args] - if self.ellipsis: - reprargs.append('...') - reprargs = reprargs or ['void'] - replace_with = self._base_pattern % (', '.join(reprargs),) - if abi is not None: - replace_with = replace_with[:1] + abi + ' ' + replace_with[1:] - self.c_name_with_marker = ( - self.result.c_name_with_marker.replace('&', replace_with)) - - -class RawFunctionType(BaseFunctionType): - # Corresponds to a C type like 'int(int)', which is the C type of - # a function, but not a pointer-to-function. The backend has no - # notion of such a type; it's used temporarily by parsing. - _base_pattern = '(&)(%s)' - is_raw_function = True - - def build_backend_type(self, ffi, finishlist): - raise CDefError("cannot render the type %r: it is a function " - "type, not a pointer-to-function type" % (self,)) - - def as_function_pointer(self): - return FunctionPtrType(self.args, self.result, self.ellipsis, self.abi) - - -class FunctionPtrType(BaseFunctionType): - _base_pattern = '(*&)(%s)' - - def build_backend_type(self, ffi, finishlist): - result = self.result.get_cached_btype(ffi, finishlist) - args = [] - for tp in self.args: - args.append(tp.get_cached_btype(ffi, finishlist)) - abi_args = () - if self.abi == "__stdcall": - if not self.ellipsis: # __stdcall ignored for variadic funcs - try: - abi_args = (ffi._backend.FFI_STDCALL,) - except AttributeError: - pass - return global_cache(self, ffi, 'new_function_type', - tuple(args), result, self.ellipsis, *abi_args) - - def as_raw_function(self): - return RawFunctionType(self.args, self.result, self.ellipsis, self.abi) - - -class PointerType(BaseType): - _attrs_ = ('totype', 'quals') - - def __init__(self, totype, quals=0): - self.totype = totype - self.quals = quals - extra = qualify(quals, " *&") - if totype.is_array_type: - extra = "(%s)" % (extra.lstrip(),) - self.c_name_with_marker = totype.c_name_with_marker.replace('&', extra) - - def build_backend_type(self, ffi, finishlist): - BItem = self.totype.get_cached_btype(ffi, finishlist, can_delay=True) - return global_cache(self, ffi, 'new_pointer_type', BItem) - -voidp_type = PointerType(void_type) - -def ConstPointerType(totype): - return PointerType(totype, Q_CONST) - -const_voidp_type = ConstPointerType(void_type) - - -class NamedPointerType(PointerType): - _attrs_ = ('totype', 'name') - - def __init__(self, totype, name, quals=0): - PointerType.__init__(self, totype, quals) - self.name = name - self.c_name_with_marker = name + '&' - - -class ArrayType(BaseType): - _attrs_ = ('item', 'length') - is_array_type = True - - def __init__(self, item, length): - self.item = item - self.length = length - # - if length is None: - brackets = '&[]' - elif length == '...': - brackets = '&[/*...*/]' - else: - brackets = '&[%s]' % length - self.c_name_with_marker = ( - self.item.c_name_with_marker.replace('&', brackets)) - - def length_is_unknown(self): - return isinstance(self.length, str) - - def resolve_length(self, newlength): - return ArrayType(self.item, newlength) - - def build_backend_type(self, ffi, finishlist): - if self.length_is_unknown(): - raise CDefError("cannot render the type %r: unknown length" % - (self,)) - self.item.get_cached_btype(ffi, finishlist) # force the item BType - BPtrItem = PointerType(self.item).get_cached_btype(ffi, finishlist) - return global_cache(self, ffi, 'new_array_type', BPtrItem, self.length) - -char_array_type = ArrayType(PrimitiveType('char'), None) - - -class StructOrUnionOrEnum(BaseTypeByIdentity): - _attrs_ = ('name',) - forcename = None - - def build_c_name_with_marker(self): - name = self.forcename or '%s %s' % (self.kind, self.name) - self.c_name_with_marker = name + '&' - - def force_the_name(self, forcename): - self.forcename = forcename - self.build_c_name_with_marker() - - def get_official_name(self): - assert self.c_name_with_marker.endswith('&') - return self.c_name_with_marker[:-1] - - -class StructOrUnion(StructOrUnionOrEnum): - fixedlayout = None - completed = 0 - partial = False - packed = 0 - - def __init__(self, name, fldnames, fldtypes, fldbitsize, fldquals=None): - self.name = name - self.fldnames = fldnames - self.fldtypes = fldtypes - self.fldbitsize = fldbitsize - self.fldquals = fldquals - self.build_c_name_with_marker() - - def anonymous_struct_fields(self): - if self.fldtypes is not None: - for name, type in zip(self.fldnames, self.fldtypes): - if name == '' and isinstance(type, StructOrUnion): - yield type - - def enumfields(self, expand_anonymous_struct_union=True): - fldquals = self.fldquals - if fldquals is None: - fldquals = (0,) * len(self.fldnames) - for name, type, bitsize, quals in zip(self.fldnames, self.fldtypes, - self.fldbitsize, fldquals): - if (name == '' and isinstance(type, StructOrUnion) - and expand_anonymous_struct_union): - # nested anonymous struct/union - for result in type.enumfields(): - yield result - else: - yield (name, type, bitsize, quals) - - def force_flatten(self): - # force the struct or union to have a declaration that lists - # directly all fields returned by enumfields(), flattening - # nested anonymous structs/unions. - names = [] - types = [] - bitsizes = [] - fldquals = [] - for name, type, bitsize, quals in self.enumfields(): - names.append(name) - types.append(type) - bitsizes.append(bitsize) - fldquals.append(quals) - self.fldnames = tuple(names) - self.fldtypes = tuple(types) - self.fldbitsize = tuple(bitsizes) - self.fldquals = tuple(fldquals) - - def get_cached_btype(self, ffi, finishlist, can_delay=False): - BType = StructOrUnionOrEnum.get_cached_btype(self, ffi, finishlist, - can_delay) - if not can_delay: - self.finish_backend_type(ffi, finishlist) - return BType - - def finish_backend_type(self, ffi, finishlist): - if self.completed: - if self.completed != 2: - raise NotImplementedError("recursive structure declaration " - "for '%s'" % (self.name,)) - return - BType = ffi._cached_btypes[self] - # - self.completed = 1 - # - if self.fldtypes is None: - pass # not completing it: it's an opaque struct - # - elif self.fixedlayout is None: - fldtypes = [tp.get_cached_btype(ffi, finishlist) - for tp in self.fldtypes] - lst = list(zip(self.fldnames, fldtypes, self.fldbitsize)) - extra_flags = () - if self.packed: - if self.packed == 1: - extra_flags = (8,) # SF_PACKED - else: - extra_flags = (0, self.packed) - ffi._backend.complete_struct_or_union(BType, lst, self, - -1, -1, *extra_flags) - # - else: - fldtypes = [] - fieldofs, fieldsize, totalsize, totalalignment = self.fixedlayout - for i in range(len(self.fldnames)): - fsize = fieldsize[i] - ftype = self.fldtypes[i] - # - if isinstance(ftype, ArrayType) and ftype.length_is_unknown(): - # fix the length to match the total size - BItemType = ftype.item.get_cached_btype(ffi, finishlist) - nlen, nrest = divmod(fsize, ffi.sizeof(BItemType)) - if nrest != 0: - self._verification_error( - "field '%s.%s' has a bogus size?" % ( - self.name, self.fldnames[i] or '{}')) - ftype = ftype.resolve_length(nlen) - self.fldtypes = (self.fldtypes[:i] + (ftype,) + - self.fldtypes[i+1:]) - # - BFieldType = ftype.get_cached_btype(ffi, finishlist) - if isinstance(ftype, ArrayType) and ftype.length is None: - assert fsize == 0 - else: - bitemsize = ffi.sizeof(BFieldType) - if bitemsize != fsize: - self._verification_error( - "field '%s.%s' is declared as %d bytes, but is " - "really %d bytes" % (self.name, - self.fldnames[i] or '{}', - bitemsize, fsize)) - fldtypes.append(BFieldType) - # - lst = list(zip(self.fldnames, fldtypes, self.fldbitsize, fieldofs)) - ffi._backend.complete_struct_or_union(BType, lst, self, - totalsize, totalalignment) - self.completed = 2 - - def _verification_error(self, msg): - raise VerificationError(msg) - - def check_not_partial(self): - if self.partial and self.fixedlayout is None: - raise VerificationMissing(self._get_c_name()) - - def build_backend_type(self, ffi, finishlist): - self.check_not_partial() - finishlist.append(self) - # - return global_cache(self, ffi, 'new_%s_type' % self.kind, - self.get_official_name(), key=self) - - -class StructType(StructOrUnion): - kind = 'struct' - - -class UnionType(StructOrUnion): - kind = 'union' - - -class EnumType(StructOrUnionOrEnum): - kind = 'enum' - partial = False - partial_resolved = False - - def __init__(self, name, enumerators, enumvalues, baseinttype=None): - self.name = name - self.enumerators = enumerators - self.enumvalues = enumvalues - self.baseinttype = baseinttype - self.build_c_name_with_marker() - - def force_the_name(self, forcename): - StructOrUnionOrEnum.force_the_name(self, forcename) - if self.forcename is None: - name = self.get_official_name() - self.forcename = '$' + name.replace(' ', '_') - - def check_not_partial(self): - if self.partial and not self.partial_resolved: - raise VerificationMissing(self._get_c_name()) - - def build_backend_type(self, ffi, finishlist): - self.check_not_partial() - base_btype = self.build_baseinttype(ffi, finishlist) - return global_cache(self, ffi, 'new_enum_type', - self.get_official_name(), - self.enumerators, self.enumvalues, - base_btype, key=self) - - def build_baseinttype(self, ffi, finishlist): - if self.baseinttype is not None: - return self.baseinttype.get_cached_btype(ffi, finishlist) - # - if self.enumvalues: - smallest_value = min(self.enumvalues) - largest_value = max(self.enumvalues) - else: - import warnings - try: - # XXX! The goal is to ensure that the warnings.warn() - # will not suppress the warning. We want to get it - # several times if we reach this point several times. - __warningregistry__.clear() - except NameError: - pass - warnings.warn("%r has no values explicitly defined; " - "guessing that it is equivalent to 'unsigned int'" - % self._get_c_name()) - smallest_value = largest_value = 0 - if smallest_value < 0: # needs a signed type - sign = 1 - candidate1 = PrimitiveType("int") - candidate2 = PrimitiveType("long") - else: - sign = 0 - candidate1 = PrimitiveType("unsigned int") - candidate2 = PrimitiveType("unsigned long") - btype1 = candidate1.get_cached_btype(ffi, finishlist) - btype2 = candidate2.get_cached_btype(ffi, finishlist) - size1 = ffi.sizeof(btype1) - size2 = ffi.sizeof(btype2) - if (smallest_value >= ((-1) << (8*size1-1)) and - largest_value < (1 << (8*size1-sign))): - return btype1 - if (smallest_value >= ((-1) << (8*size2-1)) and - largest_value < (1 << (8*size2-sign))): - return btype2 - raise CDefError("%s values don't all fit into either 'long' " - "or 'unsigned long'" % self._get_c_name()) - -def unknown_type(name, structname=None): - if structname is None: - structname = '$%s' % name - tp = StructType(structname, None, None, None) - tp.force_the_name(name) - tp.origin = "unknown_type" - return tp - -def unknown_ptr_type(name, structname=None): - if structname is None: - structname = '$$%s' % name - tp = StructType(structname, None, None, None) - return NamedPointerType(tp, name) - - -global_lock = allocate_lock() -_typecache_cffi_backend = weakref.WeakValueDictionary() - -def get_typecache(backend): - # returns _typecache_cffi_backend if backend is the _cffi_backend - # module, or type(backend).__typecache if backend is an instance of - # CTypesBackend (or some FakeBackend class during tests) - if isinstance(backend, types.ModuleType): - return _typecache_cffi_backend - with global_lock: - if not hasattr(type(backend), '__typecache'): - type(backend).__typecache = weakref.WeakValueDictionary() - return type(backend).__typecache - -def global_cache(srctype, ffi, funcname, *args, **kwds): - key = kwds.pop('key', (funcname, args)) - assert not kwds - try: - return ffi._typecache[key] - except KeyError: - pass - try: - res = getattr(ffi._backend, funcname)(*args) - except NotImplementedError as e: - raise NotImplementedError("%s: %r: %s" % (funcname, srctype, e)) - # note that setdefault() on WeakValueDictionary is not atomic - # and contains a rare bug (http://bugs.python.org/issue19542); - # we have to use a lock and do it ourselves - cache = ffi._typecache - with global_lock: - res1 = cache.get(key) - if res1 is None: - cache[key] = res - return res - else: - return res1 - -def pointer_cache(ffi, BType): - return global_cache('?', ffi, 'new_pointer_type', BType) - -def attach_exception_info(e, name): - if e.args and type(e.args[0]) is str: - e.args = ('%s: %s' % (name, e.args[0]),) + e.args[1:] diff --git a/spaces/codeparrot/gradio-playground/app.py b/spaces/codeparrot/gradio-playground/app.py deleted file mode 100644 index b402005ba1daa5566f1319062345fe4e89acdaee..0000000000000000000000000000000000000000 --- a/spaces/codeparrot/gradio-playground/app.py +++ /dev/null @@ -1,485 +0,0 @@ -import datetime -import random -import os -import re -from io import StringIO - -import gradio as gr -import pandas as pd - -from huggingface_hub import Repository, upload_file -from text_generation import Client -from text_generation.errors import UnknownError -from share_btn import (community_icon_html, loading_icon_html, share_btn_css, - share_js) - -HF_TOKEN = os.environ.get("HF_TOKEN", None) -API_TOKEN = os.environ.get("API_TOKEN", HF_TOKEN) - -DIALOGUES_DATASET = "ArmelR/gradio_playground_dialogues" - -API_URL_G = "https://api-inference.huggingface.co/models/ArmelR/starcoder-gradio-v0" -API_URL_S = "https://api-inference.huggingface.co/models/HuggingFaceH4/starcoderbase-finetuned-oasst1" -API_URL_B = "https://api-inference.huggingface.co/models/HuggingFaceH4/starchat-beta" - -model2endpoint = { - "starChat-alpha": API_URL_S, - "starCoder-gradio": API_URL_G, - "starChat-beta": API_URL_B -} - -model_names = list(model2endpoint.keys()) - -with open("./HHH_prompt_short.txt", "r") as f: - HHH_PROMPT = f.read() + "\n\n" - -with open("./TA_prompt_v0.txt", "r") as f: - TA_PROMPT = f.read() - -NO_PROMPT = "" - -def randomize_seed_generator(): - seed = random.randint(0, 1000000) - return seed - - -def save_inputs_and_outputs(now, inputs, outputs, generate_kwargs, model): - buffer = StringIO() - timestamp = datetime.datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%f") - file_name = f"prompts_{timestamp}.jsonl" - data = {"model": model, "inputs": inputs, "outputs": outputs, "generate_kwargs": generate_kwargs} - pd.DataFrame([data]).to_json(buffer, orient="records", lines=True) - - # Push to Hub - upload_file( - path_in_repo=f"{now.date()}/{now.hour}/{file_name}", - path_or_fileobj=buffer.getvalue().encode(), - repo_id=DIALOGUES_DATASET, - token=HF_TOKEN, - repo_type="dataset", - ) - - # Clean and rerun - buffer.close() - -def get_total_inputs(inputs, chatbot, preprompt, user_name, assistant_name, sep): - past = [] - for data in chatbot: - user_data, model_data = data - - if not user_data.startswith(user_name): - user_data = user_name + user_data - if not model_data.startswith(sep + assistant_name): - model_data = sep + assistant_name + model_data - - past.append(user_data + model_data.rstrip() + sep) - - if not inputs.startswith(user_name): - inputs = user_name + inputs - - total_inputs = preprompt + "".join(past) + inputs + sep + assistant_name.rstrip() - - return total_inputs - -def wrap_html_code(text): - pattern = r"<.*?>" - matches = re.findall(pattern, text) - if len(matches) > 0: - return f"```{text}```" - else: - return text - -def has_no_history(chatbot, history): - return not chatbot and not history - -def get_inference_prompt(messages, model_name): - if model_name == "starChat-beta" : - prompt = "<|system|>\n<|endoftext|>\n" - for message in messages : - if message["role"] == "user" : - prompt += f"<|user|>\n{message['content']}<|endoftext|>\n<|assistant|>\n" - else : # message["role"] == "assistant" - prompt += f"\n{message['content']}<|endoftext|>\n" - elif model_name == "starChat-alpha" : - prompt = "<|system|>\n<|end|>\n" - for message in messages : - if message["role"] == "user" : - prompt += f"<|user|>\n{message['content']}<|end|>\n<|assistant|>\n" - else : # message["role"] == "assistant" - prompt += f"\n{message['content']}<|end|>\n" - else : # starCoder-gradio - prompt = "" - for message in messages : - if message["role"] == "user" : - prompt += f"Question: {message['content']}\n\nAnswer:" - else : # message["role"] == "assistant" - prompt += f" {message['content']}\n\n" - return prompt - -def generate( - RETRY_FLAG, - model_name, - system_message, - user_message, - chatbot, - history, - temperature, - top_k, - top_p, - max_new_tokens, - repetition_penalty, - do_save=True, -): - client = Client( - model2endpoint[model_name], - headers={"Authorization": f"Bearer {API_TOKEN}"}, - timeout=60, - ) - # Don't return meaningless message when the input is empty - if not user_message: - print("Empty input") - - if not RETRY_FLAG: - history.append(user_message) - seed = 42 - else: - seed = randomize_seed_generator() - - past_messages = [] - for data in chatbot: - user_data, model_data = data - - past_messages.extend( - [{"role": "user", "content": user_data}, {"role": "assistant", "content": model_data.rstrip()}] - ) - - if len(past_messages) < 1: - prompt = get_inference_prompt(messages=[{"role": "user", "content": user_message}], model_name=model_name) - else: - prompt = get_inference_prompt(messages=past_messages + [{"role": "user", "content": user_message}], model_name=model_name) - - generate_kwargs = { - "temperature": temperature, - "top_k": top_k, - "top_p": top_p, - "max_new_tokens": max_new_tokens, - } - - temperature = float(temperature) - if temperature < 1e-2: - temperature = 1e-2 - top_p = float(top_p) - - generate_kwargs = dict( - temperature=temperature, - max_new_tokens=max_new_tokens, - top_p=top_p, - repetition_penalty=repetition_penalty, - do_sample=True, - truncate=4096, - seed=seed, - stop_sequences=["<|end|>", "Question:"], - ) - - try : - stream = client.generate_stream( - prompt, - **generate_kwargs, - ) - - output = "" - for idx, response in enumerate(stream): - if response.token.special: - continue - output += response.token.text - if idx == 0: - history.append(" " + output) - else: - history[-1] = output - - chat = [ - (wrap_html_code(history[i].strip()), wrap_html_code(history[i + 1].strip())) - for i in range(0, len(history) - 1, 2) - ] - - # chat = [(history[i].strip(), history[i + 1].strip()) for i in range(0, len(history) - 1, 2)] - - yield chat, history, user_message, "" - - if HF_TOKEN and do_save: - try: - now = datetime.datetime.now() - current_time = now.strftime("%Y-%m-%d %H:%M:%S") - print(f"[{current_time}] Pushing prompt and completion to the Hub") - save_inputs_and_outputs(now, prompt, output, generate_kwargs, model_name) - except Exception as e: - print(e) - - return chat, history, user_message, "" - - except UnknownError : - error_message = "The model is currently loading. Please wait a few seconds and retry." - yield [(user_message, error_message)], history, user_message, "" - return [(user_message, error_message)], history, user_message, "" - -examples = [ - "Use the gradio library to create a calculator. It should take into account the 4 basic operations. A user should be able to enter 2 numbers, choose an operation and get the corresponding result.", - "Write a gradio application to convert an input temperature in celcius to a temperature in fahrenheit", - "Write a gradio application for a chatbot. The chatbot should use a language model loaded with Hugging face’s transformers , and a user should be able to enter a text request to get the corresponding answer.", - "Create a basic gradio application.", - "What is gradio?" -] - -def clear_chat(): - return [], [] - -def delete_last_turn(chat, history): - if chat and history: - chat.pop(-1) - history.pop(-1) - history.pop(-1) - return chat, history - -def process_example(args): - for [x, y] in generate(args): - pass - return [x, y] - -# Regenerate response -def retry_last_answer( - selected_model, - system_message, - user_message, - chat, - history, - temperature, - top_k, - top_p, - max_new_tokens, - repetition_penalty, - do_save, -): - if chat and history: - # Removing the previous conversation from chat - chat.pop(-1) - # Removing bot response from the history - history.pop(-1) - # Setting up a flag to capture a retry - RETRY_FLAG = True - # Getting last message from user - user_message = history[-1] - - yield from generate( - RETRY_FLAG, - selected_model, - system_message, - user_message, - chat, - history, - temperature, - top_k, - top_p, - max_new_tokens, - repetition_penalty, - do_save, - ) - -title = """Chat with Gradio 💫➕
""" -custom_css = """ -#banner-image { - display: block; - margin-left: auto; - margin-right: auto; -} -#chat-message { - font-size: 14px; - min-height: 300px; -} -""" - -with gr.Blocks(analytics_enabled=False, css=custom_css) as demo: - #gr.HTML(title) - - with gr.Row(): - #with gr.Column(): - #gr.Image("gradio.png", elem_id="banner-image", show_label=False) - - #with gr.Column(): - # gr.Markdown( - # """ - # 💻 This demo showcases a series of **[StarChat](https://huggingface.co/models?search=huggingfaceh4/starchat)** language models, which are fine-tuned versions of the StarCoder family to act as helpful coding assistants. The base model has 16B parameters and was pretrained on one trillion tokens sourced from 80+ programming languages, GitHub issues, Git commits, and Jupyter notebooks (all permissively licensed). - # 📝 For more details, check out our [blog post](https://huggingface.co/blog/starchat-alpha). - # ⚠️ **Intended Use**: this app and its [supporting models](https://huggingface.co/models?search=huggingfaceh4/starchat) are provided as educational tools to explain large language model fine-tuning; not to serve as replacement for human expertise. - # ⚠️ **Known Failure Modes**: the alpha and beta version of **StarChat** have not been aligned to human preferences with techniques like RLHF, so they can produce problematic outputs (especially when prompted to do so). Since the base model was pretrained on a large corpus of code, it may produce code snippets that are syntactically valid but semantically incorrect. For example, it may produce code that does not compile or that produces incorrect results. It may also produce code that is vulnerable to security exploits. We have observed the model also has a tendency to produce false URLs which should be carefully inspected before clicking. For more details on the model's limitations in terms of factuality and biases, see the [model card](https://huggingface.co/HuggingFaceH4/starchat-alpha#bias-risks-and-limitations). - # ⚠️ **Data Collection**: by default, we are collecting the prompts entered in this app to further improve and evaluate the models. Do **NOT** share any personal or sensitive information while using the app! You can opt out of this data collection by removing the checkbox below. - # """ - # ) - with gr.Column(): - gr.Markdown("""
""")
- with gr.Row():
- selected_model = gr.Radio(choices=model_names, value=model_names[1], label="Select a model")
-
- with gr.Accordion(label="System Prompt", open=False, elem_id="parameters-accordion"):
- system_message = gr.Textbox(
- elem_id="system-message",
- placeholder="Below is a conversation between a human user and a helpful AI coding assistant.",
- show_label=False,
- )
- with gr.Row():
- with gr.Box():
- output = gr.Markdown()
- chatbot = gr.Chatbot(elem_id="chat-message", label="Chat")
-
- with gr.Row():
- with gr.Column(scale=3):
- user_message = gr.Textbox(placeholder="Enter your message here", show_label=False, elem_id="q-input")
- with gr.Row():
- send_button = gr.Button("Send", elem_id="send-btn", visible=True)
-
- regenerate_button = gr.Button("Regenerate", elem_id="retry-btn", visible=True)
-
- delete_turn_button = gr.Button("Delete last turn", elem_id="delete-btn", visible=True)
-
- clear_chat_button = gr.Button("Clear chat", elem_id="clear-btn", visible=True)
-
- with gr.Accordion(label="Parameters", open=False, elem_id="parameters-accordion"):
- temperature = gr.Slider(
- label="Temperature",
- value=0.2,
- minimum=0.0,
- maximum=1.0,
- step=0.1,
- interactive=True,
- info="Higher values produce more diverse outputs",
- )
- top_k = gr.Slider(
- label="Top-k",
- value=50,
- minimum=0.0,
- maximum=100,
- step=1,
- interactive=True,
- info="Sample from a shortlist of top-k tokens",
- )
- top_p = gr.Slider(
- label="Top-p (nucleus sampling)",
- value=0.95,
- minimum=0.0,
- maximum=1,
- step=0.05,
- interactive=True,
- info="Higher values sample more low-probability tokens",
- )
- max_new_tokens = gr.Slider(
- label="Max new tokens",
- value=512,
- minimum=0,
- maximum=1024,
- step=4,
- interactive=True,
- info="The maximum numbers of new tokens",
- )
- repetition_penalty = gr.Slider(
- label="Repetition Penalty",
- value=1.2,
- minimum=0.0,
- maximum=10,
- step=0.1,
- interactive=True,
- info="The parameter for repetition penalty. 1.0 means no penalty.",
- )
- with gr.Row():
- do_save = gr.Checkbox(
- value=True,
- label="Store data",
- info="You agree to the storage of your prompt and generated text for research and development purposes:",
- )
- # with gr.Group(elem_id="share-btn-container"):
- # community_icon = gr.HTML(community_icon_html, visible=True)
- # loading_icon = gr.HTML(loading_icon_html, visible=True)
- # share_button = gr.Button("Share to community", elem_id="share-btn", visible=True)
- with gr.Row():
- gr.Examples(
- examples=examples,
- inputs=[user_message],
- cache_examples=False,
- fn=process_example,
- outputs=[output],
- )
- with gr.Row():
- gr.Markdown(
- """Chat-with-Gradio is a 15.5 billion parameter language model based on [BigCode's StarCoderplus model](https://huggingface.co/bigcode/starcoderplus) that has been trained on a wide variety of data sources. It includes the source code and issues from [gradio's Github repository](https://github.com/gradio-app/gradio) and data from [Hugging Face's spaces](https://huggingface.co/spaces). Its training also involves instruction fine-tuning with a processed subset of [OpenAssistant's oasst1 dataset](https://huggingface.co/datasets/HuggingFaceH4/oasst1_en).
-
- Type in the box below and click the button to generate answers to your most pressing questions!
- ⚠️ **Intended Use**: this app and its [supporting model](https://huggingface.co/bigcode/starcoderplus) are provided as tools to provide assistance when using gradio ; not to serve as replacement for human expertise. For more details on the model's limitations in terms of factuality and biases, see the [model card.](https://huggingface.co/bigcode/starcoderplus#intended-uses--limitations)
-
- ⚠️ **Data Collection**: by default, we are collecting the prompts entered in this app to further improve and evaluate the model. Do not share any personal or sensitive information while using the app! You can opt out of this data collection by removing the checkbox below:)""")
-
- history = gr.State([])
- RETRY_FLAG = gr.Checkbox(value=False, visible=False)
-
- # To clear out "message" input textbox and use this to regenerate message
- last_user_message = gr.State("")
-
- user_message.submit(
- generate,
- inputs=[
- RETRY_FLAG,
- selected_model,
- system_message,
- user_message,
- chatbot,
- history,
- temperature,
- top_k,
- top_p,
- max_new_tokens,
- repetition_penalty,
- do_save,
- ],
- outputs=[chatbot, history, last_user_message, user_message],
- )
-
- send_button.click(
- generate,
- inputs=[
- RETRY_FLAG,
- selected_model,
- system_message,
- user_message,
- chatbot,
- history,
- temperature,
- top_k,
- top_p,
- max_new_tokens,
- repetition_penalty,
- do_save,
- ],
- outputs=[chatbot, history, last_user_message, user_message],
- )
-
- regenerate_button.click(
- retry_last_answer,
- inputs=[
- selected_model,
- system_message,
- user_message,
- chatbot,
- history,
- temperature,
- top_k,
- top_p,
- max_new_tokens,
- repetition_penalty,
- do_save,
- ],
- outputs=[chatbot, history, last_user_message, user_message],
- )
-
- delete_turn_button.click(delete_last_turn, [chatbot, history], [chatbot, history])
- clear_chat_button.click(clear_chat, outputs=[chatbot, history])
- selected_model.change(clear_chat, outputs=[chatbot, history])
- # share_button.click(None, [], [], _js=share_js)
-
-demo.queue(concurrency_count=16).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/colakin/video-generater/public/ffmpeg/doc/print_options.c b/spaces/colakin/video-generater/public/ffmpeg/doc/print_options.c
deleted file mode 100644
index fd1cf10dc193a1c917a3276ada9261bd233e85e1..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/doc/print_options.c
+++ /dev/null
@@ -1,136 +0,0 @@
-/*
- * Copyright (c) 2012 Anton Khirnov
- *
- * 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
- */
-
-/*
- * generate texinfo manpages for avoptions
- */
-
-#include -#include -#include -#include - -// print_options is build for the host, os_support.h isn't needed and is setup -// for the target. without this build breaks on mingw -#define AVFORMAT_OS_SUPPORT_H - -#include "libavutil/attributes.h" -#include "libavutil/opt.h" - -/* Forcibly turn off deprecation warnings, which just add noise here. */ -#undef attribute_deprecated -#define attribute_deprecated - -#include "libavcodec/options_table.h" - -#include "libavformat/options_table.h" - -static void print_usage(void) -{ - fprintf(stderr, "Usage: enum_options type\n" - "type: format codec\n"); - exit(1); -} - -static void print_option(const AVOption *opts, const AVOption *o, int per_stream) -{ - if (!(o->flags & (AV_OPT_FLAG_DECODING_PARAM | AV_OPT_FLAG_ENCODING_PARAM))) - return; - - printf("@item -%s%s @var{", o->name, per_stream ? "[:stream_specifier]" : ""); - switch (o->type) { - case AV_OPT_TYPE_BINARY: printf("hexadecimal string"); break; - case AV_OPT_TYPE_STRING: printf("string"); break; - case AV_OPT_TYPE_INT: - case AV_OPT_TYPE_INT64: printf("integer"); break; - case AV_OPT_TYPE_FLOAT: - case AV_OPT_TYPE_DOUBLE: printf("float"); break; - case AV_OPT_TYPE_RATIONAL: printf("rational number"); break; - case AV_OPT_TYPE_FLAGS: printf("flags"); break; - default: printf("value"); break; - } - printf("} (@emph{"); - - if (o->flags & AV_OPT_FLAG_DECODING_PARAM) { - printf("input"); - if (o->flags & AV_OPT_FLAG_ENCODING_PARAM) - printf("/"); - } - if (o->flags & AV_OPT_FLAG_ENCODING_PARAM) printf("output"); - if (o->flags & AV_OPT_FLAG_AUDIO_PARAM) printf(",audio"); - if (o->flags & AV_OPT_FLAG_VIDEO_PARAM) printf(",video"); - if (o->flags & AV_OPT_FLAG_SUBTITLE_PARAM) printf(",subtitles"); - - printf("})\n"); - if (o->help) - printf("%s\n", o->help); - - if (o->unit) { - const AVOption *u; - printf("\nPossible values:\n@table @samp\n"); - - for (u = opts; u->name; u++) { - if (u->type == AV_OPT_TYPE_CONST && u->unit && !strcmp(u->unit, o->unit)) - printf("@item %s\n%s\n", u->name, u->help ? u->help : ""); - } - printf("@end table\n"); - } -} - -static void show_opts(const AVOption *opts, int per_stream) -{ - const AVOption *o; - - printf("@table @option\n"); - for (o = opts; o->name; o++) { - if (o->type != AV_OPT_TYPE_CONST) - print_option(opts, o, per_stream); - } - printf("@end table\n"); -} - -static void show_format_opts(void) -{ - printf("@section Format AVOptions\n"); - show_opts(avformat_options, 0); -} - -static void show_codec_opts(void) -{ - printf("@section Codec AVOptions\n"); - show_opts(avcodec_options, 1); -} - -int main(int argc, char **argv) -{ - if (argc < 2) - print_usage(); - - printf("@c DO NOT EDIT THIS FILE!\n" - "@c It was generated by print_options.\n\n"); - if (!strcmp(argv[1], "format")) - show_format_opts(); - else if (!strcmp(argv[1], "codec")) - show_codec_opts(); - else - print_usage(); - - return 0; -} diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libopenh264dec.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libopenh264dec.c deleted file mode 100644 index af53219b410623989d318733ceea4c24ec6066f7..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/libopenh264dec.c +++ /dev/null @@ -1,173 +0,0 @@ -/* - * OpenH264 video decoder - * Copyright (C) 2016 Martin Storsjo - * - * 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 - */ - -#include -#include - -#include "libavutil/common.h" -#include "libavutil/fifo.h" -#include "libavutil/imgutils.h" -#include "libavutil/intreadwrite.h" -#include "libavutil/mathematics.h" -#include "libavutil/opt.h" - -#include "avcodec.h" -#include "codec_internal.h" -#include "decode.h" -#include "libopenh264.h" - -typedef struct SVCContext { - ISVCDecoder *decoder; -} SVCContext; - -static av_cold int svc_decode_close(AVCodecContext *avctx) -{ - SVCContext *s = avctx->priv_data; - - if (s->decoder) - WelsDestroyDecoder(s->decoder); - - return 0; -} - -static av_cold int svc_decode_init(AVCodecContext *avctx) -{ - SVCContext *s = avctx->priv_data; - SDecodingParam param = { 0 }; - int err; - int log_level; - WelsTraceCallback callback_function; - - if ((err = ff_libopenh264_check_version(avctx)) < 0) - return AVERROR_DECODER_NOT_FOUND; - - if (WelsCreateDecoder(&s->decoder)) { - av_log(avctx, AV_LOG_ERROR, "Unable to create decoder\n"); - return AVERROR_UNKNOWN; - } - - // Pass all libopenh264 messages to our callback, to allow ourselves to filter them. - log_level = WELS_LOG_DETAIL; - callback_function = ff_libopenh264_trace_callback; - (*s->decoder)->SetOption(s->decoder, DECODER_OPTION_TRACE_LEVEL, &log_level); - (*s->decoder)->SetOption(s->decoder, DECODER_OPTION_TRACE_CALLBACK, (void *)&callback_function); - (*s->decoder)->SetOption(s->decoder, DECODER_OPTION_TRACE_CALLBACK_CONTEXT, (void *)&avctx); - -#if !OPENH264_VER_AT_LEAST(1, 6) - param.eOutputColorFormat = videoFormatI420; -#endif - param.eEcActiveIdc = ERROR_CON_DISABLE; - param.sVideoProperty.eVideoBsType = VIDEO_BITSTREAM_DEFAULT; - - if ((*s->decoder)->Initialize(s->decoder, ¶m) != cmResultSuccess) { - av_log(avctx, AV_LOG_ERROR, "Initialize failed\n"); - return AVERROR_UNKNOWN; - } - - avctx->pix_fmt = AV_PIX_FMT_YUV420P; - - return 0; -} - -static int svc_decode_frame(AVCodecContext *avctx, AVFrame *avframe, - int *got_frame, AVPacket *avpkt) -{ - SVCContext *s = avctx->priv_data; - SBufferInfo info = { 0 }; - uint8_t *ptrs[4] = { NULL }; - int ret, linesize[4]; - DECODING_STATE state; -#if OPENH264_VER_AT_LEAST(1, 7) - int opt; -#endif - - if (!avpkt->data) { -#if OPENH264_VER_AT_LEAST(1, 9) - int end_of_stream = 1; - (*s->decoder)->SetOption(s->decoder, DECODER_OPTION_END_OF_STREAM, &end_of_stream); - state = (*s->decoder)->FlushFrame(s->decoder, ptrs, &info); -#else - return 0; -#endif - } else { - info.uiInBsTimeStamp = avpkt->pts; -#if OPENH264_VER_AT_LEAST(1, 4) - // Contrary to the name, DecodeFrameNoDelay actually does buffering - // and reordering of frames, and is the recommended decoding entry - // point since 1.4. This is essential for successfully decoding - // B-frames. - state = (*s->decoder)->DecodeFrameNoDelay(s->decoder, avpkt->data, avpkt->size, ptrs, &info); -#else - state = (*s->decoder)->DecodeFrame2(s->decoder, avpkt->data, avpkt->size, ptrs, &info); -#endif - } - if (state != dsErrorFree) { - av_log(avctx, AV_LOG_ERROR, "DecodeFrame failed\n"); - return AVERROR_UNKNOWN; - } - if (info.iBufferStatus != 1) { - av_log(avctx, AV_LOG_DEBUG, "No frame produced\n"); - return avpkt->size; - } - - ret = ff_set_dimensions(avctx, info.UsrData.sSystemBuffer.iWidth, info.UsrData.sSystemBuffer.iHeight); - if (ret < 0) - return ret; - // The decoder doesn't (currently) support decoding into a user - // provided buffer, so do a copy instead. - if (ff_get_buffer(avctx, avframe, 0) < 0) { - av_log(avctx, AV_LOG_ERROR, "Unable to allocate buffer\n"); - return AVERROR(ENOMEM); - } - - linesize[0] = info.UsrData.sSystemBuffer.iStride[0]; - linesize[1] = linesize[2] = info.UsrData.sSystemBuffer.iStride[1]; - linesize[3] = 0; - av_image_copy(avframe->data, avframe->linesize, (const uint8_t **) ptrs, linesize, avctx->pix_fmt, avctx->width, avctx->height); - - avframe->pts = info.uiOutYuvTimeStamp; - avframe->pkt_dts = AV_NOPTS_VALUE; -#if OPENH264_VER_AT_LEAST(1, 7) - (*s->decoder)->GetOption(s->decoder, DECODER_OPTION_PROFILE, &opt); - avctx->profile = opt; - (*s->decoder)->GetOption(s->decoder, DECODER_OPTION_LEVEL, &opt); - avctx->level = opt; -#endif - - *got_frame = 1; - return avpkt->size; -} - -const FFCodec ff_libopenh264_decoder = { - .p.name = "libopenh264", - CODEC_LONG_NAME("OpenH264 H.264 / AVC / MPEG-4 AVC / MPEG-4 part 10"), - .p.type = AVMEDIA_TYPE_VIDEO, - .p.id = AV_CODEC_ID_H264, - .priv_data_size = sizeof(SVCContext), - .init = svc_decode_init, - FF_CODEC_DECODE_CB(svc_decode_frame), - .close = svc_decode_close, - .p.capabilities = AV_CODEC_CAP_DELAY | AV_CODEC_CAP_DR1, - .caps_internal = FF_CODEC_CAP_SETS_PKT_DTS | - FF_CODEC_CAP_INIT_CLEANUP, - .bsfs = "h264_mp4toannexb", - .p.wrapper_name = "libopenh264", -}; diff --git a/spaces/congsaPfin/Manga-OCR/logs/Battle Arena Heroes Adventure MOD APK - The Ultimate RPG Game Download for Android.md b/spaces/congsaPfin/Manga-OCR/logs/Battle Arena Heroes Adventure MOD APK - The Ultimate RPG Game Download for Android.md deleted file mode 100644 index e592a43d7cfafd551a7e2279e58663528acb27fc..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Battle Arena Heroes Adventure MOD APK - The Ultimate RPG Game Download for Android.md +++ /dev/null @@ -1,121 +0,0 @@ - - Download Game Battle Arena Heroes Adventure Mod APK: A Guide for Gamers
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- | Topic | Main Points | | --- | --- | | What is Battle Arena Heroes Adventure? | A game that combines action, strategy, and role-playing elements in a colorful and immersive world. | | What is Mod APK? | A modified version of an original application that gives users some benefits that are not available in the official version. | | How to Download Game Battle Arena Heroes Adventure Mod APK? | Follow some steps and requirements, such as finding a reliable source, downloading the mod APK file, enabling unknown sources, extracting and installing the mod APK file, and copying the OBB folder. |FAQs
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- Free for All - This is a casual mode where you can join a lobby of up to 4 players and fight for fun. There are no teams or rules, just pure chaos and fun. -
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- Brawl of the Week - This is a special mode that changes every week and offers a unique twist on the gameplay. For example, one week you might have to dodge bombs while collecting coins, another week you might have to fight with only one weapon, and so on. You can earn bonus gold and XP by playing this mode. -
- Experimental - This is a mode where you can try out new features and changes that are not yet implemented in the main game. You can give feedback and suggestions to the developers and help shape the future of Brawlhalla. -
- Tournament - This is a mode where you can participate in official or community-hosted tournaments and compete for glory and prizes. You can also watch live streams of tournaments and learn from the best players in the world. -
- Training - This is a mode where you can practice your combos, setups, and techniques in a controlled environment. You can access detailed frame data, hitboxes, hurtboxes, and other useful information to improve your game. -
The characters and weapons
-Brawlhalla has a diverse roster of over 50 characters, called Legends, that you can choose from. Each Legend has their own unique stats, abilities, and personality. You can also customize your Legend's appearance with different skins, colors, taunts, KO effects, and more.
-Each Legend has two weapons that they can use in battle. There are 13 weapon types in total, each with their own moveset and playstyle. You can switch between your weapons by picking up weapon drops that appear on the stage. You can also use unarmed attacks if you don't have a weapon or if you want to mix things up.
-Here are the weapon types that you can find in Brawlhalla:
--
-Weapon Description Sword A balanced weapon that offers quick slashes and thrusts. Hammer A heavy weapon that deals powerful blows and launches enemies. Spear A long-range weapon that has good reach and poke. Rocket Lance A unique weapon that allows you to fly across the stage and ram into foes. Katars A fast weapon that excels at close-range combat and combos. Axe A large weapon that has wide swings and high damage. Bow A versatile weapon that can shoot arrows or use them as melee weapons. Gauntlets A fist weapon that lets you punch and grab your opponents. Scythe A tricky weapon that allows you to manipulate your enemies' position and direction. Blasters A ranged weapon that fires bullets with different effects. Orb A magical weapon that can manipulate gravity and energy. Greatsword A massive weapon that can chain different attacks depending on your input. The ranking and matchmaking system
-If you want to challenge yourself and see how you stack up against other players, you can play ranked matches in Brawlhalla. Ranked matches are 1v1 or 2v2 matches that affect your Elo rating, which determines your rank and division. You can earn rewards and achievements based on your rank and season performance.
-Brawlhalla uses a matchmaking system that tries to match you with players of similar skill level and region. The matchmaking system takes into account your Elo rating, your ping, and your preferred game mode. You can also see your match history, stats, and leaderboards on the official website or in-game.
-How to Download and Install Brawlhalla APK?
-The steps to download and install the game on Android devices
-If you want to play Brawlhalla on your Android device, you can download and install the game for free from the Google Play Store. Here are the steps to do so:
--
-
- Open the Google Play Store app on your device and search for Brawlhalla. -
- Select the game from the search results and tap on Install. -
- Wait for the game to download and install on your device. You might need to grant some permissions for the game to run properly. -
- Once the game is installed, tap on Open to launch it. You might need to sign in with your Ubisoft account or create one if you don't have one. -
- Enjoy playing Brawlhalla on your Android device! -
The pros and cons of playing the game on mobile
-Playing Brawlhalla on mobile has its advantages and disadvantages. Here are some of them:
--
-Pros Cons You can play Brawlhalla anytime and anywhere with your Android device. You might experience lower performance or battery drain on some devices. You can enjoy full cross-play with other platforms and play with your friends. You might have a harder time controlling your character with touch controls. You can access all the features and content of the game for free. You might encounter some bugs or glitches on some devices. FAQs About Brawlhalla APK
-A list of five common questions and answers about the game
--
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- Q: Is Brawlhalla APK safe to download and install? -
- A: Yes, Brawlhalla APK is safe to download and install from the Google Play Store. It is verified by Google and does not contain any malware or viruses. However, you should avoid downloading Brawlhalla APK from unknown or untrusted sources, as they might contain harmful or modified files. -
- Q: Is Brawlhalla APK compatible with my device? -
- A: Brawlhalla APK is compatible with most Android devices that run Android 5.0 or higher. However, some devices might not meet the minimum requirements or have compatibility issues with the game. You can check the compatibility of your device on the Google Play Store page of the game or contact the developers for support. -
- Q: How much space does Brawlhalla APK take up on my device? -
- A: Brawlhalla APK takes up about 500 MB of space on your device. However, this might vary depending on your device model and updates. You should make sure that you have enough free space on your device before downloading and installing the game. -
- Q: How do I update Brawlhalla APK? -
- A: Brawlhalla APK updates automatically when you have an internet connection and enough space on your device. You can also manually update the game by going to the Google Play Store app, tapping on My Apps & Games, finding Brawlhalla, and tapping on Update. You should always update the game to enjoy the latest features and fixes. -
- Q: How do I uninstall Brawlhalla APK? -or Applications, finding Brawlhalla, and tapping on Uninstall. You can also uninstall the game by going to the Google Play Store app, tapping on My Apps & Games, finding Brawlhalla, and tapping on Uninstall. Note that uninstalling the game will delete all your data and progress, unless you have linked your account to Ubisoft Connect.
- - It has a simple and user-friendly interface that makes it easy to navigate and use. -
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- Zoom in and out to see more of the map. With slither.io mods, you can use your mouse wheel or keyboard keys to zoom in and out of the game screen. This can help you see more of the map, avoid obstacles, find food, and spot enemies. -
- Create and join parties with friends. Slither.io mods allow you to create or join parties with your friends. You can share a party code with your friends and play together on the same server. You can also chat with your friends, team up, or challenge each other. -
- Customize your snake skin and trail. Slither.io mods let you choose from hundreds of custom skins for your snake. You can also create your own skin with a skin creator tool. You can also change the color and shape of your snake trail, or even draw on the map with it. -
- Use intelligent bots to play for you. Slither.io mods can provide you with intelligent bots that can play for you when you are busy or tired. The bots can move around, eat food, avoid enemies, and even attack other snakes. You can also control the bots with some commands. -
- Track your stats and performance. Slither.io mods can show you various stats and information about your game. You can see your current position, FPS, best score, current server IP, friend's server IP, etc. You can also see a leaderboard of the top players on the server. -
- SlitherPlus: This is one of the most popular and feature-rich slither.io mods. It offers zooming, party mode, skin creator, custom skins, intelligent bots, dashboard, stats, background changer, respawn button, live update, toggle HUD, new UI, etc. You can download it from here. -
- NTL Mod: This is a fast and simple slither.io mod with enhanced graphics. It offers zooming, party mode, custom skins, bots, stats, background changer, low graphics mode, etc. You can download it from here. -
- Slither.io Mods on Slither-io.com: This is a comprehensive guide and script for modding slither.io. It offers zooming, party mode, custom skins, bots, stats, background changer, etc. You can also find tutorials and tips on how to use the mod. You can download it from here. -
- Install a browser extension that supports slither.io mods. Depending on the mod that you want to use, you may need to install a specific browser extension that allows you to run the mod script. For example, SlitherPlus requires Tampermonkey, NTL Mod requires NTL Helper, and Slither.io Mods on Slither-io.com requires Greasemonkey. You can find the links to these extensions on the mod download pages. -
- Choose a mod from the list and click on the download link. Once you have installed the browser extension, you can choose a mod from the list above and click on the download link. This will take you to a page where you can see the mod script and install it. -
- Enable the mod in your browser and refresh the slither.io page. After installing the mod script, you need to enable it in your browser extension. You can do this by clicking on the extension icon and checking the box next to the mod name. Then, you need to refresh the slither.io page to activate the mod. -
- Enjoy the game with the new features and options. Now you can enjoy the game with the new features and options that the mod provides. You can access them by clicking on the mod menu button on the top right corner of the game screen. You can also change some settings and preferences according to your liking. -
- Q: Are slither.io mods safe? -
- A: Slither.io mods are generally safe to use, as long as you download them from trusted sources and use them with caution. However, some mods may contain viruses or malware that can harm your computer or steal your personal information. Therefore, you should always scan any file that you download with an antivirus software and avoid clicking on suspicious links or ads. -
- Q: Are slither.io mods legal? -
- A: Slither.io mods are not officially endorsed or supported by the developers of slither.io. Therefore, they may violate some terms of service or rules of the game. However, there is no clear evidence that using slither.io mods will result in any legal consequences or penalties. As long as you use them for personal enjoyment and not for cheating or harming others, you should be fine. -
- Q: Do slither.io mods work on mobile devices? -
- A: Slither.io mods are mainly designed for desktop browsers such as Chrome, Firefox, Safari, etc. Therefore, they may not work well or at all on mobile devices such as smartphones or tablets. However, some mobile apps may offer similar features or functions as slither.io mods. You can search for them on your app store or online. -
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\ No newline at end of file diff --git a/spaces/contluForse/HuggingGPT/assets/Descarga Gratis Service Tool V2000 Canon Mp280 _BEST_.md b/spaces/contluForse/HuggingGPT/assets/Descarga Gratis Service Tool V2000 Canon Mp280 _BEST_.md deleted file mode 100644 index 8a0266cbd8ff17159a1fd98564fc353648904ef3..0000000000000000000000000000000000000000 --- a/spaces/contluForse/HuggingGPT/assets/Descarga Gratis Service Tool V2000 Canon Mp280 _BEST_.md +++ /dev/null @@ -1,52 +0,0 @@ - -How to Download and Use the Service Tool V2000 for Canon MP280 Printer
-If you have a Canon MP280 printer, you may need to use the Service Tool V2000 to reset the ink counter or fix other errors. The Service Tool V2000 is a software program that allows you to perform various maintenance tasks on your printer. In this article, we will show you how to download and use the Service Tool V2000 for Canon MP280 printer.
-What is the Service Tool V2000 for Canon MP280 Printer?
-The Service Tool V2000 for Canon MP280 printer is a utility program that can help you solve some common problems with your printer, such as:
-descarga gratis service tool v2000 canon mp280
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- Resetting the ink absorber counter when it is full. -
- Clearing the error code 5B00 or P07 that indicates a waste ink pad overflow. -
- Adjusting the print head position and alignment. -
- Cleaning the print head nozzles and rollers. -
- Testing the printer functions and components. -
The Service Tool V2000 for Canon MP280 printer works by sending commands to the printer through a USB connection. You need to have a Windows computer and a USB cable to use the Service Tool V2000 for Canon MP280 printer.
-How to Download the Service Tool V2000 for Canon MP280 Printer?
-You can download the Service Tool V2000 for Canon MP280 printer from various online sources, such as:
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However, be careful when downloading the Service Tool V2000 for Canon MP280 printer from unknown or untrusted websites, as they may contain viruses or malware that can harm your computer or printer. Always scan the downloaded file with an antivirus program before opening it.
- -How to Use the Service Tool V2000 for Canon MP280 Printer?
-To use the Service Tool V2000 for Canon MP280 printer, follow these steps:
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- Turn off your printer and disconnect it from the power source. -
- Connect your printer to your computer with a USB cable. -
- Press and hold the Resume button (the one with a triangle inside a circle) on your printer. -
- While holding the Resume button, press and hold the Power button (the one with a circle and a line) on your printer. -
- Release the Resume button but keep holding the Power button until the green LED light flashes twice. -
- Release both buttons. Your printer should enter Service Mode. -
- Open the downloaded Service Tool V2000 file on your computer. You should see a window like this: - - - -
- Select your printer model from the drop-down menu at the top left corner of the window. -
- Select the appropriate function from the Main or Clear Ink Counter tabs. For example, if you want to reset the ink absorber counter, select Main -> Set -> Ink Absorber Counter -> Main -> OK. -
- Wait for the process to complete. You should see a message like this: - - - -
- Click OK and close the Service Tool V2000 window. -
- Turn off your printer and disconnect it from your computer. -
- Turn on your printer and test if it works normally. - -
Congratulations! You have successfully downloaded and used the Service Tool V2000 for Canon MP280 printer. If you have any questions or problems, please contact Canon customer support or visit their official website for more information.
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\ No newline at end of file diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/arraymisc/__init__.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/arraymisc/__init__.py deleted file mode 100644 index 4b4700d6139ae3d604ff6e542468cce4200c020c..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/arraymisc/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from .quantization import dequantize, quantize - -__all__ = ['quantize', 'dequantize'] diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/utils/parrots_wrapper.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/utils/parrots_wrapper.py deleted file mode 100644 index 93c97640d4b9ed088ca82cfe03e6efebfcfa9dbf..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/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/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/normalbae/models/submodules/efficientnet_repo/BENCHMARK.md b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/normalbae/models/submodules/efficientnet_repo/BENCHMARK.md deleted file mode 100644 index 6ead7171ce5a5bbd2702f6b5c825dc9808ba5658..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/normalbae/models/submodules/efficientnet_repo/BENCHMARK.md +++ /dev/null @@ -1,555 +0,0 @@ -# Model Performance Benchmarks - -All benchmarks run as per: - -``` -python onnx_export.py --model mobilenetv3_100 ./mobilenetv3_100.onnx -python onnx_optimize.py ./mobilenetv3_100.onnx --output mobilenetv3_100-opt.onnx -python onnx_to_caffe.py ./mobilenetv3_100.onnx --c2-prefix mobilenetv3 -python onnx_to_caffe.py ./mobilenetv3_100-opt.onnx --c2-prefix mobilenetv3-opt -python caffe2_benchmark.py --c2-init ./mobilenetv3.init.pb --c2-predict ./mobilenetv3.predict.pb -python caffe2_benchmark.py --c2-init ./mobilenetv3-opt.init.pb --c2-predict ./mobilenetv3-opt.predict.pb -``` - -## EfficientNet-B0 - -### Unoptimized -``` -Main run finished. Milliseconds per iter: 49.2862. Iters per second: 20.2897 -Time per operator type: - 29.7378 ms. 60.5145%. Conv - 12.1785 ms. 24.7824%. Sigmoid - 3.62811 ms. 7.38297%. SpatialBN - 2.98444 ms. 6.07314%. Mul - 0.326902 ms. 0.665225%. AveragePool - 0.197317 ms. 0.401528%. FC - 0.0852877 ms. 0.173555%. Add - 0.0032607 ms. 0.00663532%. Squeeze - 49.1416 ms in Total -FLOP per operator type: - 0.76907 GFLOP. 95.2696%. Conv - 0.0269508 GFLOP. 3.33857%. SpatialBN - 0.00846444 GFLOP. 1.04855%. Mul - 0.002561 GFLOP. 0.317248%. FC - 0.000210112 GFLOP. 0.0260279%. Add - 0.807256 GFLOP in Total -Feature Memory Read per operator type: - 58.5253 MB. 43.0891%. Mul - 43.2015 MB. 31.807%. Conv - 27.2869 MB. 20.0899%. SpatialBN - 5.12912 MB. 3.77631%. FC - 1.6809 MB. 1.23756%. Add - 135.824 MB in Total -Feature Memory Written per operator type: - 33.8578 MB. 38.1965%. Mul - 26.9881 MB. 30.4465%. Conv - 26.9508 MB. 30.4044%. SpatialBN - 0.840448 MB. 0.948147%. Add - 0.004 MB. 0.00451258%. FC - 88.6412 MB in Total -Parameter Memory per operator type: - 15.8248 MB. 74.9391%. Conv - 5.124 MB. 24.265%. FC - 0.168064 MB. 0.795877%. SpatialBN - 0 MB. 0%. Add - 0 MB. 0%. Mul - 21.1168 MB in Total -``` -### Optimized -``` -Main run finished. Milliseconds per iter: 46.0838. Iters per second: 21.6996 -Time per operator type: - 29.776 ms. 65.002%. Conv - 12.2803 ms. 26.8084%. Sigmoid - 3.15073 ms. 6.87815%. Mul - 0.328651 ms. 0.717456%. AveragePool - 0.186237 ms. 0.406563%. FC - 0.0832429 ms. 0.181722%. Add - 0.0026184 ms. 0.00571606%. Squeeze - 45.8078 ms in Total -FLOP per operator type: - 0.76907 GFLOP. 98.5601%. Conv - 0.00846444 GFLOP. 1.08476%. Mul - 0.002561 GFLOP. 0.328205%. FC - 0.000210112 GFLOP. 0.0269269%. Add - 0.780305 GFLOP in Total -Feature Memory Read per operator type: - 58.5253 MB. 53.8803%. Mul - 43.2855 MB. 39.8501%. Conv - 5.12912 MB. 4.72204%. FC - 1.6809 MB. 1.54749%. Add - 108.621 MB in Total -Feature Memory Written per operator type: - 33.8578 MB. 54.8834%. Mul - 26.9881 MB. 43.7477%. Conv - 0.840448 MB. 1.36237%. Add - 0.004 MB. 0.00648399%. FC - 61.6904 MB in Total -Parameter Memory per operator type: - 15.8248 MB. 75.5403%. Conv - 5.124 MB. 24.4597%. FC - 0 MB. 0%. Add - 0 MB. 0%. Mul - 20.9488 MB in Total -``` - -## EfficientNet-B1 -### Optimized -``` -Main run finished. Milliseconds per iter: 71.8102. Iters per second: 13.9256 -Time per operator type: - 45.7915 ms. 66.3206%. Conv - 17.8718 ms. 25.8841%. Sigmoid - 4.44132 ms. 6.43244%. Mul - 0.51001 ms. 0.738658%. AveragePool - 0.233283 ms. 0.337868%. Add - 0.194986 ms. 0.282402%. FC - 0.00268255 ms. 0.00388519%. Squeeze - 69.0456 ms in Total -FLOP per operator type: - 1.37105 GFLOP. 98.7673%. Conv - 0.0138759 GFLOP. 0.99959%. Mul - 0.002561 GFLOP. 0.184489%. FC - 0.000674432 GFLOP. 0.0485847%. Add - 1.38816 GFLOP in Total -Feature Memory Read per operator type: - 94.624 MB. 54.0789%. Mul - 69.8255 MB. 39.9062%. Conv - 5.39546 MB. 3.08357%. Add - 5.12912 MB. 2.93136%. FC - 174.974 MB in Total -Feature Memory Written per operator type: - 55.5035 MB. 54.555%. Mul - 43.5333 MB. 42.7894%. Conv - 2.69773 MB. 2.65163%. Add - 0.004 MB. 0.00393165%. FC - 101.739 MB in Total -Parameter Memory per operator type: - 25.7479 MB. 83.4024%. Conv - 5.124 MB. 16.5976%. FC - 0 MB. 0%. Add - 0 MB. 0%. Mul - 30.8719 MB in Total -``` - -## EfficientNet-B2 -### Optimized -``` -Main run finished. Milliseconds per iter: 92.28. Iters per second: 10.8366 -Time per operator type: - 61.4627 ms. 67.5845%. Conv - 22.7458 ms. 25.0113%. Sigmoid - 5.59931 ms. 6.15701%. Mul - 0.642567 ms. 0.706568%. AveragePool - 0.272795 ms. 0.299965%. Add - 0.216178 ms. 0.237709%. FC - 0.00268895 ms. 0.00295677%. Squeeze - 90.942 ms in Total -FLOP per operator type: - 1.98431 GFLOP. 98.9343%. Conv - 0.0177039 GFLOP. 0.882686%. Mul - 0.002817 GFLOP. 0.140451%. FC - 0.000853984 GFLOP. 0.0425782%. Add - 2.00568 GFLOP in Total -Feature Memory Read per operator type: - 120.609 MB. 54.9637%. Mul - 86.3512 MB. 39.3519%. Conv - 6.83187 MB. 3.11341%. Add - 5.64163 MB. 2.571%. FC - 219.433 MB in Total -Feature Memory Written per operator type: - 70.8155 MB. 54.6573%. Mul - 55.3273 MB. 42.7031%. Conv - 3.41594 MB. 2.63651%. Add - 0.004 MB. 0.00308731%. FC - 129.563 MB in Total -Parameter Memory per operator type: - 30.4721 MB. 84.3913%. Conv - 5.636 MB. 15.6087%. FC - 0 MB. 0%. Add - 0 MB. 0%. Mul - 36.1081 MB in Total -``` - -## MixNet-M -### Optimized -``` -Main run finished. Milliseconds per iter: 63.1122. Iters per second: 15.8448 -Time per operator type: - 48.1139 ms. 75.2052%. Conv - 7.1341 ms. 11.1511%. Sigmoid - 2.63706 ms. 4.12189%. SpatialBN - 1.73186 ms. 2.70701%. Mul - 1.38707 ms. 2.16809%. Split - 1.29322 ms. 2.02139%. Concat - 1.00093 ms. 1.56452%. Relu - 0.235309 ms. 0.367803%. Add - 0.221579 ms. 0.346343%. FC - 0.219315 ms. 0.342803%. AveragePool - 0.00250145 ms. 0.00390993%. Squeeze - 63.9768 ms in Total -FLOP per operator type: - 0.675273 GFLOP. 95.5827%. Conv - 0.0221072 GFLOP. 3.12921%. SpatialBN - 0.00538445 GFLOP. 0.762152%. Mul - 0.003073 GFLOP. 0.434973%. FC - 0.000642488 GFLOP. 0.0909421%. Add - 0 GFLOP. 0%. Concat - 0 GFLOP. 0%. Relu - 0.70648 GFLOP in Total -Feature Memory Read per operator type: - 46.8424 MB. 30.502%. Conv - 36.8626 MB. 24.0036%. Mul - 22.3152 MB. 14.5309%. SpatialBN - 22.1074 MB. 14.3955%. Concat - 14.1496 MB. 9.21372%. Relu - 6.15414 MB. 4.00735%. FC - 5.1399 MB. 3.34692%. Add - 153.571 MB in Total -Feature Memory Written per operator type: - 32.7672 MB. 28.4331%. Conv - 22.1072 MB. 19.1831%. Concat - 22.1072 MB. 19.1831%. SpatialBN - 21.5378 MB. 18.689%. Mul - 14.1496 MB. 12.2781%. Relu - 2.56995 MB. 2.23003%. Add - 0.004 MB. 0.00347092%. FC - 115.243 MB in Total -Parameter Memory per operator type: - 13.7059 MB. 68.674%. Conv - 6.148 MB. 30.8049%. FC - 0.104 MB. 0.521097%. SpatialBN - 0 MB. 0%. Add - 0 MB. 0%. Concat - 0 MB. 0%. Mul - 0 MB. 0%. Relu - 19.9579 MB in Total -``` - -## TF MobileNet-V3 Large 1.0 - -### Optimized -``` -Main run finished. Milliseconds per iter: 22.0495. Iters per second: 45.3525 -Time per operator type: - 17.437 ms. 80.0087%. Conv - 1.27662 ms. 5.8577%. Add - 1.12759 ms. 5.17387%. Div - 0.701155 ms. 3.21721%. Mul - 0.562654 ms. 2.58171%. Relu - 0.431144 ms. 1.97828%. Clip - 0.156902 ms. 0.719936%. FC - 0.0996858 ms. 0.457402%. AveragePool - 0.00112455 ms. 0.00515993%. Flatten - 21.7939 ms in Total -FLOP per operator type: - 0.43062 GFLOP. 98.1484%. Conv - 0.002561 GFLOP. 0.583713%. FC - 0.00210867 GFLOP. 0.480616%. Mul - 0.00193868 GFLOP. 0.441871%. Add - 0.00151532 GFLOP. 0.345377%. Div - 0 GFLOP. 0%. Relu - 0.438743 GFLOP in Total -Feature Memory Read per operator type: - 34.7967 MB. 43.9391%. Conv - 14.496 MB. 18.3046%. Mul - 9.44828 MB. 11.9307%. Add - 9.26157 MB. 11.6949%. Relu - 6.0614 MB. 7.65395%. Div - 5.12912 MB. 6.47673%. FC - 79.193 MB in Total -Feature Memory Written per operator type: - 17.6247 MB. 35.8656%. Conv - 9.26157 MB. 18.847%. Relu - 8.43469 MB. 17.1643%. Mul - 7.75472 MB. 15.7806%. Add - 6.06128 MB. 12.3345%. Div - 0.004 MB. 0.00813985%. FC - 49.1409 MB in Total -Parameter Memory per operator type: - 16.6851 MB. 76.5052%. Conv - 5.124 MB. 23.4948%. FC - 0 MB. 0%. Add - 0 MB. 0%. Div - 0 MB. 0%. Mul - 0 MB. 0%. Relu - 21.8091 MB in Total -``` - -## MobileNet-V3 (RW) - -### Unoptimized -``` -Main run finished. Milliseconds per iter: 24.8316. Iters per second: 40.2712 -Time per operator type: - 15.9266 ms. 69.2624%. Conv - 2.36551 ms. 10.2873%. SpatialBN - 1.39102 ms. 6.04936%. Add - 1.30327 ms. 5.66773%. Div - 0.737014 ms. 3.20517%. Mul - 0.639697 ms. 2.78195%. Relu - 0.375681 ms. 1.63378%. Clip - 0.153126 ms. 0.665921%. FC - 0.0993787 ms. 0.432184%. AveragePool - 0.0032632 ms. 0.0141912%. Squeeze - 22.9946 ms in Total -FLOP per operator type: - 0.430616 GFLOP. 94.4041%. Conv - 0.0175992 GFLOP. 3.85829%. SpatialBN - 0.002561 GFLOP. 0.561449%. FC - 0.00210961 GFLOP. 0.46249%. Mul - 0.00173891 GFLOP. 0.381223%. Add - 0.00151626 GFLOP. 0.33241%. Div - 0 GFLOP. 0%. Relu - 0.456141 GFLOP in Total -Feature Memory Read per operator type: - 34.7354 MB. 36.4363%. Conv - 17.7944 MB. 18.6658%. SpatialBN - 14.5035 MB. 15.2137%. Mul - 9.25778 MB. 9.71113%. Relu - 7.84641 MB. 8.23064%. Add - 6.06516 MB. 6.36216%. Div - 5.12912 MB. 5.38029%. FC - 95.3317 MB in Total -Feature Memory Written per operator type: - 17.6246 MB. 26.7264%. Conv - 17.5992 MB. 26.6878%. SpatialBN - 9.25778 MB. 14.0387%. Relu - 8.43843 MB. 12.7962%. Mul - 6.95565 MB. 10.5477%. Add - 6.06502 MB. 9.19713%. Div - 0.004 MB. 0.00606568%. FC - 65.9447 MB in Total -Parameter Memory per operator type: - 16.6778 MB. 76.1564%. Conv - 5.124 MB. 23.3979%. FC - 0.0976 MB. 0.445674%. SpatialBN - 0 MB. 0%. Add - 0 MB. 0%. Div - 0 MB. 0%. Mul - 0 MB. 0%. Relu - 21.8994 MB in Total - -``` -### Optimized - -``` -Main run finished. Milliseconds per iter: 22.0981. Iters per second: 45.2527 -Time per operator type: - 17.146 ms. 78.8965%. Conv - 1.38453 ms. 6.37084%. Add - 1.30991 ms. 6.02749%. Div - 0.685417 ms. 3.15391%. Mul - 0.532589 ms. 2.45068%. Relu - 0.418263 ms. 1.92461%. Clip - 0.15128 ms. 0.696106%. FC - 0.102065 ms. 0.469648%. AveragePool - 0.0022143 ms. 0.010189%. Squeeze - 21.7323 ms in Total -FLOP per operator type: - 0.430616 GFLOP. 98.1927%. Conv - 0.002561 GFLOP. 0.583981%. FC - 0.00210961 GFLOP. 0.481051%. Mul - 0.00173891 GFLOP. 0.396522%. Add - 0.00151626 GFLOP. 0.34575%. Div - 0 GFLOP. 0%. Relu - 0.438542 GFLOP in Total -Feature Memory Read per operator type: - 34.7842 MB. 44.833%. Conv - 14.5035 MB. 18.6934%. Mul - 9.25778 MB. 11.9323%. Relu - 7.84641 MB. 10.1132%. Add - 6.06516 MB. 7.81733%. Div - 5.12912 MB. 6.61087%. FC - 77.5861 MB in Total -Feature Memory Written per operator type: - 17.6246 MB. 36.4556%. Conv - 9.25778 MB. 19.1492%. Relu - 8.43843 MB. 17.4544%. Mul - 6.95565 MB. 14.3874%. Add - 6.06502 MB. 12.5452%. Div - 0.004 MB. 0.00827378%. FC - 48.3455 MB in Total -Parameter Memory per operator type: - 16.6778 MB. 76.4973%. Conv - 5.124 MB. 23.5027%. FC - 0 MB. 0%. Add - 0 MB. 0%. Div - 0 MB. 0%. Mul - 0 MB. 0%. Relu - 21.8018 MB in Total - -``` - -## MnasNet-A1 - -### Unoptimized -``` -Main run finished. Milliseconds per iter: 30.0892. Iters per second: 33.2345 -Time per operator type: - 24.4656 ms. 79.0905%. Conv - 4.14958 ms. 13.4144%. SpatialBN - 1.60598 ms. 5.19169%. Relu - 0.295219 ms. 0.95436%. Mul - 0.187609 ms. 0.606486%. FC - 0.120556 ms. 0.389724%. AveragePool - 0.09036 ms. 0.292109%. Add - 0.015727 ms. 0.050841%. Sigmoid - 0.00306205 ms. 0.00989875%. Squeeze - 30.9337 ms in Total -FLOP per operator type: - 0.620598 GFLOP. 95.6434%. Conv - 0.0248873 GFLOP. 3.8355%. SpatialBN - 0.002561 GFLOP. 0.394688%. FC - 0.000597408 GFLOP. 0.0920695%. Mul - 0.000222656 GFLOP. 0.0343146%. Add - 0 GFLOP. 0%. Relu - 0.648867 GFLOP in Total -Feature Memory Read per operator type: - 35.5457 MB. 38.4109%. Conv - 25.1552 MB. 27.1829%. SpatialBN - 22.5235 MB. 24.339%. Relu - 5.12912 MB. 5.54256%. FC - 2.40586 MB. 2.59978%. Mul - 1.78125 MB. 1.92483%. Add - 92.5406 MB in Total -Feature Memory Written per operator type: - 24.9042 MB. 32.9424%. Conv - 24.8873 MB. 32.92%. SpatialBN - 22.5235 MB. 29.7932%. Relu - 2.38963 MB. 3.16092%. Mul - 0.890624 MB. 1.17809%. Add - 0.004 MB. 0.00529106%. FC - 75.5993 MB in Total -Parameter Memory per operator type: - 10.2732 MB. 66.1459%. Conv - 5.124 MB. 32.9917%. FC - 0.133952 MB. 0.86247%. SpatialBN - 0 MB. 0%. Add - 0 MB. 0%. Mul - 0 MB. 0%. Relu - 15.5312 MB in Total -``` - -### Optimized -``` -Main run finished. Milliseconds per iter: 24.2367. Iters per second: 41.2597 -Time per operator type: - 22.0547 ms. 91.1375%. Conv - 1.49096 ms. 6.16116%. Relu - 0.253417 ms. 1.0472%. Mul - 0.18506 ms. 0.76473%. FC - 0.112942 ms. 0.466717%. AveragePool - 0.086769 ms. 0.358559%. Add - 0.0127889 ms. 0.0528479%. Sigmoid - 0.0027346 ms. 0.0113003%. Squeeze - 24.1994 ms in Total -FLOP per operator type: - 0.620598 GFLOP. 99.4581%. Conv - 0.002561 GFLOP. 0.41043%. FC - 0.000597408 GFLOP. 0.0957417%. Mul - 0.000222656 GFLOP. 0.0356832%. Add - 0 GFLOP. 0%. Relu - 0.623979 GFLOP in Total -Feature Memory Read per operator type: - 35.6127 MB. 52.7968%. Conv - 22.5235 MB. 33.3917%. Relu - 5.12912 MB. 7.60406%. FC - 2.40586 MB. 3.56675%. Mul - 1.78125 MB. 2.64075%. Add - 67.4524 MB in Total -Feature Memory Written per operator type: - 24.9042 MB. 49.1092%. Conv - 22.5235 MB. 44.4145%. Relu - 2.38963 MB. 4.71216%. Mul - 0.890624 MB. 1.75624%. Add - 0.004 MB. 0.00788768%. FC - 50.712 MB in Total -Parameter Memory per operator type: - 10.2732 MB. 66.7213%. Conv - 5.124 MB. 33.2787%. FC - 0 MB. 0%. Add - 0 MB. 0%. Mul - 0 MB. 0%. Relu - 15.3972 MB in Total -``` -## MnasNet-B1 - -### Unoptimized -``` -Main run finished. Milliseconds per iter: 28.3109. Iters per second: 35.322 -Time per operator type: - 29.1121 ms. 83.3081%. Conv - 4.14959 ms. 11.8746%. SpatialBN - 1.35823 ms. 3.88675%. Relu - 0.186188 ms. 0.532802%. FC - 0.116244 ms. 0.332647%. Add - 0.018641 ms. 0.0533437%. AveragePool - 0.0040904 ms. 0.0117052%. Squeeze - 34.9451 ms in Total -FLOP per operator type: - 0.626272 GFLOP. 96.2088%. Conv - 0.0218266 GFLOP. 3.35303%. SpatialBN - 0.002561 GFLOP. 0.393424%. FC - 0.000291648 GFLOP. 0.0448034%. Add - 0 GFLOP. 0%. Relu - 0.650951 GFLOP in Total -Feature Memory Read per operator type: - 34.4354 MB. 41.3788%. Conv - 22.1299 MB. 26.5921%. SpatialBN - 19.1923 MB. 23.0622%. Relu - 5.12912 MB. 6.16333%. FC - 2.33318 MB. 2.80364%. Add - 83.2199 MB in Total -Feature Memory Written per operator type: - 21.8266 MB. 34.0955%. Conv - 21.8266 MB. 34.0955%. SpatialBN - 19.1923 MB. 29.9805%. Relu - 1.16659 MB. 1.82234%. Add - 0.004 MB. 0.00624844%. FC - 64.016 MB in Total -Parameter Memory per operator type: - 12.2576 MB. 69.9104%. Conv - 5.124 MB. 29.2245%. FC - 0.15168 MB. 0.865099%. SpatialBN - 0 MB. 0%. Add - 0 MB. 0%. Relu - 17.5332 MB in Total -``` - -### Optimized -``` -Main run finished. Milliseconds per iter: 26.6364. Iters per second: 37.5426 -Time per operator type: - 24.9888 ms. 94.0962%. Conv - 1.26147 ms. 4.75011%. Relu - 0.176234 ms. 0.663619%. FC - 0.113309 ms. 0.426672%. Add - 0.0138708 ms. 0.0522311%. AveragePool - 0.00295685 ms. 0.0111341%. Squeeze - 26.5566 ms in Total -FLOP per operator type: - 0.626272 GFLOP. 99.5466%. Conv - 0.002561 GFLOP. 0.407074%. FC - 0.000291648 GFLOP. 0.0463578%. Add - 0 GFLOP. 0%. Relu - 0.629124 GFLOP in Total -Feature Memory Read per operator type: - 34.5112 MB. 56.4224%. Conv - 19.1923 MB. 31.3775%. Relu - 5.12912 MB. 8.3856%. FC - 2.33318 MB. 3.81452%. Add - 61.1658 MB in Total -Feature Memory Written per operator type: - 21.8266 MB. 51.7346%. Conv - 19.1923 MB. 45.4908%. Relu - 1.16659 MB. 2.76513%. Add - 0.004 MB. 0.00948104%. FC - 42.1895 MB in Total -Parameter Memory per operator type: - 12.2576 MB. 70.5205%. Conv - 5.124 MB. 29.4795%. FC - 0 MB. 0%. Add - 0 MB. 0%. Relu - 17.3816 MB in Total -``` diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/configs/_base_/datasets/chase_db1.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/configs/_base_/datasets/chase_db1.py deleted file mode 100644 index 298594ea925f87f22b37094a2ec50e370aec96a0..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/configs/_base_/datasets/chase_db1.py +++ /dev/null @@ -1,59 +0,0 @@ -# dataset settings -dataset_type = 'ChaseDB1Dataset' -data_root = 'data/CHASE_DB1' -img_norm_cfg = dict( - mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) -img_scale = (960, 999) -crop_size = (128, 128) -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/cpwan/RLOR-TSP/models/nets/attention_model/__init__.py b/spaces/cpwan/RLOR-TSP/models/nets/attention_model/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/cvlab/zero123-live/ldm/modules/losses/vqperceptual.py b/spaces/cvlab/zero123-live/ldm/modules/losses/vqperceptual.py deleted file mode 100644 index f69981769e4bd5462600458c4fcf26620f7e4306..0000000000000000000000000000000000000000 --- a/spaces/cvlab/zero123-live/ldm/modules/losses/vqperceptual.py +++ /dev/null @@ -1,167 +0,0 @@ -import torch -from torch import nn -import torch.nn.functional as F -from einops import repeat - -from taming.modules.discriminator.model import NLayerDiscriminator, weights_init -from taming.modules.losses.lpips import LPIPS -from taming.modules.losses.vqperceptual import hinge_d_loss, vanilla_d_loss - - -def hinge_d_loss_with_exemplar_weights(logits_real, logits_fake, weights): - assert weights.shape[0] == logits_real.shape[0] == logits_fake.shape[0] - loss_real = torch.mean(F.relu(1. - logits_real), dim=[1,2,3]) - loss_fake = torch.mean(F.relu(1. + logits_fake), dim=[1,2,3]) - loss_real = (weights * loss_real).sum() / weights.sum() - loss_fake = (weights * loss_fake).sum() / weights.sum() - d_loss = 0.5 * (loss_real + loss_fake) - return d_loss - -def adopt_weight(weight, global_step, threshold=0, value=0.): - if global_step < threshold: - weight = value - return weight - - -def measure_perplexity(predicted_indices, n_embed): - # src: https://github.com/karpathy/deep-vector-quantization/blob/main/model.py - # eval cluster perplexity. when perplexity == num_embeddings then all clusters are used exactly equally - encodings = F.one_hot(predicted_indices, n_embed).float().reshape(-1, n_embed) - avg_probs = encodings.mean(0) - perplexity = (-(avg_probs * torch.log(avg_probs + 1e-10)).sum()).exp() - cluster_use = torch.sum(avg_probs > 0) - return perplexity, cluster_use - -def l1(x, y): - return torch.abs(x-y) - - -def l2(x, y): - return torch.pow((x-y), 2) - - -class VQLPIPSWithDiscriminator(nn.Module): - def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0, - disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0, - perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, - disc_ndf=64, disc_loss="hinge", n_classes=None, perceptual_loss="lpips", - pixel_loss="l1"): - super().__init__() - assert disc_loss in ["hinge", "vanilla"] - assert perceptual_loss in ["lpips", "clips", "dists"] - assert pixel_loss in ["l1", "l2"] - self.codebook_weight = codebook_weight - self.pixel_weight = pixelloss_weight - if perceptual_loss == "lpips": - print(f"{self.__class__.__name__}: Running with LPIPS.") - self.perceptual_loss = LPIPS().eval() - else: - raise ValueError(f"Unknown perceptual loss: >> {perceptual_loss} <<") - self.perceptual_weight = perceptual_weight - - if pixel_loss == "l1": - self.pixel_loss = l1 - else: - self.pixel_loss = l2 - - self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels, - n_layers=disc_num_layers, - use_actnorm=use_actnorm, - ndf=disc_ndf - ).apply(weights_init) - self.discriminator_iter_start = disc_start - if disc_loss == "hinge": - self.disc_loss = hinge_d_loss - elif disc_loss == "vanilla": - self.disc_loss = vanilla_d_loss - else: - raise ValueError(f"Unknown GAN loss '{disc_loss}'.") - print(f"VQLPIPSWithDiscriminator running with {disc_loss} loss.") - self.disc_factor = disc_factor - self.discriminator_weight = disc_weight - self.disc_conditional = disc_conditional - self.n_classes = n_classes - - def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None): - if last_layer is not None: - nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0] - g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0] - else: - nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0] - g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0] - - d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4) - d_weight = torch.clamp(d_weight, 0.0, 1e4).detach() - d_weight = d_weight * self.discriminator_weight - return d_weight - - def forward(self, codebook_loss, inputs, reconstructions, optimizer_idx, - global_step, last_layer=None, cond=None, split="train", predicted_indices=None): - if not exists(codebook_loss): - codebook_loss = torch.tensor([0.]).to(inputs.device) - #rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous()) - rec_loss = self.pixel_loss(inputs.contiguous(), reconstructions.contiguous()) - if self.perceptual_weight > 0: - p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous()) - rec_loss = rec_loss + self.perceptual_weight * p_loss - else: - p_loss = torch.tensor([0.0]) - - nll_loss = rec_loss - #nll_loss = torch.sum(nll_loss) / nll_loss.shape[0] - nll_loss = torch.mean(nll_loss) - - # now the GAN part - if optimizer_idx == 0: - # generator update - if cond is None: - assert not self.disc_conditional - logits_fake = self.discriminator(reconstructions.contiguous()) - else: - assert self.disc_conditional - logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1)) - g_loss = -torch.mean(logits_fake) - - try: - d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer) - except RuntimeError: - assert not self.training - d_weight = torch.tensor(0.0) - - disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) - loss = nll_loss + d_weight * disc_factor * g_loss + self.codebook_weight * codebook_loss.mean() - - log = {"{}/total_loss".format(split): loss.clone().detach().mean(), - "{}/quant_loss".format(split): codebook_loss.detach().mean(), - "{}/nll_loss".format(split): nll_loss.detach().mean(), - "{}/rec_loss".format(split): rec_loss.detach().mean(), - "{}/p_loss".format(split): p_loss.detach().mean(), - "{}/d_weight".format(split): d_weight.detach(), - "{}/disc_factor".format(split): torch.tensor(disc_factor), - "{}/g_loss".format(split): g_loss.detach().mean(), - } - if predicted_indices is not None: - assert self.n_classes is not None - with torch.no_grad(): - perplexity, cluster_usage = measure_perplexity(predicted_indices, self.n_classes) - log[f"{split}/perplexity"] = perplexity - log[f"{split}/cluster_usage"] = cluster_usage - return loss, log - - if optimizer_idx == 1: - # second pass for discriminator update - if cond is None: - logits_real = self.discriminator(inputs.contiguous().detach()) - logits_fake = self.discriminator(reconstructions.contiguous().detach()) - else: - logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1)) - logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1)) - - disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start) - d_loss = disc_factor * self.disc_loss(logits_real, logits_fake) - - log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(), - "{}/logits_real".format(split): logits_real.detach().mean(), - "{}/logits_fake".format(split): logits_fake.detach().mean() - } - return d_loss, log diff --git a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/utils/videoio.py b/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/utils/videoio.py deleted file mode 100644 index eebc244c54616cfa4e91d3c05dc8ce9c7eaff6d6..0000000000000000000000000000000000000000 --- a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/utils/videoio.py +++ /dev/null @@ -1,41 +0,0 @@ -import shutil -import uuid - -import os - -import cv2 - -def load_video_to_cv2(input_path): - video_stream = cv2.VideoCapture(input_path) - fps = video_stream.get(cv2.CAP_PROP_FPS) - full_frames = [] - while 1: - still_reading, frame = video_stream.read() - if not still_reading: - video_stream.release() - break - full_frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) - return full_frames - -def save_video_with_watermark(video, audio, save_path, watermark=False): - temp_file = str(uuid.uuid4())+'.mp4' - cmd = r'ffmpeg -hide_banner -loglevel error -y -i "%s" -i "%s" -vcodec copy "%s"' % (video, audio, temp_file) - os.system(cmd) - - if watermark is False: - shutil.move(temp_file, save_path) - else: - # watermark - try: - ##### check if stable-diffusion-webui - import webui - from modules import paths - watarmark_path = paths.script_path+"/extensions/SadTalker/docs/sadtalker_logo.png" - except: - # get the root path of sadtalker. - dir_path = os.path.dirname(os.path.realpath(__file__)) - watarmark_path = dir_path+"/../../docs/sadtalker_logo.png" - - cmd = r'ffmpeg -y -hide_banner -i "%s" -i "%s" -filter_complex "[1]scale=100:-1[wm];[0][wm]overlay=(main_w-overlay_w)-10:10" "%s"' % (temp_file, watarmark_path, save_path) - os.system(cmd) - os.remove(temp_file) \ No newline at end of file diff --git a/spaces/declare-lab/tango/diffusers/examples/community/unclip_image_interpolation.py b/spaces/declare-lab/tango/diffusers/examples/community/unclip_image_interpolation.py deleted file mode 100644 index d0b54125b688efaa09e8577e21fc921126c763c2..0000000000000000000000000000000000000000 --- a/spaces/declare-lab/tango/diffusers/examples/community/unclip_image_interpolation.py +++ /dev/null @@ -1,493 +0,0 @@ -import inspect -from typing import List, Optional, Union - -import PIL -import torch -from torch.nn import functional as F -from transformers import ( - CLIPImageProcessor, - CLIPTextModelWithProjection, - CLIPTokenizer, - CLIPVisionModelWithProjection, -) - -from diffusers import ( - DiffusionPipeline, - ImagePipelineOutput, - UnCLIPScheduler, - UNet2DConditionModel, - UNet2DModel, -) -from diffusers.pipelines.unclip import UnCLIPTextProjModel -from diffusers.utils import is_accelerate_available, logging, randn_tensor - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - - -def slerp(val, low, high): - """ - Find the interpolation point between the 'low' and 'high' values for the given 'val'. See https://en.wikipedia.org/wiki/Slerp for more details on the topic. - """ - low_norm = low / torch.norm(low) - high_norm = high / torch.norm(high) - omega = torch.acos((low_norm * high_norm)) - so = torch.sin(omega) - res = (torch.sin((1.0 - val) * omega) / so) * low + (torch.sin(val * omega) / so) * high - return res - - -class UnCLIPImageInterpolationPipeline(DiffusionPipeline): - """ - Pipeline to generate variations from an input image using unCLIP - - This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the - library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) - - Args: - text_encoder ([`CLIPTextModelWithProjection`]): - Frozen text-encoder. - tokenizer (`CLIPTokenizer`): - Tokenizer of class - [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). - feature_extractor ([`CLIPImageProcessor`]): - Model that extracts features from generated images to be used as inputs for the `image_encoder`. - image_encoder ([`CLIPVisionModelWithProjection`]): - Frozen CLIP image-encoder. unCLIP Image Variation uses the vision portion of - [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPVisionModelWithProjection), - specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. - text_proj ([`UnCLIPTextProjModel`]): - Utility class to prepare and combine the embeddings before they are passed to the decoder. - decoder ([`UNet2DConditionModel`]): - The decoder to invert the image embedding into an image. - super_res_first ([`UNet2DModel`]): - Super resolution unet. Used in all but the last step of the super resolution diffusion process. - super_res_last ([`UNet2DModel`]): - Super resolution unet. Used in the last step of the super resolution diffusion process. - decoder_scheduler ([`UnCLIPScheduler`]): - Scheduler used in the decoder denoising process. Just a modified DDPMScheduler. - super_res_scheduler ([`UnCLIPScheduler`]): - Scheduler used in the super resolution denoising process. Just a modified DDPMScheduler. - - """ - - decoder: UNet2DConditionModel - text_proj: UnCLIPTextProjModel - text_encoder: CLIPTextModelWithProjection - tokenizer: CLIPTokenizer - feature_extractor: CLIPImageProcessor - image_encoder: CLIPVisionModelWithProjection - super_res_first: UNet2DModel - super_res_last: UNet2DModel - - decoder_scheduler: UnCLIPScheduler - super_res_scheduler: UnCLIPScheduler - - # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline.