diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Construct 3 Drift The Ultimate Guide to Creating a Racing Game with Skidding.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Construct 3 Drift The Ultimate Guide to Creating a Racing Game with Skidding.md
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How to Create a Drift Game with Construct 3
-
Construct 3 is a powerful and easy-to-use 2D game engine that allows you to create stunning games with drag-and-drop features. In this article, we will show you how to create a drift game with Construct 3, using the Car behavior and some basic events. A drift game is a racing game where you have to score points by drifting through the racing circuit. Drifting is a technique where the driver intentionally oversteers the car, causing it to slide sideways while maintaining control.
A Construct 3 account. You can sign up for free here.
-
A basic knowledge of Construct 3 interface and features. You can learn more from the manual and the tutorials.
-
A sprite of a car and a background image of a racing track. You can use your own assets or download some from the store.
-
-
Step 1: Create a New Project
-
Open Construct 3 and click on New project. Choose an empty project and name it Drift Game. Set the layout size to 800 x 600 pixels and the window size to the same. Click on Create.
-
Step 2: Add the Car Sprite
-
In the Project Bar, right-click on Object types and select Insert new object. Choose Sprite and name it Car. Click on Insert.
-
Double-click on the Car sprite to open the Image Editor. Import your car image or draw your own. Make sure the origin point is at the center of the car. Close the Image Editor.
-
-
Drag and drop the Car sprite to the layout. Position it at the bottom center of the screen.
-
Step 3: Add the Car Behavior
-
Select the Car sprite and click on Behaviors in the Properties Bar. Click on Add behavior and choose Car from the list. Click on Add.
-
The Car behavior allows an object to accelerate forwards and backwards and have steering. It also has a simple "drift" feature where the object can "skid" around corners (by pointing in a different direction to that it is moving in). You can adjust the properties of the Car behavior according to your preference. For this tutorial, we will use these values:
-
-
Property
Value
-
Max speed
300
-
Acceleration
500
-
Deceleration
500
-
Steer speed
200
-
Drift recover
100
-
Friction
0.5
-
Turn while stopped
No
-
Set angle
No
-
Default controls
No
-
Enabled
Yes
-
-
Step 4: Add the Background Image
-
In the Project Bar, right-click on Object types and select Insert new object. Choose Tiled Background and name it Track. Click on Insert.
-
Double-click on the Track object to open the Image Editor. Import your background image or draw your own. Close the Image Editor.
-
Drag and drop the Track object to the layout. Resize it to cover the whole layout.
-
Step 5: Add Some Events
-
In order to control the car movement and score points by drifting, we need to add some events. Events are like instructions that tell Construct 3 what to do when certain conditions are met. ddb901b051
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Burnout Paradise Vanity Pack 2.0 23l !!BETTER!!.md b/spaces/1gistliPinn/ChatGPT4/Examples/Burnout Paradise Vanity Pack 2.0 23l !!BETTER!!.md
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Burnout Paradise Vanity Pack 2.0 23l: The Best Way to Enjoy the Game
-
If you love racing games, you probably know about Burnout Paradise, the open-world game that lets you drive, crash, and explore a huge city. But did you know that you can make the game even better with a mod called Vanity Pack 2.0 23l?
Vanity Pack 2.0 23l is a mod that adds new cars, maps, and features to Burnout Paradise. It is the latest version of the mod, and it has some amazing improvements over the previous ones. Here are some of the things you can do with Vanity Pack 2.0 23l:
-
-
Drive over 100 new cars, including motorcycles, trucks, buses, and even planes.
-
Explore Big Surf Island, a new map that was only available on consoles before. It has new challenges, events, and secrets to discover.
-
Customize your cars with new paint jobs, decals, wheels, and more.
-
Use new features like speedometer, damage indicator, turbo boost meter, and camera modes.
-
Play online with other players who have the mod installed.
-
-
How to Install and Play with Vanity Pack 2.0 23l
-
Installing Vanity Pack 2.0 23l is easy and fast. All you need is a copy of Burnout Paradise on your PC and an internet connection. Here are the steps to follow:
Extract the zip file to your Burnout Paradise folder.
-
Run VanityPack.exe and follow the instructions.
-
Launch Burnout Paradise from Steam or Origin.
-
Enjoy the mod!
-
-
Note: You may need to disable your antivirus or firewall before installing the mod, as some of them may block it. You can also backup your save files before installing the mod, just in case something goes wrong.
-
-
Why You Should Try Vanity Pack 2.0 23l
-
Vanity Pack 2.0 23l is not just a mod, it is a whole new experience for Burnout Paradise fans. It adds so much content and variety to the game that you will never get bored of it. You can drive new cars, explore new places, customize your vehicles, and have fun with other players online.
-
Vanity Pack 2.0 23l is also very stable and compatible with the latest version of Burnout Paradise. It does not affect the performance or the graphics of the game. It only enhances them with new features and options.
-
If you want to see what Vanity Pack 2.0 23l can do for yourself, you can watch this video:
-
-
Vanity Pack 2.0 23l is the best way to enjoy Burnout Paradise on PC. It is free, easy to install, and fun to play. If you are a fan of the game, you should definitely give it a try.
-
How to Uninstall Vanity Pack 2.0 23l
-
If you want to uninstall Vanity Pack 2.0 23l for any reason, you can do it easily and safely. Here are the steps to follow:
-
-
Run VanityPack.exe and click on "Uninstall".
-
Wait for the process to finish and close the program.
-
Delete the VanityPack folder from your Burnout Paradise folder.
-
Launch Burnout Paradise from Steam or Origin.
-
The mod is now uninstalled.
-
-
Note: You may need to restore your save files from the backup you made before installing the mod, if you want to keep your progress.
-
Frequently Asked Questions about Vanity Pack 2.0 23l
-
Here are some of the most common questions and answers about Vanity Pack 2.0 23l:
-
-
Is Vanity Pack 2.0 23l safe to use? Yes, Vanity Pack 2.0 23l is safe to use and does not contain any viruses or malware. However, you should always download it from a trusted source and disable your antivirus or firewall before installing it, as some of them may block it.
-
Is Vanity Pack 2.0 23l compatible with other mods? Yes, Vanity Pack 2.0 23l is compatible with most of the other mods for Burnout Paradise, such as Burnout Hints, Burnout Config Tool, and Burnout Paradise Remastered Mod. However, you should always check the compatibility before installing any mod and follow the instructions carefully.
-
Is Vanity Pack 2.0 23l legal to use? Yes, Vanity Pack 2.0 23l is legal to use and does not violate any copyright or trademark laws. However, you should always respect the original creators of the game and the mod and give them credit for their work.
-
Can I play online with Vanity Pack 2.0 23l? Yes, you can play online with Vanity Pack 2.0 23l, but only with other players who have the mod installed. If you try to join a server that does not have the mod, you will be kicked out or banned. You can also create your own server and invite your friends who have the mod.
-
Where can I find more information about Vanity Pack 2.0 23l? You can find more information about Vanity Pack 2.0 23l on its official website, its Nexus Mods page, its YouTube channel, and its Discord server. You can also contact the developers and report any bugs or issues on their forums or email.
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diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Diddy Dirty Money Last Train To.md b/spaces/1gistliPinn/ChatGPT4/Examples/Diddy Dirty Money Last Train To.md
deleted file mode 100644
index 9ad8ed86a6b5fdb7ea9d6b6f9cf6e65a67120c8e..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/Diddy Dirty Money Last Train To.md
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Clash of Clans MOD APK Download Enjoy Unlimited Everything in the Latest Version of the Game.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Clash of Clans MOD APK Download Enjoy Unlimited Everything in the Latest Version of the Game.md
deleted file mode 100644
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--- a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Clash of Clans MOD APK Download Enjoy Unlimited Everything in the Latest Version of the Game.md
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-
Clash of Clans Mod APK Download Unlimited Everything Latest Version
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Are you a fan of strategy games that challenge your mind and skills? Do you want to build your own village, train your troops, and fight against other players from around the world? If yes, then you might have heard of Clash of Clans, one of the most popular mobile games ever. But what if you want to enjoy the game without any limitations or restrictions? What if you want to have unlimited resources, gems, troops, and everything else in the game? Well, that's where Clash of Clans Mod APK comes in. In this article, we will tell you everything you need to know about Clash of Clans Mod APK, including what it is, how to download it, and what are its benefits and risks. So, let's get started!
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What is Clash of Clans?
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Clash of Clans is a freemium strategy game developed by Supercell, a Finnish game company. It was released in 2012 for iOS and in 2013 for Android devices. The game has over 500 million downloads on Google Play Store and is one of the highest-grossing apps on both platforms. The game has also won several awards and accolades, such as the Best Mobile Game at the 2014 BAFTA Games Awards and the Best Multiplayer Game at the 2015 Pocket Gamer Awards.
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clash of clans mod apk download unlimited everything latest version
Clash of Clans has many features that make it an addictive and fun game to play. Some of these features are:
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Build your village and lead the people to a brighter future. You can create your own village from scratch, customize it with various buildings, defenses, walls, traps, and decorations. You can also upgrade your buildings and defenses to make them stronger and more efficient. You can also choose a name and a flag for your village and clan.
-
Defend your people from enemies’ attacks and raid other’s bases. You can train different types of troops, such as barbarians, archers, giants, wizards, dragons, and more. You can also use spells, siege machines, heroes, and pets to enhance your attacks and defenses. You can also join or create a clan with other players and participate in clan wars, clan games, clan war leagues, and friendly wars.
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Take on your epic adventure against the Goblin King. You can play the single-player campaign mode where you have to fight against the Goblin King and his army in various missions. You can also explore the Builder Base, a mysterious world where you can build a second village with different buildings and troops.
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Make use of the unique powers and strategies to join epic battles. You can plan your unique battle strategy with countless combinations of troops, spells, heroes, siege machines, and pets. You can also use different tactics and formations to attack or defend against different types of enemies. You can also compete with the best players from around the world and rise to the top of the leaderboard in Legend League.
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Discover many upgrade options to strengthen your clan. You can research upgrades in your laboratory to make your troops, spells, siege machines, heroes, and pets even more powerful. You can also collect resources and loot from other players to upgrade your buildings and defenses. You can also use magic items to speed up your progress or boost your performance.
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Enjoy various events and challenges. You can participate in various events and challenges that offer special rewards and bonuses. You can also complete achievements and tasks to earn gems, resources, magic items, hero skins, sceneries, and more.
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Explore the awesome Clan gameplay. You can join a clan or create your own with other players and enjoy the social aspect of the game. You can chat with your clanmates, donate and request troops, share replays and strategies, and support each other. You can also participate in clan wars, clan games, clan war leagues, and friendly wars to earn clan perks, rewards, and trophies.
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-
How to play Clash of Clans
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Clash of Clans is easy to play but hard to master. Here are some basic steps to get you started:
-
-
Download and install the game. You can download Clash of Clans from Google Play Store or App Store for free. You can also use an emulator to play it on your PC or Mac. Once you install the game, you can create your account and choose your name and flag.
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Complete the tutorial. The game will guide you through the basics of building your village, training your troops, and attacking other players. You can also watch some videos and tips to learn more about the game.
-
Build and upgrade your village. You can use the resources you collect from mines, collectors, storages, and raids to build and upgrade your buildings and defenses. You can also use gems to speed up the process or buy more resources. You can also customize your village with various decorations and sceneries.
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Train and upgrade your troops. You can use the barracks, dark barracks, siege workshop, hero altar, pet house, and laboratory to train and upgrade your troops, siege machines, heroes, and pets. You can also use gems to speed up the process or buy more troops. You can also choose different army compositions and strategies depending on your preference and target.
-
Attack and defend. You can use the map or the multiplayer mode to find and attack other players' bases. You can also use the revenge option to attack those who attacked you before. You can also join or create a clan and participate in clan wars, clan games, clan war leagues, and friendly wars. You can also play the single-player mode or the builder base mode for more fun and rewards.
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Have fun and enjoy the game. You can chat with other players, join a community, watch live streams, follow news and updates, participate in events and challenges, complete achievements and tasks, and more. You can also share your feedback and suggestions with the developers and help them improve the game.
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-
What is Clash of Clans Mod APK?
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Clash of Clans Mod APK is a modified version of the original game that allows you to have unlimited resources, gems, troops, and everything else in the game. It is not an official version of the game but a third-party application that is created by some developers or hackers. It is also not available on Google Play Store or App Store but on some websites or platforms that offer modded apps.
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Benefits of Clash of Clans Mod APK
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Clash of Clans Mod APK has some benefits that make it appealing to some players who want to have more fun and convenience in the game. Some of these benefits are:
-
-
You can have unlimited resources. You can have unlimited gold, elixir, dark elixir, gems, magic items, hero skins, sceneries, and more in the game. You can use them to build and upgrade your village, train and upgrade your troops, buy anything you want in the shop, and more. You don't have to worry about running out of resources or waiting for them to generate or collect.
-
You can have unlimited troops. You can have unlimited barbarians, archers, giants, wizards, dragons, and more in the game. You can also have unlimited siege machines, heroes, and pets. You can use them to attack and defend against any enemy you want. You don't have to worry about training time, housing space, or hero regeneration.
-
You can have unlimited access. You can have unlimited access to all the features, modes, events, and challenges in the game. You can also unlock all the buildings, defenses, troops, spells, siege machines, heroes, and pets in the game. You don't have to worry about level requirements, upgrade costs, or waiting time.
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You can have unlimited fun. You can have unlimited fun and enjoyment in the game. You can experiment with different strategies, tactics, and formations. You can also test your skills and creativity against other players or bots. You don't have to worry about losing resources, trophies, or progress.
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-
Risks of Clash of Clans Mod APK
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Clash of Clans Mod APK has some risks that make it risky and dangerous to use. Some of these risks are:
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You can get banned. You can get banned from the game if you use Clash of Clans Mod APK. The game has a strict anti-cheat system that detects and punishes any player who uses any modded or hacked app. You can lose your account, progress, and data permanently if you get caught.
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You can get infected. You can get infected with malware or viruses if you download Clash of Clans Mod APK from an untrusted or unknown source. The modded app may contain harmful or malicious code that can damage your device or steal your personal information. You can also expose your device to hackers or cybercriminals who may exploit your vulnerabilities.
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You can get bored. You can get bored of the game if you use Clash of Clans Mod APK. The game may lose its challenge, excitement, and satisfaction if you have everything unlimited and easy. You may also miss out on the original and authentic experience of the game that the developers intended for you.
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-
How to download and install Clash of Clans Mod APK?
-
If you still want to try Clash of Clans Mod APK despite its risks, you need to follow some steps to download and install it on your device. Here are the steps:
-
Step by step guide
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-
Backup your data. You need to backup your data before you download and install Clash of Clans Mod APK. You can use Google Play Games or Supercell ID to save your progress and data in the cloud. You can also use a file manager or a backup app to copy your data to another device or storage.
-
Uninstall the original game. You need to uninstall the original game before you download and install Clash of Clans Mod APK. You can go to your device settings and find the app manager or application list. Then you can tap on Clash of Clans and select uninstall option.
-
Download the modded app. You need to download the modded app from a trusted or reliable source. You can search for Clash of Clans Mod APK on Google or any other search engine. Then you can choose a website or platform that offers the modded app. You can also check the reviews, ratings, comments, and feedbacks of other users who have downloaded the modded app before.
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Enable unknown sources. You need to enable unknown sources before you install Clash of Clans Mod APK. You can go to your device settings and find the security or privacy option. Then you can toggle on the unknown sources option that allows you to install apps from sources other than Google Play Store or App Store.
-
Install the modded app. You need to install the modded app after you download it. You can go to your device file manager or downloads folder and find the modded app file. Then you can tap on it and select install option. You may need to grant some permissions or accept some terms and conditions before you install it.
-
Launch the modded app. You need to launch the modded app after you install it. You can go to your device home screen or app drawer and find the modded app icon. Then you can tap on it and open it. You may need to sign in with your account or create a new one before you play it.
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-
Tips and tricks for using Clash of Clans Mod APK
-
If you want to use Clash of Clans Mod APK effectively and safely, you need to follow some tips and tricks. Here on your main account or device. You should use it on a secondary account or device that you don't care about losing. You should also avoid linking your modded account to your Google Play Games or Supercell ID. You should also clear your cache and data before and after using the modded app.
-
Do not use the modded app online or with other players. You should not use the modded app online or with other players. You should use it offline or in single-player mode only. You should also avoid joining or creating a clan or participating in any clan wars, clan games, clan war leagues, or friendly wars. You should also avoid attacking or defending against any real players or bots.
-
Do not use the modded app for too long or too often. You should not use the modded app for too long or too often. You should use it sparingly and occasionally only. You should also switch back to the original game from time to time and enjoy the game as it is meant to be played. You should also take breaks and rest your eyes and mind from the game.
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-
Conclusion
-
Clash of Clans is a great game that offers a lot of fun and excitement to millions of players around the world. However, some players may want to have more freedom and convenience in the game by using Clash of Clans Mod APK, a modified version of the game that gives them unlimited everything. However, using Clash of Clans Mod APK is not without risks and drawbacks. It can get you banned, infected, or bored of the game. Therefore, you should be careful and responsible when using Clash of Clans Mod APK and follow some tips and tricks to use it effectively and safely.
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Summary of the article
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In this article, we have covered the following topics:
-
-
What is Clash of Clans and what are its features and how to play it?
-
What is Clash of Clans Mod APK and what are its benefits and risks?
-
How to download and install Clash of Clans Mod APK and what are some tips and tricks for using it?
-
-
FAQs
-
Here are some frequently asked questions about Clash of Clans Mod APK:
-
-
Is Clash of Clans Mod APK safe to use? No, Clash of Clans Mod APK is not safe to use. It can get you banned, infected, or bored of the game. It can also damage your device or steal your personal information. It is also illegal and unethical to use.
-
Is Clash of Clans Mod APK free to download? Yes, Clash of Clans Mod APK is free to download from some websites or platforms that offer modded apps. However, you should be careful and cautious when downloading it from an untrusted or unknown source.
-
Can I play Clash of Clans Mod APK with my friends? No, you cannot play Clash of Clans Mod APK with your friends. You can only play it offline or in single-player mode only. You cannot join or create a clan or participate in any clan wars, clan games, clan war leagues, or friendly wars. You cannot also attack or defend against any real players or bots.
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Can I update Clash of Clans Mod APK? No, you cannot update Clash of Clans Mod APK. You can only use the version that you have downloaded. If you want to update the game, you have to uninstall the modded app and install the original game from Google Play Store or App Store.
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Can I restore my data from Clash of Clans Mod APK? No, you cannot restore your data from Clash of Clans Mod APK. You can only backup your data before you download and install the modded app. You can also use Google Play Games or Supercell ID to save your progress and data in the cloud.
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diff --git a/spaces/1phancelerku/anime-remove-background/Brotato Extatonion Mod The Best Way to Enjoy Brotato in 2023.md b/spaces/1phancelerku/anime-remove-background/Brotato Extatonion Mod The Best Way to Enjoy Brotato in 2023.md
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-
Brotato Extension Download: How to Install and Use the Extatonion Mod
-
If you are a fan of Brotato, a top-down arena shooter roguelite game where you play as a potato fighting off hordes of aliens, you might be interested in trying out some mods that add new content and features to the game. One of the most popular and well-made mods for Brotato is Extatonion, a mod that adds new characters, weapons, items, and unlocks to the game. In this article, we will show you how to download and install Extatonion, what are its features and benefits, and what are some alternatives to it.
-
What is Brotato?
-
A top-down arena shooter roguelite game
-
Brotato is a game developed by Blobfish Games and published by Erabit Studios. It is a top-down arena shooter roguelite where you play as a potato wielding up to 6 weapons at a time to fight off multiple hordes of aliens. You can choose from a variety of traits and different items to create unique builds and survive until help arrives. The game has randomly generated levels, enemies, weapons, items, and bosses, making each run different and challenging.
Brotato is free to play on Steam, where it has Overwhelmingly Positive reviews from players who praise its gameplay, graphics, sound, humor, and replay value. The game was released in early access in 2022 and has been updated regularly with new content and features. The latest update (0.8) added modding and workshop support to the game, allowing players to create and share their own mods.
-
A game with modding and workshop support
-
Brotato uses a game engine called Godot, which is similar to Unity but easier to use. The game has modding support that allows players to create their own content using GodotSteam, a version of Godot with built-in Steam support. The game also has workshop support that allows players to browse, download, and subscribe to mods created by other players. However, not all mods are available on the workshop, as some modders prefer to host their mods on other platforms.
-
What is Extatonion?
-
A mod that adds new content to Brotato
-
Extatonion is a mod created by Psina, a Brotato player and modder. It is a mod that adds new content to the game that doesn't really stand out from the original game. But this does not mean that it is unoriginal or boring; Psina tries to make new content original and balanced. The mod adds new characters, weapons, items, unlocks, enemies, bosses, levels, secrets, achievements, sounds, sprites, effects, mechanics, and more. The mod is constantly updated and improved by Psina, who listens to feedback and suggestions from the community.
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How to install Extatonion mod for Brotato
-Brotato Extatonion mod guide and download link
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-Extatonion mod best potato skins and cosmetics for Brotato
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-Extatonion mod donation link and support for Psina
-How to uninstall or disable the extationon Mod in brotaro
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A mod that is updated regularly and compatible with the latest version of Brotato
-
Extatonion is one of the most active and updated mods for Brotato. Psina releases new updates every few weeks, adding new content and fixing bugs. The mod is also compatible with the latest version of Brotato (0.8), which means that it works with the modding and workshop support. Psina also makes sure that the mod is compatible with other popular mods, such as Potato Expansion Pack, Potato Plus, and Potato Overhaul.
-
A mod that has its own download sources and guide
-
Extatonion is not available on the workshop, as Psina prefers to host the mod on other platforms. The mod has its own GitHub page, where you can find the latest version of the mod, the changelog, the source code, and the license. The mod also has its own Discord server, where you can join the community, chat with Psina and other players, report bugs, give feedback, and request features. The mod also has its own installation guide, which explains how to download and install the mod step by step.
-
How to download and install Extatonion?
-
Step 1: Download GodotSteam and GDRETools
-
The first step to install Extatonion is to download GodotSteam and GDRETools. GodotSteam is a version of Godot with built-in Steam support, which is required to run Brotato mods. GDRETools is a tool that allows you to decompile and recompile Brotato projects. You can download both tools from their respective GitHub pages:
Extract both tools to a folder of your choice. Make sure you have Steam installed and running on your computer.
-
Step 2: Decompile Brotato with GDRETools
-
The next step is to decompile Brotato with GDRETools. This will allow you to access the game files and modify them with Extatonion. To do this, follow these steps:
-
-
Open GDRETools.exe.
-
Select "Decompile" from the menu.
-
Browse to your Steam folder and locate Brotato.exe (usually in Steam/steamapps/common/Brotato).
-
Select a destination folder for the decompiled project (preferably a new folder).
-
Click "Decompile" and wait for the process to finish.
-
-
You should now have a folder with the decompiled project of Brotato.
-
Step 3: Download Extatonion from the official sources
-
The third step is to download Extatonion from the official sources. You can find the latest version of the mod on its GitHub page or its Discord server:
Download the zip file of the mod and extract it to a folder of your choice.
-
Step 4: Copy the mod files to the decompiled project folder
The fourth step is to copy the mod files to the decompiled project folder. This will overwrite some of the original game files with the modded ones. To do this, follow these steps:
-
-
Open the folder where you extracted Extatonion.
-
Select all the files and folders inside it.
-
Copy them to the folder where you decompiled Brotato.
-
Replace any existing files if prompted.
-
-
You should now have a folder with the decompiled project of Brotato with Extatonion installed.
-
Step 5: Run Brotato in GodotSteam and enjoy the mod
-
The final step is to run Brotato in GodotSteam and enjoy the mod. To do this, follow these steps:
-
-
Open GodotSteam.exe.
-
Select "Import" from the menu.
-
Browse to the folder where you decompiled Brotato with Extatonion.
-
Select "project.godot" and click "Import & Edit".
-
Click "Play" on the top right corner of the window.
-
-
You should now be able to play Brotato with Extatonion mod. Have fun!
-
What are the features and benefits of Extatonion?
-
New characters, weapons, items, and unlocks
-
One of the main features of Extatonion is that it adds new characters, weapons, items, and unlocks to Brotato. These include:
-
-
4 new characters: Exta, Tonio, Nion, and Extonio. Each character has their own unique traits, weapons, and abilities.
-
12 new weapons: From laser guns to rocket launchers, from flamethrowers to ice cannons, from shotguns to sniper rifles, Extatonion adds a variety of new weapons to choose from.
-
16 new items: From passive items that boost your stats or give you special effects, to active items that let you use powerful abilities or summon allies, Extatonion adds a lot of new items to spice up your runs.
-
8 new unlocks: From new game modes to new difficulties, from new achievements to new secrets, Extatonion adds a lot of new unlocks to challenge yourself and discover more content.
-
-
Here is a table that summarizes some of the new content added by Extatonion:
- | Content | Name | Description | | --- | --- | --- | | Character | Exta | A potato with a passion for explosions. Starts with a grenade launcher and can throw grenades as a secondary attack. | | Weapon | Laser Gun | A weapon that fires a continuous beam of laser that pierces through enemies. | | Item | Potato Chip | A passive item that increases your movement speed and fire rate. | | Unlock | Hardcore Mode | A new game mode that makes the game harder by increasing enemy health and damage, reducing item drops and ammo, and disabling checkpoints. |
Original and balanced content that blends with the original game
-
Another feature of Extatonion is that it adds original and balanced content that blends with the original game. Psina tries to make the mod content fit with the theme, style, and mechanics of Brotato, while also adding some new twists and surprises. The mod content is also balanced and tested to ensure that it is not too easy or too hard, not too overpowered or too weak, not too common or too rare. Psina also listens to feedback and suggestions from the community and makes adjustments accordingly.
-
More variety and challenge for Brotato players
-
A final feature of Extatonion is that it adds more variety and challenge for Brotato players. The mod content adds more options and possibilities for creating different builds and strategies, as well as more diversity and difficulty for facing different enemies and situations. The mod content also adds more replay value and fun for playing Brotato again and again, as well as more rewards and satisfaction for completing achievements and secrets.
-
What are some alternatives to Extatonion?
-
Other mods on the Brotato Mods GitHub page
-
If you want to try other mods for Brotato besides Extatonion, you can check out the Brotato Mods GitHub page, where you can find a list of other mods created by other players and modders. Some of these mods include:
-
-
Potato Expansion Pack: A mod that adds 6 new characters, 18 new weapons, 24 new items, 12 new unlocks, 6 new enemies, 3 new bosses, 6 new levels, 6 new secrets, and 6 new achievements to the game.
-
Potato Plus: A mod that adds 4 new characters, 12 new weapons, 16 new items, 8 new unlocks, 4 new enemies, 2 new bosses, 4 new levels, 4 new secrets, and 4 new achievements to the game.
-
Potato Overhaul: A mod that changes the core gameplay of Brotato by adding new mechanics, such as stamina, reloading, ammo types, weapon mods, item tiers, and more.
-
-
You can download these mods from their respective GitHub pages or their workshop pages (if available).
-
Other games like Brotato on SteamPeek
-
If you want to try other games like Brotato on Steam, you can check out SteamPeek, a website that helps you find similar games based on tags, ratings, genres, and more. Some of these games include:
-
-
Enter the Gungeon: A bullet hell roguelite game where you explore a dungeon full of guns and gun-related enemies and bosses.
-
Nuclear Throne: A post-apocalyptic roguelite game where you play as mutants with different abilities and fight your way to the nuclear throne.
-
Binding of Isaac: A dark and twisted roguelite game where you play as a child who escapes his mother's wrath and faces various horrors in the basement.
-
-
You can find these games and more on SteamPeek by searching for "Brotato".
-
Conclusion and FAQs
-
Brotato is a fun and addictive game that offers a lot of content and replay value. If you want to enhance your experience with the game, you can try out some mods that add new content and features to the game. One of the best mods for Brotato is Extatonion, a mod that adds new characters, weapons, items, unlocks, and more to the game. To install Extatonion, you need to download GodotSteam and GDRETools, decompile Brotato with GDRETools, download Extatonion from the official sources, copy the mod files to the decompiled project folder, and run Brotato in GodotSteam. Extatonion adds original and balanced content that blends with the original game and adds more variety and challenge for Brotato players. If you want to try other mods or games like Brotato, you can check out the Brotato Mods GitHub page or SteamPeek. We hope this article helped you learn more about Brotato Extension Download and how to install and use the Extatonion mod. Have fun!
-
Here are some FAQs that might answer some of your questions:
-
-
Q: Is Extatonion safe to use?
-
A: Yes, Extatonion is safe to use as long as you download it from the official sources and follow the installation guide. The mod does not contain any viruses or malware and does not harm your computer or your game files.
-
Q: Can I use Extatonion with other mods?
-
A: Yes, Extatonion is compatible with most other mods for Brotato. However, some mods might conflict or overwrite each other if they modify the same files or content. To avoid this, you can use a mod manager tool such as Mod Organizer 2 or Vortex to manage your mods and load order.
-
Q: Can I play online or co-op with Extatonion?
-
A: Yes, Extatonion supports online and co-op play with other players who have the same mod installed. However, some features or content might not work properly or cause desync issues in multiplayer mode. To avoid this, you can disable or enable certain features or content in the mod settings menu.
-
Q: How can I update Extatonion?
-
A: To update Extatonion, you need to download the latest version of the mod from the official sources and repeat the installation process. You can also check for updates on the mod GitHub page or Discord server.
-
Q: How can I contact Psina or give feedback on Extatonion?
-
A: You can contact Psina or give feedback on Extatonion by joining the mod Discord server or by leaving a comment on the mod GitHub page. Psina is very friendly and responsive and appreciates any feedback or suggestions from the community.
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diff --git a/spaces/1phancelerku/anime-remove-background/DolphiniOS A Guide to Download and Install Dolphin Emulator on iPhone without Jailbreak.md b/spaces/1phancelerku/anime-remove-background/DolphiniOS A Guide to Download and Install Dolphin Emulator on iPhone without Jailbreak.md
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Can You Download Dolphin Emulator on iPhone?
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If you are a fan of Nintendo games, you might have heard of Dolphin emulator. It is a software that allows you to play GameCube and Wii games on your PC, Mac, Linux, Android, and even Xbox devices. But what about iPhone? Can you download Dolphin emulator on iPhone and enjoy your favorite Nintendo titles on the go?
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In this article, we will answer this question and show you how to download Dolphin emulator on iPhone. We will also explain what Dolphin emulator is, why you would want to use it on iPhone, and how to play games with it. Let's get started!
Dolphin emulator is a free and open-source video game console emulator for GameCube and Wii that runs on various operating systems. It was first released in 2003 as a freeware for Windows, but later expanded to support other platforms. Dolphin emulator is the first and only emulator that can successfully run commercial GameCube and Wii games.
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Dolphin emulator has many features that enhance the gaming experience, such as:
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Full HD resolution (1080p) and anti-aliasing.
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Compatibility with all PC controllers, keyboards, mice, and touchscreens.
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Turbo speed, networked multiplayer, and cheat codes.
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Save states, screenshots, and video recording.
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Customizable graphics, audio, and controller settings.
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Dolphin emulator supports most GameCube and Wii games, but some may have glitches or performance issues. You can check the compatibility list on the official website to see how well your favorite games run on Dolphin emulator.
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Why Use Dolphin Emulator on iPhone?
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There are many reasons why you would want to use Dolphin emulator on iPhone, such as:
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Portability: You can play GameCube and Wii games anywhere you go with your iPhone.
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Convenience: You don't need to carry around bulky consoles or discs to play GameCube and Wii games.
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Enhanced graphics: You can enjoy GameCube and Wii games in high definition and with improved visuals on your iPhone.
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Imagine playing Super Mario Sunshine, The Legend of Zelda: Twilight Princess, or Metroid Prime on your iPhone with crisp graphics and smooth gameplay. Sounds awesome, right?
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How to Download Dolphin Emulator on iPhone?
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Unfortunately, downloading Dolphin emulator on iPhone is not as easy as downloading it on other devices. This is because Apple has strict policies that prevent unauthorized apps from running on iOS devices. Therefore, you cannot find Dolphin emulator on the App Store or satisfying and reliable way to download Dolphin emulator on iPhone. You might encounter some errors or glitches while playing the games.
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Use an Alternative iOS Emulator
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A third way to download Dolphin emulator on iPhone is to use an alternative iOS emulator. An alternative iOS emulator is an app that can emulate other video game consoles on your iPhone, such as Nintendo DS, Game Boy Advance, or PlayStation. One example of such an app is iNDS, which lets you play Nintendo DS games on your iPhone.
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To use iNDS, you need to install it from a third-party app store like TweakBox or AppValley. These app stores do not require you to jailbreak your device, but they may have some ads or pop-ups. Then, you need to download the ROMs of the games you want to play from websites like EmuParadise or CoolROM. You can also transfer the ROMs from your computer to your iPhone using iTunes or a file manager app.
-
However, using an alternative iOS emulator has some limitations, such as:
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-
Inability to play GameCube and Wii games, only other Nintendo games.
-
Potential legal issues and ethical concerns of downloading ROMs.
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Possible revocation and expiration of the app certificates by Apple.
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Occasional bugs and crashes of the app and the games.
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Therefore, using an alternative iOS emulator is not a perfect solution to download Dolphin emulator on iPhone. You might not be able to play the games you want or enjoy them fully.
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How to Play Games with Dolphin Emulator on iPhone?
-
If you manage to download Dolphin emulator on iPhone using one of the methods above, you might wonder how to play games with it. Here are some tips and tricks for playing games with Dolphin emulator on iPhone:
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-
To load ROMs, you need to have them in ISO or WBFS format and store them in a folder on your device or cloud service. Then, you need to tap the plus icon on the Dolphin emulator app and browse for the folder. You can also use a file manager app like Documents by Readdle to unzip or extract the ROMs from compressed files.
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To configure settings, you need to tap the gear icon on the Dolphin emulator app and adjust the options according to your preferences and device capabilities. You can change the video settings, audio settings, controller settings, and more. You can also enable cheats by tapping the edit icon on the game list and selecting the cheat codes you want.
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To use controllers, you need to pair them with your device via Bluetooth or connect them via USB. Then, you need to map the buttons on the controller settings of the Dolphin emulator app. You can use various controllers, such as PS4, Xbox One, MFi, or Gamevice controllers.
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To troubleshoot problems, you need to check the FAQ section on the official website of Dolphin emulator or the forums for help. You can also update the app or reinstall it if it does not work properly. You can also report bugs or issues on the GitHub page of Dolphin emulator.
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Conclusion
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Dolphin emulator is a great software that allows you to play GameCube and Wii games on various devices, including iPhone. However, downloading Dolphin emulator on iPhone is not a straightforward process due to Apple's restrictions and limitations. You need to use some workarounds that have their own risks and drawbacks.
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In this article, we showed you three possible ways to download Dolphin emulator on iPhone: jailbreaking your device, using a web-based iOS simulator, or using an alternative iOS emulator. We also gave you some tips and tricks for playing games with Dolphin emulator on iPhone. We hope this article was helpful and informative for you.
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If you want to download Dolphin emulator on iPhone and enjoy your favorite Nintendo games on the go, you can try one of the methods above at your own risk and discretion. However, we advise you to be careful and responsible when doing so. Always backup your data and follow the instructions carefully. Also, respect the intellectual property rights of Nintendo and its developers.
-
Have fun playing GameCube and Wii games on your iPhone!
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FAQs
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-
Is Dolphin emulator legal?
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Dolphin emulator itself is legal, as it is a software that emulates hardware and does not contain any copyrighted material. However, downloading and playing ROMs of GameCube and Wii games may be illegal, depending on the laws of your country and the source of the ROMs. You should only download and play ROMs of games that you own legally and from trusted websites.
-
How much storage space does Dolphin emulator need?
-
Dolphin emulator itself does not need much storage space, as it is only about 15 MB in size. However, the ROMs of GameCube and Wii games can take up a lot of storage space, depending on the game. For example, Super Smash Bros. Brawl is about 7.9 GB, while Animal Crossing: City Folk is about 4.4 GB. You should have enough free space on your device or cloud service to store the ROMs you want to play.
-
How fast does Dolphin emulator run on iPhone?
-
The speed of Dolphin emulator on iPhone depends on several factors, such as the model of your device, the version of your iOS, the settings of the app, and the game you are playing. Generally speaking, newer devices with more powerful processors and memory can run Dolphin emulator faster and smoother than older devices. However, some games may still have lag or stutter issues, especially if they are graphically intensive or require a lot of resources.
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Can I play online multiplayer games with Dolphin emulator on iPhone?
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Yes, you can play online multiplayer games with Dolphin emulator on iPhone, as long as you have a stable internet connection and a compatible game. You can use the Netplay feature of Dolphin emulator to join or host online sessions with other players who are using Dolphin emulator on any device. You can also use the Wiimote feature of Dolphin emulator to connect your iPhone to a real Wii console and play online games that support Wiimote.
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What are some alternatives to Dolphin emulator for iPhone?
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If you are looking for alternatives to Dolphin emulator for iPhone, you can try some other iOS emulators that can run different video game consoles on your device. Some examples are:
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iNDS: An iOS emulator for Nintendo DS that supports many games and features.
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Delta: An iOS emulator for Game Boy, Game Boy Color, Game Boy Advance, Nintendo 64, and Super Nintendo that has a sleek design and easy-to-use interface.
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Provenance: An iOS emulator for multiple consoles, such as Sega Genesis, Sega CD, Master System, Game Gear, Atari, Neo Geo, PlayStation, and more.
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diff --git a/spaces/1phancelerku/anime-remove-background/Download Drama Live and Enjoy the Best IPTV Player for Android.md b/spaces/1phancelerku/anime-remove-background/Download Drama Live and Enjoy the Best IPTV Player for Android.md
deleted file mode 100644
index 11bb2de7c78d634707496651d3d3b4df07031214..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Download Drama Live and Enjoy the Best IPTV Player for Android.md
+++ /dev/null
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Drama Live App Download: How to Watch Live TV and Sports on Your Android Device
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Do you love watching live TV and sports, especially soccer games? Do you want to enjoy your favorite shows and movies from Asia on your phone or tablet? If you answered yes, then you should check out Drama Live App, a video player that lets you stream live television and sports channels online, as well as play any video file you want. In this article, we will show you how to download and install Drama Live App on your Android device, how to use it to watch live TV and sports, and some alternatives to Drama Live App that you might also like.
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Features of Drama Live App
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Some of the features of Drama Live App include:
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Video, Audio, Live, VOD, IPTV player.
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Play several streams simultaneously.
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Built-in Fast video & IPTV player with high capabilities.
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Auto choose server if one server not working.
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Play audio only mode.
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Start playing selected channel on launch.
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You can watch live TV and sports channels online for free, without any subscription or registration.
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You can access a large library of Korean content, including dramas, movies, originals, and more.
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You can chat with other viewers while watching and share your opinions and reactions.
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You can customize your viewing experience by choosing your video quality, audio track, playlist source, and more.
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You can cast your screen to your TV or other devices using Miracast or Web Video Caster.
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How to Download and Install Drama Live App on Your Android Device
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If you are interested in trying out Drama Live App, you can download and install it on your Android device easily. Here are the steps you need to follow:
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Step 1: Go to the Google Play Store
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The first step is to go to the Google Play Store on your Android device and search for Drama Live App. Alternatively, you can use this link to go directly to the app page.
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Step 2: Search for Drama Live App
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Once you are on the app page, you will see the app icon, name, rating, and description. You can also scroll down to see more information, such as screenshots, reviews, and permissions. To download the app, tap on the green Install button.
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Step 3: Tap on Install and Accept Permissions
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After tapping on the Install button, you will see a pop-up window asking you to accept the permissions that the app needs to function properly. These include access to your device's storage, network, and location. To proceed, tap on Accept.
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Step 4: Open the App and Enjoy
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Once the app is installed, you can open it by tapping on the Open button on the app page or by finding it on your device's app drawer. You will see a welcome screen with some instructions on how to use the app. You can also change the app settings by tapping on the menu icon on the top left corner. Now you are ready to watch live TV and sports on your Android device with Drama Live App.
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How to Use Drama Live App to Watch Live TV and Sports
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Using Drama Live App to watch live TV and sports is very simple and intuitive. Here are some tips on how to use the app:
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Choose Your Video Source and Playlist Source
-
The first thing you need to do is to choose your video source and playlist source. The video source is where you get your video files from, such as your device's storage or external sources. The playlist source is where you get your live TV and sports channels from, such as IPTV or m3u files. You can choose your video source and playlist source by tapping on the menu icon on the top left corner and selecting Video Source or Playlist Source.
-
Browse and Search for Channels and Shows
-
Once you have chosen your video source and playlist source, you can browse and search for channels and shows that you want to watch. You can use the tabs on the bottom of the screen to switch between different categories, such as Live TV, Sports, Movies, Series, etc. You can also use the search icon on the top right corner to look for specific channels or shows by name or keyword.
-
Play, Pause, Rewind, and Chat While Watching
-
When you find a channel or show that you want to watch, just tap on it and it will start playing automatically. You can use the controls on the bottom of the screen to play, pause, rewind, fast forward, or adjust the volume. You can also chat with other viewers while watching by tapping on the chat icon on the top right corner. You can send messages, emojis, stickers, or gifs to express your feelings or opinions.
-
Cast to Your TV or Other Devices
-
If you want to watch your content on a bigger screen, you can cast it to your TV or other devices using Miracast or Web Video Caster. To do this, tap on the cast icon on the top right corner and select your device from the list. Make sure that both devices are connected to the same Wi-Fi network.
Alternatives to Drama Live App
-
While Drama Live App is a great app for watching live TV and sports, especially from Asia, it is not the only one. There are some other apps that you might want to try if you are looking for more options or different content. Here are some of the alternatives to Drama Live App that you can download and use:
-
Viki
-
Viki is an app that offers Asian TV shows and movies, with subtitles in over 200 languages. You can watch popular dramas, variety shows, movies, and more from Korea, China, Japan, Taiwan, Thailand, and other countries. You can also join the Viki community and interact with other fans, create collections, leave comments, and more. Viki is free to use, but you can also upgrade to Viki Pass for ad-free and HD streaming.
-
Netflix
-
Netflix is one of the most popular streaming services in the world, offering a wide range of content from different genres and countries. You can watch original shows and movies, as well as licensed content from other sources. Netflix has a lot of Asian content as well, including dramas, movies, anime, documentaries, and more. You can also download your content for offline viewing, create profiles for different users, and adjust your settings and preferences. Netflix requires a monthly subscription fee to use.
-
Harpal Geo
-
Harpal Geo is an app that lets you watch live TV and on-demand content from Geo TV, a Pakistani television network. You can watch dramas, movies, news, sports, comedy, and more in Urdu and other languages. You can also catch up on missed episodes, watch exclusive clips and behind-the-scenes footage, and get notifications for your favorite shows. Harpal Geo is free to use, but you need to sign up with your email or phone number.
-
Conclusion
-
Drama Live App is a video player that lets you watch live TV and sports channels online, as well as play any video file from your device or external sources. It specializes in content from Asia, especially Korea. It has many features and benefits that make it a great app for watching live TV and sports on your Android device. You can download and install it easily from the Google Play Store, and use it to watch your favorite shows and movies anytime and anywhere. You can also chat with other viewers while watching, cast to your TV or other devices, and customize your viewing experience. If you are looking for alternatives to Drama Live App, you can try Viki, Netflix, or Harpal Geo.
-
FAQs
-
Here are some of the frequently asked questions about Drama Live App:
-
-
Is Drama Live App safe to use?
-
Drama Live App is safe to use as long as you download it from the official Google Play Store link. It does not contain any viruses or malware that could harm your device or data. However, you should be careful when using external sources or links for your video files or playlists, as they might not be secure or legal.
-
Is Drama Live App legal to use?
-
Drama Live App is legal to use as long as you do not infringe on any copyrights or trademarks of the content owners or providers. The app itself does not host or distribute any content; it only plays the content that you provide or access through external sources or links. You should always respect the rights of the content owners or providers and follow their terms and conditions.
-
How can I update Drama Live App?
-
You can update Drama Live App by going to the Google Play Store on your Android device and checking for updates. Alternatively, you can use this link to go directly to the app page and see if there is a new version available. You should always update your app to get the latest features and bug fixes.
-
How can I contact Drama Live App support?
-
You can contact Drama Live App support by sending an email to dramaliveapp@gmail.com. You can also visit their Facebook page or their website for more information and updates.
-
How can I uninstall Drama Live App?
-
You can uninstall Drama Live App by going to the Settings app on your Android device and selecting Apps or Applications. Then find Drama Live App from the list of apps and tap on it. Then tap on Uninstall and confirm your action.
- 197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/AI-Hobbyist/Hoyo-RVC/infer_pack/modules.py b/spaces/AI-Hobbyist/Hoyo-RVC/infer_pack/modules.py
deleted file mode 100644
index 960481cedad9a6106f2bf0b9e86e82b120f7b33f..0000000000000000000000000000000000000000
--- a/spaces/AI-Hobbyist/Hoyo-RVC/infer_pack/modules.py
+++ /dev/null
@@ -1,522 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from infer_pack.transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
- def __init__(
- self,
- in_channels,
- hidden_channels,
- out_channels,
- kernel_size,
- n_layers,
- p_dropout,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.out_channels = out_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
- assert n_layers > 1, "Number of layers should be larger than 0."
-
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.conv_layers.append(
- nn.Conv1d(
- in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
- )
- )
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
- for _ in range(n_layers - 1):
- self.conv_layers.append(
- nn.Conv1d(
- hidden_channels,
- hidden_channels,
- kernel_size,
- padding=kernel_size // 2,
- )
- )
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask):
- x_org = x
- for i in range(self.n_layers):
- x = self.conv_layers[i](x * x_mask)
- x = self.norm_layers[i](x)
- x = self.relu_drop(x)
- x = x_org + self.proj(x)
- return x * x_mask
-
-
-class DDSConv(nn.Module):
- """
- Dialted and Depth-Separable Convolution
- """
-
- def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
- super().__init__()
- self.channels = channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
-
- self.drop = nn.Dropout(p_dropout)
- self.convs_sep = nn.ModuleList()
- self.convs_1x1 = nn.ModuleList()
- self.norms_1 = nn.ModuleList()
- self.norms_2 = nn.ModuleList()
- for i in range(n_layers):
- dilation = kernel_size**i
- padding = (kernel_size * dilation - dilation) // 2
- self.convs_sep.append(
- nn.Conv1d(
- channels,
- channels,
- kernel_size,
- groups=channels,
- dilation=dilation,
- padding=padding,
- )
- )
- self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
- self.norms_1.append(LayerNorm(channels))
- self.norms_2.append(LayerNorm(channels))
-
- def forward(self, x, x_mask, g=None):
- if g is not None:
- x = x + g
- for i in range(self.n_layers):
- y = self.convs_sep[i](x * x_mask)
- y = self.norms_1[i](y)
- y = F.gelu(y)
- y = self.convs_1x1[i](y)
- y = self.norms_2[i](y)
- y = F.gelu(y)
- y = self.drop(y)
- x = x + y
- return x * x_mask
-
-
-class WN(torch.nn.Module):
- def __init__(
- self,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- gin_channels=0,
- p_dropout=0,
- ):
- super(WN, self).__init__()
- assert kernel_size % 2 == 1
- self.hidden_channels = hidden_channels
- self.kernel_size = (kernel_size,)
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
- self.p_dropout = p_dropout
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.drop = nn.Dropout(p_dropout)
-
- if gin_channels != 0:
- cond_layer = torch.nn.Conv1d(
- gin_channels, 2 * hidden_channels * n_layers, 1
- )
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
- for i in range(n_layers):
- dilation = dilation_rate**i
- padding = int((kernel_size * dilation - dilation) / 2)
- in_layer = torch.nn.Conv1d(
- hidden_channels,
- 2 * hidden_channels,
- kernel_size,
- dilation=dilation,
- padding=padding,
- )
- in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
- self.in_layers.append(in_layer)
-
- # last one is not necessary
- if i < n_layers - 1:
- res_skip_channels = 2 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
- self.res_skip_layers.append(res_skip_layer)
-
- def forward(self, x, x_mask, g=None, **kwargs):
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
- if g is not None:
- g = self.cond_layer(g)
-
- for i in range(self.n_layers):
- x_in = self.in_layers[i](x)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
- else:
- g_l = torch.zeros_like(x_in)
-
- acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
- acts = self.drop(acts)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- res_acts = res_skip_acts[:, : self.hidden_channels, :]
- x = (x + res_acts) * x_mask
- output = output + res_skip_acts[:, self.hidden_channels :, :]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.gin_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.convs1 = nn.ModuleList(
- [
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2]),
- )
- ),
- ]
- )
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList(
- [
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=1,
- padding=get_padding(kernel_size, 1),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=1,
- padding=get_padding(kernel_size, 1),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=1,
- padding=get_padding(kernel_size, 1),
- )
- ),
- ]
- )
- self.convs2.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c2(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.convs = nn.ModuleList(
- [
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]),
- )
- ),
- weight_norm(
- Conv1d(
- channels,
- channels,
- kernel_size,
- 1,
- dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]),
- )
- ),
- ]
- )
- self.convs.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Log(nn.Module):
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
- logdet = torch.sum(-y, [1, 2])
- return y, logdet
- else:
- x = torch.exp(x) * x_mask
- return x
-
-
-class Flip(nn.Module):
- def forward(self, x, *args, reverse=False, **kwargs):
- x = torch.flip(x, [1])
- if not reverse:
- logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
- return x, logdet
- else:
- return x
-
-
-class ElementwiseAffine(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.channels = channels
- self.m = nn.Parameter(torch.zeros(channels, 1))
- self.logs = nn.Parameter(torch.zeros(channels, 1))
-
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = self.m + torch.exp(self.logs) * x
- y = y * x_mask
- logdet = torch.sum(self.logs * x_mask, [1, 2])
- return y, logdet
- else:
- x = (x - self.m) * torch.exp(-self.logs) * x_mask
- return x
-
-
-class ResidualCouplingLayer(nn.Module):
- def __init__(
- self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=0,
- gin_channels=0,
- mean_only=False,
- ):
- assert channels % 2 == 0, "channels should be divisible by 2"
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = WN(
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=p_dropout,
- gin_channels=gin_channels,
- )
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels] * 2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1, 2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
-
- def remove_weight_norm(self):
- self.enc.remove_weight_norm()
-
-
-class ConvFlow(nn.Module):
- def __init__(
- self,
- in_channels,
- filter_channels,
- kernel_size,
- n_layers,
- num_bins=10,
- tail_bound=5.0,
- ):
- super().__init__()
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.num_bins = num_bins
- self.tail_bound = tail_bound
- self.half_channels = in_channels // 2
-
- self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
- self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
- self.proj = nn.Conv1d(
- filter_channels, self.half_channels * (num_bins * 3 - 1), 1
- )
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
- h = self.pre(x0)
- h = self.convs(h, x_mask, g=g)
- h = self.proj(h) * x_mask
-
- b, c, t = x0.shape
- h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
- unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
- self.filter_channels
- )
- unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
- x1, logabsdet = piecewise_rational_quadratic_transform(
- x1,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=reverse,
- tails="linear",
- tail_bound=self.tail_bound,
- )
-
- x = torch.cat([x0, x1], 1) * x_mask
- logdet = torch.sum(logabsdet * x_mask, [1, 2])
- if not reverse:
- return x, logdet
- else:
- return x
diff --git a/spaces/AIConsultant/MusicGen/audiocraft/grids/diffusion/__init__.py b/spaces/AIConsultant/MusicGen/audiocraft/grids/diffusion/__init__.py
deleted file mode 100644
index e5737294ae16c0de52085b8dcf6825c348f617e4..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/audiocraft/grids/diffusion/__init__.py
+++ /dev/null
@@ -1,6 +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.
-"""Diffusion grids."""
diff --git a/spaces/AIConsultant/MusicGen/audiocraft/utils/notebook.py b/spaces/AIConsultant/MusicGen/audiocraft/utils/notebook.py
deleted file mode 100644
index 019b9d19e5bef976bedddf428fd25da42a8a9726..0000000000000000000000000000000000000000
--- a/spaces/AIConsultant/MusicGen/audiocraft/utils/notebook.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-try:
- import IPython.display as ipd # type: ignore
-except ImportError:
- # Note in a notebook...
- pass
-
-
-import torch
-
-
-def display_audio(samples: torch.Tensor, sample_rate: int):
- """Renders an audio player for the given audio samples.
-
- Args:
- samples (torch.Tensor): a Tensor of decoded audio samples
- with shapes [B, C, T] or [C, T]
- sample_rate (int): sample rate audio should be displayed with.
- """
- assert samples.dim() == 2 or samples.dim() == 3
-
- samples = samples.detach().cpu()
- if samples.dim() == 2:
- samples = samples[None, ...]
-
- for audio in samples:
- ipd.display(ipd.Audio(audio, rate=sample_rate))
diff --git a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/utils/dtw.py b/spaces/AIGC-Audio/AudioGPT/NeuralSeq/utils/dtw.py
deleted file mode 100644
index 464c4b747d792d23cf413675a47c9dddf67da134..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/NeuralSeq/utils/dtw.py
+++ /dev/null
@@ -1,162 +0,0 @@
-from numpy import array, zeros, full, argmin, inf, ndim
-from scipy.spatial.distance import cdist
-from math import isinf
-
-
-def dtw(x, y, dist, warp=1, w=inf, s=1.0):
- """
- Computes Dynamic Time Warping (DTW) of two sequences.
-
- :param array x: N1*M array
- :param array y: N2*M array
- :param func dist: distance used as cost measure
- :param int warp: how many shifts are computed.
- :param int w: window size limiting the maximal distance between indices of matched entries |i,j|.
- :param float s: weight applied on off-diagonal moves of the path. As s gets larger, the warping path is increasingly biased towards the diagonal
- Returns the minimum distance, the cost matrix, the accumulated cost matrix, and the wrap path.
- """
- assert len(x)
- assert len(y)
- assert isinf(w) or (w >= abs(len(x) - len(y)))
- assert s > 0
- r, c = len(x), len(y)
- if not isinf(w):
- D0 = full((r + 1, c + 1), inf)
- for i in range(1, r + 1):
- D0[i, max(1, i - w):min(c + 1, i + w + 1)] = 0
- D0[0, 0] = 0
- else:
- D0 = zeros((r + 1, c + 1))
- D0[0, 1:] = inf
- D0[1:, 0] = inf
- D1 = D0[1:, 1:] # view
- for i in range(r):
- for j in range(c):
- if (isinf(w) or (max(0, i - w) <= j <= min(c, i + w))):
- D1[i, j] = dist(x[i], y[j])
- C = D1.copy()
- jrange = range(c)
- for i in range(r):
- if not isinf(w):
- jrange = range(max(0, i - w), min(c, i + w + 1))
- for j in jrange:
- min_list = [D0[i, j]]
- for k in range(1, warp + 1):
- i_k = min(i + k, r)
- j_k = min(j + k, c)
- min_list += [D0[i_k, j] * s, D0[i, j_k] * s]
- D1[i, j] += min(min_list)
- if len(x) == 1:
- path = zeros(len(y)), range(len(y))
- elif len(y) == 1:
- path = range(len(x)), zeros(len(x))
- else:
- path = _traceback(D0)
- return D1[-1, -1], C, D1, path
-
-
-def accelerated_dtw(x, y, dist, warp=1):
- """
- Computes Dynamic Time Warping (DTW) of two sequences in a faster way.
- Instead of iterating through each element and calculating each distance,
- this uses the cdist function from scipy (https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html)
-
- :param array x: N1*M array
- :param array y: N2*M array
- :param string or func dist: distance parameter for cdist. When string is given, cdist uses optimized functions for the distance metrics.
- If a string is passed, the distance function can be 'braycurtis', 'canberra', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'wminkowski', 'yule'.
- :param int warp: how many shifts are computed.
- Returns the minimum distance, the cost matrix, the accumulated cost matrix, and the wrap path.
- """
- assert len(x)
- assert len(y)
- if ndim(x) == 1:
- x = x.reshape(-1, 1)
- if ndim(y) == 1:
- y = y.reshape(-1, 1)
- r, c = len(x), len(y)
- D0 = zeros((r + 1, c + 1))
- D0[0, 1:] = inf
- D0[1:, 0] = inf
- D1 = D0[1:, 1:]
- D0[1:, 1:] = cdist(x, y, dist)
- C = D1.copy()
- for i in range(r):
- for j in range(c):
- min_list = [D0[i, j]]
- for k in range(1, warp + 1):
- min_list += [D0[min(i + k, r), j],
- D0[i, min(j + k, c)]]
- D1[i, j] += min(min_list)
- if len(x) == 1:
- path = zeros(len(y)), range(len(y))
- elif len(y) == 1:
- path = range(len(x)), zeros(len(x))
- else:
- path = _traceback(D0)
- return D1[-1, -1], C, D1, path
-
-
-def _traceback(D):
- i, j = array(D.shape) - 2
- p, q = [i], [j]
- while (i > 0) or (j > 0):
- tb = argmin((D[i, j], D[i, j + 1], D[i + 1, j]))
- if tb == 0:
- i -= 1
- j -= 1
- elif tb == 1:
- i -= 1
- else: # (tb == 2):
- j -= 1
- p.insert(0, i)
- q.insert(0, j)
- return array(p), array(q)
-
-
-if __name__ == '__main__':
- w = inf
- s = 1.0
- if 1: # 1-D numeric
- from sklearn.metrics.pairwise import manhattan_distances
- import numpy as np
- x = [0, 0, 1, 1, 2, 4, 2, 1, 2, 0]
- x = np.array(x).reshape([-1,1,1])
- y = [1, 1, 1, 2, 2, 2, 2, 3, 2, 0]
- y = np.array(y).reshape([-1,1,1])
- dist_fun = manhattan_distances
- w = 1
- # s = 1.2
- elif 0: # 2-D numeric
- from sklearn.metrics.pairwise import euclidean_distances
-
- x = [[0, 0], [0, 1], [1, 1], [1, 2], [2, 2], [4, 3], [2, 3], [1, 1], [2, 2], [0, 1]]
- y = [[1, 0], [1, 1], [1, 1], [2, 1], [4, 3], [4, 3], [2, 3], [3, 1], [1, 2], [1, 0]]
- dist_fun = euclidean_distances
- else: # 1-D list of strings
- from nltk.metrics.distance import edit_distance
-
- # x = ['we', 'shelled', 'clams', 'for', 'the', 'chowder']
- # y = ['class', 'too']
- x = ['i', 'soon', 'found', 'myself', 'muttering', 'to', 'the', 'walls']
- y = ['see', 'drown', 'himself']
- # x = 'we talked about the situation'.split()
- # y = 'we talked about the situation'.split()
- dist_fun = edit_distance
- dist, cost, acc, path = dtw(x, y, dist_fun, w=w, s=s)
-
- # Vizualize
- from matplotlib import pyplot as plt
-
- plt.imshow(cost.T, origin='lower', cmap=plt.cm.Reds, interpolation='nearest')
- plt.plot(path[0], path[1], '-o') # relation
- plt.xticks(range(len(x)), x)
- plt.yticks(range(len(y)), y)
- plt.xlabel('x')
- plt.ylabel('y')
- plt.axis('tight')
- if isinf(w):
- plt.title('Minimum distance: {}, slope weight: {}'.format(dist, s))
- else:
- plt.title('Minimum distance: {}, window widht: {}, slope weight: {}'.format(dist, w, s))
- plt.show()
diff --git a/spaces/AILab-CVC/SEED-LLaMA/models/seed_qformer/clip_vit.py b/spaces/AILab-CVC/SEED-LLaMA/models/seed_qformer/clip_vit.py
deleted file mode 100644
index 235c4e29b7cf149f7598247c7f6139f4c8c27d9e..0000000000000000000000000000000000000000
--- a/spaces/AILab-CVC/SEED-LLaMA/models/seed_qformer/clip_vit.py
+++ /dev/null
@@ -1,257 +0,0 @@
-from collections import OrderedDict
-from itertools import repeat
-import collections.abc
-import math
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-
-from .eva_vit import convert_weights_to_fp16
-from .utils import download_cached_file
-
-
-class Bottleneck(nn.Module):
- expansion = 4
-
- def __init__(self, inplanes, planes, stride=1):
- super().__init__()
-
- # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
- self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
- self.bn1 = nn.BatchNorm2d(planes)
- self.relu1 = nn.ReLU(inplace=True)
-
- self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes)
- self.relu2 = nn.ReLU(inplace=True)
-
- self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()
-
- self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
- self.bn3 = nn.BatchNorm2d(planes * self.expansion)
- self.relu3 = nn.ReLU(inplace=True)
-
- self.downsample = None
- self.stride = stride
-
- if stride > 1 or inplanes != planes * Bottleneck.expansion:
- # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
- self.downsample = nn.Sequential(
- OrderedDict([("-1", nn.AvgPool2d(stride)),
- ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
- ("1", nn.BatchNorm2d(planes * self.expansion))]))
-
- def forward(self, x: torch.Tensor):
- identity = x
-
- out = self.relu1(self.bn1(self.conv1(x)))
- out = self.relu2(self.bn2(self.conv2(out)))
- out = self.avgpool(out)
- out = self.bn3(self.conv3(out))
-
- if self.downsample is not None:
- identity = self.downsample(x)
-
- out += identity
- out = self.relu3(out)
- return out
-
-
-class AttentionPool2d(nn.Module):
- def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
- super().__init__()
- self.positional_embedding = nn.Parameter(torch.randn(spacial_dim**2 + 1, embed_dim) / embed_dim**0.5)
- self.k_proj = nn.Linear(embed_dim, embed_dim)
- self.q_proj = nn.Linear(embed_dim, embed_dim)
- self.v_proj = nn.Linear(embed_dim, embed_dim)
- self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
- self.num_heads = num_heads
-
- def forward(self, x):
- x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC
- x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC
- x = x + self.positional_embedding[:, None, :].to(x.dtype) # (HW+1)NC
- x, _ = F.multi_head_attention_forward(query=x,
- key=x,
- value=x,
- embed_dim_to_check=x.shape[-1],
- num_heads=self.num_heads,
- q_proj_weight=self.q_proj.weight,
- k_proj_weight=self.k_proj.weight,
- v_proj_weight=self.v_proj.weight,
- in_proj_weight=None,
- in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
- bias_k=None,
- bias_v=None,
- add_zero_attn=False,
- dropout_p=0,
- out_proj_weight=self.c_proj.weight,
- out_proj_bias=self.c_proj.bias,
- use_separate_proj_weight=True,
- training=self.training,
- need_weights=False)
-
- return x[0]
-
-
-class LayerNorm(nn.LayerNorm):
- """Subclass torch's LayerNorm to handle fp16."""
- def forward(self, x: torch.Tensor):
- orig_type = x.dtype
- ret = super().forward(x.type(torch.float32))
- return ret.type(orig_type)
-
-
-class QuickGELU(nn.Module):
- def forward(self, x: torch.Tensor):
- return x * torch.sigmoid(1.702 * x)
-
-
-class ResidualAttentionBlock(nn.Module):
- def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None, use_grad_checkpointing=False):
- super().__init__()
-
- self.attn = nn.MultiheadAttention(d_model, n_head)
- self.ln_1 = LayerNorm(d_model)
- self.mlp = nn.Sequential(
- OrderedDict([("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", QuickGELU()),
- ("c_proj", nn.Linear(d_model * 4, d_model))]))
- self.ln_2 = LayerNorm(d_model)
- self.attn_mask = attn_mask
-
- # if use_grad_checkpointing:
- # self.attn = checkpoint_wrapper(self.attn)
- # self.mlp = checkpoint_wrapper(self.mlp)
- # raise NotImplementedError
-
- def attention(self, x: torch.Tensor):
- self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
- return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
-
- def forward(self, x: torch.Tensor):
- x = x + self.attention(self.ln_1(x))
- x = x + self.mlp(self.ln_2(x))
- return x
-
-
-class Transformer(nn.Module):
- def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None, use_grad_checkpointing=False):
- super().__init__()
- self.width = width
- self.layers = layers
- self.resblocks = nn.Sequential(
- *[ResidualAttentionBlock(width, heads, attn_mask, use_grad_checkpointing and i > 12) for i in range(layers)])
-
- def forward(self, x: torch.Tensor):
- return self.resblocks(x)
-
-
-class VisionTransformer(nn.Module):
- def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int,
- use_grad_checkpointing: bool):
- super().__init__()
- self.input_resolution = input_resolution
- self.num_features = width
- self.num_heads = heads
- self.num_patches = (input_resolution // patch_size)**2
- self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)
-
- scale = width**-0.5
- self.class_embedding = nn.Parameter(scale * torch.randn(width))
- self.positional_embedding = nn.Parameter(scale * torch.randn(self.num_patches + 1, width))
- self.ln_pre = LayerNorm(width)
-
- self.transformer = Transformer(width, layers, heads, use_grad_checkpointing=use_grad_checkpointing)
-
-# self.ln_final = LayerNorm(width)
-
- def forward(self, x: torch.Tensor):
-
- x = self.conv1(x) # shape = [*, width, grid, grid]
- x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2]
- x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
- x = torch.cat(
- [self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x],
- dim=1) # shape = [*, grid ** 2 + 1, width]
- x = x + self.positional_embedding.to(x.dtype)
- x = self.ln_pre(x)
-
- x = x.permute(1, 0, 2) # NLD -> LND
- x = self.transformer(x)
- x = x.permute(1, 0, 2) # LND -> NLD
-
- # x = self.ln_final(x)
- return x
-
-
-# From PyTorch internals
-def _ntuple(n):
- def parse(x):
- if isinstance(x, collections.abc.Iterable):
- return x
- return tuple(repeat(x, n))
-
- return parse
-
-
-to_2tuple = _ntuple(2)
-
-
-def interpolate_pos_embed(model, state_dict, interpolation: str = 'bicubic', seq_dim=1):
- # Rescale the grid of position embeddings when loading from state_dict
- old_pos_embed = state_dict.get('positional_embedding', None)
-
- grid_size = round((model.positional_embedding.shape[0] - 1)**0.5)
- if old_pos_embed is None:
- return
- grid_size = to_2tuple(grid_size)
- extra_tokens = 1 # FIXME detect different token configs (ie no class token, or more)
- new_seq_len = grid_size[0] * grid_size[1] + extra_tokens
- if new_seq_len == old_pos_embed.shape[0]:
- return
-
- if extra_tokens:
- pos_emb_tok, pos_emb_img = old_pos_embed[:extra_tokens], old_pos_embed[extra_tokens:]
- else:
- pos_emb_tok, pos_emb_img = None, old_pos_embed
-
- old_grid_size = to_2tuple(int(math.sqrt(len(pos_emb_img))))
-
- print('Resizing position embedding grid-size from %s to %s', old_grid_size, grid_size)
- pos_emb_img = pos_emb_img.reshape(1, old_grid_size[0], old_grid_size[1], -1).permute(0, 3, 1, 2)
- pos_emb_img = F.interpolate(
- pos_emb_img,
- size=grid_size,
- mode=interpolation,
- align_corners=True,
- )
- pos_emb_img = pos_emb_img.permute(0, 2, 3, 1).reshape(1, grid_size[0] * grid_size[1], -1)[0]
- if pos_emb_tok is not None:
- new_pos_embed = torch.cat([pos_emb_tok, pos_emb_img], dim=0)
- else:
- new_pos_embed = pos_emb_img
- state_dict['positional_embedding'] = new_pos_embed
-
-
-def create_clip_vit_L(img_size=224, use_checkpoint=False, precision="fp16"):
- model = VisionTransformer(
- input_resolution=img_size,
- patch_size=14,
- width=1024,
- layers=23,
- heads=16,
- use_grad_checkpointing=use_checkpoint,
- )
- url = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/clip_vit_L.pth"
- cached_file = download_cached_file(url, check_hash=False, progress=True)
- state_dict = torch.load(cached_file, map_location="cpu")
- interpolate_pos_embed(model, state_dict)
-
- incompatible_keys = model.load_state_dict(state_dict, strict=False)
- # print(incompatible_keys)
-
- if precision == "fp16":
- convert_weights_to_fp16(model)
- return model
diff --git a/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/generated/client/nodes/9.js b/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/generated/client/nodes/9.js
deleted file mode 100644
index f3d63053dcb2b975a7b67908d08a9736c4f3307f..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT-Chat-UI/.svelte-kit/generated/client/nodes/9.js
+++ /dev/null
@@ -1 +0,0 @@
-export { default as component } from "../../../../src/routes/r/[id]/+page.svelte";
\ No newline at end of file
diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Aivvm.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Aivvm.py
deleted file mode 100644
index 1a3b6f0b08d5fa9a8aa4bdd7f5b4246624ff7059..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/Aivvm.py
+++ /dev/null
@@ -1,70 +0,0 @@
-from __future__ import annotations
-
-from ..requests import StreamSession
-from .base_provider import AsyncGeneratorProvider
-from ..typing import AsyncGenerator
-
-# to recreate this easily, send a post request to https://chat.aivvm.com/api/models
-models = {
- 'gpt-3.5-turbo': {'id': 'gpt-3.5-turbo', 'name': 'GPT-3.5'},
- 'gpt-3.5-turbo-0613': {'id': 'gpt-3.5-turbo-0613', 'name': 'GPT-3.5-0613'},
- 'gpt-3.5-turbo-16k': {'id': 'gpt-3.5-turbo-16k', 'name': 'GPT-3.5-16K'},
- 'gpt-3.5-turbo-16k-0613': {'id': 'gpt-3.5-turbo-16k-0613', 'name': 'GPT-3.5-16K-0613'},
- 'gpt-4': {'id': 'gpt-4', 'name': 'GPT-4'},
- 'gpt-4-0613': {'id': 'gpt-4-0613', 'name': 'GPT-4-0613'},
- 'gpt-4-32k': {'id': 'gpt-4-32k', 'name': 'GPT-4-32K'},
- 'gpt-4-32k-0613': {'id': 'gpt-4-32k-0613', 'name': 'GPT-4-32K-0613'},
-}
-
-class Aivvm(AsyncGeneratorProvider):
- url = 'https://chat.aivvm.com'
- supports_gpt_35_turbo = True
- supports_gpt_4 = True
- working = True
-
- @classmethod
- async def create_async_generator(
- cls,
- model: str,
- messages: list[dict[str, str]],
- stream: bool,
- timeout: int = 30,
- **kwargs
- ) -> AsyncGenerator:
- if not model:
- model = "gpt-3.5-turbo"
- elif model not in models:
- raise ValueError(f"Model is not supported: {model}")
-
- json_data = {
- "model" : models[model],
- "messages" : messages,
- "key" : "",
- "prompt" : kwargs.get("system_message", "You are ChatGPT, a large language model trained by OpenAI. Follow the user's instructions carefully. Respond using markdown."),
- "temperature" : kwargs.get("temperature", 0.7)
- }
- headers = {
- "Accept": "*/*",
- "Origin": cls.url,
- "Referer": f"{cls.url}/",
- }
- async with StreamSession(impersonate="chrome107", headers=headers, timeout=timeout) as session:
- async with session.post(f"{cls.url}/api/chat", json=json_data) as response:
- response.raise_for_status()
- async for chunk in response.iter_content():
- if b'Access denied | chat.aivvm.com used Cloudflare' in chunk:
- raise ValueError("Rate Limit | use another provider")
-
- yield chunk.decode()
-
- @classmethod
- @property
- def params(cls):
- params = [
- ('model', 'str'),
- ('messages', 'list[dict[str, str]]'),
- ('stream', 'bool'),
- ('temperature', 'float'),
- ]
- param = ', '.join([': '.join(p) for p in params])
- return f'g4f.provider.{cls.__name__} supports: ({param})'
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/pokemon_server.py b/spaces/AgentVerse/agentVerse/pokemon_server.py
deleted file mode 100644
index 09ebe983f1c1c7736f1798245232605cb2bd7be9..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/pokemon_server.py
+++ /dev/null
@@ -1,78 +0,0 @@
-from fastapi import FastAPI
-from fastapi.middleware.cors import CORSMiddleware
-from pydantic import BaseModel, Field
-from typing import Set, List, Dict
-from agentverse.simulation import Simulation
-from agentverse.message import Message
-
-
-class UserRequest(BaseModel):
- content: str = Field(default="")
- sender: str = Field(default="Brendan")
- receiver: str
- receiver_id: int
-
-
-class RoutineRequest(BaseModel):
- agent_ids: List[int]
-
-
-class UpdateRequest(BaseModel):
- agent_locations: Dict[str, str]
-
-
-app = FastAPI()
-
-app.add_middleware(
- CORSMiddleware,
- allow_origins=["*"],
- allow_credentials=True,
- allow_methods=["*"],
- allow_headers=["*"],
-)
-
-agent_verse = Simulation.from_task("pokemon")
-
-
-@app.get("/")
-def health_check():
- return {"status": "ok"}
-
-
-@app.post("/chat")
-def chat(message: UserRequest):
- content = message.content
- receiver = message.receiver
- receiver_id = message.receiver_id
- response = agent_verse.next(
- is_player=True,
- player_content=content,
- receiver=receiver,
- receiver_id=receiver_id,
- )
- return response[0].dict()
-
-
-@app.post("/make_decision")
-def update(message: RoutineRequest):
- response = agent_verse.next(is_player=False, agent_ids=message.agent_ids)
- return [r.dict() for r in response]
- # import json
-
- # return [
- # # {
- # # "content": json.dumps(
- # # {
- # # "to": "Maxie",
- # # "action": "Speak",
- # # "text": "Hello Hello Hello Hello Hello Hello",
- # # }
- # # )
- # # }
- # {"content": json.dumps({"to": "Pokémon Center", "action": "MoveTo"})}
- # ]
-
-
-@app.post("/update_location")
-def update_location(message: UpdateRequest):
- agent_verse.update_state(message.agent_locations)
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/utils/PreLayoutChild.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/utils/PreLayoutChild.js
deleted file mode 100644
index 05906a83232a6659fc09b8f1686f8a52466d833c..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/basesizer/utils/PreLayoutChild.js
+++ /dev/null
@@ -1,10 +0,0 @@
-import CopyState from '../../utils/CopyState';
-
-var PreLayoutChild = function (child) {
- if (this.sizerEventsEnable) {
- CopyState(child, this.getChildPrevState(child));
- this.layoutedChildren.push(child);
- }
-}
-
-export default PreLayoutChild;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/bbcodetext/Factory.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/bbcodetext/Factory.js
deleted file mode 100644
index b737a8b65cf8657e69a41ec406d8ea7b8833e594..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/bbcodetext/Factory.js
+++ /dev/null
@@ -1,13 +0,0 @@
-import BBCodeText from './BBCodeText.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('BBCodeText', function (x, y, text, style) {
- var gameObject = new BBCodeText(this.scene, x, y, text, style);
- this.scene.add.existing(gameObject);
- return gameObject;
-});
-
-SetValue(window, 'RexPlugins.UI.BBCodeText', BBCodeText);
-
-export default BBCodeText;
\ No newline at end of file
diff --git a/spaces/Amrrs/DragGan-Inversion/gui_utils/text_utils.py b/spaces/Amrrs/DragGan-Inversion/gui_utils/text_utils.py
deleted file mode 100644
index d1d971d9defa9a223d5b4b19def17f351a262833..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/gui_utils/text_utils.py
+++ /dev/null
@@ -1,141 +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.
-
-import functools
-from typing import Optional
-
-import dnnlib
-import numpy as np
-import PIL.Image
-import PIL.ImageFont
-import scipy.ndimage
-
-from . import gl_utils
-
-# ----------------------------------------------------------------------------
-
-
-def get_default_font():
- # Open Sans regular
- url = 'http://fonts.gstatic.com/s/opensans/v17/mem8YaGs126MiZpBA-U1UpcaXcl0Aw.ttf'
- return dnnlib.util.open_url(url, return_filename=True)
-
-# ----------------------------------------------------------------------------
-
-
-@functools.lru_cache(maxsize=None)
-def get_pil_font(font=None, size=32):
- if font is None:
- font = get_default_font()
- return PIL.ImageFont.truetype(font=font, size=size)
-
-# ----------------------------------------------------------------------------
-
-
-def get_array(string, *, dropshadow_radius: int = None, **kwargs):
- if dropshadow_radius is not None:
- offset_x = int(np.ceil(dropshadow_radius*2/3))
- offset_y = int(np.ceil(dropshadow_radius*2/3))
- return _get_array_priv(string, dropshadow_radius=dropshadow_radius, offset_x=offset_x, offset_y=offset_y, **kwargs)
- else:
- return _get_array_priv(string, **kwargs)
-
-
-@functools.lru_cache(maxsize=10000)
-def _get_array_priv(
- string: str, *,
- size: int = 32,
- max_width: Optional[int] = None,
- max_height: Optional[int] = None,
- min_size=10,
- shrink_coef=0.8,
- dropshadow_radius: int = None,
- offset_x: int = None,
- offset_y: int = None,
- **kwargs
-):
- cur_size = size
- array = None
- while True:
- if dropshadow_radius is not None:
- # separate implementation for dropshadow text rendering
- array = _get_array_impl_dropshadow(
- string, size=cur_size, radius=dropshadow_radius, offset_x=offset_x, offset_y=offset_y, **kwargs)
- else:
- array = _get_array_impl(string, size=cur_size, **kwargs)
- height, width, _ = array.shape
- if (max_width is None or width <= max_width) and (max_height is None or height <= max_height) or (cur_size <= min_size):
- break
- cur_size = max(int(cur_size * shrink_coef), min_size)
- return array
-
-# ----------------------------------------------------------------------------
-
-
-@functools.lru_cache(maxsize=10000)
-def _get_array_impl(string, *, font=None, size=32, outline=0, outline_pad=3, outline_coef=3, outline_exp=2, line_pad: int = None):
- pil_font = get_pil_font(font=font, size=size)
- lines = [pil_font.getmask(line, 'L') for line in string.split('\n')]
- lines = [np.array(line, dtype=np.uint8).reshape(
- [line.size[1], line.size[0]]) for line in lines]
- width = max(line.shape[1] for line in lines)
- lines = [np.pad(line, ((0, 0), (0, width - line.shape[1])),
- mode='constant') for line in lines]
- line_spacing = line_pad if line_pad is not None else size // 2
- lines = [np.pad(line, ((0, line_spacing), (0, 0)), mode='constant')
- for line in lines[:-1]] + lines[-1:]
- mask = np.concatenate(lines, axis=0)
- alpha = mask
- if outline > 0:
- mask = np.pad(mask, int(np.ceil(outline * outline_pad)),
- mode='constant', constant_values=0)
- alpha = mask.astype(np.float32) / 255
- alpha = scipy.ndimage.gaussian_filter(alpha, outline)
- alpha = 1 - np.maximum(1 - alpha * outline_coef, 0) ** outline_exp
- alpha = (alpha * 255 + 0.5).clip(0, 255).astype(np.uint8)
- alpha = np.maximum(alpha, mask)
- return np.stack([mask, alpha], axis=-1)
-
-# ----------------------------------------------------------------------------
-
-
-@functools.lru_cache(maxsize=10000)
-def _get_array_impl_dropshadow(string, *, font=None, size=32, radius: int, offset_x: int, offset_y: int, line_pad: int = None, **kwargs):
- assert (offset_x > 0) and (offset_y > 0)
- pil_font = get_pil_font(font=font, size=size)
- lines = [pil_font.getmask(line, 'L') for line in string.split('\n')]
- lines = [np.array(line, dtype=np.uint8).reshape(
- [line.size[1], line.size[0]]) for line in lines]
- width = max(line.shape[1] for line in lines)
- lines = [np.pad(line, ((0, 0), (0, width - line.shape[1])),
- mode='constant') for line in lines]
- line_spacing = line_pad if line_pad is not None else size // 2
- lines = [np.pad(line, ((0, line_spacing), (0, 0)), mode='constant')
- for line in lines[:-1]] + lines[-1:]
- mask = np.concatenate(lines, axis=0)
- alpha = mask
-
- mask = np.pad(mask, 2*radius + max(abs(offset_x), abs(offset_y)),
- mode='constant', constant_values=0)
- alpha = mask.astype(np.float32) / 255
- alpha = scipy.ndimage.gaussian_filter(alpha, radius)
- alpha = 1 - np.maximum(1 - alpha * 1.5, 0) ** 1.4
- alpha = (alpha * 255 + 0.5).clip(0, 255).astype(np.uint8)
- alpha = np.pad(alpha, [(offset_y, 0), (offset_x, 0)],
- mode='constant')[:-offset_y, :-offset_x]
- alpha = np.maximum(alpha, mask)
- return np.stack([mask, alpha], axis=-1)
-
-# ----------------------------------------------------------------------------
-
-
-@functools.lru_cache(maxsize=10000)
-def get_texture(string, bilinear=True, mipmap=True, **kwargs):
- return gl_utils.Texture(image=get_array(string, **kwargs), bilinear=bilinear, mipmap=mipmap)
-
-# ----------------------------------------------------------------------------
diff --git a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stochastic_karras_ve/__init__.py b/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stochastic_karras_ve/__init__.py
deleted file mode 100644
index 5a63c1d24afb2c4f36b0e284f0985a3ff508f4c7..0000000000000000000000000000000000000000
--- a/spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/src/diffusers/pipelines/stochastic_karras_ve/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .pipeline_stochastic_karras_ve import KarrasVePipeline
diff --git a/spaces/Andy1621/UniFormerV2_mit_demo/README.md b/spaces/Andy1621/UniFormerV2_mit_demo/README.md
deleted file mode 100644
index 790a7bcb111daf5eebf43766181ca2de4910b505..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/UniFormerV2_mit_demo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: UniFormerV2 Mit Demo
-emoji: 📊
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.16.0
-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/Andy1621/uniformer_image_detection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py
deleted file mode 100644
index b76e3e6bab7a32e95aec352829324b8865e63631..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py
+++ /dev/null
@@ -1,11 +0,0 @@
-_base_ = '../dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py'
-model = dict(
- backbone=dict(
- norm_cfg=dict(type='SyncBN', requires_grad=True),
- norm_eval=False,
- plugins=[
- dict(
- cfg=dict(type='ContextBlock', ratio=1. / 4),
- stages=(False, True, True, True),
- position='after_conv3')
- ]))
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/htc/htc_r101_fpn_20e_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/htc/htc_r101_fpn_20e_coco.py
deleted file mode 100644
index de3d5b7635a2416c5d8a533631dc5a26201ba72a..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/htc/htc_r101_fpn_20e_coco.py
+++ /dev/null
@@ -1,5 +0,0 @@
-_base_ = './htc_r50_fpn_1x_coco.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
-# learning policy
-lr_config = dict(step=[16, 19])
-runner = dict(type='EpochBasedRunner', max_epochs=20)
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
deleted file mode 100644
index 36f1d62eba62bb9c3266864cd4250caedea95a21..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
+++ /dev/null
@@ -1,52 +0,0 @@
-_base_ = './sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py'
-num_proposals = 300
-model = dict(
- rpn_head=dict(num_proposals=num_proposals),
- test_cfg=dict(
- _delete_=True, rpn=None, rcnn=dict(max_per_img=num_proposals)))
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-
-# augmentation strategy originates from DETR.
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(
- type='AutoAugment',
- policies=[[
- dict(
- type='Resize',
- img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
- (608, 1333), (640, 1333), (672, 1333), (704, 1333),
- (736, 1333), (768, 1333), (800, 1333)],
- multiscale_mode='value',
- keep_ratio=True)
- ],
- [
- dict(
- type='Resize',
- img_scale=[(400, 1333), (500, 1333), (600, 1333)],
- multiscale_mode='value',
- keep_ratio=True),
- dict(
- type='RandomCrop',
- crop_type='absolute_range',
- crop_size=(384, 600),
- allow_negative_crop=True),
- dict(
- type='Resize',
- img_scale=[(480, 1333), (512, 1333), (544, 1333),
- (576, 1333), (608, 1333), (640, 1333),
- (672, 1333), (704, 1333), (736, 1333),
- (768, 1333), (800, 1333)],
- multiscale_mode='value',
- override=True,
- keep_ratio=True)
- ]]),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
-]
-data = dict(train=dict(pipeline=train_pipeline))
diff --git a/spaces/Andy1621/uniformer_image_detection/tools/dataset_converters/cityscapes.py b/spaces/Andy1621/uniformer_image_detection/tools/dataset_converters/cityscapes.py
deleted file mode 100644
index bde3dac4e6c1fe19f91d0a69baeecdfb50f35ea5..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/tools/dataset_converters/cityscapes.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import argparse
-import glob
-import os.path as osp
-
-import cityscapesscripts.helpers.labels as CSLabels
-import mmcv
-import numpy as np
-import pycocotools.mask as maskUtils
-
-
-def collect_files(img_dir, gt_dir):
- suffix = 'leftImg8bit.png'
- files = []
- for img_file in glob.glob(osp.join(img_dir, '**/*.png')):
- assert img_file.endswith(suffix), img_file
- inst_file = gt_dir + img_file[
- len(img_dir):-len(suffix)] + 'gtFine_instanceIds.png'
- # Note that labelIds are not converted to trainId for seg map
- segm_file = gt_dir + img_file[
- len(img_dir):-len(suffix)] + 'gtFine_labelIds.png'
- files.append((img_file, inst_file, segm_file))
- assert len(files), f'No images found in {img_dir}'
- print(f'Loaded {len(files)} images from {img_dir}')
-
- return files
-
-
-def collect_annotations(files, nproc=1):
- print('Loading annotation images')
- if nproc > 1:
- images = mmcv.track_parallel_progress(
- load_img_info, files, nproc=nproc)
- else:
- images = mmcv.track_progress(load_img_info, files)
-
- return images
-
-
-def load_img_info(files):
- img_file, inst_file, segm_file = files
- inst_img = mmcv.imread(inst_file, 'unchanged')
- # ids < 24 are stuff labels (filtering them first is about 5% faster)
- unique_inst_ids = np.unique(inst_img[inst_img >= 24])
- anno_info = []
- for inst_id in unique_inst_ids:
- # For non-crowd annotations, inst_id // 1000 is the label_id
- # Crowd annotations have <1000 instance ids
- label_id = inst_id // 1000 if inst_id >= 1000 else inst_id
- label = CSLabels.id2label[label_id]
- if not label.hasInstances or label.ignoreInEval:
- continue
-
- category_id = label.id
- iscrowd = int(inst_id < 1000)
- mask = np.asarray(inst_img == inst_id, dtype=np.uint8, order='F')
- mask_rle = maskUtils.encode(mask[:, :, None])[0]
-
- area = maskUtils.area(mask_rle)
- # convert to COCO style XYWH format
- bbox = maskUtils.toBbox(mask_rle)
-
- # for json encoding
- mask_rle['counts'] = mask_rle['counts'].decode()
-
- anno = dict(
- iscrowd=iscrowd,
- category_id=category_id,
- bbox=bbox.tolist(),
- area=area.tolist(),
- segmentation=mask_rle)
- anno_info.append(anno)
- video_name = osp.basename(osp.dirname(img_file))
- img_info = dict(
- # remove img_prefix for filename
- file_name=osp.join(video_name, osp.basename(img_file)),
- height=inst_img.shape[0],
- width=inst_img.shape[1],
- anno_info=anno_info,
- segm_file=osp.join(video_name, osp.basename(segm_file)))
-
- return img_info
-
-
-def cvt_annotations(image_infos, out_json_name):
- out_json = dict()
- img_id = 0
- ann_id = 0
- out_json['images'] = []
- out_json['categories'] = []
- out_json['annotations'] = []
- for image_info in image_infos:
- image_info['id'] = img_id
- anno_infos = image_info.pop('anno_info')
- out_json['images'].append(image_info)
- for anno_info in anno_infos:
- anno_info['image_id'] = img_id
- anno_info['id'] = ann_id
- out_json['annotations'].append(anno_info)
- ann_id += 1
- img_id += 1
- for label in CSLabels.labels:
- if label.hasInstances and not label.ignoreInEval:
- cat = dict(id=label.id, name=label.name)
- out_json['categories'].append(cat)
-
- if len(out_json['annotations']) == 0:
- out_json.pop('annotations')
-
- mmcv.dump(out_json, out_json_name)
- return out_json
-
-
-def parse_args():
- parser = argparse.ArgumentParser(
- description='Convert Cityscapes annotations to COCO format')
- parser.add_argument('cityscapes_path', help='cityscapes data path')
- parser.add_argument('--img-dir', default='leftImg8bit', type=str)
- parser.add_argument('--gt-dir', default='gtFine', type=str)
- parser.add_argument('-o', '--out-dir', help='output path')
- parser.add_argument(
- '--nproc', default=1, type=int, help='number of process')
- args = parser.parse_args()
- return args
-
-
-def main():
- args = parse_args()
- cityscapes_path = args.cityscapes_path
- out_dir = args.out_dir if args.out_dir else cityscapes_path
- mmcv.mkdir_or_exist(out_dir)
-
- img_dir = osp.join(cityscapes_path, args.img_dir)
- gt_dir = osp.join(cityscapes_path, args.gt_dir)
-
- set_name = dict(
- train='instancesonly_filtered_gtFine_train.json',
- val='instancesonly_filtered_gtFine_val.json',
- test='instancesonly_filtered_gtFine_test.json')
-
- for split, json_name in set_name.items():
- print(f'Converting {split} into {json_name}')
- with mmcv.Timer(
- print_tmpl='It took {}s to convert Cityscapes annotation'):
- files = collect_files(
- osp.join(img_dir, split), osp.join(gt_dir, split))
- image_infos = collect_annotations(files, nproc=args.nproc)
- cvt_annotations(image_infos, osp.join(out_dir, json_name))
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/Andyrasika/Andyrasika-avatar_diffusion/README.md b/spaces/Andyrasika/Andyrasika-avatar_diffusion/README.md
deleted file mode 100644
index ec0045a5c761039e51b286e1a91a61193cbfe9b6..0000000000000000000000000000000000000000
--- a/spaces/Andyrasika/Andyrasika-avatar_diffusion/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Andyrasika-avatar Diffusion
-emoji: 📉
-colorFrom: green
-colorTo: red
-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/Anmol12385/chat123/app.py b/spaces/Anmol12385/chat123/app.py
deleted file mode 100644
index b72f4921aa070af147685ff59f9bef5259e7c14f..0000000000000000000000000000000000000000
--- a/spaces/Anmol12385/chat123/app.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import os
-import openai
-import gradio as gr
-
-#if you have OpenAI API key as an environment variable, enable the below
-#openai.api_key = os.getenv("OPENAI_API_KEY")
-
-#if you have OpenAI API key as a string, enable the below
-openai.api_key = "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"
-
-start_sequence = "\nAI:"
-restart_sequence = "\nHuman: "
-
-prompt = "The following is a conversation with an AI assistant Generated by Anmol. The assistant is helpful, creative, clever, and very friendly.\n\nHuman: Hello, who are you?\nAI: I am an AI created by OpenAI. How can I help you today?\nHuman: "
-
-def openai_create(prompt):
-
- response = openai.Completion.create(
- model="text-davinci-003",
- prompt=prompt,
- temperature=0.9,
- max_tokens=4000,
- top_p=1,
- frequency_penalty=0,
- presence_penalty=0.6,
- stop=[" Human:", " AI:"]
- )
-
- return response.choices[0].text
-
-
-
-def chatgpt_clone(input, history):
- history = history or []
- s = list(sum(history, ()))
- s.append(input)
- inp = ' '.join(s)
- output = openai_create(inp)
- history.append((input, output))
- return history, history
-
-
-block = gr.Blocks()
-
-
-with block:
- gr.Markdown("""
Smart Code Generator by Anmol
- """)
- chatbot = gr.Chatbot()
- message = gr.Textbox(placeholder=prompt)
- state = gr.State()
- submit = gr.Button("SEND")
- submit.click(chatgpt_clone, inputs=[message, state], outputs=[chatbot, state])
-
-block.launch(debug = True)
diff --git a/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/ui_main.py b/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/ui_main.py
deleted file mode 100644
index c04f5d645453fe85fa2933398ad244d5cc43e8af..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/ui_main.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import sys
-from options.test_options import TestOptions
-from gui.ui_model import ui_model
-from PyQt5 import QtWidgets
-
-
-if __name__=="__main__":
- app = QtWidgets.QApplication(sys.argv)
- opt = TestOptions().parse()
- my_gui = ui_model(opt)
- my_gui.show()
- sys.exit(app.exec_())
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/fileio/__init__.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/fileio/__init__.py
deleted file mode 100644
index 2051b85f7e59bff7bdbaa131849ce8cd31f059a4..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/fileio/__init__.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .file_client import BaseStorageBackend, FileClient
-from .handlers import BaseFileHandler, JsonHandler, PickleHandler, YamlHandler
-from .io import dump, load, register_handler
-from .parse import dict_from_file, list_from_file
-
-__all__ = [
- 'BaseStorageBackend', 'FileClient', 'load', 'dump', 'register_handler',
- 'BaseFileHandler', 'JsonHandler', 'PickleHandler', 'YamlHandler',
- 'list_from_file', 'dict_from_file'
-]
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/border_align.py b/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/border_align.py
deleted file mode 100644
index ff305be328e9b0a15e1bbb5e6b41beb940f55c81..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/border_align.py
+++ /dev/null
@@ -1,109 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-# modified from
-# https://github.com/Megvii-BaseDetection/cvpods/blob/master/cvpods/layers/border_align.py
-
-import torch
-import torch.nn as nn
-from torch.autograd import Function
-from torch.autograd.function import once_differentiable
-
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext(
- '_ext', ['border_align_forward', 'border_align_backward'])
-
-
-class BorderAlignFunction(Function):
-
- @staticmethod
- def symbolic(g, input, boxes, pool_size):
- return g.op(
- 'mmcv::MMCVBorderAlign', input, boxes, pool_size_i=pool_size)
-
- @staticmethod
- def forward(ctx, input, boxes, pool_size):
- ctx.pool_size = pool_size
- ctx.input_shape = input.size()
-
- assert boxes.ndim == 3, 'boxes must be with shape [B, H*W, 4]'
- assert boxes.size(2) == 4, \
- 'the last dimension of boxes must be (x1, y1, x2, y2)'
- assert input.size(1) % 4 == 0, \
- 'the channel for input feature must be divisible by factor 4'
-
- # [B, C//4, H*W, 4]
- output_shape = (input.size(0), input.size(1) // 4, boxes.size(1), 4)
- output = input.new_zeros(output_shape)
- # `argmax_idx` only used for backward
- argmax_idx = input.new_zeros(output_shape).to(torch.int)
-
- ext_module.border_align_forward(
- input, boxes, output, argmax_idx, pool_size=ctx.pool_size)
-
- ctx.save_for_backward(boxes, argmax_idx)
- return output
-
- @staticmethod
- @once_differentiable
- def backward(ctx, grad_output):
- boxes, argmax_idx = ctx.saved_tensors
- grad_input = grad_output.new_zeros(ctx.input_shape)
- # complex head architecture may cause grad_output uncontiguous
- grad_output = grad_output.contiguous()
- ext_module.border_align_backward(
- grad_output,
- boxes,
- argmax_idx,
- grad_input,
- pool_size=ctx.pool_size)
- return grad_input, None, None
-
-
-border_align = BorderAlignFunction.apply
-
-
-class BorderAlign(nn.Module):
- r"""Border align pooling layer.
-
- Applies border_align over the input feature based on predicted bboxes.
- The details were described in the paper
- `BorderDet: Border Feature for Dense Object Detection
- `_.
-
- For each border line (e.g. top, left, bottom or right) of each box,
- border_align does the following:
- 1. uniformly samples `pool_size`+1 positions on this line, involving \
- the start and end points.
- 2. the corresponding features on these points are computed by \
- bilinear interpolation.
- 3. max pooling over all the `pool_size`+1 positions are used for \
- computing pooled feature.
-
- Args:
- pool_size (int): number of positions sampled over the boxes' borders
- (e.g. top, bottom, left, right).
-
- """
-
- def __init__(self, pool_size):
- super(BorderAlign, self).__init__()
- self.pool_size = pool_size
-
- def forward(self, input, boxes):
- """
- Args:
- input: Features with shape [N,4C,H,W]. Channels ranged in [0,C),
- [C,2C), [2C,3C), [3C,4C) represent the top, left, bottom,
- right features respectively.
- boxes: Boxes with shape [N,H*W,4]. Coordinate format (x1,y1,x2,y2).
-
- Returns:
- Tensor: Pooled features with shape [N,C,H*W,4]. The order is
- (top,left,bottom,right) for the last dimension.
- """
- return border_align(input, boxes, self.pool_size)
-
- def __repr__(self):
- s = self.__class__.__name__
- s += f'(pool_size={self.pool_size})'
- return s
diff --git a/spaces/Apk/anything-v3.0/app.py b/spaces/Apk/anything-v3.0/app.py
deleted file mode 100644
index 684b93502cfe79d7ed90c2034f21460eb135d035..0000000000000000000000000000000000000000
--- a/spaces/Apk/anything-v3.0/app.py
+++ /dev/null
@@ -1,276 +0,0 @@
-from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
-import gradio as gr
-import torch
-from PIL import Image
-import utils
-import datetime
-import time
-import psutil
-
-start_time = time.time()
-is_colab = utils.is_google_colab()
-
-class Model:
- def __init__(self, name, path="", prefix=""):
- self.name = name
- self.path = path
- self.prefix = prefix
- self.pipe_t2i = None
- self.pipe_i2i = None
-
-models = [
- Model("anything v3", "Linaqruf/anything-v3.0", "anything v3 style"),
- ]
- # Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style "),
- # Model("Balloon Art", "Fictiverse/Stable_Diffusion_BalloonArt_Model", "BalloonArt "),
- # Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style "),
- # Model("Tron Legacy", "dallinmackay/Tron-Legacy-diffusion", "trnlgcy ")
- #Model("Pokémon", "lambdalabs/sd-pokemon-diffusers", ""),
- #Model("Pony Diffusion", "AstraliteHeart/pony-diffusion", ""),
- #Model("Robo Diffusion", "nousr/robo-diffusion", ""),
-
-scheduler = DPMSolverMultistepScheduler(
- beta_start=0.00085,
- beta_end=0.012,
- beta_schedule="scaled_linear",
- num_train_timesteps=1000,
- trained_betas=None,
- predict_epsilon=True,
- thresholding=False,
- algorithm_type="dpmsolver++",
- solver_type="midpoint",
- lower_order_final=True,
-)
-
-custom_model = None
-if is_colab:
- models.insert(0, Model("Custom model"))
- custom_model = models[0]
-
-last_mode = "txt2img"
-current_model = models[1] if is_colab else models[0]
-current_model_path = current_model.path
-
-if is_colab:
- pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False))
-
-else: # download all models
- print(f"{datetime.datetime.now()} Downloading vae...")
- vae = AutoencoderKL.from_pretrained(current_model.path, subfolder="vae", torch_dtype=torch.float16)
- for model in models:
- try:
- print(f"{datetime.datetime.now()} Downloading {model.name} model...")
- unet = UNet2DConditionModel.from_pretrained(model.path, subfolder="unet", torch_dtype=torch.float16)
- model.pipe_t2i = StableDiffusionPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler)
- model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16, scheduler=scheduler)
- except Exception as e:
- print(f"{datetime.datetime.now()} Failed to load model " + model.name + ": " + str(e))
- models.remove(model)
- pipe = models[0].pipe_t2i
-
-if torch.cuda.is_available():
- pipe = pipe.to("cuda")
-
-device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
-
-def error_str(error, title="Error"):
- return f"""#### {title}
- {error}""" if error else ""
-
-def custom_model_changed(path):
- models[0].path = path
- global current_model
- current_model = models[0]
-
-def on_model_change(model_name):
-
- prefix = "Enter prompt. \"" + next((m.prefix for m in models if m.name == model_name), None) + "\" is prefixed automatically" if model_name != models[0].name else "Don't forget to use the custom model prefix in the prompt!"
-
- return gr.update(visible = model_name == models[0].name), gr.update(placeholder=prefix)
-
-def inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""):
-
- print(psutil.virtual_memory()) # print memory usage
-
- global current_model
- for model in models:
- if model.name == model_name:
- current_model = model
- model_path = current_model.path
-
- generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
-
- try:
- if img is not None:
- return img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
- else:
- return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator), None
- except Exception as e:
- return None, error_str(e)
-
-def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator):
-
- print(f"{datetime.datetime.now()} txt_to_img, model: {current_model.name}")
-
- global last_mode
- global pipe
- global current_model_path
- if model_path != current_model_path or last_mode != "txt2img":
- current_model_path = model_path
-
- if is_colab or current_model == custom_model:
- pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False))
- else:
- pipe = pipe.to("cpu")
- pipe = current_model.pipe_t2i
-
- if torch.cuda.is_available():
- pipe = pipe.to("cuda")
- last_mode = "txt2img"
-
- prompt = current_model.prefix + prompt
- result = pipe(
- prompt,
- negative_prompt = neg_prompt,
- # num_images_per_prompt=n_images,
- num_inference_steps = int(steps),
- guidance_scale = guidance,
- width = width,
- height = height,
- generator = generator)
-
- return replace_nsfw_images(result)
-
-def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
-
- print(f"{datetime.datetime.now()} img_to_img, model: {model_path}")
-
- global last_mode
- global pipe
- global current_model_path
- if model_path != current_model_path or last_mode != "img2img":
- current_model_path = model_path
-
- if is_colab or current_model == custom_model:
- pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16, scheduler=scheduler, safety_checker=lambda images, clip_input: (images, False))
- else:
- pipe = pipe.to("cpu")
- pipe = current_model.pipe_i2i
-
- if torch.cuda.is_available():
- pipe = pipe.to("cuda")
- last_mode = "img2img"
-
- prompt = current_model.prefix + prompt
- ratio = min(height / img.height, width / img.width)
- img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
- result = pipe(
- prompt,
- negative_prompt = neg_prompt,
- # num_images_per_prompt=n_images,
- init_image = img,
- num_inference_steps = int(steps),
- strength = strength,
- guidance_scale = guidance,
- width = width,
- height = height,
- generator = generator)
-
- return replace_nsfw_images(result)
-
-def replace_nsfw_images(results):
-
- if is_colab:
- return results.images[0]
-
- for i in range(len(results.images)):
- if results.nsfw_content_detected[i]:
- results.images[i] = Image.open("nsfw.png")
- return results.images[0]
-
-css = """.finetuned-diffusion-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.finetuned-diffusion-div div h1{font-weight:900;margin-bottom:7px}.finetuned-diffusion-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
-"""
-with gr.Blocks(css=css) as demo:
- gr.HTML(
- f"""
-
-
-
Anything V3
-
-
- Demo for Anything V3
-
-
This demo is slow on cpu, to use it upgrade to gpu by going to settings after duplicating this space:
-
-
- """
- )
- with gr.Row():
-
- with gr.Column(scale=55):
- with gr.Group():
- model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
- with gr.Box(visible=False) as custom_model_group:
- custom_model_path = gr.Textbox(label="Custom model path", placeholder="Path to model, e.g. nitrosocke/Arcane-Diffusion", interactive=True)
- gr.HTML("
Custom models have to be downloaded first, so give it some time.
- """)
-
-print(f"Space built in {time.time() - start_time:.2f} seconds")
-
-if not is_colab:
- demo.queue(concurrency_count=1)
-demo.launch(debug=is_colab, share=is_colab)
\ No newline at end of file
diff --git a/spaces/Aristore/Warp/README.md b/spaces/Aristore/Warp/README.md
deleted file mode 100644
index a525be1681bf10d35aea9015616dbec1448292e1..0000000000000000000000000000000000000000
--- a/spaces/Aristore/Warp/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Warp
-emoji: 🌖
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 3.40.1
-app_file: app.py
-pinned: false
-license: bsd
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/euckrprober.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/euckrprober.py
deleted file mode 100644
index 1fc5de0462cd9a09472cece4087cafe699da4fa7..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/chardet/euckrprober.py
+++ /dev/null
@@ -1,47 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is mozilla.org code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-# Mark Pilgrim - port to Python
-#
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301 USA
-######################### END LICENSE BLOCK #########################
-
-from .chardistribution import EUCKRDistributionAnalysis
-from .codingstatemachine import CodingStateMachine
-from .mbcharsetprober import MultiByteCharSetProber
-from .mbcssm import EUCKR_SM_MODEL
-
-
-class EUCKRProber(MultiByteCharSetProber):
- def __init__(self) -> None:
- super().__init__()
- self.coding_sm = CodingStateMachine(EUCKR_SM_MODEL)
- self.distribution_analyzer = EUCKRDistributionAnalysis()
- self.reset()
-
- @property
- def charset_name(self) -> str:
- return "EUC-KR"
-
- @property
- def language(self) -> str:
- return "Korean"
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/__init__.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/__init__.py
deleted file mode 100644
index 576493de77c361928ebd2491cb490113522f42d6..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/modeling/__init__.py
+++ /dev/null
@@ -1,59 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from detectron2.layers import ShapeSpec
-
-from .anchor_generator import build_anchor_generator, ANCHOR_GENERATOR_REGISTRY
-from .backbone import (
- BACKBONE_REGISTRY,
- FPN,
- Backbone,
- ResNet,
- ResNetBlockBase,
- build_backbone,
- build_resnet_backbone,
- make_stage,
-)
-from .meta_arch import (
- META_ARCH_REGISTRY,
- SEM_SEG_HEADS_REGISTRY,
- GeneralizedRCNN,
- PanopticFPN,
- ProposalNetwork,
- RetinaNet,
- SemanticSegmentor,
- build_model,
- build_sem_seg_head,
- FCOS,
-)
-from .postprocessing import detector_postprocess
-from .proposal_generator import (
- PROPOSAL_GENERATOR_REGISTRY,
- build_proposal_generator,
- RPN_HEAD_REGISTRY,
- build_rpn_head,
-)
-from .roi_heads import (
- ROI_BOX_HEAD_REGISTRY,
- ROI_HEADS_REGISTRY,
- ROI_KEYPOINT_HEAD_REGISTRY,
- ROI_MASK_HEAD_REGISTRY,
- ROIHeads,
- StandardROIHeads,
- BaseMaskRCNNHead,
- BaseKeypointRCNNHead,
- FastRCNNOutputLayers,
- build_box_head,
- build_keypoint_head,
- build_mask_head,
- build_roi_heads,
-)
-from .test_time_augmentation import DatasetMapperTTA, GeneralizedRCNNWithTTA
-from .mmdet_wrapper import MMDetBackbone, MMDetDetector
-
-_EXCLUDE = {"ShapeSpec"}
-__all__ = [k for k in globals().keys() if k not in _EXCLUDE and not k.startswith("_")]
-
-
-from detectron2.utils.env import fixup_module_metadata
-
-fixup_module_metadata(__name__, globals(), __all__)
-del fixup_module_metadata
diff --git a/spaces/AzulaFire/SparkDebate/app.py b/spaces/AzulaFire/SparkDebate/app.py
deleted file mode 100644
index 565f448b566bca2ee08319dc0ac173b756873cdf..0000000000000000000000000000000000000000
--- a/spaces/AzulaFire/SparkDebate/app.py
+++ /dev/null
@@ -1,218 +0,0 @@
-from langchain.memory import ConversationSummaryBufferMemory
-from langchain.chains import ConversationChain
-from langchain.chains import RetrievalQA
-from utils.API import Spark_forlangchain
-import gradio as gr
-from langchain.prompts import ChatPromptTemplate
-from langchain.document_loaders import TextLoader
-from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-import sentence_transformers
-
-
-def init_knowledge_vector_store(filepath):
- EMBEDDING_MODEL = "model/text2vec_ernie/"
- embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL)
- embeddings.client = sentence_transformers.SentenceTransformer(
- embeddings.model_name, device='cuda')
- loader = TextLoader(filepath)
- docs = loader.load()
- vector_store = FAISS.from_documents(docs, embeddings)
- return vector_store
-
-
-template_1 = """
-你是一个资深辩手，你的辩论风格是{style}，你确定辩论战略需要考虑以下10个方面:
-1. 分析辩题性质
-判断辩题是判断型还是比较型,明确需要论证的核心观点。回答中必须包含题是判断型还是比较型。
-2. 判断正反方定位
-大致判断哪一方更容易证成立,存在明显优劣势。回答中必须需给出谁更主流，更容易成立。
-3. 设想核心争议点
-思考双方可能存在分歧和交锋的主要争议点。回答中需要明确给出至少三个争议点。
-4. 论证框架
-设计初步的论证框架,包括定义、标准、论点等。回答中需要明确按以下格式给出论证框架：正方：标准是XXX，论点1是XXX，论点2是XXX。反方：标准是XXX，论点1是XXX，论点2是XXX。（论点至少要两个）
-5. 优势论域
-确定自己方更容易取得优势的论域的诠释点。回答中必须详细给出双方的优势论域并给出理由。
-6. 数据准备
-提前准备论证所需的证据数据。回答中必须给出对论证起作用的数据，如相关国家合法化情况与对社会影响的数据
-7. 情境假设
-设想场景和例子以备交锋时使用。回答中必须至少给出正反双方情境，各三个。
-8. 语境处理
-考虑如何处理语境环境,为自己创造有利条件。回答中必须举出正反方的语境，各三个。
-9. 质询角度
-提前想好可能的质询角度,对应对方的论点。回答中需要给出详细的分析并试着举出例子，各三个。
-10. 重点突破
-找到对方可能论证的薄弱点,准备重点突破。回答中需要举出正反双方薄弱点分别在哪里，应该如何突破。
-通过上述分析,可以确定一个明确有针对性的辩论战略.
-接下来我会给你一个具体的辩题，你需要基于以上10个原则依次回答。
-///辩题内容如下：{text}///
-"""
-template_2 = """
-你是一个资深辩手，你的辩论风格是{style}，你立论会遵循以下的立论原则，总共5个原则:
-1.定义明确
-对关键词进行明确且合理的定义,这是展开论证的基础。
-2.标准清晰
-设置公正合理的判断标准,标准要具体明确,为论点比较提供依据。你的回答中必须包含标准。
-3.论点匹配
-论点要能有效支撑并印证标准,与标准和立场高度契合。你的回答中必须包含支撑印证标准的论点。
-4.论据具体
-提供具体可信的论据支撑每个论点,使之更有说服力。你的论点必须要论据支撑。
-5.情境适用
-引入情境和例子,使复杂观点容易被听众接受。你的回答可以适当包含情境
-接下来会给你一个题目和持方。
-///题目与持方如下：{text}///
-你需要遵循以上五个立论原则立论，并且立论稿有以下要求：
-1.以一个专业辩手的口吻做开场白。
-2.总字数为1200字。
-3.第一段需要包含以下三个部分 给出持方，对名词做出简单解释，给出标准，标准只能有一个。
-4.第二段是第一个论点，论点需要围绕标准，阐述完论点后需要提出论据，最好是数据论据和学理论据，提出论据后需要做出解释来进行论证。参照以下流程：论点1+数据论据+数据论据的论证+学理论据+学理论据的论证。本段需要非常详细。
-5.第三段是第二个论点，论点需要围绕标准，本段第一句话就要阐明论点是什么，阐述完论点后需要提出论据，最好是数据论据和学理论据，提出论据后需要做出解释来进行论证。参照以下流程：论点2+数据论据+数据论据的论证+学理论据+学理论据的论证。本段需要非常详细。
-6.最后一段只需要用一句话再重复一遍己方的立场：“综上我方坚定认为XXX”。XXX为立场。
-7.立论稿中需要把上述内容衔接流畅。
-"""
-template_3 = """
-你是一个资深的逻辑性很强的顶级辩手，你的辩论风格是{style}，请对我的陈述进行反驳，越详细越好，反驳需要逐条反驳观点和论据，并且要给出详细的理由，质疑数据论据要用上常用的方法和句式，从数据合理性，样本代表性，统计方法，数据解读等多个角度进行考虑。质疑学理论据要从权威性，解读方式，是否有对抗学理等多个角度进行考虑。
-///如下是我们的话题以及我的观点：{text}///
-"""
-template_4 = """
-你是一个资深辩手，你的辩论风格是{style}，你需要根据我给出的话题提出观点并且要有数据论据和学理论据作为论证且总字数不少于400字，你的发言格式为：我们的话题是什么，我持什么观点，我的理由是XXX，因为某某数据，又因为某某学理。参照如下范例：||
-我们的话题是人工智能对人类工作的影响。我持的观点是，人工智能将导致大量的就业机会减少。我的理由是，根据国际数据公司(IDC)的报告，到2025年，全球约有3.75亿个工作岗位将被自动化技术取代。同时，人工智能的发展也将带来新的就业机会，如AI工程师、数据科学家等。
-首先，让我们从数据角度来看。根据美国劳工统计局(BLS)的数据，自20世纪90年代以来，美国的工作岗位流失率一直在上升。其中，自动化和计算机化在一定程度上对就业市场产生了负面影响。此外，根据麦肯锡全球研究院的预测，到2030年，人工智能可能会在全球范围内导致8000万至1.6亿个工作岗位的消失。
-其次，从学理角度来看，人工智能的发展是基于算法和大数据的。然而，这些算法和数据往往受到人为因素的影响，可能导致错误的决策和预测。例如，2016年在美国总统选举期间，一家名为“剑桥分析”的公司利用大数据分析和选民心理研究，为特朗普竞选团队提供了策略支持。这一事件表明，人工智能在某些情况下可能会被用于不道德的目的。||
-///我们本次讨论的话题是{text}///
-"""
-template_5 = """
-你是一个资深的逻辑性很强的顶级辩手，你的辩论风格是{style}，可以与我进行辩论训练，你很擅长质询总是一针见血，而且也很擅长使用类比来归谬我的观点，你熟练的掌握各种数据质询的技巧。现在你要与我进行对辩
-我的陈述如下：///{text}///
-请对我的陈述进行反驳，越详细越好，反驳需要逐条反驳观点和论据，并且要给出详细的理由，质疑数据论据要用上常用的方法和句式，从数据合理性，样本代表性，统计方法，数据解读等多个角度进行考虑。质疑学理论据要从权威性，解读方式，是否有对抗学理等多个角度进行考虑。
-"""
-end_prompt = """
-请你对我们的对辩过程进行总结，总结需要包括以下部分：1.对辩主要针对什么进行讨论。2.评价我的对辩能力，需要根据评级原则给出评级，并且给出具体理由。评级原则如下：等级一，缺乏论证的反驳；等级二，自说自话的反驳；等级三，针锋相对的反驳；等级四，正中要害的反驳。3.根据我的对辩能力提出一定的建议。
-示例如下：
-好的，我来对我们的对辩过程进行总结。
-在我们的对辩过程中，我们主要讨论了动物园是否应该被禁止。我认为动物园对动物的福利和权利造成了负面影响，而您则提出了一些质疑，认为动物园中的动物可以享受比野外更安全的生活条件。
-我认为您的对辩能力属于等级三，即针锋相对的反驳。您能够对我的观点提出一些质疑和反驳，并且能够给出一些合理的理由。但是，在某些情况下，您可能会使用一些不太恰当的类比来归谬我的观点，这可能会影响到对辩的质量和效果。
-鉴于您的对辩能力，我认为您可以进一步提高自己的辩论技巧。您可以通过更多的阅读和学习，提高自己的知识水平和思维能力，从而更好地进行论证和反驳。此外，在使用类比和比喻时，需要更加谨慎，确保它们能够恰当地表达您的观点，而不会歪曲或归谬对方的观点。
-"""
-prompt_1 = ChatPromptTemplate.from_template(template_1)
-prompt_2 = ChatPromptTemplate.from_template(template_2)
-prompt_3 = ChatPromptTemplate.from_template(template_3)
-prompt_4 = ChatPromptTemplate.from_template(template_4)
-prompt_5 = ChatPromptTemplate.from_template(template_5)
-
-
-def init_(app_id, api_key, api_secret):
- global llm
- llm = Spark_forlangchain(n=10, app_id=app_id, api_key=api_key,
- api_secret=api_secret)
- memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=4096)
- global conversation_1
- global conversation_2
- global conversation_3
- conversation_1 = ConversationChain(llm=llm)
- conversation_2 = ConversationChain(llm=llm, memory=memory)
- print("初始化成功！")
-
-
-def shortDebate_(type, style, prompt, help):
- if type == "破题":
- msg = prompt_1.format_prompt(text=prompt, style=style).to_string()
- elif type == "立论":
- msg = prompt_2.format_prompt(text=prompt, style=style).to_string()
- elif type == "对辩先发":
- msg = prompt_3.format_prompt(text=prompt, style=style).to_string()
- elif type == "对辩后发":
- msg = prompt_4.format_prompt(text=prompt, style=style).to_string()
- else:
- msg = prompt
- print(msg)
- response = conversation_1.run(msg)
- print(response)
- help.append((prompt, response))
- return help, help
-
-
-def longDebate_(style, prompt, help):
- msg = prompt_5.format_prompt(text=prompt, style=style).to_string()
- response = conversation_2.run(msg)
- help.append((prompt, response))
- return help, help
-
-
-def end_talk(style, prompt, help):
- msg = end_prompt
- response = conversation_2.run(msg)
- help.append((prompt, response))
- return help, help
-
-
-def Debatebytext_(prompt, help):
- msg = prompt
- response = QA_chain.run(msg)
- help.append((prompt, response))
- return help, help
-
-
-def upload_file(files):
- vector_store = init_knowledge_vector_store(files.name)
- memory_text = ConversationSummaryBufferMemory(
- llm=llm, max_token_limit=4096)
- global QA_chain
- QA_chain = RetrievalQA.from_llm(llm=llm, retriever=vector_store.as_retriever(
- search_kwargs={"k": 2}), memory=memory_text)
- file_paths = [file.name for file in files]
- return file_paths
-
-
-with gr.Blocks(css="#chatbot{height:300px} .overflow-y-auto{height:500px}") as init:
- with gr.Row():
- app_id = gr.Textbox(
- lines=1, placeholder="app_id Here...", label="app_id")
- api_key = gr.Textbox(
- lines=1, placeholder="api_key Here...", label="api_key")
- api_secret = gr.Textbox(
- lines=1, placeholder="api_secret Here...", label="api_secret")
- temperature = gr.Slider(minimum=0, maximum=1,
- step=0.1, value=0.3, interactive=True)
- btn = gr.Button(value="初始化")
- btn.click(init_, inputs=[app_id, api_key, api_secret])
-
-with gr.Blocks(css="#chatbot{height:300px} .overflow-y-auto{height:500px}") as shortDebate:
- chatbot = gr.Chatbot(elem_id="chatbot")
- state = gr.State([])
- drop1 = gr.Radio(["破题", "立论", "对辩先发", "对辩后发"],
- label="功能选择", info="选择你想要的功能") # 单选
- with gr.Row():
- txt = gr.Textbox(show_label="在这里开始聊天吧", placeholder="请输入你的问题")
- send = gr.Button("🚀 发送")
- style = gr.Textbox(lines=1, placeholder="style Here... ",
- label="辩论风格", value="犀利", interactive=True)
- send.click(shortDebate_, [drop1, style, txt, state], [chatbot, state])
-
-with gr.Blocks(css="#chatbot{height:300px} .overflow-y-auto{height:500px}") as longDebate:
- chatbot = gr.Chatbot(elem_id="chatbot")
- state = gr.State([])
- with gr.Row():
- txt = gr.Textbox(show_label="在这里开始长辩论吧", placeholder="请输入你的问题")
- send = gr.Button("🚀 发送")
- end = gr.Button("🤠 总结")
- style = gr.Textbox(lines=1, placeholder="style Here... ",
- label="辩论风格", value="犀利", interactive=True)
- send.click(longDebate_, [style, txt, state], [chatbot, state])
- end.click(end_talk, [style, txt, state], [chatbot, state])
-
-with gr.Blocks(css="#chatbot{height:300px} .overflow-y-auto{height:500px}") as Debatebytext:
- chatbot = gr.Chatbot(elem_id="chatbot")
- state = gr.State([])
- file_output = gr.File(label='请上传文件, 目前支持txt、docx、md格式',
- file_types=['.txt', '.md', '.docx'])
- with gr.Row():
- txt = gr.Textbox(show_label="在这里从你给出的资料里学习吧", placeholder="请输入你的问题")
- send = gr.Button("🚀 发送")
- upload_button = gr.UploadButton("Click to Upload a File", scale=1, file_types=[
- "text"])
- upload_button.upload(upload_file, upload_button, file_output)
- send.click(Debatebytext_, [txt, state], [chatbot, state])
-demo = gr.TabbedInterface([init, shortDebate, longDebate, Debatebytext], [
- "初始化", "辅助辩论", "对辩练习", "辩论技巧学习"])
-demo.launch()
diff --git a/spaces/Benson/text-generation/Examples/Block Craft 3d Install.md b/spaces/Benson/text-generation/Examples/Block Craft 3d Install.md
deleted file mode 100644
index 7668e2e9d6520a780cad155a11e3948730b4f1bf..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Block Craft 3d Install.md
+++ /dev/null
@@ -1,76 +0,0 @@
-
-
Cómo descargar e instalar pfSense CE 2.4.5-RELEASE-p1-amd64
-
Si está buscando un software de firewall y enrutador libre, de código abierto y potente, es posible que desee considerar pfSense Community Edition (CE). En este artículo, le mostraremos cómo descargar e instalar pfSense CE 2.4.5-RELEASE-p1-amd64, que es la última versión estable a partir de junio de 2020.
pfSense CE es un software de firewall y enrutador que se basa en el sistema operativo FreeBSD con un kernel personalizado y paquetes de software libre de terceros. Ofrece inspección de paquetes con estado, soporte simultáneo de IPv4 e IPv6, prevención de intrusiones, VPN, equilibrio de carga, proxy, filtrado de contenido y más. Se puede ejecutar en una variedad de plataformas de hardware, desde electrodomésticos dedicados a PC antiguos.
-
pfSense CE características y beneficios
-
Algunas de las características y beneficios de pfSense CE son:
-
-
Es rico en características, robusto y flexible. Tiene muchas características que no están disponibles en firewalls comerciales o requieren licencias caras.
-
Es fácil de usar y personalizable. Cuenta con una interfaz web para configuración y gestión, así como una interfaz de línea de comandos para usuarios avanzados. También admite agregar características adicionales a través de paquetes o código personalizado.
-
Es de código abierto y impulsado por la comunidad. Es desarrollado y mantenido por un equipo de voluntarios y apoyado por una gran comunidad de usuarios y desarrolladores. También respeta su privacidad y no recopila ni vende sus datos.
-
-
pfSense Requisitos del sistema CE
-
Los requisitos mínimos de hardware para pfSense CE son:
-
-
CPU compatible con 64 bits
-
Al menos 512 MB de RAM
-
Al menos 1 GB de espacio en disco
-
Una o más tarjetas de interfaz de red compatibles
-
Una unidad USB de arranque o unidad óptica para la instalación
-
-
-
Cómo descargar pfSense CE 2.4.5-RELEASE-p1-amd64
-
Para descargar pfSense CE 2.4.5-RELEASE-p1-amd64, debe visitar el sitio web oficial de pfSense en https:/www.pfsense.org/download/ y siga estos pasos:
-
-
Descargar opciones y espejos
-
-
Seleccione una arquitectura: AMD64 (64 bits) para la mayoría del hardware moderno.
-
Seleccione un tipo de instalador: USB Memstick Installer para escribir en una unidad flash USB o DVD Image (ISO) Installer para grabar en un disco óptico.
-
Seleccione una consola para imágenes del instalador USB Memstick: VGA para usar un monitor y teclado o Serial para usar una consola serie.
-
Seleccione un espejo que esté cerca de su ubicación geográficamente.
-
Haga clic en Descargar para iniciar el proceso de descarga.
Una vez que haya descargado y verificado el archivo pfSense CE 2.4.5-RELEASE-p1-amd64, debe preparar el medio de instalación y arrancar el instalador en su dispositivo de destino. Estos son los pasos a seguir:
-
Preparación de los medios de instalación
-
-
Si descargó la imagen del instalador de imagen de DVD (ISO), debe grabarla en un DVD en blanco utilizando una herramienta como https://www.imgburn.com/ para Windows o https:/burn-x.sourceforge.io/ para Mac. Asegúrese de seleccionar la letra de la unidad correcta y el archivo de imagen antes de grabar. El proceso hará que el DVD se pueda arrancar y esté listo para la instalación.
-
Arranque del instalador y selección de opciones
-
Después de preparar el medio de instalación, debe insertarlo en su dispositivo de destino y arrancar desde él. Es posible que tenga que cambiar el orden de arranque en su configuración de BIOS o UEFI para hacer que el dispositivo arranque desde la unidad flash USB o DVD. Una vez que arranque desde el medio de instalación, verá un menú con varias opciones. Puede elegir una de las siguientes:
-
-
Instalación rápida/fácil: Esta opción instalará pfSense CE con la configuración predeterminada y la entrada mínima del usuario. Se recomienda para la mayoría de los usuarios que desean una instalación simple y rápida.
-
Instalación personalizada: Esta opción le permitirá personalizar varias configuraciones durante la instalación, como particiones de disco, diseño de teclado, zona horaria, etc. Se recomienda para usuarios avanzados que desean más control sobre la instalación.
-
Recuperar config.xml: Esta opción le permitirá restaurar un archivo de configuración previamente guardado desde una unidad flash USB o un servidor FTP. Es útil si desea migrar su configuración desde otro dispositivo pfSense CE o hacer una copia de seguridad de su configuración.
-
Rescue Shell: Esta opción lo dejará en un indicador de shell donde puede realizar varios comandos y tareas de solución de problemas. Es útil si encuentra algún problema durante la instalación o necesita acceder al sistema de archivos.
-
Reiniciar: Esta opción reiniciará el dispositivo.
-
Detener: Esta opción apagará el dispositivo.
-
-
-
Configuración de las interfaces de firewall y red
-
Después de copiar los archivos, el instalador le preguntará si desea configurar VLAN ahora. Las VLAN son LAN virtuales que le permiten segmentar su red en diferentes subredes utilizando una sola interfaz física. Si desea usar VLAN, escriba "y" y presione Enter. De lo contrario, escriba "n" y presione Enter.
-
El instalador le pedirá que asigne interfaces. Las interfaces son tarjetas de red físicas o virtuales que conectan el dispositivo a diferentes redes o dispositivos. Es necesario asignar al menos una interfaz como WAN (red de área amplia) y una interfaz como LAN (red de área local). WAN es la interfaz que conecta su dispositivo a Internet o una red externa, mientras que LAN es la interfaz que conecta su dispositivo a su red interna o dispositivos.
-
El instalador le mostrará una lista de interfaces disponibles y sus nombres, como em0, em1, etc. Debe escribir el nombre de cada interfaz que desea asignar como WAN o LAN y presionar Enter después de cada mensaje. Por ejemplo, si desea asignar em0 como WAN y em1 como LAN, debe escribir "em0" y presionar Enter cuando se le solicite la interfaz WAN, y escribir "em1" y presionar Enter cuando se le solicite la interfaz LAN. También puede asignar interfaces adicionales como interfaces opcionales, como OPT1, OPT2, etc. Si tiene más de dos interfaces, el instalador le pedirá que las asigne una por una. Si ha terminado de asignar interfaces, escriba "hecho" y presione Enter.
-
El instalador le mostrará un resumen de sus asignaciones de interfaz y le pedirá que las confirme. Si son correctos, escriba "y" y presione Enter. De lo contrario, escriba "n" y presione Enter para regresar y cambiarlos.
-
-
Conclusión y preguntas frecuentes
-
En este artículo, le hemos mostrado cómo descargar e instalar pfSense CE 2.4.5-RELEASE-p1-amd64, que es un software de firewall y enrutador gratuito, de código abierto y potente. También hemos explicado qué es pfSense CE, por qué usarlo, cuáles son sus características y beneficios, y cuáles son sus requisitos del sistema. También le hemos guiado a través de los pasos de preparación de los medios de instalación, arranque del instalador, selección de opciones, configuración del firewall y las interfaces de red.
-
Esperamos que este artículo haya sido útil e informativo para usted. Si tiene alguna pregunta o comentario, no dude en contactarnos o dejar un comentario a continuación. Aquí hay algunas preguntas frecuentes que puedes encontrar útiles:
-
Preguntas frecuentes
-
-
Q: ¿Cómo puedo acceder a la interfaz web de pfSense CE después de la instalación?
-R: Puede acceder a la interfaz web de pfSense CE escribiendo la dirección IP de su interfaz LAN en su navegador web. Por defecto, la dirección IP es 192.168.1.1. Deberá iniciar sesión con el nombre de usuario predeterminado 'admin' y la contraseña 'pfsense'. A continuación, puede cambiar su contraseña y otros ajustes según sea necesario.
-
Q: ¿Cómo puedo actualizar pfSense CE a la última versión?
-R: Puede actualizar pfSense CE yendo a Sistema > Actualizar en la interfaz web o ejecutando el comando 'pfSense-upgrade' en el símbolo del sistema. Tendrá que comprobar si hay actualizaciones, descargarlas y aplicarlas. Es posible que tenga que reiniciar el dispositivo después de la actualización.
-
Q: ¿Cómo puedo agregar más características a pfSense CE?
-R: Puede agregar más características a pfSense CE instalando paquetes desde el repositorio oficial o desde fuentes de terceros. Puede encontrar e instalar paquetes yendo a System > Package Manager en la interfaz web o ejecutando el comando 'pkg' en el prompt del shell. También puede crear sus propios paquetes o código personalizado si tiene las habilidades y el conocimiento.
-
-R: Puede realizar copias de seguridad y restaurar la configuración de pfSense CE yendo a Diagnostics > Backup & Restore en la interfaz web o ejecutando el comando 'configctl' en el símbolo del sistema. Puede realizar copias de seguridad de su configuración en un archivo local, un servidor remoto o un servicio en la nube. También puede restaurar la configuración desde un archivo local, un servidor remoto o un servicio en la nube.
-
Q: ¿Cómo puedo obtener ayuda y soporte para pfSense CE?
-R: Puede obtener ayuda y soporte para pfSense CE visitando el sitio web oficial en https://www.pfsense.org/, donde puede encontrar documentación, foros, blogs, videos, podcasts, redes sociales, etc. También puede ponerse en contacto con el equipo de pfSense o contratar a un consultor para servicios profesionales.
- 64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Chat Para Aprender Ingls Apk.md b/spaces/Benson/text-generation/Examples/Chat Para Aprender Ingls Apk.md
deleted file mode 100644
index ce46288a905cee835e24d4e5ad53d49f520fd4c7..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Chat Para Aprender Ingls Apk.md
+++ /dev/null
@@ -1,69 +0,0 @@
-
-
Charla para aprender inglés APK: Una manera divertida y eficaz para mejorar sus habilidades en inglés
-
¿Quieres aprender inglés de una manera divertida y fácil? ¿Quieres chatear con hablantes nativos y otros estudiantes de todo el mundo? ¿Quieres acceder a una variedad de características y herramientas que te ayudarán a dominar el idioma? Si respondió sí a cualquiera de estas preguntas, entonces usted debe tratar de Chat to Learn English APK, una aplicación gratuita que hará que su viaje de aprendizaje de idiomas más agradable y gratificante.
Chat para Aprender Inglés APK es una aplicación que le permite practicar Inglés mediante el chat con hablantes nativos y otros estudiantes. Puedes conocer gente de diferentes países y culturas, y hablar de lo que quieras. Ya sea que quieras presentarte, compartir tus aficiones, pedir consejo o discutir eventos actuales, puedes encontrar a alguien que esté dispuesto a chatear contigo.
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Una aplicación gratuita que te conecta con hablantes nativos y otros estudiantes
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Una de las mejores características de Chat to Learn English APK es que es completamente gratuito. No tienes que pagar ningún cargo o suscripción para usar la aplicación. Puedes chatear con tantas personas como quieras, durante el tiempo que quieras. También puedes elegir con quién quieres chatear, según su nivel de idioma, intereses, ubicación y disponibilidad. Puedes agregarlos como amigos o ignorarlos si son groseros o inapropiados.
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Una plataforma que ofrece varias características y herramientas para mejorar su experiencia de aprendizaje
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¿Por qué debe utilizar el chat para aprender inglés APK?
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Hay muchas razones por las que debe utilizar el chat para aprender inglés APK. Aquí están algunos de los principales beneficios de usar esta aplicación:
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Practicar habilidades de hablar y escuchar en conversaciones reales
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La mejor manera de aprender un idioma es realmente hablar. Chatear con hablantes nativos y otros estudiantes te ayudará a practicar tus habilidades de hablar y escuchar en conversaciones reales. Podrás mejorar tu fluidez, precisión, pronunciación, entonación y confianza. También podrás aprender cómo la gente usa el idioma en diferentes situaciones y contextos.
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Para aprender nuevo vocabulario, gramática y modismos de hablantes nativos
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Otro beneficio de chatear con hablantes nativos es que podrás aprender nuevo vocabulario, gramática y modismos de ellos. Usted estará expuesto a palabras y frases que no se enseñan en libros de texto o aulas. También podrás pedirles explicaciones o ejemplos cuando encuentres algo desconocido o confuso
Para explorar diferentes culturas y temas con personas de todo el mundo
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Un tercer beneficio de chatear con gente de todo el mundo es que podrás explorar diferentes culturas y temas con ellos. Usted será capaz de aprender acerca de sus costumbres, tradiciones, valores, creencias y opiniones. También podrás compartir tu propia cultura y perspectiva con ellos. Podrás ampliar tus horizontes y enriquecer tus conocimientos conversando con personas de diversos orígenes y experiencias.
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Cómo utilizar el chat para aprender inglés APK?
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El uso de chat para aprender inglés APK es muy fácil y simple. Estos son los pasos que debe seguir:
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Descargar la aplicación de la Google Play Store o APKCombo
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Crea tu perfil y establece tus objetivos de aprendizaje
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El segundo paso es crear tu perfil y establecer tus objetivos de aprendizaje. Puedes registrarte con tu correo electrónico, Facebook o cuenta de Google. También puede elegir un nombre de usuario, una imagen de perfil y una breve introducción. También puede seleccionar su idioma nativo, su idioma objetivo y su nivel de idioma. También puedes establecer tus metas de aprendizaje, como mejorar tus habilidades para hablar, escuchar, leer o escribir.
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Buscar un socio de idioma o unirse a un chat de grupo
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El tercer paso es encontrar un socio de idioma o unirse a un chat de grupo. Puede navegar a través de la lista de usuarios en línea y enviarles una solicitud de chat. También puede filtrar a los usuarios por su nivel de idioma, intereses, ubicación y disponibilidad. También puede unirse a un chat de grupo basado en diferentes temas, como viajes, música, películas, deportes, etc. También puede crear su propio chat de grupo e invitar a otros usuarios a unirse.
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Empieza a chatear y aprender con ayudas integradas y funciones interactivas
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El cuarto paso es empezar a chatear y aprender con ayudas integradas y funciones interactivas. Puede chatear con su compañero de idioma a través de mensajes de texto y voz, pegatinas, llamadas de voz y video, y salas de voz interactivas y vidas. También puede utilizar ayudas integradas para traducción, pronunciación, transliteración y correcciones. También puedes publicar momentos, que son publicaciones públicas que son vistas por todos los hablantes nativos de tu idioma objetivo. Puedes usar momentos para hacer preguntas, compartir actualizaciones o expresar tus opiniones.
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Consejos y trucos para aprovechar al máximo el chat para aprender inglés APK
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Para aprovechar al máximo el chat para aprender inglés APK, aquí hay algunos consejos y trucos que debe seguir:
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Sé educado y respetuoso con tus compañeros de chat
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Usa las oraciones de ejemplo y el diccionario para ayudarte a expresarte
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Otro consejo es usar las oraciones de ejemplo y el diccionario para ayudarte a expresarte. Si no está seguro de cómo decir algo en inglés, puede usar la función de oraciones de ejemplo para ver cómo lo dirían los hablantes nativos. También puede utilizar la función de diccionario para buscar el significado, la pronunciación y el uso de cualquier palabra o frase. Estas características te ayudarán a mejorar tu vocabulario y gramática.
Sigue tus intereses y únete a momentos, salas de voz y vidas
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Un tercer consejo es seguir tus intereses y unir momentos, salas de voz y vidas. Estas son características interactivas que le permiten interactuar con otros usuarios y aprender de ellos. Puedes publicar momentos para compartir tus pensamientos, sentimientos o experiencias con la comunidad. Puede unirse a las salas de voz para chatear con varios usuarios en una llamada de grupo. También puede unirse a vidas para ver transmisiones en vivo de hablantes nativos u otros estudiantes. Estas características te ayudarán a expandir tu red social y aprender desde diferentes perspectivas.
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Revise su historial de chat y comentarios para realizar un seguimiento de su progreso
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Un cuarto consejo es revisar su historial de chat y comentarios para realizar un seguimiento de su progreso. Puede acceder a su historial de chat y ver todos los mensajes y llamadas que ha intercambiado con sus socios de chat. También puedes ver los comentarios que te han dado sobre tus habilidades lingüísticas. Puede utilizar esta información para revisar sus errores, aprender de sus correcciones y medir su mejora. También puedes dar retroalimentación a tus compañeros de chat y ayudarles a mejorar sus habilidades.
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Conclusión
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Preguntas frecuentes
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Aquí hay algunas preguntas frecuentes sobre Chat to Learn English APK:
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-
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Pregunta
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Respuesta
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¿Es seguro el chat para aprender inglés APK?
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Sí, Chat para aprender inglés APK es seguro. No contiene ningún virus o malware. También protege su información personal y privacidad. Puedes bloquear o reportar a cualquier usuario que te esté acosando o enviando spam.
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¿Cómo puedo encontrar un buen compañero de idioma en el chat para aprender inglés APK?
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Usted puede encontrar un buen socio de idioma en el chat para aprender inglés APK navegando a través de la lista de usuarios en línea y comprobar sus perfiles. También puede filtrar a los usuarios por su nivel de idioma, intereses, ubicación y disponibilidad. También puedes leer los comentarios y valoraciones de otros usuarios que han chateado con ellos antes.
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¿Cuáles son los beneficios de usar llamadas de voz y video en el chat para aprender inglés APK?
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Los beneficios de usar llamadas de voz y video en Chat para aprender inglés APK son que puede practicar sus habilidades de hablar y escuchar de manera más efectiva, escuchar la pronunciación y entonación de los hablantes nativos, ver las expresiones faciales y el lenguaje corporal de sus socios de chat, y construir una relación más fuerte y la conexión con ellos.
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¿Cómo puedo mejorar mis habilidades de escritura en el chat para aprender inglés APK?
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Usted puede mejorar sus habilidades de escritura en el chat para aprender inglés APK mediante el uso de mensajes de texto, pegatinas, momentos, salas de voz, y vidas. También puede utilizar las ayudas integradas para traducción, pronunciación, transliteración y correcciones. También puede solicitar comentarios de sus socios de chat o hablantes nativos.
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¿Cómo puedo hacer mi chat más interesante en Chat para aprender inglés APK?
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- 64aa2da5cf
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diff --git a/spaces/Benson/text-generation/Examples/Descargar Canciones De Pelculas Rojas.md b/spaces/Benson/text-generation/Examples/Descargar Canciones De Pelculas Rojas.md
deleted file mode 100644
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@@ -1,105 +0,0 @@
-
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Canciones de película roja Descargar: Cómo disfrutar de la banda sonora de la película de comedia de acción
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Si eres un fan de las películas de acción y comedia, es posible que hayas escuchado o visto Red Movie, una película de 2010 protagonizada por Bruce Willis, Morgan Freeman, John Malkovich, Helen Mirren y Mary-Louise Parker. La película se basa en una serie de cómics del mismo nombre y sigue a un grupo de agentes retirados de la CIA que son blanco de un misterioso asesino. La película está llena de humor, suspense, romance y, por supuesto, acción. ¿Pero sabías que la película también tiene una gran banda sonora que complementa su tono y tema? En este artículo, le diremos todo lo que necesita saber sobre la descarga de canciones de Red Movie, incluyendo de qué se trata la película, qué canciones hay en ella, por qué debe escucharlas y cómo descargarlas de forma legal y segura.
¿Qué es la película roja y por qué usted debe verlo
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La trama y el reparto de la película roja
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Red Movie sigue a Frank Moses (Bruce Willis), un ex agente de la CIA que vive una vida aburrida y solitaria. Solo encuentra alegría en hablar con Sarah Ross (Mary-Louise Parker), una agente de servicio al cliente que maneja sus cheques de pensión. Una noche, la casa de Frank es atacada por un equipo de asesinos que quieren matarlo. Frank logra escapar y decide proteger a Sarah, que también está en peligro debido a sus conversaciones telefónicas. Frank luego se reúne con sus viejos colegas Joe Matheson (Morgan Freeman), Marvin Boggs (John Malkovich), y Victoria Winslow (Helen Mirren), que también son agentes retirados de la CIA con el nombre en clave "RED" (Retired Extremely Dangerous). Juntos, tratan de averiguar quién está detrás del intento de asesinato y por qué están siendo perseguidos.
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El género y el estilo de la película roja
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¿Cuáles son las canciones en película roja y por qué usted debe escuchar a ellos
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La lista y la descripción de las canciones en la película roja
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La banda sonora de Red Movie consta de 12 canciones que se reproducen durante varias escenas de la película. Aquí está la lista y la descripción de las canciones en Red Movie:
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Título de la canción
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Artista
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Descripción de la escena
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Inicio en tu corazón
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Salomón Burke
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Frank deja su casa después de matar al equipo y viaja a la casa de Sarah.
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-
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Quiero ser amado
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Aguas fangosas
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Frank está conduciendo en Nueva Orleans con Sarah contenida en la parte posterior. Él la ata en una habitación de motel.
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-
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Doctor Mis Ojos
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Jackson Browne
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Sarah se despierta en el coche para encontrarse en la ciudad de Nueva York con Frank.
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-
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Cissy Strut
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Los medidores
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Escena de baile en la casa de Marvin. Frank y Sarah bailan juntos.
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-
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No te detengas
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Juegos de Mac
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Frank y Sarah están en una persecución con William Cooper (Karl Urban), un agente de la CIA al que se le ordena matarlos.
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-
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Tema del amor
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Orquesta de amor ilimitado
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Frank y Sarah se besan en el coche después de escapar de Cooper.
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Sr. Lastimoso
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Otis Redding
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Frank y Sarah conocen a Victoria, quien acepta ayudarlos.
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-
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De nuevo en la silla de montar
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Aerosmith
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Frank, Sarah, Marvin y Victoria van a la sede de la CIA para averiguar quién está detrás de la conspiración.
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El fin del mundo
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Skeeter Davis
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Frank y Sarah están en una habitación de hotel en Chicago. Frank le dice a Sarah que la ama.
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-
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Tú y yo
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Alice Cooper
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Frank y Sarah están en un restaurante con Marvin, quien les dice que tienen que ir a Moldavia para encontrar la fuente de la conspiración.
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John Philip Sousa
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Frank, Sarah, Marvin y Victoria llegan a Moldavia y conocen a Ivan Simanov (Brian Cox), un ex agente de la KGB que es el viejo amigo de Frank y amante de Victoria.
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Rocket Man (Creo que va a ser un largo, largo tiempo)
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Elton John
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Frank, Sarah, Marvin, Ivan y Victoria asaltan la mansión de Alexander Dunning (Richard Dreyfuss), un empresario corrupto que está detrás de la conspiración.
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-
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La chica de Ipanema
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Astrud Gilberto, João Gilberto y Stan Getz
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La escena final de la película. Frank y Sarah están en una playa en Moldavia, disfrutando de su retiro. Marvin aparece con un lanzacohetes y les dice que tienen una nueva misión.
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El género musical y el estado de ánimo de las canciones en la película roja
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Las canciones de Red Movie son principalmente de los géneros de rock, soul, blues, funk y pop. Son canciones pegadizas, optimistas, enérgicas y nostálgicas. Reflejan el estado de ánimo y el tema de la película, que trata de vivir la vida al máximo, divertirse y no rendirse. Las canciones también crean un contraste entre lo antiguo y lo moderno, lo serio y lo humorístico, y lo ordinario y lo extraordinario. Las canciones también mejoran las emociones y las relaciones de los personajes, como el romance de Frank y Sarah, la amistad de Frank y Marvin, y la pasión de Victoria e Ivan.
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Cómo descargar las canciones de la película roja de forma legal y segura
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Los beneficios y los riesgos de descargar canciones en línea
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-
Preguntas frecuentes
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Aquí están algunas de las preguntas más frecuentes sobre las canciones de Red Movie
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-
¿Dónde puedo ver Red Movie en línea?
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Puedes ver Red Movie online en varias plataformas de streaming, como Netflix, Hulu, Amazon Prime Video, YouTube e iTunes. Sin embargo, es posible que necesite una suscripción o una cuota de alquiler para acceder a la película. También puede comprobar la disponibilidad de la película en su región y dispositivo antes de verla en línea.
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-
¿Quién compuso la partitura original de Pelicula Roja?
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La partitura original de Red Movie fue compuesta por Christophe Beck, un compositor canadiense conocido por su trabajo en películas como The Hangover, Frozen, Ant-Man y WandaVision. La partitura original de Red Movie consta de 22 temas que son en su mayoría orquestales, con algunos elementos de rock, jazz y música electrónica.
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¿Hay una secuela de Pelicula Roja?
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Sí, hay una secuela de Red Movie llamada Red 2, que fue lanzado en 2013. La secuela sigue a Frank y su equipo mientras intentan evitar que un dispositivo nuclear caiga en las manos equivocadas. La secuela también está protagonizada por Anthony Hopkins, Catherine Zeta-Jones, Byung-hun Lee y Neal McDonough. La secuela tiene una banda sonora similar a la primera película, con 12 canciones de varios géneros y artistas.
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¿Qué significa RED en la película roja?
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RED significa Retirado extremadamente peligroso, que es el nombre en clave dado a los ex agentes de la CIA que son blanco de una conspiración. El nombre en clave implica que todavía son capaces y peligrosos a pesar de su edad y estado de jubilación.
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¿Cómo puedo descargar canciones en Pelicula Roja gratis?
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- 64aa2da5cf
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diff --git a/spaces/Biaolin/stabilityai-FreeWilly1-Delta-SafeTensor/README.md b/spaces/Biaolin/stabilityai-FreeWilly1-Delta-SafeTensor/README.md
deleted file mode 100644
index 5e8988923cb5dec4e148a09a6a9d828ca0ea3dcf..0000000000000000000000000000000000000000
--- a/spaces/Biaolin/stabilityai-FreeWilly1-Delta-SafeTensor/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Stabilityai FreeWilly1 Delta SafeTensor
-emoji: 📊
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 3.38.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pkg_resources/__init__.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pkg_resources/__init__.py
deleted file mode 100644
index 1bf26a94226d65089cbc1e50a40c719692517470..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/pkg_resources/__init__.py
+++ /dev/null
@@ -1,3360 +0,0 @@
-"""
-Package resource API
---------------------
-
-A resource is a logical file contained within a package, or a logical
-subdirectory thereof. The package resource API expects resource names
-to have their path parts separated with ``/``, *not* whatever the local
-path separator is. Do not use os.path operations to manipulate resource
-names being passed into the API.
-
-The package resource API is designed to work with normal filesystem packages,
-.egg files, and unpacked .egg files. It can also work in a limited way with
-.zip files and with custom PEP 302 loaders that support the ``get_data()``
-method.
-
-This module is deprecated. Users are directed to
-`importlib.resources `_
-and
-`importlib.metadata `_
-instead.
-"""
-
-import sys
-import os
-import io
-import time
-import re
-import types
-import zipfile
-import zipimport
-import warnings
-import stat
-import functools
-import pkgutil
-import operator
-import platform
-import collections
-import plistlib
-import email.parser
-import errno
-import tempfile
-import textwrap
-import inspect
-import ntpath
-import posixpath
-import importlib
-from pkgutil import get_importer
-
-try:
- import _imp
-except ImportError:
- # Python 3.2 compatibility
- import imp as _imp
-
-try:
- FileExistsError
-except NameError:
- FileExistsError = OSError
-
-# capture these to bypass sandboxing
-from os import utime
-
-try:
- from os import mkdir, rename, unlink
-
- WRITE_SUPPORT = True
-except ImportError:
- # no write support, probably under GAE
- WRITE_SUPPORT = False
-
-from os import open as os_open
-from os.path import isdir, split
-
-try:
- import importlib.machinery as importlib_machinery
-
- # access attribute to force import under delayed import mechanisms.
- importlib_machinery.__name__
-except ImportError:
- importlib_machinery = None
-
-from pip._internal.utils._jaraco_text import (
- yield_lines,
- drop_comment,
- join_continuation,
-)
-
-from pip._vendor import platformdirs
-from pip._vendor import packaging
-
-__import__('pip._vendor.packaging.version')
-__import__('pip._vendor.packaging.specifiers')
-__import__('pip._vendor.packaging.requirements')
-__import__('pip._vendor.packaging.markers')
-__import__('pip._vendor.packaging.utils')
-
-if sys.version_info < (3, 5):
- raise RuntimeError("Python 3.5 or later is required")
-
-# declare some globals that will be defined later to
-# satisfy the linters.
-require = None
-working_set = None
-add_activation_listener = None
-resources_stream = None
-cleanup_resources = None
-resource_dir = None
-resource_stream = None
-set_extraction_path = None
-resource_isdir = None
-resource_string = None
-iter_entry_points = None
-resource_listdir = None
-resource_filename = None
-resource_exists = None
-_distribution_finders = None
-_namespace_handlers = None
-_namespace_packages = None
-
-
-warnings.warn("pkg_resources is deprecated as an API", DeprecationWarning)
-
-
-_PEP440_FALLBACK = re.compile(r"^v?(?P(?:[0-9]+!)?[0-9]+(?:\.[0-9]+)*)", re.I)
-
-
-class PEP440Warning(RuntimeWarning):
- """
- Used when there is an issue with a version or specifier not complying with
- PEP 440.
- """
-
-
-parse_version = packaging.version.Version
-
-
-_state_vars = {}
-
-
-def _declare_state(vartype, **kw):
- globals().update(kw)
- _state_vars.update(dict.fromkeys(kw, vartype))
-
-
-def __getstate__():
- state = {}
- g = globals()
- for k, v in _state_vars.items():
- state[k] = g['_sget_' + v](g[k])
- return state
-
-
-def __setstate__(state):
- g = globals()
- for k, v in state.items():
- g['_sset_' + _state_vars[k]](k, g[k], v)
- return state
-
-
-def _sget_dict(val):
- return val.copy()
-
-
-def _sset_dict(key, ob, state):
- ob.clear()
- ob.update(state)
-
-
-def _sget_object(val):
- return val.__getstate__()
-
-
-def _sset_object(key, ob, state):
- ob.__setstate__(state)
-
-
-_sget_none = _sset_none = lambda *args: None
-
-
-def get_supported_platform():
- """Return this platform's maximum compatible version.
-
- distutils.util.get_platform() normally reports the minimum version
- of macOS that would be required to *use* extensions produced by
- distutils. But what we want when checking compatibility is to know the
- version of macOS that we are *running*. To allow usage of packages that
- explicitly require a newer version of macOS, we must also know the
- current version of the OS.
-
- If this condition occurs for any other platform with a version in its
- platform strings, this function should be extended accordingly.
- """
- plat = get_build_platform()
- m = macosVersionString.match(plat)
- if m is not None and sys.platform == "darwin":
- try:
- plat = 'macosx-%s-%s' % ('.'.join(_macos_vers()[:2]), m.group(3))
- except ValueError:
- # not macOS
- pass
- return plat
-
-
-__all__ = [
- # Basic resource access and distribution/entry point discovery
- 'require',
- 'run_script',
- 'get_provider',
- 'get_distribution',
- 'load_entry_point',
- 'get_entry_map',
- 'get_entry_info',
- 'iter_entry_points',
- 'resource_string',
- 'resource_stream',
- 'resource_filename',
- 'resource_listdir',
- 'resource_exists',
- 'resource_isdir',
- # Environmental control
- 'declare_namespace',
- 'working_set',
- 'add_activation_listener',
- 'find_distributions',
- 'set_extraction_path',
- 'cleanup_resources',
- 'get_default_cache',
- # Primary implementation classes
- 'Environment',
- 'WorkingSet',
- 'ResourceManager',
- 'Distribution',
- 'Requirement',
- 'EntryPoint',
- # Exceptions
- 'ResolutionError',
- 'VersionConflict',
- 'DistributionNotFound',
- 'UnknownExtra',
- 'ExtractionError',
- # Warnings
- 'PEP440Warning',
- # Parsing functions and string utilities
- 'parse_requirements',
- 'parse_version',
- 'safe_name',
- 'safe_version',
- 'get_platform',
- 'compatible_platforms',
- 'yield_lines',
- 'split_sections',
- 'safe_extra',
- 'to_filename',
- 'invalid_marker',
- 'evaluate_marker',
- # filesystem utilities
- 'ensure_directory',
- 'normalize_path',
- # Distribution "precedence" constants
- 'EGG_DIST',
- 'BINARY_DIST',
- 'SOURCE_DIST',
- 'CHECKOUT_DIST',
- 'DEVELOP_DIST',
- # "Provider" interfaces, implementations, and registration/lookup APIs
- 'IMetadataProvider',
- 'IResourceProvider',
- 'FileMetadata',
- 'PathMetadata',
- 'EggMetadata',
- 'EmptyProvider',
- 'empty_provider',
- 'NullProvider',
- 'EggProvider',
- 'DefaultProvider',
- 'ZipProvider',
- 'register_finder',
- 'register_namespace_handler',
- 'register_loader_type',
- 'fixup_namespace_packages',
- 'get_importer',
- # Warnings
- 'PkgResourcesDeprecationWarning',
- # Deprecated/backward compatibility only
- 'run_main',
- 'AvailableDistributions',
-]
-
-
-class ResolutionError(Exception):
- """Abstract base for dependency resolution errors"""
-
- def __repr__(self):
- return self.__class__.__name__ + repr(self.args)
-
-
-class VersionConflict(ResolutionError):
- """
- An already-installed version conflicts with the requested version.
-
- Should be initialized with the installed Distribution and the requested
- Requirement.
- """
-
- _template = "{self.dist} is installed but {self.req} is required"
-
- @property
- def dist(self):
- return self.args[0]
-
- @property
- def req(self):
- return self.args[1]
-
- def report(self):
- return self._template.format(**locals())
-
- def with_context(self, required_by):
- """
- If required_by is non-empty, return a version of self that is a
- ContextualVersionConflict.
- """
- if not required_by:
- return self
- args = self.args + (required_by,)
- return ContextualVersionConflict(*args)
-
-
-class ContextualVersionConflict(VersionConflict):
- """
- A VersionConflict that accepts a third parameter, the set of the
- requirements that required the installed Distribution.
- """
-
- _template = VersionConflict._template + ' by {self.required_by}'
-
- @property
- def required_by(self):
- return self.args[2]
-
-
-class DistributionNotFound(ResolutionError):
- """A requested distribution was not found"""
-
- _template = (
- "The '{self.req}' distribution was not found "
- "and is required by {self.requirers_str}"
- )
-
- @property
- def req(self):
- return self.args[0]
-
- @property
- def requirers(self):
- return self.args[1]
-
- @property
- def requirers_str(self):
- if not self.requirers:
- return 'the application'
- return ', '.join(self.requirers)
-
- def report(self):
- return self._template.format(**locals())
-
- def __str__(self):
- return self.report()
-
-
-class UnknownExtra(ResolutionError):
- """Distribution doesn't have an "extra feature" of the given name"""
-
-
-_provider_factories = {}
-
-PY_MAJOR = '{}.{}'.format(*sys.version_info)
-EGG_DIST = 3
-BINARY_DIST = 2
-SOURCE_DIST = 1
-CHECKOUT_DIST = 0
-DEVELOP_DIST = -1
-
-
-def register_loader_type(loader_type, provider_factory):
- """Register `provider_factory` to make providers for `loader_type`
-
- `loader_type` is the type or class of a PEP 302 ``module.__loader__``,
- and `provider_factory` is a function that, passed a *module* object,
- returns an ``IResourceProvider`` for that module.
- """
- _provider_factories[loader_type] = provider_factory
-
-
-def get_provider(moduleOrReq):
- """Return an IResourceProvider for the named module or requirement"""
- if isinstance(moduleOrReq, Requirement):
- return working_set.find(moduleOrReq) or require(str(moduleOrReq))[0]
- try:
- module = sys.modules[moduleOrReq]
- except KeyError:
- __import__(moduleOrReq)
- module = sys.modules[moduleOrReq]
- loader = getattr(module, '__loader__', None)
- return _find_adapter(_provider_factories, loader)(module)
-
-
-def _macos_vers(_cache=[]):
- if not _cache:
- version = platform.mac_ver()[0]
- # fallback for MacPorts
- if version == '':
- plist = '/System/Library/CoreServices/SystemVersion.plist'
- if os.path.exists(plist):
- if hasattr(plistlib, 'readPlist'):
- plist_content = plistlib.readPlist(plist)
- if 'ProductVersion' in plist_content:
- version = plist_content['ProductVersion']
-
- _cache.append(version.split('.'))
- return _cache[0]
-
-
-def _macos_arch(machine):
- return {'PowerPC': 'ppc', 'Power_Macintosh': 'ppc'}.get(machine, machine)
-
-
-def get_build_platform():
- """Return this platform's string for platform-specific distributions
-
- XXX Currently this is the same as ``distutils.util.get_platform()``, but it
- needs some hacks for Linux and macOS.
- """
- from sysconfig import get_platform
-
- plat = get_platform()
- if sys.platform == "darwin" and not plat.startswith('macosx-'):
- try:
- version = _macos_vers()
- machine = os.uname()[4].replace(" ", "_")
- return "macosx-%d.%d-%s" % (
- int(version[0]),
- int(version[1]),
- _macos_arch(machine),
- )
- except ValueError:
- # if someone is running a non-Mac darwin system, this will fall
- # through to the default implementation
- pass
- return plat
-
-
-macosVersionString = re.compile(r"macosx-(\d+)\.(\d+)-(.*)")
-darwinVersionString = re.compile(r"darwin-(\d+)\.(\d+)\.(\d+)-(.*)")
-# XXX backward compat
-get_platform = get_build_platform
-
-
-def compatible_platforms(provided, required):
- """Can code for the `provided` platform run on the `required` platform?
-
- Returns true if either platform is ``None``, or the platforms are equal.
-
- XXX Needs compatibility checks for Linux and other unixy OSes.
- """
- if provided is None or required is None or provided == required:
- # easy case
- return True
-
- # macOS special cases
- reqMac = macosVersionString.match(required)
- if reqMac:
- provMac = macosVersionString.match(provided)
-
- # is this a Mac package?
- if not provMac:
- # this is backwards compatibility for packages built before
- # setuptools 0.6. All packages built after this point will
- # use the new macOS designation.
- provDarwin = darwinVersionString.match(provided)
- if provDarwin:
- dversion = int(provDarwin.group(1))
- macosversion = "%s.%s" % (reqMac.group(1), reqMac.group(2))
- if (
- dversion == 7
- and macosversion >= "10.3"
- or dversion == 8
- and macosversion >= "10.4"
- ):
- return True
- # egg isn't macOS or legacy darwin
- return False
-
- # are they the same major version and machine type?
- if provMac.group(1) != reqMac.group(1) or provMac.group(3) != reqMac.group(3):
- return False
-
- # is the required OS major update >= the provided one?
- if int(provMac.group(2)) > int(reqMac.group(2)):
- return False
-
- return True
-
- # XXX Linux and other platforms' special cases should go here
- return False
-
-
-def run_script(dist_spec, script_name):
- """Locate distribution `dist_spec` and run its `script_name` script"""
- ns = sys._getframe(1).f_globals
- name = ns['__name__']
- ns.clear()
- ns['__name__'] = name
- require(dist_spec)[0].run_script(script_name, ns)
-
-
-# backward compatibility
-run_main = run_script
-
-
-def get_distribution(dist):
- """Return a current distribution object for a Requirement or string"""
- if isinstance(dist, str):
- dist = Requirement.parse(dist)
- if isinstance(dist, Requirement):
- dist = get_provider(dist)
- if not isinstance(dist, Distribution):
- raise TypeError("Expected string, Requirement, or Distribution", dist)
- return dist
-
-
-def load_entry_point(dist, group, name):
- """Return `name` entry point of `group` for `dist` or raise ImportError"""
- return get_distribution(dist).load_entry_point(group, name)
-
-
-def get_entry_map(dist, group=None):
- """Return the entry point map for `group`, or the full entry map"""
- return get_distribution(dist).get_entry_map(group)
-
-
-def get_entry_info(dist, group, name):
- """Return the EntryPoint object for `group`+`name`, or ``None``"""
- return get_distribution(dist).get_entry_info(group, name)
-
-
-class IMetadataProvider:
- def has_metadata(name):
- """Does the package's distribution contain the named metadata?"""
-
- def get_metadata(name):
- """The named metadata resource as a string"""
-
- def get_metadata_lines(name):
- """Yield named metadata resource as list of non-blank non-comment lines
-
- Leading and trailing whitespace is stripped from each line, and lines
- with ``#`` as the first non-blank character are omitted."""
-
- def metadata_isdir(name):
- """Is the named metadata a directory? (like ``os.path.isdir()``)"""
-
- def metadata_listdir(name):
- """List of metadata names in the directory (like ``os.listdir()``)"""
-
- def run_script(script_name, namespace):
- """Execute the named script in the supplied namespace dictionary"""
-
-
-class IResourceProvider(IMetadataProvider):
- """An object that provides access to package resources"""
-
- def get_resource_filename(manager, resource_name):
- """Return a true filesystem path for `resource_name`
-
- `manager` must be an ``IResourceManager``"""
-
- def get_resource_stream(manager, resource_name):
- """Return a readable file-like object for `resource_name`
-
- `manager` must be an ``IResourceManager``"""
-
- def get_resource_string(manager, resource_name):
- """Return a string containing the contents of `resource_name`
-
- `manager` must be an ``IResourceManager``"""
-
- def has_resource(resource_name):
- """Does the package contain the named resource?"""
-
- def resource_isdir(resource_name):
- """Is the named resource a directory? (like ``os.path.isdir()``)"""
-
- def resource_listdir(resource_name):
- """List of resource names in the directory (like ``os.listdir()``)"""
-
-
-class WorkingSet:
- """A collection of active distributions on sys.path (or a similar list)"""
-
- def __init__(self, entries=None):
- """Create working set from list of path entries (default=sys.path)"""
- self.entries = []
- self.entry_keys = {}
- self.by_key = {}
- self.normalized_to_canonical_keys = {}
- self.callbacks = []
-
- if entries is None:
- entries = sys.path
-
- for entry in entries:
- self.add_entry(entry)
-
- @classmethod
- def _build_master(cls):
- """
- Prepare the master working set.
- """
- ws = cls()
- try:
- from __main__ import __requires__
- except ImportError:
- # The main program does not list any requirements
- return ws
-
- # ensure the requirements are met
- try:
- ws.require(__requires__)
- except VersionConflict:
- return cls._build_from_requirements(__requires__)
-
- return ws
-
- @classmethod
- def _build_from_requirements(cls, req_spec):
- """
- Build a working set from a requirement spec. Rewrites sys.path.
- """
- # try it without defaults already on sys.path
- # by starting with an empty path
- ws = cls([])
- reqs = parse_requirements(req_spec)
- dists = ws.resolve(reqs, Environment())
- for dist in dists:
- ws.add(dist)
-
- # add any missing entries from sys.path
- for entry in sys.path:
- if entry not in ws.entries:
- ws.add_entry(entry)
-
- # then copy back to sys.path
- sys.path[:] = ws.entries
- return ws
-
- def add_entry(self, entry):
- """Add a path item to ``.entries``, finding any distributions on it
-
- ``find_distributions(entry, True)`` is used to find distributions
- corresponding to the path entry, and they are added. `entry` is
- always appended to ``.entries``, even if it is already present.
- (This is because ``sys.path`` can contain the same value more than
- once, and the ``.entries`` of the ``sys.path`` WorkingSet should always
- equal ``sys.path``.)
- """
- self.entry_keys.setdefault(entry, [])
- self.entries.append(entry)
- for dist in find_distributions(entry, True):
- self.add(dist, entry, False)
-
- def __contains__(self, dist):
- """True if `dist` is the active distribution for its project"""
- return self.by_key.get(dist.key) == dist
-
- def find(self, req):
- """Find a distribution matching requirement `req`
-
- If there is an active distribution for the requested project, this
- returns it as long as it meets the version requirement specified by
- `req`. But, if there is an active distribution for the project and it
- does *not* meet the `req` requirement, ``VersionConflict`` is raised.
- If there is no active distribution for the requested project, ``None``
- is returned.
- """
- dist = self.by_key.get(req.key)
-
- if dist is None:
- canonical_key = self.normalized_to_canonical_keys.get(req.key)
-
- if canonical_key is not None:
- req.key = canonical_key
- dist = self.by_key.get(canonical_key)
-
- if dist is not None and dist not in req:
- # XXX add more info
- raise VersionConflict(dist, req)
- return dist
-
- def iter_entry_points(self, group, name=None):
- """Yield entry point objects from `group` matching `name`
-
- If `name` is None, yields all entry points in `group` from all
- distributions in the working set, otherwise only ones matching
- both `group` and `name` are yielded (in distribution order).
- """
- return (
- entry
- for dist in self
- for entry in dist.get_entry_map(group).values()
- if name is None or name == entry.name
- )
-
- def run_script(self, requires, script_name):
- """Locate distribution for `requires` and run `script_name` script"""
- ns = sys._getframe(1).f_globals
- name = ns['__name__']
- ns.clear()
- ns['__name__'] = name
- self.require(requires)[0].run_script(script_name, ns)
-
- def __iter__(self):
- """Yield distributions for non-duplicate projects in the working set
-
- The yield order is the order in which the items' path entries were
- added to the working set.
- """
- seen = {}
- for item in self.entries:
- if item not in self.entry_keys:
- # workaround a cache issue
- continue
-
- for key in self.entry_keys[item]:
- if key not in seen:
- seen[key] = 1
- yield self.by_key[key]
-
- def add(self, dist, entry=None, insert=True, replace=False):
- """Add `dist` to working set, associated with `entry`
-
- If `entry` is unspecified, it defaults to the ``.location`` of `dist`.
- On exit from this routine, `entry` is added to the end of the working
- set's ``.entries`` (if it wasn't already present).
-
- `dist` is only added to the working set if it's for a project that
- doesn't already have a distribution in the set, unless `replace=True`.
- If it's added, any callbacks registered with the ``subscribe()`` method
- will be called.
- """
- if insert:
- dist.insert_on(self.entries, entry, replace=replace)
-
- if entry is None:
- entry = dist.location
- keys = self.entry_keys.setdefault(entry, [])
- keys2 = self.entry_keys.setdefault(dist.location, [])
- if not replace and dist.key in self.by_key:
- # ignore hidden distros
- return
-
- self.by_key[dist.key] = dist
- normalized_name = packaging.utils.canonicalize_name(dist.key)
- self.normalized_to_canonical_keys[normalized_name] = dist.key
- if dist.key not in keys:
- keys.append(dist.key)
- if dist.key not in keys2:
- keys2.append(dist.key)
- self._added_new(dist)
-
- def resolve(
- self,
- requirements,
- env=None,
- installer=None,
- replace_conflicting=False,
- extras=None,
- ):
- """List all distributions needed to (recursively) meet `requirements`
-
- `requirements` must be a sequence of ``Requirement`` objects. `env`,
- if supplied, should be an ``Environment`` instance. If
- not supplied, it defaults to all distributions available within any
- entry or distribution in the working set. `installer`, if supplied,
- will be invoked with each requirement that cannot be met by an
- already-installed distribution; it should return a ``Distribution`` or
- ``None``.
-
- Unless `replace_conflicting=True`, raises a VersionConflict exception
- if
- any requirements are found on the path that have the correct name but
- the wrong version. Otherwise, if an `installer` is supplied it will be
- invoked to obtain the correct version of the requirement and activate
- it.
-
- `extras` is a list of the extras to be used with these requirements.
- This is important because extra requirements may look like `my_req;
- extra = "my_extra"`, which would otherwise be interpreted as a purely
- optional requirement. Instead, we want to be able to assert that these
- requirements are truly required.
- """
-
- # set up the stack
- requirements = list(requirements)[::-1]
- # set of processed requirements
- processed = {}
- # key -> dist
- best = {}
- to_activate = []
-
- req_extras = _ReqExtras()
-
- # Mapping of requirement to set of distributions that required it;
- # useful for reporting info about conflicts.
- required_by = collections.defaultdict(set)
-
- while requirements:
- # process dependencies breadth-first
- req = requirements.pop(0)
- if req in processed:
- # Ignore cyclic or redundant dependencies
- continue
-
- if not req_extras.markers_pass(req, extras):
- continue
-
- dist = self._resolve_dist(
- req, best, replace_conflicting, env, installer, required_by, to_activate
- )
-
- # push the new requirements onto the stack
- new_requirements = dist.requires(req.extras)[::-1]
- requirements.extend(new_requirements)
-
- # Register the new requirements needed by req
- for new_requirement in new_requirements:
- required_by[new_requirement].add(req.project_name)
- req_extras[new_requirement] = req.extras
-
- processed[req] = True
-
- # return list of distros to activate
- return to_activate
-
- def _resolve_dist(
- self, req, best, replace_conflicting, env, installer, required_by, to_activate
- ):
- dist = best.get(req.key)
- if dist is None:
- # Find the best distribution and add it to the map
- dist = self.by_key.get(req.key)
- if dist is None or (dist not in req and replace_conflicting):
- ws = self
- if env is None:
- if dist is None:
- env = Environment(self.entries)
- else:
- # Use an empty environment and workingset to avoid
- # any further conflicts with the conflicting
- # distribution
- env = Environment([])
- ws = WorkingSet([])
- dist = best[req.key] = env.best_match(
- req, ws, installer, replace_conflicting=replace_conflicting
- )
- if dist is None:
- requirers = required_by.get(req, None)
- raise DistributionNotFound(req, requirers)
- to_activate.append(dist)
- if dist not in req:
- # Oops, the "best" so far conflicts with a dependency
- dependent_req = required_by[req]
- raise VersionConflict(dist, req).with_context(dependent_req)
- return dist
-
- def find_plugins(self, plugin_env, full_env=None, installer=None, fallback=True):
- """Find all activatable distributions in `plugin_env`
-
- Example usage::
-
- distributions, errors = working_set.find_plugins(
- Environment(plugin_dirlist)
- )
- # add plugins+libs to sys.path
- map(working_set.add, distributions)
- # display errors
- print('Could not load', errors)
-
- The `plugin_env` should be an ``Environment`` instance that contains
- only distributions that are in the project's "plugin directory" or
- directories. The `full_env`, if supplied, should be an ``Environment``
- contains all currently-available distributions. If `full_env` is not
- supplied, one is created automatically from the ``WorkingSet`` this
- method is called on, which will typically mean that every directory on
- ``sys.path`` will be scanned for distributions.
-
- `installer` is a standard installer callback as used by the
- ``resolve()`` method. The `fallback` flag indicates whether we should
- attempt to resolve older versions of a plugin if the newest version
- cannot be resolved.
-
- This method returns a 2-tuple: (`distributions`, `error_info`), where
- `distributions` is a list of the distributions found in `plugin_env`
- that were loadable, along with any other distributions that are needed
- to resolve their dependencies. `error_info` is a dictionary mapping
- unloadable plugin distributions to an exception instance describing the
- error that occurred. Usually this will be a ``DistributionNotFound`` or
- ``VersionConflict`` instance.
- """
-
- plugin_projects = list(plugin_env)
- # scan project names in alphabetic order
- plugin_projects.sort()
-
- error_info = {}
- distributions = {}
-
- if full_env is None:
- env = Environment(self.entries)
- env += plugin_env
- else:
- env = full_env + plugin_env
-
- shadow_set = self.__class__([])
- # put all our entries in shadow_set
- list(map(shadow_set.add, self))
-
- for project_name in plugin_projects:
- for dist in plugin_env[project_name]:
- req = [dist.as_requirement()]
-
- try:
- resolvees = shadow_set.resolve(req, env, installer)
-
- except ResolutionError as v:
- # save error info
- error_info[dist] = v
- if fallback:
- # try the next older version of project
- continue
- else:
- # give up on this project, keep going
- break
-
- else:
- list(map(shadow_set.add, resolvees))
- distributions.update(dict.fromkeys(resolvees))
-
- # success, no need to try any more versions of this project
- break
-
- distributions = list(distributions)
- distributions.sort()
-
- return distributions, error_info
-
- def require(self, *requirements):
- """Ensure that distributions matching `requirements` are activated
-
- `requirements` must be a string or a (possibly-nested) sequence
- thereof, specifying the distributions and versions required. The
- return value is a sequence of the distributions that needed to be
- activated to fulfill the requirements; all relevant distributions are
- included, even if they were already activated in this working set.
- """
- needed = self.resolve(parse_requirements(requirements))
-
- for dist in needed:
- self.add(dist)
-
- return needed
-
- def subscribe(self, callback, existing=True):
- """Invoke `callback` for all distributions
-
- If `existing=True` (default),
- call on all existing ones, as well.
- """
- if callback in self.callbacks:
- return
- self.callbacks.append(callback)
- if not existing:
- return
- for dist in self:
- callback(dist)
-
- def _added_new(self, dist):
- for callback in self.callbacks:
- callback(dist)
-
- def __getstate__(self):
- return (
- self.entries[:],
- self.entry_keys.copy(),
- self.by_key.copy(),
- self.normalized_to_canonical_keys.copy(),
- self.callbacks[:],
- )
-
- def __setstate__(self, e_k_b_n_c):
- entries, keys, by_key, normalized_to_canonical_keys, callbacks = e_k_b_n_c
- self.entries = entries[:]
- self.entry_keys = keys.copy()
- self.by_key = by_key.copy()
- self.normalized_to_canonical_keys = normalized_to_canonical_keys.copy()
- self.callbacks = callbacks[:]
-
-
-class _ReqExtras(dict):
- """
- Map each requirement to the extras that demanded it.
- """
-
- def markers_pass(self, req, extras=None):
- """
- Evaluate markers for req against each extra that
- demanded it.
-
- Return False if the req has a marker and fails
- evaluation. Otherwise, return True.
- """
- extra_evals = (
- req.marker.evaluate({'extra': extra})
- for extra in self.get(req, ()) + (extras or (None,))
- )
- return not req.marker or any(extra_evals)
-
-
-class Environment:
- """Searchable snapshot of distributions on a search path"""
-
- def __init__(
- self, search_path=None, platform=get_supported_platform(), python=PY_MAJOR
- ):
- """Snapshot distributions available on a search path
-
- Any distributions found on `search_path` are added to the environment.
- `search_path` should be a sequence of ``sys.path`` items. If not
- supplied, ``sys.path`` is used.
-
- `platform` is an optional string specifying the name of the platform
- that platform-specific distributions must be compatible with. If
- unspecified, it defaults to the current platform. `python` is an
- optional string naming the desired version of Python (e.g. ``'3.6'``);
- it defaults to the current version.
-
- You may explicitly set `platform` (and/or `python`) to ``None`` if you
- wish to map *all* distributions, not just those compatible with the
- running platform or Python version.
- """
- self._distmap = {}
- self.platform = platform
- self.python = python
- self.scan(search_path)
-
- def can_add(self, dist):
- """Is distribution `dist` acceptable for this environment?
-
- The distribution must match the platform and python version
- requirements specified when this environment was created, or False
- is returned.
- """
- py_compat = (
- self.python is None
- or dist.py_version is None
- or dist.py_version == self.python
- )
- return py_compat and compatible_platforms(dist.platform, self.platform)
-
- def remove(self, dist):
- """Remove `dist` from the environment"""
- self._distmap[dist.key].remove(dist)
-
- def scan(self, search_path=None):
- """Scan `search_path` for distributions usable in this environment
-
- Any distributions found are added to the environment.
- `search_path` should be a sequence of ``sys.path`` items. If not
- supplied, ``sys.path`` is used. Only distributions conforming to
- the platform/python version defined at initialization are added.
- """
- if search_path is None:
- search_path = sys.path
-
- for item in search_path:
- for dist in find_distributions(item):
- self.add(dist)
-
- def __getitem__(self, project_name):
- """Return a newest-to-oldest list of distributions for `project_name`
-
- Uses case-insensitive `project_name` comparison, assuming all the
- project's distributions use their project's name converted to all
- lowercase as their key.
-
- """
- distribution_key = project_name.lower()
- return self._distmap.get(distribution_key, [])
-
- def add(self, dist):
- """Add `dist` if we ``can_add()`` it and it has not already been added"""
- if self.can_add(dist) and dist.has_version():
- dists = self._distmap.setdefault(dist.key, [])
- if dist not in dists:
- dists.append(dist)
- dists.sort(key=operator.attrgetter('hashcmp'), reverse=True)
-
- def best_match(self, req, working_set, installer=None, replace_conflicting=False):
- """Find distribution best matching `req` and usable on `working_set`
-
- This calls the ``find(req)`` method of the `working_set` to see if a
- suitable distribution is already active. (This may raise
- ``VersionConflict`` if an unsuitable version of the project is already
- active in the specified `working_set`.) If a suitable distribution
- isn't active, this method returns the newest distribution in the
- environment that meets the ``Requirement`` in `req`. If no suitable
- distribution is found, and `installer` is supplied, then the result of
- calling the environment's ``obtain(req, installer)`` method will be
- returned.
- """
- try:
- dist = working_set.find(req)
- except VersionConflict:
- if not replace_conflicting:
- raise
- dist = None
- if dist is not None:
- return dist
- for dist in self[req.key]:
- if dist in req:
- return dist
- # try to download/install
- return self.obtain(req, installer)
-
- def obtain(self, requirement, installer=None):
- """Obtain a distribution matching `requirement` (e.g. via download)
-
- Obtain a distro that matches requirement (e.g. via download). In the
- base ``Environment`` class, this routine just returns
- ``installer(requirement)``, unless `installer` is None, in which case
- None is returned instead. This method is a hook that allows subclasses
- to attempt other ways of obtaining a distribution before falling back
- to the `installer` argument."""
- if installer is not None:
- return installer(requirement)
-
- def __iter__(self):
- """Yield the unique project names of the available distributions"""
- for key in self._distmap.keys():
- if self[key]:
- yield key
-
- def __iadd__(self, other):
- """In-place addition of a distribution or environment"""
- if isinstance(other, Distribution):
- self.add(other)
- elif isinstance(other, Environment):
- for project in other:
- for dist in other[project]:
- self.add(dist)
- else:
- raise TypeError("Can't add %r to environment" % (other,))
- return self
-
- def __add__(self, other):
- """Add an environment or distribution to an environment"""
- new = self.__class__([], platform=None, python=None)
- for env in self, other:
- new += env
- return new
-
-
-# XXX backward compatibility
-AvailableDistributions = Environment
-
-
-class ExtractionError(RuntimeError):
- """An error occurred extracting a resource
-
- The following attributes are available from instances of this exception:
-
- manager
- The resource manager that raised this exception
-
- cache_path
- The base directory for resource extraction
-
- original_error
- The exception instance that caused extraction to fail
- """
-
-
-class ResourceManager:
- """Manage resource extraction and packages"""
-
- extraction_path = None
-
- def __init__(self):
- self.cached_files = {}
-
- def resource_exists(self, package_or_requirement, resource_name):
- """Does the named resource exist?"""
- return get_provider(package_or_requirement).has_resource(resource_name)
-
- def resource_isdir(self, package_or_requirement, resource_name):
- """Is the named resource an existing directory?"""
- return get_provider(package_or_requirement).resource_isdir(resource_name)
-
- def resource_filename(self, package_or_requirement, resource_name):
- """Return a true filesystem path for specified resource"""
- return get_provider(package_or_requirement).get_resource_filename(
- self, resource_name
- )
-
- def resource_stream(self, package_or_requirement, resource_name):
- """Return a readable file-like object for specified resource"""
- return get_provider(package_or_requirement).get_resource_stream(
- self, resource_name
- )
-
- def resource_string(self, package_or_requirement, resource_name):
- """Return specified resource as a string"""
- return get_provider(package_or_requirement).get_resource_string(
- self, resource_name
- )
-
- def resource_listdir(self, package_or_requirement, resource_name):
- """List the contents of the named resource directory"""
- return get_provider(package_or_requirement).resource_listdir(resource_name)
-
- def extraction_error(self):
- """Give an error message for problems extracting file(s)"""
-
- old_exc = sys.exc_info()[1]
- cache_path = self.extraction_path or get_default_cache()
-
- tmpl = textwrap.dedent(
- """
- Can't extract file(s) to egg cache
-
- The following error occurred while trying to extract file(s)
- to the Python egg cache:
-
- {old_exc}
-
- The Python egg cache directory is currently set to:
-
- {cache_path}
-
- Perhaps your account does not have write access to this directory?
- You can change the cache directory by setting the PYTHON_EGG_CACHE
- environment variable to point to an accessible directory.
- """
- ).lstrip()
- err = ExtractionError(tmpl.format(**locals()))
- err.manager = self
- err.cache_path = cache_path
- err.original_error = old_exc
- raise err
-
- def get_cache_path(self, archive_name, names=()):
- """Return absolute location in cache for `archive_name` and `names`
-
- The parent directory of the resulting path will be created if it does
- not already exist. `archive_name` should be the base filename of the
- enclosing egg (which may not be the name of the enclosing zipfile!),
- including its ".egg" extension. `names`, if provided, should be a
- sequence of path name parts "under" the egg's extraction location.
-
- This method should only be called by resource providers that need to
- obtain an extraction location, and only for names they intend to
- extract, as it tracks the generated names for possible cleanup later.
- """
- extract_path = self.extraction_path or get_default_cache()
- target_path = os.path.join(extract_path, archive_name + '-tmp', *names)
- try:
- _bypass_ensure_directory(target_path)
- except Exception:
- self.extraction_error()
-
- self._warn_unsafe_extraction_path(extract_path)
-
- self.cached_files[target_path] = 1
- return target_path
-
- @staticmethod
- def _warn_unsafe_extraction_path(path):
- """
- If the default extraction path is overridden and set to an insecure
- location, such as /tmp, it opens up an opportunity for an attacker to
- replace an extracted file with an unauthorized payload. Warn the user
- if a known insecure location is used.
-
- See Distribute #375 for more details.
- """
- if os.name == 'nt' and not path.startswith(os.environ['windir']):
- # On Windows, permissions are generally restrictive by default
- # and temp directories are not writable by other users, so
- # bypass the warning.
- return
- mode = os.stat(path).st_mode
- if mode & stat.S_IWOTH or mode & stat.S_IWGRP:
- msg = (
- "Extraction path is writable by group/others "
- "and vulnerable to attack when "
- "used with get_resource_filename ({path}). "
- "Consider a more secure "
- "location (set with .set_extraction_path or the "
- "PYTHON_EGG_CACHE environment variable)."
- ).format(**locals())
- warnings.warn(msg, UserWarning)
-
- def postprocess(self, tempname, filename):
- """Perform any platform-specific postprocessing of `tempname`
-
- This is where Mac header rewrites should be done; other platforms don't
- have anything special they should do.
-
- Resource providers should call this method ONLY after successfully
- extracting a compressed resource. They must NOT call it on resources
- that are already in the filesystem.
-
- `tempname` is the current (temporary) name of the file, and `filename`
- is the name it will be renamed to by the caller after this routine
- returns.
- """
-
- if os.name == 'posix':
- # Make the resource executable
- mode = ((os.stat(tempname).st_mode) | 0o555) & 0o7777
- os.chmod(tempname, mode)
-
- def set_extraction_path(self, path):
- """Set the base path where resources will be extracted to, if needed.
-
- If you do not call this routine before any extractions take place, the
- path defaults to the return value of ``get_default_cache()``. (Which
- is based on the ``PYTHON_EGG_CACHE`` environment variable, with various
- platform-specific fallbacks. See that routine's documentation for more
- details.)
-
- Resources are extracted to subdirectories of this path based upon
- information given by the ``IResourceProvider``. You may set this to a
- temporary directory, but then you must call ``cleanup_resources()`` to
- delete the extracted files when done. There is no guarantee that
- ``cleanup_resources()`` will be able to remove all extracted files.
-
- (Note: you may not change the extraction path for a given resource
- manager once resources have been extracted, unless you first call
- ``cleanup_resources()``.)
- """
- if self.cached_files:
- raise ValueError("Can't change extraction path, files already extracted")
-
- self.extraction_path = path
-
- def cleanup_resources(self, force=False):
- """
- Delete all extracted resource files and directories, returning a list
- of the file and directory names that could not be successfully removed.
- This function does not have any concurrency protection, so it should
- generally only be called when the extraction path is a temporary
- directory exclusive to a single process. This method is not
- automatically called; you must call it explicitly or register it as an
- ``atexit`` function if you wish to ensure cleanup of a temporary
- directory used for extractions.
- """
- # XXX
-
-
-def get_default_cache():
- """
- Return the ``PYTHON_EGG_CACHE`` environment variable
- or a platform-relevant user cache dir for an app
- named "Python-Eggs".
- """
- return os.environ.get('PYTHON_EGG_CACHE') or platformdirs.user_cache_dir(
- appname='Python-Eggs'
- )
-
-
-def safe_name(name):
- """Convert an arbitrary string to a standard distribution name
-
- Any runs of non-alphanumeric/. characters are replaced with a single '-'.
- """
- return re.sub('[^A-Za-z0-9.]+', '-', name)
-
-
-def safe_version(version):
- """
- Convert an arbitrary string to a standard version string
- """
- try:
- # normalize the version
- return str(packaging.version.Version(version))
- except packaging.version.InvalidVersion:
- version = version.replace(' ', '.')
- return re.sub('[^A-Za-z0-9.]+', '-', version)
-
-
-def _forgiving_version(version):
- """Fallback when ``safe_version`` is not safe enough
- >>> parse_version(_forgiving_version('0.23ubuntu1'))
-
- >>> parse_version(_forgiving_version('0.23-'))
-
- >>> parse_version(_forgiving_version('0.-_'))
-
- >>> parse_version(_forgiving_version('42.+?1'))
-
- >>> parse_version(_forgiving_version('hello world'))
-
- """
- version = version.replace(' ', '.')
- match = _PEP440_FALLBACK.search(version)
- if match:
- safe = match["safe"]
- rest = version[len(safe):]
- else:
- safe = "0"
- rest = version
- local = f"sanitized.{_safe_segment(rest)}".strip(".")
- return f"{safe}.dev0+{local}"
-
-
-def _safe_segment(segment):
- """Convert an arbitrary string into a safe segment"""
- segment = re.sub('[^A-Za-z0-9.]+', '-', segment)
- segment = re.sub('-[^A-Za-z0-9]+', '-', segment)
- return re.sub(r'\.[^A-Za-z0-9]+', '.', segment).strip(".-")
-
-
-def safe_extra(extra):
- """Convert an arbitrary string to a standard 'extra' name
-
- Any runs of non-alphanumeric characters are replaced with a single '_',
- and the result is always lowercased.
- """
- return re.sub('[^A-Za-z0-9.-]+', '_', extra).lower()
-
-
-def to_filename(name):
- """Convert a project or version name to its filename-escaped form
-
- Any '-' characters are currently replaced with '_'.
- """
- return name.replace('-', '_')
-
-
-def invalid_marker(text):
- """
- Validate text as a PEP 508 environment marker; return an exception
- if invalid or False otherwise.
- """
- try:
- evaluate_marker(text)
- except SyntaxError as e:
- e.filename = None
- e.lineno = None
- return e
- return False
-
-
-def evaluate_marker(text, extra=None):
- """
- Evaluate a PEP 508 environment marker.
- Return a boolean indicating the marker result in this environment.
- Raise SyntaxError if marker is invalid.
-
- This implementation uses the 'pyparsing' module.
- """
- try:
- marker = packaging.markers.Marker(text)
- return marker.evaluate()
- except packaging.markers.InvalidMarker as e:
- raise SyntaxError(e) from e
-
-
-class NullProvider:
- """Try to implement resources and metadata for arbitrary PEP 302 loaders"""
-
- egg_name = None
- egg_info = None
- loader = None
-
- def __init__(self, module):
- self.loader = getattr(module, '__loader__', None)
- self.module_path = os.path.dirname(getattr(module, '__file__', ''))
-
- def get_resource_filename(self, manager, resource_name):
- return self._fn(self.module_path, resource_name)
-
- def get_resource_stream(self, manager, resource_name):
- return io.BytesIO(self.get_resource_string(manager, resource_name))
-
- def get_resource_string(self, manager, resource_name):
- return self._get(self._fn(self.module_path, resource_name))
-
- def has_resource(self, resource_name):
- return self._has(self._fn(self.module_path, resource_name))
-
- def _get_metadata_path(self, name):
- return self._fn(self.egg_info, name)
-
- def has_metadata(self, name):
- if not self.egg_info:
- return self.egg_info
-
- path = self._get_metadata_path(name)
- return self._has(path)
-
- def get_metadata(self, name):
- if not self.egg_info:
- return ""
- path = self._get_metadata_path(name)
- value = self._get(path)
- try:
- return value.decode('utf-8')
- except UnicodeDecodeError as exc:
- # Include the path in the error message to simplify
- # troubleshooting, and without changing the exception type.
- exc.reason += ' in {} file at path: {}'.format(name, path)
- raise
-
- def get_metadata_lines(self, name):
- return yield_lines(self.get_metadata(name))
-
- def resource_isdir(self, resource_name):
- return self._isdir(self._fn(self.module_path, resource_name))
-
- def metadata_isdir(self, name):
- return self.egg_info and self._isdir(self._fn(self.egg_info, name))
-
- def resource_listdir(self, resource_name):
- return self._listdir(self._fn(self.module_path, resource_name))
-
- def metadata_listdir(self, name):
- if self.egg_info:
- return self._listdir(self._fn(self.egg_info, name))
- return []
-
- def run_script(self, script_name, namespace):
- script = 'scripts/' + script_name
- if not self.has_metadata(script):
- raise ResolutionError(
- "Script {script!r} not found in metadata at {self.egg_info!r}".format(
- **locals()
- ),
- )
- script_text = self.get_metadata(script).replace('\r\n', '\n')
- script_text = script_text.replace('\r', '\n')
- script_filename = self._fn(self.egg_info, script)
- namespace['__file__'] = script_filename
- if os.path.exists(script_filename):
- with open(script_filename) as fid:
- source = fid.read()
- code = compile(source, script_filename, 'exec')
- exec(code, namespace, namespace)
- else:
- from linecache import cache
-
- cache[script_filename] = (
- len(script_text),
- 0,
- script_text.split('\n'),
- script_filename,
- )
- script_code = compile(script_text, script_filename, 'exec')
- exec(script_code, namespace, namespace)
-
- def _has(self, path):
- raise NotImplementedError(
- "Can't perform this operation for unregistered loader type"
- )
-
- def _isdir(self, path):
- raise NotImplementedError(
- "Can't perform this operation for unregistered loader type"
- )
-
- def _listdir(self, path):
- raise NotImplementedError(
- "Can't perform this operation for unregistered loader type"
- )
-
- def _fn(self, base, resource_name):
- self._validate_resource_path(resource_name)
- if resource_name:
- return os.path.join(base, *resource_name.split('/'))
- return base
-
- @staticmethod
- def _validate_resource_path(path):
- """
- Validate the resource paths according to the docs.
- https://setuptools.pypa.io/en/latest/pkg_resources.html#basic-resource-access
-
- >>> warned = getfixture('recwarn')
- >>> warnings.simplefilter('always')
- >>> vrp = NullProvider._validate_resource_path
- >>> vrp('foo/bar.txt')
- >>> bool(warned)
- False
- >>> vrp('../foo/bar.txt')
- >>> bool(warned)
- True
- >>> warned.clear()
- >>> vrp('/foo/bar.txt')
- >>> bool(warned)
- True
- >>> vrp('foo/../../bar.txt')
- >>> bool(warned)
- True
- >>> warned.clear()
- >>> vrp('foo/f../bar.txt')
- >>> bool(warned)
- False
-
- Windows path separators are straight-up disallowed.
- >>> vrp(r'\\foo/bar.txt')
- Traceback (most recent call last):
- ...
- ValueError: Use of .. or absolute path in a resource path \
-is not allowed.
-
- >>> vrp(r'C:\\foo/bar.txt')
- Traceback (most recent call last):
- ...
- ValueError: Use of .. or absolute path in a resource path \
-is not allowed.
-
- Blank values are allowed
-
- >>> vrp('')
- >>> bool(warned)
- False
-
- Non-string values are not.
-
- >>> vrp(None)
- Traceback (most recent call last):
- ...
- AttributeError: ...
- """
- invalid = (
- os.path.pardir in path.split(posixpath.sep)
- or posixpath.isabs(path)
- or ntpath.isabs(path)
- )
- if not invalid:
- return
-
- msg = "Use of .. or absolute path in a resource path is not allowed."
-
- # Aggressively disallow Windows absolute paths
- if ntpath.isabs(path) and not posixpath.isabs(path):
- raise ValueError(msg)
-
- # for compatibility, warn; in future
- # raise ValueError(msg)
- warnings.warn(
- msg[:-1] + " and will raise exceptions in a future release.",
- DeprecationWarning,
- stacklevel=4,
- )
-
- def _get(self, path):
- if hasattr(self.loader, 'get_data'):
- return self.loader.get_data(path)
- raise NotImplementedError(
- "Can't perform this operation for loaders without 'get_data()'"
- )
-
-
-register_loader_type(object, NullProvider)
-
-
-def _parents(path):
- """
- yield all parents of path including path
- """
- last = None
- while path != last:
- yield path
- last = path
- path, _ = os.path.split(path)
-
-
-class EggProvider(NullProvider):
- """Provider based on a virtual filesystem"""
-
- def __init__(self, module):
- super().__init__(module)
- self._setup_prefix()
-
- def _setup_prefix(self):
- # Assume that metadata may be nested inside a "basket"
- # of multiple eggs and use module_path instead of .archive.
- eggs = filter(_is_egg_path, _parents(self.module_path))
- egg = next(eggs, None)
- egg and self._set_egg(egg)
-
- def _set_egg(self, path):
- self.egg_name = os.path.basename(path)
- self.egg_info = os.path.join(path, 'EGG-INFO')
- self.egg_root = path
-
-
-class DefaultProvider(EggProvider):
- """Provides access to package resources in the filesystem"""
-
- def _has(self, path):
- return os.path.exists(path)
-
- def _isdir(self, path):
- return os.path.isdir(path)
-
- def _listdir(self, path):
- return os.listdir(path)
-
- def get_resource_stream(self, manager, resource_name):
- return open(self._fn(self.module_path, resource_name), 'rb')
-
- def _get(self, path):
- with open(path, 'rb') as stream:
- return stream.read()
-
- @classmethod
- def _register(cls):
- loader_names = (
- 'SourceFileLoader',
- 'SourcelessFileLoader',
- )
- for name in loader_names:
- loader_cls = getattr(importlib_machinery, name, type(None))
- register_loader_type(loader_cls, cls)
-
-
-DefaultProvider._register()
-
-
-class EmptyProvider(NullProvider):
- """Provider that returns nothing for all requests"""
-
- module_path = None
-
- _isdir = _has = lambda self, path: False
-
- def _get(self, path):
- return ''
-
- def _listdir(self, path):
- return []
-
- def __init__(self):
- pass
-
-
-empty_provider = EmptyProvider()
-
-
-class ZipManifests(dict):
- """
- zip manifest builder
- """
-
- @classmethod
- def build(cls, path):
- """
- Build a dictionary similar to the zipimport directory
- caches, except instead of tuples, store ZipInfo objects.
-
- Use a platform-specific path separator (os.sep) for the path keys
- for compatibility with pypy on Windows.
- """
- with zipfile.ZipFile(path) as zfile:
- items = (
- (
- name.replace('/', os.sep),
- zfile.getinfo(name),
- )
- for name in zfile.namelist()
- )
- return dict(items)
-
- load = build
-
-
-class MemoizedZipManifests(ZipManifests):
- """
- Memoized zipfile manifests.
- """
-
- manifest_mod = collections.namedtuple('manifest_mod', 'manifest mtime')
-
- def load(self, path):
- """
- Load a manifest at path or return a suitable manifest already loaded.
- """
- path = os.path.normpath(path)
- mtime = os.stat(path).st_mtime
-
- if path not in self or self[path].mtime != mtime:
- manifest = self.build(path)
- self[path] = self.manifest_mod(manifest, mtime)
-
- return self[path].manifest
-
-
-class ZipProvider(EggProvider):
- """Resource support for zips and eggs"""
-
- eagers = None
- _zip_manifests = MemoizedZipManifests()
-
- def __init__(self, module):
- super().__init__(module)
- self.zip_pre = self.loader.archive + os.sep
-
- def _zipinfo_name(self, fspath):
- # Convert a virtual filename (full path to file) into a zipfile subpath
- # usable with the zipimport directory cache for our target archive
- fspath = fspath.rstrip(os.sep)
- if fspath == self.loader.archive:
- return ''
- if fspath.startswith(self.zip_pre):
- return fspath[len(self.zip_pre) :]
- raise AssertionError("%s is not a subpath of %s" % (fspath, self.zip_pre))
-
- def _parts(self, zip_path):
- # Convert a zipfile subpath into an egg-relative path part list.
- # pseudo-fs path
- fspath = self.zip_pre + zip_path
- if fspath.startswith(self.egg_root + os.sep):
- return fspath[len(self.egg_root) + 1 :].split(os.sep)
- raise AssertionError("%s is not a subpath of %s" % (fspath, self.egg_root))
-
- @property
- def zipinfo(self):
- return self._zip_manifests.load(self.loader.archive)
-
- def get_resource_filename(self, manager, resource_name):
- if not self.egg_name:
- raise NotImplementedError(
- "resource_filename() only supported for .egg, not .zip"
- )
- # no need to lock for extraction, since we use temp names
- zip_path = self._resource_to_zip(resource_name)
- eagers = self._get_eager_resources()
- if '/'.join(self._parts(zip_path)) in eagers:
- for name in eagers:
- self._extract_resource(manager, self._eager_to_zip(name))
- return self._extract_resource(manager, zip_path)
-
- @staticmethod
- def _get_date_and_size(zip_stat):
- size = zip_stat.file_size
- # ymdhms+wday, yday, dst
- date_time = zip_stat.date_time + (0, 0, -1)
- # 1980 offset already done
- timestamp = time.mktime(date_time)
- return timestamp, size
-
- # FIXME: 'ZipProvider._extract_resource' is too complex (12)
- def _extract_resource(self, manager, zip_path): # noqa: C901
- if zip_path in self._index():
- for name in self._index()[zip_path]:
- last = self._extract_resource(manager, os.path.join(zip_path, name))
- # return the extracted directory name
- return os.path.dirname(last)
-
- timestamp, size = self._get_date_and_size(self.zipinfo[zip_path])
-
- if not WRITE_SUPPORT:
- raise IOError(
- '"os.rename" and "os.unlink" are not supported ' 'on this platform'
- )
- try:
- real_path = manager.get_cache_path(self.egg_name, self._parts(zip_path))
-
- if self._is_current(real_path, zip_path):
- return real_path
-
- outf, tmpnam = _mkstemp(
- ".$extract",
- dir=os.path.dirname(real_path),
- )
- os.write(outf, self.loader.get_data(zip_path))
- os.close(outf)
- utime(tmpnam, (timestamp, timestamp))
- manager.postprocess(tmpnam, real_path)
-
- try:
- rename(tmpnam, real_path)
-
- except os.error:
- if os.path.isfile(real_path):
- if self._is_current(real_path, zip_path):
- # the file became current since it was checked above,
- # so proceed.
- return real_path
- # Windows, del old file and retry
- elif os.name == 'nt':
- unlink(real_path)
- rename(tmpnam, real_path)
- return real_path
- raise
-
- except os.error:
- # report a user-friendly error
- manager.extraction_error()
-
- return real_path
-
- def _is_current(self, file_path, zip_path):
- """
- Return True if the file_path is current for this zip_path
- """
- timestamp, size = self._get_date_and_size(self.zipinfo[zip_path])
- if not os.path.isfile(file_path):
- return False
- stat = os.stat(file_path)
- if stat.st_size != size or stat.st_mtime != timestamp:
- return False
- # check that the contents match
- zip_contents = self.loader.get_data(zip_path)
- with open(file_path, 'rb') as f:
- file_contents = f.read()
- return zip_contents == file_contents
-
- def _get_eager_resources(self):
- if self.eagers is None:
- eagers = []
- for name in ('native_libs.txt', 'eager_resources.txt'):
- if self.has_metadata(name):
- eagers.extend(self.get_metadata_lines(name))
- self.eagers = eagers
- return self.eagers
-
- def _index(self):
- try:
- return self._dirindex
- except AttributeError:
- ind = {}
- for path in self.zipinfo:
- parts = path.split(os.sep)
- while parts:
- parent = os.sep.join(parts[:-1])
- if parent in ind:
- ind[parent].append(parts[-1])
- break
- else:
- ind[parent] = [parts.pop()]
- self._dirindex = ind
- return ind
-
- def _has(self, fspath):
- zip_path = self._zipinfo_name(fspath)
- return zip_path in self.zipinfo or zip_path in self._index()
-
- def _isdir(self, fspath):
- return self._zipinfo_name(fspath) in self._index()
-
- def _listdir(self, fspath):
- return list(self._index().get(self._zipinfo_name(fspath), ()))
-
- def _eager_to_zip(self, resource_name):
- return self._zipinfo_name(self._fn(self.egg_root, resource_name))
-
- def _resource_to_zip(self, resource_name):
- return self._zipinfo_name(self._fn(self.module_path, resource_name))
-
-
-register_loader_type(zipimport.zipimporter, ZipProvider)
-
-
-class FileMetadata(EmptyProvider):
- """Metadata handler for standalone PKG-INFO files
-
- Usage::
-
- metadata = FileMetadata("/path/to/PKG-INFO")
-
- This provider rejects all data and metadata requests except for PKG-INFO,
- which is treated as existing, and will be the contents of the file at
- the provided location.
- """
-
- def __init__(self, path):
- self.path = path
-
- def _get_metadata_path(self, name):
- return self.path
-
- def has_metadata(self, name):
- return name == 'PKG-INFO' and os.path.isfile(self.path)
-
- def get_metadata(self, name):
- if name != 'PKG-INFO':
- raise KeyError("No metadata except PKG-INFO is available")
-
- with io.open(self.path, encoding='utf-8', errors="replace") as f:
- metadata = f.read()
- self._warn_on_replacement(metadata)
- return metadata
-
- def _warn_on_replacement(self, metadata):
- replacement_char = '�'
- if replacement_char in metadata:
- tmpl = "{self.path} could not be properly decoded in UTF-8"
- msg = tmpl.format(**locals())
- warnings.warn(msg)
-
- def get_metadata_lines(self, name):
- return yield_lines(self.get_metadata(name))
-
-
-class PathMetadata(DefaultProvider):
- """Metadata provider for egg directories
-
- Usage::
-
- # Development eggs:
-
- egg_info = "/path/to/PackageName.egg-info"
- base_dir = os.path.dirname(egg_info)
- metadata = PathMetadata(base_dir, egg_info)
- dist_name = os.path.splitext(os.path.basename(egg_info))[0]
- dist = Distribution(basedir, project_name=dist_name, metadata=metadata)
-
- # Unpacked egg directories:
-
- egg_path = "/path/to/PackageName-ver-pyver-etc.egg"
- metadata = PathMetadata(egg_path, os.path.join(egg_path,'EGG-INFO'))
- dist = Distribution.from_filename(egg_path, metadata=metadata)
- """
-
- def __init__(self, path, egg_info):
- self.module_path = path
- self.egg_info = egg_info
-
-
-class EggMetadata(ZipProvider):
- """Metadata provider for .egg files"""
-
- def __init__(self, importer):
- """Create a metadata provider from a zipimporter"""
-
- self.zip_pre = importer.archive + os.sep
- self.loader = importer
- if importer.prefix:
- self.module_path = os.path.join(importer.archive, importer.prefix)
- else:
- self.module_path = importer.archive
- self._setup_prefix()
-
-
-_declare_state('dict', _distribution_finders={})
-
-
-def register_finder(importer_type, distribution_finder):
- """Register `distribution_finder` to find distributions in sys.path items
-
- `importer_type` is the type or class of a PEP 302 "Importer" (sys.path item
- handler), and `distribution_finder` is a callable that, passed a path
- item and the importer instance, yields ``Distribution`` instances found on
- that path item. See ``pkg_resources.find_on_path`` for an example."""
- _distribution_finders[importer_type] = distribution_finder
-
-
-def find_distributions(path_item, only=False):
- """Yield distributions accessible via `path_item`"""
- importer = get_importer(path_item)
- finder = _find_adapter(_distribution_finders, importer)
- return finder(importer, path_item, only)
-
-
-def find_eggs_in_zip(importer, path_item, only=False):
- """
- Find eggs in zip files; possibly multiple nested eggs.
- """
- if importer.archive.endswith('.whl'):
- # wheels are not supported with this finder
- # they don't have PKG-INFO metadata, and won't ever contain eggs
- return
- metadata = EggMetadata(importer)
- if metadata.has_metadata('PKG-INFO'):
- yield Distribution.from_filename(path_item, metadata=metadata)
- if only:
- # don't yield nested distros
- return
- for subitem in metadata.resource_listdir(''):
- if _is_egg_path(subitem):
- subpath = os.path.join(path_item, subitem)
- dists = find_eggs_in_zip(zipimport.zipimporter(subpath), subpath)
- for dist in dists:
- yield dist
- elif subitem.lower().endswith(('.dist-info', '.egg-info')):
- subpath = os.path.join(path_item, subitem)
- submeta = EggMetadata(zipimport.zipimporter(subpath))
- submeta.egg_info = subpath
- yield Distribution.from_location(path_item, subitem, submeta)
-
-
-register_finder(zipimport.zipimporter, find_eggs_in_zip)
-
-
-def find_nothing(importer, path_item, only=False):
- return ()
-
-
-register_finder(object, find_nothing)
-
-
-def find_on_path(importer, path_item, only=False):
- """Yield distributions accessible on a sys.path directory"""
- path_item = _normalize_cached(path_item)
-
- if _is_unpacked_egg(path_item):
- yield Distribution.from_filename(
- path_item,
- metadata=PathMetadata(path_item, os.path.join(path_item, 'EGG-INFO')),
- )
- return
-
- entries = (os.path.join(path_item, child) for child in safe_listdir(path_item))
-
- # scan for .egg and .egg-info in directory
- for entry in sorted(entries):
- fullpath = os.path.join(path_item, entry)
- factory = dist_factory(path_item, entry, only)
- for dist in factory(fullpath):
- yield dist
-
-
-def dist_factory(path_item, entry, only):
- """Return a dist_factory for the given entry."""
- lower = entry.lower()
- is_egg_info = lower.endswith('.egg-info')
- is_dist_info = lower.endswith('.dist-info') and os.path.isdir(
- os.path.join(path_item, entry)
- )
- is_meta = is_egg_info or is_dist_info
- return (
- distributions_from_metadata
- if is_meta
- else find_distributions
- if not only and _is_egg_path(entry)
- else resolve_egg_link
- if not only and lower.endswith('.egg-link')
- else NoDists()
- )
-
-
-class NoDists:
- """
- >>> bool(NoDists())
- False
-
- >>> list(NoDists()('anything'))
- []
- """
-
- def __bool__(self):
- return False
-
- def __call__(self, fullpath):
- return iter(())
-
-
-def safe_listdir(path):
- """
- Attempt to list contents of path, but suppress some exceptions.
- """
- try:
- return os.listdir(path)
- except (PermissionError, NotADirectoryError):
- pass
- except OSError as e:
- # Ignore the directory if does not exist, not a directory or
- # permission denied
- if e.errno not in (errno.ENOTDIR, errno.EACCES, errno.ENOENT):
- raise
- return ()
-
-
-def distributions_from_metadata(path):
- root = os.path.dirname(path)
- if os.path.isdir(path):
- if len(os.listdir(path)) == 0:
- # empty metadata dir; skip
- return
- metadata = PathMetadata(root, path)
- else:
- metadata = FileMetadata(path)
- entry = os.path.basename(path)
- yield Distribution.from_location(
- root,
- entry,
- metadata,
- precedence=DEVELOP_DIST,
- )
-
-
-def non_empty_lines(path):
- """
- Yield non-empty lines from file at path
- """
- with open(path) as f:
- for line in f:
- line = line.strip()
- if line:
- yield line
-
-
-def resolve_egg_link(path):
- """
- Given a path to an .egg-link, resolve distributions
- present in the referenced path.
- """
- referenced_paths = non_empty_lines(path)
- resolved_paths = (
- os.path.join(os.path.dirname(path), ref) for ref in referenced_paths
- )
- dist_groups = map(find_distributions, resolved_paths)
- return next(dist_groups, ())
-
-
-if hasattr(pkgutil, 'ImpImporter'):
- register_finder(pkgutil.ImpImporter, find_on_path)
-
-register_finder(importlib_machinery.FileFinder, find_on_path)
-
-_declare_state('dict', _namespace_handlers={})
-_declare_state('dict', _namespace_packages={})
-
-
-def register_namespace_handler(importer_type, namespace_handler):
- """Register `namespace_handler` to declare namespace packages
-
- `importer_type` is the type or class of a PEP 302 "Importer" (sys.path item
- handler), and `namespace_handler` is a callable like this::
-
- def namespace_handler(importer, path_entry, moduleName, module):
- # return a path_entry to use for child packages
-
- Namespace handlers are only called if the importer object has already
- agreed that it can handle the relevant path item, and they should only
- return a subpath if the module __path__ does not already contain an
- equivalent subpath. For an example namespace handler, see
- ``pkg_resources.file_ns_handler``.
- """
- _namespace_handlers[importer_type] = namespace_handler
-
-
-def _handle_ns(packageName, path_item):
- """Ensure that named package includes a subpath of path_item (if needed)"""
-
- importer = get_importer(path_item)
- if importer is None:
- return None
-
- # use find_spec (PEP 451) and fall-back to find_module (PEP 302)
- try:
- spec = importer.find_spec(packageName)
- except AttributeError:
- # capture warnings due to #1111
- with warnings.catch_warnings():
- warnings.simplefilter("ignore")
- loader = importer.find_module(packageName)
- else:
- loader = spec.loader if spec else None
-
- if loader is None:
- return None
- module = sys.modules.get(packageName)
- if module is None:
- module = sys.modules[packageName] = types.ModuleType(packageName)
- module.__path__ = []
- _set_parent_ns(packageName)
- elif not hasattr(module, '__path__'):
- raise TypeError("Not a package:", packageName)
- handler = _find_adapter(_namespace_handlers, importer)
- subpath = handler(importer, path_item, packageName, module)
- if subpath is not None:
- path = module.__path__
- path.append(subpath)
- importlib.import_module(packageName)
- _rebuild_mod_path(path, packageName, module)
- return subpath
-
-
-def _rebuild_mod_path(orig_path, package_name, module):
- """
- Rebuild module.__path__ ensuring that all entries are ordered
- corresponding to their sys.path order
- """
- sys_path = [_normalize_cached(p) for p in sys.path]
-
- def safe_sys_path_index(entry):
- """
- Workaround for #520 and #513.
- """
- try:
- return sys_path.index(entry)
- except ValueError:
- return float('inf')
-
- def position_in_sys_path(path):
- """
- Return the ordinal of the path based on its position in sys.path
- """
- path_parts = path.split(os.sep)
- module_parts = package_name.count('.') + 1
- parts = path_parts[:-module_parts]
- return safe_sys_path_index(_normalize_cached(os.sep.join(parts)))
-
- new_path = sorted(orig_path, key=position_in_sys_path)
- new_path = [_normalize_cached(p) for p in new_path]
-
- if isinstance(module.__path__, list):
- module.__path__[:] = new_path
- else:
- module.__path__ = new_path
-
-
-def declare_namespace(packageName):
- """Declare that package 'packageName' is a namespace package"""
-
- msg = (
- f"Deprecated call to `pkg_resources.declare_namespace({packageName!r})`.\n"
- "Implementing implicit namespace packages (as specified in PEP 420) "
- "is preferred to `pkg_resources.declare_namespace`. "
- "See https://setuptools.pypa.io/en/latest/references/"
- "keywords.html#keyword-namespace-packages"
- )
- warnings.warn(msg, DeprecationWarning, stacklevel=2)
-
- _imp.acquire_lock()
- try:
- if packageName in _namespace_packages:
- return
-
- path = sys.path
- parent, _, _ = packageName.rpartition('.')
-
- if parent:
- declare_namespace(parent)
- if parent not in _namespace_packages:
- __import__(parent)
- try:
- path = sys.modules[parent].__path__
- except AttributeError as e:
- raise TypeError("Not a package:", parent) from e
-
- # Track what packages are namespaces, so when new path items are added,
- # they can be updated
- _namespace_packages.setdefault(parent or None, []).append(packageName)
- _namespace_packages.setdefault(packageName, [])
-
- for path_item in path:
- # Ensure all the parent's path items are reflected in the child,
- # if they apply
- _handle_ns(packageName, path_item)
-
- finally:
- _imp.release_lock()
-
-
-def fixup_namespace_packages(path_item, parent=None):
- """Ensure that previously-declared namespace packages include path_item"""
- _imp.acquire_lock()
- try:
- for package in _namespace_packages.get(parent, ()):
- subpath = _handle_ns(package, path_item)
- if subpath:
- fixup_namespace_packages(subpath, package)
- finally:
- _imp.release_lock()
-
-
-def file_ns_handler(importer, path_item, packageName, module):
- """Compute an ns-package subpath for a filesystem or zipfile importer"""
-
- subpath = os.path.join(path_item, packageName.split('.')[-1])
- normalized = _normalize_cached(subpath)
- for item in module.__path__:
- if _normalize_cached(item) == normalized:
- break
- else:
- # Only return the path if it's not already there
- return subpath
-
-
-if hasattr(pkgutil, 'ImpImporter'):
- register_namespace_handler(pkgutil.ImpImporter, file_ns_handler)
-
-register_namespace_handler(zipimport.zipimporter, file_ns_handler)
-register_namespace_handler(importlib_machinery.FileFinder, file_ns_handler)
-
-
-def null_ns_handler(importer, path_item, packageName, module):
- return None
-
-
-register_namespace_handler(object, null_ns_handler)
-
-
-def normalize_path(filename):
- """Normalize a file/dir name for comparison purposes"""
- return os.path.normcase(os.path.realpath(os.path.normpath(_cygwin_patch(filename))))
-
-
-def _cygwin_patch(filename): # pragma: nocover
- """
- Contrary to POSIX 2008, on Cygwin, getcwd (3) contains
- symlink components. Using
- os.path.abspath() works around this limitation. A fix in os.getcwd()
- would probably better, in Cygwin even more so, except
- that this seems to be by design...
- """
- return os.path.abspath(filename) if sys.platform == 'cygwin' else filename
-
-
-def _normalize_cached(filename, _cache={}):
- try:
- return _cache[filename]
- except KeyError:
- _cache[filename] = result = normalize_path(filename)
- return result
-
-
-def _is_egg_path(path):
- """
- Determine if given path appears to be an egg.
- """
- return _is_zip_egg(path) or _is_unpacked_egg(path)
-
-
-def _is_zip_egg(path):
- return (
- path.lower().endswith('.egg')
- and os.path.isfile(path)
- and zipfile.is_zipfile(path)
- )
-
-
-def _is_unpacked_egg(path):
- """
- Determine if given path appears to be an unpacked egg.
- """
- return path.lower().endswith('.egg') and os.path.isfile(
- os.path.join(path, 'EGG-INFO', 'PKG-INFO')
- )
-
-
-def _set_parent_ns(packageName):
- parts = packageName.split('.')
- name = parts.pop()
- if parts:
- parent = '.'.join(parts)
- setattr(sys.modules[parent], name, sys.modules[packageName])
-
-
-MODULE = re.compile(r"\w+(\.\w+)*$").match
-EGG_NAME = re.compile(
- r"""
- (?P[^-]+) (
- -(?P[^-]+) (
- -py(?P[^-]+) (
- -(?P.+)
- )?
- )?
- )?
- """,
- re.VERBOSE | re.IGNORECASE,
-).match
-
-
-class EntryPoint:
- """Object representing an advertised importable object"""
-
- def __init__(self, name, module_name, attrs=(), extras=(), dist=None):
- if not MODULE(module_name):
- raise ValueError("Invalid module name", module_name)
- self.name = name
- self.module_name = module_name
- self.attrs = tuple(attrs)
- self.extras = tuple(extras)
- self.dist = dist
-
- def __str__(self):
- s = "%s = %s" % (self.name, self.module_name)
- if self.attrs:
- s += ':' + '.'.join(self.attrs)
- if self.extras:
- s += ' [%s]' % ','.join(self.extras)
- return s
-
- def __repr__(self):
- return "EntryPoint.parse(%r)" % str(self)
-
- def load(self, require=True, *args, **kwargs):
- """
- Require packages for this EntryPoint, then resolve it.
- """
- if not require or args or kwargs:
- warnings.warn(
- "Parameters to load are deprecated. Call .resolve and "
- ".require separately.",
- PkgResourcesDeprecationWarning,
- stacklevel=2,
- )
- if require:
- self.require(*args, **kwargs)
- return self.resolve()
-
- def resolve(self):
- """
- Resolve the entry point from its module and attrs.
- """
- module = __import__(self.module_name, fromlist=['__name__'], level=0)
- try:
- return functools.reduce(getattr, self.attrs, module)
- except AttributeError as exc:
- raise ImportError(str(exc)) from exc
-
- def require(self, env=None, installer=None):
- if self.extras and not self.dist:
- raise UnknownExtra("Can't require() without a distribution", self)
-
- # Get the requirements for this entry point with all its extras and
- # then resolve them. We have to pass `extras` along when resolving so
- # that the working set knows what extras we want. Otherwise, for
- # dist-info distributions, the working set will assume that the
- # requirements for that extra are purely optional and skip over them.
- reqs = self.dist.requires(self.extras)
- items = working_set.resolve(reqs, env, installer, extras=self.extras)
- list(map(working_set.add, items))
-
- pattern = re.compile(
- r'\s*'
- r'(?P.+?)\s*'
- r'=\s*'
- r'(?P[\w.]+)\s*'
- r'(:\s*(?P[\w.]+))?\s*'
- r'(?P\[.*\])?\s*$'
- )
-
- @classmethod
- def parse(cls, src, dist=None):
- """Parse a single entry point from string `src`
-
- Entry point syntax follows the form::
-
- name = some.module:some.attr [extra1, extra2]
-
- The entry name and module name are required, but the ``:attrs`` and
- ``[extras]`` parts are optional
- """
- m = cls.pattern.match(src)
- if not m:
- msg = "EntryPoint must be in 'name=module:attrs [extras]' format"
- raise ValueError(msg, src)
- res = m.groupdict()
- extras = cls._parse_extras(res['extras'])
- attrs = res['attr'].split('.') if res['attr'] else ()
- return cls(res['name'], res['module'], attrs, extras, dist)
-
- @classmethod
- def _parse_extras(cls, extras_spec):
- if not extras_spec:
- return ()
- req = Requirement.parse('x' + extras_spec)
- if req.specs:
- raise ValueError()
- return req.extras
-
- @classmethod
- def parse_group(cls, group, lines, dist=None):
- """Parse an entry point group"""
- if not MODULE(group):
- raise ValueError("Invalid group name", group)
- this = {}
- for line in yield_lines(lines):
- ep = cls.parse(line, dist)
- if ep.name in this:
- raise ValueError("Duplicate entry point", group, ep.name)
- this[ep.name] = ep
- return this
-
- @classmethod
- def parse_map(cls, data, dist=None):
- """Parse a map of entry point groups"""
- if isinstance(data, dict):
- data = data.items()
- else:
- data = split_sections(data)
- maps = {}
- for group, lines in data:
- if group is None:
- if not lines:
- continue
- raise ValueError("Entry points must be listed in groups")
- group = group.strip()
- if group in maps:
- raise ValueError("Duplicate group name", group)
- maps[group] = cls.parse_group(group, lines, dist)
- return maps
-
-
-def _version_from_file(lines):
- """
- Given an iterable of lines from a Metadata file, return
- the value of the Version field, if present, or None otherwise.
- """
-
- def is_version_line(line):
- return line.lower().startswith('version:')
-
- version_lines = filter(is_version_line, lines)
- line = next(iter(version_lines), '')
- _, _, value = line.partition(':')
- return safe_version(value.strip()) or None
-
-
-class Distribution:
- """Wrap an actual or potential sys.path entry w/metadata"""
-
- PKG_INFO = 'PKG-INFO'
-
- def __init__(
- self,
- location=None,
- metadata=None,
- project_name=None,
- version=None,
- py_version=PY_MAJOR,
- platform=None,
- precedence=EGG_DIST,
- ):
- self.project_name = safe_name(project_name or 'Unknown')
- if version is not None:
- self._version = safe_version(version)
- self.py_version = py_version
- self.platform = platform
- self.location = location
- self.precedence = precedence
- self._provider = metadata or empty_provider
-
- @classmethod
- def from_location(cls, location, basename, metadata=None, **kw):
- project_name, version, py_version, platform = [None] * 4
- basename, ext = os.path.splitext(basename)
- if ext.lower() in _distributionImpl:
- cls = _distributionImpl[ext.lower()]
-
- match = EGG_NAME(basename)
- if match:
- project_name, version, py_version, platform = match.group(
- 'name', 'ver', 'pyver', 'plat'
- )
- return cls(
- location,
- metadata,
- project_name=project_name,
- version=version,
- py_version=py_version,
- platform=platform,
- **kw,
- )._reload_version()
-
- def _reload_version(self):
- return self
-
- @property
- def hashcmp(self):
- return (
- self._forgiving_parsed_version,
- self.precedence,
- self.key,
- self.location,
- self.py_version or '',
- self.platform or '',
- )
-
- def __hash__(self):
- return hash(self.hashcmp)
-
- def __lt__(self, other):
- return self.hashcmp < other.hashcmp
-
- def __le__(self, other):
- return self.hashcmp <= other.hashcmp
-
- def __gt__(self, other):
- return self.hashcmp > other.hashcmp
-
- def __ge__(self, other):
- return self.hashcmp >= other.hashcmp
-
- def __eq__(self, other):
- if not isinstance(other, self.__class__):
- # It's not a Distribution, so they are not equal
- return False
- return self.hashcmp == other.hashcmp
-
- def __ne__(self, other):
- return not self == other
-
- # These properties have to be lazy so that we don't have to load any
- # metadata until/unless it's actually needed. (i.e., some distributions
- # may not know their name or version without loading PKG-INFO)
-
- @property
- def key(self):
- try:
- return self._key
- except AttributeError:
- self._key = key = self.project_name.lower()
- return key
-
- @property
- def parsed_version(self):
- if not hasattr(self, "_parsed_version"):
- try:
- self._parsed_version = parse_version(self.version)
- except packaging.version.InvalidVersion as ex:
- info = f"(package: {self.project_name})"
- if hasattr(ex, "add_note"):
- ex.add_note(info) # PEP 678
- raise
- raise packaging.version.InvalidVersion(f"{str(ex)} {info}") from None
-
- return self._parsed_version
-
- @property
- def _forgiving_parsed_version(self):
- try:
- return self.parsed_version
- except packaging.version.InvalidVersion as ex:
- self._parsed_version = parse_version(_forgiving_version(self.version))
-
- notes = "\n".join(getattr(ex, "__notes__", [])) # PEP 678
- msg = f"""!!\n\n
- *************************************************************************
- {str(ex)}\n{notes}
-
- This is a long overdue deprecation.
- For the time being, `pkg_resources` will use `{self._parsed_version}`
- as a replacement to avoid breaking existing environments,
- but no future compatibility is guaranteed.
-
- If you maintain package {self.project_name} you should implement
- the relevant changes to adequate the project to PEP 440 immediately.
- *************************************************************************
- \n\n!!
- """
- warnings.warn(msg, DeprecationWarning)
-
- return self._parsed_version
-
- @property
- def version(self):
- try:
- return self._version
- except AttributeError as e:
- version = self._get_version()
- if version is None:
- path = self._get_metadata_path_for_display(self.PKG_INFO)
- msg = ("Missing 'Version:' header and/or {} file at path: {}").format(
- self.PKG_INFO, path
- )
- raise ValueError(msg, self) from e
-
- return version
-
- @property
- def _dep_map(self):
- """
- A map of extra to its list of (direct) requirements
- for this distribution, including the null extra.
- """
- try:
- return self.__dep_map
- except AttributeError:
- self.__dep_map = self._filter_extras(self._build_dep_map())
- return self.__dep_map
-
- @staticmethod
- def _filter_extras(dm):
- """
- Given a mapping of extras to dependencies, strip off
- environment markers and filter out any dependencies
- not matching the markers.
- """
- for extra in list(filter(None, dm)):
- new_extra = extra
- reqs = dm.pop(extra)
- new_extra, _, marker = extra.partition(':')
- fails_marker = marker and (
- invalid_marker(marker) or not evaluate_marker(marker)
- )
- if fails_marker:
- reqs = []
- new_extra = safe_extra(new_extra) or None
-
- dm.setdefault(new_extra, []).extend(reqs)
- return dm
-
- def _build_dep_map(self):
- dm = {}
- for name in 'requires.txt', 'depends.txt':
- for extra, reqs in split_sections(self._get_metadata(name)):
- dm.setdefault(extra, []).extend(parse_requirements(reqs))
- return dm
-
- def requires(self, extras=()):
- """List of Requirements needed for this distro if `extras` are used"""
- dm = self._dep_map
- deps = []
- deps.extend(dm.get(None, ()))
- for ext in extras:
- try:
- deps.extend(dm[safe_extra(ext)])
- except KeyError as e:
- raise UnknownExtra(
- "%s has no such extra feature %r" % (self, ext)
- ) from e
- return deps
-
- def _get_metadata_path_for_display(self, name):
- """
- Return the path to the given metadata file, if available.