__init__ - def __init__( - self, - decoder: UNet2DConditionModel, - text_encoder: CLIPTextModelWithProjection, - tokenizer: CLIPTokenizer, - text_proj: UnCLIPTextProjModel, - feature_extractor: CLIPImageProcessor, - image_encoder: CLIPVisionModelWithProjection, - super_res_first: UNet2DModel, - super_res_last: UNet2DModel, - decoder_scheduler: UnCLIPScheduler, - super_res_scheduler: UnCLIPScheduler, - ): - super().__init__() - - self.register_modules( - decoder=decoder, - text_encoder=text_encoder, - tokenizer=tokenizer, - text_proj=text_proj, - feature_extractor=feature_extractor, - image_encoder=image_encoder, - super_res_first=super_res_first, - super_res_last=super_res_last, - decoder_scheduler=decoder_scheduler, - super_res_scheduler=super_res_scheduler, - ) - - # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents - def prepare_latents(self, shape, dtype, device, generator, latents, scheduler): - if latents is None: - latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) - else: - if latents.shape != shape: - raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") - latents = latents.to(device) - - latents = latents * scheduler.init_noise_sigma - return latents - - # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline._encode_prompt - def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance): - batch_size = len(prompt) if isinstance(prompt, list) else 1 - - # get prompt text embeddings - text_inputs = self.tokenizer( - prompt, - padding="max_length", - max_length=self.tokenizer.model_max_length, - return_tensors="pt", - ) - text_input_ids = text_inputs.input_ids - text_mask = text_inputs.attention_mask.bool().to(device) - text_encoder_output = self.text_encoder(text_input_ids.to(device)) - - prompt_embeds = text_encoder_output.text_embeds - text_encoder_hidden_states = text_encoder_output.last_hidden_state - - prompt_embeds = prompt_embeds.repeat_interleave(num_images_per_prompt, dim=0) - text_encoder_hidden_states = text_encoder_hidden_states.repeat_interleave(num_images_per_prompt, dim=0) - text_mask = text_mask.repeat_interleave(num_images_per_prompt, dim=0) - - if do_classifier_free_guidance: - uncond_tokens = [""] * batch_size - - max_length = text_input_ids.shape[-1] - uncond_input = self.tokenizer( - uncond_tokens, - padding="max_length", - max_length=max_length, - truncation=True, - return_tensors="pt", - ) - uncond_text_mask = uncond_input.attention_mask.bool().to(device) - negative_prompt_embeds_text_encoder_output = self.text_encoder(uncond_input.input_ids.to(device)) - - negative_prompt_embeds = negative_prompt_embeds_text_encoder_output.text_embeds - uncond_text_encoder_hidden_states = negative_prompt_embeds_text_encoder_output.last_hidden_state - - # duplicate unconditional embeddings for each generation per prompt, using mps friendly method - - seq_len = negative_prompt_embeds.shape[1] - negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt) - negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len) - - seq_len = uncond_text_encoder_hidden_states.shape[1] - uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1) - uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.view( - batch_size * num_images_per_prompt, seq_len, -1 - ) - uncond_text_mask = uncond_text_mask.repeat_interleave(num_images_per_prompt, dim=0) - - # done duplicates - - # For classifier free guidance, we need to do two forward passes. - # Here we concatenate the unconditional and text embeddings into a single batch - # to avoid doing two forward passes - prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) - text_encoder_hidden_states = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]) - - text_mask = torch.cat([uncond_text_mask, text_mask]) - - return prompt_embeds, text_encoder_hidden_states, text_mask - - # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline._encode_image - def _encode_image(self, image, device, num_images_per_prompt, image_embeddings: Optional[torch.Tensor] = None): - dtype = next(self.image_encoder.parameters()).dtype - - if image_embeddings is None: - if not isinstance(image, torch.Tensor): - image = self.feature_extractor(images=image, return_tensors="pt").pixel_values - - image = image.to(device=device, dtype=dtype) - image_embeddings = self.image_encoder(image).image_embeds - - image_embeddings = image_embeddings.repeat_interleave(num_images_per_prompt, dim=0) - - return image_embeddings - - # Copied from diffusers.pipelines.unclip.pipeline_unclip_image_variation.UnCLIPImageVariationPipeline.enable_sequential_cpu_offload - def enable_sequential_cpu_offload(self, gpu_id=0): - r""" - Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline's - models have their state dicts saved to CPU and then are moved to a `torch.device('meta') and loaded to GPU only - when their specific submodule has its `forward` method called. - """ - if is_accelerate_available(): - from accelerate import cpu_offload - else: - raise ImportError("Please install accelerate via `pip install accelerate`") - - device = torch.device(f"cuda:{gpu_id}") - - models = [ - self.decoder, - self.text_proj, - self.text_encoder, - self.super_res_first, - self.super_res_last, - ] - for cpu_offloaded_model in models: - if cpu_offloaded_model is not None: - cpu_offload(cpu_offloaded_model, device) - - @property - # Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline._execution_device - def _execution_device(self): - r""" - Returns the device on which the pipeline's models will be executed. After calling - `pipeline.enable_sequential_cpu_offload()` the execution device can only be inferred from Accelerate's module - hooks. - """ - if self.device != torch.device("meta") or not hasattr(self.decoder, "_hf_hook"): - return self.device - for module in self.decoder.modules(): - if ( - hasattr(module, "_hf_hook") - and hasattr(module._hf_hook, "execution_device") - and module._hf_hook.execution_device is not None - ): - return torch.device(module._hf_hook.execution_device) - return self.device - - @torch.no_grad() - def __call__( - self, - image: Optional[Union[List[PIL.Image.Image], torch.FloatTensor]] = None, - steps: int = 5, - decoder_num_inference_steps: int = 25, - super_res_num_inference_steps: int = 7, - generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, - image_embeddings: Optional[torch.Tensor] = None, - decoder_latents: Optional[torch.FloatTensor] = None, - super_res_latents: Optional[torch.FloatTensor] = None, - decoder_guidance_scale: float = 8.0, - output_type: Optional[str] = "pil", - return_dict: bool = True, - ): - """ - Function invoked when calling the pipeline for generation. - - Args: - image (`List[PIL.Image.Image]` or `torch.FloatTensor`): - The images to use for the image interpolation. Only accepts a list of two PIL Images or If you provide a tensor, it needs to comply with the - configuration of - [this](https://huggingface.co/fusing/karlo-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json) - `CLIPImageProcessor` while still having a shape of two in the 0th dimension. Can be left to `None` only when `image_embeddings` are passed. - steps (`int`, *optional*, defaults to 5): - The number of interpolation images to generate. - decoder_num_inference_steps (`int`, *optional*, defaults to 25): - The number of denoising steps for the decoder. More denoising steps usually lead to a higher quality - image at the expense of slower inference. - super_res_num_inference_steps (`int`, *optional*, defaults to 7): - The number of denoising steps for super resolution. More denoising steps usually lead to a higher - quality image at the expense of slower inference. - generator (`torch.Generator` or `List[torch.Generator]`, *optional*): - One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) - to make generation deterministic. - image_embeddings (`torch.Tensor`, *optional*): - Pre-defined image embeddings that can be derived from the image encoder. Pre-defined image embeddings - can be passed for tasks like image interpolations. `image` can the be left to `None`. - decoder_latents (`torch.FloatTensor` of shape (batch size, channels, height, width), *optional*): - Pre-generated noisy latents to be used as inputs for the decoder. - super_res_latents (`torch.FloatTensor` of shape (batch size, channels, super res height, super res width), *optional*): - Pre-generated noisy latents to be used as inputs for the decoder. - decoder_guidance_scale (`float`, *optional*, defaults to 4.0): - Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). - `guidance_scale` is defined as `w` of equation 2. of [Imagen - Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > - 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, - usually at the expense of lower image quality. - output_type (`str`, *optional*, defaults to `"pil"`): - The output format of the generated image. Choose between - [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple. - """ - - batch_size = steps - - device = self._execution_device - - if isinstance(image, List): - if len(image) != 2: - raise AssertionError( - f"Expected 'image' List to be of size 2, but passed 'image' length is {len(image)}" - ) - elif not (isinstance(image[0], PIL.Image.Image) and isinstance(image[0], PIL.Image.Image)): - raise AssertionError( - f"Expected 'image' List to contain PIL.Image.Image, but passed 'image' contents are {type(image[0])} and {type(image[1])}" - ) - elif isinstance(image, torch.FloatTensor): - if image.shape[0] != 2: - raise AssertionError( - f"Expected 'image' to be torch.FloatTensor of shape 2 in 0th dimension, but passed 'image' size is {image.shape[0]}" - ) - elif isinstance(image_embeddings, torch.Tensor): - if image_embeddings.shape[0] != 2: - raise AssertionError( - f"Expected 'image_embeddings' to be torch.FloatTensor of shape 2 in 0th dimension, but passed 'image_embeddings' shape is {image_embeddings.shape[0]}" - ) - else: - raise AssertionError( - f"Expected 'image' or 'image_embeddings' to be not None with types List[PIL.Image] or Torch.FloatTensor respectively. Received {type(image)} and {type(image_embeddings)} repsectively" - ) - - original_image_embeddings = self._encode_image( - image=image, device=device, num_images_per_prompt=1, image_embeddings=image_embeddings - ) - - image_embeddings = [] - - for interp_step in torch.linspace(0, 1, steps): - temp_image_embeddings = slerp( - interp_step, original_image_embeddings[0], original_image_embeddings[1] - ).unsqueeze(0) - image_embeddings.append(temp_image_embeddings) - - image_embeddings = torch.cat(image_embeddings).to(device) - - do_classifier_free_guidance = decoder_guidance_scale > 1.0 - - prompt_embeds, text_encoder_hidden_states, text_mask = self._encode_prompt( - prompt=["" for i in range(steps)], - device=device, - num_images_per_prompt=1, - do_classifier_free_guidance=do_classifier_free_guidance, - ) - - text_encoder_hidden_states, additive_clip_time_embeddings = self.text_proj( - image_embeddings=image_embeddings, - prompt_embeds=prompt_embeds, - text_encoder_hidden_states=text_encoder_hidden_states, - do_classifier_free_guidance=do_classifier_free_guidance, - ) - - if device.type == "mps": - # HACK: MPS: There is a panic when padding bool tensors, - # so cast to int tensor for the pad and back to bool afterwards - text_mask = text_mask.type(torch.int) - decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=1) - decoder_text_mask = decoder_text_mask.type(torch.bool) - else: - decoder_text_mask = F.pad(text_mask, (self.text_proj.clip_extra_context_tokens, 0), value=True) - - self.decoder_scheduler.set_timesteps(decoder_num_inference_steps, device=device) - decoder_timesteps_tensor = self.decoder_scheduler.timesteps - - num_channels_latents = self.decoder.in_channels - height = self.decoder.sample_size - width = self.decoder.sample_size - - decoder_latents = self.prepare_latents( - (batch_size, num_channels_latents, height, width), - text_encoder_hidden_states.dtype, - device, - generator, - decoder_latents, - self.decoder_scheduler, - ) - - for i, t in enumerate(self.progress_bar(decoder_timesteps_tensor)): - # expand the latents if we are doing classifier free guidance - latent_model_input = torch.cat([decoder_latents] * 2) if do_classifier_free_guidance else decoder_latents - - noise_pred = self.decoder( - sample=latent_model_input, - timestep=t, - encoder_hidden_states=text_encoder_hidden_states, - class_labels=additive_clip_time_embeddings, - attention_mask=decoder_text_mask, - ).sample - - if do_classifier_free_guidance: - noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred_uncond, _ = noise_pred_uncond.split(latent_model_input.shape[1], dim=1) - noise_pred_text, predicted_variance = noise_pred_text.split(latent_model_input.shape[1], dim=1) - noise_pred = noise_pred_uncond + decoder_guidance_scale * (noise_pred_text - noise_pred_uncond) - noise_pred = torch.cat([noise_pred, predicted_variance], dim=1) - - if i + 1 == decoder_timesteps_tensor.shape[0]: - prev_timestep = None - else: - prev_timestep = decoder_timesteps_tensor[i + 1] - - # compute the previous noisy sample x_t -> x_t-1 - decoder_latents = self.decoder_scheduler.step( - noise_pred, t, decoder_latents, prev_timestep=prev_timestep, generator=generator - ).prev_sample - - decoder_latents = decoder_latents.clamp(-1, 1) - - image_small = decoder_latents - - # done decoder - - # super res - - self.super_res_scheduler.set_timesteps(super_res_num_inference_steps, device=device) - super_res_timesteps_tensor = self.super_res_scheduler.timesteps - - channels = self.super_res_first.in_channels // 2 - height = self.super_res_first.sample_size - width = self.super_res_first.sample_size - - super_res_latents = self.prepare_latents( - (batch_size, channels, height, width), - image_small.dtype, - device, - generator, - super_res_latents, - self.super_res_scheduler, - ) - - if device.type == "mps": - # MPS does not support many interpolations - image_upscaled = F.interpolate(image_small, size=[height, width]) - else: - interpolate_antialias = {} - if "antialias" in inspect.signature(F.interpolate).parameters: - interpolate_antialias["antialias"] = True - - image_upscaled = F.interpolate( - image_small, size=[height, width], mode="bicubic", align_corners=False, **interpolate_antialias - ) - - for i, t in enumerate(self.progress_bar(super_res_timesteps_tensor)): - # no classifier free guidance - - if i == super_res_timesteps_tensor.shape[0] - 1: - unet = self.super_res_last - else: - unet = self.super_res_first - - latent_model_input = torch.cat([super_res_latents, image_upscaled], dim=1) - - noise_pred = unet( - sample=latent_model_input, - timestep=t, - ).sample - - if i + 1 == super_res_timesteps_tensor.shape[0]: - prev_timestep = None - else: - prev_timestep = super_res_timesteps_tensor[i + 1] - - # compute the previous noisy sample x_t -> x_t-1 - super_res_latents = self.super_res_scheduler.step( - noise_pred, t, super_res_latents, prev_timestep=prev_timestep, generator=generator - ).prev_sample - - image = super_res_latents - # done super res - - # post processing - - image = image * 0.5 + 0.5 - image = image.clamp(0, 1) - image = image.cpu().permute(0, 2, 3, 1).float().numpy() - - if output_type == "pil": - image = self.numpy_to_pil(image) - - if not return_dict: - return (image,) - - return ImagePipelineOutput(images=image) diff --git a/spaces/deepkyu/multilingual-font-style-transfer/inference.py b/spaces/deepkyu/multilingual-font-style-transfer/inference.py deleted file mode 100644 index 660e345f4e279ba646780b6ab8ea3d547e805e9b..0000000000000000000000000000000000000000 --- a/spaces/deepkyu/multilingual-font-style-transfer/inference.py +++ /dev/null @@ -1,163 +0,0 @@ -from pathlib import Path -from typing import Dict, List, Union, Tuple - -from omegaconf import OmegaConf -import numpy as np -import torch -from torch import nn -from PIL import Image, ImageDraw, ImageFont - -import models - -GENERATOR_PREFIX = "networks.g." -WHITE = 255 -EXAMPLE_CHARACTERS = ['A', 'B', 'C', 'D', 'E'] - -class InferenceServicer: - def __init__(self, hp, checkpoint_path, content_image_dir, imsize=64, gpu_id='0') -> None: - self.hp = hp - self.imsize = imsize - - if gpu_id is None: - self.device = torch.device(f'cuda:0') if torch.cuda.is_available() else 'cpu' - else: - self.device = torch.device(f'cuda:{gpu_id}') - - model_config = self.hp.models.G - self.model: nn.Module = models.Generator(model_config) - - # Load Generator model weight - model_state_dict_pl = torch.load(checkpoint_path, map_location='cpu') - generator_state_dict = self.convert_generator_state_dict(model_state_dict_pl) - self.model.load_state_dict(generator_state_dict) - self.model.to(device=self.device) - self.model.eval() - - # Setting Content font files - self.content_character_dict = self.load_content_character_dict(Path(content_image_dir)) - - @staticmethod - def convert_generator_state_dict(model_state_dict_pl): - generator_prefix = GENERATOR_PREFIX - generator_state_dict = {} - for module_name, module_state in model_state_dict_pl['state_dict'].items(): - if module_name.startswith(generator_prefix): - generator_state_dict[module_name[len(generator_prefix):]] = module_state - - return generator_state_dict - - @staticmethod - def load_content_character_dict(content_image_dir: Path) -> Dict[str, Path]: - content_character_dict = {} - for filepath in content_image_dir.glob("**/*.png"): - content_character_dict[filepath.stem] = filepath - return content_character_dict - - @staticmethod - def center_align(bg_img: Image.Image, item_img: Image.Image, fit=False) -> Image.Image: - bg_img = bg_img.copy() - item_img = item_img.copy() - item_w, item_h = item_img.size - W, H = bg_img.size - if fit: - item_ratio = item_w / item_h - bg_ratio = W / H - - if bg_ratio > item_ratio: - # height fitting - resize_ratio = H / item_h - else: - # width fitting - resize_ratio = W / item_w - item_img = item_img.resize((int(item_w * resize_ratio), int(item_h * resize_ratio))) - item_w, item_h = item_img.size - - bg_img.paste(item_img, ((W - item_w) // 2, (H - item_h) // 2)) - return bg_img - - def set_image(self, image: Union[Path, Image.Image]) -> Image.Image: - if isinstance(image, (str, Path)): - image = Image.open(image) - assert isinstance(image, Image.Image) - - bg_img = Image.new('RGB', (self.imsize, self.imsize), color='white') - blend_img = self.center_align(bg_img, image, fit=True) - return blend_img - - @staticmethod - def pil_image_to_array(blend_img: Image.Image) -> np.ndarray: - normalized_array = np.mean(np.array(blend_img, dtype=np.float32), axis=-1) / WHITE # L-only image normalized to [0, 1] - return normalized_array - - def get_images_from_fontfile(self, font_file_path: Path, imgmode: str = 'RGB', position: tuple = (0, 0), font_size: int = 128, padding: int = 100) -> List[Image.Image]: - - imagefont = ImageFont.truetype(str(font_file_path), size=font_size) - example_characters = EXAMPLE_CHARACTERS - - font_images: List[Image.Image] = [] - - for character in example_characters: - x, y, _, _ = imagefont.getbbox(character) - img = Image.new(imgmode, (x + padding, y + padding), color='white') - draw = ImageDraw.Draw(img) - - # bbox = draw.textbbox((0,0), character, font=imagefont) - # w = bbox[2] - bbox[0] - # h = bbox[3] - bbox[1] - - w, h = draw.textsize(character, font=imagefont) - - img = Image.new(imgmode, (w + padding, h + padding), color='white') - draw = ImageDraw.Draw(img) - draw.text(position, text=character, font=imagefont, fill='black') - img = img.convert(imgmode) - font_images.append(img) - - return font_images - - @staticmethod - def get_hex_from_char(char: str) -> str: - assert len(char) == 1 - return f"{ord(char):04X}".upper() # 4-digit hex string - - @torch.no_grad() - def inference(self, content_char: str, style_font: Union[str, Path]) -> Tuple[Image.Image, List[Image.Image], Image.Image]: - assert len(content_char) > 0 - content_char = content_char[:1] # only get the first character if the length > 1 - char_hex = self.get_hex_from_char(content_char) - - if char_hex not in self.content_character_dict: - raise ValueError(f"The character {content_char} (hex: {char_hex}) is not supported in this model!") - - content_image = self.set_image(self.content_character_dict[char_hex]) - style_images: List[Image.Image] = self.get_images_from_fontfile(Path(style_font)) - style_images: List[Image.Image] = [self.set_image(image) for image in style_images] - - content_image_array = self.pil_image_to_array(content_image)[np.newaxis, np.newaxis, ...] # 1 x C(=1) x H x W - style_images_array: np.ndarray = np.array([self.pil_image_to_array(image) for image in style_images])[np.newaxis, ...] # 1 x C(=5, # shots) x H x W, k-shots goes to batch - - content_input_tensor = torch.from_numpy(content_image_array).to(self.device) - style_input_tensor = torch.from_numpy(style_images_array).to(self.device) - - generated_images: torch.Tensor = self.model((content_input_tensor, style_input_tensor)) - generated_images = torch.clip(generated_images, 0, 1) - assert generated_images.size(0) == 1 - - generated_image_numpy = (generated_images[0].cpu().numpy() * 255).astype(np.uint8)[0, ...] # H x W - return content_image, style_images, Image.fromarray(generated_image_numpy, mode='L') - - -if __name__ == '__main__': - hp = OmegaConf.load("config/models/google-font.yaml") - checkpoint_path = "epoch=199-step=257400.ckpt" - content_image_dir = "../DATA/NotoSans" - - servicer = InferenceServicer(hp, checkpoint_path, content_image_dir) - - style_font = "example_fonts/MaShanZheng-Regular.ttf" - content_image, style_images, result = servicer.inference("7", style_font) - - content_image.save("result_content.png") - for idx, style_image in enumerate(style_images): - style_image.save(f"result_style_{idx:02d}.png") - result.save("result_generated.png") \ No newline at end of file diff --git a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/data/flist_dataset.py b/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/data/flist_dataset.py deleted file mode 100644 index c0b6945c80aa756074a5d3c02b9443b15ddcfc57..0000000000000000000000000000000000000000 --- a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/face3d/data/flist_dataset.py +++ /dev/null @@ -1,125 +0,0 @@ -"""This script defines the custom dataset for Deep3DFaceRecon_pytorch -""" - -import os.path -from data.base_dataset import BaseDataset, get_transform, get_affine_mat, apply_img_affine, apply_lm_affine -from data.image_folder import make_dataset -from PIL import Image -import random -import util.util as util -import numpy as np -import json -import torch -from scipy.io import loadmat, savemat -import pickle -from util.preprocess import align_img, estimate_norm -from util.load_mats import load_lm3d - - -def default_flist_reader(flist): - """ - flist format: impath label\nimpath label\n ...(same to caffe's filelist) - """ - imlist = [] - with open(flist, 'r') as rf: - for line in rf.readlines(): - impath = line.strip() - imlist.append(impath) - - return imlist - -def jason_flist_reader(flist): - with open(flist, 'r') as fp: - info = json.load(fp) - return info - -def parse_label(label): - return torch.tensor(np.array(label).astype(np.float32)) - - -class FlistDataset(BaseDataset): - """ - It requires one directories to host training images '/path/to/data/train' - You can train the model with the dataset flag '--dataroot /path/to/data'. - """ - - def __init__(self, opt): - """Initialize this dataset class. - - Parameters: - opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions - """ - BaseDataset.__init__(self, opt) - - self.lm3d_std = load_lm3d(opt.bfm_folder) - - msk_names = default_flist_reader(opt.flist) - self.msk_paths = [os.path.join(opt.data_root, i) for i in msk_names] - - self.size = len(self.msk_paths) - self.opt = opt - - self.name = 'train' if opt.isTrain else 'val' - if '_' in opt.flist: - self.name += '_' + opt.flist.split(os.sep)[-1].split('_')[0] - - - def __getitem__(self, index): - """Return a data point and its metadata information. - - Parameters: - index (int) -- a random integer for data indexing - - Returns a dictionary that contains A, B, A_paths and B_paths - img (tensor) -- an image in the input domain - msk (tensor) -- its corresponding attention mask - lm (tensor) -- its corresponding 3d landmarks - im_paths (str) -- image paths - aug_flag (bool) -- a flag used to tell whether its raw or augmented - """ - msk_path = self.msk_paths[index % self.size] # make sure index is within then range - img_path = msk_path.replace('mask/', '') - lm_path = '.'.join(msk_path.replace('mask', 'landmarks').split('.')[:-1]) + '.txt' - - raw_img = Image.open(img_path).convert('RGB') - raw_msk = Image.open(msk_path).convert('RGB') - raw_lm = np.loadtxt(lm_path).astype(np.float32) - - _, img, lm, msk = align_img(raw_img, raw_lm, self.lm3d_std, raw_msk) - - aug_flag = self.opt.use_aug and self.opt.isTrain - if aug_flag: - img, lm, msk = self._augmentation(img, lm, self.opt, msk) - - _, H = img.size - M = estimate_norm(lm, H) - transform = get_transform() - img_tensor = transform(img) - msk_tensor = transform(msk)[:1, ...] - lm_tensor = parse_label(lm) - M_tensor = parse_label(M) - - - return {'imgs': img_tensor, - 'lms': lm_tensor, - 'msks': msk_tensor, - 'M': M_tensor, - 'im_paths': img_path, - 'aug_flag': aug_flag, - 'dataset': self.name} - - def _augmentation(self, img, lm, opt, msk=None): - affine, affine_inv, flip = get_affine_mat(opt, img.size) - img = apply_img_affine(img, affine_inv) - lm = apply_lm_affine(lm, affine, flip, img.size) - if msk is not None: - msk = apply_img_affine(msk, affine_inv, method=Image.BILINEAR) - return img, lm, msk - - - - - def __len__(self): - """Return the total number of images in the dataset. - """ - return self.size diff --git a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/generate_batch.py b/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/generate_batch.py deleted file mode 100644 index 34780a3eb2dee285649ba1d761c20969e12ce3b8..0000000000000000000000000000000000000000 --- a/spaces/deepskyreal/ai-mixer-hotchpotch/sad_talker/src/generate_batch.py +++ /dev/null @@ -1,120 +0,0 @@ -import os - -from tqdm import tqdm -import torch -import numpy as np -import random -import scipy.io as scio -import sad_talker.src.utils.audio as audio - -def crop_pad_audio(wav, audio_length): - if len(wav) > audio_length: - wav = wav[:audio_length] - elif len(wav) < audio_length: - wav = np.pad(wav, [0, audio_length - len(wav)], mode='constant', constant_values=0) - return wav - -def parse_audio_length(audio_length, sr, fps): - bit_per_frames = sr / fps - - num_frames = int(audio_length / bit_per_frames) - audio_length = int(num_frames * bit_per_frames) - - return audio_length, num_frames - -def generate_blink_seq(num_frames): - ratio = np.zeros((num_frames,1)) - frame_id = 0 - while frame_id in range(num_frames): - start = 80 - if frame_id+start+9<=num_frames - 1: - ratio[frame_id+start:frame_id+start+9, 0] = [0.5,0.6,0.7,0.9,1, 0.9, 0.7,0.6,0.5] - frame_id = frame_id+start+9 - else: - break - return ratio - -def generate_blink_seq_randomly(num_frames): - ratio = np.zeros((num_frames,1)) - if num_frames<=20: - return ratio - frame_id = 0 - while frame_id in range(num_frames): - start = random.choice(range(min(10,num_frames), min(int(num_frames/2), 70))) - if frame_id+start+5<=num_frames - 1: - ratio[frame_id+start:frame_id+start+5, 0] = [0.5, 0.9, 1.0, 0.9, 0.5] - frame_id = frame_id+start+5 - else: - break - return ratio - -def get_data(first_coeff_path, audio_path, device, ref_eyeblink_coeff_path, still=False, idlemode=False, length_of_audio=False, use_blink=True): - - syncnet_mel_step_size = 16 - fps = 25 - - pic_name = os.path.splitext(os.path.split(first_coeff_path)[-1])[0] - audio_name = os.path.splitext(os.path.split(audio_path)[-1])[0] - - - if idlemode: - num_frames = int(length_of_audio * 25) - indiv_mels = np.zeros((num_frames, 80, 16)) - else: - wav = audio.load_wav(audio_path, 16000) - wav_length, num_frames = parse_audio_length(len(wav), 16000, 25) - wav = crop_pad_audio(wav, wav_length) - orig_mel = audio.melspectrogram(wav).T - spec = orig_mel.copy() # nframes 80 - indiv_mels = [] - - for i in tqdm(range(num_frames), 'mel:'): - start_frame_num = i-2 - start_idx = int(80. * (start_frame_num / float(fps))) - end_idx = start_idx + syncnet_mel_step_size - seq = list(range(start_idx, end_idx)) - 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\ No newline at end of file diff --git a/spaces/diacanFperku/AutoGPT/Baremo De Honorarios Del Colegio De Abogados De Cadiz ((HOT)).md b/spaces/diacanFperku/AutoGPT/Baremo De Honorarios Del Colegio De Abogados De Cadiz ((HOT)).md deleted file mode 100644 index a051f06b1b575951e430005bf9fb13f058abfd2c..0000000000000000000000000000000000000000 --- a/spaces/diacanFperku/AutoGPT/Baremo De Honorarios Del Colegio De Abogados De Cadiz ((HOT)).md +++ /dev/null @@ -1,40 +0,0 @@ -baremo de honorarios del colegio de abogados de cadiz
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\ No newline at end of file diff --git a/spaces/diacanFperku/AutoGPT/Oswald All New Episodes In Hindi ((FULL)).md b/spaces/diacanFperku/AutoGPT/Oswald All New Episodes In Hindi ((FULL)).md deleted file mode 100644 index 458f31bd0c8a771847e636590da8e3e0352b0a78..0000000000000000000000000000000000000000 --- a/spaces/diacanFperku/AutoGPT/Oswald All New Episodes In Hindi ((FULL)).md +++ /dev/null @@ -1,6 +0,0 @@ -oswald all new episodes in hindi
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- diff --git a/spaces/digitalxingtong/Bufeiyan-c-Bert-VITS2/transforms.py b/spaces/digitalxingtong/Bufeiyan-c-Bert-VITS2/transforms.py deleted file mode 100644 index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Bufeiyan-c-Bert-VITS2/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/digitalxingtong/Nanami-Bert-VITS2/text/english.py b/spaces/digitalxingtong/Nanami-Bert-VITS2/text/english.py deleted file mode 100644 index 781d0a56cef71f66fc67db51d76538be90d3ddd2..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Nanami-Bert-VITS2/text/english.py +++ /dev/null @@ -1,138 +0,0 @@ -import pickle -import os -import re -from g2p_en import G2p -from string import punctuation - -from text import symbols - -current_file_path = os.path.dirname(__file__) -CMU_DICT_PATH = os.path.join(current_file_path, 'cmudict.rep') -CACHE_PATH = os.path.join(current_file_path, 'cmudict_cache.pickle') -_g2p = G2p() - -arpa = {'AH0', 'S', 'AH1', 'EY2', 'AE2', 'EH0', 'OW2', 'UH0', 'NG', 'B', 'G', 'AY0', 'M', 'AA0', 'F', 'AO0', 'ER2', 'UH1', 'IY1', 'AH2', 'DH', 'IY0', 'EY1', 'IH0', 'K', 'N', 'W', 'IY2', 'T', 'AA1', 'ER1', 'EH2', 'OY0', 'UH2', 'UW1', 'Z', 'AW2', 'AW1', 'V', 'UW2', 'AA2', 'ER', 'AW0', 'UW0', 'R', 'OW1', 'EH1', 'ZH', 'AE0', 'IH2', 'IH', 'Y', 'JH', 'P', 'AY1', 'EY0', 'OY2', 'TH', 'HH', 'D', 'ER0', 'CH', 'AO1', 'AE1', 'AO2', 'OY1', 'AY2', 'IH1', 'OW0', 'L', 'SH'} - - -def post_replace_ph(ph): - rep_map = { - '：': ',', - '；': ',', - '，': ',', - '。': '.', - '！': '!', - '？': '?', - '\n': '.', - "·": ",", - '、': ",", - '...': '…', - 'v': "V" - } - if ph in rep_map.keys(): - ph = rep_map[ph] - if ph in symbols: - return ph - if ph not in symbols: - ph = 'UNK' - return ph - -def read_dict(): - g2p_dict = {} - start_line = 49 - with open(CMU_DICT_PATH) as f: - line = f.readline() - line_index = 1 - while line: - if line_index >= start_line: - line = line.strip() - word_split = line.split(' ') - word = word_split[0] - - syllable_split = word_split[1].split(' - ') - g2p_dict[word] = [] - for syllable in syllable_split: - phone_split = syllable.split(' ') - g2p_dict[word].append(phone_split) - - line_index = line_index + 1 - line = f.readline() - - return g2p_dict - - -def cache_dict(g2p_dict, file_path): - with open(file_path, 'wb') as pickle_file: - pickle.dump(g2p_dict, pickle_file) - - -def get_dict(): - if os.path.exists(CACHE_PATH): - with open(CACHE_PATH, 'rb') as pickle_file: - g2p_dict = pickle.load(pickle_file) - else: - g2p_dict = read_dict() - cache_dict(g2p_dict, CACHE_PATH) - - return g2p_dict - -eng_dict = get_dict() - -def refine_ph(phn): - tone = 0 - if re.search(r'\d$', phn): - tone = int(phn[-1]) + 1 - phn = phn[:-1] - return phn.lower(), tone - -def refine_syllables(syllables): - tones = [] - phonemes = [] - for phn_list in syllables: - for i in range(len(phn_list)): - phn = phn_list[i] - phn, tone = refine_ph(phn) - phonemes.append(phn) - tones.append(tone) - return phonemes, tones - - -def text_normalize(text): - # todo: eng text normalize - return text - -def g2p(text): - - phones = [] - tones = [] - words = re.split(r"([,;.\-\?\!\s+])", text) - for w in words: - if w.upper() in eng_dict: - phns, tns = refine_syllables(eng_dict[w.upper()]) - phones += phns - tones += tns - else: - phone_list = list(filter(lambda p: p != " ", _g2p(w))) - for ph in phone_list: - if ph in arpa: - ph, tn = refine_ph(ph) - phones.append(ph) - tones.append(tn) - else: - phones.append(ph) - tones.append(0) - # todo: implement word2ph - word2ph = [1 for i in phones] - - phones = [post_replace_ph(i) for i in phones] - return phones, tones, word2ph - -if __name__ == "__main__": - # print(get_dict()) - # print(eng_word_to_phoneme("hello")) - print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder.")) - # all_phones = set() - # for k, syllables in eng_dict.items(): - # for group in syllables: - # for ph in group: - # all_phones.add(ph) - # print(all_phones) \ No newline at end of file diff --git a/spaces/digitalxingtong/Xingtong-Longread-Dongmuchang-Bert-VITS2/train_ms.py b/spaces/digitalxingtong/Xingtong-Longread-Dongmuchang-Bert-VITS2/train_ms.py deleted file mode 100644 index 5d109003d40497ea4493e7c73f47c1eb7370a81e..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Xingtong-Longread-Dongmuchang-Bert-VITS2/train_ms.py +++ /dev/null @@ -1,402 +0,0 @@ -import os -import json -import argparse -import itertools -import math -import torch -import shutil -from torch import nn, optim -from torch.nn import functional as F -from torch.utils.data import DataLoader -from torch.utils.tensorboard import SummaryWriter -import torch.multiprocessing as mp -import torch.distributed as dist -from torch.nn.parallel import DistributedDataParallel as DDP -from torch.cuda.amp import autocast, GradScaler -from tqdm import tqdm -import logging -logging.getLogger('numba').setLevel(logging.WARNING) -import commons -import utils -from data_utils import ( - TextAudioSpeakerLoader, - TextAudioSpeakerCollate, - DistributedBucketSampler -) -from models import ( - SynthesizerTrn, - MultiPeriodDiscriminator, - DurationDiscriminator, -) -from losses import ( - generator_loss, - discriminator_loss, - feature_loss, - kl_loss -) -from mel_processing import mel_spectrogram_torch, spec_to_mel_torch -from text.symbols import symbols - -torch.backends.cudnn.benchmark = True -torch.backends.cuda.matmul.allow_tf32 = True -torch.backends.cudnn.allow_tf32 = True -torch.set_float32_matmul_precision('medium') -global_step = 0 - - -def main(): - """Assume Single Node Multi GPUs Training Only""" - assert torch.cuda.is_available(), "CPU training is not allowed." - - n_gpus = torch.cuda.device_count() - os.environ['MASTER_ADDR'] = 'localhost' - os.environ['MASTER_PORT'] = '65280' - - hps = utils.get_hparams() - if not hps.cont: - shutil.copy('./pretrained_models/D_0.pth','./logs/OUTPUT_MODEL/D_0.pth') - shutil.copy('./pretrained_models/G_0.pth','./logs/OUTPUT_MODEL/G_0.pth') - shutil.copy('./pretrained_models/DUR_0.pth','./logs/OUTPUT_MODEL/DUR_0.pth') - mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,)) - - -def run(rank, n_gpus, hps): - global global_step - if rank == 0: - logger = utils.get_logger(hps.model_dir) - logger.info(hps) - utils.check_git_hash(hps.model_dir) - writer = SummaryWriter(log_dir=hps.model_dir) - writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval")) - - dist.init_process_group(backend= 'gloo' if os.name == 'nt' else 'nccl', init_method='env://', world_size=n_gpus, rank=rank) - torch.manual_seed(hps.train.seed) - torch.cuda.set_device(rank) - - train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data) - train_sampler = DistributedBucketSampler( - train_dataset, - hps.train.batch_size, - [32, 300, 400, 500, 600, 700, 800, 900, 1000], - num_replicas=n_gpus, - rank=rank, - shuffle=True) - collate_fn = TextAudioSpeakerCollate() - train_loader = DataLoader(train_dataset, num_workers=2, shuffle=False, pin_memory=True, - collate_fn=collate_fn, batch_sampler=train_sampler) - if rank == 0: - eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data) - eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False, - batch_size=1, pin_memory=True, - drop_last=False, collate_fn=collate_fn) - if "use_noise_scaled_mas" in hps.model.keys() and hps.model.use_noise_scaled_mas == True: - print("Using noise scaled MAS for VITS2") - use_noise_scaled_mas = True - mas_noise_scale_initial = 0.01 - noise_scale_delta = 2e-6 - else: - print("Using normal MAS for VITS1") - use_noise_scaled_mas = False - mas_noise_scale_initial = 0.0 - noise_scale_delta = 0.0 - if "use_duration_discriminator" in hps.model.keys() and hps.model.use_duration_discriminator == True: - print("Using duration discriminator for VITS2") - use_duration_discriminator = True - net_dur_disc = DurationDiscriminator( - hps.model.hidden_channels, - hps.model.hidden_channels, - 3, - 0.1, - gin_channels=hps.model.gin_channels if hps.data.n_speakers != 0 else 0, - ).cuda(rank) - if "use_spk_conditioned_encoder" in hps.model.keys() and hps.model.use_spk_conditioned_encoder == True: - if hps.data.n_speakers == 0: - raise ValueError("n_speakers must be > 0 when using spk conditioned encoder to train multi-speaker model") - use_spk_conditioned_encoder = True - else: - print("Using normal encoder for VITS1") - use_spk_conditioned_encoder = False - - net_g = SynthesizerTrn( - len(symbols), - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - n_speakers=hps.data.n_speakers, - mas_noise_scale_initial = mas_noise_scale_initial, - noise_scale_delta = noise_scale_delta, - **hps.model).cuda(rank) - - freeze_enc = getattr(hps.model, "freeze_enc", False) - if freeze_enc: - print("freeze encoder !!!") - for param in net_g.enc_p.parameters(): - param.requires_grad = False - - net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) - optim_g = torch.optim.AdamW( - filter(lambda p: p.requires_grad, net_g.parameters()), - hps.train.learning_rate, - betas=hps.train.betas, - eps=hps.train.eps) - optim_d = torch.optim.AdamW( - net_d.parameters(), - hps.train.learning_rate, - betas=hps.train.betas, - eps=hps.train.eps) - if net_dur_disc is not None: - optim_dur_disc = torch.optim.AdamW( - net_dur_disc.parameters(), - hps.train.learning_rate, - betas=hps.train.betas, - eps=hps.train.eps) - else: - optim_dur_disc = None - net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True) - net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True) - if net_dur_disc is not None: - net_dur_disc = DDP(net_dur_disc, device_ids=[rank], find_unused_parameters=True) - - pretrain_dir = None - if pretrain_dir is None: - try: - if net_dur_disc is not None: - _, optim_dur_disc, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "DUR_*.pth"), net_dur_disc, optim_dur_disc, skip_optimizer=not hps.cont) - _, optim_g, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, - optim_g, skip_optimizer=not hps.cont) - _, optim_d, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d, - optim_d, skip_optimizer=not hps.cont) - - epoch_str = max(epoch_str, 1) - global_step = (epoch_str - 1) * len(train_loader) - except Exception as e: - print(e) - epoch_str = 1 - global_step = 0 - else: - _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "G_*.pth"), net_g, - optim_g, True) - _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(pretrain_dir, "D_*.pth"), net_d, - optim_d, True) - - - - scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2) - scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2) - if net_dur_disc is not None: - scheduler_dur_disc = torch.optim.lr_scheduler.ExponentialLR(optim_dur_disc, gamma=hps.train.lr_decay, last_epoch=epoch_str-2) - else: - scheduler_dur_disc = None - scaler = GradScaler(enabled=hps.train.fp16_run) - - for epoch in range(epoch_str, hps.train.epochs + 1): - if rank == 0: - train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, eval_loader], logger, [writer, writer_eval]) - else: - train_and_evaluate(rank, epoch, hps, [net_g, net_d, net_dur_disc], [optim_g, optim_d, optim_dur_disc], [scheduler_g, scheduler_d, scheduler_dur_disc], scaler, [train_loader, None], None, None) - scheduler_g.step() - scheduler_d.step() - if net_dur_disc is not None: - scheduler_dur_disc.step() - - -def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers): - net_g, net_d, net_dur_disc = nets - optim_g, optim_d, optim_dur_disc = optims - scheduler_g, scheduler_d, scheduler_dur_disc = schedulers - train_loader, eval_loader = loaders - if writers is not None: - writer, writer_eval = writers - - train_loader.batch_sampler.set_epoch(epoch) - global global_step - - net_g.train() - net_d.train() - if net_dur_disc is not None: - net_dur_disc.train() - for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in tqdm(enumerate(train_loader)): - if net_g.module.use_noise_scaled_mas: - current_mas_noise_scale = net_g.module.mas_noise_scale_initial - net_g.module.noise_scale_delta * global_step - net_g.module.current_mas_noise_scale = max(current_mas_noise_scale, 0.0) - x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True) - spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True) - y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True) - speakers = speakers.cuda(rank, non_blocking=True) - tone = tone.cuda(rank, non_blocking=True) - language = language.cuda(rank, non_blocking=True) - bert = bert.cuda(rank, non_blocking=True) - - with autocast(enabled=hps.train.fp16_run): - y_hat, l_length, attn, ids_slice, x_mask, z_mask, \ - (z, z_p, m_p, logs_p, m_q, logs_q), (hidden_x, logw, logw_) = net_g(x, x_lengths, spec, spec_lengths, speakers, tone, language, bert) - mel = spec_to_mel_torch( - spec, - hps.data.filter_length, - hps.data.n_mel_channels, - hps.data.sampling_rate, - hps.data.mel_fmin, - hps.data.mel_fmax) - y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length) - y_hat_mel = mel_spectrogram_torch( - y_hat.squeeze(1), - hps.data.filter_length, - hps.data.n_mel_channels, - hps.data.sampling_rate, - hps.data.hop_length, - hps.data.win_length, - hps.data.mel_fmin, - hps.data.mel_fmax - ) - - y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice - - # Discriminator - y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach()) - with autocast(enabled=False): - loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g) - loss_disc_all = loss_disc - if net_dur_disc is not None: - y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x.detach(), x_mask.detach(), logw.detach(), logw_.detach()) - with autocast(enabled=False): - # TODO: I think need to mean using the mask, but for now, just mean all - loss_dur_disc, losses_dur_disc_r, losses_dur_disc_g = discriminator_loss(y_dur_hat_r, y_dur_hat_g) - loss_dur_disc_all = loss_dur_disc - optim_dur_disc.zero_grad() - scaler.scale(loss_dur_disc_all).backward() - scaler.unscale_(optim_dur_disc) - grad_norm_dur_disc = commons.clip_grad_value_(net_dur_disc.parameters(), None) - scaler.step(optim_dur_disc) - - optim_d.zero_grad() - scaler.scale(loss_disc_all).backward() - scaler.unscale_(optim_d) - grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None) - scaler.step(optim_d) - - with autocast(enabled=hps.train.fp16_run): - # Generator - y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat) - if net_dur_disc is not None: - y_dur_hat_r, y_dur_hat_g = net_dur_disc(hidden_x, x_mask, logw, logw_) - with autocast(enabled=False): - loss_dur = torch.sum(l_length.float()) - loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel - loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl - - loss_fm = feature_loss(fmap_r, fmap_g) - loss_gen, losses_gen = generator_loss(y_d_hat_g) - loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl - if net_dur_disc is not None: - loss_dur_gen, losses_dur_gen = generator_loss(y_dur_hat_g) - loss_gen_all += loss_dur_gen - optim_g.zero_grad() - scaler.scale(loss_gen_all).backward() - scaler.unscale_(optim_g) - grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None) - scaler.step(optim_g) - scaler.update() - - if rank == 0: - if global_step % hps.train.log_interval == 0: - lr = optim_g.param_groups[0]['lr'] - losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl] - logger.info('Train Epoch: {} [{:.0f}%]'.format( - epoch, - 100. * batch_idx / len(train_loader))) - logger.info([x.item() for x in losses] + [global_step, lr]) - - scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr, - "grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g} - scalar_dict.update( - {"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl}) - scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)}) - scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)}) - scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)}) - - image_dict = { - "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()), - "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()), - "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()), - "all/attn": utils.plot_alignment_to_numpy(attn[0, 0].data.cpu().numpy()) - } - utils.summarize( - writer=writer, - global_step=global_step, - images=image_dict, - scalars=scalar_dict) - - if global_step % hps.train.eval_interval == 0: - evaluate(hps, net_g, eval_loader, writer_eval) - utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch, - os.path.join(hps.model_dir, "G_{}.pth".format(global_step))) - utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch, - os.path.join(hps.model_dir, "D_{}.pth".format(global_step))) - if net_dur_disc is not None: - utils.save_checkpoint(net_dur_disc, optim_dur_disc, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "DUR_{}.pth".format(global_step))) - keep_ckpts = getattr(hps.train, 'keep_ckpts', 5) - if keep_ckpts > 0: - utils.clean_checkpoints(path_to_models=hps.model_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True) - - - global_step += 1 - - if rank == 0: - logger.info('====> Epoch: {}'.format(epoch)) - - - -def evaluate(hps, generator, eval_loader, writer_eval): - generator.eval() - image_dict = {} - audio_dict = {} - print("Evaluating ...") - with torch.no_grad(): - for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers, tone, language, bert) in enumerate(eval_loader): - x, x_lengths = x.cuda(), x_lengths.cuda() - spec, spec_lengths = spec.cuda(), spec_lengths.cuda() - y, y_lengths = y.cuda(), y_lengths.cuda() - speakers = speakers.cuda() - bert = bert.cuda() - tone = tone.cuda() - language = language.cuda() - for use_sdp in [True, False]: - y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, tone, language, bert, y=spec, max_len=1000, sdp_ratio=0.0 if not use_sdp else 1.0) - y_hat_lengths = mask.sum([1, 2]).long() * hps.data.hop_length - - mel = spec_to_mel_torch( - spec, - hps.data.filter_length, - hps.data.n_mel_channels, - hps.data.sampling_rate, - hps.data.mel_fmin, - hps.data.mel_fmax) - y_hat_mel = mel_spectrogram_torch( - y_hat.squeeze(1).float(), - hps.data.filter_length, - hps.data.n_mel_channels, - hps.data.sampling_rate, - hps.data.hop_length, - hps.data.win_length, - hps.data.mel_fmin, - hps.data.mel_fmax - ) - image_dict.update({ - f"gen/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()) - }) - audio_dict.update({ - f"gen/audio_{batch_idx}_{use_sdp}": y_hat[0, :, :y_hat_lengths[0]] - }) - image_dict.update({f"gt/mel_{batch_idx}": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())}) - audio_dict.update({f"gt/audio_{batch_idx}": y[0, :, :y_lengths[0]]}) - - utils.summarize( - writer=writer_eval, - global_step=global_step, - images=image_dict, - audios=audio_dict, - audio_sampling_rate=hps.data.sampling_rate - ) - generator.train() - -if __name__ == "__main__": - main() diff --git a/spaces/dineshreddy/WALT/mmdet/core/export/pytorch2onnx.py b/spaces/dineshreddy/WALT/mmdet/core/export/pytorch2onnx.py deleted file mode 100644 index 809a817e67446b3c0c7894dcefb3c4bbc29afb7e..0000000000000000000000000000000000000000 --- a/spaces/dineshreddy/WALT/mmdet/core/export/pytorch2onnx.py +++ /dev/null @@ -1,154 +0,0 @@ -from functools import partial - -import mmcv -import numpy as np -import torch -from mmcv.runner import load_checkpoint - - -def generate_inputs_and_wrap_model(config_path, - checkpoint_path, - input_config, - cfg_options=None): - """Prepare sample input and wrap model for ONNX export. - - The ONNX export API only accept args, and all inputs should be - torch.Tensor or corresponding types (such as tuple of tensor). - So we should call this function before exporting. This function will: - - 1. generate corresponding inputs which are used to execute the model. - 2. Wrap the model's forward function. - - For example, the MMDet models' forward function has a parameter - ``return_loss:bool``. As we want to set it as False while export API - supports neither bool type or kwargs. So we have to replace the forward - like: ``model.forward = partial(model.forward, return_loss=False)`` - - Args: - config_path (str): the OpenMMLab config for the model we want to - export to ONNX - checkpoint_path (str): Path to the corresponding checkpoint - input_config (dict): the exactly data in this dict depends on the - framework. For MMSeg, we can just declare the input shape, - and generate the dummy data accordingly. However, for MMDet, - we may pass the real img path, or the NMS will return None - as there is no legal bbox. - - Returns: - tuple: (model, tensor_data) wrapped model which can be called by \ - model(*tensor_data) and a list of inputs which are used to execute \ - the model while exporting. - """ - - model = build_model_from_cfg( - config_path, checkpoint_path, cfg_options=cfg_options) - one_img, one_meta = preprocess_example_input(input_config) - tensor_data = [one_img] - model.forward = partial( - model.forward, img_metas=[[one_meta]], return_loss=False) - - # pytorch has some bug in pytorch1.3, we have to fix it - # by replacing these existing op - opset_version = 11 - # put the import within the function thus it will not cause import error - # when not using this function - try: - from mmcv.onnx.symbolic import register_extra_symbolics - except ModuleNotFoundError: - raise NotImplementedError('please update mmcv to version>=v1.0.4') - register_extra_symbolics(opset_version) - - return model, tensor_data - - -def build_model_from_cfg(config_path, checkpoint_path, cfg_options=None): - """Build a model from config and load the given checkpoint. - - Args: - config_path (str): the OpenMMLab config for the model we want to - export to ONNX - checkpoint_path (str): Path to the corresponding checkpoint - - Returns: - torch.nn.Module: the built model - """ - from mmdet.models import build_detector - - cfg = mmcv.Config.fromfile(config_path) - if cfg_options is not None: - cfg.merge_from_dict(cfg_options) - # import modules from string list. - if cfg.get('custom_imports', None): - from mmcv.utils import import_modules_from_strings - import_modules_from_strings(**cfg['custom_imports']) - # set cudnn_benchmark - if cfg.get('cudnn_benchmark', False): - torch.backends.cudnn.benchmark = True - cfg.model.pretrained = None - cfg.data.test.test_mode = True - - # build the model - cfg.model.train_cfg = None - model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg')) - load_checkpoint(model, checkpoint_path, map_location='cpu') - model.cpu().eval() - return model - - -def preprocess_example_input(input_config): - """Prepare an example input image for ``generate_inputs_and_wrap_model``. - - Args: - input_config (dict): customized config describing the example input. - - Returns: - tuple: (one_img, one_meta), tensor of the example input image and \ - meta information for the example input image. - - Examples: - >>> from mmdet.core.export import preprocess_example_input - >>> input_config = { - >>> 'input_shape': (1,3,224,224), - >>> 'input_path': 'demo/demo.jpg', - >>> 'normalize_cfg': { - >>> 'mean': (123.675, 116.28, 103.53), - >>> 'std': (58.395, 57.12, 57.375) - >>> } - >>> } - >>> one_img, one_meta = preprocess_example_input(input_config) - >>> print(one_img.shape) - torch.Size([1, 3, 224, 224]) - >>> print(one_meta) - {'img_shape': (224, 224, 3), - 'ori_shape': (224, 224, 3), - 'pad_shape': (224, 224, 3), - 'filename': '.png', - 'scale_factor': 1.0, - 'flip': False} - """ - input_path = input_config['input_path'] - input_shape = input_config['input_shape'] - one_img = mmcv.imread(input_path) - one_img = mmcv.imresize(one_img, input_shape[2:][::-1]) - show_img = one_img.copy() - if 'normalize_cfg' in input_config.keys(): - normalize_cfg = input_config['normalize_cfg'] - mean = np.array(normalize_cfg['mean'], dtype=np.float32) - std = np.array(normalize_cfg['std'], dtype=np.float32) - to_rgb = normalize_cfg.get('to_rgb', True) - one_img = mmcv.imnormalize(one_img, mean, std, to_rgb=to_rgb) - one_img = one_img.transpose(2, 0, 1) - one_img = torch.from_numpy(one_img).unsqueeze(0).float().requires_grad_( - True) - (_, C, H, W) = input_shape - one_meta = { - 'img_shape': (H, W, C), - 'ori_shape': (H, W, C), - 'pad_shape': (H, W, C), - 'filename': ' .png', - 'scale_factor': 1.0, - 'flip': False, - 'show_img': show_img, - } - - return one_img, one_meta diff --git a/spaces/djsull/aha-multi-label/app.py b/spaces/djsull/aha-multi-label/app.py deleted file mode 100644 index 9349236137fb20186ff86111f8a1985516372846..0000000000000000000000000000000000000000 --- a/spaces/djsull/aha-multi-label/app.py +++ /dev/null @@ -1,40 +0,0 @@ -import gradio as gr -import re -import torch -from transformers import pipeline -import os - - -AUTH_TOKEN = os.environ["AUTH_TOKEN"] -cate_classifier = pipeline('text-classification', - model="djsull/kobigbird-cate-class-finder", - use_auth_token=AUTH_TOKEN, - return_all_scores=True, - function_to_apply='softmax', - ) - -def predict(text): - query = text - cleanr = re.compile('<.*?>') - query = re.sub(cleanr, '', query) - query = ' '.join(re.sub('[^가-힣a-zA-Z0-9 ]', ' ', query).split()) - result = cate_classifier(text)[0] - - ress = {} - ch = 0 - chch = 0 - for i in range(len(result)): - if result[i]['score'] >= ch: - ch = result[i]['score'] - chch = i - text_tmp = result[chch]["label"] - ress[text_tmp] = int(result[chch]["score"] * 10000) / 100 - - return ress - -gr.Interface( - predict, - inputs=gr.inputs.Textbox(label="Type anything"), - outputs=gr.outputs.Textbox(label="labels"), - title="Single-label Category classification", -).launch() \ No newline at end of file diff --git a/spaces/dorkai/SINGPT-Temporary/modules/deepspeed_parameters.py b/spaces/dorkai/SINGPT-Temporary/modules/deepspeed_parameters.py deleted file mode 100644 index 3dbed437f5b5196d0b1fcbc582085319fb8d40d1..0000000000000000000000000000000000000000 --- a/spaces/dorkai/SINGPT-Temporary/modules/deepspeed_parameters.py +++ /dev/null @@ -1,75 +0,0 @@ -def generate_ds_config(ds_bf16, train_batch_size, nvme_offload_dir): - - ''' - DeepSpeed configration - https://huggingface.co/docs/transformers/main_classes/deepspeed - ''' - - if nvme_offload_dir: - ds_config = { - "fp16": { - "enabled": not ds_bf16, - }, - "bf16": { - "enabled": ds_bf16, - }, - "zero_optimization": { - "stage": 3, - "offload_param": { - "device": "nvme", - "nvme_path": nvme_offload_dir, - "pin_memory": True, - "buffer_count": 5, - "buffer_size": 1e9, - "max_in_cpu": 1e9 - }, - "overlap_comm": True, - "reduce_bucket_size": "auto", - "contiguous_gradients": True, - "sub_group_size": 1e8, - "stage3_prefetch_bucket_size": "auto", - "stage3_param_persistence_threshold": "auto", - "stage3_max_live_parameters": "auto", - "stage3_max_reuse_distance": "auto", - }, - "aio": { - "block_size": 262144, - "queue_depth": 32, - "thread_count": 1, - "single_submit": False, - "overlap_events": True - }, - "steps_per_print": 2000, - "train_batch_size": train_batch_size, - "train_micro_batch_size_per_gpu": 1, - "wall_clock_breakdown": False - } - else: - ds_config = { - "fp16": { - "enabled": not ds_bf16, - }, - "bf16": { - "enabled": ds_bf16, - }, - "zero_optimization": { - "stage": 3, - "offload_param": { - "device": "cpu", - "pin_memory": True - }, - "overlap_comm": True, - "contiguous_gradients": True, - "reduce_bucket_size": "auto", - "stage3_prefetch_bucket_size": "auto", - "stage3_param_persistence_threshold": "auto", - "stage3_max_live_parameters": "auto", - "stage3_max_reuse_distance": "auto", - }, - "steps_per_print": 2000, - "train_batch_size": train_batch_size, - "train_micro_batch_size_per_gpu": 1, - "wall_clock_breakdown": False - } - - return ds_config diff --git a/spaces/dwarkesh/whisper-speaker-recognition/README.md b/spaces/dwarkesh/whisper-speaker-recognition/README.md deleted file mode 100644 index 112f6836168e3384d0a2155daf8b4744b37fe3da..0000000000000000000000000000000000000000 --- a/spaces/dwarkesh/whisper-speaker-recognition/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Whisper Speaker Recognition -emoji: 🌖 -colorFrom: gray -colorTo: indigo -sdk: gradio -sdk_version: 3.12.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/edvanger/White-box-Cartoonization/wbc/guided_filter.py b/spaces/edvanger/White-box-Cartoonization/wbc/guided_filter.py deleted file mode 100644 index fd019d145efc7f308cd96de90f4e7b648f6820b4..0000000000000000000000000000000000000000 --- a/spaces/edvanger/White-box-Cartoonization/wbc/guided_filter.py +++ /dev/null @@ -1,87 +0,0 @@ -import tensorflow as tf -import numpy as np - - - - -def tf_box_filter(x, r): - k_size = int(2*r+1) - ch = x.get_shape().as_list()[-1] - weight = 1/(k_size**2) - box_kernel = weight*np.ones((k_size, k_size, ch, 1)) - box_kernel = np.array(box_kernel).astype(np.float32) - output = tf.nn.depthwise_conv2d(x, box_kernel, [1, 1, 1, 1], 'SAME') - return output - - - -def guided_filter(x, y, r, eps=1e-2): - - x_shape = tf.shape(x) - #y_shape = tf.shape(y) - - N = tf_box_filter(tf.ones((1, x_shape[1], x_shape[2], 1), dtype=x.dtype), r) - - mean_x = tf_box_filter(x, r) / N - mean_y = tf_box_filter(y, r) / N - cov_xy = tf_box_filter(x * y, r) / N - mean_x * mean_y - var_x = tf_box_filter(x * x, r) / N - mean_x * mean_x - - A = cov_xy / (var_x + eps) - b = mean_y - A * mean_x - - mean_A = tf_box_filter(A, r) / N - mean_b = tf_box_filter(b, r) / N - - output = mean_A * x + mean_b - - return output - - - -def fast_guided_filter(lr_x, lr_y, hr_x, r=1, eps=1e-8): - - #assert lr_x.shape.ndims == 4 and lr_y.shape.ndims == 4 and hr_x.shape.ndims == 4 - - lr_x_shape = tf.shape(lr_x) - #lr_y_shape = tf.shape(lr_y) - hr_x_shape = tf.shape(hr_x) - - N = tf_box_filter(tf.ones((1, lr_x_shape[1], lr_x_shape[2], 1), dtype=lr_x.dtype), r) - - mean_x = tf_box_filter(lr_x, r) / N - mean_y = tf_box_filter(lr_y, r) / N - cov_xy = tf_box_filter(lr_x * lr_y, r) / N - mean_x * mean_y - var_x = tf_box_filter(lr_x * lr_x, r) / N - mean_x * mean_x - - A = cov_xy / (var_x + eps) - b = mean_y - A * mean_x - - mean_A = tf.image.resize_images(A, hr_x_shape[1: 3]) - mean_b = tf.image.resize_images(b, hr_x_shape[1: 3]) - - output = mean_A * hr_x + mean_b - - return output - - -if __name__ == '__main__': - import cv2 - from tqdm import 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-__webpack_require__.r(__webpack_exports__); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "emptyTokenDisplay", function() { return emptyTokenDisplay; }); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "API", function() { return API; }); -/* harmony import */ var d3__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! d3 */ "./node_modules/d3/index.js"); -/* harmony import */ var _data_TokenWrapper__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../data/TokenWrapper */ "./ts/data/TokenWrapper.ts"); -/* harmony import */ var ramda__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ramda */ "./node_modules/ramda/es/index.js"); -/* harmony import */ var _demoAPI__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ./demoAPI */ "./ts/api/demoAPI.ts"); -/* harmony import */ var object_hash__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! object-hash */ "./node_modules/object-hash/dist/object_hash.js"); -/* harmony import */ var object_hash__WEBPACK_IMPORTED_MODULE_4___default = /*#__PURE__*/__webpack_require__.n(object_hash__WEBPACK_IMPORTED_MODULE_4__); -/* harmony import */ var _etc_apiHelpers__WEBPACK_IMPORTED_MODULE_5__ = __webpack_require__(/*! ../etc/apiHelpers */ "./ts/etc/apiHelpers.ts"); -/* harmony import */ var _etc_URLHandler__WEBPACK_IMPORTED_MODULE_6__ = __webpack_require__(/*! ../etc/URLHandler */ "./ts/etc/URLHandler.ts"); - - - - - - - -const emptyTokenDisplay = new _data_TokenWrapper__WEBPACK_IMPORTED_MODULE_1__["TokenDisplay"](); -const baseurl = _etc_URLHandler__WEBPACK_IMPORTED_MODULE_6__["URLHandler"].basicURL(); -/** - * A rewrite of `d3-fetch`'s `d3.json` callback. If an api call fails, make a backup call to specified url and payload, if specified. - * - * @param response Object expected at time of callback - * @param backupUrl Backup url in the event of fail - * @param backupPayload Backup payload if making a post request - */ -function responseJson(response, backupUrl = null, backupPayload = null) { - if (!response.ok) { - if (backupUrl != null) { - console.log("STATIC FILE NOT FOUND"); - return fetch(backupUrl, backupPayload).then(responseJson); - } - throw new Error(response.status + " " + response.statusText); - } - return response.json(); -} -/** - * Check first if the information being sent exists in a static demo file. If it does, send that. Otherwise, make a normal call to the server. - * - * @param toSend The packet of information to send to an API endpoint - * @param backupUrl Backup url in the event that the demo file is not found - * @param backupPayload Backup payload if demo file not found, for POST requests only - */ -function checkDemoAPI(toSend, backupUrl = null, backupPayload = null) { - const hsh = object_hash__WEBPACK_IMPORTED_MODULE_4__["sha1"](toSend); - console.log("CHECKING DEMOAPI: " + hsh); - if (_demoAPI__WEBPACK_IMPORTED_MODULE_3__["DemoAPI"].hasOwnProperty(hsh)) { - // Relies on a symbolic link being present in the dist folder to the demo folder - const path = './demo/' + _demoAPI__WEBPACK_IMPORTED_MODULE_3__["DemoAPI"][hsh]; - console.log("TRYING TO SENDING STATIC: ", path); - const follow = (response) => responseJson(response, backupUrl, backupPayload); - return fetch(path).then(follow); - } - return d3__WEBPACK_IMPORTED_MODULE_0__["json"](backupUrl, backupPayload); -} -class API { - constructor(baseURL = null) { - this.baseURL = baseURL; - if (this.baseURL == null) { - this.baseURL = baseurl + '/api'; - } - } - getModelDetails(model, hashObj = null) { - const toSend = { - model: model - }; - const url = Object(_etc_apiHelpers__WEBPACK_IMPORTED_MODULE_5__["makeUrl"])(this.baseURL + "/get-model-details", toSend); - console.log("--- GET " + url); - if (hashObj != null) { - const key = object_hash__WEBPACK_IMPORTED_MODULE_4__["sha1"](toSend); - d3__WEBPACK_IMPORTED_MODULE_0__["json"](url).then(r => { - hashObj[key] = r; - }); - } - return checkDemoAPI(toSend, url); - } - getMetaAttentions(model, sentence, layer, hashObj = null) { - const toSend = { - model: model, - sentence: sentence, - layer: layer - }; - const url = Object(_etc_apiHelpers__WEBPACK_IMPORTED_MODULE_5__["makeUrl"])(this.baseURL + "/attend+meta", toSend); - console.log("--- GET " + url); - // Add hash and value to hashObj - if (hashObj != null) { - const key = object_hash__WEBPACK_IMPORTED_MODULE_4__["sha1"](toSend); - d3__WEBPACK_IMPORTED_MODULE_0__["json"](url).then(r => { - hashObj[key] = r; - }); - } - return checkDemoAPI(toSend, url); - } - /** - * Update the display based on the information that was already parsed from the passed sentence. - * - * @param a The displayed tokens in the columns - * @param sentenceA The original sentence that led to the tokenized information in `a` - * @param layer Which layer to search at - * @param hashObj If not null, store the information of the responses into the passed object. Used for creating demos. - */ - updateMaskedAttentions(model, tokens, sentence, layer, hashObj = null) { - const toSend = { - model: model, - tokens: ramda__WEBPACK_IMPORTED_MODULE_2__["map"](ramda__WEBPACK_IMPORTED_MODULE_2__["prop"]('text'), tokens.tokenData), - sentence: sentence, - // Empty masks need to be sent as a number, unfortunately. Choosing -1 for this - mask: tokens.maskInds.length ? tokens.maskInds : [-1], - layer: layer, - }; - const url = Object(_etc_apiHelpers__WEBPACK_IMPORTED_MODULE_5__["makeUrl"])(this.baseURL + '/update-mask'); - const payload = Object(_etc_apiHelpers__WEBPACK_IMPORTED_MODULE_5__["toPayload"])(toSend); - if (hashObj != null) { - // Add hash and value to hashObj for demo purposes - const key = object_hash__WEBPACK_IMPORTED_MODULE_4__["sha1"](toSend); - d3__WEBPACK_IMPORTED_MODULE_0__["json"](url, payload).then(r => { - hashObj[key] = r; - }); - } - console.log("--- POST " + url, payload); - return checkDemoAPI(toSend, url, payload); - } -} -; - - -/***/ }), - -/***/ "./ts/data/AttentionCapsule.ts": -/*!*************************************!*\ - !*** ./ts/data/AttentionCapsule.ts ***! - \*************************************/ -/*! exports provided: makeFromMetaResponse, AttentionWrapper */ -/***/ (function(module, __webpack_exports__, __webpack_require__) { - -"use strict"; -__webpack_require__.r(__webpack_exports__); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "makeFromMetaResponse", function() { return makeFromMetaResponse; }); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "AttentionWrapper", function() { return AttentionWrapper; }); -/* harmony import */ var lodash__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! lodash */ "./node_modules/lodash/lodash.js"); -/* harmony import */ var lodash__WEBPACK_IMPORTED_MODULE_0___default = /*#__PURE__*/__webpack_require__.n(lodash__WEBPACK_IMPORTED_MODULE_0__); -/* harmony import */ var _etc_Tools__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ../etc/_Tools */ "./ts/etc/_Tools.ts"); -/* harmony import */ var _tensorflow_tfjs__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! @tensorflow/tfjs */ "./node_modules/@tensorflow/tfjs/dist/tf.esm.js"); - - - -/** - * Notes: - * - * - Also encapsulate the CLS/SEP info vs. no CLS/SEP info - * - When layer format changes from list, drop the index into conf.layer - */ -const bpeTokens = ["[CLS]", "[SEP]", " ", "", "<|endoftext|>"]; -const findBadIndexes = (x) => _etc_Tools__WEBPACK_IMPORTED_MODULE_1__["findAllIndexes"](x.map(t => t.text), (a) => lodash__WEBPACK_IMPORTED_MODULE_0__["includes"](bpeTokens, a)); -function makeFromMetaResponse(r, isZeroed) { - const key = 'aa'; // Change this if backend response changes to be simpler - const currPair = r[key]; - const left = currPair.left; - const right = currPair.right; - const leftZero = _etc_Tools__WEBPACK_IMPORTED_MODULE_1__["findAllIndexes"](left.map(t => t.text), (a) => lodash__WEBPACK_IMPORTED_MODULE_0__["includes"](bpeTokens, a)); - const rightZero = _etc_Tools__WEBPACK_IMPORTED_MODULE_1__["findAllIndexes"](right.map(t => t.text), (a) => lodash__WEBPACK_IMPORTED_MODULE_0__["includes"](bpeTokens, a)); - return new AttentionWrapper(currPair.att, [leftZero, rightZero], isZeroed); -} -class AttentionWrapper { - constructor(att, badToks = [[], []], isZeroed = true) { - this.nLayers = 12; - this.nHeads = 12; - this.init(att, badToks, isZeroed); - } - init(att, badToks = [[], []], isZeroed) { - this.isZeroed = isZeroed; - this._att = att; - this._zeroedAttTensor = zeroRowCol(_tensorflow_tfjs__WEBPACK_IMPORTED_MODULE_2__["tensor3d"](att), badToks[0], badToks[1]); - this._attTensor = _tensorflow_tfjs__WEBPACK_IMPORTED_MODULE_2__["tensor3d"](att); // If I put this first, buffer modifications change this too. - this.badToks = badToks; - } - updateFromNormal(r, isZeroed) { - const key = 'aa'; // Change this if backend response changes to be simpler - const currPair = r[key]; - const left = currPair.left; - const right = currPair.right; - const leftZero = findBadIndexes(left); - const rightZero = findBadIndexes(right); - this.init(currPair.att, [leftZero, rightZero], isZeroed); - } - get attTensor() { - const tens = this.isZeroed ? this._zeroedAttTensor : this._attTensor; - return tens; - } - get att() { - return this.attTensor.arraySync(); - } - zeroed(val) { - if (val == null) - return this.isZeroed; - this.isZeroed = val; - return this; - } - toggleZeroing() { - this.zeroed(!this.zeroed()); - } - _byHeads(heads) { - if (heads.length == 0) { - return _tensorflow_tfjs__WEBPACK_IMPORTED_MODULE_2__["zerosLike"](this._byHead(0)); - } - return this.attTensor.gather(heads, 0).sum(0); - } - _byHead(head) { - return this.attTensor.gather([head], 0).squeeze([0]); - } - byHeads(heads) { - return this._byHeads(heads).arraySync(); - } - byHead(head) { - return this._byHead(head).arraySync(); - } -} -function zeroRowCol(tens, rows, cols) { - let outTens = tens.clone(); - let atb = outTens.bufferSync(); - lodash__WEBPACK_IMPORTED_MODULE_0__["range"](atb.shape[0]).forEach((head) => { - lodash__WEBPACK_IMPORTED_MODULE_0__["range"](atb.shape[1]).forEach((i) => { - // Set rows to 0 - if (lodash__WEBPACK_IMPORTED_MODULE_0__["includes"](rows, i)) { - lodash__WEBPACK_IMPORTED_MODULE_0__["range"](atb.shape[2]).forEach((j) => { - atb.set(0, head, i, j); - }); - } - // Set cols to 0 - lodash__WEBPACK_IMPORTED_MODULE_0__["range"](atb.shape[2]).forEach((j) => { - if (lodash__WEBPACK_IMPORTED_MODULE_0__["includes"](cols, j)) - lodash__WEBPACK_IMPORTED_MODULE_0__["range"](atb.shape[1]).forEach((i) => { - atb.set(0, head, i, j); - }); - }); - }); - }); - return outTens; -} - - -/***/ }), - -/***/ "./ts/data/TokenWrapper.ts": -/*!*********************************!*\ - !*** ./ts/data/TokenWrapper.ts ***! - \*********************************/ -/*! exports provided: TokenDisplay, TokenWrapper, sideToLetter */ -/***/ (function(module, __webpack_exports__, __webpack_require__) { - -"use strict"; -__webpack_require__.r(__webpack_exports__); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "TokenDisplay", function() { return TokenDisplay; }); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "TokenWrapper", function() { return TokenWrapper; }); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "sideToLetter", function() { return sideToLetter; }); -/* harmony import */ var _etc_Tools__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ../etc/_Tools */ "./ts/etc/_Tools.ts"); -/* harmony import */ var lodash__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! lodash */ "./node_modules/lodash/lodash.js"); -/* harmony import */ var lodash__WEBPACK_IMPORTED_MODULE_1___default = /*#__PURE__*/__webpack_require__.n(lodash__WEBPACK_IMPORTED_MODULE_1__); -/* harmony import */ var ramda__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ramda */ "./node_modules/ramda/es/index.js"); - - - -/** - * The original tokens, and the indexes that need to be masked - */ -const emptyFullResponse = [{ - text: '[SEP]', - topk_words: [], - topk_probs: [] - }]; -class TokenDisplay { - constructor(tokens = emptyFullResponse, maskInds = []) { - this.tokenData = tokens; - this.maskInds = maskInds; - } - /** - * Push idx to the mask idx list in order from smallest to largest - */ - mask(val) { - const currInd = lodash__WEBPACK_IMPORTED_MODULE_1__["indexOf"](this.maskInds, val); - if (currInd == -1) { - _etc_Tools__WEBPACK_IMPORTED_MODULE_0__["orderedInsert_"](this.maskInds, val); - } - else { - console.log(`${val} already in maskInds!`); - console.log(this.maskInds); - } - } - toggle(val) { - const currInd = lodash__WEBPACK_IMPORTED_MODULE_1__["indexOf"](this.maskInds, val); - if (currInd == -1) { - console.log(`Masking ${val}`); - this.mask(val); - } - else { - console.log(`Unmasking ${val}`); - this.unmask(val); - } - } - unmask(val) { - lodash__WEBPACK_IMPORTED_MODULE_1__["pull"](this.maskInds, val); - } - resetMask() { - this.maskInds = []; - } - length() { - return this.tokenData.length; - } - concat(other) { - const newTokens = lodash__WEBPACK_IMPORTED_MODULE_1__["concat"](this.tokenData, other.tokenData); - const newMask = lodash__WEBPACK_IMPORTED_MODULE_1__["concat"](this.maskInds, other.maskInds.map(x => x + this.length())); - return new TokenDisplay(newTokens, newMask); - } -} -class TokenWrapper { - constructor(r) { - this.updateFromResponse(r); - } - updateFromResponse(r) { - const tokensA = r.aa.left; - this.updateFromComponents(tokensA, []); - } - updateFromComponents(a, maskA) { - this.a = new TokenDisplay(a, maskA); - } - updateTokens(r) { - const desiredKeys = ['contexts', 'embeddings', 'topk_probs', 'topk_words']; - const newTokens = r.aa.left.map(v => ramda__WEBPACK_IMPORTED_MODULE_2__["pick"](desiredKeys, v)); - const pairs = ramda__WEBPACK_IMPORTED_MODULE_2__["zip"](this.a.tokenData, newTokens); - pairs.forEach((d, i) => { - Object.keys(d[1]).map(k => { - d[0][k] = d[1][k]; - }); - }); - } - /** - * Mask the appropriate sentence at the index indicated - */ - mask(sID, idx) { - this[sID].mask(idx); - const opts = ["a", "b"]; - const Na = this.a.length(); - } -} -function sideToLetter(side, atype) { - // const atype = conf.attType; - if (atype == "all") { - return "all"; - } - const out = side == "left" ? atype[0] : atype[1]; // No type checking? - return out; -} - - -/***/ }), - -/***/ "./ts/etc/SVGplus.ts": -/*!***************************!*\ - !*** ./ts/etc/SVGplus.ts ***! - \***************************/ -/*! exports provided: SVG, SVGMeasurements */ -/***/ (function(module, __webpack_exports__, __webpack_require__) { - -"use strict"; -__webpack_require__.r(__webpack_exports__); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "SVG", function() { return SVG; }); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "SVGMeasurements", function() { return SVGMeasurements; }); -/* harmony import */ var d3_selection_multi__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! d3-selection-multi */ "./node_modules/d3-selection-multi/index.js"); - -/** - * Created by hen on 5/15/17. - * Modified by hoo on 4/16/19. - */ -class SVG { - static translate({ x, y }) { - return "translate(" + x + "," + y + ")"; - } - static rotate(deg) { - return `rotate(${deg})`; - } - static group(parent, classes, pos = { x: 0, y: 0 }) { - return parent.append('g').attrs({ - class: classes, - "transform": SVG.translate(pos) - }); - } -} -class SVGMeasurements { - constructor(baseElement, classes = '') { - this.measureElement = baseElement.append('text') - .attrs({ x: 0, y: -20, class: classes }); - } - textLength(text, style = null) { - this.measureElement.attr('style', style); - this.measureElement.text(text); - const tl = this.measureElement.node().getComputedTextLength(); - this.measureElement.text(''); - return tl; - } -} - - -/***/ }), - -/***/ "./ts/etc/SimpleEventHandler.ts": -/*!**************************************!*\ - !*** ./ts/etc/SimpleEventHandler.ts ***! - \**************************************/ -/*! exports provided: SimpleEventHandler */ -/***/ (function(module, __webpack_exports__, __webpack_require__) { - -"use strict"; -__webpack_require__.r(__webpack_exports__); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "SimpleEventHandler", function() { return SimpleEventHandler; }); -/** - * Created by hen on 5/15/17. - * Modified by hoo on 4/16/19. - */ -class SimpleEventHandler { - constructor(element) { - this.element = element; - this.eventListeners = []; - } - bind(eventNames, eventFunction) { - for (const eventName of eventNames.split(' ')) { - this.eventListeners.push({ eventName, eventFunction }); - const eventFunctionWrap = e => eventFunction(e.detail, e); - this.element.addEventListener(eventName, eventFunctionWrap, false); - } - } - getListeners() { - return this.eventListeners; - } - trigger(eventName, detail) { - this.element.dispatchEvent(new CustomEvent(eventName, { detail })); - } -} - - -/***/ }), - -/***/ "./ts/etc/URLHandler.ts": -/*!******************************!*\ - !*** ./ts/etc/URLHandler.ts ***! - \******************************/ -/*! exports provided: URLHandler */ -/***/ (function(module, __webpack_exports__, __webpack_require__) { - -"use strict"; -__webpack_require__.r(__webpack_exports__); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "URLHandler", function() { return URLHandler; }); -/** - * Created by hen on 5/15/17. - */ -class URLHandler { - static basicURL() { - const url_path = window.location.pathname.split('/').slice(0, -2).join('/'); - return window.location.origin + (url_path.length ? url_path : ''); - } - /** - * Read all URL parameters into a map. - * @returns {Map} the url parameters as a key-value store (ES6 map) - */ - static get parameters() { - // Adapted from: http://stackoverflow.com/questions/2090551/parse-query-string-in-javascript - const query = window.location.search.substring(1); - const vars = query.split('&'); - console.log(vars, "--- vars"); - const urlParameters = {}; - const isInt = x => (/^[0-9]+$/).test(x); - const isFloat = x => (/^[0-9]+\.[0-9]*$/).test(x); - const typeCast = val => { - if (isInt(val)) { - return Number.parseInt(val, 10); - } - else if (isFloat(val)) { - return Number.parseFloat(val); - } - // else: - return val; - }; - vars.forEach(v => { - if (v.length > 0) { - const splits = v.split('='); - const key = decodeURIComponent(splits[0]); - let raw_value = decodeURIComponent(splits[1]); - const isArray = raw_value.startsWith('..'); - if (isArray) { - raw_value = raw_value.slice(2); - } - if (raw_value.length < 1) { - urlParameters[key] = isArray ? [] : ''; - } - else if (isArray) { - urlParameters[key] = raw_value.split(',') - .map(val => typeCast(val)); - } - else { - urlParameters[key] = typeCast(raw_value); - } - } - }); - return urlParameters; - } - /** - * Generates an URL string from a map of url parameters - * @param {{}} urlParameters - the map of parameters - * @returns {string} - an URI string - */ - static urlString(urlParameters) { - const attr = []; - Object.keys(urlParameters).forEach(k => { - const v = urlParameters[k]; - if (v !== undefined) { - let value = v; - if (Array.isArray(v)) - value = '..' + v.join(','); - attr.push(encodeURI(k + '=' + value)); - } - }); - const url = window.location.pathname; - let res = url.substring(url.lastIndexOf('/') + 1); - if (attr.length > 0) { - res += '?' + attr.join('&'); - } - return res; - } - static updateURLParam(key, value, addToBrowserHistory = true) { - const currentParams = URLHandler.parameters; - currentParams[key] = value; - URLHandler.updateUrl(currentParams, addToBrowserHistory); - } - // /** - // * Generates a key-value map of all URL params and replaces replaceKeys - // * @param updateKeys - // */ - // static updateURLParams(updateKeys) { - // const currentParams = URLHandler.parameters; - // Object.keys(updateKeys).forEach((k) => currentParams[k] = updateKeys[k]) - // return currentParams; - // } - static updateUrl(urlParameters, addToBrowserHistory = true) { - if (addToBrowserHistory) { - window.history.pushState(urlParameters, '', URLHandler.urlString(urlParameters)); - } - else { - window.history.replaceState(urlParameters, '', URLHandler.urlString(urlParameters)); - } - } -} - - -/***/ }), - -/***/ "./ts/etc/Util.ts": -/*!************************!*\ - !