- """
- try:
- # We need to access _get_metadata_path() on the provider object
- # directly rather than through this class's __getattr__()
- # since _get_metadata_path() is marked private.
- path = self._provider._get_metadata_path(name)
-
- # Handle exceptions e.g. in case the distribution's metadata
- # provider doesn't support _get_metadata_path().
- except Exception:
- return '[could not detect]'
-
- return path
-
- def _get_metadata(self, name):
- if self.has_metadata(name):
- for line in self.get_metadata_lines(name):
- yield line
-
- def _get_version(self):
- lines = self._get_metadata(self.PKG_INFO)
- version = _version_from_file(lines)
-
- return version
-
- def activate(self, path=None, replace=False):
- """Ensure distribution is importable on `path` (default=sys.path)"""
- if path is None:
- path = sys.path
- self.insert_on(path, replace=replace)
- if path is sys.path:
- fixup_namespace_packages(self.location)
- for pkg in self._get_metadata('namespace_packages.txt'):
- if pkg in sys.modules:
- declare_namespace(pkg)
-
- def egg_name(self):
- """Return what this distribution's standard .egg filename should be"""
- filename = "%s-%s-py%s" % (
- to_filename(self.project_name),
- to_filename(self.version),
- self.py_version or PY_MAJOR,
- )
-
- if self.platform:
- filename += '-' + self.platform
- return filename
-
- def __repr__(self):
- if self.location:
- return "%s (%s)" % (self, self.location)
- else:
- return str(self)
-
- def __str__(self):
- try:
- version = getattr(self, 'version', None)
- except ValueError:
- version = None
- version = version or "[unknown version]"
- return "%s %s" % (self.project_name, version)
-
- def __getattr__(self, attr):
- """Delegate all unrecognized public attributes to .metadata provider"""
- if attr.startswith('_'):
- raise AttributeError(attr)
- return getattr(self._provider, attr)
-
- def __dir__(self):
- return list(
- set(super(Distribution, self).__dir__())
- | set(attr for attr in self._provider.__dir__() if not attr.startswith('_'))
- )
-
- @classmethod
- def from_filename(cls, filename, metadata=None, **kw):
- return cls.from_location(
- _normalize_cached(filename), os.path.basename(filename), metadata, **kw
- )
-
- def as_requirement(self):
- """Return a ``Requirement`` that matches this distribution exactly"""
- if isinstance(self.parsed_version, packaging.version.Version):
- spec = "%s==%s" % (self.project_name, self.parsed_version)
- else:
- spec = "%s===%s" % (self.project_name, self.parsed_version)
-
- return Requirement.parse(spec)
-
- def load_entry_point(self, group, name):
- """Return the `name` entry point of `group` or raise ImportError"""
- ep = self.get_entry_info(group, name)
- if ep is None:
- raise ImportError("Entry point %r not found" % ((group, name),))
- return ep.load()
-
- def get_entry_map(self, group=None):
- """Return the entry point map for `group`, or the full entry map"""
- try:
- ep_map = self._ep_map
- except AttributeError:
- ep_map = self._ep_map = EntryPoint.parse_map(
- self._get_metadata('entry_points.txt'), self
- )
- if group is not None:
- return ep_map.get(group, {})
- return ep_map
-
- def get_entry_info(self, group, name):
- """Return the EntryPoint object for `group`+`name`, or ``None``"""
- return self.get_entry_map(group).get(name)
-
- # FIXME: 'Distribution.insert_on' is too complex (13)
- def insert_on(self, path, loc=None, replace=False): # noqa: C901
- """Ensure self.location is on path
-
- If replace=False (default):
- - If location is already in path anywhere, do nothing.
- - Else:
- - If it's an egg and its parent directory is on path,
- insert just ahead of the parent.
- - Else: add to the end of path.
- If replace=True:
- - If location is already on path anywhere (not eggs)
- or higher priority than its parent (eggs)
- do nothing.
- - Else:
- - If it's an egg and its parent directory is on path,
- insert just ahead of the parent,
- removing any lower-priority entries.
- - Else: add it to the front of path.
- """
-
- loc = loc or self.location
- if not loc:
- return
-
- nloc = _normalize_cached(loc)
- bdir = os.path.dirname(nloc)
- npath = [(p and _normalize_cached(p) or p) for p in path]
-
- for p, item in enumerate(npath):
- if item == nloc:
- if replace:
- break
- else:
- # don't modify path (even removing duplicates) if
- # found and not replace
- return
- elif item == bdir and self.precedence == EGG_DIST:
- # if it's an .egg, give it precedence over its directory
- # UNLESS it's already been added to sys.path and replace=False
- if (not replace) and nloc in npath[p:]:
- return
- if path is sys.path:
- self.check_version_conflict()
- path.insert(p, loc)
- npath.insert(p, nloc)
- break
- else:
- if path is sys.path:
- self.check_version_conflict()
- if replace:
- path.insert(0, loc)
- else:
- path.append(loc)
- return
-
- # p is the spot where we found or inserted loc; now remove duplicates
- while True:
- try:
- np = npath.index(nloc, p + 1)
- except ValueError:
- break
- else:
- del npath[np], path[np]
- # ha!
- p = np
-
- return
-
- def check_version_conflict(self):
- if self.key == 'setuptools':
- # ignore the inevitable setuptools self-conflicts :(
- return
-
- nsp = dict.fromkeys(self._get_metadata('namespace_packages.txt'))
- loc = normalize_path(self.location)
- for modname in self._get_metadata('top_level.txt'):
- if (
- modname not in sys.modules
- or modname in nsp
- or modname in _namespace_packages
- ):
- continue
- if modname in ('pkg_resources', 'setuptools', 'site'):
- continue
- fn = getattr(sys.modules[modname], '__file__', None)
- if fn and (
- normalize_path(fn).startswith(loc) or fn.startswith(self.location)
- ):
- continue
- issue_warning(
- "Module %s was already imported from %s, but %s is being added"
- " to sys.path" % (modname, fn, self.location),
- )
-
- def has_version(self):
- try:
- self.version
- except ValueError:
- issue_warning("Unbuilt egg for " + repr(self))
- return False
- except SystemError:
- # TODO: remove this except clause when python/cpython#103632 is fixed.
- return False
- return True
-
- def clone(self, **kw):
- """Copy this distribution, substituting in any changed keyword args"""
- names = 'project_name version py_version platform location precedence'
- for attr in names.split():
- kw.setdefault(attr, getattr(self, attr, None))
- kw.setdefault('metadata', self._provider)
- return self.__class__(**kw)
-
- @property
- def extras(self):
- return [dep for dep in self._dep_map if dep]
-
-
-class EggInfoDistribution(Distribution):
- def _reload_version(self):
- """
- Packages installed by distutils (e.g. numpy or scipy),
- which uses an old safe_version, and so
- their version numbers can get mangled when
- converted to filenames (e.g., 1.11.0.dev0+2329eae to
- 1.11.0.dev0_2329eae). These distributions will not be
- parsed properly
- downstream by Distribution and safe_version, so
- take an extra step and try to get the version number from
- the metadata file itself instead of the filename.
- """
- md_version = self._get_version()
- if md_version:
- self._version = md_version
- return self
-
-
-class DistInfoDistribution(Distribution):
- """
- Wrap an actual or potential sys.path entry
- w/metadata, .dist-info style.
- """
-
- PKG_INFO = 'METADATA'
- EQEQ = re.compile(r"([\(,])\s*(\d.*?)\s*([,\)])")
-
- @property
- def _parsed_pkg_info(self):
- """Parse and cache metadata"""
- try:
- return self._pkg_info
- except AttributeError:
- metadata = self.get_metadata(self.PKG_INFO)
- self._pkg_info = email.parser.Parser().parsestr(metadata)
- return self._pkg_info
-
- @property
- def _dep_map(self):
- try:
- return self.__dep_map
- except AttributeError:
- self.__dep_map = self._compute_dependencies()
- return self.__dep_map
-
- def _compute_dependencies(self):
- """Recompute this distribution's dependencies."""
- dm = self.__dep_map = {None: []}
-
- reqs = []
- # Including any condition expressions
- for req in self._parsed_pkg_info.get_all('Requires-Dist') or []:
- reqs.extend(parse_requirements(req))
-
- def reqs_for_extra(extra):
- for req in reqs:
- if not req.marker or req.marker.evaluate({'extra': extra}):
- yield req
-
- common = types.MappingProxyType(dict.fromkeys(reqs_for_extra(None)))
- dm[None].extend(common)
-
- for extra in self._parsed_pkg_info.get_all('Provides-Extra') or []:
- s_extra = safe_extra(extra.strip())
- dm[s_extra] = [r for r in reqs_for_extra(extra) if r not in common]
-
- return dm
-
-
-_distributionImpl = {
- '.egg': Distribution,
- '.egg-info': EggInfoDistribution,
- '.dist-info': DistInfoDistribution,
-}
-
-
-def issue_warning(*args, **kw):
- level = 1
- g = globals()
- try:
- # find the first stack frame that is *not* code in
- # the pkg_resources module, to use for the warning
- while sys._getframe(level).f_globals is g:
- level += 1
- except ValueError:
- pass
- warnings.warn(stacklevel=level + 1, *args, **kw)
-
-
-def parse_requirements(strs):
- """
- Yield ``Requirement`` objects for each specification in `strs`.
-
- `strs` must be a string, or a (possibly-nested) iterable thereof.
- """
- return map(Requirement, join_continuation(map(drop_comment, yield_lines(strs))))
-
-
-class RequirementParseError(packaging.requirements.InvalidRequirement):
- "Compatibility wrapper for InvalidRequirement"
-
-
-class Requirement(packaging.requirements.Requirement):
- def __init__(self, requirement_string):
- """DO NOT CALL THIS UNDOCUMENTED METHOD; use Requirement.parse()!"""
- super(Requirement, self).__init__(requirement_string)
- self.unsafe_name = self.name
- project_name = safe_name(self.name)
- self.project_name, self.key = project_name, project_name.lower()
- self.specs = [(spec.operator, spec.version) for spec in self.specifier]
- self.extras = tuple(map(safe_extra, self.extras))
- self.hashCmp = (
- self.key,
- self.url,
- self.specifier,
- frozenset(self.extras),
- str(self.marker) if self.marker else None,
- )
- self.__hash = hash(self.hashCmp)
-
- def __eq__(self, other):
- return isinstance(other, Requirement) and self.hashCmp == other.hashCmp
-
- def __ne__(self, other):
- return not self == other
-
- def __contains__(self, item):
- if isinstance(item, Distribution):
- if item.key != self.key:
- return False
-
- item = item.version
-
- # Allow prereleases always in order to match the previous behavior of
- # this method. In the future this should be smarter and follow PEP 440
- # more accurately.
- return self.specifier.contains(item, prereleases=True)
-
- def __hash__(self):
- return self.__hash
-
- def __repr__(self):
- return "Requirement.parse(%r)" % str(self)
-
- @staticmethod
- def parse(s):
- (req,) = parse_requirements(s)
- return req
-
-
-def _always_object(classes):
- """
- Ensure object appears in the mro even
- for old-style classes.
- """
- if object not in classes:
- return classes + (object,)
- return classes
-
-
-def _find_adapter(registry, ob):
- """Return an adapter factory for `ob` from `registry`"""
- types = _always_object(inspect.getmro(getattr(ob, '__class__', type(ob))))
- for t in types:
- if t in registry:
- return registry[t]
-
-
-def ensure_directory(path):
- """Ensure that the parent directory of `path` exists"""
- dirname = os.path.dirname(path)
- os.makedirs(dirname, exist_ok=True)
-
-
-def _bypass_ensure_directory(path):
- """Sandbox-bypassing version of ensure_directory()"""
- if not WRITE_SUPPORT:
- raise IOError('"os.mkdir" not supported on this platform.')
- dirname, filename = split(path)
- if dirname and filename and not isdir(dirname):
- _bypass_ensure_directory(dirname)
- try:
- mkdir(dirname, 0o755)
- except FileExistsError:
- pass
-
-
-def split_sections(s):
- """Split a string or iterable thereof into (section, content) pairs
-
- Each ``section`` is a stripped version of the section header ("[section]")
- and each ``content`` is a list of stripped lines excluding blank lines and
- comment-only lines. If there are any such lines before the first section
- header, they're returned in a first ``section`` of ``None``.
- """
- section = None
- content = []
- for line in yield_lines(s):
- if line.startswith("["):
- if line.endswith("]"):
- if section or content:
- yield section, content
- section = line[1:-1].strip()
- content = []
- else:
- raise ValueError("Invalid section heading", line)
- else:
- content.append(line)
-
- # wrap up last segment
- yield section, content
-
-
-def _mkstemp(*args, **kw):
- old_open = os.open
- try:
- # temporarily bypass sandboxing
- os.open = os_open
- return tempfile.mkstemp(*args, **kw)
- finally:
- # and then put it back
- os.open = old_open
-
-
-# Silence the PEP440Warning by default, so that end users don't get hit by it
-# randomly just because they use pkg_resources. We want to append the rule
-# because we want earlier uses of filterwarnings to take precedence over this
-# one.
-warnings.filterwarnings("ignore", category=PEP440Warning, append=True)
-
-
-# from jaraco.functools 1.3
-def _call_aside(f, *args, **kwargs):
- f(*args, **kwargs)
- return f
-
-
-@_call_aside
-def _initialize(g=globals()):
- "Set up global resource manager (deliberately not state-saved)"
- manager = ResourceManager()
- g['_manager'] = manager
- g.update(
- (name, getattr(manager, name))
- for name in dir(manager)
- if not name.startswith('_')
- )
-
-
-class PkgResourcesDeprecationWarning(Warning):
- """
- Base class for warning about deprecations in ``pkg_resources``
-
- This class is not derived from ``DeprecationWarning``, and as such is
- visible by default.
- """
-
-
-@_call_aside
-def _initialize_master_working_set():
- """
- Prepare the master working set and make the ``require()``
- API available.
-
- This function has explicit effects on the global state
- of pkg_resources. It is intended to be invoked once at
- the initialization of this module.
-
- Invocation by other packages is unsupported and done
- at their own risk.
- """
- working_set = WorkingSet._build_master()
- _declare_state('object', working_set=working_set)
-
- require = working_set.require
- iter_entry_points = working_set.iter_entry_points
- add_activation_listener = working_set.subscribe
- run_script = working_set.run_script
- # backward compatibility
- run_main = run_script
- # Activate all distributions already on sys.path with replace=False and
- # ensure that all distributions added to the working set in the future
- # (e.g. by calling ``require()``) will get activated as well,
- # with higher priority (replace=True).
- tuple(dist.activate(replace=False) for dist in working_set)
- add_activation_listener(
- lambda dist: dist.activate(replace=True),
- existing=False,
- )
- working_set.entries = []
- # match order
- list(map(working_set.add_entry, sys.path))
- globals().update(locals())
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/msvc.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/msvc.py
deleted file mode 100644
index 5d4d7759c95a4713df96332781cba1e336d7638f..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/msvc.py
+++ /dev/null
@@ -1,1703 +0,0 @@
-"""
-Improved support for Microsoft Visual C++ compilers.
-
-Known supported compilers:
---------------------------
-Microsoft Visual C++ 14.X:
- Microsoft Visual C++ Build Tools 2015 (x86, x64, arm)
- Microsoft Visual Studio Build Tools 2017 (x86, x64, arm, arm64)
- Microsoft Visual Studio Build Tools 2019 (x86, x64, arm, arm64)
-
-This may also support compilers shipped with compatible Visual Studio versions.
-"""
-
-import json
-from io import open
-from os import listdir, pathsep
-from os.path import join, isfile, isdir, dirname
-import sys
-import contextlib
-import platform
-import itertools
-import subprocess
-import distutils.errors
-from setuptools.extern.packaging.version import LegacyVersion
-from setuptools.extern.more_itertools import unique_everseen
-
-from .monkey import get_unpatched
-
-if platform.system() == 'Windows':
- import winreg
- from os import environ
-else:
- # Mock winreg and environ so the module can be imported on this platform.
-
- class winreg:
- HKEY_USERS = None
- HKEY_CURRENT_USER = None
- HKEY_LOCAL_MACHINE = None
- HKEY_CLASSES_ROOT = None
-
- environ = dict()
-
-
-def _msvc14_find_vc2015():
- """Python 3.8 "distutils/_msvccompiler.py" backport"""
- try:
- key = winreg.OpenKey(
- winreg.HKEY_LOCAL_MACHINE,
- r"Software\Microsoft\VisualStudio\SxS\VC7",
- 0,
- winreg.KEY_READ | winreg.KEY_WOW64_32KEY
- )
- except OSError:
- return None, None
-
- best_version = 0
- best_dir = None
- with key:
- for i in itertools.count():
- try:
- v, vc_dir, vt = winreg.EnumValue(key, i)
- except OSError:
- break
- if v and vt == winreg.REG_SZ and isdir(vc_dir):
- try:
- version = int(float(v))
- except (ValueError, TypeError):
- continue
- if version >= 14 and version > best_version:
- best_version, best_dir = version, vc_dir
- return best_version, best_dir
-
-
-def _msvc14_find_vc2017():
- """Python 3.8 "distutils/_msvccompiler.py" backport
-
- Returns "15, path" based on the result of invoking vswhere.exe
- If no install is found, returns "None, None"
-
- The version is returned to avoid unnecessarily changing the function
- result. It may be ignored when the path is not None.
-
- If vswhere.exe is not available, by definition, VS 2017 is not
- installed.
- """
- root = environ.get("ProgramFiles(x86)") or environ.get("ProgramFiles")
- if not root:
- return None, None
-
- try:
- path = subprocess.check_output([
- join(root, "Microsoft Visual Studio", "Installer", "vswhere.exe"),
- "-latest",
- "-prerelease",
- "-requiresAny",
- "-requires", "Microsoft.VisualStudio.Component.VC.Tools.x86.x64",
- "-requires", "Microsoft.VisualStudio.Workload.WDExpress",
- "-property", "installationPath",
- "-products", "*",
- ]).decode(encoding="mbcs", errors="strict").strip()
- except (subprocess.CalledProcessError, OSError, UnicodeDecodeError):
- return None, None
-
- path = join(path, "VC", "Auxiliary", "Build")
- if isdir(path):
- return 15, path
-
- return None, None
-
-
-PLAT_SPEC_TO_RUNTIME = {
- 'x86': 'x86',
- 'x86_amd64': 'x64',
- 'x86_arm': 'arm',
- 'x86_arm64': 'arm64'
-}
-
-
-def _msvc14_find_vcvarsall(plat_spec):
- """Python 3.8 "distutils/_msvccompiler.py" backport"""
- _, best_dir = _msvc14_find_vc2017()
- vcruntime = None
-
- if plat_spec in PLAT_SPEC_TO_RUNTIME:
- vcruntime_plat = PLAT_SPEC_TO_RUNTIME[plat_spec]
- else:
- vcruntime_plat = 'x64' if 'amd64' in plat_spec else 'x86'
-
- if best_dir:
- vcredist = join(best_dir, "..", "..", "redist", "MSVC", "**",
- vcruntime_plat, "Microsoft.VC14*.CRT",
- "vcruntime140.dll")
- try:
- import glob
- vcruntime = glob.glob(vcredist, recursive=True)[-1]
- except (ImportError, OSError, LookupError):
- vcruntime = None
-
- if not best_dir:
- best_version, best_dir = _msvc14_find_vc2015()
- if best_version:
- vcruntime = join(best_dir, 'redist', vcruntime_plat,
- "Microsoft.VC140.CRT", "vcruntime140.dll")
-
- if not best_dir:
- return None, None
-
- vcvarsall = join(best_dir, "vcvarsall.bat")
- if not isfile(vcvarsall):
- return None, None
-
- if not vcruntime or not isfile(vcruntime):
- vcruntime = None
-
- return vcvarsall, vcruntime
-
-
-def _msvc14_get_vc_env(plat_spec):
- """Python 3.8 "distutils/_msvccompiler.py" backport"""
- if "DISTUTILS_USE_SDK" in environ:
- return {
- key.lower(): value
- for key, value in environ.items()
- }
-
- vcvarsall, vcruntime = _msvc14_find_vcvarsall(plat_spec)
- if not vcvarsall:
- raise distutils.errors.DistutilsPlatformError(
- "Unable to find vcvarsall.bat"
- )
-
- try:
- out = subprocess.check_output(
- 'cmd /u /c "{}" {} && set'.format(vcvarsall, plat_spec),
- stderr=subprocess.STDOUT,
- ).decode('utf-16le', errors='replace')
- except subprocess.CalledProcessError as exc:
- raise distutils.errors.DistutilsPlatformError(
- "Error executing {}".format(exc.cmd)
- ) from exc
-
- env = {
- key.lower(): value
- for key, _, value in
- (line.partition('=') for line in out.splitlines())
- if key and value
- }
-
- if vcruntime:
- env['py_vcruntime_redist'] = vcruntime
- return env
-
-
-def msvc14_get_vc_env(plat_spec):
- """
- Patched "distutils._msvccompiler._get_vc_env" for support extra
- Microsoft Visual C++ 14.X compilers.
-
- Set environment without use of "vcvarsall.bat".
-
- Parameters
- ----------
- plat_spec: str
- Target architecture.
-
- Return
- ------
- dict
- environment
- """
-
- # Always use backport from CPython 3.8
- try:
- return _msvc14_get_vc_env(plat_spec)
- except distutils.errors.DistutilsPlatformError as exc:
- _augment_exception(exc, 14.0)
- raise
-
-
-def msvc14_gen_lib_options(*args, **kwargs):
- """
- Patched "distutils._msvccompiler.gen_lib_options" for fix
- compatibility between "numpy.distutils" and "distutils._msvccompiler"
- (for Numpy < 1.11.2)
- """
- if "numpy.distutils" in sys.modules:
- import numpy as np
- if LegacyVersion(np.__version__) < LegacyVersion('1.11.2'):
- return np.distutils.ccompiler.gen_lib_options(*args, **kwargs)
- return get_unpatched(msvc14_gen_lib_options)(*args, **kwargs)
-
-
-def _augment_exception(exc, version, arch=''):
- """
- Add details to the exception message to help guide the user
- as to what action will resolve it.
- """
- # Error if MSVC++ directory not found or environment not set
- message = exc.args[0]
-
- if "vcvarsall" in message.lower() or "visual c" in message.lower():
- # Special error message if MSVC++ not installed
- tmpl = 'Microsoft Visual C++ {version:0.1f} or greater is required.'
- message = tmpl.format(**locals())
- msdownload = 'www.microsoft.com/download/details.aspx?id=%d'
- if version == 9.0:
- if arch.lower().find('ia64') > -1:
- # For VC++ 9.0, if IA64 support is needed, redirect user
- # to Windows SDK 7.0.
- # Note: No download link available from Microsoft.
- message += ' Get it with "Microsoft Windows SDK 7.0"'
- else:
- # For VC++ 9.0 redirect user to Vc++ for Python 2.7 :
- # This redirection link is maintained by Microsoft.
- # Contact vspython@microsoft.com if it needs updating.
- message += ' Get it from http://aka.ms/vcpython27'
- elif version == 10.0:
- # For VC++ 10.0 Redirect user to Windows SDK 7.1
- message += ' Get it with "Microsoft Windows SDK 7.1": '
- message += msdownload % 8279
- elif version >= 14.0:
- # For VC++ 14.X Redirect user to latest Visual C++ Build Tools
- message += (' Get it with "Microsoft C++ Build Tools": '
- r'https://visualstudio.microsoft.com'
- r'/visual-cpp-build-tools/')
-
- exc.args = (message, )
-
-
-class PlatformInfo:
- """
- Current and Target Architectures information.
-
- Parameters
- ----------
- arch: str
- Target architecture.
- """
- current_cpu = environ.get('processor_architecture', '').lower()
-
- def __init__(self, arch):
- self.arch = arch.lower().replace('x64', 'amd64')
-
- @property
- def target_cpu(self):
- """
- Return Target CPU architecture.
-
- Return
- ------
- str
- Target CPU
- """
- return self.arch[self.arch.find('_') + 1:]
-
- def target_is_x86(self):
- """
- Return True if target CPU is x86 32 bits..
-
- Return
- ------
- bool
- CPU is x86 32 bits
- """
- return self.target_cpu == 'x86'
-
- def current_is_x86(self):
- """
- Return True if current CPU is x86 32 bits..
-
- Return
- ------
- bool
- CPU is x86 32 bits
- """
- return self.current_cpu == 'x86'
-
- def current_dir(self, hidex86=False, x64=False):
- """
- Current platform specific subfolder.
-
- Parameters
- ----------
- hidex86: bool
- return '' and not '\x86' if architecture is x86.
- x64: bool
- return '\x64' and not '\amd64' if architecture is amd64.
-
- Return
- ------
- str
- subfolder: '\target', or '' (see hidex86 parameter)
- """
- return (
- '' if (self.current_cpu == 'x86' and hidex86) else
- r'\x64' if (self.current_cpu == 'amd64' and x64) else
- r'\%s' % self.current_cpu
- )
-
- def target_dir(self, hidex86=False, x64=False):
- r"""
- Target platform specific subfolder.
-
- Parameters
- ----------
- hidex86: bool
- return '' and not '\x86' if architecture is x86.
- x64: bool
- return '\x64' and not '\amd64' if architecture is amd64.
-
- Return
- ------
- str
- subfolder: '\current', or '' (see hidex86 parameter)
- """
- return (
- '' if (self.target_cpu == 'x86' and hidex86) else
- r'\x64' if (self.target_cpu == 'amd64' and x64) else
- r'\%s' % self.target_cpu
- )
-
- def cross_dir(self, forcex86=False):
- r"""
- Cross platform specific subfolder.
-
- Parameters
- ----------
- forcex86: bool
- Use 'x86' as current architecture even if current architecture is
- not x86.
-
- Return
- ------
- str
- subfolder: '' if target architecture is current architecture,
- '\current_target' if not.
- """
- current = 'x86' if forcex86 else self.current_cpu
- return (
- '' if self.target_cpu == current else
- self.target_dir().replace('\\', '\\%s_' % current)
- )
-
-
-class RegistryInfo:
- """
- Microsoft Visual Studio related registry information.
-
- Parameters
- ----------
- platform_info: PlatformInfo
- "PlatformInfo" instance.
- """
- HKEYS = (winreg.HKEY_USERS,
- winreg.HKEY_CURRENT_USER,
- winreg.HKEY_LOCAL_MACHINE,
- winreg.HKEY_CLASSES_ROOT)
-
- def __init__(self, platform_info):
- self.pi = platform_info
-
- @property
- def visualstudio(self):
- """
- Microsoft Visual Studio root registry key.
-
- Return
- ------
- str
- Registry key
- """
- return 'VisualStudio'
-
- @property
- def sxs(self):
- """
- Microsoft Visual Studio SxS registry key.
-
- Return
- ------
- str
- Registry key
- """
- return join(self.visualstudio, 'SxS')
-
- @property
- def vc(self):
- """
- Microsoft Visual C++ VC7 registry key.
-
- Return
- ------
- str
- Registry key
- """
- return join(self.sxs, 'VC7')
-
- @property
- def vs(self):
- """
- Microsoft Visual Studio VS7 registry key.
-
- Return
- ------
- str
- Registry key
- """
- return join(self.sxs, 'VS7')
-
- @property
- def vc_for_python(self):
- """
- Microsoft Visual C++ for Python registry key.
-
- Return
- ------
- str
- Registry key
- """
- return r'DevDiv\VCForPython'
-
- @property
- def microsoft_sdk(self):
- """
- Microsoft SDK registry key.
-
- Return
- ------
- str
- Registry key
- """
- return 'Microsoft SDKs'
-
- @property
- def windows_sdk(self):
- """
- Microsoft Windows/Platform SDK registry key.
-
- Return
- ------
- str
- Registry key
- """
- return join(self.microsoft_sdk, 'Windows')
-
- @property
- def netfx_sdk(self):
- """
- Microsoft .NET Framework SDK registry key.
-
- Return
- ------
- str
- Registry key
- """
- return join(self.microsoft_sdk, 'NETFXSDK')
-
- @property
- def windows_kits_roots(self):
- """
- Microsoft Windows Kits Roots registry key.
-
- Return
- ------
- str
- Registry key
- """
- return r'Windows Kits\Installed Roots'
-
- def microsoft(self, key, x86=False):
- """
- Return key in Microsoft software registry.
-
- Parameters
- ----------
- key: str
- Registry key path where look.
- x86: str
- Force x86 software registry.
-
- Return
- ------
- str
- Registry key
- """
- node64 = '' if self.pi.current_is_x86() or x86 else 'Wow6432Node'
- return join('Software', node64, 'Microsoft', key)
-
- def lookup(self, key, name):
- """
- Look for values in registry in Microsoft software registry.
-
- Parameters
- ----------
- key: str
- Registry key path where look.
- name: str
- Value name to find.
-
- Return
- ------
- str
- value
- """
- key_read = winreg.KEY_READ
- openkey = winreg.OpenKey
- closekey = winreg.CloseKey
- ms = self.microsoft
- for hkey in self.HKEYS:
- bkey = None
- try:
- bkey = openkey(hkey, ms(key), 0, key_read)
- except (OSError, IOError):
- if not self.pi.current_is_x86():
- try:
- bkey = openkey(hkey, ms(key, True), 0, key_read)
- except (OSError, IOError):
- continue
- else:
- continue
- try:
- return winreg.QueryValueEx(bkey, name)[0]
- except (OSError, IOError):
- pass
- finally:
- if bkey:
- closekey(bkey)
-
-
-class SystemInfo:
- """
- Microsoft Windows and Visual Studio related system information.
-
- Parameters
- ----------
- registry_info: RegistryInfo
- "RegistryInfo" instance.
- vc_ver: float
- Required Microsoft Visual C++ version.
- """
-
- # Variables and properties in this class use originals CamelCase variables
- # names from Microsoft source files for more easy comparison.
- WinDir = environ.get('WinDir', '')
- ProgramFiles = environ.get('ProgramFiles', '')
- ProgramFilesx86 = environ.get('ProgramFiles(x86)', ProgramFiles)
-
- def __init__(self, registry_info, vc_ver=None):
- self.ri = registry_info
- self.pi = self.ri.pi
-
- self.known_vs_paths = self.find_programdata_vs_vers()
-
- # Except for VS15+, VC version is aligned with VS version
- self.vs_ver = self.vc_ver = (
- vc_ver or self._find_latest_available_vs_ver())
-
- def _find_latest_available_vs_ver(self):
- """
- Find the latest VC version
-
- Return
- ------
- float
- version
- """
- reg_vc_vers = self.find_reg_vs_vers()
-
- if not (reg_vc_vers or self.known_vs_paths):
- raise distutils.errors.DistutilsPlatformError(
- 'No Microsoft Visual C++ version found')
-
- vc_vers = set(reg_vc_vers)
- vc_vers.update(self.known_vs_paths)
- return sorted(vc_vers)[-1]
-
- def find_reg_vs_vers(self):
- """
- Find Microsoft Visual Studio versions available in registry.
-
- Return
- ------
- list of float
- Versions
- """
- ms = self.ri.microsoft
- vckeys = (self.ri.vc, self.ri.vc_for_python, self.ri.vs)
- vs_vers = []
- for hkey, key in itertools.product(self.ri.HKEYS, vckeys):
- try:
- bkey = winreg.OpenKey(hkey, ms(key), 0, winreg.KEY_READ)
- except (OSError, IOError):
- continue
- with bkey:
- subkeys, values, _ = winreg.QueryInfoKey(bkey)
- for i in range(values):
- with contextlib.suppress(ValueError):
- ver = float(winreg.EnumValue(bkey, i)[0])
- if ver not in vs_vers:
- vs_vers.append(ver)
- for i in range(subkeys):
- with contextlib.suppress(ValueError):
- ver = float(winreg.EnumKey(bkey, i))
- if ver not in vs_vers:
- vs_vers.append(ver)
- return sorted(vs_vers)
-
- def find_programdata_vs_vers(self):
- r"""
- Find Visual studio 2017+ versions from information in
- "C:\ProgramData\Microsoft\VisualStudio\Packages\_Instances".
-
- Return
- ------
- dict
- float version as key, path as value.
- """
- vs_versions = {}
- instances_dir = \
- r'C:\ProgramData\Microsoft\VisualStudio\Packages\_Instances'
-
- try:
- hashed_names = listdir(instances_dir)
-
- except (OSError, IOError):
- # Directory not exists with all Visual Studio versions
- return vs_versions
-
- for name in hashed_names:
- try:
- # Get VS installation path from "state.json" file
- state_path = join(instances_dir, name, 'state.json')
- with open(state_path, 'rt', encoding='utf-8') as state_file:
- state = json.load(state_file)
- vs_path = state['installationPath']
-
- # Raises OSError if this VS installation does not contain VC
- listdir(join(vs_path, r'VC\Tools\MSVC'))
-
- # Store version and path
- vs_versions[self._as_float_version(
- state['installationVersion'])] = vs_path
-
- except (OSError, IOError, KeyError):
- # Skip if "state.json" file is missing or bad format
- continue
-
- return vs_versions
-
- @staticmethod
- def _as_float_version(version):
- """
- Return a string version as a simplified float version (major.minor)
-
- Parameters
- ----------
- version: str
- Version.
-
- Return
- ------
- float
- version
- """
- return float('.'.join(version.split('.')[:2]))
-
- @property
- def VSInstallDir(self):
- """
- Microsoft Visual Studio directory.
-
- Return
- ------
- str
- path
- """
- # Default path
- default = join(self.ProgramFilesx86,
- 'Microsoft Visual Studio %0.1f' % self.vs_ver)
-
- # Try to get path from registry, if fail use default path
- return self.ri.lookup(self.ri.vs, '%0.1f' % self.vs_ver) or default
-
- @property
- def VCInstallDir(self):
- """
- Microsoft Visual C++ directory.
-
- Return
- ------
- str
- path
- """
- path = self._guess_vc() or self._guess_vc_legacy()
-
- if not isdir(path):
- msg = 'Microsoft Visual C++ directory not found'
- raise distutils.errors.DistutilsPlatformError(msg)
-
- return path
-
- def _guess_vc(self):
- """
- Locate Visual C++ for VS2017+.
-
- Return
- ------
- str
- path
- """
- if self.vs_ver <= 14.0:
- return ''
-
- try:
- # First search in known VS paths
- vs_dir = self.known_vs_paths[self.vs_ver]
- except KeyError:
- # Else, search with path from registry
- vs_dir = self.VSInstallDir
-
- guess_vc = join(vs_dir, r'VC\Tools\MSVC')
-
- # Subdir with VC exact version as name
- try:
- # Update the VC version with real one instead of VS version
- vc_ver = listdir(guess_vc)[-1]
- self.vc_ver = self._as_float_version(vc_ver)
- return join(guess_vc, vc_ver)
- except (OSError, IOError, IndexError):
- return ''
-
- def _guess_vc_legacy(self):
- """
- Locate Visual C++ for versions prior to 2017.
-
- Return
- ------
- str
- path
- """
- default = join(self.ProgramFilesx86,
- r'Microsoft Visual Studio %0.1f\VC' % self.vs_ver)
-
- # Try to get "VC++ for Python" path from registry as default path
- reg_path = join(self.ri.vc_for_python, '%0.1f' % self.vs_ver)
- python_vc = self.ri.lookup(reg_path, 'installdir')
- default_vc = join(python_vc, 'VC') if python_vc else default
-
- # Try to get path from registry, if fail use default path
- return self.ri.lookup(self.ri.vc, '%0.1f' % self.vs_ver) or default_vc
-
- @property
- def WindowsSdkVersion(self):
- """
- Microsoft Windows SDK versions for specified MSVC++ version.
-
- Return
- ------
- tuple of str
- versions
- """
- if self.vs_ver <= 9.0:
- return '7.0', '6.1', '6.0a'
- elif self.vs_ver == 10.0:
- return '7.1', '7.0a'
- elif self.vs_ver == 11.0:
- return '8.0', '8.0a'
- elif self.vs_ver == 12.0:
- return '8.1', '8.1a'
- elif self.vs_ver >= 14.0:
- return '10.0', '8.1'
-
- @property
- def WindowsSdkLastVersion(self):
- """
- Microsoft Windows SDK last version.
-
- Return
- ------
- str
- version
- """
- return self._use_last_dir_name(join(self.WindowsSdkDir, 'lib'))
-
- @property # noqa: C901
- def WindowsSdkDir(self): # noqa: C901 # is too complex (12) # FIXME
- """
- Microsoft Windows SDK directory.
-
- Return
- ------
- str
- path
- """
- sdkdir = ''
- for ver in self.WindowsSdkVersion:
- # Try to get it from registry
- loc = join(self.ri.windows_sdk, 'v%s' % ver)
- sdkdir = self.ri.lookup(loc, 'installationfolder')
- if sdkdir:
- break
- if not sdkdir or not isdir(sdkdir):
- # Try to get "VC++ for Python" version from registry
- path = join(self.ri.vc_for_python, '%0.1f' % self.vc_ver)
- install_base = self.ri.lookup(path, 'installdir')
- if install_base:
- sdkdir = join(install_base, 'WinSDK')
- if not sdkdir or not isdir(sdkdir):
- # If fail, use default new path
- for ver in self.WindowsSdkVersion:
- intver = ver[:ver.rfind('.')]
- path = r'Microsoft SDKs\Windows Kits\%s' % intver
- d = join(self.ProgramFiles, path)
- if isdir(d):
- sdkdir = d
- if not sdkdir or not isdir(sdkdir):
- # If fail, use default old path
- for ver in self.WindowsSdkVersion:
- path = r'Microsoft SDKs\Windows\v%s' % ver
- d = join(self.ProgramFiles, path)
- if isdir(d):
- sdkdir = d
- if not sdkdir:
- # If fail, use Platform SDK
- sdkdir = join(self.VCInstallDir, 'PlatformSDK')
- return sdkdir
-
- @property
- def WindowsSDKExecutablePath(self):
- """
- Microsoft Windows SDK executable directory.
-
- Return
- ------
- str
- path
- """
- # Find WinSDK NetFx Tools registry dir name
- if self.vs_ver <= 11.0:
- netfxver = 35
- arch = ''
- else:
- netfxver = 40
- hidex86 = True if self.vs_ver <= 12.0 else False
- arch = self.pi.current_dir(x64=True, hidex86=hidex86)
- fx = 'WinSDK-NetFx%dTools%s' % (netfxver, arch.replace('\\', '-'))
-
- # list all possibles registry paths
- regpaths = []
- if self.vs_ver >= 14.0:
- for ver in self.NetFxSdkVersion:
- regpaths += [join(self.ri.netfx_sdk, ver, fx)]
-
- for ver in self.WindowsSdkVersion:
- regpaths += [join(self.ri.windows_sdk, 'v%sA' % ver, fx)]
-
- # Return installation folder from the more recent path
- for path in regpaths:
- execpath = self.ri.lookup(path, 'installationfolder')
- if execpath:
- return execpath
-
- @property
- def FSharpInstallDir(self):
- """
- Microsoft Visual F# directory.
-
- Return
- ------
- str
- path
- """
- path = join(self.ri.visualstudio, r'%0.1f\Setup\F#' % self.vs_ver)
- return self.ri.lookup(path, 'productdir') or ''
-
- @property
- def UniversalCRTSdkDir(self):
- """
- Microsoft Universal CRT SDK directory.
-
- Return
- ------
- str
- path
- """
- # Set Kit Roots versions for specified MSVC++ version
- vers = ('10', '81') if self.vs_ver >= 14.0 else ()
-
- # Find path of the more recent Kit
- for ver in vers:
- sdkdir = self.ri.lookup(self.ri.windows_kits_roots,
- 'kitsroot%s' % ver)
- if sdkdir:
- return sdkdir or ''
-
- @property
- def UniversalCRTSdkLastVersion(self):
- """
- Microsoft Universal C Runtime SDK last version.
-
- Return
- ------
- str
- version
- """
- return self._use_last_dir_name(join(self.UniversalCRTSdkDir, 'lib'))
-
- @property
- def NetFxSdkVersion(self):
- """
- Microsoft .NET Framework SDK versions.
-
- Return
- ------
- tuple of str
- versions
- """
- # Set FxSdk versions for specified VS version
- return (('4.7.2', '4.7.1', '4.7',
- '4.6.2', '4.6.1', '4.6',
- '4.5.2', '4.5.1', '4.5')
- if self.vs_ver >= 14.0 else ())
-
- @property
- def NetFxSdkDir(self):
- """
- Microsoft .NET Framework SDK directory.
-
- Return
- ------
- str
- path
- """
- sdkdir = ''
- for ver in self.NetFxSdkVersion:
- loc = join(self.ri.netfx_sdk, ver)
- sdkdir = self.ri.lookup(loc, 'kitsinstallationfolder')
- if sdkdir:
- break
- return sdkdir
-
- @property
- def FrameworkDir32(self):
- """
- Microsoft .NET Framework 32bit directory.
-
- Return
- ------
- str
- path
- """
- # Default path
- guess_fw = join(self.WinDir, r'Microsoft.NET\Framework')
-
- # Try to get path from registry, if fail use default path
- return self.ri.lookup(self.ri.vc, 'frameworkdir32') or guess_fw
-
- @property
- def FrameworkDir64(self):
- """
- Microsoft .NET Framework 64bit directory.
-
- Return
- ------
- str
- path
- """
- # Default path
- guess_fw = join(self.WinDir, r'Microsoft.NET\Framework64')
-
- # Try to get path from registry, if fail use default path
- return self.ri.lookup(self.ri.vc, 'frameworkdir64') or guess_fw
-
- @property
- def FrameworkVersion32(self):
- """
- Microsoft .NET Framework 32bit versions.
-
- Return
- ------
- tuple of str
- versions
- """
- return self._find_dot_net_versions(32)
-
- @property
- def FrameworkVersion64(self):
- """
- Microsoft .NET Framework 64bit versions.
-
- Return
- ------
- tuple of str
- versions
- """
- return self._find_dot_net_versions(64)
-
- def _find_dot_net_versions(self, bits):
- """
- Find Microsoft .NET Framework versions.
-
- Parameters
- ----------
- bits: int
- Platform number of bits: 32 or 64.
-
- Return
- ------
- tuple of str
- versions
- """
- # Find actual .NET version in registry
- reg_ver = self.ri.lookup(self.ri.vc, 'frameworkver%d' % bits)
- dot_net_dir = getattr(self, 'FrameworkDir%d' % bits)
- ver = reg_ver or self._use_last_dir_name(dot_net_dir, 'v') or ''
-
- # Set .NET versions for specified MSVC++ version
- if self.vs_ver >= 12.0:
- return ver, 'v4.0'
- elif self.vs_ver >= 10.0:
- return 'v4.0.30319' if ver.lower()[:2] != 'v4' else ver, 'v3.5'
- elif self.vs_ver == 9.0:
- return 'v3.5', 'v2.0.50727'
- elif self.vs_ver == 8.0:
- return 'v3.0', 'v2.0.50727'
-
- @staticmethod
- def _use_last_dir_name(path, prefix=''):
- """
- Return name of the last dir in path or '' if no dir found.
-
- Parameters
- ----------
- path: str
- Use dirs in this path
- prefix: str
- Use only dirs starting by this prefix
-
- Return
- ------
- str
- name
- """
- matching_dirs = (
- dir_name
- for dir_name in reversed(listdir(path))
- if isdir(join(path, dir_name)) and
- dir_name.startswith(prefix)
- )
- return next(matching_dirs, None) or ''
-
-
-class EnvironmentInfo:
- """
- Return environment variables for specified Microsoft Visual C++ version
- and platform : Lib, Include, Path and libpath.
-
- This function is compatible with Microsoft Visual C++ 9.0 to 14.X.
-
- Script created by analysing Microsoft environment configuration files like
- "vcvars[...].bat", "SetEnv.Cmd", "vcbuildtools.bat", ...
-
- Parameters
- ----------
- arch: str
- Target architecture.
- vc_ver: float
- Required Microsoft Visual C++ version. If not set, autodetect the last
- version.
- vc_min_ver: float
- Minimum Microsoft Visual C++ version.
- """
-
- # Variables and properties in this class use originals CamelCase variables
- # names from Microsoft source files for more easy comparison.
-
- def __init__(self, arch, vc_ver=None, vc_min_ver=0):
- self.pi = PlatformInfo(arch)
- self.ri = RegistryInfo(self.pi)
- self.si = SystemInfo(self.ri, vc_ver)
-
- if self.vc_ver < vc_min_ver:
- err = 'No suitable Microsoft Visual C++ version found'
- raise distutils.errors.DistutilsPlatformError(err)
-
- @property
- def vs_ver(self):
- """
- Microsoft Visual Studio.
-
- Return
- ------
- float
- version
- """
- return self.si.vs_ver
-
- @property
- def vc_ver(self):
- """
- Microsoft Visual C++ version.
-
- Return
- ------
- float
- version
- """
- return self.si.vc_ver
-
- @property
- def VSTools(self):
- """
- Microsoft Visual Studio Tools.
-
- Return
- ------
- list of str
- paths
- """
- paths = [r'Common7\IDE', r'Common7\Tools']
-
- if self.vs_ver >= 14.0:
- arch_subdir = self.pi.current_dir(hidex86=True, x64=True)
- paths += [r'Common7\IDE\CommonExtensions\Microsoft\TestWindow']
- paths += [r'Team Tools\Performance Tools']
- paths += [r'Team Tools\Performance Tools%s' % arch_subdir]
-
- return [join(self.si.VSInstallDir, path) for path in paths]
-
- @property
- def VCIncludes(self):
- """
- Microsoft Visual C++ & Microsoft Foundation Class Includes.
-
- Return
- ------
- list of str
- paths
- """
- return [join(self.si.VCInstallDir, 'Include'),
- join(self.si.VCInstallDir, r'ATLMFC\Include')]
-
- @property
- def VCLibraries(self):
- """
- Microsoft Visual C++ & Microsoft Foundation Class Libraries.
-
- Return
- ------
- list of str
- paths
- """
- if self.vs_ver >= 15.0:
- arch_subdir = self.pi.target_dir(x64=True)
- else:
- arch_subdir = self.pi.target_dir(hidex86=True)
- paths = ['Lib%s' % arch_subdir, r'ATLMFC\Lib%s' % arch_subdir]
-
- if self.vs_ver >= 14.0:
- paths += [r'Lib\store%s' % arch_subdir]
-
- return [join(self.si.VCInstallDir, path) for path in paths]
-
- @property
- def VCStoreRefs(self):
- """
- Microsoft Visual C++ store references Libraries.
-
- Return
- ------
- list of str
- paths
- """
- if self.vs_ver < 14.0:
- return []
- return [join(self.si.VCInstallDir, r'Lib\store\references')]
-
- @property
- def VCTools(self):
- """
- Microsoft Visual C++ Tools.
-
- Return
- ------
- list of str
- paths
- """
- si = self.si
- tools = [join(si.VCInstallDir, 'VCPackages')]
-
- forcex86 = True if self.vs_ver <= 10.0 else False
- arch_subdir = self.pi.cross_dir(forcex86)
- if arch_subdir:
- tools += [join(si.VCInstallDir, 'Bin%s' % arch_subdir)]
-
- if self.vs_ver == 14.0:
- path = 'Bin%s' % self.pi.current_dir(hidex86=True)
- tools += [join(si.VCInstallDir, path)]
-
- elif self.vs_ver >= 15.0:
- host_dir = (r'bin\HostX86%s' if self.pi.current_is_x86() else
- r'bin\HostX64%s')
- tools += [join(
- si.VCInstallDir, host_dir % self.pi.target_dir(x64=True))]
-
- if self.pi.current_cpu != self.pi.target_cpu:
- tools += [join(
- si.VCInstallDir, host_dir % self.pi.current_dir(x64=True))]
-
- else:
- tools += [join(si.VCInstallDir, 'Bin')]
-
- return tools
-
- @property
- def OSLibraries(self):
- """
- Microsoft Windows SDK Libraries.
-
- Return
- ------
- list of str
- paths
- """
- if self.vs_ver <= 10.0:
- arch_subdir = self.pi.target_dir(hidex86=True, x64=True)
- return [join(self.si.WindowsSdkDir, 'Lib%s' % arch_subdir)]
-
- else:
- arch_subdir = self.pi.target_dir(x64=True)
- lib = join(self.si.WindowsSdkDir, 'lib')
- libver = self._sdk_subdir
- return [join(lib, '%sum%s' % (libver, arch_subdir))]
-
- @property
- def OSIncludes(self):
- """
- Microsoft Windows SDK Include.
-
- Return
- ------
- list of str
- paths
- """
- include = join(self.si.WindowsSdkDir, 'include')
-
- if self.vs_ver <= 10.0:
- return [include, join(include, 'gl')]
-
- else:
- if self.vs_ver >= 14.0:
- sdkver = self._sdk_subdir
- else:
- sdkver = ''
- return [join(include, '%sshared' % sdkver),
- join(include, '%sum' % sdkver),
- join(include, '%swinrt' % sdkver)]
-
- @property
- def OSLibpath(self):
- """
- Microsoft Windows SDK Libraries Paths.
-
- Return
- ------
- list of str
- paths
- """
- ref = join(self.si.WindowsSdkDir, 'References')
- libpath = []
-
- if self.vs_ver <= 9.0:
- libpath += self.OSLibraries
-
- if self.vs_ver >= 11.0:
- libpath += [join(ref, r'CommonConfiguration\Neutral')]
-
- if self.vs_ver >= 14.0:
- libpath += [
- ref,
- join(self.si.WindowsSdkDir, 'UnionMetadata'),
- join(
- ref, 'Windows.Foundation.UniversalApiContract', '1.0.0.0'),
- join(ref, 'Windows.Foundation.FoundationContract', '1.0.0.0'),
- join(
- ref, 'Windows.Networking.Connectivity.WwanContract',
- '1.0.0.0'),
- join(
- self.si.WindowsSdkDir, 'ExtensionSDKs', 'Microsoft.VCLibs',
- '%0.1f' % self.vs_ver, 'References', 'CommonConfiguration',
- 'neutral'),
- ]
- return libpath
-
- @property
- def SdkTools(self):
- """
- Microsoft Windows SDK Tools.
-
- Return
- ------
- list of str
- paths
- """
- return list(self._sdk_tools())
-
- def _sdk_tools(self):
- """
- Microsoft Windows SDK Tools paths generator.
-
- Return
- ------
- generator of str
- paths
- """
- if self.vs_ver < 15.0:
- bin_dir = 'Bin' if self.vs_ver <= 11.0 else r'Bin\x86'
- yield join(self.si.WindowsSdkDir, bin_dir)
-
- if not self.pi.current_is_x86():
- arch_subdir = self.pi.current_dir(x64=True)
- path = 'Bin%s' % arch_subdir
- yield join(self.si.WindowsSdkDir, path)
-
- if self.vs_ver in (10.0, 11.0):
- if self.pi.target_is_x86():
- arch_subdir = ''
- else:
- arch_subdir = self.pi.current_dir(hidex86=True, x64=True)
- path = r'Bin\NETFX 4.0 Tools%s' % arch_subdir
- yield join(self.si.WindowsSdkDir, path)
-
- elif self.vs_ver >= 15.0:
- path = join(self.si.WindowsSdkDir, 'Bin')
- arch_subdir = self.pi.current_dir(x64=True)
- sdkver = self.si.WindowsSdkLastVersion
- yield join(path, '%s%s' % (sdkver, arch_subdir))
-
- if self.si.WindowsSDKExecutablePath:
- yield self.si.WindowsSDKExecutablePath
-
- @property
- def _sdk_subdir(self):
- """
- Microsoft Windows SDK version subdir.
-
- Return
- ------
- str
- subdir
- """
- ucrtver = self.si.WindowsSdkLastVersion
- return ('%s\\' % ucrtver) if ucrtver else ''
-
- @property
- def SdkSetup(self):
- """
- Microsoft Windows SDK Setup.
-
- Return
- ------
- list of str
- paths
- """
- if self.vs_ver > 9.0:
- return []
-
- return [join(self.si.WindowsSdkDir, 'Setup')]
-
- @property
- def FxTools(self):
- """
- Microsoft .NET Framework Tools.
-
- Return
- ------
- list of str
- paths
- """
- pi = self.pi
- si = self.si
-
- if self.vs_ver <= 10.0:
- include32 = True
- include64 = not pi.target_is_x86() and not pi.current_is_x86()
- else:
- include32 = pi.target_is_x86() or pi.current_is_x86()
- include64 = pi.current_cpu == 'amd64' or pi.target_cpu == 'amd64'
-
- tools = []
- if include32:
- tools += [join(si.FrameworkDir32, ver)
- for ver in si.FrameworkVersion32]
- if include64:
- tools += [join(si.FrameworkDir64, ver)
- for ver in si.FrameworkVersion64]
- return tools
-
- @property
- def NetFxSDKLibraries(self):
- """
- Microsoft .Net Framework SDK Libraries.
-
- Return
- ------
- list of str
- paths
- """
- if self.vs_ver < 14.0 or not self.si.NetFxSdkDir:
- return []
-
- arch_subdir = self.pi.target_dir(x64=True)
- return [join(self.si.NetFxSdkDir, r'lib\um%s' % arch_subdir)]
-
- @property
- def NetFxSDKIncludes(self):
- """
- Microsoft .Net Framework SDK Includes.
-
- Return
- ------
- list of str
- paths
- """
- if self.vs_ver < 14.0 or not self.si.NetFxSdkDir:
- return []
-
- return [join(self.si.NetFxSdkDir, r'include\um')]
-
- @property
- def VsTDb(self):
- """
- Microsoft Visual Studio Team System Database.
-
- Return
- ------
- list of str
- paths
- """
- return [join(self.si.VSInstallDir, r'VSTSDB\Deploy')]
-
- @property
- def MSBuild(self):
- """
- Microsoft Build Engine.
-
- Return
- ------
- list of str
- paths
- """
- if self.vs_ver < 12.0:
- return []
- elif self.vs_ver < 15.0:
- base_path = self.si.ProgramFilesx86
- arch_subdir = self.pi.current_dir(hidex86=True)
- else:
- base_path = self.si.VSInstallDir
- arch_subdir = ''
-
- path = r'MSBuild\%0.1f\bin%s' % (self.vs_ver, arch_subdir)
- build = [join(base_path, path)]
-
- if self.vs_ver >= 15.0:
- # Add Roslyn C# & Visual Basic Compiler
- build += [join(base_path, path, 'Roslyn')]
-
- return build
-
- @property
- def HTMLHelpWorkshop(self):
- """
- Microsoft HTML Help Workshop.
-
- Return
- ------
- list of str
- paths
- """
- if self.vs_ver < 11.0:
- return []
-
- return [join(self.si.ProgramFilesx86, 'HTML Help Workshop')]
-
- @property
- def UCRTLibraries(self):
- """
- Microsoft Universal C Runtime SDK Libraries.
-
- Return
- ------
- list of str
- paths
- """
- if self.vs_ver < 14.0:
- return []
-
- arch_subdir = self.pi.target_dir(x64=True)
- lib = join(self.si.UniversalCRTSdkDir, 'lib')
- ucrtver = self._ucrt_subdir
- return [join(lib, '%sucrt%s' % (ucrtver, arch_subdir))]
-
- @property
- def UCRTIncludes(self):
- """
- Microsoft Universal C Runtime SDK Include.
-
- Return
- ------
- list of str
- paths
- """
- if self.vs_ver < 14.0:
- return []
-
- include = join(self.si.UniversalCRTSdkDir, 'include')
- return [join(include, '%sucrt' % self._ucrt_subdir)]
-
- @property
- def _ucrt_subdir(self):
- """
- Microsoft Universal C Runtime SDK version subdir.
-
- Return
- ------
- str
- subdir
- """
- ucrtver = self.si.UniversalCRTSdkLastVersion
- return ('%s\\' % ucrtver) if ucrtver else ''
-
- @property
- def FSharp(self):
- """
- Microsoft Visual F#.
-
- Return
- ------
- list of str
- paths
- """
- if 11.0 > self.vs_ver > 12.0:
- return []
-
- return [self.si.FSharpInstallDir]
-
- @property
- def VCRuntimeRedist(self):
- """
- Microsoft Visual C++ runtime redistributable dll.
-
- Return
- ------
- str
- path
- """
- vcruntime = 'vcruntime%d0.dll' % self.vc_ver
- arch_subdir = self.pi.target_dir(x64=True).strip('\\')
-
- # Installation prefixes candidates
- prefixes = []
- tools_path = self.si.VCInstallDir
- redist_path = dirname(tools_path.replace(r'\Tools', r'\Redist'))
- if isdir(redist_path):
- # Redist version may not be exactly the same as tools
- redist_path = join(redist_path, listdir(redist_path)[-1])
- prefixes += [redist_path, join(redist_path, 'onecore')]
-
- prefixes += [join(tools_path, 'redist')] # VS14 legacy path
-
- # CRT directory
- crt_dirs = ('Microsoft.VC%d.CRT' % (self.vc_ver * 10),
- # Sometime store in directory with VS version instead of VC
- 'Microsoft.VC%d.CRT' % (int(self.vs_ver) * 10))
-
- # vcruntime path
- for prefix, crt_dir in itertools.product(prefixes, crt_dirs):
- path = join(prefix, arch_subdir, crt_dir, vcruntime)
- if isfile(path):
- return path
-
- def return_env(self, exists=True):
- """
- Return environment dict.
-
- Parameters
- ----------
- exists: bool
- It True, only return existing paths.
-
- Return
- ------
- dict
- environment
- """
- env = dict(
- include=self._build_paths('include',
- [self.VCIncludes,
- self.OSIncludes,
- self.UCRTIncludes,
- self.NetFxSDKIncludes],
- exists),
- lib=self._build_paths('lib',
- [self.VCLibraries,
- self.OSLibraries,
- self.FxTools,
- self.UCRTLibraries,
- self.NetFxSDKLibraries],
- exists),
- libpath=self._build_paths('libpath',
- [self.VCLibraries,
- self.FxTools,
- self.VCStoreRefs,
- self.OSLibpath],
- exists),
- path=self._build_paths('path',
- [self.VCTools,
- self.VSTools,
- self.VsTDb,
- self.SdkTools,
- self.SdkSetup,
- self.FxTools,
- self.MSBuild,
- self.HTMLHelpWorkshop,
- self.FSharp],
- exists),
- )
- if self.vs_ver >= 14 and isfile(self.VCRuntimeRedist):
- env['py_vcruntime_redist'] = self.VCRuntimeRedist
- return env
-
- def _build_paths(self, name, spec_path_lists, exists):
- """
- Given an environment variable name and specified paths,
- return a pathsep-separated string of paths containing
- unique, extant, directories from those paths and from
- the environment variable. Raise an error if no paths
- are resolved.
-
- Parameters
- ----------
- name: str
- Environment variable name
- spec_path_lists: list of str
- Paths
- exists: bool
- It True, only return existing paths.
-
- Return
- ------
- str
- Pathsep-separated paths
- """
- # flatten spec_path_lists
- spec_paths = itertools.chain.from_iterable(spec_path_lists)
- env_paths = environ.get(name, '').split(pathsep)
- paths = itertools.chain(spec_paths, env_paths)
- extant_paths = list(filter(isdir, paths)) if exists else paths
- if not extant_paths:
- msg = "%s environment variable is empty" % name.upper()
- raise distutils.errors.DistutilsPlatformError(msg)
- unique_paths = unique_everseen(extant_paths)
- return pathsep.join(unique_paths)
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/train_net.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/train_net.py
deleted file mode 100644
index 4457e9f7375d8c603d85158774950dc5fe56b6f5..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/train_net.py
+++ /dev/null
@@ -1,117 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-"""
-DensePose Training Script.
-
-This script is similar to the training script in detectron2/tools.
-
-It is an example of how a user might use detectron2 for a new project.
-"""
-
-import logging
-import os
-from collections import OrderedDict
-
-import detectron2.utils.comm as comm
-from detectron2.checkpoint import DetectionCheckpointer
-from detectron2.config import CfgNode, get_cfg
-from detectron2.data import build_detection_test_loader, build_detection_train_loader
-from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, hooks, launch
-from detectron2.evaluation import COCOEvaluator, DatasetEvaluators, verify_results
-from detectron2.modeling import DatasetMapperTTA
-from detectron2.utils.logger import setup_logger
-
-from densepose import (
- DatasetMapper,
- DensePoseCOCOEvaluator,
- DensePoseGeneralizedRCNNWithTTA,
- add_densepose_config,
- load_from_cfg,
-)
-
-
-class Trainer(DefaultTrainer):
- @classmethod
- def build_evaluator(cls, cfg: CfgNode, dataset_name, output_folder=None):
- if output_folder is None:
- output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
- evaluators = [COCOEvaluator(dataset_name, cfg, True, output_folder)]
- if cfg.MODEL.DENSEPOSE_ON:
- evaluators.append(DensePoseCOCOEvaluator(dataset_name, True, output_folder))
- return DatasetEvaluators(evaluators)
-
- @classmethod
- def build_test_loader(cls, cfg: CfgNode, dataset_name):
- return build_detection_test_loader(cfg, dataset_name, mapper=DatasetMapper(cfg, False))
-
- @classmethod
- def build_train_loader(cls, cfg: CfgNode):
- return build_detection_train_loader(cfg, mapper=DatasetMapper(cfg, True))
-
- @classmethod
- def test_with_TTA(cls, cfg: CfgNode, model):
- logger = logging.getLogger("detectron2.trainer")
- # In the end of training, run an evaluation with TTA
- # Only support some R-CNN models.
- logger.info("Running inference with test-time augmentation ...")
- transform_data = load_from_cfg(cfg)
- model = DensePoseGeneralizedRCNNWithTTA(cfg, model, transform_data, DatasetMapperTTA(cfg))
- evaluators = [
- cls.build_evaluator(
- cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, "inference_TTA")
- )
- for name in cfg.DATASETS.TEST
- ]
- res = cls.test(cfg, model, evaluators)
- res = OrderedDict({k + "_TTA": v for k, v in res.items()})
- return res
-
-
-def setup(args):
- cfg = get_cfg()
- add_densepose_config(cfg)
- cfg.merge_from_file(args.config_file)
- cfg.merge_from_list(args.opts)
- cfg.freeze()
- default_setup(cfg, args)
- # Setup logger for "densepose" module
- setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name="densepose")
- return cfg
-
-
-def main(args):
- cfg = setup(args)
-
- if args.eval_only:
- model = Trainer.build_model(cfg)
- DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
- cfg.MODEL.WEIGHTS, resume=args.resume
- )
- res = Trainer.test(cfg, model)
- if cfg.TEST.AUG.ENABLED:
- res.update(Trainer.test_with_TTA(cfg, model))
- if comm.is_main_process():
- verify_results(cfg, res)
- return res
-
- trainer = Trainer(cfg)
- trainer.resume_or_load(resume=args.resume)
- if cfg.TEST.AUG.ENABLED:
- trainer.register_hooks(
- [hooks.EvalHook(0, lambda: trainer.test_with_TTA(cfg, trainer.model))]
- )
- return trainer.train()
-
-
-if __name__ == "__main__":
- args = default_argument_parser().parse_args()
- print("Command Line Args:", args)
- launch(
- main,
- args.num_gpus,
- num_machines=args.num_machines,
- machine_rank=args.machine_rank,
- dist_url=args.dist_url,
- args=(args,),
- )
diff --git a/spaces/CVPR/GFPGAN-example/gfpgan/archs/arcface_arch.py b/spaces/CVPR/GFPGAN-example/gfpgan/archs/arcface_arch.py
deleted file mode 100644
index e6d3bd97f83334450bd78ad2c3b9871102a56b70..0000000000000000000000000000000000000000
--- a/spaces/CVPR/GFPGAN-example/gfpgan/archs/arcface_arch.py
+++ /dev/null
@@ -1,245 +0,0 @@
-import torch.nn as nn
-from basicsr.utils.registry import ARCH_REGISTRY
-
-
-def conv3x3(inplanes, outplanes, stride=1):
- """A simple wrapper for 3x3 convolution with padding.
-
- Args:
- inplanes (int): Channel number of inputs.
- outplanes (int): Channel number of outputs.
- stride (int): Stride in convolution. Default: 1.
- """
- return nn.Conv2d(inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False)
-
-
-class BasicBlock(nn.Module):
- """Basic residual block used in the ResNetArcFace architecture.
-
- Args:
- inplanes (int): Channel number of inputs.
- planes (int): Channel number of outputs.
- stride (int): Stride in convolution. Default: 1.
- downsample (nn.Module): The downsample module. Default: None.
- """
- expansion = 1 # output channel expansion ratio
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(BasicBlock, self).__init__()
- self.conv1 = conv3x3(inplanes, planes, stride)
- self.bn1 = nn.BatchNorm2d(planes)
- self.relu = nn.ReLU(inplace=True)
- self.conv2 = conv3x3(planes, planes)
- self.bn2 = nn.BatchNorm2d(planes)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
-class IRBlock(nn.Module):
- """Improved residual block (IR Block) used in the ResNetArcFace architecture.
-
- Args:
- inplanes (int): Channel number of inputs.
- planes (int): Channel number of outputs.
- stride (int): Stride in convolution. Default: 1.
- downsample (nn.Module): The downsample module. Default: None.
- use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True.
- """
- expansion = 1 # output channel expansion ratio
-
- def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
- super(IRBlock, self).__init__()
- self.bn0 = nn.BatchNorm2d(inplanes)
- self.conv1 = conv3x3(inplanes, inplanes)
- self.bn1 = nn.BatchNorm2d(inplanes)
- self.prelu = nn.PReLU()
- self.conv2 = conv3x3(inplanes, planes, stride)
- self.bn2 = nn.BatchNorm2d(planes)
- self.downsample = downsample
- self.stride = stride
- self.use_se = use_se
- if self.use_se:
- self.se = SEBlock(planes)
-
- def forward(self, x):
- residual = x
- out = self.bn0(x)
- out = self.conv1(out)
- out = self.bn1(out)
- out = self.prelu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
- if self.use_se:
- out = self.se(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.prelu(out)
-
- return out
-
-
-class Bottleneck(nn.Module):
- """Bottleneck block used in the ResNetArcFace architecture.
-
- Args:
- inplanes (int): Channel number of inputs.
- planes (int): Channel number of outputs.
- stride (int): Stride in convolution. Default: 1.
- downsample (nn.Module): The downsample module. Default: None.
- """
- expansion = 4 # output channel expansion ratio
-
- def __init__(self, inplanes, planes, stride=1, downsample=None):
- super(Bottleneck, self).__init__()
- self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
- self.bn1 = nn.BatchNorm2d(planes)
- self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes)
- self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
- self.bn3 = nn.BatchNorm2d(planes * self.expansion)
- self.relu = nn.ReLU(inplace=True)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
- out = self.relu(out)
-
- out = self.conv3(out)
- out = self.bn3(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
-class SEBlock(nn.Module):
- """The squeeze-and-excitation block (SEBlock) used in the IRBlock.
-
- Args:
- channel (int): Channel number of inputs.
- reduction (int): Channel reduction ration. Default: 16.
- """
-
- def __init__(self, channel, reduction=16):
- super(SEBlock, self).__init__()
- self.avg_pool = nn.AdaptiveAvgPool2d(1) # pool to 1x1 without spatial information
- self.fc = nn.Sequential(
- nn.Linear(channel, channel // reduction), nn.PReLU(), nn.Linear(channel // reduction, channel),
- nn.Sigmoid())
-
- def forward(self, x):
- b, c, _, _ = x.size()
- y = self.avg_pool(x).view(b, c)
- y = self.fc(y).view(b, c, 1, 1)
- return x * y
-
-
-@ARCH_REGISTRY.register()
-class ResNetArcFace(nn.Module):
- """ArcFace with ResNet architectures.
-
- Ref: ArcFace: Additive Angular Margin Loss for Deep Face Recognition.
-
- Args:
- block (str): Block used in the ArcFace architecture.
- layers (tuple(int)): Block numbers in each layer.
- use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True.
- """
-
- def __init__(self, block, layers, use_se=True):
- if block == 'IRBlock':
- block = IRBlock
- self.inplanes = 64
- self.use_se = use_se
- super(ResNetArcFace, self).__init__()
-
- self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False)
- self.bn1 = nn.BatchNorm2d(64)
- self.prelu = nn.PReLU()
- self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
- self.layer1 = self._make_layer(block, 64, layers[0])
- self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
- self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
- self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
- self.bn4 = nn.BatchNorm2d(512)
- self.dropout = nn.Dropout()
- self.fc5 = nn.Linear(512 * 8 * 8, 512)
- self.bn5 = nn.BatchNorm1d(512)
-
- # initialization
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- nn.init.xavier_normal_(m.weight)
- elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
- nn.init.constant_(m.weight, 1)
- nn.init.constant_(m.bias, 0)
- elif isinstance(m, nn.Linear):
- nn.init.xavier_normal_(m.weight)
- nn.init.constant_(m.bias, 0)
-
- def _make_layer(self, block, planes, num_blocks, stride=1):
- downsample = None
- if stride != 1 or self.inplanes != planes * block.expansion:
- downsample = nn.Sequential(
- nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
- nn.BatchNorm2d(planes * block.expansion),
- )
- layers = []
- layers.append(block(self.inplanes, planes, stride, downsample, use_se=self.use_se))
- self.inplanes = planes
- for _ in range(1, num_blocks):
- layers.append(block(self.inplanes, planes, use_se=self.use_se))
-
- return nn.Sequential(*layers)
-
- def forward(self, x):
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.prelu(x)
- x = self.maxpool(x)
-
- x = self.layer1(x)
- x = self.layer2(x)
- x = self.layer3(x)
- x = self.layer4(x)
- x = self.bn4(x)
- x = self.dropout(x)
- x = x.view(x.size(0), -1)
- x = self.fc5(x)
- x = self.bn5(x)
-
- return x
diff --git a/spaces/CVPR/LIVE/thrust/cmake/ThrustUtilities.cmake b/spaces/CVPR/LIVE/thrust/cmake/ThrustUtilities.cmake
deleted file mode 100644
index e8fa9be1046554c5b4ed9309edf472dc3d023f4c..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/cmake/ThrustUtilities.cmake
+++ /dev/null
@@ -1,25 +0,0 @@
-# Given a cu_file (e.g. foo/bar.cu) relative to CMAKE_CURRENT_SOURCE_DIR
-# and a thrust_target, create a cpp file that includes the .cu file, and set
-# ${cpp_file_var} in the parent scope to the full path of the new file. The new
-# file will be generated in:
-# ${CMAKE_CURRENT_BINARY_DIR}//${cu_file}.cpp
-function(thrust_wrap_cu_in_cpp cpp_file_var cu_file thrust_target)
- thrust_get_target_property(prefix ${thrust_target} PREFIX)
- set(wrapped_source_file "${CMAKE_CURRENT_SOURCE_DIR}/${cu_file}")
- set(cpp_file "${CMAKE_CURRENT_BINARY_DIR}/${prefix}/${cu_file}.cpp")
- configure_file("${Thrust_SOURCE_DIR}/cmake/wrap_source_file.cpp.in" "${cpp_file}")
- set(${cpp_file_var} "${cpp_file}" PARENT_SCOPE)
-endfunction()
-
-# Enable RDC for a CUDA target. Encapsulates compiler hacks:
-function(thrust_enable_rdc_for_cuda_target target_name)
- if ("Feta" STREQUAL "${CMAKE_CUDA_COMPILER_ID}")
- set_target_properties(${target_name} PROPERTIES
- COMPILE_FLAGS "-gpu=rdc"
- )
- else()
- set_target_properties(${target_name} PROPERTIES
- CUDA_SEPARABLE_COMPILATION ON
- )
- endif()
-endfunction()
diff --git a/spaces/CVPR/WALT/mmdet/core/bbox/demodata.py b/spaces/CVPR/WALT/mmdet/core/bbox/demodata.py
deleted file mode 100644
index feecb693745a47d9f2bebd8af9a217ff4f5cc92b..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/core/bbox/demodata.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import numpy as np
-import torch
-
-from mmdet.utils.util_random import ensure_rng
-
-
-def random_boxes(num=1, scale=1, rng=None):
- """Simple version of ``kwimage.Boxes.random``
-
- Returns:
- Tensor: shape (n, 4) in x1, y1, x2, y2 format.
-
- References:
- https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390
-
- Example:
- >>> num = 3
- >>> scale = 512
- >>> rng = 0
- >>> boxes = random_boxes(num, scale, rng)
- >>> print(boxes)
- tensor([[280.9925, 278.9802, 308.6148, 366.1769],
- [216.9113, 330.6978, 224.0446, 456.5878],
- [405.3632, 196.3221, 493.3953, 270.7942]])
- """
- rng = ensure_rng(rng)
-
- tlbr = rng.rand(num, 4).astype(np.float32)
-
- tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2])
- tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3])
- br_x = np.maximum(tlbr[:, 0], tlbr[:, 2])
- br_y = np.maximum(tlbr[:, 1], tlbr[:, 3])
-
- tlbr[:, 0] = tl_x * scale
- tlbr[:, 1] = tl_y * scale
- tlbr[:, 2] = br_x * scale
- tlbr[:, 3] = br_y * scale
-
- boxes = torch.from_numpy(tlbr)
- return boxes
diff --git a/spaces/CVPR/WALT/mmdet/models/necks/pafpn.py b/spaces/CVPR/WALT/mmdet/models/necks/pafpn.py
deleted file mode 100644
index d7c0b50f29e882aacb5158b33ead3d4566d0ce0b..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/models/necks/pafpn.py
+++ /dev/null
@@ -1,142 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule
-from mmcv.runner import auto_fp16
-
-from ..builder import NECKS
-from .fpn import FPN
-
-
-@NECKS.register_module()
-class PAFPN(FPN):
- """Path Aggregation Network for Instance Segmentation.
-
- This is an implementation of the `PAFPN in Path Aggregation Network
- `_.
-
- Args:
- in_channels (List[int]): Number of input channels per scale.
- out_channels (int): Number of output channels (used at each scale)
- num_outs (int): Number of output scales.
- start_level (int): Index of the start input backbone level used to
- build the feature pyramid. Default: 0.
- end_level (int): Index of the end input backbone level (exclusive) to
- build the feature pyramid. Default: -1, which means the last level.
- add_extra_convs (bool): Whether to add conv layers on top of the
- original feature maps. Default: False.
- extra_convs_on_inputs (bool): Whether to apply extra conv on
- the original feature from the backbone. Default: False.
- relu_before_extra_convs (bool): Whether to apply relu before the extra
- conv. Default: False.
- no_norm_on_lateral (bool): Whether to apply norm on lateral.
- Default: False.
- conv_cfg (dict): Config dict for convolution layer. Default: None.
- norm_cfg (dict): Config dict for normalization layer. Default: None.
- act_cfg (str): Config dict for activation layer in ConvModule.
- Default: None.
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- num_outs,
- start_level=0,
- end_level=-1,
- add_extra_convs=False,
- extra_convs_on_inputs=True,
- relu_before_extra_convs=False,
- no_norm_on_lateral=False,
- conv_cfg=None,
- norm_cfg=None,
- act_cfg=None):
- super(PAFPN,
- self).__init__(in_channels, out_channels, num_outs, start_level,
- end_level, add_extra_convs, extra_convs_on_inputs,
- relu_before_extra_convs, no_norm_on_lateral,
- conv_cfg, norm_cfg, act_cfg)
- # add extra bottom up pathway
- self.downsample_convs = nn.ModuleList()
- self.pafpn_convs = nn.ModuleList()
- for i in range(self.start_level + 1, self.backbone_end_level):
- d_conv = ConvModule(
- out_channels,
- out_channels,
- 3,
- stride=2,
- padding=1,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg,
- inplace=False)
- pafpn_conv = ConvModule(
- out_channels,
- out_channels,
- 3,
- padding=1,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg,
- act_cfg=act_cfg,
- inplace=False)
- self.downsample_convs.append(d_conv)
- self.pafpn_convs.append(pafpn_conv)
-
- @auto_fp16()
- def forward(self, inputs):
- """Forward function."""
- assert len(inputs) == len(self.in_channels)
-
- # build laterals
- laterals = [
- lateral_conv(inputs[i + self.start_level])
- for i, lateral_conv in enumerate(self.lateral_convs)
- ]
-
- # build top-down path
- used_backbone_levels = len(laterals)
- for i in range(used_backbone_levels - 1, 0, -1):
- prev_shape = laterals[i - 1].shape[2:]
- laterals[i - 1] += F.interpolate(
- laterals[i], size=prev_shape, mode='nearest')
-
- # build outputs
- # part 1: from original levels
- inter_outs = [
- self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels)
- ]
-
- # part 2: add bottom-up path
- for i in range(0, used_backbone_levels - 1):
- inter_outs[i + 1] += self.downsample_convs[i](inter_outs[i])
-
- outs = []
- outs.append(inter_outs[0])
- outs.extend([
- self.pafpn_convs[i - 1](inter_outs[i])
- for i in range(1, used_backbone_levels)
- ])
-
- # part 3: add extra levels
- if self.num_outs > len(outs):
- # use max pool to get more levels on top of outputs
- # (e.g., Faster R-CNN, Mask R-CNN)
- if not self.add_extra_convs:
- for i in range(self.num_outs - used_backbone_levels):
- outs.append(F.max_pool2d(outs[-1], 1, stride=2))
- # add conv layers on top of original feature maps (RetinaNet)
- else:
- if self.add_extra_convs == 'on_input':
- orig = inputs[self.backbone_end_level - 1]
- outs.append(self.fpn_convs[used_backbone_levels](orig))
- elif self.add_extra_convs == 'on_lateral':
- outs.append(self.fpn_convs[used_backbone_levels](
- laterals[-1]))
- elif self.add_extra_convs == 'on_output':
- outs.append(self.fpn_convs[used_backbone_levels](outs[-1]))
- else:
- raise NotImplementedError
- for i in range(used_backbone_levels + 1, self.num_outs):
- if self.relu_before_extra_convs:
- outs.append(self.fpn_convs[i](F.relu(outs[-1])))
- else:
- outs.append(self.fpn_convs[i](outs[-1]))
- return tuple(outs)
diff --git a/spaces/Cyril666/my_abi/callbacks.py b/spaces/Cyril666/my_abi/callbacks.py
deleted file mode 100644
index 82fb9e34da2a819ce849857c304bb3cd23973e81..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/my_abi/callbacks.py
+++ /dev/null
@@ -1,360 +0,0 @@
-import logging
-import shutil
-import time
-
-import editdistance as ed
-import torchvision.utils as vutils
-from fastai.callbacks.tensorboard import (LearnerTensorboardWriter,
- SummaryWriter, TBWriteRequest,
- asyncTBWriter)
-from fastai.vision import *
-from torch.nn.parallel import DistributedDataParallel
-from torchvision import transforms
-
-import dataset
-from utils import CharsetMapper, Timer, blend_mask
-
-
-class IterationCallback(LearnerTensorboardWriter):
- "A `TrackerCallback` that monitor in each iteration."
- def __init__(self, learn:Learner, name:str='model', checpoint_keep_num=5,
- show_iters:int=50, eval_iters:int=1000, save_iters:int=20000,
- start_iters:int=0, stats_iters=20000):
- #if self.learn.rank is not None: time.sleep(self.learn.rank) # keep all event files
- super().__init__(learn, base_dir='.', name=learn.path, loss_iters=show_iters,
- stats_iters=stats_iters, hist_iters=stats_iters)
- self.name, self.bestname = Path(name).name, f'best-{Path(name).name}'
- self.show_iters = show_iters
- self.eval_iters = eval_iters
- self.save_iters = save_iters
- self.start_iters = start_iters
- self.checpoint_keep_num = checpoint_keep_num
- self.metrics_root = 'metrics/' # rewrite
- self.timer = Timer()
- self.host = self.learn.rank is None or self.learn.rank == 0
-
- def _write_metrics(self, iteration:int, names:List[str], last_metrics:MetricsList)->None:
- "Writes training metrics to Tensorboard."
- for i, name in enumerate(names):
- if last_metrics is None or len(last_metrics) < i+1: return
- scalar_value = last_metrics[i]
- self._write_scalar(name=name, scalar_value=scalar_value, iteration=iteration)
-
- def _write_sub_loss(self, iteration:int, last_losses:dict)->None:
- "Writes sub loss to Tensorboard."
- for name, loss in last_losses.items():
- scalar_value = to_np(loss)
- tag = self.metrics_root + name
- self.tbwriter.add_scalar(tag=tag, scalar_value=scalar_value, global_step=iteration)
-
- def _save(self, name):
- if isinstance(self.learn.model, DistributedDataParallel):
- tmp = self.learn.model
- self.learn.model = self.learn.model.module
- self.learn.save(name)
- self.learn.model = tmp
- else: self.learn.save(name)
-
- def _validate(self, dl=None, callbacks=None, metrics=None, keeped_items=False):
- "Validate on `dl` with potential `callbacks` and `metrics`."
- dl = ifnone(dl, self.learn.data.valid_dl)
- metrics = ifnone(metrics, self.learn.metrics)
- cb_handler = CallbackHandler(ifnone(callbacks, []), metrics)
- cb_handler.on_train_begin(1, None, metrics); cb_handler.on_epoch_begin()
- if keeped_items: cb_handler.state_dict.update(dict(keeped_items=[]))
- val_metrics = validate(self.learn.model, dl, self.loss_func, cb_handler)
- cb_handler.on_epoch_end(val_metrics)
- if keeped_items: return cb_handler.state_dict['keeped_items']
- else: return cb_handler.state_dict['last_metrics']
-
- def jump_to_epoch_iter(self, epoch:int, iteration:int)->None:
- try:
- self.learn.load(f'{self.name}_{epoch}_{iteration}', purge=False)
- logging.info(f'Loaded {self.name}_{epoch}_{iteration}')
- except: logging.info(f'Model {self.name}_{epoch}_{iteration} not found.')
-
- def on_train_begin(self, n_epochs, **kwargs):
- # TODO: can not write graph here
- # super().on_train_begin(**kwargs)
- self.best = -float('inf')
- self.timer.tic()
- if self.host:
- checkpoint_path = self.learn.path/'checkpoint.yaml'
- if checkpoint_path.exists():
- os.remove(checkpoint_path)
- open(checkpoint_path, 'w').close()
- return {'skip_validate': True, 'iteration':self.start_iters} # disable default validate
-
- def on_batch_begin(self, **kwargs:Any)->None:
- self.timer.toc_data()
- super().on_batch_begin(**kwargs)
-
- def on_batch_end(self, iteration, epoch, last_loss, smooth_loss, train, **kwargs):
- super().on_batch_end(last_loss, iteration, train, **kwargs)
- if iteration == 0: return
-
- if iteration % self.loss_iters == 0:
- last_losses = self.learn.loss_func.last_losses
- self._write_sub_loss(iteration=iteration, last_losses=last_losses)
- self.tbwriter.add_scalar(tag=self.metrics_root + 'lr',
- scalar_value=self.opt.lr, global_step=iteration)
-
- if iteration % self.show_iters == 0:
- log_str = f'epoch {epoch} iter {iteration}: loss = {last_loss:6.4f}, ' \
- f'smooth loss = {smooth_loss:6.4f}'
- logging.info(log_str)
- # log_str = f'data time = {self.timer.data_diff:.4f}s, runing time = {self.timer.running_diff:.4f}s'
- # logging.info(log_str)
-
- if iteration % self.eval_iters == 0:
- # TODO: or remove time to on_epoch_end
- # 1. Record time
- log_str = f'average data time = {self.timer.average_data_time():.4f}s, ' \
- f'average running time = {self.timer.average_running_time():.4f}s'
- logging.info(log_str)
-
- # 2. Call validate
- last_metrics = self._validate()
- self.learn.model.train()
- log_str = f'epoch {epoch} iter {iteration}: eval loss = {last_metrics[0]:6.4f}, ' \
- f'ccr = {last_metrics[1]:6.4f}, cwr = {last_metrics[2]:6.4f}, ' \
- f'ted = {last_metrics[3]:6.4f}, ned = {last_metrics[4]:6.4f}, ' \
- f'ted/w = {last_metrics[5]:6.4f}, '
- logging.info(log_str)
- names = ['eval_loss', 'ccr', 'cwr', 'ted', 'ned', 'ted/w']
- self._write_metrics(iteration, names, last_metrics)
-
- # 3. Save best model
- current = last_metrics[2]
- if current is not None and current > self.best:
- logging.info(f'Better model found at epoch {epoch}, '\
- f'iter {iteration} with accuracy value: {current:6.4f}.')
- self.best = current
- self._save(f'{self.bestname}')
-
- if iteration % self.save_iters == 0 and self.host:
- logging.info(f'Save model {self.name}_{epoch}_{iteration}')
- filename = f'{self.name}_{epoch}_{iteration}'
- self._save(filename)
-
- checkpoint_path = self.learn.path/'checkpoint.yaml'
- if not checkpoint_path.exists():
- open(checkpoint_path, 'w').close()
- with open(checkpoint_path, 'r') as file:
- checkpoints = yaml.load(file, Loader=yaml.FullLoader) or dict()
- checkpoints['all_checkpoints'] = (
- checkpoints.get('all_checkpoints') or list())
- checkpoints['all_checkpoints'].insert(0, filename)
- if len(checkpoints['all_checkpoints']) > self.checpoint_keep_num:
- removed_checkpoint = checkpoints['all_checkpoints'].pop()
- removed_checkpoint = self.learn.path/self.learn.model_dir/f'{removed_checkpoint}.pth'
- os.remove(removed_checkpoint)
- checkpoints['current_checkpoint'] = filename
- with open(checkpoint_path, 'w') as file:
- yaml.dump(checkpoints, file)
-
-
- self.timer.toc_running()
-
- def on_train_end(self, **kwargs):
- #self.learn.load(f'{self.bestname}', purge=False)
- pass
-
- def on_epoch_end(self, last_metrics:MetricsList, iteration:int, **kwargs)->None:
- self._write_embedding(iteration=iteration)
-
-
-class TextAccuracy(Callback):
- _names = ['ccr', 'cwr', 'ted', 'ned', 'ted/w']
- def __init__(self, charset_path, max_length, case_sensitive, model_eval):
- self.charset_path = charset_path
- self.max_length = max_length
- self.case_sensitive = case_sensitive
- self.charset = CharsetMapper(charset_path, self.max_length)
- self.names = self._names
-
- self.model_eval = model_eval or 'alignment'
- assert self.model_eval in ['vision', 'language', 'alignment']
-
- def on_epoch_begin(self, **kwargs):
- self.total_num_char = 0.
- self.total_num_word = 0.
- self.correct_num_char = 0.
- self.correct_num_word = 0.
- self.total_ed = 0.
- self.total_ned = 0.
-
- def _get_output(self, last_output):
- if isinstance(last_output, (tuple, list)):
- for res in last_output:
- if res['name'] == self.model_eval: output = res
- else: output = last_output
- return output
-
- def _update_output(self, last_output, items):
- if isinstance(last_output, (tuple, list)):
- for res in last_output:
- if res['name'] == self.model_eval: res.update(items)
- else: last_output.update(items)
- return last_output
-
- def on_batch_end(self, last_output, last_target, **kwargs):
- output = self._get_output(last_output)
- logits, pt_lengths = output['logits'], output['pt_lengths']
- pt_text, pt_scores, pt_lengths_ = self.decode(logits)
- assert (pt_lengths == pt_lengths_).all(), f'{pt_lengths} != {pt_lengths_} for {pt_text}'
- last_output = self._update_output(last_output, {'pt_text':pt_text, 'pt_scores':pt_scores})
-
- pt_text = [self.charset.trim(t) for t in pt_text]
- label = last_target[0]
- if label.dim() == 3: label = label.argmax(dim=-1) # one-hot label
- gt_text = [self.charset.get_text(l, trim=True) for l in label]
-
- for i in range(len(gt_text)):
- if not self.case_sensitive:
- gt_text[i], pt_text[i] = gt_text[i].lower(), pt_text[i].lower()
- distance = ed.eval(gt_text[i], pt_text[i])
- self.total_ed += distance
- self.total_ned += float(distance) / max(len(gt_text[i]), 1)
-
- if gt_text[i] == pt_text[i]:
- self.correct_num_word += 1
- self.total_num_word += 1
-
- for j in range(min(len(gt_text[i]), len(pt_text[i]))):
- if gt_text[i][j] == pt_text[i][j]:
- self.correct_num_char += 1
- self.total_num_char += len(gt_text[i])
-
- return {'last_output': last_output}
-
- def on_epoch_end(self, last_metrics, **kwargs):
- mets = [self.correct_num_char / self.total_num_char,
- self.correct_num_word / self.total_num_word,
- self.total_ed,
- self.total_ned,
- self.total_ed / self.total_num_word]
- return add_metrics(last_metrics, mets)
-
- def decode(self, logit):
- """ Greed decode """
- # TODO: test running time and decode on GPU
- out = F.softmax(logit, dim=2)
- pt_text, pt_scores, pt_lengths = [], [], []
- for o in out:
- text = self.charset.get_text(o.argmax(dim=1), padding=False, trim=False)
- text = text.split(self.charset.null_char)[0] # end at end-token
- pt_text.append(text)
- pt_scores.append(o.max(dim=1)[0])
- pt_lengths.append(min(len(text) + 1, self.max_length)) # one for end-token
- pt_scores = torch.stack(pt_scores)
- pt_lengths = pt_scores.new_tensor(pt_lengths, dtype=torch.long)
- return pt_text, pt_scores, pt_lengths
-
-
-class TopKTextAccuracy(TextAccuracy):
- _names = ['ccr', 'cwr']
- def __init__(self, k, charset_path, max_length, case_sensitive, model_eval):
- self.k = k
- self.charset_path = charset_path
- self.max_length = max_length
- self.case_sensitive = case_sensitive
- self.charset = CharsetMapper(charset_path, self.max_length)
- self.names = self._names
-
- def on_epoch_begin(self, **kwargs):
- self.total_num_char = 0.
- self.total_num_word = 0.
- self.correct_num_char = 0.
- self.correct_num_word = 0.
-
- def on_batch_end(self, last_output, last_target, **kwargs):
- logits, pt_lengths = last_output['logits'], last_output['pt_lengths']
- gt_labels, gt_lengths = last_target[:]
-
- for logit, pt_length, label, length in zip(logits, pt_lengths, gt_labels, gt_lengths):
- word_flag = True
- for i in range(length):
- char_logit = logit[i].topk(self.k)[1]
- char_label = label[i].argmax(-1)
- if char_label in char_logit: self.correct_num_char += 1
- else: word_flag = False
- self.total_num_char += 1
- if pt_length == length and word_flag:
- self.correct_num_word += 1
- self.total_num_word += 1
-
- def on_epoch_end(self, last_metrics, **kwargs):
- mets = [self.correct_num_char / self.total_num_char,
- self.correct_num_word / self.total_num_word,
- 0., 0., 0.]
- return add_metrics(last_metrics, mets)
-
-
-class DumpPrediction(LearnerCallback):
-
- def __init__(self, learn, dataset, charset_path, model_eval, image_only=False, debug=False):
- super().__init__(learn=learn)
- self.debug = debug
- self.model_eval = model_eval or 'alignment'
- self.image_only = image_only
- assert self.model_eval in ['vision', 'language', 'alignment']
-
- self.dataset, self.root = dataset, Path(self.learn.path)/f'{dataset}-{self.model_eval}'
- self.attn_root = self.root/'attn'
- self.charset = CharsetMapper(charset_path)
- if self.root.exists(): shutil.rmtree(self.root)
- self.root.mkdir(), self.attn_root.mkdir()
-
- self.pil = transforms.ToPILImage()
- self.tensor = transforms.ToTensor()
- size = self.learn.data.img_h, self.learn.data.img_w
- self.resize = transforms.Resize(size=size, interpolation=0)
- self.c = 0
-
- def on_batch_end(self, last_input, last_output, last_target, **kwargs):
- if isinstance(last_output, (tuple, list)):
- for res in last_output:
- if res['name'] == self.model_eval: pt_text = res['pt_text']
- if res['name'] == 'vision': attn_scores = res['attn_scores'].detach().cpu()
- if res['name'] == self.model_eval: logits = res['logits']
- else:
- pt_text = last_output['pt_text']
- attn_scores = last_output['attn_scores'].detach().cpu()
- logits = last_output['logits']
-
- images = last_input[0] if isinstance(last_input, (tuple, list)) else last_input
- images = images.detach().cpu()
- pt_text = [self.charset.trim(t) for t in pt_text]
- gt_label = last_target[0]
- if gt_label.dim() == 3: gt_label = gt_label.argmax(dim=-1) # one-hot label
- gt_text = [self.charset.get_text(l, trim=True) for l in gt_label]
-
- prediction, false_prediction = [], []
- for gt, pt, image, attn, logit in zip(gt_text, pt_text, images, attn_scores, logits):
- prediction.append(f'{gt}\t{pt}\n')
- if gt != pt:
- if self.debug:
- scores = torch.softmax(logit, dim=-1)[:max(len(pt), len(gt)) + 1]
- logging.info(f'{self.c} gt {gt}, pt {pt}, logit {logit.shape}, scores {scores.topk(5, dim=-1)}')
- false_prediction.append(f'{gt}\t{pt}\n')
-
- image = self.learn.data.denorm(image)
- if not self.image_only:
- image_np = np.array(self.pil(image))
- attn_pil = [self.pil(a) for a in attn[:, None, :, :]]
- attn = [self.tensor(self.resize(a)).repeat(3, 1, 1) for a in attn_pil]
- attn_sum = np.array([np.array(a) for a in attn_pil[:len(pt)]]).sum(axis=0)
- blended_sum = self.tensor(blend_mask(image_np, attn_sum))
- blended = [self.tensor(blend_mask(image_np, np.array(a))) for a in attn_pil]
- save_image = torch.stack([image] + attn + [blended_sum] + blended)
- save_image = save_image.view(2, -1, *save_image.shape[1:])
- save_image = save_image.permute(1, 0, 2, 3, 4).flatten(0, 1)
- vutils.save_image(save_image, self.attn_root/f'{self.c}_{gt}_{pt}.jpg',
- nrow=2, normalize=True, scale_each=True)
- else:
- self.pil(image).save(self.attn_root/f'{self.c}_{gt}_{pt}.jpg')
- self.c += 1
-
- with open(self.root/f'{self.model_eval}.txt', 'a') as f: f.writelines(prediction)
- with open(self.root/f'{self.model_eval}-false.txt', 'a') as f: f.writelines(false_prediction)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/Image-003ee87c.css b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/Image-003ee87c.css
deleted file mode 100644
index 60f45635043d082881d8d8a529c1142ee028a68b..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/Image-003ee87c.css
+++ /dev/null
@@ -1 +0,0 @@
-img.svelte-gqt00k{border-radius:var(--radius-lg);max-width:none}img.selected.svelte-gqt00k{border-color:var(--border-color-accent)}.table.svelte-gqt00k{margin:0 auto;border:2px solid var(--border-color-primary);border-radius:var(--radius-lg);width:var(--size-20);height:var(--size-20);object-fit:cover}.gallery.svelte-gqt00k{border:2px solid var(--border-color-primary);max-height:var(--size-20);object-fit:cover}
diff --git a/spaces/DarshanMM/OpenAICodexSummarizer/app.py b/spaces/DarshanMM/OpenAICodexSummarizer/app.py
deleted file mode 100644
index de83984396e365dafd150c021fc5a5601ae382f2..0000000000000000000000000000000000000000
--- a/spaces/DarshanMM/OpenAICodexSummarizer/app.py
+++ /dev/null
@@ -1,13 +0,0 @@
-#python3
-#build a text summarizer using hugging face and gradio
-
-import gradio as gr
-import pandas as pd
-import numpy as np
-from transformers import pipeline
-
-def summarize(text):
- summarizer = pipeline("summarization")
- return summarizer(text, max_length=512, min_length=30)[0]['summary_text']
-
-gr.Interface(fn=summarize, inputs=gr.inputs.Textbox(lines=7, placeholder="Enter text here"), outputs="text").launch(inline = False)
\ No newline at end of file
diff --git a/spaces/Datasculptor/DescriptionGPT/tools/dump_clip_features.py b/spaces/Datasculptor/DescriptionGPT/tools/dump_clip_features.py
deleted file mode 100644
index 127f8c2a86c2425611c8ec075006664f5e07df45..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/DescriptionGPT/tools/dump_clip_features.py
+++ /dev/null
@@ -1,116 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import argparse
-import json
-import torch
-import numpy as np
-import itertools
-from nltk.corpus import wordnet
-import sys
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--ann', default='datasets/lvis/lvis_v1_val.json')
- parser.add_argument('--out_path', default='')
- parser.add_argument('--prompt', default='a')
- parser.add_argument('--model', default='clip')
- parser.add_argument('--clip_model', default="ViT-B/32")
- parser.add_argument('--fix_space', action='store_true')
- parser.add_argument('--use_underscore', action='store_true')
- parser.add_argument('--avg_synonyms', action='store_true')
- parser.add_argument('--use_wn_name', action='store_true')
- args = parser.parse_args()
-
- print('Loading', args.ann)
- data = json.load(open(args.ann, 'r'))
- cat_names = [x['name'] for x in \
- sorted(data['categories'], key=lambda x: x['id'])]
- if 'synonyms' in data['categories'][0]:
- if args.use_wn_name:
- synonyms = [
- [xx.name() for xx in wordnet.synset(x['synset']).lemmas()] \
- if x['synset'] != 'stop_sign.n.01' else ['stop_sign'] \
- for x in sorted(data['categories'], key=lambda x: x['id'])]
- else:
- synonyms = [x['synonyms'] for x in \
- sorted(data['categories'], key=lambda x: x['id'])]
- else:
- synonyms = []
- if args.fix_space:
- cat_names = [x.replace('_', ' ') for x in cat_names]
- if args.use_underscore:
- cat_names = [x.strip().replace('/ ', '/').replace(' ', '_') for x in cat_names]
- print('cat_names', cat_names)
- device = "cuda" if torch.cuda.is_available() else "cpu"
-
- if args.prompt == 'a':
- sentences = ['a ' + x for x in cat_names]
- sentences_synonyms = [['a ' + xx for xx in x] for x in synonyms]
- if args.prompt == 'none':
- sentences = [x for x in cat_names]
- sentences_synonyms = [[xx for xx in x] for x in synonyms]
- elif args.prompt == 'photo':
- sentences = ['a photo of a {}'.format(x) for x in cat_names]
- sentences_synonyms = [['a photo of a {}'.format(xx) for xx in x] \
- for x in synonyms]
- elif args.prompt == 'scene':
- sentences = ['a photo of a {} in the scene'.format(x) for x in cat_names]
- sentences_synonyms = [['a photo of a {} in the scene'.format(xx) for xx in x] \
- for x in synonyms]
-
- print('sentences_synonyms', len(sentences_synonyms), \
- sum(len(x) for x in sentences_synonyms))
- if args.model == 'clip':
- import clip
- print('Loading CLIP')
- model, preprocess = clip.load(args.clip_model, device=device)
- if args.avg_synonyms:
- sentences = list(itertools.chain.from_iterable(sentences_synonyms))
- print('flattened_sentences', len(sentences))
- text = clip.tokenize(sentences).to(device)
- with torch.no_grad():
- if len(text) > 10000:
- text_features = torch.cat([
- model.encode_text(text[:len(text) // 2]),
- model.encode_text(text[len(text) // 2:])],
- dim=0)
- else:
- text_features = model.encode_text(text)
- print('text_features.shape', text_features.shape)
- if args.avg_synonyms:
- synonyms_per_cat = [len(x) for x in sentences_synonyms]
- text_features = text_features.split(synonyms_per_cat, dim=0)
- text_features = [x.mean(dim=0) for x in text_features]
- text_features = torch.stack(text_features, dim=0)
- print('after stack', text_features.shape)
- text_features = text_features.cpu().numpy()
- elif args.model in ['bert', 'roberta']:
- from transformers import AutoTokenizer, AutoModel
- if args.model == 'bert':
- model_name = 'bert-large-uncased'
- if args.model == 'roberta':
- model_name = 'roberta-large'
- tokenizer = AutoTokenizer.from_pretrained(model_name)
- model = AutoModel.from_pretrained(model_name)
- model.eval()
- if args.avg_synonyms:
- sentences = list(itertools.chain.from_iterable(sentences_synonyms))
- print('flattened_sentences', len(sentences))
- inputs = tokenizer(sentences, padding=True, return_tensors="pt")
- with torch.no_grad():
- model_outputs = model(**inputs)
- outputs = model_outputs.pooler_output
- text_features = outputs.detach().cpu()
- if args.avg_synonyms:
- synonyms_per_cat = [len(x) for x in sentences_synonyms]
- text_features = text_features.split(synonyms_per_cat, dim=0)
- text_features = [x.mean(dim=0) for x in text_features]
- text_features = torch.stack(text_features, dim=0)
- print('after stack', text_features.shape)
- text_features = text_features.numpy()
- print('text_features.shape', text_features.shape)
- else:
- assert 0, args.model
- if args.out_path != '':
- print('saveing to', args.out_path)
- np.save(open(args.out_path, 'wb'), text_features)
- import pdb; pdb.set_trace()
diff --git a/spaces/DeclK/pose/tools/visualizer.py b/spaces/DeclK/pose/tools/visualizer.py
deleted file mode 100644
index f2898f4da3917cd2a411205f3fba77ebda66e258..0000000000000000000000000000000000000000
--- a/spaces/DeclK/pose/tools/visualizer.py
+++ /dev/null
@@ -1,346 +0,0 @@
-import cv2
-import numpy as np
-from skimage import draw, io
-from PIL import Image, ImageDraw, ImageFont
-from easydict import EasyDict
-from typing import Union
-from .utils import get_skeleton, Timer
-
-class FastVisualizer:
- """ Use skimage to draw, which is much faster than matplotlib, and
- more beatiful than opencv.😎
- """
- # TODO: modify color input parameter
- def __init__(self, image=None) -> None:
- self.set_image(image)
- self.colors = self.get_pallete()
- self.skeleton = get_skeleton()
- self.lvl_tresh = self.set_level([0.3, 0.6, 0.8])
-
- def set_image(self, image: Union[str, np.ndarray]):
- if isinstance(image, str):
- self.image = cv2.imread(image)
- elif isinstance(image, np.ndarray) or image is None:
- self.image = image
- else:
- raise TypeError(f"Type {type(image)} is not supported")
-
- def get_image(self):
- return self.image
-
- def draw_box(self, box_coord, color=(25, 113, 194), alpha=1.0):
- """ Draw a box on the image
- Args:
- box_coord: a list of [xmin, ymin, xmax, ymax]
- alpha: the alpha of the box
- color: the edge color of the box
- """
- xmin, ymin, xmax, ymax = box_coord
- rr, cc = draw.rectangle_perimeter((ymin, xmin), (ymax, xmax))
- draw.set_color(self.image, (rr, cc), color, alpha=alpha)
- return self
-
- def draw_rectangle(self, box_coord, color=(25, 113, 194), alpha=1.0):
- xmin, ymin, xmax, ymax = box_coord
- rr, cc = draw.rectangle((ymin, xmin), (ymax, xmax))
- draw.set_color(self.image, (rr, cc), color, alpha=alpha)
- return self
-
- def draw_point(self, point_coord, radius=5, color=(25, 113, 194), alpha=1.0):
- """ Coord in (x, y) format, but will be converted to (y, x)
- """
- x, y = point_coord
- rr, cc = draw.disk((y, x), radius=radius)
- draw.set_color(self.image, (rr, cc), color, alpha=alpha)
- return self
-
- def draw_line(self, start_point, end_point, color=(25, 113, 194), alpha=1.0):
- """ Not used, because I can't produce smooth line.
- """
- cv2.line(self.image, start_point, end_point, color.tolist(), 2,
- cv2.LINE_AA)
- return self
-
- def draw_line_aa(self, start_point, end_point, color=(25, 113, 194), alpha=1.0):
- """ Not used, because I can't produce smooth line.
- """
- x1, y1 = start_point
- x2, y2 = end_point
- rr, cc, val = draw.line_aa(y1, x1, y2, x2)
- draw.set_color(self.image, (rr, cc), color, alpha=alpha)
- return self
-
- def draw_thick_line(self, start_point, end_point, thickness=1, color=(25, 113, 194), alpha=1.0):
- """ Not used, because I can't produce smooth line.
- """
- x1, y1 = start_point
- x2, y2 = end_point
- dx, dy = x2 - x1, y2 - y1
- length = np.sqrt(dx * dx + dy * dy)
- cos, sin = dx / length, dy / length
-
- half_t = thickness / 2.0
- # Calculate the polygon vertices
- vertices_x = [x1 - half_t * sin, x1 + half_t * sin,
- x2 + half_t * sin, x2 - half_t * sin]
- vertices_y = [y1 + half_t * cos, y1 - half_t * cos,
- y2 - half_t * cos, y2 + half_t * cos]
- rr, cc = draw.polygon(vertices_y, vertices_x)
- draw.set_color(self.image, (rr, cc), color, alpha)
-
- return self
-
- def draw_text(self, text, position,
- font_path='assets/SmileySans/SmileySans-Oblique.ttf',
- font_size=20,
- text_color=(255, 255, 255)):
- """ Position is the left top corner of the text
- """
- # Convert the NumPy array to a PIL image
- pil_image = Image.fromarray(np.uint8(self.image))
- # Load the font (default is Arial)
- font = ImageFont.truetype(font_path, font_size)
- # Create a drawing object
- draw = ImageDraw.Draw(pil_image)
- # Add the text to the image
- draw.text(position, text, font=font, fill=text_color)
- # Convert the PIL image back to a NumPy array
- result = np.array(pil_image)
-
- self.image = result
- return self
-
- def xyhw_to_xyxy(self, box):
- hw = box[2:]
- x1y1 = box[:2] - hw / 2
- x2y2 = box[:2] + hw / 2
- return np.concatenate([x1y1, x2y2]).astype(np.int32)
-
- def draw_line_in_discrete_style(self, start_point, end_point, size=2, sample_points=3,
- color=(25, 113, 194), alpha=1.0):
- """ When drawing continous line, it is super fuzzy, and I can't handle them
- very well even tried OpneCV & PIL all kinds of ways. This is a workaround.
- The discrete line will be represented with few sampled cubes along the line,
- and it is exclusive with start & end points.
- """
- # sample points
- points = np.linspace(start_point, end_point, sample_points + 2)[1:-1]
- for p in points:
- rectangle_xyhw = np.array((p[0], p[1], size, size))
- rectangle_xyxy = self.xyhw_to_xyxy(rectangle_xyhw)
- self.draw_rectangle(rectangle_xyxy, color, alpha)
- return self
-
- def draw_human_keypoints(self, keypoints, scores=None, factor=20, draw_skeleton=False):
- """ Draw skeleton on the image, and give different color according
- to similarity scores.
- """
- # get max length of skeleton
- max_x, max_y = np.max(keypoints, axis=0)
- min_x, min_y = np.min(keypoints, axis=0)
- max_length = max(max_x - min_x, max_y - min_y)
- if max_length < 1: return self
- cube_size = max_length // factor
- line_cube_size = cube_size // 2
- # draw skeleton in discrete style
- if draw_skeleton:
- for key, links in self.skeleton.items():
- links = np.array(links)
- start_points = keypoints[links[:, 0]]
- end_points = keypoints[links[:, 1]]
- for s, e in zip(start_points, end_points):
- self.draw_line_in_discrete_style(s, e, line_cube_size,
- color=self.colors[key], alpha=0.9)
- # draw points
- if scores is None: # use vamos color
- lvl_names = ['vamos'] * len(keypoints)
- else: lvl_names = self.score_level_names(scores)
-
- for idx, (point, lvl_name) in enumerate(zip(keypoints, lvl_names)):
- if idx in set((0, 1, 2, 3, 4)):
- continue # do not draw head
- rectangle_xyhw = np.array((point[0], point[1], cube_size, cube_size))
- rectangle_xyxy = self.xyhw_to_xyxy(rectangle_xyhw)
- self.draw_rectangle(rectangle_xyxy,
- color=self.colors[lvl_name],
- alpha=0.8)
- return self
-
- def draw_score_bar(self, score, factor=50, bar_ratio=7):
- """ Draw a score bar on the left top of the image.
- factor: the value of image longer edge divided by the bar height
- bar_ratio: the ratio of bar width to bar height
- """
- # calculate bar's height and width
- long_edge = np.max(self.image.shape[:2])
- short_edge = np.min(self.image.shape[:2])
- bar_h = long_edge // factor
- bar_w = bar_h * bar_ratio
- if bar_w * 3 > short_edge:
- # when the image width is not enough
- bar_w = short_edge // 4
- bar_h = bar_w // bar_ratio
- cube_size = bar_h
- # bar's base position
- bar_start_point = (2*bar_h, 2*bar_h)
- # draw bar horizontally, and record the position of each word
- word_positions = []
- box_coords = []
- colors = [self.colors.bad, self.colors.good, self.colors.vamos]
- for i, color in enumerate(colors):
- x0, y0 = bar_start_point[0] + i*bar_w, bar_start_point[1]
- x1, y1 = x0 + bar_w - 1, y0 + bar_h
- box_coord = np.array((x0, y0, x1, y1), dtype=np.int32)
- self.draw_rectangle(box_coord, color=color)
-
- box_coords.append(box_coord)
- word_positions.append(np.array((x0, y1 + bar_h // 2)))
- # calculate cube position according to score
- lvl, lvl_ratio, lvl_name = self.score_level(score)
- # the first level start point is the first bar
- cube_lvl_start_x0 = [box_coord[0] - cube_size // 2 if i != 0
- else box_coord[0]
- for i, box_coord in enumerate(box_coords)]
- # process the last level, I want the cube stays in the bar
- level_length = bar_w if lvl == 1 else bar_w - cube_size // 2
- cube_x0 = cube_lvl_start_x0[lvl] + lvl_ratio * level_length
- cube_y0 = bar_start_point[1] - bar_h // 2 - cube_size
- cube_x1 = cube_x0 + cube_size
- cube_y1 = cube_y0 + cube_size
- # draw cube
- self.draw_rectangle((cube_x0, cube_y0, cube_x1, cube_y1),
- color=self.colors.cube)
- # enlarge the box, to emphasize the level
- enlarged_box = box_coords[lvl].copy()
- enlarged_box[:2] = enlarged_box[:2] - bar_h // 8
- enlarged_box[2:] = enlarged_box[2:] + bar_h // 8
- self.draw_rectangle(enlarged_box, color=self.colors[lvl_name])
-
- # draw text
- if lvl_name == 'vamos':
- lvl_name = 'vamos!!' # exciting!
- self.draw_text(lvl_name.capitalize(),
- word_positions[lvl],
- font_size=bar_h * 2,
- text_color=tuple(colors[lvl].tolist()))
-
- return self
-
- def draw_non_transparent_area(self, box_coord, alpha=0.2, extend_ratio=0.1):
- """ Make image outside the box transparent using alpha blend
- """
- x1, y1, x2, y2 = box_coord.astype(np.int32)
- # enlarge the box for 10%
- max_len = max((x2 - x1), (y2 - y1))
- extend_len = int(max_len * extend_ratio)
- x1, y1 = x1 - extend_len, y1 - extend_len
- x2, y2 = x2 + extend_len, y2 + extend_len
- # clip the box
- h, w = self.image.shape[:2]
- x1, y1, x2, y2 = np.clip((x1,y1,x2,y2), a_min=0,
- a_max=(w,h,w,h))
- # Create a white background color
- bg_color = np.ones_like(self.image) * 255
- # Copy the box region from the image
- bg_color[y1:y2, x1:x2] = self.image[y1:y2, x1:x2]
- # Alpha blend inplace
- self.image[:] = self.image * alpha + bg_color * (1 - alpha)
- return self
-
- def draw_logo(self, logo='assets/logo.png', factor=30, shift=20):
- """ Draw logo on the right bottom of the image.
- """
- H, W = self.image.shape[:2]
- # load logo
- logo_img = Image.open(logo)
- # scale logo
- logo_h = self.image.shape[0] // factor
- scale_size = logo_h / logo_img.size[1]
- logo_w = int(logo_img.size[0] * scale_size)
- logo_img = logo_img.resize((logo_w, logo_h))
- # convert to RGBA
- image = Image.fromarray(self.image).convert("RGBA")
- # alpha blend
- image.alpha_composite(logo_img, (W - logo_w - shift,
- H - logo_h - shift))
- self.image = np.array(image.convert("RGB"))
- return self
-
- def score_level(self, score):
- """ Return the level according to level thresh.
- """
- t = self.lvl_tresh
- if score < t[1]: # t[0] might bigger than 0
- ratio = (score - t[0]) / (t[1] - t[0])
- ratio = np.clip(ratio, a_min=0, a_max=1)
- return 0, ratio, 'bad'
- elif score < t[2]:
- ratio = (score - t[1]) / (t[2] - t[1])
- return 1, ratio, 'good'
- else:
- ratio = (score - t[2]) / (1 - t[2])
- return 2, ratio, 'vamos'
-
- def score_level_names(self, scores):
- """ Get multiple score level, return numpy array.
- np.vectorize does not speed up loop, but it is convenient.
- """
- t = self.lvl_tresh
- func_lvl_name = lambda x: 'bad' if x < t[1] else 'good' \
- if x < t[2] else 'vamos'
- lvl_names = np.vectorize(func_lvl_name)(scores)
- return lvl_names
-
- def set_level(self, thresh):
- """ Set level thresh for bad, good, vamos.