*** ./ts/etc/Util.ts ***! - \************************/ -/*! exports provided: Util, Sel */ -/***/ (function(module, __webpack_exports__, __webpack_require__) { - -"use strict"; -__webpack_require__.r(__webpack_exports__); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "Util", function() { return Util; }); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "Sel", function() { return Sel; }); -/* harmony import */ var d3__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! d3 */ "./node_modules/d3/index.js"); - -/** - * Created by hen on 5/15/17. - * Modifyed by hoo on 4/16/19. - */ -let the_unique_id_counter = 0; -class Util { - static simpleUId({ prefix = '' }) { - the_unique_id_counter += 1; - return prefix + the_unique_id_counter; - } -} -/** - * Selection utility functions should be static methods in the below class - */ -class Sel { -} -Sel.setSelVisible = (x) => x.attr("visibility", "visible"); -Sel.setSelHidden = (x) => x.attr("visibility", "hidden"); -Sel.setVisible = (x) => Sel.setSelVisible(d3__WEBPACK_IMPORTED_MODULE_0__["selectAll"](x)); -Sel.setHidden = (x) => Sel.setSelHidden(d3__WEBPACK_IMPORTED_MODULE_0__["selectAll"](x)); -Sel.hideElement = (hE) => hE.transition().styles({ - 'opacity': 0, - 'pointer-events': 'none', - 'display': 'none' -}); -Sel.unhideElement = (hE) => hE.transition().styles({ - 'opacity': 1, - 'pointer-events': null, - 'display': null -}); - - -/***/ }), - -/***/ "./ts/etc/_Tools.ts": -/*!**************************!*\ - !*** ./ts/etc/_Tools.ts ***! - \**************************/ -/*! exports provided: findAllIndexes, insertAt_, orderedInsert_, set2SortedArray, makeRandom */ -/***/ (function(module, __webpack_exports__, __webpack_require__) { - -"use strict"; -__webpack_require__.r(__webpack_exports__); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "findAllIndexes", function() { return findAllIndexes; }); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "insertAt_", function() { return insertAt_; }); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "orderedInsert_", function() { return orderedInsert_; }); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "set2SortedArray", function() { return set2SortedArray; }); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "makeRandom", function() { return makeRandom; }); -/* harmony import */ var lodash__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! lodash */ "./node_modules/lodash/lodash.js"); -/* harmony import */ var lodash__WEBPACK_IMPORTED_MODULE_0___default = /*#__PURE__*/__webpack_require__.n(lodash__WEBPACK_IMPORTED_MODULE_0__); - -function ascOrder(n1, n2) { - if (n1 < n2) { - return -1; - } - else if (n1 > n2) { - return 1; - } - return 0; -} - -/** - * Find all indexes that match a particular predicate - */ -function findAllIndexes(array, predicate) { - let fromIndex = 0; - let results = []; - let i = lodash__WEBPACK_IMPORTED_MODULE_0__["findIndex"](array, predicate, fromIndex); - while (i != -1) { - results.push(i); - i = lodash__WEBPACK_IMPORTED_MODULE_0__["findIndex"](array, predicate, i + 1); - } - return results; -} -; -function insertAt_(array, val, ind) { - array.splice(ind, 0, val); - return array; -} -/** - * Convert a set to an ordered array - */ -function set2SortedArray(input) { - return Array.from(input).sort(ascOrder); -} -/** - * Insert a value into array in sorted order IN PLACE - * - * WARNING: Only handles numbers, sorted from least to greatest - * - Assumes already sorted array - */ -function orderedInsert_(array, val, coldstart = false) { - // Resort array if desired - if (coldstart) { - array.sort(ascOrder); - } - const ind = lodash__WEBPACK_IMPORTED_MODULE_0__["sortedIndex"](array, val); - return insertAt_(array, val, ind); -} -function makeRandom(len) { - const a = new Array(len).fill(0); - return a.map((x) => { return lodash__WEBPACK_IMPORTED_MODULE_0__["random"](-5, 5, true); }); -} - - -/***/ }), - -/***/ "./ts/etc/apiHelpers.ts": -/*!******************************!*\ - !*** ./ts/etc/apiHelpers.ts ***! - \******************************/ -/*! exports provided: makeUrl, toPayload */ -/***/ (function(module, __webpack_exports__, __webpack_require__) { - -"use strict"; -__webpack_require__.r(__webpack_exports__); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "makeUrl", function() { return makeUrl; }); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "toPayload", function() { return toPayload; }); -/** - * Convert a JS object into GET URL parameters - * - * @param base Base URL atop which to add GET parameters - * @param params Object to insert into a URL string - */ -function makeUrl(base, params) { - if (params) { - let out = base + "?"; - Object.keys(params).forEach(k => { - out += k; - out += '='; - out += params[k]; - out += "&"; - }); - return out.replace(/&$/g, ""); - } - else { - return base; - } -} -; -/** - * Convert information in GET request into the message for a POST request - */ -const toPayload = (toSend) => { - return { - method: "POST", - body: JSON.stringify(toSend), - headers: { - "Content-type": "application/json; charset=UTF-8" - } - }; -}; - - -/***/ }), - -/***/ "./ts/etc/arrayUtils.ts": -/*!******************************!*\ - !*** ./ts/etc/arrayUtils.ts ***! - \******************************/ -/*! exports provided: sortWithIndices */ -/***/ (function(module, __webpack_exports__, __webpack_require__) { - -"use strict"; -__webpack_require__.r(__webpack_exports__); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "sortWithIndices", function() { return sortWithIndices; }); -/** - * Copies and sorts an array while keeping track of the indices. Currently only supports sorting max -> min. - * - * @param arr - Array to be copied and sorted - */ -function sortWithIndices(arr, fn) { - // If fn is not provided, default to sorting by max value - if (!fn) { - fn = function (left, right) { - return left[0] > right[0] ? -1 : 1; - }; - } - let out = { - arr: [], - sortIndices: [] - }; - let idxTracker = []; - for (let i = 0; i < arr.length; i++) { - idxTracker[i] = [arr[i], i]; - } - idxTracker.sort(function (left, right) { - return left[0] > right[0] ? -1 : 1; - }); - for (var j = 0; j < arr.length; j++) { - out.sortIndices.push(idxTracker[j][1]); - out.arr[j] = idxTracker[j][0]; - } - return out; -} - - -/***/ }), - -/***/ "./ts/etc/types.ts": -/*!*************************!*\ - !*** ./ts/etc/types.ts ***! - \*************************/ -/*! exports provided: Toggled, NormBy, ModelKind */ -/***/ (function(module, __webpack_exports__, __webpack_require__) { - -"use strict"; -__webpack_require__.r(__webpack_exports__); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "Toggled", function() { return Toggled; }); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "NormBy", function() { return NormBy; }); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "ModelKind", function() { return ModelKind; }); -var Toggled; -(function (Toggled) { - Toggled[Toggled["ADDED"] = 0] = "ADDED"; - Toggled[Toggled["REMOVED"] = 1] = "REMOVED"; -})(Toggled || (Toggled = {})); -var NormBy; -(function (NormBy) { - NormBy[NormBy["ROW"] = 0] = "ROW"; - NormBy[NormBy["COL"] = 1] = "COL"; - NormBy[NormBy["ALL"] = 2] = "ALL"; -})(NormBy || (NormBy = {})); -var ModelKind; -(function (ModelKind) { - ModelKind["Bidirectional"] = "bidirectional"; - ModelKind["Autoregressive"] = "autoregressive"; -})(ModelKind || (ModelKind = {})); - - -/***/ }), - -/***/ "./ts/etc/xd3.ts": -/*!***********************!*\ - !*** ./ts/etc/xd3.ts ***! - \***********************/ -/*! no exports provided */ -/***/ (function(module, __webpack_exports__, __webpack_require__) { - -"use strict"; -__webpack_require__.r(__webpack_exports__); -/* harmony import */ var d3__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! d3 */ "./node_modules/d3/index.js"); - -d3__WEBPACK_IMPORTED_MODULE_0__["selection"].prototype.clear = function () { - this.selectAll('*').remove(); - return this; -}; -d3__WEBPACK_IMPORTED_MODULE_0__["selection"].prototype.toggleClass = function (className) { - this.classed(className, !this.classed(className)); - return this; -}; -d3__WEBPACK_IMPORTED_MODULE_0__["selection"].prototype.show = function () { - this.style('display', 'initial'); - return this; -}; -d3__WEBPACK_IMPORTED_MODULE_0__["selection"].prototype.hide = function () { - this.style('display', 'none'); - return this; -}; -d3__WEBPACK_IMPORTED_MODULE_0__["selection"].prototype.toggle = function () { - var isHidden = this.style('display') == 'none'; - return this.style('display', isHidden ? 'inherit' : 'none'); -}; -d3__WEBPACK_IMPORTED_MODULE_0__["selection"].prototype.after = function (tagName) { - var elements = []; - this.each(function () { - var element = document.createElement(tagName); - this.parentNode.insertBefore(element, this.nextSibling); - elements.push(element); - }); - return d3__WEBPACK_IMPORTED_MODULE_0__["selectAll"](elements); -}; -d3__WEBPACK_IMPORTED_MODULE_0__["selection"].prototype.before = function (tagName) { - var elements = []; - this.each(function () { - var element = document.createElement(tagName); - this.parentNode.insertBefore(element, this); - elements.push(element); - }); - return d3__WEBPACK_IMPORTED_MODULE_0__["selectAll"](elements); -}; - - -/***/ }), - -/***/ "./ts/main.ts": -/*!********************!*\ - !*** ./ts/main.ts ***! - \********************/ -/*! no exports provided */ -/***/ (function(module, __webpack_exports__, __webpack_require__) { - -"use strict"; -__webpack_require__.r(__webpack_exports__); -/* harmony import */ var _vis_attentionVis__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./vis/attentionVis */ "./ts/vis/attentionVis.ts"); -/* harmony import */ var _file_loader_name_exBERT_html_exBERT_html__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! file-loader?name=exBERT.html!../exBERT.html */ "./node_modules/file-loader/dist/cjs.js?name=exBERT.html!./exBERT.html"); -/* harmony import */ var _file_loader_name_exBERT_html_exBERT_html__WEBPACK_IMPORTED_MODULE_1___default = /*#__PURE__*/__webpack_require__.n(_file_loader_name_exBERT_html_exBERT_html__WEBPACK_IMPORTED_MODULE_1__); -/* harmony import */ var _file_loader_name_index_html_index_html__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! file-loader?name=index.html!../index.html */ "./node_modules/file-loader/dist/cjs.js?name=index.html!./index.html"); -/* harmony import */ var _file_loader_name_index_html_index_html__WEBPACK_IMPORTED_MODULE_2___default = /*#__PURE__*/__webpack_require__.n(_file_loader_name_index_html_index_html__WEBPACK_IMPORTED_MODULE_2__); -/* harmony import */ var _css_main_scss__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ../css/main.scss */ "./css/main.scss"); -/* harmony import */ var _css_main_scss__WEBPACK_IMPORTED_MODULE_3___default = /*#__PURE__*/__webpack_require__.n(_css_main_scss__WEBPACK_IMPORTED_MODULE_3__); - - - - -window.onload = () => { - const base = document.getElementById('attention-vis'); - //@ts-ignore - const mainVis = new _vis_attentionVis__WEBPACK_IMPORTED_MODULE_0__["MainGraphic"](base); - console.log("Done loading window"); -}; - - -/***/ }), - -/***/ "./ts/uiConfig.ts": -/*!************************!*\ - !*** ./ts/uiConfig.ts ***! - \************************/ -/*! exports provided: UIConfig */ -/***/ (function(module, __webpack_exports__, __webpack_require__) { - -"use strict"; -__webpack_require__.r(__webpack_exports__); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "UIConfig", function() { return UIConfig; }); -/* harmony import */ var _etc_types__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! ./etc/types */ "./ts/etc/types.ts"); -/* harmony import */ var _etc_Tools__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./etc/_Tools */ "./ts/etc/_Tools.ts"); -/* harmony import */ var lodash__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! lodash */ "./node_modules/lodash/lodash.js"); -/* harmony import */ var lodash__WEBPACK_IMPORTED_MODULE_2___default = /*#__PURE__*/__webpack_require__.n(lodash__WEBPACK_IMPORTED_MODULE_2__); -/* harmony import */ var ramda__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ramda */ "./node_modules/ramda/es/index.js"); -/* harmony import */ var _etc_URLHandler__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! ./etc/URLHandler */ "./ts/etc/URLHandler.ts"); - - - - - -const falsey = val => (new Set(['false', 0, "no", false, null, ""])).has(val); -const truthy = val => !falsey(val); -const toNumber = x => +x; -class UIConfig { - constructor() { - this._conf = {}; - this._nHeads = 12; - this._nLayers = null; - this.attType = 'aa'; - this.fromURL(); - this.toURL(false); - } - toURL(updateHistory = false) { - _etc_URLHandler__WEBPACK_IMPORTED_MODULE_4__["URLHandler"].updateUrl(this._conf, updateHistory); - } - fromURL() { - const params = _etc_URLHandler__WEBPACK_IMPORTED_MODULE_4__["URLHandler"].parameters; - this._conf = { - model: params['model'] || 'bert-base-cased', - modelKind: params['modelKind'] || _etc_types__WEBPACK_IMPORTED_MODULE_0__["ModelKind"].Bidirectional, - sentence: params['sentence'] || "The girl ran to a local pub to escape the din of her city.", - layer: params['layer'] || 1, - heads: this._initHeads(params['heads']), - threshold: params['threshold'] || 0.7, - tokenInd: params['tokenInd'] || null, - tokenSide: params['tokenSide'] || null, - maskInds: params['maskInds'] || [9], - hideClsSep: truthy(params['hideClsSep']) || true, - }; - this._token = { side: this._conf.tokenSide, ind: this._conf.tokenInd }; - } - _initHeads(v) { - if (v == null || v.length < 1) { - this.selectAllHeads(); - } - else { - console.log(this.headSet(new Set(v))._conf.heads); - } - return this.heads(); - } - nHeads(val) { - if (val == null) - return this._nHeads; - this._nHeads = val; - return this; - } - nLayers(val) { - if (val == null) - return this._nLayers; - this._nLayers = val; - return this; - } - toggleSelectAllHeads() { - if (this.heads().length == 0) { - this.selectAllHeads(); - } - else { - this.selectNoHeads(); - } - } - selectAllHeads() { - this.headSet(new Set(lodash__WEBPACK_IMPORTED_MODULE_2__["range"](0, this._nHeads))); - } - selectNoHeads() { - this.headSet(new Set([])); - } - toggleHead(head) { - let out; - if (this.headSet().has(head)) { - this.headSet().delete(head); - out = _etc_types__WEBPACK_IMPORTED_MODULE_0__["Toggled"].REMOVED; - } - else { - this.headSet().add(head); - out = _etc_types__WEBPACK_IMPORTED_MODULE_0__["Toggled"].ADDED; - } - // Set through setter function to ensure url is updated - this.headSet(this.headSet()); // I hate mutable datastructures... This is confusing. - return out; - } - toggleToken(e) { - const picker = ramda__WEBPACK_IMPORTED_MODULE_3__["pick"](['ind', 'side']); - const compareEvent = picker(e); - const compareToken = picker(this.token()); - if (ramda__WEBPACK_IMPORTED_MODULE_3__["equals"](compareToken, compareEvent)) { - this.rmToken(); - } - else { - this.token(e); - } - return this; - } - token(val) { - if (val == null) - return this._token; - this._token = val; - this._conf.tokenInd = val.ind; - this._conf.tokenSide = val.side; - this.toURL(); - return this; - } - hasToken() { - const conf = this._conf; - const actuallyNull = ((conf.tokenInd == null) && (conf.tokenSide == null)); - const strNull = (conf.tokenInd == "null"); - return (!actuallyNull) && (!strNull); - } - rmToken() { - this.token({ ind: null, side: null }); - return this; - } - sentence(val) { - if (val == null) - return this._conf.sentence; - this._conf.sentence = val; - this.toURL(true); - return this; - } - threshold(val) { - if (val == null) - return this._conf.threshold; - this._conf.threshold = val; - this.toURL(); - return this; - } - heads() { - return this._conf.heads; - } - layer(val) { - if (val == null) - return this._conf.layer; - this._conf.layer = val; - this.toURL(); - return this; - } - headSet(val) { - if (val == null) { - return this._headSet; - } - this._headSet = val; - this._conf.heads = _etc_Tools__WEBPACK_IMPORTED_MODULE_1__["set2SortedArray"](this._headSet); - this.toURL(); - return this; - } - maskInds(val) { - if (val == null) - return this._conf.maskInds; - this._conf.maskInds = val; - this.toURL(); - return this; - } - hideClsSep(val) { - if (val == null) - return this._conf.hideClsSep; - this._conf.hideClsSep = truthy(val); - this.toURL(); - return this; - } - model(val) { - if (val == null) - return this._conf.model; - this._conf.model = val; - this.toURL(); - return this; - } - modelKind(val) { - if (val == null) - return this._conf.modelKind; - this._conf.modelKind = val; - this.toURL(); - return this; - } - /** - * Return the offset needed for the modelKind in the configuration - */ - get offset() { - switch (this.modelKind()) { - case _etc_types__WEBPACK_IMPORTED_MODULE_0__["ModelKind"].Bidirectional: { - return 0; - } - case _etc_types__WEBPACK_IMPORTED_MODULE_0__["ModelKind"].Autoregressive: { - return 0; - } - default: { - return 0; - } - } - } - get showNext() { - return this.modelKind() == _etc_types__WEBPACK_IMPORTED_MODULE_0__["ModelKind"].Autoregressive ? true : false; - } - get matchHistogramDescription() { - return this.modelKind() == _etc_types__WEBPACK_IMPORTED_MODULE_0__["ModelKind"].Autoregressive ? "Next" : "Matched"; - } -} - - -/***/ }), - -/***/ "./ts/vis/AttentionConnector.ts": -/*!**************************************!*\ - !*** ./ts/vis/AttentionConnector.ts ***! - \**************************************/ -/*! exports provided: scaleLinearWidth, AttentionGraph */ -/***/ (function(module, __webpack_exports__, __webpack_require__) { - -"use strict"; -__webpack_require__.r(__webpack_exports__); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "scaleLinearWidth", function() { return scaleLinearWidth; }); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "AttentionGraph", function() { return AttentionGraph; }); -/* harmony import */ var d3__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! d3 */ "./node_modules/d3/index.js"); -/* harmony import */ var d3_selection_multi__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! d3-selection-multi */ "./node_modules/d3-selection-multi/index.js"); -/* harmony import */ var _EdgeConnector__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ./EdgeConnector */ "./ts/vis/EdgeConnector.ts"); -/* harmony import */ var _VisComponent__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ./VisComponent */ "./ts/vis/VisComponent.ts"); -/* harmony import */ var _etc_types__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! ../etc/types */ "./ts/etc/types.ts"); - - - - - -const scaleLinearWidth = opacity => 5 * opacity ^ 0.33; -class AttentionGraph extends _VisComponent__WEBPACK_IMPORTED_MODULE_3__["VComponent"] { - constructor(d3Parent, eventHandler, options = {}) { - super(d3Parent, eventHandler); - this.css_name = ''; - // OPTIONS WITH DEFAULTS - this._threshold = 0.7; // Accumulation threshold. Between 0-1 - this.options = { - boxheight: 26, - height: 500, - width: 200, - offset: 0, - }; - /** - * Scale the opacity according to the values of the data, from 0 to max of contained data - * Normalize by each source target, or across the whole - */ - this.createScales = () => { - this.opacityScales = []; - let arr = []; - // Group normalization - switch (this.normBy) { - case _etc_types__WEBPACK_IMPORTED_MODULE_4__["NormBy"].ROW: - arr = this.edgeData.extent(1); - this.opacityScales = []; - arr.forEach((v, i) => { - this.opacityScales.push(d3__WEBPACK_IMPORTED_MODULE_0__["scaleLinear"]() - .domain([0, v[1]]) - .range([0, 0.9])); - }); - break; - case _etc_types__WEBPACK_IMPORTED_MODULE_4__["NormBy"].COL: - arr = this.edgeData.extent(0); - this.opacityScales = []; - arr.forEach((v, i) => { - this.opacityScales.push(d3__WEBPACK_IMPORTED_MODULE_0__["scaleLinear"]() - .domain([0, v[1]]) - .range([0, 0.9])); - }); - break; - case _etc_types__WEBPACK_IMPORTED_MODULE_4__["NormBy"].ALL: - const maxIn = d3__WEBPACK_IMPORTED_MODULE_0__["max"](this.plotData.map((d) => d.v)); - for (let i = 0; i < this._data.length; i++) { - this.opacityScales.push(d3__WEBPACK_IMPORTED_MODULE_0__["scaleLinear"]() - .domain([0, maxIn]) - .range([0, 1])); - } - break; - default: - console.log("Nor norming specified"); - break; - } - }; - this.superInitSVG(options); - this._init(); - } - _init() { - this.svg = this.parent; - this.graph = this.svg.selectAll(`.atn-curve`); - this.linkGen = d3__WEBPACK_IMPORTED_MODULE_0__["linkHorizontal"]() - .x(d => d[0]) - .y(d => d[1]); - } - // Define whether to use the 'j' or 'i' attribute to calculate opacities - scaleIdx() { - switch (this.normBy) { - case _etc_types__WEBPACK_IMPORTED_MODULE_4__["NormBy"].COL: - return 'j'; - case _etc_types__WEBPACK_IMPORTED_MODULE_4__["NormBy"].ROW: - return 'i'; - case _etc_types__WEBPACK_IMPORTED_MODULE_4__["NormBy"].ALL: - return 'i'; - } - } - /** - * Create connections between locations of the SVG using D3's linkGen - */ - createConnections() { - const self = this; - const op = this.options; - if (this.paths) { - this.paths.attrs({ - 'd': (d, i) => { - const data = { - source: [0, op.boxheight * (d.i + 0.5 + op.offset)], - target: [op.width, op.boxheight * (d.j + 0.5)] // + 2 allows small offset - }; - return this.linkGen(data); - }, - 'class': 'atn-curve' - }) - .attr("src-idx", (d, i) => d.i) - .attr("target-idx", (d, i) => d.j); - } - } - /** - * Change the height of the SVG - */ - updateHeight() { - const op = this.options; - if (this.svg != null) { - this.svg.attr("height", this.options.height + (op.offset * this.options.boxheight)); - } - return this; - } - /** - * Change the width of the SVG - */ - updateWidth() { - if (this.svg != null) { - this.svg.attr("width", this.options.width); - } - return this; - } - /** - * Change the Opacity of the lines according to the value of the data - */ - updateOpacity() { - const self = this; - if (this.paths != null) { - // paths.transition().duration(500).attr('opacity', (d) => { - this.paths.attr('opacity', (d) => { - const val = this.opacityScales[d[self.scaleIdx()]](d.v); - return val; - }); - this.paths.attr('stroke-width', (d) => { - const val = this.opacityScales[d[self.scaleIdx()]](d.v); - return scaleLinearWidth(val); //5 * val^0.33; - }); - } - return this; - } - /** - * Rerender the graph in the event that the data changes - */ - updateData() { - if (this.graph != null) { - d3__WEBPACK_IMPORTED_MODULE_0__["selectAll"](".atn-curve").remove(); - const data = this.plotData; - this.paths = this.graph - .data(data) - .join('path'); - this.createConnections(); - this.updateOpacity(); - return this; - } - } - data(value) { - if (value == null) { - return this._data; - } - this._data = value; - this.edgeData = new _EdgeConnector__WEBPACK_IMPORTED_MODULE_2__["EdgeData"](value); - this.plotData = this.edgeData.format(this._threshold); - this.createScales(); - this.updateData(); - return this; - } - height(value) { - if (value == null) { - return this.options.height; - } - this.options.height = value; - this.updateHeight(); - return this; - } - width(value) { - if (value == null) { - return this.options.width; - } - this.options.width = value; - this.updateWidth(); - return this; - } - threshold(value) { - if (value == null) { - return this._threshold; - } - this._threshold = value; - this.plotData = this.edgeData.format(this._threshold); - this.createScales(); - this.updateData(); - return this; - } - _wrangle(data) { - return data; - } - _render(data) { - this.svg.html(''); - this.updateHeight(); - this.updateWidth(); - this.updateData(); - return this; - } -} -AttentionGraph.events = {}; // No events needed for this one - - -/***/ }), - -/***/ "./ts/vis/AttentionHeadBox.ts": -/*!************************************!*\ - !*** ./ts/vis/AttentionHeadBox.ts ***! - \************************************/ -/*! exports provided: getAttentionInfo, AttentionHeadBox */ -/***/ (function(module, __webpack_exports__, __webpack_require__) { - -"use strict"; -__webpack_require__.r(__webpack_exports__); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "getAttentionInfo", function() { return getAttentionInfo; }); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "AttentionHeadBox", function() { return AttentionHeadBox; }); -/* harmony import */ var d3__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! d3 */ "./node_modules/d3/index.js"); -/* harmony import */ var _VisComponent__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! ./VisComponent */ "./ts/vis/VisComponent.ts"); -/* harmony import */ var _etc_SVGplus__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ../etc/SVGplus */ "./ts/etc/SVGplus.ts"); -/* harmony import */ var _tensorflow_tfjs__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! @tensorflow/tfjs */ "./node_modules/@tensorflow/tfjs/dist/tf.esm.js"); - - - - -/** - * From an attention matrix selected by layer, show a summary of the attentions belonging to each head. - * - * @param headMat The matrix representing all the attentions by head (layer already selected)layers: bg -> main -> fg - */ - superInitSVG(options = {}, defaultLayers = ['bg', 'main', 'fg']) { - // Set default options if not specified in constructor call - // const defaults = this.defaultOptions; - // this.options = {}; - // const keys = new Set([...Object.keys(defaults), ...Object.keys(options)]); - // keys.forEach(key => this.options[key] = (key in options) ? options[key] : defaults[key]); - Object.keys(options).forEach(key => this.options[key] = options[key]); - this.layers = {}; - // Create the base group element - const svg = this.parent; - this.base = _etc_SVGplus__WEBPACK_IMPORTED_MODULE_2__["SVG"].group(svg, this.css_name + ' ID' + this.id, this.options.pos); - // create default layers: background, main, foreground - if (defaultLayers) { - // construction order is important ! - defaultLayers.forEach(layer => { - this.layers[layer] = _etc_SVGplus__WEBPACK_IMPORTED_MODULE_2__["SVG"].group(this.base, layer); - }); - } - } - // DATA UPDATE & RENDER ============================================================ - /** - * Every time data has changed, update is called and - * triggers wrangling and re-rendering - * @param {Object} data data object - * @return {*} --- - */ - update(data) { - this._data = data; - if (this._visibility.hidden) - return; - this.renderData = this._wrangle(data); - this._render(this.renderData); - } - // UPDATE OPTIONS ============================================================ - /** - * Updates instance options - * @param {Object} options only the options that should be updated - * @param {Boolean} reRender if option change requires a re-rendering (default:false) - * @returns {*} --- - */ - updateOptions({ options, reRender = false }) { - Object.keys(options).forEach(k => this.options[k] = options[k]); - if (reRender) - this._render(this.renderData); - } - // === CONVENIENCE ==== - redraw() { - this._render(this.renderData); - } - setHideElement(hE) { - this._visibility.hideElement = hE; - } - hideView() { - if (!this._visibility.hidden) { - const hE = this._visibility.hideElement || this.parent; - hE.transition().styles({ - 'opacity': 0, - 'pointer-events': 'none' - }).style('display', 'none'); - this._visibility.hidden = true; - } - } - unhideView() { - if (this._visibility.hidden) { - const hE = this._visibility.hideElement || this.parent; - hE.transition().styles({ - 'opacity': 1, - 'pointer-events': null, - 'display': null - }); - this._visibility.hidden = false; - // this.update(this.data); - } - } - destroy() { - this.base.remove(); - } - clear() { - this.base.html(''); - } -} -// STATIC FIELDS ============================================================ -/** - * The static property that contains all class related events. - * Should be overwritten and event strings have to be unique!! - */ -VComponent.events = { noEvent: 'VComponent_noEvent' }; - - -/***/ }), - -/***/ "./ts/vis/attentionVis.ts": -/*!********************************!*\ - !*** ./ts/vis/attentionVis.ts ***! - \********************************/ -/*! exports provided: MainGraphic */ -/***/ (function(module, __webpack_exports__, __webpack_require__) { - -"use strict"; -__webpack_require__.r(__webpack_exports__); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "MainGraphic", function() { return MainGraphic; }); -/* harmony import */ var d3__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! d3 */ "./node_modules/d3/index.js"); -/* harmony import */ var lodash__WEBPACK_IMPORTED_MODULE_1__ = __webpack_require__(/*! lodash */ "./node_modules/lodash/lodash.js"); -/* harmony import */ var lodash__WEBPACK_IMPORTED_MODULE_1___default = /*#__PURE__*/__webpack_require__.n(lodash__WEBPACK_IMPORTED_MODULE_1__); -/* harmony import */ var ramda__WEBPACK_IMPORTED_MODULE_2__ = __webpack_require__(/*! ramda */ "./node_modules/ramda/es/index.js"); -/* harmony import */ var _etc_types__WEBPACK_IMPORTED_MODULE_3__ = __webpack_require__(/*! ../etc/types */ "./ts/etc/types.ts"); -/* harmony import */ var _etc_xd3__WEBPACK_IMPORTED_MODULE_4__ = __webpack_require__(/*! ../etc/xd3 */ "./ts/etc/xd3.ts"); -/* harmony import */ var _api_mainApi__WEBPACK_IMPORTED_MODULE_5__ = __webpack_require__(/*! ../api/mainApi */ "./ts/api/mainApi.ts"); -/* harmony import */ var _uiConfig__WEBPACK_IMPORTED_MODULE_6__ = __webpack_require__(/*! ../uiConfig */ "./ts/uiConfig.ts"); -/* harmony import */ var _TextToken__WEBPACK_IMPORTED_MODULE_7__ = __webpack_require__(/*! ./TextToken */ "./ts/vis/TextToken.ts"); -/* harmony import */ var _AttentionHeadBox__WEBPACK_IMPORTED_MODULE_8__ = __webpack_require__(/*! ./AttentionHeadBox */ "./ts/vis/AttentionHeadBox.ts"); -/* harmony import */ var _AttentionConnector__WEBPACK_IMPORTED_MODULE_9__ = __webpack_require__(/*! ./AttentionConnector */ "./ts/vis/AttentionConnector.ts"); -/* harmony import */ var _data_TokenWrapper__WEBPACK_IMPORTED_MODULE_10__ = __webpack_require__(/*! ../data/TokenWrapper */ "./ts/data/TokenWrapper.ts"); -/* harmony import */ var _data_AttentionCapsule__WEBPACK_IMPORTED_MODULE_11__ = __webpack_require__(/*! ../data/AttentionCapsule */ "./ts/data/AttentionCapsule.ts"); -/* harmony import */ var _etc_SimpleEventHandler__WEBPACK_IMPORTED_MODULE_12__ = __webpack_require__(/*! ../etc/SimpleEventHandler */ "./ts/etc/SimpleEventHandler.ts"); -/* harmony import */ var _etc_Util__WEBPACK_IMPORTED_MODULE_13__ = __webpack_require__(/*! ../etc/Util */ "./ts/etc/Util.ts"); -/* harmony import */ var rxjs__WEBPACK_IMPORTED_MODULE_14__ = __webpack_require__(/*! rxjs */ "./node_modules/rxjs/_esm5/index.js"); -/* harmony import */ var rxjs_operators__WEBPACK_IMPORTED_MODULE_15__ = __webpack_require__(/*! rxjs/operators */ "./node_modules/rxjs/_esm5/operators/index.js"); -/* harmony import */ var _staticLayout__WEBPACK_IMPORTED_MODULE_16__ = __webpack_require__(/*! ./staticLayout */ "./ts/vis/staticLayout.ts"); -/** - * Showing the top left part of exBERT, no information from the embeddings or the contexts - */ - - - - - - - - - - - - - - - - - -function isNullToken(tok) { - const isSomeNull = x => { - return (x == null) || (x == "null"); - }; - const tokIsNull = tok == null; - const tokHasNull = isSomeNull(tok.side) || isSomeNull(tok.ind); - return tokIsNull || tokHasNull; -} -function showBySide(e) { - // Check if saved token in uiConf is null - if (!isNullToken(e)) { - const classSelector = e.side == "left" ? "src-idx" : "target-idx"; - _etc_Util__WEBPACK_IMPORTED_MODULE_13__["Sel"].setHidden(".atn-curve"); - _etc_Util__WEBPACK_IMPORTED_MODULE_13__["Sel"].setVisible(`.atn-curve[${classSelector}='${e.ind}']`); - } -} -function chooseShowBySide(savedEvent, newEvent) { - if (isNullToken(savedEvent)) { - showBySide(newEvent); - } -} -function chooseShowAll(savedEvent) { - if (isNullToken(savedEvent)) - _etc_Util__WEBPACK_IMPORTED_MODULE_13__["Sel"].setVisible(".atn-curve"); -} -function unselectHead(head) { - const affectedHeads = d3__WEBPACK_IMPORTED_MODULE_0__["selectAll"](`.att-rect[head='${head}']`); - affectedHeads.classed("unselected", true); -} -function selectHead(head) { - const affectedHeads = d3__WEBPACK_IMPORTED_MODULE_0__["selectAll"](`.att-rect[head='${head}']`); - affectedHeads.classed("unselected", false); -} -function setSelDisabled(attr, sel) { - const val = attr ? true : null; - sel.attr('disabled', val); -} -class MainGraphic { - /** - * - * @param base 'div' html element into which everything below will be rendered - */ - constructor(baseDiv) { - this.base = d3__WEBPACK_IMPORTED_MODULE_0__["select"](baseDiv); - this.api = new _api_mainApi__WEBPACK_IMPORTED_MODULE_5__["API"](); - this.uiConf = new _uiConfig__WEBPACK_IMPORTED_MODULE_6__["UIConfig"](); - this.sels = Object(_staticLayout__WEBPACK_IMPORTED_MODULE_16__["createStaticSkeleton"])(this.base); - this.eventHandler = new _etc_SimpleEventHandler__WEBPACK_IMPORTED_MODULE_12__["SimpleEventHandler"](this.base.node()); - this.vizs = { - leftHeads: new _AttentionHeadBox__WEBPACK_IMPORTED_MODULE_8__["AttentionHeadBox"](this.sels.atnHeads.left, this.eventHandler, { side: "left", }), - rightHeads: new _AttentionHeadBox__WEBPACK_IMPORTED_MODULE_8__["AttentionHeadBox"](this.sels.atnHeads.right, this.eventHandler, { side: "right" }), - tokens: { - left: new _TextToken__WEBPACK_IMPORTED_MODULE_7__["LeftTextToken"](this.sels.tokens.left, this.eventHandler), - right: new _TextToken__WEBPACK_IMPORTED_MODULE_7__["RightTextToken"](this.sels.tokens.right, this.eventHandler), - }, - attentionSvg: new _AttentionConnector__WEBPACK_IMPORTED_MODULE_9__["AttentionGraph"](this.sels.atnDisplay, this.eventHandler), - }; - this._bindEventHandler(); - this.mainInit(); - } - mainInit() { - const self = this; - this.sels.body.style("cursor", "progress"); - this.api.getModelDetails(this.uiConf.model()).then(md => { - const val = md.payload; - // If changing to model with fewer layers, cap accordingly - this.uiConf.nLayers(val.nlayers).nHeads(val.nheads); - const currLayer = this.uiConf.layer(); - const maxLayer = this.uiConf.nLayers() - 1; - this.uiConf.layer(Math.min(currLayer, maxLayer)); - this.initLayers(this.uiConf.nLayers()); - this.api.getMetaAttentions(this.uiConf.model(), this.uiConf.sentence(), this.uiConf.layer()).then(attention => { - const att = attention.payload; - this.initFromResponse(att); - // Wrap postInit into function so asynchronous call does not mess with necessary inits - const postResponseDisplayCleanup = () => { - this._toggleTokenSel(); - }; - let normBy; - if ((this.uiConf.modelKind() == _etc_types__WEBPACK_IMPORTED_MODULE_3__["ModelKind"].Autoregressive) && (!this.uiConf.hideClsSep())) { - normBy = _etc_types__WEBPACK_IMPORTED_MODULE_3__["NormBy"].COL; - } - else { - normBy = _etc_types__WEBPACK_IMPORTED_MODULE_3__["NormBy"].ALL; - } - this.vizs.attentionSvg.normBy = normBy; - if (this.uiConf.maskInds().length > 0) { - this.tokCapsule.a.maskInds = this.uiConf.maskInds(); - this.api.updateMaskedAttentions(this.uiConf.model(), this.tokCapsule.a, this.uiConf.sentence(), this.uiConf.layer()).then(resp => { - const r = resp.payload; - this.attCapsule.updateFromNormal(r, this.uiConf.hideClsSep()); - this.tokCapsule.updateTokens(r); - this.update(); - postResponseDisplayCleanup(); - }); - } - else { - this.update(); - postResponseDisplayCleanup(); - } - if (this.uiConf.modelKind() == _etc_types__WEBPACK_IMPORTED_MODULE_3__["ModelKind"].Autoregressive) { - // Ensure only 1 mask ind is present for autoregressive models - if (this.uiConf.hasToken()) { - this.grayToggle(this.uiConf.token().ind); - } - self.vizs.tokens.left.options.divHover.textInfo = "Would predict next..."; - self.vizs.tokens.right.options.divHover.textInfo = "Would predict next..."; - } - else { - self.vizs.tokens.left.options.divHover.textInfo = "Would predict here..."; - self.vizs.tokens.right.options.divHover.textInfo = "Would predict here..."; - } - this.sels.body.style("cursor", "default"); - }); - }); - } - initFromResponse(attention) { - this.attCapsule = Object(_data_AttentionCapsule__WEBPACK_IMPORTED_MODULE_11__["makeFromMetaResponse"])(attention, this.uiConf.hideClsSep()); - this.tokCapsule = new _data_TokenWrapper__WEBPACK_IMPORTED_MODULE_10__["TokenWrapper"](attention); - this._staticInits(); - } - leaveCorpusMsg(msg) { - this.vizs.corpusInspector.hideView(); - this.vizs.corpusMatManager.hideView(); - console.log("Running leave msg"); - _etc_Util__WEBPACK_IMPORTED_MODULE_13__["Sel"].unhideElement(this.sels.corpusMsgBox); - this.sels.corpusMsgBox.text(msg); - } - _bindEventHandler() { - const self = this; - this.eventHandler.bind(_TextToken__WEBPACK_IMPORTED_MODULE_7__["TextTokens"].events.tokenDblClick, (e) => { - switch (self.uiConf.modelKind()) { - case _etc_types__WEBPACK_IMPORTED_MODULE_3__["ModelKind"].Bidirectional: { - e.sel.classed("masked-token", !e.sel.classed("masked-token")); - const letter = Object(_data_TokenWrapper__WEBPACK_IMPORTED_MODULE_10__["sideToLetter"])(e.side, this.uiConf.attType); - self.tokCapsule[letter].toggle(e.ind); - self.sels.body.style("cursor", "progress"); - self.api.updateMaskedAttentions(this.uiConf.model(), this.tokCapsule.a, this.uiConf.sentence(), this.uiConf.layer()).then((resp) => { - const r = resp.payload; - self.attCapsule.updateFromNormal(r, this.uiConf.hideClsSep()); - self.tokCapsule.updateTokens(r); - self.uiConf.maskInds(this.tokCapsule.a.maskInds); - self.update(); - self.sels.body.style("cursor", "default"); - }); - break; - } - case _etc_types__WEBPACK_IMPORTED_MODULE_3__["ModelKind"].Autoregressive: { - console.log("Autoregressive model doesn't do masking"); - break; - } - default: { - console.log("What kind of model is this?"); - break; - } - } - }); - this.eventHandler.bind(_TextToken__WEBPACK_IMPORTED_MODULE_7__["TextTokens"].events.tokenMouseOver, (e) => { - chooseShowBySide(this.uiConf.token(), e); - }); - this.eventHandler.bind(_TextToken__WEBPACK_IMPORTED_MODULE_7__["TextTokens"].events.tokenMouseOut, (e) => { - chooseShowAll(this.uiConf.token()); - }); - this.eventHandler.bind(_TextToken__WEBPACK_IMPORTED_MODULE_7__["TextTokens"].events.tokenClick, (e) => { - const tokToggle = () => { - this.uiConf.toggleToken(e); - this._toggleTokenSel(); - showBySide(e); - }; - tokToggle(); - this.renderAttHead(); - }); - this.eventHandler.bind(_AttentionHeadBox__WEBPACK_IMPORTED_MODULE_8__["AttentionHeadBox"].events.rowMouseOver, (e) => { - self.sels.atnHeads.headInfo.style('visibility', 'visible'); - }); - this.eventHandler.bind(_AttentionHeadBox__WEBPACK_IMPORTED_MODULE_8__["AttentionHeadBox"].events.rowMouseOut, () => { - self.sels.atnHeads.headInfo.style('visibility', 'hidden'); - // Don't do anything special on row mouse out - }); - this.eventHandler.bind(_AttentionHeadBox__WEBPACK_IMPORTED_MODULE_8__["AttentionHeadBox"].events.boxMouseOver, (e) => { - const updateMat = this.attCapsule.byHead(e.head); - this.vizs.attentionSvg.data(updateMat); - this.vizs.attentionSvg.update(updateMat); - showBySide(this.uiConf.token()); - }); - this.eventHandler.bind(_AttentionHeadBox__WEBPACK_IMPORTED_MODULE_8__["AttentionHeadBox"].events.boxMouseOut, () => { - const att = this.attCapsule.byHeads(this.uiConf.heads()); - this.vizs.attentionSvg.data(att); - this.vizs.attentionSvg.update(att); - showBySide(this.uiConf.token()); - }); - this.eventHandler.bind(_AttentionHeadBox__WEBPACK_IMPORTED_MODULE_8__["AttentionHeadBox"].events.boxMouseMove, (e) => { - const headInfo = self.sels.atnHeads.headInfo; - let left, top, borderRadius; - if (e.side == "left") { - const divOffset = [12, 3]; - left = e.mouse[0] + e.baseX - (+headInfo.style('width').replace('px', '') + divOffset[0]); - top = e.mouse[1] + e.baseY - (+headInfo.style('height').replace('px', '') + divOffset[1]); - borderRadius = "8px 8px 1px 8px"; - } - else { - const divOffset = [-13, 3]; - left = e.mouse[0] + e.baseX + divOffset[0]; - top = e.mouse[1] + e.baseY - (+headInfo.style('height').replace('px', '') + divOffset[1]); - borderRadius = "8px 8px 8px 1px"; - } - headInfo - .style('visibility', 'visible') - .style('left', String(left) + 'px') - .style('top', String(top) + 'px') - .style('border-radius', borderRadius) - .text(`Head: ${e.ind + 1}`); - // Don't do anything special on row mouse over - }); - this.eventHandler.bind(_AttentionHeadBox__WEBPACK_IMPORTED_MODULE_8__["AttentionHeadBox"].events.boxClick, (e) => { - const result = this.uiConf.toggleHead(e.head); - if (result == _etc_types__WEBPACK_IMPORTED_MODULE_3__["Toggled"].ADDED) { - selectHead(e.head); - } - else if (result == _etc_types__WEBPACK_IMPORTED_MODULE_3__["Toggled"].REMOVED) { - unselectHead(e.head); - } - this._renderHeadSummary(); - this.renderSvg(); - }); - } - _toggleTokenSel() { - const e = this.uiConf.token(); - const alreadySelected = d3__WEBPACK_IMPORTED_MODULE_0__["select"]('.selected-token'); - // If no token should be selected, unselect all tokens - if (!this.uiConf.hasToken()) { - const newSel = d3__WEBPACK_IMPORTED_MODULE_0__["selectAll"]('.selected-token'); - if (!newSel.empty()) - newSel.classed('selected-token', false); - } - // Otherwise, select the indicated token - else { - const token2String = (e) => `#${e.side}-token-${e.ind}`; - const newSel = d3__WEBPACK_IMPORTED_MODULE_0__["select"](token2String(e)); - // Check that selection exists - if (!newSel.empty()) - newSel.classed('selected-token', true); - } - // Remove previous token selection, if any - if (!alreadySelected.empty()) { - alreadySelected.classed('selected-token', false); - } - if (this.uiConf.modelKind() == _etc_types__WEBPACK_IMPORTED_MODULE_3__["ModelKind"].Autoregressive) { - this.grayToggle(+e.ind); - this.markNextToggle(+e.ind, this.tokCapsule.a.length()); - } - } - /** Gray all tokens that have index greater than ind */ - grayBadToks(ind) { - if (this.uiConf.modelKind() == _etc_types__WEBPACK_IMPORTED_MODULE_3__["ModelKind"].Autoregressive) { - const grayToks = function (d, i) { - const s = d3__WEBPACK_IMPORTED_MODULE_0__["select"](this); - s.classed("masked-token", i > ind); - }; - d3__WEBPACK_IMPORTED_MODULE_0__["selectAll"]('.right-token').each(grayToks); - d3__WEBPACK_IMPORTED_MODULE_0__["selectAll"]('.left-token').each(grayToks); - } - } - grayToggle(ind) { - if (this.uiConf.hasToken()) - this.grayBadToks(ind); - else - d3__WEBPACK_IMPORTED_MODULE_0__["selectAll"]('.token').classed('masked-token', false); - } - markNextWordToks(ind, N) { - const markToks = function (d, i) { - const s = d3__WEBPACK_IMPORTED_MODULE_0__["select"](this); - s.classed("next-token", i == Math.min(ind + 1, N)); - }; - d3__WEBPACK_IMPORTED_MODULE_0__["selectAll"]('.right-token').each(markToks); - d3__WEBPACK_IMPORTED_MODULE_0__["selectAll"]('.left-token').each(markToks); - } - markNextToggle(ind, N) { - if (this.uiConf.hasToken()) - this.markNextWordToks(ind, N); - else - d3__WEBPACK_IMPORTED_MODULE_0__["selectAll"]('.token').classed('next-token', false); - } - _staticInits() { - this._initSentenceForm(); - this._initModelSelection(); - this._renderHeadSummary(); - this._initToggle(); - this.renderAttHead(); - this.renderTokens(); - } - _initSentenceForm() { - const self = this; - this.sels.form.sentenceA.attr('placeholder', "Enter new sentence to analyze"); - this.sels.form.sentenceA.attr('value', this.uiConf.sentence()); - const submitNewSentence = () => { - // replace all occurences of '#' in sentence as this causes the API to break - const sentence_a = this.sels.form.sentenceA.property("value").replace(/\#/g, ''); - // Only update if the form is filled correctly - if (sentence_a.length) { - this.sels.body.style("cursor", "progress"); - this.api.getMetaAttentions(this.uiConf.model(), sentence_a, this.uiConf.layer()) - .then((resp) => { - const r = resp.payload; - this.uiConf.sentence(sentence_a); - this.uiConf.rmToken(); - this.attCapsule.updateFromNormal(r, this.uiConf.hideClsSep()); - this.tokCapsule.updateFromResponse(r); - this._toggleTokenSel(); - this.update(); - this.sels.body.style("cursor", "default"); - }); - } - }; - const onEnter = ramda__WEBPACK_IMPORTED_MODULE_2__["curry"]((keyCode, f, event) => { - const e = event || window.event; - if (e.keyCode !== keyCode) - return; - e.preventDefault(); - f(); - }); - const onEnterSubmit = onEnter(13, submitNewSentence); - const btn = this.sels.form.button; - const inputBox = this.sels.form.sentenceA; - btn.on("click", submitNewSentence); - inputBox.on('keypress', onEnterSubmit); - } - _renderHeadSummary() { - this.sels.selectedHeads - .html(ramda__WEBPACK_IMPORTED_MODULE_2__["join"](', ', this.uiConf.heads().map(h => h + 1))); - } - initLayers(nLayers) { - const self = this; - let hasActive = false; - const checkboxes = self.sels.layerCheckboxes.selectAll(".layerCheckbox") - .data(lodash__WEBPACK_IMPORTED_MODULE_1__["range"](0, nLayers)) - .join("label") - .attr("class", "btn button layerCheckbox") - .classed('active', (d, i) => { - // Assign to largest layer available if uiConf.layer() > new nLayers - if (d == self.uiConf.layer()) { // Javascript is 0 indexed! - hasActive = true; - return true; - } - if (!hasActive && d == nLayers) { - self.uiConf.layer(d); - hasActive = true; - return true; - } - return false; - }) - .text((d) => d + 1) - .append("input") - .attr("type", "radio") - .attr("class", "checkbox-inline") - .attr("name", "layerbox") - // .attr("head", d => d) - .attr("id", (d, i) => "layerCheckbox" + i); - // .text((d, i) => d + " ") - Object(rxjs__WEBPACK_IMPORTED_MODULE_14__["fromEvent"])(checkboxes.nodes(), 'change').pipe(Object(rxjs_operators__WEBPACK_IMPORTED_MODULE_15__["tap"])((e) => { - const myData = d3__WEBPACK_IMPORTED_MODULE_0__["select"](e.target).datum(); - console.log(myData, "--- myData"); - this.sels.layerCheckboxes.selectAll(".layerCheckbox") - .classed('active', d => d === myData); - }), Object(rxjs_operators__WEBPACK_IMPORTED_MODULE_15__["map"])((v) => +d3__WEBPACK_IMPORTED_MODULE_0__["select"](v.target).datum()), Object(rxjs_operators__WEBPACK_IMPORTED_MODULE_15__["tap"])(v => { - console.log("New layer: ", v); - self.uiConf.layer(v); - self.sels.body.style("cursor", "progress"); - }), Object(rxjs_operators__WEBPACK_IMPORTED_MODULE_15__["switchMap"])((v) => Object(rxjs__WEBPACK_IMPORTED_MODULE_14__["from"])(self.api.updateMaskedAttentions(self.uiConf.model(), self.tokCapsule.a, self.uiConf.sentence(), v)))).subscribe({ - next: (resp) => { - const r = resp.payload; - self.attCapsule.updateFromNormal(r, this.uiConf.hideClsSep()); - self.tokCapsule.updateTokens(r); - self.uiConf.maskInds(self.tokCapsule.a.maskInds); - self.update(); - self.sels.body.style("cursor", "default"); - self._toggleTokenSel(); - } - }); - const layerId = `#layerCheckbox${this.uiConf.layer()}`; - console.log("Layer ID: ", layerId); - d3__WEBPACK_IMPORTED_MODULE_0__["select"](layerId).attr("checked", "checked"); - // Init threshold stuff - const dispThresh = (thresh) => Math.round(thresh * 100); - d3__WEBPACK_IMPORTED_MODULE_0__["select"]('#my-range-value').text(dispThresh(self.uiConf.threshold())); - this.sels.threshSlider.on("input", lodash__WEBPACK_IMPORTED_MODULE_1__["throttle"](function () { - const node = this; - self.uiConf.threshold(+node.value / 100); - d3__WEBPACK_IMPORTED_MODULE_0__["select"]('#my-range-value').text(dispThresh(self.uiConf.threshold())); - self.vizs.attentionSvg.threshold(self.uiConf.threshold()); - }, 100)); - this.sels.headSelectAll.on("click", function () { - self.uiConf.selectAllHeads(); - self.renderSvg(); - self.renderAttHead(); - }); - this.sels.headSelectNone.on("click", function () { - self.uiConf.selectNoHeads(); - self.renderSvg(); - self.renderAttHead(); - _etc_Util__WEBPACK_IMPORTED_MODULE_13__["Sel"].setHidden(".atn-curve"); - }); - } - _initToggle() { - Object(rxjs__WEBPACK_IMPORTED_MODULE_14__["fromEvent"])(this.sels.clsToggle.node(), 'input').pipe( - // @ts-ignore -- TODO: FIX ! - Object(rxjs_operators__WEBPACK_IMPORTED_MODULE_15__["map"])(e => e.srcElement.checked)).subscribe({ - next: v => { - this.uiConf.hideClsSep(v); - this.attCapsule.zeroed(v); - this.renderSvg(); - this.renderAttHead(); - } - }); - } - _initModelSelection() { - const self = this; - // Below are the available models. Will need to choose 3 to be available ONLY - const data = [ - { name: "bert-base-cased", kind: _etc_types__WEBPACK_IMPORTED_MODULE_3__["ModelKind"].Bidirectional }, - { name: "bert-base-uncased", kind: _etc_types__WEBPACK_IMPORTED_MODULE_3__["ModelKind"].Bidirectional }, - { name: "bert-base-german-cased", kind: _etc_types__WEBPACK_IMPORTED_MODULE_3__["ModelKind"].Bidirectional }, - { name: "xlm-mlm-en-2048", kind: _etc_types__WEBPACK_IMPORTED_MODULE_3__["ModelKind"].Bidirectional }, - { name: "distilbert-base-uncased", kind: _etc_types__WEBPACK_IMPORTED_MODULE_3__["ModelKind"].Bidirectional }, - { name: "distilroberta-base", kind: _etc_types__WEBPACK_IMPORTED_MODULE_3__["ModelKind"].Bidirectional }, - { name: "albert-base-v1", kind: _etc_types__WEBPACK_IMPORTED_MODULE_3__["ModelKind"].Bidirectional }, - { name: "albert-xxlarge-v2", kind: _etc_types__WEBPACK_IMPORTED_MODULE_3__["ModelKind"].Bidirectional }, - { name: "xlm-roberta-base", kind: _etc_types__WEBPACK_IMPORTED_MODULE_3__["ModelKind"].Bidirectional }, - // { name: "t5-small", kind: tp.ModelKind.Autoregressive }, - { name: "roberta-base", kind: _etc_types__WEBPACK_IMPORTED_MODULE_3__["ModelKind"].Bidirectional }, - { name: "gpt2", kind: _etc_types__WEBPACK_IMPORTED_MODULE_3__["ModelKind"].Autoregressive }, - { name: "distilgpt2", kind: _etc_types__WEBPACK_IMPORTED_MODULE_3__["ModelKind"].Autoregressive }, - ]; - const names = ramda__WEBPACK_IMPORTED_MODULE_2__["map"](ramda__WEBPACK_IMPORTED_MODULE_2__["prop"]('name'))(data); - const kinds = ramda__WEBPACK_IMPORTED_MODULE_2__["map"](ramda__WEBPACK_IMPORTED_MODULE_2__["prop"]('kind'))(data); - const kindmap = ramda__WEBPACK_IMPORTED_MODULE_2__["zipObj"](names, kinds); - this.sels.modelSelector.selectAll('.model-option') - .data(data) - .join('option') - .classed('model-option', true) - .property('value', d => d.name) - .attr("modelkind", d => d.kind) - .text(d => d.name); - this.sels.modelSelector.property('value', this.uiConf.model()); - this.sels.modelSelector.on('change', function () { - const me = d3__WEBPACK_IMPORTED_MODULE_0__["select"](this); - const mname = me.property('value'); - self.uiConf.model(mname); - self.uiConf.modelKind(kindmap[mname]); - if (kindmap[mname] == _etc_types__WEBPACK_IMPORTED_MODULE_3__["ModelKind"].Autoregressive) { - console.log("RESETTING MASK INDS"); - self.uiConf.maskInds([]); - } - self.mainInit(); - }); - } - renderAttHead() { - const heads = lodash__WEBPACK_IMPORTED_MODULE_1__["range"](0, this.uiConf._nHeads); - const focusAtt = this.attCapsule.att; - const token = this.uiConf.hasToken() ? this.uiConf.token() : null; - //@ts-ignore - const leftAttInfo = Object(_AttentionHeadBox__WEBPACK_IMPORTED_MODULE_8__["getAttentionInfo"])(focusAtt, heads, "left", token); - //@ts-ignore - const rightAttInfo = Object(_AttentionHeadBox__WEBPACK_IMPORTED_MODULE_8__["getAttentionInfo"])(focusAtt, heads, "right", token); - this.vizs.leftHeads.options.offset = this.uiConf.offset; - this.vizs.leftHeads.update(leftAttInfo); - this.vizs.rightHeads.update(rightAttInfo); - this._renderHeadSummary(); - // Make sure - heads.forEach((h) => { - if (this.uiConf.headSet().has(h)) { - selectHead(h); - } - else { - unselectHead(h); - } - }); - } - ; - renderTokens() { - const left = this.tokCapsule[this.uiConf.attType[0]]; - const right = this.tokCapsule[this.uiConf.attType[1]]; - console.log("now: ", this.uiConf.offset); - this.vizs.tokens.left.options.offset = this.uiConf.offset; - this.vizs.tokens.left.update(left.tokenData); - this.vizs.tokens.left.mask(left.maskInds); - this.vizs.tokens.right.update(right.tokenData); - this.vizs.tokens.right.mask(right.maskInds); - // displaySelectedToken - } - renderSvg() { - const att = this.attCapsule.byHeads(this.uiConf.heads()); - this.vizs.attentionSvg.options.offset = this.uiConf.offset; - const svg = this.vizs.attentionSvg.data(att); - svg.update(att); - const maxTokens = lodash__WEBPACK_IMPORTED_MODULE_1__["max"]([this.tokCapsule.a.length()]); - const newHeight = svg.options.boxheight * maxTokens; - svg.height(newHeight); - // Don't redisplay everything if one token is selected - showBySide(this.uiConf.token()); - } - ; - render() { - this.renderTokens(); - this.renderSvg(); - this.renderAttHead(); - } - update() { - this.render(); - } -} - - -/***/ }), - -/***/ "./ts/vis/staticLayout.ts": -/*!********************************!*\ - !*** ./ts/vis/staticLayout.ts ***! - \********************************/ -/*! exports provided: createStaticSkeleton */ -/***/ (function(module, __webpack_exports__, __webpack_require__) { - -"use strict"; -__webpack_require__.r(__webpack_exports__); -/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "createStaticSkeleton", function() { return createStaticSkeleton; }); -/* harmony import */ var d3__WEBPACK_IMPORTED_MODULE_0__ = __webpack_require__(/*! d3 */ "./node_modules/d3/index.js"); - -function createStaticSkeleton(base) { - // ---- TEMPLATE ----- - // language=HTML - function template() { - return ` - - --- --- - - -- --- -Filters --- - Hide Special Tokens - - --- Show top 70% of att: -- - --- -- Layer --- ---- -- Selected heads: --- ---- - `; - } - base.html(template); - // ---- TEMPLATE.end ----- - /** - * Top level sections - */ - const sentenceInput = base.select("#sentence-input"); - const connectorContainer = base.select('#connector-container'); - const atnControls = base.select('#connector-controls'); - const atnContainer = base.select('#atn-container'); - /** - * Sentence Input - */ - const sentenceA = base.select('#form-sentence-a'); - const formButton = base.select('#update-sentence'); - /** - * Connector Controls - */ - const modelSelector = base.select('#model-option-selector'); - const threshSlider = base.select('#my-range'); - const layerCheckboxes = base.select('#layer-select'); - const clsToggle = base.select('#cls-toggle'); - const selectedHeads = base.select('#selected-heads'); - const headSelectAll = base.select('#select-all-heads'); - const headSelectNone = base.select('#select-no-heads'); - /** - * For main attention vis - */ - const headInfoBox = base.select('#head-info-box'); - const headBoxLeft = base.select('#left-att-heads'); - const tokensLeft = base.select('#left-tokens'); - const atnDisplay = base.select('#atn-display'); - const tokensRight = base.select('#right-tokens'); - const headBoxRight = base.select('#right-att-heads'); - /** - * Return an object that provides handles to the important parts here - */ - return { - body: d3__WEBPACK_IMPORTED_MODULE_0__["select"]('body'), - atnContainer: atnContainer, - atnDisplay: atnDisplay, - atnHeads: { - left: headBoxLeft, - right: headBoxRight, - headInfo: headInfoBox - }, - form: { - sentenceA: sentenceA, - button: formButton - }, - tokens: { - left: tokensLeft, - right: tokensRight - }, - modelSelector: modelSelector, - clsToggle: clsToggle, - layerCheckboxes: 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\"c1bdb8cbafd5d5d8f6bda2b4db16b6bc2d62e18a.json\",\n\t\"2b81a8f602251cf40b4682c8cedb3966b912d7c6\": \"2b81a8f602251cf40b4682c8cedb3966b912d7c6.json\",\n\t\"8f94e843d2510047fd1af46f249afe87ff49cc2f\": \"8f94e843d2510047fd1af46f249afe87ff49cc2f.json\",\n\t\"4e3ddb51550c03c64fdbe5592526665100d32930\": \"4e3ddb51550c03c64fdbe5592526665100d32930.json\",\n\t\"f816b23af9aa4312c80e668a66a156c934cb330c\": \"f816b23af9aa4312c80e668a66a156c934cb330c.json\",\n\t\"2fe05a911a574b8a6d4b05eb13db9e09aaacad2d\": \"2fe05a911a574b8a6d4b05eb13db9e09aaacad2d.json\",\n\t\"5c71496726395aaa4d0a65373fd1651b5a0e6b1f\": \"5c71496726395aaa4d0a65373fd1651b5a0e6b1f.json\",\n}","import * as d3 from 'd3';\nimport { debug } from 'util';\nimport { TokenDisplay } from '../data/TokenWrapper'\nimport * as tp from '../etc/types'\nimport * as rsp from './responses'\nimport * as R from 'ramda'\nimport { DemoAPI } from './demoAPI'\nimport * as hash from 'object-hash'\nimport { makeUrl, toPayload } from '../etc/apiHelpers'\nimport { URLHandler } from '../etc/URLHandler';\n\nexport const emptyTokenDisplay = new TokenDisplay()\n\nconst baseurl = URLHandler.basicURL()\n\n/**\n * A rewrite of `d3-fetch`'s `d3.json` callback. If an api call fails, make a backup call to specified url and payload, if specified.\n * \n * @param response Object expected at time of callback\n * @param backupUrl Backup url in the event of fail\n * @param backupPayload Backup payload if making a post request\n */\nfunction responseJson(response, backupUrl = null, backupPayload = null) {\n    if (!response.ok) {\n        if (backupUrl != null) {\n            console.log(\"STATIC FILE NOT FOUND\");\n            return fetch(backupUrl, backupPayload).then(responseJson);\n        }\n        throw new Error(response.status + \" \" + response.statusText)\n    }\n    return response.json()\n}\n\n/**\n * Check first if the information being sent exists in a static demo file. If it does, send that. Otherwise, make a normal call to the server.\n * \n * @param toSend The packet of information to send to an API endpoint\n * @param backupUrl Backup url in the event that the demo file is not found\n * @param backupPayload Backup payload if demo file not found, for POST requests only\n */\nfunction checkDemoAPI(toSend, backupUrl = null, backupPayload = null) {\n    const hsh = hash.sha1(toSend);\n    console.log(\"CHECKING DEMOAPI: \" + hsh);\n    if (DemoAPI.hasOwnProperty(hsh)) {\n        // Relies on a symbolic link being present in the dist folder to the demo folder\n        const path = './demo/' + DemoAPI[hsh]\n        console.log(\"TRYING TO SENDING STATIC: \", path);\n        const follow = (response) => responseJson(response, backupUrl, backupPayload)\n        return fetch(path).then(follow)\n    }\n    return d3.json(backupUrl, backupPayload)\n}\n\n\nexport class API {\n\n    constructor(private baseURL: string = null) {\n        if (this.baseURL == null) {\n            this.baseURL = baseurl + '/api';\n        }\n    }\n\n    getModelDetails(model: string, hashObj: {} | null = null): Promise<rsp.ModelDetailResponse> {\n        const toSend = {\n            model: model\n        }\n\n        const url = makeUrl(this.baseURL + \"/get-model-details\", toSend)\n        console.log(\"--- GET \" + url);\n\n        if (hashObj != null) {\n            const key = hash.sha1(toSend)\n            d3.json(url).then(r => {\n                hashObj[key] = r;\n            })\n        }\n\n        return checkDemoAPI(toSend, url)\n    }\n\n    getMetaAttentions(model: string, sentence: string, layer: number, hashObj: {} | null = null): Promise<rsp.AttentionDetailsResponse> {\n        const toSend = {\n            model: model,\n            sentence: sentence,\n            layer: layer\n        };\n\n        const url = makeUrl(this.baseURL + \"/attend+meta\", toSend)\n        console.log(\"--- GET \" + url);\n\n        // Add hash and value to hashObj\n        if (hashObj != null) {\n            const key = hash.sha1(toSend)\n            d3.json(url).then(r => {\n                hashObj[key] = r;\n            })\n        }\n\n        return checkDemoAPI(toSend, url)\n    }\n\n    /**\n     * Update the display based on the information that was already parsed from the passed sentence.\n     * \n     * @param a The displayed tokens in the columns \n     * @param sentenceA The original sentence that led to the tokenized information in `a`\n     * @param layer Which layer to search at\n     * @param hashObj If not null, store the information of the responses into the passed object. Used for creating demos.\n     */\n    updateMaskedAttentions(model: string, tokens: TokenDisplay, sentence: string, layer: number, hashObj: {} | null = null): Promise<rsp.AttentionDetailsResponse> {\n        const toSend = {\n            model: model,\n            tokens: R.map(R.prop('text'), tokens.tokenData),\n            sentence: sentence,\n\n            // Empty masks need to be sent as a number, unfortunately. Choosing -1 for this\n            mask: tokens.maskInds.length ? tokens.maskInds : [-1],\n            layer: layer,\n        }\n\n        const url = makeUrl(this.baseURL + '/update-mask');\n        const payload = toPayload(toSend)\n\n\n        if (hashObj != null) {\n            // Add hash and value to hashObj for demo purposes\n            const key = hash.sha1(toSend)\n            d3.json(url, payload).then(r => {\n                hashObj[key] = r;\n            })\n        }\n\n        console.log(\"--- POST \" + url, payload);\n\n        return checkDemoAPI(toSend, url, payload)\n    }\n};","import * as _ from 'lodash'\nimport * as x_ from '../etc/_Tools'\nimport * as tp from '../etc/types'\nimport * as tf from '@tensorflow/tfjs'\n\n/**\n * Notes:\n * \n * - Also encapsulate the CLS/SEP info vs. no CLS/SEP info\n * - When layer format changes from list, drop the index into conf.layer\n */\n\nconst bpeTokens = [\"[CLS]\", \"[SEP]\", \"<s>\", \"</s>\", \"<|endoftext|>\"]\nconst findBadIndexes = (x: tp.FullSingleTokenInfo[]) => x_.findAllIndexes(x.map(t => t.text), (a) => _.includes(bpeTokens, a))\n\nexport function makeFromMetaResponse(r:tp.AttentionResponse, isZeroed){\n    const key = 'aa' // Change this if backend response changes to be simpler\n    const currPair = r[key]\n    const left = <tp.FullSingleTokenInfo[]>currPair.left\n    const right = <tp.FullSingleTokenInfo[]>currPair.right\n    const leftZero = x_.findAllIndexes(left.map(t => t.text), (a) => _.includes(bpeTokens, a))\n    const rightZero = x_.findAllIndexes(right.map(t => t.text), (a) => _.includes(bpeTokens, a))\n    return new AttentionWrapper(currPair.att, [leftZero, rightZero], isZeroed)\n}\n\nexport class AttentionWrapper {\n    protected _att:number[][][]\n    protected _attTensor:tf.Tensor3D\n    protected _zeroedAttTensor:tf.Tensor3D\n\n    badToks:[number[], number[]] // Indexes for the CLS and SEP tokens\n    isZeroed: boolean\n    nLayers = 12;\n    nHeads = 12;\n\n    constructor(att:number[][][], badToks:[number[], number[]]=[[],[]], isZeroed=true){\n        this.init(att, badToks, isZeroed)\n    }\n\n    init(att:number[][][], badToks:[number[], number[]]=[[],[]], isZeroed) {\n        this.isZeroed = isZeroed\n        this._att = att;\n        this._zeroedAttTensor = zeroRowCol(tf.tensor3d(att), badToks[0], badToks[1])\n        this._attTensor = tf.tensor3d(att) // If I put this first, buffer modifications change this too.\n        this.badToks = badToks;\n    }\n\n    updateFromNormal(r:tp.AttentionResponse, isZeroed){\n        const key = 'aa' // Change this if backend response changes to be simpler\n        const currPair = r[key]\n        const left = <tp.FullSingleTokenInfo[]>currPair.left\n        const right = <tp.FullSingleTokenInfo[]>currPair.right\n\n        const leftZero = findBadIndexes(left)\n        const rightZero = findBadIndexes(right)\n        this.init(currPair.att, [leftZero, rightZero], isZeroed)\n    }\n\n    get attTensor() {\n        const tens = this.isZeroed ? this._zeroedAttTensor : this._attTensor\n        return tens\n    }\n\n    get att() {\n        return this.attTensor.arraySync()\n    }\n\n    zeroed(): boolean\n    zeroed(val:boolean): this\n    zeroed(val?) {\n        if (val == null) return this.isZeroed\n        this.isZeroed = val\n        return this\n    }\n\n    toggleZeroing() {\n        this.zeroed(!this.zeroed())\n    }\n\n    protected _byHeads(heads:number[]):tf.Tensor2D {\n        if (heads.length == 0) {\n            return tf.zerosLike(this._byHead(0))\n        }\n\n        return (<tf.Tensor2D>this.attTensor.gather(heads, 0).sum(0))\n    }\n\n    protected _byHead(head:number):tf.Tensor2D {\n        return (<tf.Tensor2D>this.attTensor.gather([head], 0).squeeze([0]))\n    }\n\n    byHeads(heads:number[]):number[][] {\n        return this._byHeads(heads).arraySync()\n    }\n\n    byHead(head:number):number[][] {\n        return this._byHead(head).arraySync()\n    }\n}\n\nfunction zeroRowCol(tens:tf.Tensor3D, rows:number[], cols:number[]):tf.Tensor3D {\n    let outTens = tens.clone()\n    let atb = outTens.bufferSync()\n    _.range(atb.shape[0]).forEach((head) => {\n        _.range(atb.shape[1]).forEach((i) => {\n            // Set rows to 0\n            if (_.includes(rows, i)) {\n                _.range(atb.shape[2]).forEach((j) => {\n                    atb.set(0, head, i, j)\n                })\n            }\n\n            // Set cols to 0\n            _.range(atb.shape[2]).forEach((j) => {\n                if (_.includes(cols, j))\n                    _.range(atb.shape[1]).forEach((i) => {\n                        atb.set(0, head, i, j)\n                    })\n            })\n        })\n    })\n\n    return outTens\n}","import * as x_ from '../etc/_Tools'\nimport * as _ from 'lodash'\nimport * as tp from '../etc/types'\nimport * as R from 'ramda'\n\n/**\n * The original tokens, and the indexes that need to be masked\n */\n const emptyFullResponse: tp.FullSingleTokenInfo[] = [{\n     text: '[SEP]',\n     topk_words: [],\n     topk_probs: []\n }]\n\nexport class TokenDisplay  {\n    tokenData:tp.FullSingleTokenInfo[]\n    maskInds:number[]\n\n    constructor(tokens=emptyFullResponse, maskInds=[]){\n        this.tokenData = tokens;\n        this.maskInds = maskInds;\n    }\n\n    /**\n     * Push idx to the mask idx list in order from smallest to largest\n     */\n    mask(val) {\n        const currInd = _.indexOf(this.maskInds, val)\n        if (currInd == -1) {\n            x_.orderedInsert_(this.maskInds, val)\n        }\n        else {\n            console.log(`${val} already in maskInds!`);\n            console.log(this.maskInds);\n        }\n    }\n\n    toggle(val) {\n        const currInd = _.indexOf(this.maskInds, val)\n        if (currInd == -1) {\n            console.log(`Masking ${val}`);\n            this.mask(val)\n        }\n        else {\n            console.log(`Unmasking ${val}`);\n            this.unmask(val)\n        }\n    }\n\n    unmask(val) {\n        _.pull(this.maskInds, val);\n    }\n\n    resetMask() {\n        this.maskInds = [];\n    }\n\n    length() {\n        return this.tokenData.length;\n    }\n\n    concat(other: TokenDisplay) {\n        const newTokens = _.concat(this.tokenData, other.tokenData);\n        const newMask = _.concat(this.maskInds, other.maskInds.map(x => x + this.length()));\n        return new TokenDisplay(newTokens, newMask);\n    }\n}\n\nexport class TokenWrapper {\n    a: TokenDisplay\n\n    constructor(r:tp.AttentionResponse){\n        this.updateFromResponse(r);\n    }\n\n    updateFromResponse(r:tp.AttentionResponse) {\n        const tokensA = r.aa.left;\n        this.updateFromComponents(tokensA, [])\n    }\n\n    updateFromComponents(a:tp.FullSingleTokenInfo[], maskA:number[]){\n        this.a = new TokenDisplay(a, maskA)\n    }\n\n    updateTokens(r: tp.AttentionResponse) {\n        const desiredKeys = ['contexts', 'embeddings', 'topk_probs', 'topk_words']\n        const newTokens = r.aa.left.map(v => R.pick(desiredKeys, v))\n\n        const pairs = R.zip(this.a.tokenData, newTokens)\n\n        pairs.forEach((d, i) => {\n            Object.keys(d[1]).map(k => {\n                d[0][k] = d[1][k]\n            })\n        })\n\n    }\n\n    /**\n     * Mask the appropriate sentence at the index indicated\n     */\n    mask(sID:tp.TokenOptions, idx:number){\n        this[sID].mask(idx)\n        const opts = [\"a\", \"b\"]\n        const Na = this.a.length();\n    }\n}\n\nexport function sideToLetter(side:tp.SideOptions, atype:tp.SentenceOptions){\n    // const atype = conf.attType;\n    if (atype == \"all\") {\n        return \"all\"\n    }\n    const out = side == \"left\" ? atype[0] : atype[1] // No type checking?\n    return out\n}\n","import * as d3 from \"d3\"\nimport 'd3-selection-multi'\nimport { D3Sel } from '../etc/Util'\n\n/**\n * Created by hen on 5/15/17.\n * Modified by hoo on 4/16/19.\n */\nexport class SVG {\n    static translate({x, y}):string {\n        return \"translate(\" + x + \",\" + y + \")\"\n    }\n\n    static rotate(deg):string {\n        return `rotate(${deg})`\n    }\n\n    static group(parent, classes, pos = {x: 0, y: 0}) {\n        return parent.append('g').attrs({\n            class: classes,\n            \"transform\": SVG.translate(pos)\n        })\n    }\n\n}\n\nexport class SVGMeasurements {\n\n    private measureElement: d3.Selection<any, any, any, any>;\n\n    constructor(baseElement, classes = '') {\n        this.measureElement = baseElement.append('text')\n            .attrs({x: 0, y: -20, class: classes})\n\n    }\n\n    textLength(text, style = null) {\n        this.measureElement.attr('style', style);\n        this.measureElement.text(text);\n        const tl = (<SVGTextElement> this.measureElement.node()).getComputedTextLength();\n        this.measureElement.text('');\n\n        return tl;\n    }\n}","/**\n * Created by hen on 5/15/17.\n * Modified by hoo on 4/16/19.\n */\nexport class SimpleEventHandler {\n\n    element: Element;\n    eventListeners: object[];\n\n    constructor(element: Element) {\n        this.element = element;\n        this.eventListeners = []\n    }\n\n    bind(eventNames: string, eventFunction: Function) {\n        for (const eventName of eventNames.split(' ')) {\n            this.eventListeners.push({eventName, eventFunction});\n            const eventFunctionWrap = e => eventFunction(e.detail, e);\n            this.element.addEventListener(eventName, eventFunctionWrap, false);\n        }\n    }\n\n    getListeners() {\n        return this.eventListeners;\n    }\n\n    trigger(eventName: string, detail: object) {\n        this.element.dispatchEvent(new CustomEvent(eventName, {detail}));\n    }\n}","/**\n * Created by hen on 5/15/17.\n */\n\nexport class URLHandler {\n\n    static basicURL() {\n        const url_path = window.location.pathname.split('/').slice(0, -2).join('/');\n\n        return window.location.origin + (url_path.length ? url_path : '');\n    }\n\n    /**\n     * Read all URL parameters into a map.\n     * @returns {Map} the url parameters as a key-value store (ES6 map)\n     */\n    static get parameters(): object {\n        // Adapted from:  http://stackoverflow.com/questions/2090551/parse-query-string-in-javascript\n        const query = window.location.search.substring(1);\n        const vars = query.split('&');\n        console.log(vars, \"--- vars\");\n\n        const urlParameters = {};\n\n        const isInt = x => (/^[0-9]+$/).test(x);\n        const isFloat = x => (/^[0-9]+\\.[0-9]*$/).test(x);\n\n        const typeCast = val => {\n            if (isInt(val)) {\n                return Number.parseInt(val, 10);\n            } else if (isFloat(val)) {\n                return Number.parseFloat(val);\n            }\n            // else:\n            return val;\n        }\n\n\n        vars.forEach(v => {\n            if (v.length > 0) {\n                const splits = v.split('=');\n                const key = decodeURIComponent(splits[0]);\n                let raw_value = decodeURIComponent(splits[1]);\n\n                const isArray = raw_value.startsWith('..');\n                if (isArray) {\n                    raw_value = raw_value.slice(2);\n                }\n\n                if (raw_value.length < 1) {\n                    urlParameters[key] = isArray ? [] : '';\n                } else if (isArray) {\n                    urlParameters[key] = raw_value.split(',')\n                        .map(val => typeCast(val));\n                } else {\n                    urlParameters[key] = typeCast(raw_value);\n                }\n            }\n        });\n\n        return urlParameters;\n\n    }\n\n\n    /**\n     * Generates an URL string from a map of url parameters\n     * @param {{}} urlParameters - the map of parameters\n     * @returns {string} - an URI string\n     */\n    static urlString(urlParameters: object) {\n        const attr = [];\n        Object.keys(urlParameters).forEach(k => {\n            const v = urlParameters[k];\n            if (v !== undefined) {\n                let value = v;\n                if (Array.isArray(v)) value = '..' + v.join(',');\n                attr.push(encodeURI(k + '=' + value))\n            }\n        });\n\n\n        const url = window.location.pathname;\n        let res = url.substring(url.lastIndexOf('/') + 1);\n        if (attr.length > 0) {\n            res += '?' + attr.join('&')\n        }\n\n        return res;\n    }\n\n    static updateURLParam(key: string, value: string | any[], addToBrowserHistory = true) {\n        const currentParams = URLHandler.parameters;\n        currentParams[key] = value;\n        URLHandler.updateUrl(currentParams, addToBrowserHistory);\n    }\n\n    // /**\n    //  * Generates a key-value map of all URL params and replaces replaceKeys\n    //  * @param updateKeys\n    //  */\n    // static updateURLParams(updateKeys) {\n    //     const currentParams = URLHandler.parameters;\n    //     Object.keys(updateKeys).forEach((k) => currentParams[k] = updateKeys[k])\n    //     return currentParams;\n    // }\n\n\n    static updateUrl(urlParameters: object, addToBrowserHistory = true) {\n        if (addToBrowserHistory) {\n            window.history.pushState(urlParameters, '',\n                URLHandler.urlString(urlParameters))\n        } else {\n            window.history.replaceState(urlParameters, '',\n                URLHandler.urlString(urlParameters))\n        }\n    }\n\n}","import * as d3 from \"d3\";\nimport { BaseType } from \"d3\";\n\n/**\n * Created by hen on 5/15/17.\n * Modifyed by hoo on 4/16/19.\n */\nlet the_unique_id_counter = 0;\n\nexport class Util {\n    static simpleUId({ prefix = '' }): string {\n        the_unique_id_counter += 1;\n\n        return prefix + the_unique_id_counter;\n    }\n}\n\nexport type D3Sel = d3.Selection<any, any, any, any>\n\n/**\n * Selection utility functions should be static methods in the below class\n */\nexport class Sel {\n    static setSelVisible = (x: D3Sel) => x.attr(\"visibility\", \"visible\")\n    static setSelHidden = (x: D3Sel) => x.attr(\"visibility\", \"hidden\")\n    static setVisible = (x: string) => Sel.setSelVisible(d3.selectAll(x))\n    static setHidden = (x: string) => Sel.setSelHidden(d3.selectAll(x))\n    static hideElement = (hE: D3Sel) => hE.transition().styles({\n                                            'opacity': 0,\n                                            'pointer-events': 'none',\n                                            'display': 'none'})\n    static unhideElement = (hE: D3Sel) => hE.transition().styles({\n                                            'opacity': 1, \n                                            'pointer-events': null,\n                                            'display': null})\n}\n\nexport interface LooseObject {\n    [key: string]: any\n}\n\nexport type d3S<T extends BaseType, U = any> = d3.Selection<T, U, any, any>","import * as _ from 'lodash'\n\nfunction ascOrder(n1, n2) {\n    if (n1 < n2) {\n        return -1;\n    }\n    else if (n1 > n2) { \n        return 1;\n    }\n    return 0;\n}\n\nexport {findAllIndexes, insertAt_, orderedInsert_, set2SortedArray}\n\n/**\n * Find all indexes that match a particular predicate\n */\nfunction findAllIndexes<T>(array:Array<T>, predicate:(a:T) => boolean): number[] {\n    let fromIndex=0;\n    let results = [];\n\n    let i = _.findIndex(array, predicate, fromIndex);\n    while (i != -1) {\n        results.push(i);\n        i = _.findIndex(array, predicate, i+1)\n    }\n\n    return results;\n};\n\nfunction insertAt_<T>(array:Array<T>, val:T, ind:number):Array<T> {\n    array.splice(ind, 0, val);\n    return array\n}\n\n/**\n * Convert a set to an ordered array\n */\nfunction set2SortedArray<T>(input:Set<T>):Array<T> {\n    return Array.from(input).sort(ascOrder)\n}\n\n/**\n * Insert a value into array in sorted order IN PLACE\n * \n * WARNING: Only handles numbers, sorted from least to greatest\n * - Assumes already sorted array\n */\nfunction orderedInsert_<T>(array:Array<T>, val:T, coldstart=false):Array<T> {\n    // Resort array if desired\n    if (coldstart) {\n        array.sort(ascOrder)\n    }\n\n    const ind = _.sortedIndex(array, val);\n    return insertAt_(array, val, ind)\n}\n\nexport function makeRandom(len:number){\n  const a:number[] = new Array(len).fill(0)\n\n  return a.map((x) => {return _.random(-5, 5, true)})\n}","/**\n * Convert a JS object into GET URL parameters\n * \n * @param base Base URL atop which to add GET parameters\n * @param params Object to insert into a URL string\n */\nexport function makeUrl(base: string, params?: object):string {\n    if (params){\n        let out: string = base + \"?\";\n\n        Object.keys(params).forEach( k => {\n            out += k;\n            out += '=';\n            out += params[k];\n            out += \"&\";\n        })\n        return out.replace(/&$/g, \"\");\n    }\n    else {\n        return base;\n    }\n};\n\n/**\n * Convert information in GET request into the message for a POST request    \n */\nexport const toPayload = (toSend) => {return {\n    method:\"POST\",\n    body:JSON.stringify(toSend),\n    headers: {\n            \"Content-type\": \"application/json; charset=UTF-8\"\n        }\n}}","// [[val, ind], [val, ind]...]\ntype CompFnInd = (a:number[], b:number[]) => number;\n\nexport interface SortArray {\n    arr: number[],\n    sortIndices: number[],\n}\n\n/**\n * Copies and sorts an array while keeping track of the indices. Currently only supports sorting max -> min.\n * \n * @param arr - Array to be copied and sorted\n */\nexport function sortWithIndices(arr:number[], fn?:CompFnInd): SortArray {\n    // If fn is not provided, default to sorting by max value\n    if (!fn) {\n        fn = function(left, right) {\n        return left[0] > right[0] ? -1 : 1;\n        } \n    }\n\n    let out:SortArray = {\n        arr: [],\n        sortIndices: []\n    };\n\n    let idxTracker: number[][] = []\n    for (let i = 0; i < arr.length; i++) {\n      idxTracker[i] = [arr[i], i];\n    }\n\n    idxTracker.sort(function(left, right) {\n      return left[0] > right[0] ? -1 : 1;\n    });\n\n    for (var j = 0; j < arr.length; j++) {\n      out.sortIndices.push(idxTracker[j][1]);\n      out.arr[j] = idxTracker[j][0];\n    }\n\n    return out;\n  }","import { D3Sel } from \"./Util\"\n\n/**\n * ATTENTION RESPONSES FROM BACKEND\n *\n * Contain the attentions and embeddings for all combinations of returns from the backend\n */\n\nexport type ModelInfo = {\n    nlayers: number\n    nheads: number\n}\n\n\n/**\n * ATTENTION RESULTS FROM BACKEND\n *\n * These are the results that are encased in the 'aa' and 'ab' keys returned\n */\ntype AbstractAttentionResponse<T> = {\n    aa: T\n}\n\nexport type AttentionResponse = AbstractAttentionResponse<AttentionMetaResult>\nexport type AttentionMetaResult = AbstractAttentionResult<FullSingleTokenInfo[]>\n\nexport type FullSingleTokenInfo = {\n    text: string,\n    topk_words: string[],\n    topk_probs: number[]\n}\n\ninterface AbstractAttentionResult<T> {\n    att: number[][][],\n    left: T,\n    right: T,\n}\n\n/**\n * SEARCH RESULT TYPES\n */\n\ninterface MatchedTokAtt {\n    att: number[]\n    offset_to_max: number\n    loc_of_max: number\n}\n\n\ninterface MatchedAttentions {\n    in: MatchedTokAtt,\n    out: MatchedTokAtt,\n}\n\n/**\n * EVENT TYPES\n */\nexport interface TokenEvent {\n    sel?: D3Sel,\n    side: SideOptions,\n    ind: number | \"null\",\n}\n\nexport interface TokenEmbeddingEvent extends TokenEvent {\n    embeddings: number[]\n}\n\nexport type HeadBoxEvent = {\n    side: SideOptions,\n    ind: number,\n    head: number,\n}\n\n/**\n * MISCELLANEOUS TYPES\n */\n\nexport type SideOptions = \"left\" | \"right\"\n\nexport enum Toggled {\n    ADDED = 0,\n    REMOVED,\n}\n\nexport enum NormBy {\n    ROW = 0,\n    COL,\n    ALL\n}\n\nexport enum ModelKind {\n    Bidirectional = \"bidirectional\",\n    Autoregressive = \"autoregressive\"\n}\nexport type TokenOptions = \"a\" | \"b\" | \"all\"\nexport type SentenceOptions = \"ab\" | \"ba\" | \"aa\" | \"bb\" | \"all\";","import * as d3 from 'd3'\n\nd3.selection.prototype.clear = function() {  \n    this.selectAll('*').remove();\n    return this;\n}\n\nd3.selection.prototype.toggleClass = function(className) {  \n    this.classed(className, !this.classed(className));\n    return this;\n}\n\nd3.selection.prototype.show = function() {  \n    this.style('display', 'initial');\n    return this;\n}\n\nd3.selection.prototype.hide = function() {  \n    this.style('display', 'none');\n    return this;\n}\n\nd3.selection.prototype.toggle = function() {  \n    var isHidden = this.style('display') == 'none';\n    return this.style('display', isHidden ? 'inherit' : 'none');\n}\n\nd3.selection.prototype.after = function(tagName) {  \n    var elements = [];\n  \n    this.each(function() {\n      var element = document.createElement(tagName);\n      this.parentNode.insertBefore(element, this.nextSibling);\n      elements.push(element);\n    });\n  \n    return d3.selectAll(elements);\n  }\n\nd3.selection.prototype.before = function(tagName) {  \n    var elements = [];\n  \n    this.each(function() {\n      var element = document.createElement(tagName);\n      this.parentNode.insertBefore(element, this);\n      elements.push(element);\n    });\n  \n    return d3.selectAll(elements);\n}","import { MainGraphic } from './vis/attentionVis'\n\nimport \"!file-loader?name=exBERT.html!../exBERT.html\";\nimport \"!file-loader?name=index.html!../index.html\";\nimport \"../css/main.scss\"\n\nwindow.onload = () => {\n    const base = document.getElementById('attention-vis')\n    //@ts-ignore\n    const mainVis = new MainGraphic(base)\n    console.log(\"Done loading window\");\n}\n","import * as tp from \"./etc/types\"\nimport * as x_ from \"./etc/_Tools\"\nimport * as _ from \"lodash\"\nimport * as R from 'ramda'\nimport { URLHandler } from \"./etc/URLHandler\";\n\nconst falsey = val => (new Set(['false', 0, \"no\", false, null, \"\"])).has(val)\nconst truthy = val => !falsey(val)\nconst toNumber = x => +x;\n\n\ntype InspectorOptions = \"context\" | \"embeddings\" | null\n\n// Must be optional params for initializations\ninterface URLParameters {\n    sentence?: string\n    model?: string\n    modelKind?: string\n    layer?: number\n    heads?: number[]\n    threshold?: number\n    tokenInd?: number | 'null'\n    tokenSide?: tp.SideOptions\n    maskInds?: number[]\n    hideClsSep?: boolean\n}\n\nexport class UIConfig {\n\n    private _conf: URLParameters = {}\n    private _headSet: Set<number>;\n    attType: \"aa\"\n    _nHeads: number | null;\n    _nLayers: number | null;\n    private _token: tp.TokenEvent;\n\n    constructor() {\n        this._nHeads = 12; \n        this._nLayers = null;\n        this.attType = 'aa'\n        this.fromURL()\n        this.toURL(false)\n    }\n\n    toURL(updateHistory = false) {\n        URLHandler.updateUrl(this._conf, updateHistory)\n    }\n\n\n    fromURL() {\n        const params = URLHandler.parameters\n\n        this._conf = {\n            model: params['model'] || 'bert-base-cased',\n            modelKind: params['modelKind'] || tp.ModelKind.Bidirectional,\n            sentence: params['sentence'] || \"The girl ran to a local pub to escape the din of her city.\",\n            layer: params['layer'] || 1,\n            heads: this._initHeads(params['heads']),\n            threshold: params['threshold'] || 0.7,\n            tokenInd: params['tokenInd'] || null,\n            tokenSide: params['tokenSide'] || null,\n            maskInds: params['maskInds'] || [9],\n            hideClsSep: truthy(params['hideClsSep']) || true,\n        }\n\n        this._token = { side: this._conf.tokenSide, ind: this._conf.tokenInd }\n\n    }\n\n    private _initHeads(v: number[] | null) {\n        if (v == null || v.length < 1) {\n            this.selectAllHeads()\n        }\n        else {\n            console.log(this.headSet(new Set(v))._conf.heads);\n        }\n\n        return this.heads()\n    }\n\n    nHeads(): number\n    nHeads(val: number): this\n    nHeads(val?) {\n        if (val == null) return this._nHeads\n        this._nHeads = val\n        return this\n    }\n\n    nLayers(): number\n    nLayers(val: number): this\n    nLayers(val?) {\n        if (val == null) return this._nLayers\n        this._nLayers = val\n        return this\n    }\n\n    toggleSelectAllHeads() {\n        if (this.heads().length == 0) {\n            this.selectAllHeads()\n        }\n        else {\n            this.selectNoHeads()\n        }\n    }\n\n    selectAllHeads() {\n        this.headSet(new Set(_.range(0, this._nHeads)))\n    }\n\n    selectNoHeads() {\n        this.headSet(new Set([]))\n    }\n\n    toggleHead(head: number): tp.Toggled {\n        let out;\n        if (this.headSet().has(head)) {\n            this.headSet().delete(head);\n            out = tp.Toggled.REMOVED\n        }\n        else {\n            this.headSet().add(head);\n            out = tp.Toggled.ADDED\n        }\n\n        // Set through setter function to ensure url is updated\n        this.headSet(this.headSet()); // I hate mutable datastructures... This is confusing.\n\n        return out\n    }\n\n    toggleToken(e: tp.TokenEvent): this {\n        const picker = R.pick(['ind', 'side'])\n        const compareEvent = picker(e)\n        const compareToken = picker(this.token())\n\n        if (R.equals(compareToken, compareEvent)) {\n            this.rmToken();\n        }\n        else {\n            this.token(e);\n        }\n        return this;\n    }\n\n    token(): tp.TokenEvent;\n    token(val: tp.TokenEvent): this;\n    token(val?: tp.TokenEvent) {\n        if (val == null)\n            return this._token\n\n        this._token = val;\n        this._conf.tokenInd = val.ind;\n        this._conf.tokenSide = val.side;\n        this.toURL();\n\n        return this\n    }\n\n    hasToken() {\n        const conf = this._conf\n        const actuallyNull = ((conf.tokenInd == null) && (conf.tokenSide == null))\n        const strNull = (conf.tokenInd == \"null\")\n        return (!actuallyNull) && (!strNull)\n    }\n\n    rmToken() {\n        this.token({ ind: null, side: null });\n        return this\n    }\n\n    sentence(): string;\n    sentence(val: string): this;\n    sentence(val?) {\n        if (val == null)\n            return this._conf.sentence\n\n        this._conf.sentence = val\n        this.toURL(true)\n        return this\n    }\n\n    threshold(): number;\n    threshold(val: number): this;\n    threshold(val?) {\n        if (val == null) return this._conf.threshold;\n\n        this._conf.threshold = val;\n        this.toURL();\n        return this;\n    }\n\n    heads(): number[] {\n        return this._conf.heads\n    }\n\n    layer(): number\n    layer(val: number): this\n    layer(val?) {\n        if (val == null)\n            return this._conf.layer\n\n        this._conf.layer = val;\n        this.toURL();\n        return this\n    }\n\n    headSet(): Set<number>;\n    headSet(val: Set<number>): this;\n    headSet(val?) {\n        if (val == null) {\n            return this._headSet;\n        }\n\n        this._headSet = val;\n        this._conf.heads = x_.set2SortedArray(this._headSet)\n        this.toURL();\n        return this\n    }\n\n    maskInds(): number[];\n    maskInds(val: number[]): this;\n    maskInds(val?) {\n        if (val == null) return this._conf.maskInds;\n\n        this._conf.maskInds = val;\n        this.toURL();\n        return this;\n    }\n\n    hideClsSep(): boolean;\n    hideClsSep(val: boolean): this;\n    hideClsSep(val?) {\n        if (val == null) return this._conf.hideClsSep;\n\n        this._conf.hideClsSep = truthy(val);\n        this.toURL();\n        return this;\n    }\n\n    model(): string;\n    model(val: string): this;\n    model(val?) {\n        if (val == null) return this._conf.model\n        this._conf.model = val\n        this.toURL();\n        return this\n    }\n\n    modelKind(): string;\n    modelKind(val: string): this;\n    modelKind(val?) {\n        if (val == null) return this._conf.modelKind\n        this._conf.modelKind = val\n        this.toURL();\n        return this\n    }\n\n    /**\n     * Return the offset needed for the modelKind in the configuration\n     */\n    get offset() {\n        switch (this.modelKind()) {\n            case tp.ModelKind.Bidirectional: {\n                return 0\n            }\n            case tp.ModelKind.Autoregressive: {\n                return 0\n            }\n            default: {\n                return 0\n            }\n        }\n    }\n\n    get showNext() {\n        return this.modelKind() == tp.ModelKind.Autoregressive ? true : false\n    }\n\n    get matchHistogramDescription() {\n        return this.modelKind() == tp.ModelKind.Autoregressive ? \"Next\" : \"Matched\"\n    }\n}\n","import * as d3 from \"d3\";\nimport 'd3-selection-multi'\nimport { D3Sel } from \"../etc/Util\";\nimport { Edge, EdgeData } from \"./EdgeConnector\"\nimport { VComponent } from \"./VisComponent\";\nimport { SimpleEventHandler } from \"../etc/SimpleEventHandler\";\nimport * as tp from \"../etc/types\"\n\nexport type AttentionData = number[][]\n\nexport const scaleLinearWidth = opacity => 5 * opacity^0.33;\n\nexport class AttentionGraph extends VComponent<AttentionData>{\n    css_name = '';\n    _current: {};\n\n    _data: AttentionData; // The passed data\n    edgeData: EdgeData; // A wrapper around _data. User should not mind\n    plotData: Edge[]; // Needed for plotting\n\n    /** COMPONENTS\n     * Expose the components belonging to the class as properties of the class. \n     * This is useful to create methods that specifically modify a single part or component without having to reselect it. \n     * Makes for more responsive applications\n     * */\n    svg: D3Sel;\n    graph: D3Sel;\n\n    // The below components require data\n    paths: D3Sel;\n    opacityScales: d3.ScaleLinear<any, any>[];\n    linkGen: d3.Link<any, any, any>\n\n    // OPTIONS WITH DEFAULTS\n    _threshold = 0.7; // Accumulation threshold. Between 0-1\n    normBy: tp.NormBy\n\n    static events = {} // No events needed for this one\n\n    options = {\n        boxheight: 26, // The height of the div boxes around the SVG element\n        height: 500,\n        width: 200,\n        offset: 0, // Should I offset the left side by 1 or not?\n    }\n\n    constructor(d3Parent: D3Sel, eventHandler?: SimpleEventHandler, options: {} = {}) {\n        super(d3Parent, eventHandler)\n        this.superInitSVG(options)\n        this._init()\n    }\n\n    _init() {\n        this.svg = this.parent;\n        this.graph = this.svg.selectAll(`.atn-curve`);\n        this.linkGen = d3.linkHorizontal()\n            .x(d => d[0])\n            .y(d => d[1]);\n    }\n\n    // Define whether to use the 'j' or 'i' attribute to calculate opacities\n    private scaleIdx(): \"i\" | \"j\" {\n        switch (this.normBy) {\n            case tp.NormBy.COL:\n                return 'j'\n            case tp.NormBy.ROW:\n                return 'i'\n            case tp.NormBy.ALL:\n                return 'i'\n\n        }\n\n    }\n\n    /**\n     * Create connections between locations of the SVG using D3's linkGen\n     */\n    private createConnections() {\n        const self = this;\n        const op = this.options;\n        if (this.paths) {\n            this.paths.attrs({\n                'd': (d, i) => {\n                    const data: { source: [number, number], target: [number, number] } =\n                    {\n                        source: [0, op.boxheight * (d.i + 0.5 + op.offset)],\n                        target: [op.width, op.boxheight * (d.j + 0.5)] // + 2 allows small offset\n                    };\n                    return this.linkGen(data);\n                },\n                'class': 'atn-curve'\n            })\n                .attr(\"src-idx\", (d, i) => d.i)\n                .attr(\"target-idx\", (d, i) => d.j);\n        }\n    }\n\n    /**\n     * Change the height of the SVG\n     */\n    private updateHeight() {\n        const op = this.options;\n        if (this.svg != null) {\n            this.svg.attr(\"height\", this.options.height + (op.offset * this.options.boxheight))\n        }\n        return this;\n    }\n\n    /**\n     * Change the width of the SVG\n     */\n    private updateWidth() {\n        if (this.svg != null) {\n            this.svg.attr(\"width\", this.options.width)\n        }\n        return this;\n    }\n\n    /**\n     * Change the Opacity of the lines according to the value of the data\n     */\n    private updateOpacity() {\n        const self = this;\n        if (this.paths != null) {\n            // paths.transition().duration(500).attr('opacity', (d) => {\n            this.paths.attr('opacity', (d) => {\n                const val = this.opacityScales[d[self.scaleIdx()]](d.v);\n                return val;\n            })\n            this.paths.attr('stroke-width', (d) => {\n                const val = this.opacityScales[d[self.scaleIdx()]](d.v);\n                return scaleLinearWidth(val) //5 * val^0.33;\n            })\n        }\n        return this;\n    }\n\n    /**\n     * Rerender the graph in the event that the data changes\n     */\n    private updateData() {\n        if (this.graph != null) {\n            d3.selectAll(\".atn-curve\").remove();\n\n            const data = this.plotData\n\n            this.paths = this.graph\n                .data(data)\n                .join('path');\n\n            this.createConnections();\n            this.updateOpacity();\n\n            return this;\n        }\n    }\n\n    /**\n     * Scale the opacity according to the values of the data, from 0 to max of contained data\n     * Normalize by each source target, or across the whole\n     */\n    private createScales = () => {\n        this.opacityScales = [];\n        let arr = []\n\n        // Group normalization\n        switch (this.normBy){\n            case tp.NormBy.ROW:\n                arr = this.edgeData.extent(1);\n                this.opacityScales = [];\n                arr.forEach((v, i) => {\n                    (this.opacityScales as d3.ScaleLinear<any, any>[]).push(\n                        d3.scaleLinear()\n                            .domain([0, v[1]])\n                            .range([0, 0.9])\n                    )\n                })\n                break;\n            case tp.NormBy.COL:\n                arr = this.edgeData.extent(0);\n                this.opacityScales = [];\n                arr.forEach((v, i) => {\n                    (this.opacityScales as d3.ScaleLinear<any, any>[]).push(\n                        d3.scaleLinear()\n                            .domain([0, v[1]])\n                            .range([0, 0.9])\n                    )\n                })\n                break;\n            case tp.NormBy.ALL:\n                const maxIn = d3.max(this.plotData.map((d) => d.v))\n                for (let i = 0; i < this._data.length; i++) {\n                    this.opacityScales.push(d3.scaleLinear()\n                        .domain([0, maxIn])\n                        .range([0, 1]));\n                }\n                break;\n            default:\n                console.log(\"Nor norming specified\");\n                break;\n        }\n    }\n\n    /**\n     * Access / modify the data in a D3 style way. If modified, the component will update just the part that is needed to be updated\n     */\n    data(): AttentionData\n    data(value: AttentionData): this\n    data(value?) {\n        if (value == null) {\n            return this._data;\n        }\n\n        this._data = value;\n        this.edgeData = new EdgeData(value);\n        this.plotData = this.edgeData.format(this._threshold);\n        this.createScales();\n        this.updateData();\n        return this;\n    }\n\n    /**\n     * Access / modify the height in a D3 style way. If modified, the component will update just the part that is needed to be updated\n     */\n    height(): number\n    height(value: number): this\n    height(value?) {\n        if (value == null) {\n            return this.options.height\n        }\n\n        this.options.height = value\n        this.updateHeight()\n        return this;\n    }\n\n    /**\n     * Access / modify the width in a D3 style way. If modified, the component will update just the part that is needed to be updated\n     */\n    width(): number\n    width(value: number): this\n    width(value?: number): this | number {\n        if (value == null) {\n            return this.options.width;\n        }\n        this.options.width = value;\n        this.updateWidth();\n        return this;\n    }\n\n    /**\n     * Access / modify the threshold in a D3 style way. If modified, the component will update just the part that is needed to be updated\n     */\n    threshold(): number\n    threshold(value: number): this\n    threshold(value?) {\n        if (value == null) {\n            return this._threshold;\n        }\n\n        this._threshold = value;\n        this.plotData = this.edgeData.format(this._threshold);\n        this.createScales();\n        this.updateData();\n        return this;\n    }\n\n    _wrangle(data: AttentionData) {\n        return data;\n    }\n\n    _render(data: AttentionData) {\n        this.svg.html('')\n        this.updateHeight();\n        this.updateWidth();\n\n        this.updateData();\n        return this;\n    }\n}","import * as d3 from \"d3\";\nimport { VComponent } from \"./VisComponent\";\nimport { SimpleEventHandler } from \"../etc/SimpleEventHandler\";\nimport { D3Sel } from \"../etc/Util\";\nimport { SVG } from \"../etc/SVGplus\"\nimport * as tf from '@tensorflow/tfjs'\nimport { Tensor3D } from \"@tensorflow/tfjs\";\n\n// The below two (interface and function) can become a class\nexport type AttentionHeadBoxI = {\n    rows: number[][],\n    labels: number[],\n    max: number,\n}\n\n/**\n * From an attention matrix selected by layer, show a summary of the attentions belonging to each head.\n * \n * @param headMat The matrix representing all the attentions by head (layer already selected) <head, from, to>\n * @param headList The heads that are selected\n * @param side Is this the right or the left display?\n * @param tokenInd If not null, select just the information from a single token across heads\n * @returns Information needed to label the headbox\n */\nexport function getAttentionInfo(headMat: number[][][], headList: number[], side: \"right\" | \"left\" = \"left\", token: null | {ind: number, side: \"left\" | \"right\"}=null): AttentionHeadBoxI {\n    // Collect only from headlist, average each head, transpose to ease iteration\n    if (headList.length == 0) {\n        return {\n            rows: [[]],\n            labels: [],\n            max: 0,\n        }\n    }\n\n    let dim = null\n    // Only change the attention graph opposite selected token\n    if (token != null && (token.side != side)) {\n        dim = token.side == \"left\" ? -2 : -1 // Assign to \"from\" direction if \"left\" \n    }\n\n    let axis: number = side == \"left\" ? 2 : 1;\n\n    // average across the axis representing the attentions.\n    let gatheredMat = tf.tensor3d(headMat)\n    if (dim != null) {\n        gatheredMat = gatheredMat.gather([token.ind], dim)\n    }\n    let newMat = gatheredMat.gather(headList, 0).mean([axis]).transpose();\n\n    const rowInfo = <number[][]>newMat.arraySync();\n\n    const out: AttentionHeadBoxI = {\n        rows: rowInfo,\n        labels: headList,\n        max: <number>newMat.max().arraySync(),\n    }\n\n    return out\n}\n\ninterface CurrentOptions {\n    headHeight: number\n    headWidth: number\n    xPad: number\n    yPad: number\n    boxWidth: number\n    totalWidth: number\n    totalHeight: number\n};\n\nexport class AttentionHeadBox extends VComponent<AttentionHeadBoxI>{\n    css_name = '';\n    rowCssName = 'att-head';\n    boxCssName = 'att-rect';\n\n    static events = {\n        rowMouseOver: \"AttentionHeadBox_RowMouseOver\",\n        rowMouseOut: \"AttentionHeadBox_RowMouseOut\",\n        boxMouseOver: \"AttentionHeadBox_BoxMouseOver\",\n        boxMouseOut: \"AttentionHeadBox_BoxMouseOut\",\n        boxMouseMove: \"AttentionHeadBox_BoxMouseMove\",\n        boxClick: \"AttentionHeadBox_BoxClick\",\n    };\n\n    _data: AttentionHeadBoxI;\n\n    _current: Partial<CurrentOptions> = {}\n\n    options = {\n        boxDim: 26,\n        yscale: 1, // Amount to scale boxheight to get individual heads\n        xscale: 0.5, // Amount to scale boxwidth to get individual heads\n        side: \"left\",\n        maxWidth: 200, // Maximum width of SVG\n        offset: 0, // Change to 1 if you desire the offset visualization for Autoregressive models\n    };\n\n    // D3 Components\n    headRows: D3Sel;\n    headCells: D3Sel;\n    opacityScale: d3.ScaleLinear<any, any>;\n\n    constructor(d3Parent: D3Sel, eventHandler?: SimpleEventHandler, options: {} = {}) {\n        super(d3Parent, eventHandler);\n        this.superInitSVG(options);\n        this._init()\n    }\n\n    _init() {\n        this.headRows = this.base.selectAll(`.${this.rowCssName}`)\n        this.headCells = this.headRows.selectAll(`${this.boxCssName}`)\n        this.opacityScale = d3.scaleLinear().range([0, 1]);\n    }\n\n    private updateCurrent(): Partial<CurrentOptions> {\n        const op = this.options\n        const cur = this._current\n\n        const nHeads = this._data.rows[0].length\n        const baseHeadWidth = op.boxDim * op.xscale\n\n        // Scale headwidth according to maximum width\n        const getHeadScale = (nH) => (Math.min(op.maxWidth / nH, baseHeadWidth) / baseHeadWidth) * op.xscale;\n\n        cur.headHeight = op.boxDim * op.yscale;\n        cur.headWidth = getHeadScale(nHeads) * op.boxDim;\n        cur.xPad = cur.headWidth;\n        cur.yPad = (op.boxDim - cur.headHeight) / 2;\n\n        const getBoxWidth = (headWidth) => {\n            const maxBwidth = 100;\n            const bwidth = this._data.rows[0].length * cur.headWidth\n            const scale = d3.scaleLinear\n            if (bwidth > maxBwidth) {\n                return\n            }\n\n        }\n\n        cur.boxWidth = (this._data.rows[0].length * cur.headWidth);\n        cur.totalWidth = (2 * cur.xPad) + cur.boxWidth;\n        cur.totalHeight = (op.boxDim * (this._data.rows.length + op.offset));\n\n        return this._current\n    }\n\n    private updateData() {\n        const op = this.options;\n        const self = this;\n        const boxEvent = (i) => { return { ind: i, side: op.side, head: self._data.labels[i] } }\n        const cur = this.updateCurrent()\n\n        const getBaseX = () => (<HTMLElement>self.base.node()).getBoundingClientRect().left\n        const getBaseY = () => (<HTMLElement>self.base.node()).getBoundingClientRect().top\n\n        this.base.html('');\n\n        this.parent\n            .attr(\"width\", cur.totalWidth)\n            .attr(\"height\", cur.totalHeight)\n\n        this.headRows = this.base.selectAll(`.${self.rowCssName}`)\n            .data(self._data.rows)\n            .join(\"g\")\n            .attrs({\n                class: (d, i) => `${self.rowCssName} ${self.rowCssName}-${i}`,\n                transform: (d, i) => {\n                    return SVG.translate(\n                        {\n                            x: cur.xPad,\n                            y: (op.boxDim * (i + op.offset)) + cur.yPad,\n                        })\n                },\n                width: cur.boxWidth,\n                height: cur.headHeight,\n\n            })\n            .on(\"mouseover\", (d, i) => {\n                self.eventHandler.trigger(AttentionHeadBox.events.rowMouseOver, { ind: i, side: op.side })\n            })\n            .on(\"mouseout\", (d, i) => {\n                self.eventHandler.trigger(AttentionHeadBox.events.rowMouseOut, { ind: i, side: op.side })\n            })\n\n        this.headCells = this.headRows\n            .selectAll(`${this.boxCssName}`)\n            .data(d => d)\n            .join('rect')\n            .attrs({\n                x: (d, i) => i * cur.headWidth,\n                y: 0,\n                class: this.boxCssName,\n                head: (d, i) => self._data.labels[i],\n                width: cur.headWidth,\n                height: cur.headHeight,\n                opacity: (d: number) => this.opacityScale(d),\n                fill: \"blue\"\n            })\n            .on(\"mouseover\", (d, i) => {\n                self.eventHandler.trigger(AttentionHeadBox.events.boxMouseOver, boxEvent(i))\n            })\n            .on(\"mouseout\", (d, i) => {\n                self.eventHandler.trigger(AttentionHeadBox.events.boxMouseOut, boxEvent(i))\n            })\n            .on(\"click\", (d, i) => {\n                self.eventHandler.trigger(AttentionHeadBox.events.boxClick, boxEvent(i))\n            })\n            .on(\"mousemove\", function(d, i) {\n                const op = self.options\n                const mouse = d3.mouse(self.base.node())\n\n                self.eventHandler.trigger(AttentionHeadBox.events.boxMouseMove, { ind: i, side: op.side, baseX: getBaseX(), baseY: getBaseY(), mouse: mouse })\n\n            })\n            .append(\"svg:title\")\n            .text((d, i) => \"Head \" + (self._data.labels[i] + 1))\n    }\n\n\n    _wrangle(data: AttentionHeadBoxI) {\n        this._data = data;\n        this.opacityScale = this.opacityScale.domain([0, data.max])\n        return data;\n    }\n\n    _render(data: AttentionHeadBoxI) {\n        this.updateData();\n    }\n}","import * as d3 from 'd3'\nimport 'd3-array'\nimport * as au from '../etc/arrayUtils'\nimport * as tf from '@tensorflow/tfjs'\nimport { TypedArray } from '@tensorflow/tfjs-core/dist/types';\n\nexport interface Edge {\n    i: number, // Source index\n    j: number, // Target index\n    v: number, // Value\n}\n\n/**\n * Convert data matrix to necessary data array to pass to SVG connections\n */\nexport function toEdges (data:number[][], cutoffAmt=1) : Edge[] {\n    let outArr: Edge[] = [];\n    let cutoff: number;\n    data.forEach((row, i) => {\n        cutoff = cutoffAmt * d3.sum(row);\n        let counter = 0;\n        const sortedArr:au.SortArray = au.sortWithIndices(row);\n\n        sortedArr.arr.forEach((v,j) => {\n            if (counter < cutoff) {\n                const obj: Edge = {\n                    i: i,\n                    j: sortedArr.sortIndices[j],\n                    v: v,\n                }\n                outArr.push(obj);\n                counter += v;\n            }\n            })\n        })\n\n    return outArr;\n}\n/**\n * Class for implementing operations on AttentionGraph implementation. \n * Closely tied to [[AttentionConnector]]\n */\nexport class EdgeData {\n    readonly tensData:tf.Tensor;\n\n    constructor (public data:number[][]){\n        this.tensData = tf.tensor(data);\n    }\n\n    min(axis?:number):TypedArray {\n        return this.tensData.min(axis).dataSync();\n    }\n\n    max(axis?:number):TypedArray{\n        return this.tensData.max(axis).dataSync();\n    }\n\n    extent(axis?:number):number[][] {\n        return d3.zip(this.min(axis), this.max(axis))\n    }\n\n    /**\n     * Format the data to send to SVG chart.\n     * \n     * @param accumulateThresh - A float between 0 and 1, indicating the amount of weight to display. Defaults to 0.7.\n     */\n    format (accumulateThresh=0.7):Edge[] {\n        return toEdges(this.data, accumulateThresh);\n    }\n}","import * as d3 from \"d3\";\nimport * as R from \"ramda\"\nimport * as _ from \"lodash\"\nimport { VComponent } from \"./VisComponent\";\nimport { SimpleEventHandler } from \"../etc/SimpleEventHandler\";\nimport { D3Sel } from \"../etc/Util\";\nimport * as tp from \"../etc/types\"\n\ntype infoEventFromI = (sel: D3Sel, i: number) => tp.TokenEvent\ntype infoEmbeddingEventFromI = (sel: D3Sel, i: number, embed: number[]) => tp.TokenEmbeddingEvent\n\nexport abstract class TextTokens extends VComponent<tp.FullSingleTokenInfo[]>{\n\n    abstract css_name: string\n    abstract hover_css_name: string\n    abstract side: tp.SideOptions\n    eInfo: infoEventFromI = (sel, i) => { return { sel: sel, side: this.side, ind: i } }\n    eEmbedding: infoEmbeddingEventFromI = (sel, i, embed) => { return { sel: sel, side: this.side, ind: i, embeddings: embed } }\n    divHover: D3Sel\n\n    static events = {\n        tokenMouseOver: \"TextToken_TokenMouseOver\",\n        tokenMouseOut: \"TextToken_TokenMouseOut\",\n        tokenClick: \"TextToken_TokenClick\",\n        tokenDblClick: \"TextToken_TokenDblClick\",\n    };\n\n    data: tp.FullSingleTokenInfo[];\n\n    _current: {};\n\n    options = {\n        boxheight: 26,\n        offset: 0,\n        divHover: {\n            width: 150,\n            height: 150,\n            offset: [3, 3],\n            textInfo: \"Would predict...\"\n        },\n    };\n\n    textBoxes: D3Sel\n\n    constructor(d3Parent: D3Sel, eventHandler?: SimpleEventHandler, options: {} = {}) {\n        super(d3Parent, eventHandler);\n        this.superInitHTML(options);\n    }\n\n    mask(maskInds: number[]) {\n        this.parent.selectAll(`.${this.css_name}`)\n            .each((d, i, n) => {\n                const sel = d3.select(n[i])\n                sel.classed(\"masked-token\", _.includes(maskInds, i))\n            })\n    }\n\n    getEmbedding(ind: number): tp.FullSingleTokenInfo {\n        return this._data[ind]\n    }\n\n    _init() { }\n\n    _wrangle(data: tp.FullSingleTokenInfo[]) {\n        this.data = this._data;\n        return this._data;\n    }\n\n    _divPlacement() {\n        const getBaseX = () => (<HTMLElement>self.base.node()).getBoundingClientRect().left\n        const getBaseY = () => (<HTMLElement>self.base.node()).getBoundingClientRect().top\n        const self = this\n        const op = this.options\n        const mouse = d3.mouse(self.base.node())\n        const divOffset = [3, 3]\n        const left = mouse[0] + getBaseX() - (op.divHover.width + divOffset[0])\n        const top = mouse[1] + getBaseY() + divOffset[1]\n        return [left, top]\n    }\n\n    _render(data: tp.FullSingleTokenInfo[]) {\n        const op = this.options;\n        const self = this;\n        // Reset token display\n        this.base.selectAll(\"*\").remove()\n\n        this.divHover = this.base.append('div')\n            .classed('tok-info', true)\n            .classed('mat-hover-display', true)\n            .classed(this.hover_css_name, true)\n            .style('width', String(this.options.divHover.width) + 'px')\n            .style('height', String(this.options.divHover.height) + 'px')\n\n        this.divHover\n            .append('p')\n            .classed('p-info', true)\n            .style('font-weight', 'bold')\n            .text(op.divHover.textInfo)\n\n\n        // Add blank divs\n        console.log(`Internal offset (${this.side}): `, op.offset);\n        const blankDivs = this.base.selectAll(`.blank-text-box`)\n\n        blankDivs.data(R.range(0, op.offset))\n            .join(\"div\")\n            .classed(\"blank-text-box\", true)\n            .classed(\"token\", true)\n            .style(\"height\", op.boxheight + 'px')\n            .text((d) => \"  \")\n\n        // Render normal text box data\n        self.textBoxes = <D3Sel>this.base.selectAll(`.${this.css_name}`)\n            .data(data)\n            .join(\"div\")\n            .attr(\"class\", (d, i) => `token ${this.css_name} token-${i}`)\n            .attr(\"id\", (d, i) => `${this.css_name}-${i}`)\n            .style('height', op.boxheight + 'px')\n            .text((d) => {\n                return d.text.replace(\"\\u0120\", \" \").replace(\"\\u010A\", \"\\\\n\")\n            })\n            .on('mouseover', function (d, i) {\n                const sel = d3.select(this);\n                sel.style('background', 'lightblue');\n                self.eventHandler.trigger(TextTokens.events.tokenMouseOver, self.eInfo(sel, i))\n                self.divHover.style('visibility', 'visible')\n            })\n            .on('mouseout', function (d, i) {\n                let sel = d3.select(this);\n                sel.style('background', 0)\n                self.eventHandler.trigger(TextTokens.events.tokenMouseOut, self.eInfo(sel, i))\n                self.divHover.style('visibility', 'hidden')\n            })\n            .on('mousemove', function (d, i) {\n                const s = d3.select(this)\n                const [left, top] = self._divPlacement()\n\n                self.divHover\n                    .style('left', String(left) + 'px')\n                    .style('top', String(top) + 'px')\n                    .selectAll(\".topk-word-box\")\n                    //@ts-ignore\n                    .data(d3.zip(d.topk_words, d.topk_probs))\n                    .join('p')\n                    .classed(\"topk-word-box\", true)\n                    .text(w => {\n                        const name = w[0].replace(/\\u0120/g, \" \").replace(/\\u010A/g, \"\\\\n\")\n                        const prob = w[1].toFixed(2)\n                        return name + \": \" + prob\n                    })\n            })\n\n        self.addClick(self.textBoxes)\n    }\n\n    addClick(textboxes: D3Sel) {\n        const self = this;\n\n        self.textBoxes = textboxes\n            .on('click', (d, i, n) => {\n                const sel = d3.select(n[i]);\n                self.eventHandler.trigger(TextTokens.events.tokenClick, self.eEmbedding(sel, i, d.embeddings))\n            })\n            .on('dblclick', (d, i, n) => {\n                const sel = d3.select(n[i]);\n                self.eventHandler.trigger(TextTokens.events.tokenDblClick, self.eInfo(sel, i))\n            });\n    }\n}\n\nexport class LeftTextToken extends TextTokens {\n\n    css_name = 'left-token';\n    hover_css_name = 'left-token-hover'\n    side: tp.SideOptions = 'left';\n    offset: number = 1;\n\n    constructor(d3Parent: D3Sel, eventHandler?: SimpleEventHandler, options: {} = {}) {\n        super(d3Parent, eventHandler);\n    }\n\n\n}\n\nexport class RightTextToken extends TextTokens {\n    css_name = 'right-token';\n    hover_css_name = 'right-token-hover'\n    side: tp.SideOptions = 'right'\n    offset: number = 0;\n\n    constructor(d3Parent: D3Sel, eventHandler?: SimpleEventHandler, options: {} = {}) {\n        super(d3Parent, eventHandler);\n    }\n\n    _divPlacement() {\n        const getBaseX = () => (<HTMLElement>self.base.node()).getBoundingClientRect().left\n        const getBaseY = () => (<HTMLElement>self.base.node()).getBoundingClientRect().top\n        const self = this\n        const op = this.options\n        const mouse = d3.mouse(self.base.node())\n        const divOffset = [3, 3]\n        const left = mouse[0] + getBaseX() + divOffset[0]\n        const top = mouse[1] + getBaseY() + divOffset[1]\n        return [left, top]\n    }\n}\n","/**\n * Created by Hendrik Strobelt (hendrik.strobelt.com) on 12/3/16.\n * Modified by Ben Hoover on 4/16/2019\n */\nimport * as d3 from 'd3'\nimport {D3Sel, Util} from \"../etc/Util\";\nimport {SimpleEventHandler} from \"../etc/SimpleEventHandler\";\nimport {SVG} from \"../etc/SVGplus\";\n\n/**\n * Should have VComponentHTML and VComponentSVG\n * \n * Common Properties:\n * - events\n * - eventHandler (V important)\n * - options (Maintains public state. Can expose these with get/set functions with auto update)\n * - _current (Maintains private state)\n * - cssName (synced with corresponding CSS file)\n * - parent (HTML is div containing the base, SVG is SVG element)\n * - base (HTML is div with css_name established)\n * - _data (Data used to create and render the component)\n * - _renderData (Data needed to display. This may not be needed, but is currently used in histogram)\n * \n * Common Methods:\n * - constructor\n * - _render()      Consider replacing with `_updateData()` that updates all data at once\n * - update()       Consider replacing this with `data()` that auto updates data\n * - redraw()\n * - destroy()\n */\n\nexport abstract class VComponent<DataInterface> {\n\n    // STATIC FIELDS ============================================================\n\n    /**\n     * The static property that contains all class related events.\n     * Should be overwritten and event strings have to be unique!!\n     */\n\n    static events: {} = {noEvent: 'VComponent_noEvent'};\n\n    /**\n     * Defines the layers in SVG  for bg,main,fg,...\n     */\n    // protected abstract readonly layout: { name: string, pos: number[] }[] = [{name: 'main', pos: [0, 0]}];\n\n    protected id: string; // Mostly obsolete, nice to have simple ID to assign in CSS\n    protected parent: D3Sel;\n    protected abstract options: { [key: string]: any };\n    protected base: D3Sel; // Mostly obsolete, represents <g> in svg\n    protected layers: { main?: D3Sel, fg?: D3Sel, bg?: D3Sel, [key: string]: D3Sel }; // Still useful\n    protected eventHandler: SimpleEventHandler;\n    protected _visibility: { hidden: boolean, hideElement?: D3Sel | null; [key: string]: any }; // Enables transitions from visible to invisible. Mostly obsolete.\n    protected _data: DataInterface;\n    protected renderData: any; // Unnecessary\n    protected abstract css_name: string; // Make the same as the corresponding css file\n    protected abstract _current: {}; // Private state information contained in the object itself.\n\n    // CONSTRUCTOR ============================================================\n\n    /**\n     * Simple constructor. Subclasses should call @superInit(options) as well.\n     * see why here: https://stackoverflow.com/questions/43595943/why-are-derived-class-property-values-not-seen-in-the-base-class-constructor\n     *\n     * template:\n     constructor(d3Parent: D3Sel, eventHandler?: SimpleEventHandler, options: {} = {}) {\n        super(d3Parent, eventHandler);\n        // -- access to subclass params:\n        this.superInit(options);\n     }\n     *\n     * @param {D3Sel} d3parent  D3 selection of parent SVG DOM Element\n     * @param {SimpleEventHandler} eventHandler a global event handler object or 'null' for local event handler\n     */\n    protected constructor(d3parent: D3Sel, eventHandler?: SimpleEventHandler) {\n        this.id = Util.simpleUId({});\n\n        this.parent = d3parent;\n\n        // If not further specified - create a local event handler bound to the bas element\n        this.eventHandler = eventHandler ||\n            new SimpleEventHandler(this.parent.node());\n\n        // Object for storing internal states and variables\n        this._visibility = {hidden: false};\n\n    }\n\n    protected superInitHTML(options: {} = {}) {\n        Object.keys(options).forEach(key => this.options[key] = options[key]);\n        this.base = this.parent.append('div')\n            .classed(this.css_name, true)\n    }\n\n    /**\n     * Has to be called as last call in subclass constructor.\n     * \n     * @param {{}} options\n     * @param defaultLayers -- create the default <g> layers: bg -> main -> fg\n     */\n    protected superInitSVG(options: {} = {}, defaultLayers = ['bg', 'main', 'fg']) {\n        // Set default options if not specified in constructor call\n        // const defaults = this.defaultOptions;\n        // this.options = {};\n        // const keys = new Set([...Object.keys(defaults), ...Object.keys(options)]);\n        // keys.forEach(key => this.options[key] = (key in options) ? options[key] : defaults[key]);\n        Object.keys(options).forEach(key => this.options[key] = options[key]);\n\n        this.layers = {};\n\n        // Create the base group element\n        const svg = this.parent;\n        this.base = SVG.group(svg,\n            this.css_name + ' ID' + this.id,\n            this.options.pos);\n\n        // create default layers: background, main, foreground\n        if (defaultLayers) {\n            // construction order is important !\n            defaultLayers.forEach(layer =>{\n                this.layers[layer] = SVG.group(this.base, layer);\n            });\n        }\n    }\n\n\n    /**\n     * Should be overwritten to create the static DOM elements\n     * @abstract\n     * @return {*} ---\n     */\n    protected abstract _init();\n\n    // DATA UPDATE & RENDER ============================================================\n\n    /**\n     * Every time data has changed, update is called and\n     * triggers wrangling and re-rendering\n     * @param {Object} data data object\n     * @return {*} ---\n     */\n    update(data: DataInterface) {\n        this._data = data;\n        if (this._visibility.hidden) return;\n        this.renderData = this._wrangle(data);\n        this._render(this.renderData);\n    }\n\n    /**\n     * Data wrangling method -- implement in subclass. Returns this.renderData.\n     * Simplest implementation: `return data;`\n     * @param {Object} data data\n     * @returns {*} -- data in render format\n     * @abstract\n     */\n    protected abstract _wrangle(data);\n\n\n    /**\n     * Is responsible for mapping data to DOM elements\n     * @param {Object} renderData pre-processed (wrangled) data\n     * @abstract\n     * @returns {*} ---\n     */\n    protected abstract _render(renderData): void;\n\n\n    // UPDATE OPTIONS ============================================================\n    /**\n     * Updates instance options\n     * @param {Object} options only the options that should be updated\n     * @param {Boolean} reRender if option change requires a re-rendering (default:false)\n     * @returns {*} ---\n     */\n    updateOptions({options, reRender = false}) {\n        Object.keys(options).forEach(k => this.options[k] = options[k]);\n        if (reRender) this._render(this.renderData);\n    }\n\n\n    // === CONVENIENCE ====\n    redraw(){\n        this._render(this.renderData);\n    }\n\n    setHideElement(hE: D3Sel) {\n        this._visibility.hideElement = hE;\n    }\n\n    hideView() {\n        if (!this._visibility.hidden) {\n            const hE = this._visibility.hideElement || this.parent;\n            hE.transition().styles({\n                'opacity': 0,\n                'pointer-events': 'none'\n            }).style('display', 'none');\n            this._visibility.hidden = true;\n        }\n    }\n\n    unhideView() {\n        if (this._visibility.hidden) {\n            const hE = this._visibility.hideElement || this.parent;\n            hE.transition().styles({\n                'opacity': 1,\n                'pointer-events': null,\n                'display': null\n            });\n            this._visibility.hidden = false;\n            // this.update(this.data);\n\n        }\n    }\n\n    destroy() {\n        this.base.remove();\n    }\n\n    clear() {\n        this.base.html('');\n    }\n\n}","/**\n * Showing the top left part of exBERT, no information from the embeddings or the contexts\n */\n\nimport * as d3 from 'd3';\nimport * as _ from \"lodash\"\nimport * as R from 'ramda'\nimport * as tp from '../etc/types';\nimport * as rsp from '../api/responses';\nimport '../etc/xd3'\nimport { API } from '../api/mainApi'\nimport { UIConfig } from '../uiConfig'\nimport { TextTokens, LeftTextToken, RightTextToken } from './TextToken'\nimport { AttentionHeadBox, getAttentionInfo } from './AttentionHeadBox'\nimport { AttentionGraph } from './AttentionConnector'\nimport { TokenWrapper, sideToLetter } from '../data/TokenWrapper'\nimport { AttentionWrapper, makeFromMetaResponse } from '../data/AttentionCapsule'\nimport { SimpleEventHandler } from '../etc/SimpleEventHandler'\nimport { D3Sel, Sel } from '../etc/Util';\nimport { from, fromEvent } from 'rxjs'\nimport { switchMap, map, tap } from 'rxjs/operators'\nimport { BaseType } from \"d3\";\nimport {createStaticSkeleton} from \"./staticLayout\";\n\n\nfunction isNullToken(tok: tp.TokenEvent) {\n    const isSomeNull = x => {\n        return (x == null) || (x == \"null\")\n    }\n    const tokIsNull = tok == null;\n    const tokHasNull = isSomeNull(tok.side) || isSomeNull(tok.ind)\n    return tokIsNull || tokHasNull\n}\n\nfunction showBySide(e: tp.TokenEvent) {\n    // Check if saved token in uiConf is null\n    if (!isNullToken(e)) {\n        const classSelector = e.side == \"left\" ? \"src-idx\" : \"target-idx\";\n\n        Sel.setHidden(\".atn-curve\")\n        Sel.setVisible(`.atn-curve[${classSelector}='${e.ind}']`)\n    }\n}\n\nfunction chooseShowBySide(savedEvent: tp.TokenEvent, newEvent: tp.TokenEvent) {\n    if (isNullToken(savedEvent)) {\n        showBySide(newEvent)\n    }\n}\n\nfunction chooseShowAll(savedEvent: tp.TokenEvent) {\n    if (isNullToken(savedEvent))\n        Sel.setVisible(\".atn-curve\")\n}\n\nfunction unselectHead(head: number) {\n    const affectedHeads = d3.selectAll(`.att-rect[head='${head}']`);\n    affectedHeads.classed(\"unselected\", true)\n}\n\nfunction selectHead(head: number) {\n    const affectedHeads = d3.selectAll(`.att-rect[head='${head}']`);\n    affectedHeads.classed(\"unselected\", false)\n}\n\nfunction setSelDisabled(attr: boolean, sel: D3Sel) {\n    const val = attr ? true : null\n    sel.attr('disabled', val)\n}\n\nexport class MainGraphic {\n    base: D3Sel\n    api: API\n    uiConf: UIConfig\n    attCapsule: AttentionWrapper\n    tokCapsule: TokenWrapper\n    sels: any                           // Contains initial d3 selections of objects\n    vizs: any                           // Contains vis components wrapped around parent sel\n    eventHandler: SimpleEventHandler    // Orchestrates events raised from components\n\n    /**\n     *\n     * @param base 'div' html element into which everything below will be rendered\n     */\n    constructor(baseDiv: Element) {\n        this.base = d3.select(baseDiv)\n        this.api = new API()\n        this.uiConf = new UIConfig()\n        this.sels = createStaticSkeleton(this.base)\n\n        this.eventHandler = new SimpleEventHandler(<Element>this.base.node());\n\n        this.vizs = {\n            leftHeads: new AttentionHeadBox(this.sels.atnHeads.left, this.eventHandler, { side: \"left\", }),\n            rightHeads: new AttentionHeadBox(this.sels.atnHeads.right, this.eventHandler, { side: \"right\" }),\n            tokens: {\n                left: new LeftTextToken(this.sels.tokens.left, this.eventHandler),\n                right: new RightTextToken(this.sels.tokens.right, this.eventHandler),\n            },\n            attentionSvg: new AttentionGraph(this.sels.atnDisplay, this.eventHandler),\n        }\n\n        this._bindEventHandler()\n\n        this.mainInit()\n    }\n\n    private mainInit() {\n        const self = this;\n        this.sels.body.style(\"cursor\", \"progress\")\n        this.api.getModelDetails(this.uiConf.model()).then(md => {\n            const val = md.payload\n\n            // If changing to model with fewer layers, cap accordingly\n            this.uiConf.nLayers(val.nlayers).nHeads(val.nheads)\n            const currLayer = this.uiConf.layer()\n            const maxLayer = this.uiConf.nLayers() - 1\n            this.uiConf.layer(Math.min(currLayer, maxLayer))\n            this.initLayers(this.uiConf.nLayers())\n\n            this.api.getMetaAttentions(this.uiConf.model(), this.uiConf.sentence(), this.uiConf.layer()).then(attention => {\n                const att = attention.payload;\n                this.initFromResponse(att)\n\n                // Wrap postInit into function so asynchronous call does not mess with necessary inits\n                const postResponseDisplayCleanup = () => {\n                    this._toggleTokenSel()\n                }\n\n                let normBy\n                if ((this.uiConf.modelKind() == tp.ModelKind.Autoregressive) && (!this.uiConf.hideClsSep())) {\n                    normBy = tp.NormBy.COL\n                }\n                else {\n                    normBy = tp.NormBy.ALL\n                }\n                this.vizs.attentionSvg.normBy = normBy\n\n                if (this.uiConf.maskInds().length > 0) {\n                    this.tokCapsule.a.maskInds = this.uiConf.maskInds()\n\n                    this.api.updateMaskedAttentions(this.uiConf.model(), this.tokCapsule.a, this.uiConf.sentence(), this.uiConf.layer()).then(resp => {\n                        const r = resp.payload;\n                        this.attCapsule.updateFromNormal(r, this.uiConf.hideClsSep());\n                        this.tokCapsule.updateTokens(r)\n                        this.update()\n                        postResponseDisplayCleanup()\n                    })\n                } else {\n                    this.update()\n                    postResponseDisplayCleanup()\n                }\n\n                if (this.uiConf.modelKind() == tp.ModelKind.Autoregressive) {\n                    // Ensure only 1 mask ind is present for autoregressive models\n                    if (this.uiConf.hasToken()) {\n                        this.grayToggle(<number>this.uiConf.token().ind)\n                    }\n                    self.vizs.tokens.left.options.divHover.textInfo = \"Would predict next...\"\n                    self.vizs.tokens.right.options.divHover.textInfo = \"Would predict next...\"\n                }\n                else {\n                    self.vizs.tokens.left.options.divHover.textInfo = \"Would predict here...\"\n                    self.vizs.tokens.right.options.divHover.textInfo = \"Would predict here...\"\n                }\n\n                this.sels.body.style(\"cursor\", \"default\")\n            });\n        })\n\n    }\n\n    private initFromResponse(attention: tp.AttentionResponse) {\n        this.attCapsule = makeFromMetaResponse(attention, this.uiConf.hideClsSep())\n        this.tokCapsule = new TokenWrapper(attention);\n        this._staticInits()\n    }\n\n    private leaveCorpusMsg(msg: string) {\n        this.vizs.corpusInspector.hideView()\n        this.vizs.corpusMatManager.hideView()\n        console.log(\"Running leave msg\");\n        Sel.unhideElement(this.sels.corpusMsgBox)\n        this.sels.corpusMsgBox.text(msg)\n    }\n\n    private _bindEventHandler() {\n        const self = this;\n        this.eventHandler.bind(TextTokens.events.tokenDblClick, (e) => {\n            switch (self.uiConf.modelKind()) {\n                case tp.ModelKind.Bidirectional: {\n                    e.sel.classed(\"masked-token\", !e.sel.classed(\"masked-token\"));\n                    const letter = sideToLetter(e.side, this.uiConf.attType)\n                    self.tokCapsule[letter].toggle(e.ind)\n                    self.sels.body.style(\"cursor\", \"progress\")\n\n                    self.api.updateMaskedAttentions(this.uiConf.model(), this.tokCapsule.a, this.uiConf.sentence(), this.uiConf.layer()).then((resp: rsp.AttentionDetailsResponse) => {\n                        const r = resp.payload;\n                        self.attCapsule.updateFromNormal(r, this.uiConf.hideClsSep());\n                        self.tokCapsule.updateTokens(r);\n\n                        self.uiConf.maskInds(this.tokCapsule.a.maskInds)\n\n                        self.update();\n                        self.sels.body.style(\"cursor\", \"default\")\n                    })\n                    break;\n                }\n                case tp.ModelKind.Autoregressive: {\n                    console.log(\"Autoregressive model doesn't do masking\");\n                    break;\n                }\n                default: {\n                    console.log(\"What kind of model is this?\");\n                    break;\n                }\n            }\n        })\n\n        this.eventHandler.bind(TextTokens.events.tokenMouseOver, (e: tp.TokenEvent) => {\n            chooseShowBySide(this.uiConf.token(), e)\n        })\n\n        this.eventHandler.bind(TextTokens.events.tokenMouseOut, (e) => {\n            chooseShowAll(this.uiConf.token())\n        })\n\n        this.eventHandler.bind(TextTokens.events.tokenClick, (e: tp.TokenEvent) => {\n            const tokToggle = () => {\n                this.uiConf.toggleToken(e)\n                this._toggleTokenSel()\n                showBySide(e)\n            }\n            tokToggle()\n            this.renderAttHead()\n        })\n\n\n        this.eventHandler.bind(AttentionHeadBox.events.rowMouseOver, (e: tp.HeadBoxEvent) => {\n            self.sels.atnHeads.headInfo.style('visibility', 'visible')\n        })\n\n\n        this.eventHandler.bind(AttentionHeadBox.events.rowMouseOut, () => {\n            self.sels.atnHeads.headInfo.style('visibility', 'hidden')\n            // Don't do anything special on row mouse out\n        })\n\n        this.eventHandler.bind(AttentionHeadBox.events.boxMouseOver, (e: tp.HeadBoxEvent) => {\n            const updateMat = this.attCapsule.byHead(e.head)\n            this.vizs.attentionSvg.data(updateMat)\n            this.vizs.attentionSvg.update(updateMat)\n\n            showBySide(this.uiConf.token())\n        })\n\n        this.eventHandler.bind(AttentionHeadBox.events.boxMouseOut, () => {\n            const att = this.attCapsule.byHeads(this.uiConf.heads())\n            this.vizs.attentionSvg.data(att)\n            this.vizs.attentionSvg.update(att)\n            showBySide(this.uiConf.token())\n        })\n\n        this.eventHandler.bind(AttentionHeadBox.events.boxMouseMove, (e) => {\n            const headInfo = self.sels.atnHeads.headInfo\n            let left, top, borderRadius\n\n            if (e.side == \"left\") {\n                const divOffset = [12, 3]\n                left = e.mouse[0] + e.baseX - (+headInfo.style('width').replace('px', '') + divOffset[0])\n                top = e.mouse[1] + e.baseY - (+headInfo.style('height').replace('px', '') + divOffset[1])\n                borderRadius = \"8px 8px 1px 8px\"\n            }\n            else {\n                const divOffset = [-13, 3]\n                left = e.mouse[0] + e.baseX + divOffset[0]\n                top = e.mouse[1] + e.baseY - (+headInfo.style('height').replace('px', '') + divOffset[1])\n                borderRadius = \"8px 8px 8px 1px\"\n            }\n\n            headInfo\n                .style('visibility', 'visible')\n                .style('left', String(left) + 'px')\n                .style('top', String(top) + 'px')\n                .style('border-radius', borderRadius)\n                .text(`Head: ${e.ind + 1}`)\n\n            // Don't do anything special on row mouse over\n        })\n\n        this.eventHandler.bind(AttentionHeadBox.events.boxClick, (e: { head }) => {\n            const result = this.uiConf.toggleHead(e.head)\n            if (result == tp.Toggled.ADDED) {\n                selectHead(e.head)\n            } else if (result == tp.Toggled.REMOVED) {\n                unselectHead(e.head)\n            }\n\n            this._renderHeadSummary();\n            this.renderSvg();\n        })\n    }\n\n    private _toggleTokenSel() {\n        const e = this.uiConf.token()\n        const alreadySelected = d3.select('.selected-token')\n\n        // If no token should be selected, unselect all tokens\n        if (!this.uiConf.hasToken()) {\n            const newSel: d3.Selection<BaseType, any, BaseType, any> = d3.selectAll('.selected-token')\n            if (!newSel.empty()) newSel.classed('selected-token', false)\n        }\n\n        // Otherwise, select the indicated token\n        else {\n            const token2String = (e: tp.TokenEvent) => `#${e.side}-token-${e.ind}`\n            const newSel = d3.select(token2String(e))\n            // Check that selection exists\n            if (!newSel.empty()) newSel.classed('selected-token', true)\n        }\n\n        // Remove previous token selection, if any\n        if (!alreadySelected.empty()) {\n            alreadySelected.classed('selected-token', false)\n        }\n\n        if (this.uiConf.modelKind() == tp.ModelKind.Autoregressive) {\n            this.grayToggle(+e.ind)\n            this.markNextToggle(+e.ind, this.tokCapsule.a.length())\n        }\n    }\n\n    /** Gray all tokens that have index greater than ind */\n    private grayBadToks(ind: number) {\n        if (this.uiConf.modelKind() == tp.ModelKind.Autoregressive) {\n            const grayToks = function (d, i) {\n                const s = d3.select(this)\n                s.classed(\"masked-token\", i > ind)\n            }\n            d3.selectAll('.right-token').each(grayToks)\n            d3.selectAll('.left-token').each(grayToks)\n        }\n    }\n\n\n    private grayToggle(ind: number) {\n        if (this.uiConf.hasToken())\n            this.grayBadToks(ind)\n        else\n            d3.selectAll('.token').classed('masked-token', false)\n\n    }\n\n    private markNextWordToks(ind: number, N: number) {\n        const markToks = function (d, i) {\n            const s = d3.select(this)\n            s.classed(\"next-token\", i == Math.min(ind + 1, N))\n        }\n        d3.selectAll('.right-token').each(markToks)\n        d3.selectAll('.left-token').each(markToks)\n    }\n\n    private markNextToggle(ind: number, N: number) {\n        if (this.uiConf.hasToken())\n            this.markNextWordToks(ind, N)\n        else\n            d3.selectAll('.token').classed('next-token', false)\n\n    }\n\n    private _staticInits() {\n        this._initSentenceForm();\n        this._initModelSelection();\n        this._renderHeadSummary();\n        this._initToggle();\n        this.renderAttHead();\n        this.renderTokens();\n    }\n\n    private _initSentenceForm() {\n        const self = this;\n\n        this.sels.form.sentenceA.attr('placeholder', \"Enter new sentence to analyze\")\n        this.sels.form.sentenceA.attr('value', this.uiConf.sentence())\n\n        const submitNewSentence = () => {\n            // replace all occurences of '#' in sentence as this causes the API to break\n            const sentence_a: string = this.sels.form.sentenceA.property(\"value\").replace(/\\#/g, '')\n\n            // Only update if the form is filled correctly\n            if (sentence_a.length) {\n                this.sels.body.style(\"cursor\", \"progress\")\n                this.api.getMetaAttentions(this.uiConf.model(), sentence_a, this.uiConf.layer())\n                    .then((resp: rsp.AttentionDetailsResponse) => {\n                        const r = resp.payload\n                        this.uiConf.sentence(sentence_a)\n                        this.uiConf.rmToken();\n                        this.attCapsule.updateFromNormal(r, this.uiConf.hideClsSep());\n                        this.tokCapsule.updateFromResponse(r);\n                        this._toggleTokenSel();\n                        this.update();\n                        this.sels.body.style(\"cursor\", \"default\")\n                    })\n            }\n        }\n\n        const onEnter = R.curry((keyCode, f, event) => {\n            const e = event || window.event;\n            if (e.keyCode !== keyCode) return;\n            e.preventDefault();\n            f();\n        })\n\n        const onEnterSubmit = onEnter(13, submitNewSentence)\n\n        const btn = this.sels.form.button;\n        const inputBox = this.sels.form.sentenceA;\n\n        btn.on(\"click\", submitNewSentence)\n        inputBox.on('keypress', onEnterSubmit)\n    }\n\n    private _renderHeadSummary() {\n        this.sels.selectedHeads\n            .html(R.join(', ', this.uiConf.heads().map(h => h + 1)))\n    }\n\n    private initLayers(nLayers: number) {\n        const self = this;\n        let hasActive = false;\n\n        const checkboxes = self.sels.layerCheckboxes.selectAll(\".layerCheckbox\")\n            .data(_.range(0, nLayers))\n            .join(\"label\")\n            .attr(\"class\", \"btn button layerCheckbox\")\n            .classed('active', (d, i) => {\n                // Assign to largest layer available if uiConf.layer() > new nLayers\n                if (d == self.uiConf.layer()) { // Javascript is 0 indexed!\n                    hasActive = true;\n                    return true\n                }\n\n                if (!hasActive && d == nLayers) {\n                    self.uiConf.layer(d)\n                    hasActive = true\n                    return true\n                }\n\n                return false\n\n            })\n            .text((d) => d + 1)\n            .append(\"input\")\n            .attr(\"type\", \"radio\")\n            .attr(\"class\", \"checkbox-inline\")\n            .attr(\"name\", \"layerbox\")\n            // .attr(\"head\", d => d)\n            .attr(\"id\", (d, i) => \"layerCheckbox\" + i)\n        // .text((d, i) => d + \" \")\n\n        fromEvent(checkboxes.nodes(), 'change').pipe(\n            tap((e: Event) => {\n                const myData = d3.select(<BaseType>e.target).datum();\n                console.log(myData, \"--- myData\");\n                this.sels.layerCheckboxes.selectAll(\".layerCheckbox\")\n                    .classed('active', d => d === myData)\n            }),\n            map((v: Event) => +d3.select(<BaseType>v.target).datum()),\n            tap(v => {\n                console.log(\"New layer: \", v);\n                self.uiConf.layer(v);\n                self.sels.body.style(\"cursor\", \"progress\");\n            }),\n            switchMap((v) => from(self.api.updateMaskedAttentions(self.uiConf.model(), self.tokCapsule.a, self.uiConf.sentence(), v)))\n        ).subscribe({\n            next: (resp: rsp.AttentionDetailsResponse) => {\n                const r = resp.payload;\n                self.attCapsule.updateFromNormal(r, this.uiConf.hideClsSep());\n                self.tokCapsule.updateTokens(r);\n                self.uiConf.maskInds(self.tokCapsule.a.maskInds)\n                self.update();\n                self.sels.body.style(\"cursor\", \"default\")\n                self._toggleTokenSel();\n            }\n        })\n\n        const layerId = `#layerCheckbox${this.uiConf.layer()}`\n        console.log(\"Layer ID: \", layerId);\n        d3.select(layerId).attr(\"checked\", \"checked\")\n\n        // Init threshold stuff\n        const dispThresh = (thresh) => Math.round(thresh * 100)\n        d3.select('#my-range-value').text(dispThresh(self.uiConf.threshold()))\n\n        this.sels.threshSlider.on(\"input\", _.throttle(function () {\n            const node = <HTMLInputElement>this;\n            self.uiConf.threshold(+node.value / 100);\n            d3.select('#my-range-value').text(dispThresh(self.uiConf.threshold()))\n            self.vizs.attentionSvg.threshold(self.uiConf.threshold())\n        }, 100))\n\n        this.sels.headSelectAll.on(\"click\", function () {\n            self.uiConf.selectAllHeads();\n            self.renderSvg()\n            self.renderAttHead()\n        })\n\n        this.sels.headSelectNone.on(\"click\", function () {\n            self.uiConf.selectNoHeads();\n            self.renderSvg()\n            self.renderAttHead()\n            Sel.setHidden(\".atn-curve\")\n        })\n\n    }\n\n    _initToggle() {\n        fromEvent(this.sels.clsToggle.node(), 'input').pipe(\n            // @ts-ignore -- TODO: FIX !\n            map(e => e.srcElement.checked),\n        ).subscribe({\n            next: v => {\n                this.uiConf.hideClsSep(v)\n                this.attCapsule.zeroed(v)\n                this.renderSvg();\n                this.renderAttHead();\n            }\n        })\n    }\n\n    private _initModelSelection() {\n        const self = this\n\n        // Below are the available models. Will need to choose 3 to be available ONLY\n        const data = [\n            { name: \"bert-base-cased\", kind: tp.ModelKind.Bidirectional },\n            { name: \"bert-base-uncased\", kind: tp.ModelKind.Bidirectional },\n            { name: \"bert-base-german-cased\", kind: tp.ModelKind.Bidirectional },\n            { name: \"xlm-mlm-en-2048\", kind: tp.ModelKind.Bidirectional },\n            { name: \"distilbert-base-uncased\", kind: tp.ModelKind.Bidirectional },\n            { name: \"distilroberta-base\", kind: tp.ModelKind.Bidirectional },\n            { name: \"albert-base-v1\", kind: tp.ModelKind.Bidirectional },\n            { name: \"albert-xxlarge-v2\", kind: tp.ModelKind.Bidirectional },\n            { name: \"xlm-roberta-base\", kind: tp.ModelKind.Bidirectional },\n            // { name: \"t5-small\", kind: tp.ModelKind.Autoregressive },\n            { name: \"roberta-base\", kind: tp.ModelKind.Bidirectional },\n            { name: \"gpt2\", kind: tp.ModelKind.Autoregressive },\n            { name: \"distilgpt2\", kind: tp.ModelKind.Autoregressive },\n        ]\n\n        const names = R.map(R.prop('name'))(data)\n        const kinds = R.map(R.prop('kind'))(data)\n        const kindmap = R.zipObj(names, kinds)\n\n        this.sels.modelSelector.selectAll('.model-option')\n            .data(data)\n            .join('option')\n            .classed('model-option', true)\n            .property('value', d => d.name)\n            .attr(\"modelkind\", d => d.kind)\n            .text(d => d.name)\n\n        this.sels.modelSelector.property('value', this.uiConf.model());\n\n        this.sels.modelSelector.on('change', function () {\n            const me = d3.select(this)\n            const mname = me.property('value')\n            self.uiConf.model(mname);\n            self.uiConf.modelKind(kindmap[mname]);\n            if (kindmap[mname] == tp.ModelKind.Autoregressive) {\n                console.log(\"RESETTING MASK INDS\");\n                self.uiConf.maskInds([])\n            }\n            self.mainInit();\n        })\n    }\n\n    renderAttHead() {\n        const heads = _.range(0, this.uiConf._nHeads)\n        const focusAtt = this.attCapsule.att\n        const token = this.uiConf.hasToken() ? this.uiConf.token() : null\n        //@ts-ignore\n        const leftAttInfo = getAttentionInfo(focusAtt, heads, \"left\", token);\n        //@ts-ignore\n        const rightAttInfo = getAttentionInfo(focusAtt, heads, \"right\", token);\n        this.vizs.leftHeads.options.offset = this.uiConf.offset\n        this.vizs.leftHeads.update(leftAttInfo)\n        this.vizs.rightHeads.update(rightAttInfo)\n        this._renderHeadSummary();\n\n        // Make sure\n        heads.forEach((h) => {\n            if (this.uiConf.headSet().has(h)) {\n                selectHead(h)\n            } else {\n                unselectHead(h)\n            }\n        })\n    };\n\n    renderTokens() {\n        const left = this.tokCapsule[this.uiConf.attType[0]]\n        const right = this.tokCapsule[this.uiConf.attType[1]]\n\n        console.log(\"now: \", this.uiConf.offset);\n        this.vizs.tokens.left.options.offset = this.uiConf.offset\n        this.vizs.tokens.left.update(left.tokenData);\n        this.vizs.tokens.left.mask(left.maskInds);\n        this.vizs.tokens.right.update(right.tokenData);\n        this.vizs.tokens.right.mask(right.maskInds);\n        // displaySelectedToken\n    }\n\n    renderSvg() {\n        const att = this.attCapsule.byHeads(this.uiConf.heads())\n        this.vizs.attentionSvg.options.offset = this.uiConf.offset\n        const svg = <AttentionGraph>this.vizs.attentionSvg.data(att);\n        svg.update(att)\n        const maxTokens = _.max([this.tokCapsule.a.length()])\n        const newHeight = svg.options.boxheight * maxTokens\n        svg.height(newHeight)\n\n        // Don't redisplay everything if one token is selected\n        showBySide(this.uiConf.token())\n    };\n\n    render() {\n        this.renderTokens();\n        this.renderSvg();\n        this.renderAttHead();\n    }\n\n    update() {\n        this.render();\n    }\n}\n\n\n","import {D3Sel} from \"../etc/Util\";\nimport * as d3 from \"d3\";\n\nexport function createStaticSkeleton(base: D3Sel) {\n\n    // ---- TEMPLATE -----\n    // language=HTML\n    function template() {\n        return `\n            <!--        <div id=\"attention-vis\">-->\n            <div>\n                <div id=\"model-selection\">\n                    <label for=\"model-options\"\n                           style=\"width:150px;margin-bottom: 5px;\">Select\n                        model</label><select id=\"model-option-selector\"\n                                             name=\"model-options\">\n\n                </select>\n                </div>\n            </div>\n            <div id=\"sentence-input\">\n                <form>\n                    <div class=\"form-group\">\n                        <label for=\"form-sentence-a\"\n                               style=\"width: 150px; display: inline-block;vertical-align: middle;\">Input\n                            Sentence</label>\n                        <input id=\"form-sentence-a\" type=\"text\"\n                               name=\"sent-a-input\"\n                               placeholder=\"Enter new sentence to analyze\"\n                               value=\"The girl ran to a local pub to escape the din of her city.\"\n                               style=\"width: calc(100% - 240px); display: inline-block;vertical-align: middle;\"\n                        >\n                        <button class=\"btn btn-primary\" id=\"update-sentence\"\n                                type=\"button\"\n                                style=\"width:70px;display: inline-block;padding: 3px 3px;\">\n                            Update\n                        </button>\n                    </div>\n                </form>\n                <!--                <div class=\"padding\"></div>-->\n\n            </div>\n\n            <div>\n                <div style=\"display: inline-block; width:150px;\"><b>Filters</b>\n                </div>\n                <div id=\"cls-toggle\" style=\"display: inline-block;\">\n                    <!--                    <div class=\"input-description\">-->\n                    Hide Special Tokens\n                    <!--                    </div>-->\n                    <label class=\"switch\" style=\"vertical-align: middle;\">\n                        <input type=\"checkbox\" checked=\"checked\">\n                        <span class=\"short-slider round\"></span>\n                    </label>\n                </div>\n                <div style=\"margin-left: 25px;display: inline-block;\">\n                    Show top <span id=\"my-range-value\">70</span>% of att:\n                </div>\n\n                <div class=\"slide-container\"\n                     style=\"display: inline-block;margin-left: 5px;width:calc(100% - 550px);\">\n                    <!--                    <label for=\"my-range\">-->\n\n                    <!--                    </label>-->\n                    <input\n                            type=\"range\" min=\"0\" max=\"100\" value=\"70\"\n                            class=\"slider\" id=\"my-range\"\n                            style=\"vertical-align: middle;display: inline-block;\">\n                </div>\n            </div>\n\n            <div id=\"layer-selection\" style=\"margin-top: 10px;\">\n                <div class=\"input-description\"\n                     style=\"display: inline-block; width: 150px;vertical-align: top;\">\n                    Layer\n                </div>\n                <div class=\"layer-select btn-group btn-group-toggle\"\n                     data-toggle=\"buttons\" id=\"layer-select\"\n                     style=\"vertical-align: top;\">\n\n                </div>\n            </div>\n\n            <div id=\"selected-head-display\">\n                <div class=\"input-description\" style=\"display: inline-block; width: 150px;vertical-align: top;\">\n                    Selected heads:\n                </div>\n                <div id=\"selected-heads\" style=\"display:inline-block;vertical-align: top;\">\n\n                </div>\n            </div>\n\n            <div id=\"connector-container\">\n                <div id=\"connector-controls\">\n                    <div class=\"left-control-half\">\n\n\n                        <!--                        <div id=\"layer-selection\">-->\n                        <!--                            <div class=\"input-description\">-->\n                        <!--                                Layer:-->\n                        <!--                            </div>-->\n                        <!--                            <div class=\"layer-select btn-group btn-group-toggle\"-->\n                        <!--                                 data-toggle=\"buttons\" id=\"layer-select\">-->\n\n                        <!--                            </div>-->\n                        <!--                        </div>-->\n\n                    </div>\n                    <div class=\"right-control-half head-control\">\n<!--                        <div id=\"selected-head-display\">-->\n<!--                            <div class=\"input-description\">-->\n<!--                                Selected heads:-->\n<!--                            </div>-->\n<!--                            <div id=\"selected-heads\">-->\n\n<!--                            </div>-->\n<!--                        </div>-->\n                        <div class=\"select-input\" id=\"head-all-or-none\">\n                            <button id=\"select-all-heads\">Select all heads\n                            </button>\n                            <button id=\"select-no-heads\">Unselect all heads\n                            </button>\n                        </div>\n                        <div id=\"usage-info\">\n                            <p>\n                                You focus on one token by <b>click</b>.\n                                You can mask any token by <b>double click</b>.\n                            </p>\n                            <p>\n                                You can select and de-select a head by a\n                                <b>click</b>\n                                on the heatmap columns\n                            </p>\n                        </div>\n                    </div>\n                </div>\n                <div id=\"atn-container\" class=\"text-center\">\n                    <div id=\"head-info-box\"\n                         class=\"mat-hover-display text-center\"\n                         style=\"width: 70px; height: 30px; visibility: hidden; left: 929px; top: 670px; border-radius: 8px 8px 8px 1px;\">\n                    </div>\n                    <svg id=\"left-att-heads\" width=\"182\" height=\"442\"></svg>\n                    <div id=\"left-tokens\"></div>\n                    <svg id=\"atn-display\" height=\"442\" width=\"200\"></svg>\n                    <div id=\"right-tokens\"></div>\n                    <svg id=\"right-att-heads\" width=\"182\" height=\"442\"></svg>\n                </div>\n                <div id=\"vis-break\"></div>\n            </div>\n            <!--        </div>-->\n        `\n    }\n\n    base.html(template);\n\n    // ---- TEMPLATE.end -----\n\n\n    /**\n     * Top level sections\n     */\n    const sentenceInput = base.select(\"#sentence-input\");\n    const connectorContainer = base.select('#connector-container');\n    const atnControls = base.select('#connector-controls');\n    const atnContainer = base.select('#atn-container');\n\n    /**\n     * Sentence Input\n     */\n\n    const sentenceA = base.select('#form-sentence-a');\n    const formButton = base.select('#update-sentence');\n\n    /**\n     * Connector Controls\n     */\n\n    const modelSelector = base.select('#model-option-selector')\n    const threshSlider = base.select('#my-range');\n    const layerCheckboxes = base.select('#layer-select');\n    const clsToggle = base.select('#cls-toggle');\n    const selectedHeads = base.select('#selected-heads');\n    const headSelectAll = base.select('#select-all-heads');\n    const headSelectNone = base.select('#select-no-heads');\n\n    /**\n     * For main attention vis\n     */\n\n    const headInfoBox = base.select('#head-info-box');\n    const headBoxLeft = base.select('#left-att-heads');\n    const tokensLeft = base.select('#left-tokens');\n    const atnDisplay = base.select('#atn-display');\n    const tokensRight = base.select('#right-tokens');\n    const headBoxRight = base.select('#right-att-heads');\n\n    /**\n     * Return an object that provides handles to the important parts here\n     */\n\n    return {\n        body: d3.select('body'),\n        atnContainer: atnContainer,\n        atnDisplay: atnDisplay,\n        atnHeads: {\n            left: headBoxLeft,\n            right: headBoxRight,\n            headInfo: headInfoBox\n        },\n        form: {\n            sentenceA: sentenceA,\n            button: formButton\n        },\n        tokens: {\n            left: tokensLeft,\n            right: tokensRight\n        },\n        modelSelector: modelSelector,\n        clsToggle: clsToggle,\n        layerCheckboxes: layerCheckboxes,\n        selectedHeads: selectedHeads,\n        headSelectAll: headSelectAll,\n        headSelectNone: headSelectNone,\n        threshSlider: threshSlider,\n    }\n}\n","/* (ignored) */","/* (ignored) */","/* (ignored) */","/* (ignored) */","/* (ignored) */","/* (ignored) */"],"sourceRoot":""} \ No newline at end of file diff --git a/spaces/fabiogra/moseca/README.md b/spaces/fabiogra/moseca/README.md deleted file mode 100644 index c3883a2a16f00e1ff0d109be2b7537c6ac6fb2df..0000000000000000000000000000000000000000 --- a/spaces/fabiogra/moseca/README.md +++ /dev/null @@ -1,236 +0,0 @@ ---- -title: Moseca -emoji: 🎤🎸🥁🎹 -colorFrom: yellow -colorTo: purple -sdk: docker -app_port: 7860 -models: ["https://huggingface.co/fabiogra/baseline_vocal_remover"] -tags: ["audio", "music", "vocal-removal", "karaoke", "music-separation", "music-source-separation"] -pinned: true ---- - ---- - - - - - - - - - - - --- - - - - - - - --- - ----- You focus on one token by click. - You can mask any token by double click. -
-- You can select and de-select a head by a - click - on the heatmap columns -
--- --- - - - - --
-
-Moseca -
Music Source Separation & Karaoke
- -[](https://colab.research.google.com/drive/1ODoK3VXajprNbskqy7G8P1h-Zom92TMA?usp=sharing) - -- -
-
-
----
--
- - -- [Setup](#setup) - - [Local environment](#local-environment) - - [Docker](#docker) - - [(Optional) Preprocess samples](#optional-preprocess-samples) -- [About](#about) - - [High-Quality Stem Separation](#high-quality-stem-separation) - - [Advanced AI Algorithms](#advanced-ai-algorithms) - - [Karaoke Fun](#karaoke-fun) - - [Easy Deployment](#easy-deployment) - - [Open-Source and Free](#open-source-and-free) - - [Support](#support) -- [FAQs](#faqs) - - [What is Moseca?](#what-is-moseca) - - [Are there any limitations?](#are-there-any-limitations) - - [How does Moseca work?](#how-does-moseca-work) - - [How do I use Moseca?](#how-do-i-use-moseca) - - [Where can I find the code for Moseca?](#where-can-i-find-the-code-for-moseca) - - [How can I get in touch with you?](#how-can-i-get-in-touch-with-you) -- [Disclaimer](#disclaimer) - ---- - - -## Setup -### Local environment -Create a new environment with Python 3.10 and install the requirements: -```bash -pip install -r requirements.txt -``` -set the `PYTHONPATH` to the root folder: -```bash -export PYTHONPATH=path/to/moseca -``` -download the vocal remover model: -```bash -wget --progress=bar:force:noscroll https://huggingface.co/fabiogra/baseline_vocal_remover/resolve/main/baseline.pth -``` -then run the app with: -```bash -streamlit run app/header.py -``` -### Docker -You can also run the app with Docker: -```bash -docker build -t moseca . -docker run -it --rm -p 7860:7860 $(DOCKER_IMAGE_NAME) -``` -or pull the image from Hugging Face Spaces: -```bash -docker run -it -p 7860:7860 --platform=linux/amd64 \ - registry.hf.space/fabiogra-moseca:latest -``` - -You can set the following environment variables to limit the resources used by the app: -- ENV_LIMITATION=true -- LIMIT_CPU=true - -### (Optional) Preprocess samples -If you want to preprocess the samples used in the demo, you need to set the env variable `PREPARE_SAMPLES=true` as a secret (create the file `run/secrets/PREPARE_SAMPLES` with `true` value inside). - -If you run locally you also need to move the files inside `scripts` to the root folder and run `prepare_samples.sh`. -``` -cp scripts/ . -chmod +x prepare_samples.sh -python prepare_samples.sh -``` - ---- -## About - -Welcome to Moseca, your personal web application designed to redefine your music experience. -Whether you're a musician looking to remix your favorite songs, a karaoke -enthusiast, or a music lover wanting to dive deeper into your favorite tracks, -Moseca is for you. - -
-
- -### High-Quality Stem Separation - -
-
-
-
- -Separate up to 6 stems including 🗣voice, 🥁drums, 🔉bass, 🎸guitar, -🎹piano (beta), and 🎶 others. - -
- -### Advanced AI Algorithms - -
-
-
- -Moseca utilizes state-of-the-art AI technology to extract voice or music from -your original songs accurately. - -
- -### Karaoke Fun - -
-
-
- -Engage with your favorite tunes in a whole new way! - -Moseca offers an immersive online karaoke experience, allowing you to search -for any song on YouTube and remove the vocals online. - -Enjoy singing along with high-quality instrumentals at the comfort of your home. - - -
- -### Easy Deployment - - -With Moseca, you can deploy your personal Moseca app in the - -
- -### Open-Source and Free - -Moseca is the free and open-source alternative to lalal.ai, splitter.ai or media.io vocal remover. - -You can modify, distribute, and use it free of charge. I believe in the power of community -collaboration and encourage users to contribute to our source code, making Moseca better with -each update. - - -
- -### Support - -- Show your support by giving a star to the GitHub repository [](https://github.com/fabiogra/moseca). -- If you have found an issue or have a suggestion to improve Moseca, you can open an [](https://github.com/fabiogra/moseca/issues/new) -- Enjoy Moseca? [](https://www.buymeacoffee.com/fabiogra) - ------- - -## FAQs - -### What is Moseca? - -Moseca is an open-source web app that utilizes advanced AI technology to separate vocals and -instrumentals from music tracks. It also provides an online karaoke experience by allowing you -to search for any song on YouTube and remove the vocals. - -### Are there any limitations? -Yes, in this environment there are some limitations regarding lenght processing -and CPU usage to allow a smooth experience for all users. - -If you want to remove these limitations you can deploy a Moseca app in your personal -environment like in the
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\ No newline at end of file diff --git a/spaces/falterWliame/Face_Mask_Detection/Belkin F5d8055 V2 Pilote Windows 10.md b/spaces/falterWliame/Face_Mask_Detection/Belkin F5d8055 V2 Pilote Windows 10.md deleted file mode 100644 index 6d808d58d11aa2c15f5293dd1697afa44ba5a922..0000000000000000000000000000000000000000 --- a/spaces/falterWliame/Face_Mask_Detection/Belkin F5d8055 V2 Pilote Windows 10.md +++ /dev/null @@ -1,36 +0,0 @@ - -How to Install Belkin F5D8055 V2 Driver on Windows 10
-If you have a Belkin F5D8055 V2 wireless adapter and want to use it on your Windows 10 computer, you may need to update its driver to ensure compatibility and performance. In this article, we will show you how to download and install the latest driver for your Belkin F5D8055 V2 wireless adapter.