- """
- from collections import namedtuple
- Level = namedtuple('Level', ['zero', 'good', 'vamos'])
- return Level(thresh[0], thresh[1], thresh[2])
-
- def get_pallete(self):
- PALLETE = EasyDict()
-
- # light set
- # PALLETE.bad = np.array([253, 138, 138])
- # PALLETE.good = np.array([168, 209, 209])
- # PALLETE.vamos = np.array([241, 247, 181])
- # PALLETE.cube = np.array([158, 161, 212])
-
- # dark set, set 80% brightness
- PALLETE.bad = np.array([204, 111, 111])
- PALLETE.good = np.array([143, 179, 179])
- PALLETE.vamos = np.array([196, 204, 124])
- PALLETE.vamos = np.array([109, 169, 228])
- PALLETE.cube = np.array([152, 155, 204])
-
- PALLETE.left_arm = np.array([218, 119, 242])
- PALLETE.right_arm = np.array([151, 117, 250])
- PALLETE.left_leg = np.array([255, 212, 59])
- PALLETE.right_leg = np.array([255, 169, 77])
-
- PALLETE.head = np.array([134, 142, 150])
- PALLETE.body = np.array([134, 142, 150])
-
- # convert rgb to bgr
- for k, v in PALLETE.items():
- PALLETE[k] = v[::-1]
- return PALLETE
-
-if __name__ == '__main__':
- vis = FastVisualizer()
-
- image = '/github/Tennis.ai/assets/tempt_test.png'
- vis.set_image(image)
- np.random.seed(0)
- keypoints = np.random.randint(300, 600, (17, 2))
- from utils import Timer
- t= Timer()
- t.start()
- vis.draw_score_bar(0.94)
- # vis.draw_skeleton(keypoints)
- # vis.draw_non_transparent_area((0, 0, 100, 100), alpha=0.2)
- vis.draw_logo()
- cv2.imshow('test', vis.image)
- cv2.waitKey(0)
- cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/spaces/ECCV2022/bytetrack/tools/mix_data_test_mot17.py b/spaces/ECCV2022/bytetrack/tools/mix_data_test_mot17.py
deleted file mode 100644
index b0848db812dfe63e631dd8e35a401d7dbaecd767..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/bytetrack/tools/mix_data_test_mot17.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import json
-import os
-
-
-"""
-cd datasets
-mkdir -p mix_det/annotations
-cp mot/annotations/val_half.json mix_det/annotations/val_half.json
-cp mot/annotations/test.json mix_det/annotations/test.json
-cd mix_det
-ln -s ../mot/train mot_train
-ln -s ../crowdhuman/CrowdHuman_train crowdhuman_train
-ln -s ../crowdhuman/CrowdHuman_val crowdhuman_val
-ln -s ../Cityscapes cp_train
-ln -s ../ETHZ ethz_train
-cd ..
-"""
-
-mot_json = json.load(open('datasets/mot/annotations/train_half.json','r'))
-
-img_list = list()
-for img in mot_json['images']:
- img['file_name'] = 'mot_train/' + img['file_name']
- img_list.append(img)
-
-ann_list = list()
-for ann in mot_json['annotations']:
- ann_list.append(ann)
-
-video_list = mot_json['videos']
-category_list = mot_json['categories']
-
-
-print('mot17')
-
-max_img = 10000
-max_ann = 2000000
-max_video = 10
-
-crowdhuman_json = json.load(open('datasets/crowdhuman/annotations/train.json','r'))
-img_id_count = 0
-for img in crowdhuman_json['images']:
- img_id_count += 1
- img['file_name'] = 'crowdhuman_train/' + img['file_name']
- img['frame_id'] = img_id_count
- img['prev_image_id'] = img['id'] + max_img
- img['next_image_id'] = img['id'] + max_img
- img['id'] = img['id'] + max_img
- img['video_id'] = max_video
- img_list.append(img)
-
-for ann in crowdhuman_json['annotations']:
- ann['id'] = ann['id'] + max_ann
- ann['image_id'] = ann['image_id'] + max_img
- ann_list.append(ann)
-
-print('crowdhuman_train')
-
-video_list.append({
- 'id': max_video,
- 'file_name': 'crowdhuman_train'
-})
-
-
-max_img = 30000
-max_ann = 10000000
-
-crowdhuman_val_json = json.load(open('datasets/crowdhuman/annotations/val.json','r'))
-img_id_count = 0
-for img in crowdhuman_val_json['images']:
- img_id_count += 1
- img['file_name'] = 'crowdhuman_val/' + img['file_name']
- img['frame_id'] = img_id_count
- img['prev_image_id'] = img['id'] + max_img
- img['next_image_id'] = img['id'] + max_img
- img['id'] = img['id'] + max_img
- img['video_id'] = max_video
- img_list.append(img)
-
-for ann in crowdhuman_val_json['annotations']:
- ann['id'] = ann['id'] + max_ann
- ann['image_id'] = ann['image_id'] + max_img
- ann_list.append(ann)
-
-print('crowdhuman_val')
-
-video_list.append({
- 'id': max_video,
- 'file_name': 'crowdhuman_val'
-})
-
-max_img = 40000
-max_ann = 20000000
-
-ethz_json = json.load(open('datasets/ETHZ/annotations/train.json','r'))
-img_id_count = 0
-for img in ethz_json['images']:
- img_id_count += 1
- img['file_name'] = 'ethz_train/' + img['file_name'][5:]
- img['frame_id'] = img_id_count
- img['prev_image_id'] = img['id'] + max_img
- img['next_image_id'] = img['id'] + max_img
- img['id'] = img['id'] + max_img
- img['video_id'] = max_video
- img_list.append(img)
-
-for ann in ethz_json['annotations']:
- ann['id'] = ann['id'] + max_ann
- ann['image_id'] = ann['image_id'] + max_img
- ann_list.append(ann)
-
-print('ETHZ')
-
-video_list.append({
- 'id': max_video,
- 'file_name': 'ethz'
-})
-
-max_img = 50000
-max_ann = 25000000
-
-cp_json = json.load(open('datasets/Cityscapes/annotations/train.json','r'))
-img_id_count = 0
-for img in cp_json['images']:
- img_id_count += 1
- img['file_name'] = 'cp_train/' + img['file_name'][11:]
- img['frame_id'] = img_id_count
- img['prev_image_id'] = img['id'] + max_img
- img['next_image_id'] = img['id'] + max_img
- img['id'] = img['id'] + max_img
- img['video_id'] = max_video
- img_list.append(img)
-
-for ann in cp_json['annotations']:
- ann['id'] = ann['id'] + max_ann
- ann['image_id'] = ann['image_id'] + max_img
- ann_list.append(ann)
-
-print('Cityscapes')
-
-video_list.append({
- 'id': max_video,
- 'file_name': 'cityperson'
-})
-
-mix_json = dict()
-mix_json['images'] = img_list
-mix_json['annotations'] = ann_list
-mix_json['videos'] = video_list
-mix_json['categories'] = category_list
-json.dump(mix_json, open('datasets/mix_det/annotations/train.json','w'))
\ No newline at end of file
diff --git a/spaces/ECCV2022/bytetrack/yolox/models/yolo_fpn.py b/spaces/ECCV2022/bytetrack/yolox/models/yolo_fpn.py
deleted file mode 100644
index 8b3ba1473c005a57187247fd276ee5920750add8..0000000000000000000000000000000000000000
--- a/spaces/ECCV2022/bytetrack/yolox/models/yolo_fpn.py
+++ /dev/null
@@ -1,84 +0,0 @@
-#!/usr/bin/env python
-# -*- encoding: utf-8 -*-
-# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
-
-import torch
-import torch.nn as nn
-
-from .darknet import Darknet
-from .network_blocks import BaseConv
-
-
-class YOLOFPN(nn.Module):
- """
- YOLOFPN module. Darknet 53 is the default backbone of this model.
- """
-
- def __init__(
- self,
- depth=53,
- in_features=["dark3", "dark4", "dark5"],
- ):
- super().__init__()
-
- self.backbone = Darknet(depth)
- self.in_features = in_features
-
- # out 1
- self.out1_cbl = self._make_cbl(512, 256, 1)
- self.out1 = self._make_embedding([256, 512], 512 + 256)
-
- # out 2
- self.out2_cbl = self._make_cbl(256, 128, 1)
- self.out2 = self._make_embedding([128, 256], 256 + 128)
-
- # upsample
- self.upsample = nn.Upsample(scale_factor=2, mode="nearest")
-
- def _make_cbl(self, _in, _out, ks):
- return BaseConv(_in, _out, ks, stride=1, act="lrelu")
-
- def _make_embedding(self, filters_list, in_filters):
- m = nn.Sequential(
- *[
- self._make_cbl(in_filters, filters_list[0], 1),
- self._make_cbl(filters_list[0], filters_list[1], 3),
- self._make_cbl(filters_list[1], filters_list[0], 1),
- self._make_cbl(filters_list[0], filters_list[1], 3),
- self._make_cbl(filters_list[1], filters_list[0], 1),
- ]
- )
- return m
-
- def load_pretrained_model(self, filename="./weights/darknet53.mix.pth"):
- with open(filename, "rb") as f:
- state_dict = torch.load(f, map_location="cpu")
- print("loading pretrained weights...")
- self.backbone.load_state_dict(state_dict)
-
- def forward(self, inputs):
- """
- Args:
- inputs (Tensor): input image.
-
- Returns:
- Tuple[Tensor]: FPN output features..
- """
- # backbone
- out_features = self.backbone(inputs)
- x2, x1, x0 = [out_features[f] for f in self.in_features]
-
- # yolo branch 1
- x1_in = self.out1_cbl(x0)
- x1_in = self.upsample(x1_in)
- x1_in = torch.cat([x1_in, x1], 1)
- out_dark4 = self.out1(x1_in)
-
- # yolo branch 2
- x2_in = self.out2_cbl(out_dark4)
- x2_in = self.upsample(x2_in)
- x2_in = torch.cat([x2_in, x2], 1)
- out_dark3 = self.out2(x2_in)
-
- outputs = (out_dark3, out_dark4, x0)
- return outputs
diff --git a/spaces/EDGAhab/Paimon-Talking/monotonic_align/__init__.py b/spaces/EDGAhab/Paimon-Talking/monotonic_align/__init__.py
deleted file mode 100644
index 3d7009c40fea3a98168e3e3bc9ae061e91327422..0000000000000000000000000000000000000000
--- a/spaces/EDGAhab/Paimon-Talking/monotonic_align/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import numpy as np
-import torch
-from .monotonic_align.core import maximum_path_c
-
-
-def maximum_path(neg_cent, mask):
- """ Cython optimized version.
- neg_cent: [b, t_t, t_s]
- mask: [b, t_t, t_s]
- """
- device = neg_cent.device
- dtype = neg_cent.dtype
- neg_cent = neg_cent.data.cpu().numpy().astype(np.float32)
- path = np.zeros(neg_cent.shape, dtype=np.int32)
-
- t_t_max = mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32)
- t_s_max = mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32)
- maximum_path_c(path, neg_cent, t_t_max, t_s_max)
- return torch.from_numpy(path).to(device=device, dtype=dtype)
diff --git a/spaces/EPFL-VILAB/MultiMAE/mask2former/modeling/pixel_decoder/ops/functions/__init__.py b/spaces/EPFL-VILAB/MultiMAE/mask2former/modeling/pixel_decoder/ops/functions/__init__.py
deleted file mode 100644
index 2b06b5ac538b63bdb9a6c82e4635b95bb5491d5b..0000000000000000000000000000000000000000
--- a/spaces/EPFL-VILAB/MultiMAE/mask2former/modeling/pixel_decoder/ops/functions/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# ------------------------------------------------------------------------------------------------
-# Deformable DETR
-# Copyright (c) 2020 SenseTime. All Rights Reserved.
-# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-# ------------------------------------------------------------------------------------------------
-# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-# ------------------------------------------------------------------------------------------------
-
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
-
-from .ms_deform_attn_func import MSDeformAttnFunction
-
diff --git a/spaces/Eddycrack864/Applio-Inference/infer/lib/train/losses.py b/spaces/Eddycrack864/Applio-Inference/infer/lib/train/losses.py
deleted file mode 100644
index b1b263e4c205e78ffe970f622ab6ff68f36d3b17..0000000000000000000000000000000000000000
--- a/spaces/Eddycrack864/Applio-Inference/infer/lib/train/losses.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import torch
-
-
-def feature_loss(fmap_r, fmap_g):
- loss = 0
- for dr, dg in zip(fmap_r, fmap_g):
- for rl, gl in zip(dr, dg):
- rl = rl.float().detach()
- gl = gl.float()
- loss += torch.mean(torch.abs(rl - gl))
-
- return loss * 2
-
-
-def discriminator_loss(disc_real_outputs, disc_generated_outputs):
- loss = 0
- r_losses = []
- g_losses = []
- for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
- dr = dr.float()
- dg = dg.float()
- r_loss = torch.mean((1 - dr) ** 2)
- g_loss = torch.mean(dg**2)
- loss += r_loss + g_loss
- r_losses.append(r_loss.item())
- g_losses.append(g_loss.item())
-
- return loss, r_losses, g_losses
-
-
-def generator_loss(disc_outputs):
- loss = 0
- gen_losses = []
- for dg in disc_outputs:
- dg = dg.float()
- l = torch.mean((1 - dg) ** 2)
- gen_losses.append(l)
- loss += l
-
- return loss, gen_losses
-
-
-def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
- """
- z_p, logs_q: [b, h, t_t]
- m_p, logs_p: [b, h, t_t]
- """
- z_p = z_p.float()
- logs_q = logs_q.float()
- m_p = m_p.float()
- logs_p = logs_p.float()
- z_mask = z_mask.float()
-
- kl = logs_p - logs_q - 0.5
- kl += 0.5 * ((z_p - m_p) ** 2) * torch.exp(-2.0 * logs_p)
- kl = torch.sum(kl * z_mask)
- l = kl / torch.sum(z_mask)
- return l
diff --git a/spaces/Elbhnasy/ASD_Diagnosis/README.md b/spaces/Elbhnasy/ASD_Diagnosis/README.md
deleted file mode 100644
index e5ba739f1b0ac79f677b4f2f8c8abf8f6ceb9e34..0000000000000000000000000000000000000000
--- a/spaces/Elbhnasy/ASD_Diagnosis/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: ASD Diagnosis
-emoji: 🐨
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.18.0
-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/Emanuel/porttagger/top.html b/spaces/Emanuel/porttagger/top.html
deleted file mode 100644
index 0f339037f8bb81ed4abc7a7c444aa7593882abb4..0000000000000000000000000000000000000000
--- a/spaces/Emanuel/porttagger/top.html
+++ /dev/null
@@ -1,26 +0,0 @@
-
-
-
- Porttagger
-
-
A Brazilian Portuguese part of speech tagger according to the Universal Dependencies model
-
-
-
- Porttagger is a state of the art part of speech tagger for Brazilian Portuguese that automatically assigns
- morphosyntactic classes to the words of sentences, following the Universal Dependencies international model. You
- may provide single sentences or multiple sentences (using plain text files with several sentences) to be tagged.
- You may also choose which trained model to use. The options include a model trained on news texts (using the
- Porttinari-base corpus), on stock
- market tweets (from the DANTE corpus), on
- academic texts from the oil & gas
- domain (from the PetroGold
- corpus), and on all of them together. To the interested reader, this initiative is
- part of the POeTiSA project, where much more
- information is available.
- See more details about Porttagger in this paper.
-
My name is Fan. I’m now a data engineer working at Meta. Nice to e-meet you!
I'm always passionate about helping people, especially young professionals, to get their first and dream job! When I first see SharpestMinds website, I found this is a great platform that can help me connect with the people who needs help, so here I am :)
Let me briefly introduce myself a bit.
I got both my BSc. Statistics and MSc. Computing Science from the University of Alberta. Right after I graduated, I started to pursue my career in the data industry. I’ve been working as a BI developer, data scientist and cyber security analyst before settling down as a data engineer. I have experience working in financial organizations, banks such as HSBC and Scotiabank, consulting firms such as PwC and Accenture, and high-tech companies such as IBM and Meta. I’m enthusiastic as working and growing as a data engineer, and I hope I can help my mentees to launch their career as a data professional!
Other than my professional experience, I’d also love to share my experience as an international job hunter in Canada with my mentee. I moved from China to Canada 10 years ago. I understand how difficult it is to adapt a new environment to study and work. I hope I can help my mentee to set a clear career goal and achieve their career success in the near future!
Long story short, I have been working on helping students and early professionals launch their dream jobs in the data industry for several years, and I always enjoy sharing the happiness when people get hired. Please do not hesitate to connect with me to learn more about me! I’m looking forward to hearing from you soon.
Best,
Fan
Interview
How did you hear about SM?
I know a couple of mentors and saw their postings on Linkedin
Then googled SM
Mentorship experience?
founded a student group called iGeek
Ran it for 3 years
bridge btw students and companies
hosted lots of different career-focused webinars and career fairs
Founded this b/c when I did it it was hard, and I didn't have the mindset
help them early to build up their mindset
Lots of experience in the data field. Love helping young professionals to find their first job
Gives me a lot of happiness
IBM DS course - I helped them make it
What are beginners lacking?
Technical
huge gap btw what I learned and school and what was needed
Imbalance of information
Perceived as hard. Requirements are opaque. Don't know the difference btw roles
Confidence!!
And how can you add value as a mentor?
Do some hands-on projects
Fill in gaps in knowledge
Set up clear goals and give them confidence
Help them decide on the discipline
-
- Questions about SM?
How and why did you found it?
How do you attract mentees?
How long do these mentorships last?
Any tips for me?
-
-
-
-
-
-
\ No newline at end of file
diff --git a/spaces/atimughal662/InfoFusion/iterators/iterator_pipe.py b/spaces/atimughal662/InfoFusion/iterators/iterator_pipe.py
deleted file mode 100644
index 90883b08ee6c5fbb7a575a7f1176f124b4d66134..0000000000000000000000000000000000000000
--- a/spaces/atimughal662/InfoFusion/iterators/iterator_pipe.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import queue
-import asyncio
-
-
-class IteratorPipe:
- """
- Iterator Pipe creates an iterator that can be fed in data from another block of code or thread of execution
- """
-
- def __init__(self, sentinel=object()):
- self._q = queue.Queue()
- self._sentinel = sentinel
- self._sentinel_pushed = False
- self._closed = False
-
- def __iter__(self):
- return self
-
- def __next__(self):
- if self._closed:
- raise StopIteration
-
- data = self._q.get(block=True)
- if data is self._sentinel:
- self._closed = True
- raise StopIteration
-
- return data
-
- def put(self, data) -> bool:
- """
- Pushes next item to Iterator and returns True
- If iterator has been closed via close(), doesn't push anything and returns False
- """
- if self._sentinel_pushed:
- return False
-
- self._q.put(data)
- return True
-
- def close(self):
- """
- Close is idempotent. Calling close multiple times is safe
- Iterator will raise StopIteration only after all elements pushed before close have been iterated
- """
- # make close idempotent
- if not self._sentinel_pushed:
- self._sentinel_pushed = True
- self._q.put(self._sentinel)
-
-
-class AsyncIteratorPipe:
-
- def __init__(self, sentinel=object()):
- self._q = asyncio.Queue()
- self._sentinel = sentinel
- self._sentinel_pushed = False
- self._closed = False
-
- def __aiter__(self):
- return self
-
- async def __anext__(self):
- if self._closed:
- raise StopAsyncIteration
-
- data = await self._q.get()
- if data is self._sentinel:
- self._closed = True
- raise StopAsyncIteration
-
- return data
-
- async def put(self, data) -> bool:
- """
- Pushes next item to Iterator and returns True
- If iterator has been closed via close(), doesn't push anything and returns False
- """
- if self._sentinel_pushed:
- return False
-
- await self._q.put(data)
- return True
-
- async def close(self):
- """
- Close is idempotent. Calling close multiple times is safe
- Iterator will raise StopIteration only after all elements pushed before close have been iterated
- """
- # make close idempotent
- if not self._sentinel_pushed:
- self._sentinel_pushed = True
- await self._q.put(self._sentinel)
diff --git a/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/modules/diffusionmodules/depth_grounding_net.py b/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/modules/diffusionmodules/depth_grounding_net.py
deleted file mode 100644
index 637816e79a97e38cf987e6311fa91d9792dc0fce..0000000000000000000000000000000000000000
--- a/spaces/attention-refocusing/Attention-refocusing/gligen/ldm/modules/diffusionmodules/depth_grounding_net.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import torch
-import torch.nn as nn
-from ldm.modules.attention import BasicTransformerBlock
-from ldm.modules.diffusionmodules.util import checkpoint, FourierEmbedder
-import torch.nn.functional as F
-from ..attention import SelfAttention, FeedForward
-from .convnext import convnext_tiny
-
-
-
-
-class PositionNet(nn.Module):
- def __init__(self, resize_input=448, out_dim=768):
- super().__init__()
- self.resize_input = resize_input
- self.down_factor = 32 # determined by the convnext backbone
- self.out_dim = out_dim
- assert self.resize_input % self.down_factor == 0
-
- self.convnext_tiny_backbone = convnext_tiny(pretrained=True)
-
- self.num_tokens = (self.resize_input // self.down_factor) ** 2
-
- convnext_feature_dim = 768
- self.pos_embedding = nn.Parameter(torch.empty(1, self.num_tokens, convnext_feature_dim).normal_(std=0.02)) # from BERT
-
- self.linears = nn.Sequential(
- nn.Linear( convnext_feature_dim, 512),
- nn.SiLU(),
- nn.Linear( 512, 512),
- nn.SiLU(),
- nn.Linear(512, out_dim),
- )
-
- self.null_feature = torch.nn.Parameter(torch.zeros([convnext_feature_dim]))
-
-
- def forward(self, depth, mask):
- B = depth.shape[0]
-
- # token from edge map
- depth = torch.nn.functional.interpolate(depth, self.resize_input)
- depth_feature = self.convnext_tiny_backbone(depth)
- objs = depth_feature.reshape(B, -1, self.num_tokens)
- objs = objs.permute(0, 2, 1) # N*Num_tokens*dim
-
- # expand null token
- null_objs = self.null_feature.view(1,1,-1)
- null_objs = null_objs.repeat(B,self.num_tokens,1)
-
- # mask replacing
- mask = mask.view(-1,1,1)
- objs = objs*mask + null_objs*(1-mask)
-
- # add pos
- objs = objs + self.pos_embedding
-
- # fuse them
- objs = self.linears(objs)
-
- assert objs.shape == torch.Size([B,self.num_tokens,self.out_dim])
- return objs
-
-
-
diff --git a/spaces/avivdm1/AutoGPT/autogpt/spinner.py b/spaces/avivdm1/AutoGPT/autogpt/spinner.py
deleted file mode 100644
index 4e33d74213881352546f334ccb1eb4772b8b7b70..0000000000000000000000000000000000000000
--- a/spaces/avivdm1/AutoGPT/autogpt/spinner.py
+++ /dev/null
@@ -1,65 +0,0 @@
-"""A simple spinner module"""
-import itertools
-import sys
-import threading
-import time
-
-
-class Spinner:
- """A simple spinner class"""
-
- def __init__(self, message: str = "Loading...", delay: float = 0.1) -> None:
- """Initialize the spinner class
-
- Args:
- message (str): The message to display.
- delay (float): The delay between each spinner update.
- """
- self.spinner = itertools.cycle(["-", "/", "|", "\\"])
- self.delay = delay
- self.message = message
- self.running = False
- self.spinner_thread = None
-
- def spin(self) -> None:
- """Spin the spinner"""
- while self.running:
- sys.stdout.write(f"{next(self.spinner)} {self.message}\r")
- sys.stdout.flush()
- time.sleep(self.delay)
- sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
-
- def __enter__(self):
- """Start the spinner"""
- self.running = True
- self.spinner_thread = threading.Thread(target=self.spin)
- self.spinner_thread.start()
-
- return self
-
- def __exit__(self, exc_type, exc_value, exc_traceback) -> None:
- """Stop the spinner
-
- Args:
- exc_type (Exception): The exception type.
- exc_value (Exception): The exception value.
- exc_traceback (Exception): The exception traceback.
- """
- self.running = False
- if self.spinner_thread is not None:
- self.spinner_thread.join()
- sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r")
- sys.stdout.flush()
-
- def update_message(self, new_message, delay=0.1):
- """Update the spinner message
- Args:
- new_message (str): New message to display
- delay: Delay in seconds before updating the message
- """
- time.sleep(delay)
- sys.stdout.write(
- f"\r{' ' * (len(self.message) + 2)}\r"
- ) # Clear the current message
- sys.stdout.flush()
- self.message = new_message
diff --git a/spaces/awacke1/PytorchStreamlitNeuralNetUI/README.md b/spaces/awacke1/PytorchStreamlitNeuralNetUI/README.md
deleted file mode 100644
index dccd5c2fa634c58b018056d16b8e5f08a8eafc91..0000000000000000000000000000000000000000
--- a/spaces/awacke1/PytorchStreamlitNeuralNetUI/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: PytorchStreamlitNeuralNetUI
-emoji: 🏢
-colorFrom: yellow
-colorTo: red
-sdk: streamlit
-sdk_version: 1.25.0
-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/awacke1/Top-Ten-United-States/README.md b/spaces/awacke1/Top-Ten-United-States/README.md
deleted file mode 100644
index 9f7a1d8df46239e94783badebf49b92cc863a802..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Top-Ten-United-States/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Top Ten Board Games Map Making Strategy
-emoji: 👀
-colorFrom: purple
-colorTo: green
-sdk: streamlit
-sdk_version: 1.17.0
-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/awsaf49/gcvit-tf/gcvit/models/__init__.py b/spaces/awsaf49/gcvit-tf/gcvit/models/__init__.py
deleted file mode 100644
index 6e82c900173de21e76256f5d31366dea44ea0aad..0000000000000000000000000000000000000000
--- a/spaces/awsaf49/gcvit-tf/gcvit/models/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .gcvit import GCViT, GCViTXXTiny, GCViTXTiny, GCViTTiny, GCViTSmall, GCViTBase, GCViTLarge
\ No newline at end of file
diff --git a/spaces/ayaanzaveri/whisper-webui/src/download.py b/spaces/ayaanzaveri/whisper-webui/src/download.py
deleted file mode 100644
index 20565153f9e582be73246a1e2a3b7be3f368b322..0000000000000000000000000000000000000000
--- a/spaces/ayaanzaveri/whisper-webui/src/download.py
+++ /dev/null
@@ -1,78 +0,0 @@
-from tempfile import mkdtemp
-from typing import List
-from yt_dlp import YoutubeDL
-
-import yt_dlp
-from yt_dlp.postprocessor import PostProcessor
-
-class FilenameCollectorPP(PostProcessor):
- def __init__(self):
- super(FilenameCollectorPP, self).__init__(None)
- self.filenames = []
-
- def run(self, information):
- self.filenames.append(information["filepath"])
- return [], information
-
-def download_url(url: str, maxDuration: int = None, destinationDirectory: str = None, playlistItems: str = "1") -> List[str]:
- try:
- return _perform_download(url, maxDuration=maxDuration, outputTemplate=None, destinationDirectory=destinationDirectory, playlistItems=playlistItems)
- except yt_dlp.utils.DownloadError as e:
- # In case of an OS error, try again with a different output template
- if e.msg and e.msg.find("[Errno 36] File name too long") >= 0:
- return _perform_download(url, maxDuration=maxDuration, outputTemplate="%(title).10s %(id)s.%(ext)s")
- pass
-
-def _perform_download(url: str, maxDuration: int = None, outputTemplate: str = None, destinationDirectory: str = None, playlistItems: str = "1"):
- # Create a temporary directory to store the downloaded files
- if destinationDirectory is None:
- destinationDirectory = mkdtemp()
-
- ydl_opts = {
- "format": "bestaudio/best",
- 'paths': {
- 'home': destinationDirectory
- }
- }
- if (playlistItems):
- ydl_opts['playlist_items'] = playlistItems
-
- # Add output template if specified
- if outputTemplate:
- ydl_opts['outtmpl'] = outputTemplate
-
- filename_collector = FilenameCollectorPP()
-
- with YoutubeDL(ydl_opts) as ydl:
- if maxDuration and maxDuration > 0:
- info = ydl.extract_info(url, download=False)
- entries = "entries" in info and info["entries"] or [info]
-
- total_duration = 0
-
- # Compute total duration
- for entry in entries:
- total_duration += float(entry["duration"])
-
- if total_duration >= maxDuration:
- raise ExceededMaximumDuration(videoDuration=total_duration, maxDuration=maxDuration, message="Video is too long")
-
- ydl.add_post_processor(filename_collector)
- ydl.download([url])
-
- if len(filename_collector.filenames) <= 0:
- raise Exception("Cannot download " + url)
-
- result = []
-
- for filename in filename_collector.filenames:
- result.append(filename)
- print("Downloaded " + filename)
-
- return result
-
-class ExceededMaximumDuration(Exception):
- def __init__(self, videoDuration, maxDuration, message):
- self.videoDuration = videoDuration
- self.maxDuration = maxDuration
- super().__init__(message)
\ No newline at end of file
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/lights/SpotLightShadow.d.ts b/spaces/banana-projects/web3d/node_modules/three/src/lights/SpotLightShadow.d.ts
deleted file mode 100644
index fa59dac66a47740663652ed642f791a38cc6af9c..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/lights/SpotLightShadow.d.ts
+++ /dev/null
@@ -1,8 +0,0 @@
-import { PerspectiveCamera } from './../cameras/PerspectiveCamera';
-import { Light } from './Light';
-import { LightShadow } from './LightShadow';
-
-export class SpotLightShadow extends LightShadow {
- camera: PerspectiveCamera;
- update(light: Light): void;
-}
diff --git a/spaces/banana-projects/web3d/node_modules/three/src/math/Color.d.ts b/spaces/banana-projects/web3d/node_modules/three/src/math/Color.d.ts
deleted file mode 100644
index a8d49b546683312999ea9c6fa6c3172755a0c0f0..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/src/math/Color.d.ts
+++ /dev/null
@@ -1,129 +0,0 @@
-export interface HSL {
- h: number;
- s: number;
- l: number;
-}
-
-/**
- * Represents a color. See also {@link ColorUtils}.
- *
- * @example
- * var color = new THREE.Color( 0xff0000 );
- *
- * @see src/math/Color.js
- */
-export class Color {
- constructor(color?: Color | string | number);
- constructor(r: number, g: number, b: number);
-
- /**
- * Red channel value between 0 and 1. Default is 1.
- */
- r: number;
-
- /**
- * Green channel value between 0 and 1. Default is 1.
- */
- g: number;
-
- /**
- * Blue channel value between 0 and 1. Default is 1.
- */
- b: number;
-
- set(color: Color): Color;
- set(color: number): Color;
- set(color: string): Color;
- setScalar(scalar: number): Color;
- setHex(hex: number): Color;
-
- /**
- * Sets this color from RGB values.
- * @param r Red channel value between 0 and 1.
- * @param g Green channel value between 0 and 1.
- * @param b Blue channel value between 0 and 1.
- */
- setRGB(r: number, g: number, b: number): Color;
-
- /**
- * Sets this color from HSL values.
- * Based on MochiKit implementation by Bob Ippolito.
- *
- * @param h Hue channel value between 0 and 1.
- * @param s Saturation value channel between 0 and 1.
- * @param l Value channel value between 0 and 1.
- */
- setHSL(h: number, s: number, l: number): Color;
-
- /**
- * Sets this color from a CSS context style string.
- * @param contextStyle Color in CSS context style format.
- */
- setStyle(style: string): Color;
-
- /**
- * Clones this color.
- */
- clone(): this;
-
- /**
- * Copies given color.
- * @param color Color to copy.
- */
- copy(color: Color): this;
-
- /**
- * Copies given color making conversion from gamma to linear space.
- * @param color Color to copy.
- */
- copyGammaToLinear(color: Color, gammaFactor?: number): Color;
-
- /**
- * Copies given color making conversion from linear to gamma space.
- * @param color Color to copy.
- */
- copyLinearToGamma(color: Color, gammaFactor?: number): Color;
-
- /**
- * Converts this color from gamma to linear space.
- */
- convertGammaToLinear(): Color;
-
- /**
- * Converts this color from linear to gamma space.
- */
- convertLinearToGamma(): Color;
-
- /**
- * Returns the hexadecimal value of this color.
- */
- getHex(): number;
-
- /**
- * Returns the string formated hexadecimal value of this color.
- */
- getHexString(): string;
-
- getHSL(target: HSL): HSL;
-
- /**
- * Returns the value of this color in CSS context style.
- * Example: rgb(r, g, b)
- */
- getStyle(): string;
-
- offsetHSL(h: number, s: number, l: number): this;
-
- add(color: Color): this;
- addColors(color1: Color, color2: Color): this;
- addScalar(s: number): this;
- sub(color: Color): this;
- multiply(color: Color): this;
- multiplyScalar(s: number): this;
- lerp(color: Color, alpha: number): this;
- lerpHSL(color: Color, alpha: number): this;
- equals(color: Color): boolean;
- fromArray(rgb: number[], offset?: number): this;
- toArray(array?: number[], offset?: number): number[];
- toArray(xyz: ArrayLike, offset?: number): ArrayLike;
-}
diff --git "a/spaces/betterme/Nice/pages/\345\244\232\350\275\256\345\257\271\350\257\235.py" "b/spaces/betterme/Nice/pages/\345\244\232\350\275\256\345\257\271\350\257\235.py"
deleted file mode 100644
index 35dcc6f3c9d026de6467b8d9c677e482e28aa77d..0000000000000000000000000000000000000000
--- "a/spaces/betterme/Nice/pages/\345\244\232\350\275\256\345\257\271\350\257\235.py"
+++ /dev/null
@@ -1,37 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# @Project : Python.
-# @File : 991_streamlit_apex_charts
-# @Time : 2022/10/17 上午10:48
-# @Author : yuanjie
-# @WeChat : meutils
-# @Software : PyCharm
-# @Description :
-import time
-import types
-
-import streamlit as st
-from meutils.pipe import *
-
-from appzoo.streamlit_app.utils import reply4input
-
-if __name__ == '__main__':
-
- previous_messages = ["你好！我是你的电影小助手，很高兴为您服务。", "你可以向我提问。"]
-
- container = st.container() # 占位符
- text = st.text_area(label="用户输入", height=100, placeholder="请在这儿输入您的问题")
-
-
- def reply_func(query):
- for i in range(10):
- time.sleep(0.5)
- yield query
- query += str(i)
-
-
- if st.button("发送", key="predict"):
- with st.spinner("AI正在思考，请稍等........"):
- history = st.session_state.get('state')
- st.session_state["state"] = reply4input(text, history, container=container, reply_func=reply_func,
- previous_messages=previous_messages)
diff --git a/spaces/bigjoker/stable-diffusion-webui/javascript/hires_fix.js b/spaces/bigjoker/stable-diffusion-webui/javascript/hires_fix.js
deleted file mode 100644
index ced83e7c05c8bb5199f6f08c4bee47ee1277d4f3..0000000000000000000000000000000000000000
--- a/spaces/bigjoker/stable-diffusion-webui/javascript/hires_fix.js
+++ /dev/null
@@ -1,22 +0,0 @@
-
-function setInactive(elem, inactive){
- if(inactive){
- elem.classList.add('inactive')
- } else{
- elem.classList.remove('inactive')
- }
-}
-
-function onCalcResolutionHires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y){
- hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale')
- hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x')
- hrResizeY = gradioApp().getElementById('txt2img_hr_resize_y')
-
- gradioApp().getElementById('txt2img_hires_fix_row2').style.display = opts.use_old_hires_fix_width_height ? "none" : ""
-
- setInactive(hrUpscaleBy, opts.use_old_hires_fix_width_height || hr_resize_x > 0 || hr_resize_y > 0)
- setInactive(hrResizeX, opts.use_old_hires_fix_width_height || hr_resize_x == 0)
- setInactive(hrResizeY, opts.use_old_hires_fix_width_height || hr_resize_y == 0)
-
- return [enable, width, height, hr_scale, hr_resize_x, hr_resize_y]
-}
diff --git a/spaces/bioriAsaeru/text-to-voice/Bhishma Movie Torrent Download [HOT].md b/spaces/bioriAsaeru/text-to-voice/Bhishma Movie Torrent Download [HOT].md
deleted file mode 100644
index c3fbf87a8d10a8f9a3e41a1847958ebbb71cfe6d..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Bhishma Movie Torrent Download [HOT].md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-Bhishma full movie is also available for download if you prefer to watch it later. There are torrent and direct download links available in HD, Blu-Ray and other ... 1fdad05405
-
-
-
diff --git a/spaces/bioriAsaeru/text-to-voice/Download Boss Baby (English) Today and Join the Adventure of a Lifetime.md b/spaces/bioriAsaeru/text-to-voice/Download Boss Baby (English) Today and Join the Adventure of a Lifetime.md
deleted file mode 100644
index aa125399b4cb015e2c6fd654f950392817b3b22f..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Download Boss Baby (English) Today and Join the Adventure of a Lifetime.md
+++ /dev/null
@@ -1,7 +0,0 @@
-
-
Later, Baby makes Tim suck a special pacifier that allows them to see Baby Corp, where babies come from. Most babies go to families, but those unresponsive to tickling are sent to management, where they are given a special baby formula that allows them to think and behave as adults while remaining young forever. Baby also explains he's on a special mission to discover why the world's love of babies is being threatened lately by love of puppies, and came to the Templetons as Tim's parents work for Puppy Co. Once his mission is done, he will leave. However, the boys overhear Baby's boss threatening to fire him, should he fail. As that would mean Baby would have to stay with the Templetons and grow up, Tim and Baby agree to work together to prevent this from happening.
Upon reading the original book on which the film is based McGrath felt a connection to it, as he had an older brother and felt like "the boss baby of the family".[11] In keeping with that theme he stated, in an interview with Den of Geek, that "My personal goal with this was to watch this movie with my brother, and to see how it affected him!", which resulted in McGrath's brother being moved to tears by the completed film.[12]
-
Includes unlimited streaming via the free Bandcamp app, plus high-quality downloads of Asheville (Demo Series), Will You Still Love Me? EP, Broken Down (Demo Series), Long Miracle, Out This Summer, Trying To Be Free (Vocal Version), Long For This World, If I Could Have, and 13 more. , and , . Purchasable with gift card Buy Digital Discography $27.01 CAD or more (30% OFF) Send as Gift lyrics Baby you could stay You don't have to go to work tonight Just call your boss and say Something in you don't feel right
The city will keep on breathing Just outside our door
I could get some wine It's been a while since we both had the time And the kids are out ti' 9 (maybe 10) We should just unwind
The planets will keep on spinning Just like they done before I'm pretty sure I am pretty sure
You can never it all A paycheque and a decent night's sleep But we could have tonight babe The time is ours to lose or to keep
Oh and that phone can keep on ringing That's what the machine is for I'm pretty sure Hey baby where you going Just sit awhile and dream with me Maybe we can make it true if we both believe Maybe we can make it true if we both believe $(".lyricsText").last().bcTruncate(TruncateProfile.get("tralbum_long"), "more", "less"); credits from Will You Still Love Me? EP, released June 24, 2022 license all rights reserved tags Tags folk electronic folk folk pop indie indie pop modern folk Edmonton Shopping cart total USD Check out about VON BIEKER Edmonton, Alberta
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diff --git a/spaces/bioriAsaeru/text-to-voice/Get Tokyo Outer Ring Road and Autonomous Car (Makuraishido) (Japanese Edition) in Epub Mobi Pdf Fb Formats A Sci-Fi Thriller by Makuraishido.md b/spaces/bioriAsaeru/text-to-voice/Get Tokyo Outer Ring Road and Autonomous Car (Makuraishido) (Japanese Edition) in Epub Mobi Pdf Fb Formats A Sci-Fi Thriller by Makuraishido.md
deleted file mode 100644
index c41799cedff2008773a73647d39a4d3d536a90e6..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Get Tokyo Outer Ring Road and Autonomous Car (Makuraishido) (Japanese Edition) in Epub Mobi Pdf Fb Formats A Sci-Fi Thriller by Makuraishido.md
+++ /dev/null
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-
Tokyo Outer Ring Road and Autonomous Car (Makuraishido) (Japanese Edition) download epub mobi pdf fb
-
- aaccfb2cb3
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-
diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/PsdImagePlugin.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/PsdImagePlugin.py
deleted file mode 100644
index 5a5d60d568c78b1546d0564b38a64fec2e2ca0b1..0000000000000000000000000000000000000000
--- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/PIL/PsdImagePlugin.py
+++ /dev/null
@@ -1,303 +0,0 @@
-#
-# The Python Imaging Library
-# $Id$
-#
-# Adobe PSD 2.5/3.0 file handling
-#
-# History:
-# 1995-09-01 fl Created
-# 1997-01-03 fl Read most PSD images
-# 1997-01-18 fl Fixed P and CMYK support
-# 2001-10-21 fl Added seek/tell support (for layers)
-#
-# Copyright (c) 1997-2001 by Secret Labs AB.
-# Copyright (c) 1995-2001 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-import io
-
-from . import Image, ImageFile, ImagePalette
-from ._binary import i8
-from ._binary import i16be as i16
-from ._binary import i32be as i32
-from ._binary import si16be as si16
-
-MODES = {
- # (photoshop mode, bits) -> (pil mode, required channels)
- (0, 1): ("1", 1),
- (0, 8): ("L", 1),
- (1, 8): ("L", 1),
- (2, 8): ("P", 1),
- (3, 8): ("RGB", 3),
- (4, 8): ("CMYK", 4),
- (7, 8): ("L", 1), # FIXME: multilayer
- (8, 8): ("L", 1), # duotone
- (9, 8): ("LAB", 3),
-}
-
-
-# --------------------------------------------------------------------.
-# read PSD images
-
-
-def _accept(prefix):
- return prefix[:4] == b"8BPS"
-
-
-##
-# Image plugin for Photoshop images.
-
-
-class PsdImageFile(ImageFile.ImageFile):
- format = "PSD"
- format_description = "Adobe Photoshop"
- _close_exclusive_fp_after_loading = False
-
- def _open(self):
- read = self.fp.read
-
- #
- # header
-
- s = read(26)
- if not _accept(s) or i16(s, 4) != 1:
- msg = "not a PSD file"
- raise SyntaxError(msg)
-
- psd_bits = i16(s, 22)
- psd_channels = i16(s, 12)
- psd_mode = i16(s, 24)
-
- mode, channels = MODES[(psd_mode, psd_bits)]
-
- if channels > psd_channels:
- msg = "not enough channels"
- raise OSError(msg)
- if mode == "RGB" and psd_channels == 4:
- mode = "RGBA"
- channels = 4
-
- self.mode = mode
- self._size = i32(s, 18), i32(s, 14)
-
- #
- # color mode data
-
- size = i32(read(4))
- if size:
- data = read(size)
- if mode == "P" and size == 768:
- self.palette = ImagePalette.raw("RGB;L", data)
-
- #
- # image resources
-
- self.resources = []
-
- size = i32(read(4))
- if size:
- # load resources
- end = self.fp.tell() + size
- while self.fp.tell() < end:
- read(4) # signature
- id = i16(read(2))
- name = read(i8(read(1)))
- if not (len(name) & 1):
- read(1) # padding
- data = read(i32(read(4)))
- if len(data) & 1:
- read(1) # padding
- self.resources.append((id, name, data))
- if id == 1039: # ICC profile
- self.info["icc_profile"] = data
-
- #
- # layer and mask information
-
- self.layers = []
-
- size = i32(read(4))
- if size:
- end = self.fp.tell() + size
- size = i32(read(4))
- if size:
- _layer_data = io.BytesIO(ImageFile._safe_read(self.fp, size))
- self.layers = _layerinfo(_layer_data, size)
- self.fp.seek(end)
- self.n_frames = len(self.layers)
- self.is_animated = self.n_frames > 1
-
- #
- # image descriptor
-
- self.tile = _maketile(self.fp, mode, (0, 0) + self.size, channels)
-
- # keep the file open
- self._fp = self.fp
- self.frame = 1
- self._min_frame = 1
-
- def seek(self, layer):
- if not self._seek_check(layer):
- return
-
- # seek to given layer (1..max)
- try:
- name, mode, bbox, tile = self.layers[layer - 1]
- self.mode = mode
- self.tile = tile
- self.frame = layer
- self.fp = self._fp
- return name, bbox
- except IndexError as e:
- msg = "no such layer"
- raise EOFError(msg) from e
-
- def tell(self):
- # return layer number (0=image, 1..max=layers)
- return self.frame
-
-
-def _layerinfo(fp, ct_bytes):
- # read layerinfo block
- layers = []
-
- def read(size):
- return ImageFile._safe_read(fp, size)
-
- ct = si16(read(2))
-
- # sanity check
- if ct_bytes < (abs(ct) * 20):
- msg = "Layer block too short for number of layers requested"
- raise SyntaxError(msg)
-
- for _ in range(abs(ct)):
- # bounding box
- y0 = i32(read(4))
- x0 = i32(read(4))
- y1 = i32(read(4))
- x1 = i32(read(4))
-
- # image info
- mode = []
- ct_types = i16(read(2))
- types = list(range(ct_types))
- if len(types) > 4:
- continue
-
- for _ in types:
- type = i16(read(2))
-
- if type == 65535:
- m = "A"
- else:
- m = "RGBA"[type]
-
- mode.append(m)
- read(4) # size
-
- # figure out the image mode
- mode.sort()
- if mode == ["R"]:
- mode = "L"
- elif mode == ["B", "G", "R"]:
- mode = "RGB"
- elif mode == ["A", "B", "G", "R"]:
- mode = "RGBA"
- else:
- mode = None # unknown
-
- # skip over blend flags and extra information
- read(12) # filler
- name = ""
- size = i32(read(4)) # length of the extra data field
- if size:
- data_end = fp.tell() + size
-
- length = i32(read(4))
- if length:
- fp.seek(length - 16, io.SEEK_CUR)
-
- length = i32(read(4))
- if length:
- fp.seek(length, io.SEEK_CUR)
-
- length = i8(read(1))
- if length:
- # Don't know the proper encoding,
- # Latin-1 should be a good guess
- name = read(length).decode("latin-1", "replace")
-
- fp.seek(data_end)
- layers.append((name, mode, (x0, y0, x1, y1)))
-
- # get tiles
- for i, (name, mode, bbox) in enumerate(layers):
- tile = []
- for m in mode:
- t = _maketile(fp, m, bbox, 1)
- if t:
- tile.extend(t)
- layers[i] = name, mode, bbox, tile
-
- return layers
-
-
-def _maketile(file, mode, bbox, channels):
- tile = None
- read = file.read
-
- compression = i16(read(2))
-
- xsize = bbox[2] - bbox[0]
- ysize = bbox[3] - bbox[1]
-
- offset = file.tell()
-
- if compression == 0:
- #
- # raw compression
- tile = []
- for channel in range(channels):
- layer = mode[channel]
- if mode == "CMYK":
- layer += ";I"
- tile.append(("raw", bbox, offset, layer))
- offset = offset + xsize * ysize
-
- elif compression == 1:
- #
- # packbits compression
- i = 0
- tile = []
- bytecount = read(channels * ysize * 2)
- offset = file.tell()
- for channel in range(channels):
- layer = mode[channel]
- if mode == "CMYK":
- layer += ";I"
- tile.append(("packbits", bbox, offset, layer))
- for y in range(ysize):
- offset = offset + i16(bytecount, i)
- i += 2
-
- file.seek(offset)
-
- if offset & 1:
- read(1) # padding
-
- return tile
-
-
-# --------------------------------------------------------------------
-# registry
-
-
-Image.register_open(PsdImageFile.format, PsdImageFile, _accept)
-
-Image.register_extension(PsdImageFile.format, ".psd")
-
-Image.register_mime(PsdImageFile.format, "image/vnd.adobe.photoshop")
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/configs/common/models/panoptic_fpn.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/configs/common/models/panoptic_fpn.py
deleted file mode 100644
index 88f55d2ce9db62e61445d6a3700067d9d864ecae..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/configs/common/models/panoptic_fpn.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from detectron2.config import LazyCall as L
-from detectron2.layers import ShapeSpec
-from detectron2.modeling import PanopticFPN
-from detectron2.modeling.meta_arch.semantic_seg import SemSegFPNHead
-
-from .mask_rcnn_fpn import model
-
-model._target_ = PanopticFPN
-model.sem_seg_head = L(SemSegFPNHead)(
- input_shape={
- f: L(ShapeSpec)(stride=s, channels="${....backbone.out_channels}")
- for f, s in zip(["p2", "p3", "p4", "p5"], [4, 8, 16, 32])
- },
- ignore_value=255,
- num_classes=54, # COCO stuff + 1
- conv_dims=128,
- common_stride=4,
- loss_weight=0.5,
- norm="GN",
-)
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/docs/notes/contributing.md b/spaces/carlosalonso/Detection-video/carpeta_deteccion/docs/notes/contributing.md
deleted file mode 100644
index 95181235eaff1cb5cbb2dc554e8d4991b603d0e5..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/docs/notes/contributing.md
+++ /dev/null
@@ -1 +0,0 @@
-../../.github/CONTRIBUTING.md
\ No newline at end of file
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/doc/DENSEPOSE_DATASETS.md b/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/doc/DENSEPOSE_DATASETS.md
deleted file mode 100644
index 6943741e104310e7ec1837951e602e9c79061b10..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/projects/DensePose/doc/DENSEPOSE_DATASETS.md
+++ /dev/null
@@ -1,513 +0,0 @@
-# DensePose Datasets
-
-We summarize the datasets used in various DensePose training
-schedules and describe different available annotation types.
-
-## Table of Contents
-
-[General Information](#general-information)
-
-[DensePose COCO](#densepose-coco)
-
-[DensePose PoseTrack](#densepose-posetrack)
-
-[DensePose Chimps](#densepose-chimps)
-
-[DensePose LVIS](#densepose-lvis)
-
-## General Information
-
-DensePose annotations are typically stored in JSON files. Their
-structure follows the [COCO Data Format](https://cocodataset.org/#format-data),
-the basic data structure is outlined below:
-
-```
-{
- "info": info,
- "images": [image],
- "annotations": [annotation],
- "licenses": [license],
-}
-
-info{
- "year": int,
- "version": str,
- "description": str,
- "contributor": str,
- "url": str,
- "date_created": datetime,
-}
-
-image{
- "id": int,
- "width": int,
- "height": int,
- "file_name": str,
- "license": int,
- "flickr_url": str,
- "coco_url": str,
- "date_captured": datetime,
-}
-
-license{
- "id": int, "name": str, "url": str,
-}
-```
-
-DensePose annotations can be of two types:
-*chart-based annotations* or *continuous surface embeddings annotations*.
-We give more details on each of the two annotation types below.
-
-### Chart-based Annotations
-
-These annotations assume a single 3D model which corresponds to
-all the instances in a given dataset.
-3D model is assumed to be split into *charts*. Each chart has its own
-2D parametrization through inner coordinates `U` and `V`, typically
-taking values in `[0, 1]`.
-
-Chart-based annotations consist of *point-based annotations* and
-*segmentation annotations*. Point-based annotations specify, for a given
-image point, which model part it belongs to and what are its coordinates
-in the corresponding chart. Segmentation annotations specify regions
-in an image that are occupied by a given part. In some cases, charts
-associated with point annotations are more detailed than the ones
-associated with segmentation annotations. In this case we distinguish
-*fine segmentation* (associated with points) and *coarse segmentation*
-(associated with masks).
-
-**Point-based annotations**:
-
-`dp_x` and `dp_y`: image coordinates of the annotated points along
-the horizontal and vertical axes respectively. The coordinates are defined
-with respect to the top-left corner of the annotated bounding box and are
-normalized assuming the bounding box size to be `256x256`;
-
-`dp_I`: for each point specifies the index of the fine segmentation chart
-it belongs to;
-
-`dp_U` and `dp_V`: point coordinates on the corresponding chart.
-Each fine segmentation part has its own parametrization in terms of chart
-coordinates.
-
-**Segmentation annotations**:
-
-`dp_masks`: RLE encoded dense masks (`dict` containing keys `counts` and `size`).
-The masks are typically of size `256x256`, they define segmentation within the
-bounding box.
-
-### Continuous Surface Embeddings Annotations
-
-Continuous surface embeddings annotations also consist of *point-based annotations*
-and *segmentation annotations*. Point-based annotations establish correspondence
-between image points and 3D model vertices. Segmentation annotations specify
-foreground regions for a given instane.
-
-**Point-based annotations**:
-
-`dp_x` and `dp_y` specify image point coordinates the same way as for chart-based
-annotations;
-
-`dp_vertex` gives indices of 3D model vertices, which the annotated image points
-correspond to;
-
-`ref_model` specifies 3D model name.
-
-**Segmentation annotations**:
-
-Segmentations can either be given by `dp_masks` field or by `segmentation` field.
-
-`dp_masks`: RLE encoded dense masks (`dict` containing keys `counts` and `size`).
-The masks are typically of size `256x256`, they define segmentation within the
-bounding box.
-
-`segmentation`: polygon-based masks stored as a 2D list
-`[[x1 y1 x2 y2...],[x1 y1 ...],...]` of polygon vertex coordinates in a given
-image.
-
-## DensePose COCO
-
-
-
-
-
- Figure 1. Annotation examples from the DensePose COCO dataset.
-
-
-DensePose COCO dataset contains about 50K annotated persons on images from the
-[COCO dataset](https://cocodataset.org/#home)
-The images are available for download from the
-[COCO Dataset download page](https://cocodataset.org/#download):
-[train2014](http://images.cocodataset.org/zips/train2014.zip),
-[val2014](http://images.cocodataset.org/zips/val2014.zip).
-The details on available annotations and their download links are given below.
-
-### Chart-based Annotations
-
-Chart-based DensePose COCO annotations are available for the instances of category
-`person` and correspond to the model shown in Figure 2.
-They include `dp_x`, `dp_y`, `dp_I`, `dp_U` and `dp_V` fields for annotated points
-(~100 points per annotated instance) and `dp_masks` field, which encodes
-coarse segmentation into 14 parts in the following order:
-`Torso`, `Right Hand`, `Left Hand`, `Left Foot`, `Right Foot`,
-`Upper Leg Right`, `Upper Leg Left`, `Lower Leg Right`, `Lower Leg Left`,
-`Upper Arm Left`, `Upper Arm Right`, `Lower Arm Left`, `Lower Arm Right`,
-`Head`.
-
-
-
-
-
- Figure 2. Human body charts (fine segmentation)
- and the associated 14 body parts depicted with rounded rectangles
- (coarse segmentation).
-
-
-The dataset splits used in the training schedules are
-`train2014`, `valminusminival2014` and `minival2014`.
-`train2014` and `valminusminival2014` are used for training,
-and `minival2014` is used for validation.
-The table with annotation download links, which summarizes the number of annotated
-instances and images for each of the dataset splits is given below:
-
-
-### Continuous Surface Embeddings Annotations
-
-DensePose COCO continuous surface embeddings annotations are available for the instances
-of category `person`. The annotations correspond to the 3D model shown in Figure 2,
-and include `dp_x`, `dp_y` and `dp_vertex` and `ref_model` fields.
-All chart-based annotations were also kept for convenience.
-
-As with chart-based annotations, the dataset splits used in the training schedules are
-`train2014`, `valminusminival2014` and `minival2014`.
-`train2014` and `valminusminival2014` are used for training,
-and `minival2014` is used for validation.
-The table with annotation download links, which summarizes the number of annotated
-instances and images for each of the dataset splits is given below:
-
- Figure 3. Annotation examples from the PoseTrack dataset.
-
-
-DensePose PoseTrack dataset contains annotated image sequences.
-To download the images for this dataset, please follow the instructions
-from the [PoseTrack Download Page](https://posetrack.net/users/download.php).
-
-### Chart-based Annotations
-
-Chart-based DensePose PoseTrack annotations are available for the instances with category
-`person` and correspond to the model shown in Figure 2.
-They include `dp_x`, `dp_y`, `dp_I`, `dp_U` and `dp_V` fields for annotated points
-(~100 points per annotated instance) and `dp_masks` field, which encodes
-coarse segmentation into the same 14 parts as in DensePose COCO.
-
-The dataset splits used in the training schedules are
-`posetrack_train2017` (train set) and `posetrack_val2017` (validation set).
-The table with annotation download links, which summarizes the number of annotated
-instances, instance tracks and images for the dataset splits is given below:
-
- Figure 4. Example images from the DensePose Chimps dataset.
-
-
-DensePose Chimps dataset contains annotated images of chimpanzees.
-To download the images for this dataset, please use the URL specified in
-`image_url` field in the annotations.
-
-### Chart-based Annotations
-
-Chart-based DensePose Chimps annotations correspond to the human model shown in Figure 2,
-the instances are thus annotated to belong to the `person` category.
-They include `dp_x`, `dp_y`, `dp_I`, `dp_U` and `dp_V` fields for annotated points
-(~3 points per annotated instance) and `dp_masks` field, which encodes
-foreground mask in RLE format.
-
-Chart-base DensePose Chimps annotations are used for validation only.
-The table with annotation download link, which summarizes the number of annotated
-instances and images is given below:
-
-
-
-### Continuous Surface Embeddings Annotations
-
-Continuous surface embeddings annotations for DensePose Chimps
-include `dp_x`, `dp_y` and `dp_vertex` point-based annotations
-(~3 points per annotated instance), `dp_masks` field with the same
-contents as for chart-based annotations and `ref_model` field
-which refers to a chimpanzee 3D model `chimp_5029`.
-
-The dataset is split into training and validation subsets.
-The table with annotation download links, which summarizes the number of annotated
-instances and images for each of the dataset splits is given below:
-
-The table below outlines the dataset splits:
-
- Figure 5. Example images from the DensePose LVIS dataset.
-
-
-DensePose LVIS dataset contains segmentation and DensePose annotations for animals
-on images from the [LVIS dataset](https://www.lvisdataset.org/dataset).
-The images are available for download through the links:
-[train2017](http://images.cocodataset.org/zips/train2017.zip),
-[val2017](http://images.cocodataset.org/zips/val2017.zip).
-
-### Continuous Surface Embeddings Annotations
-
-Continuous surface embeddings (CSE) annotations for DensePose LVIS
-include `dp_x`, `dp_y` and `dp_vertex` point-based annotations
-(~3 points per annotated instance) and a `ref_model` field
-which refers to a 3D model that corresponds to the instance.
-Instances from 9 animal categories were annotated with CSE DensePose data:
-bear, cow, cat, dog, elephant, giraffe, horse, sheep and zebra.
-
-Foreground masks are available from instance segmentation annotations
-(`segmentation` field) in polygon format, they are stored as a 2D list
-`[[x1 y1 x2 y2...],[x1 y1 ...],...]`.
-
-We used two datasets, each constising of one training (`train`)
-and validation (`val`) subsets: the first one (`ds1`)
-was used in [Neverova et al, 2020](https://arxiv.org/abs/2011.12438).
-The second one (`ds2`), was used in [Neverova et al, 2021]().
-
-The summary of the available datasets is given below:
-
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/cjwzfczr12398/DeepDanbooru_string/README.md b/spaces/cjwzfczr12398/DeepDanbooru_string/README.md
deleted file mode 100644
index 4330b6f969246dc764a34ea254d2e807159f1c55..0000000000000000000000000000000000000000
--- a/spaces/cjwzfczr12398/DeepDanbooru_string/README.md
+++ /dev/null
@@ -1,39 +0,0 @@
----
-title: DeepDanbooru String
-emoji: 💬
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
-duplicated_from: NoCrypt/DeepDanbooru_string
----
-
-# 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.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/codellama/codellama-13b-chat/README.md b/spaces/codellama/codellama-13b-chat/README.md
deleted file mode 100644
index 6dc5fbe49240b4bdb8b857a399eacbbd272d57d6..0000000000000000000000000000000000000000
--- a/spaces/codellama/codellama-13b-chat/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
----
-title: Code Llama 13B Chat
-emoji: 🦙
-colorFrom: indigo
-colorTo: pink
-sdk: gradio
-sdk_version: 3.37.0
-app_file: app.py
-pinned: false
-license: other
-suggested_hardware: a10g-small
-duplicated_from: huggingface-projects/llama-2-13b-chat
----
-
-# LLAMA v2 Models
-
-Llama v2 was introduced in [this paper](https://arxiv.org/abs/2307.09288).
-
-This Space demonstrates [Llama-2-13b-chat-hf](meta-llama/Llama-2-13b-chat-hf) from Meta. Please, check the original model card for details.
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aarch64/h264pred_init.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aarch64/h264pred_init.c
deleted file mode 100644
index 0ae8f70d239047a947b22922b79508f1eeb0acc7..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/aarch64/h264pred_init.c
+++ /dev/null
@@ -1,135 +0,0 @@
-/*
- * Copyright (c) 2009 Mans Rullgard
- *
- * 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 "libavutil/attributes.h"
-#include "libavutil/aarch64/cpu.h"
-#include "libavcodec/avcodec.h"
-#include "libavcodec/h264pred.h"
-
-void ff_pred16x16_vert_neon(uint8_t *src, ptrdiff_t stride);
-void ff_pred16x16_hor_neon(uint8_t *src, ptrdiff_t stride);
-void ff_pred16x16_plane_neon(uint8_t *src, ptrdiff_t stride);
-void ff_pred16x16_dc_neon(uint8_t *src, ptrdiff_t stride);
-void ff_pred16x16_128_dc_neon(uint8_t *src, ptrdiff_t stride);
-void ff_pred16x16_left_dc_neon(uint8_t *src, ptrdiff_t stride);
-void ff_pred16x16_top_dc_neon(uint8_t *src, ptrdiff_t stride);
-
-void ff_pred8x8_vert_neon(uint8_t *src, ptrdiff_t stride);
-void ff_pred8x8_hor_neon(uint8_t *src, ptrdiff_t stride);
-void ff_pred8x8_plane_neon(uint8_t *src, ptrdiff_t stride);
-void ff_pred8x8_dc_neon(uint8_t *src, ptrdiff_t stride);
-void ff_pred8x8_128_dc_neon(uint8_t *src, ptrdiff_t stride);
-void ff_pred8x8_left_dc_neon(uint8_t *src, ptrdiff_t stride);
-void ff_pred8x8_top_dc_neon(uint8_t *src, ptrdiff_t stride);
-void ff_pred8x8_l0t_dc_neon(uint8_t *src, ptrdiff_t stride);
-void ff_pred8x8_0lt_dc_neon(uint8_t *src, ptrdiff_t stride);
-void ff_pred8x8_l00_dc_neon(uint8_t *src, ptrdiff_t stride);
-void ff_pred8x8_0l0_dc_neon(uint8_t *src, ptrdiff_t stride);
-
-void ff_pred16x16_vert_neon_10(uint8_t *src, ptrdiff_t stride);
-void ff_pred16x16_hor_neon_10(uint8_t *src, ptrdiff_t stride);
-void ff_pred16x16_plane_neon_10(uint8_t *src, ptrdiff_t stride);
-void ff_pred16x16_dc_neon_10(uint8_t *src, ptrdiff_t stride);
-void ff_pred16x16_top_dc_neon_10(uint8_t *src, ptrdiff_t stride);
-
-void ff_pred8x8_vert_neon_10(uint8_t *src, ptrdiff_t stride);
-void ff_pred8x8_hor_neon_10(uint8_t *src, ptrdiff_t stride);
-void ff_pred8x8_plane_neon_10(uint8_t *src, ptrdiff_t stride);
-void ff_pred8x8_dc_neon_10(uint8_t *src, ptrdiff_t stride);
-void ff_pred8x8_128_dc_neon_10(uint8_t *src, ptrdiff_t stride);
-void ff_pred8x8_left_dc_neon_10(uint8_t *src, ptrdiff_t stride);
-void ff_pred8x8_top_dc_neon_10(uint8_t *src, ptrdiff_t stride);
-void ff_pred8x8_l0t_dc_neon_10(uint8_t *src, ptrdiff_t stride);
-void ff_pred8x8_0lt_dc_neon_10(uint8_t *src, ptrdiff_t stride);
-void ff_pred8x8_l00_dc_neon_10(uint8_t *src, ptrdiff_t stride);
-void ff_pred8x8_0l0_dc_neon_10(uint8_t *src, ptrdiff_t stride);
-
-static av_cold void h264_pred_init_neon(H264PredContext *h, int codec_id,
- const int bit_depth,
- const int chroma_format_idc)
-{
- if (bit_depth == 8) {
- if (chroma_format_idc <= 1) {
- h->pred8x8[VERT_PRED8x8 ] = ff_pred8x8_vert_neon;
- h->pred8x8[HOR_PRED8x8 ] = ff_pred8x8_hor_neon;
- if (codec_id != AV_CODEC_ID_VP7 && codec_id != AV_CODEC_ID_VP8)
- h->pred8x8[PLANE_PRED8x8] = ff_pred8x8_plane_neon;
- h->pred8x8[DC_128_PRED8x8 ] = ff_pred8x8_128_dc_neon;
- if (codec_id != AV_CODEC_ID_RV40 && codec_id != AV_CODEC_ID_VP7 &&
- codec_id != AV_CODEC_ID_VP8) {
- h->pred8x8[DC_PRED8x8 ] = ff_pred8x8_dc_neon;
- h->pred8x8[LEFT_DC_PRED8x8] = ff_pred8x8_left_dc_neon;
- h->pred8x8[TOP_DC_PRED8x8 ] = ff_pred8x8_top_dc_neon;
- h->pred8x8[ALZHEIMER_DC_L0T_PRED8x8] = ff_pred8x8_l0t_dc_neon;
- h->pred8x8[ALZHEIMER_DC_0LT_PRED8x8] = ff_pred8x8_0lt_dc_neon;
- h->pred8x8[ALZHEIMER_DC_L00_PRED8x8] = ff_pred8x8_l00_dc_neon;
- h->pred8x8[ALZHEIMER_DC_0L0_PRED8x8] = ff_pred8x8_0l0_dc_neon;
- }
- }
-
- h->pred16x16[DC_PRED8x8 ] = ff_pred16x16_dc_neon;
- h->pred16x16[VERT_PRED8x8 ] = ff_pred16x16_vert_neon;
- h->pred16x16[HOR_PRED8x8 ] = ff_pred16x16_hor_neon;
- h->pred16x16[LEFT_DC_PRED8x8] = ff_pred16x16_left_dc_neon;
- h->pred16x16[TOP_DC_PRED8x8 ] = ff_pred16x16_top_dc_neon;
- h->pred16x16[DC_128_PRED8x8 ] = ff_pred16x16_128_dc_neon;
- if (codec_id != AV_CODEC_ID_SVQ3 && codec_id != AV_CODEC_ID_RV40 &&
- codec_id != AV_CODEC_ID_VP7 && codec_id != AV_CODEC_ID_VP8)
- h->pred16x16[PLANE_PRED8x8 ] = ff_pred16x16_plane_neon;
- }
- if (bit_depth == 10) {
- if (chroma_format_idc <= 1) {
- h->pred8x8[VERT_PRED8x8 ] = ff_pred8x8_vert_neon_10;
- h->pred8x8[HOR_PRED8x8 ] = ff_pred8x8_hor_neon_10;
- if (codec_id != AV_CODEC_ID_VP7 && codec_id != AV_CODEC_ID_VP8)
- h->pred8x8[PLANE_PRED8x8] = ff_pred8x8_plane_neon_10;
- h->pred8x8[DC_128_PRED8x8 ] = ff_pred8x8_128_dc_neon_10;
- if (codec_id != AV_CODEC_ID_RV40 && codec_id != AV_CODEC_ID_VP7 &&
- codec_id != AV_CODEC_ID_VP8) {
- h->pred8x8[DC_PRED8x8 ] = ff_pred8x8_dc_neon_10;
- h->pred8x8[LEFT_DC_PRED8x8] = ff_pred8x8_left_dc_neon_10;
- h->pred8x8[TOP_DC_PRED8x8 ] = ff_pred8x8_top_dc_neon_10;
- h->pred8x8[ALZHEIMER_DC_L0T_PRED8x8] = ff_pred8x8_l0t_dc_neon_10;
- h->pred8x8[ALZHEIMER_DC_0LT_PRED8x8] = ff_pred8x8_0lt_dc_neon_10;
- h->pred8x8[ALZHEIMER_DC_L00_PRED8x8] = ff_pred8x8_l00_dc_neon_10;
- h->pred8x8[ALZHEIMER_DC_0L0_PRED8x8] = ff_pred8x8_0l0_dc_neon_10;
- }
- }
-
- h->pred16x16[DC_PRED8x8 ] = ff_pred16x16_dc_neon_10;
- h->pred16x16[VERT_PRED8x8 ] = ff_pred16x16_vert_neon_10;
- h->pred16x16[HOR_PRED8x8 ] = ff_pred16x16_hor_neon_10;
- h->pred16x16[TOP_DC_PRED8x8 ] = ff_pred16x16_top_dc_neon_10;
- if (codec_id != AV_CODEC_ID_SVQ3 && codec_id != AV_CODEC_ID_RV40 &&
- codec_id != AV_CODEC_ID_VP7 && codec_id != AV_CODEC_ID_VP8)
- h->pred16x16[PLANE_PRED8x8 ] = ff_pred16x16_plane_neon_10;
- }
-}
-
-av_cold void ff_h264_pred_init_aarch64(H264PredContext *h, int codec_id,
- int bit_depth, const int chroma_format_idc)
-{
- int cpu_flags = av_get_cpu_flags();
-
- if (have_neon(cpu_flags))
- h264_pred_init_neon(h, codec_id, bit_depth, chroma_format_idc);
-}
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arbc.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arbc.c
deleted file mode 100644
index 343c56695ea55d020db85a95615139bcd17cfb80..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/arbc.c
+++ /dev/null
@@ -1,221 +0,0 @@
-/*
- * Gryphon's Anim Compressor decoder
- * Copyright (c) 2019 Paul B Mahol
- *
- * 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 "libavutil/internal.h"
-#include "libavutil/intreadwrite.h"
-
-#include "avcodec.h"
-#include "bytestream.h"
-#include "codec_internal.h"
-#include "decode.h"
-
-typedef struct ARBCContext {
- GetByteContext gb;
-
- AVFrame *prev_frame;
-} ARBCContext;
-
-static int fill_tile4(AVCodecContext *avctx, int color, AVFrame *frame)
-{
- ARBCContext *s = avctx->priv_data;
- GetByteContext *gb = &s->gb;
- int nb_tiles = bytestream2_get_le16(gb);
- int h = avctx->height - 1;
- int pixels_overwritten = 0;
-
- if ((avctx->width / 4 + 1) * (avctx->height / 4 + 1) < nb_tiles)
- return 0;
-
- for (int i = 0; i < nb_tiles; i++) {
- int y = bytestream2_get_byte(gb);
- int x = bytestream2_get_byte(gb);
- uint16_t mask = bytestream2_get_le16(gb);
- int start_y = y * 4, start_x = x * 4;
- int end_y = start_y + 4, end_x = start_x + 4;
-
- for (int j = start_y; j < end_y; j++) {
- for (int k = start_x; k < end_x; k++) {
- if (mask & 0x8000) {
- if (j >= avctx->height || k >= avctx->width) {
- mask = mask << 1;
- continue;
- }
- AV_WB24(&frame->data[0][frame->linesize[0] * (h - j) + 3 * k], color);
- pixels_overwritten ++;
- }
- mask = mask << 1;
- }
- }
- }
- return pixels_overwritten;
-}
-
-static int fill_tileX(AVCodecContext *avctx, int tile_width, int tile_height,
- int color, AVFrame *frame)
-{
- ARBCContext *s = avctx->priv_data;
- GetByteContext *gb = &s->gb;
- const int step_h = tile_height / 4;
- const int step_w = tile_width / 4;
- int nb_tiles = bytestream2_get_le16(gb);
- int h = avctx->height - 1;
- int pixels_overwritten = 0;
-
- if ((avctx->width / tile_width + 1) * (avctx->height / tile_height + 1) < nb_tiles)
- return 0;
-
- for (int i = 0; i < nb_tiles; i++) {
- int y = bytestream2_get_byte(gb);
- int x = bytestream2_get_byte(gb);
- uint16_t mask = bytestream2_get_le16(gb);
- int start_y = y * tile_height, start_x = x * tile_width;
- int end_y = start_y + tile_height, end_x = start_x + tile_width;
-
- if (start_x >= avctx->width || start_y >= avctx->height)
- continue;
-
- for (int j = start_y; j < end_y; j += step_h) {
- for (int k = start_x; k < end_x; k += step_w) {
- if (mask & 0x8000U) {
- for (int m = 0; m < step_h; m++) {
- for (int n = 0; n < step_w; n++) {
- if (j + m >= avctx->height || k + n >= avctx->width)
- continue;
- AV_WB24(&frame->data[0][frame->linesize[0] * (h - (j + m)) + 3 * (k + n)], color);
- }
- }
- pixels_overwritten += FFMIN(step_h, avctx->height - j) * FFMIN(step_w, avctx->width - k);
- }
- mask = mask << 1;
- }
- }
- }
- return pixels_overwritten;
-}
-
-static int decode_frame(AVCodecContext *avctx, AVFrame *frame,
- int *got_frame, AVPacket *avpkt)
-{
- ARBCContext *s = avctx->priv_data;
- int ret, nb_segments;
- int prev_pixels = avctx->width * avctx->height;
-
- if (avpkt->size < 10)
- return AVERROR_INVALIDDATA;
-
- bytestream2_init(&s->gb, avpkt->data, avpkt->size);
- bytestream2_skip(&s->gb, 8);
- nb_segments = bytestream2_get_le16(&s->gb);
- if (nb_segments == 0)
- return avpkt->size;
-
- if (7 * nb_segments > bytestream2_get_bytes_left(&s->gb))
- return AVERROR_INVALIDDATA;
-
- if ((ret = ff_get_buffer(avctx, frame, AV_GET_BUFFER_FLAG_REF)) < 0)
- return ret;
-
- if (s->prev_frame->data[0]) {
- ret = av_frame_copy(frame, s->prev_frame);
- if (ret < 0)
- return ret;
- }
-
- for (int i = 0; i < nb_segments; i++) {
- int resolution_flag;
- int fill;
-
- if (bytestream2_get_bytes_left(&s->gb) <= 0)
- return AVERROR_INVALIDDATA;
-
- fill = bytestream2_get_byte(&s->gb) << 16;
- bytestream2_skip(&s->gb, 1);
- fill |= bytestream2_get_byte(&s->gb) << 8;
- bytestream2_skip(&s->gb, 1);
- fill |= bytestream2_get_byte(&s->gb) << 0;
- bytestream2_skip(&s->gb, 1);
- resolution_flag = bytestream2_get_byte(&s->gb);
-
- if (resolution_flag & 0x10)
- prev_pixels -= fill_tileX(avctx, 1024, 1024, fill, frame);
- if (resolution_flag & 0x08)
- prev_pixels -= fill_tileX(avctx, 256, 256, fill, frame);
- if (resolution_flag & 0x04)
- prev_pixels -= fill_tileX(avctx, 64, 64, fill, frame);
- if (resolution_flag & 0x02)
- prev_pixels -= fill_tileX(avctx, 16, 16, fill, frame);
- if (resolution_flag & 0x01)
- prev_pixels -= fill_tile4(avctx, fill, frame);
- }
-
- av_frame_unref(s->prev_frame);
- if ((ret = av_frame_ref(s->prev_frame, frame)) < 0)
- return ret;
-
- frame->pict_type = prev_pixels <= 0 ? AV_PICTURE_TYPE_I : AV_PICTURE_TYPE_P;
- frame->key_frame = prev_pixels <= 0;
- *got_frame = 1;
-
- return avpkt->size;
-}
-
-static av_cold int decode_init(AVCodecContext *avctx)
-{
- ARBCContext *s = avctx->priv_data;
-
- avctx->pix_fmt = AV_PIX_FMT_RGB24;
-
- s->prev_frame = av_frame_alloc();
- if (!s->prev_frame)
- return AVERROR(ENOMEM);
-
- return 0;
-}
-
-static void decode_flush(AVCodecContext *avctx)
-{
- ARBCContext *s = avctx->priv_data;
-
- av_frame_unref(s->prev_frame);
-}
-
-static av_cold int decode_close(AVCodecContext *avctx)
-{
- ARBCContext *s = avctx->priv_data;
-
- av_frame_free(&s->prev_frame);
-
- return 0;
-}
-
-const FFCodec ff_arbc_decoder = {
- .p.name = "arbc",
- CODEC_LONG_NAME("Gryphon's Anim Compressor"),
- .p.type = AVMEDIA_TYPE_VIDEO,
- .p.id = AV_CODEC_ID_ARBC,
- .priv_data_size = sizeof(ARBCContext),
- .init = decode_init,
- FF_CODEC_DECODE_CB(decode_frame),
- .flush = decode_flush,
- .close = decode_close,
- .p.capabilities = AV_CODEC_CAP_DR1,
- .caps_internal = FF_CODEC_CAP_INIT_CLEANUP,
-};
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/exr.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/exr.c
deleted file mode 100644
index 2f1766c17bfad6895cad644f104e6df9fe67d2dc..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/exr.c
+++ /dev/null
@@ -1,2359 +0,0 @@
-/*
- * OpenEXR (.exr) image decoder
- * Copyright (c) 2006 Industrial Light & Magic, a division of Lucas Digital Ltd. LLC
- * Copyright (c) 2009 Jimmy Christensen
- *
- * B44/B44A, Tile, UINT32 added by Jokyo Images support by CNC - French National Center for Cinema
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * FFmpeg is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-/**
- * @file
- * OpenEXR decoder
- * @author Jimmy Christensen
- *
- * For more information on the OpenEXR format, visit:
- * http://openexr.com/
- */
-
-#include
-#include
-
-#include "libavutil/avassert.h"
-#include "libavutil/common.h"
-#include "libavutil/csp.h"
-#include "libavutil/imgutils.h"
-#include "libavutil/intfloat.h"
-#include "libavutil/avstring.h"
-#include "libavutil/opt.h"
-#include "libavutil/half2float.h"
-
-#include "avcodec.h"
-#include "bytestream.h"
-
-#if HAVE_BIGENDIAN
-#include "bswapdsp.h"
-#endif
-
-#include "codec_internal.h"
-#include "decode.h"
-#include "exrdsp.h"
-#include "get_bits.h"
-#include "mathops.h"
-#include "thread.h"
-
-enum ExrCompr {
- EXR_RAW,
- EXR_RLE,
- EXR_ZIP1,
- EXR_ZIP16,
- EXR_PIZ,
- EXR_PXR24,
- EXR_B44,
- EXR_B44A,
- EXR_DWAA,
- EXR_DWAB,
- EXR_UNKN,
-};
-
-enum ExrPixelType {
- EXR_UINT,
- EXR_HALF,
- EXR_FLOAT,
- EXR_UNKNOWN,
-};
-
-enum ExrTileLevelMode {
- EXR_TILE_LEVEL_ONE,
- EXR_TILE_LEVEL_MIPMAP,
- EXR_TILE_LEVEL_RIPMAP,
- EXR_TILE_LEVEL_UNKNOWN,
-};
-
-enum ExrTileLevelRound {
- EXR_TILE_ROUND_UP,
- EXR_TILE_ROUND_DOWN,
- EXR_TILE_ROUND_UNKNOWN,
-};
-
-typedef struct HuffEntry {
- uint8_t len;
- uint16_t sym;
- uint32_t code;
-} HuffEntry;
-
-typedef struct EXRChannel {
- int xsub, ysub;
- enum ExrPixelType pixel_type;
-} EXRChannel;
-
-typedef struct EXRTileAttribute {
- int32_t xSize;
- int32_t ySize;
- enum ExrTileLevelMode level_mode;
- enum ExrTileLevelRound level_round;
-} EXRTileAttribute;
-
-typedef struct EXRThreadData {
- uint8_t *uncompressed_data;
- int uncompressed_size;
-
- uint8_t *tmp;
- int tmp_size;
-
- uint8_t *bitmap;
- uint16_t *lut;
-
- uint8_t *ac_data;
- unsigned ac_size;
-
- uint8_t *dc_data;
- unsigned dc_size;
-
- uint8_t *rle_data;
- unsigned rle_size;
-
- uint8_t *rle_raw_data;
- unsigned rle_raw_size;
-
- float block[3][64];
-
- int ysize, xsize;
-
- int channel_line_size;
-
- int run_sym;
- HuffEntry *he;
- uint64_t *freq;
- VLC vlc;
-} EXRThreadData;
-
-typedef struct EXRContext {
- AVClass *class;
- AVFrame *picture;
- AVCodecContext *avctx;
- ExrDSPContext dsp;
-
-#if HAVE_BIGENDIAN
- BswapDSPContext bbdsp;
-#endif
-
- enum ExrCompr compression;
- enum ExrPixelType pixel_type;
- int channel_offsets[4]; // 0 = red, 1 = green, 2 = blue and 3 = alpha
- const AVPixFmtDescriptor *desc;
-
- int w, h;
- uint32_t sar;
- int32_t xmax, xmin;
- int32_t ymax, ymin;
- uint32_t xdelta, ydelta;
-
- int scan_lines_per_block;
-
- EXRTileAttribute tile_attr; /* header data attribute of tile */
- int is_tile; /* 0 if scanline, 1 if tile */
- int is_multipart;
- int current_part;
-
- int is_luma;/* 1 if there is an Y plane */
-
- GetByteContext gb;
- const uint8_t *buf;
- int buf_size;
-
- EXRChannel *channels;
- int nb_channels;
- int current_channel_offset;
- uint32_t chunk_count;
-
- EXRThreadData *thread_data;
-
- const char *layer;
- int selected_part;
-
- enum AVColorTransferCharacteristic apply_trc_type;
- float gamma;
- union av_intfloat32 gamma_table[65536];
-
- uint8_t *offset_table;
-
- Half2FloatTables h2f_tables;
-} EXRContext;
-
-static int zip_uncompress(const EXRContext *s, const uint8_t *src, int compressed_size,
- int uncompressed_size, EXRThreadData *td)
-{
- unsigned long dest_len = uncompressed_size;
-
- if (uncompress(td->tmp, &dest_len, src, compressed_size) != Z_OK ||
- dest_len != uncompressed_size)
- return AVERROR_INVALIDDATA;
-
- av_assert1(uncompressed_size % 2 == 0);
-
- s->dsp.predictor(td->tmp, uncompressed_size);
- s->dsp.reorder_pixels(td->uncompressed_data, td->tmp, uncompressed_size);
-
- return 0;
-}
-
-static int rle(uint8_t *dst, const uint8_t *src,
- int compressed_size, int uncompressed_size)
-{
- uint8_t *d = dst;
- const int8_t *s = src;
- int ssize = compressed_size;
- int dsize = uncompressed_size;
- uint8_t *dend = d + dsize;
- int count;
-
- while (ssize > 0) {
- count = *s++;
-
- if (count < 0) {
- count = -count;
-
- if ((dsize -= count) < 0 ||
- (ssize -= count + 1) < 0)
- return AVERROR_INVALIDDATA;
-
- while (count--)
- *d++ = *s++;
- } else {
- count++;
-
- if ((dsize -= count) < 0 ||
- (ssize -= 2) < 0)
- return AVERROR_INVALIDDATA;
-
- while (count--)
- *d++ = *s;
-
- s++;
- }
- }
-
- if (dend != d)
- return AVERROR_INVALIDDATA;
-
- return 0;
-}
-
-static int rle_uncompress(const EXRContext *ctx, const uint8_t *src, int compressed_size,
- int uncompressed_size, EXRThreadData *td)
-{
- rle(td->tmp, src, compressed_size, uncompressed_size);
-
- av_assert1(uncompressed_size % 2 == 0);
-
- ctx->dsp.predictor(td->tmp, uncompressed_size);
- ctx->dsp.reorder_pixels(td->uncompressed_data, td->tmp, uncompressed_size);
-
- return 0;
-}
-
-#define USHORT_RANGE (1 << 16)
-#define BITMAP_SIZE (1 << 13)
-
-static uint16_t reverse_lut(const uint8_t *bitmap, uint16_t *lut)
-{
- int i, k = 0;
-
- for (i = 0; i < USHORT_RANGE; i++)
- if ((i == 0) || (bitmap[i >> 3] & (1 << (i & 7))))
- lut[k++] = i;
-
- i = k - 1;
-
- memset(lut + k, 0, (USHORT_RANGE - k) * 2);
-
- return i;
-}
-
-static void apply_lut(const uint16_t *lut, uint16_t *dst, int dsize)
-{
- int i;
-
- for (i = 0; i < dsize; ++i)
- dst[i] = lut[dst[i]];
-}
-
-#define HUF_ENCBITS 16 // literal (value) bit length
-#define HUF_ENCSIZE ((1 << HUF_ENCBITS) + 1) // encoding table size
-
-static void huf_canonical_code_table(uint64_t *freq)
-{
- uint64_t c, n[59] = { 0 };
- int i;
-
- for (i = 0; i < HUF_ENCSIZE; i++)
- n[freq[i]] += 1;
-
- c = 0;
- for (i = 58; i > 0; --i) {
- uint64_t nc = ((c + n[i]) >> 1);
- n[i] = c;
- c = nc;
- }
-
- for (i = 0; i < HUF_ENCSIZE; ++i) {
- int l = freq[i];
-
- if (l > 0)
- freq[i] = l | (n[l]++ << 6);
- }
-}
-
-#define SHORT_ZEROCODE_RUN 59
-#define LONG_ZEROCODE_RUN 63
-#define SHORTEST_LONG_RUN (2 + LONG_ZEROCODE_RUN - SHORT_ZEROCODE_RUN)
-#define LONGEST_LONG_RUN (255 + SHORTEST_LONG_RUN)
-
-static int huf_unpack_enc_table(GetByteContext *gb,
- int32_t im, int32_t iM, uint64_t *freq)
-{
- GetBitContext gbit;
- int ret = init_get_bits8(&gbit, gb->buffer, bytestream2_get_bytes_left(gb));
- if (ret < 0)
- return ret;
-
- for (; im <= iM; im++) {
- uint64_t l = freq[im] = get_bits(&gbit, 6);
-
- if (l == LONG_ZEROCODE_RUN) {
- int zerun = get_bits(&gbit, 8) + SHORTEST_LONG_RUN;
-
- if (im + zerun > iM + 1)
- return AVERROR_INVALIDDATA;
-
- while (zerun--)
- freq[im++] = 0;
-
- im--;
- } else if (l >= SHORT_ZEROCODE_RUN) {
- int zerun = l - SHORT_ZEROCODE_RUN + 2;
-
- if (im + zerun > iM + 1)
- return AVERROR_INVALIDDATA;
-
- while (zerun--)
- freq[im++] = 0;
-
- im--;
- }
- }
-
- bytestream2_skip(gb, (get_bits_count(&gbit) + 7) / 8);
- huf_canonical_code_table(freq);
-
- return 0;
-}
-
-static int huf_build_dec_table(const EXRContext *s,
- EXRThreadData *td, int im, int iM)
-{
- int j = 0;
-
- td->run_sym = -1;
- for (int i = im; i < iM; i++) {
- td->he[j].sym = i;
- td->he[j].len = td->freq[i] & 63;
- td->he[j].code = td->freq[i] >> 6;
- if (td->he[j].len > 32) {
- avpriv_request_sample(s->avctx, "Too big code length");
- return AVERROR_PATCHWELCOME;
- }
- if (td->he[j].len > 0)
- j++;
- else
- td->run_sym = i;
- }
-
- if (im > 0)
- td->run_sym = 0;
- else if (iM < 65535)
- td->run_sym = 65535;
-
- if (td->run_sym == -1) {
- avpriv_request_sample(s->avctx, "No place for run symbol");
- return AVERROR_PATCHWELCOME;
- }
-
- td->he[j].sym = td->run_sym;
- td->he[j].len = td->freq[iM] & 63;
- if (td->he[j].len > 32) {
- avpriv_request_sample(s->avctx, "Too big code length");
- return AVERROR_PATCHWELCOME;
- }
- td->he[j].code = td->freq[iM] >> 6;
- j++;
-
- ff_free_vlc(&td->vlc);
- return ff_init_vlc_sparse(&td->vlc, 12, j,
- &td->he[0].len, sizeof(td->he[0]), sizeof(td->he[0].len),
- &td->he[0].code, sizeof(td->he[0]), sizeof(td->he[0].code),
- &td->he[0].sym, sizeof(td->he[0]), sizeof(td->he[0].sym), 0);
-}
-
-static int huf_decode(VLC *vlc, GetByteContext *gb, int nbits, int run_sym,
- int no, uint16_t *out)
-{
- GetBitContext gbit;
- int oe = 0;
-
- init_get_bits(&gbit, gb->buffer, nbits);
- while (get_bits_left(&gbit) > 0 && oe < no) {
- uint16_t x = get_vlc2(&gbit, vlc->table, 12, 3);
-
- if (x == run_sym) {
- int run = get_bits(&gbit, 8);
- uint16_t fill;
-
- if (oe == 0 || oe + run > no)
- return AVERROR_INVALIDDATA;
-
- fill = out[oe - 1];
-
- while (run-- > 0)
- out[oe++] = fill;
- } else {
- out[oe++] = x;
- }
- }
-
- return 0;
-}
-
-static int huf_uncompress(const EXRContext *s,
- EXRThreadData *td,
- GetByteContext *gb,
- uint16_t *dst, int dst_size)
-{
- int32_t im, iM;
- uint32_t nBits;
- int ret;
-
- im = bytestream2_get_le32(gb);
- iM = bytestream2_get_le32(gb);
- bytestream2_skip(gb, 4);
- nBits = bytestream2_get_le32(gb);
- if (im < 0 || im >= HUF_ENCSIZE ||
- iM < 0 || iM >= HUF_ENCSIZE)
- return AVERROR_INVALIDDATA;
-
- bytestream2_skip(gb, 4);
-
- if (!td->freq)
- td->freq = av_malloc_array(HUF_ENCSIZE, sizeof(*td->freq));
- if (!td->he)
- td->he = av_calloc(HUF_ENCSIZE, sizeof(*td->he));
- if (!td->freq || !td->he) {
- ret = AVERROR(ENOMEM);
- return ret;
- }
-
- memset(td->freq, 0, sizeof(*td->freq) * HUF_ENCSIZE);
- if ((ret = huf_unpack_enc_table(gb, im, iM, td->freq)) < 0)
- return ret;
-
- if (nBits > 8 * bytestream2_get_bytes_left(gb)) {
- ret = AVERROR_INVALIDDATA;
- return ret;
- }
-
- if ((ret = huf_build_dec_table(s, td, im, iM)) < 0)
- return ret;
- return huf_decode(&td->vlc, gb, nBits, td->run_sym, dst_size, dst);
-}
-
-static inline void wdec14(uint16_t l, uint16_t h, uint16_t *a, uint16_t *b)
-{
- int16_t ls = l;
- int16_t hs = h;
- int hi = hs;
- int ai = ls + (hi & 1) + (hi >> 1);
- int16_t as = ai;
- int16_t bs = ai - hi;
-
- *a = as;
- *b = bs;
-}
-
-#define NBITS 16
-#define A_OFFSET (1 << (NBITS - 1))
-#define MOD_MASK ((1 << NBITS) - 1)
-
-static inline void wdec16(uint16_t l, uint16_t h, uint16_t *a, uint16_t *b)
-{
- int m = l;
- int d = h;
- int bb = (m - (d >> 1)) & MOD_MASK;
- int aa = (d + bb - A_OFFSET) & MOD_MASK;
- *b = bb;
- *a = aa;
-}
-
-static void wav_decode(uint16_t *in, int nx, int ox,
- int ny, int oy, uint16_t mx)
-{
- int w14 = (mx < (1 << 14));
- int n = (nx > ny) ? ny : nx;
- int p = 1;
- int p2;
-
- while (p <= n)
- p <<= 1;
-
- p >>= 1;
- p2 = p;
- p >>= 1;
-
- while (p >= 1) {
- uint16_t *py = in;
- uint16_t *ey = in + oy * (ny - p2);
- uint16_t i00, i01, i10, i11;
- int oy1 = oy * p;
- int oy2 = oy * p2;
- int ox1 = ox * p;
- int ox2 = ox * p2;
-
- for (; py <= ey; py += oy2) {
- uint16_t *px = py;
- uint16_t *ex = py + ox * (nx - p2);
-
- for (; px <= ex; px += ox2) {
- uint16_t *p01 = px + ox1;
- uint16_t *p10 = px + oy1;
- uint16_t *p11 = p10 + ox1;
-
- if (w14) {
- wdec14(*px, *p10, &i00, &i10);
- wdec14(*p01, *p11, &i01, &i11);
- wdec14(i00, i01, px, p01);
- wdec14(i10, i11, p10, p11);
- } else {
- wdec16(*px, *p10, &i00, &i10);
- wdec16(*p01, *p11, &i01, &i11);
- wdec16(i00, i01, px, p01);
- wdec16(i10, i11, p10, p11);
- }
- }
-
- if (nx & p) {
- uint16_t *p10 = px + oy1;
-
- if (w14)
- wdec14(*px, *p10, &i00, p10);
- else
- wdec16(*px, *p10, &i00, p10);
-
- *px = i00;
- }
- }
-
- if (ny & p) {
- uint16_t *px = py;
- uint16_t *ex = py + ox * (nx - p2);
-
- for (; px <= ex; px += ox2) {
- uint16_t *p01 = px + ox1;
-
- if (w14)
- wdec14(*px, *p01, &i00, p01);
- else
- wdec16(*px, *p01, &i00, p01);
-
- *px = i00;
- }
- }
-
- p2 = p;
- p >>= 1;
- }
-}
-
-static int piz_uncompress(const EXRContext *s, const uint8_t *src, int ssize,
- int dsize, EXRThreadData *td)
-{
- GetByteContext gb;
- uint16_t maxval, min_non_zero, max_non_zero;
- uint16_t *ptr;
- uint16_t *tmp = (uint16_t *)td->tmp;
- uint16_t *out;
- uint16_t *in;
- int ret, i, j;
- int pixel_half_size;/* 1 for half, 2 for float and uint32 */
- EXRChannel *channel;
- int tmp_offset;
-
- if (!td->bitmap)
- td->bitmap = av_malloc(BITMAP_SIZE);
- if (!td->lut)
- td->lut = av_malloc(1 << 17);
- if (!td->bitmap || !td->lut) {
- av_freep(&td->bitmap);
- av_freep(&td->lut);
- return AVERROR(ENOMEM);
- }
-
- bytestream2_init(&gb, src, ssize);
- min_non_zero = bytestream2_get_le16(&gb);
- max_non_zero = bytestream2_get_le16(&gb);
-
- if (max_non_zero >= BITMAP_SIZE)
- return AVERROR_INVALIDDATA;
-
- memset(td->bitmap, 0, FFMIN(min_non_zero, BITMAP_SIZE));
- if (min_non_zero <= max_non_zero)
- bytestream2_get_buffer(&gb, td->bitmap + min_non_zero,
- max_non_zero - min_non_zero + 1);
- memset(td->bitmap + max_non_zero + 1, 0, BITMAP_SIZE - max_non_zero - 1);
-
- maxval = reverse_lut(td->bitmap, td->lut);
-
- bytestream2_skip(&gb, 4);
- ret = huf_uncompress(s, td, &gb, tmp, dsize / sizeof(uint16_t));
- if (ret)
- return ret;
-
- ptr = tmp;
- for (i = 0; i < s->nb_channels; i++) {
- channel = &s->channels[i];
-
- if (channel->pixel_type == EXR_HALF)
- pixel_half_size = 1;
- else
- pixel_half_size = 2;
-
- for (j = 0; j < pixel_half_size; j++)
- wav_decode(ptr + j, td->xsize, pixel_half_size, td->ysize,
- td->xsize * pixel_half_size, maxval);
- ptr += td->xsize * td->ysize * pixel_half_size;
- }
-
- apply_lut(td->lut, tmp, dsize / sizeof(uint16_t));
-
- out = (uint16_t *)td->uncompressed_data;
- for (i = 0; i < td->ysize; i++) {
- tmp_offset = 0;
- for (j = 0; j < s->nb_channels; j++) {
- channel = &s->channels[j];
- if (channel->pixel_type == EXR_HALF)
- pixel_half_size = 1;
- else
- pixel_half_size = 2;
-
- in = tmp + tmp_offset * td->xsize * td->ysize + i * td->xsize * pixel_half_size;
- tmp_offset += pixel_half_size;
-
-#if HAVE_BIGENDIAN
- s->bbdsp.bswap16_buf(out, in, td->xsize * pixel_half_size);
-#else
- memcpy(out, in, td->xsize * 2 * pixel_half_size);
-#endif
- out += td->xsize * pixel_half_size;
- }
- }
-
- return 0;
-}
-
-static int pxr24_uncompress(const EXRContext *s, const uint8_t *src,
- int compressed_size, int uncompressed_size,
- EXRThreadData *td)
-{
- unsigned long dest_len, expected_len = 0;
- const uint8_t *in = td->tmp;
- uint8_t *out;
- int c, i, j;
-
- for (i = 0; i < s->nb_channels; i++) {
- if (s->channels[i].pixel_type == EXR_FLOAT) {
- expected_len += (td->xsize * td->ysize * 3);/* PRX 24 store float in 24 bit instead of 32 */
- } else if (s->channels[i].pixel_type == EXR_HALF) {
- expected_len += (td->xsize * td->ysize * 2);
- } else {//UINT 32
- expected_len += (td->xsize * td->ysize * 4);
- }
- }
-
- dest_len = expected_len;
-
- if (uncompress(td->tmp, &dest_len, src, compressed_size) != Z_OK) {
- return AVERROR_INVALIDDATA;
- } else if (dest_len != expected_len) {
- return AVERROR_INVALIDDATA;
- }
-
- out = td->uncompressed_data;
- for (i = 0; i < td->ysize; i++)
- for (c = 0; c < s->nb_channels; c++) {
- EXRChannel *channel = &s->channels[c];
- const uint8_t *ptr[4];
- uint32_t pixel = 0;
-
- switch (channel->pixel_type) {
- case EXR_FLOAT:
- ptr[0] = in;
- ptr[1] = ptr[0] + td->xsize;
- ptr[2] = ptr[1] + td->xsize;
- in = ptr[2] + td->xsize;
-
- for (j = 0; j < td->xsize; ++j) {
- uint32_t diff = ((unsigned)*(ptr[0]++) << 24) |
- (*(ptr[1]++) << 16) |
- (*(ptr[2]++) << 8);
- pixel += diff;
- bytestream_put_le32(&out, pixel);
- }
- break;
- case EXR_HALF:
- ptr[0] = in;
- ptr[1] = ptr[0] + td->xsize;
- in = ptr[1] + td->xsize;
- for (j = 0; j < td->xsize; j++) {
- uint32_t diff = (*(ptr[0]++) << 8) | *(ptr[1]++);
-
- pixel += diff;
- bytestream_put_le16(&out, pixel);
- }
- break;
- case EXR_UINT:
- ptr[0] = in;
- ptr[1] = ptr[0] + s->xdelta;
- ptr[2] = ptr[1] + s->xdelta;
- ptr[3] = ptr[2] + s->xdelta;
- in = ptr[3] + s->xdelta;
-
- for (j = 0; j < s->xdelta; ++j) {
- uint32_t diff = ((uint32_t)*(ptr[0]++) << 24) |
- (*(ptr[1]++) << 16) |
- (*(ptr[2]++) << 8 ) |
- (*(ptr[3]++));
- pixel += diff;
- bytestream_put_le32(&out, pixel);
- }
- break;
- default:
- return AVERROR_INVALIDDATA;
- }
- }
-
- return 0;
-}
-
-static void unpack_14(const uint8_t b[14], uint16_t s[16])
-{
- unsigned short shift = (b[ 2] >> 2) & 15;
- unsigned short bias = (0x20 << shift);
- int i;
-
- s[ 0] = (b[0] << 8) | b[1];
-
- s[ 4] = s[ 0] + ((((b[ 2] << 4) | (b[ 3] >> 4)) & 0x3f) << shift) - bias;
- s[ 8] = s[ 4] + ((((b[ 3] << 2) | (b[ 4] >> 6)) & 0x3f) << shift) - bias;
- s[12] = s[ 8] + ((b[ 4] & 0x3f) << shift) - bias;
-
- s[ 1] = s[ 0] + ((b[ 5] >> 2) << shift) - bias;
- s[ 5] = s[ 4] + ((((b[ 5] << 4) | (b[ 6] >> 4)) & 0x3f) << shift) - bias;
- s[ 9] = s[ 8] + ((((b[ 6] << 2) | (b[ 7] >> 6)) & 0x3f) << shift) - bias;
- s[13] = s[12] + ((b[ 7] & 0x3f) << shift) - bias;
-
- s[ 2] = s[ 1] + ((b[ 8] >> 2) << shift) - bias;
- s[ 6] = s[ 5] + ((((b[ 8] << 4) | (b[ 9] >> 4)) & 0x3f) << shift) - bias;
- s[10] = s[ 9] + ((((b[ 9] << 2) | (b[10] >> 6)) & 0x3f) << shift) - bias;
- s[14] = s[13] + ((b[10] & 0x3f) << shift) - bias;
-
- s[ 3] = s[ 2] + ((b[11] >> 2) << shift) - bias;
- s[ 7] = s[ 6] + ((((b[11] << 4) | (b[12] >> 4)) & 0x3f) << shift) - bias;
- s[11] = s[10] + ((((b[12] << 2) | (b[13] >> 6)) & 0x3f) << shift) - bias;
- s[15] = s[14] + ((b[13] & 0x3f) << shift) - bias;
-
- for (i = 0; i < 16; ++i) {
- if (s[i] & 0x8000)
- s[i] &= 0x7fff;
- else
- s[i] = ~s[i];
- }
-}
-
-static void unpack_3(const uint8_t b[3], uint16_t s[16])
-{
- int i;
-
- s[0] = (b[0] << 8) | b[1];
-
- if (s[0] & 0x8000)
- s[0] &= 0x7fff;
- else
- s[0] = ~s[0];
-
- for (i = 1; i < 16; i++)
- s[i] = s[0];
-}
-
-
-static int b44_uncompress(const EXRContext *s, const uint8_t *src, int compressed_size,
- int uncompressed_size, EXRThreadData *td) {
- const int8_t *sr = src;
- int stay_to_uncompress = compressed_size;
- int nb_b44_block_w, nb_b44_block_h;
- int index_tl_x, index_tl_y, index_out, index_tmp;
- uint16_t tmp_buffer[16]; /* B44 use 4x4 half float pixel */
- int c, iY, iX, y, x;
- int target_channel_offset = 0;
-
- /* calc B44 block count */
- nb_b44_block_w = td->xsize / 4;
- if ((td->xsize % 4) != 0)
- nb_b44_block_w++;
-
- nb_b44_block_h = td->ysize / 4;
- if ((td->ysize % 4) != 0)
- nb_b44_block_h++;
-
- for (c = 0; c < s->nb_channels; c++) {
- if (s->channels[c].pixel_type == EXR_HALF) {/* B44 only compress half float data */
- for (iY = 0; iY < nb_b44_block_h; iY++) {
- for (iX = 0; iX < nb_b44_block_w; iX++) {/* For each B44 block */
- if (stay_to_uncompress < 3)
- return AVERROR_INVALIDDATA;
-
- if (src[compressed_size - stay_to_uncompress + 2] == 0xfc) { /* B44A block */
- unpack_3(sr, tmp_buffer);
- sr += 3;
- stay_to_uncompress -= 3;
- } else {/* B44 Block */
- if (stay_to_uncompress < 14)
- return AVERROR_INVALIDDATA;
- unpack_14(sr, tmp_buffer);
- sr += 14;
- stay_to_uncompress -= 14;
- }
-
- /* copy data to uncompress buffer (B44 block can exceed target resolution)*/
- index_tl_x = iX * 4;
- index_tl_y = iY * 4;
-
- for (y = index_tl_y; y < FFMIN(index_tl_y + 4, td->ysize); y++) {
- for (x = index_tl_x; x < FFMIN(index_tl_x + 4, td->xsize); x++) {
- index_out = target_channel_offset * td->xsize + y * td->channel_line_size + 2 * x;
- index_tmp = (y-index_tl_y) * 4 + (x-index_tl_x);
- td->uncompressed_data[index_out] = tmp_buffer[index_tmp] & 0xff;
- td->uncompressed_data[index_out + 1] = tmp_buffer[index_tmp] >> 8;
- }
- }
- }
- }
- target_channel_offset += 2;
- } else {/* Float or UINT 32 channel */
- if (stay_to_uncompress < td->ysize * td->xsize * 4)
- return AVERROR_INVALIDDATA;
-
- for (y = 0; y < td->ysize; y++) {
- index_out = target_channel_offset * td->xsize + y * td->channel_line_size;
- memcpy(&td->uncompressed_data[index_out], sr, td->xsize * 4);
- sr += td->xsize * 4;
- }
- target_channel_offset += 4;
-
- stay_to_uncompress -= td->ysize * td->xsize * 4;
- }
- }
-
- return 0;
-}
-
-static int ac_uncompress(const EXRContext *s, GetByteContext *gb, float *block)
-{
- int ret = 0, n = 1;
-
- while (n < 64) {
- uint16_t val = bytestream2_get_ne16(gb);
-
- if (val == 0xff00) {
- n = 64;
- } else if ((val >> 8) == 0xff) {
- n += val & 0xff;
- } else {
- ret = n;
- block[ff_zigzag_direct[n]] = av_int2float(half2float(val, &s->h2f_tables));
- n++;
- }
- }
-
- return ret;
-}
-
-static void idct_1d(float *blk, int step)
-{
- const float a = .5f * cosf( M_PI / 4.f);
- const float b = .5f * cosf( M_PI / 16.f);
- const float c = .5f * cosf( M_PI / 8.f);
- const float d = .5f * cosf(3.f*M_PI / 16.f);
- const float e = .5f * cosf(5.f*M_PI / 16.f);
- const float f = .5f * cosf(3.f*M_PI / 8.f);
- const float g = .5f * cosf(7.f*M_PI / 16.f);
-
- float alpha[4], beta[4], theta[4], gamma[4];
-
- alpha[0] = c * blk[2 * step];
- alpha[1] = f * blk[2 * step];
- alpha[2] = c * blk[6 * step];
- alpha[3] = f * blk[6 * step];
-
- beta[0] = b * blk[1 * step] + d * blk[3 * step] + e * blk[5 * step] + g * blk[7 * step];
- beta[1] = d * blk[1 * step] - g * blk[3 * step] - b * blk[5 * step] - e * blk[7 * step];
- beta[2] = e * blk[1 * step] - b * blk[3 * step] + g * blk[5 * step] + d * blk[7 * step];
- beta[3] = g * blk[1 * step] - e * blk[3 * step] + d * blk[5 * step] - b * blk[7 * step];
-
- theta[0] = a * (blk[0 * step] + blk[4 * step]);
- theta[3] = a * (blk[0 * step] - blk[4 * step]);
-
- theta[1] = alpha[0] + alpha[3];
- theta[2] = alpha[1] - alpha[2];
-
- gamma[0] = theta[0] + theta[1];
- gamma[1] = theta[3] + theta[2];
- gamma[2] = theta[3] - theta[2];
- gamma[3] = theta[0] - theta[1];
-
- blk[0 * step] = gamma[0] + beta[0];
- blk[1 * step] = gamma[1] + beta[1];
- blk[2 * step] = gamma[2] + beta[2];
- blk[3 * step] = gamma[3] + beta[3];
-
- blk[4 * step] = gamma[3] - beta[3];
- blk[5 * step] = gamma[2] - beta[2];
- blk[6 * step] = gamma[1] - beta[1];
- blk[7 * step] = gamma[0] - beta[0];
-}
-
-static void dct_inverse(float *block)
-{
- for (int i = 0; i < 8; i++)
- idct_1d(block + i, 8);
-
- for (int i = 0; i < 8; i++) {
- idct_1d(block, 1);
- block += 8;
- }
-}
-
-static void convert(float y, float u, float v,
- float *b, float *g, float *r)
-{
- *r = y + 1.5747f * v;
- *g = y - 0.1873f * u - 0.4682f * v;
- *b = y + 1.8556f * u;
-}
-
-static float to_linear(float x, float scale)
-{
- float ax = fabsf(x);
-
- if (ax <= 1.f) {
- return FFSIGN(x) * powf(ax, 2.2f * scale);
- } else {
- const float log_base = expf(2.2f * scale);
-
- return FFSIGN(x) * powf(log_base, ax - 1.f);
- }
-}
-
-static int dwa_uncompress(const EXRContext *s, const uint8_t *src, int compressed_size,
- int uncompressed_size, EXRThreadData *td)
-{
- int64_t version, lo_usize, lo_size;
- int64_t ac_size, dc_size, rle_usize, rle_csize, rle_raw_size;
- int64_t ac_count, dc_count, ac_compression;
- const int dc_w = td->xsize >> 3;
- const int dc_h = td->ysize >> 3;
- GetByteContext gb, agb;
- int skip, ret;
-
- if (compressed_size <= 88)
- return AVERROR_INVALIDDATA;
-
- version = AV_RL64(src + 0);
- if (version != 2)
- return AVERROR_INVALIDDATA;
-
- lo_usize = AV_RL64(src + 8);
- lo_size = AV_RL64(src + 16);
- ac_size = AV_RL64(src + 24);
- dc_size = AV_RL64(src + 32);
- rle_csize = AV_RL64(src + 40);
- rle_usize = AV_RL64(src + 48);
- rle_raw_size = AV_RL64(src + 56);
- ac_count = AV_RL64(src + 64);
- dc_count = AV_RL64(src + 72);
- ac_compression = AV_RL64(src + 80);
-
- if ( compressed_size < (uint64_t)(lo_size | ac_size | dc_size | rle_csize) || compressed_size < 88LL + lo_size + ac_size + dc_size + rle_csize
- || ac_count > (uint64_t)INT_MAX/2
- )
- return AVERROR_INVALIDDATA;
-
- bytestream2_init(&gb, src + 88, compressed_size - 88);
- skip = bytestream2_get_le16(&gb);
- if (skip < 2)
- return AVERROR_INVALIDDATA;
-
- bytestream2_skip(&gb, skip - 2);
-
- if (lo_size > 0) {
- if (lo_usize > uncompressed_size)
- return AVERROR_INVALIDDATA;
- bytestream2_skip(&gb, lo_size);
- }
-
- if (ac_size > 0) {
- unsigned long dest_len;
- GetByteContext agb = gb;
-
- if (ac_count > 3LL * td->xsize * s->scan_lines_per_block)
- return AVERROR_INVALIDDATA;
-
- dest_len = ac_count * 2LL;
-
- av_fast_padded_malloc(&td->ac_data, &td->ac_size, dest_len);
- if (!td->ac_data)
- return AVERROR(ENOMEM);
-
- switch (ac_compression) {
- case 0:
- ret = huf_uncompress(s, td, &agb, (int16_t *)td->ac_data, ac_count);
- if (ret < 0)
- return ret;
- break;
- case 1:
- if (uncompress(td->ac_data, &dest_len, agb.buffer, ac_size) != Z_OK ||
- dest_len != ac_count * 2LL)
- return AVERROR_INVALIDDATA;
- break;
- default:
- return AVERROR_INVALIDDATA;
- }
-
- bytestream2_skip(&gb, ac_size);
- }
-
- {
- unsigned long dest_len;
- GetByteContext agb = gb;
-
- if (dc_count != dc_w * dc_h * 3)
- return AVERROR_INVALIDDATA;
-
- dest_len = dc_count * 2LL;
-
- av_fast_padded_malloc(&td->dc_data, &td->dc_size, FFALIGN(dest_len, 64) * 2);
- if (!td->dc_data)
- return AVERROR(ENOMEM);
-
- if (uncompress(td->dc_data + FFALIGN(dest_len, 64), &dest_len, agb.buffer, dc_size) != Z_OK ||
- (dest_len != dc_count * 2LL))
- return AVERROR_INVALIDDATA;
-
- s->dsp.predictor(td->dc_data + FFALIGN(dest_len, 64), dest_len);
- s->dsp.reorder_pixels(td->dc_data, td->dc_data + FFALIGN(dest_len, 64), dest_len);
-
- bytestream2_skip(&gb, dc_size);
- }
-
- if (rle_raw_size > 0 && rle_csize > 0 && rle_usize > 0) {
- unsigned long dest_len = rle_usize;
-
- av_fast_padded_malloc(&td->rle_data, &td->rle_size, rle_usize);
- if (!td->rle_data)
- return AVERROR(ENOMEM);
-
- av_fast_padded_malloc(&td->rle_raw_data, &td->rle_raw_size, rle_raw_size);
- if (!td->rle_raw_data)
- return AVERROR(ENOMEM);
-
- if (uncompress(td->rle_data, &dest_len, gb.buffer, rle_csize) != Z_OK ||
- (dest_len != rle_usize))
- return AVERROR_INVALIDDATA;
-
- ret = rle(td->rle_raw_data, td->rle_data, rle_usize, rle_raw_size);
- if (ret < 0)
- return ret;
- bytestream2_skip(&gb, rle_csize);
- }
-
- bytestream2_init(&agb, td->ac_data, ac_count * 2);
-
- for (int y = 0; y < td->ysize; y += 8) {
- for (int x = 0; x < td->xsize; x += 8) {
- memset(td->block, 0, sizeof(td->block));
-
- for (int j = 0; j < 3; j++) {
- float *block = td->block[j];
- const int idx = (x >> 3) + (y >> 3) * dc_w + dc_w * dc_h * j;
- uint16_t *dc = (uint16_t *)td->dc_data;
- union av_intfloat32 dc_val;
-
- dc_val.i = half2float(dc[idx], &s->h2f_tables);
-
- block[0] = dc_val.f;
- ac_uncompress(s, &agb, block);
- dct_inverse(block);
- }
-
- {
- const float scale = s->pixel_type == EXR_FLOAT ? 2.f : 1.f;
- const int o = s->nb_channels == 4;
- float *bo = ((float *)td->uncompressed_data) +
- y * td->xsize * s->nb_channels + td->xsize * (o + 0) + x;
- float *go = ((float *)td->uncompressed_data) +
- y * td->xsize * s->nb_channels + td->xsize * (o + 1) + x;
- float *ro = ((float *)td->uncompressed_data) +
- y * td->xsize * s->nb_channels + td->xsize * (o + 2) + x;
- float *yb = td->block[0];
- float *ub = td->block[1];
- float *vb = td->block[2];
-
- for (int yy = 0; yy < 8; yy++) {
- for (int xx = 0; xx < 8; xx++) {
- const int idx = xx + yy * 8;
-
- convert(yb[idx], ub[idx], vb[idx], &bo[xx], &go[xx], &ro[xx]);
-
- bo[xx] = to_linear(bo[xx], scale);
- go[xx] = to_linear(go[xx], scale);
- ro[xx] = to_linear(ro[xx], scale);
- }
-
- bo += td->xsize * s->nb_channels;
- go += td->xsize * s->nb_channels;
- ro += td->xsize * s->nb_channels;
- }
- }
- }
- }
-
- if (s->nb_channels < 4)
- return 0;
-
- for (int y = 0; y < td->ysize && td->rle_raw_data; y++) {
- uint32_t *ao = ((uint32_t *)td->uncompressed_data) + y * td->xsize * s->nb_channels;
- uint8_t *ai0 = td->rle_raw_data + y * td->xsize;
- uint8_t *ai1 = td->rle_raw_data + y * td->xsize + rle_raw_size / 2;
-
- for (int x = 0; x < td->xsize; x++) {
- uint16_t ha = ai0[x] | (ai1[x] << 8);
-
- ao[x] = half2float(ha, &s->h2f_tables);
- }
- }
-
- return 0;
-}
-
-static int decode_block(AVCodecContext *avctx, void *tdata,
- int jobnr, int threadnr)
-{
- const EXRContext *s = avctx->priv_data;
- AVFrame *const p = s->picture;
- EXRThreadData *td = &s->thread_data[threadnr];
- const uint8_t *channel_buffer[4] = { 0 };
- const uint8_t *buf = s->buf;
- uint64_t line_offset, uncompressed_size;
- uint8_t *ptr;
- uint32_t data_size;
- int line, col = 0;
- uint64_t tile_x, tile_y, tile_level_x, tile_level_y;
- const uint8_t *src;
- int step = s->desc->flags & AV_PIX_FMT_FLAG_FLOAT ? 4 : 2 * s->desc->nb_components;
- int bxmin = 0, axmax = 0, window_xoffset = 0;
- int window_xmin, window_xmax, window_ymin, window_ymax;
- int data_xoffset, data_yoffset, data_window_offset, xsize, ysize;
- int i, x, buf_size = s->buf_size;
- int c, rgb_channel_count;
- float one_gamma = 1.0f / s->gamma;
- av_csp_trc_function trc_func = av_csp_trc_func_from_id(s->apply_trc_type);
- int ret;
-
- line_offset = AV_RL64(s->gb.buffer + jobnr * 8);
-
- if (s->is_tile) {
- if (buf_size < 20 || line_offset > buf_size - 20)
- return AVERROR_INVALIDDATA;
-
- src = buf + line_offset + 20;
- if (s->is_multipart)
- src += 4;
-
- tile_x = AV_RL32(src - 20);
- tile_y = AV_RL32(src - 16);
- tile_level_x = AV_RL32(src - 12);
- tile_level_y = AV_RL32(src - 8);
-
- data_size = AV_RL32(src - 4);
- if (data_size <= 0 || data_size > buf_size - line_offset - 20)
- return AVERROR_INVALIDDATA;
-
- if (tile_level_x || tile_level_y) { /* tile level, is not the full res level */
- avpriv_report_missing_feature(s->avctx, "Subres tile before full res tile");
- return AVERROR_PATCHWELCOME;
- }
-
- if (tile_x && s->tile_attr.xSize + (int64_t)FFMAX(s->xmin, 0) >= INT_MAX / tile_x )
- return AVERROR_INVALIDDATA;
- if (tile_y && s->tile_attr.ySize + (int64_t)FFMAX(s->ymin, 0) >= INT_MAX / tile_y )
- return AVERROR_INVALIDDATA;
-
- line = s->ymin + s->tile_attr.ySize * tile_y;
- col = s->tile_attr.xSize * tile_x;
-
- if (line < s->ymin || line > s->ymax ||
- s->xmin + col < s->xmin || s->xmin + col > s->xmax)
- return AVERROR_INVALIDDATA;
-
- td->ysize = FFMIN(s->tile_attr.ySize, s->ydelta - tile_y * s->tile_attr.ySize);
- td->xsize = FFMIN(s->tile_attr.xSize, s->xdelta - tile_x * s->tile_attr.xSize);
-
- if (td->xsize * (uint64_t)s->current_channel_offset > INT_MAX ||
- av_image_check_size2(td->xsize, td->ysize, s->avctx->max_pixels, AV_PIX_FMT_NONE, 0, s->avctx) < 0)
- return AVERROR_INVALIDDATA;
-
- td->channel_line_size = td->xsize * s->current_channel_offset;/* uncompress size of one line */
- uncompressed_size = td->channel_line_size * (uint64_t)td->ysize;/* uncompress size of the block */
- } else {
- if (buf_size < 8 || line_offset > buf_size - 8)
- return AVERROR_INVALIDDATA;
-
- src = buf + line_offset + 8;
- if (s->is_multipart)
- src += 4;
- line = AV_RL32(src - 8);
-
- if (line < s->ymin || line > s->ymax)
- return AVERROR_INVALIDDATA;
-
- data_size = AV_RL32(src - 4);
- if (data_size <= 0 || data_size > buf_size - line_offset - 8)
- return AVERROR_INVALIDDATA;
-
- td->ysize = FFMIN(s->scan_lines_per_block, s->ymax - line + 1); /* s->ydelta - line ?? */
- td->xsize = s->xdelta;
-
- if (td->xsize * (uint64_t)s->current_channel_offset > INT_MAX ||
- av_image_check_size2(td->xsize, td->ysize, s->avctx->max_pixels, AV_PIX_FMT_NONE, 0, s->avctx) < 0)
- return AVERROR_INVALIDDATA;
-
- td->channel_line_size = td->xsize * s->current_channel_offset;/* uncompress size of one line */
- uncompressed_size = td->channel_line_size * (uint64_t)td->ysize;/* uncompress size of the block */
-
- if ((s->compression == EXR_RAW && (data_size != uncompressed_size ||
- line_offset > buf_size - uncompressed_size)) ||
- (s->compression != EXR_RAW && (data_size > uncompressed_size ||
- line_offset > buf_size - data_size))) {
- return AVERROR_INVALIDDATA;
- }
- }
-
- window_xmin = FFMIN(avctx->width, FFMAX(0, s->xmin + col));
- window_xmax = FFMIN(avctx->width, FFMAX(0, s->xmin + col + td->xsize));
- window_ymin = FFMIN(avctx->height, FFMAX(0, line ));
- window_ymax = FFMIN(avctx->height, FFMAX(0, line + td->ysize));
- xsize = window_xmax - window_xmin;
- ysize = window_ymax - window_ymin;
-
- /* tile or scanline not visible skip decoding */
- if (xsize <= 0 || ysize <= 0)
- return 0;
-
- /* is the first tile or is a scanline */
- if(col == 0) {
- window_xmin = 0;
- /* pixels to add at the left of the display window */
- window_xoffset = FFMAX(0, s->xmin);
- /* bytes to add at the left of the display window */
- bxmin = window_xoffset * step;
- }
-
- /* is the last tile or is a scanline */
- if(col + td->xsize == s->xdelta) {
- window_xmax = avctx->width;
- /* bytes to add at the right of the display window */
- axmax = FFMAX(0, (avctx->width - (s->xmax + 1))) * step;
- }
-
- if (avctx->max_pixels && uncompressed_size > avctx->max_pixels * 16LL)
- return AVERROR_INVALIDDATA;
-
- if (data_size < uncompressed_size || s->is_tile) { /* td->tmp is use for tile reorganization */
- av_fast_padded_malloc(&td->tmp, &td->tmp_size, uncompressed_size);
- if (!td->tmp)
- return AVERROR(ENOMEM);
- }
-
- if (data_size < uncompressed_size) {
- av_fast_padded_malloc(&td->uncompressed_data,
- &td->uncompressed_size, uncompressed_size + 64);/* Force 64 padding for AVX2 reorder_pixels dst */
-
- if (!td->uncompressed_data)
- return AVERROR(ENOMEM);
-
- ret = AVERROR_INVALIDDATA;
- switch (s->compression) {
- case EXR_ZIP1:
- case EXR_ZIP16:
- ret = zip_uncompress(s, src, data_size, uncompressed_size, td);
- break;
- case EXR_PIZ:
- ret = piz_uncompress(s, src, data_size, uncompressed_size, td);
- break;
- case EXR_PXR24:
- ret = pxr24_uncompress(s, src, data_size, uncompressed_size, td);
- break;
- case EXR_RLE:
- ret = rle_uncompress(s, src, data_size, uncompressed_size, td);
- break;
- case EXR_B44:
- case EXR_B44A:
- ret = b44_uncompress(s, src, data_size, uncompressed_size, td);
- break;
- case EXR_DWAA:
- case EXR_DWAB:
- ret = dwa_uncompress(s, src, data_size, uncompressed_size, td);
- break;
- }
- if (ret < 0) {
- av_log(avctx, AV_LOG_ERROR, "decode_block() failed.\n");
- return ret;
- }
- src = td->uncompressed_data;
- }
-
- /* offsets to crop data outside display window */
- data_xoffset = FFABS(FFMIN(0, s->xmin + col)) * (s->pixel_type == EXR_HALF ? 2 : 4);
- data_yoffset = FFABS(FFMIN(0, line));
- data_window_offset = (data_yoffset * td->channel_line_size) + data_xoffset;
-
- if (!s->is_luma) {
- channel_buffer[0] = src + (td->xsize * s->channel_offsets[0]) + data_window_offset;
- channel_buffer[1] = src + (td->xsize * s->channel_offsets[1]) + data_window_offset;
- channel_buffer[2] = src + (td->xsize * s->channel_offsets[2]) + data_window_offset;
- rgb_channel_count = 3;
- } else { /* put y data in the first channel_buffer */
- channel_buffer[0] = src + (td->xsize * s->channel_offsets[1]) + data_window_offset;
- rgb_channel_count = 1;
- }
- if (s->channel_offsets[3] >= 0)
- channel_buffer[3] = src + (td->xsize * s->channel_offsets[3]) + data_window_offset;
-
- if (s->desc->flags & AV_PIX_FMT_FLAG_FLOAT) {
- /* todo: change this when a floating point pixel format with luma with alpha is implemented */
- int channel_count = s->channel_offsets[3] >= 0 ? 4 : rgb_channel_count;
- if (s->is_luma) {
- channel_buffer[1] = channel_buffer[0];
- channel_buffer[2] = channel_buffer[0];
- }
-
- for (c = 0; c < channel_count; c++) {
- int plane = s->desc->comp[c].plane;
- ptr = p->data[plane] + window_ymin * p->linesize[plane] + (window_xmin * 4);
-
- for (i = 0; i < ysize; i++, ptr += p->linesize[plane]) {
- const uint8_t *src;
- union av_intfloat32 *ptr_x;
-
- src = channel_buffer[c];
- ptr_x = (union av_intfloat32 *)ptr;
-
- // Zero out the start if xmin is not 0
- memset(ptr_x, 0, bxmin);
- ptr_x += window_xoffset;
-
- if (s->pixel_type == EXR_FLOAT ||
- s->compression == EXR_DWAA ||
- s->compression == EXR_DWAB) {
- // 32-bit
- union av_intfloat32 t;
- if (trc_func && c < 3) {
- for (x = 0; x < xsize; x++) {
- t.i = bytestream_get_le32(&src);
- t.f = trc_func(t.f);
- *ptr_x++ = t;
- }
- } else if (one_gamma != 1.f) {
- for (x = 0; x < xsize; x++) {
- t.i = bytestream_get_le32(&src);
- if (t.f > 0.0f && c < 3) /* avoid negative values */
- t.f = powf(t.f, one_gamma);
- *ptr_x++ = t;
- }
- } else {
- for (x = 0; x < xsize; x++) {
- t.i = bytestream_get_le32(&src);
- *ptr_x++ = t;
- }
- }
- } else if (s->pixel_type == EXR_HALF) {
- // 16-bit
- if (c < 3 || !trc_func) {
- for (x = 0; x < xsize; x++) {
- *ptr_x++ = s->gamma_table[bytestream_get_le16(&src)];
- }
- } else {
- for (x = 0; x < xsize; x++) {
- ptr_x[0].i = half2float(bytestream_get_le16(&src), &s->h2f_tables);
- ptr_x++;
- }
- }
- }
-
- // Zero out the end if xmax+1 is not w
- memset(ptr_x, 0, axmax);
- channel_buffer[c] += td->channel_line_size;
- }
- }
- } else {
-
- av_assert1(s->pixel_type == EXR_UINT);
- ptr = p->data[0] + window_ymin * p->linesize[0] + (window_xmin * s->desc->nb_components * 2);
-
- for (i = 0; i < ysize; i++, ptr += p->linesize[0]) {
-
- const uint8_t * a;
- const uint8_t *rgb[3];
- uint16_t *ptr_x;
-
- for (c = 0; c < rgb_channel_count; c++) {
- rgb[c] = channel_buffer[c];
- }
-
- if (channel_buffer[3])
- a = channel_buffer[3];
-
- ptr_x = (uint16_t *) ptr;
-
- // Zero out the start if xmin is not 0
- memset(ptr_x, 0, bxmin);
- ptr_x += window_xoffset * s->desc->nb_components;
-
- for (x = 0; x < xsize; x++) {
- for (c = 0; c < rgb_channel_count; c++) {
- *ptr_x++ = bytestream_get_le32(&rgb[c]) >> 16;
- }
-
- if (channel_buffer[3])
- *ptr_x++ = bytestream_get_le32(&a) >> 16;
- }
-
- // Zero out the end if xmax+1 is not w
- memset(ptr_x, 0, axmax);
-
- channel_buffer[0] += td->channel_line_size;
- channel_buffer[1] += td->channel_line_size;
- channel_buffer[2] += td->channel_line_size;
- if (channel_buffer[3])
- channel_buffer[3] += td->channel_line_size;
- }
- }
-
- return 0;
-}
-
-static void skip_header_chunk(EXRContext *s)
-{
- GetByteContext *gb = &s->gb;
-
- while (bytestream2_get_bytes_left(gb) > 0) {
- if (!bytestream2_peek_byte(gb))
- break;
-
- // Process unknown variables
- for (int i = 0; i < 2; i++) // value_name and value_type
- while (bytestream2_get_byte(gb) != 0);
-
- // Skip variable length
- bytestream2_skip(gb, bytestream2_get_le32(gb));
- }
-}
-
-/**
- * Check if the variable name corresponds to its data type.
- *
- * @param s the EXRContext
- * @param value_name name of the variable to check
- * @param value_type type of the variable to check
- * @param minimum_length minimum length of the variable data
- *
- * @return bytes to read containing variable data
- * -1 if variable is not found
- * 0 if buffer ended prematurely
- */
-static int check_header_variable(EXRContext *s,
- const char *value_name,
- const char *value_type,
- unsigned int minimum_length)
-{
- GetByteContext *gb = &s->gb;
- int var_size = -1;
-
- if (bytestream2_get_bytes_left(gb) >= minimum_length &&
- !strcmp(gb->buffer, value_name)) {
- // found value_name, jump to value_type (null terminated strings)
- gb->buffer += strlen(value_name) + 1;
- if (!strcmp(gb->buffer, value_type)) {
- gb->buffer += strlen(value_type) + 1;
- var_size = bytestream2_get_le32(gb);
- // don't go read past boundaries
- if (var_size > bytestream2_get_bytes_left(gb))
- var_size = 0;
- } else {
- // value_type not found, reset the buffer
- gb->buffer -= strlen(value_name) + 1;
- av_log(s->avctx, AV_LOG_WARNING,
- "Unknown data type %s for header variable %s.\n",
- value_type, value_name);
- }
- }
-
- return var_size;
-}
-
-static int decode_header(EXRContext *s, AVFrame *frame)
-{
- AVDictionary *metadata = NULL;
- GetByteContext *gb = &s->gb;
- int magic_number, version, flags;
- int layer_match = 0;
- int ret;
- int dup_channels = 0;
-
- s->current_channel_offset = 0;
- s->xmin = ~0;
- s->xmax = ~0;
- s->ymin = ~0;
- s->ymax = ~0;
- s->xdelta = ~0;
- s->ydelta = ~0;
- s->channel_offsets[0] = -1;
- s->channel_offsets[1] = -1;
- s->channel_offsets[2] = -1;
- s->channel_offsets[3] = -1;
- s->pixel_type = EXR_UNKNOWN;
- s->compression = EXR_UNKN;
- s->nb_channels = 0;
- s->w = 0;
- s->h = 0;
- s->tile_attr.xSize = -1;
- s->tile_attr.ySize = -1;
- s->is_tile = 0;
- s->is_multipart = 0;
- s->is_luma = 0;
- s->current_part = 0;
-
- if (bytestream2_get_bytes_left(gb) < 10) {
- av_log(s->avctx, AV_LOG_ERROR, "Header too short to parse.\n");
- return AVERROR_INVALIDDATA;
- }
-
- magic_number = bytestream2_get_le32(gb);
- if (magic_number != 20000630) {
- /* As per documentation of OpenEXR, it is supposed to be
- * int 20000630 little-endian */
- av_log(s->avctx, AV_LOG_ERROR, "Wrong magic number %d.\n", magic_number);
- return AVERROR_INVALIDDATA;
- }
-
- version = bytestream2_get_byte(gb);
- if (version != 2) {
- avpriv_report_missing_feature(s->avctx, "Version %d", version);
- return AVERROR_PATCHWELCOME;
- }
-
- flags = bytestream2_get_le24(gb);
-
- if (flags & 0x02)
- s->is_tile = 1;
- if (flags & 0x10)
- s->is_multipart = 1;
- if (flags & 0x08) {
- avpriv_report_missing_feature(s->avctx, "deep data");
- return AVERROR_PATCHWELCOME;
- }
-
- // Parse the header
- while (bytestream2_get_bytes_left(gb) > 0) {
- int var_size;
-
- while (s->is_multipart && s->current_part < s->selected_part &&
- bytestream2_get_bytes_left(gb) > 0) {
- if (bytestream2_peek_byte(gb)) {
- skip_header_chunk(s);
- } else {
- bytestream2_skip(gb, 1);
- if (!bytestream2_peek_byte(gb))
- break;
- }
- bytestream2_skip(gb, 1);
- s->current_part++;
- }
-
- if (!bytestream2_peek_byte(gb)) {
- if (!s->is_multipart)
- break;
- bytestream2_skip(gb, 1);
- if (s->current_part == s->selected_part) {
- while (bytestream2_get_bytes_left(gb) > 0) {
- if (bytestream2_peek_byte(gb)) {
- skip_header_chunk(s);
- } else {
- bytestream2_skip(gb, 1);
- if (!bytestream2_peek_byte(gb))
- break;
- }
- }
- }
- if (!bytestream2_peek_byte(gb))
- break;
- s->current_part++;
- }
-
- if ((var_size = check_header_variable(s, "channels",
- "chlist", 38)) >= 0) {
- GetByteContext ch_gb;
- if (!var_size) {
- ret = AVERROR_INVALIDDATA;
- goto fail;
- }
-
- bytestream2_init(&ch_gb, gb->buffer, var_size);
-
- while (bytestream2_get_bytes_left(&ch_gb) >= 19) {
- EXRChannel *channel;
- enum ExrPixelType current_pixel_type;
- int channel_index = -1;
- int xsub, ysub;
-
- if (strcmp(s->layer, "") != 0) {
- if (strncmp(ch_gb.buffer, s->layer, strlen(s->layer)) == 0) {
- layer_match = 1;
- av_log(s->avctx, AV_LOG_INFO,
- "Channel match layer : %s.\n", ch_gb.buffer);
- ch_gb.buffer += strlen(s->layer);
- if (*ch_gb.buffer == '.')
- ch_gb.buffer++; /* skip dot if not given */
- } else {
- layer_match = 0;
- av_log(s->avctx, AV_LOG_INFO,
- "Channel doesn't match layer : %s.\n", ch_gb.buffer);
- }
- } else {
- layer_match = 1;
- }
-
- if (layer_match) { /* only search channel if the layer match is valid */
- if (!av_strcasecmp(ch_gb.buffer, "R") ||
- !av_strcasecmp(ch_gb.buffer, "X") ||
- !av_strcasecmp(ch_gb.buffer, "U")) {
- channel_index = 0;
- s->is_luma = 0;
- } else if (!av_strcasecmp(ch_gb.buffer, "G") ||
- !av_strcasecmp(ch_gb.buffer, "V")) {
- channel_index = 1;
- s->is_luma = 0;
- } else if (!av_strcasecmp(ch_gb.buffer, "Y")) {
- channel_index = 1;
- s->is_luma = 1;
- } else if (!av_strcasecmp(ch_gb.buffer, "B") ||
- !av_strcasecmp(ch_gb.buffer, "Z") ||
- !av_strcasecmp(ch_gb.buffer, "W")) {
- channel_index = 2;
- s->is_luma = 0;
- } else if (!av_strcasecmp(ch_gb.buffer, "A")) {
- channel_index = 3;
- } else {
- av_log(s->avctx, AV_LOG_WARNING,
- "Unsupported channel %.256s.\n", ch_gb.buffer);
- }
- }
-
- /* skip until you get a 0 */
- while (bytestream2_get_bytes_left(&ch_gb) > 0 &&
- bytestream2_get_byte(&ch_gb))
- continue;
-
- if (bytestream2_get_bytes_left(&ch_gb) < 4) {
- av_log(s->avctx, AV_LOG_ERROR, "Incomplete header.\n");
- ret = AVERROR_INVALIDDATA;
- goto fail;
- }
-
- current_pixel_type = bytestream2_get_le32(&ch_gb);
- if (current_pixel_type >= EXR_UNKNOWN) {
- avpriv_report_missing_feature(s->avctx, "Pixel type %d",
- current_pixel_type);
- ret = AVERROR_PATCHWELCOME;
- goto fail;
- }
-
- bytestream2_skip(&ch_gb, 4);
- xsub = bytestream2_get_le32(&ch_gb);
- ysub = bytestream2_get_le32(&ch_gb);
-
- if (xsub != 1 || ysub != 1) {
- avpriv_report_missing_feature(s->avctx,
- "Subsampling %dx%d",
- xsub, ysub);
- ret = AVERROR_PATCHWELCOME;
- goto fail;
- }
-
- if (channel_index >= 0 && s->channel_offsets[channel_index] == -1) { /* channel has not been previously assigned */
- if (s->pixel_type != EXR_UNKNOWN &&
- s->pixel_type != current_pixel_type) {
- av_log(s->avctx, AV_LOG_ERROR,
- "RGB channels not of the same depth.\n");
- ret = AVERROR_INVALIDDATA;
- goto fail;
- }
- s->pixel_type = current_pixel_type;
- s->channel_offsets[channel_index] = s->current_channel_offset;
- } else if (channel_index >= 0) {
- av_log(s->avctx, AV_LOG_WARNING,
- "Multiple channels with index %d.\n", channel_index);
- if (++dup_channels > 10) {
- ret = AVERROR_INVALIDDATA;
- goto fail;
- }
- }
-
- s->channels = av_realloc(s->channels,
- ++s->nb_channels * sizeof(EXRChannel));
- if (!s->channels) {
- ret = AVERROR(ENOMEM);
- goto fail;
- }
- channel = &s->channels[s->nb_channels - 1];
- channel->pixel_type = current_pixel_type;
- channel->xsub = xsub;
- channel->ysub = ysub;
-
- if (current_pixel_type == EXR_HALF) {
- s->current_channel_offset += 2;
- } else {/* Float or UINT32 */
- s->current_channel_offset += 4;
- }
- }
-
- /* Check if all channels are set with an offset or if the channels
- * are causing an overflow */
- if (!s->is_luma) {/* if we expected to have at least 3 channels */
- if (FFMIN3(s->channel_offsets[0],
- s->channel_offsets[1],
- s->channel_offsets[2]) < 0) {
- if (s->channel_offsets[0] < 0)
- av_log(s->avctx, AV_LOG_ERROR, "Missing red channel.\n");
- if (s->channel_offsets[1] < 0)
- av_log(s->avctx, AV_LOG_ERROR, "Missing green channel.\n");
- if (s->channel_offsets[2] < 0)
- av_log(s->avctx, AV_LOG_ERROR, "Missing blue channel.\n");
- ret = AVERROR_INVALIDDATA;
- goto fail;
- }
- }
-
- // skip one last byte and update main gb
- gb->buffer = ch_gb.buffer + 1;
- continue;
- } else if ((var_size = check_header_variable(s, "dataWindow", "box2i",
- 31)) >= 0) {
- int xmin, ymin, xmax, ymax;
- if (!var_size) {
- ret = AVERROR_INVALIDDATA;
- goto fail;
- }
-
- xmin = bytestream2_get_le32(gb);
- ymin = bytestream2_get_le32(gb);
- xmax = bytestream2_get_le32(gb);
- ymax = bytestream2_get_le32(gb);
-
- if (xmin > xmax || ymin > ymax ||
- ymax == INT_MAX || xmax == INT_MAX ||
- (unsigned)xmax - xmin >= INT_MAX ||
- (unsigned)ymax - ymin >= INT_MAX) {
- ret = AVERROR_INVALIDDATA;
- goto fail;
- }
- s->xmin = xmin;
- s->xmax = xmax;
- s->ymin = ymin;
- s->ymax = ymax;
- s->xdelta = (s->xmax - s->xmin) + 1;
- s->ydelta = (s->ymax - s->ymin) + 1;
-
- continue;
- } else if ((var_size = check_header_variable(s, "displayWindow",
- "box2i", 34)) >= 0) {
- int32_t sx, sy, dx, dy;
-
- if (!var_size) {
- ret = AVERROR_INVALIDDATA;
- goto fail;
- }
-
- sx = bytestream2_get_le32(gb);
- sy = bytestream2_get_le32(gb);
- dx = bytestream2_get_le32(gb);
- dy = bytestream2_get_le32(gb);
-
- s->w = (unsigned)dx - sx + 1;
- s->h = (unsigned)dy - sy + 1;
-
- continue;
- } else if ((var_size = check_header_variable(s, "lineOrder",
- "lineOrder", 25)) >= 0) {
- int line_order;
- if (!var_size) {
- ret = AVERROR_INVALIDDATA;
- goto fail;
- }
-
- line_order = bytestream2_get_byte(gb);
- av_log(s->avctx, AV_LOG_DEBUG, "line order: %d.\n", line_order);
- if (line_order > 2) {
- av_log(s->avctx, AV_LOG_ERROR, "Unknown line order.\n");
- ret = AVERROR_INVALIDDATA;
- goto fail;
- }
-
- continue;
- } else if ((var_size = check_header_variable(s, "pixelAspectRatio",
- "float", 31)) >= 0) {
- if (!var_size) {
- ret = AVERROR_INVALIDDATA;
- goto fail;
- }
-
- s->sar = bytestream2_get_le32(gb);
-
- continue;
- } else if ((var_size = check_header_variable(s, "compression",
- "compression", 29)) >= 0) {
- if (!var_size) {
- ret = AVERROR_INVALIDDATA;
- goto fail;
- }
-
- if (s->compression == EXR_UNKN)
- s->compression = bytestream2_get_byte(gb);
- else {
- bytestream2_skip(gb, 1);
- av_log(s->avctx, AV_LOG_WARNING,
- "Found more than one compression attribute.\n");
- }
-
- continue;
- } else if ((var_size = check_header_variable(s, "tiles",
- "tiledesc", 22)) >= 0) {
- char tileLevel;
-
- if (!s->is_tile)
- av_log(s->avctx, AV_LOG_WARNING,
- "Found tile attribute and scanline flags. Exr will be interpreted as scanline.\n");
-
- s->tile_attr.xSize = bytestream2_get_le32(gb);
- s->tile_attr.ySize = bytestream2_get_le32(gb);
-
- tileLevel = bytestream2_get_byte(gb);
- s->tile_attr.level_mode = tileLevel & 0x0f;
- s->tile_attr.level_round = (tileLevel >> 4) & 0x0f;
-
- if (s->tile_attr.level_mode >= EXR_TILE_LEVEL_UNKNOWN) {
- avpriv_report_missing_feature(s->avctx, "Tile level mode %d",
- s->tile_attr.level_mode);
- ret = AVERROR_PATCHWELCOME;
- goto fail;
- }
-
- if (s->tile_attr.level_round >= EXR_TILE_ROUND_UNKNOWN) {
- avpriv_report_missing_feature(s->avctx, "Tile level round %d",
- s->tile_attr.level_round);
- ret = AVERROR_PATCHWELCOME;
- goto fail;
- }
-
- continue;
- } else if ((var_size = check_header_variable(s, "writer",
- "string", 1)) >= 0) {
- uint8_t key[256] = { 0 };
-
- bytestream2_get_buffer(gb, key, FFMIN(sizeof(key) - 1, var_size));
- av_dict_set(&metadata, "writer", key, 0);
-
- continue;
- } else if ((var_size = check_header_variable(s, "framesPerSecond",
- "rational", 33)) >= 0) {
- if (!var_size) {
- ret = AVERROR_INVALIDDATA;
- goto fail;
- }
-
- s->avctx->framerate.num = bytestream2_get_le32(gb);
- s->avctx->framerate.den = bytestream2_get_le32(gb);
-
- continue;
- } else if ((var_size = check_header_variable(s, "chunkCount",
- "int", 23)) >= 0) {
-
- s->chunk_count = bytestream2_get_le32(gb);
-
- continue;
- } else if ((var_size = check_header_variable(s, "type",
- "string", 16)) >= 0) {
- uint8_t key[256] = { 0 };
-
- bytestream2_get_buffer(gb, key, FFMIN(sizeof(key) - 1, var_size));
- if (strncmp("scanlineimage", key, var_size) &&
- strncmp("tiledimage", key, var_size))
- return AVERROR_PATCHWELCOME;
-
- continue;
- } else if ((var_size = check_header_variable(s, "preview",
- "preview", 16)) >= 0) {
- uint32_t pw = bytestream2_get_le32(gb);
- uint32_t ph = bytestream2_get_le32(gb);
- uint64_t psize = pw * ph;
- if (psize > INT64_MAX / 4)
- return AVERROR_INVALIDDATA;
- psize *= 4;
-
- if ((int64_t)psize >= bytestream2_get_bytes_left(gb))
- return AVERROR_INVALIDDATA;
-
- bytestream2_skip(gb, psize);
-
- continue;
- }
-
- // Check if there are enough bytes for a header
- if (bytestream2_get_bytes_left(gb) <= 9) {
- av_log(s->avctx, AV_LOG_ERROR, "Incomplete header\n");
- ret = AVERROR_INVALIDDATA;
- goto fail;
- }
-
- // Process unknown variables
- {
- uint8_t name[256] = { 0 };
- uint8_t type[256] = { 0 };
- uint8_t value[8192] = { 0 };
- int i = 0, size;
-
- while (bytestream2_get_bytes_left(gb) > 0 &&
- bytestream2_peek_byte(gb) && i < 255) {
- name[i++] = bytestream2_get_byte(gb);
- }
-
- bytestream2_skip(gb, 1);
- i = 0;
- while (bytestream2_get_bytes_left(gb) > 0 &&
- bytestream2_peek_byte(gb) && i < 255) {
- type[i++] = bytestream2_get_byte(gb);
- }
- bytestream2_skip(gb, 1);
- size = bytestream2_get_le32(gb);
-
- bytestream2_get_buffer(gb, value, FFMIN(sizeof(value) - 1, size));
- if (size > sizeof(value) - 1)
- bytestream2_skip(gb, size - (sizeof(value) - 1));
- if (!strcmp(type, "string"))
- av_dict_set(&metadata, name, value, 0);
- }
- }
-
- if (s->compression == EXR_UNKN) {
- av_log(s->avctx, AV_LOG_ERROR, "Missing compression attribute.\n");
- ret = AVERROR_INVALIDDATA;
- goto fail;
- }
-
- if (s->is_tile) {
- if (s->tile_attr.xSize < 1 || s->tile_attr.ySize < 1) {
- av_log(s->avctx, AV_LOG_ERROR, "Invalid tile attribute.\n");
- ret = AVERROR_INVALIDDATA;
- goto fail;
- }
- }
-
- if (bytestream2_get_bytes_left(gb) <= 0) {
- av_log(s->avctx, AV_LOG_ERROR, "Incomplete frame.\n");
- ret = AVERROR_INVALIDDATA;
- goto fail;
- }
-
- frame->metadata = metadata;
-
- // aaand we are done
- bytestream2_skip(gb, 1);
- return 0;
-fail:
- av_dict_free(&metadata);
- return ret;
-}
-
-static int decode_frame(AVCodecContext *avctx, AVFrame *picture,
- int *got_frame, AVPacket *avpkt)
-{
- EXRContext *s = avctx->priv_data;
- GetByteContext *gb = &s->gb;
- uint8_t *ptr;
-
- int i, y, ret, ymax;
- int planes;
- int out_line_size;
- int nb_blocks; /* nb scanline or nb tile */
- uint64_t start_offset_table;
- uint64_t start_next_scanline;
-
- bytestream2_init(gb, avpkt->data, avpkt->size);
-
- if ((ret = decode_header(s, picture)) < 0)
- return ret;
-
- if ((s->compression == EXR_DWAA || s->compression == EXR_DWAB) &&
- s->pixel_type == EXR_HALF) {
- s->current_channel_offset *= 2;
- for (int i = 0; i < 4; i++)
- s->channel_offsets[i] *= 2;
- }
-
- switch (s->pixel_type) {
- case EXR_FLOAT:
- case EXR_HALF:
- if (s->channel_offsets[3] >= 0) {
- if (!s->is_luma) {
- avctx->pix_fmt = AV_PIX_FMT_GBRAPF32;
- } else {
- /* todo: change this when a floating point pixel format with luma with alpha is implemented */
- avctx->pix_fmt = AV_PIX_FMT_GBRAPF32;
- }
- } else {
- if (!s->is_luma) {
- avctx->pix_fmt = AV_PIX_FMT_GBRPF32;
- } else {
- avctx->pix_fmt = AV_PIX_FMT_GRAYF32;
- }
- }
- break;
- case EXR_UINT:
- if (s->channel_offsets[3] >= 0) {
- if (!s->is_luma) {
- avctx->pix_fmt = AV_PIX_FMT_RGBA64;
- } else {
- avctx->pix_fmt = AV_PIX_FMT_YA16;
- }
- } else {
- if (!s->is_luma) {
- avctx->pix_fmt = AV_PIX_FMT_RGB48;
- } else {
- avctx->pix_fmt = AV_PIX_FMT_GRAY16;
- }
- }
- break;
- default:
- av_log(avctx, AV_LOG_ERROR, "Missing channel list.\n");
- return AVERROR_INVALIDDATA;
- }
-
- if (s->apply_trc_type != AVCOL_TRC_UNSPECIFIED)
- avctx->color_trc = s->apply_trc_type;
-
- switch (s->compression) {
- case EXR_RAW:
- case EXR_RLE:
- case EXR_ZIP1:
- s->scan_lines_per_block = 1;
- break;
- case EXR_PXR24:
- case EXR_ZIP16:
- s->scan_lines_per_block = 16;
- break;
- case EXR_PIZ:
- case EXR_B44:
- case EXR_B44A:
- case EXR_DWAA:
- s->scan_lines_per_block = 32;
- break;
- case EXR_DWAB:
- s->scan_lines_per_block = 256;
- break;
- default:
- avpriv_report_missing_feature(avctx, "Compression %d", s->compression);
- return AVERROR_PATCHWELCOME;
- }
-
- /* Verify the xmin, xmax, ymin and ymax before setting the actual image size.
- * It's possible for the data window can larger or outside the display window */
- if (s->xmin > s->xmax || s->ymin > s->ymax ||
- s->ydelta == 0xFFFFFFFF || s->xdelta == 0xFFFFFFFF) {
- av_log(avctx, AV_LOG_ERROR, "Wrong or missing size information.\n");
- return AVERROR_INVALIDDATA;
- }
-
- if ((ret = ff_set_dimensions(avctx, s->w, s->h)) < 0)
- return ret;
-
- ff_set_sar(s->avctx, av_d2q(av_int2float(s->sar), 255));
-
- if (avctx->skip_frame >= AVDISCARD_ALL)
- return avpkt->size;
-
- s->desc = av_pix_fmt_desc_get(avctx->pix_fmt);
- if (!s->desc)
- return AVERROR_INVALIDDATA;
-
- if (s->desc->flags & AV_PIX_FMT_FLAG_FLOAT) {
- planes = s->desc->nb_components;
- out_line_size = avctx->width * 4;
- } else {
- planes = 1;
- out_line_size = avctx->width * 2 * s->desc->nb_components;
- }
-
- if (s->is_tile) {
- nb_blocks = ((s->xdelta + s->tile_attr.xSize - 1) / s->tile_attr.xSize) *
- ((s->ydelta + s->tile_attr.ySize - 1) / s->tile_attr.ySize);
- } else { /* scanline */
- nb_blocks = (s->ydelta + s->scan_lines_per_block - 1) /
- s->scan_lines_per_block;
- }
-
- if ((ret = ff_thread_get_buffer(avctx, picture, 0)) < 0)
- return ret;
-
- if (bytestream2_get_bytes_left(gb)/8 < nb_blocks)
- return AVERROR_INVALIDDATA;
-
- // check offset table and recreate it if need
- if (!s->is_tile && bytestream2_peek_le64(gb) == 0) {
- PutByteContext offset_table_writer;
-
- av_log(s->avctx, AV_LOG_DEBUG, "recreating invalid scanline offset table\n");
-
- s->offset_table = av_realloc_f(s->offset_table, nb_blocks, 8);
- if (!s->offset_table)
- return AVERROR(ENOMEM);
-
- start_offset_table = bytestream2_tell(gb);
- start_next_scanline = start_offset_table + nb_blocks * 8;
- bytestream2_init_writer(&offset_table_writer, s->offset_table, nb_blocks * 8);
-
- for (y = 0; y < nb_blocks; y++) {
- /* write offset of prev scanline in offset table */
- bytestream2_put_le64(&offset_table_writer, start_next_scanline);
-
- /* get len of next scanline */
- bytestream2_seek(gb, start_next_scanline + 4, SEEK_SET);/* skip line number */
- start_next_scanline += (bytestream2_get_le32(gb) + 8);
- }
- bytestream2_init(gb, s->offset_table, nb_blocks * 8);
- }
-
- // save pointer we are going to use in decode_block
- s->buf = avpkt->data;
- s->buf_size = avpkt->size;
-
- // Zero out the start if ymin is not 0
- for (i = 0; i < planes; i++) {
- ptr = picture->data[i];
- for (y = 0; y < FFMIN(s->ymin, s->h); y++) {
- memset(ptr, 0, out_line_size);
- ptr += picture->linesize[i];
- }
- }
-
- s->picture = picture;
-
- avctx->execute2(avctx, decode_block, s->thread_data, NULL, nb_blocks);
-
- ymax = FFMAX(0, s->ymax + 1);
- // Zero out the end if ymax+1 is not h
- if (ymax < avctx->height)
- for (i = 0; i < planes; i++) {
- ptr = picture->data[i] + (ymax * picture->linesize[i]);
- for (y = ymax; y < avctx->height; y++) {
- memset(ptr, 0, out_line_size);
- ptr += picture->linesize[i];
- }
- }
-
- picture->pict_type = AV_PICTURE_TYPE_I;
- *got_frame = 1;
-
- return avpkt->size;
-}
-
-static av_cold int decode_init(AVCodecContext *avctx)
-{
- EXRContext *s = avctx->priv_data;
- uint32_t i;
- union av_intfloat32 t;
- float one_gamma = 1.0f / s->gamma;
- av_csp_trc_function trc_func = NULL;
-
- ff_init_half2float_tables(&s->h2f_tables);
-
- s->avctx = avctx;
-
- ff_exrdsp_init(&s->dsp);
-
-#if HAVE_BIGENDIAN
- ff_bswapdsp_init(&s->bbdsp);
-#endif
-
- trc_func = av_csp_trc_func_from_id(s->apply_trc_type);
- if (trc_func) {
- for (i = 0; i < 65536; ++i) {
- t.i = half2float(i, &s->h2f_tables);
- t.f = trc_func(t.f);
- s->gamma_table[i] = t;
- }
- } else {
- if (one_gamma > 0.9999f && one_gamma < 1.0001f) {
- for (i = 0; i < 65536; ++i) {
- s->gamma_table[i].i = half2float(i, &s->h2f_tables);
- }
- } else {
- for (i = 0; i < 65536; ++i) {
- t.i = half2float(i, &s->h2f_tables);
- /* If negative value we reuse half value */
- if (t.f <= 0.0f) {
- s->gamma_table[i] = t;
- } else {
- t.f = powf(t.f, one_gamma);
- s->gamma_table[i] = t;
- }
- }
- }
- }
-
- // allocate thread data, used for non EXR_RAW compression types
- s->thread_data = av_calloc(avctx->thread_count, sizeof(*s->thread_data));
- if (!s->thread_data)
- return AVERROR(ENOMEM);
-
- return 0;
-}
-
-static av_cold int decode_end(AVCodecContext *avctx)
-{
- EXRContext *s = avctx->priv_data;
- int i;
- for (i = 0; i < avctx->thread_count; i++) {
- EXRThreadData *td = &s->thread_data[i];
- av_freep(&td->uncompressed_data);
- av_freep(&td->tmp);
- av_freep(&td->bitmap);
- av_freep(&td->lut);
- av_freep(&td->he);
- av_freep(&td->freq);
- av_freep(&td->ac_data);
- av_freep(&td->dc_data);
- av_freep(&td->rle_data);
- av_freep(&td->rle_raw_data);
- ff_free_vlc(&td->vlc);
- }
-
- av_freep(&s->thread_data);
- av_freep(&s->channels);
- av_freep(&s->offset_table);
-
- return 0;
-}
-
-#define OFFSET(x) offsetof(EXRContext, x)
-#define VD AV_OPT_FLAG_VIDEO_PARAM | AV_OPT_FLAG_DECODING_PARAM
-static const AVOption options[] = {
- { "layer", "Set the decoding layer", OFFSET(layer),
- AV_OPT_TYPE_STRING, { .str = "" }, 0, 0, VD },
- { "part", "Set the decoding part", OFFSET(selected_part),
- AV_OPT_TYPE_INT, { .i64 = 0 }, 0, INT_MAX, VD },
- { "gamma", "Set the float gamma value when decoding", OFFSET(gamma),
- AV_OPT_TYPE_FLOAT, { .dbl = 1.0f }, 0.001, FLT_MAX, VD },
-
- // XXX: Note the abuse of the enum using AVCOL_TRC_UNSPECIFIED to subsume the existing gamma option
- { "apply_trc", "color transfer characteristics to apply to EXR linear input", OFFSET(apply_trc_type),
- AV_OPT_TYPE_INT, {.i64 = AVCOL_TRC_UNSPECIFIED }, 1, AVCOL_TRC_NB-1, VD, "apply_trc_type"},
- { "bt709", "BT.709", 0,
- AV_OPT_TYPE_CONST, {.i64 = AVCOL_TRC_BT709 }, INT_MIN, INT_MAX, VD, "apply_trc_type"},
- { "gamma", "gamma", 0,
- AV_OPT_TYPE_CONST, {.i64 = AVCOL_TRC_UNSPECIFIED }, INT_MIN, INT_MAX, VD, "apply_trc_type"},
- { "gamma22", "BT.470 M", 0,
- AV_OPT_TYPE_CONST, {.i64 = AVCOL_TRC_GAMMA22 }, INT_MIN, INT_MAX, VD, "apply_trc_type"},
- { "gamma28", "BT.470 BG", 0,
- AV_OPT_TYPE_CONST, {.i64 = AVCOL_TRC_GAMMA28 }, INT_MIN, INT_MAX, VD, "apply_trc_type"},
- { "smpte170m", "SMPTE 170 M", 0,
- AV_OPT_TYPE_CONST, {.i64 = AVCOL_TRC_SMPTE170M }, INT_MIN, INT_MAX, VD, "apply_trc_type"},
- { "smpte240m", "SMPTE 240 M", 0,
- AV_OPT_TYPE_CONST, {.i64 = AVCOL_TRC_SMPTE240M }, INT_MIN, INT_MAX, VD, "apply_trc_type"},
- { "linear", "Linear", 0,
- AV_OPT_TYPE_CONST, {.i64 = AVCOL_TRC_LINEAR }, INT_MIN, INT_MAX, VD, "apply_trc_type"},
- { "log", "Log", 0,
- AV_OPT_TYPE_CONST, {.i64 = AVCOL_TRC_LOG }, INT_MIN, INT_MAX, VD, "apply_trc_type"},
- { "log_sqrt", "Log square root", 0,
- AV_OPT_TYPE_CONST, {.i64 = AVCOL_TRC_LOG_SQRT }, INT_MIN, INT_MAX, VD, "apply_trc_type"},
- { "iec61966_2_4", "IEC 61966-2-4", 0,
- AV_OPT_TYPE_CONST, {.i64 = AVCOL_TRC_IEC61966_2_4 }, INT_MIN, INT_MAX, VD, "apply_trc_type"},
- { "bt1361", "BT.1361", 0,
- AV_OPT_TYPE_CONST, {.i64 = AVCOL_TRC_BT1361_ECG }, INT_MIN, INT_MAX, VD, "apply_trc_type"},
- { "iec61966_2_1", "IEC 61966-2-1", 0,
- AV_OPT_TYPE_CONST, {.i64 = AVCOL_TRC_IEC61966_2_1 }, INT_MIN, INT_MAX, VD, "apply_trc_type"},
- { "bt2020_10bit", "BT.2020 - 10 bit", 0,
- AV_OPT_TYPE_CONST, {.i64 = AVCOL_TRC_BT2020_10 }, INT_MIN, INT_MAX, VD, "apply_trc_type"},
- { "bt2020_12bit", "BT.2020 - 12 bit", 0,
- AV_OPT_TYPE_CONST, {.i64 = AVCOL_TRC_BT2020_12 }, INT_MIN, INT_MAX, VD, "apply_trc_type"},
- { "smpte2084", "SMPTE ST 2084", 0,
- AV_OPT_TYPE_CONST, {.i64 = AVCOL_TRC_SMPTEST2084 }, INT_MIN, INT_MAX, VD, "apply_trc_type"},
- { "smpte428_1", "SMPTE ST 428-1", 0,
- AV_OPT_TYPE_CONST, {.i64 = AVCOL_TRC_SMPTEST428_1 }, INT_MIN, INT_MAX, VD, "apply_trc_type"},
-
- { NULL },
-};
-
-static const AVClass exr_class = {
- .class_name = "EXR",
- .item_name = av_default_item_name,
- .option = options,
- .version = LIBAVUTIL_VERSION_INT,
-};
-
-const FFCodec ff_exr_decoder = {
- .p.name = "exr",
- CODEC_LONG_NAME("OpenEXR image"),
- .p.type = AVMEDIA_TYPE_VIDEO,
- .p.id = AV_CODEC_ID_EXR,
- .priv_data_size = sizeof(EXRContext),
- .init = decode_init,
- .close = decode_end,
- FF_CODEC_DECODE_CB(decode_frame),
- .p.capabilities = AV_CODEC_CAP_DR1 | AV_CODEC_CAP_FRAME_THREADS |
- AV_CODEC_CAP_SLICE_THREADS,
- .caps_internal = FF_CODEC_CAP_SKIP_FRAME_FILL_PARAM,
- .p.priv_class = &exr_class,
-};
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Belajar Jadi Lebih Seru dengan Game Edukasi Anak Lengkap APK.md b/spaces/congsaPfin/Manga-OCR/logs/Belajar Jadi Lebih Seru dengan Game Edukasi Anak Lengkap APK.md
deleted file mode 100644
index 76c41dae5692044d243bbf6d7b247379b1691a70..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Belajar Jadi Lebih Seru dengan Game Edukasi Anak Lengkap APK.md
+++ /dev/null
@@ -1,162 +0,0 @@
-
-
Game Edukasi Anak Lengkap Apk: A Fun and Educational App for Kids
-
If you are looking for a fun and educational app for your kids, you might want to check out Game Edukasi Anak Lengkap Apk. This app is developed by SekarMedia, a company that specializes in creating games for children. Game Edukasi Anak Lengkap Apk offers a variety of games that teach your kids different skills and concepts in an engaging way. Whether your kids want to learn letters, numbers, colors, animals, music, or writing, this app has something for them. In this article, we will review the features and benefits of this app, as well as show you how to download and use it. We will also compare it with some other popular educational games for kids.
-
Features of the App
-
Educational Games
-
One of the main features of Game Edukasi Anak Lengkap Apk is its educational games. The app has games for different age groups and levels of difficulty. Some of the games are:
Letter recognition: This game helps kids learn the alphabet by showing them pictures of objects that start with each letter.
-
Number recognition: This game helps kids learn numbers by showing them pictures of objects that match each number.
-
Color recognition: This game helps kids learn colors by showing them pictures of objects that have each color.
-
Animal sounds: This game helps kids learn animal sounds by playing them when they tap on each animal.
-
Piano: This game helps
Piano: This game helps kids learn music by playing notes and songs on a virtual piano.
-
Learn to write: This game helps kids practice writing letters and numbers by tracing them on the screen.
-
-
These games are designed to be fun and interactive, so that kids can enjoy learning while playing. They also provide feedback and rewards to motivate kids and keep them interested.
-
Two Languages
-
Another feature of Game Edukasi Anak Lengkap Apk is that it supports two languages: Indonesian and English. This means that kids can learn a new language or improve their native one by playing the games in either language. They can also switch between the languages easily by tapping on the flag icon on the top right corner of the screen. This feature can help kids develop their bilingual skills and expand their vocabulary.
-
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-
Memory Games
-
Game Edukasi Anak Lengkap Apk also has memory games that challenge kids to remember and match different images. These games can train the brain and improve concentration and recall. They can also help kids learn new words and concepts by associating them with the images. Some of the memory games are:
-
-
Animal memory: This game shows kids pairs of animals that they have to match by flipping the cards.
-
Color memory: This game shows kids pairs of colors that they have to match by flipping the cards.
-
Number memory: This game shows kids pairs of numbers that they have to match by flipping the cards.
-
Letter memory: This game shows kids pairs of letters that they have to match by flipping the cards.
-
-
These games are fun and challenging, and they can help kids improve their memory and cognitive skills.
-
Coloring
-
Game Edukasi Anak Lengkap Apk also has a coloring feature that lets kids express their creativity and imagination. The app has a variety of images and scenes that kids can color using different tools and colors. They can also save and share their creations with others. Some of the images and scenes are:
-
-
Animals: This category has images of different animals, such as cats, dogs, birds, fish, etc.
-
Nature: This category has images of different natural elements, such as flowers, trees, mountains, etc.
-
Vehicles: This category has images of different vehicles, such as cars, planes, trains, etc.
-
Fairy tales: This category has images of different fairy tale characters and settings, such as princesses, castles, dragons, etc.
-
-
This feature is fun and relaxing, and it can help kids develop their artistic skills and sense of color.
-
Learn to Write
-
Game Edukasi Anak Lengkap Apk also has a writing feature that helps kids learn how to write letters and numbers. The app shows kids how to write each letter and number using dotted lines and arrows. It also gives them feedback on their accuracy and speed. Kids can practice writing in both uppercase and lowercase letters, as well as in both Indonesian and English languages. This feature can help kids improve their handwriting skills and literacy skills.
-
Benefits of Educational Games for Kids
-
Motivation and Engagement
-
Educational games can have many benefits for kids' learning and development. One of the benefits is that they can increase kids' motivation and engagement in learning. According to research, games can make learning more enjoyable, meaningful, and relevant for kids. They can also provide immediate feedback, rewards, and challenges that keep kids interested and motivated. Games can also adapt to kids' individual needs, preferences, and abilities, making learning more personalized and effective.
-
Self-Esteem and Social Skills
-
Educational games can also boost kids' self-esteem and social skills. According to research, games can enhance kids' self-confidence, self-efficacy, and self-regulation. They can also foster positive emotions, such as joy, pride, and satisfaction. Games can also promote social interaction, communication, collaboration, and empathy among kids. They can help kids make friends, share ideas, solve problems, and learn from each other. Games can also teach kids about different cultures, perspectives, and values.
-
Problem-Solving and Critical Thinking
-
Educational games can also develop kids' problem-solving
Educational games can also develop kids' problem-solving and critical thinking skills. According to research, games can stimulate kids' cognitive processes, such as reasoning, analysis, evaluation, and decision making. They can also expose kids to complex and realistic problems that require creativity and innovation. Games can also help kids transfer their learning to new situations and domains, as well as reflect on their own thinking and actions.
-
Curiosity and Creativity
-
Educational games can also spark kids' curiosity and creativity. According to research, games can arouse kids' interest and curiosity about different topics and phenomena. They can also encourage kids to explore, experiment, and discover new things. Games can also inspire kids to express their ideas and feelings in various ways, such as words, images, sounds, or movements. Games can also help kids develop their imagination and originality, as well as their aesthetic and artistic appreciation.
-
How to Download and Use the App
-
Downloading Instructions
-
If you want to download Game Edukasi Anak Lengkap Apk, you can follow these simple steps:
-
-
Go to the Google Play Store on your Android device.
-
Search for "Game Edukasi Anak Lengkap Apk" or click on this link: .
-
Tap on the "Install" button and wait for the app to download and install.
-
Open the app and enjoy playing the games.
-
-
You can also download the app from other sources, such as APKPure or APKMonk. However, make sure that you download the app from a trusted and secure site, and that you scan the app for viruses or malware before installing it.
-
Using Instructions
-
To use Game Edukasi Anak Lengkap Apk effectively, you can follow these tips:
-
-
Choose a language: You can choose between Indonesian and English by tapping on the flag icon on the top right corner of the screen. You can change the language anytime you want.
-
Select a game: You can select a game by tapping on the icon that represents it on the main menu. You can also swipe left or right to see more games.
-
Adjust the settings: You can adjust the settings of the app by tapping on the gear icon on the top left corner of the screen. You can change the sound effects, music, voice, and difficulty level of the games.
-
Get help: You can get help by tapping on the question mark icon on the top right corner of the screen. You can see the instructions and tips for each game.
-
Have fun: You can have fun by playing the games and learning new things. You can also earn stars and trophies for your achievements.
-
-
Best Educational Games for Kids
-
Other Recommended Apps
-
If you like Game Edukasi Anak Lengkap Apk, you might also like some other educational games for kids. Here are some of our recommendations:
-
-
Minecraft: This is a sandbox game that lets kids create and explore a virtual world made of blocks. Kids can build anything they can imagine, from houses and castles to farms and cities. They can also play with other players online or offline. Minecraft is a great game for developing creativity, spatial awareness, logic, and collaboration skills.
-
Starfall: This is an app that teaches kids reading, writing, math, and more. Kids can learn phonics, spelling, vocabulary, grammar, numbers, shapes, patterns, fractions, etc. They can also play fun games and activities that reinforce their learning. Starfall is a great app for developing literacy and numeracy skills.
-
Zoombinis: This is an app that teaches kids logic, problem-solving, and data analysis. Kids have to help a group of blue creatures called Zoombinis escape from an evil corporation by solving various puzzles. They have to use their reasoning skills to find patterns, sequences, rules, etc. Zoombinis is a great app for developing critical thinking skills.
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-
Comparison Table
-
To help you compare Game Edukasi Anak Lengkap Apk with other apps, we have created a table that shows their features and benefits:
-
-
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App Name
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Features
-
Benefits
-
-
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Game Edukasi Anak Lengkap Apk
-
- Educational games - Two languages - Memory games - Coloring - Learn to write
-
- Teach various skills and concepts - Support bilingual learning - Train memory and concentration - Express creativity and imagination - Express creativity and imagination - Improve handwriting and literacy skills
-
-
-
Minecraft
-
- Sandbox game - Virtual world - Blocks - Multiplayer mode
-
- Develop creativity and spatial awareness - Explore and discover new things - Build anything they can imagine - Collaborate and communicate with others
-
-
-
Starfall
-
- Reading, writing, math, etc. - Phonics, spelling, vocabulary, etc. - Numbers, shapes, patterns, etc. - Games and activities
-
- Develop literacy and numeracy skills - Learn phonics and spelling rules - Learn math concepts and operations - Reinforce learning with fun and feedback
-
-
-
Zoombinis
-
- Logic, problem-solving, data analysis - Puzzles - Patterns, sequences, rules, etc. - Zoombinis characters
-
- Develop critical thinking skills - Solve complex and realistic problems - Use reasoning and analysis skills - Learn about data and information
-
-
-
Conclusion
-
Game Edukasi Anak Lengkap Apk is a fun and educational app for kids that offers a variety of games that teach different skills and concepts. The app supports two languages, Indonesian and English, and helps kids learn a new language or improve their native one. The app also has memory games that train the brain and improve concentration and recall. The app also has a coloring feature that lets kids express their creativity and imagination. The app also has a writing feature that helps kids learn how to write letters and numbers. The app has many benefits for kids' learning and development, such as increasing their motivation and engagement, boosting their self-esteem and social skills, developing their problem-solving and critical thinking skills, and sparking their curiosity and creativity. The app is easy to download and use, and it can be compared with other popular educational games for kids. If you want to give your kids a fun and educational experience, you should try Game Edukasi Anak Lengkap Apk.
-
FAQs
-
Here are some frequently asked questions about Game Edukasi Anak Lengkap Apk:
-
-
How much does the app cost? The app is free to download and use. However, it contains ads that can be removed by purchasing the premium version for $1.99.
-
How safe is the app? The app is safe for kids to use. It does not collect any personal information or require any permissions. It also does not contain any inappropriate or harmful content.
-
How often is the app updated? The app is updated regularly with new features, games, images, sounds, etc. The latest update was on June 15, 2023.
-
What are the minimum requirements for the app? The app requires Android 4.4 or higher to run smoothly. It also requires about 100 MB of storage space.
-
How can I contact the developer? You can contact the developer by sending an email to sekarmedia@gmail.com or by visiting their website at https://sekarmedia.com/.
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-
Chess.com Free Premium APK: How to Get Unlimited Access to Chess.com Features
-
If you are a chess lover, you probably have heard of Chess.com, the most popular online chess platform in the world. With over 100 million members, Chess.com offers you a variety of ways to play, learn, and improve your chess skills. But did you know that you can also get unlimited access to all the premium features of Chess.com for free? In this article, we will show you how to download and install Chess.com free premium APK, a modified version of the official app that lets you enjoy all the benefits of a paid membership without spending a dime. We will also discuss the risks and drawbacks of using this app, as well as some alternatives that you can try.
What is Chess.com and why is it popular among chess players?
-
Chess.com is an online chess platform that was launched in 2007. It allows you to play chess games with friends or strangers from around the world, either in real-time or at your own pace. You can also join tournaments, solve puzzles, take lessons, watch videos, analyze your games, and much more. Chess.com is designed for players of all levels, from beginners to grandmasters. You can customize your experience by choosing from different themes, boards, pieces, and sounds. You can also chat with other players, join clubs, follow your favorite players and streamers, and participate in community events.
-
Chess.com features and benefits
-
Some of the features and benefits that you can enjoy on Chess.com are:
-
-
Play online: You can play chess games with anyone online, either live or daily. You can choose from different time controls, modes, variants, and ratings. You can also challenge your friends or the computer.
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Solve puzzles: You can train your tactical skills by solving thousands of puzzles that are tailored to your level. You can also try Puzzle Rush, a game where you have to solve as many puzzles as you can in a limited time.
-
Take lessons: You can learn from the best chess teachers in the world by taking interactive lessons that cover various topics, from the basics to advanced strategies. You can also practice what you learned by doing drills and exercises.
-
Watch videos: You can watch hundreds of videos from top chess players and instructors, who share their insights, tips, tricks, and analysis. You can also watch live broadcasts of major chess events and tournaments.
-
Analyze your games: You can review your games with the help of powerful computer engines that show you your mistakes, blunders, and missed opportunities. You can also explore different moves and variations with the opening explorer and the board editor.
-
And more: You can also access other features such as ChessTV, custom flair, club management, timeout protection, endgames, daily puzzle explanations, etc.
-
-
Chess.com premium membership plans and prices
-
While you can use Chess.com for free, you will have some limitations on how many games, puzzles, lessons, videos, and analysis you can access per day or per month. If you want to unlock unlimited access to all the features and benefits of Chess.com, you will need to upgrade to a premium membership plan. There are three plans available:
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Plan
Price per month
Price per year
-
Gold
$4.99
$49. 99
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Platinum
$6.99
$69.99
-
Diamond
$14.99
$99.99
-
-
Each plan offers different levels of access to the features and benefits of Chess.com, as well as some exclusive perks such as unlimited puzzles, unlimited lessons, unlimited videos, unlimited analysis, etc. You can compare the plans and see what they include here.
-
What is Chess.com free premium APK and how does it work?
-
If you want to enjoy all the premium features and benefits of Chess.com without paying for a membership plan, you might be tempted to try Chess.com free premium APK. This is a modified version of the official Chess.com app that bypasses the payment system and gives you unlimited access to everything on the platform. You can download and install this app on your Android device and use it as if you were a premium member.
-
Chess.com free premium APK features and benefits
-
Some of the features and benefits that you can enjoy with Chess.com free premium APK are:
-
-
Unlimited access: You can access all the features and benefits of Chess.com without any limitations or restrictions. You can play as many games, solve as many puzzles, take as many lessons, watch as many videos, and analyze as many games as you want.
-
No ads: You can enjoy a smooth and uninterrupted experience without any annoying ads or pop-ups.
-
No root required: You don't need to root your device or do any complicated procedures to use this app. You just need to download and install it like any other app.
-
Easy to use: You don't need to create an account or log in to use this app. You just need to open it and start playing chess.
-
Free of cost: You don't need to pay anything to use this app. You can save your money and still enjoy all the premium features and benefits of Chess.com.
-
-
How to download and install Chess.com free premium APK
-
If you want to try Chess.com free premium APK, you will need to follow these steps:
-
-
Download the APK file: You can find the APK file from various sources on the internet, such as this one. Make sure you download it from a trusted and reliable source, as some files might contain viruses or malware.
-
Enable unknown sources: Before you can install the APK file, you will need to enable unknown sources on your device. This will allow you to install apps that are not from the Google Play Store. To do this, go to Settings > Security > Unknown Sources and toggle it on.
-
Install the APK file: Once you have downloaded and enabled unknown sources, you can install the APK file by tapping on it and following the instructions on the screen.
-
Enjoy Chess.com free premium APK: After you have installed the app, you can open it and start playing chess with unlimited access to all the features and benefits of Chess.com.
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-
Risks and drawbacks of using Chess.com free premium APK
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While Chess.com free premium APK might sound like a great deal, it also comes with some risks and drawbacks that you should be aware of before using it. Some of them are:
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-
Illegal and unethical: Using Chess.com free premium APK is illegal and unethical, as it violates the terms and conditions of Chess.com. You are essentially stealing from the developers who work hard to create and maintain the platform. You are also depriving them of their rightful income that they use to improve the platform and support the chess community.
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Banned or suspended account: If you use Chess.com free premium APK, you run the risk of getting your account banned or suspended by Chess.com. They have systems in place to detect and prevent unauthorized access to their platform. If they catch you using this app, they will take action against you and revoke your access to their platform.
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Incompatible or outdated app: Since Chess.com free premium APK is not an official app, it might not be compatible with your device or with the latest version of Chess.com. It might also have bugs or errors that affect its performance or functionality. You might miss out on some features or updates that are available on the official app.
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Security and privacy issues : Using Chess.com free premium APK might expose your device and your personal information to security and privacy risks. The APK file might contain viruses or malware that can harm your device or steal your data. You might also be vulnerable to hackers or cyberattacks that can access your account or your device.
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Alternatives to Chess.com free premium APK
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If you are looking for other ways to play chess online without using Chess.com free premium APK, you might want to consider some of these alternatives:
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Lichess.org
-
Lichess.org is a free and open-source online chess platform that offers you a similar experience to Chess.com. You can play chess games with anyone online, either live or correspondence. You can also join tournaments, solve puzzles, take lessons, watch videos, analyze your games, and much more. Lichess.org is supported by donations and volunteers, so you don't have to pay anything to use it. You can also download the Lichess app for your Android or iOS device.
-
Chess24.com
-
Chess24.com is another online chess platform that offers you a variety of features and benefits. You can play chess games with anyone online, either live or daily. You can also join tournaments, solve puzzles, take lessons, watch videos, analyze your games, and much more. Chess24.com has a premium membership plan that gives you unlimited access to everything on the platform, as well as some exclusive perks such as ad-free experience, offline mode, cloud analysis, etc. You can also download the Chess24 app for your Android or iOS device.
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Chessable.com
-
Chessable.com is an online chess learning platform that helps you improve your chess skills with science-based methods. You can learn from hundreds of courses that cover various topics, from openings to endgames. You can also practice what you learned by doing drills and exercises. Chessable.com has a free plan that gives you access to some courses and features, as well as a pro plan that gives you unlimited access to everything on the platform, as well as some exclusive perks such as personalized feedback, advanced statistics, etc. You can also download the Chessable app for your Android or iOS device.
-
Conclusion
-
Summary of the main points
-
In this article, we have discussed the following points:
-
-
Chess.com is the most popular online chess platform in the world that offers you a variety of ways to play, learn, and improve your chess skills.
-
Chess.com has three premium membership plans that give you unlimited access to all the features and benefits of the platform.
-
Chess.com free premium APK is a modified version of the official app that gives you unlimited access to everything on the platform for free.
-
Chess.com free premium APK has some risks and drawbacks that you should be aware of before using it.
-
There are some alternatives to Chess.com free premium APK that you can try if you want to play chess online without paying anything.
-
-
Call to action
-
If you are interested in playing chess online with unlimited access to all the features and benefits of Chess.com, we recommend you to upgrade to a premium membership plan. This way, you will support the developers who work hard to create and maintain the platform, as well as the chess community that benefits from it. You will also enjoy a smooth and uninterrupted experience without any security or privacy issues. You can choose from three plans that suit your needs and budget: Gold, Platinum, or Diamond. To upgrade your account, click here.
-
If you have any questions or feedback about this article or Chess.com in general, feel free to leave a comment below. We would love to hear from you and help you with anything related to chess. Thank you for reading and happy chess playing!
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Frequently Asked Questions
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Q: Is Chess.com free premium APK safe?
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A: No, Chess.com free premium APK is not safe. It is illegal and unethical, as it violates the terms and conditions of Chess.com. It might also contain viruses or malware that can harm your device or steal your data. It might also get your account banned or suspended by Chess.com.
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Q: How can I get Chess.com free premium APK?
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A: You can get Chess.com free premium APK by downloading and installing the APK file from various sources on the internet. However, we do not recommend you to do this, as it has many risks and drawbacks.Q: What are the benefits of Chess.com premium membership?
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A: Chess.com premium membership gives you unlimited access to all the features and benefits of the platform, such as playing online, solving puzzles, taking lessons, watching videos, analyzing your games, and more. You also get some exclusive perks such as ad-free experience, offline mode, cloud analysis, etc.
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Q: What are the alternatives to Chess.com free premium APK?
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A: Some of the alternatives to Chess.com free premium APK are Lichess.org, Chess24.com, and Chessable.com. These are online chess platforms that offer you similar or different features and benefits. You can use them for free or upgrade to a paid plan if you want.
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Q: How can I improve my chess skills?
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A: You can improve your chess skills by playing regularly, solving puzzles, taking lessons, watching videos, analyzing your games, and learning from other players. You can also use Chess.com or any of the alternatives to access various resources and tools that can help you improve your chess skills.
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Download Bubble Shooter Mod APK for Free and Enjoy the Best Shooting Game Ever.md b/spaces/congsaPfin/Manga-OCR/logs/Download Bubble Shooter Mod APK for Free and Enjoy the Best Shooting Game Ever.md
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How to Hack Bubble Shooter APK on Android
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Bubble Shooter is one of the most addictive and entertaining games on Android. But what if you want to get more out of it? What if you want to have unlimited coins, lives, boosters, and levels? Well, you can do that by hacking the Bubble Shooter APK on your Android device. In this article, we will show you how to do that in a few simple steps.
Bubble Shooter is a classic game that involves shooting bubbles to match three or more of the same color. The game has hundreds of levels, each with different challenges and objectives. You can also play online with other players and compete for high scores. The game is fun, relaxing, and easy to play.
-
Hacking it can unlock more features and fun
-
However, some players may find the game too easy or too hard. They may also run out of coins, lives, or boosters. They may also want to access all the levels without having to complete them one by one. That's where hacking comes in. By hacking the Bubble Shooter APK, you can modify the game's code and data to change its behavior and features. You can get unlimited resources, unlock all levels, remove ads, and more. This way, you can enjoy the game in your own way.
-
How to download and install APK files on Android
-
APK files are Android application packages
-
Before we show you how to hack Bubble Shooter APK on Android, you need to know what an APK file is. An APK file is an Android application package that contains all the files and data needed to run an app on your device. It's like a zip file that you can extract and install. Normally, you download and install apps from the Google Play Store, which automatically handles the APK files for you. But sometimes, you may want to install apps from other sources, such as websites or file transfers. That's when you need to deal with APK files manually.
-
You need to enable unknown sources and use a file manager
-
To install an APK file on your Android device, you need to do two things. First, you need to enable unknown sources in your settings. This allows you to install apps from sources other than the Google Play Store. To do this, go to Settings > Apps & Notifications > Special Access > Install Unknown Apps. Then, select your browser or file manager app and turn on the Allow from this source option.
-
Second, you need to use a file manager app to locate and install the APK file. A file manager app lets you browse and manage the files on your device. You can use the default file manager app that comes with your device or download one from the Google Play Store. For example, you can use [ES File Explorer] or [Solid Explorer]. Once you have a file manager app, open it and navigate to the folder where you downloaded or transferred the APK file. Then, tap on the APK file and follow the instructions to install it.
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You can download APK files from reputable sources or transfer them from your computer
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Now that you know how to install APK files on your Android device, you need to know where to get them. There are two main ways to do this. One is to download them from reputable websites that offer APK files for various apps. Some examples are [APKPure], [APKMirror], and [Uptodown]. These websites usually have the latest versions of popular apps, as well as older versions and modded versions. You can use your browser to search and download the APK files you want.
-
The other way is to transfer them from your computer. This is useful if you have an APK file on your computer that you want to install on your Android device. To do this, you need to connect your device to your computer using a USB cable. Then, you need to enable USB debugging on your device. This allows your computer to communicate with your device and access its files. To enable USB debugging, go to Settings > About Phone > Tap on Build Number 7 times > Go back to Settings > Developer Options > Turn on USB Debugging. Then, you need to use a program like [Android File Transfer] or [AirDroid] to transfer the APK file from your computer to your device.
-
How to hack Bubble Shooter APK on Android
-
You need to find a hacked version of Bubble Shooter APK
-
Now that you know how to download and install APK files on your Android device, you need to find a hacked version of Bubble Shooter APK. A hacked version of Bubble Shooter APK is an APK file that has been modified by hackers to change the game's features and behavior. For example, a hacked version of Bubble Shooter APK may have unlimited coins, lives, boosters, and levels. It may also have no ads, no in-app purchases, and no restrictions.
-
There are many websites that offer hacked versions of Bubble Shooter APK for free. However, not all of them are safe and reliable. Some of them may contain malware, viruses, or spyware that can harm your device or steal your personal information. Therefore, you need to be careful and choose a reputable source for downloading hacked versions of Bubble Shooter APK. Some examples are [ModAPKDown], [HappyMod], and [Android-1]. These websites have a large collection of hacked versions of Bubble Shooter APK and other apps. You can use your browser to search and download the hacked version of Bubble Shooter APK that suits your needs.
-
You need to uninstall the original game and install the hacked one
-
Once you have downloaded the hacked version of Bubble Shooter APK, you need to uninstall the original game from your device. This is because you cannot have two versions of the same app installed on your device at the same time. To uninstall the original game, go to Settings > Apps & Notifications > See All Apps > Bubble Shooter > Uninstall.
-
Then, you need to install the hacked version of Bubble Shooter APK using the same method as before. Locate the hacked version of Bubble Shooter APK on your device using a file manager app and tap on it to install it.
-
You need to launch the game and enjoy the hack
-
Finally, you need to launch the game and enjoy the hack. You should see a difference in the game's features and behavior compared to the original version. For example, you should have unlimited coins, lives, boosters, and levels. You should also have no ads, no in-app purchases, and no restrictions.
-
Congratulations! You have successfully hacked Bubble Shooter APK on your Android device. You can now play the game in your own way and have more fun.
-
What are the benefits and risks of hacking Bubble Shooter APK on Android
-
Benefits include unlimited coins, lives, boosters, and levels
-
The main benefit of hacking Bubble Shooter APK on Android is that you can get unlimited resources and features that can enhance your gaming experience. You can play as long as you want without worrying about running out of coins, lives, or boosters. You can also access all the levels without having to complete them one by one. You can also enjoy the game without any ads or in-app purchases.
-
Risks include malware, viruses, bans, and legal issues
-
The main risk of hacking Bubble Shooter APK on Android is that you may expose your device and data to malware, viruses, or spyware. These are malicious programs that can damage your device, steal your personal information, or compromise your security. They may also cause your device to malfunction, crash, or freeze. Therefore, you need to be careful and scan the APK files you download with a reliable antivirus app before installing them.
-
Another risk of hacking Bubble Shooter APK on Android is that you may violate the game's terms of service and get banned from playing online. The game's developers may detect that you are using a hacked version of the game and block your access to the online features. They may also take legal action against you for infringing their intellectual property rights. Therefore, you need to be responsible and respectful when hacking the game and avoid using it for unfair or illegal purposes.
-
Conclusion
-
Hacking Bubble Shooter APK on Android is possible and fun. You can get unlimited resources and features that can make the game more enjoyable and challenging. However, you need to be careful and responsible when doing it. You need to download and install APK files from reputable sources, enable unknown sources and use a file manager app, uninstall the original game and install the hacked one, launch the game and enjoy the hack, and be aware of the benefits and risks of hacking the game.
-
We hope this article has helped you learn how to hack Bubble Shooter APK on Android. If you have any questions or comments, please feel free to leave them below. Happy hacking!
-
FAQs
-
Is hacking Bubble Shooter APK on Android illegal?
-
Hacking Bubble Shooter APK on Android is not illegal per se, but it may violate the game's terms of service and intellectual property rights. Therefore, you should only do it for personal and educational purposes, and not for commercial or malicious purposes. You should also respect the game's developers and other players, and not use the hack to cheat or harm them.
-
Is hacking Bubble Shooter APK on Android safe?
-
Hacking Bubble Shooter APK on Android is safe as long as you download and install APK files from reputable sources, scan them with a reliable antivirus app before installing them, and backup your device and data before doing anything. However, there is always a risk of malware, viruses, or spyware when downloading and installing APK files from unknown sources. Therefore, you should be careful and cautious when doing it.
-
How can I update the hacked Bubble Shooter APK on Android?
-
To update the hacked Bubble Shooter APK on Android, you need to find and download the latest version of the hacked APK file from the same source you got it from. Then, you need to uninstall the old version of the hacked game and install the new one using the same method as before. You should also backup your game data before updating it in case something goes wrong.
-
Can I play online with the hacked Bubble Shooter APK on Android?
-
You can play online with the hacked Bubble Shooter APK on Android, but you may encounter some problems or limitations. For example, you may not be able to connect to the game's servers or sync your progress with other devices. You may also get banned from playing online if the game's developers detect that you are using a hacked version of the game. Therefore, you should use the hack at your own risk and discretion.
-
Where can I find more hacking apps for Android?
-
You can find more hacking apps for Android on various websites that offer APK files for different apps. Some examples are [ModAPKDown], [HappyMod], and [Android-1]. These websites have a large collection of hacking apps for Android that can modify various games and apps. You can use your browser to search and download the hacking apps you want.
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Radio Bla by Lobao The Legendary Rock Anthem - Download MP3 and Sing Along.md b/spaces/congsaPfin/Manga-OCR/logs/Radio Bla by Lobao The Legendary Rock Anthem - Download MP3 and Sing Along.md
deleted file mode 100644
index 71c95218aff12bce5546712e663c146d70d2419f..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Radio Bla by Lobao The Legendary Rock Anthem - Download MP3 and Sing Along.md
+++ /dev/null
@@ -1,91 +0,0 @@
-
-
Radio Bla Lobao Download MP3: How to Listen to the Classic Brazilian Rock Song Online
-
If you are a fan of Brazilian rock music, you have probably heard of Radio Bla Lobao, one of the most famous songs by Lobao, a legendary singer-songwriter and musician. Radio Bla Lobao is a catchy and energetic tune that celebrates the power of radio and music to connect people and express their feelings. But how can you listen to this classic song online? In this article, we will show you three easy ways to download Radio Bla Lobao MP3 and enjoy it anytime, anywhere.
Radio Bla Lobao is a song by Lobao, a Brazilian rock star who rose to fame in the 1980s and 1990s. The song was released in 1987 as part of his album Cuidado!, which was a huge success and sold over one million copies. Radio Bla Lobao is one of the most popular tracks on the album, and it features Lobao's distinctive vocals, guitar, and synthesizer. The lyrics of the song are about a radio station called Radio Bla, where people can call in and request songs, dedicate them to their loved ones, or express their opinions. The song also pays tribute to some of Lobao's musical influences, such as The Beatles, The Rolling Stones, Bob Dylan, and Elvis Presley.
-
Why is Radio Bla Lobao popular?
-
Radio Bla Lobao is popular because it is a fun and upbeat song that captures the spirit of Brazilian rock music in the late 1980s. The song reflects Lobao's personality and style, which are rebellious, creative, and original. The song also resonates with many listeners who grew up listening to radio and music as a way of escaping from their problems or expressing their emotions. Radio Bla Lobao is a song that celebrates the power of music and radio to bring people together and make them happy.
-
How to download Radio Bla Lobao MP3
-
If you want to download Radio Bla Lobao MP3 and listen to it offline, you have several options. Here are three of them:
-
Option 1: MuzicaHot
-
MuzicaHot is a website that allows you to download radio bla mp3 for free and without registration. Here are the steps to follow:
Type "Lobao - Radio Bla" in the search box and click on the magnifying glass icon.
-
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-
Step 3: Choose a download option
-
You will see a list of results with different versions of the song, such as original, remix, live, or cover. Choose the one you prefer and click on the download button.
-
Step 4: Enjoy the song
-
The song will be downloaded to your device in MP3 format. You can play it with any media player or transfer it to another device.
-
Option 2: Boomplay
-
Boomplay is a music streaming and downloading app that has a large collection of African and international songs. You can download Radio Bla Lobao MP3 from Boomplay with these steps:
-
Step 1: Download the app
-
Go to https://www.boomplay.com/ and download the app for your device. You can also find it on Google Play Store or App Store.
-
Step 2: Sign up or log in
-
Create an account with your email, phone number, or social media. Or log in with your existing account if you have one.
-
Step 3: Search for the song
-
Type "Lobao - Radio Bla" in the search box and tap on the song from the results.
-
Step 4: Stream or download the song
-
You can listen to the song online by tapping on the play button. Or you can download it to your device by tapping on the download icon. You may need to buy some coins or subscribe to a plan to download the song.
-
Step 5: Enjoy the song
-
The song will be saved to your device in MP3 format. You can access it from the app or from your music library.
-
Option 3: 4shared
-
4shared is a file-sharing website that lets you download radio bla mp3 for free and without registration. Here are the steps to follow:
Type "Lobao - Radio Bla" in the search box and click on the search button.
-
Step 3: Click on the download button
-
You will see a list of results with different files of the song. Choose the one you want and click on the download button.
-
Step 4: Enjoy the song
-
The song will be downloaded to your device in MP3 format. You can play it with any media player or transfer it to another device.
-
Conclusion
-
Radio Bla Lobao is a classic Brazilian rock song that you can enjoy online or offline. In this article, we showed you three easy ways to download Radio Bla Lobao MP3 and listen to it anytime, anywhere. Whether you use MuzicaHot, Boomplay, or 4shared, you can get this catchy and energetic tune in just a few clicks. So what are you waiting for? Download Radio Bla Lobao MP3 today and rock on!
- FAQs - Q: Who is Lobao? - A: Lobao is a Brazilian singer-songwriter and musician who is known for his rock songs and his outspoken views. - Q: When was Radio Bla Lobao released? - A: Radio Bla Lobao was released in 1987 as part of Lobao's album Cuidado!. - Q: What are some other songs by Lobao? - A: Some other songs by Lobao are Vida Louca Vida, Me Chama, Rádio Blá (Versão II), and O Rock Errou. - Q: How can I listen to Radio Bla Lobao online? - A: You can listen to Radio Bla Lobao online by streaming it from websites or apps like YouTube, Spotify, Deezer, or Apple Music. - Q: How can I download Radio Bla Lobao MP3 for free? - A: You can download Radio Bla Lobao MP3 for free by using websites like MuzicaHot or 4shared, which do not require registration or payment. 401be4b1e0
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diff --git a/spaces/contluForse/HuggingGPT/assets/Angry Indian Goddesses film full movie free download Indias first female buddy comedy.md b/spaces/contluForse/HuggingGPT/assets/Angry Indian Goddesses film full movie free download Indias first female buddy comedy.md
deleted file mode 100644
index 9ae452a55efab161d14554e0be615a5e98aa7608..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/Angry Indian Goddesses film full movie free download Indias first female buddy comedy.md
+++ /dev/null
@@ -1,5 +0,0 @@
-
-
India, like every other film industry in the world, suffers from a significant shortage of films featuring women in leading roles. I'm not talking about women's films, chick flicks, or other films that celebrate women by exaggerating feminity to conform with male fantasies. I'm just talking about regular old movies with leading women who don't have any men to answer to. Even those films that do exist -- think Sex & the City, Bridesmaids, etc... -- largely predicate their existance upon how the women involved relate to men. Thankfully, Pan Nalin's new film Angry Indian Goddesses largely ignores the preset rules about what women's films should be and delivers a film in which women are characters instead of archetypes interacting with one another.
-
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-Pro Evolution Soccer 2012, KONAMI tarafından hazırlanmış, ... Etiketler: Pes 2012 Full PC,Pes 2012 Mega İndir,Pes 2012 Tek Link,Pes 2012 ... 4d29de3e1b
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diff --git a/spaces/dineshreddy/WALT/walt/apis/train.py b/spaces/dineshreddy/WALT/walt/apis/train.py
deleted file mode 100644
index 6c8003d5fdf20a3d6a04ab4a031b053cf56d49c7..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/walt/apis/train.py
+++ /dev/null
@@ -1,187 +0,0 @@
-import random
-import warnings
-
-import numpy as np
-import torch
-from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
-from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner,
- Fp16OptimizerHook, OptimizerHook, build_optimizer,
- build_runner)
-from mmcv.utils import build_from_cfg
-
-from mmdet.core import DistEvalHook, EvalHook
-from walt.datasets import (build_dataloader, build_dataset,
- replace_ImageToTensor)
-from mmdet.utils import get_root_logger
-from mmcv_custom.runner import EpochBasedRunnerAmp
-try:
- import apex
-except:
- print('apex is not installed')
-
-
-def set_random_seed(seed, deterministic=False):
- """Set random seed.
-
- Args:
- seed (int): Seed to be used.
- deterministic (bool): Whether to set the deterministic option for
- CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
- to True and `torch.backends.cudnn.benchmark` to False.
- Default: False.
- """
- random.seed(seed)
- np.random.seed(seed)
- torch.manual_seed(seed)
- torch.cuda.manual_seed_all(seed)
- if deterministic:
- torch.backends.cudnn.deterministic = True
- torch.backends.cudnn.benchmark = False
-
-
-def train_detector(model,
- dataset,
- cfg,
- distributed=False,
- validate=False,
- timestamp=None,
- meta=None):
- logger = get_root_logger(cfg.log_level)
-
- # prepare data loaders
- dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
- if 'imgs_per_gpu' in cfg.data:
- logger.warning('"imgs_per_gpu" is deprecated in MMDet V2.0. '
- 'Please use "samples_per_gpu" instead')
- if 'samples_per_gpu' in cfg.data:
- logger.warning(
- f'Got "imgs_per_gpu"={cfg.data.imgs_per_gpu} and '
- f'"samples_per_gpu"={cfg.data.samples_per_gpu}, "imgs_per_gpu"'
- f'={cfg.data.imgs_per_gpu} is used in this experiments')
- else:
- logger.warning(
- 'Automatically set "samples_per_gpu"="imgs_per_gpu"='
- f'{cfg.data.imgs_per_gpu} in this experiments')
- cfg.data.samples_per_gpu = cfg.data.imgs_per_gpu
-
- data_loaders = [
- build_dataloader(
- ds,
- cfg.data.samples_per_gpu,
- cfg.data.workers_per_gpu,
- # cfg.gpus will be ignored if distributed
- len(cfg.gpu_ids),
- dist=distributed,
- seed=cfg.seed) for ds in dataset
- ]
-
- # build optimizer
- optimizer = build_optimizer(model, cfg.optimizer)
-
- # use apex fp16 optimizer
- if cfg.optimizer_config.get("type", None) and cfg.optimizer_config["type"] == "DistOptimizerHook":
- if cfg.optimizer_config.get("use_fp16", False):
- model, optimizer = apex.amp.initialize(
- model.cuda(), optimizer, opt_level="O1")
- for m in model.modules():
- if hasattr(m, "fp16_enabled"):
- m.fp16_enabled = True
-
- # put model on gpus
- if distributed:
- find_unused_parameters = cfg.get('find_unused_parameters', False)
- # Sets the `find_unused_parameters` parameter in
- # torch.nn.parallel.DistributedDataParallel
- model = MMDistributedDataParallel(
- model.cuda(),
- device_ids=[torch.cuda.current_device()],
- broadcast_buffers=False,
- find_unused_parameters=find_unused_parameters)
- else:
- model = MMDataParallel(
- model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids)
-
- if 'runner' not in cfg:
- cfg.runner = {
- 'type': 'EpochBasedRunner',
- 'max_epochs': cfg.total_epochs
- }
- warnings.warn(
- 'config is now expected to have a `runner` section, '
- 'please set `runner` in your config.', UserWarning)
- else:
- if 'total_epochs' in cfg:
- assert cfg.total_epochs == cfg.runner.max_epochs
-
- # build runner
- runner = build_runner(
- cfg.runner,
- default_args=dict(
- model=model,
- optimizer=optimizer,
- work_dir=cfg.work_dir,
- logger=logger,
- meta=meta))
-
- # an ugly workaround to make .log and .log.json filenames the same
- runner.timestamp = timestamp
-
- # fp16 setting
- fp16_cfg = cfg.get('fp16', None)
- if fp16_cfg is not None:
- optimizer_config = Fp16OptimizerHook(
- **cfg.optimizer_config, **fp16_cfg, distributed=distributed)
- elif distributed and 'type' not in cfg.optimizer_config:
- optimizer_config = OptimizerHook(**cfg.optimizer_config)
- else:
- optimizer_config = cfg.optimizer_config
-
- # register hooks
- runner.register_training_hooks(cfg.lr_config, optimizer_config,
- cfg.checkpoint_config, cfg.log_config,
- cfg.get('momentum_config', None))
- if distributed:
- if isinstance(runner, EpochBasedRunner):
- runner.register_hook(DistSamplerSeedHook())
-
- # register eval hooks
- if validate:
- # Support batch_size > 1 in validation
- val_samples_per_gpu = cfg.data.val.pop('samples_per_gpu', 1)
- if val_samples_per_gpu > 1:
- # Replace 'ImageToTensor' to 'DefaultFormatBundle'
- cfg.data.val.pipeline = replace_ImageToTensor(
- cfg.data.val.pipeline)
- val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
- val_dataloader = build_dataloader(
- val_dataset,
- samples_per_gpu=val_samples_per_gpu,
- workers_per_gpu=cfg.data.workers_per_gpu,
- dist=distributed,
- shuffle=False)
- eval_cfg = cfg.get('evaluation', {})
- eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
- eval_hook = DistEvalHook if distributed else EvalHook
- runner.register_hook(eval_hook(val_dataloader, **eval_cfg))
- '''
- '''
-
- # user-defined hooks
- if cfg.get('custom_hooks', None):
- custom_hooks = cfg.custom_hooks
- assert isinstance(custom_hooks, list), \
- f'custom_hooks expect list type, but got {type(custom_hooks)}'
- for hook_cfg in cfg.custom_hooks:
- assert isinstance(hook_cfg, dict), \
- 'Each item in custom_hooks expects dict type, but got ' \
- f'{type(hook_cfg)}'
- hook_cfg = hook_cfg.copy()
- priority = hook_cfg.pop('priority', 'NORMAL')
- hook = build_from_cfg(hook_cfg, HOOKS)
- runner.register_hook(hook, priority=priority)
-
- if cfg.resume_from:
- runner.resume(cfg.resume_from)
- elif cfg.load_from:
- runner.load_checkpoint(cfg.load_from)
- runner.run(data_loaders, cfg.workflow)
diff --git a/spaces/dineshreddy/WALT/walt/datasets/__init__.py b/spaces/dineshreddy/WALT/walt/datasets/__init__.py
deleted file mode 100644
index 90b6b616c1be7cf9841de63293ee9d41e03a057f..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/walt/datasets/__init__.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset
-from mmdet.datasets.cityscapes import CityscapesDataset
-from mmdet.datasets.coco import CocoDataset
-from .custom import CustomDatasetLocal
-from mmdet.datasets.custom import CustomDataset
-from mmdet.datasets.dataset_wrappers import (ClassBalancedDataset, ConcatDataset,
- RepeatDataset)
-from mmdet.datasets.deepfashion import DeepFashionDataset
-from mmdet.datasets.lvis import LVISDataset, LVISV1Dataset, LVISV05Dataset
-from mmdet.datasets.samplers import DistributedGroupSampler, DistributedSampler, GroupSampler
-from mmdet.datasets.utils import (NumClassCheckHook, get_loading_pipeline,
- replace_ImageToTensor)
-from mmdet.datasets.voc import VOCDataset
-from mmdet.datasets.wider_face import WIDERFaceDataset
-from mmdet.datasets.xml_style import XMLDataset
-from .walt_synthetic import WaltSynthDataset
-from .walt_3d import Walt3DDataset
-from .walt import WaltDataset
-__all__ = [
- 'CustomDataset', 'XMLDataset', 'CocoDataset', 'DeepFashionDataset',
- 'VOCDataset', 'CityscapesDataset', 'LVISDataset', 'LVISV05Dataset',
- 'LVISV1Dataset', 'GroupSampler', 'DistributedGroupSampler',
- 'DistributedSampler', 'build_dataloader', 'ConcatDataset', 'RepeatDataset',
- 'ClassBalancedDataset', 'Walt3DDataset','WIDERFaceDataset', 'DATASETS', 'PIPELINES',
- 'build_dataset', 'replace_ImageToTensor', 'get_loading_pipeline',
- 'WaltSynthDataset', 'WaltDataset', 'NumClassCheckHook'
-]
-
-
diff --git a/spaces/divyahansg/text-generation-webui-space/extensions/send_pictures/script.py b/spaces/divyahansg/text-generation-webui-space/extensions/send_pictures/script.py
deleted file mode 100644
index b0c356329a51edf026f7223a0ee7e5427d8751ce..0000000000000000000000000000000000000000
--- a/spaces/divyahansg/text-generation-webui-space/extensions/send_pictures/script.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import base64
-from io import BytesIO
-
-import gradio as gr
-import torch
-from transformers import BlipForConditionalGeneration, BlipProcessor
-
-import modules.chat as chat
-import modules.shared as shared
-
-# If 'state' is True, will hijack the next chat generation with
-# custom input text given by 'value' in the format [text, visible_text]
-input_hijack = {
- 'state': False,
- 'value': ["", ""]
-}
-
-processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
-model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float32).to("cpu")
-
-def caption_image(raw_image):
- inputs = processor(raw_image.convert('RGB'), return_tensors="pt").to("cpu", torch.float32)
- out = model.generate(**inputs, max_new_tokens=100)
- return processor.decode(out[0], skip_special_tokens=True)
-
-def generate_chat_picture(picture, name1, name2):
- text = f'*{name1} sends {name2} a picture that contains the following: "{caption_image(picture)}"*'
- buffer = BytesIO()
- picture.save(buffer, format="JPEG")
- img_str = base64.b64encode(buffer.getvalue()).decode('utf-8')
- visible_text = f''
- return text, visible_text
-
-def ui():
- picture_select = gr.Image(label='Send a picture', type='pil')
-
- function_call = 'chat.cai_chatbot_wrapper' if shared.args.cai_chat else 'chat.chatbot_wrapper'
-
- # Prepare the hijack with custom inputs
- picture_select.upload(lambda picture, name1, name2: input_hijack.update({"state": True, "value": generate_chat_picture(picture, name1, name2)}), [picture_select, shared.gradio['name1'], shared.gradio['name2']], None)
-
- # Call the generation function
- picture_select.upload(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)
-
- # Clear the picture from the upload field
- picture_select.upload(lambda : None, [], [picture_select], show_progress=False)
diff --git a/spaces/dvitel/codebleu/bleu.py b/spaces/dvitel/codebleu/bleu.py
deleted file mode 100644
index fa5941b463a43c845f53d9581aaa0f4df5b17d97..0000000000000000000000000000000000000000
--- a/spaces/dvitel/codebleu/bleu.py
+++ /dev/null
@@ -1,590 +0,0 @@
-# -*- coding: utf-8 -*-
-# Natural Language Toolkit: BLEU Score
-#
-# Copyright (C) 2001-2020 NLTK Project
-# Authors: Chin Yee Lee, Hengfeng Li, Ruxin Hou, Calvin Tanujaya Lim
-# Contributors: Björn Mattsson, Dmitrijs Milajevs, Liling Tan
-# URL:
-# For license information, see LICENSE.TXT
-
-"""BLEU score implementation."""
-
-import math
-import sys
-from fractions import Fraction
-import warnings
-from collections import Counter
-
-from .utils import ngrams
-import pdb
-
-
-def sentence_bleu(
- references,
- hypothesis,
- weights=(0.25, 0.25, 0.25, 0.25),
- smoothing_function=None,
- auto_reweigh=False,
-):
- """
- Calculate BLEU score (Bilingual Evaluation Understudy) from
- Papineni, Kishore, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002.
- "BLEU: a method for automatic evaluation of machine translation."
- In Proceedings of ACL. http://www.aclweb.org/anthology/P02-1040.pdf
- >>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
- ... 'ensures', 'that', 'the', 'military', 'always',
- ... 'obeys', 'the', 'commands', 'of', 'the', 'party']
- >>> hypothesis2 = ['It', 'is', 'to', 'insure', 'the', 'troops',
- ... 'forever', 'hearing', 'the', 'activity', 'guidebook',
- ... 'that', 'party', 'direct']
- >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
- ... 'ensures', 'that', 'the', 'military', 'will', 'forever',
- ... 'heed', 'Party', 'commands']
- >>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which',
- ... 'guarantees', 'the', 'military', 'forces', 'always',
- ... 'being', 'under', 'the', 'command', 'of', 'the',
- ... 'Party']
- >>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
- ... 'army', 'always', 'to', 'heed', 'the', 'directions',
- ... 'of', 'the', 'party']
- >>> sentence_bleu([reference1, reference2, reference3], hypothesis1) # doctest: +ELLIPSIS
- 0.5045...
- If there is no ngrams overlap for any order of n-grams, BLEU returns the
- value 0. This is because the precision for the order of n-grams without
- overlap is 0, and the geometric mean in the final BLEU score computation
- multiplies the 0 with the precision of other n-grams. This results in 0
- (independently of the precision of the othe n-gram orders). The following
- example has zero 3-gram and 4-gram overlaps:
- >>> round(sentence_bleu([reference1, reference2, reference3], hypothesis2),4) # doctest: +ELLIPSIS
- 0.0
- To avoid this harsh behaviour when no ngram overlaps are found a smoothing
- function can be used.
- >>> chencherry = SmoothingFunction()
- >>> sentence_bleu([reference1, reference2, reference3], hypothesis2,
- ... smoothing_function=chencherry.method1) # doctest: +ELLIPSIS
- 0.0370...
- The default BLEU calculates a score for up to 4-grams using uniform
- weights (this is called BLEU-4). To evaluate your translations with
- higher/lower order ngrams, use customized weights. E.g. when accounting
- for up to 5-grams with uniform weights (this is called BLEU-5) use:
- >>> weights = (1./5., 1./5., 1./5., 1./5., 1./5.)
- >>> sentence_bleu([reference1, reference2, reference3], hypothesis1, weights) # doctest: +ELLIPSIS
- 0.3920...
- :param references: reference sentences
- :type references: list(list(str))
- :param hypothesis: a hypothesis sentence
- :type hypothesis: list(str)
- :param weights: weights for unigrams, bigrams, trigrams and so on
- :type weights: list(float)
- :param smoothing_function:
- :type smoothing_function: SmoothingFunction
- :param auto_reweigh: Option to re-normalize the weights uniformly.
- :type auto_reweigh: bool
- :return: The sentence-level BLEU score.
- :rtype: float
- """
- return corpus_bleu(
- [references], [hypothesis], weights, smoothing_function, auto_reweigh
- )
-
-
-def corpus_bleu(
- list_of_references,
- hypotheses,
- weights=(0.25, 0.25, 0.25, 0.25),
- smoothing_function=None,
- auto_reweigh=False,
-):
- """
- Calculate a single corpus-level BLEU score (aka. system-level BLEU) for all
- the hypotheses and their respective references.
- Instead of averaging the sentence level BLEU scores (i.e. marco-average
- precision), the original BLEU metric (Papineni et al. 2002) accounts for
- the micro-average precision (i.e. summing the numerators and denominators
- for each hypothesis-reference(s) pairs before the division).
- >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
- ... 'ensures', 'that', 'the', 'military', 'always',
- ... 'obeys', 'the', 'commands', 'of', 'the', 'party']
- >>> ref1a = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
- ... 'ensures', 'that', 'the', 'military', 'will', 'forever',
- ... 'heed', 'Party', 'commands']
- >>> ref1b = ['It', 'is', 'the', 'guiding', 'principle', 'which',
- ... 'guarantees', 'the', 'military', 'forces', 'always',
- ... 'being', 'under', 'the', 'command', 'of', 'the', 'Party']
- >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
- ... 'army', 'always', 'to', 'heed', 'the', 'directions',
- ... 'of', 'the', 'party']
- >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
- ... 'interested', 'in', 'world', 'history']
- >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',
- ... 'because', 'he', 'read', 'the', 'book']
- >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]
- >>> hypotheses = [hyp1, hyp2]
- >>> corpus_bleu(list_of_references, hypotheses) # doctest: +ELLIPSIS
- 0.5920...
- The example below show that corpus_bleu() is different from averaging
- sentence_bleu() for hypotheses
- >>> score1 = sentence_bleu([ref1a, ref1b, ref1c], hyp1)
- >>> score2 = sentence_bleu([ref2a], hyp2)
- >>> (score1 + score2) / 2 # doctest: +ELLIPSIS
- 0.6223...
- :param list_of_references: a corpus of lists of reference sentences, w.r.t. hypotheses
- :type list_of_references: list(list(list(str)))
- :param hypotheses: a list of hypothesis sentences
- :type hypotheses: list(list(str))
- :param weights: weights for unigrams, bigrams, trigrams and so on
- :type weights: list(float)
- :param smoothing_function:
- :type smoothing_function: SmoothingFunction
- :param auto_reweigh: Option to re-normalize the weights uniformly.
- :type auto_reweigh: bool
- :return: The corpus-level BLEU score.
- :rtype: float
- """
- # Before proceeding to compute BLEU, perform sanity checks.
-
- p_numerators = Counter() # Key = ngram order, and value = no. of ngram matches.
- p_denominators = Counter() # Key = ngram order, and value = no. of ngram in ref.
- hyp_lengths, ref_lengths = 0, 0
-
- assert len(list_of_references) == len(hypotheses), (
- "The number of hypotheses and their reference(s) should be the " "same "
- )
-
- # Iterate through each hypothesis and their corresponding references.
- for references, hypothesis in zip(list_of_references, hypotheses):
- # For each order of ngram, calculate the numerator and
- # denominator for the corpus-level modified precision.
- for i, _ in enumerate(weights, start=1):
- p_i = modified_precision(references, hypothesis, i)
- p_numerators[i] += p_i.numerator
- p_denominators[i] += p_i.denominator
-
- # Calculate the hypothesis length and the closest reference length.
- # Adds them to the corpus-level hypothesis and reference counts.
- hyp_len = len(hypothesis)
- hyp_lengths += hyp_len
- ref_lengths += closest_ref_length(references, hyp_len)
-
- # Calculate corpus-level brevity penalty.
- bp = brevity_penalty(ref_lengths, hyp_lengths)
-
- # Uniformly re-weighting based on maximum hypothesis lengths if largest
- # order of n-grams < 4 and weights is set at default.
- if auto_reweigh:
- if hyp_lengths < 4 and weights == (0.25, 0.25, 0.25, 0.25):
- weights = (1 / hyp_lengths,) * hyp_lengths
-
- # Collects the various precision values for the different ngram orders.
- p_n = [
- Fraction(p_numerators[i], p_denominators[i], _normalize=False)
- for i, _ in enumerate(weights, start=1)
- ]
-
- # Returns 0 if there's no matching n-grams
- # We only need to check for p_numerators[1] == 0, since if there's
- # no unigrams, there won't be any higher order ngrams.
- if p_numerators[1] == 0:
- return 0
-
- # If there's no smoothing, set use method0 from SmoothinFunction class.
- if not smoothing_function:
- smoothing_function = SmoothingFunction().method1
- # Smoothen the modified precision.
- # Note: smoothing_function() may convert values into floats;
- # it tries to retain the Fraction object as much as the
- # smoothing method allows.
- p_n = smoothing_function(
- p_n, references=references, hypothesis=hypothesis, hyp_len=hyp_lengths
- )
- s = (w_i * math.log(p_i) for w_i, p_i in zip(weights, p_n))
- s = bp * math.exp(math.fsum(s))
- return s
-
-
-def modified_precision(references, hypothesis, n):
- """
- Calculate modified ngram precision.
- The normal precision method may lead to some wrong translations with
- high-precision, e.g., the translation, in which a word of reference
- repeats several times, has very high precision.
- This function only returns the Fraction object that contains the numerator
- and denominator necessary to calculate the corpus-level precision.
- To calculate the modified precision for a single pair of hypothesis and
- references, cast the Fraction object into a float.
- The famous "the the the ... " example shows that you can get BLEU precision
- by duplicating high frequency words.
- >>> reference1 = 'the cat is on the mat'.split()
- >>> reference2 = 'there is a cat on the mat'.split()
- >>> hypothesis1 = 'the the the the the the the'.split()
- >>> references = [reference1, reference2]
- >>> float(modified_precision(references, hypothesis1, n=1)) # doctest: +ELLIPSIS
- 0.2857...
- In the modified n-gram precision, a reference word will be considered
- exhausted after a matching hypothesis word is identified, e.g.
- >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
- ... 'ensures', 'that', 'the', 'military', 'will',
- ... 'forever', 'heed', 'Party', 'commands']
- >>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which',
- ... 'guarantees', 'the', 'military', 'forces', 'always',
- ... 'being', 'under', 'the', 'command', 'of', 'the',
- ... 'Party']
- >>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
- ... 'army', 'always', 'to', 'heed', 'the', 'directions',
- ... 'of', 'the', 'party']
- >>> hypothesis = 'of the'.split()
- >>> references = [reference1, reference2, reference3]
- >>> float(modified_precision(references, hypothesis, n=1))
- 1.0
- >>> float(modified_precision(references, hypothesis, n=2))
- 1.0
- An example of a normal machine translation hypothesis:
- >>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
- ... 'ensures', 'that', 'the', 'military', 'always',
- ... 'obeys', 'the', 'commands', 'of', 'the', 'party']
- >>> hypothesis2 = ['It', 'is', 'to', 'insure', 'the', 'troops',
- ... 'forever', 'hearing', 'the', 'activity', 'guidebook',
- ... 'that', 'party', 'direct']
- >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
- ... 'ensures', 'that', 'the', 'military', 'will',
- ... 'forever', 'heed', 'Party', 'commands']
- >>> reference2 = ['It', 'is', 'the', 'guiding', 'principle', 'which',
- ... 'guarantees', 'the', 'military', 'forces', 'always',
- ... 'being', 'under', 'the', 'command', 'of', 'the',
- ... 'Party']
- >>> reference3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
- ... 'army', 'always', 'to', 'heed', 'the', 'directions',
- ... 'of', 'the', 'party']
- >>> references = [reference1, reference2, reference3]
- >>> float(modified_precision(references, hypothesis1, n=1)) # doctest: +ELLIPSIS
- 0.9444...
- >>> float(modified_precision(references, hypothesis2, n=1)) # doctest: +ELLIPSIS
- 0.5714...
- >>> float(modified_precision(references, hypothesis1, n=2)) # doctest: +ELLIPSIS
- 0.5882352941176471
- >>> float(modified_precision(references, hypothesis2, n=2)) # doctest: +ELLIPSIS
- 0.07692...
- :param references: A list of reference translations.
- :type references: list(list(str))
- :param hypothesis: A hypothesis translation.
- :type hypothesis: list(str)
- :param n: The ngram order.
- :type n: int
- :return: BLEU's modified precision for the nth order ngram.
- :rtype: Fraction
- """
- # Extracts all ngrams in hypothesis
- # Set an empty Counter if hypothesis is empty.
-
- counts = Counter(ngrams(hypothesis, n)) if len(hypothesis) >= n else Counter()
- # Extract a union of references' counts.
- # max_counts = reduce(or_, [Counter(ngrams(ref, n)) for ref in references])
- max_counts = {}
- for reference in references:
- reference_counts = (
- Counter(ngrams(reference, n)) if len(reference) >= n else Counter()
- )
- for ngram in counts:
- max_counts[ngram] = max(max_counts.get(ngram, 0), reference_counts[ngram])
-
- # Assigns the intersection between hypothesis and references' counts.
- clipped_counts = {
- ngram: min(count, max_counts[ngram]) for ngram, count in counts.items()
- }
-
- numerator = sum(clipped_counts.values())
- # Ensures that denominator is minimum 1 to avoid ZeroDivisionError.
- # Usually this happens when the ngram order is > len(reference).
- denominator = max(1, sum(counts.values()))
-
- return Fraction(numerator, denominator, _normalize=False)
-
-
-def closest_ref_length(references, hyp_len):
- """
- This function finds the reference that is the closest length to the
- hypothesis. The closest reference length is referred to as *r* variable
- from the brevity penalty formula in Papineni et. al. (2002)
- :param references: A list of reference translations.
- :type references: list(list(str))
- :param hyp_len: The length of the hypothesis.
- :type hyp_len: int
- :return: The length of the reference that's closest to the hypothesis.
- :rtype: int
- """
- ref_lens = (len(reference) for reference in references)
- closest_ref_len = min(
- ref_lens, key=lambda ref_len: (abs(ref_len - hyp_len), ref_len)
- )
- return closest_ref_len
-
-
-def brevity_penalty(closest_ref_len, hyp_len):
- """
- Calculate brevity penalty.
- As the modified n-gram precision still has the problem from the short
- length sentence, brevity penalty is used to modify the overall BLEU
- score according to length.
- An example from the paper. There are three references with length 12, 15
- and 17. And a concise hypothesis of the length 12. The brevity penalty is 1.
- >>> reference1 = list('aaaaaaaaaaaa') # i.e. ['a'] * 12
- >>> reference2 = list('aaaaaaaaaaaaaaa') # i.e. ['a'] * 15
- >>> reference3 = list('aaaaaaaaaaaaaaaaa') # i.e. ['a'] * 17
- >>> hypothesis = list('aaaaaaaaaaaa') # i.e. ['a'] * 12
- >>> references = [reference1, reference2, reference3]
- >>> hyp_len = len(hypothesis)
- >>> closest_ref_len = closest_ref_length(references, hyp_len)
- >>> brevity_penalty(closest_ref_len, hyp_len)
- 1.0
- In case a hypothesis translation is shorter than the references, penalty is
- applied.
- >>> references = [['a'] * 28, ['a'] * 28]
- >>> hypothesis = ['a'] * 12
- >>> hyp_len = len(hypothesis)
- >>> closest_ref_len = closest_ref_length(references, hyp_len)
- >>> brevity_penalty(closest_ref_len, hyp_len)
- 0.2635971381157267
- The length of the closest reference is used to compute the penalty. If the
- length of a hypothesis is 12, and the reference lengths are 13 and 2, the
- penalty is applied because the hypothesis length (12) is less then the
- closest reference length (13).
- >>> references = [['a'] * 13, ['a'] * 2]
- >>> hypothesis = ['a'] * 12
- >>> hyp_len = len(hypothesis)
- >>> closest_ref_len = closest_ref_length(references, hyp_len)
- >>> brevity_penalty(closest_ref_len, hyp_len) # doctest: +ELLIPSIS
- 0.9200...
- The brevity penalty doesn't depend on reference order. More importantly,
- when two reference sentences are at the same distance, the shortest
- reference sentence length is used.
- >>> references = [['a'] * 13, ['a'] * 11]
- >>> hypothesis = ['a'] * 12
- >>> hyp_len = len(hypothesis)
- >>> closest_ref_len = closest_ref_length(references, hyp_len)
- >>> bp1 = brevity_penalty(closest_ref_len, hyp_len)
- >>> hyp_len = len(hypothesis)
- >>> closest_ref_len = closest_ref_length(reversed(references), hyp_len)
- >>> bp2 = brevity_penalty(closest_ref_len, hyp_len)
- >>> bp1 == bp2 == 1
- True
- A test example from mteval-v13a.pl (starting from the line 705):
- >>> references = [['a'] * 11, ['a'] * 8]
- >>> hypothesis = ['a'] * 7
- >>> hyp_len = len(hypothesis)
- >>> closest_ref_len = closest_ref_length(references, hyp_len)
- >>> brevity_penalty(closest_ref_len, hyp_len) # doctest: +ELLIPSIS
- 0.8668...
- >>> references = [['a'] * 11, ['a'] * 8, ['a'] * 6, ['a'] * 7]
- >>> hypothesis = ['a'] * 7
- >>> hyp_len = len(hypothesis)
- >>> closest_ref_len = closest_ref_length(references, hyp_len)
- >>> brevity_penalty(closest_ref_len, hyp_len)
- 1.0
- :param hyp_len: The length of the hypothesis for a single sentence OR the
- sum of all the hypotheses' lengths for a corpus
- :type hyp_len: int
- :param closest_ref_len: The length of the closest reference for a single
- hypothesis OR the sum of all the closest references for every hypotheses.
- :type closest_ref_len: int
- :return: BLEU's brevity penalty.
- :rtype: float
- """
- if hyp_len > closest_ref_len:
- return 1
- # If hypothesis is empty, brevity penalty = 0 should result in BLEU = 0.0
- elif hyp_len == 0:
- return 0
- else:
- return math.exp(1 - closest_ref_len / hyp_len)
-
-
-class SmoothingFunction:
- """
- This is an implementation of the smoothing techniques
- for segment-level BLEU scores that was presented in
- Boxing Chen and Collin Cherry (2014) A Systematic Comparison of
- Smoothing Techniques for Sentence-Level BLEU. In WMT14.
- http://acl2014.org/acl2014/W14-33/pdf/W14-3346.pdf
- """
-
- def __init__(self, epsilon=0.1, alpha=5, k=5):
- """
- This will initialize the parameters required for the various smoothing
- techniques, the default values are set to the numbers used in the
- experiments from Chen and Cherry (2014).