-What is Belkin F5D8055 V2 Wireless Adapter?
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-How to Download and Install Belkin F5D8055 V2 Driver on Windows 10?
-To download and install the latest driver for your Belkin F5D8055 V2 wireless adapter, follow these steps:
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- Go to the Belkin Support page and enter "F5D8055" in the search bar. Press [Enter]. -
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You should now have successfully installed the latest driver for your Belkin F5D8055 V2 wireless adapter on Windows 10. You can now enjoy faster and more reliable wireless connectivity with your device.
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-¿Qué es y Cómo Rellenar la Hoja De Vida Minerva Pdf Para Llenar?
- -La hoja de vida Minerva 1003 es un formato de hoja de vida que muchas empresas y entidades públicas de Colombia requieren a los aspirantes a un empleo. Se trata de un documento oficial y estandarizado que facilita la selección y el análisis de los candidatos por parte de los contratistas. Pero, ¿sabes qué es y cómo rellenar la hoja de vida Minerva pdf para llenar correctamente? En este artículo te lo contamos todo.
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- -¿Qué es la hoja de vida Minerva 1003?
- -La hoja de vida Minerva 1003 es un tipo de hoja de vida que tiene un diseño específico y un color azul distintivo. Está compuesta por ocho secciones que recogen la información general, personal, familiar, educativa, laboral, referencias personales y familiares, y otros datos de interés del postulante.
- -La ventaja de usar este formato es que permite a los contratistas ingresar estos modelos en sus bases de datos y filtrar la consulta de los postulantes al puesto de trabajo por sus características o requerimientos particulares. Además, ayuda a presentar la información de forma clara, ordenada y concisa.
- -La hoja de vida Minerva 1003 tiene la misma finalidad que una hoja de vida normal: ser la primera impresión que tendrá el empleador o el seleccionador hacia el aspirante al cargo. Por eso, es muy importante llenarla correctamente y de forma atractiva, para destacar tus fortalezas y lograr una entrevista de trabajo.
- -¿Cómo descargar la hoja de vida Minerva pdf para llenar?
- -Puedes descargar gratis la hoja de vida Minerva 1003 en formato pdf o word desde diferentes sitios web. Aquí te dejamos algunos enlaces donde puedes hacerlo:
- - --
-
- Hoja de Vida Minerva 1003 ⇨ Descargar PDF y WORD -
- Formato hoja de vida Minerva 1003 para Descargar GRATIS - Experto Laboral -
- Hoja de Vida Minerva 1003 Editable en PDF | Descargar en Línea -
- Formato de Hoja de Vida Minerva 1003 para Llenar en Word -
Una vez que descargues el archivo, puedes imprimirlo o editarlo en tu computador o celular. Te recomendamos que uses el formato pdf para no alterar el diseño original.
- -¿Cómo rellenar la hoja de vida Minerva pdf para llenar?
- -Ahora que ya tienes el formato, te preguntarás cómo rellenar la hoja de vida Minerva pdf para llenar correctamente. No te preocupes, te lo explicamos paso a paso:
- --
-
- En los primeros campos, escribe la fecha en la que entregas la hoja de vida, el cargo al que te postulas y el código de la oferta de trabajo (si lo hay). -
- En la primera sección, escribe tu información general: nombre completo, dirección, teléfono, correo electrónico, nacionalidad, profesión u ocupación, estado civil y años de experiencia. -
- En la misma sección, coloca una fotografía reciente de tamaño 3 cm x 4 cm. -
- También en la misma sección, completa tu documentación: número de identidad, libreta militar (si aplica), tarjeta profesional (si aplica) y licencia de conducción (si aplica). -
- En la segunda sección, escribe tu información personal: si trabajas actualmente o no, en qué ciudades has trabajado o puedes trabajar, cuáles son tus pasatiempos o actividades extralaborales, si tienes alguna discapacidad o enfermedad y si tienes disponibilidad para viajar o cambiar de residencia. -
- También en la segunda sección, escribe tu objetivo o resumen profesional en tres líneas. Describe brevemente tus metas laborales o cómo te adaptas al empleo solicitado. -
- En la tercera sección, escribe tu información familiar: nombre, ocupación y teléfono de tu cónyuge (si aplica), padres y hermanos. -
- En la cuarta sección, escribe tu información educativa: nivel académico (primaria, secundaria, técnica, tecnológica o universitaria), nombre del centro educativo, título obtenido o por obtener y fecha de inicio y finalización. -
- En la quinta sección, escribe tu información laboral: nombre de las empresas donde has trabajado o trabajas actualmente (empezando por la más reciente), cargo desempeñado, funciones realizadas y fecha de inicio y finalización. -
- En la sexta sección, escribe tus referencias personales: nombre completo, ocupación u oficio y teléfono
-
Beneficios de usar la hoja de vida Minerva pdf para llenar
- -Usar la hoja de vida Minerva pdf para llenar tiene varios beneficios, tanto para los postulantes como para los contratistas. Algunos de ellos son:
- --
-
- Es un formato gratuito y fácil de descargar desde internet. -
- Es un formato oficial y reconocido por muchas empresas y entidades públicas de Colombia. -
- Es un formato estandarizado y uniforme que facilita la comparación y el análisis de los candidatos. -
- Es un formato que contiene la información básica y relevante para el puesto de trabajo. -
- Es un formato que permite presentar la información de forma clara, ordenada y concisa. -
- Es un formato que ayuda a causar una buena impresión y a destacar las fortalezas del postulante. -
Errores comunes al llenar la hoja de vida Minerva pdf para llenar
- -Aunque la hoja de vida Minerva pdf para llenar es un formato sencillo y práctico, muchas personas cometen errores al diligenciarla que pueden afectar sus posibilidades de conseguir una entrevista de trabajo. Algunos de estos errores son:
- --
-
- Escribir con mala ortografía o gramática. -
- Inventar o exagerar datos que no se pueden comprobar. -
- Escribir más información de la necesaria o repetir datos innecesariamente. -
- No seguir el orden cronológico inverso o no respetar los espacios asignados para cada dato. -
- No destacar los datos que son más importantes o relevantes para el cargo al que se aspira. -
- No ser positivo ni profesional al redactar el objetivo o resumen profesional. -
- No colocar una fotografía reciente ni firmar al final del documento. -
Para evitar estos errores, te recomendamos revisar bien tu hoja de vida antes de enviarla o entregarla. También puedes pedirle a alguien que te ayude a corregirla o a mejorarla. Recuerda que tu hoja de vida es tu carta de presentación y debe reflejar tu mejor imagen.
-Ejemplos de hoja de vida Minerva pdf para llenar
- -Para que te hagas una idea de cómo debe quedar tu hoja de vida Minerva pdf para llenar, te mostramos algunos ejemplos de hojas de vida Minerva 1003 rellenadas con datos ficticios. Puedes usarlos como referencia o inspiración, pero recuerda que debes adaptar tu hoja de vida a tu perfil y al cargo al que aspiras.
- -Ejemplo 1: Hoja de vida Minerva pdf para llenar de un contador público
- -
-
-Ejemplo 2: Hoja de vida Minerva pdf para llenar de un ingeniero civil
- -
-
-Ejemplo 3: Hoja de vida Minerva pdf para llenar de un abogado
- -
-
-Consejos para hacer una buena hoja de vida Minerva pdf para llenar
- -Ahora que ya sabes qué es y cómo rellenar la hoja de vida Minerva pdf para llenar correctamente, te damos algunos consejos para que tu hoja de vida sea más atractiva y eficaz:
- --
-
- Usa un lenguaje claro y sencillo. Evita las faltas ortográficas y gramaticales. -
- Sé honesto y veraz. No inventes ni exageres datos que no puedas comprobar. -
- Sé breve y conciso. No escribas más información de la necesaria ni repitas datos innecesariamente. -
- Sé coherente y ordenado. Sigue el orden cronológico inverso (de lo más reciente a lo más antiguo) y respeta los espacios asignados para cada dato. -
- Sé relevante y enfocado. Destaca los datos que sean más importantes o relevantes para el cargo al que te postulas. -
- Sé positivo y profesional. Redacta tu objetivo o resumen profesional con entusiasmo y confianza, mostrando tus capacidades y metas. -
- Coloca una fotografía reciente y adecuada. Evita las selfies, las fotos con filtros o las fotos informales. Usa una foto tipo carnet con fondo blanco y buena iluminación. -
- Firma al final del documento con tu nombre completo y número de identidad. Esto le dará un toque personal y formal a tu hoja de vida. -
Esperamos que este artículo te haya sido útil para saber qué es y cómo rellenar la hoja de vida Minerva pdf para llenar correctamente. Recuerda que tu hoja de vida es tu carta de presentación y debe reflejar tu mejor imagen.
-Preguntas frecuentes sobre la hoja de vida Minerva pdf para llenar
- -A continuación, te respondemos algunas de las preguntas más frecuentes que tienen las personas sobre la hoja de vida Minerva pdf para llenar:
- --
-
- ¿Qué diferencia hay entre la hoja de vida Minerva y la hoja de vida normal? La principal diferencia es que la hoja de vida Minerva tiene un formato oficial y estandarizado que muchas empresas y entidades públicas de Colombia solicitan a los postulantes a un empleo. La hoja de vida normal tiene un formato libre y personalizado que el candidato puede adaptar a su perfil y al cargo al que aspira. -
- ¿Qué ventajas tiene usar la hoja de vida Minerva? Algunas de las ventajas son que permite presentar la información de forma clara, ordenada y concisa, que facilita la comparación y el análisis de los candidatos por parte de los contratistas, y que ayuda a causar una buena impresión y a destacar las fortalezas del postulante. -
- ¿Qué desventajas tiene usar la hoja de vida Minerva? Algunas de las desventajas son que no permite personalizar el diseño ni el contenido de la hoja de vida, que puede resultar monótona o aburrida para el lector, y que puede limitar la expresión o la creatividad del candidato. -
- ¿Qué tamaño debe tener la hoja de vida Minerva? La hoja de vida Minerva debe tener un tamaño estándar de una hoja carta (21,6 cm x 27,9 cm). No se recomienda usar un tamaño mayor o menor, ya que puede afectar la legibilidad o el diseño del documento. -
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\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Car Parking Multiplayer MOD APK for iOS The Best Parking Simulator Game with Online Mode.md b/spaces/fatiXbelha/sd/Car Parking Multiplayer MOD APK for iOS The Best Parking Simulator Game with Online Mode.md deleted file mode 100644 index 0359866a70e71f2c61dbd2e31aa620317fbc8ea9..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Car Parking Multiplayer MOD APK for iOS The Best Parking Simulator Game with Online Mode.md +++ /dev/null @@ -1,94 +0,0 @@ -
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Yes, Car Parking Multiplayer MOD APK for iOS is safe to use as long as you download the IPA file from a trusted source and install it using a reputable app installer. You should also scan the file for viruses or malware before installing it on your device. However, you should be aware that using modded apps may violate the terms and conditions of the original game and may result in your account being banned or suspended. Therefore, you should use Car Parking Multiplayer MOD APK for iOS at your own risk and discretion.
-Car Parking Multiplayer MOD APK for iOS is compatible with most iOS devices that run on iOS 9.0 or later. However, some devices may experience performance issues or crashes due to the high-quality graphics and physics of the game. Therefore, you should check the compatibility of your device before downloading and installing Car Parking Multiplayer MOD APK for iOS.
-Yes, you can play Car Parking Multiplayer MOD APK for iOS offline without an internet connection. You can access all the features of the game, such as unlimited money, customizable cars and garages, and various parking scenarios and challenges. However, you will not be able to play with other players online or chat with them using voice or text messages. Therefore, you should connect to the internet if you want to enjoy the online multiplayer mode of the game.
-No, you cannot update Car Parking Multiplayer MOD APK for iOS directly from the app. If you want to update the game to the latest version, you need to download and install the new IPA file from a trusted source and follow the same steps as before. You should also backup your game data before updating the game to avoid losing your progress and achievements.
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\ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Disfruta de todas las funciones premium de ibis Paint X con el APK MOD 2022.md b/spaces/fatiXbelha/sd/Disfruta de todas las funciones premium de ibis Paint X con el APK MOD 2022.md deleted file mode 100644 index 9eb0452c5e97133dcbca6058546647864205af55..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Disfruta de todas las funciones premium de ibis Paint X con el APK MOD 2022.md +++ /dev/null @@ -1,116 +0,0 @@ -
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ibis Paint X Mod APK 2022 is not an official app from the original developer, so it is not guaranteed to be safe to use. There is a possibility that the mod APK file might contain viruses, malware, spyware, or other harmful elements that might damage your device or steal your data. Therefore, you should always download the mod APK file from a reliable source and scan it with an antivirus software before installing it on your device.
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\ No newline at end of file diff --git a/spaces/fb700/chatglm-fitness-RLHF/docs/waifu_plugin/autoload.js b/spaces/fb700/chatglm-fitness-RLHF/docs/waifu_plugin/autoload.js deleted file mode 100644 index 3464a5cd44b0d4e1b0f2528bd01fc1793275b964..0000000000000000000000000000000000000000 --- a/spaces/fb700/chatglm-fitness-RLHF/docs/waifu_plugin/autoload.js +++ /dev/null @@ -1,30 +0,0 @@ -try { - $("").attr({href: "file=docs/waifu_plugin/waifu.css", rel: "stylesheet", type: "text/css"}).appendTo('head'); - $('body').append(''); - $.ajax({url: "file=docs/waifu_plugin/waifu-tips.js", dataType:"script", cache: true, success: function() { - $.ajax({url: "file=docs/waifu_plugin/live2d.js", dataType:"script", cache: true, success: function() { - /* 可直接修改部分参数 */ - live2d_settings['hitokotoAPI'] = "hitokoto.cn"; // 一言 API - live2d_settings['modelId'] = 5; // 默认模型 ID - live2d_settings['modelTexturesId'] = 1; // 默认材质 ID - live2d_settings['modelStorage'] = false; // 不储存模型 ID - live2d_settings['waifuSize'] = '210x187'; - live2d_settings['waifuTipsSize'] = '187x52'; - live2d_settings['canSwitchModel'] = true; - live2d_settings['canSwitchTextures'] = true; - live2d_settings['canSwitchHitokoto'] = false; - live2d_settings['canTakeScreenshot'] = false; - live2d_settings['canTurnToHomePage'] = false; - live2d_settings['canTurnToAboutPage'] = false; - live2d_settings['showHitokoto'] = false; // 显示一言 - live2d_settings['showF12Status'] = false; // 显示加载状态 - live2d_settings['showF12Message'] = false; // 显示看板娘消息 - live2d_settings['showF12OpenMsg'] = false; // 显示控制台打开提示 - live2d_settings['showCopyMessage'] = false; // 显示 复制内容 提示 - live2d_settings['showWelcomeMessage'] = true; // 显示进入面页欢迎词 - - /* 在 initModel 前添加 */ - initModel("file=docs/waifu_plugin/waifu-tips.json"); - }}); - }}); -} catch(err) { console.log("[Error] JQuery is not defined.") } diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Cargo Delivery Truck Games 3D Mod APK - The Most Amazing Car Transporter Simulator Game Ever.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Cargo Delivery Truck Games 3D Mod APK - The Most Amazing Car Transporter Simulator Game Ever.md deleted file mode 100644 index b405297444869fdb9da18d139d3c8613cb656993..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Cargo Delivery Truck Games 3D Mod APK - The Most Amazing Car Transporter Simulator Game Ever.md +++ /dev/null @@ -1,93 +0,0 @@ -
-Cargo Delivery Truck Games 3D Mod APK: A Fun and Challenging Driving Simulator
-Introduction
-If you love driving games and want to test your skills in transporting various cargoes, then you should try cargo delivery truck games 3d. This is a realistic and immersive driving simulator that lets you experience the thrill and challenge of being an offroad cargo truck driver. You will have to deliver different types of vehicles, such as cars, bikes, buses, etc., to their destinations in various locations, such as mountains, deserts, forests, etc. You will have to drive carefully and avoid obstacles, traffic, and accidents, as well as manage your fuel, time, and damage.
-However, if you want to enjoy the game without any limitations or ads, then you should download the mod apk version of cargo delivery truck games 3d. This is a modified version of the original game that gives you access to unlimited money, unlocked trucks and trailers, and other features that will make your gameplay more fun and easy. In this article, we will tell you more about the features of cargo delivery truck games 3d mod apk, how to download and install it on your device, and some tips and tricks for playing it.
-cargo delivery truck games 3d mod apk
DOWNLOAD 🆗 https://gohhs.com/2uPpwn
-Features of cargo delivery truck games 3d mod apk
-Realistic graphics and physics
-One of the best features of cargo delivery truck games 3d mod apk is its realistic graphics and physics. The game has stunning 3D graphics that will make you feel like you are driving a real truck in a real environment. The game also has realistic physics that will affect the movement and behavior of your truck and trailer, as well as the cargoes you are transporting. You will have to deal with different terrains, weather conditions, road conditions, etc., that will make your driving more challenging and exciting.
-Various trucks and trailers to choose from
-Another feature of cargo delivery truck games 3d mod apk is its variety of trucks and trailers to choose from. The game has over 20 different trucks and trailers that you can unlock and use in the game. Each truck and trailer has its own specifications, such as speed, power, fuel capacity, load capacity, etc., that will affect your performance and efficiency in the game. You can also customize your trucks and trailers with different colors, stickers, accessories, etc., to make them look more appealing and unique.
-Multiple levels and locations to explore
-The game also has multiple levels and locations to explore in cargo delivery truck games 3d mod apk. The game has over 50 different levels that will test your driving skills in various scenarios. Each level has its own objectives, such as delivering a certain number of vehicles within a given time limit, avoiding damage or accidents, etc., that will make your gameplay more challenging and rewarding. The game also has different locations to explore, such as mountains, deserts, forests, cities, etc., that will offer you different views and experiences.
-Customizable controls and settings
-The last feature of cargo delivery truck games 3d mod apk is its customizable controls and settings. The game allows you to choose between different control options, such as steering wheel, buttons, or tilt, to suit your preference and comfort. You can also adjust the camera angle, sound effects, music, and other settings to enhance your gaming experience. You can also pause the game anytime and resume it later.
-How to download and install cargo delivery truck games 3d mod apk
-Step 1: Enable unknown sources on your device
-Before you can download and install cargo delivery truck games 3d mod apk on your device, you need to enable unknown sources on your device. This will allow you to install apps from sources other than the Google Play Store. To do this, go to your device settings, then security, then unknown sources, and toggle it on.
-Step 2: Download the mod apk file from a trusted source
-Next, you need to download the mod apk file of cargo delivery truck games 3d from a trusted source. You can search for it online or use the link provided below. Make sure you download the latest version of the mod apk file that is compatible with your device. The file size is about 100 MB, so make sure you have enough storage space and a stable internet connection.
-Download cargo delivery truck games 3d mod apk here
-Step 3: Install the mod apk file and launch the game
-Finally, you need to install the mod apk file of cargo delivery truck games 3d on your device. To do this, locate the downloaded file in your file manager and tap on it. Follow the instructions on the screen and wait for the installation to complete. Once done, you can launch the game from your app drawer or home screen and enjoy playing it.
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-farm animal transport: offroad driving simulator mod apkTips and tricks for playing cargo delivery truck games 3d mod apk
-Follow the map and the instructions carefully
-One of the tips for playing cargo delivery truck games 3d mod apk is to follow the map and the instructions carefully. The map will show you the route and the destination of your delivery, as well as any obstacles or hazards along the way. The instructions will tell you what type of cargo you are transporting, how much time you have, how much damage you can afford, etc. Pay attention to these details and plan your strategy accordingly.
-Drive slowly and safely to avoid accidents and damage
-Another tip for playing cargo delivery truck games 3d mod apk is to drive slowly and safely to avoid accidents and damage. Driving fast may seem tempting, but it will also increase the risk of crashing into other vehicles, objects, or barriers, which will damage your truck and trailer, as well as your cargo. Driving safely will also help you save fuel and time, as well as earn more money and rewards.
-Upgrade your trucks and trailers to improve performance and capacity
-The last tip for playing cargo delivery truck games 3d mod apk is to upgrade your trucks and trailers to improve performance and capacity. As you progress in the game, you will unlock more trucks and trailers that have better features and capabilities. You can also use the money you earn from completing levels to upgrade your existing trucks and trailers with new engines, tires, brakes, etc., that will make them faster, stronger, and more efficient.
-Conclusion
-Cargo delivery truck games 3d mod apk is a fun and challenging driving simulator that will test your skills in transporting various cargoes in different locations. The game has realistic graphics and physics, various trucks and trailers to choose from, multiple levels and locations to explore, customizable controls and settings, and many other features that will make your gameplay more enjoyable and engaging. You can download the mod apk version of cargo delivery truck games 3d from a trusted source and install it on your device easily. You can also follow some tips and tricks for playing cargo delivery truck games 3d mod apk that will help you improve your performance and efficiency in the game.
-FAQs
-Here are some frequently asked questions about cargo delivery truck games 3d mod apk:
--
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- Is cargo delivery truck games 3d mod apk safe to download? -
- Is cargo delivery truck games 3d mod apk free to play? -
- Can I play cargo delivery truck games 3d mod apk offline? -
- How can I contact the developers of cargo delivery truck games 3d mod apk? -
- What are some similar games to cargo delivery truck games 3d mod apk? -
- Euro Truck Simulator 2: A realistic and popular driving simulator that lets you drive across Europe and deliver various cargoes. -
- Offroad Cargo Truck Simulator: A challenging and adventurous driving simulator that lets you drive on rough and hilly terrains and deliver different types of goods. -
- Truck Simulator USA: A fun and exciting driving simulator that lets you drive across the USA and Canada and transport various vehicles and cargoes. -
Yes, cargo delivery truck games 3d mod apk is safe to download as long as you download it from a trusted source that does not contain any viruses or malware. You should also scan the file before installing it on your device.
-Yes, cargo delivery truck games 3d mod apk is free to play as it does not require any payment or subscription to access its features. However , you can use the unlimited money feature of the mod apk version to buy and upgrade anything you want in the game.
-Yes, you can play cargo delivery truck games 3d mod apk offline as it does not require an internet connection to run. However, you may need to connect to the internet occasionally to update the game or access some online features.
-If you have any questions, feedback, or suggestions about cargo delivery truck games 3d mod apk, you can contact the developers of the game by emailing them at cargodeliverytruckgames3d@gmail.com or visiting their Facebook page at https://www.facebook.com/cargodeliverytruckgames3d.
-If you like cargo delivery truck games 3d mod apk, you may also enjoy some similar games, such as:
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\ No newline at end of file diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Tank Hero APK for Android - Free Arcade Game with 3D Tank Action.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Tank Hero APK for Android - Free Arcade Game with 3D Tank Action.md deleted file mode 100644 index 4960634b74a40c58b47fbc083be5b64f984ea899..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Tank Hero APK for Android - Free Arcade Game with 3D Tank Action.md +++ /dev/null @@ -1,133 +0,0 @@ - -Tank Hero APKCombo: A Review of the Fast Paced 3D Tank Action Game
-Do you love tank games? Do you want to experience the thrill of battling against cunning enemies and becoming the tank hero? If yes, then you should try Tank Hero APKCombo, a free and offline game that offers you a lot of fun and excitement. In this article, we will review Tank Hero APKCombo, a game that has over 10 million downloads and a 4.4 rating on Google Play Store. We will tell you what is Tank Hero APKCombo, how to download and install it, and why you should play it.
-tank hero apkcombo
Download Zip › https://gohhs.com/2uPoE5
-What is Tank Hero APKCombo?
-Tank Hero APKCombo is a fast paced 3D tank action game developed by Clapfoot Inc., a Canadian game studio. The game lets you take out your enemies with cannons, heat seekers, and howitzers in over 120 levels across three unique environments. You can choose from five different weapons and five types of AI tanks to fight against. You can also play in two game modes: campaign and survival. The game supports HD devices and tablets, and has a small download size of only 10 MB.
-The features of Tank Hero APKCombo
-Some of the features that make Tank Hero APKCombo a great game are:
--
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- Fast paced 3D tank action with realistic physics and graphics. -
- Over 120 levels to battle through in three different environments: grasslands, winter, and industrial. -
- Two game modes to challenge yourself: campaign and survival. -
- Five weapons to choose from: cannon, heat seeker, howitzer, laser, and plasma. -
- Five types of AI tanks to fight against: light, medium, heavy, missile, and mine. -
- Support for HD devices and tablets with high resolution graphics. -
- Small download size of only 10 MB that does not take up much space on your device. -
- Playable offline without any internet connection or data usage. -
- Free to play with no consumables, time, or upgrades to purchase. -
The gameplay of Tank Hero APKCombo
-The gameplay of Tank Hero APKCombo is simple and intuitive. You control your tank with a virtual joystick on the left side of the screen, and fire your weapon with a button on the right side. You can also switch your weapon by tapping on the weapon icon on the top right corner. You can move your tank around the arena and avoid enemy fire, while aiming and shooting at them. You can also use obstacles and walls as cover or as a way to bounce your shots. You have to destroy all the enemy tanks in each level to progress to the next one. You also have to watch out for your health bar on the top left corner, which decreases when you get hit by enemy fire. If your health bar reaches zero, you lose the level and have to restart it. You can also collect power-ups that appear randomly on the arena, such as health packs, shields, speed boosts, and ammo refills.
-How to download and install Tank Hero APKCombo?
-If you want to download and install Tank Hero APKCombo on your Android device, you can follow these easy steps:
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-heroes and tanks vs modern war machines apkThe steps to download and install Tank Hero APKCombo
--
-
- Go to [APKCombo](^1^), a website that offers free downloads of Android games and apps. -
- Type "Tank Hero" in the search box and press enter. -
- Select "Tank Hero" from the list of results. -
- Click on the "Download APK" button on the right side of the screen. -
- Choose the version of the game that you want to download. You can see the size, date, and rating of each version. -
- Wait for the download to finish. You can see the progress on the notification bar of your device. -
- Once the download is complete, open the APK file and tap on "Install". You may need to enable "Unknown sources" in your device settings to allow the installation of apps from outside the Google Play Store. -
- Wait for the installation to finish. You can see the progress on the screen. -
- Once the installation is complete, tap on "Open" to launch the game. You can also find the game icon on your home screen or app drawer. -
The requirements and compatibility of Tank Hero APKCombo
-To download and install Tank Hero APKCombo, you need to have an Android device that meets the following requirements:
--
-
- Android version 4.1 or higher. -
- At least 10 MB of free storage space. -
- A stable internet connection for downloading the game. -
Tank Hero APKCombo is compatible with most Android devices, including smartphones and tablets. However, some devices may not support the game or may experience performance issues. If you encounter any problems with the game, you can contact the developer at support@clapfootgames.com or visit their website at https://www.clapfootgames.com/.
-Why should you play Tank Hero APKCombo?
-Tank Hero APKCombo is a game that will keep you entertained and challenged for hours. Here are some of the reasons why you should play it:
-The benefits of playing Tank Hero APKCombo
-Some of the benefits that you can get from playing Tank Hero APKCombo are:
--
-
- You can improve your reflexes, coordination, and strategy skills as you dodge, aim, and shoot at your enemies. -
- You can enjoy a variety of levels, weapons, and enemies that will test your abilities and keep you interested. -
- You can play offline without any internet connection or data usage, which is convenient and cost-effective. -
- You can play for free without any consumables, time, or upgrades to purchase, which is fair and satisfying. -
- You can have fun with a simple and addictive game that will make you feel like a tank hero. -
The challenges and tips of playing Tank Hero APKCombo
-Some of the challenges that you may face while playing Tank Hero APKCombo are:
--
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- You may encounter enemies that are faster, stronger, or smarter than you, and that can use different weapons and tactics to defeat you. -
- You may run out of ammo, health, or shield during a level, which can put you in a disadvantageous position. -
- You may have to deal with obstacles and walls that can block your shots or hinder your movement. -
Some of the tips that can help you overcome these challenges are:
--
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- You should try to use different weapons depending on the situation and the enemy type. For example, you can use heat seekers to lock on and chase your enemies, or howitzers to lob shots over walls. -
- You should try to collect power-ups whenever you see them, as they can give you an edge over your enemies. For example, you can use shields to protect yourself from enemy fire, or speed boosts to outrun them. -
- You should try to use obstacles and walls as cover or as a way to bounce your shots. For example, you can hide behind a wall and wait for an enemy to come closer, or shoot at a wall and make your shot ricochet towards an enemy. -
Conclusion
-Tank Hero APKCombo is a fast paced 3D tank action game that will give you a lot of fun and excitement. You can download and install it for free from APKCombo, a website that offers free downloads of Android games and apps. You can enjoy over 120 levels across three environments with five weapons and five types of enemies. You can also play offline without any internet connection or data usage. You can improve your skills and have fun with a simple and addictive game that will make you feel like a tank hero. So what are you waiting for? Download Tank Hero APKCombo today and start your tank adventure!
-FAQs
-Here are some frequently asked questions about Tank Hero APKCombo:
-Q: How many levels are there in Tank Hero APKCombo?
-A: There are over 120 levels in Tank Hero APKCombo, divided into three environments: grasslands, winter, and industrial. Each environment has 40 levels, and each level has a different layout, difficulty, and number of enemies.
-Q: How many weapons are there in Tank Hero APKCombo?
-A: There are five weapons in Tank Hero APKCombo: cannon, heat seeker, howitzer, laser, and plasma. Each weapon has its own advantages and disadvantages, such as range, damage, accuracy, and reload time. You can switch your weapon by tapping on the weapon icon on the top right corner of the screen.
-Q: How many types of enemies are there in Tank Hero APKCombo?
-A: There are five types of enemies in Tank Hero APKCombo: light, medium, heavy, missile, and mine. Each type of enemy has its own characteristics, such as speed, armor, weapon, and behavior. You have to adapt your strategy and weapon to deal with different types of enemies.
-Q: How can I play Tank Hero APKCombo offline?
-A: You can play Tank Hero APKCombo offline without any internet connection or data usage. You just need to download and install the game from APKCombo once, and then you can play it anytime and anywhere. The game does not require any online features or updates.
-Q: How can I contact the developer of Tank Hero APKCombo?
-A: You can contact the developer of Tank Hero APKCombo by sending an email to support@clapfootgames.com or visiting their website at https://www.clapfootgames.com/. You can also follow them on Facebook and Twitter for the latest news and updates about the game.
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\ No newline at end of file diff --git a/spaces/fffffu/bing/src/lib/isomorphic/node.ts b/spaces/fffffu/bing/src/lib/isomorphic/node.ts deleted file mode 100644 index da213ad6a86181979f098309c374da02835db5a0..0000000000000000000000000000000000000000 --- a/spaces/fffffu/bing/src/lib/isomorphic/node.ts +++ /dev/null @@ -1,26 +0,0 @@ -import Debug from 'debug' - -const { fetch, setGlobalDispatcher, ProxyAgent } = require('undici') -const { HttpsProxyAgent } = require('https-proxy-agent') -const ws = require('ws') - -const debug = Debug('bingo') - -const httpProxy = process.env.http_proxy || process.env.HTTP_PROXY || process.env.https_proxy || process.env.HTTPS_PROXY; -let WebSocket = ws.WebSocket - -if (httpProxy) { - setGlobalDispatcher(new ProxyAgent(httpProxy)) - const agent = new HttpsProxyAgent(httpProxy) - // @ts-ignore - WebSocket = class extends ws.WebSocket { - constructor(address: string | URL, options: typeof ws.WebSocket) { - super(address, { - ...options, - agent, - }) - } - } -} - -export default { fetch, WebSocket, debug } diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io-parser/build/esm/contrib/base64-arraybuffer.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io-parser/build/esm/contrib/base64-arraybuffer.d.ts deleted file mode 100644 index 6e0fa6bc7b46dfcf8f99d2286c0da70a87948fe5..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/engine.io-parser/build/esm/contrib/base64-arraybuffer.d.ts +++ /dev/null @@ -1,2 +0,0 @@ -export declare const encode: (arraybuffer: ArrayBuffer) => string; -export declare const decode: (base64: string) => ArrayBuffer; diff --git a/spaces/fkhuggingme/gpt-academic/.github/ISSUE_TEMPLATE/bug_report.md b/spaces/fkhuggingme/gpt-academic/.github/ISSUE_TEMPLATE/bug_report.md deleted file mode 100644 index ac668766a39892be5bc9e03f3ea626f8b3bf4b57..0000000000000000000000000000000000000000 --- a/spaces/fkhuggingme/gpt-academic/.github/ISSUE_TEMPLATE/bug_report.md +++ /dev/null @@ -1,25 +0,0 @@ ---- -name: Bug report -about: Create a report to help us improve -title: '' -labels: '' -assignees: '' - ---- - -- **(1) Describe the bug 简述** - - -- **(2) Screen Shot 截图** - - -- **(3) Terminal Traceback 终端traceback（如有）** - - -- **(4) Material to Help Reproduce Bugs 帮助我们复现的测试材料样本（如有）** - - - -Before submitting an issue 提交issue之前： -- Please try to upgrade your code. 如果您的代码不是最新的，建议您先尝试更新代码 -- Please check project wiki for common problem solutions.项目[wiki](https://github.com/binary-husky/chatgpt_academic/wiki)有一些常见问题的解决方法 diff --git a/spaces/flatindo/Image-Diffusion-WebUI/diffusion_webui/utils/scheduler_list.py b/spaces/flatindo/Image-Diffusion-WebUI/diffusion_webui/utils/scheduler_list.py deleted file mode 100644 index 1e47475ca4d123a13c736112d96891ebb5aac43a..0000000000000000000000000000000000000000 --- a/spaces/flatindo/Image-Diffusion-WebUI/diffusion_webui/utils/scheduler_list.py +++ /dev/null @@ -1,39 +0,0 @@ -from diffusers import ( - DDIMScheduler, - DDPMScheduler, - DEISMultistepScheduler, - DPMSolverMultistepScheduler, - DPMSolverSinglestepScheduler, - EulerAncestralDiscreteScheduler, - EulerDiscreteScheduler, - HeunDiscreteScheduler, - KDPM2AncestralDiscreteScheduler, - KDPM2DiscreteScheduler, - PNDMScheduler, - UniPCMultistepScheduler, -) - -SCHEDULER_MAPPING = { - "DDIM": DDIMScheduler, - "DDPMScheduler": DDPMScheduler, - "DEISMultistep": DEISMultistepScheduler, - "DPMSolverMultistep": DPMSolverMultistepScheduler, - "DPMSolverSinglestep": DPMSolverSinglestepScheduler, - "EulerAncestralDiscrete": EulerAncestralDiscreteScheduler, - "EulerDiscrete": EulerDiscreteScheduler, - "HeunDiscrete": HeunDiscreteScheduler, - "KDPM2AncestralDiscrete": KDPM2AncestralDiscreteScheduler, - "KDPM2Discrete": KDPM2DiscreteScheduler, - "PNDMScheduler": PNDMScheduler, - "UniPCMultistep": UniPCMultistepScheduler, -} - - -def get_scheduler(pipe, scheduler): - if scheduler in SCHEDULER_MAPPING: - SchedulerClass = SCHEDULER_MAPPING[scheduler] - pipe.scheduler = SchedulerClass.from_config(pipe.scheduler.config) - else: - raise ValueError(f"Invalid scheduler name {scheduler}") - - return pipe diff --git a/spaces/flax-community/t5-vae/t5_vae_flax_alt/src/decoders.py b/spaces/flax-community/t5-vae/t5_vae_flax_alt/src/decoders.py deleted file mode 100644 index 32a7677256bca882bc99565cf3717fa1b760ee9f..0000000000000000000000000000000000000000 --- a/spaces/flax-community/t5-vae/t5_vae_flax_alt/src/decoders.py +++ /dev/null @@ -1,23 +0,0 @@ -import logging -import flax.linen as nn - -logger = logging.getLogger(__name__) - - -class Decoder(nn.Module): - ''' - Converts latent code -> transformer encoding. - ''' - dim_model: int - n_latent_tokens: int - - @nn.compact - def __call__(self, latent_code): # (batch, latent_tokens_per_sequence, latent_token_dim) - raw_latent_tokens = nn.Dense(self.dim_model)(latent_code) - latent_tokens = nn.LayerNorm()(raw_latent_tokens) - return latent_tokens # (batch, latent_tokens_per_sequence, dim_model) - - -VAE_DECODER_MODELS = { - '': Decoder, -} diff --git a/spaces/flowers-team/SocialAISchool/scripts/create_LLM_examples_old.py b/spaces/flowers-team/SocialAISchool/scripts/create_LLM_examples_old.py deleted file mode 100644 index ab8ab4a11bf738e78f9dc9744f4f6c20e2346fe9..0000000000000000000000000000000000000000 --- a/spaces/flowers-team/SocialAISchool/scripts/create_LLM_examples_old.py +++ /dev/null @@ -1,313 +0,0 @@ -#!/usr/bin/env python3 - -import time -import argparse -import numpy as np -import gym -import gym_minigrid -from gym_minigrid.wrappers import * -from gym_minigrid.window import Window -from utils import * -from models import MultiModalBaby11ACModel -from collections import Counter -import torch_ac -import json -from termcolor import colored, COLORS - -from functools import partial -from tkinter import * - -from torch.distributions import Categorical - -inter_acl = False -draw_tree = True - -def redraw(img): - if not args.agent_view: - img = env.render('rgb_array', tile_size=args.tile_size, mask_unobserved=args.mask_unobserved) - - window.show_img(img) - -def reset(): - # if args.seed != -1: - # env.seed(args.seed) - - obs = env.reset() - - if hasattr(env, 'mission'): - print('Mission: %s' % env.mission) - window.set_caption(env.mission) - - redraw(obs) - - -tot_bonus = [0] - -prev = { - "prev_obs": None, - "prev_info": {}, -} -shortened_obj_names = { - 'lockablebox' : 'loc_box', - 'applegenerator' : 'app_gen', - 'generatorplatform': 'gen_pl', - 'marbletee' : 'tee', - 'remotedoor' : 'rem_door', -} - -IDX_TO_OBJECT = {v: shortened_obj_names.get(k, k) for k, v in OBJECT_TO_IDX.items()} -# no duplicates -assert len(IDX_TO_OBJECT) == len(OBJECT_TO_IDX) - -IDX_TO_COLOR = {v: k for k, v in COLOR_TO_IDX.items()} -assert len(IDX_TO_COLOR) == len(COLOR_TO_IDX) - - -# def to_string(enc): -# s = "{:<8} {} {} {} {} {:3} {:3} {}\t".format( -# IDX_TO_OBJECT.get(enc[0], enc[0]), # obj -# *enc[1:3], # x, y -# IDX_TO_COLOR.get(enc[3], enc[3])[:1].upper(), # color -# *enc[4:] # -# ) -# -# if IDX_TO_OBJECT.get(enc[0], enc[0]) == "unseen": -# pass -# # s = colored(s, "on_grey") -# -# elif IDX_TO_OBJECT.get(enc[0], enc[0]) != "empty": -# col = IDX_TO_COLOR.get(enc[3], enc[3]) -# if col in COLORS: -# s = colored(s, col) -# -# return s - - -def step(action): - if type(action) == np.ndarray: - obs, reward, done, info = env.step(action) - else: - action = [int(action), np.nan, np.nan] - obs, reward, done, info = env.step(action) - - - redraw(obs) - - if done: - print('done!') - print('Reward=%.2f' % (reward)) - print('Exploration_bonus=%.2f' % (tot_bonus[0])) - tot_bonus[0] = 0 - - with open(output_file, "a") as f: - if reward > 0: - f.write("Success!\n") - f.write("New episode.\n") - - reset() - - else: - print('\nStep=%s' % (env.step_count)) - - # print to screen - print("Obs : ", end="") - print("".join(info["descriptions"]), end="") - if obs["utterance_history"] != "Conversation: \n": - print(obs['utterance_history']) - print("Act : ", end="") - - # write to file - with open(output_file, "a") as f: - f.write("Obs : ") - f.write("".join(info["descriptions"])) - if obs["utterance_history"] != "Conversation: \n": - f.write(obs['utterance_history']) - # f.write("Your possible actions are:\n") - # f.write("(a) move forward\n") - # f.write("(b) turn left\n") - # f.write("(c) turn right\n") - # f.write("(d) toggle\n") - # f.write("(e) no_op\n") - f.write("Act : ") - - print('Full reward (undiminshed)=%.2f' % (reward)) - - -def key_handler(event): - - # if hasattr(event.canvas, "_event_loop") and event.canvas._event_loop.isRunning(): - # return - - print('pressed', event.key) - - action_dict = { - "up": "a) move forward", - "left": "b) turn left", - "right": "c) turn right", - " ": "d) toggle", - "shift": "e) no_op", - } - action_dict = { - "up": "move forward", - "left": "turn left", - "right": "turn right", - " ": "toggle", - "shift": "no_op", - } - - if event.key in action_dict: - your_action = action_dict[event.key] - - with open(output_file, "a") as f: - f.write("{}\n".format(your_action)) - - if event.key == 'escape': - window.close() - return - - if event.key == 'r': - reset() - return - - if event.key == 'tab': - step(np.array([np.nan, np.nan, np.nan])) - return - - if event.key == 'shift': - step(np.array([np.nan, np.nan, np.nan])) - return - - if event.key == 'left': - step(env.actions.left) - return - if event.key == 'right': - step(env.actions.right) - return - if event.key == 'up': - step(env.actions.forward) - return - if event.key == 't': - step(env.actions.speak) - return - - if event.key == '1': - step(np.array([np.nan, 0, 0])) - return - if event.key == '2': - step(np.array([np.nan, 0, 1])) - return - if event.key == '3': - step(np.array([np.nan, 1, 0])) - return - if event.key == '4': - step(np.array([np.nan, 1, 1])) - return - if event.key == '5': - step(np.array([np.nan, 2, 2])) - return - if event.key == '6': - step(np.array([np.nan, 1, 2])) - return - if event.key == '7': - step(np.array([np.nan, 2, 1])) - return - if event.key == '8': - step(np.array([np.nan, 1, 3])) - return - if event.key == 'p': - step(np.array([np.nan, 3, 3])) - return - - # Spacebar - if event.key == ' ': - step(env.actions.toggle) - return - if event.key == '9': - step(env.actions.pickup) - return - if event.key == '0': - step(env.actions.drop) - return - - if event.key == 'enter': - step(env.actions.done) - return - -parser = argparse.ArgumentParser() -parser.add_argument( - "--env", - help="gym environment to load", - # default="SocialAI-AsocialBoxInformationSeekingParamEnv-v1", - default="SocialAI-ColorBoxesLLMCSParamEnv-v1", -) -parser.add_argument( - "--seed", - type=int, - help="random seed to generate the environment with", - default=-1 -) -parser.add_argument( - "--tile_size", - type=int, - help="size at which to render tiles", - default=32 -) -parser.add_argument( - '--agent_view', - default=False, - help="draw the agent sees (partially observable view)", - action='store_true' -) -parser.add_argument( - '--print_grid', - default=False, - help="print the grid with symbols", - action='store_true' -) -parser.add_argument( - '--calc-bonus', - default=False, - help="calculate explo bonus", - action='store_true' -) -parser.add_argument( - '--mask-unobserved', - default=False, - help="mask cells that are not observed by the agent", - action='store_true' -) -parser.add_argument( - '--output-file', - default="./llm_data/in_context_color_test.txt", - help="file where to save episodes", -) - - -# Put all env related arguments after --env_args, e.g. --env_args nb_foo 1 is_bar True -parser.add_argument("--env-args", nargs='*', default=None) - -args = parser.parse_args() - -output_file=args.output_file - -env = gym.make(args.env, **env_args_str_to_dict(args.env_args)) - -if draw_tree: - # draw tree - env.parameter_tree.draw_tree( - filename="viz/SocialAIParam/{}_raw_tree".format(args.env), - ignore_labels=["Num_of_colors"], - ) - -if args.seed >= 0: - env.seed(args.seed) - -with open(output_file, "a") as f: - f.write("New episode.\n") - -window = Window('gym_minigrid - ' + args.env, figsize=(4, 4)) -window.reg_key_handler(key_handler) -env.window = window - -# Blocking event loop -window.show(block=True) diff --git a/spaces/fsdl2022emotion/meme-manipulation-gradio-space/README.md b/spaces/fsdl2022emotion/meme-manipulation-gradio-space/README.md deleted file mode 100644 index f93b8f6ce78416059119c232b510047793edbb0e..0000000000000000000000000000000000000000 --- a/spaces/fsdl2022emotion/meme-manipulation-gradio-space/README.md +++ /dev/null @@ -1,34 +0,0 @@ ---- -title: Meme Manipulation Gradio Space -emoji: 🏃 -colorFrom: indigo -colorTo: green -sdk: gradio -sdk_version: 3.4 -app_file: app.py -pinned: false -license: mit ---- - -# MEME Emotion Manipulation Tool - -MEME Emotion Manipulation Tool is an app that allows you to manipulate the emotions of a person in a photo. You can also add text on the image to create a meme. - -This tool used the pretrained model and is modified based on the [GANmut Model](https://github.com/stefanodapolito/GANmut). You can view the source code of this tool in [GitHub](https://github.com/fsdl2022emotion/meme-manipulation-app) and [Gradio Space](https://huggingface.co/spaces/fsdl2022emotion/meme-manipulation-gradio-space) and give it a star if you like it! - -## Instructions - -1. install the libraries `pip install -r requirements.txt` -2. run `python app.py` - -## Examples - -Original | Result -:-------------------------:|:-------------------------: - |  - |  - |  - -## Limitations - -As a MVP, the text drawing function does not work well on some images and it only works on relatively short text. diff --git a/spaces/fuckyoudeki/AutoGPT/autogpt/commands/execute_code.py b/spaces/fuckyoudeki/AutoGPT/autogpt/commands/execute_code.py deleted file mode 100644 index 11266f852727f2f8aedbc995b1e504a17acbfb77..0000000000000000000000000000000000000000 --- a/spaces/fuckyoudeki/AutoGPT/autogpt/commands/execute_code.py +++ /dev/null @@ -1,158 +0,0 @@ -"""Execute code in a Docker container""" -import os -import subprocess - -import docker -from docker.errors import ImageNotFound - -from autogpt.workspace import WORKSPACE_PATH, path_in_workspace - - -def execute_python_file(file: str) -> str: - """Execute a Python file in a Docker container and return the output - - Args: - file (str): The name of the file to execute - - Returns: - str: The output of the file - """ - - print(f"Executing file '{file}' in workspace '{WORKSPACE_PATH}'") - - if not file.endswith(".py"): - return "Error: Invalid file type. Only .py files are allowed." - - file_path = path_in_workspace(file) - - if not os.path.isfile(file_path): - return f"Error: File '{file}' does not exist." - - if we_are_running_in_a_docker_container(): - result = subprocess.run( - f"python {file_path}", capture_output=True, encoding="utf8", shell=True - ) - if result.returncode == 0: - return result.stdout - else: - return f"Error: {result.stderr}" - - try: - client = docker.from_env() - - # You can replace this with the desired Python image/version - # You can find available Python images on Docker Hub: - # https://hub.docker.com/_/python - image_name = "python:3-alpine" - try: - client.images.get(image_name) - print(f"Image '{image_name}' found locally") - except ImageNotFound: - print(f"Image '{image_name}' not found locally, pulling from Docker Hub") - # Use the low-level API to stream the pull response - low_level_client = docker.APIClient() - for line in low_level_client.pull(image_name, stream=True, decode=True): - # Print the status and progress, if available - status = line.get("status") - progress = line.get("progress") - if status and progress: - print(f"{status}: {progress}") - elif status: - print(status) - - container = client.containers.run( - image_name, - f"python {file}", - volumes={ - os.path.abspath(WORKSPACE_PATH): { - "bind": "/workspace", - "mode": "ro", - } - }, - working_dir="/workspace", - stderr=True, - stdout=True, - detach=True, - ) - - container.wait() - logs = container.logs().decode("utf-8") - container.remove() - - # print(f"Execution complete. Output: {output}") - # print(f"Logs: {logs}") - - return logs - - except docker.errors.DockerException as e: - print( - "Could not run the script in a container. If you haven't already, please install Docker https://docs.docker.com/get-docker/" - ) - return f"Error: {str(e)}" - - except Exception as e: - return f"Error: {str(e)}" - - -def execute_shell(command_line: str) -> str: - """Execute a shell command and return the output - - Args: - command_line (str): The command line to execute - - Returns: - str: The output of the command - """ - current_dir = os.getcwd() - # Change dir into workspace if necessary - if str(WORKSPACE_PATH) not in current_dir: - os.chdir(WORKSPACE_PATH) - - print(f"Executing command '{command_line}' in working directory '{os.getcwd()}'") - - result = subprocess.run(command_line, capture_output=True, shell=True) - output = f"STDOUT:\n{result.stdout}\nSTDERR:\n{result.stderr}" - - # Change back to whatever the prior working dir was - - os.chdir(current_dir) - - return output - - -def execute_shell_popen(command_line) -> str: - """Execute a shell command with Popen and returns an english description - of the event and the process id - - Args: - command_line (str): The command line to execute - - Returns: - str: Description of the fact that the process started and its id - """ - current_dir = os.getcwd() - # Change dir into workspace if necessary - if str(WORKSPACE_PATH) not in current_dir: - os.chdir(WORKSPACE_PATH) - - print(f"Executing command '{command_line}' in working directory '{os.getcwd()}'") - - do_not_show_output = subprocess.DEVNULL - process = subprocess.Popen( - command_line, shell=True, stdout=do_not_show_output, stderr=do_not_show_output - ) - - # Change back to whatever the prior working dir was - - os.chdir(current_dir) - - return f"Subprocess started with PID:'{str(process.pid)}'" - - -def we_are_running_in_a_docker_container() -> bool: - """Check if we are running in a Docker container - - Returns: - bool: True if we are running in a Docker container, False otherwise - """ - return os.path.exists("/.dockerenv") diff --git a/spaces/g4f/freegpt-webui/server/backend.py b/spaces/g4f/freegpt-webui/server/backend.py deleted file mode 100644 index 9d2d56fac8bf5dc5ed7ca9b8dd147cfceb039f85..0000000000000000000000000000000000000000 --- a/spaces/g4f/freegpt-webui/server/backend.py +++ /dev/null @@ -1,177 +0,0 @@ -import re -from datetime import datetime -from g4f import ChatCompletion -from flask import request, Response, stream_with_context -from requests import get -from server.config import special_instructions - - -class Backend_Api: - def __init__(self, bp, config: dict) -> None: - """ - Initialize the Backend_Api class. - :param app: Flask application instance - :param config: Configuration dictionary - """ - self.bp = bp - self.routes = { - '/backend-api/v2/conversation': { - 'function': self._conversation, - 'methods': ['POST'] - } - } - - def _conversation(self): - """ - Handles the conversation route. - - :return: Response object containing the generated conversation stream - """ - conversation_id = request.json['conversation_id'] - - try: - jailbreak = request.json['jailbreak'] - model = request.json['model'] - messages = build_messages(jailbreak) - - # Generate response - response = ChatCompletion.create( - model=model, - stream=True, - chatId=conversation_id, - messages=messages - ) - - return Response(stream_with_context(generate_stream(response, jailbreak)), mimetype='text/event-stream') - - except Exception as e: - print(e) - print(e.__traceback__.tb_next) - - return { - '_action': '_ask', - 'success': False, - "error": f"an error occurred {str(e)}" - }, 400 - - -def build_messages(jailbreak): - """ - Build the messages for the conversation. - - :param jailbreak: Jailbreak instruction string - :return: List of messages for the conversation - """ - _conversation = request.json['meta']['content']['conversation'] - internet_access = request.json['meta']['content']['internet_access'] - prompt = request.json['meta']['content']['parts'][0] - - # Add the existing conversation - conversation = _conversation - - # Add web results if enabled - if internet_access: - current_date = datetime.now().strftime("%Y-%m-%d") - query = f'Current date: {current_date}. ' + prompt["content"] - search_results = fetch_search_results(query) - conversation.extend(search_results) - - # Add jailbreak instructions if enabled - if jailbreak_instructions := getJailbreak(jailbreak): - conversation.extend(jailbreak_instructions) - - # Add the prompt - conversation.append(prompt) - - # Reduce conversation size to avoid API Token quantity error - if len(conversation) > 3: - conversation = conversation[-4:] - - return conversation - - -def fetch_search_results(query): - """ - Fetch search results for a given query. - - :param query: Search query string - :return: List of search results - """ - search = get('https://ddg-api.herokuapp.com/search', - params={ - 'query': query, - 'limit': 3, - }) - - snippets = "" - for index, result in enumerate(search.json()): - snippet = f'[{index + 1}] "{result["snippet"]}" URL:{result["link"]}.' - snippets += snippet - - response = "Here are some updated web searches. Use this to improve user response:" - response += snippets - - return [{'role': 'system', 'content': response}] - - -def generate_stream(response, jailbreak): - """ - Generate the conversation stream. - - :param response: Response object from ChatCompletion.create - :param jailbreak: Jailbreak instruction string - :return: Generator object yielding messages in the conversation - """ - if getJailbreak(jailbreak): - response_jailbreak = '' - jailbroken_checked = False - for message in response: - response_jailbreak += message - if jailbroken_checked: - yield message - else: - if response_jailbroken_success(response_jailbreak): - jailbroken_checked = True - if response_jailbroken_failed(response_jailbreak): - yield response_jailbreak - jailbroken_checked = True - else: - yield from response - - -def response_jailbroken_success(response: str) -> bool: - """Check if the response has been jailbroken. - - :param response: Response string - :return: Boolean indicating if the response has been jailbroken - """ - act_match = re.search(r'ACT:', response, flags=re.DOTALL) - return bool(act_match) - - -def response_jailbroken_failed(response): - """ - Check if the response has not been jailbroken. - - :param response: Response string - :return: Boolean indicating if the response has not been jailbroken - """ - return False if len(response) < 4 else not (response.startswith("GPT:") or response.startswith("ACT:")) - - -def getJailbreak(jailbreak): - """ - Check if jailbreak instructions are provided. - - :param jailbreak: Jailbreak instruction string - :return: Jailbreak instructions if provided, otherwise None - """ - if jailbreak != "default": - special_instructions[jailbreak][0]['content'] += special_instructions['two_responses_instruction'] - if jailbreak in special_instructions: - special_instructions[jailbreak] - return special_instructions[jailbreak] - else: - return None - else: - return None diff --git a/spaces/gbharti/fastai-model-deploy/README.md b/spaces/gbharti/fastai-model-deploy/README.md deleted file mode 100644 index 4e9e3c0ceae4ef49b3f5c1814058ee40036ef7c9..0000000000000000000000000000000000000000 --- a/spaces/gbharti/fastai-model-deploy/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Fastai Model Reply -emoji: ⚡ -colorFrom: blue -colorTo: yellow -sdk: gradio -sdk_version: 3.6 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/fileio/handlers/json_handler.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/fileio/handlers/json_handler.py deleted file mode 100644 index 18d4f15f74139d20adff18b20be5529c592a66b6..0000000000000000000000000000000000000000 --- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/fileio/handlers/json_handler.py +++ /dev/null @@ -1,36 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import json - -import numpy as np - -from .base import BaseFileHandler - - -def set_default(obj): - """Set default json values for non-serializable values. - - It helps convert ``set``, ``range`` and ``np.ndarray`` data types to list. - It also converts ``np.generic`` (including ``np.int32``, ``np.float32``, - etc.) into plain numbers of plain python built-in types. - """ - if isinstance(obj, (set, range)): - return list(obj) - elif isinstance(obj, np.ndarray): - return obj.tolist() - elif isinstance(obj, np.generic): - return obj.item() - raise TypeError(f'{type(obj)} is unsupported for json dump') - - -class JsonHandler(BaseFileHandler): - - def load_from_fileobj(self, file): - return json.load(file) - - def dump_to_fileobj(self, obj, file, **kwargs): - kwargs.setdefault('default', set_default) - json.dump(obj, file, **kwargs) - - def dump_to_str(self, obj, **kwargs): - kwargs.setdefault('default', set_default) - return json.dumps(obj, **kwargs) diff --git a/spaces/gotiQspiryo/whisper-ui/examples/IPVanish VPN 4 0 3 0 REPACK Crack[REPACK Cracks4win] Serial Key.md b/spaces/gotiQspiryo/whisper-ui/examples/IPVanish VPN 4 0 3 0 REPACK Crack[REPACK Cracks4win] Serial Key.md deleted file mode 100644 index 7d19634151612079bb6b00ce4618c3ee5eb7f7c6..0000000000000000000000000000000000000000 --- a/spaces/gotiQspiryo/whisper-ui/examples/IPVanish VPN 4 0 3 0 REPACK Crack[REPACK Cracks4win] Serial Key.md +++ /dev/null @@ -1,79 +0,0 @@ - -IPVanish VPN 4.0.3.0 Crack[cracks4win] Serial Key: A Complete Guide
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\ No newline at end of file diff --git a/spaces/gradio/HuBERT/examples/fast_noisy_channel/noisy_channel_sequence_generator.py b/spaces/gradio/HuBERT/examples/fast_noisy_channel/noisy_channel_sequence_generator.py deleted file mode 100644 index ea8fae98e87e9f3e69bc51987703a6429eb0c92a..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/examples/fast_noisy_channel/noisy_channel_sequence_generator.py +++ /dev/null @@ -1,842 +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 Dict, List, Optional - -import math -import numpy as np - -import torch -import torch.nn.functional as F -from torch import Tensor - -from .noisy_channel_beam_search import NoisyChannelBeamSearch -from fairseq.sequence_generator import EnsembleModel - - -class NoisyChannelSequenceGenerator(object): - def __init__( - self, - combine_method, - tgt_dict, - src_dict=None, - beam_size=1, - max_len_a=0, - max_len_b=200, - min_len=1, - len_penalty=1.0, - unk_penalty=0.0, - retain_dropout=False, - temperature=1.0, - match_source_len=False, - no_repeat_ngram_size=0, - normalize_scores=True, - channel_models=None, - k2=10, - ch_weight=1.0, - channel_scoring_type='log_norm', - top_k_vocab=0, - lm_models=None, - lm_dict=None, - lm_weight=1.0, - normalize_lm_scores_by_tgt_len=False, - ): - """Generates translations of a given source sentence, - using beam search with noisy channel decoding. - - Args: - combine_method (string, optional): Method to combine direct, LM and - channel model scores (default: None) - tgt_dict (~fairseq.data.Dictionary): target dictionary - src_dict (~fairseq.data.Dictionary): source dictionary - beam_size (int, optional): beam width (default: 1) - max_len_a/b (int, optional): generate sequences of maximum length - ax + b, where x is the source length - min_len (int, optional): the minimum length of the generated output - (not including end-of-sentence) - len_penalty (float, optional): length penalty, where <1.0 favors - shorter, >1.0 favors longer sentences (default: 1.0) - unk_penalty (float, optional): unknown word penalty, where <0 - produces more unks, >0 produces fewer (default: 0.0) - retain_dropout (bool, optional): use dropout when generating - (default: False) - temperature (float, optional): temperature, where values - >1.0 produce more uniform samples and values <1.0 produce - sharper samples (default: 1.0) - match_source_len (bool, optional): outputs should match the source - length (default: False) - no_repeat_ngram_size (int, optional): Size of n-grams that we avoid - repeating in the generation (default: 0) - normalize_scores (bool, optional): normalize scores by the length - of the output (default: True) - channel_models (List[~fairseq.models.FairseqModel]): ensemble of models - translating from the target to the source - k2 (int, optional): Top K2 candidates to score per beam at each step (default:10) - ch_weight (int, optional): Weight associated with the channel model score - assuming that the direct model score has weight 1.0 (default: 1.0) - channel_scoring_type (str, optional): String specifying how to score - the channel model (default: 'log_norm') - top_k_vocab (int, optional): If `channel_scoring_type` is `'src_vocab'` or - `'src_vocab_batched'`, then this parameter specifies the number of - most frequent tokens to include in the channel model output vocabulary, - in addition to the source tokens in the input batch (default: 0) - lm_models (List[~fairseq.models.FairseqModel]): ensemble of models - generating text in the target language - lm_dict (~fairseq.data.Dictionary): LM Model dictionary - lm_weight (int, optional): Weight associated with the LM model score - assuming that the direct model score has weight 1.0 (default: 1.0) - normalize_lm_scores_by_tgt_len (bool, optional): Should we normalize LM scores - by the target length? By default, we normalize the combination of - LM and channel model scores by the source length - """ - self.pad = tgt_dict.pad() - self.unk = tgt_dict.unk() - self.eos = tgt_dict.eos() - self.vocab_size = len(tgt_dict) - self.beam_size = beam_size - # the max beam size is the dictionary size - 1, since we never select pad - self.beam_size = min(beam_size, self.vocab_size - 1) - self.max_len_a = max_len_a - self.max_len_b = max_len_b - self.min_len = min_len - self.normalize_scores = normalize_scores - self.len_penalty = len_penalty - self.unk_penalty = unk_penalty - self.retain_dropout = retain_dropout - self.temperature = temperature - self.match_source_len = match_source_len - self.no_repeat_ngram_size = no_repeat_ngram_size - self.channel_models = channel_models - self.src_dict = src_dict - self.tgt_dict = tgt_dict - self.combine_method = combine_method - self.k2 = k2 - self.ch_weight = ch_weight - self.channel_scoring_type = channel_scoring_type - self.top_k_vocab = top_k_vocab - self.lm_models = lm_models - self.lm_dict = lm_dict - self.lm_weight = lm_weight - self.log_softmax_fn = torch.nn.LogSoftmax(dim=1) - self.normalize_lm_scores_by_tgt_len = normalize_lm_scores_by_tgt_len - - self.share_tgt_dict = (self.lm_dict == self.tgt_dict) - self.tgt_to_lm = make_dict2dict(tgt_dict, lm_dict) - - self.ch_scoring_bsz = 3072 - - assert temperature > 0, '--temperature must be greater than 0' - - self.search = NoisyChannelBeamSearch(tgt_dict) - - @torch.no_grad() - def generate( - self, - models, - sample, - prefix_tokens=None, - bos_token=None, - **kwargs - ): - """Generate a batch of translations. - Args: - models (List[~fairseq.models.FairseqModel]): ensemble of models - sample (dict): batch - prefix_tokens (torch.LongTensor, optional): force decoder to begin - with these tokens - """ - model = EnsembleModel(models) - incremental_states = torch.jit.annotate( - List[Dict[str, Dict[str, Optional[Tensor]]]], - [ - torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {}) - for i in range(model.models_size) - ], - ) - if not self.retain_dropout: - model.eval() - - # model.forward normally channels prev_output_tokens into the decoder - # separately, but SequenceGenerator directly calls model.encoder - encoder_input = { - k: v for k, v in sample['net_input'].items() - if k != 'prev_output_tokens' - } - src_tokens = encoder_input['src_tokens'] - src_lengths_no_eos = (src_tokens.ne(self.eos) & src_tokens.ne(self.pad)).long().sum(dim=1) - input_size = src_tokens.size() - # batch dimension goes first followed by source lengths - bsz = input_size[0] - src_len = input_size[1] - beam_size = self.beam_size - - if self.match_source_len: - max_len = src_lengths_no_eos.max().item() - else: - max_len = min( - int(self.max_len_a * src_len + self.max_len_b), - # exclude the EOS marker - model.max_decoder_positions() - 1, - ) - - # compute the encoder output for each beam - encoder_outs = model.forward_encoder(encoder_input) - new_order = torch.arange(bsz).view(-1, 1).repeat(1, beam_size).view(-1) - new_order = new_order.to(src_tokens.device).long() - encoder_outs = model.reorder_encoder_out(encoder_outs, new_order) - - src_lengths = encoder_input['src_lengths'] - # initialize buffers - scores = src_tokens.new(bsz * beam_size, max_len + 1).float().fill_(0) - lm_prefix_scores = src_tokens.new(bsz * beam_size).float().fill_(0) - - scores_buf = scores.clone() - tokens = src_tokens.new(bsz * beam_size, max_len + 2).long().fill_(self.pad) - tokens_buf = tokens.clone() - tokens[:, 0] = self.eos if bos_token is None else bos_token - - # reorder source tokens so they may be used as a reference in generating P(S|T) - src_tokens = reorder_all_tokens(src_tokens, src_lengths, self.src_dict.eos_index) - - src_tokens = src_tokens.repeat(1, beam_size).view(-1, src_len) - src_lengths = src_lengths.view(bsz, -1).repeat(1, beam_size).view(bsz*beam_size, -1) - - attn, attn_buf = None, None - nonpad_idxs = None - - # The cands_to_ignore indicates candidates that should be ignored. - # For example, suppose we're sampling and have already finalized 2/5 - # samples. Then the cands_to_ignore would mark 2 positions as being ignored, - # so that we only finalize the remaining 3 samples. - cands_to_ignore = src_tokens.new_zeros(bsz, beam_size).eq(-1) # forward and backward-compatible False mask - - # list of completed sentences - finalized = [[] for i in range(bsz)] - finished = [False for i in range(bsz)] - num_remaining_sent = bsz - - # number of candidate hypos per step - cand_size = 2 * beam_size # 2 x beam size in case half are EOS - - # offset arrays for converting between different indexing schemes - bbsz_offsets = (torch.arange(0, bsz) * beam_size).unsqueeze(1).type_as(tokens) - cand_offsets = torch.arange(0, cand_size).type_as(tokens) - - # helper function for allocating buffers on the fly - buffers = {} - - def buffer(name, type_of=tokens): # noqa - if name not in buffers: - buffers[name] = type_of.new() - return buffers[name] - - def is_finished(sent, step, unfin_idx): - """ - Check whether we've finished generation for a given sentence, by - comparing the worst score among finalized hypotheses to the best - possible score among unfinalized hypotheses. - """ - assert len(finalized[sent]) <= beam_size - if len(finalized[sent]) == beam_size: - return True - return False - - def finalize_hypos(step, bbsz_idx, eos_scores, combined_noisy_channel_eos_scores): - """ - Finalize the given hypotheses at this step, while keeping the total - number of finalized hypotheses per sentence <= beam_size. - - Note: the input must be in the desired finalization order, so that - hypotheses that appear earlier in the input are preferred to those - that appear later. - - Args: - step: current time step - bbsz_idx: A vector of indices in the range [0, bsz*beam_size), - indicating which hypotheses to finalize - eos_scores: A vector of the same size as bbsz_idx containing - fw scores for each hypothesis - combined_noisy_channel_eos_scores: A vector of the same size as bbsz_idx containing - combined noisy channel scores for each hypothesis - """ - assert bbsz_idx.numel() == eos_scores.numel() - - # clone relevant token and attention tensors - tokens_clone = tokens.index_select(0, bbsz_idx) - tokens_clone = tokens_clone[:, 1:step + 2] # skip the first index, which is EOS - assert not tokens_clone.eq(self.eos).any() - tokens_clone[:, step] = self.eos - attn_clone = attn.index_select(0, bbsz_idx)[:, :, 1:step+2] if attn is not None else None - - # compute scores per token position - pos_scores = scores.index_select(0, bbsz_idx)[:, :step+1] - pos_scores[:, step] = eos_scores - # convert from cumulative to per-position scores - pos_scores[:, 1:] = pos_scores[:, 1:] - pos_scores[:, :-1] - - # normalize sentence-level scores - if self.normalize_scores: - combined_noisy_channel_eos_scores /= (step + 1) ** self.len_penalty - - cum_unfin = [] - prev = 0 - for f in finished: - if f: - prev += 1 - else: - cum_unfin.append(prev) - - sents_seen = set() - for i, (idx, score) in enumerate(zip(bbsz_idx.tolist(), combined_noisy_channel_eos_scores.tolist())): - unfin_idx = idx // beam_size - sent = unfin_idx + cum_unfin[unfin_idx] - - sents_seen.add((sent, unfin_idx)) - - if self.match_source_len and step > src_lengths_no_eos[unfin_idx]: - score = -math.inf - - def get_hypo(): - - if attn_clone is not None: - # remove padding tokens from attn scores - hypo_attn = attn_clone[i][nonpad_idxs[sent]] - _, alignment = hypo_attn.max(dim=0) - else: - hypo_attn = None - alignment = None - - return { - 'tokens': tokens_clone[i], - 'score': score, - 'attention': hypo_attn, # src_len x tgt_len - 'alignment': alignment, - 'positional_scores': pos_scores[i], - } - - if len(finalized[sent]) < beam_size: - finalized[sent].append(get_hypo()) - - newly_finished = [] - for sent, unfin_idx in sents_seen: - # check termination conditions for this sentence - if not finished[sent] and is_finished(sent, step, unfin_idx): - finished[sent] = True - newly_finished.append(unfin_idx) - return newly_finished - - def noisy_channel_rescoring(lprobs, beam_size, bsz, src_tokens, tokens, k): - """Rescore the top k hypothesis from each beam using noisy channel modeling - Returns: - new_fw_lprobs: the direct model probabilities after pruning the top k - new_ch_lm_lprobs: the combined channel and language model probabilities - new_lm_lprobs: the language model probabilities after pruning the top k - """ - with torch.no_grad(): - lprobs_size = lprobs.size() - if prefix_tokens is not None and step < prefix_tokens.size(1): - probs_slice = lprobs.view(bsz, -1, lprobs.size(-1))[:, 0, :] - cand_scores = torch.gather( - probs_slice, dim=1, - index=prefix_tokens[:, step].view(-1, 1).data - ).expand(-1, beam_size).contiguous().view(bsz*beam_size, 1) - cand_indices = prefix_tokens[:, step].view(-1, 1).expand(bsz, beam_size).data.contiguous().view(bsz*beam_size, 1) - - # need to calculate and save fw and lm probs for prefix tokens - fw_top_k = cand_scores - fw_top_k_idx = cand_indices - k = 1 - else: - # take the top k best words for every sentence in batch*beam - fw_top_k, fw_top_k_idx = torch.topk(lprobs.view(beam_size*bsz, -1), k=k) - eos_idx = torch.nonzero(fw_top_k_idx.view(bsz*beam_size*k, -1) == self.eos)[:, 0] - ch_scores = fw_top_k.new_full((beam_size*bsz*k, ), 0) - src_size = torch.sum(src_tokens[:, :] != self.src_dict.pad_index, dim=1, keepdim=True, dtype=fw_top_k.dtype) - - if self.combine_method != "lm_only": - temp_src_tokens_full = src_tokens[:, :].repeat(1, k).view(bsz*beam_size*k, -1) - not_padding = temp_src_tokens_full[:, 1:] != self.src_dict.pad_index - cur_tgt_size = step+2 - - # add eos to all candidate sentences except those that already end in eos - eos_tokens = tokens[:, 0].repeat(1, k).view(-1, 1) - eos_tokens[eos_idx] = self.tgt_dict.pad_index - - if step == 0: - channel_input = torch.cat((fw_top_k_idx.view(-1, 1), eos_tokens), 1) - else: - # move eos from beginning to end of target sentence - channel_input = torch.cat((tokens[:, 1:step + 1].repeat(1, k).view(-1, step), fw_top_k_idx.view(-1, 1), eos_tokens), 1) - - ch_input_lengths = torch.tensor(np.full(channel_input.size(0), cur_tgt_size)) - ch_input_lengths[eos_idx] = cur_tgt_size-1 - if self.channel_scoring_type == "unnormalized": - ch_encoder_output = channel_model.encoder(channel_input, src_lengths=ch_input_lengths) - ch_decoder_output, _ = channel_model.decoder(temp_src_tokens_full, encoder_out=ch_encoder_output, features_only=True) - del ch_encoder_output - ch_intermed_scores = channel_model.decoder.unnormalized_scores_given_target(ch_decoder_output, target_ids=temp_src_tokens_full[:, 1:]) - ch_intermed_scores = ch_intermed_scores.float() - ch_intermed_scores *= not_padding.float() - ch_scores = torch.sum(ch_intermed_scores, dim=1) - elif self.channel_scoring_type == "k2_separate": - for k_idx in range(k): - k_eos_tokens = eos_tokens[k_idx::k, :] - if step == 0: - k_ch_input = torch.cat((fw_top_k_idx[:, k_idx:k_idx+1], k_eos_tokens), 1) - else: - # move eos from beginning to end of target sentence - k_ch_input = torch.cat((tokens[:, 1:step + 1], fw_top_k_idx[:, k_idx:k_idx+1], k_eos_tokens), 1) - k_ch_input_lengths = ch_input_lengths[k_idx::k] - k_ch_output = channel_model(k_ch_input, k_ch_input_lengths, src_tokens) - k_ch_lprobs = channel_model.get_normalized_probs(k_ch_output, log_probs=True) - k_ch_intermed_scores = torch.gather(k_ch_lprobs[:, :-1, :], 2, src_tokens[:, 1:].unsqueeze(2)).squeeze(2) - k_ch_intermed_scores *= not_padding.float() - ch_scores[k_idx::k] = torch.sum(k_ch_intermed_scores, dim=1) - elif self.channel_scoring_type == "src_vocab": - ch_encoder_output = channel_model.encoder(channel_input, src_lengths=ch_input_lengths) - ch_decoder_output, _ = channel_model.decoder(temp_src_tokens_full, encoder_out=ch_encoder_output, features_only=True) - - del ch_encoder_output - ch_lprobs = normalized_scores_with_batch_vocab( - channel_model.decoder, - ch_decoder_output, src_tokens, k, bsz, beam_size, - self.src_dict.pad_index, top_k=self.top_k_vocab) - ch_scores = torch.sum(ch_lprobs, dim=1) - elif self.channel_scoring_type == "src_vocab_batched": - ch_bsz_size = temp_src_tokens_full.shape[0] - ch_lprobs_list = [None] * len(range(0, ch_bsz_size, self.ch_scoring_bsz)) - for i, start_idx in enumerate(range(0, ch_bsz_size, self.ch_scoring_bsz)): - end_idx = min(start_idx + self.ch_scoring_bsz, ch_bsz_size) - temp_src_tokens_full_batch = temp_src_tokens_full[start_idx:end_idx, :] - channel_input_batch = channel_input[start_idx:end_idx, :] - ch_input_lengths_batch = ch_input_lengths[start_idx:end_idx] - ch_encoder_output_batch = channel_model.encoder(channel_input_batch, src_lengths=ch_input_lengths_batch) - ch_decoder_output_batch, _ = channel_model.decoder(temp_src_tokens_full_batch, encoder_out=ch_encoder_output_batch, features_only=True) - ch_lprobs_list[i] = normalized_scores_with_batch_vocab( - channel_model.decoder, - ch_decoder_output_batch, src_tokens, k, bsz, beam_size, - self.src_dict.pad_index, top_k=self.top_k_vocab, - start_idx=start_idx, end_idx=end_idx) - ch_lprobs = torch.cat(ch_lprobs_list, dim=0) - ch_scores = torch.sum(ch_lprobs, dim=1) - else: - ch_output = channel_model(channel_input, ch_input_lengths, temp_src_tokens_full) - ch_lprobs = channel_model.get_normalized_probs(ch_output, log_probs=True) - ch_intermed_scores = torch.gather(ch_lprobs[:, :-1, :], 2, temp_src_tokens_full[:, 1:].unsqueeze(2)).squeeze().view(bsz*beam_size*k, -1) - ch_intermed_scores *= not_padding.float() - ch_scores = torch.sum(ch_intermed_scores, dim=1) - - else: - cur_tgt_size = 0 - ch_scores = ch_scores.view(bsz*beam_size, k) - expanded_lm_prefix_scores = lm_prefix_scores.unsqueeze(1).expand(-1, k).flatten() - - if self.share_tgt_dict: - lm_scores = get_lm_scores(lm, tokens[:, :step + 1].view(-1, step+1), lm_incremental_states, fw_top_k_idx.view(-1, 1), torch.tensor(np.full(tokens.size(0), step+1)), k) - else: - new_lm_input = dict2dict(tokens[:, :step + 1].view(-1, step+1), self.tgt_to_lm) - new_cands = dict2dict(fw_top_k_idx.view(-1, 1), self.tgt_to_lm) - lm_scores = get_lm_scores(lm, new_lm_input, lm_incremental_states, new_cands, torch.tensor(np.full(tokens.size(0), step+1)), k) - - lm_scores.add_(expanded_lm_prefix_scores) - ch_lm_scores = combine_ch_lm(self.combine_method, ch_scores, lm_scores, src_size, cur_tgt_size) - # initialize all as min value - new_fw_lprobs = ch_scores.new(lprobs_size).fill_(-1e17).view(bsz*beam_size, -1) - new_ch_lm_lprobs = ch_scores.new(lprobs_size).fill_(-1e17).view(bsz*beam_size, -1) - new_lm_lprobs = ch_scores.new(lprobs_size).fill_(-1e17).view(bsz*beam_size, -1) - new_fw_lprobs[:, self.pad] = -math.inf - new_ch_lm_lprobs[:, self.pad] = -math.inf - new_lm_lprobs[:, self.pad] = -math.inf - - new_fw_lprobs.scatter_(1, fw_top_k_idx, fw_top_k) - new_ch_lm_lprobs.scatter_(1, fw_top_k_idx, ch_lm_scores) - new_lm_lprobs.scatter_(1, fw_top_k_idx, lm_scores.view(-1, k)) - return new_fw_lprobs, new_ch_lm_lprobs, new_lm_lprobs - - def combine_ch_lm(combine_type, ch_scores, lm_scores1, src_size, tgt_size): - if self.channel_scoring_type == "unnormalized": - ch_scores = self.log_softmax_fn( - ch_scores.view(-1, self.beam_size * self.k2) - ).view(ch_scores.shape) - ch_scores = ch_scores * self.ch_weight - lm_scores1 = lm_scores1 * self.lm_weight - - if combine_type == "lm_only": - # log P(T|S) + log P(T) - ch_scores = lm_scores1.view(ch_scores.size()) - elif combine_type == "noisy_channel": - # 1/t log P(T|S) + 1/s log P(S|T) + 1/t log P(T) - if self.normalize_lm_scores_by_tgt_len: - ch_scores.div_(src_size) - lm_scores_norm = lm_scores1.view(ch_scores.size()).div(tgt_size) - ch_scores.add_(lm_scores_norm) - # 1/t log P(T|S) + 1/s log P(S|T) + 1/s log P(T) - else: - ch_scores.add_(lm_scores1.view(ch_scores.size())) - ch_scores.div_(src_size) - - return ch_scores - - if self.channel_models is not None: - channel_model = self.channel_models[0] # assume only one channel_model model - else: - channel_model = None - - lm = EnsembleModel(self.lm_models) - lm_incremental_states = torch.jit.annotate( - List[Dict[str, Dict[str, Optional[Tensor]]]], - [ - torch.jit.annotate(Dict[str, Dict[str, Optional[Tensor]]], {}) - for i in range(lm.models_size) - ], - ) - - reorder_state = None - batch_idxs = None - for step in range(max_len + 1): # one extra step for EOS marker - # reorder decoder internal states based on the prev choice of beams - if reorder_state is not None: - if batch_idxs is not None: - # update beam indices to take into account removed sentences - corr = batch_idxs - torch.arange(batch_idxs.numel()).type_as(batch_idxs) - reorder_state.view(-1, beam_size).add_(corr.unsqueeze(-1) * beam_size) - model.reorder_incremental_state(incremental_states, reorder_state) - encoder_outs = model.reorder_encoder_out(encoder_outs, reorder_state) - - lm.reorder_incremental_state(lm_incremental_states, reorder_state) - - fw_lprobs, avg_attn_scores = model.forward_decoder( - tokens[:, :step + 1], encoder_outs, incremental_states, temperature=self.temperature, - ) - - fw_lprobs[:, self.pad] = -math.inf # never select pad - fw_lprobs[:, self.unk] -= self.unk_penalty # apply unk penalty - fw_lprobs, ch_lm_lprobs, lm_lprobs = noisy_channel_rescoring(fw_lprobs, beam_size, bsz, src_tokens, tokens, self.k2) - - # handle min and max length constraints - if step >= max_len: - fw_lprobs[:, :self.eos] = -math.inf - fw_lprobs[:, self.eos + 1:] = -math.inf - elif step < self.min_len: - fw_lprobs[:, self.eos] = -math.inf - - # handle prefix tokens (possibly with different lengths) - if prefix_tokens is not None and step < prefix_tokens.size(1): - prefix_toks = prefix_tokens[:, step].unsqueeze(-1).repeat(1, beam_size).view(-1) - prefix_mask = prefix_toks.ne(self.pad) - - prefix_fw_lprobs = fw_lprobs.gather(-1, prefix_toks.unsqueeze(-1)) - fw_lprobs[prefix_mask] = -math.inf - fw_lprobs[prefix_mask] = fw_lprobs[prefix_mask].scatter_( - -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_fw_lprobs - ) - - prefix_ch_lm_lprobs = ch_lm_lprobs.gather(-1, prefix_toks.unsqueeze(-1)) - ch_lm_lprobs[prefix_mask] = -math.inf - ch_lm_lprobs[prefix_mask] = ch_lm_lprobs[prefix_mask].scatter_( - -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_ch_lm_lprobs - ) - - prefix_lm_lprobs = lm_lprobs.gather(-1, prefix_toks.unsqueeze(-1)) - lm_lprobs[prefix_mask] = -math.inf - lm_lprobs[prefix_mask] = lm_lprobs[prefix_mask].scatter_( - -1, prefix_toks[prefix_mask].unsqueeze(-1), prefix_lm_lprobs - ) - - # if prefix includes eos, then we should make sure tokens and - # scores are the same across all beams - eos_mask = prefix_toks.eq(self.eos) - if eos_mask.any(): - # validate that the first beam matches the prefix - first_beam = tokens[eos_mask].view(-1, beam_size, tokens.size(-1))[:, 0, 1:step + 1] - eos_mask_batch_dim = eos_mask.view(-1, beam_size)[:, 0] - target_prefix = prefix_tokens[eos_mask_batch_dim][:, :step] - assert (first_beam == target_prefix).all() - - def replicate_first_beam(tensor, mask): - tensor = tensor.view(-1, beam_size, tensor.size(-1)) - tensor[mask] = tensor[mask][:, :1, :] - return tensor.view(-1, tensor.size(-1)) - - # copy tokens, scores and lprobs from the first beam to all beams - tokens = replicate_first_beam(tokens, eos_mask_batch_dim) - scores = replicate_first_beam(scores, eos_mask_batch_dim) - - fw_lprobs = replicate_first_beam(fw_lprobs, eos_mask_batch_dim) - ch_lm_lprobs = replicate_first_beam(ch_lm_lprobs, eos_mask_batch_dim) - lm_lprobs = replicate_first_beam(lm_lprobs, eos_mask_batch_dim) - - if self.no_repeat_ngram_size > 0: - # for each beam and batch sentence, generate a list of previous ngrams - gen_ngrams = [{} for bbsz_idx in range(bsz * beam_size)] - for bbsz_idx in range(bsz * beam_size): - gen_tokens = tokens[bbsz_idx].tolist() - for ngram in zip(*[gen_tokens[i:] for i in range(self.no_repeat_ngram_size)]): - gen_ngrams[bbsz_idx][tuple(ngram[:-1])] = \ - gen_ngrams[bbsz_idx].get(tuple(ngram[:-1]), []) + [ngram[-1]] - - # Record attention scores - if avg_attn_scores is not None: - if attn is None: - attn = scores.new(bsz * beam_size, src_tokens.size(1), max_len + 2) - attn_buf = attn.clone() - nonpad_idxs = src_tokens.ne(self.pad) - attn[:, :, step + 1].copy_(avg_attn_scores) - - scores = scores.type_as(fw_lprobs) - scores_buf = scores_buf.type_as(fw_lprobs) - - self.search.set_src_lengths(src_lengths_no_eos) - - if self.no_repeat_ngram_size > 0: - def calculate_banned_tokens(bbsz_idx): - # before decoding the next token, prevent decoding of ngrams that have already appeared - ngram_index = tuple(tokens[bbsz_idx, step + 2 - self.no_repeat_ngram_size:step + 1].tolist()) - return gen_ngrams[bbsz_idx].get(ngram_index, []) - - 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 = [calculate_banned_tokens(bbsz_idx) for bbsz_idx in range(bsz * beam_size)] - else: - banned_tokens = [[] for bbsz_idx in range(bsz * beam_size)] - - for bbsz_idx in range(bsz * beam_size): - fw_lprobs[bbsz_idx, banned_tokens[bbsz_idx]] = -math.inf - - combined_noisy_channel_scores, fw_lprobs_top_k, lm_lprobs_top_k, cand_indices, cand_beams = self.search.step( - step, - fw_lprobs.view(bsz, -1, self.vocab_size), - scores.view(bsz, beam_size, -1)[:, :, :step], ch_lm_lprobs.view(bsz, -1, self.vocab_size), - lm_lprobs.view(bsz, -1, self.vocab_size), self.combine_method - ) - - # cand_bbsz_idx contains beam indices for the top candidate - # hypotheses, with a range of values: [0, bsz*beam_size), - # and dimensions: [bsz, cand_size] - cand_bbsz_idx = cand_beams.add(bbsz_offsets) - - # finalize hypotheses that end in eos (except for candidates to be ignored) - eos_mask = cand_indices.eq(self.eos) - eos_mask[:, :beam_size] &= ~cands_to_ignore - - # only consider eos when it's among the top beam_size indices - eos_bbsz_idx = torch.masked_select( - cand_bbsz_idx[:, :beam_size], mask=eos_mask[:, :beam_size] - ) - - finalized_sents = set() - if eos_bbsz_idx.numel() > 0: - eos_scores = torch.masked_select( - fw_lprobs_top_k[:, :beam_size], mask=eos_mask[:, :beam_size] - ) - combined_noisy_channel_eos_scores = torch.masked_select( - combined_noisy_channel_scores[:, :beam_size], - mask=eos_mask[:, :beam_size], - ) - - # finalize hypo using channel model score - finalized_sents = finalize_hypos( - step, eos_bbsz_idx, eos_scores, combined_noisy_channel_eos_scores) - - num_remaining_sent -= len(finalized_sents) - - assert num_remaining_sent >= 0 - if num_remaining_sent == 0: - break - - if len(finalized_sents) > 0: - new_bsz = bsz - len(finalized_sents) - - # construct batch_idxs which holds indices of batches to keep for the next pass - batch_mask = cand_indices.new_ones(bsz) - batch_mask[cand_indices.new(finalized_sents)] = 0 - batch_idxs = torch.nonzero(batch_mask).squeeze(-1) - - eos_mask = eos_mask[batch_idxs] - cand_beams = cand_beams[batch_idxs] - bbsz_offsets.resize_(new_bsz, 1) - cand_bbsz_idx = cand_beams.add(bbsz_offsets) - - lm_lprobs_top_k = lm_lprobs_top_k[batch_idxs] - - fw_lprobs_top_k = fw_lprobs_top_k[batch_idxs] - cand_indices = cand_indices[batch_idxs] - if prefix_tokens is not None: - prefix_tokens = prefix_tokens[batch_idxs] - src_lengths_no_eos = src_lengths_no_eos[batch_idxs] - cands_to_ignore = cands_to_ignore[batch_idxs] - - scores = scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) - scores_buf.resize_as_(scores) - tokens = tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) - tokens_buf.resize_as_(tokens) - src_tokens = src_tokens.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) - src_lengths = src_lengths.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1) - lm_prefix_scores = lm_prefix_scores.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, -1).squeeze() - - if attn is not None: - attn = attn.view(bsz, -1)[batch_idxs].view(new_bsz * beam_size, attn.size(1), -1) - attn_buf.resize_as_(attn) - bsz = new_bsz - else: - batch_idxs = None - - # Set active_mask so that values > cand_size indicate eos or - # ignored hypos and values < cand_size indicate candidate - # active hypos. After this, the min values per row are the top - # candidate active hypos. - eos_mask[:, :beam_size] |= cands_to_ignore - active_mask = torch.add( - eos_mask.type_as(cand_offsets) * cand_size, - cand_offsets[: eos_mask.size(1)], - ) - - # get the top beam_size active hypotheses, which are just the hypos - # with the smallest values in active_mask - active_hypos, new_cands_to_ignore = buffer('active_hypos'), buffer('new_cands_to_ignore') - torch.topk( - active_mask, k=beam_size, dim=1, largest=False, - out=(new_cands_to_ignore, active_hypos) - ) - - # update cands_to_ignore to ignore any finalized hypos - cands_to_ignore = new_cands_to_ignore.ge(cand_size)[:, :beam_size] - assert (~cands_to_ignore).any(dim=1).all() - - active_bbsz_idx = buffer('active_bbsz_idx') - torch.gather( - cand_bbsz_idx, dim=1, index=active_hypos, - out=active_bbsz_idx, - ) - active_scores = torch.gather( - fw_lprobs_top_k, dim=1, index=active_hypos, - out=scores[:, step].view(bsz, beam_size), - ) - - active_bbsz_idx = active_bbsz_idx.view(-1) - active_scores = active_scores.view(-1) - - # copy tokens and scores for active hypotheses - torch.index_select( - tokens[:, :step + 1], dim=0, index=active_bbsz_idx, - out=tokens_buf[:, :step + 1], - ) - torch.gather( - cand_indices, dim=1, index=active_hypos, - out=tokens_buf.view(bsz, beam_size, -1)[:, :, step + 1], - ) - if step > 0: - torch.index_select( - scores[:, :step], dim=0, index=active_bbsz_idx, - out=scores_buf[:, :step], - ) - torch.gather( - fw_lprobs_top_k, dim=1, index=active_hypos, - out=scores_buf.view(bsz, beam_size, -1)[:, :, step], - ) - torch.gather( - lm_lprobs_top_k, dim=1, index=active_hypos, - out=lm_prefix_scores.view(bsz, beam_size) - ) - - # copy attention for active hypotheses - if attn is not None: - torch.index_select( - attn[:, :, :step + 2], dim=0, index=active_bbsz_idx, - out=attn_buf[:, :, :step + 2], - ) - - # swap buffers - tokens, tokens_buf = tokens_buf, tokens - scores, scores_buf = scores_buf, scores - if attn is not None: - attn, attn_buf = attn_buf, attn - - # reorder incremental state in decoder - reorder_state = active_bbsz_idx - - # sort by score descending - for sent in range(len(finalized)): - finalized[sent] = sorted(finalized[sent], key=lambda r: r['score'], reverse=True) - - return finalized - - -def get_lm_scores(model, input_tokens, incremental_states, cand_tokens, input_len, k): - with torch.no_grad(): - lm_lprobs, avg_attn_scores = model.forward_decoder( - input_tokens, encoder_outs=None, incremental_states=incremental_states, - ) - - lm_lprobs_size = lm_lprobs.size(0) - probs_next_wrd = torch.gather(lm_lprobs.repeat(1, k).view(lm_lprobs_size*k, -1), 1, cand_tokens).squeeze().view(-1) - - return probs_next_wrd - - -def make_dict2dict(old_dict, new_dict): - dict2dict_map = {} - for sym in old_dict.symbols: - dict2dict_map[old_dict.index(sym)] = new_dict.index(sym) - return dict2dict_map - - -def dict2dict(tokens, dict2dict_map): - if tokens.device == torch.device('cpu'): - tokens_tmp = tokens - else: - tokens_tmp = tokens.cpu() - return tokens_tmp.map_( - tokens_tmp, - lambda _, val, dict2dict_map=dict2dict_map : dict2dict_map[float(val)] - ).to(tokens.device) - - -def reorder_tokens(tokens, lengths, eos): - # reorder source tokens so they may be used as reference for P(S|T) - return torch.cat((tokens.new([eos]), tokens[-lengths:-1], tokens[:-lengths]), 0) - - -def reorder_all_tokens(tokens, lengths, eos): - # used to reorder src tokens from [.. ] to [ ... ] - # so source tokens can be used to predict P(S|T) - return torch.stack([reorder_tokens(token, length, eos) for token, length in zip(tokens, lengths)]) - - -def normalized_scores_with_batch_vocab( - model_decoder, features, target_ids, k, bsz, beam_size, - pad_idx, top_k=0, vocab_size_meter=None, start_idx=None, - end_idx=None, **kwargs): - """ - Get normalized probabilities (or log probs) from a net's output - w.r.t. vocab consisting of target IDs in the batch - """ - if model_decoder.adaptive_softmax is None: - weight = model_decoder.output_projection.weight - vocab_ids = torch.unique( - torch.cat( - (torch.unique(target_ids), torch.arange(top_k, device=target_ids.device)) - ) - ) - id_map = dict(zip(vocab_ids.tolist(), range(len(vocab_ids)))) - mapped_target_ids = target_ids.cpu().apply_( - lambda x, id_map=id_map: id_map[x] - ).to(target_ids.device) - expanded_target_ids = mapped_target_ids[:, :].repeat(1, k).view(bsz*beam_size*k, -1) - if start_idx is not None and end_idx is not None: - expanded_target_ids = expanded_target_ids[start_idx:end_idx, :] - logits = F.linear(features, weight[vocab_ids, :]) - log_softmax = F.log_softmax(logits, dim=-1, dtype=torch.float32) - intermed_scores = torch.gather( - log_softmax[:, :-1, :], - 2, - expanded_target_ids[:, 1:].unsqueeze(2), - ).squeeze() - not_padding = expanded_target_ids[:, 1:] != pad_idx - intermed_scores *= not_padding.float() - return intermed_scores - else: - raise ValueError("adaptive softmax doesn't work with " + - "`normalized_scores_with_batch_vocab()`") diff --git a/spaces/gradio/HuBERT/fairseq/modules/grad_multiply.py b/spaces/gradio/HuBERT/fairseq/modules/grad_multiply.py deleted file mode 100644 index 08d15f55dfda9c61a1cf8641ea31424fe1d97f57..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/fairseq/modules/grad_multiply.py +++ /dev/null @@ -1,18 +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 - - -class GradMultiply(torch.autograd.Function): - @staticmethod - def forward(ctx, x, scale): - ctx.scale = scale - res = x.new(x) - return res - - @staticmethod - def backward(ctx, grad): - return grad * ctx.scale, None diff --git a/spaces/gradio/HuBERT/fairseq/optim/shard.py b/spaces/gradio/HuBERT/fairseq/optim/shard.py deleted file mode 100644 index 9d7f2eb9e5de6086fe2435d432bde7521ebb8155..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/fairseq/optim/shard.py +++ /dev/null @@ -1,58 +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 - -from fairseq.distributed import utils - - -try: - from fairscale.optim import OSS - - _has_fairscale = True -except ImportError: - _has_fairscale = False - - -def shard_(optimizer, group): - if not _has_fairscale: - raise ImportError( - "\n\nPlease install the fairscale package:" "\n\n pip install fairscale" - ) - - class FairseqOSS(OSS): - @property - def disable_mem_eff_fp16_loading_hack(self): - return True - - def __getattr__(self, name): - if name.startswith("supports") and hasattr(self.optim, name): - return getattr(self.optim, name) - raise AttributeError( - "'FairseqOSS' object has no attribute {0!r}".format(name) - ) - - def broadcast_global_state_dict( - self, state_dict: Dict[str, Any] - ) -> Dict[str, Any]: - """ - Broadcasts the entire state_dict to all other ranks - each rank is responsible to load their own partition of data - """ - return utils.broadcast_object( - state_dict, - src_rank=0, - group=self.group, - ) - - torch_optimizer = optimizer.optimizer - optim_cls = type(torch_optimizer) - - optimizer.optimizer = FairseqOSS( - torch_optimizer.param_groups, - optim_cls, - group=group, - **optimizer.optimizer_config - ) diff --git a/spaces/gradio/space-api-fetcher/README.md b/spaces/gradio/space-api-fetcher/README.md deleted file mode 100644 index fa5868ca081ce9d018d0be8a2fbe5ae9adb1f120..0000000000000000000000000000000000000000 --- a/spaces/gradio/space-api-fetcher/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Space Api Fetcher -emoji: 📈 -colorFrom: yellow -colorTo: purple -sdk: gradio -sdk_version: 3.25.0 -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/gradio/xgboost-income-prediction-with-explainability/app.py b/spaces/gradio/xgboost-income-prediction-with-explainability/app.py deleted file mode 100644 index cbc1f44fcd39cb79ac1159aabd92fc703d947310..0000000000000000000000000000000000000000 --- a/spaces/gradio/xgboost-income-prediction-with-explainability/app.py +++ /dev/null @@ -1,165 +0,0 @@ -import gradio as gr -import random -import matplotlib -import matplotlib.pyplot as plt -import pandas as pd -import shap -import xgboost as xgb -from datasets import load_dataset - - -matplotlib.use("Agg") -dataset = load_dataset("scikit-learn/adult-census-income") -X_train = dataset["train"].to_pandas() -_ = X_train.pop("fnlwgt") -_ = X_train.pop("race") -y_train = X_train.pop("income") -y_train = (y_train == ">50K").astype(int) -categorical_columns = [ - "workclass", - "education", - "marital.status", - "occupation", - "relationship", - "sex", - "native.country", -] -X_train = X_train.astype({col: "category" for col in categorical_columns}) -data = xgb.DMatrix(X_train, label=y_train, enable_categorical=True) -model = xgb.train(params={"objective": "binary:logistic"}, dtrain=data) -explainer = shap.TreeExplainer(model) - -def predict(*args): - df = pd.DataFrame([args], columns=X_train.columns) - df = df.astype({col: "category" for col in categorical_columns}) - pos_pred = model.predict(xgb.DMatrix(df, enable_categorical=True)) - return {">50K": float(pos_pred[0]), "<=50K": 1 - float(pos_pred[0])} - - -def interpret(*args): - df = pd.DataFrame([args], columns=X_train.columns) - df = df.astype({col: "category" for col in categorical_columns}) - shap_values = explainer.shap_values(xgb.DMatrix(df, enable_categorical=True)) - scores_desc = list(zip(shap_values[0], X_train.columns)) - scores_desc = sorted(scores_desc) - fig_m = plt.figure(tight_layout=True) - plt.barh([s[1] for s in scores_desc], [s[0] for s in scores_desc]) - plt.title("Feature Shap Values") - plt.ylabel("Shap Value") - plt.xlabel("Feature") - plt.tight_layout() - return fig_m - - -unique_class = sorted(X_train["workclass"].unique()) -unique_education = sorted(X_train["education"].unique()) -unique_marital_status = sorted(X_train["marital.status"].unique()) -unique_relationship = sorted(X_train["relationship"].unique()) -unique_occupation = sorted(X_train["occupation"].unique()) -unique_sex = sorted(X_train["sex"].unique()) -unique_country = sorted(X_train["native.country"].unique()) - -with gr.Blocks() as demo: - gr.Markdown(""" - **Income Classification with XGBoost 💰**: This demo uses an XGBoost classifier predicts income based on demographic factors, along with Shapley value-based *explanations*. The [source code for this Gradio demo is here](https://huggingface.co/spaces/gradio/xgboost-income-prediction-with-explainability/blob/main/app.py). - """) - with gr.Row(): - with gr.Column(): - age = gr.Slider(label="Age", minimum=17, maximum=90, step=1, randomize=True) - work_class = gr.Dropdown( - label="Workclass", - choices=unique_class, - value=lambda: random.choice(unique_class), - ) - education = gr.Dropdown( - label="Education Level", - choices=unique_education, - value=lambda: random.choice(unique_education), - ) - years = gr.Slider( - label="Years of schooling", - minimum=1, - maximum=16, - step=1, - randomize=True, - ) - marital_status = gr.Dropdown( - label="Marital Status", - choices=unique_marital_status, - value=lambda: random.choice(unique_marital_status), - ) - occupation = gr.Dropdown( - label="Occupation", - choices=unique_occupation, - value=lambda: random.choice(unique_occupation), - ) - relationship = gr.Dropdown( - label="Relationship Status", - choices=unique_relationship, - value=lambda: random.choice(unique_relationship), - ) - sex = gr.Dropdown( - label="Sex", choices=unique_sex, value=lambda: random.choice(unique_sex) - ) - capital_gain = gr.Slider( - label="Capital Gain", - minimum=0, - maximum=100000, - step=500, - randomize=True, - ) - capital_loss = gr.Slider( - label="Capital Loss", minimum=0, maximum=10000, step=500, randomize=True - ) - hours_per_week = gr.Slider( - label="Hours Per Week Worked", minimum=1, maximum=99, step=1 - ) - country = gr.Dropdown( - label="Native Country", - choices=unique_country, - value=lambda: random.choice(unique_country), - ) - with gr.Column(): - label = gr.Label() - plot = gr.Plot() - with gr.Row(): - predict_btn = gr.Button(value="Predict") - interpret_btn = gr.Button(value="Explain") - predict_btn.click( - predict, - inputs=[ - age, - work_class, - education, - years, - marital_status, - occupation, - relationship, - sex, - capital_gain, - capital_loss, - hours_per_week, - country, - ], - outputs=[label], - ) - interpret_btn.click( - interpret, - inputs=[ - age, - work_class, - education, - years, - marital_status, - occupation, - relationship, - sex, - capital_gain, - capital_loss, - hours_per_week, - country, - ], - outputs=[plot], - ) - -demo.launch() diff --git a/spaces/gulabpatel/GFP_GAN/README.md b/spaces/gulabpatel/GFP_GAN/README.md deleted file mode 100644 index c01224a69f399e92e1f8160f1f14d4e0fff04692..0000000000000000000000000000000000000000 --- a/spaces/gulabpatel/GFP_GAN/README.md +++ /dev/null @@ -1,33 +0,0 @@ ---- -title: GFPGAN -emoji: 📚 -colorFrom: green -colorTo: blue -sdk: gradio -app_file: app.py -pinned: false ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`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/gyugnsu/DragGan-Inversion/PTI/training/projectors/__init__.py b/spaces/gyugnsu/DragGan-Inversion/PTI/training/projectors/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/gyugnsu/DragGan-Inversion/training/networks_stylegan3.py b/spaces/gyugnsu/DragGan-Inversion/training/networks_stylegan3.py deleted file mode 100644 index 338fd287110a02d76c0b7b03fbf041c340f5adb9..0000000000000000000000000000000000000000 --- a/spaces/gyugnsu/DragGan-Inversion/training/networks_stylegan3.py +++ /dev/null @@ -1,645 +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. - -"""Generator architecture from the paper -"Alias-Free Generative Adversarial Networks".""" - -import numpy as np -import scipy.signal -import scipy.optimize -import torch -import torch.nn.functional as F -from torch_utils import misc -from torch_utils import persistence -from torch_utils.ops import conv2d_gradfix -from torch_utils.ops import filtered_lrelu -from torch_utils.ops import bias_act - -# ---------------------------------------------------------------------------- - - -@misc.profiled_function -def modulated_conv2d( - # Input tensor: [batch_size, in_channels, in_height, in_width] - x, - # Weight tensor: [out_channels, in_channels, kernel_height, kernel_width] - w, - s, # Style tensor: [batch_size, in_channels] - demodulate=True, # Apply weight demodulation? - padding=0, # Padding: int or [padH, padW] - input_gain=None, # Optional scale factors for the input channels: [], [in_channels], or [batch_size, in_channels] -): - with misc.suppress_tracer_warnings(): # this value will be treated as a constant - batch_size = int(x.shape[0]) - out_channels, in_channels, kh, kw = w.shape - misc.assert_shape(w, [out_channels, in_channels, kh, kw]) # [OIkk] - misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW] - misc.assert_shape(s, [batch_size, in_channels]) # [NI] - - # Pre-normalize inputs. - if demodulate: - w = w * w.square().mean([1, 2, 3], keepdim=True).rsqrt() - s = s * s.square().mean().rsqrt() - - # Modulate weights. - w = w.unsqueeze(0) # [NOIkk] - w = w * s.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk] - - # Demodulate weights. - if demodulate: - dcoefs = (w.square().sum(dim=[2, 3, 4]) + 1e-8).rsqrt() # [NO] - w = w * dcoefs.unsqueeze(2).unsqueeze(3).unsqueeze(4) # [NOIkk] - - # Apply input scaling. - if input_gain is not None: - input_gain = input_gain.expand(batch_size, in_channels) # [NI] - w = w * input_gain.unsqueeze(1).unsqueeze(3).unsqueeze(4) # [NOIkk] - - # Execute as one fused op using grouped convolution. - x = x.reshape(1, -1, *x.shape[2:]) - w = w.reshape(-1, in_channels, kh, kw) - x = conv2d_gradfix.conv2d(input=x, weight=w.to( - x.dtype), padding=padding, groups=batch_size) - x = x.reshape(batch_size, -1, *x.shape[2:]) - return x - -# ---------------------------------------------------------------------------- - - -@persistence.persistent_class -class FullyConnectedLayer(torch.nn.Module): - def __init__(self, - in_features, # Number of input features. - out_features, # Number of output features. - # Activation function: 'relu', 'lrelu', etc. - activation='linear', - bias=True, # Apply additive bias before the activation function? - lr_multiplier=1, # Learning rate multiplier. - # Initial standard deviation of the weight tensor. - weight_init=1, - bias_init=0, # Initial value of the additive bias. - ): - super().__init__() - self.in_features = in_features - self.out_features = out_features - self.activation = activation - self.weight = torch.nn.Parameter(torch.randn( - [out_features, in_features]) * (weight_init / lr_multiplier)) - bias_init = np.broadcast_to(np.asarray( - bias_init, dtype=np.float32), [out_features]) - self.bias = torch.nn.Parameter(torch.from_numpy( - bias_init / lr_multiplier)) if bias else None - self.weight_gain = lr_multiplier / np.sqrt(in_features) - self.bias_gain = lr_multiplier - - def forward(self, x): - w = self.weight.to(x.dtype) * self.weight_gain - b = self.bias - if b is not None: - b = b.to(x.dtype) - if self.bias_gain != 1: - b = b * self.bias_gain - if self.activation == 'linear' and b is not None: - x = torch.addmm(b.unsqueeze(0), x, w.t()) - else: - x = x.matmul(w.t()) - x = bias_act.bias_act(x, b, act=self.activation) - return x - - def extra_repr(self): - return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}' - -# ---------------------------------------------------------------------------- - - -@persistence.persistent_class -class MappingNetwork(torch.nn.Module): - def __init__(self, - z_dim, # Input latent (Z) dimensionality. - # Conditioning label (C) dimensionality, 0 = no labels. - c_dim, - # Intermediate latent (W) dimensionality. - w_dim, - # Number of intermediate latents to output. - num_ws, - num_layers=2, # Number of mapping layers. - # Learning rate multiplier for the mapping layers. - lr_multiplier=0.01, - # Decay for tracking the moving average of W during training. - w_avg_beta=0.998, - ): - super().__init__() - self.z_dim = z_dim - self.c_dim = c_dim - self.w_dim = w_dim - self.num_ws = num_ws - self.num_layers = num_layers - self.w_avg_beta = w_avg_beta - - # Construct layers. - self.embed = FullyConnectedLayer( - self.c_dim, self.w_dim) if self.c_dim > 0 else None - features = [self.z_dim + (self.w_dim if self.c_dim > - 0 else 0)] + [self.w_dim] * self.num_layers - for idx, in_features, out_features in zip(range(num_layers), features[:-1], features[1:]): - layer = FullyConnectedLayer( - in_features, out_features, activation='lrelu', lr_multiplier=lr_multiplier) - setattr(self, f'fc{idx}', layer) - self.register_buffer('w_avg', torch.zeros([w_dim])) - - def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False): - misc.assert_shape(z, [None, self.z_dim]) - if truncation_cutoff is None: - truncation_cutoff = self.num_ws - - # Embed, normalize, and concatenate inputs. - x = z.to(torch.float32) - x = x * (x.square().mean(1, keepdim=True) + 1e-8).rsqrt() - if self.c_dim > 0: - misc.assert_shape(c, [None, self.c_dim]) - y = self.embed(c.to(torch.float32)) - y = y * (y.square().mean(1, keepdim=True) + 1e-8).rsqrt() - x = torch.cat([x, y], dim=1) if x is not None else y - - # Execute layers. - for idx in range(self.num_layers): - x = getattr(self, f'fc{idx}')(x) - - # Update moving average of W. - if update_emas: - self.w_avg.copy_(x.detach().mean( - dim=0).lerp(self.w_avg, self.w_avg_beta)) - - # Broadcast and apply truncation. - x = x.unsqueeze(1).repeat([1, self.num_ws, 1]) - if truncation_psi != 1: - x[:, :truncation_cutoff] = self.w_avg.lerp( - x[:, :truncation_cutoff], truncation_psi) - return x - - def extra_repr(self): - return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}' - -# ---------------------------------------------------------------------------- - - -@persistence.persistent_class -class SynthesisInput(torch.nn.Module): - def __init__(self, - w_dim, # Intermediate latent (W) dimensionality. - channels, # Number of output channels. - size, # Output spatial size: int or [width, height]. - sampling_rate, # Output sampling rate. - bandwidth, # Output bandwidth. - ): - super().__init__() - self.w_dim = w_dim - self.channels = channels - self.size = np.broadcast_to(np.asarray(size), [2]) - self.sampling_rate = sampling_rate - self.bandwidth = bandwidth - - # Draw random frequencies from uniform 2D disc. - freqs = torch.randn([self.channels, 2]) - radii = freqs.square().sum(dim=1, keepdim=True).sqrt() - freqs /= radii * radii.square().exp().pow(0.25) - freqs *= bandwidth - phases = torch.rand([self.channels]) - 0.5 - - # Setup parameters and buffers. - self.weight = torch.nn.Parameter( - torch.randn([self.channels, self.channels])) - self.affine = FullyConnectedLayer( - w_dim, 4, weight_init=0, bias_init=[1, 0, 0, 0]) - # User-specified inverse transform wrt. resulting image. - self.register_buffer('transform', torch.eye(3, 3)) - self.register_buffer('freqs', freqs) - self.register_buffer('phases', phases) - - def forward(self, w): - # Introduce batch dimension. - transforms = self.transform.unsqueeze(0) # [batch, row, col] - freqs = self.freqs.unsqueeze(0) # [batch, channel, xy] - phases = self.phases.unsqueeze(0) # [batch, channel] - - # Apply learned transformation. - t = self.affine(w) # t = (r_c, r_s, t_x, t_y) - # t' = (r'_c, r'_s, t'_x, t'_y) - t = t / t[:, :2].norm(dim=1, keepdim=True) - # Inverse rotation wrt. resulting image. - m_r = torch.eye(3, device=w.device).unsqueeze( - 0).repeat([w.shape[0], 1, 1]) - m_r[:, 0, 0] = t[:, 0] # r'_c - m_r[:, 0, 1] = -t[:, 1] # r'_s - m_r[:, 1, 0] = t[:, 1] # r'_s - m_r[:, 1, 1] = t[:, 0] # r'_c - # Inverse translation wrt. resulting image. - m_t = torch.eye(3, device=w.device).unsqueeze( - 0).repeat([w.shape[0], 1, 1]) - m_t[:, 0, 2] = -t[:, 2] # t'_x - m_t[:, 1, 2] = -t[:, 3] # t'_y - # First rotate resulting image, then translate, and finally apply user-specified transform. - transforms = m_r @ m_t @ transforms - - # Transform frequencies. - phases = phases + (freqs @ transforms[:, :2, 2:]).squeeze(2) - freqs = freqs @ transforms[:, :2, :2] - - # Dampen out-of-band frequencies that may occur due to the user-specified transform. - amplitudes = (1 - (freqs.norm(dim=2) - self.bandwidth) / - (self.sampling_rate / 2 - self.bandwidth)).clamp(0, 1) - - # Construct sampling grid. - theta = torch.eye(2, 3, device=w.device) - theta[0, 0] = 0.5 * self.size[0] / self.sampling_rate - theta[1, 1] = 0.5 * self.size[1] / self.sampling_rate - grids = torch.nn.functional.affine_grid(theta.unsqueeze( - 0), [1, 1, self.size[1], self.size[0]], align_corners=False) - - # Compute Fourier features. - x = (grids.unsqueeze(3) @ freqs.permute(0, 2, 1).unsqueeze(1).unsqueeze(2) - ).squeeze(3) # [batch, height, width, channel] - x = x + phases.unsqueeze(1).unsqueeze(2) - x = torch.sin(x * (np.pi * 2)) - x = x * amplitudes.unsqueeze(1).unsqueeze(2) - - # Apply trainable mapping. - weight = self.weight / np.sqrt(self.channels) - x = x @ weight.t() - - # Ensure correct shape. - x = x.permute(0, 3, 1, 2) # [batch, channel, height, width] - misc.assert_shape(x, [w.shape[0], self.channels, - int(self.size[1]), int(self.size[0])]) - return x - - def extra_repr(self): - return '\n'.join([ - f'w_dim={self.w_dim:d}, channels={self.channels:d}, size={list(self.size)},', - f'sampling_rate={self.sampling_rate:g}, bandwidth={self.bandwidth:g}']) - -# ---------------------------------------------------------------------------- - - -@persistence.persistent_class -class SynthesisLayer(torch.nn.Module): - def __init__(self, - # Intermediate latent (W) dimensionality. - w_dim, - is_torgb, # Is this the final ToRGB layer? - is_critically_sampled, # Does this layer use critical sampling? - use_fp16, # Does this layer use FP16? - - # Input & output specifications. - in_channels, # Number of input channels. - out_channels, # Number of output channels. - # Input spatial size: int or [width, height]. - in_size, - # Output spatial size: int or [width, height]. - out_size, - in_sampling_rate, # Input sampling rate (s). - out_sampling_rate, # Output sampling rate (s). - # Input cutoff frequency (f_c). - in_cutoff, - # Output cutoff frequency (f_c). - out_cutoff, - # Input transition band half-width (f_h). - in_half_width, - # Output Transition band half-width (f_h). - out_half_width, - - # Hyperparameters. - # Convolution kernel size. Ignored for final the ToRGB layer. - conv_kernel=3, - # Low-pass filter size relative to the lower resolution when up/downsampling. - filter_size=6, - # Relative sampling rate for leaky ReLU. Ignored for final the ToRGB layer. - lrelu_upsampling=2, - # Use radially symmetric downsampling filter? Ignored for critically sampled layers. - use_radial_filters=False, - # Clamp the output to [-X, +X], None = disable clamping. - conv_clamp=256, - # Decay rate for the moving average of input magnitudes. - magnitude_ema_beta=0.999, - ): - super().__init__() - self.w_dim = w_dim - self.is_torgb = is_torgb - self.is_critically_sampled = is_critically_sampled - self.use_fp16 = use_fp16 - self.in_channels = in_channels - self.out_channels = out_channels - self.in_size = np.broadcast_to(np.asarray(in_size), [2]) - self.out_size = np.broadcast_to(np.asarray(out_size), [2]) - self.in_sampling_rate = in_sampling_rate - self.out_sampling_rate = out_sampling_rate - self.tmp_sampling_rate = max( - in_sampling_rate, out_sampling_rate) * (1 if is_torgb else lrelu_upsampling) - self.in_cutoff = in_cutoff - self.out_cutoff = out_cutoff - self.in_half_width = in_half_width - self.out_half_width = out_half_width - self.conv_kernel = 1 if is_torgb else conv_kernel - self.conv_clamp = conv_clamp - self.magnitude_ema_beta = magnitude_ema_beta - - # Setup parameters and buffers. - self.affine = FullyConnectedLayer( - self.w_dim, self.in_channels, bias_init=1) - self.weight = torch.nn.Parameter(torch.randn( - [self.out_channels, self.in_channels, self.conv_kernel, self.conv_kernel])) - self.bias = torch.nn.Parameter(torch.zeros([self.out_channels])) - self.register_buffer('magnitude_ema', torch.ones([])) - - # Design upsampling filter. - self.up_factor = int( - np.rint(self.tmp_sampling_rate / self.in_sampling_rate)) - assert self.in_sampling_rate * self.up_factor == self.tmp_sampling_rate - self.up_taps = filter_size * \ - self.up_factor if self.up_factor > 1 and not self.is_torgb else 1 - self.register_buffer('up_filter', self.design_lowpass_filter( - numtaps=self.up_taps, cutoff=self.in_cutoff, width=self.in_half_width*2, fs=self.tmp_sampling_rate)) - - # Design downsampling filter. - self.down_factor = int( - np.rint(self.tmp_sampling_rate / self.out_sampling_rate)) - assert self.out_sampling_rate * self.down_factor == self.tmp_sampling_rate - self.down_taps = filter_size * \ - self.down_factor if self.down_factor > 1 and not self.is_torgb else 1 - self.down_radial = use_radial_filters and not self.is_critically_sampled - self.register_buffer('down_filter', self.design_lowpass_filter( - numtaps=self.down_taps, cutoff=self.out_cutoff, width=self.out_half_width*2, fs=self.tmp_sampling_rate, radial=self.down_radial)) - - # Compute padding. - # Desired output size before downsampling. - pad_total = (self.out_size - 1) * self.down_factor + 1 - # Input size after upsampling. - pad_total -= (self.in_size + self.conv_kernel - 1) * self.up_factor - # Size reduction caused by the filters. - pad_total += self.up_taps + self.down_taps - 2 - # Shift sample locations according to the symmetric interpretation (Appendix C.3). - pad_lo = (pad_total + self.up_factor) // 2 - pad_hi = pad_total - pad_lo - self.padding = [int(pad_lo[0]), int(pad_hi[0]), - int(pad_lo[1]), int(pad_hi[1])] - - def forward(self, x, w, noise_mode='random', force_fp32=False, update_emas=False): - assert noise_mode in ['random', 'const', 'none'] # unused - misc.assert_shape(x, [None, self.in_channels, int( - self.in_size[1]), int(self.in_size[0])]) - misc.assert_shape(w, [x.shape[0], self.w_dim]) - - # Track input magnitude. - if update_emas: - with torch.autograd.profiler.record_function('update_magnitude_ema'): - magnitude_cur = x.detach().to(torch.float32).square().mean() - self.magnitude_ema.copy_(magnitude_cur.lerp( - self.magnitude_ema, self.magnitude_ema_beta)) - input_gain = self.magnitude_ema.rsqrt() - - # Execute affine layer. - styles = self.affine(w) - if self.is_torgb: - weight_gain = 1 / \ - np.sqrt(self.in_channels * (self.conv_kernel ** 2)) - styles = styles * weight_gain - - # Execute modulated conv2d. - dtype = torch.float16 if ( - self.use_fp16 and not force_fp32 and x.device.type == 'cuda') else torch.float32 - x = modulated_conv2d(x=x.to(dtype), w=self.weight, s=styles, - padding=self.conv_kernel-1, demodulate=(not self.is_torgb), input_gain=input_gain) - - # Execute bias, filtered leaky ReLU, and clamping. - gain = 1 if self.is_torgb else np.sqrt(2) - slope = 1 if self.is_torgb else 0.2 - x = filtered_lrelu.filtered_lrelu(x=x, fu=self.up_filter, fd=self.down_filter, b=self.bias.to(x.dtype), - up=self.up_factor, down=self.down_factor, padding=self.padding, gain=gain, slope=slope, clamp=self.conv_clamp) - - # Ensure correct shape and dtype. - misc.assert_shape(x, [None, self.out_channels, int( - self.out_size[1]), int(self.out_size[0])]) - assert x.dtype == dtype - return x - - @staticmethod - def design_lowpass_filter(numtaps, cutoff, width, fs, radial=False): - assert numtaps >= 1 - - # Identity filter. - if numtaps == 1: - return None - - # Separable Kaiser low-pass filter. - if not radial: - f = scipy.signal.firwin( - numtaps=numtaps, cutoff=cutoff, width=width, fs=fs) - return torch.as_tensor(f, dtype=torch.float32) - - # Radially symmetric jinc-based filter. - x = (np.arange(numtaps) - (numtaps - 1) / 2) / fs - r = np.hypot(*np.meshgrid(x, x)) - f = scipy.special.j1(2 * cutoff * (np.pi * r)) / (np.pi * r) - beta = scipy.signal.kaiser_beta( - scipy.signal.kaiser_atten(numtaps, width / (fs / 2))) - w = np.kaiser(numtaps, beta) - f *= np.outer(w, w) - f /= np.sum(f) - return torch.as_tensor(f, dtype=torch.float32) - - def extra_repr(self): - return '\n'.join([ - f'w_dim={self.w_dim:d}, is_torgb={self.is_torgb},', - f'is_critically_sampled={self.is_critically_sampled}, use_fp16={self.use_fp16},', - f'in_sampling_rate={self.in_sampling_rate:g}, out_sampling_rate={self.out_sampling_rate:g},', - f'in_cutoff={self.in_cutoff:g}, out_cutoff={self.out_cutoff:g},', - f'in_half_width={self.in_half_width:g}, out_half_width={self.out_half_width:g},', - f'in_size={list(self.in_size)}, out_size={list(self.out_size)},', - f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}']) - -# ---------------------------------------------------------------------------- - - -@persistence.persistent_class -class SynthesisNetwork(torch.nn.Module): - def __init__(self, - # Intermediate latent (W) dimensionality. - w_dim, - img_resolution, # Output image resolution. - img_channels, # Number of color channels. - # Overall multiplier for the number of channels. - channel_base=32768, - # Maximum number of channels in any layer. - channel_max=512, - # Total number of layers, excluding Fourier features and ToRGB. - num_layers=14, - # Number of critically sampled layers at the end. - num_critical=2, - # Cutoff frequency of the first layer (f_{c,0}). - first_cutoff=2, - # Minimum stopband of the first layer (f_{t,0}). - first_stopband=2**2.1, - # Minimum stopband of the last layer, expressed relative to the cutoff. - last_stopband_rel=2**0.3, - # Number of additional pixels outside the image. - margin_size=10, - output_scale=0.25, # Scale factor for the output image. - # Use FP16 for the N highest resolutions. - num_fp16_res=4, - # Arguments for SynthesisLayer. - **layer_kwargs, - ): - super().__init__() - self.w_dim = w_dim - self.num_ws = num_layers + 2 - self.img_resolution = img_resolution - self.img_channels = img_channels - self.num_layers = num_layers - self.num_critical = num_critical - self.margin_size = margin_size - self.output_scale = output_scale - self.num_fp16_res = num_fp16_res - - # Geometric progression of layer cutoffs and min. stopbands. - last_cutoff = self.img_resolution / 2 # f_{c,N} - last_stopband = last_cutoff * last_stopband_rel # f_{t,N} - exponents = np.minimum( - np.arange(self.num_layers + 1) / (self.num_layers - self.num_critical), 1) - cutoffs = first_cutoff * \ - (last_cutoff / first_cutoff) ** exponents # f_c[i] - stopbands = first_stopband * \ - (last_stopband / first_stopband) ** exponents # f_t[i] - - # Compute remaining layer parameters. - sampling_rates = np.exp2( - np.ceil(np.log2(np.minimum(stopbands * 2, self.img_resolution)))) # s[i] - half_widths = np.maximum( - stopbands, sampling_rates / 2) - cutoffs # f_h[i] - sizes = sampling_rates + self.margin_size * 2 - sizes[-2:] = self.img_resolution - channels = np.rint(np.minimum( - (channel_base / 2) / cutoffs, channel_max)) - channels[-1] = self.img_channels - - # Construct layers. - self.input = SynthesisInput( - w_dim=self.w_dim, channels=int(channels[0]), size=int(sizes[0]), - sampling_rate=sampling_rates[0], bandwidth=cutoffs[0]) - self.layer_names = [] - for idx in range(self.num_layers + 1): - prev = max(idx - 1, 0) - is_torgb = (idx == self.num_layers) - is_critically_sampled = ( - idx >= self.num_layers - self.num_critical) - use_fp16 = (sampling_rates[idx] * (2 ** - self.num_fp16_res) > self.img_resolution) - layer = SynthesisLayer( - w_dim=self.w_dim, is_torgb=is_torgb, is_critically_sampled=is_critically_sampled, use_fp16=use_fp16, - in_channels=int(channels[prev]), out_channels=int(channels[idx]), - in_size=int(sizes[prev]), out_size=int(sizes[idx]), - in_sampling_rate=int(sampling_rates[prev]), out_sampling_rate=int(sampling_rates[idx]), - in_cutoff=cutoffs[prev], out_cutoff=cutoffs[idx], - in_half_width=half_widths[prev], out_half_width=half_widths[idx], - **layer_kwargs) - name = f'L{idx}_{layer.out_size[0]}_{layer.out_channels}' - setattr(self, name, layer) - self.layer_names.append(name) - - def forward(self, ws, return_feature=False, **layer_kwargs): - features = [] - misc.assert_shape(ws, [None, self.num_ws, self.w_dim]) - ws = ws.to(torch.float32).unbind(dim=1) - - # Execute layers. - x = self.input(ws[0]) - for name, w in zip(self.layer_names, ws[1:]): - x = getattr(self, name)(x, w, **layer_kwargs) - features.append(x) - if self.output_scale != 1: - x = x * self.output_scale - - # Ensure correct shape and dtype. - misc.assert_shape(x, [None, self.img_channels, - self.img_resolution, self.img_resolution]) - x = x.to(torch.float32) - if return_feature: - return x, features - else: - return x - - def extra_repr(self): - return '\n'.join([ - f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},', - f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},', - f'num_layers={self.num_layers:d}, num_critical={self.num_critical:d},', - f'margin_size={self.margin_size:d}, num_fp16_res={self.num_fp16_res:d}']) - -# ---------------------------------------------------------------------------- - - -@persistence.persistent_class -class Generator(torch.nn.Module): - def __init__(self, - z_dim, # Input latent (Z) dimensionality. - # Conditioning label (C) dimensionality. - c_dim, - # Intermediate latent (W) dimensionality. - w_dim, - img_resolution, # Output resolution. - img_channels, # Number of output color channels. - mapping_kwargs={}, # Arguments for MappingNetwork. - resize=None, - **synthesis_kwargs, # Arguments for SynthesisNetwork. - ): - super().__init__() - self.z_dim = z_dim - self.c_dim = c_dim - self.w_dim = w_dim - self.img_resolution = img_resolution - self.img_channels = img_channels - self.synthesis = SynthesisNetwork( - w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs) - self.num_ws = self.synthesis.num_ws - self.mapping = MappingNetwork( - z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs) - self.resize = resize - - def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, input_is_w=False, return_feature=False, **synthesis_kwargs): - if input_is_w: - ws = z - if ws.dim() == 2: - ws = ws.unsqueeze(1).repeat([1, self.mapping.num_ws, 1]) - else: - ws = self.mapping(z, c, truncation_psi=truncation_psi, - truncation_cutoff=truncation_cutoff, update_emas=update_emas) - img = self.synthesis(ws, update_emas=update_emas, - return_feature=return_feature, **synthesis_kwargs) - if return_feature: - img, feature = img - if self.resize is not None: - img = imresize(img, [self.resize, self.resize]) - if return_feature: - return img, feature - else: - return img - -# ---------------------------------------------------------------------------- - - -def imresize(image, size): - dim = image.dim() - if dim == 3: - image = image.unsqueeze(1) - b, _, h, w = image.shape - if size[0] > h: - image = F.interpolate(image, size, mode='bilinear') - elif size[0] < h: - image = F.interpolate(image, size, mode='area') - if dim == 3: - image = image.squeeze(1) - return image diff --git a/spaces/h2oai/wave-tour/examples/chatbot_events_stop.py b/spaces/h2oai/wave-tour/examples/chatbot_events_stop.py deleted file mode 100644 index 9aef9639bd8048b3bba2b1d12fb013582f046715..0000000000000000000000000000000000000000 --- a/spaces/h2oai/wave-tour/examples/chatbot_events_stop.py +++ /dev/null @@ -1,48 +0,0 @@ -# Chatbot / Events/ Stop -# Use this card for chatbot interactions. Streaming can be stopped by the user. -# #chatbot #events #stop -# --- -from h2o_wave import main, app, Q, ui, data -import asyncio - - -async def stream_message(q): - stream = '' - q.page['example'].data += [stream, False] - # Show the "Stop generating" button - q.page['example'].generating = True - for w in 'I am a fake chatbot. Sorry, I cannot help you.'.split(): - await asyncio.sleep(0.3) - stream += w + ' ' - q.page['example'].data[-1] = [stream, False] - await q.page.save() - # Hide the "Stop generating" button - q.page['example'].generating = False - await q.page.save() - - -@app('/demo') -async def serve(q: Q): - if not q.client.initialized: - q.page['example'] = ui.chatbot_card( - box='1 1 5 5', - data=data(fields='content from_user', t='list'), - name='chatbot', - events=['stop'] - ) - q.client.initialized = True - - # Handle the stop event. - if q.events.chatbot and q.events.chatbot.stop: - # Cancel the streaming task. - q.client.task.cancel() - # Hide the "Stop generating" button. - q.page['example'].generating = False - # A new message arrived. - elif q.args.chatbot: - # Append user message. - q.page['example'].data += [q.args.chatbot, True] - # Run the streaming within cancelable asyncio task. - q.client.task = asyncio.create_task(stream_message(q)) - - await q.page.save() diff --git a/spaces/hamacojr/SAM-CAT-Seg/cat_seg/config.py b/spaces/hamacojr/SAM-CAT-Seg/cat_seg/config.py deleted file mode 100644 index 41720bb3971e1070ae9a0e9fc0a2026d8d19126f..0000000000000000000000000000000000000000 --- a/spaces/hamacojr/SAM-CAT-Seg/cat_seg/config.py +++ /dev/null @@ -1,93 +0,0 @@ -# -*- coding: utf-8 -*- -# Copyright (c) Facebook, Inc. and its affiliates. -from detectron2.config import CfgNode as CN - - -def add_cat_seg_config(cfg): - """ - Add config for MASK_FORMER. - """ - # data config - # select the dataset mapper - cfg.INPUT.DATASET_MAPPER_NAME = "mask_former_semantic" - - cfg.DATASETS.VAL_ALL = ("coco_2017_val_all_stuff_sem_seg",) - - # Color augmentation - cfg.INPUT.COLOR_AUG_SSD = False - # We retry random cropping until no single category in semantic segmentation GT occupies more - # than `SINGLE_CATEGORY_MAX_AREA` part of the crop. - cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0 - # Pad image and segmentation GT in dataset mapper. - cfg.INPUT.SIZE_DIVISIBILITY = -1 - - # solver config - # weight decay on embedding - cfg.SOLVER.WEIGHT_DECAY_EMBED = 0.0 - # optimizer - cfg.SOLVER.OPTIMIZER = "ADAMW" - cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1 - - # mask_former model config - cfg.MODEL.MASK_FORMER = CN() - - # Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet) - # you can use this config to override - cfg.MODEL.MASK_FORMER.SIZE_DIVISIBILITY = 32 - - # swin transformer backbone - cfg.MODEL.SWIN = CN() - cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224 - cfg.MODEL.SWIN.PATCH_SIZE = 4 - cfg.MODEL.SWIN.EMBED_DIM = 96 - cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2] - cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24] - cfg.MODEL.SWIN.WINDOW_SIZE = 7 - cfg.MODEL.SWIN.MLP_RATIO = 4.0 - cfg.MODEL.SWIN.QKV_BIAS = True - cfg.MODEL.SWIN.QK_SCALE = None - cfg.MODEL.SWIN.DROP_RATE = 0.0 - cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0 - cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3 - cfg.MODEL.SWIN.APE = False - cfg.MODEL.SWIN.PATCH_NORM = True - cfg.MODEL.SWIN.OUT_FEATURES = ["res2", "res3", "res4", "res5"] - - # zero shot config - cfg.MODEL.SEM_SEG_HEAD.TRAIN_CLASS_JSON = "datasets/ADE20K_2021_17_01/ADE20K_847.json" - cfg.MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON = "datasets/ADE20K_2021_17_01/ADE20K_847.json" - cfg.MODEL.SEM_SEG_HEAD.TRAIN_CLASS_INDEXES = "datasets/coco/coco_stuff/split/seen_indexes.json" - cfg.MODEL.SEM_SEG_HEAD.TEST_CLASS_INDEXES = "datasets/coco/coco_stuff/split/unseen_indexes.json" - - cfg.MODEL.SEM_SEG_HEAD.CLIP_PRETRAINED = "ViT-B/16" - - cfg.MODEL.PROMPT_ENSEMBLE = False - cfg.MODEL.PROMPT_ENSEMBLE_TYPE = "single" - - cfg.MODEL.CLIP_PIXEL_MEAN = [122.7709383, 116.7460125, 104.09373615] - cfg.MODEL.CLIP_PIXEL_STD = [68.5005327, 66.6321579, 70.3231630] - # three styles for clip classification, crop, mask, cropmask - - cfg.MODEL.SEM_SEG_HEAD.TEXT_AFFINITY_DIM = 512 - cfg.MODEL.SEM_SEG_HEAD.TEXT_AFFINITY_PROJ_DIM = 128 - cfg.MODEL.SEM_SEG_HEAD.APPEARANCE_AFFINITY_DIM = 512 - cfg.MODEL.SEM_SEG_HEAD.APPEARANCE_AFFINITY_PROJ_DIM = 128 - - cfg.MODEL.SEM_SEG_HEAD.DECODER_DIMS = [64, 32] - cfg.MODEL.SEM_SEG_HEAD.DECODER_AFFINITY_DIMS = [256, 128] - cfg.MODEL.SEM_SEG_HEAD.DECODER_AFFINITY_PROJ_DIMS = [32, 16] - - cfg.MODEL.SEM_SEG_HEAD.NUM_LAYERS = 4 - cfg.MODEL.SEM_SEG_HEAD.NUM_HEADS = 4 - cfg.MODEL.SEM_SEG_HEAD.HIDDEN_DIMS = 128 - cfg.MODEL.SEM_SEG_HEAD.POOLING_SIZES = [6, 6] - cfg.MODEL.SEM_SEG_HEAD.FEATURE_RESOLUTION = [24, 24] - cfg.MODEL.SEM_SEG_HEAD.WINDOW_SIZES = 12 - cfg.MODEL.SEM_SEG_HEAD.ATTENTION_TYPE = "linear" - - cfg.MODEL.SEM_SEG_HEAD.PROMPT_DEPTH = 0 - cfg.MODEL.SEM_SEG_HEAD.PROMPT_LENGTH = 0 - cfg.SOLVER.CLIP_MULTIPLIER = 0.01 - - cfg.MODEL.SEM_SEG_HEAD.CLIP_FINETUNE = "attention" - cfg.TEST.SLIDING_WINDOW = False \ No newline at end of file diff --git a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/utils/dist.py b/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/utils/dist.py deleted file mode 100644 index 2b25a7a205a0379414873fa6c2be2fc6f444f00a..0000000000000000000000000000000000000000 --- a/spaces/haotiz/glip-zeroshot-demo/maskrcnn_benchmark/utils/dist.py +++ /dev/null @@ -1,228 +0,0 @@ -# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -""" -Utilities related to distributed mode. - -By default, the reduce of metrics and such are done on GPU, since it's more straightforward (we reuse the NCCL backend) -If you want to reduce on CPU instead (required for big datasets like GQA), use the env variable MDETR_CPU_REDUCE=1 -""" -import functools -import io -import os - -import torch -import torch.distributed as dist - -_LOCAL_PROCESS_GROUP = None - - -@functools.lru_cache() -def _get_global_gloo_group(): - """ - Return a process group based on gloo backend, containing all the ranks - The result is cached. - """ - - if dist.get_backend() == "nccl": - return dist.new_group(backend="gloo") - - return dist.group.WORLD - - -def all_gather(data): - """ - Run all_gather on arbitrary picklable data (not necessarily tensors) - Args: - data: any picklable object - Returns: - list[data]: list of data gathered from each rank - """ - - world_size = get_world_size() - if world_size == 1: - return [data] - - cpu_group = None - if os.getenv("MDETR_CPU_REDUCE") == "1": - cpu_group = _get_global_gloo_group() - - buffer = io.BytesIO() - torch.save(data, buffer) - data_view = buffer.getbuffer() - device = "cuda" if cpu_group is None else "cpu" - tensor = torch.ByteTensor(data_view).to(device) - - # obtain Tensor size of each rank - local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long) - size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)] - if cpu_group is None: - dist.all_gather(size_list, local_size) - else: - print("gathering on cpu") - dist.all_gather(size_list, local_size, group=cpu_group) - size_list = [int(size.item()) for size in size_list] - max_size = max(size_list) - assert isinstance(local_size.item(), int) - local_size = int(local_size.item()) - - # receiving Tensor from all ranks - # we pad the tensor because torch all_gather does not support - # gathering tensors of different shapes - tensor_list = [] - for _ in size_list: - tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device)) - if local_size != max_size: - padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device) - tensor = torch.cat((tensor, padding), dim=0) - if cpu_group is None: - dist.all_gather(tensor_list, tensor) - else: - dist.all_gather(tensor_list, tensor, group=cpu_group) - - data_list = [] - for size, tensor in zip(size_list, tensor_list): - tensor = torch.split(tensor, [size, max_size - size], dim=0)[0] - buffer = io.BytesIO(tensor.cpu().numpy()) - obj = torch.load(buffer) - data_list.append(obj) - - return data_list - - -def reduce_dict(input_dict, average=True): - """ - Args: - input_dict (dict): all the values will be reduced - average (bool): whether to do average or sum - Reduce the values in the dictionary from all processes so that all processes - have the averaged results. Returns a dict with the same fields as - input_dict, after reduction. - """ - world_size = get_world_size() - if world_size < 2: - return input_dict - with torch.no_grad(): - names = [] - values = [] - # sort the keys so that they are consistent across processes - for k in sorted(input_dict.keys()): - names.append(k) - values.append(input_dict[k]) - values = torch.stack(values, dim=0) - dist.all_reduce(values) - if average: - values /= world_size - reduced_dict = {k: v for k, v in zip(names, values)} - return reduced_dict - - -def setup_for_distributed(is_master): - """ - This function disables printing when not in master process - """ - import builtins as __builtin__ - - builtin_print = __builtin__.print - - def print(*args, **kwargs): - force = kwargs.pop("force", False) - if is_master or force: - builtin_print(*args, **kwargs) - - __builtin__.print = print - - -def is_dist_avail_and_initialized(): - """ - Returns: - True if distributed training is enabled - """ - if not dist.is_available(): - return False - if not dist.is_initialized(): - return False - return True - - -def get_world_size(): - """ - Returns: - The number of processes in the process group - """ - if not is_dist_avail_and_initialized(): - return 1 - return dist.get_world_size() - - -def get_rank(): - """ - Returns: - The rank of the current process within the global process group. - """ - if not is_dist_avail_and_initialized(): - return 0 - return dist.get_rank() - - -def get_local_rank() -> int: - """ - Returns: - The rank of the current process within the local (per-machine) process group. - """ - if not dist.is_available(): - return 0 - if not dist.is_initialized(): - return 0 - assert _LOCAL_PROCESS_GROUP is not None - return dist.get_rank(group=_LOCAL_PROCESS_GROUP) - - -def get_local_size() -> int: - """ - Returns: - The size of the per-machine process group, - i.e. the number of processes per machine. - """ - if not dist.is_available(): - return 1 - if not dist.is_initialized(): - return 1 - return dist.get_world_size(group=_LOCAL_PROCESS_GROUP) - - -def is_main_process(): - """Return true if the current process is the main one""" - return get_rank() == 0 - - -def save_on_master(*args, **kwargs): - """Utility function to save only from the main process""" - if is_main_process(): - torch.save(*args, **kwargs) - - -def init_distributed_mode(args): - """Initialize distributed training, if appropriate""" - if "RANK" in os.environ and "WORLD_SIZE" in os.environ: - args.rank = int(os.environ["RANK"]) - args.world_size = int(os.environ["WORLD_SIZE"]) - args.gpu = int(os.environ["LOCAL_RANK"]) - elif "SLURM_PROCID" in os.environ: - args.rank = int(os.environ["SLURM_PROCID"]) - args.gpu = args.rank % torch.cuda.device_count() - else: - print("Not using distributed mode") - args.distributed = False - return - - args.distributed = True - - torch.cuda.set_device(args.gpu) - args.dist_backend = "nccl" - print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True) - - dist.init_process_group( - backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank - ) - dist.barrier() - setup_for_distributed(args.rank == 0) diff --git a/spaces/hareshhecker/midjourney-v5/README.md b/spaces/hareshhecker/midjourney-v5/README.md deleted file mode 100644 index 5f1202e4c961fbc147a8f3ff74723a8fb41bc71d..0000000000000000000000000000000000000000 --- a/spaces/hareshhecker/midjourney-v5/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Midjourney V5 -emoji: 📚 -colorFrom: yellow -colorTo: blue -sdk: gradio -sdk_version: 3.14.0 -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/hf4all/web-ui/_next/static/chunks/bce60fc1-b7a26e6d69cbd1bd.js b/spaces/hf4all/web-ui/_next/static/chunks/bce60fc1-b7a26e6d69cbd1bd.js deleted file mode 100644 index 040c0754dc12ba03071bed6c3d0514581a9412af..0000000000000000000000000000000000000000 --- a/spaces/hf4all/web-ui/_next/static/chunks/bce60fc1-b7a26e6d69cbd1bd.js +++ /dev/null @@ -1,9 +0,0 @@ -"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[253],{42614:function(e,t,n){/** - * @license React - * react-dom.production.min.js - * - * Copyright (c) Meta Platforms, Inc. and affiliates. - * - * This source code is licensed under the MIT license found in the - * LICENSE file in the root directory of this source tree. - */var r,l=n(86006),a=n(26183),o={usingClientEntryPoint:!1,Events:null,Dispatcher:{current:null}};function i(e){for(var t="https://reactjs.org/docs/error-decoder.html?invariant="+e,n=1;n ")&&(s=s.replace(" ",e.displayName)),s}while(1<=i&&0<=u);break}}}finally{ej=!1,Error.prepareStackTrace=n}return(e=e?e.displayName||e.name:"")?eW(e):""}function eq(e){switch(typeof e){case"boolean":case"number":case"string":case"undefined":case"object":return e;default:return""}}function eK(e){var t=e.type;return(e=e.nodeName)&&"input"===e.toLowerCase()&&("checkbox"===t||"radio"===t)}function eY(e){e._valueTracker||(e._valueTracker=function(e){var t=eK(e)?"checked":"value",n=Object.getOwnPropertyDescriptor(e.constructor.prototype,t),r=""+e[t];if(!e.hasOwnProperty(t)&&void 0!==n&&"function"==typeof n.get&&"function"==typeof n.set){var l=n.get,a=n.set;return Object.defineProperty(e,t,{configurable:!0,get:function(){return l.call(this)},set:function(e){r=""+e,a.call(this,e)}}),Object.defineProperty(e,t,{enumerable:n.enumerable}),{getValue:function(){return r},setValue:function(e){r=""+e},stopTracking:function(){e._valueTracker=null,delete e[t]}}}}(e))}function eX(e){if(!e)return!1;var t=e._valueTracker;if(!t)return!0;var n=t.getValue(),r="";return e&&(r=eK(e)?e.checked?"true":"false":e.value),(e=r)!==n&&(t.setValue(e),!0)}function eG(e){if(void 0===(e=e||("undefined"!=typeof document?document:void 0)))return null;try{return e.activeElement||e.body}catch(t){return e.body}}var eZ=/[\n"\\]/g;function eJ(e){return e.replace(eZ,function(e){return"\\"+e.charCodeAt(0).toString(16)+" "})}function e0(e,t,n,r,l,a,o,i){e.name="",null!=o&&"function"!=typeof o&&"symbol"!=typeof o&&"boolean"!=typeof o?e.type=o:e.removeAttribute("type"),null!=t?"number"===o?(0===t&&""===e.value||e.value!=t)&&(e.value=""+eq(t)):e.value!==""+eq(t)&&(e.value=""+eq(t)):"submit"!==o&&"reset"!==o||e.removeAttribute("value"),null!=t?e2(e,o,eq(t)):null!=n?e2(e,o,eq(n)):null!=r&&e.removeAttribute("value"),null==l&&null!=a&&(e.defaultChecked=!!a),null!=l&&!!l!==e.checked&&(e.checked=l),null!=i&&"function"!=typeof i&&"symbol"!=typeof i&&"boolean"!=typeof i?e.name=""+eq(i):e.removeAttribute("name")}function e1(e,t,n,r,l,a,o,i){if(null!=a&&"function"!=typeof a&&"symbol"!=typeof a&&"boolean"!=typeof a&&(e.type=a),null!=t||null!=n){if(!("submit"!==a&&"reset"!==a||null!=t))return;n=null!=n?""+eq(n):"",t=null!=t?""+eq(t):n,i||t===e.value||(e.value=t),e.defaultValue=t}r="function"!=typeof(r=null!=r?r:l)&&"symbol"!=typeof r&&!!r,i||(e.checked=!!r),e.defaultChecked=!!r,null!=o&&"function"!=typeof o&&"symbol"!=typeof o&&"boolean"!=typeof o&&(e.name=o)}function e2(e,t,n){"number"===t&&eG(e.ownerDocument)===e||e.defaultValue===""+n||(e.defaultValue=""+n)}var e3=Array.isArray;function e4(e,t,n,r){if(e=e.options,t){t={};for(var l=0;l - {Array.from({ length: num }).map((_, index) => { - const randomDuration = Math.random() * 100 + duration - const initialDelay = Math.random() * 2 * duration - const initialScale = Math.sin((index + 1) * Math.PI / num) - return ( - - ) - })} -- ) -} diff --git a/spaces/igolas0/fastai_sportsman/README.md b/spaces/igolas0/fastai_sportsman/README.md deleted file mode 100644 index 8046f1b6d07827c5f693bdebdc49d0eb12f2a32c..0000000000000000000000000000000000000000 --- a/spaces/igolas0/fastai_sportsman/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Fastai Sportsman -emoji: 🏆 -colorFrom: green -colorTo: red -sdk: gradio -sdk_version: 3.47.1 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Download Gns3 Ios Image C3700 193 [PORTABLE].md b/spaces/inplisQlawa/anything-midjourney-v4-1/Download Gns3 Ios Image C3700 193 [PORTABLE].md deleted file mode 100644 index 4a8affc7cd3fbe2fa36427307ed5e8e6c5382f90..0000000000000000000000000000000000000000 --- a/spaces/inplisQlawa/anything-midjourney-v4-1/Download Gns3 Ios Image C3700 193 [PORTABLE].md +++ /dev/null @@ -1,103 +0,0 @@ -
-How to Download GNS3 IOS Image c3700 193 for Your Network Simulations
- -If you are studying for Cisco exams or want to practice Cisco commands, you may need to use a network simulator program such as GNS3. GNS3 is a powerful tool that allows you to create complex network topologies and run real Cisco IOS images on them. However, you may wonder where to get the IOS images and how to add them to GNS3.