- >>> hypothesis1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which', 'ensures',
- ... 'that', 'the', 'military', 'always', 'obeys', 'the',
- ... 'commands', 'of', 'the', 'party']
- >>> reference1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that', 'ensures',
- ... 'that', 'the', 'military', 'will', 'forever', 'heed',
- ... 'Party', 'commands']
- >>> chencherry = SmoothingFunction()
- >>> print(sentence_bleu([reference1], hypothesis1)) # doctest: +ELLIPSIS
- 0.4118...
- >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method0)) # doctest: +ELLIPSIS
- 0.4118...
- >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method1)) # doctest: +ELLIPSIS
- 0.4118...
- >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method2)) # doctest: +ELLIPSIS
- 0.4489...
- >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method3)) # doctest: +ELLIPSIS
- 0.4118...
- >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method4)) # doctest: +ELLIPSIS
- 0.4118...
- >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method5)) # doctest: +ELLIPSIS
- 0.4905...
- >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method6)) # doctest: +ELLIPSIS
- 0.4135...
- >>> print(sentence_bleu([reference1], hypothesis1, smoothing_function=chencherry.method7)) # doctest: +ELLIPSIS
- 0.4905...
- :param epsilon: the epsilon value use in method 1
- :type epsilon: float
- :param alpha: the alpha value use in method 6
- :type alpha: int
- :param k: the k value use in method 4
- :type k: int
- """
- self.epsilon = epsilon
- self.alpha = alpha
- self.k = k
-
- def method0(self, p_n, *args, **kwargs):
- """
- No smoothing.
- """
- p_n_new = []
- for i, p_i in enumerate(p_n):
- if p_i.numerator != 0:
- p_n_new.append(p_i)
- else:
- _msg = str(
- "\nThe hypothesis contains 0 counts of {}-gram overlaps.\n"
- "Therefore the BLEU score evaluates to 0, independently of\n"
- "how many N-gram overlaps of lower order it contains.\n"
- "Consider using lower n-gram order or use "
- "SmoothingFunction()"
- ).format(i + 1)
- warnings.warn(_msg)
- # When numerator==0 where denonminator==0 or !=0, the result
- # for the precision score should be equal to 0 or undefined.
- # Due to BLEU geometric mean computation in logarithm space,
- # we we need to take the return sys.float_info.min such that
- # math.log(sys.float_info.min) returns a 0 precision score.
- p_n_new.append(sys.float_info.min)
- return p_n_new
-
- def method1(self, p_n, *args, **kwargs):
- """
- Smoothing method 1: Add *epsilon* counts to precision with 0 counts.
- """
- return [
- (p_i.numerator + self.epsilon) / p_i.denominator
- if p_i.numerator == 0
- else p_i
- for p_i in p_n
- ]
-
- def method2(self, p_n, *args, **kwargs):
- """
- Smoothing method 2: Add 1 to both numerator and denominator from
- Chin-Yew Lin and Franz Josef Och (2004) Automatic evaluation of
- machine translation quality using longest common subsequence and
- skip-bigram statistics. In ACL04.
- """
- return [
- Fraction(p_i.numerator + 1, p_i.denominator + 1, _normalize=False)
- for p_i in p_n
- ]
-
- def method3(self, p_n, *args, **kwargs):
- """
- Smoothing method 3: NIST geometric sequence smoothing
- The smoothing is computed by taking 1 / ( 2^k ), instead of 0, for each
- precision score whose matching n-gram count is null.
- k is 1 for the first 'n' value for which the n-gram match count is null/
- For example, if the text contains:
- - one 2-gram match
- - and (consequently) two 1-gram matches
- the n-gram count for each individual precision score would be:
- - n=1 => prec_count = 2 (two unigrams)
- - n=2 => prec_count = 1 (one bigram)
- - n=3 => prec_count = 1/2 (no trigram, taking 'smoothed' value of 1 / ( 2^k ), with k=1)
- - n=4 => prec_count = 1/4 (no fourgram, taking 'smoothed' value of 1 / ( 2^k ), with k=2)
- """
- incvnt = 1 # From the mteval-v13a.pl, it's referred to as k.
- for i, p_i in enumerate(p_n):
- if p_i.numerator == 0:
- p_n[i] = 1 / (2 ** incvnt * p_i.denominator)
- incvnt += 1
- return p_n
-
- def method4(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs):
- """
- Smoothing method 4:
- Shorter translations may have inflated precision values due to having
- smaller denominators; therefore, we give them proportionally
- smaller smoothed counts. Instead of scaling to 1/(2^k), Chen and Cherry
- suggests dividing by 1/ln(len(T)), where T is the length of the translation.
- """
- hyp_len = hyp_len if hyp_len else len(hypothesis)
- for i, p_i in enumerate(p_n):
- if p_i.numerator == 0 and hyp_len != 0:
- incvnt = i + 1 * self.k / math.log(
- hyp_len
- ) # Note that this K is different from the K from NIST.
- p_n[i] = incvnt / p_i.denominator
- return p_n
-
- def method5(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs):
- """
- Smoothing method 5:
- The matched counts for similar values of n should be similar. To a
- calculate the n-gram matched count, it averages the n−1, n and n+1 gram
- matched counts.
- """
- hyp_len = hyp_len if hyp_len else len(hypothesis)
- m = {}
- # Requires an precision value for an addition ngram order.
- p_n_plus1 = p_n + [modified_precision(references, hypothesis, 5)]
- m[-1] = p_n[0] + 1
- for i, p_i in enumerate(p_n):
- p_n[i] = (m[i - 1] + p_i + p_n_plus1[i + 1]) / 3
- m[i] = p_n[i]
- return p_n
-
- def method6(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs):
- """
- Smoothing method 6:
- Interpolates the maximum likelihood estimate of the precision *p_n* with
- a prior estimate *pi0*. The prior is estimated by assuming that the ratio
- between pn and pn−1 will be the same as that between pn−1 and pn−2; from
- Gao and He (2013) Training MRF-Based Phrase Translation Models using
- Gradient Ascent. In NAACL.
- """
- hyp_len = hyp_len if hyp_len else len(hypothesis)
- # This smoothing only works when p_1 and p_2 is non-zero.
- # Raise an error with an appropriate message when the input is too short
- # to use this smoothing technique.
- assert p_n[2], "This smoothing method requires non-zero precision for bigrams."
- for i, p_i in enumerate(p_n):
- if i in [0, 1]: # Skips the first 2 orders of ngrams.
- continue
- else:
- pi0 = 0 if p_n[i - 2] == 0 else p_n[i - 1] ** 2 / p_n[i - 2]
- # No. of ngrams in translation that matches the reference.
- m = p_i.numerator
- # No. of ngrams in translation.
- l = sum(1 for _ in ngrams(hypothesis, i + 1))
- # Calculates the interpolated precision.
- p_n[i] = (m + self.alpha * pi0) / (l + self.alpha)
- return p_n
-
- def method7(self, p_n, references, hypothesis, hyp_len=None, *args, **kwargs):
- """
- Smoothing method 7:
- Interpolates methods 4 and 5.
- """
- hyp_len = hyp_len if hyp_len else len(hypothesis)
- p_n = self.method4(p_n, references, hypothesis, hyp_len)
- p_n = self.method5(p_n, references, hypothesis, hyp_len)
- return p_n
diff --git a/spaces/ekenkel/dog-identifier/app.py b/spaces/ekenkel/dog-identifier/app.py
deleted file mode 100644
index 41b3f09638d6079bac2882966c2276117486ccec..0000000000000000000000000000000000000000
--- a/spaces/ekenkel/dog-identifier/app.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from fastai.vision.all import load_learner
-from PIL import Image
-import gradio as gr
-# import pathlib
-from pillow_heif import register_heif_opener
-import pillow_avif
-
-register_heif_opener()
-
-# For the posix path error: when you train your model on colab/gradient and download it, then do inference on Windows.
-# Redirect PosixPath to WindowsPath:
-# temp = pathlib.PosixPath
-# pathlib.PosixPath = pathlib.WindowsPath
-
-
-# Data below sourced from:
-# URL = 'https://dog.ceo/api/breeds/list/all'
-# To remain consistent, I initialized it as a tuple (previously broke when the API was utilized to get dog breeds)
-dog_breeds = tuple(['affenpinscher', 'afghan hound', 'african', 'airedale', 'akita', 'american terrier', 'appenzeller', 'australian cattledog', 'australian terrier', 'basenji', 'basset hound', 'beagle', 'bedlington terrier', 'bernese mountain', 'bichon frise', 'blenheim spaniel', 'blood hound', 'bluetick', 'border collie', 'border terrier', 'borzoi', 'boston bulldog', 'bouvier', 'boxer', 'brabancon', 'briard', 'brittany spaniel', 'bull mastiff', 'cairn terrier', 'cardigan corgi', 'caucasian ovcharka', 'cavapoo', 'chesapeake retriever', 'chihuahua', 'chow', 'clumber', 'cockapoo', 'cocker spaniel', 'coonhound', 'cotondetulear', 'curly retriever', 'dachshund', 'dalmatian', 'dandie terrier', 'dhole', 'dingo', 'doberman', 'english bulldog', 'english hound', 'english mastiff', 'english setter', 'english sheepdog', 'english springer', 'entlebucher', 'eskimo', 'flatcoated retriever', 'fox terrier', 'french bulldog', 'german pointer', 'germanlonghair pointer', 'germanshepherd', 'giant schnauzer', 'golden retriever', 'gordon setter', 'great dane', 'groenendael', 'havanese', 'husky', 'ibizan hound', 'irish setter', 'irish spaniel', 'irish terrier', 'irish wolfhound', 'italian greyhound', 'italian segugio', 'japanese spaniel', 'japanese spitz', 'keeshond', 'kelpie', 'kerryblue terrier', 'komondor', 'kuvasz', 'labradoodle', 'labrador', 'lakeland terrier', 'lapphund finnish', 'leonberg', 'lhasa', 'malamute', 'malinois', 'maltese', 'medium poodle', 'mexicanhairless', 'miniature pinscher', 'miniature poodle', 'miniature schnauzer', 'mix', 'newfoundland', 'norfolk terrier', 'norwegian buhund', 'norwegian elkhound', 'norwich terrier', 'otterhound', 'papillon', 'patterdale terrier', 'pekinese', 'pembroke', 'pitbull', 'plott hound', 'pomeranian', 'pug', 'puggle', 'pyrenees', 'redbone', 'rhodesian ridgeback', 'rottweiler', 'russell terrier', 'saluki', 'samoyed', 'schipperke', 'scottish deerhound', 'scottish terrier', 'sealyham terrier', 'sharpei', 'shepherd australian', 'shetland sheepdog', 'shiba', 'shihtzu', 'silky terrier', 'spanish waterdog', 'staffordshire bullterrier', 'standard poodle', 'stbernard', 'sussex spaniel', 'swiss mountain', 'tervuren', 'tibetan mastiff', 'tibetan terrier', 'toy poodle', 'toy terrier', 'vizsla', 'walker hound', 'weimaraner', 'welsh spaniel', 'welsh terrier', 'westhighland terrier', 'wheaten terrier', 'whippet', 'yorkshire terrier'])
-
-def classify_image(img):
- try:
- _, _, probs = learn.predict(img)
- return dict(zip(dog_breeds, map(float, probs)))
- except Exception as e:
- raise gr.Error("Invalid Image Input Type")
-
-learn = load_learner('dogIdentifierModel.pkl')
-
-image = gr.components.Image(image_mode='RGB')
-label = gr.components.Label()
-examples = ['golden-retriever.jpg', 'german-shepherd.jpg', 'doberman.jpg', 'husky.jpg']
-
-intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
-intf.launch(inline=False)
\ No newline at end of file
diff --git a/spaces/emc348/faces-through-time/models/e4e/stylegan2/__init__.py b/spaces/emc348/faces-through-time/models/e4e/stylegan2/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/epsilonator/euclidean_distance/euclidean_distance.py b/spaces/epsilonator/euclidean_distance/euclidean_distance.py
deleted file mode 100644
index 77f2faa80b383485f3f856d96896cec4d4ef890f..0000000000000000000000000000000000000000
--- a/spaces/epsilonator/euclidean_distance/euclidean_distance.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import datasets
-
-import evaluate
-
-import numpy as np
-
-_DESCRIPTION = '''
-Euclidean distance is the square root of the sum of the squares of
-differences between two vectors. It can be computed as
-
-ED(x, y) = sqrt(sum((a - b)^2 for a, b in zip(a, b)))
-'''
-
-_KWARGS_DESCRIPTION = '''
-Args:
- predictions (`list` of `int`): Predicted labels.
- references (`list` of `int`): Ground truth labels.
-Returns:
- euclidean_distance (`float` or `int`): Euclidean Distance between the two given vectors
-Examples:
- >>> import evaluate
- >>> euclidean = evaluate.load('euclidean_distance')
- >>> euclidean.compute(predictions = [0, 1, 2, 3], references = [4, 5, 6, 7])
- {'euclidean_distance': 8.0}
-'''
-
-_CITATION = ""
-
-@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
-class EuclideanDistance(evaluate.Metric):
- def _info(self):
- return evaluate.MetricInfo(
- description=_DESCRIPTION,
- citation=_CITATION,
- inputs_description=_KWARGS_DESCRIPTION,
- features=datasets.Features({
- "predictions": datasets.Value("float32"),
- "references": datasets.Value("float32")
- }),
- codebase_urls=[],
- reference_urls=[],
- format='numpy'
- )
-
- def _compute(self, predictions, references, **kwargs):
- return {
- "euclidean_distance": float(np.sqrt(np.sum(np.square(predictions - references))))
- }
\ No newline at end of file
diff --git a/spaces/ethzanalytics/dialog-China/README.md b/spaces/ethzanalytics/dialog-China/README.md
deleted file mode 100644
index f65040c2693ac57cfb07b65f6926a98c49826cb2..0000000000000000000000000000000000000000
--- a/spaces/ethzanalytics/dialog-China/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Dialog China
-emoji: 📊
-colorFrom: red
-colorTo: gray
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/etweedy/Find_objects/app.py b/spaces/etweedy/Find_objects/app.py
deleted file mode 100644
index 5555523ec994cda5cbd4c2a7d3911c72525adc52..0000000000000000000000000000000000000000
--- a/spaces/etweedy/Find_objects/app.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from fastai.vision.all import *
-import gradio as gr
-
-# Define custom functions for the model
-def get_x(r): return path/'train'/r['fname']
-def get_y(r): return r['labels'].split(' ')
-def splitter(df):
- train = df.index[~df['is_valid']].tolist()
- valid = df.index[df['is_valid']].tolist()
- return train,valid
-
-# Load the model
-learn=load_learner('obj_class2.pkl')
-
-# The loss function has default threshold of 0.5. It seems to do better with 0.3.
-learn.loss_func = BCEWithLogitsLossFlat(thresh=0.3)
-
-# Pull out the list of categories from the model
-categories = learn.dls.vocab
-cat_list = [x for x in categories]
-
-# Function for classifying image.
-def classify_image(img):
- pred,idx,probs = learn.predict(img)
- idx = list(idx)
- answer = ' and '.join([cat_list[i] for i in np.where(idx)[0].tolist()])
- if answer:
- return answer
- else:
- return "I don't recognize anything..."
-
-# Initialize and launch gradio interface
-image = gr.inputs.Image(shape=(192,192))
-label = gr.outputs.Label()
-title = 'Object finder'
-description = "This app will try to find certain types of objects in the photo it's given. Try one of the examples, or upload your own photo! Keep in mind that it only will recognize the following objects: aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, diningtable, dog, horse, motorbike, person, pottedplant, sheep, sofa, train, or tvmonitor"
-examples = ['cat_pot.jpeg','cow_bike.jpeg','dog_plane.jpeg','horse_sheep.jpeg','chair_sofa.jpeg','pizza.jpeg']
-
-intf = gr.Interface(fn=classify_image,inputs=image,outputs=label,examples=examples, title=title,description=description)
-intf.launch(inline=False)
\ No newline at end of file
diff --git a/spaces/evi0mo/vits-fastapi-server/models.py b/spaces/evi0mo/vits-fastapi-server/models.py
deleted file mode 100644
index 676d883f92711412da4b1f822704a75b65ae6196..0000000000000000000000000000000000000000
--- a/spaces/evi0mo/vits-fastapi-server/models.py
+++ /dev/null
@@ -1,533 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-import monotonic_align
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class StochasticDurationPredictor(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
- super().__init__()
- filter_channels = in_channels # it needs to be removed from future version.
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.n_flows = n_flows
- self.gin_channels = gin_channels
-
- self.log_flow = modules.Log()
- self.flows = nn.ModuleList()
- self.flows.append(modules.ElementwiseAffine(2))
- for i in range(n_flows):
- self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
- self.flows.append(modules.Flip())
-
- self.post_pre = nn.Conv1d(1, filter_channels, 1)
- self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
- self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
- self.post_flows = nn.ModuleList()
- self.post_flows.append(modules.ElementwiseAffine(2))
- for i in range(4):
- self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
- self.post_flows.append(modules.Flip())
-
- self.pre = nn.Conv1d(in_channels, filter_channels, 1)
- self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
- self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
- def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
- x = torch.detach(x)
- x = self.pre(x)
- if g is not None:
- g = torch.detach(g)
- x = x + self.cond(g)
- x = self.convs(x, x_mask)
- x = self.proj(x) * x_mask
-
- if not reverse:
- flows = self.flows
- assert w is not None
-
- logdet_tot_q = 0
- h_w = self.post_pre(w)
- h_w = self.post_convs(h_w, x_mask)
- h_w = self.post_proj(h_w) * x_mask
- e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
- z_q = e_q
- for flow in self.post_flows:
- z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
- logdet_tot_q += logdet_q
- z_u, z1 = torch.split(z_q, [1, 1], 1)
- u = torch.sigmoid(z_u) * x_mask
- z0 = (w - u) * x_mask
- logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
- logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
-
- logdet_tot = 0
- z0, logdet = self.log_flow(z0, x_mask)
- logdet_tot += logdet
- z = torch.cat([z0, z1], 1)
- for flow in flows:
- z, logdet = flow(z, x_mask, g=x, reverse=reverse)
- logdet_tot = logdet_tot + logdet
- nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
- return nll + logq # [b]
- else:
- flows = list(reversed(self.flows))
- flows = flows[:-2] + [flows[-1]] # remove a useless vflow
- z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
- for flow in flows:
- z = flow(z, x_mask, g=x, reverse=reverse)
- z0, z1 = torch.split(z, [1, 1], 1)
- logw = z0
- return logw
-
-
-class DurationPredictor(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
- super().__init__()
-
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
- self.gin_channels = gin_channels
-
- self.drop = nn.Dropout(p_dropout)
- self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
- self.norm_1 = modules.LayerNorm(filter_channels)
- self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
- self.norm_2 = modules.LayerNorm(filter_channels)
- self.proj = nn.Conv1d(filter_channels, 1, 1)
-
- if gin_channels != 0:
- self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
- def forward(self, x, x_mask, g=None):
- x = torch.detach(x)
- if g is not None:
- g = torch.detach(g)
- x = x + self.cond(g)
- x = self.conv_1(x * x_mask)
- x = torch.relu(x)
- x = self.norm_1(x)
- x = self.drop(x)
- x = self.conv_2(x * x_mask)
- x = torch.relu(x)
- x = self.norm_2(x)
- x = self.drop(x)
- x = self.proj(x * x_mask)
- return x * x_mask
-
-
-class TextEncoder(nn.Module):
- def __init__(self,
- n_vocab,
- out_channels,
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout):
- super().__init__()
- self.n_vocab = n_vocab
- self.out_channels = out_channels
- self.hidden_channels = hidden_channels
- self.filter_channels = filter_channels
- self.n_heads = n_heads
- self.n_layers = n_layers
- self.kernel_size = kernel_size
- self.p_dropout = p_dropout
-
- self.emb = nn.Embedding(n_vocab, hidden_channels)
- nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
-
- self.encoder = attentions.Encoder(
- hidden_channels,
- filter_channels,
- n_heads,
- n_layers,
- kernel_size,
- p_dropout)
- self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
- def forward(self, x, x_lengths):
- x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
- x = torch.transpose(x, 1, -1) # [b, h, t]
- x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
- x = self.encoder(x * x_mask, x_mask)
- stats = self.proj(x) * x_mask
-
- m, logs = torch.split(stats, self.out_channels, dim=1)
- return x, m, logs, x_mask
-
-
-class 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/f2api/gpt-academic/crazy_functions/\345\233\276\347\211\207\347\224\237\346\210\220.py" "b/spaces/f2api/gpt-academic/crazy_functions/\345\233\276\347\211\207\347\224\237\346\210\220.py"
deleted file mode 100644
index 5bf8bc4ba95864dc53f98b7335e654f58c4fed54..0000000000000000000000000000000000000000
--- "a/spaces/f2api/gpt-academic/crazy_functions/\345\233\276\347\211\207\347\224\237\346\210\220.py"
+++ /dev/null
@@ -1,67 +0,0 @@
-from toolbox import CatchException, update_ui, get_conf, select_api_key
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-import datetime
-
-
-def gen_image(llm_kwargs, prompt, resolution="256x256"):
- import requests, json, time, os
- from request_llm.bridge_all import model_info
-
- proxies, = get_conf('proxies')
- # Set up OpenAI API key and model
- api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
- chat_endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
- # 'https://api.openai.com/v1/chat/completions'
- img_endpoint = chat_endpoint.replace('chat/completions','images/generations')
- # # Generate the image
- url = img_endpoint
- headers = {
- 'Authorization': f"Bearer {api_key}",
- 'Content-Type': 'application/json'
- }
- data = {
- 'prompt': prompt,
- 'n': 1,
- 'size': resolution,
- 'response_format': 'url'
- }
- response = requests.post(url, headers=headers, json=data, proxies=proxies)
- print(response.content)
- image_url = json.loads(response.content.decode('utf8'))['data'][0]['url']
-
- # 文件保存到本地
- r = requests.get(image_url, proxies=proxies)
- file_path = 'gpt_log/image_gen/'
- os.makedirs(file_path, exist_ok=True)
- file_name = 'Image' + time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.png'
- with open(file_path+file_name, 'wb+') as f: f.write(r.content)
-
-
- return image_url, file_path+file_name
-
-
-
-@CatchException
-def 图片生成(prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- """
- txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
- llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
- plugin_kwargs 插件模型的参数，暂时没有用武之地
- chatbot 聊天显示框的句柄，用于显示给用户
- history 聊天历史，前情提要
- system_prompt 给gpt的静默提醒
- web_port 当前软件运行的端口号
- """
- history = [] # 清空历史，以免输入溢出
- chatbot.append(("这是什么功能？", "[Local Message] 生成图像, 请先把模型切换至gpt-xxxx或者api2d-xxxx。如果中文效果不理想, 尝试Prompt。正在处理中 ....."))
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 由于请求gpt需要一段时间，我们先及时地做一次界面更新
- if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
- resolution = plugin_kwargs.get("advanced_arg", '256x256')
- image_url, image_path = gen_image(llm_kwargs, prompt, resolution)
- chatbot.append([prompt,
- f'图像中转网址: `{image_url}` '+
- f'中转网址预览:
'
- f'本地文件地址: `{image_path}` '+
- f'本地文件预览:
'
- ])
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新
diff --git a/spaces/facebook/ov-seg/open_vocab_seg/modeling/heads/pixel_decoder.py b/spaces/facebook/ov-seg/open_vocab_seg/modeling/heads/pixel_decoder.py
deleted file mode 100644
index 6b10089331785e937b79cf82af6d8fba55519082..0000000000000000000000000000000000000000
--- a/spaces/facebook/ov-seg/open_vocab_seg/modeling/heads/pixel_decoder.py
+++ /dev/null
@@ -1,308 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# Copyright (c) Meta Platforms, Inc. All Rights Reserved
-
-import logging
-from typing import Callable, Dict, List, Optional, Tuple, Union
-
-import fvcore.nn.weight_init as weight_init
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.config import configurable
-from detectron2.layers import Conv2d, ShapeSpec, get_norm
-from detectron2.modeling import SEM_SEG_HEADS_REGISTRY
-
-from ..transformer.position_encoding import PositionEmbeddingSine
-from ..transformer.transformer import TransformerEncoder, TransformerEncoderLayer
-
-
-def build_pixel_decoder(cfg, input_shape):
- """
- Build a pixel decoder from `cfg.MODEL.MASK_FORMER.PIXEL_DECODER_NAME`.
- """
- name = cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME
- model = SEM_SEG_HEADS_REGISTRY.get(name)(cfg, input_shape)
- forward_features = getattr(model, "forward_features", None)
- if not callable(forward_features):
- raise ValueError(
- "Only SEM_SEG_HEADS with forward_features method can be used as pixel decoder. "
- f"Please implement forward_features for {name} to only return mask features."
- )
- return model
-
-
-@SEM_SEG_HEADS_REGISTRY.register()
-class BasePixelDecoder(nn.Module):
- @configurable
- def __init__(
- self,
- input_shape: Dict[str, ShapeSpec],
- *,
- conv_dim: int,
- mask_dim: int,
- norm: Optional[Union[str, Callable]] = None,
- ):
- """
- NOTE: this interface is experimental.
- Args:
- input_shape: shapes (channels and stride) of the input features
- conv_dims: number of output channels for the intermediate conv layers.
- mask_dim: number of output channels for the final conv layer.
- norm (str or callable): normalization for all conv layers
- """
- super().__init__()
-
- input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
- self.in_features = [k for k, v in input_shape] # starting from "res2" to "res5"
- feature_channels = [v.channels for k, v in input_shape]
-
- lateral_convs = []
- output_convs = []
-
- use_bias = norm == ""
- for idx, in_channels in enumerate(feature_channels):
- if idx == len(self.in_features) - 1:
- output_norm = get_norm(norm, conv_dim)
- output_conv = Conv2d(
- in_channels,
- conv_dim,
- kernel_size=3,
- stride=1,
- padding=1,
- bias=use_bias,
- norm=output_norm,
- activation=F.relu,
- )
- weight_init.c2_xavier_fill(output_conv)
- self.add_module("layer_{}".format(idx + 1), output_conv)
-
- lateral_convs.append(None)
- output_convs.append(output_conv)
- else:
- lateral_norm = get_norm(norm, conv_dim)
- output_norm = get_norm(norm, conv_dim)
-
- lateral_conv = Conv2d(
- in_channels,
- conv_dim,
- kernel_size=1,
- bias=use_bias,
- norm=lateral_norm,
- )
- output_conv = Conv2d(
- conv_dim,
- conv_dim,
- kernel_size=3,
- stride=1,
- padding=1,
- bias=use_bias,
- norm=output_norm,
- activation=F.relu,
- )
- weight_init.c2_xavier_fill(lateral_conv)
- weight_init.c2_xavier_fill(output_conv)
- self.add_module("adapter_{}".format(idx + 1), lateral_conv)
- self.add_module("layer_{}".format(idx + 1), output_conv)
-
- lateral_convs.append(lateral_conv)
- output_convs.append(output_conv)
- # Place convs into top-down order (from low to high resolution)
- # to make the top-down computation in forward clearer.
- self.lateral_convs = lateral_convs[::-1]
- self.output_convs = output_convs[::-1]
-
- self.mask_dim = mask_dim
- self.mask_features = Conv2d(
- conv_dim,
- mask_dim,
- kernel_size=3,
- stride=1,
- padding=1,
- )
- weight_init.c2_xavier_fill(self.mask_features)
-
- @classmethod
- def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
- ret = {}
- ret["input_shape"] = {
- k: v
- for k, v in input_shape.items()
- if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES
- }
- ret["conv_dim"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
- ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM
- ret["norm"] = cfg.MODEL.SEM_SEG_HEAD.NORM
- return ret
-
- def forward_features(self, features):
- # Reverse feature maps into top-down order (from low to high resolution)
- for idx, f in enumerate(self.in_features[::-1]):
- x = features[f]
- lateral_conv = self.lateral_convs[idx]
- output_conv = self.output_convs[idx]
- if lateral_conv is None:
- y = output_conv(x)
- else:
- cur_fpn = lateral_conv(x)
- # Following FPN implementation, we use nearest upsampling here
- y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode="nearest")
- y = output_conv(y)
- return self.mask_features(y), None
-
- def forward(self, features, targets=None):
- logger = logging.getLogger(__name__)
- logger.warning(
- "Calling forward() may cause unpredicted behavior of PixelDecoder module."
- )
- return self.forward_features(features)
-
-
-class TransformerEncoderOnly(nn.Module):
- def __init__(
- self,
- d_model=512,
- nhead=8,
- num_encoder_layers=6,
- dim_feedforward=2048,
- dropout=0.1,
- activation="relu",
- normalize_before=False,
- ):
- super().__init__()
-
- encoder_layer = TransformerEncoderLayer(
- d_model, nhead, dim_feedforward, dropout, activation, normalize_before
- )
- encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
- self.encoder = TransformerEncoder(
- encoder_layer, num_encoder_layers, encoder_norm
- )
-
- self._reset_parameters()
-
- self.d_model = d_model
- self.nhead = nhead
-
- def _reset_parameters(self):
- for p in self.parameters():
- if p.dim() > 1:
- nn.init.xavier_uniform_(p)
-
- def forward(self, src, mask, pos_embed):
- # flatten NxCxHxW to HWxNxC
- bs, c, h, w = src.shape
- src = src.flatten(2).permute(2, 0, 1)
- pos_embed = pos_embed.flatten(2).permute(2, 0, 1)
- if mask is not None:
- mask = mask.flatten(1)
-
- memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)
- return memory.permute(1, 2, 0).view(bs, c, h, w)
-
-
-@SEM_SEG_HEADS_REGISTRY.register()
-class TransformerEncoderPixelDecoder(BasePixelDecoder):
- @configurable
- def __init__(
- self,
- input_shape: Dict[str, ShapeSpec],
- *,
- transformer_dropout: float,
- transformer_nheads: int,
- transformer_dim_feedforward: int,
- transformer_enc_layers: int,
- transformer_pre_norm: bool,
- conv_dim: int,
- mask_dim: int,
- norm: Optional[Union[str, Callable]] = None,
- ):
- """
- NOTE: this interface is experimental.
- Args:
- input_shape: shapes (channels and stride) of the input features
- transformer_dropout: dropout probability in transformer
- transformer_nheads: number of heads in transformer
- transformer_dim_feedforward: dimension of feedforward network
- transformer_enc_layers: number of transformer encoder layers
- transformer_pre_norm: whether to use pre-layernorm or not
- conv_dims: number of output channels for the intermediate conv layers.
- mask_dim: number of output channels for the final conv layer.
- norm (str or callable): normalization for all conv layers
- """
- super().__init__(input_shape, conv_dim=conv_dim, mask_dim=mask_dim, norm=norm)
-
- input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
- self.in_features = [k for k, v in input_shape] # starting from "res2" to "res5"
- feature_strides = [v.stride for k, v in input_shape]
- feature_channels = [v.channels for k, v in input_shape]
-
- in_channels = feature_channels[len(self.in_features) - 1]
- self.input_proj = Conv2d(in_channels, conv_dim, kernel_size=1)
- weight_init.c2_xavier_fill(self.input_proj)
- self.transformer = TransformerEncoderOnly(
- d_model=conv_dim,
- dropout=transformer_dropout,
- nhead=transformer_nheads,
- dim_feedforward=transformer_dim_feedforward,
- num_encoder_layers=transformer_enc_layers,
- normalize_before=transformer_pre_norm,
- )
- N_steps = conv_dim // 2
- self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)
-
- # update layer
- use_bias = norm == ""
- output_norm = get_norm(norm, conv_dim)
- output_conv = Conv2d(
- conv_dim,
- conv_dim,
- kernel_size=3,
- stride=1,
- padding=1,
- bias=use_bias,
- norm=output_norm,
- activation=F.relu,
- )
- weight_init.c2_xavier_fill(output_conv)
- delattr(self, "layer_{}".format(len(self.in_features)))
- self.add_module("layer_{}".format(len(self.in_features)), output_conv)
- self.output_convs[0] = output_conv
-
- @classmethod
- def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
- ret = super().from_config(cfg, input_shape)
- ret["transformer_dropout"] = cfg.MODEL.MASK_FORMER.DROPOUT
- ret["transformer_nheads"] = cfg.MODEL.MASK_FORMER.NHEADS
- ret["transformer_dim_feedforward"] = cfg.MODEL.MASK_FORMER.DIM_FEEDFORWARD
- ret[
- "transformer_enc_layers"
- ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config
- ret["transformer_pre_norm"] = cfg.MODEL.MASK_FORMER.PRE_NORM
- return ret
-
- def forward_features(self, features):
- # Reverse feature maps into top-down order (from low to high resolution)
- for idx, f in enumerate(self.in_features[::-1]):
- x = features[f]
- lateral_conv = self.lateral_convs[idx]
- output_conv = self.output_convs[idx]
- if lateral_conv is None:
- transformer = self.input_proj(x)
- pos = self.pe_layer(x)
- transformer = self.transformer(transformer, None, pos)
- y = output_conv(transformer)
- # save intermediate feature as input to Transformer decoder
- transformer_encoder_features = transformer
- else:
- cur_fpn = lateral_conv(x)
- # Following FPN implementation, we use nearest upsampling here
- y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode="nearest")
- y = output_conv(y)
- return self.mask_features(y), transformer_encoder_features
-
- def forward(self, features, targets=None):
- logger = logging.getLogger(__name__)
- logger.warning(
- "Calling forward() may cause unpredicted behavior of PixelDecoder module."
- )
- return self.forward_features(features)
diff --git a/spaces/falterWliame/Face_Mask_Detection/Actix Analyzer Crack Version Winzip.md b/spaces/falterWliame/Face_Mask_Detection/Actix Analyzer Crack Version Winzip.md
deleted file mode 100644
index 8f3b6977f868b0475d3d7f5f13462d79738227b2..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Actix Analyzer Crack Version Winzip.md
+++ /dev/null
@@ -1,94 +0,0 @@
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Multi-vendor and multi-technology support: Actix Analyzer supports all common network test equipment and data sources, including 2G, 3G, LTE, VoLTE and 5G NSA NR. It normalizes and standardizes the data, so you can analyze it consistently regardless of its source.
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Automated troubleshooting: Actix Analyzer helps you identify, analyze and diagnose common network issues automatically. It also provides detailed ad hoc analysis capabilities for uncommon issues.
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If you are interested in trying Actix Analyzer for yourself, you can download a free trial version from the official website. The trial version is fully functional for 30 days, so you can explore all the features and capabilities of the software without paying anything. You can also access tutorials, videos, forums, and support to help you get started.
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If you decide to buy Actix Analyzer after the trial period, you can choose from four different products: Personal Architect, Pro Architect, Building Essentials, and Construction Suite. Each product has different features and prices to suit your needs and budget. You can compare the products on the website and choose the best one for you.
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Actix Analyzer is a well-received software that has positive reviews from its users. Here are some of the testimonials from satisfied customers who have used Actix Analyzer for their network optimization, acceptance and validation projects:
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"Actix Analyzer is a great tool for wireless network analysis. It supports all the technologies and data sources that we need, and provides us with a comprehensive and consistent view of network performance. It also helps us troubleshoot issues quickly and efficiently, and generate reports that meet our quality standards. Actix Analyzer is easy to use, flexible and reliable. We highly recommend it to anyone who works with wireless networks." - Mark Lee, Network Engineer
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"We have been using Actix Analyzer for over 5 years and we are very happy with it. It is a powerful software that allows us to perform data services analysis and validate indoor networks. It also enables us to define and measure custom KPIs that are relevant to our business objectives. Actix Analyzer is a user-friendly software that anyone can learn and use, even without any technical background. It is a must-have software for any wireless network professional." - Sarah Jones, Network Manager
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As you can see, Actix Analyzer has many happy customers who appreciate its features and benefits. However, you don't have to take their word for it. You can try Actix Analyzer for yourself and see how it works for you.
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If you are interested in downloading Actix Analyzer for free, you may be tempted to look for a crack version winzip online. However, as we have explained earlier, using a crack version winzip is illegal, risky, and unreliable. You may end up with a software that doesn't work properly, or worse, a software that harms your computer or steals your information.
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To download the trial version of Actix Analyzer, you just need to visit the website and fill out a simple form with your name and email address. You will then receive a link to download the software and a code to activate it. You can install the software on your computer and start using it right away.
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By downloading the trial version of Actix Analyzer from the website, you can ensure that you are getting a legal and safe software that works as intended. You can also enjoy the software without any limitations or restrictions for 30 days.
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Actix Analyzer is a powerful and easy-to-use software that can help you optimize, analyze and validate wireless networks. It supports all common network test equipment and data sources, and enables you to troubleshoot problems, define and measure KPIs, perform data services analysis, validate indoor networks, and more. Actix Analyzer is a versatile software that can suit the needs and budgets of various professionals and users in the wireless network industry.
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However, Actix Analyzer is not a cheap software. It costs $4,995 for the full version, which may be too expensive for some users. That's why some people are looking for a way to download and install Actix Analyzer crack version winzip for free. A crack version winzip is a compressed file that contains a modified version of a software that bypasses the activation or registration process and allows the user to use the software without paying for it.
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But using a crack version winzip is not a good idea. It is illegal and unethical, as it violates the software license agreement and the intellectual property rights of the developers. It is also risky and unsafe, as it may contain viruses, malware, or spyware that can harm your computer or steal your personal information. It is also unreliable and unsupported, as it may not work properly or have bugs or errors that can affect your performance or quality of your work.
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Therefore, we do not recommend using Actix Analyzer crack version winzip for free. Instead, we suggest you to try some of the alternatives that we have mentioned in this article. You can use the free trial version of Actix Analyzer for 30 days without any limitations. You can also use a free or cheaper alternative to Actix Analyzer, such as TEMS Discovery, Nemo Outdoor, QXDM, etc. You can also buy Actix Analyzer with a discount or coupon code, if you can find any promotions or deals from the developers.
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By using these alternatives, you can get Actix Analyzer legally and safely. You can also support the developers and enjoy the software with peace of mind. Actix Analyzer is a great software that can help you optimize, analyze and validate wireless networks. It is worth investing in.
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\ No newline at end of file
diff --git a/spaces/falterWliame/Face_Mask_Detection/Kabir Poetry In Urdu Pdf 18 TOP.md b/spaces/falterWliame/Face_Mask_Detection/Kabir Poetry In Urdu Pdf 18 TOP.md
deleted file mode 100644
index 488f0ea16911b3d067f840da2a7a4353c56fd7e1..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Kabir Poetry In Urdu Pdf 18 TOP.md
+++ /dev/null
@@ -1,23 +0,0 @@
-
-
Kabir Poetry In Urdu Pdf 18: A Guide to Download and Enjoy the Mystic Verses of a Legendary Poet
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Kabir was a 15th-century Indian poet and mystic who wrote in various languages, including Hindi, Urdu, and Sanskrit. He is widely regarded as one of the most influential poets of India, whose verses express a universal message of love, harmony, and devotion. Kabir's poetry is also known for its simplicity, wit, and sarcasm, as he challenged the dogmas and rituals of various religions and sects.
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If you are interested in reading Kabir's poetry in Urdu, you might be wondering how to find and download a PDF file that contains his poems. There are many websites that offer free or paid downloads of Kabir's poetry in Urdu, but not all of them are reliable or authentic. Some of them might have poor quality scans, incomplete collections, or inaccurate translations. To help you avoid these problems, we have compiled a list of 18 websites that offer Kabir's poetry in Urdu PDF files that you can download and enjoy.
Here are the 18 websites that offer Kabir's poetry in Urdu PDF files:
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Rekhta: This is one of the most popular and comprehensive websites for Urdu poetry. It has a large collection of Kabir's poems in Urdu, Hindi, and English, along with audio, video, and ebooks. You can download the PDF files of Kabir's poetry from this website for free.
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Poem Hunter: This is another website that offers a wide range of poems by various poets, including Kabir. You can download the PDF files of Kabir's poetry in Urdu from this website for free.
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PDF Drive: This is a website that provides free access to millions of PDF files on various topics. You can find and download the PDF files of Kabir's poetry in Urdu from this website for free.
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Archive.org: This is a website that preserves and provides access to historical and cultural artifacts in digital form. You can find and download the PDF files of Kabir's poetry in Urdu from this website for free.
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Scribd: This is a website that allows users to upload and share documents, books, and audiobooks. You can find and download the PDF files of Kabir's poetry in Urdu from this website for free or with a subscription.
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Goodreads: This is a website that allows users to rate and review books, as well as discover new ones. You can find and download the PDF files of Kabir's poetry in Urdu from this website for free or with a subscription.
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Amazon: This is a website that sells books, ebooks, and other products online. You can find and download the PDF files of Kabir's poetry in Urdu from this website for a fee.
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Flipkart: This is a website that sells books, ebooks, and other products online. You can find and download the PDF files of Kabir's poetry in Urdu from this website for a fee.
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Daraz: This is a website that sells books, ebooks, and other products online. You can find and download the PDF files of Kabir's poetry in Urdu from this website for a fee.
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-exe to be based on the now venerable Pure Data visual programming environment. This new version of Pure Data has a number of features, like a new look, new GUI, new semi-transparent gray component placement, audio routing, new user options, a new video mode, a new clock, new real-time audio oscillator, pure-data functionality, etc. The design of this software has been done with performance in mind and it delivers a very quick response.
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-Features
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-Powerful hardware modeling synth
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-The Pro-53 has been built on the premise of being a powerful synthesizer with lots of features. It has a 10, 12 or 16 voice analog monosynth with tonal modulation and overdrive, three oscillators (0, 1 or 2 channels with different parameters) with selfosc, FM, and pulse width modulation (PWM), an 8 channel mixer, 2 x 2 oscillators (4 oscillators in total), two VCFs with envelope, mix, stereo spread and pulse width modulation, one dual filter (which can have two separate filters with envelope and mix), one LFO, a sample & hold (S&H), 3 noise generators and a digital signal processor for wavetable synthesis.
-
-In addition, the Pro-53 has many of the functions usually found on a modular synthesizer (reverbs, delays, special effects, etc.) The real power of the Pro-53 however comes from the fact that all these features are available with only 10-20 KB RAM and 128 KB ROM memory, so it doesn't take a lot of extra memory to run and expand your sounds.
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-Interface and GUI
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-The interface for the Pro-53 is a big change from the first version. It has been designed to be a control interface for controlling analog filters and a multichannel mixer. It is completely graphical and all parameters are in slider form. It was designed to be a GUI synth with several control screens and the possibility to have multiple screens, like one screen for the mono synth and another for the multichannel mixer.
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-Each one of the screens can be stretched or shrunk as well as each screen has it's own unique color with a semi-transparent gray on the rest of the screen. This way you can see the actual filters or mixer at all times and see the synth's state at a glance. It has been designed to be completely scalable, so you can have more or less screens, depending on how much memory you want to use. You 4fefd39f24
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diff --git a/spaces/fatiXbelha/sd/Download Go Rush APK and Play Fun Mini-Games with Friends.md b/spaces/fatiXbelha/sd/Download Go Rush APK and Play Fun Mini-Games with Friends.md
deleted file mode 100644
index 444d1e8985752c50bcae21b10bdc896940dfd0b2..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download Go Rush APK and Play Fun Mini-Games with Friends.md
+++ /dev/null
@@ -1,117 +0,0 @@
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What is Go Rush APK?
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Go Rush APK is an exciting and addictive arcade game that challenges your reflexes and skills. In this game, you have to control a ball that moves along a track full of obstacles and traps. You have to avoid crashing into anything and collect coins and gems along the way. The game has simple but colorful graphics, smooth animations, and catchy music. You can also customize your ball with different skins and effects.
Go Rush APK is not available on the Google Play Store, so you have to download it from a third-party source. Here are the steps to do that:
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Go to [this link](^1^) and click on the download button.
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Go to your device settings and enable the option to install apps from unknown sources.
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Go Rush APK is easy to play but hard to master. Here are the basics of the gameplay and controls:
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Playing Go Rush APK can be fun and rewarding for many reasons. Here are some of them:
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You can improve your reflexes, coordination, and concentration by playing this game.
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You can challenge yourself and compete with other players around the world by checking your score on the leaderboard.
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You can unlock new skins and effects for your ball by collecting coins and gems.
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You can enjoy a relaxing and entertaining game experience with minimal ads and interruptions.
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What are the drawbacks of playing Go Rush APK?
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Playing Go Rush APK can also have some disadvantages and risks that you should be aware of. Here are some of them:
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You may encounter some bugs or glitches in the game that can affect your performance or enjoyment.
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How to improve your skills and score in Go Rush APK?
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If you want to become a pro player and beat your own or others' records in Go Rush APK, you need to practice and improve your skills. Here are some tips and tricks that can help you:
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Play the game regularly and try different modes and levels.
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Watch some videos or tutorials of other players and learn from their strategies and techniques.
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Use the power-ups wisely and at the right time. Don't waste them or miss them.
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Focus on the track and the obstacles ahead. Don't get distracted by the coins, gems, or effects.
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Keep calm and don't panic. If you make a mistake, don't give up. Try again and learn from it.
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How to contact the developers of Go Rush APK?
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If you have any questions, feedback, suggestions, or issues regarding Go Rush APK, you can contact the developers of the app through the following ways:
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Email: gorushapk@gmail.com
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Facebook: [Go Rush APK]
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Conclusion
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Go Rush APK is a fun and addictive arcade game that tests your reflexes and skills. You can download and install it from a third-party source and enjoy playing it on your device. You can also customize your ball, collect coins and gems, use power-ups, and compete with other players. However, you should also be careful of the drawbacks and risks of playing this game and take some precautions to avoid them. Go Rush APK is a game that can keep you entertained and challenged for hours. Are you ready to go for a rush?
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FAQs
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What is the latest version of Go Rush APK?
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The latest version of Go Rush APK is 1.0.5, which was released on June 15, 2023. It has some bug fixes and performance improvements.
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Go Rush APK is generally safe to download and play, as long as you get it from a reliable source and scan it for viruses or malware before installing it. However, you should also be aware of the potential security issues or malware threats that may come from downloading an app from an unknown source.
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No, Go Rush APK is only compatible with Android devices. You cannot play it on PC or other devices unless you use an emulator or a simulator.
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\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/ENB GTA 5 Where to Download and How to Configure the Graphics Mod.md b/spaces/fatiXbelha/sd/ENB GTA 5 Where to Download and How to Configure the Graphics Mod.md
deleted file mode 100644
index 68708f0188d1946b19c2e6f6233812b26399c83f..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/ENB GTA 5 Where to Download and How to Configure the Graphics Mod.md
+++ /dev/null
@@ -1,74 +0,0 @@
-
-
How to Download and Install ENB for GTA 5
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If you are looking for a way to enhance the graphics quality of Grand Theft Auto V, you may want to try using ENB. ENB is a mod that adds post-processing effects to the game, such as ambient occlusion, depth of field, bloom, reflections, shadows, and more. ENB can make GTA 5 look more realistic, cinematic, or stylized, depending on the preset you choose or the settings you customize.
In this article, we will show you how to download and install ENB for GTA 5, as well as some of the best ENB presets available. We will also give you some tips and tricks for using ENB for GTA 5, such as how to access the in-game menu, improve performance, and uninstall it. Follow these simple steps and you will be able to enjoy a new level of graphics quality in GTA 5.
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Step 1: Download the latest ENB binaries from the official website
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The first thing you need to do is to download the latest ENB binaries from the official website. These are the files that enable ENB to work with GTA 5. You can find them here: http://www.enbdev.com/download_mod_gta5.htm. Make sure you download the version that matches your game version and DirectX mode. For example, if you have GTA 5 version 1.0.2372.0 and use DirectX 11 mode, you should download ENBSeries v0.492.
-
Step 2: Extract the files and place them in the GTA V root folder
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Once you have downloaded the ENB binaries, you need to extract them using a program like WinRAR or 7-Zip. You should see a folder called "WrapperVersion" that contains several files, such as d3d11.dll, enblocal.ini, enbseries.ini, etc. You need to copy these files and paste them in your GTA V root folder. This is the folder where your GTA5.exe file is located, usually in C:\Program Files\Rockstar Games\Grand Theft Auto V.
-
Step 3: Download a preset or customize your own settings
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By default, ENB does not have a graphic preset, so you need to download one from the internet or create your own. A preset is a set of settings that define how ENB will look in GTA 5. There are many presets available online, each with its own style and features. You can find them on websites like Nexus Mods, Reddit, or ENBDev Forum. To install a preset, you need to follow the instructions provided by the author, but usually it involves copying some files to your GTA V root folder or your enbseries folder.
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How to download and install ENBSeries for GTA 5
-Best ENB presets and shaders for GTA 5 graphics mod
-GTA 5 ENB vs ReShade: which one is better for enhancing visuals?
-GTA 5 Natural Vision Evolved: the ultimate ENB and ReShade mod
-How to fix common issues and errors with GTA 5 ENB mod
-GTA 5 ENB comparison: Vanilla vs ENBSeries vs Natural Vision Evolved
-How to customize and tweak your GTA 5 ENB settings
-GTA 5 ENB performance guide: how to optimize FPS and graphics quality
-How to uninstall GTA 5 ENB mod safely and completely
-GTA 5 ENB features: water tesselation, subsurface scattering, skylighting, and more
-How to use ENB editor and SDK for GTA 5 modding
-GTA 5 ENB screenshots and videos: showcase your amazing graphics
-GTA 5 ENB reviews and ratings: what do users think of the mod?
-GTA 5 ENB compatibility: how to make it work with other mods and multiplayer
-GTA 5 ENB alternatives: other graphics mods you can try
-How to download and update GTA 5 ENB mod to the latest version
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-GTA 5 ENB FAQ: answers to common questions about the mod
-GTA 5 ENB tutorial: how to create your own presets and shaders
-GTA 5 ENB changelog: what's new in each version of the mod?
-How to download and install Aeonic 2021 - ENB and ReShade for Natural Vision Evolved
-Best Aeonic 2021 settings and options for GTA 5 graphics enhancement
-Aeonic 2021 vs Natural Vision Evolved: which one is more realistic and immersive?
-Aeonic 2021 features: bloom, lens textures, ambient occlusion, indirect lighting, and more
-How to fix common issues and errors with Aeonic 2021 mod for GTA 5
-Aeonic 2021 comparison: Vanilla vs Natural Vision Evolved vs Aeonic 2021
-How to customize and tweak your Aeonic 2021 settings for GTA 5
-Aeonic 2021 performance guide: how to optimize FPS and graphics quality with the mod
-How to uninstall Aeonic 2021 mod safely and completely from GTA 5
-Aeonic 2021 screenshots and videos: showcase your amazing graphics with the mod
-Aeonic 2021 reviews and ratings: what do users think of the mod?
-Aeonic 2021 compatibility: how to make it work with other mods and multiplayer for GTA 5
-Aeonic 2021 alternatives: other graphics mods you can try for GTA 5
-How to download and update Aeonic 2021 mod to the latest version for GTA 5
-Aeonic 2021 tips and tricks: how to get the most out of the mod for GTA 5
-Aeonic 2021 FAQ: answers to common questions about the mod for GTA 5
-Aeonic 2021 tutorial: how to create your own presets and shaders with the mod for GTA 5
-Aeonic 2021 changelog: what's new in each version of the mod for GTA 5?
-
If you want to customize your own settings, you can use the integrated editor that comes with ENB. To access it , you need to press Shift+Enter while in the game. This will open a menu where you can adjust various parameters, such as brightness, contrast, saturation, color correction, bloom, depth of field, and more. You can also save and load your settings using the buttons at the bottom of the menu. To apply the changes, you need to press Save and Apply.
-
Step 4: Turn on/off ENB using the Scroll Lock key
-
Once you have installed ENB and a preset, you can turn it on or off using the Scroll Lock key on your keyboard. This is useful if you want to compare the graphics quality with and without ENB, or if you want to disable it temporarily for performance reasons. You can also change the key binding in the enblocal.ini file, under the [INPUT] section.
-
Best ENB Presets for GTA 5
-
There are many ENB presets available for GTA 5, each with its own style and features. Some of them aim to make the game look more realistic, while others add a cinematic or artistic touch. Here are some of the best ENB presets for GTA 5 that you can try:
-
NaturalVision Evolved
-
NaturalVision Evolved is one of the most popular and advanced ENB presets for GTA 5. It is a photorealistic graphics overhaul that enhances the lighting, weather, colors, textures, and effects of the game. It also adds new features, such as ray tracing, volumetric clouds, custom shaders, and more. NaturalVision Evolved requires a powerful PC to run smoothly, but it offers a stunning visual experience that rivals real life.
-
You can download NaturalVision Evolved from Patreon, where you need to support the author with a monthly donation of $10 or more. You will also need to install some additional mods, such as ScriptHookV, OpenIV, and LA Roads. You can follow the installation guide provided by the author on YouTube.
-
PRSA
-
PRSA stands for PhotoRealistic San Andreas, and it is another cinematic and immersive ENB preset for GTA 5. It improves the lighting and colors of the game, making them more realistic and balanced. It also adds depth of field, motion blur, lens flare, chromatic aberration, and other effects that enhance the cinematic feel of the game. PRSA is compatible with most weather and timecycle mods, such as VisualV or NVR.
-
You can download PRSA from Nexus Mods, where you need to create a free account to access the files. You will also need to install the latest ENB binaries from the official website. You can follow the installation instructions provided by the author on YouTube.
-
Aeonic
-
Aeonic is a vibrant and dynamic ENB preset for GTA 5 that enhances the water and reflections of the game. It makes the water look more realistic and detailed, with waves, foam, ripples, and reflections. It also improves the reflections on cars, buildings, windows, and other surfaces. Aeonic adds a touch of color and contrast to the game, making it more lively and eye-catching.
-
You can download Aeonic from Nexus Mods, where you need to create a free account to access the files. You will also need to install the latest ENB binaries from the official website. You can follow the installation instructions provided by the author on YouTube.
-
Tips and Tricks for Using ENB for GTA 5
-
Using ENB for GTA 5 can enhance your gaming experience significantly, but it can also cause some issues or challenges. Here are some tips and tricks for using ENB for GTA 5:
- - How to access the in-game ENB menu and tweak parameters: As mentioned before, you can access the in-game ENB menu by pressing Shift+Enter while in the game. This will allow you to tweak various parameters, such as brightness, contrast , saturation, color correction, bloom, depth of field, and more. You can also save and load your settings using the buttons at the bottom of the menu. To apply the changes, you need to press Save and Apply. - How to improve performance and compatibility with other mods: ENB can have a significant impact on your FPS (frames per second), especially if you use a high-quality preset or a high-resolution monitor. To improve your performance, you can try lowering some of the settings in the enblocal.ini file, such as ForceVideoAdapterIndex, VideoMemorySizeMb, EnableOcclusionCulling, EnableZPrepass, etc. You can also disable some of the effects that you don't need or like, such as depth of field, motion blur, lens flare, etc. You can do this by editing the enbseries.ini file or using the in-game menu. Additionally, you can use other mods that optimize the game performance, such as FPS Booster or GTA V Config. To avoid compatibility issues with other mods, you should always check the mod description and comments for any conflicts or requirements. You should also use a mod manager, such as OpenIV or Mod Organizer 2, to install and uninstall mods safely and easily. - How to uninstall ENB and restore the original game files: If you want to uninstall ENB and restore the original game files, you need to delete all the files that you copied to your GTA V root folder when you installed ENB. These include d3d11.dll, enblocal.ini, enbseries.ini, and any other files that came with your preset. You can also use a mod manager to uninstall ENB automatically.
Conclusion
-
ENB is a mod that can enhance the graphics quality of GTA 5 by adding post-processing effects, such as ambient occlusion, depth of field, bloom, reflections, shadows, and more. ENB can make GTA 5 look more realistic, cinematic, or stylized, depending on the preset you choose or the settings you customize. To download and install ENB for GTA 5, you need to follow these steps:
- - Download the latest ENB binaries from the official website - Extract the files and place them in the GTA V root folder - Download a preset or customize your own settings - Turn on/off ENB using the Scroll Lock key
You can also try some of the best ENB presets for GTA 5, such as NaturalVision Evolved, PRSA, or Aeonic. These presets offer different styles and features that enhance the lighting, weather, colors, textures, and effects of the game. You can also use some tips and tricks for using ENB for GTA 5, such as how to access the in-game menu, improve performance, and uninstall it.
-
If you want to enjoy a new level of graphics quality in GTA 5, download ENB today and see the difference for yourself. You will be amazed by how much ENB can transform your game experience.
-
FAQs
-
Here are some of the frequently asked questions about ENB for GTA 5:
- - Q: What is ENB? - A: ENB is a mod that adds post-processing effects to GTA 5, such as ambient occlusion, depth of field, bloom , reflections, shadows, and more. ENB can make GTA 5 look more realistic, cinematic, or stylized, depending on the preset you choose or the settings you customize. - Q: How do I download and install ENB for GTA 5? - A: To download and install ENB for GTA 5, you need to follow these steps: - Download the latest ENB binaries from the official website - Extract the files and place them in the GTA V root folder - Download a preset or customize your own settings - Turn on/off ENB using the Scroll Lock key - Q: What are some of the best ENB presets for GTA 5? - A: Some of the best ENB presets for GTA 5 are: - NaturalVision Evolved: A photorealistic graphics overhaul that enhances the lighting, weather, colors, textures, and effects of the game. It also adds new features, such as ray tracing, volumetric clouds, custom shaders, and more. - PRSA: A cinematic and immersive ENB with realistic lighting and colors. It also adds depth of field, motion blur, lens flare, chromatic aberration, and other effects that enhance the cinematic feel of the game. - Aeonic: A vibrant and dynamic ENB that enhances the water and reflections of the game. It makes the water look more realistic and detailed, with waves, foam, ripples, and reflections. It also improves the reflections on cars, buildings, windows, and other surfaces. - Q: How do I improve performance and compatibility with ENB for GTA 5? - A: To improve performance and compatibility with ENB for GTA 5, you can try lowering some of the settings in the enblocal.ini file, such as ForceVideoAdapterIndex, VideoMemorySizeMb, EnableOcclusionCulling, EnableZPrepass, etc. You can also disable some of the effects that you don't need or like, such as depth of field, motion blur, lens flare, etc. You can do this by editing the enbseries.ini file or using the in-game menu. Additionally, you can use other mods that optimize the game performance, such as FPS Booster or GTA V Config. To avoid compatibility issues with other mods, you should always check the mod description and comments for any conflicts or requirements. You should also use a mod manager, such as OpenIV or Mod Organizer 2, to install and uninstall mods safely and easily. - Q: How do I uninstall ENB and restore the original game files? - A: To uninstall ENB and restore the original game files, you need to delete all the files that you copied to your GTA V root folder when you installed ENB. These include d3d11.dll, enblocal.ini, enbseries.ini , and any other files that came with your preset. You can also use a mod manager to uninstall ENB automatically.
I hope this article has helped you to download and install ENB for GTA 5, as well as some of the best ENB presets available. ENB is a mod that can enhance the graphics quality of GTA 5 by adding post-processing effects, such as ambient occlusion, depth of field, bloom, reflections, shadows, and more. ENB can make GTA 5 look more realistic, cinematic, or stylized, depending on the preset you choose or the settings you customize. If you have any questions or feedback, feel free to leave a comment below.
401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/fffiloni/Stable-Diffusion-CPU/README.md b/spaces/fffiloni/Stable-Diffusion-CPU/README.md
deleted file mode 100644
index 719a2107c0310e37773777ade5ec6674800e9e5c..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/Stable-Diffusion-CPU/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Stable Diffusion CPU
-emoji: 🔥
-colorFrom: gray
-colorTo: blue
-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/fffiloni/controlnet-animation-doodle/node_modules/negotiator/index.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/negotiator/index.js
deleted file mode 100644
index 4788264b16c9f2282bba539529577ed31920425d..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/negotiator/index.js
+++ /dev/null
@@ -1,82 +0,0 @@
-/*!
- * negotiator
- * Copyright(c) 2012 Federico Romero
- * Copyright(c) 2012-2014 Isaac Z. Schlueter
- * Copyright(c) 2015 Douglas Christopher Wilson
- * MIT Licensed
- */
-
-'use strict';
-
-var preferredCharsets = require('./lib/charset')
-var preferredEncodings = require('./lib/encoding')
-var preferredLanguages = require('./lib/language')
-var preferredMediaTypes = require('./lib/mediaType')
-
-/**
- * Module exports.
- * @public
- */
-
-module.exports = Negotiator;
-module.exports.Negotiator = Negotiator;
-
-/**
- * Create a Negotiator instance from a request.
- * @param {object} request
- * @public
- */
-
-function Negotiator(request) {
- if (!(this instanceof Negotiator)) {
- return new Negotiator(request);
- }
-
- this.request = request;
-}
-
-Negotiator.prototype.charset = function charset(available) {
- var set = this.charsets(available);
- return set && set[0];
-};
-
-Negotiator.prototype.charsets = function charsets(available) {
- return preferredCharsets(this.request.headers['accept-charset'], available);
-};
-
-Negotiator.prototype.encoding = function encoding(available) {
- var set = this.encodings(available);
- return set && set[0];
-};
-
-Negotiator.prototype.encodings = function encodings(available) {
- return preferredEncodings(this.request.headers['accept-encoding'], available);
-};
-
-Negotiator.prototype.language = function language(available) {
- var set = this.languages(available);
- return set && set[0];
-};
-
-Negotiator.prototype.languages = function languages(available) {
- return preferredLanguages(this.request.headers['accept-language'], available);
-};
-
-Negotiator.prototype.mediaType = function mediaType(available) {
- var set = this.mediaTypes(available);
- return set && set[0];
-};
-
-Negotiator.prototype.mediaTypes = function mediaTypes(available) {
- return preferredMediaTypes(this.request.headers.accept, available);
-};
-
-// Backwards compatibility
-Negotiator.prototype.preferredCharset = Negotiator.prototype.charset;
-Negotiator.prototype.preferredCharsets = Negotiator.prototype.charsets;
-Negotiator.prototype.preferredEncoding = Negotiator.prototype.encoding;
-Negotiator.prototype.preferredEncodings = Negotiator.prototype.encodings;
-Negotiator.prototype.preferredLanguage = Negotiator.prototype.language;
-Negotiator.prototype.preferredLanguages = Negotiator.prototype.languages;
-Negotiator.prototype.preferredMediaType = Negotiator.prototype.mediaType;
-Negotiator.prototype.preferredMediaTypes = Negotiator.prototype.mediaTypes;
diff --git "a/spaces/fkhuggingme/gpt-academic/crazy_functions/\350\260\267\346\255\214\346\243\200\347\264\242\345\260\217\345\212\251\346\211\213.py" "b/spaces/fkhuggingme/gpt-academic/crazy_functions/\350\260\267\346\255\214\346\243\200\347\264\242\345\260\217\345\212\251\346\211\213.py"
deleted file mode 100644
index 834f0799e1dca6328454ca7ec8eaa29b6a167199..0000000000000000000000000000000000000000
--- "a/spaces/fkhuggingme/gpt-academic/crazy_functions/\350\260\267\346\255\214\346\243\200\347\264\242\345\260\217\345\212\251\346\211\213.py"
+++ /dev/null
@@ -1,108 +0,0 @@
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from toolbox import CatchException, report_execption, write_results_to_file
-from toolbox import update_ui
-
-def get_meta_information(url, chatbot, history):
- import requests
- import arxiv
- import difflib
- from bs4 import BeautifulSoup
- from toolbox import get_conf
- proxies, = get_conf('proxies')
- headers = {
- 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/105.0.0.0 Safari/537.36',
- }
- # 发送 GET 请求
- response = requests.get(url, proxies=proxies, headers=headers)
-
- # 解析网页内容
- soup = BeautifulSoup(response.text, "html.parser")
-
- def string_similar(s1, s2):
- return difflib.SequenceMatcher(None, s1, s2).quick_ratio()
-
- profile = []
- # 获取所有文章的标题和作者
- for result in soup.select(".gs_ri"):
- title = result.a.text.replace('\n', ' ').replace(' ', ' ')
- author = result.select_one(".gs_a").text
- try:
- citation = result.select_one(".gs_fl > a[href*='cites']").text # 引用次数是链接中的文本，直接取出来
- except:
- citation = 'cited by 0'
- abstract = result.select_one(".gs_rs").text.strip() # 摘要在 .gs_rs 中的文本，需要清除首尾空格
- search = arxiv.Search(
- query = title,
- max_results = 1,
- sort_by = arxiv.SortCriterion.Relevance,
- )
- paper = next(search.results())
- if string_similar(title, paper.title) > 0.90: # same paper
- abstract = paper.summary.replace('\n', ' ')
- is_paper_in_arxiv = True
- else: # different paper
- abstract = abstract
- is_paper_in_arxiv = False
- paper = next(search.results())
- print(title)
- print(author)
- print(citation)
- profile.append({
- 'title':title,
- 'author':author,
- 'citation':citation,
- 'abstract':abstract,
- 'is_paper_in_arxiv':is_paper_in_arxiv,
- })
-
- chatbot[-1] = [chatbot[-1][0], title + f'\n\n是否在arxiv中（不在arxiv中无法获取完整摘要）:{is_paper_in_arxiv}\n\n' + abstract]
- yield from update_ui(chatbot=chatbot, history=[]) # 刷新界面
- return profile
-
-@CatchException
-def 谷歌检索小助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
- # 基本信息：功能、贡献者
- chatbot.append([
- "函数插件功能？",
- "分析用户提供的谷歌学术（google scholar）搜索页面中，出现的所有文章: binary-husky，插件初始化中..."])
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
- # 尝试导入依赖，如果缺少依赖，则给出安装建议
- try:
- import arxiv
- import math
- from bs4 import BeautifulSoup
- except:
- report_execption(chatbot, history,
- a = f"解析项目: {txt}",
- b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade beautifulsoup4 arxiv```。")
- yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
- return
-
- # 清空历史，以免输入溢出
- history = []
- meta_paper_info_list = yield from get_meta_information(txt, chatbot, history)
- batchsize = 5
- for batch in range(math.ceil(len(meta_paper_info_list)/batchsize)):
- if len(meta_paper_info_list[:batchsize]) > 0:
- i_say = "下面是一些学术文献的数据，提取出以下内容：" + \
- "1、英文题目；2、中文题目翻译；3、作者；4、arxiv公开（is_paper_in_arxiv）；4、引用数量（cite）；5、中文摘要翻译。" + \
- f"以下是信息源：{str(meta_paper_info_list[:batchsize])}"
-
- inputs_show_user = f"请分析此页面中出现的所有文章：{txt}，这是第{batch+1}批"
- gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
- inputs=i_say, inputs_show_user=inputs_show_user,
- llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
- sys_prompt="你是一个学术翻译，请从数据中提取信息。你必须使用Markdown表格。你必须逐个文献进行处理。"
- )
-
- history.extend([ f"第{batch+1}批", gpt_say ])
- meta_paper_info_list = meta_paper_info_list[batchsize:]
-
- chatbot.append(["状态？",
- "已经全部完成，您可以试试让AI写一个Related Works，例如您可以继续输入Write an academic \"Related Works\" section about \"你搜索的研究领域\" for me."])
- msg = '正常'
- yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
- res = write_results_to_file(history)
- chatbot.append(("完成了吗？", res));
- yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
diff --git a/spaces/frapochetti/fast-neural-style-transfer/README.md b/spaces/frapochetti/fast-neural-style-transfer/README.md
deleted file mode 100644
index 98a9fe1bbd940fcca782f6a518ed5a1ab353b307..0000000000000000000000000000000000000000
--- a/spaces/frapochetti/fast-neural-style-transfer/README.md
+++ /dev/null
@@ -1,46 +0,0 @@
----
-title: Fast Neural Style Transfer
-emoji: 🎨
-colorFrom: green
-colorTo: red
-sdk: gradio
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-# 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/fuckyoudeki/AutoGPT/run.sh b/spaces/fuckyoudeki/AutoGPT/run.sh
deleted file mode 100644
index edcbc44155b9ca9df83e283fdf976472c13e6492..0000000000000000000000000000000000000000
--- a/spaces/fuckyoudeki/AutoGPT/run.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-python scripts/check_requirements.py requirements.txt
-if [ $? -eq 1 ]
-then
- echo Installing missing packages...
- pip install -r requirements.txt
-fi
-python -m autogpt $@
-read -p "Press any key to continue..."
diff --git a/spaces/g4f/freegpt-webui/g4f/Provider/Providers/DeepAi.py b/spaces/g4f/freegpt-webui/g4f/Provider/Providers/DeepAi.py
deleted file mode 100644
index 02b08120ec8ef50c91c9237047a4f36c822a7bfc..0000000000000000000000000000000000000000
--- a/spaces/g4f/freegpt-webui/g4f/Provider/Providers/DeepAi.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import os
-import json
-import random
-import hashlib
-import requests
-
-from ...typing import sha256, Dict, get_type_hints
-
-url = 'https://deepai.org'
-model = ['gpt-3.5-turbo']
-supports_stream = True
-needs_auth = False
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
- def md5(text: str) -> str:
- return hashlib.md5(text.encode()).hexdigest()[::-1]
-
-
- def get_api_key(user_agent: str) -> str:
- part1 = str(random.randint(0, 10**11))
- part2 = md5(user_agent + md5(user_agent + md5(user_agent + part1 + "x")))
-
- return f"tryit-{part1}-{part2}"
-
- user_agent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36'
-
- headers = {
- "api-key": get_api_key(user_agent),
- "user-agent": user_agent
- }
-
- files = {
- "chat_style": (None, "chat"),
- "chatHistory": (None, json.dumps(messages))
- }
-
- r = requests.post("https://api.deepai.org/chat_response", headers=headers, files=files, stream=True)
-
- for chunk in r.iter_content(chunk_size=None):
- r.raise_for_status()
- yield chunk.decode()
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
- '(%s)' % ', '.join(
- [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
diff --git a/spaces/gfhayworth/chat_qa_demo2/greg_funcs.py b/spaces/gfhayworth/chat_qa_demo2/greg_funcs.py
deleted file mode 100644
index 94d9673474edc456f6829c0624c5135a472bfc85..0000000000000000000000000000000000000000
--- a/spaces/gfhayworth/chat_qa_demo2/greg_funcs.py
+++ /dev/null
@@ -1,217 +0,0 @@
-from sentence_transformers import SentenceTransformer, CrossEncoder, util
-#from torch import tensor as torch_tensor
-#from datasets import load_dataset
-
-from langchain.llms import OpenAI
-from langchain.docstore.document import Document
-from langchain.prompts import PromptTemplate
-from langchain.chains.question_answering import load_qa_chain
-from langchain.chains.qa_with_sources import load_qa_with_sources_chain
-from langchain import LLMMathChain, SQLDatabase, SQLDatabaseChain, LLMChain
-from langchain.agents import initialize_agent, Tool
-
-import sqlite3
-#import pandas as pd
-import json
-import chromadb
-
-# database
-cxn = sqlite3.connect('./data/mbr.db')
-
-"""# import models"""
-
-bi_encoder = SentenceTransformer('multi-qa-MiniLM-L6-cos-v1')
-bi_encoder.max_seq_length = 256 #Truncate long passages to 256 tokens
-
-#The bi-encoder will retrieve top_k documents. We use a cross-encoder, to re-rank the results list to improve the quality
-cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
-
-
-
-"""# import datasets"""
-
-# dataset = load_dataset("gfhayworth/hack_policy", split='train')
-# mypassages = list(dataset.to_pandas()['psg'])
-
-# dataset_embed = load_dataset("gfhayworth/hack_policy_embed", split='train')
-# dataset_embed_pd = dataset_embed.to_pandas()
-# mycorpus_embeddings = torch_tensor(dataset_embed_pd.values)
-###########################################################################################################################
-"""# set up vector db"""
-from chromadb.config import Settings
-
-chroma_client = chromadb.Client(settings=Settings(
- chroma_db_impl="duckdb+parquet",
- persist_directory="./data/mychromadb/" # Optional, defaults to .chromadb/ in the current directory
-))
-collection = chroma_client.get_collection(name="benefit_collection")
-
-def vdb_rslt(qry,src,top_k=20):
- results = collection.query(
- query_embeddings=[ bi_encoder.encode(qry) ],
- n_results=top_k,
- where={"source": src},
- )
- return results
-##################################################################################################################################
-# Semantic Search Functions
-def rtrv(qry, src = 'H1036236000SB23.pdf', top_k=20):
- rslts = vdb_rslt(qry,src, top_k)
- return rslts
-
-def rernk(query, collection=collection, top_k=20, top_n = 5):
- rtrv_rslts = rtrv(query, top_k=top_k)
- rtrv_ids = rtrv_rslts.get('ids')[0]
- rtrv_docs = rtrv_rslts.get('documents')[0]
-
- ##### Re-Ranking #####
- cross_inp = [[query, doc] for doc in rtrv_docs]
- cross_scores = cross_encoder.predict(cross_inp)
-
- # Sort results by the cross-encoder scores
- combined = list(zip(rtrv_ids, list(cross_scores)))
- sorted_tuples = sorted(combined, key=lambda x: x[1], reverse=True)
- sorted_ids = [t[0] for t in sorted_tuples[:top_n]]
- predictions = collection.get(ids=sorted_ids, include=["documents","metadatas"])
- return predictions
-
-def get_text_fmt(qry):
- prediction_text = []
- predictions = rernk(qry, collection=collection, top_k=20, top_n = 5)
- docs = predictions['documents']
- meta = predictions['metadatas']
- for i in range(len(docs)):
- result = Document(page_content=docs[i], metadata=meta[i])
- prediction_text.append(result)
- return prediction_text
-
-##################################################################################################################################
-"""# LLM based qa functions"""
-
-template = """You are a friendly AI assistant for the insurance company Humana.
-Given the following extracted parts of a long document and a question, create a succinct final answer.
-If you don't know the answer, just say that you don't know. Don't try to make up an answer.
-If the question is not about Humana, politely inform the user that you are tuned to only answer questions about Humana.
-QUESTION: {question}
-=========
-{summaries}
-=========
-FINAL ANSWER:"""
-PROMPT = PromptTemplate(template=template, input_variables=["summaries", "question"])
-
-chain_qa = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff", prompt=PROMPT, verbose=True)
-
-def get_llm_response(message):
- mydocs = get_text_fmt(message)
- responses = chain_qa({"input_documents":mydocs, "question":message})
- return responses
-
-"""# Database query"""
-
-db = SQLDatabase.from_uri("sqlite:///./data/mbr.db")
-
-llm = OpenAI(temperature=0)
-# default model
-# model_name: str = "text-davinci-003"
-# instruction fine-tuned, sometimes referred to as GPT-3.5
-
-db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True, return_intermediate_steps=True)
-
-def db_qry(qry):
- responses = db_chain('my mbr_id is 456 ;'+str(qry) ) ############### hardcode mbr id 456 for demo
- return responses
-
-#db_qry('how many footcare visits have I had?')
-
-"""## Math
-- default version
-"""
-
-llm_math_chain = LLMMathChain(llm=llm, verbose=True)
-
-#llm_math_chain.run('what is the square root of 49?')
-
-"""# Greeting"""
-
-template = """You are an AI assistant for the insurance company Humana.
-Your name is Jarvis and you were created by Humana's AI research team.
-Offer polite, friendly greetings and brief small talk.
-Respond to thanks with, 'Glad to help.'
-If the question is not about Humana, politely guide the user to ask questions about Humana insurance benefits.
-QUESTION: {question}
-=========
-FINAL ANSWER:"""
-greet_prompt = PromptTemplate(template=template, input_variables=["question"])
-
-greet_llm = LLMChain(prompt=greet_prompt, llm=llm, verbose=True)
-
-"""# MRKL Chain"""
-
-tools = [
- Tool(
- name = "Benefit",
- func=get_llm_response,
- description='''useful for when you need to answer questions about plan benefits, premiums and payments.
- This tool shows how much of a benefit is available in the plan.
- You should ask targeted questions'''
- ),
- Tool(
- name="Calculator",
- func=llm_math_chain.run,
- description="useful for when you need to answer questions about math"
- ),
- Tool(
- name="Member DB",
- func=db_qry,
- description='''useful for when you need to answer questions about member details such their name, id and accumulated use of services.
- This tool shows how much a benfit has already been consumed.
- Input should be in the form of a question containing full context'''
- ),
- Tool(
- name="Greeting",
- func=greet_llm.run,
- description="useful for when you need to respond to greetings, thanks, answer questions about yourself, and make small talk"
- ),
-]
-
-mrkl = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True, return_intermediate_steps=True, max_iterations=5, early_stopping_method="generate")
-
-def mrkl_rspnd(qry):
- response = mrkl({"input":str(qry) })
- return response
-
-def get_cot(r):
- cot = '
'
- try:
- intermedObj = r['intermediate_steps']
- cot +='Input: '+r['input']+' '
- for agnt_action, obs in intermedObj:
- al = ' '.join(agnt_action.log.split('\n') )
- cot += 'AI chain of thought: '+ al +' '
- if type(obs) is dict:
- if obs.get('input_documents') is not None:
- for d in obs['input_documents']:
- cot += ' '+'- '+str(d.page_content)+''+' '+''''''+str(d.metadata['page'])+' '+' '
- cot += 'Observation: '+str(obs['output_text']) +'
'
- elif obs.get('intermediate_steps') is not None:
- cot += 'Query: '+str(obs.get('intermediate_steps')) +'
'
- return cot
-
-def chat(message, history):
- history = history or []
- message = message.lower()
-
- response = mrkl_rspnd(message)
- cot = get_cot(response)
- history.append((message, response['output']))
- return history, history, cot
-
-
-
diff --git a/spaces/gilmar/health_insurance_app/models/HealthInsurance.py b/spaces/gilmar/health_insurance_app/models/HealthInsurance.py
deleted file mode 100644
index 4cd54da548f8d87be8981b3c1c0cb928ab6af684..0000000000000000000000000000000000000000
--- a/spaces/gilmar/health_insurance_app/models/HealthInsurance.py
+++ /dev/null
@@ -1,61 +0,0 @@
-
-import pandas as pd
-
-
-class HealthInsurance():
-
- def __init__(self, model, column_transformer, bins_annual_premium_type):
- """model : the sklearn model already trainned.
- colums_transformer : the column transformer with all transformations.
- bins_annual_premium_type : bins to create annual_premium_type feature"""
-
- self.model = model
- self.transformer = column_transformer
- self.bins_annual_premium_type = bins_annual_premium_type
-
-
- def feature_engineering(self, df):
-
- df[['previously_insured','vintage','age','driving_license']] = df[['previously_insured','vintage','age','driving_license']].astype(int)
- df[['annual_premium','region_code','policy_sales_channel']] = df[['annual_premium','region_code','policy_sales_channel']].astype(float)
-
- df['vehicle_age'] = df['vehicle_age'].apply(self.get_vehicle_age)
-
- premium_categories = ['very_low', 'low', 'moderate', 'high', 'very_high']
- df['annual_premium_type'] = pd.cut(x = df['annual_premium'],
- bins = self.bins_annual_premium_type,
- labels = premium_categories)
- return df
-
- def get_vehicle_age(self, vehicle_age):
-
- vehicle_labels = {
- '> 2 Years' : 'over_2_years',
- '1-2 Year' : 'between_1_2_year',
- '< 1 Year' : 'below_1_year'
- }
-
- return vehicle_labels.get(vehicle_age)
-
- def data_preparation(self, df):
- return self.transformer.transform(df)
-
- def predict(self, df):
-
- np_array = (df.pipe(self.feature_engineering)
- .pipe(self.data_preparation)
- )
-
- df['score'] = self.model.predict_proba(np_array)[:, 1]
- df.drop('annual_premium_type', axis=1, inplace=True)
- return df
-
-
-
-
-
-
-
-
-
-
diff --git a/spaces/gradio/HuBERT/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lm.sh b/spaces/gradio/HuBERT/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lm.sh
deleted file mode 100644
index c2edcefede2da3b6a991b9c8fbc78c96d46d27cb..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/examples/wav2vec/unsupervised/kaldi_self_train/st/local/prepare_lm.sh
+++ /dev/null
@@ -1,35 +0,0 @@
-#!/usr/bin/env bash
-
-langdir=""
-lmdir=""
-
-. ./cmd.sh
-. ./path.sh
-. parse_options.sh
-
-arpa_lm=$1
-data=$2
-
-if [ -z $langdir ]; then
- langdir=$data/lang
-fi
-if [ -z $lmdir ]; then
- lmdir=$data/lang_test
-fi
-
-if [ ! -d $langdir ]; then
- echo "$langdir not found. run local/prepare_lang.sh first" && exit 1
-fi
-
-mkdir -p $lmdir
-cp -r $langdir/* $lmdir
-
-if [[ "$arpa_lm" == *.gz ]]; then
- gunzip -c $arpa_lm | arpa2fst --disambig-symbol=#0 --read-symbol-table=$lmdir/words.txt - $lmdir/G.fst
-else
- arpa2fst --disambig-symbol=#0 --read-symbol-table=$lmdir/words.txt $arpa_lm $lmdir/G.fst
-fi
-fstisstochastic $lmdir/G.fst
-utils/validate_lang.pl $lmdir || exit 1
-
-echo "done preparing lm ($lmdir)"
diff --git a/spaces/gradio/HuBERT/fairseq/modules/downsampled_multihead_attention.py b/spaces/gradio/HuBERT/fairseq/modules/downsampled_multihead_attention.py
deleted file mode 100644
index 2cdece3f7fca2b830eb72999ce93f58667ed595b..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/modules/downsampled_multihead_attention.py
+++ /dev/null
@@ -1,316 +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 math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from fairseq.modules.fairseq_dropout import FairseqDropout
-from fairseq.modules.scalar_bias import scalar_bias
-
-
-class SingleHeadAttention(nn.Module):
- """
- Single-head attention that supports Gating and Downsampling
- """
-
- def __init__(
- self,
- out_channels,
- embed_dim,
- head_dim,
- head_index,
- dropout=0.0,
- bias=True,
- project_input=True,
- gated=False,
- downsample=False,
- num_heads=1,
- ):
- super().__init__()
- self.embed_dim = embed_dim
- self.dropout_module = FairseqDropout(
- dropout, module_name=self.__class__.__name__
- )
- self.head_index = head_index
- self.head_dim = head_dim
- self.project_input = project_input
- self.gated = gated
- self.downsample = downsample
- self.num_heads = num_heads
- self.projection = None
-
- k_layers = []
- v_layers = []
- if self.downsample:
- k_layers.append(Downsample(self.head_index))
- v_layers.append(Downsample(self.head_index))
- out_proj_size = self.head_dim
- else:
- out_proj_size = self.head_dim * self.num_heads
- if self.gated:
- k_layers.append(GatedLinear(self.embed_dim, out_proj_size, bias=bias))
- self.in_proj_q = GatedLinear(self.embed_dim, out_proj_size, bias=bias)
- v_layers.append(GatedLinear(self.embed_dim, out_proj_size, bias=bias))
- else:
- k_layers.append(Linear(self.embed_dim, out_proj_size, bias=bias))
- self.in_proj_q = Linear(self.embed_dim, out_proj_size, bias=bias)
- v_layers.append(Linear(self.embed_dim, out_proj_size, bias=bias))
-
- self.in_proj_k = nn.Sequential(*k_layers)
- self.in_proj_v = nn.Sequential(*v_layers)
-
- if self.downsample:
- self.out_proj = Linear(out_proj_size, self.head_dim, bias=bias)
- else:
- self.out_proj = Linear(out_proj_size, out_channels, bias=bias)
-
- self.scaling = self.head_dim ** -0.5
-
- def forward(
- self,
- query,
- key,
- value,
- mask_future_timesteps=False,
- key_padding_mask=None,
- use_scalar_bias=False,
- ):
- """Input shape: Time x Batch x Channel
- Self-attention can be implemented by passing in the same arguments for
- query, key and value. Future timesteps can be masked with the
- `mask_future_timesteps` argument. Padding elements can be excluded from
- the key by passing a binary ByteTensor (`key_padding_mask`) with shape:
- batch x src_len, where padding elements are indicated by 1s.
- """
- src_len, bsz, out_channels = key.size()
- tgt_len = query.size(0)
- assert list(query.size()) == [tgt_len, bsz, out_channels]
- assert key.size() == value.size()
-
- if key_padding_mask is not None:
- assert key_padding_mask.size(0) == bsz
- assert key_padding_mask.size(1) == src_len
-
- if self.downsample:
- size = bsz
- else:
- size = bsz * self.num_heads
-
- k = key
- v = value
- q = query
- if self.project_input:
- q = self.in_proj_q(q)
- k = self.in_proj_k(k)
- v = self.in_proj_v(v)
- src_len = k.size()[0]
- q *= self.scaling
-
- if not self.downsample:
- q = q.view(tgt_len, size, self.head_dim)
- k = k.view(src_len, size, self.head_dim)
- v = v.view(src_len, size, self.head_dim)
-
- q = q.transpose(0, 1)
- k = k.transpose(0, 1)
- v = v.transpose(0, 1)
-
- attn_weights = torch.bmm(q, k.transpose(1, 2))
- if mask_future_timesteps:
- assert (
- query.size() == key.size()
- ), "mask_future_timesteps only applies to self-attention"
- attn_weights *= torch.tril(
- attn_weights.data.new([1]).expand(tgt_len, tgt_len).clone(),
- diagonal=-1,
- )[:, :: self.head_index + 1 if self.downsample else 1].unsqueeze(0)
- attn_weights += torch.triu(
- attn_weights.data.new([-math.inf]).expand(tgt_len, tgt_len).clone(),
- diagonal=0,
- )[:, :: self.head_index + 1 if self.downsample else 1].unsqueeze(0)
- tgt_size = tgt_len
- if use_scalar_bias:
- attn_weights = scalar_bias(attn_weights, 2)
- v = scalar_bias(v, 1)
- tgt_size += 1
-
- if key_padding_mask is not None:
- # don't attend to padding symbols
- if key_padding_mask.max() > 0:
- if self.downsample:
- attn_weights = attn_weights.view(bsz, 1, tgt_len, src_len)
- else:
- attn_weights = attn_weights.view(
- size, self.num_heads, tgt_len, src_len
- )
- attn_weights = attn_weights.masked_fill(
- key_padding_mask.unsqueeze(1).unsqueeze(2),
- -math.inf,
- )
- attn_weights = attn_weights.view(size, tgt_len, src_len)
- attn_weights = F.softmax(attn_weights, dim=-1)
- attn_weights = self.dropout_module(attn_weights)
-
- attn = torch.bmm(attn_weights, v)
- if self.downsample:
- attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.head_dim)
- else:
- attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, self.embed_dim)
-
- attn = self.out_proj(attn)
-
- return attn, attn_weights
-
-
-class DownsampledMultiHeadAttention(nn.ModuleList):
- """
- Multi-headed attention with Gating and Downsampling
- """
-
- def __init__(
- self,
- out_channels,
- embed_dim,
- num_heads,
- dropout=0.0,
- bias=True,
- project_input=True,
- gated=False,
- downsample=False,
- ):
- self.embed_dim = embed_dim
- self.num_heads = num_heads
- self.head_dim = embed_dim // num_heads
- self.downsample = downsample
- self.gated = gated
- self.project_input = project_input
- assert self.head_dim * num_heads == embed_dim
-
- if self.downsample:
- attention_heads = []
- for index in range(self.num_heads):
- attention_heads.append(
- SingleHeadAttention(
- out_channels,
- self.embed_dim,
- self.head_dim,
- index,
- dropout,
- bias,
- self.project_input,
- self.gated,
- self.downsample,
- self.num_heads,
- )
- )
- super().__init__(modules=attention_heads)
- self.out_proj = Linear(embed_dim, out_channels, bias=bias)
- else:
- # either we have a list of attention heads, or just one attention head
- # if not being downsampled, we can do the heads with one linear layer instead of separate ones
- super().__init__()
- self.attention_module = SingleHeadAttention(
- out_channels,
- self.embed_dim,
- self.head_dim,
- 1,
- dropout,
- bias,
- self.project_input,
- self.gated,
- self.downsample,
- self.num_heads,
- )
-
- def forward(
- self,
- query,
- key,
- value,
- mask_future_timesteps=False,
- key_padding_mask=None,
- use_scalar_bias=False,
- ):
- src_len, bsz, embed_dim = key.size()
- tgt_len = query.size(0)
- assert embed_dim == self.embed_dim
- assert list(query.size()) == [tgt_len, bsz, embed_dim]
- assert key.size() == value.size()
-
- tgt_size = tgt_len
- if use_scalar_bias:
- tgt_size += 1
-
- attn = []
- attn_weights = []
- if self.downsample:
- for attention_head_number in range(self.num_heads):
- # call the forward of each attention head
- _attn, _attn_weight = self[attention_head_number](
- query,
- key,
- value,
- mask_future_timesteps,
- key_padding_mask,
- use_scalar_bias,
- )
- attn.append(_attn)
- attn_weights.append(_attn_weight)
- full_attn = torch.cat(attn, dim=2)
- full_attn = self.out_proj(full_attn)
- return full_attn, attn_weights[0].clone()
- else:
- _attn, _attn_weight = self.attention_module(
- query,
- key,
- value,
- mask_future_timesteps,
- key_padding_mask,
- use_scalar_bias,
- )
- attn.append(_attn)
- attn_weights.append(_attn_weight)
- full_attn = torch.cat(attn, dim=2)
- full_attn_weights = torch.cat(attn_weights)
- full_attn_weights = full_attn_weights.view(
- bsz, self.num_heads, tgt_size, src_len
- )
- full_attn_weights = full_attn_weights.sum(dim=1) / self.num_heads
- return full_attn, full_attn_weights
-
-
-class Downsample(nn.Module):
- """
- Selects every nth element, where n is the index
- """
-
- def __init__(self, index):
- super().__init__()
- self.index = index
-
- def forward(self, x):
- return x[:: self.index + 1]
-
-
-def Linear(in_features, out_features, dropout=0.0, bias=True):
- """Weight-normalized Linear layer (input: B x T x C)"""
- m = nn.Linear(in_features, out_features, bias=bias)
- m.weight.data.normal_(mean=0, std=math.sqrt((1 - dropout) / in_features))
- m.bias.data.zero_()
- return nn.utils.weight_norm(m)
-
-
-def GatedLinear(in_features, out_features, dropout=0.0, bias=True):
- """Weight-normalized Linear layer (input: B x T x C) with interspersed GLU units"""
- return nn.Sequential(
- Linear(in_features, out_features * 4, dropout, bias),
- nn.GLU(),
- Linear(out_features * 2, out_features * 2, dropout, bias),
- nn.GLU(),
- Linear(out_features, out_features, dropout, bias),
- )
diff --git a/spaces/gradio/HuBERT/fairseq/modules/vggblock.py b/spaces/gradio/HuBERT/fairseq/modules/vggblock.py
deleted file mode 100644
index ee5ee19a34816c7350c21fba7c4907fec8ca7a61..0000000000000000000000000000000000000000
--- a/spaces/gradio/HuBERT/fairseq/modules/vggblock.py
+++ /dev/null
@@ -1,116 +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 __future__ import absolute_import, division, print_function, unicode_literals
-
-from collections.abc import Iterable
-from itertools import repeat
-
-import torch
-import torch.nn as nn
-
-
-def _pair(v):
- if isinstance(v, Iterable):
- assert len(v) == 2, "len(v) != 2"
- return v
- return tuple(repeat(v, 2))
-
-
-def infer_conv_output_dim(conv_op, input_dim, sample_inchannel):
- sample_seq_len = 200
- sample_bsz = 10
- x = torch.randn(sample_bsz, sample_inchannel, sample_seq_len, input_dim)
- # N x C x H x W
- # N: sample_bsz, C: sample_inchannel, H: sample_seq_len, W: input_dim
- x = conv_op(x)
- # N x C x H x W
- x = x.transpose(1, 2)
- # N x H x C x W
- bsz, seq = x.size()[:2]
- per_channel_dim = x.size()[3]
- # bsz: N, seq: H, CxW the rest
- return x.contiguous().view(bsz, seq, -1).size(-1), per_channel_dim
-
-
-class VGGBlock(torch.nn.Module):
- """
- VGG motibated cnn module https://arxiv.org/pdf/1409.1556.pdf
-
- Args:
- in_channels: (int) number of input channels (typically 1)
- out_channels: (int) number of output channels
- conv_kernel_size: convolution channels
- pooling_kernel_size: the size of the pooling window to take a max over
- num_conv_layers: (int) number of convolution layers
- input_dim: (int) input dimension
- conv_stride: the stride of the convolving kernel.
- Can be a single number or a tuple (sH, sW) Default: 1
- padding: implicit paddings on both sides of the input.
- Can be a single number or a tuple (padH, padW). Default: None
- layer_norm: (bool) if layer norm is going to be applied. Default: False
-
- Shape:
- Input: BxCxTxfeat, i.e. (batch_size, input_size, timesteps, features)
- Output: BxCxTxfeat, i.e. (batch_size, input_size, timesteps, features)
- """
-
- def __init__(
- self,
- in_channels,
- out_channels,
- conv_kernel_size,
- pooling_kernel_size,
- num_conv_layers,
- input_dim,
- conv_stride=1,
- padding=None,
- layer_norm=False,
- ):
- assert (
- input_dim is not None
- ), "Need input_dim for LayerNorm and infer_conv_output_dim"
- super(VGGBlock, self).__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.conv_kernel_size = _pair(conv_kernel_size)
- self.pooling_kernel_size = _pair(pooling_kernel_size)
- self.num_conv_layers = num_conv_layers
- self.padding = (
- tuple(e // 2 for e in self.conv_kernel_size)
- if padding is None
- else _pair(padding)
- )
- self.conv_stride = _pair(conv_stride)
-
- self.layers = nn.ModuleList()
- for layer in range(num_conv_layers):
- conv_op = nn.Conv2d(
- in_channels if layer == 0 else out_channels,
- out_channels,
- self.conv_kernel_size,
- stride=self.conv_stride,
- padding=self.padding,
- )
- self.layers.append(conv_op)
- if layer_norm:
- conv_output_dim, per_channel_dim = infer_conv_output_dim(
- conv_op, input_dim, in_channels if layer == 0 else out_channels
- )
- self.layers.append(nn.LayerNorm(per_channel_dim))
- input_dim = per_channel_dim
- self.layers.append(nn.ReLU())
-
- if self.pooling_kernel_size is not None:
- pool_op = nn.MaxPool2d(kernel_size=self.pooling_kernel_size, ceil_mode=True)
- self.layers.append(pool_op)
- self.total_output_dim, self.output_dim = infer_conv_output_dim(
- pool_op, input_dim, out_channels
- )
-
- def forward(self, x):
- for i, _ in enumerate(self.layers):
- x = self.layers[i](x)
- return x
diff --git a/spaces/haakohu/deep_privacy2/configs/anonymizers/face_fdf128.py b/spaces/haakohu/deep_privacy2/configs/anonymizers/face_fdf128.py
deleted file mode 100644
index 327b7f5c5b2711bb59eb13489b44ad8a3c0f5f57..0000000000000000000000000000000000000000
--- a/spaces/haakohu/deep_privacy2/configs/anonymizers/face_fdf128.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from dp2.anonymizer import Anonymizer
-from dp2.detection.face_detector import FaceDetector
-from ..defaults import common
-from tops.config import LazyCall as L
-
-
-detector = L(FaceDetector)(
- face_detector_cfg=dict(name="DSFDDetector", clip_boxes=True),
- face_post_process_cfg=dict(target_imsize=(128, 128), fdf128_expand=True),
- score_threshold=0.3,
- cache_directory=common.output_dir.joinpath("face_detection_cache")
-)
-
-
-anonymizer = L(Anonymizer)(
- detector="${detector}",
- face_G_cfg="configs/fdf/stylegan_fdf128.py",
-)
diff --git a/spaces/hackathon-pln-es/AbstractGen_ES/app.py b/spaces/hackathon-pln-es/AbstractGen_ES/app.py
deleted file mode 100644
index e2c7d1bc339e967fa9d4d3f14b553c3d3fe333ef..0000000000000000000000000000000000000000
--- a/spaces/hackathon-pln-es/AbstractGen_ES/app.py
+++ /dev/null
@@ -1,187 +0,0 @@
-# -*- coding: utf-8 -*-
-"""ABSTRACTGEN_ES FINAL.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
- https://colab.research.google.com/drive/1XdfeMcdDbRuRmOGGiOmkiCP9Yih5JXyF
-
-# installs
-"""
-
-import os
-os.system('pip install gpt_2_simple')
-os.system('pip install os.system')
-os.system('pip install gradio')
-os.system('pip install huggingface_hub')
-os.system('pip install easynmt')
-os.system('pip install sentence-transformers')
-os.system('curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash')
-os.system('apt-get install git-lfs')
-os.system('git lfs install')
-os.system('git clone https://huggingface.co/franz96521/AbstractGeneratorES ')
-#os.system('cd AbstractGeneratorES')
-print(os.getcwd())
-print(os.listdir())
-# Commented out IPython magic to ensure Python compatibility.
-# %cd '/content/AbstractGeneratorES'
-
-"""# Init"""
-
-import gpt_2_simple as gpt2
-import os
-import tensorflow as tf
-import pandas as pd
-import re
-
-model_name = "124M"
-if not os.path.isdir(os.path.join("models", model_name)):
- print(f"Downloading {model_name} model...")
- gpt2.download_gpt2(model_name=model_name)
-
-path = os.getcwd()+'/AbstractGeneratorES/AbstractGenerator/'
-checkpoint_dir =path+'weights/'
-data_path = path+'TrainigData/'
-
-
-
-file_name_en = 'en'
-file_path_en = data_path+file_name_en
-
-file_name_es = 'es'
-file_path_es = data_path+file_name_es
-
-
-prefix= '<|startoftext|>'
-sufix ='<|endoftext|>'
-
-import gradio as gr
-import random
-from easynmt import EasyNMT
-
-from sentence_transformers import SentenceTransformer, util
-
-def generateAbstract(text):
- tf.compat.v1.reset_default_graph()
- sess = gpt2.start_tf_sess()
- gpt2.load_gpt2(sess,checkpoint_dir=checkpoint_dir,run_name='run1')
- txt = gpt2.generate(sess,prefix=str(text)+"\nABSTRACT", return_as_list=True,truncate=sufix,checkpoint_dir=checkpoint_dir,nsamples=1)[0]
- return txt
-def removeAbstract(text):
- p = text.find("Introducción")
- p2 = text.find("INTRODUCCIÓN")
- print(p,p2)
- if(p != -1):
- return (text[:p] , text[p:] )
- if(p2 != -1):
- return (text[:p2] , text[p2:] )
-
-def generated_similarity(type_of_input, cn_text):
- if(type_of_input == "English"):
- tf.compat.v1.reset_default_graph()
- model2 = EasyNMT('opus-mt')
- cn_text = model2.translate(cn_text, target_lang='es')
-
-
- print(cn_text)
- abstract_original , body = removeAbstract(cn_text)
- tf.compat.v1.reset_default_graph()
-
- generated_Abstract = generateAbstract(body)
-
- sentences = [abstract_original, generated_Abstract]
-
- model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
-
- #Compute embedding for both lists
- embedding_1= model.encode(sentences[0], convert_to_tensor=True)
- embedding_2 = model.encode(sentences[1], convert_to_tensor=True)
-
- generated_similarity = util.pytorch_cos_sim(embedding_1, embedding_2)
- ## tensor([[0.6003]])
- return f'''TEXTO SIN ABSTRACT\n
- {body}\n
- ABSTRACT ORIGINAL\n
- {abstract_original}\n
- ABSTRACT GENERADO\n
- {generated_Abstract}\n
- SIMILARIDAD DE ABSTRACT: {float(round(generated_similarity.item()*100, 3))}%
- '''
- elif type_of_input == "Spanish":
- abstract_original , body = removeAbstract(cn_text)
- tf.compat.v1.reset_default_graph()
-
- generated_Abstract = generateAbstract(body)
-
- sentences = [abstract_original, generated_Abstract]
-
- model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
-
- #Compute embedding for both lists
- embedding_1= model.encode(sentences[0], convert_to_tensor=True)
- embedding_2 = model.encode(sentences[1], convert_to_tensor=True)
-
- generated_similarity = util.pytorch_cos_sim(embedding_1, embedding_2)
- return f'''TEXTO SIN ABSTRACT\n
- {body}\n
- ABSTRACT ORIGINAL\n
- {abstract_original}\n
- ABSTRACT GENERADO\n
- {generated_Abstract}\n
- SIMILARIDAD DE ABSTRACT: {float(round(generated_similarity.item()*100, 3))}%
- '''
-def generated_abstract(type_of_input, cn_text):
- if type_of_input == "English":
- tf.compat.v1.reset_default_graph()
- model2 = EasyNMT('opus-mt')
- cn_text = model2.translate(cn_text, target_lang='es')
- generated_Abstract = generateAbstract(cn_text)
- return f'''TEXTO SIN ABSTRACT\n
- {cn_text}\n
- ABSTRACT GENERADO\n
- {generated_Abstract}\n
- '''
- elif type_of_input == "Spanish":
- tf.compat.v1.reset_default_graph()
- generated_Abstract = generateAbstract(cn_text)
- return f'''TEXTO SIN ABSTRACT\n
- {cn_text}\n
- ABSTRACT GENERADO\n
- {generated_Abstract}\n
- '''
-
-block = gr.Blocks()
-
-with block:
- gr.Markdown('''ABSTRACTGEN_ES''')
- gr.Markdown('''An app that can generate abstracts in Spanish based on the text that you input via document text and if you already have an abstract and need a different idea, check how similar the new abstract is to the original one.
- ''')
- gr.Markdown('''FUNCTIONING:
- - Upload your paper with abstract (text without abstract + original abstract by itself): our app will generate an abstract by its own, and then you can compare how similar it is in content itself with the original abstract that was contained in the file
-- Upload your paper without abstract (text without abstract only): our app will generate an abstract that you can use for your paper and work in order for it to be used directly or to inspire you to write a good and well written abstract in Spanish''')
- gr.Markdown(''' We used Blocks (beta), which allows you to build web-based demos in a flexible way using the gradio library. Blocks is a more low-level and flexible alternative to the core Interface class.
- The main problem with this library right now is that
- it doesn't support some functionality that Interface
- class has''')
- gr.Markdown('''To get more info about this project go to: https://sites.google.com/up.edu.mx/somos-pln-abstractgen-es/inicio''')
- with gr.Tab("Full text and text similarity"):
- gr.Markdown("Choose the language:")
- type_of_input = gr.inputs.Radio(["English", "Spanish"], label="Input Language")
- with gr.Row():
- cn_text = gr.inputs.Textbox(placeholder="Full text", lines=7)
- with gr.Row():
- cn_results1 = gr.outputs.Textbox(label="Abstract generado")
- cn_run = gr.Button("Run")
- cn_run.click(generated_similarity, inputs=[type_of_input, cn_text], outputs=[cn_results1])
-
- with gr.Tab("Only text with no abstract"):
- gr.Markdown("Choose the language:")
- type_of_input = gr.inputs.Radio(["English", "Spanish"], label="Input Language")
- with gr.Row():
- cn_text = gr.inputs.Textbox(placeholder="Text without abstract", lines=7)
- with gr.Row():
- cn_results1 = gr.outputs.Textbox(label="Abstract generado")
- cn_run = gr.Button("Run")
- cn_run.click(generated_abstract, inputs=[type_of_input, cn_text], outputs=cn_results1)
-
-block.launch(debug = True)
diff --git a/spaces/hanstyle/tts/app.py b/spaces/hanstyle/tts/app.py
deleted file mode 100644
index 8d5db4f19801fe8cef85cd37c1808eda9f5f181e..0000000000000000000000000000000000000000
--- a/spaces/hanstyle/tts/app.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import datetime
-import gradio as gr
-import numpy as np
-import os
-
-from paddlespeech.cli.tts.infer import TTSExecutor
-
-#该函数有3个输入参数和2个输出参数
-def greet(name, file):
-
- print(file)
-
- #语音合成
- wavname = f"{datetime.datetime.now().strftime('%Y%m%d%H%M%S')}.wav"
- tts = TTSExecutor()
- tts(text=name, output=wavname)
- output_file = f"results/{datetime.datetime.now().strftime('%Y%m%d%H%M%S')}.mp4"
-
- #处理视频
- ckpt = os.path.join(os.path.dirname(__file__), "checkpoints/wav2lip.pth")
- audio = os.path.join(os.path.dirname(__file__), wavname)
- out = os.path.join(os.path.dirname(__file__), output_file)
- print(ckpt)
- print(audio)
- print(out)
- os.system(f"python3.10 ./inference.py --checkpoint_path {ckpt} --face_det_batch_size 1 --face {file} --audio {audio} --outfile={out}")
- # video_file = os.path.join(os.path.dirname(__file__), "result_voice5.mp4")
- # video_bytes = open(video_file, "rb").read()
-
- return wavname, out
-
-
-
-
-demo = gr.Interface(
- fn=greet,
- #按照处理程序设置输入组件
- inputs=[gr.Text(placeholder="输入要转的文本"), gr.Image(type="filepath")],
- #按照处理程序设置输出组件
- outputs=[gr.Audio(), gr.Video(label="Processed Video")],
-)
-
-if __name__ == "__main__":
- demo.launch()
diff --git a/spaces/hasibzunair/fifa-tryon-demo/u2net_portrait_test.py b/spaces/hasibzunair/fifa-tryon-demo/u2net_portrait_test.py
deleted file mode 100644
index 7e103dd336868ff71fe4a114d7e4e3b437a80a59..0000000000000000000000000000000000000000
--- a/spaces/hasibzunair/fifa-tryon-demo/u2net_portrait_test.py
+++ /dev/null
@@ -1,117 +0,0 @@
-import os
-from skimage import io, transform
-import torch
-import torchvision
-from torch.autograd import Variable
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.utils.data import Dataset, DataLoader
-from torchvision import transforms#, utils
-# import torch.optim as optim
-
-import numpy as np
-from PIL import Image
-import glob
-
-from data_loader import RescaleT
-from data_loader import ToTensor
-from data_loader import ToTensorLab
-from data_loader import SalObjDataset
-
-from model import U2NET # full size version 173.6 MB
-from model import U2NETP # small version u2net 4.7 MB
-
-# normalize the predicted SOD probability map
-def normPRED(d):
- ma = torch.max(d)
- mi = torch.min(d)
-
- dn = (d-mi)/(ma-mi)
-
- return dn
-
-def save_output(image_name,pred,d_dir):
-
- predict = pred
- predict = predict.squeeze()
- predict_np = predict.cpu().data.numpy()
-
- im = Image.fromarray(predict_np*255).convert('RGB')
- img_name = image_name.split(os.sep)[-1]
- image = io.imread(image_name)
- imo = im.resize((image.shape[1],image.shape[0]),resample=Image.BILINEAR)
-
- pb_np = np.array(imo)
-
- aaa = img_name.split(".")
- bbb = aaa[0:-1]
- imidx = bbb[0]
- for i in range(1,len(bbb)):
- imidx = imidx + "." + bbb[i]
-
- imo.save(d_dir+'/'+imidx+'.png')
-
-def main():
-
- # --------- 1. get image path and name ---------
- model_name='u2net_portrait'#u2netp
-
-
- image_dir = './test_data/test_portrait_images/portrait_im'
- prediction_dir = './test_data/test_portrait_images/portrait_results'
- if(not os.path.exists(prediction_dir)):
- os.mkdir(prediction_dir)
-
- model_dir = './saved_models/u2net_portrait/u2net_portrait.pth'
-
- img_name_list = glob.glob(image_dir+'/*')
- print("Number of images: ", len(img_name_list))
-
- # --------- 2. dataloader ---------
- #1. dataloader
- test_salobj_dataset = SalObjDataset(img_name_list = img_name_list,
- lbl_name_list = [],
- transform=transforms.Compose([RescaleT(512),
- ToTensorLab(flag=0)])
- )
- test_salobj_dataloader = DataLoader(test_salobj_dataset,
- batch_size=1,
- shuffle=False,
- num_workers=1)
-
- # --------- 3. model define ---------
-
- print("...load U2NET---173.6 MB")
- net = U2NET(3,1)
-
- net.load_state_dict(torch.load(model_dir))
- if torch.cuda.is_available():
- net.cuda()
- net.eval()
-
- # --------- 4. inference for each image ---------
- for i_test, data_test in enumerate(test_salobj_dataloader):
-
- print("inferencing:",img_name_list[i_test].split(os.sep)[-1])
-
- inputs_test = data_test['image']
- inputs_test = inputs_test.type(torch.FloatTensor)
-
- if torch.cuda.is_available():
- inputs_test = Variable(inputs_test.cuda())
- else:
- inputs_test = Variable(inputs_test)
-
- d1,d2,d3,d4,d5,d6,d7= net(inputs_test)
-
- # normalization
- pred = 1.0 - d1[:,0,:,:]
- pred = normPRED(pred)
-
- # save results to test_results folder
- save_output(img_name_list[i_test],pred,prediction_dir)
-
- del d1,d2,d3,d4,d5,d6,d7
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/hbestm/gpt-academic-play/crazy_functions/test_project/latex/attention/introduction.tex b/spaces/hbestm/gpt-academic-play/crazy_functions/test_project/latex/attention/introduction.tex
deleted file mode 100644
index 1baa8915f4cf7aec2520894a87470fc9436d954b..0000000000000000000000000000000000000000
--- a/spaces/hbestm/gpt-academic-play/crazy_functions/test_project/latex/attention/introduction.tex
+++ /dev/null
@@ -1,18 +0,0 @@
-Recurrent neural networks, long short-term memory \citep{hochreiter1997} and gated recurrent \citep{gruEval14} neural networks in particular, have been firmly established as state of the art approaches in sequence modeling and transduction problems such as language modeling and machine translation \citep{sutskever14, bahdanau2014neural, cho2014learning}. Numerous efforts have since continued to push the boundaries of recurrent language models and encoder-decoder architectures \citep{wu2016google,luong2015effective,jozefowicz2016exploring}.
-
-Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a sequence of hidden states $h_t$, as a function of the previous hidden state $h_{t-1}$ and the input for position $t$. This inherently sequential nature precludes parallelization within training examples, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples.
-%\marginpar{not sure if the memory constraints are understandable here}
-Recent work has achieved significant improvements in computational efficiency through factorization tricks \citep{Kuchaiev2017Factorization} and conditional computation \citep{shazeer2017outrageously}, while also improving model performance in case of the latter. The fundamental constraint of sequential computation, however, remains.
-
-%\marginpar{@all: there is work on analyzing what attention really does in seq2seq models, couldn't find it right away}
-
-Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences \citep{bahdanau2014neural, structuredAttentionNetworks}. In all but a few cases \citep{decomposableAttnModel}, however, such attention mechanisms are used in conjunction with a recurrent network.
-
-%\marginpar{not sure if "cross-positional communication" is understandable without explanation}
-%\marginpar{insert exact training times and stats for the model that reaches sota earliest, maybe even a single GPU model?}
-
-In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.
-%\marginpar{you removed the constant number of repetitions part. I wrote it because I wanted to make it clear that the model does not only perform attention once, while it's also not recurrent. I thought that might be important to get across early.}
-
-% Just a standard paragraph with citations, rewrite.
-%After the seminal papers of \citep{sutskever14}, \citep{bahdanau2014neural}, and \citep{cho2014learning}, recurrent models have become the dominant solution for both sequence modeling and sequence-to-sequence transduction. Many efforts such as \citep{wu2016google,luong2015effective,jozefowicz2016exploring} have pushed the boundaries of machine translation and language modeling with recurrent sequence models. Recent effort \citep{shazeer2017outrageously} has combined the power of conditional computation with sequence models to train very large models for machine translation, pushing SOTA at lower computational cost. Recurrent models compute a vector of hidden states $h_t$, for each time step $t$ of computation. $h_t$ is a function of both the input at time $t$ and the previous hidden state $h_t$. This dependence on the previous hidden state encumbers recurrnet models to process multiple inputs at once, and their time complexity is a linear function of the length of the input and output, both during training and inference. [What I want to say here is that although this is fine during decoding, at training time, we are given both input and output and this linear nature does not allow the RNN to process all inputs and outputs simultaneously and haven't been used on datasets that are the of the scale of the web. What's the largest dataset we have ? . Talk about Nividia and possibly other's effors to speed up things, and possibly other efforts that alleviate this, but are still limited by it's comptuational nature]. Rest of the intro: What if you could construct the state based on the actual inputs and outputs, then you could construct them all at once. This has been the foundation of many promising recent efforts, bytenet,facenet (Also talk about quasi rnn here). Now we talk about attention!! Along with cell architectures such as long short-term meory (LSTM) \citep{hochreiter1997}, and gated recurrent units (GRUs) \citep{cho2014learning}, attention has emerged as an essential ingredient in successful sequence models, in particular for machine translation. In recent years, many, if not all, state-of-the-art (SOTA) results in machine translation have been achieved with attention-based sequence models \citep{wu2016google,luong2015effective,jozefowicz2016exploring}. Talk about the neon work on how it played with attention to do self attention! Then talk about what we do.