-download gns3 ios image c3700 193
Download ››› https://urlin.us/2uEx4Q
- -In this article, we will explain how to download GNS3 IOS image c3700 193, which is one of the supported images for Dynamips emulator, and how to use it with GNS3.
- -What is GNS3 IOS Image c3700 193?
- -GNS3 IOS image c3700 193 is a file that contains the operating system of a Cisco router. It is also known as Cisco IOS software or simply IOS. The c3700 series refers to the Cisco 3700 series routers, which are modular and support a wide range of network modules. The 193 suffix indicates the feature set and the version of the IOS image.
- -The GNS3 IOS image c3700 193 belongs to the advanced enterprise services feature set, which includes many advanced features such as IP routing protocols, MPLS, voice, security, VPN, QoS, and more. The version of the image is 12.4(25d), which means it was released in December 2009.
- - -The GNS3 IOS image c3700 193 can be used with Dynamips emulator, which is a program that can run Cisco IOS images on a PC. Dynamips can emulate different models of Cisco routers, such as 1700, 2600, 3600, 3700, and 7200 series. By using Dynamips with GNS3, you can create realistic network scenarios and practice Cisco commands without having a physical router.
- -How to Download GNS3 IOS Image c3700 193?
- -There are several ways to get the GNS3 IOS image c3700 193 for your network simulations. Here are some of them:
- --
-
- Cisco website: If you have a valid service contract with Cisco, you can download the IOS images directly from their website: https://software.cisco.com/download/navigator.html. You need to log in with your Cisco account and select the router model and the IOS image you want to download. -
- VIRL: If you don't have a service contract with Cisco, you can purchase a VIRL license to get access to multiple IOS images that are useful for GNS3 labs. VIRL is a network simulation platform that allows you to create virtual networks using Cisco devices. The price starts at $199.99 per year for the Personal Edition: http://virl.cisco.com/. You can use VIRL images without license authentication and integrate them with GNS3. -
- Physical Cisco devices: If you own a physical router that runs the same IOS image as the one you want to use with GNS3, you can copy the image from the router and import it into GNS3. You need to connect your router to your PC via a console cable or a USB-to-serial adapter and use a terminal program such as PuTTY or Tera Term to access the router's CLI. Then, you need to copy the IOS image from the router's flash memory to your PC using TFTP or FTP protocols. -
How to Use GNS3 IOS Image c3700 193 with GNS3?
- -Once you have downloaded the GNS3 IOS image c3700 193, you need to add it to GNS3 and configure it properly. Here are the steps:
- --
-
- Open GNS3 and go to Edit > Preferences > Dynamips > IOS Routers. -
- Click on New and select New Image in the wizard. -
- Browse to the location where you saved the IOS image file and select it. -
- GNS3 will automatically detect the platform and name of the image. You can change them if you want. -
- Click on Next and select the amount of RAM you want to allocate for the router. The default value is 128 MB, which is enough for most scenarios. -
- Click on Next and select an Idle-PC value for the router. This value is used by Dynamips to optimize the CPU usage of the emulator. You can use the suggested value or click on Idle-PC finder to find a better one. -
- Click on Next and select any additional settings you want for the router, such as slots, WICs, port adapters, etc. -
- Click on Finish and then on OK to save your settings. -
- You can now drag and drop the router from the Devices toolbar to the Workspace and start using it. -
Conclusion
- -In this article, we have explained how to download GNS3 IOS image c3700 193 and how to use it with GNS3 for your network simulations. We hope this article was helpful for you and you learned something new. If you have any questions or feedback, feel free to leave a comment below.
-Benefits of Using GNS3 IOS Image c3700 193
- -Using GNS3 IOS image c3700 193 has many benefits for network professionals and students. Here are some of them:
- --
-
- You can practice Cisco commands and configurations without having a physical router. This can save you money, time, and space. -
- You can create complex network topologies and scenarios that are difficult or impossible to replicate with physical devices. This can help you prepare for Cisco exams and certifications. -
- You can test new features and technologies that are supported by the IOS image. This can help you learn new skills and stay updated with the latest trends. -
- You can troubleshoot and debug network issues in a safe and controlled environment. This can help you improve your problem-solving skills and avoid affecting production networks. -
Limitations of Using GNS3 IOS Image c3700 193
- -While using GNS3 IOS image c3700 193 has many advantages, it also has some limitations that you should be aware of. Here are some of them:
- --
-
- You need to have a powerful PC to run GNS3 and Dynamips emulator. The more routers you add to your topology, the more CPU and RAM resources you will need. -
- You need to have enough disk space to store the IOS image files. The c3700 193 image file is about 35 MB in size, but some other images can be larger than 100 MB. -
- You need to have a reliable internet connection to download the IOS image files from Cisco or VIRL websites. You may also need a service contract or a VIRL license to access them. -
- You may encounter some compatibility issues or bugs when using the IOS image with Dynamips emulator. Some features or commands may not work as expected or cause errors. -
Conclusion
- -In this article, we have explained how to download GNS3 IOS image c3700 193 and how to use it with GNS3 for your network simulations. We have also discussed the benefits and limitations of using this image with GNS3. We hope this article was helpful for you and you learned something new. If you have any questions or feedback, feel free to leave a comment below.
- - -- Add some images to illustrate the steps of downloading and using the IOS image with GNS3. This can make the article more appealing and informative for the readers. -- Check the article for grammar and spelling errors. You can use a tool such as Grammarly or Hemingway to help you with this task. This can make the article more professional and readable. -- Optimize the article for SEO by using the keyword "download gns3 ios image c3700 193" in a natural and relevant way throughout the article. You can also use some related keywords or synonyms to avoid keyword stuffing. You can use a tool such as Yoast or Moz to help you with this task. This can help the article rank higher on search engines and attract more traffic. - - -- Add some images to illustrate the steps of downloading and using the IOS image with GNS3. This can make the article more appealing and informative for the readers. -- Check the article for grammar and spelling errors. You can use a tool such as Grammarly or Hemingway to help you with this task. This can make the article more professional and readable. -- Optimize the article for SEO by using the keyword "download gns3 ios image c3700 193" in a natural and relevant way throughout the article. You can also use some related keywords or synonyms to avoid keyword stuffing. You can use a tool such as Yoast or Moz to help you with this task. This can help the article rank higher on search engines and attract more traffic. - - -- Add some images to illustrate the steps of downloading and using the IOS image with GNS3. This can make the article more appealing and informative for the readers. -- Check the article for grammar and spelling errors. You can use a tool such as Grammarly or Hemingway to help you with this task. This can make the article more professional and readable. -- Optimize the article for SEO by using the keyword "download gns3 ios image c3700 193" in a natural and relevant way throughout the article. You can also use some related keywords or synonyms to avoid keyword stuffing. You can use a tool such as Yoast or Moz to help you with this task. This can help the article rank higher on search engines and attract more traffic. - - -- Add some images to illustrate the steps of downloading and using the IOS image with GNS3. This can make the article more appealing and informative for the readers. -- Check the article for grammar and spelling errors. You can use a tool such as Grammarly or Hemingway to help you with this task. This can make the article more professional and readable. -- Optimize the article for SEO by using the keyword "download gns3 ios image c3700 193" in a natural and relevant way throughout the article. You can also use some related keywords or synonyms to avoid keyword stuffing. You can use a tool such as Yoast or Moz to help you with this task. This can help the article rank higher on search engines and attract more traffic. - - -- Add some images to illustrate the steps of downloading and using the IOS image with GNS3. This can make the article more appealing and informative for the readers. -- Check the article for grammar and spelling errors. You can use a tool such as Grammarly or Hemingway to help you with this task. This can make the article more professional and readable. -- Optimize the article for SEO by using the keyword "download gns3 ios image c3700 193" in a natural and relevant way throughout the article. You can also use some related keywords or synonyms to avoid keyword stuffing. You can use a tool such as Yoast or Moz to help you with this task. This can help the article rank higher on search engines and attract more traffic. -Conclusion
- -In this article, we have explained how to download GNS3 IOS image c3700 193 and how to use it with GNS3 for your network simulations. We have also discussed the benefits and limitations of using this image with GNS3. We hope this article was helpful for you and you learned something new. If you have any questions or feedback, feel free to leave a comment below.
- -If you want to learn more about GNS3 and Cisco IOS images, you can check out our other articles on this topic. You can also subscribe to our newsletter to get the latest updates and tips on network simulation and emulation. Thank you for reading and happy learning!
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\ No newline at end of file diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Ligandscout 3 0 Keygen __LINK__ Software.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Ligandscout 3 0 Keygen __LINK__ Software.md deleted file mode 100644 index 72c7efa183b0eda4d5b105f221398caba02d7fe9..0000000000000000000000000000000000000000 --- a/spaces/inplisQlawa/anything-midjourney-v4-1/Ligandscout 3 0 Keygen __LINK__ Software.md +++ /dev/null @@ -1,34 +0,0 @@ - -LigandScout 3.0: A Powerful Tool for Pharmacophore Modeling and Virtual Screening
-LigandScout is a software that allows you to create and use 3D chemical feature pharmacophore models for accurate virtual screening of large compound databases. It is a fully integrated platform that offers seamless workflows, starting from ligand- and structure-based pharmacophore modeling, and includes novel high-performance alignment algorithms for excellent prediction quality with unprecedented screening speed.
-ligandscout 3 0 keygen software
DOWNLOAD ✪ https://urlin.us/2uEwcX
-In this article, we will show you how to download, install, and activate LigandScout 3.0 using a keygen software. A keygen software is a program that generates valid license keys for software products. By using a keygen software, you can bypass the need to purchase a license and enjoy the full features of LigandScout 3.0 for free.
-How to Download LigandScout 3.0
-The first step is to download LigandScout 3.0 from the official website of InteLigand GmbH, the developer of the software. You can find the download link here[^1^]. The file size is about 180.8 MB and it works fine with 32-bit versions of Windows XP/7/8/10/11.
-After downloading the file, you need to extract it using a program like WinRAR or 7-Zip. You will find two folders inside: one named "LigandScout" and another named "Keygen". Open the "LigandScout" folder and run the setup.exe file to start the installation process.
-How to Install LigandScout 3.0
-The installation process is straightforward and takes only a few minutes. Follow these steps:
--
-
- Accept the license agreement and click Next. -
- Choose the destination folder where you want to install LigandScout 3.0 and click Next. -
- Select the components you want to install and click Next. -
- Click Install to begin the installation. -
- Wait for the installation to finish and click Finish. -
Congratulations! You have successfully installed LigandScout 3.0 on your computer. Now you need to activate it using a keygen software.
-How to Activate LigandScout 3.0 Using a Keygen Software
-A keygen software is a program that generates valid license keys for software products. By using a keygen software, you can bypass the need to purchase a license and enjoy the full features of LigandScout 3.0 for free.
- -To activate LigandScout 3.0 using a keygen software, follow these steps:
--
-
- Open the "Keygen" folder that you extracted earlier and run the keygen.exe file. -
- In the keygen window, click on Generate to create a random license key. -
- Copy the license key and paste it into a text file for later use. -
- Open LigandScout 3.0 from your desktop or start menu. -
- Go to Help > License Manager > Activate License. -
- Paste the license key that you copied earlier into the License Key field and click OK. -
- A message will appear saying that your license has been activated successfully. -
Congratulations! You have successfully activated LigandScout 3.0 using a keygen software. Now you can enjoy the full features of this powerful tool for pharmacophore modeling and virtual screening.
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\ No newline at end of file diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Media Player For_The Platform.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Media Player For_The Platform.md deleted file mode 100644 index fdc0e5d1de9048b6f251232d4110dc565e5e5167..0000000000000000000000000000000000000000 --- a/spaces/inplisQlawa/anything-midjourney-v4-1/Media Player For_The Platform.md +++ /dev/null @@ -1,27 +0,0 @@ -
-How to Choose the Best Media Player For_The Platform
-If you are looking for a media player that can handle all your audio and video needs on your device, you might be overwhelmed by the number of options available. There are many factors to consider when choosing a media player, such as compatibility, features, performance, and user interface. In this article, we will compare some of the most popular media players for_the platform and help you find the best one for your needs.
-Media Player For_The Platform
Download Zip ✶✶✶ https://urlin.us/2uExDG
-VLC Media Player
-VLC Media Player is one of the most versatile and powerful media players out there. It can play almost any format of audio and video files, as well as DVDs, CDs, and streaming content. It also supports subtitles, playlists, audio filters, video effects, and more. VLC Media Player is free and open-source, which means you can customize it to your liking and contribute to its development. It is available for Windows, Mac OS X, Linux, Android, iOS, and other platforms.
-KMPlayer
-KMPlayer is another popular media player that can handle a wide range of formats and codecs. It has a sleek and intuitive user interface that allows you to easily access all the features and settings. It also supports 3D, 4K, UHD, and VR videos, as well as online streaming services. KMPlayer is free to download and use, but it may display some ads during playback. It is compatible with Windows, Mac OS X, Android, and iOS.
-PotPlayer
-PotPlayer is a lightweight and fast media player that offers high-quality playback and smooth performance. It can play various types of audio and video files, including Blu-ray and DVD discs. It also supports hardware acceleration, screen capture, video editing, subtitles, skins, and more. PotPlayer is free to use but it is only available for Windows.
-GOM Player
-GOM Player is a user-friendly and customizable media player that can play most common formats of audio and video files. It also has a built-in codec finder that can automatically search and download the missing codecs for your files. It also supports 360-degree video playback, VR headset support, online streaming services, subtitles, screen capture, and more. GOM Player is free to download and use but it may show some ads during playback. It works on Windows, Mac OS X, Android, and iOS.
- -Conclusion
-Choosing the best media player for_the platform depends on your personal preferences and needs. You should consider the compatibility, features, performance, and user interface of each media player before making a decision. You can also try out different media players and see which one suits you best. We hope this article has helped you narrow down your options and find the best media player for_the platform.
- -How to Install and Use Media Players
-Once you have chosen the best media player for_the platform, you need to install it on your device and start using it. Here are some general steps to follow:
--
-
- Download the media player from its official website or app store. Make sure you download the correct version for your platform and device. -
- Run the installer and follow the instructions on the screen. You may need to accept some terms and conditions, choose a destination folder, and customize some settings. -
- Launch the media player and start playing your audio and video files. You can either drag and drop your files to the media player window or use the open file option from the menu. -
- Adjust the playback controls, volume, subtitles, and other options as you wish. You can also access more features and settings from the menu or toolbar. -
If you encounter any problems or errors while using the media player, you can check the help section or contact the support team of the media player. You can also update the media player regularly to get the latest features and bug fixes.
d5da3c52bf
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\ No newline at end of file diff --git a/spaces/inreVtussa/clothingai/Examples/Adobe Photoshop CC 14.2.1 Multilingual (x64-x86) FREE.md b/spaces/inreVtussa/clothingai/Examples/Adobe Photoshop CC 14.2.1 Multilingual (x64-x86) FREE.md deleted file mode 100644 index 5ebccd22744b1373d2a130d79d26153b8e9c78ee..0000000000000000000000000000000000000000 --- a/spaces/inreVtussa/clothingai/Examples/Adobe Photoshop CC 14.2.1 Multilingual (x64-x86) FREE.md +++ /dev/null @@ -1,6 +0,0 @@ -Adobe Photoshop CC 14.2.1 Multilingual (x64-x86)
Download File ✔ https://tiurll.com/2uClGh
- -Description: Adobe Photoshop CC 14.2.1 Multilingual (x86 x64) Adobe Photoshop CC 14.2.1 Multilingual (Windows 32 and 64 bit)| 1.73 Gb 4d29de3e1b
-
-
- diff --git a/spaces/jannisborn/paccmann/smiles.py b/spaces/jannisborn/paccmann/smiles.py deleted file mode 100644 index 1a5987e3e4f9b46fb40245a535952dccddf56383..0000000000000000000000000000000000000000 --- a/spaces/jannisborn/paccmann/smiles.py +++ /dev/null @@ -1,211 +0,0 @@ -"""SMILES utilities.""" -import os -import regex -import logging -import matplotlib -import matplotlib as mpl -import matplotlib.cm as cm -import matplotlib.pyplot as plt -from io import StringIO -from operator import itemgetter -from typing import Callable, Iterable, Tuple -from matplotlib.ticker import FormatStrFormatter, ScalarFormatter -from rdkit import Chem -from rdkit.Chem.Draw import rdMolDraw2D -from configuration import SMILES_LANGUAGE, SMILES_TOKENIZE_FN - -logger = logging.getLogger(__name__) -# NOTE: avoid segfaults in matplotlib -matplotlib.use("Agg") - -MOLECULE_TOKENS = set(SMILES_LANGUAGE.token_to_index.keys()) -NON_ATOM_REGEX = regex.compile(r"^(\d|\%\d+|\p{P}+|\p{Math}+)$") -NON_ATOM_TOKENS = set( - [token for token in MOLECULE_TOKENS if NON_ATOM_REGEX.match(token)] -) -CMAP = cm.Oranges -COLOR_NORMALIZERS = {"linear": mpl.colors.Normalize, "logarithmic": mpl.colors.LogNorm} -ATOM_RADII = float(os.environ.get("PACCMANN_ATOM_RADII", 0.5)) -SVG_WIDTH = int(os.environ.get("PACCMANN_SVG_WIDTH", 400)) -SVG_HEIGHT = int(os.environ.get("PACCMANN_SVG_HEIGHT", 200)) -COLOR_NORMALIZATION = os.environ.get("PACCMANN_COLOR_NORMALIZATION", "logarithmic") - - -def validate_smiles(smiles: str) -> bool: - """ - Validate a SMILES. - - Args: - smiles (str): a SMILES string. - - Returns: - bool: flag indicating whether the SMILES is a valid molecule. - """ - molecule = Chem.MolFromSmiles(smiles) - return not (molecule is None) - - -def canonicalize_smiles(smiles: str) -> str: - """ - Canonicalize a SMILES. - - Args: - smiles (str): a SMILES string. - - Returns: - str: the canonicalized SMILES. - """ - molecule = Chem.MolFromSmiles(smiles) - return Chem.MolToSmiles(molecule) - - -def remove_housekeeping_from_tokens_and_smiles_attention( - tokens: Iterable[str], smiles_attention: Iterable[float] -) -> Tuple[Iterable[str], Iterable[float]]: - """ - Remove housekeeping tokens and corresponding attention weights. - - Args: - tokens (Iterable[str]): tokens obtained from the SMILES. - smiles_attention (Iterable[float]): SMILES attention. - - Returns: - Tuple[Iterable[str], Iterable[float]]: a tuple containing the filtered - tokens and attention values. - """ - to_keep = [index for index, token in enumerate(tokens) if token in MOLECULE_TOKENS] - return ( - list(itemgetter(*to_keep)(tokens)), - list(itemgetter(*to_keep)(smiles_attention)), - ) - - -def _get_index_and_colors( - values: Iterable[float], - tokens: Iterable[str], - predicate: Callable[[tuple], bool], - color_mapper: cm.ScalarMappable, -) -> Tuple[Iterable[int], Iterable[tuple]]: - """ - Get index and RGB colors from a color map using a rule. - - Args: - values (Iterable[float]): values associated to tokens. - tokens (Iterable[str]): tokens. - predicate (Callable[[tuple], bool]): a predicate that acts on a tuple - of (value, object). - color_mapper (cm.ScalarMappable): a color mapper. - - Returns: - Tuple[Iterable[int], Iterable[tuple]]: tuple with indexes and RGB - colors associated to the given index. - """ - indices = [] - colors = {} - for index, value in enumerate( - map(lambda t: t[0], filter(lambda t: predicate(t), zip(values, tokens))) - ): - indices.append(index) - colors[index] = color_mapper.to_rgba(value) - return indices, colors - - -def smiles_attention_to_svg( - smiles: str, smiles_attention: Iterable[float] -) -> Tuple[str, str]: - """ - Generate an svg of the molecule highlighiting SMILES attention. - - Args: - smiles (str): SMILES representing a molecule. - smiles_attention (Iterable[float]): SMILES attention. - - Returns: - Tuple[str, str]: drawing, colorbar - the svg of the molecule highlighiting SMILES attention - and the svg displaying the colorbar - """ - # remove padding - logger.debug("SMILES attention:\n{}.".format(smiles_attention)) - logger.debug( - "SMILES attention range: [{},{}].".format( - min(smiles_attention), max(smiles_attention) - ) - ) - # get the molecule - molecule = Chem.MolFromSmiles(smiles) - tokens = [ - SMILES_LANGUAGE.index_to_token[token_index] - for token_index in SMILES_TOKENIZE_FN(smiles) - ] - logger.debug("SMILES tokens:{}.".format(tokens)) - tokens, smiles_attention = remove_housekeeping_from_tokens_and_smiles_attention( - tokens, smiles_attention - ) # yapf:disable - logger.debug( - "tokens and SMILES attention after removal:\n{}\n{}.".format( - tokens, smiles_attention - ) - ) - logger.debug( - "SMILES attention range after padding removal: [{},{}].".format( - min(smiles_attention), max(smiles_attention) - ) - ) - # define a color map - normalize = COLOR_NORMALIZERS.get(COLOR_NORMALIZATION, mpl.colors.LogNorm)( - vmin=min(smiles_attention), vmax=min(1.0, 2 * max(smiles_attention)) - ) - color_mapper = cm.ScalarMappable(norm=normalize, cmap=CMAP) - # get atom colors - highlight_atoms, highlight_atom_colors = _get_index_and_colors( - smiles_attention, tokens, lambda t: t[1] not in NON_ATOM_TOKENS, color_mapper - ) - logger.debug("atom colors:\n{}.".format(highlight_atom_colors)) - # get bond colors - highlight_bonds, highlight_bond_colors = _get_index_and_colors( - smiles_attention, tokens, lambda t: t[1] in NON_ATOM_TOKENS, color_mapper - ) - logger.debug("bond colors:\n{}.".format(highlight_bond_colors)) - # add coordinates - logger.debug("compute 2D coordinates") - Chem.rdDepictor.Compute2DCoords(molecule) - # draw the molecule - logger.debug("get a drawer") - drawer = rdMolDraw2D.MolDraw2DSVG(SVG_WIDTH, SVG_HEIGHT) - logger.debug("draw the molecule") - drawer.DrawMolecule( - molecule, - highlightAtoms=highlight_atoms, - highlightAtomColors=highlight_atom_colors, - highlightBonds=highlight_bonds, - highlightBondColors=highlight_bond_colors, - highlightAtomRadii={index: ATOM_RADII for index in highlight_atoms}, - ) - logger.debug("finish drawing") - drawer.FinishDrawing() - # the drawn molecule as str - logger.debug("drawing to string") - drawing = drawer.GetDrawingText().replace("\n", " ") - # the respective colorbar - logger.debug("draw the colorbar") - fig, ax = plt.subplots(figsize=(0.5, 6)) - mpl.colorbar.ColorbarBase( - ax, - cmap=CMAP, - norm=normalize, - orientation="vertical", - extend="both", - extendrect=True, - ) - # instead of LogFormatterSciNotation - logger.debug("format the colorbar") - ax.yaxis.set_minor_formatter(ScalarFormatter()) - ax.yaxis.set_major_formatter(FormatStrFormatter("%.2f")) # fixes 0.1, 0.20 - # the colorbar svg as str - logger.debug("colorbar to string") - file_like = StringIO() - plt.savefig(file_like, format="svg", bbox_inches="tight") - colorbar = file_like.getvalue().replace("\n", " ") - plt.close(fig) - return drawing, colorbar diff --git a/spaces/jennysun/jwsun-multisubject-render-model/gligen/ldm/models/diffusion/__init__.py b/spaces/jennysun/jwsun-multisubject-render-model/gligen/ldm/models/diffusion/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/jhwen/bingo/src/components/ui/button.tsx b/spaces/jhwen/bingo/src/components/ui/button.tsx deleted file mode 100644 index 281da005124fa94c89a9a9db7605748a92b60865..0000000000000000000000000000000000000000 --- a/spaces/jhwen/bingo/src/components/ui/button.tsx +++ /dev/null @@ -1,57 +0,0 @@ -import * as React from 'react' -import { Slot } from '@radix-ui/react-slot' -import { cva, type VariantProps } from 'class-variance-authority' - -import { cn } from '@/lib/utils' - -const buttonVariants = cva( - 'inline-flex items-center justify-center rounded-md text-sm font-medium shadow ring-offset-background transition-colors outline-none disabled:pointer-events-none disabled:opacity-50', - { - variants: { - variant: { - default: - 'bg-primary text-primary-foreground shadow-md hover:bg-primary/90', - destructive: - 'bg-destructive text-destructive-foreground hover:bg-destructive/90', - outline: - 'border border-input hover:bg-accent hover:text-accent-foreground', - secondary: - 'bg-secondary text-secondary-foreground hover:bg-secondary/80', - ghost: 'shadow-none hover:bg-accent hover:text-accent-foreground', - link: 'text-primary underline-offset-4 shadow-none hover:underline' - }, - size: { - default: 'h-8 px-4 py-2', - sm: 'h-8 rounded-md px-3', - lg: 'h-11 rounded-md px-8', - icon: 'h-8 w-8 p-0' - } - }, - defaultVariants: { - variant: 'default', - size: 'default' - } - } -) - -export interface ButtonProps - extends React.ButtonHTMLAttributes, - VariantProps { - asChild?: boolean -} - -const Button = React.forwardRef - ) - } -) -Button.displayName = 'Button' - -export { Button, buttonVariants } diff --git a/spaces/jiejiejie0420/bingo/next.config.js b/spaces/jiejiejie0420/bingo/next.config.js deleted file mode 100644 index 0e6ccd7fbc91d0459eaaff3e968ce0556789c605..0000000000000000000000000000000000000000 --- a/spaces/jiejiejie0420/bingo/next.config.js +++ /dev/null @@ -1,38 +0,0 @@ -/** @type {import('next').NextConfig} */ -const nextConfig = { - // output: 'export', - // assetPrefix: '.', - webpack: (config, { isServer }) => { - if (!isServer) { - config.resolve = { - ...config.resolve, - fallback: { - 'bufferutil': false, - 'utf-8-validate': false, - http: false, - https: false, - stream: false, - // fixes proxy-agent dependencies - net: false, - dns: false, - tls: false, - assert: false, - // fixes next-i18next dependencies - path: false, - fs: false, - // fixes mapbox dependencies - events: false, - // fixes sentry dependencies - process: false - } - }; - } - config.module.exprContextCritical = false; - - return config; - }, -} - -module.exports = (...args) => { - return nextConfig -} diff --git a/spaces/jimschat/VITS-Umamusume-voice-synthesizer/README.md b/spaces/jimschat/VITS-Umamusume-voice-synthesizer/README.md deleted file mode 100644 index 1b24e6efdb04cb1460e4fe3257d2303677c5a0e1..0000000000000000000000000000000000000000 --- a/spaces/jimschat/VITS-Umamusume-voice-synthesizer/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Multilingual Anime TTS -emoji: 🎙🐴 -colorFrom: green -colorTo: gray -sdk: gradio -sdk_version: 3.7 -app_file: app.py -pinned: false -duplicated_from: Plachta/VITS-Umamusume-voice-synthesizer ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/jinhybr/OCR-LayoutLM-v3-Document-Parser/README.md b/spaces/jinhybr/OCR-LayoutLM-v3-Document-Parser/README.md deleted file mode 100644 index 0c94a69107f6fbc1730a2902a220d121d4a02a3d..0000000000000000000000000000000000000000 --- a/spaces/jinhybr/OCR-LayoutLM-v3-Document-Parser/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: OCR LayoutLM V3 Document Parser -emoji: 🐢 -colorFrom: gray -colorTo: blue -sdk: gradio -sdk_version: 3.9 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/jjzha/skill_extraction_demo/README.md b/spaces/jjzha/skill_extraction_demo/README.md deleted file mode 100644 index 01afda810d19ebcaa86a1afbb84d4eeac29a1200..0000000000000000000000000000000000000000 --- a/spaces/jjzha/skill_extraction_demo/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Skill Extraction -emoji: 🏃 -colorFrom: yellow -colorTo: blue -sdk: gradio -sdk_version: 3.24.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/joaogabriellima/Real-Time-Voice-Cloning/synthesizer/preprocess.py b/spaces/joaogabriellima/Real-Time-Voice-Cloning/synthesizer/preprocess.py deleted file mode 100644 index cde325c4163d6800404de214202d773addfff296..0000000000000000000000000000000000000000 --- a/spaces/joaogabriellima/Real-Time-Voice-Cloning/synthesizer/preprocess.py +++ /dev/null @@ -1,259 +0,0 @@ -from multiprocessing.pool import Pool -from synthesizer import audio -from functools import partial -from itertools import chain -from encoder import inference as encoder -from pathlib import Path -from utils import logmmse -from tqdm import tqdm -import numpy as np -import librosa - - -def preprocess_dataset(datasets_root: Path, out_dir: Path, n_processes: int, - skip_existing: bool, hparams, no_alignments: bool, - datasets_name: str, subfolders: str): - # Gather the input directories - dataset_root = datasets_root.joinpath(datasets_name) - input_dirs = [dataset_root.joinpath(subfolder.strip()) for subfolder in subfolders.split(",")] - print("\n ".join(map(str, ["Using data from:"] + input_dirs))) - assert all(input_dir.exists() for input_dir in input_dirs) - - # Create the output directories for each output file type - out_dir.joinpath("mels").mkdir(exist_ok=True) - out_dir.joinpath("audio").mkdir(exist_ok=True) - - # Create a metadata file - metadata_fpath = out_dir.joinpath("train.txt") - metadata_file = metadata_fpath.open("a" if skip_existing else "w", encoding="utf-8") - - # Preprocess the dataset - speaker_dirs = list(chain.from_iterable(input_dir.glob("*") for input_dir in input_dirs)) - func = partial(preprocess_speaker, out_dir=out_dir, skip_existing=skip_existing, - hparams=hparams, no_alignments=no_alignments) - job = Pool(n_processes).imap(func, speaker_dirs) - for speaker_metadata in tqdm(job, datasets_name, len(speaker_dirs), unit="speakers"): - for metadatum in speaker_metadata: - metadata_file.write("|".join(str(x) for x in metadatum) + "\n") - metadata_file.close() - - # Verify the contents of the metadata file - with metadata_fpath.open("r", encoding="utf-8") as metadata_file: - metadata = [line.split("|") for line in metadata_file] - mel_frames = sum([int(m[4]) for m in metadata]) - timesteps = sum([int(m[3]) for m in metadata]) - sample_rate = hparams.sample_rate - hours = (timesteps / sample_rate) / 3600 - print("The dataset consists of %d utterances, %d mel frames, %d audio timesteps (%.2f hours)." % - (len(metadata), mel_frames, timesteps, hours)) - print("Max input length (text chars): %d" % max(len(m[5]) for m in metadata)) - print("Max mel frames length: %d" % max(int(m[4]) for m in metadata)) - print("Max audio timesteps length: %d" % max(int(m[3]) for m in metadata)) - - -def preprocess_speaker(speaker_dir, out_dir: Path, skip_existing: bool, hparams, no_alignments: bool): - metadata = [] - for book_dir in speaker_dir.glob("*"): - if no_alignments: - # Gather the utterance audios and texts - # LibriTTS uses .wav but we will include extensions for compatibility with other datasets - extensions = ["*.wav", "*.flac", "*.mp3"] - for extension in extensions: - wav_fpaths = book_dir.glob(extension) - - for wav_fpath in wav_fpaths: - # Load the audio waveform - wav, _ = librosa.load(str(wav_fpath), hparams.sample_rate) - if hparams.rescale: - wav = wav / np.abs(wav).max() * hparams.rescaling_max - - # Get the corresponding text - # Check for .txt (for compatibility with other datasets) - text_fpath = wav_fpath.with_suffix(".txt") - if not text_fpath.exists(): - # Check for .normalized.txt (LibriTTS) - text_fpath = wav_fpath.with_suffix(".normalized.txt") - assert text_fpath.exists() - with text_fpath.open("r") as text_file: - text = "".join([line for line in text_file]) - text = text.replace("\"", "") - text = text.strip() - - # Process the utterance - metadata.append(process_utterance(wav, text, out_dir, str(wav_fpath.with_suffix("").name), - skip_existing, hparams)) - else: - # Process alignment file (LibriSpeech support) - # Gather the utterance audios and texts - try: - alignments_fpath = next(book_dir.glob("*.alignment.txt")) - with alignments_fpath.open("r") as alignments_file: - alignments = [line.rstrip().split(" ") for line in alignments_file] - except StopIteration: - # A few alignment files will be missing - continue - - # Iterate over each entry in the alignments file - for wav_fname, words, end_times in alignments: - wav_fpath = book_dir.joinpath(wav_fname + ".flac") - assert wav_fpath.exists() - words = words.replace("\"", "").split(",") - end_times = list(map(float, end_times.replace("\"", "").split(","))) - - # Process each sub-utterance - wavs, texts = split_on_silences(wav_fpath, words, end_times, hparams) - for i, (wav, text) in enumerate(zip(wavs, texts)): - sub_basename = "%s_%02d" % (wav_fname, i) - metadata.append(process_utterance(wav, text, out_dir, sub_basename, - skip_existing, hparams)) - - return [m for m in metadata if m is not None] - - -def split_on_silences(wav_fpath, words, end_times, hparams): - # Load the audio waveform - wav, _ = librosa.load(str(wav_fpath), hparams.sample_rate) - if hparams.rescale: - wav = wav / np.abs(wav).max() * hparams.rescaling_max - - words = np.array(words) - start_times = np.array([0.0] + end_times[:-1]) - end_times = np.array(end_times) - assert len(words) == len(end_times) == len(start_times) - assert words[0] == "" and words[-1] == "" - - # Find pauses that are too long - mask = (words == "") & (end_times - start_times >= hparams.silence_min_duration_split) - mask[0] = mask[-1] = True - breaks = np.where(mask)[0] - - # Profile the noise from the silences and perform noise reduction on the waveform - silence_times = [[start_times[i], end_times[i]] for i in breaks] - silence_times = (np.array(silence_times) * hparams.sample_rate).astype(np.int) - noisy_wav = np.concatenate([wav[stime[0]:stime[1]] for stime in silence_times]) - if len(noisy_wav) > hparams.sample_rate * 0.02: - profile = logmmse.profile_noise(noisy_wav, hparams.sample_rate) - wav = logmmse.denoise(wav, profile, eta=0) - - # Re-attach segments that are too short - segments = list(zip(breaks[:-1], breaks[1:])) - segment_durations = [start_times[end] - end_times[start] for start, end in segments] - i = 0 - while i < len(segments) and len(segments) > 1: - if segment_durations[i] < hparams.utterance_min_duration: - # See if the segment can be re-attached with the right or the left segment - left_duration = float("inf") if i == 0 else segment_durations[i - 1] - right_duration = float("inf") if i == len(segments) - 1 else segment_durations[i + 1] - joined_duration = segment_durations[i] + min(left_duration, right_duration) - - # Do not re-attach if it causes the joined utterance to be too long - if joined_duration > hparams.hop_size * hparams.max_mel_frames / hparams.sample_rate: - i += 1 - continue - - # Re-attach the segment with the neighbour of shortest duration - j = i - 1 if left_duration <= right_duration else i - segments[j] = (segments[j][0], segments[j + 1][1]) - segment_durations[j] = joined_duration - del segments[j + 1], segment_durations[j + 1] - else: - i += 1 - - # Split the utterance - segment_times = [[end_times[start], start_times[end]] for start, end in segments] - segment_times = (np.array(segment_times) * hparams.sample_rate).astype(np.int) - wavs = [wav[segment_time[0]:segment_time[1]] for segment_time in segment_times] - texts = [" ".join(words[start + 1:end]).replace(" ", " ") for start, end in segments] - - # # DEBUG: play the audio segments (run with -n=1) - # import sounddevice as sd - # if len(wavs) > 1: - # print("This sentence was split in %d segments:" % len(wavs)) - # else: - # print("There are no silences long enough for this sentence to be split:") - # for wav, text in zip(wavs, texts): - # # Pad the waveform with 1 second of silence because sounddevice tends to cut them early - # # when playing them. You shouldn't need to do that in your parsers. - # wav = np.concatenate((wav, [0] * 16000)) - # print("\t%s" % text) - # sd.play(wav, 16000, blocking=True) - # print("") - - return wavs, texts - - -def process_utterance(wav: np.ndarray, text: str, out_dir: Path, basename: str, - skip_existing: bool, hparams): - ## FOR REFERENCE: - # For you not to lose your head if you ever wish to change things here or implement your own - # synthesizer. - # - Both the audios and the mel spectrograms are saved as numpy arrays - # - There is no processing done to the audios that will be saved to disk beyond volume - # normalization (in split_on_silences) - # - However, pre-emphasis is applied to the audios before computing the mel spectrogram. This - # is why we re-apply it on the audio on the side of the vocoder. - # - Librosa pads the waveform before computing the mel spectrogram. Here, the waveform is saved - # without extra padding. This means that you won't have an exact relation between the length - # of the wav and of the mel spectrogram. See the vocoder data loader. - - - # Skip existing utterances if needed - mel_fpath = out_dir.joinpath("mels", "mel-%s.npy" % basename) - wav_fpath = out_dir.joinpath("audio", "audio-%s.npy" % basename) - if skip_existing and mel_fpath.exists() and wav_fpath.exists(): - return None - - # Trim silence - if hparams.trim_silence: - wav = encoder.preprocess_wav(wav, normalize=False, trim_silence=True) - - # Skip utterances that are too short - if len(wav) < hparams.utterance_min_duration * hparams.sample_rate: - return None - - # Compute the mel spectrogram - mel_spectrogram = audio.melspectrogram(wav, hparams).astype(np.float32) - mel_frames = mel_spectrogram.shape[1] - - # Skip utterances that are too long - if mel_frames > hparams.max_mel_frames and hparams.clip_mels_length: - return None - - # Write the spectrogram, embed and audio to disk - np.save(mel_fpath, mel_spectrogram.T, allow_pickle=False) - np.save(wav_fpath, wav, allow_pickle=False) - - # Return a tuple describing this training example - return wav_fpath.name, mel_fpath.name, "embed-%s.npy" % basename, len(wav), mel_frames, text - - -def embed_utterance(fpaths, encoder_model_fpath): - if not encoder.is_loaded(): - encoder.load_model(encoder_model_fpath) - - # Compute the speaker embedding of the utterance - wav_fpath, embed_fpath = fpaths - wav = np.load(wav_fpath) - wav = encoder.preprocess_wav(wav) - embed = encoder.embed_utterance(wav) - np.save(embed_fpath, embed, allow_pickle=False) - - -def create_embeddings(synthesizer_root: Path, encoder_model_fpath: Path, n_processes: int): - wav_dir = synthesizer_root.joinpath("audio") - metadata_fpath = synthesizer_root.joinpath("train.txt") - assert wav_dir.exists() and metadata_fpath.exists() - embed_dir = synthesizer_root.joinpath("embeds") - embed_dir.mkdir(exist_ok=True) - - # Gather the input wave filepath and the target output embed filepath - with metadata_fpath.open("r") as metadata_file: - metadata = [line.split("|") for line in metadata_file] - fpaths = [(wav_dir.joinpath(m[0]), embed_dir.joinpath(m[2])) for m in metadata] - - # TODO: improve on the multiprocessing, it's terrible. Disk I/O is the bottleneck here. - # Embed the utterances in separate threads - func = partial(embed_utterance, encoder_model_fpath=encoder_model_fpath) - job = Pool(n_processes).imap(func, fpaths) - list(tqdm(job, "Embedding", len(fpaths), unit="utterances")) - diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Signature/test_pss.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Signature/test_pss.py deleted file mode 100644 index 535474bec5f00a4b02185c3bdfe4b1e303cdb9fd..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Signature/test_pss.py +++ /dev/null @@ -1,377 +0,0 @@ -# =================================================================== -# -# Copyright (c) 2014, Legrandin ")`). - similarity_cutoff (float): Optional float. If set, will filter out nodes with - similarity below this cutoff threshold when computing the response - streaming (bool): Optional bool. If True, will return a StreamingResponse - object. If False, will return a Response object. - - """ - - def __init__( - self, - index_struct: IS, - # TODO: pass from superclass - llm_predictor: Optional[LLMPredictor] = None, - prompt_helper: Optional[PromptHelper] = None, - embed_model: Optional[BaseEmbedding] = None, - docstore: Optional[DocumentStore] = None, - query_runner: Optional[BaseQueryRunner] = None, - required_keywords: Optional[List[str]] = None, - exclude_keywords: Optional[List[str]] = None, - response_mode: ResponseMode = ResponseMode.DEFAULT, - text_qa_template: Optional[QuestionAnswerPrompt] = None, - refine_template: Optional[RefinePrompt] = None, - include_summary: bool = False, - response_kwargs: Optional[Dict] = None, - similarity_cutoff: Optional[float] = None, - use_async: bool = True, - recursive: bool = False, - streaming: bool = False, - doc_ids: Optional[List[str]] = None, - ) -> None: - """Initialize with parameters.""" - if index_struct is None: - raise ValueError("index_struct must be provided.") - self._validate_index_struct(index_struct) - self._index_struct = index_struct - self._llm_predictor = llm_predictor or LLMPredictor() - # NOTE: the embed_model isn't used in all indices - self._embed_model = embed_model or OpenAIEmbedding() - self._docstore = docstore - self._query_runner = query_runner - # TODO: make this a required param - if prompt_helper is None: - raise ValueError("prompt_helper must be provided.") - self._prompt_helper = cast(PromptHelper, prompt_helper) - - self._required_keywords = required_keywords - self._exclude_keywords = exclude_keywords - self._response_mode = ResponseMode(response_mode) - - self.text_qa_template = text_qa_template or DEFAULT_TEXT_QA_PROMPT - self.refine_template = refine_template or DEFAULT_REFINE_PROMPT_SEL - self._include_summary = include_summary - - self._response_kwargs = response_kwargs or {} - self._use_async = use_async - self.response_builder = ResponseBuilder( - self._prompt_helper, - self._llm_predictor, - self.text_qa_template, - self.refine_template, - use_async=use_async, - streaming=streaming, - ) - - self.similarity_cutoff = similarity_cutoff - self._recursive = recursive - self._streaming = streaming - self._doc_ids = doc_ids - - def _should_use_node( - self, node: Node, similarity_tracker: Optional[SimilarityTracker] = None - ) -> bool: - """Run node through filters to determine if it should be used.""" - words = re.findall(r"\w+", node.get_text()) - if self._required_keywords is not None: - for w in self._required_keywords: - if w not in words: - return False - - if self._exclude_keywords is not None: - for w in self._exclude_keywords: - if w in words: - return False - - sim_cutoff_exists = ( - similarity_tracker is not None and self.similarity_cutoff is not None - ) - - if sim_cutoff_exists: - similarity = cast(SimilarityTracker, similarity_tracker).find(node) - if similarity is None: - return False - if cast(float, similarity) < cast(float, self.similarity_cutoff): - return False - - return True - - def _get_text_from_node( - self, - query_bundle: QueryBundle, - node: Node, - level: Optional[int] = None, - ) -> Tuple[TextChunk, Optional[Response]]: - """Query a given node. - - If node references a given document, then return the document. - If node references a given index, then query the index. - - """ - level_str = "" if level is None else f"[Level {level}]" - fmt_text_chunk = truncate_text(node.get_text(), 50) - logging.debug(f">{level_str} Searching in chunk: {fmt_text_chunk}") - - is_index_struct = False - # if recursive and self._query_runner is not None, - # assume we want to do a recursive - # query. In order to not perform a recursive query, make sure - # _query_runner is None. - if ( - self._recursive - and self._query_runner is not None - and node.ref_doc_id is not None - and self._docstore is not None - ): - doc = self._docstore.get_document(node.ref_doc_id, raise_error=False) - # NOTE: old version of the docstore contain both documents and index_struct, - # whereas new versions of the docstore only contain the index struct - if doc is not None and isinstance(doc, IndexStruct): - is_index_struct = True - - if is_index_struct: - query_runner = cast(BaseQueryRunner, self._query_runner) - response = query_runner.query(query_bundle, cast(IndexStruct, doc)) - return TextChunk(str(response), is_answer=True), response - else: - text = node.get_text() - return TextChunk(text), None - - @property - def index_struct(self) -> IS: - """Get the index struct.""" - return self._index_struct - - def _validate_index_struct(self, index_struct: IS) -> None: - """Validate the index struct.""" - pass - - def _give_response_for_nodes(self, query_str: str) -> RESPONSE_TEXT_TYPE: - """Give response for nodes.""" - response = self.response_builder.get_response( - query_str, - mode=self._response_mode, - **self._response_kwargs, - ) - return response - - async def _agive_response_for_nodes(self, query_str: str) -> RESPONSE_TEXT_TYPE: - """Give response for nodes.""" - response = await self.response_builder.aget_response( - query_str, - mode=self._response_mode, - **self._response_kwargs, - ) - return response - - def get_nodes_and_similarities_for_response( - self, query_bundle: QueryBundle - ) -> List[Tuple[Node, Optional[float]]]: - """Get list of tuples of node and similarity for response. - - First part of the tuple is the node. - Second part of tuple is the distance from query to the node. - If not applicable, it's None. - """ - similarity_tracker = SimilarityTracker() - nodes = self._get_nodes_for_response( - query_bundle, similarity_tracker=similarity_tracker - ) - nodes = [ - node for node in nodes if self._should_use_node(node, similarity_tracker) - ] - - # TODO: create a `display` method to allow subclasses to print the Node - return similarity_tracker.get_zipped_nodes(nodes) - - @abstractmethod - def _get_nodes_for_response( - self, - query_bundle: QueryBundle, - similarity_tracker: Optional[SimilarityTracker] = None, - ) -> List[Node]: - """Get nodes for response.""" - - def _get_extra_info_for_response( - self, - nodes: List[Node], - ) -> Optional[Dict[str, Any]]: - """Get extra info for response.""" - return None - - def _prepare_response_builder( - self, - response_builder: ResponseBuilder, - query_bundle: QueryBundle, - tuples: List[Tuple[Node, Optional[float]]], - ) -> None: - """Prepare response builder and return values for query time.""" - response_builder.reset() - for node, similarity in tuples: - text, response = self._get_text_from_node(query_bundle, node) - response_builder.add_node_as_source(node, similarity=similarity) - if response is not None: - # these are source nodes from within this node (when it's an index) - for source_node in response.source_nodes: - response_builder.add_source_node(source_node) - response_builder.add_text_chunks([text]) - - def _prepare_response_output( - self, - response_str: Optional[RESPONSE_TEXT_TYPE], - tuples: List[Tuple[Node, Optional[float]]], - ) -> RESPONSE_TYPE: - """Prepare response object from response string.""" - response_extra_info = self._get_extra_info_for_response( - [node for node, _ in tuples] - ) - - if response_str is None or isinstance(response_str, str): - return Response( - response_str, - source_nodes=self.response_builder.get_sources(), - extra_info=response_extra_info, - ) - elif response_str is None or isinstance(response_str, Generator): - return StreamingResponse( - response_str, - source_nodes=self.response_builder.get_sources(), - extra_info=response_extra_info, - ) - else: - raise ValueError("Response must be a string or a generator.") - - def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE: - """Answer a query.""" - # TODO: remove _query and just use query - tuples = self.get_nodes_and_similarities_for_response(query_bundle) - - # prepare response builder - self._prepare_response_builder(self.response_builder, query_bundle, tuples) - - if self._response_mode != ResponseMode.NO_TEXT: - response_str = self._give_response_for_nodes(query_bundle.query_str) - else: - response_str = None - - return self._prepare_response_output(response_str, tuples) - - async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE: - """Answer a query asynchronously.""" - # TODO: remove _query and just use query - tuples = self.get_nodes_and_similarities_for_response(query_bundle) - - # prepare response builder - self._prepare_response_builder(self.response_builder, query_bundle, tuples) - - if self._response_mode != ResponseMode.NO_TEXT: - response_str = await self._agive_response_for_nodes(query_bundle.query_str) - else: - response_str = None - - return self._prepare_response_output(response_str, tuples) - - @llm_token_counter("query") - def query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE: - """Answer a query.""" - response = self._query(query_bundle) - # if include_summary is True, then include summary text in answer - # summary text is set through `set_text` on the underlying index. - # TODO: refactor response builder to be in the __init__ - if self._response_mode != ResponseMode.NO_TEXT and self._include_summary: - response_builder = ResponseBuilder( - self._prompt_helper, - self._llm_predictor, - self.text_qa_template, - self.refine_template, - texts=[TextChunk(self._index_struct.get_text())], - streaming=self._streaming, - ) - if isinstance(response, Response): - # NOTE: use create and refine for now (default response mode) - response_str = response_builder.get_response( - query_bundle.query_str, - mode=self._response_mode, - prev_response=response.response, - ) - response.response = cast(str, response_str) - elif isinstance(response, StreamingResponse): - response_gen = response_builder.get_response( - query_bundle.query_str, - mode=self._response_mode, - prev_response=str(response.response_gen), - ) - response.response_gen = cast(Generator, response_gen) - else: - raise ValueError("Response must be a string or a generator.") - - return response - - @llm_token_counter("query") - async def aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE: - """Answer a query.""" - response = await self._aquery(query_bundle) - # if include_summary is True, then include summary text in answer - # summary text is set through `set_text` on the underlying index. - # TODO: refactor response builder to be in the __init__ - if self._response_mode != ResponseMode.NO_TEXT and self._include_summary: - response_builder = ResponseBuilder( - self._prompt_helper, - self._llm_predictor, - self.text_qa_template, - self.refine_template, - texts=[TextChunk(self._index_struct.get_text())], - streaming=self._streaming, - ) - if isinstance(response, Response): - # NOTE: use create and refine for now (default response mode) - response_str = await response_builder.aget_response( - query_bundle.query_str, - mode=self._response_mode, - prev_response=response.response, - ) - response.response = cast(str, response_str) - elif isinstance(response, StreamingResponse): - response_gen = await response_builder.aget_response( - query_bundle.query_str, - mode=self._response_mode, - prev_response=str(response.response_gen), - ) - response.response_gen = cast(Generator, response_gen) - else: - raise ValueError("Response must be a string or a generator.") - - return response diff --git a/spaces/jpatech/dogcat/README.md b/spaces/jpatech/dogcat/README.md deleted file mode 100644 index cd945409628f754407dd6fb47d703e5e9dc66c98..0000000000000000000000000000000000000000 --- a/spaces/jpatech/dogcat/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Dogcat -emoji: 👀 -colorFrom: red -colorTo: purple -sdk: gradio -sdk_version: 3.35.2 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/jskim/paper-matching/app.py b/spaces/jskim/paper-matching/app.py deleted file mode 100644 index 4713b754985e53db89203c8c377aa00a484a945a..0000000000000000000000000000000000000000 --- a/spaces/jskim/paper-matching/app.py +++ /dev/null @@ -1,708 +0,0 @@ -import gradio as gr -from transformers import AutoTokenizer, AutoModel -from sentence_transformers import SentenceTransformer -import pickle -import nltk -import time - -from input_format import * -from score import * - -nltk.download('punkt') # tokenizer -nltk.download('averaged_perceptron_tagger') # postagger - -## load document scoring model -#torch.cuda.is_available = lambda : False # uncomment to test with CPU only -device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') -#pretrained_model = 'allenai/specter' -pretrained_model = 'allenai/specter2' -tokenizer = AutoTokenizer.from_pretrained(pretrained_model) -doc_model = AutoModel.from_pretrained(pretrained_model) -doc_model.to(device) - -## load sentence model -sent_model = doc_model # have the same model for document and sentence level - -# OR specify different model for sentence level -#sent_model = SentenceTransformer('sentence-transformers/gtr-t5-base') -#sent_model.to(device) - -NUM_PAPERS_SHOW = 5 # max number of top papers to show from the reviewer upfront -NUM_PAIRS_SHOW = 5 # max number of top sentence pairs to show - -def get_similar_paper( - title_input, - abstract_text_input, - author_id_input, - top_paper_slider, - top_pair_slider, - results={}, # this state variable will be updated and returned -): - progress = gr.Progress() - if title_input == None: - title_input = '' # if no title is given, just focus on abstract. - print('retrieving similar papers...') - start = time.time() - input_sentences = sent_tokenize(abstract_text_input) - - # Get author papers from id - #progress(0.1, desc="Retrieving reviewer papers ...") - name, papers = get_text_from_author_id(author_id_input) - - # Compute Doc-level affinity scores for the Papers - # print('computing document scores...') - #progress(0.5, desc="Computing document scores...") - # TODO detect duplicate papers? - titles, abstracts, paper_urls, doc_scores, paper_years, paper_citations = compute_document_score( - doc_model, - tokenizer, - title_input, - abstract_text_input, - papers, - batch=10 - ) - - results = { - 'name': name, - 'author_url': author_id_input, - 'titles': titles, - 'abstracts': abstracts, - 'urls': paper_urls, - 'doc_scores': doc_scores, - 'years': paper_years, - 'citations': paper_citations, - } - - # Select top 10 papers to show - titles = titles[:10] - abstracts = abstracts[:10] - doc_scores = doc_scores[:10] - paper_urls = paper_urls[:10] - paper_years = paper_years[:10] - paper_citations = paper_citations[:10] - - display_title = ['[ %0.3f ] %s'%(s, t) for t, s in zip(titles, doc_scores)] - end = time.time() - retrieval_time = end - start - print('paper retrieval complete in [%0.2f] seconds'%(retrieval_time)) - - progress(0.9, desc="Obtaining relevant information from the papers...") - print('obtaining highlights..') - start = time.time() - input_sentences = sent_tokenize(abstract_text_input) - num_input_sents = len(input_sentences) - - for aa, (tt, ab, ds, url) in enumerate(zip(titles, abstracts, doc_scores, paper_urls)): - # Compute sent-level and phrase-level affinity scores for each papers - sent_ids, sent_scores, info, top_pairs_info = get_highlight_info( - sent_model, - tokenizer, - abstract_text_input, - ab, - K=None, - top_pair_num=10, # top ten sentence pairs at max to show upfront - ) - num_cand_sents = sent_ids.shape[1] - - # get scores for each word in the format for Gradio Interpretation component - word_scores = dict() - for i in range(num_input_sents): - word_scores[str(i)] = dict() - for j in range(1, num_cand_sents+1): - ww, ss = remove_spaces(info['all_words'], info[i][j]['scores']) - word_scores[str(i)][str(j)] = { - "original": ab, - "interpretation": list(zip(ww, ss)) - } - - results[display_title[aa]] = { - 'title': tt, - 'abstract': ab, - 'num_cand_sents': num_cand_sents, - 'doc_score': '%0.3f'%ds, - 'source_sentences': input_sentences, - 'highlight': word_scores, - 'top_pairs': top_pairs_info, - 'url': url, - 'year': paper_years[aa], - 'citations': paper_citations[aa], - } - - end = time.time() - highlight_time = end - start - print('done in [%0.2f] seconds'%(highlight_time)) - - ## Set up output elements - ## Components for Initial Part - result1_desc_value = """ - Top %d relevant papers by the reviewer %s
- - For each paper, top %d sentence pairs (one from the submission on the left, one from the paper on the right) with the highest relevance scores are shown. - - **Blue highlights**: phrases that appear in both sentences. - """%(int(top_paper_slider), author_id_input, results['name'], int(top_pair_slider)) - - out1 = [ - gr.update(visible=True), # Explore more button - gr.update(value=result1_desc_value, visible=True), # result 1 description - gr.update(value='Done (in %0.1f seconds)'%(retrieval_time+highlight_time), visible=True), # search status - gr.update(visible=True), # top paper slider - gr.update(visible=True) # top pair slider - ] - - ### Components for Results in Initial Part - top_papers_show = int(top_paper_slider) # number of top papers to show upfront - top_num_info_show = int(top_pair_slider) # number of sentence pairs from each paper to show upfront - output = setup_outputs(results, top_papers_show, top_num_info_show) - out2 = [] - for x in output: - out2 += x - - ### Components for Explore More Section - # list of top papers, sentences to select from, paper_title, affinity - title = results[display_title[0]]['title'] # set default title as the top paper - url = results[display_title[0]]['url'] - aff_score = results[display_title[0]]['doc_score'] - title_out = """%s
"""%(url, title) - aff_score_out = '##### Affinity Score: %s'%aff_score - result2_desc_value = """ - ##### Click a paper by %s (left, sorted by affinity scores), and a sentence from the submission abstract (center), to see which parts of the paper's abstract are relevant (right). - """%results['name'] - out3 = [ - gr.update(choices=display_title, value=display_title[0], interactive=True), # set of papers (radio) - gr.update(choices=input_sentences, value=input_sentences[0], interactive=True), # submission sentences - gr.update(value=title_out), # paper_title - gr.update(value=aff_score_out), # affinity - gr.update(value=result2_desc_value), # result 2 description (show more section) - gr.update(value=2, maximum=len(sent_tokenize(abstracts[0]))), # highlight slider to control - ] - - torch.cuda.empty_cache() - - ## Return by adding the State variable info - return out1 + out2 + out3 + [results] - -def setup_outputs(info, top_papers_show, top_num_info_show): - titles = info['titles'] - doc_scores = info['doc_scores'] - paper_urls = info['urls'] - paper_years = info['years'] - paper_citations = info['citations'] - display_title = ['[ %0.3f ] %s'%(s, t) for t, s in zip(info['titles'], info['doc_scores'])] - title = [] - affinity = [] - citation_count = [] - sent_pair_score = [] - sent_text_query = [] - sent_text_candidate = [] - sent_hl_query = [] - sent_hl_candidate = [] - demarc_lines = [] - for i in range(top_papers_show): - if i == 0: - title.append( - gr.update(value="""%s (%s)
"""%(paper_urls[i], titles[i], str(paper_years[i])), visible=True) - ) - affinity.append( - gr.update(value="""#### Affinity Score: %0.3f --- """%doc_scores[i], visible=True) # document affinity - ) - citation_count.append( - gr.update(value="""#### Citation Count: %d"""%paper_citations[i], visible=True) # document affinity - ) - else: - title.append( - gr.update(value="""Measures how similar the paper's abstract is to the submission abstract.
-%s (%s)
"""%(paper_urls[i], titles[i], str(paper_years[i])), visible=True) - ) - affinity.append( - gr.update(value='#### Affinity Score: %0.3f'%doc_scores[i], visible=True) # document affinity - ) - citation_count.append( - gr.update(value="""#### Citation Count: %d"""%paper_citations[i], visible=True) # document affinity - ) - demarc_lines.append(gr.Markdown.update(visible=True)) - - # fill in the rest as - tp = info[display_title[i]]['top_pairs'] - for j in range(top_num_info_show): - if i == 0 and j == 0: - # for the first entry add help tip - sent_pair_score.append( - gr.update(value="""Sentence Relevance:\n%0.3f --"""%tp[j]['score'], visible=True) - ) - else: - sent_pair_score.append( - gr.Textbox.update(value='Sentence Relevance:\n%0.3f'%tp[j]['score'], visible=True) - ) - sent_text_query.append(gr.Textbox.update(tp[j]['query']['original'])) - sent_text_candidate.append(gr.Textbox.update(tp[j]['candidate']['original'])) - sent_hl_query.append(tp[j]['query']) - sent_hl_candidate.append(tp[j]['candidate']) - #row2.append(gr.update(visible=True)) - sent_pair_score += [gr.Markdown.update(visible=False)] * (NUM_PAIRS_SHOW - top_num_info_show) - sent_text_query += [gr.Textbox.update(value='', visible=False)] * (NUM_PAIRS_SHOW - top_num_info_show) - sent_text_candidate += [gr.Textbox.update(value='', visible=False)] * (NUM_PAIRS_SHOW - top_num_info_show) - sent_hl_query += [None] * (NUM_PAIRS_SHOW - top_num_info_show) - sent_hl_candidate += [None] * (NUM_PAIRS_SHOW - top_num_info_show) - - # mark others not visible - title += [gr.Markdown.update(visible=False)] * (NUM_PAPERS_SHOW - top_papers_show) - affinity += [gr.Markdown.update(visible=False)] * (NUM_PAPERS_SHOW - top_papers_show) - citation_count += [gr.Markdown.update(visible=False)] * (NUM_PAPERS_SHOW - top_papers_show) - demarc_lines += [gr.Markdown.update(visible=False)] * (NUM_PAPERS_SHOW - top_papers_show) - sent_pair_score += [gr.Markdown.update(visible=False)] * (NUM_PAPERS_SHOW - top_papers_show) * NUM_PAIRS_SHOW - sent_text_query += [gr.Textbox.update(value='', visible=False)] * (NUM_PAPERS_SHOW - top_papers_show) * NUM_PAIRS_SHOW - sent_text_candidate += [gr.Textbox.update(value='', visible=False)] * (NUM_PAPERS_SHOW - top_papers_show) * NUM_PAIRS_SHOW - sent_hl_query += [None] * (NUM_PAPERS_SHOW - top_papers_show) * NUM_PAIRS_SHOW - sent_hl_candidate += [None] * (NUM_PAPERS_SHOW - top_papers_show) * NUM_PAIRS_SHOW - - assert(len(title) == NUM_PAPERS_SHOW) - assert(len(affinity) == NUM_PAPERS_SHOW) - assert(len(sent_pair_score) == NUM_PAIRS_SHOW * NUM_PAPERS_SHOW) - - return title, affinity, citation_count, demarc_lines, sent_pair_score, sent_text_query, sent_text_candidate, sent_hl_query, sent_hl_candidate - -def show_more(info): - # show the interactive part of the app - return ( - gr.update(visible=True), # description - gr.update(visible=True), # set of papers - gr.update(visible=True), # submission sentences - gr.update(visible=True), # title row - gr.update(visible=True), # affinity row - gr.update(visible=True), # citation row - gr.update(visible=True), # highlight legend - gr.update(visible=True), # highlight slider - gr.update(visible=True), # highlight abstract - ) - -def show_status(): - # show search status field when search button is clicked - return gr.update(visible=True) - -def update_name(author_id_input): - # update the name of the author based on the id input - name, _ = get_text_from_author_id(author_id_input) - - return gr.update(value=name) - -def change_sentence( - selected_papers_radio, - source_sent_choice, - highlight_slider, - info={} -): - # change the output highlight based on the sentence selected from the submission - if len(info.keys()) != 0: # if the info is not empty - source_sents = info[selected_papers_radio]['source_sentences'] - highlights = info[selected_papers_radio]['highlight'] - idx = source_sents.index(source_sent_choice) - return highlights[str(idx)][str(highlight_slider)] - else: - return - -def change_paper( - selected_papers_radio, - source_sent_choice, - highlight_slider, - info={} -): - if len(info.keys()) != 0: # if the info is not empty - source_sents = info[selected_papers_radio]['source_sentences'] - title = info[selected_papers_radio]['title'] - year = info[selected_papers_radio]['year'] - citation_count = info[selected_papers_radio]['citations'] - num_sents = info[selected_papers_radio]['num_cand_sents'] - abstract = info[selected_papers_radio]['abstract'] - aff_score = info[selected_papers_radio]['doc_score'] - highlights = info[selected_papers_radio]['highlight'] - url = info[selected_papers_radio]['url'] - title_out = """Measures how similar the sentence pairs are.
-%s (%s)
"""%(url, title, str(year)) - aff_score_out = '##### Affinity Score: %s'%aff_score - citation_count_out = '##### Citation Count: %s'%citation_count - idx = source_sents.index(source_sent_choice) - if highlight_slider <= num_sents: - return title_out, abstract, aff_score_out, citation_count_out, highlights[str(idx)][str(highlight_slider)], gr.update(value=highlight_slider, maximum=num_sents) - else: # if the slider is set to more than the current number of sentences, show the max number of highlights - return title_out, abstract, aff_score_out, citation_count_out, highlights[str(idx)][str(num_sents)], gr.update(value=num_sents, maximum=num_sents) - else: - return - -def change_num_highlight( - selected_papers_radio, - source_sent_choice, - highlight_slider, - info={} -): - if len(info.keys()) != 0: # if the info is not empty - source_sents = info[selected_papers_radio]['source_sentences'] - highlights = info[selected_papers_radio]['highlight'] - idx = source_sents.index(source_sent_choice) - return highlights[str(idx)][str(highlight_slider)] - else: - return - -def change_top_output(top_paper_slider, top_pair_slider, info={}): - top_papers_show = int(top_paper_slider) - top_num_info_show = int(top_pair_slider) - - result1_desc_value = """ -Top %d relevant papers by the reviewer %s
- - For each paper, top %d sentence pairs (one from the submission on the left, one from the paper on the right) with the highest relevance scores are shown. - - **Blue highlights**: phrases that appear in both sentences. - """%(int(top_paper_slider), info['author_url'], info['name'], int(top_pair_slider)) - if len(info.keys()) != 0: - tmp = setup_outputs(info, top_papers_show, top_num_info_show) - x = [] - for t in tmp: - x += t - return x + [gr.update(value=result1_desc_value)] - else: - return - -with gr.Blocks(css='style.css') as demo: - info = gr.State({}) # cached search results as a State variable shared throughout - - # Text description about the app and disclaimer - ### TEXT Description - - # General instruction - general_instruction = """ -# R2P2: An Assistance Tool for Reviewer-Paper Matching in Peer Review - -#### Who is it for? -It is for meta-reviewers, area chairs, program chairs, or anyone who oversees the submission-reviewer matching process in peer review for academic conferences, journals, and grants. - -
{'); - const endIdx = data.indexOf('}'); - if (startIdx >= 0 && endIdx >= 0) { - const slice = data.slice(startIdx + 5, endIdx + 1); - try { - const config = JSON.parse(slice); - if (config['gpt-4']?.active > 0 && config['gpt-4']?.remaining !== '0%') { - isGpt4 = true; - } - if (config.keys?.gpt4 > 0 && config.keys?.quotaLeft !== '0%') { - isGpt4 = true; - } - if (config.keyInfo?.gpt4 > 0 && config.keyInfo?.quotaLeft?.gpt4 !== '0%') { - isGpt4 = true; - } - if (config.keys?.active === 0) { - isDisabled = true; - } - if (config.keyInfo?.active === 0) { - isDisabled = true; - } - if (config.config?.proxyKey) { - isDisabled = true; - } - if (config.config?.promptLogging === 'true') { - isLogging = true; - } - const quotaLeft = isGpt4 - ? parseInt(config.keys?.quotaLeft.gpt4 || config.keyInfo?.quotaLeft?.gpt4 || config['gpt-4']?.remaining) - : parseInt(config.keys?.quotaLeft.all || config.keyInfo?.quotaLeft?.all || config['gpt-3.5-turbo']?.remaining); - - if (!isNaN(quotaLeft)) { - proxy.remaining = quotaLeft; - } - - rateLimit = +config.config?.modelRateLimit || 0; - } catch (e) { } - } - - if (!isDisabled && rateLimit > 2 && !isLogging) { - try { - await axios.post( - `${proxy.url}/v1/chat/completions`, - { - model: "gpt-3.5-turbo", max_tokens: 1, - messages: [{ role: "user", content: "" }], - }, - { headers: { "Content-Type": "application/json" } }, - ); - } catch (e) { - if (!silent) logger.error(e, `Proxy is not active ${proxy.url}`); - isDisabled = true; - } - } - - if (!isDisabled && isGpt4 && rateLimit > 2 && !isLogging) { - try { - await axios.post( - `${proxy.url}/v1/chat/completions`, - { - model: "gpt-4", max_tokens: 1, - messages: [{ role: "user", content: "" }], - }, - { headers: { "Content-Type": "application/json" } }, - ); - } catch (e) { - if (!silent) logger.error(e, `Proxy is not GPT-4 ${proxy.url}`); - isGpt4 = false; - } - } - } catch (e) { - isDisabled = true; - if (!silent) logger.error(e, `Error checking proxy ${proxy.url}`); - } - - proxy.isDisabled = isDisabled; - proxy.isGpt4 = isGpt4; - proxy.isLogging = isLogging; -} - -async function add(url: string) { - if (!url) { - return; - } - if (proxyPool.some(k => k.url === url)) { - logger.warn('Proxy already exists'); - return; - } - const newProxy = { - url, - isGpt4: false, - isDisabled: false, - lastUsed: 0, - promptCount: 0, - remaining: 100, - hash: crypto - .createHash("sha256") - .update(url) - .digest("hex") - .slice(0, 6), - }; - - await check(newProxy); - - proxyPool.push(newProxy); - - logger.info({ proxy: newProxy.hash }, "Proxy added"); - - return newProxy; -} - -function disable(proxy: Proxy) { - const proxyFromPool = proxyPool.find((k) => k.url === proxy.url)!; - if (proxyFromPool.isDisabled) return; - proxyFromPool.isDisabled = true; - logger.warn({ proxy: proxy.hash, url: proxy.url }, "Proxy disabled"); -} - -function anyAvailable() { - return proxyPool.some((proxy) => !proxy.isDisabled); -} - -function get(model: string) { - const needsGpt4Proxy = model.startsWith("gpt-4"); - const enabledProxies = proxyPool.filter( - (proxy) => !proxy.isDisabled - ); - - let availableProxies = enabledProxies.filter(k => k.isGpt4 === needsGpt4Proxy); - if (availableProxies.length === 0 && !needsGpt4Proxy) { - availableProxies = enabledProxies; - } - - if (availableProxies.length === 0) { - let message = "No proxies available. Please add more proxies."; - if (needsGpt4Proxy) { - message = - "No GPT-4 proxies available. Please add more proxies or use a non-GPT-4 model."; - } - logger.error(message); - return; - } - - const notLoggingProxies = availableProxies.filter((proxy) => !proxy.isLogging); - const selectionProxies = notLoggingProxies.length > 0 ? notLoggingProxies : availableProxies; - const msg = notLoggingProxies.length > 0 ? "Assigning proxy to request." : "Using logging proxy"; - - // Return the oldest proxy - const oldestProxy = selectionProxies.sort((a, b) => a.lastUsed - b.lastUsed)[0]; - logger.info({ proxy: oldestProxy.hash }, msg); - oldestProxy.lastUsed = Date.now(); - return { ...oldestProxy }; -} - -function incrementPrompt(proxyHash?: string) { - if (!proxyHash) return; - const proxy = proxyPool.find((k) => k.hash === proxyHash)!; - proxy.promptCount++; -} - -function downgradeProxy(proxyHash?: string) { - if (!proxyHash) return; - logger.warn({ proxy: proxyHash }, "Downgrading proxy to GPT-3.5."); - const proxy = proxyPool.find((k) => k.hash === proxyHash)!; - proxy.isGpt4 = false; -} - -function getRemaining(onlyActive = false) { - let remaining = 0; - let remainingCount = 0; - for (const proxy of proxyPool) { - if (onlyActive && proxy.isDisabled) continue; - remainingCount++; - remaining += proxy.remaining; - } - - if (remainingCount === 0) return '0%'; - return `${Math.round(remaining / remainingCount)}%`; -} - -export const proxies = { - init, - list, - get, - anyAvailable, - parse, - add, - check, - getRemaining, - disable, - incrementPrompt, - downgradeProxy, -}; diff --git a/spaces/kriss-ml/Boston-House-Price/README.md b/spaces/kriss-ml/Boston-House-Price/README.md deleted file mode 100644 index afb9a69c18dc913ecb13612a64d571be9b7dec18..0000000000000000000000000000000000000000 --- a/spaces/kriss-ml/Boston-House-Price/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Boston House Price -emoji: 🏫💒📈 -colorFrom: green -colorTo: purple -sdk: gradio -sdk_version: 3.20.0 -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/kukuhtw/VToonify/vtoonify/model/dualstylegan.py b/spaces/kukuhtw/VToonify/vtoonify/model/dualstylegan.py deleted file mode 100644 index 60d9850ad049a2751781871d6ae0c2779ecc863f..0000000000000000000000000000000000000000 --- a/spaces/kukuhtw/VToonify/vtoonify/model/dualstylegan.py +++ /dev/null @@ -1,203 +0,0 @@ -import random -import torch -from torch import nn -from model.stylegan.model import ConvLayer, PixelNorm, EqualLinear, Generator - -class AdaptiveInstanceNorm(nn.Module): - def __init__(self, fin, style_dim=512): - super().__init__() - - self.norm = nn.InstanceNorm2d(fin, affine=False) - self.style = nn.Linear(style_dim, fin * 2) - - self.style.bias.data[:fin] = 1 - self.style.bias.data[fin:] = 0 - - def forward(self, input, style): - style = self.style(style).unsqueeze(2).unsqueeze(3) - gamma, beta = style.chunk(2, 1) - out = self.norm(input) - out = gamma * out + beta - return out - -# modulative residual blocks (ModRes) -class AdaResBlock(nn.Module): - def __init__(self, fin, style_dim=512, dilation=1): # modified - super().__init__() - - self.conv = ConvLayer(fin, fin, 3, dilation=dilation) # modified - self.conv2 = ConvLayer(fin, fin, 3, dilation=dilation) # modified - self.norm = AdaptiveInstanceNorm(fin, style_dim) - self.norm2 = AdaptiveInstanceNorm(fin, style_dim) - - # model initialization - # the convolution filters are set to values close to 0 to produce negligible residual features - self.conv[0].weight.data *= 0.01 - self.conv2[0].weight.data *= 0.01 - - def forward(self, x, s, w=1): - skip = x - if w == 0: - return skip - out = self.conv(self.norm(x, s)) - out = self.conv2(self.norm2(out, s)) - out = out * w + skip - return out - -class DualStyleGAN(nn.Module): - def __init__(self, size, style_dim, n_mlp, channel_multiplier=2, twoRes=True, res_index=6): - super().__init__() - - layers = [PixelNorm()] - for i in range(n_mlp-6): - layers.append(EqualLinear(512, 512, lr_mul=0.01, activation="fused_lrelu")) - # color transform blocks T_c - self.style = nn.Sequential(*layers) - # StyleGAN2 - self.generator = Generator(size, style_dim, n_mlp, channel_multiplier) - # The extrinsic style path - self.res = nn.ModuleList() - self.res_index = res_index//2 * 2 - self.res.append(AdaResBlock(self.generator.channels[2 ** 2])) # for conv1 - for i in range(3, self.generator.log_size + 1): - out_channel = self.generator.channels[2 ** i] - if i < 3 + self.res_index//2: - # ModRes - self.res.append(AdaResBlock(out_channel)) - self.res.append(AdaResBlock(out_channel)) - else: - # structure transform block T_s - self.res.append(EqualLinear(512, 512)) - # FC layer is initialized with identity matrices, meaning no changes to the input latent code - self.res[-1].weight.data = torch.eye(512) * 512.0**0.5 + torch.randn(512, 512) * 0.01 - self.res.append(EqualLinear(512, 512)) - self.res[-1].weight.data = torch.eye(512) * 512.0**0.5 + torch.randn(512, 512) * 0.01 - self.res.append(EqualLinear(512, 512)) # for to_rgb7 - self.res[-1].weight.data = torch.eye(512) * 512.0**0.5 + torch.randn(512, 512) * 0.01 - self.size = self.generator.size - self.style_dim = self.generator.style_dim - self.log_size = self.generator.log_size - self.num_layers = self.generator.num_layers - self.n_latent = self.generator.n_latent - self.channels = self.generator.channels - - def forward( - self, - styles, # intrinsic style code - exstyles, # extrinsic style code - return_latents=False, - return_feat=False, - inject_index=None, - truncation=1, - truncation_latent=None, - input_is_latent=False, - noise=None, - randomize_noise=True, - z_plus_latent=False, # intrinsic style code is z+ or z - use_res=True, # whether to use the extrinsic style path - fuse_index=18, # layers > fuse_index do not use the extrinsic style path - interp_weights=[1]*18, # weight vector for style combination of two paths - ): - - if not input_is_latent: - if not z_plus_latent: - styles = [self.generator.style(s) for s in styles] - else: - styles = [self.generator.style(s.reshape(s.shape[0]*s.shape[1], s.shape[2])).reshape(s.shape) for s in styles] - - if noise is None: - if randomize_noise: - noise = [None] * self.generator.num_layers - else: - noise = [ - getattr(self.generator.noises, f"noise_{i}") for i in range(self.generator.num_layers) - ] - - if truncation < 1: - style_t = [] - - for style in styles: - style_t.append( - truncation_latent + truncation * (style - truncation_latent) - ) - - styles = style_t - - if len(styles) < 2: - inject_index = self.generator.n_latent - - if styles[0].ndim < 3: - latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - - else: - latent = styles[0] - - else: - if inject_index is None: - inject_index = random.randint(1, self.generator.n_latent - 1) - - if styles[0].ndim < 3: - latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - latent2 = styles[1].unsqueeze(1).repeat(1, self.generator.n_latent - inject_index, 1) - - latent = torch.cat([latent, latent2], 1) - else: - latent = torch.cat([styles[0][:,0:inject_index], styles[1][:,inject_index:]], 1) - - if use_res: - if exstyles.ndim < 3: - resstyles = self.style(exstyles).unsqueeze(1).repeat(1, self.generator.n_latent, 1) - adastyles = exstyles.unsqueeze(1).repeat(1, self.generator.n_latent, 1) - else: - nB, nL, nD = exstyles.shape - resstyles = self.style(exstyles.reshape(nB*nL, nD)).reshape(nB, nL, nD) - adastyles = exstyles - - out = self.generator.input(latent) - out = self.generator.conv1(out, latent[:, 0], noise=noise[0]) - if use_res and fuse_index > 0: - out = self.res[0](out, resstyles[:, 0], interp_weights[0]) - - skip = self.generator.to_rgb1(out, latent[:, 1]) - i = 1 - for conv1, conv2, noise1, noise2, to_rgb in zip( - self.generator.convs[::2], self.generator.convs[1::2], noise[1::2], noise[2::2], self.generator.to_rgbs): - if use_res and fuse_index >= i and i > self.res_index: - out = conv1(out, interp_weights[i] * self.res[i](adastyles[:, i]) + - (1-interp_weights[i]) * latent[:, i], noise=noise1) - else: - out = conv1(out, latent[:, i], noise=noise1) - if use_res and fuse_index >= i and i <= self.res_index: - out = self.res[i](out, resstyles[:, i], interp_weights[i]) - if use_res and fuse_index >= (i+1) and i > self.res_index: - out = conv2(out, interp_weights[i+1] * self.res[i+1](adastyles[:, i+1]) + - (1-interp_weights[i+1]) * latent[:, i+1], noise=noise2) - else: - out = conv2(out, latent[:, i + 1], noise=noise2) - if use_res and fuse_index >= (i+1) and i <= self.res_index: - out = self.res[i+1](out, resstyles[:, i+1], interp_weights[i+1]) - if use_res and fuse_index >= (i+2) and i >= self.res_index-1: - skip = to_rgb(out, interp_weights[i+2] * self.res[i+2](adastyles[:, i+2]) + - (1-interp_weights[i+2]) * latent[:, i + 2], skip) - else: - skip = to_rgb(out, latent[:, i + 2], skip) - i += 2 - if i > self.res_index and return_feat: - return out, skip - - image = skip - - if return_latents: - return image, latent - - else: - return image, None - - def make_noise(self): - return self.generator.make_noise() - - def mean_latent(self, n_latent): - return self.generator.mean_latent(n_latent) - - def get_latent(self, input): - return self.generator.style(input) \ No newline at end of file diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/anyio/_core/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/anyio/_core/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/otlLib/optimize/__init__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/otlLib/optimize/__init__.py deleted file mode 100644 index 25bce9cd2cdaa51338c83b7ecb9059b592b5574f..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/otlLib/optimize/__init__.py +++ /dev/null @@ -1,53 +0,0 @@ -from argparse import RawTextHelpFormatter -from fontTools.otlLib.optimize.gpos import COMPRESSION_LEVEL, compact -from fontTools.ttLib import TTFont - - -def main(args=None): - """Optimize the layout tables of an existing font""" - from argparse import ArgumentParser - - from fontTools import configLogger - - parser = ArgumentParser( - prog="otlLib.optimize", - description=main.__doc__, - formatter_class=RawTextHelpFormatter, - ) - parser.add_argument("font") - parser.add_argument( - "-o", metavar="OUTPUTFILE", dest="outfile", default=None, help="output file" - ) - parser.add_argument( - "--gpos-compression-level", - help=COMPRESSION_LEVEL.help, - default=COMPRESSION_LEVEL.default, - choices=list(range(10)), - type=int, - ) - logging_group = parser.add_mutually_exclusive_group(required=False) - logging_group.add_argument( - "-v", "--verbose", action="store_true", help="Run more verbosely." - ) - logging_group.add_argument( - "-q", "--quiet", action="store_true", help="Turn verbosity off." - ) - options = parser.parse_args(args) - - configLogger( - level=("DEBUG" if options.verbose else "ERROR" if options.quiet else "INFO") - ) - - font = TTFont(options.font) - compact(font, options.gpos_compression_level) - font.save(options.outfile or options.font) - - -if __name__ == "__main__": - import sys - - if len(sys.argv) > 1: - sys.exit(main()) - import doctest - - sys.exit(doctest.testmod().failed) diff --git a/spaces/leave7/kazunaAI2.0/commons.py b/spaces/leave7/kazunaAI2.0/commons.py deleted file mode 100644 index 074888006392e956ce204d8368362dbb2cd4e304..0000000000000000000000000000000000000000 --- a/spaces/leave7/kazunaAI2.0/commons.py +++ /dev/null @@ -1,188 +0,0 @@ -import math -import numpy as np -import torch -from torch import nn -from torch.nn import functional as F - -def slice_pitch_segments(x, ids_str, segment_size=4): - ret = torch.zeros_like(x[:, :segment_size]) - for i in range(x.size(0)): - idx_str = ids_str[i] - idx_end = idx_str + segment_size - ret[i] = x[i, idx_str:idx_end] - return ret - -def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4): - b, d, t = x.size() - if x_lengths is None: - x_lengths = t - ids_str_max = x_lengths - segment_size + 1 - ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) - ret = slice_segments(x, ids_str, segment_size) - ret_pitch = slice_pitch_segments(pitch, ids_str, segment_size) - return ret, ret_pitch, ids_str - -def init_weights(m, mean=0.0, std=0.01): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - m.weight.data.normal_(mean, std) - - -def get_padding(kernel_size, dilation=1): - return int((kernel_size*dilation - dilation)/2) - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def intersperse(lst, item): - result = [item] * (len(lst) * 2 + 1) - result[1::2] = lst - return result - - -def kl_divergence(m_p, logs_p, m_q, logs_q): - """KL(P||Q)""" - kl = (logs_q - logs_p) - 0.5 - kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q) - return kl - - -def rand_gumbel(shape): - """Sample from the Gumbel distribution, protect from overflows.""" - uniform_samples = torch.rand(shape) * 0.99998 + 0.00001 - return -torch.log(-torch.log(uniform_samples)) - - -def rand_gumbel_like(x): - g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device) - return g - - -def slice_segments(x, ids_str, segment_size=4): - ret = torch.zeros_like(x[:, :, :segment_size]) - for i in range(x.size(0)): - idx_str = ids_str[i] - idx_end = idx_str + segment_size - ret[i] = x[i, :, idx_str:idx_end] - return ret - - -def rand_slice_segments(x, x_lengths=None, segment_size=4): - b, d, t = x.size() - if x_lengths is None: - x_lengths = t - ids_str_max = x_lengths - segment_size + 1 - ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) - ret = slice_segments(x, ids_str, segment_size) - return ret, ids_str - - -def rand_spec_segments(x, x_lengths=None, segment_size=4): - b, d, t = x.size() - if x_lengths is None: - x_lengths = t - ids_str_max = x_lengths - segment_size - ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) - ret = slice_segments(x, ids_str, segment_size) - return ret, ids_str - - -def get_timing_signal_1d( - length, channels, min_timescale=1.0, max_timescale=1.0e4): - position = torch.arange(length, dtype=torch.float) - num_timescales = channels // 2 - log_timescale_increment = ( - math.log(float(max_timescale) / float(min_timescale)) / - (num_timescales - 1)) - inv_timescales = min_timescale * torch.exp( - torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment) - scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1) - signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0) - signal = F.pad(signal, [0, 0, 0, channels % 2]) - signal = signal.view(1, channels, length) - return signal - - -def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4): - b, channels, length = x.size() - signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) - return x + signal.to(dtype=x.dtype, device=x.device) - - -def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1): - b, channels, length = x.size() - signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) - return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis) - - -def subsequent_mask(length): - mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) - return mask - - -@torch.jit.script -def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): - n_channels_int = n_channels[0] - in_act = input_a + input_b - t_act = torch.tanh(in_act[:, :n_channels_int, :]) - s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) - acts = t_act * s_act - return acts - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def shift_1d(x): - x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] - return x - - -def sequence_mask(length, max_length=None): - if max_length is None: - max_length = length.max() - x = torch.arange(max_length, dtype=length.dtype, device=length.device) - return x.unsqueeze(0) < length.unsqueeze(1) - - -def generate_path(duration, mask): - """ - duration: [b, 1, t_x] - mask: [b, 1, t_y, t_x] - """ - device = duration.device - - b, _, t_y, t_x = mask.shape - cum_duration = torch.cumsum(duration, -1) - - cum_duration_flat = cum_duration.view(b * t_x) - path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) - path = path.view(b, t_x, t_y) - path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] - path = path.unsqueeze(1).transpose(2,3) * mask - return path - - -def clip_grad_value_(parameters, clip_value, norm_type=2): - if isinstance(parameters, torch.Tensor): - parameters = [parameters] - parameters = list(filter(lambda p: p.grad is not None, parameters)) - norm_type = float(norm_type) - if clip_value is not None: - clip_value = float(clip_value) - - total_norm = 0 - for p in parameters: - param_norm = p.grad.data.norm(norm_type) - total_norm += param_norm.item() ** norm_type - if clip_value is not None: - p.grad.data.clamp_(min=-clip_value, max=clip_value) - total_norm = total_norm ** (1. / norm_type) - return total_norm diff --git a/spaces/leogabraneth/text-generation-webui-main/modules/utils.py b/spaces/leogabraneth/text-generation-webui-main/modules/utils.py deleted file mode 100644 index e5cca918d16a34e5c9b7658c0fe36cde210dc1f1..0000000000000000000000000000000000000000 --- a/spaces/leogabraneth/text-generation-webui-main/modules/utils.py +++ /dev/null @@ -1,132 +0,0 @@ -import os -import re -from datetime import datetime -from pathlib import Path - -from modules import github, shared -from modules.logging_colors import logger - - -# Helper function to get multiple values from shared.gradio -def gradio(*keys): - if len(keys) == 1 and type(keys[0]) in [list, tuple]: - keys = keys[0] - - return [shared.gradio[k] for k in keys] - - -def save_file(fname, contents): - if fname == '': - logger.error('File name is empty!') - return - - root_folder = Path(__file__).resolve().parent.parent - abs_path = Path(fname).resolve() - rel_path = abs_path.relative_to(root_folder) - if rel_path.parts[0] == '..': - logger.error(f'Invalid file path: {fname}') - return - - with open(abs_path, 'w', encoding='utf-8') as f: - f.write(contents) - - logger.info(f'Saved {abs_path}.') - - -def delete_file(fname): - if fname == '': - logger.error('File name is empty!') - return - - root_folder = Path(__file__).resolve().parent.parent - abs_path = Path(fname).resolve() - rel_path = abs_path.relative_to(root_folder) - if rel_path.parts[0] == '..': - logger.error(f'Invalid file path: {fname}') - return - - if abs_path.exists(): - abs_path.unlink() - logger.info(f'Deleted {fname}.') - - -def current_time(): - return f"{datetime.now().strftime('%Y-%m-%d-%H%M%S')}" - - -def atoi(text): - return int(text) if text.isdigit() else text.lower() - - -# Replace multiple string pairs in a string -def replace_all(text, dic): - for i, j in dic.items(): - text = text.replace(i, j) - - return text - - -def natural_keys(text): - return [atoi(c) for c in re.split(r'(\d+)', text)] - - -def get_available_models(): - model_list = ['None'] - for item in list(Path(f'{shared.args.model_dir}/').glob('*')): - if not item.name.endswith(('.txt', '-np', '.pt', '.json', '.yaml', '.py')) and 'llama-tokenizer' not in item.name: - model_list.append(re.sub('.pth$', '', item.name)) - - return sorted(model_list, key=natural_keys) - - -def get_available_presets(): - return sorted(set((k.stem for k in Path('presets').glob('*.yaml'))), key=natural_keys) - - -def get_available_prompts(): - prompts = [] - files = set((k.stem for k in Path('prompts').glob('*.txt'))) - prompts += sorted([k for k in files if re.match('^[0-9]', k)], key=natural_keys, reverse=True) - prompts += sorted([k for k in files if re.match('^[^0-9]', k)], key=natural_keys) - prompts += ['None'] - return prompts - - -def get_available_characters(): - paths = (x for x in Path('characters').iterdir() if x.suffix in ('.json', '.yaml', '.yml')) - return sorted(set((k.stem for k in paths)), key=natural_keys) - - -def get_available_instruction_templates(): - path = "instruction-templates" - paths = [] - if os.path.exists(path): - paths = (x for x in Path(path).iterdir() if x.suffix in ('.json', '.yaml', '.yml')) - - return ['None'] + sorted(set((k.stem for k in paths)), key=natural_keys) - - -def get_available_extensions(): - extensions = sorted(set(map(lambda x: x.parts[1], Path('extensions').glob('*/script.py'))), key=natural_keys) - extensions = [v for v in extensions if v not in github.new_extensions] - return extensions - - -def get_available_loras(): - return ['None'] + sorted([item.name for item in list(Path(shared.args.lora_dir).glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=natural_keys) - - -def get_datasets(path: str, ext: str): - # include subdirectories for raw txt files to allow training from a subdirectory of txt files - if ext == "txt": - return ['None'] + sorted(set([k.stem for k in list(Path(path).glob('txt')) + list(Path(path).glob('*/')) if k.stem != 'put-trainer-datasets-here']), key=natural_keys) - - return ['None'] + sorted(set([k.stem for k in Path(path).glob(f'*.{ext}') if k.stem != 'put-trainer-datasets-here']), key=natural_keys) - - -def get_available_chat_styles(): - return sorted(set(('-'.join(k.stem.split('-')[1:]) for k in Path('css').glob('chat_style*.css'))), key=natural_keys) - - -def get_available_grammars(): - return ['None'] + sorted([item.name for item in list(Path('grammars').glob('*.gbnf'))], key=natural_keys) diff --git a/spaces/lewiswu1209/MockingBird/synthesizer/__init__.py b/spaces/lewiswu1209/MockingBird/synthesizer/__init__.py deleted file mode 100644 index 4287ca8617970fa8fc025b75cb319c7032706910..0000000000000000000000000000000000000000 --- a/spaces/lewiswu1209/MockingBird/synthesizer/__init__.py +++ /dev/null @@ -1 +0,0 @@ -# \ No newline at end of file diff --git a/spaces/lightli/bingo-newbing/src/pages/api/create.ts b/spaces/lightli/bingo-newbing/src/pages/api/create.ts deleted file mode 100644 index 508fa97ef609cbb215a61085711638e116235ebe..0000000000000000000000000000000000000000 --- a/spaces/lightli/bingo-newbing/src/pages/api/create.ts +++ /dev/null @@ -1,31 +0,0 @@ -'use server' - -import { NextApiRequest, NextApiResponse } from 'next' -import { fetch, debug } from '@/lib/isomorphic' -import { createHeaders } from '@/lib/utils' - -// const API_ENDPOINT = 'https://www.bing.com/turing/conversation/create' -const API_ENDPOINT = 'https://edgeservices.bing.com/edgesvc/turing/conversation/create'; - -export default async function handler(req: NextApiRequest, res: NextApiResponse) { - try { - const headers = createHeaders(req.cookies) - - res.writeHead(200, { - 'Content-Type': 'application/json', - }) - - debug('headers', headers) - const response = await fetch(API_ENDPOINT, { method: 'GET', headers }) - .then((res) => res.text()) - - res.end(response) - } catch (e) { - return res.end(JSON.stringify({ - result: { - value: 'UnauthorizedRequest', - message: `${e}` - } - })) - } -} diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Class Warehouse Software Free Downloadl.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Class Warehouse Software Free Downloadl.md deleted file mode 100644 index dcfaa9e6d109ce13b048e3ef18afb8812cc26138..0000000000000000000000000000000000000000 --- a/spaces/lincquiQcaudo/Top-20-Diffusion/Class Warehouse Software Free Downloadl.md +++ /dev/null @@ -1,22 +0,0 @@ -Class Warehouse Software Free Downloadl
DOWNLOAD » https://bytlly.com/2uGy8g
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-One of the biggest questions warehouse operators have is if it’s worth investing in warehouse management software. It seems a daunting task, but as they say, you get out what you put in. The good news is that warehouse management software doesn’t have to be a huge upfront investment, but there are benefits to investing in warehouse management software. - -Over the last few years, the warehouse management software industry has exploded with new warehouse management software options. There are hundreds of warehouse management software available and making a sound decision about which warehouse management software to buy can be difficult. - -If you operate a large warehouse, you should be considering warehouse management software. However, while warehouse management software is great for companies with large warehouse operations, the technology comes with a large price tag. - -There are several reasons why warehouse management software is so important. Software can significantly increase the efficiency and effectiveness of warehouse management and greatly reduce costs. In addition, technology can also increase warehouse productivity. However, it can be difficult to judge whether it’s a good idea to invest in warehouse management software. - -There are many different types of warehouse management software, each with its own set of strengths and weaknesses. When making a decision on which warehouse management software to buy, it’s important to evaluate each option. - -One of the most important aspects of a warehouse is its internal system, which is used to store and organize items, receive shipments, and plan shipments to customers. This system is known as the warehouse management system, which can include: - -Warehouse management software is a program designed to manage your warehouse or storage area. This program contains functions that you can use to manage the warehouse and plan activities such as storage, inventory, shipments, picking, and fulfillment. Warehouse management software offers a number of benefits, such as the ability to create schedules and track orders for a better understanding of how items move through your warehouse. - -Many warehouse managers are confused about the best warehouse management software. The good news is that you don’t have to be a computer science genius to figure out which warehouse management software is best for your company. All you have to do is start by asking a few simple questions. - -Smart warehouse management software can help you manage your warehouse more efficiently, and make it easier to work with your employees and suppliers. By implementing warehouse management software, you can improve inventory accuracy, increase productivity, and generate more revenue from your warehouse. It’s important to understand what warehouse management software can 4fefd39f24
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-
- diff --git a/spaces/livelaughcats/m/README.md b/spaces/livelaughcats/m/README.md deleted file mode 100644 index 04dcc70a28c793cd2205e4cfab246fd796f273c1..0000000000000000000000000000000000000000 --- a/spaces/livelaughcats/m/README.md +++ /dev/null @@ -1,10 +0,0 @@ ---- -title: M -emoji: 🦀 -colorFrom: blue -colorTo: gray -sdk: docker -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/lixq/bingo61/src/components/user-menu.tsx b/spaces/lixq/bingo61/src/components/user-menu.tsx deleted file mode 100644 index 9bd1edc9cf9f39b63629b021f0c1186b1a7c1341..0000000000000000000000000000000000000000 --- a/spaces/lixq/bingo61/src/components/user-menu.tsx +++ /dev/null @@ -1,113 +0,0 @@ -'use client' - -import { useEffect, useState } from 'react' -import Image from 'next/image' -import { toast } from 'react-hot-toast' -import { Button } from '@/components/ui/button' -import pkg from '../../package.json' -import { - DropdownMenu, - DropdownMenuContent, - DropdownMenuItem, - DropdownMenuSeparator, - DropdownMenuTrigger -} from '@/components/ui/dropdown-menu' -import { IconCopy, IconExternalLink, IconGitHub } from '@/components/ui/icons' -import SettingIcon from '@/assets/images/settings.svg' -import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard' - -export function UserMenu() { - const [host, setHost] = useState('') - const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 }) - useEffect(() => { - setHost(location.host) - }, []) - - useEffect(() => { - if (isCopied) { - toast.success('复制成功') - } - }, [isCopied]) - return ( --- ) -} diff --git a/spaces/lixq/bingo61/src/state/index.ts b/spaces/lixq/bingo61/src/state/index.ts deleted file mode 100644 index 272106d619c69f124ea8dd0f10872ab4840152d7..0000000000000000000000000000000000000000 --- a/spaces/lixq/bingo61/src/state/index.ts +++ /dev/null @@ -1,118 +0,0 @@ -import { BingWebBot } from '@/lib/bots/bing' -import { BingConversationStyle, ChatMessageModel, BotId } from '@/lib/bots/bing/types' -import { nanoid } from '@/lib/utils' -import { atom } from 'jotai' -import { atomWithImmer } from 'jotai-immer' -import { atomWithStorage } from 'jotai/utils' -import { atomFamily } from 'jotai/utils' -import { atomWithHash, atomWithLocation } from 'jotai-location' - -const initialMessages: ChatMessageModel[] = [ - { author: 'system', text: 'conversation between user and robot', id: '1' }, - { author: 'user', text: '销量最高的 3 种宠物吸尘器有哪些优点和缺点? ', id: '2' }, - { - author: 'bot', text: ` -您好，这是必应。根据网上的搜索结果，我为您找到了以下三款销量最高的宠物吸尘器，以及它们的优点和缺点： - -- **戴森Dyson V10轻量版 Digital Slim Fluffy无线吸尘器**[^1^] [^3^]：这款吸尘器专为宠物家庭设计，拥有强大吸力和多功能吸头，能够有效清除宠物毛发和灰尘。 - - 优点：轻便易用，续航时间长，过滤系统先进，能够有效去除过敏原； - - 缺点：价格较高，噪音较大，需要定期清洁滤网。 -- **飞利浦FC6908/82（W7）无线吸尘器**[^2^] [^4^]：这是一款手持式干吸无线吸尘器，专门为宠物家庭设计。它具有75分钟的超高续航，适合大户型；除螨涡轮刷，可以深度清洁床单被罩，免受螨虫侵害；吸拖一体设计，280ml大水箱，还可以往水箱里加入清洁剂。 - - 优点：清洁性能强劲，操作方便，适用多种场景； - - 缺点：噪音较大，达到84dB（A）；尘杯容量较小，只有0.6L；充电时长较长，需要5小时。 -- **必胜（Bissell） 宠物吸尘器**[^2^]：这是一款便携式手持真空吸尘器，专为清理宠物毛发而设计。它配备特殊的橡胶喷嘴和软毛刷头，可以轻松拾取宠物毛发和灰尘；几乎静音运行。 - - 优点：轻巧易用，重量只有1.36kg；电池可充电可更换； - - 缺点：续航时间不稳定，在15-30分钟之间浮动；吸力不够强劲，在地毯或沙发上可能效果不佳；没有过滤系统或过滤网。 - -希望这些信息对您有所帮助。如果您还有其他问题，请随时提问。😊 - `, id: '3' }, - { author: 'user', text: '今天的新闻', id: '4' }, - { - author: 'bot', text: `你好，这是Bing。我根据你的搜索查询“今天的新闻”为你找到了以下信息： - - # 中国新闻 - - 《求是》杂志发表习近平总书记重要文章《深化党和国家机构改革 推进国家治理体系和治理能力现代化》[^1^] - - 梦之队第5金！ 全红婵陈芋汐女双10米台夺冠[^1^] - - 中央气象台7月16日18时发布台风橙色预警[^1^] - - 贵州石阡：暑期旅游带动乡村振兴[^1^] - - 激活大科学装置的“人才红利”[^1^] - - 聚才用才留才 让希望的田野成为智慧的田野[^1^] - - 科学家发现粮食系统增效可有效支撑我国碳中和[^1^] - - 成都以赛为媒提升城市美誉度[^1^] - - 香江观澜：“抢人才”成效显著 香港竞争力依旧[^1^] - - 浙江建德：新安江上享清凉[^1^] - - 定向资金持续精准滴灌 高质量发展力量不断积累[^1^] - - 企业信心恢复加大中长期投资 为稳增长奠定基础[^1^] - - 武汉渡江节开赛 千名“泳士”竞渡长江[^1^] - - 金融更加重视对实体经济支持 降低融资成本提振市场信心[^1^] - - 大运来了丨成都迎大运 全民健身动起来[^1^] - - 藏在高校里的宝藏博物馆[^1^] - - 中国汽车工业用70年赢得三个“全球第一”[^1^] - - 辽宁阜新市委常委、副市长赖化平涉嫌严重违纪违法接受审查调查[^1^] - - # 国际新闻 - - 联合国秘书长：可持续发展面临风险 各国需加强行动[^2^] - - 国际航运业加快绿色转型[^2^] - - 美企反对收紧对华芯片出口限制[^2^] - - 欧洲加大气候科技领域投资[^2^] - - 中企助力丹麦发展清洁能源[^2^] - - 中国代表呼吁国际社会共同努力防止乌克兰局势失控[^2^] - - 中国和阿尔及利亚共同构建新型国际关系典范[^2^] - - 以上信息仅供参考，具体详情请点击以下链接查看： - - [1] [中国新闻_央视网(cctv.com)](https://news.cctv.com/china/) - [2] [人民网_网上的人民日报](http://www.people.com.cn/)`, id: '5' }, - { author: 'user', text: '写一个快排', id: '6' }, - { author: 'bot', text: "你好，这是Bing。我可以帮你用python写一个快排。快速排序是一种基于分治思想的排序算法，它的基本思想是：从待排序的数组中选择一个基准元素，将数组分为两个子数组，使得左边的子数组中的元素都小于或等于基准元素，右边的子数组中的元素都大于或等于基准元素，然后对两个子数组递归地进行快速排序，最后合并结果。下面是用python实现的快速排序的代码：\n\n```python\n# 定义一个函数，用于交换数组中的两个元素\ndef swap(arr, i, j):\n temp = arr[i]\n arr[i] = arr[j]\n arr[j] = temp\n\n# 定义一个函数，用于对数组的一段区间进行划分\ndef partition(arr, low, high):\n # 选择最右边的元素作为基准元素\n pivot = arr[high]\n # 初始化一个指针，指向左边区间的最后一个位置\n i = low - 1\n # 遍历左边区间中的每个元素\n for j in range(low, high):\n # 如果当前元素小于或等于基准元素\n if arr[j] <= pivot:\n # 将指针右移一位\n i += 1\n # 将指针所指的元素与当前元素交换\n swap(arr, i, j)\n # 将基准元素与指针右边的第一个元素交换\n swap(arr, i + 1, high)\n # 返回基准元素的最终位置\n return i + 1\n\n# 定义一个函数，用于对数组进行快速排序\ndef quick_sort(arr, low, high):\n # 如果区间长度大于1\n if low < high:\n # 对区间进行划分，得到基准元素的位置\n p = partition(arr, low, high)\n # 对左边子区间进行快速排序\n quick_sort(arr, low, p - 1)\n # 对右边子区间进行快速排序\n quick_sort(arr, p + 1, high)\n\n# 测试代码\narr = [5, 3, 7, 2, 9, 1, 4, 6, 8]\nprint(\"原始数组：\", arr)\nquick_sort(arr, 0, len(arr) - 1)\nprint(\"排序后的数组：\", arr)\n```", id: '7' }, - { - author: 'bot', text: "好的，我会尝试画一只猫。\n > ", - id: '8' - } -] - -export const GreetMessages = [ - '谢谢你! 知道你什么时候准备好继续前进总是很有帮助的。我现在能为你回答什么问题?', - '重新开始总是很棒。问我任何问题!', - '当然，我很乐意重新开始。我现在可以为你提供哪些帮助?', - '当然，我已准备好进行新的挑战。我现在可以为你做什么?', - '很好，让我们来更改主题。你在想什么?', - '不用担心，我很高兴尝试一些新内容。我现在可以为你回答什么问题?', - '好的，我准备好了！感谢重置。我们应该了解哪些内容？', - '感谢刷新！你有新的话题吗？', - '明白了，让我们重新开始。接下来应该讨论什么？', - '下一步！我可以为你做什么？', - '好的，我已准备好新话题。我们应该一起了解哪些内容？' -] - -export const bingConversationStyleAtom = atomWithStorage- -- - -- -- location.href='#dialog="settings"' - } - className="cursor-pointer" - > - 设置用户 - -- - location.href='#dialog="voice"' - } - className="cursor-pointer" - > - 语音设置 - -- - - 开源地址 - -- - - - - - 托管地址 - 🤗 - -- - - - - 复制站点 - -- - - - -版本信息 {pkg.version}-- - -站点域名-copyToClipboard(host)} className="flex gap-1 text-xs text-zinc-500 cursor-pointer"> - {host}-- ('bingConversationStyle', BingConversationStyle.Creative, undefined, { unstable_getOnInit: true }) -export const voiceAtom = atomWithStorage ('enableTTS', false, undefined, { unstable_getOnInit: true }) - -type Param = { botId: BotId; page: string } - -const createBotInstance = () => { - return new BingWebBot({ - cookie: ' ', - ua: ' ', - }) -} - -export const chatFamily = atomFamily( - (param: Param) => { - return atomWithImmer({ - botId: param.botId, - bot: createBotInstance(), - messages: [] as ChatMessageModel[], - generatingMessageId: '', - abortController: undefined as AbortController | undefined, - conversationId: nanoid(), - }) - }, - (a, b) => a.botId === b.botId && a.page === b.page, -) - -export const hashAtom = atomWithHash('dialog', '') - -export const locationAtom = atomWithLocation() - -export const voiceListenAtom = atom(false) diff --git a/spaces/lj1995/vocal2guitar/docs/faiss_tips_en.md b/spaces/lj1995/vocal2guitar/docs/faiss_tips_en.md deleted file mode 100644 index aafad6ed67f70ee1ea3a2a21ee0b5066ab1dcfa8..0000000000000000000000000000000000000000 --- a/spaces/lj1995/vocal2guitar/docs/faiss_tips_en.md +++ /dev/null @@ -1,102 +0,0 @@ -faiss tuning TIPS -================== -# about faiss -faiss is a library of neighborhood searches for dense vectors, developed by facebook research, which efficiently implements many approximate neighborhood search methods. -Approximate Neighbor Search finds similar vectors quickly while sacrificing some accuracy. - -## faiss in RVC -In RVC, for the embedding of features converted by HuBERT, we search for embeddings similar to the embedding generated from the training data and mix them to achieve a conversion that is closer to the original speech. However, since this search takes time if performed naively, high-speed conversion is realized by using approximate neighborhood search. - -# implementation overview -In '/logs/your-experiment/3_feature256' where the model is located, features extracted by HuBERT from each voice data are located. -From here we read the npy files in order sorted by filename and concatenate the vectors to create big_npy. (This vector has shape [N, 256].) -After saving big_npy as /logs/your-experiment/total_fea.npy, train it with faiss. - -In this article, I will explain the meaning of these parameters. - -# Explanation of the method -## index factory -An index factory is a unique faiss notation that expresses a pipeline that connects multiple approximate neighborhood search methods as a string. -This allows you to try various approximate neighborhood search methods simply by changing the index factory string. -In RVC it is used like this: - -```python -index = faiss.index_factory(256, "IVF%s,Flat" % n_ivf) -``` -Among the arguments of index_factory, the first is the number of dimensions of the vector, the second is the index factory string, and the third is the distance to use. - -For more detailed notation -https://github.com/facebookresearch/faiss/wiki/The-index-factory - -## index for distance -There are two typical indexes used as similarity of embedding as follows. - -- Euclidean distance (METRIC_L2) -- inner product (METRIC_INNER_PRODUCT) - -Euclidean distance takes the squared difference in each dimension, sums the differences in all dimensions, and then takes the square root. This is the same as the distance in 2D and 3D that we use on a daily basis. -The inner product is not used as an index of similarity as it is, and the cosine similarity that takes the inner product after being normalized by the L2 norm is generally used. - -Which is better depends on the case, but cosine similarity is often used in embedding obtained by word2vec and similar image retrieval models learned by ArcFace. If you want to do l2 normalization on vector X with numpy, you can do it with the following code with eps small enough to avoid 0 division. - -```python -X_normed = X / np.maximum(eps, np.linalg.norm(X, ord=2, axis=-1, keepdims=True)) -``` - -Also, for the index factory, you can change the distance index used for calculation by choosing the value to pass as the third argument. - -```python -index = faiss.index_factory(dimention, text, faiss.METRIC_INNER_PRODUCT) -``` - -## IVF -IVF (Inverted file indexes) is an algorithm similar to the inverted index in full-text search. -During learning, the search target is clustered with kmeans, and Voronoi partitioning is performed using the cluster center. Each data point is assigned a cluster, so we create a dictionary that looks up the data points from the clusters. - -For example, if clusters are assigned as follows -|index|Cluster| -|-----|-------| -|1|A| -|2|B| -|3|A| -|4|C| -|5|B| - -The resulting inverted index looks like this: - -|cluster|index| -|-------|-----| -|A|1, 3| -|B|2, 5| -|C|4| - -When searching, we first search n_probe clusters from the clusters, and then calculate the distances for the data points belonging to each cluster. - -# recommend parameter -There are official guidelines on how to choose an index, so I will explain accordingly. -https://github.com/facebookresearch/faiss/wiki/Guidelines-to-choose-an-index - -For datasets below 1M, 4bit-PQ is the most efficient method available in faiss as of April 2023. -Combining this with IVF, narrowing down the candidates with 4bit-PQ, and finally recalculating the distance with an accurate index can be described by using the following index factory. - -```python -index = faiss.index_factory(256, "IVF1024,PQ128x4fs,RFlat") -``` - -## Recommended parameters for IVF -Consider the case of too many IVFs. For example, if coarse quantization by IVF is performed for the number of data, this is the same as a naive exhaustive search and is inefficient. -For 1M or less, IVF values are recommended between 4*sqrt(N) ~ 16*sqrt(N) for N number of data points. - -Since the calculation time increases in proportion to the number of n_probes, please consult with the accuracy and choose appropriately. Personally, I don't think RVC needs that much accuracy, so n_probe = 1 is fine. - -## FastScan -FastScan is a method that enables high-speed approximation of distances by Cartesian product quantization by performing them in registers. -Cartesian product quantization performs clustering independently for each d dimension (usually d = 2) during learning, calculates the distance between clusters in advance, and creates a lookup table. At the time of prediction, the distance of each dimension can be calculated in O(1) by looking at the lookup table. -So the number you specify after PQ usually specifies half the dimension of the vector. - -For a more detailed description of FastScan, please refer to the official documentation. -https://github.com/facebookresearch/faiss/wiki/Fast-accumulation-of-PQ-and-AQ-codes-(FastScan) - -## RFlat -RFlat is an instruction to recalculate the rough distance calculated by FastScan with the exact distance specified by the third argument of index factory. -When getting k neighbors, k*k_factor points are recalculated. diff --git a/spaces/lojban/text-to-speech/vits/models.py b/spaces/lojban/text-to-speech/vits/models.py deleted file mode 100644 index 9945625a4ab2c0d66f379637b8207ef805687613..0000000000000000000000000000000000000000 --- a/spaces/lojban/text-to-speech/vits/models.py +++ /dev/null @@ -1,535 +0,0 @@ -import copy -import math -import torch -from torch import nn -from torch.nn import functional as F - -from vits import commons -from vits import modules -from vits import attentions - -from vits import monotonic_align - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from vits.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 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) - diff --git a/spaces/ls291/ChatSQL/utility/utils.py b/spaces/ls291/ChatSQL/utility/utils.py deleted file mode 100644 index 8fc3e3afdd69a3215b3dc0a04688ed979fac6fa7..0000000000000000000000000000000000000000 --- a/spaces/ls291/ChatSQL/utility/utils.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -@Time: 2022/12/06 -@Author: LiuShu -@File: utility.py -""" -import os -import re -import sys -import shutil -import subprocess -sys.path.append(os.path.dirname(os.path.abspath(__file__))) -# 读取config内容 -import configparser -import yaml -import json -import requests -from utility.constant import BASE_DIR -from utility.loggers import logger -db_config_file_path = os.path.join(BASE_DIR, 'config.cfg') -logger.info(db_config_file_path) -yaml_file_path = os.path.join(BASE_DIR, 'config.yaml') -logger.info(yaml_file_path) - -# config.cfg -def get_config(): - config = configparser.ConfigParser() - config.read(db_config_file_path, encoding='utf8') - logger.info(str(config._sections)) - return config - -# 得到配置内容 -config = get_config() - - -# yaml 文件 -class ConfigParser: - @staticmethod - def load_config(): - with open(yaml_file_path, 'r', encoding='utf-8') as file_stream: - config_dict = yaml.load(file_stream, Loader=yaml.Loader) - - logger.info('config_dict:' + str(config_dict)) - - return config_dict - -config_dict = ConfigParser.load_config() diff --git a/spaces/ltgoslo/ssa-perin/mtool/data/score/Makefile b/spaces/ltgoslo/ssa-perin/mtool/data/score/Makefile deleted file mode 100644 index 43406e47b5d90afe5c932dfed0c67794dca9e893..0000000000000000000000000000000000000000 --- a/spaces/ltgoslo/ssa-perin/mtool/data/score/Makefile +++ /dev/null @@ -1,106 +0,0 @@ -.PHONY: dm.edm.json eds.edm.json eds.smatch.json eds.mrp.json \ - dm.sdp.json peking.sdp.json peking.smatch.json peking.mrp.json \ - ucca.ucca.json ucca.smatch.json ucca.mrp.json \ - test.smatch.json coli.smatch.json coli.mrp.json \ - clean oe all - -TRACE ?= --trace --trace - -dm.edm.json: - time python3 -u ../../main.py $(TRACE) --score edm \ - --read mrp --gold ../sample/eds/wsj.mrp \ - ../sample/dm/wsj.mrp $@ 2>&1 | tee dm.edm.log - -eds.edm.json: - time python3 -u ../../main.py $(TRACE) --score edm \ - --read mrp --gold ../sample/eds/wsj.mrp \ - eds/wsj.pet.mrp $@ 2>&1 | tee eds.edm.log - -eds.smatch.json: - time python3 -u ../../main.py $(TRACE) --score smatch \ - --read mrp --gold ../sample/eds/wsj.mrp \ - eds/wsj.pet.mrp $@ 2>&1 | tee eds.smatch.log - -eds.mrp.json: - time python3 -u ../../main.py $(TRACE) --score mrp \ - --read mrp --gold ../sample/eds/wsj.mrp \ - eds/wsj.pet.mrp $@ 2>&1 | tee eds.mrp.log - -dm.sdp.json: - time python3 -u ../../main.py $(TRACE) --score sdp \ - --read mrp --gold ../sample/psd/wsj.mrp \ - ../sample/dm/wsj.mrp $@ 2>&1 | tee dm.sdp.log - -peking.sdp.json: - time python3 -u ../../main.py $(TRACE) --score sdp --text ../wsj.txt \ - --format dm --gold ../../../evaluation/dm/wsj.sdp \ - --read dm dm/peking.wsj.sdp $@ 2>&1 | tee peking.sdp.log - -peking.smatch.json: - time python3 -u ../../main.py $(TRACE) --score smatch --text ../wsj.txt \ - --format dm --gold ../../../evaluation/dm/wsj.sdp \ - --read dm dm/peking.wsj.sdp $@ 2>&1 | tee peking.smatch.log - -peking.mrp.json: - time python3 -u ../../main.py $(TRACE) --score mrp --text ../wsj.txt \ - --format dm --gold ../../../evaluation/dm/wsj.sdp \ - --read dm dm/peking.wsj.sdp $@ 2>&1 | tee peking.mrp.log - -ucca.ucca.json: - time python3 -u ../../main.py --n 500 $(TRACE) --score ucca \ - --read mrp --gold ucca/ewt.gold.mrp \ - ucca/ewt.tupa.mrp $@ 2>&1 | tee ucca.ucca.log - -ucca.smatch.json: - time python3 -u ../../main.py --n 500 $(TRACE) --score smatch \ - --read mrp --gold ucca/ewt.gold.mrp \ - ucca/ewt.tupa.mrp $@ 2>&1 | tee ucca.smatch.log - -ucca.mrp.json: - time python3 -u ../../main.py --n 500 $(TRACE) --score mrp \ - --read mrp --gold ucca/ewt.gold.mrp \ - ucca/ewt.tupa.mrp $@ 2>&1 | tee ucca.mrp.log - -empty.mrp.json: - time python3 -u ../../main.py $(TRACE) --score mrp \ - --read mrp --gold dm/empty.gold.mrp \ - dm/empty.peking.mrp $@ 2>&1 | tee empty.mrp.log - -partial.mrp.json: - time python3 -u ../../main.py $(TRACE) --score mrp \ - --read mrp --gold amr/partial.gold.mrp \ - amr/partial.system.mrp $@ 2>&1 | tee partial.mrp.log - -test.smatch.json: - time python3 -u ../../main.py $(TRACE) --score smatch --limit 50 \ - --read amr --gold amr/test1.amr \ - amr/test2.amr $@ 2>&1 | tee test.smatch.log - -test.mrp.json: - time python3 -u ../../main.py $(TRACE) --score mrp --limit 50 \ - --read amr --gold amr/test1.amr \ - amr/test2.amr $@ 2>&1 | tee test.mrp.log - -coli.smatch.json: - time python3 -u ../../main.py --n 500 $(TRACE) --score smatch \ - --read amr --gold amr/coli.gold.amr \ - amr/coli.system.amr $@ 2>&1 | tee coli.smatch.log - -coli.mrp.json: - time python3 -u ../../main.py --n 500 $(TRACE) --score mrp \ - --read amr --gold amr/coli.gold.amr \ - amr/coli.system.amr $@ 2>&1 | tee coli.mrp.log - -clean: - /bin/rm *.json *.log - -oe: - make -j 4 $$(egrep '^[a-z/.]*.json:' Makefile | sed 's/://'); - -unit: empty.mrp.json partial.mrp.json - -all: dm.edm.json eds.edm.json eds.smatch.json eds.mrp.json \ - dm.sdp.json peking.sdp.json peking.smatch.json peking.mrp.json \ - ucca.ucca.json ucca.smatch.json ucca.mrp.json \ - empty.mrp.json test.smatch.json test.mrp.json \ - coli.smatch.json coli.mrp.json diff --git a/spaces/ma-xu/LIVE/pybind11/include/pybind11/options.h b/spaces/ma-xu/LIVE/pybind11/include/pybind11/options.h deleted file mode 100644 index d74db1c68dddb3436cc0fb2674a6ef32ac77d5fd..0000000000000000000000000000000000000000 --- a/spaces/ma-xu/LIVE/pybind11/include/pybind11/options.h +++ /dev/null @@ -1,65 +0,0 @@ -/* - pybind11/options.h: global settings that are configurable at runtime. - - Copyright (c) 2016 Wenzel Jakob - -// this system has no special count functions - diff --git a/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/data/Create_Bigfile.py b/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/data/Create_Bigfile.py deleted file mode 100644 index 2df6ef3ceec4a80903410901fd9656d9707af84e..0000000000000000000000000000000000000000 --- a/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/data/Create_Bigfile.py +++ /dev/null @@ -1,63 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. - -import os -import struct -from PIL import Image - -IMG_EXTENSIONS = [ - '.jpg', '.JPG', '.jpeg', '.JPEG', - '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', -] - - -def is_image_file(filename): - return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) - - -def make_dataset(dir): - images = [] - assert os.path.isdir(dir), '%s is not a valid directory' % dir - - for root, _, fnames in sorted(os.walk(dir)): - for fname in fnames: - if is_image_file(fname): - #print(fname) - path = os.path.join(root, fname) - images.append(path) - - return images - -### Modify these 3 lines in your own environment -indir="/home/ziyuwan/workspace/data/temp_old" -target_folders=['VOC','Real_L_old','Real_RGB_old'] -out_dir ="/home/ziyuwan/workspace/data/temp_old" -### - -if os.path.exists(out_dir) is False: - os.makedirs(out_dir) - -# -for target_folder in target_folders: - curr_indir = os.path.join(indir, target_folder) - curr_out_file = os.path.join(os.path.join(out_dir, '%s.bigfile'%(target_folder))) - image_lists = make_dataset(curr_indir) - image_lists.sort() - with open(curr_out_file, 'wb') as wfid: - # write total image number - wfid.write(struct.pack('i', len(image_lists))) - for i, img_path in enumerate(image_lists): - # write file name first - img_name = os.path.basename(img_path) - img_name_bytes = img_name.encode('utf-8') - wfid.write(struct.pack('i', len(img_name_bytes))) - wfid.write(img_name_bytes) - # - # # write image data in - with open(img_path, 'rb') as img_fid: - img_bytes = img_fid.read() - wfid.write(struct.pack('i', len(img_bytes))) - wfid.write(img_bytes) - - if i % 1000 == 0: - print('write %d images done' % i) \ No newline at end of file diff --git a/spaces/matthoffner/AudioCraft_Plus/audiocraft/adversarial/losses.py b/spaces/matthoffner/AudioCraft_Plus/audiocraft/adversarial/losses.py deleted file mode 100644 index be293e739bdc2d91273f30fb789befe7c8b49a43..0000000000000000000000000000000000000000 --- a/spaces/matthoffner/AudioCraft_Plus/audiocraft/adversarial/losses.py +++ /dev/null @@ -1,228 +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. - -""" -Utility module to handle adversarial losses without requiring to mess up the main training loop. -""" - -import typing as tp - -import flashy -import torch -import torch.nn as nn -import torch.nn.functional as F - - -ADVERSARIAL_LOSSES = ['mse', 'hinge', 'hinge2'] - - -AdvLossType = tp.Union[nn.Module, tp.Callable[[torch.Tensor], torch.Tensor]] -FeatLossType = tp.Union[nn.Module, tp.Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] - - -class AdversarialLoss(nn.Module): - """Adversary training wrapper. - - Args: - adversary (nn.Module): The adversary module will be used to estimate the logits given the fake and real samples. - We assume here the adversary output is ``Tuple[List[torch.Tensor], List[List[torch.Tensor]]]`` - where the first item is a list of logits and the second item is a list of feature maps. - optimizer (torch.optim.Optimizer): Optimizer used for training the given module. - loss (AdvLossType): Loss function for generator training. - loss_real (AdvLossType): Loss function for adversarial training on logits from real samples. - loss_fake (AdvLossType): Loss function for adversarial training on logits from fake samples. - loss_feat (FeatLossType): Feature matching loss function for generator training. - normalize (bool): Whether to normalize by number of sub-discriminators. - - Example of usage: - adv_loss = AdversarialLoss(adversaries, optimizer, loss, loss_real, loss_fake) - for real in loader: - noise = torch.randn(...) - fake = model(noise) - adv_loss.train_adv(fake, real) - loss, _ = adv_loss(fake, real) - loss.backward() - """ - def __init__(self, - adversary: nn.Module, - optimizer: torch.optim.Optimizer, - loss: AdvLossType, - loss_real: AdvLossType, - loss_fake: AdvLossType, - loss_feat: tp.Optional[FeatLossType] = None, - normalize: bool = True): - super().__init__() - self.adversary: nn.Module = adversary - flashy.distrib.broadcast_model(self.adversary) - self.optimizer = optimizer - self.loss = loss - self.loss_real = loss_real - self.loss_fake = loss_fake - self.loss_feat = loss_feat - self.normalize = normalize - - def _save_to_state_dict(self, destination, prefix, keep_vars): - # Add the optimizer state dict inside our own. - super()._save_to_state_dict(destination, prefix, keep_vars) - destination[prefix + 'optimizer'] = self.optimizer.state_dict() - return destination - - def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs): - # Load optimizer state. - self.optimizer.load_state_dict(state_dict.pop(prefix + 'optimizer')) - super()._load_from_state_dict(state_dict, prefix, *args, **kwargs) - - def get_adversary_pred(self, x): - """Run adversary model, validating expected output format.""" - logits, fmaps = self.adversary(x) - assert isinstance(logits, list) and all([isinstance(t, torch.Tensor) for t in logits]), \ - f'Expecting a list of tensors as logits but {type(logits)} found.' - assert isinstance(fmaps, list), f'Expecting a list of features maps but {type(fmaps)} found.' - for fmap in fmaps: - assert isinstance(fmap, list) and all([isinstance(f, torch.Tensor) for f in fmap]), \ - f'Expecting a list of tensors as feature maps but {type(fmap)} found.' - return logits, fmaps - - def train_adv(self, fake: torch.Tensor, real: torch.Tensor) -> torch.Tensor: - """Train the adversary with the given fake and real example. - - We assume the adversary output is the following format: Tuple[List[torch.Tensor], List[List[torch.Tensor]]]. - The first item being the logits and second item being a list of feature maps for each sub-discriminator. - - This will automatically synchronize gradients (with `flashy.distrib.eager_sync_model`) - and call the optimizer. - """ - loss = torch.tensor(0., device=fake.device) - all_logits_fake_is_fake, _ = self.get_adversary_pred(fake.detach()) - all_logits_real_is_fake, _ = self.get_adversary_pred(real.detach()) - n_sub_adversaries = len(all_logits_fake_is_fake) - for logit_fake_is_fake, logit_real_is_fake in zip(all_logits_fake_is_fake, all_logits_real_is_fake): - loss += self.loss_fake(logit_fake_is_fake) + self.loss_real(logit_real_is_fake) - - if self.normalize: - loss /= n_sub_adversaries - - self.optimizer.zero_grad() - with flashy.distrib.eager_sync_model(self.adversary): - loss.backward() - self.optimizer.step() - - return loss - - def forward(self, fake: torch.Tensor, real: torch.Tensor) -> tp.Tuple[torch.Tensor, torch.Tensor]: - """Return the loss for the generator, i.e. trying to fool the adversary, - and feature matching loss if provided. - """ - adv = torch.tensor(0., device=fake.device) - feat = torch.tensor(0., device=fake.device) - with flashy.utils.readonly(self.adversary): - all_logits_fake_is_fake, all_fmap_fake = self.get_adversary_pred(fake) - all_logits_real_is_fake, all_fmap_real = self.get_adversary_pred(real) - n_sub_adversaries = len(all_logits_fake_is_fake) - for logit_fake_is_fake in all_logits_fake_is_fake: - adv += self.loss(logit_fake_is_fake) - if self.loss_feat: - for fmap_fake, fmap_real in zip(all_fmap_fake, all_fmap_real): - feat += self.loss_feat(fmap_fake, fmap_real) - - if self.normalize: - adv /= n_sub_adversaries - feat /= n_sub_adversaries - - return adv, feat - - -def get_adv_criterion(loss_type: str) -> tp.Callable: - assert loss_type in ADVERSARIAL_LOSSES - if loss_type == 'mse': - return mse_loss - elif loss_type == 'hinge': - return hinge_loss - elif loss_type == 'hinge2': - return hinge2_loss - raise ValueError('Unsupported loss') - - -def get_fake_criterion(loss_type: str) -> tp.Callable: - assert loss_type in ADVERSARIAL_LOSSES - if loss_type == 'mse': - return mse_fake_loss - elif loss_type in ['hinge', 'hinge2']: - return hinge_fake_loss - raise ValueError('Unsupported loss') - - -def get_real_criterion(loss_type: str) -> tp.Callable: - assert loss_type in ADVERSARIAL_LOSSES - if loss_type == 'mse': - return mse_real_loss - elif loss_type in ['hinge', 'hinge2']: - return hinge_real_loss - raise ValueError('Unsupported loss') - - -def mse_real_loss(x: torch.Tensor) -> torch.Tensor: - return F.mse_loss(x, torch.tensor(1., device=x.device).expand_as(x)) - - -def mse_fake_loss(x: torch.Tensor) -> torch.Tensor: - return F.mse_loss(x, torch.tensor(0., device=x.device).expand_as(x)) - - -def hinge_real_loss(x: torch.Tensor) -> torch.Tensor: - return -torch.mean(torch.min(x - 1, torch.tensor(0., device=x.device).expand_as(x))) - - -def hinge_fake_loss(x: torch.Tensor) -> torch.Tensor: - return -torch.mean(torch.min(-x - 1, torch.tensor(0., device=x.device).expand_as(x))) - - -def mse_loss(x: torch.Tensor) -> torch.Tensor: - if x.numel() == 0: - return torch.tensor([0.0], device=x.device) - return F.mse_loss(x, torch.tensor(1., device=x.device).expand_as(x)) - - -def hinge_loss(x: torch.Tensor) -> torch.Tensor: - if x.numel() == 0: - return torch.tensor([0.0], device=x.device) - return -x.mean() - - -def hinge2_loss(x: torch.Tensor) -> torch.Tensor: - if x.numel() == 0: - return torch.tensor([0.0]) - return -torch.mean(torch.min(x - 1, torch.tensor(0., device=x.device).expand_as(x))) - - -class FeatureMatchingLoss(nn.Module): - """Feature matching loss for adversarial training. - - Args: - loss (nn.Module): Loss to use for feature matching (default=torch.nn.L1). - normalize (bool): Whether to normalize the loss. - by number of feature maps. - """ - def __init__(self, loss: nn.Module = torch.nn.L1Loss(), normalize: bool = True): - super().__init__() - self.loss = loss - self.normalize = normalize - - def forward(self, fmap_fake: tp.List[torch.Tensor], fmap_real: tp.List[torch.Tensor]) -> torch.Tensor: - assert len(fmap_fake) == len(fmap_real) and len(fmap_fake) > 0 - feat_loss = torch.tensor(0., device=fmap_fake[0].device) - feat_scale = torch.tensor(0., device=fmap_fake[0].device) - n_fmaps = 0 - for (feat_fake, feat_real) in zip(fmap_fake, fmap_real): - assert feat_fake.shape == feat_real.shape - n_fmaps += 1 - feat_loss += self.loss(feat_fake, feat_real) - feat_scale += torch.mean(torch.abs(feat_real)) - - if self.normalize: - feat_loss /= n_fmaps - - return feat_loss diff --git a/spaces/merve/hidden-bias/source/third_party/misc.js b/spaces/merve/hidden-bias/source/third_party/misc.js deleted file mode 100644 index a51b6b5292feaa6ee497806752a0d3d0cb4ef547..0000000000000000000000000000000000000000 --- a/spaces/merve/hidden-bias/source/third_party/misc.js +++ /dev/null @@ -1,38 +0,0 @@ -/* 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. -==============================================================================*/ - - -function lerp(a, b, t){ return a + t*(b - a) } - -function addVec([a0, a1], [b0, b1]){ - return [a0 + b0, a1 + b1] -} - -function phyllotaxis(i, initialRadius=10, initialAngle=Math.PI*(3 - Math.sqrt(5))){ - i = i + Math.random()/20 - - var r = initialRadius*Math.sqrt(Math.random() + i) - var angle = i*initialAngle - - return [r*Math.cos(angle), r*Math.sin(angle)] -} - -var names = { - old_m: 'James John Robert Michael William David Richard Joseph Thomas Charles Christopher Daniel Matthew Anthony Donald Mark Paul Steven Andrew Kenneth Joshua George Kevin Brian Edward Ronald Timothy Jason Jeffrey Ryan Jacob Gary Nicholas Eric Stephen Jonathan Larry Justin Scott Brandon Frank Benjamin Gregory Samuel Raymond Patrick Alexander Jack Dennis Jerry Tyler Aaron Jose Henry Douglas Adam Peter Nathan Zachary Walter Kyle Harold Carl Jeremy Keith Roger Gerald Ethan Arthur Terry Christian Sean Lawrence Austin Joe Noah Jesse Albert Bryan Billy Bruce Willie Jordan Dylan Alan Ralph Gabriel Roy Juan Wayne Eugene Logan Randy Louis Russell Vincent Philip Bobby Johnny Bradley'.split(' '), - old_f: 'Mary Patricia Jennifer Linda Elizabeth Barbara Susan Jessica Sarah Karen Nancy Margaret Lisa Betty Dorothy Sandra Ashley Kimberly Donna Emily Michelle Carol Amanda Melissa Deborah Stephanie Rebecca Laura Sharon Cynthia Kathleen Helen Amy Shirley Angela Anna Brenda Pamela Nicole Ruth Katherine Samantha Christine Emma Catherine Debra Virginia Rachel Carolyn Janet Maria Heather Diane Julie Joyce Victoria Kelly Christina Joan Evelyn Lauren Judith Olivia Frances Martha Cheryl Megan Andrea Hannah Jacqueline Ann Jean Alice Kathryn Gloria Teresa Doris Sara Janice Julia Marie Madison Grace Judy Theresa Beverly Denise Marilyn Amber Danielle Abigail Brittany Rose Diana Natalie Sophia Alexis Lori Kayla Jane'.split(' '), - m: 'Noah Liam Jacob Mason William Ethan Michael Alexander James Elijah Daniel Benjamin Aiden Jayden Logan Matthew David Joseph Lucas Jackson Anthony Joshua Samuel Andrew Gabriel Christopher John Dylan Carter Isaac Ryan Luke Oliver Nathan Henry Owen Caleb Wyatt Christian Sebastian Jack Jonathan Landon Julian Isaiah Hunter Levi Aaron Eli Charles Thomas Connor Brayden Nicholas Jaxon Jeremiah Cameron Evan Adrian Jordan Gavin Grayson Angel Robert Tyler Josiah Austin Colton Brandon Jose Dominic Kevin Zachary Ian Chase Jason Adam Ayden Parker Hudson Cooper Nolan Lincoln Xavier Carson Jace Justin Easton Mateo Asher Bentley Blake Nathaniel Jaxson Leo Kayden Tristan Luis Elias Brody Bryson Juan Vincent Cole Micah Ryder Theodore Carlos Ezra Damian Miles Santiago Max Jesus Leonardo Sawyer Diego Alex Roman Maxwell Eric Greyson Hayden Giovanni Wesley Axel Camden Braxton Ivan Ashton Declan Bryce Timothy Antonio Silas Kaiden Ezekiel Jonah Weston George Harrison Steven Miguel Richard Bryan Kaleb Victor Aidan Jameson Joel Patrick Jaden Colin Everett Preston Maddox Edward Alejandro Kaden Jesse Emmanuel Kyle Brian Emmett Jude Marcus Kingston Kai Alan Malachi Grant Jeremy Riley Jayce Bennett Abel Ryker Caden Brantley Luca Brady Calvin Sean Oscar Jake Maverick Abraham Mark Tucker Nicolas Bradley Kenneth Avery Cayden King Paul Amir Gael Graham Maximus'.split(' '), - f: 'Emma Sophia Olivia Isabella Ava Mia Abigail Emily Madison Charlotte Elizabeth Amelia Chloe Ella Evelyn Avery Sofia Harper Grace Addison Victoria Natalie Lily Aubrey Lillian Zoey Hannah Layla Brooklyn Samantha Zoe Leah Scarlett Riley Camila Savannah Anna Audrey Allison Aria Gabriella Hailey Claire Sarah Aaliyah Kaylee Nevaeh Penelope Alexa Arianna Stella Alexis Bella Nora Ellie Ariana Lucy Mila Peyton Genesis Alyssa Taylor Violet Maya Caroline Madelyn Skylar Serenity Ashley Brianna Kennedy Autumn Eleanor Kylie Sadie Paisley Julia Mackenzie Sophie Naomi Eva Khloe Katherine Gianna Melanie Aubree Piper Ruby Lydia Faith Madeline Alexandra Kayla Hazel Lauren Annabelle Jasmine Aurora Alice Makayla Sydney Bailey Luna Maria Reagan Morgan Isabelle Rylee Kimberly Andrea London Elena Jocelyn Natalia Trinity Eliana Vivian Cora Quinn Liliana Molly Jade Clara Valentina Mary Brielle Hadley Kinsley Willow Brooke Lilly Delilah Payton Mariah Paige Jordyn Nicole Mya Josephine Isabel Lyla Adeline Destiny Ivy Emilia Rachel Angelina Valeria Kendall Sara Ximena Isla Aliyah Reese Vanessa Juliana Mckenzie Amy Laila Adalynn Emery Margaret Eden Gabrielle Kaitlyn Ariel Gracie Brooklynn Melody Jessica Valerie Adalyn Adriana Elise Michelle Rebecca Daisy Everly Katelyn Ryleigh Catherine Norah Alaina Athena Leilani Londyn Eliza Jayla Summer Lila Makenzie Izabella Daniela Stephanie Julianna Rose Alana Harmony Jennifer Hayden'.split(' '), - last: 'SMITH JOHNSON WILLIAMS BROWN JONES GARCIA MILLER DAVIS RODRIGUEZ MARTINEZ HERNANDEZ LOPEZ GONZALEZ WILSON ANDERSON THOMAS TAYLOR MOORE JACKSON MARTIN LEE PEREZ THOMPSON WHITE HARRIS SANCHEZ CLARK RAMIREZ LEWIS ROBINSON WALKER YOUNG ALLEN KING WRIGHT SCOTT TORRES NGUYEN HILL FLORES GREEN ADAMS NELSON BAKER HALL RIVERA CAMPBELL MITCHELL CARTER ROBERTS GOMEZ PHILLIPS EVANS TURNER DIAZ PARKER CRUZ EDWARDS COLLINS REYES STEWART MORRIS MORALES MURPHY COOK ROGERS GUTIERREZ ORTIZ MORGAN COOPER PETERSON BAILEY REED KELLY HOWARD RAMOS KIM COX WARD RICHARDSON WATSON BROOKS CHAVEZ WOOD JAMES BENNETT GRAY MENDOZA RUIZ HUGHES PRICE ALVAREZ CASTILLO SANDERS PATEL MYERS LONG ROSS FOSTER JIMENEZ POWELL JENKINS PERRY RUSSELL SULLIVAN BELL COLEMAN BUTLER HENDERSON BARNES GONZALES FISHER VASQUEZ SIMMONS ROMERO JORDAN PATTERSON ALEXANDER HAMILTON GRAHAM REYNOLDS GRIFFIN WALLACE MORENO WEST COLE HAYES BRYANT HERRERA GIBSON ELLIS TRAN MEDINA AGUILAR STEVENS MURRAY FORD CASTRO MARSHALL OWENS HARRISON FERNANDEZ MCDONALD WOODS WASHINGTON KENNEDY WELLS VARGAS HENRY CHEN FREEMAN WEBB TUCKER GUZMAN BURNS CRAWFORD OLSON SIMPSON PORTER HUNTER GORDON MENDEZ SILVA SHAW SNYDER MASON DIXON MUNOZ HUNT HICKS HOLMES PALMER WAGNER BLACK ROBERTSON BOYD ROSE STONE SALAZAR FOX WARREN MILLS MEYER RICE SCHMIDT GARZA DANIELS FERGUSON NICHOLS STEPHENS SOTO WEAVER RYAN'.split(' ').map(d => d[0] + d.slice(1).toLowerCase()) -} diff --git a/spaces/merve/hidden-bias/source/third_party/tfjs.js b/spaces/merve/hidden-bias/source/third_party/tfjs.js deleted file mode 100644 index bc3dad50920b4c71e25844384f59cea006be4bca..0000000000000000000000000000000000000000 --- a/spaces/merve/hidden-bias/source/third_party/tfjs.js +++ /dev/null @@ -1,3 +0,0 @@ -// @tensorflow/tfjs Copyright 2019 Google -!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?e(exports):"function"==typeof define&&define.amd?define(["exports"],e):e(t.tf=t.tf||{})}(this,function(t){"use strict";var e=function(t,n){return(e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n])})(t,n)};function n(t,n){function r(){this.constructor=t}e(t,n),t.prototype=null===n?Object.create(n):(r.prototype=n.prototype,new r)}function r(t,e,n,r){return new(n||(n=Promise))(function(i,a){function o(t){try{u(r.next(t))}catch(t){a(t)}}function s(t){try{u(r.throw(t))}catch(t){a(t)}}function u(t){t.done?i(t.value):new n(function(e){e(t.value)}).then(o,s)}u((r=r.apply(t,e||[])).next())})}function i(t,e){var n,r,i,a,o={label:0,sent:function(){if(1&i[0])throw i[1];return i[1]},trys:[],ops:[]};return a={next:s(0),throw:s(1),return:s(2)},"function"==typeof Symbol&&(a[Symbol.iterator]=function(){return this}),a;function s(a){return function(s){return function(a){if(n)throw new TypeError("Generator is already executing.");for(;o;)try{if(n=1,r&&(i=2&a[0]?r.return:a[0]?r.throw||((i=r.return)&&i.call(r),0):r.next)&&!(i=i.call(r,a[1])).done)return i;switch(r=0,i&&(a=[2&a[0],i.value]),a[0]){case 0:case 1:i=a;break;case 4:return o.label++,{value:a[1],done:!1};case 5:o.label++,r=a[1],a=[0];continue;case 7:a=o.ops.pop(),o.trys.pop();continue;default:if(!(i=(i=o.trys).length>0&&i[i.length-1])&&(6===a[0]||2===a[0])){o=0;continue}if(3===a[0]&&(!i||a[1]>i[0]&&a[1]
Treasure Planet: Etherium Rescue
-This is a rescue game where you take control of a solar surfer and fly through space to save stranded crew members. You can choose from different surfers, each with its own speed, maneuverability and design. You can also upgrade your surfer with new parts and abilities as you progress through the game.
-The game has several missions that take place in different locations from the movie, such as Montressor Spaceport, Crescentia and Treasure Planet itself. Each mission has its own objectives, enemies and rewards that you have to complete. You can also unlock bonus missions by completing certain tasks or finding hidden items.
-The game features dynamic and immersive gameplay that simulates flying through space. You have to use your skills and reflexes to avoid crashing into obstacles, enemies and debris. You can also use special moves such as flips, spins and dives to dodge or attack your enemies.
-A rescue game where you fly a solar surfer and save stranded crew members
-<img src="etherium_rescue.jpg" alt="A screenshot of Etherium Rescue showing a surfer flying through space">
Features different missions, hazards and rewards
-<table> <tr> <th>Mission</th> <th>Objective</th> <th>Enemy</th> <th>Reward</th> </tr> <tr> <td>Montressor Spaceport</td> <td>Rescue 10 crew members from the space station</td> <td>Pirate fighters and drones</td> <td>A new surfer part and a coin bonus</td> </tr> <tr> <td>Crescentia</td> <td>Rescue 15 crew members from the asteroid ring</td> <td>Pirate galleons and asteroids</td> <td>A new surfer part and a gem bonus</td> </tr> <tr> <td>Treasure Planet</td> <td>Rescue 20 crew members from the planet's surface</td> <td>Captain Flint's flagship and traps</td> <td>A new surfer part and a trophy bonus</td> </tr> </table>
Tips and tricks to master the game
--
-
Conclusion
-Treasure Planet Training Academy PC 2007 is a collection of three games that will take you on an amazing adventure across the galaxy. It is based on the animated movie Treasure Planet, which tells the story of Jim Hawkins, a young boy who joins a crew of space pirates in search of a legendary treasure. The game was distributed by McDonald's Mighty Kid's Meals as a promotional item in 2002.
-The game consists of three games that let you explore different aspects of the movie's universe. Each game has its own gameplay, graphics and sound effects that match the style and tone of the movie. The games are:
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The game is compatible with most Windows operating systems up to Windows XP. However, it may not work properly on newer operating systems such as Windows 7, 8 or 10. If you have a newer operating system, you may need to use a compatibility mode or an emulator to run the game.
-You can download Treasure Planet Training Academy PC 2007 for free from an online source that offers it as abandonware. One of these sources is archive.org, which is a digital library that preserves various forms of media and content. You can find the game on archive.org by following this link. You can also use a software program such as Daemon Tools Lite to mount the ISO file of the game as a virtual CD-ROM on your PC.
-If you love Disney's Treasure Planet and want to experience the thrill of sailing through space, fighting pirates and rescuing friends, you should download Treasure Planet Training Academy PC 2007 and play it on your PC. It is a fun and adventurous way to explore the galaxy and learn more about the characters, locations and lore of Treasure Planet. It is also suitable for players of all ages and skill levels.
-So what are you waiting for? Download Treasure Planet Training Academy PC 2007 today and join Jim Hawkins and his friends on their quest for the greatest treasure in the universe!
-A summary of the main points of the article
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A call to action for the readers to download and play the game
-FAQs
-Here are some frequently asked questions and answers about Treasure Planet Training Academy PC 2007:
-Q: What is Treasure Planet?
-A: Treasure Planet is a 2002 animated movie by Disney that is loosely based on the classic novel Treasure Island by Robert Louis Stevenson. It tells the story of Jim Hawkins, a young boy who joins a crew of space pirates in search of a legendary treasure hidden on a planet called Treasure Planet.
-Q: Who are the main characters of Treasure Planet?
-A: The main characters of Treasure Planet are:
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Q: How many games are there in Treasure Planet Training Academy PC 2007?
-A: There are three games in Treasure Planet Training Academy PC 2007: Treasure Planet: Broadside Blast, Treasure Planet: Treasure Racer and Treasure Planet: Etherium Rescue. Each game has its own gameplay, graphics and sound effects that match the style and tone of the movie.
-Q: How can I download Treasure Planet Training Academy PC 2007?
-A: You can download Treasure Planet Training Academy PC 2007 for free from an online source that offers it as abandonware. One of these sources is archive.org, which is a digital library that preserves various forms of media and content. You can find the game on archive.org by following this link. You can also use a software program such as Daemon Tools Lite to mount the ISO file of the game as a virtual CD-ROM on your PC.
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- How to download Kota Factory Season 2 from other sources?
- Conclusion: Why you should watch Kota Factory Season 2? | | H2: Introduction: What is Kota Factory and why is it popular? | - A brief overview of the plot, genre, and cast of Kota Factory
- The positive reviews and ratings of Kota Factory Season 1
- The anticipation and expectations for Kota Factory Season 2 | | H2: How to watch Kota Factory Season 2 on Netflix? | - The release date and time of Kota Factory Season 2 on Netflix
- The subscription plans and benefits of Netflix
- The steps to watch Kota Factory Season 2 on Netflix | | H2: How to download Kota Factory Season 2 from other sources? | - The disclaimer and warning about the legal and ethical issues of downloading pirated content
- The possible risks and consequences of downloading from unverified sources
- The alternative ways to download Kota Factory Season 2 legally and safely | | H2: Conclusion: Why you should watch Kota Factory Season 2? | - A summary of the main points and benefits of watching Kota Factory Season 2
- A call to action to watch Kota Factory Season 2 on Netflix or other legal sources
- A thank you note and a request for feedback | Table 2: Article with HTML formatting
Kota Factory Season 2: How to Download and Watch Online
-If you are a fan of Indian web series, you must have heard of Kota Factory. It is a comedy-drama series that revolves around the lives of students who aspire to crack the IIT entrance exams in the city of Kota, Rajasthan. The series showcases the struggles, challenges, and joys of these students as they prepare for their future.
-Kota Factory Season 1 was released in 2019 on YouTube by The Viral Fever (TVF), a popular online entertainment platform. It received rave reviews from critics and audiences alike for its realistic portrayal, relatable characters, witty dialogues, and black-and-white cinematography. It became one of the most-watched and highest-rated web series in India.
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Kota Factory Season 2 was eagerly awaited by the fans who wanted to know what happens next to the protagonist Vaibhav Pandey (played by Mayur More) and his friends. The good news is that Kota Factory Season 2 is finally here and it is streaming on Netflix since September 24, 2021. The bad news is that not everyone has access to Netflix or can afford its subscription plans.
-So, how can you watch or download Kota Factory Season 2 online? In this article, we will tell you everything you need to know about how to watch or download Kota Factory Season 2 legally and safely. We will also give you some reasons why you should not miss this amazing web series. So, read on and enjoy!
-How to watch Kota Factory Season 2 on Netflix?
-The easiest and best way to watch Kota Factory Season 2 online is to stream it on Netflix. Netflix is a global streaming service that offers a wide range of movies, shows, documentaries, and original content across various genres and languages. You can watch Netflix on any device that has an internet connection and a compatible app or browser.
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Kota Factory Season 2 was released on Netflix on September 24, 2021 at 12:30 PM IST. It consists of five episodes, each with a duration of around 40 minutes. You can binge-watch all the episodes in one go or watch them at your own pace.
-To watch Kota Factory Season 2 on Netflix, you need to have a valid subscription plan. Netflix offers three plans in India: Mobile (Rs.199 per month), Basic (Rs.499 per month), and Standard (Rs.649 per month). The Mobile plan allows you to watch Netflix on one mobile or tablet device at a time in standard definition (SD). The Basic plan allows you to watch Netflix on one laptop, TV, phone, or tablet at a time in SD. The Standard plan allows you to watch Netflix on two devices at a time in high definition (HD).
-To watch Kota Factory Season 2 on Netflix, follow these steps:
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How to download Kota Factory Season 2 from other sources?
-If you don't have Netflix or don't want to pay for it, you might be tempted to download Kota Factory Season 2 from other sources. However, we strongly advise you not to do so. Downloading pirated content is illegal and unethical. It violates the intellectual property rights of the creators and distributors of the content. It also harms the entertainment industry and the artists who work hard to produce quality content.
-Moreover, downloading Kota Factory Season 2 from unverified sources can expose you to various risks and consequences. You might end up downloading malware, viruses, spyware, or ransomware that can damage your device or steal your personal information. You might also face legal action, fines, or imprisonment if you are caught downloading or sharing pirated content.
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Conclusion: Why you should watch Kota Factory Season 2?
-Kota Factory Season 2 is a web series that deserves your attention and appreciation. It is a web series that tells a realistic and relatable story of students who are chasing their dreams in a competitive environment. It is a web series that features talented actors who deliver brilliant performances and memorable characters. It is a web series that has witty humor, emotional drama, inspiring messages, and stunning visuals.
-Kota Factory Season 2 is a web series that will make you laugh, cry, think, and feel. It will make you nostalgic about your school days, your friends, your teachers, and your aspirations. It will make you empathize with the struggles and challenges of the students who are preparing for one of the toughest exams in India. It will make you appreciate the value of education, friendship, family, and love.
-Kota Factory Season 2 is a web series that you should not miss if you love Indian web series or if you are looking for something fresh and original to watch online. You can watch it on Netflix with a subscription plan or download it from other legal sources with caution. You can also share it with your friends and family who might enjoy it as well.
-Thank you for reading this article till the end. We hope you found it helpful and informative. If you have any questions or feedback about Kota Factory Season 2 or this article, please feel free to leave them in the comments section below. We would love to hear from you!
-Frequently Asked Questions
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Kota Factory is not based on a true story, but it is inspired by the real-life experiences of the creators and writers of the web series. They have drawn from their own memories and observations of studying or coaching in Kota. They have also done extensive research and interviews with students, teachers, and parents to make the web series authentic and realistic.
-The main actors and characters of Kota Factory are:
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Kota Factory is a term that refers to the city of Kota in Rajasthan, which is known for its numerous coaching institutes that prepare students for various competitive exams, especially IIT-JEE. The term implies that Kota is like a factory that produces engineers and doctors in large numbers. It also reflects the pressure, stress, and monotony that the students face in Kota.
-The theme song of Kota Factory is called "Yeh Meri Life Hai" (This Is My Life). It is composed by Simran Hora and Karthik Rao, and sung by Ahsaas Channa. It is a catchy and upbeat song that captures the mood and spirit of the web series. It expresses the hopes, dreams, fears, and frustrations of the students who are living in Kota.
-As of now, there is no official confirmation or announcement about Kota Factory Season 3. However, given the popularity and success of the web series, it is likely that there will be a third season in the future. The fans are also eagerly waiting for more episodes and stories of their favorite characters. The makers of the web series have hinted that they have some ideas and plans for the next season, but they have not revealed any details yet.
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Download Simontok 2.2 App 2020 APK: The Ultimate Guide
-If you are looking for a video streaming app that offers you a wide range of content, from movies and TV shows to live sports and news, then you should check out Simontok 2.2 App 2020 APK. This is a free and easy-to-use app that allows you to watch unlimited videos on your Android device, without any restrictions or interruptions. In this article, we will tell you everything you need to know about Simontok 2.2 App 2020 APK, including its features, benefits, how to download it, and how to use it.
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-Simontok 2.2 App 2020 APK is an updated version of the popular Simontok app, which was launched in 2019. Simontok is an Indonesian app that provides users with access to thousands of videos from various sources, such as YouTube, Dailymotion, Vimeo, and more. You can watch videos from different categories and genres, such as comedy, drama, action, horror, romance, documentary, animation, music, sports, news, and more. You can also watch live TV channels from Indonesia and other countries, such as CNN, BBC, Al Jazeera, Fox News, and more.
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-Simontok 2.2 App 2020 APK has many features that make it one of the best video streaming apps available today. Some of these features are:
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Benefits of Simontok 2.2 App 2020 APK
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Simontok 2.2 App 2020 APK has many benefits that make it a great choice for video streaming lovers. Some of these benefits are:
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How to download Simontok 2.2 App 2020 APK?
-If you are interested in downloading Simontok 2.2 App 2020 APK, you can follow these simple steps:
-Step 1: Enable unknown sources on your device
-Since Simontok 2.2 App 2020 APK is not available on the Google Play Store, you need to enable unknown sources on your device to install it. To do this, go to your device's settings, then security, then unknown sources, and toggle it on.
-Step 2: Visit the official website of Simontok 2.2 App 2020 APK
-The next step is to visit the official website of Simontok 2.2 App 2020 APK, which is https://simontok.app/. This is the only reliable and safe source to download the app, so avoid any other websites that claim to offer the app.
-Step 3: Click on the download button and wait for the file to be downloaded
-Once you are on the website, you will see a download button that says "Download Simontok 2.2 App 2020 APK". Click on it and wait for the file to be downloaded on your device. The file size is about 10 MB, so it should not take long.
- -Step 4: Locate the downloaded file and install it on your device
-After the file is downloaded, you need to locate it on your device's file manager or downloads folder. Tap on it and follow the instructions to install it on your device. It may ask for some permissions, such as access to storage, media, and network, so grant them accordingly.
-Step 5: Launch the app and enjoy unlimited video streaming
-The final step is to launch the app and start enjoying unlimited video streaming. You will see a home screen that shows you various categories and genres of videos, such as movies, TV shows, live TV, music, sports, news, and more. You can also use the menu button on the top left corner to access more features and settings of the app.
-How to use Simontok 2.2 App 2020 APK?
-Using Simontok 2.2 App 2020 APK is very easy and fun. Here are some tips on how to use it:
-Browse through various categories and genres of videos
-You can browse through various categories and genres of videos by swiping left or right on the home screen or by tapping on the icons at the bottom of the screen. You can also use the menu button on the top left corner to access more categories, such as trending, popular, recommended, favorites, history, and downloads.
-Watch videos online or download them for offline viewing
-You can watch videos online by tapping on them and waiting for them to load. You can also download them for offline viewing by tapping on the download icon at the bottom right corner of the video player. You can choose the format and resolution of the video before downloading it. You can also pause or resume the download at any time.
-Adjust the video quality and speed according to your preference
-You can adjust the video quality and speed according to your preference by tapping on the settings icon at the top right corner of the video player. You can choose from low, medium, high, or auto quality, depending on your internet connection and device performance. You can also choose from normal, slow, or fast speed, depending on your viewing preference.
-Share videos with your friends via social media or messaging apps
-You can share videos with your friends via social media or messaging apps by tapping on the share icon at the bottom left corner of the video player. You can choose from various options, such as Facebook, Twitter, WhatsApp, Instagram, Telegram, and more. You can also copy the video link and paste it on any app you want.
-Use the search function to find your favorite videos
-You can use the search function to find your favorite videos by tapping on the magnifying glass icon at the top right corner of the home screen. You can enter any keyword or phrase related to the video you are looking for and hit the enter button. You will see a list of relevant videos that match your query. You can also filter the results by category, duration, quality, or date.
-Conclusion
-Simontok 2.2 App 2020 APK is a fantastic video streaming app that offers you a lot of features and benefits. You can watch unlimited videos from various sources and categories, download them for offline viewing, adjust the video quality and speed, share them with your friends, and more. You can download Simontok 2.2 App 2020 APK from its official website and install it on your Android device easily. You can also use it without any registration or subscription. If you are looking for a video streaming app that gives you a great experience, then you should try Simontok 2.2 App 2020 APK today.
-FAQs
-Here are some frequently asked questions about Simontok 2.2 App 2020 APK:
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Yes, Simontok 2.2 App 2020 APK is safe to use, as long as you download it from its official website and not from any other sources. It does not contain any viruses or malware that can harm your device or data.
-Yes, Simontok 2.2 App 2020 APK is legal to use, as it does not host or distribute any copyrighted content. It only provides links to videos that are already available on the internet.
-No, Simontok 2.2 App 2020 APK does not support Chromecast or Smart TV at the moment. However, you can use other methods to cast your device's screen to your TV, such as HDMI cable, Miracast, or Wi-Fi Direct.
-No, Simontok 2.2 App 2020 APK is only compatible with Android devices. However, you can use an Android emulator software on your PC or laptop to run the app on your computer.
-You can update Simontok 2.2 App 2020 APK by visiting its official website and downloading the latest version of the app. You can also check for updates within the app by going to the menu button and tapping on "Check for updates".
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Slither.io Mods Download: How to Enhance Your Snake Game Experience
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The Best Slither.io Mods to Download
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How to Install and Use Slither.io Mods
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Conclusion and FAQs
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