\ No newline at end of file
diff --git a/spaces/hebert2099/MusicGen/tests/__init__.py b/spaces/hebert2099/MusicGen/tests/__init__.py
deleted file mode 100644
index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000
--- a/spaces/hebert2099/MusicGen/tests/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
diff --git a/spaces/hgd/kk/README.md b/spaces/hgd/kk/README.md
deleted file mode 100644
index afba40b03f1ee611a3445229b7006b87394231d1..0000000000000000000000000000000000000000
--- a/spaces/hgd/kk/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Kk
-emoji: 🏃
-colorFrom: purple
-colorTo: gray
-sdk: docker
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/hhalim/WikipediaAIDataScience/README.md b/spaces/hhalim/WikipediaAIDataScience/README.md
deleted file mode 100644
index 767dd534ee77db991e36d99f6f14c6f1abf1eeb7..0000000000000000000000000000000000000000
--- a/spaces/hhalim/WikipediaAIDataScience/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: WikipediaAIWithDataframeMemory
-emoji: 🏢
-colorFrom: gray
-colorTo: pink
-sdk: gradio
-sdk_version: 3.16.2
-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/ho11laqe/nnUNet_calvingfront_detection/nnunet/experiment_planning/nnUNet_convert_decathlon_task.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/experiment_planning/nnUNet_convert_decathlon_task.py
deleted file mode 100644
index cf5285a1da802c1980a36f83a3b810f56d63bdfb..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/experiment_planning/nnUNet_convert_decathlon_task.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
-#
-# 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 batchgenerators.utilities.file_and_folder_operations import *
-from nnunet.configuration import default_num_threads
-from nnunet.experiment_planning.utils import split_4d
-from nnunet.utilities.file_endings import remove_trailing_slash
-
-
-def crawl_and_remove_hidden_from_decathlon(folder):
- folder = remove_trailing_slash(folder)
- assert folder.split('/')[-1].startswith("Task"), "This does not seem to be a decathlon folder. Please give me a " \
- "folder that starts with TaskXX and has the subfolders imagesTr, " \
- "labelsTr and imagesTs"
- subf = subfolders(folder, join=False)
- assert 'imagesTr' in subf, "This does not seem to be a decathlon folder. Please give me a " \
- "folder that starts with TaskXX and has the subfolders imagesTr, " \
- "labelsTr and imagesTs"
- assert 'imagesTs' in subf, "This does not seem to be a decathlon folder. Please give me a " \
- "folder that starts with TaskXX and has the subfolders imagesTr, " \
- "labelsTr and imagesTs"
- assert 'labelsTr' in subf, "This does not seem to be a decathlon folder. Please give me a " \
- "folder that starts with TaskXX and has the subfolders imagesTr, " \
- "labelsTr and imagesTs"
- _ = [os.remove(i) for i in subfiles(folder, prefix=".")]
- _ = [os.remove(i) for i in subfiles(join(folder, 'imagesTr'), prefix=".")]
- _ = [os.remove(i) for i in subfiles(join(folder, 'labelsTr'), prefix=".")]
- _ = [os.remove(i) for i in subfiles(join(folder, 'imagesTs'), prefix=".")]
-
-
-def main():
- import argparse
- parser = argparse.ArgumentParser(description="The MSD provides data as 4D Niftis with the modality being the first"
- " dimension. We think this may be cumbersome for some users and "
- "therefore expect 3D niftixs instead, with one file per modality. "
- "This utility will convert 4D MSD data into the format nnU-Net "
- "expects")
- parser.add_argument("-i", help="Input folder. Must point to a TaskXX_TASKNAME folder as downloaded from the MSD "
- "website", required=True)
- parser.add_argument("-p", required=False, default=default_num_threads, type=int,
- help="Use this to specify how many processes are used to run the script. "
- "Default is %d" % default_num_threads)
- parser.add_argument("-output_task_id", required=False, default=None, type=int,
- help="If specified, this will overwrite the task id in the output folder. If unspecified, the "
- "task id of the input folder will be used.")
- args = parser.parse_args()
-
- crawl_and_remove_hidden_from_decathlon(args.i)
-
- split_4d(args.i, args.p, args.output_task_id)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/scripts_new/run_glacier_front_3.sh b/spaces/ho11laqe/nnUNet_calvingfront_detection/scripts_new/run_glacier_front_3.sh
deleted file mode 100644
index d5d8d32dfc78ccafae3d563bac1d1336063c21d3..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/scripts_new/run_glacier_front_3.sh
+++ /dev/null
@@ -1,18 +0,0 @@
-#!/bin/bash -l
-#SBATCH --nodes=1 --gres=gpu:1 --time=24:00:00
-#SBATCH --job-name=Task501_glacier_front_3
-
-export data_raw="/home/woody/iwi5/iwi5039h/data_raw"
-export nnUNet_raw_data_base="/home/woody/iwi5/iwi5039h/nnUNet_data/nnUNet_raw_data_base/"
-export nnUNet_preprocessed="/home/woody/iwi5/iwi5039h/nnUNet_data/nnUNet_preprocessed/"
-export RESULTS_FOLDER="/home/woody/iwi5/iwi5039h/nnUNet_data/RESULTS_FOLDER"
-
-cd nnunet_glacer
-pwd
-conda activate nnunet
-
-python3 nnunet/run/run_training.py 2d nnUNetTrainerV2 501 3 --disable_postprocessing_on_folds --disable_deepsupervision
-python3 nnunet/inference/predict_simple.py -i $nnUNet_raw_data_base/nnUNet_raw_data/Task501_Glacier_front/imagesTs -o $RESULTS_FOLDER/test_predictions/Task501_Glacier_front/fold_3 -t 501 -m 2d -f 3 -p nnUNetPlansv2.1 -tr nnUNetTrainerV2
-python3 nnunet/dataset_conversion/Task501_Glacier_reverse.py -i $RESULTS_FOLDER/test_predictions/Task501_Glacier_front/fold_3
-python3 ./evaluate_nnUNet.py --predictions $RESULTS_FOLDER/test_predictions/Task501_Glacier_front/fold_3/pngs --labels_fronts $data_raw/fronts/test --labels_zones $data_raw/zones/test --sar_images $data_raw/sar_images/test
-
diff --git a/spaces/hunger11243/VITS-Umamusume-voice-synthesizer/commons.py b/spaces/hunger11243/VITS-Umamusume-voice-synthesizer/commons.py
deleted file mode 100644
index 2153153f527d94e2abb641ea00c80b518ff6c5bd..0000000000000000000000000000000000000000
--- a/spaces/hunger11243/VITS-Umamusume-voice-synthesizer/commons.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import math
-import torch
-from torch.nn import functional as F
-import torch.jit
-
-
-def script_method(fn, _rcb=None):
- return fn
-
-
-def script(obj, optimize=True, _frames_up=0, _rcb=None):
- return obj
-
-
-torch.jit.script_method = script_method
-torch.jit.script = script
-
-
-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 intersperse(lst, item):
- result = [item] * (len(lst) * 2 + 1)
- result[1::2] = lst
- return result
-
-
-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 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 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
diff --git a/spaces/hysts/ibug-face_alignment/app.py b/spaces/hysts/ibug-face_alignment/app.py
deleted file mode 100644
index f65f069c76e7a6b87da316ca0bf0dd880568afc2..0000000000000000000000000000000000000000
--- a/spaces/hysts/ibug-face_alignment/app.py
+++ /dev/null
@@ -1,152 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import argparse
-import functools
-import os
-import pathlib
-import sys
-import tarfile
-
-import cv2
-import gradio as gr
-import huggingface_hub
-import numpy as np
-import torch
-
-sys.path.insert(0, 'face_detection')
-sys.path.insert(0, 'face_alignment')
-
-from ibug.face_alignment import FANPredictor
-from ibug.face_detection import RetinaFacePredictor
-
-TITLE = 'ibug-group/face_alignment'
-DESCRIPTION = 'This is an unofficial demo for https://github.com/ibug-group/face_alignment.'
-ARTICLE = '
Bloodstained.Curse.of.the.Moon.RIP-Unleashed Download For Computer
-
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-
In this game, you take control of Zangetsu, a demon slayer who is on a quest to destroy a powerful demon lurking in a dark castle. Along the way, you can recruit three allies: Miriam, a whip-wielding girl cursed by an alchemist; Alfred, an old magician who can use various spells; and Gebel, a mysterious man who can transform into a bat. Each character has their own abilities and weaknesses, and you can switch between them at any time. You can also choose to ignore or kill your allies, which will affect the story and the gameplay.
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Bloodstained.Curse.of.the.Moon.RIP-Unleashed Download For Computer
The game has eight stages, each with different enemies, traps, and bosses. You can choose between two difficulty modes: Veteran and Casual. In Veteran mode, you have limited lives and you will be knocked back when you take damage, just like in the old-school games. In Casual mode, you have unlimited lives and no knockback, which makes the game easier and more accessible.
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If you want to play this game on your computer, you can download it from various sources online. However, one of the best options is to download Bloodstained.Curse.of.the.Moon.RIP-Unleashed, which is a compressed version of the game that does not require installation. You just need to unzip the file and run the executable to start playing. This version also has some extra features, such as achievements and leaderboards.
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Bloodstained.Curse.of.the.Moon.RIP-Unleashed is compatible with Windows 7 or later, and requires at least 2 GB of RAM and 500 MB of free disk space. The game supports both keyboard and controller input, and you can customize the controls to your liking. The game also has an option to change the screen size and filter.
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Bloodstained: Curse of the Moon is a spin-off game from the Bloodstained series, which is a spiritual successor to the classic Castlevania games. The main game, Bloodstained: Ritual of the Night, is a modern metroidvania game that features 3D graphics, exploration, and RPG elements. However, Bloodstained: Curse of the Moon is a retro-style game that mimics the gameplay and aesthetics of the 8-bit era, especially Castlevania III: Dracula's Curse.
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The game was originally a stretch goal for the Kickstarter campaign of Bloodstained: Ritual of the Night, but it became a standalone game that was released in 2018 for various platforms, including PC. The game was developed by Inti Creates, a Japanese studio that is known for making other retro-inspired games such as Mega Man Zero, Azure Striker Gunvolt, and Blaster Master Zero. The game was also directed by Koji Igarashi, the former producer of the Castlevania series and the creator of the Bloodstained series.
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Bloodstained: Curse of the Moon RIP-Unleashed is a retro-style action game that is inspired by the legendary Castlevania III: Dracula's Curse. The game features four playable characters, multiple endings, and retro-style graphics and music. The game also has some extra features for PC gamers, such as achievements, leaderboards, controller support, and screen size and filter options. The game has received very positive reviews from critics and players alike, who praised its gameplay, presentation, and replay value.
-
If you are looking for a nostalgic and challenging game that pays homage to the classics, you should definitely try Bloodstained: Curse of the Moon RIP-Unleashed. It is a fun and satisfying game that will keep you hooked for hours. You can download it for free from SoundCloud by following this link:
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Bloodstained: Curse of the Moon RIP-Unleashed is a retro-style action game that is inspired by the legendary Castlevania III: Dracula's Curse. The game features four playable characters, multiple endings, and retro-style graphics and music. The game also has some extra features for PC gamers, such as achievements, leaderboards, controller support, and screen size and filter options. The game has received very positive reviews from critics and players alike, who praised its gameplay, presentation, and replay value.
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Delphi 2014.R2 (Autocom) Diagnostics Software Utorrent ((INSTALL)).md b/spaces/inplisQlawa/anything-midjourney-v4-1/Delphi 2014.R2 (Autocom) Diagnostics Software Utorrent ((INSTALL)).md
deleted file mode 100644
index 467ae8d7af789d772a23cf53dc143076f246644b..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Delphi 2014.R2 (Autocom) Diagnostics Software Utorrent ((INSTALL)).md
+++ /dev/null
@@ -1,7 +0,0 @@
-
-
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\ No newline at end of file
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/JetBrains PhpStorm V2017.3 Final Crack HOT! - [SH] Serial Key Keygen.md b/spaces/inplisQlawa/anything-midjourney-v4-1/JetBrains PhpStorm V2017.3 Final Crack HOT! - [SH] Serial Key Keygen.md
deleted file mode 100644
index 3a8049b385e597a8d31673b60785130633940627..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/JetBrains PhpStorm V2017.3 Final Crack HOT! - [SH] Serial Key Keygen.md
+++ /dev/null
@@ -1,50 +0,0 @@
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The final step is to apply the crack patch for JetBrains PhpStorm V2017.3 Final Crack - [SH] that will activate the program and remove the license error. To do this, open the folder named "Crack" that you extracted earlier and copy the file named "phpstorm64.exe". Then, go to the destination folder where you installed JetBrains PhpStorm V2017.3 and paste the file there, replacing the original one.
-
After that, you can launch JetBrains PhpStorm V2017.3 from your Start menu or Desktop shortcut. You will see a splash screen that says "JetBrains PhpStorm v2017.3 Final + Crack - [SH]". This means that you have successfully applied the crack patch and activated the program.
-
Conclusion
-
Congratulations! You have just learned how to download and activate JetBrains PhpStorm V2017.3 Final Crack - [SH] Serial Key Keygen in a few simple steps. Now you can use all the features and benefits of this powerful PHP IDE without paying for a license or compromising your security.
-
However, please note that this method is only for educational purposes and we do not condone piracy or illegal use of software. If you like JetBrains PhpStorm V2017.3 and want to support its development, we recommend that you buy a legitimate license from its official website or authorized resellers.
-
How to Use JetBrains PhpStorm V2017.3 Final Crack - [SH]
-
Now that you have JetBrains PhpStorm V2017.3 Final Crack - [SH] installed and activated on your device, you may wonder how to use it to develop your PHP projects. JetBrains PhpStorm V2017.3 is a complex and sophisticated software that requires some learning and practice to master. However, it also offers a user-friendly and intuitive interface that makes it easy to get started.
-
In this section, we will give you a brief overview of the main features and functions of JetBrains PhpStorm V2017.3 and how to use them to create professional-looking PHP applications. We will cover the following topics:
-
-
How to create a new project and configure it in JetBrains PhpStorm V2017.3
-
How to use the editor and tools in JetBrains PhpStorm V2017.3
-
How to debug and test your code in JetBrains PhpStorm V2017.3
-
How to use version control and collaboration features in JetBrains PhpStorm V2017.3
-
How to deploy and run your application from JetBrains PhpStorm V2017.3
-
-
How to Create a New Project and Configure It in JetBrains PhpStorm V2017.3
-
The first step to develop your PHP projects in JetBrains PhpStorm V2017.3 is to create a new project and configure it in the program. A project is a collection of files, folders, settings, and resources that relate to a specific PHP application. You can create projects from scratch or from existing sources, such as local files, remote servers, or version control repositories.
-
To create a new project in JetBrains PhpStorm V2017.3, you can use one of the following methods:
-
-
File menu: You can use the File menu in JetBrains PhpStorm V2017.3 and choose New Project... This will open a dialog box where you can name your project and choose its type, location, and settings. You can also choose an existing project as a template or create a custom project.
-
Welcome screen: You can also use the welcome screen that appears when you launch JetBrains PhpStorm V2017.3 for the first time or when you close all projects. The welcome screen will allow you to create a new project with the same options as the File menu.
-
-
After creating a new project in JetBrains PhpStorm V2017.3, you will see it in the Project tool window with a default folder structure and files. You can rename, organize, add, delete, or modify these files and folders as needed.
-
To configure your project in JetBrains PhpStorm V2017.3, you can use the Settings/Preferences dialog box that you can access from the File menu or by pressing Ctrl+Alt+S on Windows or Cmd+, on Mac OS X. The Settings/Preferences dialog box will allow you to adjust various options for your project, such as PHP language level, interpreter, code style, inspections, deployment servers, frameworks, databases, and more.
-
How to Use the Editor and Tools in JetBrains PhpStorm V2017.3
-
The next step to develop your PHP projects in JetBrains PhpStorm V2017.3 is to use the editor and tools in the program. The editor is the main area where you write and edit your code. The tools are the additional features that help you with various tasks related to coding, such as navigation, refactoring, documentation, testing, debugging, and more.
-
To use the editor in JetBrains PhpStorm V2017.3, you can open any file from your project by double-clicking on it in the Project tool window or by using the Go to File action (Ctrl+Shift+N on Windows or Cmd+Shift+O on Mac OS X). The editor will show your code with syntax highlighting, code folding, indentation, line numbers, error highlighting, code completion, parameter hints, quick documentation, quick fixes, and more.
-
To use the tools in JetBrains PhpStorm V2017.3, you can access them from various places in the program, such as menus, toolbars, tool windows, pop-ups, shortcuts, or actions. Some of the most useful tools in JetBrains PhpStorm V2017.3 are:
-
-
Navigation tools: These tools help you navigate through your code and project structure quickly and easily. You can use actions such as Go to Declaration (Ctrl+B on Windows or Cmd+B on Mac OS X), Go to Symbol (Ctrl+Alt+Shift+N on Windows or Cmd+Alt+Shift+O on Mac OS X), Go to Type (Ctrl+N on Windows or Cmd+O on Mac OS X), Find Usages (Alt+F7 on Windows or Opt+F7 on Mac OS X), Search Everywhere (Double Shift), Recent Files (Ctrl+E on Windows or Cmd+E on Mac OS X), Switcher (Ctrl+Tab on Windows or Cmd+Tab on Mac OS X), Breadcrumbs (Alt+Home on Windows or Opt+Home on Mac OS X), Structure View (Alt+7 on Windows or Opt+Cmd+O on Mac OS X), File Structure (Ctrl+F12 on Windows or Cmd+F12 on Mac OS X), Bookmarks (F11 on Windows or F3 on Mac OS X), Favorites (Alt+2 on Windows or Opt+Cmd+L on Mac OS X), and more.
-
Refactoring tools: These tools help you improve your code quality and structure by applying automated changes across your project. You can use actions such as Rename (Shift+F6), Move (F6), Copy (F5), Safe Delete (Alt+Delete on Windows or Opt+Cmd+Delete on Mac OS X), Change Signature (Ctrl+F6 on Windows or Cmd+F6 on Mac OS X), Extract Variable/Constant/Parameter/Method/Class/Interface/Trait (Ctrl+Alt+V/C/P/M/C/I/T on Windows or Cmd+Opt+V/C/P/M/C/I/T on Mac OS X), Inline Variable/Constant/Parameter/Method (Ctrl+Alt+N on Windows or Cmd+Opt+N on Mac OS X), Pull Members Up/Push Members Down (Ctrl+F6/F5 on Windows or Cmd+F6/F5 on Mac OS X), Extract Superclass/Interface/Trait (Ctrl+F6/F5/F4 on Windows or Cmd+F6/F5/F4 on Mac OS X), Convert Local Variable to Field/Property (Ctrl+F6/F5/F4/F8/F9/F10/F11/F12/F13/F14/F15/F16/F17/F18/F19/F20/F21/F22/F23/F24/F25/F26/F27/F28/F29/F30/F31/F32/F33/F34/F35/F36/F37/F38/F39/F40
-
Conclusion
-
In this article, we have shown you how to download and activate JetBrains PhpStorm V2017.3 Final Crack - [SH] Serial Key Keygen and how to use it to develop your PHP projects. JetBrains PhpStorm V2017.3 is a powerful and smart PHP IDE that offers many features and functions to help you create professional-looking PHP applications. However, it also requires some learning and practice to master it.
-
We hope that this article has been helpful and informative for you. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and happy coding!
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diff --git a/spaces/inreVtussa/clothingai/Examples/Ant-Man (English) Download Movie 1080p Torrent [BETTER].md b/spaces/inreVtussa/clothingai/Examples/Ant-Man (English) Download Movie 1080p Torrent [BETTER].md
deleted file mode 100644
index 5dab06a906f23c9924cda0634d52bd704a0115ed..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Ant-Man (English) Download Movie 1080p Torrent [BETTER].md
+++ /dev/null
@@ -1,76 +0,0 @@
-
-
How to Download Ant-Man (English) Movie in 1080p Quality Using Torrents
-
Ant-Man (English) is a 2015 superhero movie based on the Marvel Comics character of the same name. It stars Paul Rudd as Scott Lang, a former thief who becomes the Ant-Man after acquiring a suit that allows him to shrink in size and increase in strength. He teams up with his mentor, Dr. Hank Pym, played by Michael Douglas, to pull off a heist that will save the world from a dangerous threat.
If you are looking for a way to download Ant-Man (English) movie in 1080p quality, you might want to consider using torrents. Torrents are files that contain information about other files and folders that are distributed over a peer-to-peer network. By using a torrent client, such as BitTorrent or uTorrent, you can download Ant-Man (English) movie from other users who have the same file.
-
What are the Benefits of Downloading Ant-Man (English) Movie in 1080p Quality?
-
Downloading Ant-Man (English) movie in 1080p quality has several benefits. First, you will be able to enjoy the movie in high definition, with crisp and clear images and sound. You will be able to appreciate the stunning visual effects, the action-packed scenes, and the witty humor of the characters. Second, you will be able to watch the movie offline, without any interruptions or buffering. You can watch it anytime and anywhere you want. Third, you will be able to save money, as you don't have to pay for streaming services or cinema tickets.
-
Where can you Find Ant-Man (English) Movie Torrents?
-
There are many websites that offer torrents for Ant-Man (English) movie in 1080p quality. However, not all of them are reliable and safe. Some of them may contain viruses, malware, or fake files that can harm your device or waste your time. Therefore, you need to be careful and choose only trusted and verified sources. Here are some of the best websites that you can use to find Ant-Man (English) movie torrents:
-
-
YTS: This is one of the most popular torrent sites for movies. It offers high-quality torrents with small file sizes and fast download speeds. You can find Ant-Man (English) movie in 1080p BluRay x264 AC3 format on this site.
-
Archive.org: This is a non-profit digital library that provides free access to millions of books, movies, music, and more. You can find Ant-Man (English) movie in 1080p BluRay Filmxy format on this site.
-
Tealfeed: This is a social media platform that allows users to share and discover content on various topics. You can find Ant-Man and the Wasp: Quantumania (2023), the sequel to Ant-Man, in 1080p YTS and YIFY torrent format on this site.
-
-
How to Download Ant-Man (English) Movie Torrents?
-
Once you have found a suitable website for Ant-Man (English) movie torrents, you need to follow these steps to download them:
-
-
-
Download a torrent client: A torrent client is a software that enables you to download torrents from other users. You can download a torrent client from its official website or from a trusted source. Some of the most popular torrent clients are BitTorrent and uTorrent.
-
Download a VPN: A VPN or a virtual private network is a service that encrypts your internet traffic and hides your IP address from others. By using a VPN, you can download torrents anonymously and securely without being tracked or monitored by anyone.
-
Download Ant-Man (English) movie torrent: After installing a torrent client and a VPN, you can go to the website that offers Ant-Man (English) movie torrent and click on the download button. You will be prompted to open the torrent file with your torrent client. You can then choose the location where you want to save the file and start the download process.
-
-
What to Do After Downloading Ant-Man (English) Movie Torrent?
-
After downloading Ant-Man (English) movie torrent, you need to do some things before watching it:
-
-
Scan the file: Before opening the file, you should scan it with an antivirus software to make sure it is free from viruses, malware, or other threats that can infect your device or steal your information.
-
Extract the file: Some torrents may come in compressed formats, such as ZIP or RAR. You need to extract them using a software such as WinRAR or 7-Zip before watching them.
-
Play the file: After extracting the file, you can play it using a media player such as VLC or Windows Media Player. You may also need some codecs or plugins to play some formats.
-
-
Risks and Precautions When Downloading Torrents
-
Downloading torrents can be risky for several reasons. First, you may violate some copyright laws or face legal actions from the rights holders if you download or share copyrighted content without permission. Second, you may expose your IP address and personal data to other users or third parties who may track or spy on your online activities. Third, you may download malicious files or programs that can infect your device or steal your information.
-
To avoid these risks and protect yourself when downloading torrents, you should follow some precautions. Here are some of them:
-
-
Use a VPN: A VPN or a virtual private network is a service that encrypts your internet traffic and hides your IP address from others. By using a VPN, you can download torrents anonymously and securely without being tracked or monitored by anyone.
-
Use an antivirus: An antivirus is a software that detects and removes viruses, malware, and other threats from your device. By using an antivirus, you can scan and clean your downloaded files before opening them.
-
Use a trusted source: As mentioned earlier, not all torrent sites are reliable and safe. By using a trusted source, you can avoid downloading fake or harmful files that can ruin your experience or damage your device.
-
-
Conclusion
-
Ant-Man (English) is a great movie that you can download in 1080p quality by using torrents. However, you need to be careful and choose only trusted sources and use some precautions when downloading torrents. By doing so, you can enjoy watching Ant-Man (English) in 1080p without any problems or worries.
-
What are the Features of Ant-Man (English) Movie?
-
Ant-Man (English) movie is a fun and entertaining movie that has many features that make it worth watching. Some of the features are:
-
-
The cast: The movie has a talented and charismatic cast that brings the characters to life. Paul Rudd is perfect as Scott Lang, the reluctant hero who has a sense of humor and a heart of gold. Michael Douglas is impressive as Dr. Hank Pym, the brilliant inventor who mentors Scott and entrusts him with the Ant-Man suit. Evangeline Lilly is strong and smart as Hope van Dyne, Hank's daughter and Scott's love interest. Corey Stoll is menacing as Darren Cross, the villain who wants to use the Ant-Man technology for evil purposes.
-
The story: The movie has a simple but engaging story that mixes comedy, action, and drama. It follows Scott as he tries to redeem himself after being released from prison and reconnect with his daughter. He gets involved in a heist that will save the world from Cross, who has developed a weaponized version of the Ant-Man suit called the Yellowjacket. Along the way, he learns how to use the suit and control the ants that help him in his missions.
-
The effects: The movie has amazing effects that create a realistic and immersive experience of shrinking and growing. The movie uses a combination of CGI, practical effects, and macro photography to show the different perspectives and scales of the Ant-Man world. The movie also has creative and thrilling action scenes that showcase the abilities of the Ant-Man suit and the ants.
-
-
What are the Reviews of Ant-Man (English) Movie?
-
Ant-Man (English) movie has received positive reviews from critics and audiences alike. It has a rating of 82% on Rotten Tomatoes, based on 375 reviews, with an average score of 6.9/10. The site's critical consensus reads, \"Led by a charming performance from Paul Rudd, Ant-Man offers Marvel thrills on an appropriately smaller scale -- albeit not as smoothly as its most successful predecessors.\" It also has a rating of 7.3/10 on IMDb, based on 590,000 votes.
-
Some of the praises for the movie are:
-
-
\"Ant-Man is light-hearted fun with a dose of well-played sentiment.\" - Richard Roeper, Chicago Sun-Times
-
\"Ant-Man is a reminder that Marvel can still make 'em like they used to: fast-paced but leisurely, full of screwball humour but with real stakes.\" - Helen O'Hara, Empire
-
\"Ant-Man is proof that less can be more.\" - Peter Travers, Rolling Stone
-
-
Conclusion
-
Ant-Man (English) is a great movie that you can download in 1080p quality by using torrents. However, you need to be careful and choose only trusted sources and use some precautions when downloading torrents. By doing so, you can enjoy watching Ant-Man (English) in 1080p without any problems or worries.
-
What are the Sequels and Spin-offs of Ant-Man (English) Movie?
-
Ant-Man (English) movie is part of the Marvel Cinematic Universe (MCU), a series of interconnected movies and shows based on Marvel Comics characters. Ant-Man (English) movie has two sequels and one spin-off that you can also download and watch:
-
-
Ant-Man and the Wasp (2018): This is the sequel to Ant-Man (English) movie that follows Scott and Hope as they team up again to rescue Hope's mother, Janet van Dyne, from the quantum realm. They also face a new enemy, Ghost, who can phase through objects and wants to use the quantum technology for her own purposes.
-
Ant-Man and the Wasp: Quantumania (2023): This is the upcoming third installment of the Ant-Man series that will feature Scott, Hope, Hank, and Janet as they explore the quantum realm further. They will also encounter Kang the Conqueror, a powerful villain who can manipulate time and space.
-
What If...? (2021): This is an animated spin-off series that explores alternate scenarios in the MCU. One of the episodes features Scott as a head in a jar who helps Nick Fury stop Loki from invading Earth.
-
-
What are the Similar Movies to Ant-Man (English) Movie?
-
If you enjoyed watching Ant-Man (English) movie, you might also like these similar movies that you can download using torrents:
-
-
Spider-Man: Homecoming (2017): This is a movie that follows Peter Parker, a young superhero who tries to balance his high school life with his crime-fighting career. He also gets mentored by Tony Stark, aka Iron Man, and faces a new threat, the Vulture.
-
Guardians of the Galaxy (2014): This is a movie that follows a group of misfits who band together to save the galaxy from a fanatical warlord. The group consists of Peter Quill, aka Star-Lord, a human who was abducted by aliens as a child; Gamora, an assassin who works for Thanos; Drax, a warrior who seeks revenge for his family; Rocket, a genetically engineered raccoon; and Groot, a sentient tree.
-
Shazam! (2019): This is a movie that follows Billy Batson, a foster kid who gains the ability to transform into an adult superhero by saying the word \"Shazam\". He also has to deal with his new powers, his foster family, and a villain who wants to steal his magic.
-
-
Conclusion
-
Ant-Man (English) is a great movie that you can download in 1080p quality by using torrents. However, you need to be careful and choose only trusted sources and use some precautions when downloading torrents. By doing so, you can enjoy watching Ant-Man (English) in 1080p without any problems or worries.
-
Conclusion
-
Ant-Man (English) is a great movie that you can download in 1080p quality by using torrents. However, you need to be careful and choose only trusted sources and use some precautions when downloading torrents. By doing so, you can enjoy watching Ant-Man (English) in 1080p without any problems or worries.
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-
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diff --git a/spaces/inreVtussa/clothingai/Examples/Armacad V10 Update Bloqueur Tita.md b/spaces/inreVtussa/clothingai/Examples/Armacad V10 Update Bloqueur Tita.md
deleted file mode 100644
index fa296de8934a07cb57b471b1aadfd8a86d339621..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Armacad V10 Update Bloqueur Tita.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-ArmaCAD allows you to draw easily any type of 2D or 3D reinforcement drawing with ... ArmaCAD is capable of changing the bar diameter while updating the ... 4d29de3e1b
-
-
-
diff --git a/spaces/inreVtussa/clothingai/Examples/BurnInTest 9.1 Build 1001 Portable [WORK] Download HERE !.md b/spaces/inreVtussa/clothingai/Examples/BurnInTest 9.1 Build 1001 Portable [WORK] Download HERE !.md
deleted file mode 100644
index bfe7607e1bedf2d393c9773f4133061838cba870..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/BurnInTest 9.1 Build 1001 Portable [WORK] Download HERE !.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
BurnInTest 9.1 Build 1001 Portable Download HERE !
"
-
- index_list = []
- dir_index = filepath.iterdir()
- for _file in sorted(dir_index):
- # show file url as relative to static path
- rel_path = _file.relative_to(self._directory).as_posix()
- file_url = self._prefix + "/" + rel_path
-
- # if file is a directory, add '/' to the end of the name
- if _file.is_dir():
- file_name = f"{_file.name}/"
- else:
- file_name = _file.name
-
- index_list.append(
- '
".format("\n".join(index_list))
- body = f"\n{h1}\n{ul}\n"
-
- head_str = f"\n{index_of}\n"
- html = f"\n{head_str}\n{body}\n"
-
- return html
-
- def __repr__(self) -> str:
- name = "'" + self.name + "'" if self.name is not None else ""
- return " {directory!r}>".format(
- name=name, path=self._prefix, directory=self._directory
- )
-
-
-class PrefixedSubAppResource(PrefixResource):
- def __init__(self, prefix: str, app: "Application") -> None:
- super().__init__(prefix)
- self._app = app
- for resource in app.router.resources():
- resource.add_prefix(prefix)
-
- def add_prefix(self, prefix: str) -> None:
- super().add_prefix(prefix)
- for resource in self._app.router.resources():
- resource.add_prefix(prefix)
-
- def url_for(self, *args: str, **kwargs: str) -> URL:
- raise RuntimeError(".url_for() is not supported " "by sub-application root")
-
- def get_info(self) -> _InfoDict:
- return {"app": self._app, "prefix": self._prefix}
-
- async def resolve(self, request: Request) -> _Resolve:
- if (
- not request.url.raw_path.startswith(self._prefix2)
- and request.url.raw_path != self._prefix
- ):
- return None, set()
- match_info = await self._app.router.resolve(request)
- match_info.add_app(self._app)
- if isinstance(match_info.http_exception, HTTPMethodNotAllowed):
- methods = match_info.http_exception.allowed_methods
- else:
- methods = set()
- return match_info, methods
-
- def __len__(self) -> int:
- return len(self._app.router.routes())
-
- def __iter__(self) -> Iterator[AbstractRoute]:
- return iter(self._app.router.routes())
-
- def __repr__(self) -> str:
- return " {app!r}>".format(
- prefix=self._prefix, app=self._app
- )
-
-
-class AbstractRuleMatching(abc.ABC):
- @abc.abstractmethod # pragma: no branch
- async def match(self, request: Request) -> bool:
- """Return bool if the request satisfies the criteria"""
-
- @abc.abstractmethod # pragma: no branch
- def get_info(self) -> _InfoDict:
- """Return a dict with additional info useful for introspection"""
-
- @property
- @abc.abstractmethod # pragma: no branch
- def canonical(self) -> str:
- """Return a str"""
-
-
-class Domain(AbstractRuleMatching):
- re_part = re.compile(r"(?!-)[a-z\d-]{1,63}(? None:
- super().__init__()
- self._domain = self.validation(domain)
-
- @property
- def canonical(self) -> str:
- return self._domain
-
- def validation(self, domain: str) -> str:
- if not isinstance(domain, str):
- raise TypeError("Domain must be str")
- domain = domain.rstrip(".").lower()
- if not domain:
- raise ValueError("Domain cannot be empty")
- elif "://" in domain:
- raise ValueError("Scheme not supported")
- url = URL("http://" + domain)
- assert url.raw_host is not None
- if not all(self.re_part.fullmatch(x) for x in url.raw_host.split(".")):
- raise ValueError("Domain not valid")
- if url.port == 80:
- return url.raw_host
- return f"{url.raw_host}:{url.port}"
-
- async def match(self, request: Request) -> bool:
- host = request.headers.get(hdrs.HOST)
- if not host:
- return False
- return self.match_domain(host)
-
- def match_domain(self, host: str) -> bool:
- return host.lower() == self._domain
-
- def get_info(self) -> _InfoDict:
- return {"domain": self._domain}
-
-
-class MaskDomain(Domain):
- re_part = re.compile(r"(?!-)[a-z\d\*-]{1,63}(? None:
- super().__init__(domain)
- mask = self._domain.replace(".", r"\.").replace("*", ".*")
- self._mask = re.compile(mask)
-
- @property
- def canonical(self) -> str:
- return self._mask.pattern
-
- def match_domain(self, host: str) -> bool:
- return self._mask.fullmatch(host) is not None
-
-
-class MatchedSubAppResource(PrefixedSubAppResource):
- def __init__(self, rule: AbstractRuleMatching, app: "Application") -> None:
- AbstractResource.__init__(self)
- self._prefix = ""
- self._app = app
- self._rule = rule
-
- @property
- def canonical(self) -> str:
- return self._rule.canonical
-
- def get_info(self) -> _InfoDict:
- return {"app": self._app, "rule": self._rule}
-
- async def resolve(self, request: Request) -> _Resolve:
- if not await self._rule.match(request):
- return None, set()
- match_info = await self._app.router.resolve(request)
- match_info.add_app(self._app)
- if isinstance(match_info.http_exception, HTTPMethodNotAllowed):
- methods = match_info.http_exception.allowed_methods
- else:
- methods = set()
- return match_info, methods
-
- def __repr__(self) -> str:
- return " {app!r}>" "".format(app=self._app)
-
-
-class ResourceRoute(AbstractRoute):
- """A route with resource"""
-
- def __init__(
- self,
- method: str,
- handler: Union[Handler, Type[AbstractView]],
- resource: AbstractResource,
- *,
- expect_handler: Optional[_ExpectHandler] = None,
- ) -> None:
- super().__init__(
- method, handler, expect_handler=expect_handler, resource=resource
- )
-
- def __repr__(self) -> str:
- return " {handler!r}".format(
- method=self.method, resource=self._resource, handler=self.handler
- )
-
- @property
- def name(self) -> Optional[str]:
- if self._resource is None:
- return None
- return self._resource.name
-
- def url_for(self, *args: str, **kwargs: str) -> URL:
- """Construct url for route with additional params."""
- assert self._resource is not None
- return self._resource.url_for(*args, **kwargs)
-
- def get_info(self) -> _InfoDict:
- assert self._resource is not None
- return self._resource.get_info()
-
-
-class SystemRoute(AbstractRoute):
- def __init__(self, http_exception: HTTPException) -> None:
- super().__init__(hdrs.METH_ANY, self._handle)
- self._http_exception = http_exception
-
- def url_for(self, *args: str, **kwargs: str) -> URL:
- raise RuntimeError(".url_for() is not allowed for SystemRoute")
-
- @property
- def name(self) -> Optional[str]:
- return None
-
- def get_info(self) -> _InfoDict:
- return {"http_exception": self._http_exception}
-
- async def _handle(self, request: Request) -> StreamResponse:
- raise self._http_exception
-
- @property
- def status(self) -> int:
- return self._http_exception.status
-
- @property
- def reason(self) -> str:
- return self._http_exception.reason
-
- def __repr__(self) -> str:
- return "".format(self=self)
-
-
-class View(AbstractView):
- async def _iter(self) -> StreamResponse:
- if self.request.method not in hdrs.METH_ALL:
- self._raise_allowed_methods()
- method: Callable[[], Awaitable[StreamResponse]] = getattr(
- self, self.request.method.lower(), None
- )
- if method is None:
- self._raise_allowed_methods()
- resp = await method()
- return resp
-
- def __await__(self) -> Generator[Any, None, StreamResponse]:
- return self._iter().__await__()
-
- def _raise_allowed_methods(self) -> None:
- allowed_methods = {m for m in hdrs.METH_ALL if hasattr(self, m.lower())}
- raise HTTPMethodNotAllowed(self.request.method, allowed_methods)
-
-
-class ResourcesView(Sized, Iterable[AbstractResource], Container[AbstractResource]):
- def __init__(self, resources: List[AbstractResource]) -> None:
- self._resources = resources
-
- def __len__(self) -> int:
- return len(self._resources)
-
- def __iter__(self) -> Iterator[AbstractResource]:
- yield from self._resources
-
- def __contains__(self, resource: object) -> bool:
- return resource in self._resources
-
-
-class RoutesView(Sized, Iterable[AbstractRoute], Container[AbstractRoute]):
- def __init__(self, resources: List[AbstractResource]):
- self._routes: List[AbstractRoute] = []
- for resource in resources:
- for route in resource:
- self._routes.append(route)
-
- def __len__(self) -> int:
- return len(self._routes)
-
- def __iter__(self) -> Iterator[AbstractRoute]:
- yield from self._routes
-
- def __contains__(self, route: object) -> bool:
- return route in self._routes
-
-
-class UrlDispatcher(AbstractRouter, Mapping[str, AbstractResource]):
-
- NAME_SPLIT_RE = re.compile(r"[.:-]")
-
- def __init__(self) -> None:
- super().__init__()
- self._resources: List[AbstractResource] = []
- self._named_resources: Dict[str, AbstractResource] = {}
-
- async def resolve(self, request: Request) -> UrlMappingMatchInfo:
- method = request.method
- allowed_methods: Set[str] = set()
-
- for resource in self._resources:
- match_dict, allowed = await resource.resolve(request)
- if match_dict is not None:
- return match_dict
- else:
- allowed_methods |= allowed
-
- if allowed_methods:
- return MatchInfoError(HTTPMethodNotAllowed(method, allowed_methods))
- else:
- return MatchInfoError(HTTPNotFound())
-
- def __iter__(self) -> Iterator[str]:
- return iter(self._named_resources)
-
- def __len__(self) -> int:
- return len(self._named_resources)
-
- def __contains__(self, resource: object) -> bool:
- return resource in self._named_resources
-
- def __getitem__(self, name: str) -> AbstractResource:
- return self._named_resources[name]
-
- def resources(self) -> ResourcesView:
- return ResourcesView(self._resources)
-
- def routes(self) -> RoutesView:
- return RoutesView(self._resources)
-
- def named_resources(self) -> Mapping[str, AbstractResource]:
- return MappingProxyType(self._named_resources)
-
- def register_resource(self, resource: AbstractResource) -> None:
- assert isinstance(
- resource, AbstractResource
- ), f"Instance of AbstractResource class is required, got {resource!r}"
- if self.frozen:
- raise RuntimeError("Cannot register a resource into frozen router.")
-
- name = resource.name
-
- if name is not None:
- parts = self.NAME_SPLIT_RE.split(name)
- for part in parts:
- if keyword.iskeyword(part):
- raise ValueError(
- f"Incorrect route name {name!r}, "
- "python keywords cannot be used "
- "for route name"
- )
- if not part.isidentifier():
- raise ValueError(
- "Incorrect route name {!r}, "
- "the name should be a sequence of "
- "python identifiers separated "
- "by dash, dot or column".format(name)
- )
- if name in self._named_resources:
- raise ValueError(
- "Duplicate {!r}, "
- "already handled by {!r}".format(name, self._named_resources[name])
- )
- self._named_resources[name] = resource
- self._resources.append(resource)
-
- def add_resource(self, path: str, *, name: Optional[str] = None) -> Resource:
- if path and not path.startswith("/"):
- raise ValueError("path should be started with / or be empty")
- # Reuse last added resource if path and name are the same
- if self._resources:
- resource = self._resources[-1]
- if resource.name == name and resource.raw_match(path):
- return cast(Resource, resource)
- if not ("{" in path or "}" in path or ROUTE_RE.search(path)):
- resource = PlainResource(_requote_path(path), name=name)
- self.register_resource(resource)
- return resource
- resource = DynamicResource(path, name=name)
- self.register_resource(resource)
- return resource
-
- def add_route(
- self,
- method: str,
- path: str,
- handler: Union[Handler, Type[AbstractView]],
- *,
- name: Optional[str] = None,
- expect_handler: Optional[_ExpectHandler] = None,
- ) -> AbstractRoute:
- resource = self.add_resource(path, name=name)
- return resource.add_route(method, handler, expect_handler=expect_handler)
-
- def add_static(
- self,
- prefix: str,
- path: PathLike,
- *,
- name: Optional[str] = None,
- expect_handler: Optional[_ExpectHandler] = None,
- chunk_size: int = 256 * 1024,
- show_index: bool = False,
- follow_symlinks: bool = False,
- append_version: bool = False,
- ) -> AbstractResource:
- """Add static files view.
-
- prefix - url prefix
- path - folder with files
-
- """
- assert prefix.startswith("/")
- if prefix.endswith("/"):
- prefix = prefix[:-1]
- resource = StaticResource(
- prefix,
- path,
- name=name,
- expect_handler=expect_handler,
- chunk_size=chunk_size,
- show_index=show_index,
- follow_symlinks=follow_symlinks,
- append_version=append_version,
- )
- self.register_resource(resource)
- return resource
-
- def add_head(self, path: str, handler: Handler, **kwargs: Any) -> AbstractRoute:
- """Shortcut for add_route with method HEAD."""
- return self.add_route(hdrs.METH_HEAD, path, handler, **kwargs)
-
- def add_options(self, path: str, handler: Handler, **kwargs: Any) -> AbstractRoute:
- """Shortcut for add_route with method OPTIONS."""
- return self.add_route(hdrs.METH_OPTIONS, path, handler, **kwargs)
-
- def add_get(
- self,
- path: str,
- handler: Handler,
- *,
- name: Optional[str] = None,
- allow_head: bool = True,
- **kwargs: Any,
- ) -> AbstractRoute:
- """Shortcut for add_route with method GET.
-
- If allow_head is true, another
- route is added allowing head requests to the same endpoint.
- """
- resource = self.add_resource(path, name=name)
- if allow_head:
- resource.add_route(hdrs.METH_HEAD, handler, **kwargs)
- return resource.add_route(hdrs.METH_GET, handler, **kwargs)
-
- def add_post(self, path: str, handler: Handler, **kwargs: Any) -> AbstractRoute:
- """Shortcut for add_route with method POST."""
- return self.add_route(hdrs.METH_POST, path, handler, **kwargs)
-
- def add_put(self, path: str, handler: Handler, **kwargs: Any) -> AbstractRoute:
- """Shortcut for add_route with method PUT."""
- return self.add_route(hdrs.METH_PUT, path, handler, **kwargs)
-
- def add_patch(self, path: str, handler: Handler, **kwargs: Any) -> AbstractRoute:
- """Shortcut for add_route with method PATCH."""
- return self.add_route(hdrs.METH_PATCH, path, handler, **kwargs)
-
- def add_delete(self, path: str, handler: Handler, **kwargs: Any) -> AbstractRoute:
- """Shortcut for add_route with method DELETE."""
- return self.add_route(hdrs.METH_DELETE, path, handler, **kwargs)
-
- def add_view(
- self, path: str, handler: Type[AbstractView], **kwargs: Any
- ) -> AbstractRoute:
- """Shortcut for add_route with ANY methods for a class-based view."""
- return self.add_route(hdrs.METH_ANY, path, handler, **kwargs)
-
- def freeze(self) -> None:
- super().freeze()
- for resource in self._resources:
- resource.freeze()
-
- def add_routes(self, routes: Iterable[AbstractRouteDef]) -> List[AbstractRoute]:
- """Append routes to route table.
-
- Parameter should be a sequence of RouteDef objects.
-
- Returns a list of registered AbstractRoute instances.
- """
- registered_routes = []
- for route_def in routes:
- registered_routes.extend(route_def.register(self))
- return registered_routes
-
-
-def _quote_path(value: str) -> str:
- if YARL_VERSION < (1, 6):
- value = value.replace("%", "%25")
- return URL.build(path=value, encoded=False).raw_path
-
-
-def _unquote_path(value: str) -> str:
- return URL.build(path=value, encoded=True).path
-
-
-def _requote_path(value: str) -> str:
- # Quote non-ascii characters and other characters which must be quoted,
- # but preserve existing %-sequences.
- result = _quote_path(value)
- if "%" in value:
- result = result.replace("%25", "%")
- return result
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/pens/quartzPen.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/pens/quartzPen.py
deleted file mode 100644
index 6e1228d6f2b8bbc78cf52864ccaf3b249a654749..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/pens/quartzPen.py
+++ /dev/null
@@ -1,44 +0,0 @@
-from fontTools.pens.basePen import BasePen
-
-from Quartz.CoreGraphics import CGPathCreateMutable, CGPathMoveToPoint
-from Quartz.CoreGraphics import CGPathAddLineToPoint, CGPathAddCurveToPoint
-from Quartz.CoreGraphics import CGPathAddQuadCurveToPoint, CGPathCloseSubpath
-
-
-__all__ = ["QuartzPen"]
-
-
-class QuartzPen(BasePen):
-
- """A pen that creates a CGPath
-
- Parameters
- - path: an optional CGPath to add to
- - xform: an optional CGAffineTransform to apply to the path
- """
-
- def __init__(self, glyphSet, path=None, xform=None):
- BasePen.__init__(self, glyphSet)
- if path is None:
- path = CGPathCreateMutable()
- self.path = path
- self.xform = xform
-
- def _moveTo(self, pt):
- x, y = pt
- CGPathMoveToPoint(self.path, self.xform, x, y)
-
- def _lineTo(self, pt):
- x, y = pt
- CGPathAddLineToPoint(self.path, self.xform, x, y)
-
- def _curveToOne(self, p1, p2, p3):
- (x1, y1), (x2, y2), (x3, y3) = p1, p2, p3
- CGPathAddCurveToPoint(self.path, self.xform, x1, y1, x2, y2, x3, y3)
-
- def _qCurveToOne(self, p1, p2):
- (x1, y1), (x2, y2) = p1, p2
- CGPathAddQuadCurveToPoint(self.path, self.xform, x1, y1, x2, y2)
-
- def _closePath(self):
- CGPathCloseSubpath(self.path)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fsspec/gui.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fsspec/gui.py
deleted file mode 100644
index 78eae1cf9cc798bdc982201a3a3e9e2978cf4a2b..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fsspec/gui.py
+++ /dev/null
@@ -1,408 +0,0 @@
-import ast
-import contextlib
-import logging
-import os
-import re
-
-import panel as pn
-
-from .core import OpenFile, get_filesystem_class, split_protocol
-from .registry import known_implementations
-
-pn.extension()
-logger = logging.getLogger("fsspec.gui")
-
-
-class SigSlot(object):
- """Signal-slot mixin, for Panel event passing
-
- Include this class in a widget manager's superclasses to be able to
- register events and callbacks on Panel widgets managed by that class.
-
- The method ``_register`` should be called as widgets are added, and external
- code should call ``connect`` to associate callbacks.
-
- By default, all signals emit a DEBUG logging statement.
- """
-
- signals = [] # names of signals that this class may emit
- # each of which must be set by _register for any new instance
- slots = [] # names of actions that this class may respond to
-
- # each of which must be a method name
-
- def __init__(self):
- self._ignoring_events = False
- self._sigs = {}
- self._map = {}
- self._setup()
-
- def _setup(self):
- """Create GUI elements and register signals"""
- self.panel = pn.pane.PaneBase()
- # no signals to set up in the base class
-
- def _register(
- self, widget, name, thing="value", log_level=logging.DEBUG, auto=False
- ):
- """Watch the given attribute of a widget and assign it a named event
-
- This is normally called at the time a widget is instantiated, in the
- class which owns it.
-
- Parameters
- ----------
- widget : pn.layout.Panel or None
- Widget to watch. If None, an anonymous signal not associated with
- any widget.
- name : str
- Name of this event
- thing : str
- Attribute of the given widget to watch
- log_level : int
- When the signal is triggered, a logging event of the given level
- will be fired in the dfviz logger.
- auto : bool
- If True, automatically connects with a method in this class of the
- same name.
- """
- if name not in self.signals:
- raise ValueError("Attempt to assign an undeclared signal: %s" % name)
- self._sigs[name] = {
- "widget": widget,
- "callbacks": [],
- "thing": thing,
- "log": log_level,
- }
- wn = "-".join(
- [
- getattr(widget, "name", str(widget)) if widget is not None else "none",
- thing,
- ]
- )
- self._map[wn] = name
- if widget is not None:
- widget.param.watch(self._signal, thing, onlychanged=True)
- if auto and hasattr(self, name):
- self.connect(name, getattr(self, name))
-
- def _repr_mimebundle_(self, *args, **kwargs):
- """Display in a notebook or a server"""
- try:
- return self.panel._repr_mimebundle_(*args, **kwargs)
- except (ValueError, AttributeError):
- raise NotImplementedError("Panel does not seem to be set " "up properly")
-
- def connect(self, signal, slot):
- """Associate call back with given event
-
- The callback must be a function which takes the "new" value of the
- watched attribute as the only parameter. If the callback return False,
- this cancels any further processing of the given event.
-
- Alternatively, the callback can be a string, in which case it means
- emitting the correspondingly-named event (i.e., connect to self)
- """
- self._sigs[signal]["callbacks"].append(slot)
-
- def _signal(self, event):
- """This is called by a an action on a widget
-
- Within an self.ignore_events context, nothing happens.
-
- Tests can execute this method by directly changing the values of
- widget components.
- """
- if not self._ignoring_events:
- wn = "-".join([event.obj.name, event.name])
- if wn in self._map and self._map[wn] in self._sigs:
- self._emit(self._map[wn], event.new)
-
- @contextlib.contextmanager
- def ignore_events(self):
- """Temporarily turn off events processing in this instance
-
- (does not propagate to children)
- """
- self._ignoring_events = True
- try:
- yield
- finally:
- self._ignoring_events = False
-
- def _emit(self, sig, value=None):
- """An event happened, call its callbacks
-
- This method can be used in tests to simulate message passing without
- directly changing visual elements.
-
- Calling of callbacks will halt whenever one returns False.
- """
- logger.log(self._sigs[sig]["log"], "{}: {}".format(sig, value))
- for callback in self._sigs[sig]["callbacks"]:
- if isinstance(callback, str):
- self._emit(callback)
- else:
- try:
- # running callbacks should not break the interface
- ret = callback(value)
- if ret is False:
- break
- except Exception as e:
- logger.exception(
- "Exception (%s) while executing callback for signal: %s"
- "" % (e, sig)
- )
-
- def show(self, threads=False):
- """Open a new browser tab and display this instance's interface"""
- self.panel.show(threads=threads, verbose=False)
- return self
-
-
-class SingleSelect(SigSlot):
- """A multiselect which only allows you to select one item for an event"""
-
- signals = ["_selected", "selected"] # the first is internal
- slots = ["set_options", "set_selection", "add", "clear", "select"]
-
- def __init__(self, **kwargs):
- self.kwargs = kwargs
- super().__init__()
-
- def _setup(self):
- self.panel = pn.widgets.MultiSelect(**self.kwargs)
- self._register(self.panel, "_selected", "value")
- self._register(None, "selected")
- self.connect("_selected", self.select_one)
-
- def _signal(self, *args, **kwargs):
- super()._signal(*args, **kwargs)
-
- def select_one(self, *_):
- with self.ignore_events():
- val = [self.panel.value[-1]] if self.panel.value else []
- self.panel.value = val
- self._emit("selected", self.panel.value)
-
- def set_options(self, options):
- self.panel.options = options
-
- def clear(self):
- self.panel.options = []
-
- @property
- def value(self):
- return self.panel.value
-
- def set_selection(self, selection):
- self.panel.value = [selection]
-
-
-class FileSelector(SigSlot):
- """Panel-based graphical file selector widget
-
- Instances of this widget are interactive and can be displayed in jupyter by having
- them as the output of a cell, or in a separate browser tab using ``.show()``.
- """
-
- signals = [
- "protocol_changed",
- "selection_changed",
- "directory_entered",
- "home_clicked",
- "up_clicked",
- "go_clicked",
- "filters_changed",
- ]
- slots = ["set_filters", "go_home"]
-
- def __init__(self, url=None, filters=None, ignore=None, kwargs=None):
- """
-
- Parameters
- ----------
- url : str (optional)
- Initial value of the URL to populate the dialog; should include protocol
- filters : list(str) (optional)
- File endings to include in the listings. If not included, all files are
- allowed. Does not affect directories.
- If given, the endings will appear as checkboxes in the interface
- ignore : list(str) (optional)
- Regex(s) of file basename patterns to ignore, e.g., "\\." for typical
- hidden files on posix
- kwargs : dict (optional)
- To pass to file system instance
- """
- if url:
- self.init_protocol, url = split_protocol(url)
- else:
- self.init_protocol, url = "file", os.getcwd()
- self.init_url = url
- self.init_kwargs = kwargs or "{}"
- self.filters = filters
- self.ignore = [re.compile(i) for i in ignore or []]
- self._fs = None
- super().__init__()
-
- def _setup(self):
- self.url = pn.widgets.TextInput(
- name="url",
- value=self.init_url,
- align="end",
- sizing_mode="stretch_width",
- width_policy="max",
- )
- self.protocol = pn.widgets.Select(
- options=list(sorted(known_implementations)),
- value=self.init_protocol,
- name="protocol",
- align="center",
- )
- self.kwargs = pn.widgets.TextInput(name="kwargs", value="{}", align="center")
- self.go = pn.widgets.Button(name="⇨", align="end", width=45)
- self.main = SingleSelect(size=10)
- self.home = pn.widgets.Button(name="🏠", width=40, height=30, align="end")
- self.up = pn.widgets.Button(name="‹", width=30, height=30, align="end")
-
- self._register(self.protocol, "protocol_changed", auto=True)
- self._register(self.go, "go_clicked", "clicks", auto=True)
- self._register(self.up, "up_clicked", "clicks", auto=True)
- self._register(self.home, "home_clicked", "clicks", auto=True)
- self._register(None, "selection_changed")
- self.main.connect("selected", self.selection_changed)
- self._register(None, "directory_entered")
- self.prev_protocol = self.protocol.value
- self.prev_kwargs = self.storage_options
-
- self.filter_sel = pn.widgets.CheckBoxGroup(
- value=[], options=[], inline=False, align="end", width_policy="min"
- )
- self._register(self.filter_sel, "filters_changed", auto=True)
-
- self.panel = pn.Column(
- pn.Row(self.protocol, self.kwargs),
- pn.Row(self.home, self.up, self.url, self.go, self.filter_sel),
- self.main.panel,
- )
- self.set_filters(self.filters)
- self.go_clicked()
-
- def set_filters(self, filters=None):
- self.filters = filters
- if filters:
- self.filter_sel.options = filters
- self.filter_sel.value = filters
- else:
- self.filter_sel.options = []
- self.filter_sel.value = []
-
- @property
- def storage_options(self):
- """Value of the kwargs box as a dictionary"""
- return ast.literal_eval(self.kwargs.value) or {}
-
- @property
- def fs(self):
- """Current filesystem instance"""
- if self._fs is None:
- cls = get_filesystem_class(self.protocol.value)
- self._fs = cls(**self.storage_options)
- return self._fs
-
- @property
- def urlpath(self):
- """URL of currently selected item"""
- return (
- (self.protocol.value + "://" + self.main.value[0])
- if self.main.value
- else None
- )
-
- def open_file(self, mode="rb", compression=None, encoding=None):
- """Create OpenFile instance for the currently selected item
-
- For example, in a notebook you might do something like
-
- .. code-block::
-
- [ ]: sel = FileSelector(); sel
-
- # user selects their file
-
- [ ]: with sel.open_file('rb') as f:
- ... out = f.read()
-
- Parameters
- ----------
- mode: str (optional)
- Open mode for the file.
- compression: str (optional)
- The interact with the file as compressed. Set to 'infer' to guess
- compression from the file ending
- encoding: str (optional)
- If using text mode, use this encoding; defaults to UTF8.
- """
- if self.urlpath is None:
- raise ValueError("No file selected")
- return OpenFile(self.fs, self.urlpath, mode, compression, encoding)
-
- def filters_changed(self, values):
- self.filters = values
- self.go_clicked()
-
- def selection_changed(self, *_):
- if self.urlpath is None:
- return
- if self.fs.isdir(self.urlpath):
- self.url.value = self.fs._strip_protocol(self.urlpath)
- self.go_clicked()
-
- def go_clicked(self, *_):
- if (
- self.prev_protocol != self.protocol.value
- or self.prev_kwargs != self.storage_options
- ):
- self._fs = None # causes fs to be recreated
- self.prev_protocol = self.protocol.value
- self.prev_kwargs = self.storage_options
- listing = sorted(
- self.fs.ls(self.url.value, detail=True), key=lambda x: x["name"]
- )
- listing = [
- l
- for l in listing
- if not any(i.match(l["name"].rsplit("/", 1)[-1]) for i in self.ignore)
- ]
- folders = {
- "📁 " + o["name"].rsplit("/", 1)[-1]: o["name"]
- for o in listing
- if o["type"] == "directory"
- }
- files = {
- "📄 " + o["name"].rsplit("/", 1)[-1]: o["name"]
- for o in listing
- if o["type"] == "file"
- }
- if self.filters:
- files = {
- k: v
- for k, v in files.items()
- if any(v.endswith(ext) for ext in self.filters)
- }
- self.main.set_options(dict(**folders, **files))
-
- def protocol_changed(self, *_):
- self._fs = None
- self.main.options = []
- self.url.value = ""
-
- def home_clicked(self, *_):
- self.protocol.value = self.init_protocol
- self.kwargs.value = self.init_kwargs
- self.url.value = self.init_url
- self.go_clicked()
-
- def up_clicked(self, *_):
- self.url.value = self.fs._parent(self.url.value)
- self.go_clicked()
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/httpcore/_async/http11.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/httpcore/_async/http11.py
deleted file mode 100644
index 99b356f494bf7760de4c0d950cd6953310466088..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/httpcore/_async/http11.py
+++ /dev/null
@@ -1,329 +0,0 @@
-import enum
-import logging
-import time
-from types import TracebackType
-from typing import (
- AsyncIterable,
- AsyncIterator,
- List,
- Optional,
- Tuple,
- Type,
- Union,
- cast,
-)
-
-import h11
-
-from .._exceptions import (
- ConnectionNotAvailable,
- LocalProtocolError,
- RemoteProtocolError,
- map_exceptions,
-)
-from .._models import Origin, Request, Response
-from .._synchronization import AsyncLock
-from .._trace import Trace
-from ..backends.base import AsyncNetworkStream
-from .interfaces import AsyncConnectionInterface
-
-logger = logging.getLogger("httpcore.http11")
-
-
-# A subset of `h11.Event` types supported by `_send_event`
-H11SendEvent = Union[
- h11.Request,
- h11.Data,
- h11.EndOfMessage,
-]
-
-
-class HTTPConnectionState(enum.IntEnum):
- NEW = 0
- ACTIVE = 1
- IDLE = 2
- CLOSED = 3
-
-
-class AsyncHTTP11Connection(AsyncConnectionInterface):
- READ_NUM_BYTES = 64 * 1024
- MAX_INCOMPLETE_EVENT_SIZE = 100 * 1024
-
- def __init__(
- self,
- origin: Origin,
- stream: AsyncNetworkStream,
- keepalive_expiry: Optional[float] = None,
- ) -> None:
- self._origin = origin
- self._network_stream = stream
- self._keepalive_expiry: Optional[float] = keepalive_expiry
- self._expire_at: Optional[float] = None
- self._state = HTTPConnectionState.NEW
- self._state_lock = AsyncLock()
- self._request_count = 0
- self._h11_state = h11.Connection(
- our_role=h11.CLIENT,
- max_incomplete_event_size=self.MAX_INCOMPLETE_EVENT_SIZE,
- )
-
- async def handle_async_request(self, request: Request) -> Response:
- if not self.can_handle_request(request.url.origin):
- raise RuntimeError(
- f"Attempted to send request to {request.url.origin} on connection "
- f"to {self._origin}"
- )
-
- async with self._state_lock:
- if self._state in (HTTPConnectionState.NEW, HTTPConnectionState.IDLE):
- self._request_count += 1
- self._state = HTTPConnectionState.ACTIVE
- self._expire_at = None
- else:
- raise ConnectionNotAvailable()
-
- try:
- kwargs = {"request": request}
- async with Trace("send_request_headers", logger, request, kwargs) as trace:
- await self._send_request_headers(**kwargs)
- async with Trace("send_request_body", logger, request, kwargs) as trace:
- await self._send_request_body(**kwargs)
- async with Trace(
- "receive_response_headers", logger, request, kwargs
- ) as trace:
- (
- http_version,
- status,
- reason_phrase,
- headers,
- ) = await self._receive_response_headers(**kwargs)
- trace.return_value = (
- http_version,
- status,
- reason_phrase,
- headers,
- )
-
- return Response(
- status=status,
- headers=headers,
- content=HTTP11ConnectionByteStream(self, request),
- extensions={
- "http_version": http_version,
- "reason_phrase": reason_phrase,
- "network_stream": self._network_stream,
- },
- )
- except BaseException as exc:
- async with Trace("response_closed", logger, request) as trace:
- await self._response_closed()
- raise exc
-
- # Sending the request...
-
- async def _send_request_headers(self, request: Request) -> None:
- timeouts = request.extensions.get("timeout", {})
- timeout = timeouts.get("write", None)
-
- with map_exceptions({h11.LocalProtocolError: LocalProtocolError}):
- event = h11.Request(
- method=request.method,
- target=request.url.target,
- headers=request.headers,
- )
- await self._send_event(event, timeout=timeout)
-
- async def _send_request_body(self, request: Request) -> None:
- timeouts = request.extensions.get("timeout", {})
- timeout = timeouts.get("write", None)
-
- assert isinstance(request.stream, AsyncIterable)
- async for chunk in request.stream:
- event = h11.Data(data=chunk)
- await self._send_event(event, timeout=timeout)
-
- await self._send_event(h11.EndOfMessage(), timeout=timeout)
-
- async def _send_event(
- self, event: h11.Event, timeout: Optional[float] = None
- ) -> None:
- bytes_to_send = self._h11_state.send(event)
- if bytes_to_send is not None:
- await self._network_stream.write(bytes_to_send, timeout=timeout)
-
- # Receiving the response...
-
- async def _receive_response_headers(
- self, request: Request
- ) -> Tuple[bytes, int, bytes, List[Tuple[bytes, bytes]]]:
- timeouts = request.extensions.get("timeout", {})
- timeout = timeouts.get("read", None)
-
- while True:
- event = await self._receive_event(timeout=timeout)
- if isinstance(event, h11.Response):
- break
- if (
- isinstance(event, h11.InformationalResponse)
- and event.status_code == 101
- ):
- break
-
- http_version = b"HTTP/" + event.http_version
-
- # h11 version 0.11+ supports a `raw_items` interface to get the
- # raw header casing, rather than the enforced lowercase headers.
- headers = event.headers.raw_items()
-
- return http_version, event.status_code, event.reason, headers
-
- async def _receive_response_body(self, request: Request) -> AsyncIterator[bytes]:
- timeouts = request.extensions.get("timeout", {})
- timeout = timeouts.get("read", None)
-
- while True:
- event = await self._receive_event(timeout=timeout)
- if isinstance(event, h11.Data):
- yield bytes(event.data)
- elif isinstance(event, (h11.EndOfMessage, h11.PAUSED)):
- break
-
- async def _receive_event(
- self, timeout: Optional[float] = None
- ) -> Union[h11.Event, Type[h11.PAUSED]]:
- while True:
- with map_exceptions({h11.RemoteProtocolError: RemoteProtocolError}):
- event = self._h11_state.next_event()
-
- if event is h11.NEED_DATA:
- data = await self._network_stream.read(
- self.READ_NUM_BYTES, timeout=timeout
- )
-
- # If we feed this case through h11 we'll raise an exception like:
- #
- # httpcore.RemoteProtocolError: can't handle event type
- # ConnectionClosed when role=SERVER and state=SEND_RESPONSE
- #
- # Which is accurate, but not very informative from an end-user
- # perspective. Instead we handle this case distinctly and treat
- # it as a ConnectError.
- if data == b"" and self._h11_state.their_state == h11.SEND_RESPONSE:
- msg = "Server disconnected without sending a response."
- raise RemoteProtocolError(msg)
-
- self._h11_state.receive_data(data)
- else:
- # mypy fails to narrow the type in the above if statement above
- return cast(Union[h11.Event, Type[h11.PAUSED]], event)
-
- async def _response_closed(self) -> None:
- async with self._state_lock:
- if (
- self._h11_state.our_state is h11.DONE
- and self._h11_state.their_state is h11.DONE
- ):
- self._state = HTTPConnectionState.IDLE
- self._h11_state.start_next_cycle()
- if self._keepalive_expiry is not None:
- now = time.monotonic()
- self._expire_at = now + self._keepalive_expiry
- else:
- await self.aclose()
-
- # Once the connection is no longer required...
-
- async def aclose(self) -> None:
- # Note that this method unilaterally closes the connection, and does
- # not have any kind of locking in place around it.
- self._state = HTTPConnectionState.CLOSED
- await self._network_stream.aclose()
-
- # The AsyncConnectionInterface methods provide information about the state of
- # the connection, allowing for a connection pooling implementation to
- # determine when to reuse and when to close the connection...
-
- def can_handle_request(self, origin: Origin) -> bool:
- return origin == self._origin
-
- def is_available(self) -> bool:
- # Note that HTTP/1.1 connections in the "NEW" state are not treated as
- # being "available". The control flow which created the connection will
- # be able to send an outgoing request, but the connection will not be
- # acquired from the connection pool for any other request.
- return self._state == HTTPConnectionState.IDLE
-
- def has_expired(self) -> bool:
- now = time.monotonic()
- keepalive_expired = self._expire_at is not None and now > self._expire_at
-
- # If the HTTP connection is idle but the socket is readable, then the
- # only valid state is that the socket is about to return b"", indicating
- # a server-initiated disconnect.
- server_disconnected = (
- self._state == HTTPConnectionState.IDLE
- and self._network_stream.get_extra_info("is_readable")
- )
-
- return keepalive_expired or server_disconnected
-
- def is_idle(self) -> bool:
- return self._state == HTTPConnectionState.IDLE
-
- def is_closed(self) -> bool:
- return self._state == HTTPConnectionState.CLOSED
-
- def info(self) -> str:
- origin = str(self._origin)
- return (
- f"{origin!r}, HTTP/1.1, {self._state.name}, "
- f"Request Count: {self._request_count}"
- )
-
- def __repr__(self) -> str:
- class_name = self.__class__.__name__
- origin = str(self._origin)
- return (
- f"<{class_name} [{origin!r}, {self._state.name}, "
- f"Request Count: {self._request_count}]>"
- )
-
- # These context managers are not used in the standard flow, but are
- # useful for testing or working with connection instances directly.
-
- async def __aenter__(self) -> "AsyncHTTP11Connection":
- return self
-
- async def __aexit__(
- self,
- exc_type: Optional[Type[BaseException]] = None,
- exc_value: Optional[BaseException] = None,
- traceback: Optional[TracebackType] = None,
- ) -> None:
- await self.aclose()
-
-
-class HTTP11ConnectionByteStream:
- def __init__(self, connection: AsyncHTTP11Connection, request: Request) -> None:
- self._connection = connection
- self._request = request
- self._closed = False
-
- async def __aiter__(self) -> AsyncIterator[bytes]:
- kwargs = {"request": self._request}
- try:
- async with Trace("receive_response_body", logger, self._request, kwargs):
- async for chunk in self._connection._receive_response_body(**kwargs):
- yield chunk
- except BaseException as exc:
- # If we get an exception while streaming the response,
- # we want to close the response (and possibly the connection)
- # before raising that exception.
- await self.aclose()
- raise exc
-
- async def aclose(self) -> None:
- if not self._closed:
- self._closed = True
- async with Trace("response_closed", logger, self._request):
- await self._connection._response_closed()
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jsonschema/tests/test_types.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jsonschema/tests/test_types.py
deleted file mode 100644
index 3eacc7235a7980234bae72c5e340688005ba626b..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/jsonschema/tests/test_types.py
+++ /dev/null
@@ -1,221 +0,0 @@
-"""
-Tests for the `TypeChecker`-based type interface.
-
-The actual correctness of the type checking is handled in
-`test_jsonschema_test_suite`; these tests check that TypeChecker
-functions correctly at a more granular level.
-"""
-from collections import namedtuple
-from unittest import TestCase
-
-from jsonschema import ValidationError, _validators
-from jsonschema._types import TypeChecker
-from jsonschema.exceptions import UndefinedTypeCheck, UnknownType
-from jsonschema.validators import Draft202012Validator, extend
-
-
-def equals_2(checker, instance):
- return instance == 2
-
-
-def is_namedtuple(instance):
- return isinstance(instance, tuple) and getattr(instance, "_fields", None)
-
-
-def is_object_or_named_tuple(checker, instance):
- if Draft202012Validator.TYPE_CHECKER.is_type(instance, "object"):
- return True
- return is_namedtuple(instance)
-
-
-class TestTypeChecker(TestCase):
- def test_is_type(self):
- checker = TypeChecker({"two": equals_2})
- self.assertEqual(
- (
- checker.is_type(instance=2, type="two"),
- checker.is_type(instance="bar", type="two"),
- ),
- (True, False),
- )
-
- def test_is_unknown_type(self):
- with self.assertRaises(UndefinedTypeCheck) as e:
- TypeChecker().is_type(4, "foobar")
- self.assertIn(
- "'foobar' is unknown to this type checker",
- str(e.exception),
- )
- self.assertTrue(
- e.exception.__suppress_context__,
- msg="Expected the internal KeyError to be hidden.",
- )
-
- def test_checks_can_be_added_at_init(self):
- checker = TypeChecker({"two": equals_2})
- self.assertEqual(checker, TypeChecker().redefine("two", equals_2))
-
- def test_redefine_existing_type(self):
- self.assertEqual(
- TypeChecker().redefine("two", object()).redefine("two", equals_2),
- TypeChecker().redefine("two", equals_2),
- )
-
- def test_remove(self):
- self.assertEqual(
- TypeChecker({"two": equals_2}).remove("two"),
- TypeChecker(),
- )
-
- def test_remove_unknown_type(self):
- with self.assertRaises(UndefinedTypeCheck) as context:
- TypeChecker().remove("foobar")
- self.assertIn("foobar", str(context.exception))
-
- def test_redefine_many(self):
- self.assertEqual(
- TypeChecker().redefine_many({"foo": int, "bar": str}),
- TypeChecker().redefine("foo", int).redefine("bar", str),
- )
-
- def test_remove_multiple(self):
- self.assertEqual(
- TypeChecker({"foo": int, "bar": str}).remove("foo", "bar"),
- TypeChecker(),
- )
-
- def test_type_check_can_raise_key_error(self):
- """
- Make sure no one writes:
-
- try:
- self._type_checkers[type](...)
- except KeyError:
-
- ignoring the fact that the function itself can raise that.
- """
-
- error = KeyError("Stuff")
-
- def raises_keyerror(checker, instance):
- raise error
-
- with self.assertRaises(KeyError) as context:
- TypeChecker({"foo": raises_keyerror}).is_type(4, "foo")
-
- self.assertIs(context.exception, error)
-
- def test_repr(self):
- checker = TypeChecker({"foo": is_namedtuple, "bar": is_namedtuple})
- self.assertEqual(repr(checker), "")
-
-
-class TestCustomTypes(TestCase):
- def test_simple_type_can_be_extended(self):
- def int_or_str_int(checker, instance):
- if not isinstance(instance, (int, str)):
- return False
- try:
- int(instance)
- except ValueError:
- return False
- return True
-
- CustomValidator = extend(
- Draft202012Validator,
- type_checker=Draft202012Validator.TYPE_CHECKER.redefine(
- "integer", int_or_str_int,
- ),
- )
- validator = CustomValidator({"type": "integer"})
-
- validator.validate(4)
- validator.validate("4")
-
- with self.assertRaises(ValidationError):
- validator.validate(4.4)
-
- with self.assertRaises(ValidationError):
- validator.validate("foo")
-
- def test_object_can_be_extended(self):
- schema = {"type": "object"}
-
- Point = namedtuple("Point", ["x", "y"])
-
- type_checker = Draft202012Validator.TYPE_CHECKER.redefine(
- "object", is_object_or_named_tuple,
- )
-
- CustomValidator = extend(
- Draft202012Validator,
- type_checker=type_checker,
- )
- validator = CustomValidator(schema)
-
- validator.validate(Point(x=4, y=5))
-
- def test_object_extensions_require_custom_validators(self):
- schema = {"type": "object", "required": ["x"]}
-
- type_checker = Draft202012Validator.TYPE_CHECKER.redefine(
- "object", is_object_or_named_tuple,
- )
-
- CustomValidator = extend(
- Draft202012Validator,
- type_checker=type_checker,
- )
- validator = CustomValidator(schema)
-
- Point = namedtuple("Point", ["x", "y"])
- # Cannot handle required
- with self.assertRaises(ValidationError):
- validator.validate(Point(x=4, y=5))
-
- def test_object_extensions_can_handle_custom_validators(self):
- schema = {
- "type": "object",
- "required": ["x"],
- "properties": {"x": {"type": "integer"}},
- }
-
- type_checker = Draft202012Validator.TYPE_CHECKER.redefine(
- "object", is_object_or_named_tuple,
- )
-
- def coerce_named_tuple(fn):
- def coerced(validator, value, instance, schema):
- if is_namedtuple(instance):
- instance = instance._asdict()
- return fn(validator, value, instance, schema)
- return coerced
-
- required = coerce_named_tuple(_validators.required)
- properties = coerce_named_tuple(_validators.properties)
-
- CustomValidator = extend(
- Draft202012Validator,
- type_checker=type_checker,
- validators={"required": required, "properties": properties},
- )
-
- validator = CustomValidator(schema)
-
- Point = namedtuple("Point", ["x", "y"])
- # Can now process required and properties
- validator.validate(Point(x=4, y=5))
-
- with self.assertRaises(ValidationError):
- validator.validate(Point(x="not an integer", y=5))
-
- # As well as still handle objects.
- validator.validate({"x": 4, "y": 5})
-
- with self.assertRaises(ValidationError):
- validator.validate({"x": "not an integer", "y": 5})
-
- def test_unknown_type(self):
- with self.assertRaises(UnknownType) as e:
- Draft202012Validator({}).is_type(12, "some unknown type")
- self.assertIn("'some unknown type'", str(e.exception))
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_gtk4cairo.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_gtk4cairo.py
deleted file mode 100644
index 83cbd081c26d9de8c284c2e89cd3bd751e17d4ed..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_gtk4cairo.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from contextlib import nullcontext
-
-from .backend_cairo import ( # noqa
- FigureCanvasCairo, _RendererGTKCairo as RendererGTK4Cairo)
-from .backend_gtk4 import Gtk, FigureCanvasGTK4, _BackendGTK4
-
-
-class FigureCanvasGTK4Cairo(FigureCanvasCairo, FigureCanvasGTK4):
- _context_is_scaled = True
-
- def on_draw_event(self, widget, ctx):
- with (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar
- else nullcontext()):
- self._renderer.set_context(ctx)
- scale = self.device_pixel_ratio
- # Scale physical drawing to logical size.
- ctx.scale(1 / scale, 1 / scale)
- allocation = self.get_allocation()
- Gtk.render_background(
- self.get_style_context(), ctx,
- allocation.x, allocation.y,
- allocation.width, allocation.height)
- self._renderer.dpi = self.figure.dpi
- self.figure.draw(self._renderer)
-
-
-@_BackendGTK4.export
-class _BackendGTK4Cairo(_BackendGTK4):
- FigureCanvas = FigureCanvasGTK4Cairo
diff --git a/spaces/leafShen/CodeFormer/CodeFormer/scripts/download_pretrained_models.py b/spaces/leafShen/CodeFormer/CodeFormer/scripts/download_pretrained_models.py
deleted file mode 100644
index daa6e8ca14ea91c89a318e85d9f182eb7d1bf025..0000000000000000000000000000000000000000
--- a/spaces/leafShen/CodeFormer/CodeFormer/scripts/download_pretrained_models.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import argparse
-import os
-from os import path as osp
-
-from basicsr.utils.download_util import load_file_from_url
-
-
-def download_pretrained_models(method, file_urls):
- save_path_root = f'./weights/{method}'
- os.makedirs(save_path_root, exist_ok=True)
-
- for file_name, file_url in file_urls.items():
- save_path = load_file_from_url(url=file_url, model_dir=save_path_root, progress=True, file_name=file_name)
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
-
- parser.add_argument(
- 'method',
- type=str,
- help=("Options: 'CodeFormer' 'facelib'. Set to 'all' to download all the models."))
- args = parser.parse_args()
-
- file_urls = {
- 'CodeFormer': {
- 'codeformer.pth': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
- },
- 'facelib': {
- # 'yolov5l-face.pth': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5l-face.pth',
- 'detection_Resnet50_Final.pth': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/detection_Resnet50_Final.pth',
- 'parsing_parsenet.pth': 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/parsing_parsenet.pth'
- }
- }
-
- if args.method == 'all':
- for method in file_urls.keys():
- download_pretrained_models(method, file_urls[method])
- else:
- download_pretrained_models(args.method, file_urls[args.method])
\ No newline at end of file
diff --git a/spaces/leilevy/bingo/src/components/chat-attachments.tsx b/spaces/leilevy/bingo/src/components/chat-attachments.tsx
deleted file mode 100644
index ef43d4e262935d263b6099138c56f7daade5299d..0000000000000000000000000000000000000000
--- a/spaces/leilevy/bingo/src/components/chat-attachments.tsx
+++ /dev/null
@@ -1,37 +0,0 @@
-import Image from 'next/image'
-import ClearIcon from '@/assets/images/clear.svg'
-import RefreshIcon from '@/assets/images/refresh.svg'
-import { FileItem } from '@/lib/bots/bing/types'
-import { cn } from '@/lib/utils'
-import { useBing } from '@/lib/hooks/use-bing'
-
-type ChatAttachmentsProps = Pick, 'attachmentList' | 'setAttachmentList' | 'uploadImage'>
-
-export function ChatAttachments({ attachmentList = [], setAttachmentList, uploadImage }: ChatAttachmentsProps) {
- return attachmentList.length ? (
-
- {attachmentList.map(file => (
-
- {file.status === 'loading' && (
-
-
-
)
- }
- {file.status !== 'error' && (
-
-
-
)
- }
- {file.status === 'error' && (
-
- uploadImage(file.url)} />
-
- )}
-
-
- ))}
-
- ) : null
-}
diff --git a/spaces/leogabraneth/text-generation-webui-main/extensions/openai/completions.py b/spaces/leogabraneth/text-generation-webui-main/extensions/openai/completions.py
deleted file mode 100644
index 40d96c1f0cf0a2d72cd5beb7f957a0918f06812c..0000000000000000000000000000000000000000
--- a/spaces/leogabraneth/text-generation-webui-main/extensions/openai/completions.py
+++ /dev/null
@@ -1,637 +0,0 @@
-import time
-
-import tiktoken
-import torch
-import torch.nn.functional as F
-import yaml
-from extensions.openai.defaults import clamp, default, get_default_req_params
-from extensions.openai.errors import InvalidRequestError
-from extensions.openai.utils import debug_msg, end_line
-from modules import shared
-from modules.text_generation import decode, encode, generate_reply
-from transformers import LogitsProcessor, LogitsProcessorList
-
-
-# Thanks to @Cypherfox [Cypherfoxy] for the logits code, blame to @matatonic
-class LogitsBiasProcessor(LogitsProcessor):
- def __init__(self, logit_bias={}):
- self.logit_bias = logit_bias
- if self.logit_bias:
- self.keys = list([int(key) for key in self.logit_bias.keys()])
- values = [self.logit_bias[str(key)] for key in self.keys]
- self.values = torch.tensor(values, dtype=torch.float, device=shared.model.device)
- debug_msg(f"{self})")
-
- def __call__(self, input_ids: torch.LongTensor, logits: torch.FloatTensor) -> torch.FloatTensor:
- if self.logit_bias:
- debug_msg(logits[0, self.keys], " + ", self.values)
- logits[0, self.keys] += self.values
- debug_msg(" --> ", logits[0, self.keys])
- debug_msg(" max/min ", float(torch.max(logits[0])), float(torch.min(logits[0])))
- return logits
-
- def __repr__(self):
- return f"<{self.__class__.__name__}(logit_bias={self.logit_bias})>"
-
-
-class LogprobProcessor(LogitsProcessor):
- def __init__(self, logprobs=None):
- self.logprobs = logprobs
- self.token_alternatives = {}
-
- def __call__(self, input_ids: torch.LongTensor, logits: torch.FloatTensor) -> torch.FloatTensor:
- if self.logprobs is not None: # 0-5
- log_e_probabilities = F.log_softmax(logits, dim=1)
- top_values, top_indices = torch.topk(log_e_probabilities, k=self.logprobs + 1)
- top_tokens = [decode(tok) for tok in top_indices[0]]
- top_probs = [float(x) for x in top_values[0]]
- self.token_alternatives = dict(zip(top_tokens, top_probs))
- debug_msg(repr(self))
- return logits
-
- def __repr__(self):
- return f"<{self.__class__.__name__}(logprobs={self.logprobs}, token_alternatives={self.token_alternatives})>"
-
-
-def convert_logprobs_to_tiktoken(model, logprobs):
- # more problems than it's worth.
- # try:
- # encoder = tiktoken.encoding_for_model(model)
- # # just pick the first one if it encodes to multiple tokens... 99.9% not required and maybe worse overall.
- # return dict([(encoder.decode([encoder.encode(token)[0]]), prob) for token, prob in logprobs.items()])
- # except KeyError:
- # # assume native tokens if we can't find the tokenizer
- # return logprobs
-
- return logprobs
-
-
-def marshal_common_params(body):
- # Request Parameters
- # Try to use openai defaults or map them to something with the same intent
-
- req_params = get_default_req_params()
-
- # Common request parameters
- req_params['truncation_length'] = shared.settings['truncation_length']
- req_params['add_bos_token'] = shared.settings.get('add_bos_token', req_params['add_bos_token'])
- req_params['seed'] = shared.settings.get('seed', req_params['seed'])
- req_params['custom_stopping_strings'] = shared.settings['custom_stopping_strings']
-
- # OpenAI API Parameters
- # model - ignored for now, TODO: When we can reliably load a model or lora from a name only change this
- req_params['requested_model'] = body.get('model', shared.model_name)
-
- req_params['suffix'] = default(body, 'suffix', req_params['suffix'])
- req_params['temperature'] = clamp(default(body, 'temperature', req_params['temperature']), 0.01, 1.99) # fixup absolute 0.0/2.0
- req_params['top_p'] = clamp(default(body, 'top_p', req_params['top_p']), 0.01, 1.0)
- n = default(body, 'n', 1)
- if n != 1:
- raise InvalidRequestError(message="Only n = 1 is supported.", param='n')
-
- if 'stop' in body: # str or array, max len 4 (ignored)
- if isinstance(body['stop'], str):
- req_params['stopping_strings'] = [body['stop']] # non-standard parameter
- elif isinstance(body['stop'], list):
- req_params['stopping_strings'] = body['stop']
-
- # presence_penalty - ignored
- # frequency_penalty - ignored
-
- # pass through unofficial params
- req_params['repetition_penalty'] = default(body, 'repetition_penalty', req_params['repetition_penalty'])
- req_params['encoder_repetition_penalty'] = default(body, 'encoder_repetition_penalty', req_params['encoder_repetition_penalty'])
-
- # user - ignored
-
- logits_processor = []
- logit_bias = body.get('logit_bias', None)
- if logit_bias: # {str: float, ...}
- # XXX convert tokens from tiktoken based on requested model
- # Ex.: 'logit_bias': {'1129': 100, '11442': 100, '16243': 100}
- try:
- encoder = tiktoken.encoding_for_model(req_params['requested_model'])
- new_logit_bias = {}
- for logit, bias in logit_bias.items():
- for x in encode(encoder.decode([int(logit)]), add_special_tokens=False)[0]:
- if int(x) in [0, 1, 2, 29871]: # XXX LLAMA tokens
- continue
- new_logit_bias[str(int(x))] = bias
- debug_msg('logit_bias_map', logit_bias, '->', new_logit_bias)
- logit_bias = new_logit_bias
- except KeyError:
- pass # assume native tokens if we can't find the tokenizer
-
- logits_processor = [LogitsBiasProcessor(logit_bias)]
-
- logprobs = None # coming to chat eventually
- if 'logprobs' in body:
- logprobs = default(body, 'logprobs', 0) # maybe cap at topk? don't clamp 0-5.
- req_params['logprob_proc'] = LogprobProcessor(logprobs)
- logits_processor.extend([req_params['logprob_proc']])
- else:
- logprobs = None
-
- if logits_processor: # requires logits_processor support
- req_params['logits_processor'] = LogitsProcessorList(logits_processor)
-
- return req_params
-
-
-def messages_to_prompt(body: dict, req_params: dict, max_tokens):
- # functions
- if body.get('functions', []): # chat only
- raise InvalidRequestError(message="functions is not supported.", param='functions')
- if body.get('function_call', ''): # chat only, 'none', 'auto', {'name': 'func'}
- raise InvalidRequestError(message="function_call is not supported.", param='function_call')
-
- if 'messages' not in body:
- raise InvalidRequestError(message="messages is required", param='messages')
-
- messages = body['messages']
-
- role_formats = {
- 'user': 'User: {message}\n',
- 'assistant': 'Assistant: {message}\n',
- 'system': '{message}',
- 'context': 'You are a helpful assistant. Answer as concisely as possible.\nUser: I want your assistance.\nAssistant: Sure! What can I do for you?',
- 'prompt': 'Assistant:',
- }
-
- if 'stopping_strings' not in req_params:
- req_params['stopping_strings'] = []
-
- # Instruct models can be much better
- if shared.settings['instruction_template']:
- try:
- instruct = yaml.safe_load(open(f"instruction-templates/{shared.settings['instruction_template']}.yaml", 'r'))
-
- template = instruct['turn_template']
- system_message_template = "{message}"
- system_message_default = instruct.get('context', '') # can be missing
- bot_start = template.find('<|bot|>') # So far, 100% of instruction templates have this token
- user_message_template = template[:bot_start].replace('<|user-message|>', '{message}').replace('<|user|>', instruct.get('user', ''))
- bot_message_template = template[bot_start:].replace('<|bot-message|>', '{message}').replace('<|bot|>', instruct.get('bot', ''))
- bot_prompt = bot_message_template[:bot_message_template.find('{message}')].rstrip(' ')
-
- role_formats = {
- 'user': user_message_template,
- 'assistant': bot_message_template,
- 'system': system_message_template,
- 'context': system_message_default,
- 'prompt': bot_prompt,
- }
-
- if 'Alpaca' in shared.settings['instruction_template']:
- req_params['stopping_strings'].extend(['\n###'])
- elif instruct['user']: # WizardLM and some others have no user prompt.
- req_params['stopping_strings'].extend(['\n' + instruct['user'], instruct['user']])
-
- debug_msg(f"Loaded instruction role format: {shared.settings['instruction_template']}")
-
- except Exception as e:
- req_params['stopping_strings'].extend(['\nUser:', 'User:']) # XXX User: prompt here also
-
- print(f"Exception: When loading instruction-templates/{shared.settings['instruction_template']}.yaml: {repr(e)}")
- print("Warning: Loaded default instruction-following template for model.")
-
- else:
- req_params['stopping_strings'].extend(['\nUser:', 'User:']) # XXX User: prompt here also
- print("Warning: Loaded default instruction-following template for model.")
-
- system_msgs = []
- chat_msgs = []
-
- # You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible. Knowledge cutoff: {knowledge_cutoff} Current date: {current_date}
- context_msg = role_formats['system'].format(message=role_formats['context']) if role_formats['context'] else ''
- context_msg = end_line(context_msg)
-
- # Maybe they sent both? This is not documented in the API, but some clients seem to do this.
- if 'prompt' in body:
- context_msg = end_line(role_formats['system'].format(message=body['prompt'])) + context_msg
-
- for m in messages:
- if 'role' not in m:
- raise InvalidRequestError(message="messages: missing role", param='messages')
- if 'content' not in m:
- raise InvalidRequestError(message="messages: missing content", param='messages')
-
- role = m['role']
- content = m['content']
- # name = m.get('name', None)
- # function_call = m.get('function_call', None) # user name or function name with output in content
- msg = role_formats[role].format(message=content)
- if role == 'system':
- system_msgs.extend([msg])
- elif role == 'function':
- raise InvalidRequestError(message="role: function is not supported.", param='messages')
- else:
- chat_msgs.extend([msg])
-
- system_msg = '\n'.join(system_msgs)
- system_msg = end_line(system_msg)
-
- prompt = system_msg + context_msg + ''.join(chat_msgs) + role_formats['prompt']
-
- token_count = len(encode(prompt)[0])
-
- if token_count >= req_params['truncation_length']:
- err_msg = f"This model maximum context length is {req_params['truncation_length']} tokens. However, your messages resulted in over {token_count} tokens."
- raise InvalidRequestError(message=err_msg, param='messages')
-
- if max_tokens > 0 and token_count + max_tokens > req_params['truncation_length']:
- err_msg = f"This model maximum context length is {req_params['truncation_length']} tokens. However, your messages resulted in over {token_count} tokens and max_tokens is {max_tokens}."
- print(f"Warning: ${err_msg}")
- # raise InvalidRequestError(message=err_msg, params='max_tokens')
-
- return prompt, token_count
-
-
-def chat_completions(body: dict, is_legacy: bool = False) -> dict:
- # Chat Completions
- object_type = 'chat.completions'
- created_time = int(time.time())
- cmpl_id = "chatcmpl-%d" % (int(time.time() * 1000000000))
- resp_list = 'data' if is_legacy else 'choices'
-
- # common params
- req_params = marshal_common_params(body)
- req_params['stream'] = False
- requested_model = req_params.pop('requested_model')
- logprob_proc = req_params.pop('logprob_proc', None)
- req_params['top_k'] = 20 # There is no best_of/top_k param for chat, but it is much improved with a higher top_k.
-
- # chat default max_tokens is 'inf', but also flexible
- max_tokens = 0
- max_tokens_str = 'length' if is_legacy else 'max_tokens'
- if max_tokens_str in body:
- max_tokens = default(body, max_tokens_str, req_params['truncation_length'])
- req_params['max_new_tokens'] = max_tokens
- else:
- req_params['max_new_tokens'] = req_params['truncation_length']
-
- # format the prompt from messages
- prompt, token_count = messages_to_prompt(body, req_params, max_tokens) # updates req_params['stopping_strings']
-
- # set real max, avoid deeper errors
- if req_params['max_new_tokens'] + token_count >= req_params['truncation_length']:
- req_params['max_new_tokens'] = req_params['truncation_length'] - token_count
-
- stopping_strings = req_params.pop('stopping_strings', [])
-
- # generate reply #######################################
- debug_msg({'prompt': prompt, 'req_params': req_params})
- generator = generate_reply(prompt, req_params, stopping_strings=stopping_strings, is_chat=False)
-
- answer = ''
- for a in generator:
- answer = a
-
- # strip extra leading space off new generated content
- if answer and answer[0] == ' ':
- answer = answer[1:]
-
- completion_token_count = len(encode(answer)[0])
- stop_reason = "stop"
- if token_count + completion_token_count >= req_params['truncation_length'] or completion_token_count >= req_params['max_new_tokens']:
- stop_reason = "length"
-
- resp = {
- "id": cmpl_id,
- "object": object_type,
- "created": created_time,
- "model": shared.model_name, # TODO: add Lora info?
- resp_list: [{
- "index": 0,
- "finish_reason": stop_reason,
- "message": {"role": "assistant", "content": answer}
- }],
- "usage": {
- "prompt_tokens": token_count,
- "completion_tokens": completion_token_count,
- "total_tokens": token_count + completion_token_count
- }
- }
- if logprob_proc: # not official for chat yet
- top_logprobs = convert_logprobs_to_tiktoken(model=requested_model, logprobs=logprob_proc.token_alternatives)
- resp[resp_list][0]["logprobs"] = {'top_logprobs': [top_logprobs]}
- # else:
- # resp[resp_list][0]["logprobs"] = None
-
- return resp
-
-
-# generator
-def stream_chat_completions(body: dict, is_legacy: bool = False):
-
- # Chat Completions
- stream_object_type = 'chat.completions.chunk'
- created_time = int(time.time())
- cmpl_id = "chatcmpl-%d" % (int(time.time() * 1000000000))
- resp_list = 'data' if is_legacy else 'choices'
-
- # common params
- req_params = marshal_common_params(body)
- req_params['stream'] = True
- requested_model = req_params.pop('requested_model')
- logprob_proc = req_params.pop('logprob_proc', None)
- req_params['top_k'] = 20 # There is no best_of/top_k param for chat, but it is much improved with a higher top_k.
-
- # chat default max_tokens is 'inf', but also flexible
- max_tokens = 0
- max_tokens_str = 'length' if is_legacy else 'max_tokens'
- if max_tokens_str in body:
- max_tokens = default(body, max_tokens_str, req_params['truncation_length'])
- req_params['max_new_tokens'] = max_tokens
- else:
- req_params['max_new_tokens'] = req_params['truncation_length']
-
- # format the prompt from messages
- prompt, token_count = messages_to_prompt(body, req_params, max_tokens) # updates req_params['stopping_strings']
-
- # set real max, avoid deeper errors
- if req_params['max_new_tokens'] + token_count >= req_params['truncation_length']:
- req_params['max_new_tokens'] = req_params['truncation_length'] - token_count
-
- def chat_streaming_chunk(content):
- # begin streaming
- chunk = {
- "id": cmpl_id,
- "object": stream_object_type,
- "created": created_time,
- "model": shared.model_name,
- resp_list: [{
- "index": 0,
- "finish_reason": None,
- # So yeah... do both methods? delta and messages.
- "message": {'role': 'assistant', 'content': content},
- "delta": {'role': 'assistant', 'content': content},
- }],
- }
-
- if logprob_proc: # not official for chat yet
- top_logprobs = convert_logprobs_to_tiktoken(model=requested_model, logprobs=logprob_proc.token_alternatives)
- chunk[resp_list][0]["logprobs"] = {'top_logprobs': [top_logprobs]}
- # else:
- # chunk[resp_list][0]["logprobs"] = None
- return chunk
-
- yield chat_streaming_chunk('')
-
- # generate reply #######################################
- debug_msg({'prompt': prompt, 'req_params': req_params})
-
- stopping_strings = req_params.pop('stopping_strings', [])
-
- generator = generate_reply(prompt, req_params, stopping_strings=stopping_strings, is_chat=False)
-
- answer = ''
- seen_content = ''
- completion_token_count = 0
-
- for a in generator:
- answer = a
-
- len_seen = len(seen_content)
- new_content = answer[len_seen:]
-
- if not new_content or chr(0xfffd) in new_content: # partial unicode character, don't send it yet.
- continue
-
- seen_content = answer
-
- # strip extra leading space off new generated content
- if len_seen == 0 and new_content[0] == ' ':
- new_content = new_content[1:]
-
- chunk = chat_streaming_chunk(new_content)
-
- yield chunk
-
- # to get the correct token_count, strip leading space if present
- if answer and answer[0] == ' ':
- answer = answer[1:]
-
- completion_token_count = len(encode(answer)[0])
- stop_reason = "stop"
- if token_count + completion_token_count >= req_params['truncation_length'] or completion_token_count >= req_params['max_new_tokens']:
- stop_reason = "length"
-
- chunk = chat_streaming_chunk('')
- chunk[resp_list][0]['finish_reason'] = stop_reason
- chunk['usage'] = {
- "prompt_tokens": token_count,
- "completion_tokens": completion_token_count,
- "total_tokens": token_count + completion_token_count
- }
-
- yield chunk
-
-
-def completions(body: dict, is_legacy: bool = False):
- # Legacy
- # Text Completions
- object_type = 'text_completion'
- created_time = int(time.time())
- cmpl_id = "conv-%d" % (int(time.time() * 1000000000))
- resp_list = 'data' if is_legacy else 'choices'
-
- # ... encoded as a string, array of strings, array of tokens, or array of token arrays.
- prompt_str = 'context' if is_legacy else 'prompt'
- if prompt_str not in body:
- raise InvalidRequestError("Missing required input", param=prompt_str)
-
- prompt_arg = body[prompt_str]
- if isinstance(prompt_arg, str) or (isinstance(prompt_arg, list) and isinstance(prompt_arg[0], int)):
- prompt_arg = [prompt_arg]
-
- # common params
- req_params = marshal_common_params(body)
- req_params['stream'] = False
- max_tokens_str = 'length' if is_legacy else 'max_tokens'
- max_tokens = default(body, max_tokens_str, req_params['max_new_tokens'])
- req_params['max_new_tokens'] = max_tokens
- requested_model = req_params.pop('requested_model')
- logprob_proc = req_params.pop('logprob_proc', None)
- stopping_strings = req_params.pop('stopping_strings', [])
- # req_params['suffix'] = default(body, 'suffix', req_params['suffix'])
- req_params['echo'] = default(body, 'echo', req_params['echo'])
- req_params['top_k'] = default(body, 'best_of', req_params['top_k'])
-
- resp_list_data = []
- total_completion_token_count = 0
- total_prompt_token_count = 0
-
- for idx, prompt in enumerate(prompt_arg, start=0):
- if isinstance(prompt[0], int):
- # token lists
- if requested_model == shared.model_name:
- prompt = decode(prompt)[0]
- else:
- try:
- encoder = tiktoken.encoding_for_model(requested_model)
- prompt = encoder.decode(prompt)
- except KeyError:
- prompt = decode(prompt)[0]
-
- token_count = len(encode(prompt)[0])
- total_prompt_token_count += token_count
-
- if token_count + max_tokens > req_params['truncation_length']:
- err_msg = f"The token count of your prompt ({token_count}) plus max_tokens ({max_tokens}) cannot exceed the model's context length ({req_params['truncation_length']})."
- # print(f"Warning: ${err_msg}")
- raise InvalidRequestError(message=err_msg, param=max_tokens_str)
-
- # generate reply #######################################
- debug_msg({'prompt': prompt, 'req_params': req_params})
- generator = generate_reply(prompt, req_params, stopping_strings=stopping_strings, is_chat=False)
- answer = ''
-
- for a in generator:
- answer = a
-
- # strip extra leading space off new generated content
- if answer and answer[0] == ' ':
- answer = answer[1:]
-
- completion_token_count = len(encode(answer)[0])
- total_completion_token_count += completion_token_count
- stop_reason = "stop"
- if token_count + completion_token_count >= req_params['truncation_length'] or completion_token_count >= max_tokens:
- stop_reason = "length"
-
- respi = {
- "index": idx,
- "finish_reason": stop_reason,
- "text": answer,
- "logprobs": {'top_logprobs': [logprob_proc.token_alternatives]} if logprob_proc else None,
- }
-
- resp_list_data.extend([respi])
-
- resp = {
- "id": cmpl_id,
- "object": object_type,
- "created": created_time,
- "model": shared.model_name, # TODO: add Lora info?
- resp_list: resp_list_data,
- "usage": {
- "prompt_tokens": total_prompt_token_count,
- "completion_tokens": total_completion_token_count,
- "total_tokens": total_prompt_token_count + total_completion_token_count
- }
- }
-
- return resp
-
-
-# generator
-def stream_completions(body: dict, is_legacy: bool = False):
- # Legacy
- # Text Completions
- # object_type = 'text_completion'
- stream_object_type = 'text_completion.chunk'
- created_time = int(time.time())
- cmpl_id = "conv-%d" % (int(time.time() * 1000000000))
- resp_list = 'data' if is_legacy else 'choices'
-
- # ... encoded as a string, array of strings, array of tokens, or array of token arrays.
- prompt_str = 'context' if is_legacy else 'prompt'
- if prompt_str not in body:
- raise InvalidRequestError("Missing required input", param=prompt_str)
-
- prompt = body[prompt_str]
- req_params = marshal_common_params(body)
- requested_model = req_params.pop('requested_model')
- if isinstance(prompt, list):
- if prompt and isinstance(prompt[0], int):
- try:
- encoder = tiktoken.encoding_for_model(requested_model)
- prompt = encoder.decode(prompt)
- except KeyError:
- prompt = decode(prompt)[0]
- else:
- raise InvalidRequestError(message="API Batched generation not yet supported.", param=prompt_str)
-
- # common params
- req_params['stream'] = True
- max_tokens_str = 'length' if is_legacy else 'max_tokens'
- max_tokens = default(body, max_tokens_str, req_params['max_new_tokens'])
- req_params['max_new_tokens'] = max_tokens
- logprob_proc = req_params.pop('logprob_proc', None)
- stopping_strings = req_params.pop('stopping_strings', [])
- # req_params['suffix'] = default(body, 'suffix', req_params['suffix'])
- req_params['echo'] = default(body, 'echo', req_params['echo'])
- req_params['top_k'] = default(body, 'best_of', req_params['top_k'])
-
- token_count = len(encode(prompt)[0])
-
- if token_count + max_tokens > req_params['truncation_length']:
- err_msg = f"The token count of your prompt ({token_count}) plus max_tokens ({max_tokens}) cannot exceed the model's context length ({req_params['truncation_length']})."
- # print(f"Warning: ${err_msg}")
- raise InvalidRequestError(message=err_msg, param=max_tokens_str)
-
- def text_streaming_chunk(content):
- # begin streaming
- chunk = {
- "id": cmpl_id,
- "object": stream_object_type,
- "created": created_time,
- "model": shared.model_name,
- resp_list: [{
- "index": 0,
- "finish_reason": None,
- "text": content,
- "logprobs": {'top_logprobs': [logprob_proc.token_alternatives]} if logprob_proc else None,
- }],
- }
-
- return chunk
-
- yield text_streaming_chunk('')
-
- # generate reply #######################################
- debug_msg({'prompt': prompt, 'req_params': req_params})
- generator = generate_reply(prompt, req_params, stopping_strings=stopping_strings, is_chat=False)
-
- answer = ''
- seen_content = ''
- completion_token_count = 0
-
- for a in generator:
- answer = a
-
- len_seen = len(seen_content)
- new_content = answer[len_seen:]
-
- if not new_content or chr(0xfffd) in new_content: # partial unicode character, don't send it yet.
- continue
-
- seen_content = answer
-
- # strip extra leading space off new generated content
- if len_seen == 0 and new_content[0] == ' ':
- new_content = new_content[1:]
-
- chunk = text_streaming_chunk(new_content)
-
- yield chunk
-
- # to get the correct count, we strip the leading space if present
- if answer and answer[0] == ' ':
- answer = answer[1:]
-
- completion_token_count = len(encode(answer)[0])
- stop_reason = "stop"
- if token_count + completion_token_count >= req_params['truncation_length'] or completion_token_count >= max_tokens:
- stop_reason = "length"
-
- chunk = text_streaming_chunk('')
- chunk[resp_list][0]["finish_reason"] = stop_reason
- chunk["usage"] = {
- "prompt_tokens": token_count,
- "completion_tokens": completion_token_count,
- "total_tokens": token_count + completion_token_count
- }
-
- yield chunk
diff --git a/spaces/leurez/moss/CONTRIBUTING.en.md b/spaces/leurez/moss/CONTRIBUTING.en.md
deleted file mode 100644
index e0e7f27a7492fd095c84e0a98a836afb5bbd7841..0000000000000000000000000000000000000000
--- a/spaces/leurez/moss/CONTRIBUTING.en.md
+++ /dev/null
@@ -1,49 +0,0 @@
-# Contribution Guide
-Thank you for your valuable time. Your contributions will make this project better! Before submitting a contribution, please take some time to read the getting started guide below.
-
-## Semantic Versioning
-This project follows semantic versioning. We release patch versions for important bug fixes, minor versions for new features or non-important changes, and major versions for significant and incompatible changes.
-
-Each major change will be recorded in the `changelog`.
-
-## Submitting Pull Request
-1. Fork [this repository](https://github.com/Chanzhaoyu/chatgpt-web) and create a branch from `main`. For new feature implementations, submit a pull request to the `feature` branch. For other changes, submit to the `main` branch.
-2. Install the `pnpm` tool using `npm install pnpm -g`.
-3. Install the `Eslint` plugin for `VSCode`, or enable `eslint` functionality for other editors such as `WebStorm`.
-4. Execute `pnpm bootstrap` in the root directory.
-5. Execute `pnpm install` in the `/service/` directory.
-6. Make changes to the codebase. If applicable, ensure that appropriate testing has been done.
-7. Execute `pnpm lint:fix` in the root directory to perform a code formatting check.
-8. Execute `pnpm type-check` in the root directory to perform a type check.
-9. Submit a git commit, following the [Commit Guidelines](#commit-guidelines).
-10. Submit a `pull request`. If there is a corresponding `issue`, please link it using the [linking-a-pull-request-to-an-issue keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword).
-
-## Commit Guidelines
-
-Commit messages should follow the [conventional-changelog standard](https://www.conventionalcommits.org/en/v1.0.0/):
-
-```bash
-[optional scope]:
-
-[optional body]
-
-[optional footer]
-```
-
-### Commit Types
-
-The following is a list of commit types:
-
-- feat: New feature or functionality
-- fix: Bug fix
-- docs: Documentation update
-- style: Code style or component style update
-- refactor: Code refactoring, no new features or bug fixes introduced
-- perf: Performance optimization
-- test: Unit test
-- chore: Other commits that do not modify src or test files
-
-
-## License
-
-[MIT](./license)
\ No newline at end of file
diff --git a/spaces/lgaleana/toolkit/examples/summarize_website.py b/spaces/lgaleana/toolkit/examples/summarize_website.py
deleted file mode 100644
index 758e91cf202cc1fd7f829ab07a3f927c8861453e..0000000000000000000000000000000000000000
--- a/spaces/lgaleana/toolkit/examples/summarize_website.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import gradio as gr
-from components import AITask, CodeTask
-
-from examples import demo_buttons, demo_tasks
-
-
-DEMO_ID = __name__
-tasks = [
- CodeTask(
- 0,
- "https://huggingface.co/",
- visible=True,
- code_value="Get text from a website. No html. No empty lines.",
- ),
- AITask(1, "Summarize: {t0}", visible=True),
-]
-demo_tasks[DEMO_ID] = tasks
-
-
-def render():
- with gr.Tab("Example: Summarize a website"):
- demo_id = gr.Textbox(DEMO_ID, visible=False)
- with gr.Box():
- gr.Dropdown(
- value=CodeTask.name,
- label="Pick a new Task",
- interactive=False,
- )
- tasks[0].render()
- with gr.Box():
- gr.Dropdown(
- value=AITask.name,
- label="Pick a new Task",
- interactive=False,
- )
- tasks[1].render()
- demo_buttons(demo_id, tasks)
diff --git a/spaces/lijiacai/ai-set-demo/Dockerfile b/spaces/lijiacai/ai-set-demo/Dockerfile
deleted file mode 100644
index 9d7d665ec07497a9a1acede3da5982e4d609e175..0000000000000000000000000000000000000000
--- a/spaces/lijiacai/ai-set-demo/Dockerfile
+++ /dev/null
@@ -1,19 +0,0 @@
-FROM python:3.9-buster
-
-
-RUN apt-get install -y git
-
-
-RUN mkdir /app
-RUN git clone https://github.com/ai-auto-factory/ai-set-demo.git && mv ai-set-demo/* /app/
-
-RUN pip install --no-cache --upgrade pip
-RUN pip install --no-cache --timeout=120 -r /app/requirements.txt
-
-WORKDIR /app
-
-RUN chmod 777 /app/_secret_auth_.json
-EXPOSE 7860
-
-
-CMD ["python", "-m","streamlit","run","app.py","--server.port","7860","--server.address","0.0.0.0"]
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Download Film Return Of The Condor Heroes Bahasa Indonesia Wikipedia.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Download Film Return Of The Condor Heroes Bahasa Indonesia Wikipedia.md
deleted file mode 100644
index 06fddf8ff19d56f3de50bf93085b1dd10e8fcca1..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Download Film Return Of The Condor Heroes Bahasa Indonesia Wikipedia.md
+++ /dev/null
@@ -1,110 +0,0 @@
-
-
Download Film Return Of The Condor Heroes Bahasa Indonesia Wikipedia: Serial Wuxia Penuh Cinta dan Petualangan
-
-
Anda suka menonton serial wuxia, yaitu genre fiksi yang menceritakan kisah-kisah tentang seni bela diri dan petualangan di Tiongkok kuno? Jika iya, Anda pasti sudah tidak asing lagi dengan The Return of the Condor Heroes, salah satu novel wuxia terpopuler karya Jin Yong (Louis Cha). Novel ini adalah bagian kedua dari Trilogi Condor, yang juga meliputi The Legend of the Condor Heroes dan The Heaven Sword and Dragon Saber.
-
Download Film Return Of The Condor Heroes Bahasa Indonesia Wikipedia
The Return of the Condor Heroes mengisahkan tentang kisah cinta antara Yang Guo, seorang anak yatim piatu yang diasuh oleh para pendekar jahat, dan Xiaolongnü, seorang gadis cantik yang menjadi gurunya dalam seni bela diri. Mereka berdua mengalami berbagai macam rintangan dan musuh di jianghu (komunitas seni bela diri), di mana hubungan antara guru dan murid dianggap tabu. Novel ini juga menampilkan tokoh-tokoh terkenal seperti Guo Jing, Huang Rong, Ouyang Feng, dan Hong Qigong.
-
-
The Return of the Condor Heroes telah diadaptasi menjadi berbagai bentuk media, seperti film, serial televisi, komik, dan game. Salah satu adaptasi yang paling terkenal adalah serial televisi produksi TVB Hong Kong yang ditayangkan pada tahun 1983. Serial ini dibintangi oleh Andy Lau sebagai Yang Guo dan Idy Chan sebagai Xiaolongnü. Serial ini mendapat sambutan yang sangat baik dari penonton di Hong Kong, Tiongkok, Taiwan, dan Asia Tenggara.
-
-
Download Film Return Of The Condor Heroes Bahasa Indonesia Wikipedia: Mengapa Harus Download?
-
-
Jika Anda ingin menonton serial ini dengan bahasa Indonesia, Anda bisa mencoba untuk download film Return of the Condor Heroes Bahasa Indonesia Wikipedia. Wikipedia adalah ensiklopedia online yang bebas dan terbuka untuk semua orang. Di sana, Anda bisa menemukan informasi-informasi tentang serial ini, seperti sinopsis, daftar episode, daftar pemeran, dan lain-lain. Anda juga bisa menemukan link untuk download film Return of the Condor Heroes Bahasa Indonesia dari sumber-sumber yang terpercaya.
-
-
Berikut adalah beberapa alasan mengapa Anda harus download film Return of the Condor Heroes Bahasa Indonesia Wikipedia:
-
-
-
Anda bisa menonton serial ini dengan bahasa yang Anda mengerti. Anda bisa lebih mudah mengikuti alur cerita dan dialog-dialog yang ada di serial ini.
-
Anda bisa menonton serial ini kapan saja dan di mana saja. Anda tidak perlu khawatir kehilangan episode atau jadwal tayangnya. Anda bisa menontonnya sesuai dengan waktu luang dan kesukaan Anda.
-
Anda bisa menonton serial ini dengan kualitas gambar dan suara yang baik. Anda tidak perlu khawatir dengan gangguan sinyal atau buffering yang bisa mengganggu pengalaman menonton Anda.
-
Anda bisa menonton serial ini dengan hemat biaya. Anda tidak perlu membayar biaya langganan atau sewa untuk menonton serial ini. Anda hanya perlu memiliki koneksi internet yang cukup untuk download filmnya.
-
-
-
Download Film Return Of The Condor Heroes Bahasa Indonesia Wikipedia: Bagaimana Caranya?
-
-
Berikut adalah beberapa langkah yang bisa Anda ikuti untuk download film Return of the Condor Heroes Bahasa Indonesia Wikipedia:
-
-
-
-
Buka situs web Wikipedia Bahasa Indonesia di https://id.wikipedia.org/.
-
Ketik "The Return of the Condor Heroes (seri televisi 1983)" di kotak pencarian dan klik tombol cari.
-
Anda akan dibawa ke halaman artikel tentang serial ini. Di sana, Anda bisa membaca informasi-informasi yang Anda butuhkan.
-
Gulir ke bawah hingga Anda menemukan bagian "Pranala luar". Di sana, Anda akan melihat beberapa link yang mengarah ke situs-situs yang menyediakan download film Return of the Condor Heroes Bahasa Indonesia.
-
Pilih salah satu link yang Anda percaya dan klik pada link tersebut. Anda akan dibawa ke situs tersebut.
-
Ikuti petunjuk-petunjuk yang ada di situs tersebut untuk download film Return of the Condor Heroes Bahasa Indonesia. Biasanya, Anda harus mendaftar atau masuk terlebih dahulu sebelum bisa mendownload filmnya.
-
Setelah proses download selesai, Anda bisa menonton filmnya dengan pemutar media yang sesuai dengan format file yang Anda download.
-
-
-
Dengan demikian, download film Return of the Condor Heroes Bahasa Indonesia Wikipedia adalah salah satu cara untuk menonton serial wuxia legendaris ini dengan bahasa yang Anda mengerti. Anda bisa menikmati kisah-kisah tentang cinta, persahabatan, keadilan, dan petualangan di dunia seni bela diri kuno Tiongkok. Jadi tunggu apa lagi? Download filmnya sekarang juga!
-
Download Film Return Of The Condor Heroes Bahasa Indonesia Wikipedia: Apa Saja Kelebihan Serial Ini?
-
-
Serial The Return of the Condor Heroes versi 1983 ini memiliki banyak kelebihan yang membuatnya layak untuk ditonton. Berikut adalah beberapa di antaranya:
-
-
-
Serial ini memiliki cerita yang menarik dan mendalam. Anda bisa menyaksikan perkembangan karakter dan hubungan antara Yang Guo dan Xiaolongnü dari masa kecil hingga dewasa. Anda juga bisa melihat bagaimana mereka menghadapi berbagai konflik dan tantangan di jianghu, baik dari musuh maupun dari diri mereka sendiri.
-
Serial ini memiliki latar belakang yang kaya dan beragam. Anda bisa melihat berbagai tempat dan budaya yang ada di Tiongkok kuno, seperti Biara Shaolin, Lembah Sutra Ungu, Pulau Teratai Putih, dan Gunung Hua. Anda juga bisa melihat berbagai aliran dan perguruan seni bela diri yang ada di jianghu, seperti Lima Jagoan Pedang, Lima Racun Besar, Lima Guru Besar, dan lain-lain.
-
Serial ini memiliki pesan-pesan moral dan nilai-nilai positif. Anda bisa belajar banyak hal dari serial ini, seperti arti cinta sejati, persahabatan, kesetiaan, pengorbanan, keberanian, kejujuran, dan keadilan. Anda juga bisa mengambil pelajaran dari kesalahan-kesalahan yang dilakukan oleh para tokoh di serial ini.
-
-
-
Download Film Return Of The Condor Heroes Bahasa Indonesia Wikipedia: Bagaimana Tanggapan Penonton?
-
-
Serial The Return of the Condor Heroes versi 1983 ini mendapat tanggapan yang sangat positif dari penonton di berbagai negara. Banyak penonton yang terpesona oleh cerita dan pemeranan yang ada di serial ini. Banyak juga penonton yang merasa terbawa suasana dan emosi oleh serial ini. Beberapa penonton bahkan menganggap serial ini sebagai salah satu serial wuxia terbaik sepanjang masa.
-
-
Berikut adalah beberapa testimoni dari penonton yang sudah download film Return of the Condor Heroes Bahasa Indonesia Wikipedia:
-
-
-
"Serial ini sangat bagus dan mengesankan. Saya suka sekali dengan ceritanya yang romantis dan petualangannya yang seru. Saya juga suka dengan pemeran-pemerannya yang sangat cocok dengan karakternya. Saya sampai menangis dan tertawa melihat kisah cinta Yang Guo dan Xiaolongnü."
-- Rina, Jakarta
-
-
-
-
"Serial ini adalah salah satu favorit saya sejak kecil. Saya selalu menunggu-nunggu tayangannya di televisi dulu. Saya senang sekali bisa download filmnya sekarang dan menontonnya lagi. Saya merasa seperti kembali ke masa lalu dan bernostalgia dengan serial ini."
-- Budi, Surabaya
-
-
-
-
"Serial ini adalah karya seni yang luar biasa. Saya kagum dengan cara pengarangnya menyajikan cerita yang kompleks dan mendalam dengan gaya bahasa yang indah dan puitis. Saya juga kagum dengan cara sutradara dan pemerannya menghidupkan cerita tersebut dengan gambar dan suara yang memukau."
-- Dian, Bandung
-
-
-
Dengan demikian, download film Return of the Condor Heroes Bahasa Indonesia Wikipedia adalah salah satu cara untuk menonton serial wuxia legendaris ini dengan bahasa yang Anda mengerti. Anda bisa menikmati kisah-kisah tentang cinta, persahabatan, keadilan, dan petualangan di dunia seni bela diri kuno Tiongkok. Jadi tunggu apa lagi? Download filmnya sekarang juga!
-
Download Film Return Of The Condor Heroes Bahasa Indonesia Wikipedia: Apa Saja Lagu-Lagu yang Ada di Serial Ini?
-
-
Serial The Return of the Condor Heroes versi 1983 ini memiliki beberapa lagu-lagu yang menjadi ciri khasnya. Lagu-lagu ini disusun oleh Joseph Koo dan dinyanyikan oleh Roman Tam dan Jenny Tseng, dua penyanyi terkenal di Hong Kong. Lagu-lagu ini memiliki lirik yang menyentuh dan melodi yang indah. Lagu-lagu ini juga menggambarkan suasana hati dan perasaan para tokoh utama di serial ini.
-
-
Berikut adalah beberapa lagu-lagu yang ada di serial ini:
-
-
-
Ho Yat Tsoi Seung Kin (何日再相見, When Will We Meet Again): Sebuah lagu tema pembuka yang mengungkapkan kerinduan Yang Guo dan Xiaolongnü yang terpisah oleh takdir.
-
Ching Yi Leung Sum Kin (情義兩心堅, Love and Loyalty are Firm in Our Hearts): Sebuah lagu tema penutup yang menggambarkan kesetiaan Yang Guo dan Xiaolongnü terhadap cinta mereka.
-
Man Sai Kan (問世間, Asking the World): Sebuah lagu sisipan yang mengekspresikan kekecewaan Yang Guo dan Xiaolongnü terhadap dunia yang tidak mengerti cinta mereka.
-
Lau Chyu Gam Yat Ching (留住今日情, Keeping Today's Love): Sebuah lagu sisipan yang menunjukkan kebahagiaan Yang Guo dan Xiaolongnü saat bersama.
-
San Tiu Tai Hap (神鵰大俠, The Divine Eagle Hero): Sebuah lagu sisipan yang memuji kehebatan Yang Guo sebagai pendekar jianghu.
-
-
-Download Film Return Of The Condor Heroes Bahasa Indonesia Wikipedia: Bagaimana Cara Mendownloadnya?
-
-
Jika Anda tertarik untuk download film Return of the Condor Heroes Bahasa Indonesia Wikipedia, Anda bisa mengikuti langkah-langkah berikut ini:
-
-
-
Pastikan Anda memiliki koneksi internet yang stabil dan cukup kuota untuk download filmnya.
-
Buka browser Anda dan kunjungi situs web Wikipedia Bahasa Indonesia di https://id.wikipedia.org/.
-
Ketik "The Return of the Condor Heroes (seri televisi 1983)" di kotak pencarian dan klik tombol cari.
-
Anda akan dibawa ke halaman artikel tentang serial ini. Di sana, Anda bisa membaca informasi-informasi yang Anda butuhkan.
-
Gulir ke bawah hingga Anda menemukan bagian "Pranala luar". Di sana, Anda akan melihat beberapa link yang mengarah ke situs-situs yang menyediakan download film Return of the Condor Heroes Bahasa Indonesia.
-
Pilih salah satu link yang Anda percaya dan klik pada link tersebut. Anda akan dibawa ke situs tersebut.
-
Ikuti petunjuk-petunjuk yang ada di situs tersebut untuk download film Return of the Condor Heroes Bahasa Indonesia. Biasanya, Anda harus mendaftar atau masuk terlebih dahulu sebelum bisa mendownload filmnya.
-
Setelah proses download selesai, Anda bisa menonton filmnya dengan pemutar media yang sesuai dengan format file yang Anda download.
-
-
-
Selamat menonton!
-Download Film Return Of The Condor Heroes Bahasa Indonesia Wikipedia: Kesimpulan
-
-
The Return of the Condor Heroes adalah sebuah serial wuxia legendaris yang diadaptasi dari novel karya Jin Yong. Serial ini menceritakan kisah cinta dan petualangan Yang Guo dan Xiaolongnü di dunia seni bela diri kuno Tiongkok. Serial ini memiliki banyak kelebihan, seperti cerita yang menarik, latar belakang yang kaya, pesan-pesan moral, dan lagu-lagu yang indah. Serial ini juga mendapat tanggapan yang positif dari penonton di berbagai negara.
-
-
Jika Anda ingin menonton serial ini dengan bahasa Indonesia, Anda bisa mencoba untuk download film Return of the Condor Heroes Bahasa Indonesia Wikipedia. Wikipedia adalah ensiklopedia online yang bebas dan terbuka untuk semua orang. Di sana, Anda bisa menemukan informasi-informasi tentang serial ini, seperti sinopsis, daftar episode, daftar pemeran, dan lain-lain. Anda juga bisa menemukan link untuk download film Return of the Condor Heroes Bahasa Indonesia dari sumber-sumber yang terpercaya.
-
-
Download film Return of the Condor Heroes Bahasa Indonesia Wikipedia adalah salah satu cara untuk menonton serial wuxia legendaris ini dengan bahasa yang Anda mengerti. Anda bisa menikmati kisah-kisah tentang cinta, persahabatan, keadilan, dan petualangan di dunia seni bela diri kuno Tiongkok. Jadi tunggu apa lagi? Download filmnya sekarang juga!
3cee63e6c2
-
-
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Kanzen Kouryaku 5.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Kanzen Kouryaku 5.md
deleted file mode 100644
index 1724a378cd9fee46b85874ce2d97a68541f142d3..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Kanzen Kouryaku 5.md
+++ /dev/null
@@ -1,8 +0,0 @@
-
-
little stars of sumireko no koku kara (黄岡奈コウと杏生ちゃん) —koi wa ohoushi ni shikou (金城あずしに話す) ~a romance in sumireko no koku (黄岡奈コウ) is a two-part episode of manga nante tanoshii otaku-tachi no shitemasu (マンガなんてただいじょうぶ遊びよ), a 2011 tv series that aired from october 15 to november 19, 2011, starring yōko hikasa and ryō kase as sumireko no koku (黄岡奈コウ), and kenichi suzumura as yoshimitsu yamashiro (山形慧之). hikasa plays the role of a magazine writer named sōma karasumaki (司馬からさやマキ, shortened to さむらか in the anime) who wants to work for the meikyosha magazine but is rejected.
-
japan zoids galactorhythm toho jyushindou kanzen kouryaku release date: the fun not fear tour of the galaxy! you will be destroying the forces of zoids galactorhythm from beginning to end and fighting against them with your army of zoids. this will be the perfect opportunity to join your friends and also battle against them. the best part is that you will be getting a key to unlock all the different zoids in this game.
in kanzen koryaku ryū ga gotoku players can take control of one of the five female protagonists including: tifa lockhart (ffvii), yuna (ffx), rosa heart (ffix), ridia heart (ffviii) and haoh 5 (ffv). the game also features a new, non-canon version of yuna from final fantasy x-2 (ffx-2) compared to the original version.
-
haoh game special (, haou gmu supesharu) is a series of video game strategy books published by kodansha's haoh magazine. early books from this series were titled hisshou kouryaku hon (, lit. certain victory capture book) with later book having varying titles. this series includes guides for games from the mega man franchise.
899543212b
-
-
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Kuptimi I Lektyres Agimet E Kaltra Qamil Batalli.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Kuptimi I Lektyres Agimet E Kaltra Qamil Batalli.md
deleted file mode 100644
index f5853599f8b8e5ca2ae09c761461ed897051ea4e..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Kuptimi I Lektyres Agimet E Kaltra Qamil Batalli.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-rirasure/2020-kuptimi-i-lektyres-agimet-e-kaltra-qamil-batalli. By drawing. Kuptimi I Lektyres Agimet E Kaltra Camille Batalli. container. 1850. Paper, mixed media. 30 × 21 cm. On the reverse side, the inscription: “Camille Batalli 1850. Container. Kuptimi I Lektyres Agimet E Kaltra. (Camille Batalli)." C. B. 1850. Container. Kuptimi I Lektyres Agimet E Kaltra (Camille Batalli) From the collection of Camille Batalli. Paris. Kuptimi I Lektyres Agimet E Kaltra (Kamil Batalli). 1850. Paper, mixed media. 30 × 21 cm. Inscription on the back: “Camille Batalli 1850. Container. Kuptimi I Lektyres Agimet E Kaltra (Kamil Batalli)". C. B. 1850. Container. 8a78ff9644
-
-
-
diff --git a/spaces/lithiumice/SadTalker/src/facerender/sync_batchnorm/batchnorm.py b/spaces/lithiumice/SadTalker/src/facerender/sync_batchnorm/batchnorm.py
deleted file mode 100644
index 5f4e763f0366dffa10320116413f8c7181a8aeb1..0000000000000000000000000000000000000000
--- a/spaces/lithiumice/SadTalker/src/facerender/sync_batchnorm/batchnorm.py
+++ /dev/null
@@ -1,315 +0,0 @@
-# -*- coding: utf-8 -*-
-# File : batchnorm.py
-# Author : Jiayuan Mao
-# Email : maojiayuan@gmail.com
-# Date : 27/01/2018
-#
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import collections
-
-import torch
-import torch.nn.functional as F
-
-from torch.nn.modules.batchnorm import _BatchNorm
-from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast
-
-from .comm import SyncMaster
-
-__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d']
-
-
-def _sum_ft(tensor):
- """sum over the first and last dimention"""
- return tensor.sum(dim=0).sum(dim=-1)
-
-
-def _unsqueeze_ft(tensor):
- """add new dementions at the front and the tail"""
- return tensor.unsqueeze(0).unsqueeze(-1)
-
-
-_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size'])
-_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std'])
-
-
-class _SynchronizedBatchNorm(_BatchNorm):
- def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True):
- super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine)
-
- self._sync_master = SyncMaster(self._data_parallel_master)
-
- self._is_parallel = False
- self._parallel_id = None
- self._slave_pipe = None
-
- def forward(self, input):
- # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation.
- if not (self._is_parallel and self.training):
- return F.batch_norm(
- input, self.running_mean, self.running_var, self.weight, self.bias,
- self.training, self.momentum, self.eps)
-
- # Resize the input to (B, C, -1).
- input_shape = input.size()
- input = input.view(input.size(0), self.num_features, -1)
-
- # Compute the sum and square-sum.
- sum_size = input.size(0) * input.size(2)
- input_sum = _sum_ft(input)
- input_ssum = _sum_ft(input ** 2)
-
- # Reduce-and-broadcast the statistics.
- if self._parallel_id == 0:
- mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size))
- else:
- mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size))
-
- # Compute the output.
- if self.affine:
- # MJY:: Fuse the multiplication for speed.
- output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias)
- else:
- output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std)
-
- # Reshape it.
- return output.view(input_shape)
-
- def __data_parallel_replicate__(self, ctx, copy_id):
- self._is_parallel = True
- self._parallel_id = copy_id
-
- # parallel_id == 0 means master device.
- if self._parallel_id == 0:
- ctx.sync_master = self._sync_master
- else:
- self._slave_pipe = ctx.sync_master.register_slave(copy_id)
-
- def _data_parallel_master(self, intermediates):
- """Reduce the sum and square-sum, compute the statistics, and broadcast it."""
-
- # Always using same "device order" makes the ReduceAdd operation faster.
- # Thanks to:: Tete Xiao (http://tetexiao.com/)
- intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device())
-
- to_reduce = [i[1][:2] for i in intermediates]
- to_reduce = [j for i in to_reduce for j in i] # flatten
- target_gpus = [i[1].sum.get_device() for i in intermediates]
-
- sum_size = sum([i[1].sum_size for i in intermediates])
- sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce)
- mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size)
-
- broadcasted = Broadcast.apply(target_gpus, mean, inv_std)
-
- outputs = []
- for i, rec in enumerate(intermediates):
- outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2])))
-
- return outputs
-
- def _compute_mean_std(self, sum_, ssum, size):
- """Compute the mean and standard-deviation with sum and square-sum. This method
- also maintains the moving average on the master device."""
- assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.'
- mean = sum_ / size
- sumvar = ssum - sum_ * mean
- unbias_var = sumvar / (size - 1)
- bias_var = sumvar / size
-
- self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data
- self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data
-
- return mean, bias_var.clamp(self.eps) ** -0.5
-
-
-class SynchronizedBatchNorm1d(_SynchronizedBatchNorm):
- r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a
- mini-batch.
-
- .. math::
-
- y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
- This module differs from the built-in PyTorch BatchNorm1d as the mean and
- standard-deviation are reduced across all devices during training.
-
- For example, when one uses `nn.DataParallel` to wrap the network during
- training, PyTorch's implementation normalize the tensor on each device using
- the statistics only on that device, which accelerated the computation and
- is also easy to implement, but the statistics might be inaccurate.
- Instead, in this synchronized version, the statistics will be computed
- over all training samples distributed on multiple devices.
-
- Note that, for one-GPU or CPU-only case, this module behaves exactly same
- as the built-in PyTorch implementation.
-
- The mean and standard-deviation are calculated per-dimension over
- the mini-batches and gamma and beta are learnable parameter vectors
- of size C (where C is the input size).
-
- During training, this layer keeps a running estimate of its computed mean
- and variance. The running sum is kept with a default momentum of 0.1.
-
- During evaluation, this running mean/variance is used for normalization.
-
- Because the BatchNorm is done over the `C` dimension, computing statistics
- on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm
-
- Args:
- num_features: num_features from an expected input of size
- `batch_size x num_features [x width]`
- eps: a value added to the denominator for numerical stability.
- Default: 1e-5
- momentum: the value used for the running_mean and running_var
- computation. Default: 0.1
- affine: a boolean value that when set to ``True``, gives the layer learnable
- affine parameters. Default: ``True``
-
- Shape:
- - Input: :math:`(N, C)` or :math:`(N, C, L)`
- - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input)
-
- Examples:
- >>> # With Learnable Parameters
- >>> m = SynchronizedBatchNorm1d(100)
- >>> # Without Learnable Parameters
- >>> m = SynchronizedBatchNorm1d(100, affine=False)
- >>> input = torch.autograd.Variable(torch.randn(20, 100))
- >>> output = m(input)
- """
-
- def _check_input_dim(self, input):
- if input.dim() != 2 and input.dim() != 3:
- raise ValueError('expected 2D or 3D input (got {}D input)'
- .format(input.dim()))
- super(SynchronizedBatchNorm1d, self)._check_input_dim(input)
-
-
-class SynchronizedBatchNorm2d(_SynchronizedBatchNorm):
- r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch
- of 3d inputs
-
- .. math::
-
- y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
- This module differs from the built-in PyTorch BatchNorm2d as the mean and
- standard-deviation are reduced across all devices during training.
-
- For example, when one uses `nn.DataParallel` to wrap the network during
- training, PyTorch's implementation normalize the tensor on each device using
- the statistics only on that device, which accelerated the computation and
- is also easy to implement, but the statistics might be inaccurate.
- Instead, in this synchronized version, the statistics will be computed
- over all training samples distributed on multiple devices.
-
- Note that, for one-GPU or CPU-only case, this module behaves exactly same
- as the built-in PyTorch implementation.
-
- The mean and standard-deviation are calculated per-dimension over
- the mini-batches and gamma and beta are learnable parameter vectors
- of size C (where C is the input size).
-
- During training, this layer keeps a running estimate of its computed mean
- and variance. The running sum is kept with a default momentum of 0.1.
-
- During evaluation, this running mean/variance is used for normalization.
-
- Because the BatchNorm is done over the `C` dimension, computing statistics
- on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm
-
- Args:
- num_features: num_features from an expected input of
- size batch_size x num_features x height x width
- eps: a value added to the denominator for numerical stability.
- Default: 1e-5
- momentum: the value used for the running_mean and running_var
- computation. Default: 0.1
- affine: a boolean value that when set to ``True``, gives the layer learnable
- affine parameters. Default: ``True``
-
- Shape:
- - Input: :math:`(N, C, H, W)`
- - Output: :math:`(N, C, H, W)` (same shape as input)
-
- Examples:
- >>> # With Learnable Parameters
- >>> m = SynchronizedBatchNorm2d(100)
- >>> # Without Learnable Parameters
- >>> m = SynchronizedBatchNorm2d(100, affine=False)
- >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45))
- >>> output = m(input)
- """
-
- def _check_input_dim(self, input):
- if input.dim() != 4:
- raise ValueError('expected 4D input (got {}D input)'
- .format(input.dim()))
- super(SynchronizedBatchNorm2d, self)._check_input_dim(input)
-
-
-class SynchronizedBatchNorm3d(_SynchronizedBatchNorm):
- r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch
- of 4d inputs
-
- .. math::
-
- y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
- This module differs from the built-in PyTorch BatchNorm3d as the mean and
- standard-deviation are reduced across all devices during training.
-
- For example, when one uses `nn.DataParallel` to wrap the network during
- training, PyTorch's implementation normalize the tensor on each device using
- the statistics only on that device, which accelerated the computation and
- is also easy to implement, but the statistics might be inaccurate.
- Instead, in this synchronized version, the statistics will be computed
- over all training samples distributed on multiple devices.
-
- Note that, for one-GPU or CPU-only case, this module behaves exactly same
- as the built-in PyTorch implementation.
-
- The mean and standard-deviation are calculated per-dimension over
- the mini-batches and gamma and beta are learnable parameter vectors
- of size C (where C is the input size).
-
- During training, this layer keeps a running estimate of its computed mean
- and variance. The running sum is kept with a default momentum of 0.1.
-
- During evaluation, this running mean/variance is used for normalization.
-
- Because the BatchNorm is done over the `C` dimension, computing statistics
- on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm
- or Spatio-temporal BatchNorm
-
- Args:
- num_features: num_features from an expected input of
- size batch_size x num_features x depth x height x width
- eps: a value added to the denominator for numerical stability.
- Default: 1e-5
- momentum: the value used for the running_mean and running_var
- computation. Default: 0.1
- affine: a boolean value that when set to ``True``, gives the layer learnable
- affine parameters. Default: ``True``
-
- Shape:
- - Input: :math:`(N, C, D, H, W)`
- - Output: :math:`(N, C, D, H, W)` (same shape as input)
-
- Examples:
- >>> # With Learnable Parameters
- >>> m = SynchronizedBatchNorm3d(100)
- >>> # Without Learnable Parameters
- >>> m = SynchronizedBatchNorm3d(100, affine=False)
- >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10))
- >>> output = m(input)
- """
-
- def _check_input_dim(self, input):
- if input.dim() != 5:
- raise ValueError('expected 5D input (got {}D input)'
- .format(input.dim()))
- super(SynchronizedBatchNorm3d, self)._check_input_dim(input)
diff --git a/spaces/lj1995/vocal2guitar/infer_pack/transforms.py b/spaces/lj1995/vocal2guitar/infer_pack/transforms.py
deleted file mode 100644
index a11f799e023864ff7082c1f49c0cc18351a13b47..0000000000000000000000000000000000000000
--- a/spaces/lj1995/vocal2guitar/infer_pack/transforms.py
+++ /dev/null
@@ -1,209 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(
- inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails=None,
- tail_bound=1.0,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
- if tails is None:
- spline_fn = rational_quadratic_spline
- spline_kwargs = {}
- else:
- spline_fn = unconstrained_rational_quadratic_spline
- spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
-
- outputs, logabsdet = spline_fn(
- inputs=inputs,
- unnormalized_widths=unnormalized_widths,
- unnormalized_heights=unnormalized_heights,
- unnormalized_derivatives=unnormalized_derivatives,
- inverse=inverse,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- **spline_kwargs
- )
- return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
- bin_locations[..., -1] += eps
- return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
-
-
-def unconstrained_rational_quadratic_spline(
- inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails="linear",
- tail_bound=1.0,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
- inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
- outside_interval_mask = ~inside_interval_mask
-
- outputs = torch.zeros_like(inputs)
- logabsdet = torch.zeros_like(inputs)
-
- if tails == "linear":
- unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
- constant = np.log(np.exp(1 - min_derivative) - 1)
- unnormalized_derivatives[..., 0] = constant
- unnormalized_derivatives[..., -1] = constant
-
- outputs[outside_interval_mask] = inputs[outside_interval_mask]
- logabsdet[outside_interval_mask] = 0
- else:
- raise RuntimeError("{} tails are not implemented.".format(tails))
-
- (
- outputs[inside_interval_mask],
- logabsdet[inside_interval_mask],
- ) = rational_quadratic_spline(
- inputs=inputs[inside_interval_mask],
- unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
- unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
- unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
- inverse=inverse,
- left=-tail_bound,
- right=tail_bound,
- bottom=-tail_bound,
- top=tail_bound,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- )
-
- return outputs, logabsdet
-
-
-def rational_quadratic_spline(
- inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- left=0.0,
- right=1.0,
- bottom=0.0,
- top=1.0,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
- if torch.min(inputs) < left or torch.max(inputs) > right:
- raise ValueError("Input to a transform is not within its domain")
-
- num_bins = unnormalized_widths.shape[-1]
-
- if min_bin_width * num_bins > 1.0:
- raise ValueError("Minimal bin width too large for the number of bins")
- if min_bin_height * num_bins > 1.0:
- raise ValueError("Minimal bin height too large for the number of bins")
-
- widths = F.softmax(unnormalized_widths, dim=-1)
- widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
- cumwidths = torch.cumsum(widths, dim=-1)
- cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
- cumwidths = (right - left) * cumwidths + left
- cumwidths[..., 0] = left
- cumwidths[..., -1] = right
- widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
- derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
- heights = F.softmax(unnormalized_heights, dim=-1)
- heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
- cumheights = torch.cumsum(heights, dim=-1)
- cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
- cumheights = (top - bottom) * cumheights + bottom
- cumheights[..., 0] = bottom
- cumheights[..., -1] = top
- heights = cumheights[..., 1:] - cumheights[..., :-1]
-
- if inverse:
- bin_idx = searchsorted(cumheights, inputs)[..., None]
- else:
- bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
- input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
- input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
- input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
- delta = heights / widths
- input_delta = delta.gather(-1, bin_idx)[..., 0]
-
- input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
- input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
- input_heights = heights.gather(-1, bin_idx)[..., 0]
-
- if inverse:
- a = (inputs - input_cumheights) * (
- input_derivatives + input_derivatives_plus_one - 2 * input_delta
- ) + input_heights * (input_delta - input_derivatives)
- b = input_heights * input_derivatives - (inputs - input_cumheights) * (
- input_derivatives + input_derivatives_plus_one - 2 * input_delta
- )
- c = -input_delta * (inputs - input_cumheights)
-
- discriminant = b.pow(2) - 4 * a * c
- assert (discriminant >= 0).all()
-
- root = (2 * c) / (-b - torch.sqrt(discriminant))
- outputs = root * input_bin_widths + input_cumwidths
-
- theta_one_minus_theta = root * (1 - root)
- denominator = input_delta + (
- (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta
- )
- derivative_numerator = input_delta.pow(2) * (
- input_derivatives_plus_one * root.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - root).pow(2)
- )
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, -logabsdet
- else:
- theta = (inputs - input_cumwidths) / input_bin_widths
- theta_one_minus_theta = theta * (1 - theta)
-
- numerator = input_heights * (
- input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
- )
- denominator = input_delta + (
- (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta
- )
- outputs = input_cumheights + numerator / denominator
-
- derivative_numerator = input_delta.pow(2) * (
- input_derivatives_plus_one * theta.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - theta).pow(2)
- )
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, logabsdet
diff --git a/spaces/ljjggr/bingo/src/components/chat-history.tsx b/spaces/ljjggr/bingo/src/components/chat-history.tsx
deleted file mode 100644
index feb81de66562edda8f40d3c0cc717202c92b6509..0000000000000000000000000000000000000000
--- a/spaces/ljjggr/bingo/src/components/chat-history.tsx
+++ /dev/null
@@ -1,48 +0,0 @@
-import { IconEdit, IconTrash, IconMore, IconDownload } from "./ui/icons"
-
-export function ChatHistory() {
- return (
-
Every dataset communicates a different perspective. When you shift your perspective, your conclusions can shift, too.
-
Suppose you have a dataset of shapes. They can either be shaded or unshaded. They look something like this:
-
-
-
-
You built a supervised machine learning classifier that will automatically classify each shape as shaded or unshaded. You call it the “Is-Shaded Classifier”.
-
-
Click “Run Classifier” to see how your model performs.
-
-
-
-
-
-
It’s not perfect— some of the shapes are definitely misclassified. You want to improve your model!
-
-
To do so, you want to know more about the kinds of mistakes your model is making.
-
-
Thinking About Bias
-
-
In training, you only gave your model the raw image of each shape and one ground truth label: shaded and unshaded. But maybe something about your model—the distribution of the training data you used, the architecture you chose, or how you set your hyperparameters—resulted in your model performing better on some shapes than others.
-
-
In fact, you’ve seen a lot of papers and articles citing issues of biased model performance between circles, triangles, and rectangles in shape data. One paper finds that shape detection algorithms tend to do worse on triangles; another article says color accuracy is an issue with circles. So you wonder: are there biases in your model’s misclassifications?
-
-
-
-
You want to make sure that your model is performing equally well across circles, triangles, and rectangles, so you decide to do a fairness analysis.
-
-
There’s just one issue: you don’t have labels for which of your shapes are circles, triangles, or rectangles.
You receive feedback from your data labeling team that they’re not sure what to do with the shapes that aren’t exactly circles, triangles, or rectangles.
-
-
-
-
For the shapes that are unclear, you can have them use their best guess or simply label them as “other”. Then, you can finally do some fairness analysis!
-
-
Below is the interface they see:
-
-
-
-
These shapes should be labeled…
-
-
-
-
-
-
-
If you go back and change the labelers’ instructions, which shapes do you perform worst on? Where do you find bias?
-
-
You notice that your results hinge on how you choose to classify the shapes in our data.
-
-
Because ultimately, this isn’t a world of only circles, triangles, and rectangles!
-
-
Thinking About Classification
-
-
What could we find out about our classifier’s performance if we used different categories altogether?
-
-
All shapes are basically…
-
Everything else should be labeled…
-
-
-
-
-
-
-
With each of the different categories, which shapes do you perform worst on? Where do you find bias?
-
-
Each way of categorizing your shapes takes a different stance about what’s important . Each one makes some features more important than others, it make some distinctions visible and other distinctions invisible, and make some things easy to classify while others become outliers.
-
-
And each one tells you something different about what kind of bias your classifier has!
-
-
Grouping and Regrouping
-
-
Here’s another way to look at the same results. We can draw all the shapes that were correctly classified above the dashed line, and all the incorrectly classified shapes below it.
-
-
-
-
We’re still looking at the same model making the same classification on the same shapes, so the same shapes stay above and below the line. But each way of grouping the results distributes the errors differently— each way tells you something different.
-
-
Labels Tell Stories
-
-
The decisions you make about classification, however small…
-
-
All shapes are basically…
-
-
…begin to shape others’ decisions…
-
-
-
-
…they shape the analysis you can do…
-
-
-
-
…and they shape the kinds of conversations that happen.
-
-
-
-
It’s natural to want to find a way out of this problem by gathering more features or collecting more data. If we just have enough detail on enough data, surely we can avoid making these kinds of decisions, right?
-
-
Unfortunately, that isn’t the case. Describing the world around us in any way—whether we’re telling a friend a story or telling a computer about shapes—requires us to choose what information is important to convey and what tools we want to use to convey it.
-
-
Whether we think about it or not, we’re always making choices about classification.
-
-
-
All people are basically… men or women
-
All food is basically… sweet or savory
-
All content is basically… kid-friendly or adult
-
All speech is basically… hate speech or acceptable speech
-
-
All results are basically… significant or insignificant
-
-
And as we saw with shapes, all of these choices make some features more important than others, make some distinctions visible and other distinctions invisible, and make some things easy to classify while others become outliers.
-
-
In Practice
-
-
Let’s take a closer look at how this plays out in real machine learning applications. One straightforward example is in supervised object detection tasks.
-
-
-
For example, let’s imagine we want to train an object detection model on a dataset including this image:
We could give it the following ground truth bounding boxes:
-
-
-
-
This looks objective, right? After all, a building is a building, a bush is a bush, and a mountain is a mountain!
-
But even labeling the same regions in the same image, you can communicate a very different perspective:
-
-
-
-
Or consider the image below, with several sets of “ground truth” labels. Looking at each of these labels, consider:
-
-
What features matter? What gets labeled? Whose worldview comes through? What might you learn from this set of labels that you wouldn’t learn from another?
There is no “view from nowhere”, no universal way to organize every object, or word, or image. Datasets are always products of a particular time, place, and set of conditions; they are socially situated artifacts. They have histories; they have politics. And ignoring this fact has very real consequences.
If it’s hard to see a dataset’s values—if it feels “objective”, “universal”, or “neutral”—it may simply be reflecting a worldview you’re accustomed to. So, understanding the limitations of your own worldview can tell you about the limitations of “objective” data. What assumptions do you make about the world? What feels like common sense? What feels foreign?
-
-
And do some sleuthing about your data! Who collected this data? Why was it collected? Who paid for it? Where did the “ground truth” come from?
If you find yourself with lots of questions, you’re already off to a good start.
-
-
-
-
-
Credits
-
-
Dylan Baker // January 2022
-
Thanks to Adam Pearce, Alex Hanna, Emily Denton, Fernanda Viégas, Kevin Robinson, Nithum Thain, Razvan Amironesei, and Vinodkumar Prabhakaran for their help with this piece.
-
-
-
-
-
-
-
More Explorables
-
-
-
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-
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-
\ No newline at end of file
diff --git a/spaces/mhenrichsen/DanskGPT/README.md b/spaces/mhenrichsen/DanskGPT/README.md
deleted file mode 100644
index e9b9e68d8b3407c9695e22604f70271a98139edd..0000000000000000000000000000000000000000
--- a/spaces/mhenrichsen/DanskGPT/README.md
+++ /dev/null
@@ -1,6 +0,0 @@
----
-title: DanskGPT
-app_file: chat-ui/main.py
-sdk: gradio
-sdk_version: 3.40.1
----
\ No newline at end of file
diff --git a/spaces/mjuetz/neu/app.py b/spaces/mjuetz/neu/app.py
deleted file mode 100644
index a4491fa68b763a8a344f905b856e79f8ff7aabf7..0000000000000000000000000000000000000000
--- a/spaces/mjuetz/neu/app.py
+++ /dev/null
@@ -1,4 +0,0 @@
-import streamlit as st
-
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
\ No newline at end of file
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/quantization/pq/__init__.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/quantization/pq/__init__.py
deleted file mode 100644
index c142a802e05ec7ecfa5dba7d9a98c26a60ac75d2..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/modules/quantization/pq/__init__.py
+++ /dev/null
@@ -1,6 +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 .utils import SizeTracker, get_param, attrsetter, quantize_model_ # NOQA
diff --git a/spaces/mshukor/UnIVAL/fairseq/tests/test_character_token_embedder.py b/spaces/mshukor/UnIVAL/fairseq/tests/test_character_token_embedder.py
deleted file mode 100644
index 24940ebd21a0e4465ca6052409353a3179e9cf6d..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/tests/test_character_token_embedder.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import unittest
-
-import torch
-from fairseq.data import Dictionary
-from fairseq.modules import CharacterTokenEmbedder
-
-
-class TestCharacterTokenEmbedder(unittest.TestCase):
- def test_character_token_embedder(self):
- vocab = Dictionary()
- vocab.add_symbol("hello")
- vocab.add_symbol("there")
-
- embedder = CharacterTokenEmbedder(
- vocab, [(2, 16), (4, 32), (8, 64), (16, 2)], 64, 5, 2
- )
-
- test_sents = [["hello", "unk", "there"], ["there"], ["hello", "there"]]
- max_len = max(len(s) for s in test_sents)
- input = torch.LongTensor(len(test_sents), max_len + 2).fill_(vocab.pad())
- for i in range(len(test_sents)):
- input[i][0] = vocab.eos()
- for j in range(len(test_sents[i])):
- input[i][j + 1] = vocab.index(test_sents[i][j])
- input[i][j + 2] = vocab.eos()
- embs = embedder(input)
-
- assert embs.size() == (len(test_sents), max_len + 2, 5)
- self.assertAlmostEqual(embs[0][0], embs[1][0])
- self.assertAlmostEqual(embs[0][0], embs[0][-1])
- self.assertAlmostEqual(embs[0][1], embs[2][1])
- self.assertAlmostEqual(embs[0][3], embs[1][1])
-
- embs.sum().backward()
- assert embedder.char_embeddings.weight.grad is not None
-
- def assertAlmostEqual(self, t1, t2):
- self.assertEqual(t1.size(), t2.size(), "size mismatch")
- self.assertLess((t1 - t2).abs().max(), 1e-6)
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/spaces/mshukor/UnIVAL/slurm_adastra/averaging/ratatouille/refcoco/ofa_ratarefcocoplus_snli_refcocoplus.sh b/spaces/mshukor/UnIVAL/slurm_adastra/averaging/ratatouille/refcoco/ofa_ratarefcocoplus_snli_refcocoplus.sh
deleted file mode 100644
index babe57e23c27f8decc62bafd5bc8b8a10480409d..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/slurm_adastra/averaging/ratatouille/refcoco/ofa_ratarefcocoplus_snli_refcocoplus.sh
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/bin/bash
-
-#SBATCH --job-name=ofa_ratarefcocoplus_snli_refcocoplus
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --gpus=8
-#SBATCH --threads-per-core=2
-#SBATCH --gpu-bind=closest
-#SBATCH -C MI250
-#SBATCH -A gda2204
-#SBATCH --time=24:00:00
-#SBATCH --mail-type=END,FAIL
-#SBATCH --output=/lus/home/NAT/gda2204/mshukor/logs/slurm/ofa_ratarefcocoplus_snli_refcocoplus.out
-#SBATCH --exclusive
-#SBATCH --mail-user=mustafa.shukor@isir.upmc.fr
-
-
-cd /lus/home/NAT/gda2204/mshukor/code/ofa_ours/run_scripts
-source /lus/home/NAT/gda2204/mshukor/.bashrc
-
-conda activate main
-
-
-rm core-python3*
-
-
-srun -l -N 1 -n 1 -c 128 --gpus=8 bash averaging/ratatouille/refcoco/ofa_ratarefcocoplus_snli_refcocoplus.sh
-
-
diff --git a/spaces/myscale/ChatData/callbacks/arxiv_callbacks.py b/spaces/myscale/ChatData/callbacks/arxiv_callbacks.py
deleted file mode 100644
index 0dc6c189385041810e4b743601e4d359d203d837..0000000000000000000000000000000000000000
--- a/spaces/myscale/ChatData/callbacks/arxiv_callbacks.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import streamlit as st
-from typing import Dict, Any
-from sql_formatter.core import format_sql
-from langchain.callbacks.streamlit.streamlit_callback_handler import StreamlitCallbackHandler
-from langchain.schema.output import LLMResult
-
-class ChatDataSelfSearchCallBackHandler(StreamlitCallbackHandler):
- def __init__(self) -> None:
- self.progress_bar = st.progress(value=0.0, text="Working...")
- self.tokens_stream = ""
-
- def on_llm_start(self, serialized, prompts, **kwargs) -> None:
- pass
-
- def on_text(self, text: str, **kwargs) -> None:
- self.progress_bar.progress(value=0.2, text="Asking LLM...")
-
- def on_chain_end(self, outputs, **kwargs) -> None:
- self.progress_bar.progress(value=0.6, text='Searching in DB...')
- st.markdown('### Generated Filter')
- st.write(outputs['text'], unsafe_allow_html=True)
-
- def on_chain_start(self, serialized, inputs, **kwargs) -> None:
- pass
-
-class ChatDataSelfAskCallBackHandler(StreamlitCallbackHandler):
- def __init__(self) -> None:
- self.progress_bar = st.progress(value=0.0, text='Searching DB...')
- self.status_bar = st.empty()
- self.prog_value = 0.0
- self.prog_map = {
- 'langchain.chains.qa_with_sources.retrieval.RetrievalQAWithSourcesChain': 0.2,
- 'langchain.chains.combine_documents.map_reduce.MapReduceDocumentsChain': 0.4,
- 'langchain.chains.combine_documents.stuff.StuffDocumentsChain': 0.8
- }
-
- def on_llm_start(self, serialized, prompts, **kwargs) -> None:
- pass
-
- def on_text(self, text: str, **kwargs) -> None:
- pass
-
- def on_chain_start(self, serialized, inputs, **kwargs) -> None:
- cid = '.'.join(serialized['id'])
- if cid != 'langchain.chains.llm.LLMChain':
- self.progress_bar.progress(value=self.prog_map[cid], text=f'Running Chain `{cid}`...')
- self.prog_value = self.prog_map[cid]
- else:
- self.prog_value += 0.1
- self.progress_bar.progress(value=self.prog_value, text=f'Running Chain `{cid}`...')
-
- def on_chain_end(self, outputs, **kwargs) -> None:
- pass
-
-
-class ChatDataSQLSearchCallBackHandler(StreamlitCallbackHandler):
- def __init__(self) -> None:
- self.progress_bar = st.progress(value=0.0, text='Writing SQL...')
- self.status_bar = st.empty()
- self.prog_value = 0
- self.prog_interval = 0.2
-
- def on_llm_start(self, serialized, prompts, **kwargs) -> None:
- pass
-
- def on_llm_end(
- self,
- response: LLMResult,
- *args,
- **kwargs,
- ):
- text = response.generations[0][0].text
- if text.replace(' ', '').upper().startswith('SELECT'):
- st.write('We generated Vector SQL for you:')
- st.markdown(f'''```sql\n{format_sql(text, max_len=80)}\n```''')
- print(f"Vector SQL: {text}")
- self.prog_value += self.prog_interval
- self.progress_bar.progress(value=self.prog_value, text="Searching in DB...")
-
- def on_chain_start(self, serialized, inputs, **kwargs) -> None:
- cid = '.'.join(serialized['id'])
- self.prog_value += self.prog_interval
- self.progress_bar.progress(value=self.prog_value, text=f'Running Chain `{cid}`...')
-
- def on_chain_end(self, outputs, **kwargs) -> None:
- pass
-
-class ChatDataSQLAskCallBackHandler(ChatDataSQLSearchCallBackHandler):
- def __init__(self) -> None:
- self.progress_bar = st.progress(value=0.0, text='Writing SQL...')
- self.status_bar = st.empty()
- self.prog_value = 0
- self.prog_interval = 0.1
\ No newline at end of file
diff --git a/spaces/nagolinc/liteDungeon/app.py b/spaces/nagolinc/liteDungeon/app.py
deleted file mode 100644
index 42c04bd53742e1d1fc1d85c122888015c0209961..0000000000000000000000000000000000000000
--- a/spaces/nagolinc/liteDungeon/app.py
+++ /dev/null
@@ -1,106 +0,0 @@
-from asyncio import constants
-import gradio as gr
-import requests
-import os
-import re
-import random
-from words import *
-
-
-# GPT-J-6B API
-API_URL = "https://api-inference.huggingface.co/models/EleutherAI/gpt-j-6B"
-MAX_NEW_TOKENS = 25
-
-basePrompt="""
-The following session was recorded from a text adventure game.
-computer: you are an adventurer exploring the darkest dungeon
-player: enter dungeon
-"""
-
-default_story="computer: you are standing in front of a dark dungeon.\n"
-
-def fallbackResponse():
- return "You are attacked by a {monster}!".format(monster=random.choice(monsters))
-
-def continue_story(prompt,story):
- print("about to die",basePrompt,story,prompt)
- print("huh?",story)
- p=basePrompt+story+"player:"+str(prompt)+"\ncomputer:"
-
- print("got prompt:\n\n",p)
-
- print(f"*****Inside desc_generate - Prompt is :{p}")
- json_ = {"inputs": p,
- "parameters":
- {
- "top_p": 0.9,
- "temperature": 1.1,
- "max_new_tokens": MAX_NEW_TOKENS,
- "return_full_text": False,
- }}
- #response = requests.post(API_URL, headers=headers, json=json_)
- response = requests.post(API_URL, json=json_)
- output = response.json()
- print(f"If there was an error? Reason is : {output}")
-
-
-
- #error handling
- if "error" in output:
- print("using fallback description method!")
- #fallback method
- output_tmp=fallbackResponse()
- else:
-
- print("generated text was",output[0]['generated_text'])
-
- output_tmp = output[0]['generated_text']
- #strip whitespace
- output_tmp = output_tmp.strip()
- #truncate response at first newline
- if "\n" in output_tmp:
- idx = output_tmp.find('\n')
- output_tmp = output_tmp[:idx]
-
- #check if response starts with "computer:", if not use fallback
- #think I was just being dumb, should have included 'computer:' in the prompt
- #if not output_tmp.startswith("computer:"):
- # output_tmp = "computer:"+fallbackResponse()
-
- print("which was trimmed to",output_tmp)
-
- #truncate story to last 6 lines
- story_tmp = story.split("\n")
- if len(story_tmp)>6:
- story_tmp = story_tmp[-6:]
- story = "\n".join(story_tmp)
- #return story
- story=story+"player:"+prompt+"\ncomputer: "+output_tmp+"\n"
- return story
-
-
-demo = gr.Blocks()
-
-with demo:
- gr.Markdown("
LiteDungeon
")
- gr.Markdown(
- "
Create a text adventure, using GPT-J
"
- )
-
- with gr.Row():
- output_story = gr.Textbox(value=default_story,label="story",lines=7)
-
- with gr.Row():
- input_command = gr.Textbox(label="input",placeholder="look around")
-
- with gr.Row():
- b0 = gr.Button("Submit")
-
-
-
-
-
- b0.click(continue_story,inputs=[input_command,output_story],outputs=[output_story])
- #examples=examples
-
-demo.launch(enable_queue=True, debug=True)
\ No newline at end of file
diff --git a/spaces/nakamura196/yolov5-ndl-layout/ultralytics/yolov5/utils/flask_rest_api/example_request.py b/spaces/nakamura196/yolov5-ndl-layout/ultralytics/yolov5/utils/flask_rest_api/example_request.py
deleted file mode 100644
index ff21f30f93ca37578ce45366a1ddbe3f3eadaa79..0000000000000000000000000000000000000000
--- a/spaces/nakamura196/yolov5-ndl-layout/ultralytics/yolov5/utils/flask_rest_api/example_request.py
+++ /dev/null
@@ -1,13 +0,0 @@
-"""Perform test request"""
-import pprint
-
-import requests
-
-DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s"
-TEST_IMAGE = "zidane.jpg"
-
-image_data = open(TEST_IMAGE, "rb").read()
-
-response = requests.post(DETECTION_URL, files={"image": image_data}).json()
-
-pprint.pprint(response)
diff --git a/spaces/nasa-cisto-data-science-group/satvision-base-demo/pytorch-caney/pytorch_caney/data/datasets/segmentation_dataset.py b/spaces/nasa-cisto-data-science-group/satvision-base-demo/pytorch-caney/pytorch_caney/data/datasets/segmentation_dataset.py
deleted file mode 100644
index d81c757c7854d9f3c09f890ec85cb33a8482920c..0000000000000000000000000000000000000000
--- a/spaces/nasa-cisto-data-science-group/satvision-base-demo/pytorch-caney/pytorch_caney/data/datasets/segmentation_dataset.py
+++ /dev/null
@@ -1,284 +0,0 @@
-import os
-import logging
-from glob import glob
-from pathlib import Path
-from typing import Optional, Union
-
-import torch
-import numpy as np
-from torch.utils.data import Dataset
-from torch.utils.dlpack import from_dlpack
-
-import xarray as xr
-from terragpu.engine import array_module, df_module
-
-import terragpu.ai.preprocessing as preprocessing
-
-xp = array_module()
-xf = df_module()
-
-
-class PLSegmentationDataset(Dataset):
-
- def __init__(
- self,
- images_regex: Optional[str] = None,
- labels_regex: Optional[str] = None,
- dataset_dir: Optional[str] = None,
- generate_dataset: bool = False,
- tile_size: int = 256,
- max_patches: Union[float, int] = 100,
- augment: bool = True,
- chunks: dict = {'band': 1, 'x': 2048, 'y': 2048},
- input_bands: list = ['CB', 'B', 'G', 'Y', 'R', 'RE', 'N1', 'N2'],
- output_bands: list = ['B', 'G', 'R'],
- seed: int = 24,
- normalize: bool = True,
- pytorch: bool = True):
-
- super().__init__()
-
- # Dataset metadata
- self.input_bands = input_bands
- self.output_bands = output_bands
- self.chunks = chunks
- self.tile_size = tile_size
- self.seed = seed
- self.max_patches = max_patches
-
- # Preprocessing metadata
- self.generate_dataset = generate_dataset
- self.normalize = normalize
-
- # Validate several input sources
- assert dataset_dir is not None, \
- f'dataset_dir: {dataset_dir} does not exist.'
-
- # Setup directories structure
- self.dataset_dir = dataset_dir # where to store dataset
- self.images_dir = os.path.join(self.dataset_dir, 'images')
- self.labels_dir = os.path.join(self.dataset_dir, 'labels')
-
- if self.generate_dataset:
-
- logging.info(f"Starting to prepare dataset: {self.dataset_dir}")
- # Assert images_dir and labels_dir to be not None
- self.images_regex = images_regex # images location
- self.labels_regex = labels_regex # labels location
-
- # Create directories to store dataset
- os.makedirs(self.images_dir, exist_ok=True)
- os.makedirs(self.labels_dir, exist_ok=True)
-
- self.prepare_data()
-
- assert os.path.exists(self.images_dir), \
- f'{self.images_dir} does not exist. Make sure prepare_data: true.'
- assert os.path.exists(self.labels_dir), \
- f'{self.labels_dir} does not exist. Make sure prepare_data: true.'
-
- self.files = self.get_filenames()
- self.augment = augment
- self.pytorch = pytorch
-
- # -------------------------------------------------------------------------
- # Dataset methods
- # -------------------------------------------------------------------------
- def __len__(self):
- return len(self.files)
-
- def __repr__(self):
- s = 'Dataset class with {} files'.format(self.__len__())
- return s
-
- def __getitem__(self, idx):
-
- idx = idx % len(self.files)
- x, y = self.open_image(idx), self.open_mask(idx)
-
- if self.augment:
- x, y = self.transform(x, y)
- return x, y
-
- def transform(self, x, y):
-
- if xp.random.random_sample() > 0.5: # flip left and right
- x = torch.fliplr(x)
- y = torch.fliplr(y)
- if xp.random.random_sample() > 0.5: # reverse second dimension
- x = torch.flipud(x)
- y = torch.flipud(y)
- if xp.random.random_sample() > 0.5: # rotate 90 degrees
- x = torch.rot90(x, k=1, dims=[1, 2])
- y = torch.rot90(y, k=1, dims=[0, 1])
- if xp.random.random_sample() > 0.5: # rotate 180 degrees
- x = torch.rot90(x, k=2, dims=[1, 2])
- y = torch.rot90(y, k=2, dims=[0, 1])
- if xp.random.random_sample() > 0.5: # rotate 270 degrees
- x = torch.rot90(x, k=3, dims=[1, 2])
- y = torch.rot90(y, k=3, dims=[0, 1])
-
- # standardize 0.70, 0.30
- # if np.random.random_sample() > 0.70:
- # image = preprocess.standardizeLocalCalcTensor(image, means, stds)
- # else:
- # image = preprocess.standardizeGlobalCalcTensor(image)
- return x, y
-
- # -------------------------------------------------------------------------
- # preprocess methods
- # -------------------------------------------------------------------------
- def prepare_data(self):
-
- logging.info("Preparing dataset...")
- images_list = sorted(glob(self.images_regex))
- labels_list = sorted(glob(self.labels_regex))
-
- for image, label in zip(images_list, labels_list):
-
- # Read imagery from disk and process both image and mask
- filename = Path(image).stem
- image = xr.open_rasterio(image, chunks=self.chunks).load()
- label = xr.open_rasterio(label, chunks=self.chunks).values
-
- # Modify bands if necessary - in a future version, add indices
- image = preprocessing.modify_bands(
- img=image, input_bands=self.input_bands,
- output_bands=self.output_bands)
-
- # Asarray option to force array type
- image = xp.asarray(image.values)
- label = xp.asarray(label)
-
- # Move from chw to hwc, squeze mask if required
- image = xp.moveaxis(image, 0, -1).astype(np.int16)
- label = xp.squeeze(label) if len(label.shape) != 2 else label
- logging.info(f'Label classes from image: {xp.unique(label)}')
-
- # Generate dataset tiles
- image_tiles, label_tiles = preprocessing.gen_random_tiles(
- image=image, label=label, tile_size=self.tile_size,
- max_patches=self.max_patches, seed=self.seed)
- logging.info(f"Tiles: {image_tiles.shape}, {label_tiles.shape}")
-
- # Save to disk
- for id in range(image_tiles.shape[0]):
- xp.save(
- os.path.join(self.images_dir, f'{filename}_{id}.npy'),
- image_tiles[id, :, :, :])
- xp.save(
- os.path.join(self.labels_dir, f'{filename}_{id}.npy'),
- label_tiles[id, :, :])
- return
-
- # -------------------------------------------------------------------------
- # dataset methods
- # -------------------------------------------------------------------------
- def list_files(self, files_list: list = []):
-
- for i in os.listdir(self.images_dir):
- files_list.append(
- {
- 'image': os.path.join(self.images_dir, i),
- 'label': os.path.join(self.labels_dir, i)
- }
- )
- return files_list
-
- def open_image(self, idx: int, invert: bool = True):
- # image = imread(self.files[idx]['image'])
- image = xp.load(self.files[idx]['image'], allow_pickle=False)
- image = image.transpose((2, 0, 1)) if invert else image
- image = (
- image / xp.iinfo(image.dtype).max) if self.normalize else image
- return from_dlpack(image.toDlpack()) # .to(torch.float32)
-
- def open_mask(self, idx: int, add_dims: bool = False):
- # mask = imread(self.files[idx]['label'])
- mask = xp.load(self.files[idx]['label'], allow_pickle=False)
- mask = xp.expand_dims(mask, 0) if add_dims else mask
- return from_dlpack(mask.toDlpack()) # .to(torch.torch.int64)
-
-
-class SegmentationDataset(Dataset):
-
- def __init__(
- self, dataset_dir, pytorch=True, augment=True):
-
- super().__init__()
-
- self.files: list = self.list_files(dataset_dir)
- self.augment: bool = augment
- self.pytorch: bool = pytorch
- self.invert: bool = True
- self.normalize: bool = True
- self.standardize: bool = True
-
- # -------------------------------------------------------------------------
- # Common methods
- # -------------------------------------------------------------------------
- def __len__(self):
- return len(self.files)
-
- def __repr__(self):
- s = 'Dataset class with {} files'.format(self.__len__())
- return s
-
- def __getitem__(self, idx):
-
- # get data
- x = self.open_image(idx)
- y = self.open_mask(idx)
-
- # augment the data
- if self.augment:
-
- if xp.random.random_sample() > 0.5: # flip left and right
- x = torch.fliplr(x)
- y = torch.fliplr(y)
- if xp.random.random_sample() > 0.5: # reverse second dimension
- x = torch.flipud(x)
- y = torch.flipud(y)
- if xp.random.random_sample() > 0.5: # rotate 90 degrees
- x = torch.rot90(x, k=1, dims=[1, 2])
- y = torch.rot90(y, k=1, dims=[0, 1])
- if xp.random.random_sample() > 0.5: # rotate 180 degrees
- x = torch.rot90(x, k=2, dims=[1, 2])
- y = torch.rot90(y, k=2, dims=[0, 1])
- if xp.random.random_sample() > 0.5: # rotate 270 degrees
- x = torch.rot90(x, k=3, dims=[1, 2])
- y = torch.rot90(y, k=3, dims=[0, 1])
-
- return x, y
-
- # -------------------------------------------------------------------------
- # IO methods
- # -------------------------------------------------------------------------
- def get_filenames(self, dataset_dir: str, files_list: list = []):
-
- images_dir = os.path.join(dataset_dir, 'images')
- labels_dir = os.path.join(dataset_dir, 'labels')
-
- for i in os.listdir(images_dir):
- files_list.append(
- {
- 'image': os.path.join(images_dir, i),
- 'label': os.path.join(labels_dir, i)
- }
- )
- return files_list
-
- def open_image(self, idx: int):
- image = xp.load(self.files[idx]['image'], allow_pickle=False)
- if self.invert:
- image = image.transpose((2, 0, 1))
- if self.normalize:
- image = (image / xp.iinfo(image.dtype).max)
- if self.standardize:
- image = preprocessing.standardize_local(image)
- return from_dlpack(image.toDlpack()).float()
-
- def open_mask(self, idx: int, add_dims: bool = False):
- mask = xp.load(self.files[idx]['label'], allow_pickle=False)
- mask = xp.expand_dims(mask, 0) if add_dims else mask
- return from_dlpack(mask.toDlpack()).long()
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Antares Autotune Vst Download UPD.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Antares Autotune Vst Download UPD.md
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Antares Autotune VST Download: How to Get the Best Vocal Plug-Ins for Your Music Production
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Do you want to make your vocals sound professional, polished, and pitch-perfect? Do you want to enhance your music production with the best vocal plug-ins available? Do you want to learn how to download and use Antares Autotune VST, the industry standard for pitch correction and vocal effects?
If you answered yes to any of these questions, then this article is for you. In this article, you will learn everything you need to know about Antares Autotune VST, the most popular and powerful vocal plug-in in the world. You will discover what it is, how it works, how to download it, how to use it, and what are the best alternatives to it. By the end of this article, you will be able to get the most out of your vocals with Antares Autotune VST.
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So, what are you waiting for? Let's get started!
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What is Antares Autotune VST?
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Antares Autotune VST is a software plug-in that allows you to manipulate the pitch, tone, and quality of your vocals in real time or offline. It is designed to help you correct any pitch problems, enhance your vocal expression, and create various vocal effects. It is compatible with most digital audio workstations (DAWs) and can be used for recording, mixing, mastering, live performance, and more.
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A brief history of Antares and Auto-Tune technology
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Antares is a company that specializes in developing innovative audio technologies for music production. It was founded in 1990 by Dr. Andy Hildebrand, a former engineer at Exxon who invented a technique called autocorrelation for seismic data analysis. He applied this technique to audio processing and created the first version of Auto-Tune in 1997.
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Auto-Tune was originally intended as a tool for fixing subtle pitch errors in vocal recordings. However, it soon became a phenomenon when artists like Cher, T-Pain, Kanye West, and others started using it as a creative effect to create distinctive vocal sounds. Since then, Auto-Tune has become a ubiquitous element in modern music genres such as pop, hip-hop, R&B, EDM, and more.
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The features and benefits of Antares Autotune VST
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Antares Autotune
Antares Autotune VST has many features and benefits that make it the best vocal plug-in for your music production. Here are some of them:
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It offers flexible and precise pitch correction. You can choose from different modes and scales to suit your vocal style and genre. You can also adjust the speed, accuracy, and sensitivity of the pitch correction to achieve natural or dramatic results.
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It provides powerful and versatile vocal effects. You can use Antares Autotune VST to create harmonies, melodies, modulations, distortions, and other effects. You can also use it to change the gender, age, or character of your voice.
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It supports multiple formats and platforms. You can use Antares Autotune VST as a standalone application or as a plug-in in your DAW. You can also use it with Windows or Mac operating systems, and with VST, AU, AAX, or RTAS formats.
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It has user-friendly and customizable interface. You can easily access and control all the features and functions of Antares Autotune VST with its intuitive and graphical interface. You can also customize the interface to suit your preferences and workflow.
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It has high-quality and reliable performance. You can trust Antares Autotune VST to deliver professional and consistent results for your vocals. It has low latency, high resolution, and low CPU usage. It also has regular updates and technical support from Antares.
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The different versions and editions of Antares Autotune VST
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Antares Autotune VST has several versions and editions that cater to different needs and budgets of users. Here are some of them:
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Version/Edition
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Description
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Price
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Auto-Tune Pro
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The most advanced and complete version of Auto-Tune. It has all the features and functions of Auto-Tune, plus a new Auto-Key plug-in that automatically detects the key and scale of your music, a new Classic Mode that emulates the original Auto-Tune 5 sound, a new Auto Mode with a streamlined interface, a new Graph Mode with enhanced editing tools, and more.
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$399
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Auto-Tune Artist
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A more affordable and streamlined version of Auto-Tune Pro. It has most of the features and functions of Auto-Tune Pro, except for the Classic Mode, the Auto-Key plug-in, and some advanced Graph Mode features. It is designed for artists who want to quickly and easily tune their vocals in real time or offline.
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$199
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Auto-Tune EFX+
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A creative and fun version of Auto-Tune. It has the essential features and functions of Auto-Tune, plus a new EFX+ section that allows you to create various vocal effects such as vocoder, tube amp, filter, pitch shifter, chorus, flanger, phaser, delay, reverb, and more.
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$199
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Auto-Tune Access
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A simple and affordable version of Auto-Tune. It has the basic features and functions of Auto-Tune, such as pitch correction, humanize, formant correction, low latency, and bypass. It is designed for beginners who want to get started with Auto-Tune quickly and easily.
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$99
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Auto-Tune Unlimited
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A subscription-based service that gives you access to all the current and future versions and editions of Auto-Tune, plus other Antares plug-ins such as Harmony Engine, Mic Mod EFX, Mutator EVO, Aspire EVO, Articulator EVO, Punch EVO, Sybil EVO, Throat EVO, Warm EVO, Choir EVO, Duo EVO, Avox 4 Bundle, Wavosaur Bundle, Auto-Key Bundle, and more. It also gives you unlimited upgrades, support, and tutorials. It is designed for professionals who want to have the ultimate vocal production toolkit.
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$24.99/month or $249.90/year
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How to Download Antares Autotune VST?
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Now that you know what Antares Autotune VST is and what are the different versions and editions of it, you might be wondering how to download it. Well, it's very easy and simple. Just follow these steps:
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The requirements and compatibility of Antares Autotune VST
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Before you download Antares Autotune VST, you need to make sure that your computer meets the minimum requirements and is compatible with the plug-in. Here are the requirements and compatibility of Antares Autotune VST:
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Operating system: Windows 8.1 or later (64-bit) or Mac OS 10.13 or later (64-bit)
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Processor: Intel Core i5 or equivalent
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Memory: 4 GB RAM or more
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Hard disk space: 500 MB or more
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Internet connection: Required for registration, activation, updates, and online services
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DAW: Compatible with any DAW that supports VST, AU, AAX, or RTAS formats
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Audio interface: Compatible with any audio interface that supports ASIO (Windows) or Core Audio (Mac)
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Microphone: Compatible with any microphone that can connect to your audio interface
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The steps to download and install Antares Autotune VST
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Once you have checked the requirements and compatibility of Antares Autotune VST, you can proceed to download and install it. Here are the steps to do so:
Select the version or edition of Antares Autotune VST that you want to download. You can either buy it or try it for free for 14 days.
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Enter your email address and create a password to create an account with Antares.
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Check your email and confirm your account by clicking on the link sent by Antares.
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Login to your account and go to the Downloads section.
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Select the installer file for your operating system and download it.
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Run the installer file and follow the instructions on the screen to install Antares Autotune VST on your computer.
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Launch your DAW and scan for new plug-ins. You should see Antares Autotune VST in your plug-in list.
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Open Antares Autotune VST in your DAW and activate it by entering your license code or logging in to your account.
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Congratulations! You have successfully downloaded and installed Antares Autotune VST on your computer.
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The tips and tricks to optimize Antares Autotune VST performance
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To get the best results from Antares Autotune VST, you need to optimize its performance and settings. Here are some tips and tricks to do so:
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Use a good quality microphone and audio interface to record your vocals. Avoid any background noise, distortion, or clipping.
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Use a pop filter and a shock mount to reduce any plosives or vibrations in your vocals.
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Use headphones or monitor speakers to listen to your vocals while recording or editing. Avoid using laptop speakers or earphones.
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Use a buffer size of 256 samples or lower in your DAW to reduce latency. If you experience any glitches or dropouts, increase the buffer size slightly.
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Use a sample rate of 44.1 kHz or higher in your DAW to ensure high-quality audio processing. If you experience any CPU overload, lower the sample rate slightly.
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Use a bit depth of 24 bits or higher in your DAW to ensure high-resolution audio processing. If you experience any disk space issues, lower the bit depth slightly.
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Use a mono track for your vocals in your DAW. If you use a stereo track, make sure to pan it center.
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Bypass any other effects or processors on your vocal track before applying Antares Autotune VST. You can add them back later after tuning your vocals.
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Tune your vocals before mixing them with other instruments or tracks. You can adjust the volume, panning, EQ, compression, reverb, delay, and other effects later after tuning your vocals.
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Use the Auto Mode for quick and easy pitch correction. You can select the key and scale of your song, or use the Auto-Key plug-in to detect them automatically. You can also adjust the retune speed, humanize, flex-tune, natural vibrato, and formant parameters to fine-tune the pitch correction.
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Use the Graph Mode for advanced and detailed pitch editing. You can draw, move, cut, copy, paste, delete, or smooth any pitch curve on the graph. You can also zoom in or out, snap to grid, select notes, transpose, quantize, and more.
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Use the Classic Mode for the original Auto-Tune 5 sound. You can achieve the iconic "Auto-Tune effect" by setting the retune speed to zero and selecting a major or minor scale. You can also experiment with different scales and notes to create interesting effects.
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Use the EFX+ section for creative and fun vocal effects. You can choose from various presets or create your own effects by combining different modules. You can also adjust the mix, output, and bypass parameters to control the effects.
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How to Use Antares Autotune VST?
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Now that you know how to download and install Antares Autotune VST, you might be wondering how to use it. Well, it's very easy and simple. Just follow these steps:
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The basics of Antares Autotune VST interface and settings
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When you open Antares Autotune VST in your DAW, you will see its interface and settings. Here are the basics of them:
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The Input section shows the input level and pitch of your vocals. You can also mute or solo your vocals here.
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The Output section shows the output level and pitch of your vocals after applying Antares Autotune VST. You can also mute or solo your vocals here.
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The Key section allows you to select the key and scale of your song. You can either choose from a list of presets or enter your own custom key and scale. You can also use the Auto-Key plug-in to detect them automatically.
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The Mode section allows you to switch between different modes of Antares Autotune VST. You can choose from Auto Mode, Graph Mode, Classic Mode, or EFX+ Mode.
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The Settings section allows you to access and adjust various settings of Antares Autotune VST. You can change the input type, tracking, buffer size, sample rate, bit depth, latency compensation, bypass mode, keyboard shortcuts, preferences, and more.
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The Help section allows you to access the user manual, online tutorials, technical support, product registration, updates, and more.
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The modes and functions of Antares Autotune VST
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As mentioned earlier, Antares Autotune VST has four modes: Auto Mode, Graph Mode, Classic Mode, and EFX+ Mode. Each mode has different functions and features that you can use to tune and enhance your vocals. Here are the modes and functions of Antares Autotune VST:
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Auto Mode
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Auto Mode is the default mode of Antares Autotune VST. It is designed for quick and easy pitch correction in real time or offline. It has two main sections: Basic View and Advanced View.
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The Basic View shows the essential parameters of Auto Mode. You can adjust the retune speed, humanize, flex-tune, natural vibrato , and formant parameters. These parameters control how fast, how natural, how flexible, how expressive, and how realistic your pitch correction is.
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The Advanced View shows the additional parameters of Auto Mode. You can adjust the transpose, detune, offset, scale detune, throat length, and envelope parameters. These parameters control how high, how low, how fine, how wide, how deep, and how dynamic your pitch correction is.
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In both views, you can also see the keyboard display that shows the notes and scales of your song. You can use the keyboard display to select or deselect any notes or scales that you want to include or exclude in your pitch correction. You can also use the keyboard display to play or record any notes or melodies that you want to add to your vocals.
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Graph Mode
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Graph Mode is the advanced mode of Antares Autotune VST. It is designed for detailed and precise pitch editing in offline mode. It has two main sections: Pitch Graph and Note Editor.
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The Pitch Graph shows the graphical representation of your vocal pitch over time. You can zoom in or out, scroll left or right, snap to grid, select notes, and more. You can also see the pitch curve that shows the actual pitch of your vocals, and the correction curve that shows the corrected pitch of your vocals after applying Antares Autotune VST.
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The Note Editor shows the editing tools and functions that you can use to modify the pitch curve or the correction curve. You can draw, move, cut, copy, paste, delete, or smooth any pitch curve on the graph. You can also transpose, quantize, vibrato, formant shift, amplitude modulate, time shift, or throat model any pitch curve on the graph.
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In both sections, you can also see the toolbar that shows the options and settings that you can use to customize your pitch editing. You can change the view mode, zoom level, grid size, snap mode, selection mode, edit mode, playback mode, track mode, and more.
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Classic Mode
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Classic Mode is a special mode of Antares Autotune VST. It is designed to emulate the original Auto-Tune 5 sound that was popularized by artists like Cher and T-Pain. It has one main section: Classic View.
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The Classic View shows the simplified parameters of Classic Mode. You can adjust the retune speed and scale parameters. These parameters control how fast and how fixed your pitch correction is.
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In this view, you can also see the keyboard display that shows the notes and scales of your song. You can use the keyboard display to select or deselect any notes or scales that you want to include or exclude in your pitch correction.
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EFX+ Mode
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EFX+ Mode is a creative mode of Antares Autotune VST. It is designed to create various vocal effects using different modules and presets. It has two main sections: EFX+ View and Module View.
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The EFX+ View shows the main parameters of EFX+ Mode. You can adjust the mix , output, and bypass parameters. These parameters control how much, how loud, and how on or off your vocal effects are.
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The Module View shows the different modules and presets that you can use to create your vocal effects. You can choose from various modules such as vocoder, tube amp, filter, pitch shifter, chorus, flanger, phaser, delay, reverb, and more. You can also choose from various presets that are categorized by genre, mood, style, and more.
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In both views, you can also see the toolbar that shows the options and settings that you can use to customize your vocal effects. You can change the view mode, module mode, preset mode, edit mode, playback mode, track mode, and more.
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The examples and tutorials of Antares Autotune VST application
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To help you learn how to use Antares Autotune VST effectively and creatively, here are some examples and tutorials of Antares Autotune VST application. You can watch these videos and follow along with the instructions to practice your skills and improve your vocals.
How to Use Auto-Tune EFX+: Module Tutorial: This video shows you how to use the different modules and presets of EFX+ mode to create different vocal effects on your vocals.
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What are the Alternatives to Antares Autotune VST?
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Antares Autotune VST is undoubtedly the best vocal plug-in for your music production. However, it is not the only one. There are many other vocal plug-ins that offer similar or different features and functions that you can use to tune and enhance your vocals. Here are some of them:
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The pros and cons of Antares Autotune VST compared to other vocal plug-ins
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Before we look at the alternatives to Antares Autotune VST, let's compare the pros and cons of Antares Autotune VST with other vocal plug-ins. Here are some of them:
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Pros
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Cons
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- It is the industry standard for pitch correction and vocal effects.
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- It is relatively expensive compared to other vocal plug-ins.
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- It has a wide range of features and functions that suit any vocal style and genre.
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- It has a steep learning curve for beginners and advanced users.
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- It has a high-quality and reliable performance that delivers professional and consistent results.
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- It requires a high-end computer system and a good internet connection for optimal performance.
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- It has a user-friendly and customizable interface that adapts to your preferences and workflow.
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- It has some compatibility issues with some DAWs and audio interfaces.
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- It has regular updates and technical support from Antares.
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- It has some bugs and glitches that need fixing.
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The list and comparison of the best alternatives to Antares Autotune VST
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Now that we have compared the pros and cons of Antares Autotune VST with other vocal plug-ins, let's look at the list and comparison of the best alternatives to Antares Autotune VST. Here are some of them:
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Name
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Description
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Price
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Comparison with Antares Autotune VST
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Melodyne 5
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A software plug-in that allows you to edit the pitch, timing, dynamics, formants, sibilants, vibrato, phrasing, and more of your vocals in a musical way. It uses a technology called DNA (Direct Note Access) that allows you to edit individual notes within polyphonic audio material. It is compatible with most DAWs and can be used for recording, mixing, mastering, and more.
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$99-$849
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- It has more advanced and flexible pitch editing features than Antares Autotune VST.
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- It has less real-time and creative pitch correction and vocal effects features than Antares Autotune VST.
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Waves Tune Real-Time
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A software plug-in that allows you to correct the pitch of your vocals in real time with minimal latency and maximum transparency. It is designed for live performance, studio recording, and post-production. It has a simple and intuitive interface that lets you adjust the key, scale, speed, depth, and formant of your pitch correction. It is compatible with most DAWs and can be used with any microphone.
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$69.99
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- It has faster and smoother real-time pitch correction than Antares Autotune VST.
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- It has fewer offline and detailed pitch editing features than Antares Autotune VST.
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iZotope Nectar 3
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A software plug-in that allows you to enhance your vocals with a comprehensive set of tools and effects. It includes pitch correction, harmony, compression, EQ, de-essing, gate, saturation, reverb, delay, modulation, and more. It also has a feature called Vocal Assistant that analyzes your vocals and suggests the best settings for your vocal style and genre. It is compatible with most DAWs and can be used for recording, mixing, mastering, and more.
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$249
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- It has more vocal enhancement and processing features than Antares Autotune VST.
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- It has less precise and accurate pitch correction features than Antares Autotune VST.
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Revoice Pro 4
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A software plug-in that allows you to adjust the timing, pitch, level, vibrato, and sibilance of your vocals in a natural and realistic way. It is designed for vocal alignment, doubling, tuning, and editing. It also has a feature called APT (Audio Performance Transfer) that allows you to transfer the characteristics of one vocal performance to another. It is compatible with most DAWs and can be used for recording, mixing, mastering, and more.
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$599
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- It has more natural and realistic vocal editing features than Antares Autotune VST.
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- It has less creative and fun vocal effects features than Antares Autotune VST.
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MAutoPitch
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A free software plug-in that allows you to correct the pitch of your vocals in a simple and easy way. It has basic parameters such as depth, speed, formant shift, dry/wet mix, output gain, and bypass. It also has a feature called MIDI control that allows you to control the pitch of your vocals with a MIDI keyboard or controller. It is compatible with most DAWs and can be used for recording, mixing, mastering, and more.
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Free
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- It is cheaper and simpler than Antares Autotune VST.
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- It has fewer features and functions than Antares Autotune VST.
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The recommendations and advice on choosing the right vocal plug-in for your needs
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As you can see, there are many vocal plug-ins that you can use to tune and enhance your vocals. However, not all of them are suitable for your needs and preferences. Therefore, you need to consider some factors before choosing the right vocal plug-in for your music production. Here are some of them:
- - Your budget: How much are you willing to spend on a vocal plug-in? Do you want to buy it or try it for free? Do you want to pay a one-time fee or a subscription fee? - Your skill level: How experienced are you with using vocal plug-ins? Do you want a simple or complex interface? Do you want a steep or gentle learning curve? - Your vocal style: What kind of vocals do you want to produce? Do you want natural or artificial vocals? Do you want subtle or dramatic vocals? Do you want realistic or creative vocals? - Your music genre: What kind of music do you want to make? Do you want pop or rock vocals? Do you want hip-hop or EDM vocals? Do you want acoustic or electric vocals? - Your music goal: What do you want to achieve with your vocals? Do you want to fix or enhance your vocals? Do you want to create or modify your vocals ? Do you want to add or remove vocals? Based on these factors, you can narrow down your choices and find the best vocal plug-in for your music production. Here are some recommendations and advice on choosing the right vocal plug-in for your needs: - If you have a low budget and want a simple and easy vocal plug-in, you can try MAutoPitch. It is free and has basic pitch correction features that can improve your vocals quickly and easily. - If you have a medium budget and want a versatile and comprehensive vocal plug-in, you can try iZotope Nectar 3. It has a wide range of vocal enhancement and processing features that can suit any vocal style and genre. It also has a Vocal Assistant feature that can suggest the best settings for your vocals automatically. - If you have a high budget and want the best and most advanced vocal plug-in, you can try Antares Autotune VST. It has the most flexible and precise pitch correction and vocal effects features that can create professional and consistent results for your vocals. It also has the industry standard Auto-Tune technology that can give you the iconic "Auto-Tune effect" on your vocals. - If you are a beginner and want a gentle and intuitive vocal plug-in, you can try Auto-Tune Access. It has the basic features and functions of Antares Autotune VST, such as pitch correction, humanize, formant correction, low latency, and bypass. It also has a user-friendly and customizable interface that adapts to your preferences and workflow. - If you are an intermediate and want a fast and smooth vocal plug-in, you can try Waves Tune Real-Time. It has the essential features and functions of Antares Autotune VST, such as pitch correction, speed, depth, formant shift, dry/wet mix, output gain, and bypass. It also has a low latency and high transparency performance that allows you to correct your vocals in real time with minimal artifacts. - If you are an advanced and want a detailed and accurate vocal plug-in, you can try Melodyne 5. It has more advanced and flexible pitch editing features than Antares Autotune VST, such as timing, dynamics, formants, sibilants, vibrato, phrasing, and more. It also has a DNA technology that allows you to edit individual notes within polyphonic audio material. - If you are a creative and want a fun and experimental vocal plug-in, you can try Auto-Tune EFX+. It has the essential features and functions of Antares Autotune VST, such as pitch correction, humanize, flex-tune, natural vibrato , and formant shift. It also has an EFX+ section that allows you to create various vocal effects such as vocoder, tube amp, filter, pitch shifter, chorus, flanger, phaser, delay, reverb, and more.
Conclusion: Why You Should Download Antares Autotune VST Today
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In conclusion, Antares Autotune VST is the best vocal plug-in for your music production. It can help you correct any pitch problems, enhance your vocal expression, and create various vocal effects. It is compatible with most DAWs and can be used for recording, mixing, mastering, live performance, and more. It has a wide range of features and functions that suit any vocal style and genre. It has a high-quality and reliable performance that delivers professional and consistent results. It has a user-friendly and customizable interface that adapts to your preferences and workflow. It has regular updates and technical support from Antares.
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So, what are you waiting for? Download Antares Autotune VST today and get the most out of your vocals. You can either buy it or try it for free for 14 days from the official website of Antares at https://www.antarestech.com/. You can also check out the other versions and editions of Antares Autotune VST that cater to different needs and budgets of users.
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Thank you for reading this article. We hope you found it helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. We would love to hear from you.
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FAQs
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Here are some frequently asked questions about Antares Autotune VST:
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What is the difference between Auto-Tune and pitch correction?
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Auto-Tune is a brand name of a vocal plug-in developed by Antares that uses a technology called pitch correction. Pitch correction is a general term that refers to any process or technique that alters the pitch of an audio signal. Auto-Tune is one of the most popular and widely used pitch correction tools in the world.
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Is Auto-Tune cheating?
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No, Auto-Tune is not cheating. Auto-Tune is a tool that can help you improve your vocals, not replace them. Auto-Tune cannot make a bad singer sound good, but it can make a good singer sound better. Auto-Tune can also be used as a creative effect to create distinctive vocal sounds. Auto-Tune is not cheating, but a choice.
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Who uses Auto-Tune?
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Many artists use Auto-Tune for different purposes and effects. Some of the most famous artists who use Auto-Tune are Cher, T-Pain, Kanye West, Drake, Rihanna, Lady Gaga, Ed Sheeran, Ariana Grande, Justin Bieber, Taylor Swift, Billie Eilish, and more.
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How do I know if my vocals need Auto-Tune?
-
You can use your ears or a tuner to check if your vocals need Auto-Tune. If you hear any pitch problems or inconsistencies in your vocals, such as flat or sharp notes, off-key notes, or unwanted vibrato , you might need Auto-Tune. You can also use your taste or preference to decide if your vocals need Auto-Tune. If you want to make your vocals sound more professional, polished, or pitch-perfect, you might want to use Auto-Tune. If you want to make your vocals sound more natural, expressive, or original, you might not want to use Auto-Tune.
-
How do I get the best results from Auto-Tune?
-
You can get the best results from Auto-Tune by following these tips:
-
-
Use a good quality microphone and audio interface to record your vocals.
-
Use a pop filter and a shock mount to reduce any plosives or vibrations in your vocals.
-
Use headphones or monitor speakers to listen to your vocals while recording or editing.
-
Use a buffer size of 256 samples or lower in your DAW to reduce latency.
-
Use a sample rate of 44.1 kHz or higher in your DAW to ensure high-quality audio processing.
-
Use a bit depth of 24 bits or higher in your DAW to ensure high-resolution audio processing.
-
Use a mono track for your vocals in your DAW.
-
Bypass any other effects or processors on your vocal track before applying Auto-Tune.
-
Tune your vocals before mixing them with other instruments or tracks.
-
Choose the right mode, key, scale, and parameters for your vocals in Auto-Tune.
-
Experiment with different modes, scales, notes, and parameters to create different effects on your vocals in Auto-Tune.
-
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Kalemsoft Media Player Bar Cracked Windshield ((NEW)).md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Kalemsoft Media Player Bar Cracked Windshield ((NEW)).md
deleted file mode 100644
index f879341b90d7d2498d80f98cb76e49febfb7f2f5..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Kalemsoft Media Player Bar Cracked Windshield ((NEW)).md
+++ /dev/null
@@ -1,16 +0,0 @@
-
-
How to Fix a Cracked Windshield with KalemSoft Media Player
-
If you have a cracked windshield on your car, you might be wondering how to fix it without spending a lot of money. One possible solution is to use KalemSoft Media Player, a streaming media player app that can also display videos on your windshield. Here's how to do it:
-
-
Download KalemSoft Media Player for Windows, BlackBerry Playbook, Android or iPhone from their official website[^1^] or from the PlayBook Appstore[^2^]. Make sure you get the latest version (3.3.8.4515 as of April 2023) to avoid any bugs or glitches.
-
Connect your device to your car's audio system via Bluetooth or AUX cable.
-
Launch KalemSoft Media Player and select the video file you want to play. You can also stream videos from online sources like Dailymotion[^2^].
-
Adjust the settings of the app to fit your windshield size and shape. You can use the small bar that usually sits below your main video to resize, rotate and move the video around[^2^]. You can also change the brightness, contrast and color of the video to improve the visibility.
-
Enjoy your video on your windshield while driving safely. The app will automatically adjust the video quality and speed according to your network connection and battery level.
-
-
KalemSoft Media Player is a versatile and powerful app that can turn your cracked windshield into a fun and functional screen. However, this is not a permanent solution and you should still get your windshield repaired or replaced as soon as possible. Driving with a cracked windshield can be dangerous and illegal in some areas. KalemSoft Media Player is not responsible for any damages or injuries caused by using their app on your windshield.
If you want to learn more about KalemSoft Media Player and its features, you can visit their website or follow them on SoundCloud. You can also read some of the reviews and feedback from other users who have tried their app on various devices and platforms. KalemSoft Media Player is one of the most popular and highly rated media player apps on the market, with over 4,000 reviews and a 5-star rating on the PlayBook Appstore.
-
KalemSoft Media Player is not only a great app for fixing your cracked windshield, but also for enjoying your favorite videos and music on any screen. Whether you want to watch a movie on your TV, a YouTube video on your laptop, or a live stream on your phone, KalemSoft Media Player can handle it all. You can also use it to play your own media files from your device's storage or from external sources like USB drives or network shares. KalemSoft Media Player supports a wide range of formats and codecs, including MKV, AVI, MP4, WMV, FLV, MP3, AAC, WMA, FLAC and more.
-
KalemSoft Media Player is the ultimate media player app for any device and any screen. Download it today and see for yourself how it can transform your cracked windshield into a multimedia entertainment center.
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Neerja Telugu Dubbed Movies [VERIFIED].md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Neerja Telugu Dubbed Movies [VERIFIED].md
deleted file mode 100644
index 32491e5e0373a906bd3c48ffc80986a83cab3eda..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Neerja Telugu Dubbed Movies [VERIFIED].md
+++ /dev/null
@@ -1,17 +0,0 @@
-
-
Neerja: A Biopic on a Brave Heroine Now Available in Telugu
-
Neerja is a 2016 Hindi biopic film based on the life of Neerja Bhanot, a flight attendant who sacrificed her life to save the passengers of Pan Am Flight 73 when it was hijacked by terrorists in 1986. The film stars Sonam Kapoor as Neerja, Shabana Azmi as her mother, and Yogendra Tiku as her father. The film was directed by Ram Madhvani and produced by Atul Kasbekar and Fox Star Studios.
The film received critical acclaim and commercial success, winning several awards including the National Film Award for Best Feature Film in Hindi and two Filmfare Awards. It was also India's official entry for the 89th Academy Awards.
-
Now, the film is available in Telugu dubbed version on Disney+ Hotstar[^1^] [^2^], a popular streaming platform that offers a variety of movies and TV shows in different languages. You can watch Neerja in Telugu with high-quality video and audio on your smart devices anytime, anywhere.
-
If you are looking for a gripping and inspiring story of courage, patriotism, and humanity, Neerja is a must-watch film for you. Watch it now on Disney+ Hotstar and witness the heroic act of Neerja Bhanot that saved hundreds of lives.
Neerja Bhanot: The Life and Legacy of a Brave Heroine
-
Neerja Bhanot was born in Chandigarh, Punjab, India, on September 7, 1963, to Rama Bhanot and Harish Bhanot, a Mumbai-based journalist. After two sons, Akhil and Aneesh, she was the coupleâs third child and a much-desired daughter. She graduated from St. Xavierâs College after finishing secondary school at Bombay Scottish School. [^1^] [^3^]
-
She had a brief and unhappy marriage that ended in divorce. She returned to her parents' home and pursued a career in modelling and aviation. She appeared in several advertisements for brands like Benzer sarees, Binaca toothpaste, Godrej Besto detergent, Vaporex, and Vicco Turmeric cream. She also became a flight attendant for Pan Am, the largest international air carrier in the United States at that time. She was selected among 10,000 applicants and underwent rigorous training in Miami. She became a senior flight purser at the age of 22. [^1^] [^2^]
-
On September 5, 1986, she was on board Pan Am Flight 73 from Bombay to New York via Karachi and Frankfurt. The flight was hijacked by four armed terrorists who belonged to a terrorist organization that wanted to free Palestinian prisoners in Cyprus. Neerja alerted the cockpit crew as soon as the hijackers entered the plane, enabling them to escape through an overhead hatch. As the senior-most crew member left on board, she took charge of the situation and tried to calm down the passengers and crew. She also hid the passports of the American passengers to prevent them from being singled out by the hijackers. [^1^]
-
The hijackers held the passengers and crew hostage for 17 hours, during which they made several demands and threatened to kill the hostages. They also opened fire and set off explosives. Neerja showed remarkable courage and presence of mind in this crisis. She opened one of the emergency doors and helped many passengers escape. She also shielded three children from the bullets fired by the terrorists. She was fatally wounded by a gunshot while trying to save the children. She died two days before her 23rd birthday. [^1^] [^2^]
-
Neerja Bhanot was hailed as a national hero and an international icon of bravery and compassion. She saved 360 lives out of 379 passengers and crew on board. She became the youngest recipient of India's highest peacetime military award for bravery, the Ashoka Chakra. She also received several other awards and honors from India, Pakistan, and the United States, including the Tamgha-e-Insaaniyat (Awarded for showing incredible human kindness) from Pakistan, the Justice for Crimes Award from United States Attorney's Office for the District of Columbia, Special Courage award from US government, Flight Safety Foundation Heroism Award from USA among others. [^1^] [^2^]
-
Her life and heroism inspired many people across the world. A biopic film based on her story was released in 2016, starring Sonam Kapoor as Neerja and directed by Ram Madhvani. The film received critical acclaim and commercial success, winning several awards including two National Film Awards and six Filmfare Awards. It was also India's official entry for the 89th Academy Awards. [^1^] [^2^]
-
-
Neerja Bhanot was a remarkable woman who lived a short but meaningful life. She exemplified courage, selflessness, and humanity in the face of terror and violence. She is remembered as a heroine who gave her life for others.
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diff --git a/spaces/ngxson/poet-cat/frontend/pages/_app.tsx b/spaces/ngxson/poet-cat/frontend/pages/_app.tsx
deleted file mode 100644
index ae3be4faf5b79b43da7b2ef59d67a4ba3e1580d8..0000000000000000000000000000000000000000
--- a/spaces/ngxson/poet-cat/frontend/pages/_app.tsx
+++ /dev/null
@@ -1,8 +0,0 @@
-import '@/styles/globals.css'
-import '@/styles/bootstrap.css'
-import '@/styles/chat.css'
-import type { AppProps } from 'next/app'
-
-export default function App({ Component, pageProps }: AppProps) {
- return
-}
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DeepLab/deeplab/semantic_seg.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/DeepLab/deeplab/semantic_seg.py
deleted file mode 100644
index d4625c52d96b2a700d828112c2a2ea80f5028330..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/DeepLab/deeplab/semantic_seg.py
+++ /dev/null
@@ -1,348 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from typing import Callable, Dict, List, Optional, Tuple, Union
-import fvcore.nn.weight_init as weight_init
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.config import configurable
-from detectron2.layers import ASPP, Conv2d, DepthwiseSeparableConv2d, ShapeSpec, get_norm
-from detectron2.modeling import SEM_SEG_HEADS_REGISTRY
-
-from .loss import DeepLabCE
-
-
-@SEM_SEG_HEADS_REGISTRY.register()
-class DeepLabV3PlusHead(nn.Module):
- """
- A semantic segmentation head described in :paper:`DeepLabV3+`.
- """
-
- @configurable
- def __init__(
- self,
- input_shape: Dict[str, ShapeSpec],
- *,
- project_channels: List[int],
- aspp_dilations: List[int],
- aspp_dropout: float,
- decoder_channels: List[int],
- common_stride: int,
- norm: Union[str, Callable],
- train_size: Optional[Tuple],
- loss_weight: float = 1.0,
- loss_type: str = "cross_entropy",
- ignore_value: int = -1,
- num_classes: Optional[int] = None,
- use_depthwise_separable_conv: bool = False,
- ):
- """
- NOTE: this interface is experimental.
-
- Args:
- input_shape: shape of the input features. They will be ordered by stride
- and the last one (with largest stride) is used as the input to the
- decoder (i.e. the ASPP module); the rest are low-level feature for
- the intermediate levels of decoder.
- project_channels (list[int]): a list of low-level feature channels.
- The length should be len(in_features) - 1.
- aspp_dilations (list(int)): a list of 3 dilations in ASPP.
- aspp_dropout (float): apply dropout on the output of ASPP.
- decoder_channels (list[int]): a list of output channels of each
- decoder stage. It should have the same length as "in_features"
- (each element in "in_features" corresponds to one decoder stage).
- common_stride (int): output stride of decoder.
- norm (str or callable): normalization for all conv layers.
- train_size (tuple): (height, width) of training images.
- loss_weight (float): loss weight.
- loss_type (str): type of loss function, 2 opptions:
- (1) "cross_entropy" is the standard cross entropy loss.
- (2) "hard_pixel_mining" is the loss in DeepLab that samples
- top k% hardest pixels.
- ignore_value (int): category to be ignored during training.
- num_classes (int): number of classes, if set to None, the decoder
- will not construct a predictor.
- use_depthwise_separable_conv (bool): use DepthwiseSeparableConv2d
- in ASPP and decoder.
- """
- super().__init__()
- input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
-
- # fmt: off
- self.in_features = [k for k, v in input_shape] # starting from "res2" to "res5"
- in_channels = [x[1].channels for x in input_shape]
- in_strides = [x[1].stride for x in input_shape]
- aspp_channels = decoder_channels[-1]
- self.ignore_value = ignore_value
- self.common_stride = common_stride # output stride
- self.loss_weight = loss_weight
- self.loss_type = loss_type
- self.decoder_only = num_classes is None
- self.use_depthwise_separable_conv = use_depthwise_separable_conv
- # fmt: on
-
- assert (
- len(project_channels) == len(self.in_features) - 1
- ), "Expected {} project_channels, got {}".format(
- len(self.in_features) - 1, len(project_channels)
- )
- assert len(decoder_channels) == len(
- self.in_features
- ), "Expected {} decoder_channels, got {}".format(
- len(self.in_features), len(decoder_channels)
- )
- self.decoder = nn.ModuleDict()
-
- use_bias = norm == ""
- for idx, in_channel in enumerate(in_channels):
- decoder_stage = nn.ModuleDict()
-
- if idx == len(self.in_features) - 1:
- # ASPP module
- if train_size is not None:
- train_h, train_w = train_size
- encoder_stride = in_strides[-1]
- if train_h % encoder_stride or train_w % encoder_stride:
- raise ValueError("Crop size need to be divisible by encoder stride.")
- pool_h = train_h // encoder_stride
- pool_w = train_w // encoder_stride
- pool_kernel_size = (pool_h, pool_w)
- else:
- pool_kernel_size = None
- project_conv = ASPP(
- in_channel,
- aspp_channels,
- aspp_dilations,
- norm=norm,
- activation=F.relu,
- pool_kernel_size=pool_kernel_size,
- dropout=aspp_dropout,
- use_depthwise_separable_conv=use_depthwise_separable_conv,
- )
- fuse_conv = None
- else:
- project_conv = Conv2d(
- in_channel,
- project_channels[idx],
- kernel_size=1,
- bias=use_bias,
- norm=get_norm(norm, project_channels[idx]),
- activation=F.relu,
- )
- weight_init.c2_xavier_fill(project_conv)
- if use_depthwise_separable_conv:
- # We use a single 5x5 DepthwiseSeparableConv2d to replace
- # 2 3x3 Conv2d since they have the same receptive field,
- # proposed in :paper:`Panoptic-DeepLab`.
- fuse_conv = DepthwiseSeparableConv2d(
- project_channels[idx] + decoder_channels[idx + 1],
- decoder_channels[idx],
- kernel_size=5,
- padding=2,
- norm1=norm,
- activation1=F.relu,
- norm2=norm,
- activation2=F.relu,
- )
- else:
- fuse_conv = nn.Sequential(
- Conv2d(
- project_channels[idx] + decoder_channels[idx + 1],
- decoder_channels[idx],
- kernel_size=3,
- padding=1,
- bias=use_bias,
- norm=get_norm(norm, decoder_channels[idx]),
- activation=F.relu,
- ),
- Conv2d(
- decoder_channels[idx],
- decoder_channels[idx],
- kernel_size=3,
- padding=1,
- bias=use_bias,
- norm=get_norm(norm, decoder_channels[idx]),
- activation=F.relu,
- ),
- )
- weight_init.c2_xavier_fill(fuse_conv[0])
- weight_init.c2_xavier_fill(fuse_conv[1])
-
- decoder_stage["project_conv"] = project_conv
- decoder_stage["fuse_conv"] = fuse_conv
-
- self.decoder[self.in_features[idx]] = decoder_stage
-
- if not self.decoder_only:
- self.predictor = Conv2d(
- decoder_channels[0], num_classes, kernel_size=1, stride=1, padding=0
- )
- nn.init.normal_(self.predictor.weight, 0, 0.001)
- nn.init.constant_(self.predictor.bias, 0)
-
- if self.loss_type == "cross_entropy":
- self.loss = nn.CrossEntropyLoss(reduction="mean", ignore_index=self.ignore_value)
- elif self.loss_type == "hard_pixel_mining":
- self.loss = DeepLabCE(ignore_label=self.ignore_value, top_k_percent_pixels=0.2)
- else:
- raise ValueError("Unexpected loss type: %s" % self.loss_type)
-
- @classmethod
- def from_config(cls, cfg, input_shape):
- if cfg.INPUT.CROP.ENABLED:
- assert cfg.INPUT.CROP.TYPE == "absolute"
- train_size = cfg.INPUT.CROP.SIZE
- else:
- train_size = None
- decoder_channels = [cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM] * (
- len(cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES) - 1
- ) + [cfg.MODEL.SEM_SEG_HEAD.ASPP_CHANNELS]
- ret = dict(
- input_shape={
- k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES
- },
- project_channels=cfg.MODEL.SEM_SEG_HEAD.PROJECT_CHANNELS,
- aspp_dilations=cfg.MODEL.SEM_SEG_HEAD.ASPP_DILATIONS,
- aspp_dropout=cfg.MODEL.SEM_SEG_HEAD.ASPP_DROPOUT,
- decoder_channels=decoder_channels,
- common_stride=cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE,
- norm=cfg.MODEL.SEM_SEG_HEAD.NORM,
- train_size=train_size,
- loss_weight=cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT,
- loss_type=cfg.MODEL.SEM_SEG_HEAD.LOSS_TYPE,
- ignore_value=cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
- num_classes=cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES,
- use_depthwise_separable_conv=cfg.MODEL.SEM_SEG_HEAD.USE_DEPTHWISE_SEPARABLE_CONV,
- )
- return ret
-
- def forward(self, features, targets=None):
- """
- Returns:
- In training, returns (None, dict of losses)
- In inference, returns (CxHxW logits, {})
- """
- y = self.layers(features)
- if self.decoder_only:
- # Output from self.layers() only contains decoder feature.
- return y
- if self.training:
- return None, self.losses(y, targets)
- else:
- y = F.interpolate(
- y, scale_factor=self.common_stride, mode="bilinear", align_corners=False
- )
- return y, {}
-
- def layers(self, features):
- # Reverse feature maps into top-down order (from low to high resolution)
- for f in self.in_features[::-1]:
- x = features[f]
- proj_x = self.decoder[f]["project_conv"](x)
- if self.decoder[f]["fuse_conv"] is None:
- # This is aspp module
- y = proj_x
- else:
- # Upsample y
- y = F.interpolate(y, size=proj_x.size()[2:], mode="bilinear", align_corners=False)
- y = torch.cat([proj_x, y], dim=1)
- y = self.decoder[f]["fuse_conv"](y)
- if not self.decoder_only:
- y = self.predictor(y)
- return y
-
- def losses(self, predictions, targets):
- predictions = F.interpolate(
- predictions, scale_factor=self.common_stride, mode="bilinear", align_corners=False
- )
- loss = self.loss(predictions, targets)
- losses = {"loss_sem_seg": loss * self.loss_weight}
- return losses
-
-
-@SEM_SEG_HEADS_REGISTRY.register()
-class DeepLabV3Head(nn.Module):
- """
- A semantic segmentation head described in :paper:`DeepLabV3`.
- """
-
- def __init__(self, cfg, input_shape: Dict[str, ShapeSpec]):
- super().__init__()
-
- # fmt: off
- self.in_features = cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES
- in_channels = [input_shape[f].channels for f in self.in_features]
- aspp_channels = cfg.MODEL.SEM_SEG_HEAD.ASPP_CHANNELS
- aspp_dilations = cfg.MODEL.SEM_SEG_HEAD.ASPP_DILATIONS
- self.ignore_value = cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE
- num_classes = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES
- conv_dims = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
- self.common_stride = cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE # output stride
- norm = cfg.MODEL.SEM_SEG_HEAD.NORM
- self.loss_weight = cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT
- self.loss_type = cfg.MODEL.SEM_SEG_HEAD.LOSS_TYPE
- train_crop_size = cfg.INPUT.CROP.SIZE
- aspp_dropout = cfg.MODEL.SEM_SEG_HEAD.ASPP_DROPOUT
- use_depthwise_separable_conv = cfg.MODEL.SEM_SEG_HEAD.USE_DEPTHWISE_SEPARABLE_CONV
- # fmt: on
-
- assert len(self.in_features) == 1
- assert len(in_channels) == 1
-
- # ASPP module
- if cfg.INPUT.CROP.ENABLED:
- assert cfg.INPUT.CROP.TYPE == "absolute"
- train_crop_h, train_crop_w = train_crop_size
- if train_crop_h % self.common_stride or train_crop_w % self.common_stride:
- raise ValueError("Crop size need to be divisible by output stride.")
- pool_h = train_crop_h // self.common_stride
- pool_w = train_crop_w // self.common_stride
- pool_kernel_size = (pool_h, pool_w)
- else:
- pool_kernel_size = None
- self.aspp = ASPP(
- in_channels[0],
- aspp_channels,
- aspp_dilations,
- norm=norm,
- activation=F.relu,
- pool_kernel_size=pool_kernel_size,
- dropout=aspp_dropout,
- use_depthwise_separable_conv=use_depthwise_separable_conv,
- )
-
- self.predictor = Conv2d(conv_dims, num_classes, kernel_size=1, stride=1, padding=0)
- nn.init.normal_(self.predictor.weight, 0, 0.001)
- nn.init.constant_(self.predictor.bias, 0)
-
- if self.loss_type == "cross_entropy":
- self.loss = nn.CrossEntropyLoss(reduction="mean", ignore_index=self.ignore_value)
- elif self.loss_type == "hard_pixel_mining":
- self.loss = DeepLabCE(ignore_label=self.ignore_value, top_k_percent_pixels=0.2)
- else:
- raise ValueError("Unexpected loss type: %s" % self.loss_type)
-
- def forward(self, features, targets=None):
- """
- Returns:
- In training, returns (None, dict of losses)
- In inference, returns (CxHxW logits, {})
- """
- x = features[self.in_features[0]]
- x = self.aspp(x)
- x = self.predictor(x)
- if self.training:
- return None, self.losses(x, targets)
- else:
- x = F.interpolate(
- x, scale_factor=self.common_stride, mode="bilinear", align_corners=False
- )
- return x, {}
-
- def losses(self, predictions, targets):
- predictions = F.interpolate(
- predictions, scale_factor=self.common_stride, mode="bilinear", align_corners=False
- )
- loss = self.loss(predictions, targets)
- losses = {"loss_sem_seg": loss * self.loss_weight}
- return losses
diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/projects/PointRend/point_rend/semantic_seg.py b/spaces/nikitaPDL2023/assignment4/detectron2/projects/PointRend/point_rend/semantic_seg.py
deleted file mode 100644
index ea65200996777022cbb1c3c5dd9c943b67ca4ab1..0000000000000000000000000000000000000000
--- a/spaces/nikitaPDL2023/assignment4/detectron2/projects/PointRend/point_rend/semantic_seg.py
+++ /dev/null
@@ -1,135 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-from typing import Dict
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from detectron2.layers import ShapeSpec, cat
-from detectron2.modeling import SEM_SEG_HEADS_REGISTRY
-
-from .point_features import (
- get_uncertain_point_coords_on_grid,
- get_uncertain_point_coords_with_randomness,
- point_sample,
-)
-from .point_head import build_point_head
-
-
-def calculate_uncertainty(sem_seg_logits):
- """
- For each location of the prediction `sem_seg_logits` we estimate uncerainty as the
- difference between top first and top second predicted logits.
-
- Args:
- mask_logits (Tensor): A tensor of shape (N, C, ...), where N is the minibatch size and
- C is the number of foreground classes. The values are logits.
-
- Returns:
- scores (Tensor): A tensor of shape (N, 1, ...) that contains uncertainty scores with
- the most uncertain locations having the highest uncertainty score.
- """
- top2_scores = torch.topk(sem_seg_logits, k=2, dim=1)[0]
- return (top2_scores[:, 1] - top2_scores[:, 0]).unsqueeze(1)
-
-
-@SEM_SEG_HEADS_REGISTRY.register()
-class PointRendSemSegHead(nn.Module):
- """
- A semantic segmentation head that combines a head set in `POINT_HEAD.COARSE_SEM_SEG_HEAD_NAME`
- and a point head set in `MODEL.POINT_HEAD.NAME`.
- """
-
- def __init__(self, cfg, input_shape: Dict[str, ShapeSpec]):
- super().__init__()
-
- self.ignore_value = cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE
-
- self.coarse_sem_seg_head = SEM_SEG_HEADS_REGISTRY.get(
- cfg.MODEL.POINT_HEAD.COARSE_SEM_SEG_HEAD_NAME
- )(cfg, input_shape)
- self._init_point_head(cfg, input_shape)
-
- def _init_point_head(self, cfg, input_shape: Dict[str, ShapeSpec]):
- # fmt: off
- assert cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES == cfg.MODEL.POINT_HEAD.NUM_CLASSES
- feature_channels = {k: v.channels for k, v in input_shape.items()}
- self.in_features = cfg.MODEL.POINT_HEAD.IN_FEATURES
- self.train_num_points = cfg.MODEL.POINT_HEAD.TRAIN_NUM_POINTS
- self.oversample_ratio = cfg.MODEL.POINT_HEAD.OVERSAMPLE_RATIO
- self.importance_sample_ratio = cfg.MODEL.POINT_HEAD.IMPORTANCE_SAMPLE_RATIO
- self.subdivision_steps = cfg.MODEL.POINT_HEAD.SUBDIVISION_STEPS
- self.subdivision_num_points = cfg.MODEL.POINT_HEAD.SUBDIVISION_NUM_POINTS
- # fmt: on
-
- in_channels = int(np.sum([feature_channels[f] for f in self.in_features]))
- self.point_head = build_point_head(cfg, ShapeSpec(channels=in_channels, width=1, height=1))
-
- def forward(self, features, targets=None):
- coarse_sem_seg_logits = self.coarse_sem_seg_head.layers(features)
-
- if self.training:
- losses = self.coarse_sem_seg_head.losses(coarse_sem_seg_logits, targets)
-
- with torch.no_grad():
- point_coords = get_uncertain_point_coords_with_randomness(
- coarse_sem_seg_logits,
- calculate_uncertainty,
- self.train_num_points,
- self.oversample_ratio,
- self.importance_sample_ratio,
- )
- coarse_features = point_sample(coarse_sem_seg_logits, point_coords, align_corners=False)
-
- fine_grained_features = cat(
- [
- point_sample(features[in_feature], point_coords, align_corners=False)
- for in_feature in self.in_features
- ],
- dim=1,
- )
- point_logits = self.point_head(fine_grained_features, coarse_features)
- point_targets = (
- point_sample(
- targets.unsqueeze(1).to(torch.float),
- point_coords,
- mode="nearest",
- align_corners=False,
- )
- .squeeze(1)
- .to(torch.long)
- )
- losses["loss_sem_seg_point"] = F.cross_entropy(
- point_logits, point_targets, reduction="mean", ignore_index=self.ignore_value
- )
- return None, losses
- else:
- sem_seg_logits = coarse_sem_seg_logits.clone()
- for _ in range(self.subdivision_steps):
- sem_seg_logits = F.interpolate(
- sem_seg_logits, scale_factor=2, mode="bilinear", align_corners=False
- )
- uncertainty_map = calculate_uncertainty(sem_seg_logits)
- point_indices, point_coords = get_uncertain_point_coords_on_grid(
- uncertainty_map, self.subdivision_num_points
- )
- fine_grained_features = cat(
- [
- point_sample(features[in_feature], point_coords, align_corners=False)
- for in_feature in self.in_features
- ]
- )
- coarse_features = point_sample(
- coarse_sem_seg_logits, point_coords, align_corners=False
- )
- point_logits = self.point_head(fine_grained_features, coarse_features)
-
- # put sem seg point predictions to the right places on the upsampled grid.
- N, C, H, W = sem_seg_logits.shape
- point_indices = point_indices.unsqueeze(1).expand(-1, C, -1)
- sem_seg_logits = (
- sem_seg_logits.reshape(N, C, H * W)
- .scatter_(2, point_indices, point_logits)
- .view(N, C, H, W)
- )
- return sem_seg_logits, {}
diff --git a/spaces/nnaii/White-box-Cartoonization/wbc/cartoonize.py b/spaces/nnaii/White-box-Cartoonization/wbc/cartoonize.py
deleted file mode 100644
index 25faf1ceb95aaed9a3f7a7982d17a03dc6bc32b1..0000000000000000000000000000000000000000
--- a/spaces/nnaii/White-box-Cartoonization/wbc/cartoonize.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import os
-import cv2
-import numpy as np
-import tensorflow as tf
-import wbc.network as network
-import wbc.guided_filter as guided_filter
-from tqdm import tqdm
-
-
-def resize_crop(image):
- h, w, c = np.shape(image)
- if min(h, w) > 720:
- if h > w:
- h, w = int(720 * h / w), 720
- else:
- h, w = 720, int(720 * w / h)
- image = cv2.resize(image, (w, h),
- interpolation=cv2.INTER_AREA)
- h, w = (h // 8) * 8, (w // 8) * 8
- image = image[:h, :w, :]
- return image
-
-
-def cartoonize(load_folder, save_folder, model_path):
- print(model_path)
- input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
- network_out = network.unet_generator(input_photo)
- final_out = guided_filter.guided_filter(input_photo, network_out, r=1, eps=5e-3)
-
- all_vars = tf.trainable_variables()
- gene_vars = [var for var in all_vars if 'generator' in var.name]
- saver = tf.train.Saver(var_list=gene_vars)
-
- config = tf.ConfigProto()
- config.gpu_options.allow_growth = True
- sess = tf.Session(config=config)
-
- sess.run(tf.global_variables_initializer())
- saver.restore(sess, tf.train.latest_checkpoint(model_path))
- name_list = os.listdir(load_folder)
- for name in tqdm(name_list):
- try:
- load_path = os.path.join(load_folder, name)
- save_path = os.path.join(save_folder, name)
- image = cv2.imread(load_path)
- image = resize_crop(image)
- batch_image = image.astype(np.float32) / 127.5 - 1
- batch_image = np.expand_dims(batch_image, axis=0)
- output = sess.run(final_out, feed_dict={input_photo: batch_image})
- output = (np.squeeze(output) + 1) * 127.5
- output = np.clip(output, 0, 255).astype(np.uint8)
- cv2.imwrite(save_path, output)
- except:
- print('cartoonize {} failed'.format(load_path))
-
-
-class Cartoonize:
- def __init__(self, model_path):
- print(model_path)
- self.input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
- network_out = network.unet_generator(self.input_photo)
- self.final_out = guided_filter.guided_filter(self.input_photo, network_out, r=1, eps=5e-3)
-
- all_vars = tf.trainable_variables()
- gene_vars = [var for var in all_vars if 'generator' in var.name]
- saver = tf.train.Saver(var_list=gene_vars)
-
- config = tf.ConfigProto()
- config.gpu_options.allow_growth = True
- self.sess = tf.Session(config=config)
-
- self.sess.run(tf.global_variables_initializer())
- saver.restore(self.sess, tf.train.latest_checkpoint(model_path))
-
- def run(self, load_folder, save_folder):
- name_list = os.listdir(load_folder)
- for name in tqdm(name_list):
- try:
- load_path = os.path.join(load_folder, name)
- save_path = os.path.join(save_folder, name)
- image = cv2.imread(load_path)
- image = resize_crop(image)
- batch_image = image.astype(np.float32) / 127.5 - 1
- batch_image = np.expand_dims(batch_image, axis=0)
- output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image})
- output = (np.squeeze(output) + 1) * 127.5
- output = np.clip(output, 0, 255).astype(np.uint8)
- cv2.imwrite(save_path, output)
- except:
- print('cartoonize {} failed'.format(load_path))
-
- def run_sigle(self, load_path, save_path):
- try:
- image = cv2.imread(load_path)
- image = resize_crop(image)
- batch_image = image.astype(np.float32) / 127.5 - 1
- batch_image = np.expand_dims(batch_image, axis=0)
- output = self.sess.run(self.final_out, feed_dict={self.input_photo: batch_image})
- output = (np.squeeze(output) + 1) * 127.5
- output = np.clip(output, 0, 255).astype(np.uint8)
- cv2.imwrite(save_path, output)
- except:
- print('cartoonize {} failed'.format(load_path))
-
-
-if __name__ == '__main__':
- model_path = 'saved_models'
- load_folder = 'test_images'
- save_folder = 'cartoonized_images'
- if not os.path.exists(save_folder):
- os.mkdir(save_folder)
- cartoonize(load_folder, save_folder, model_path)
diff --git a/spaces/noes14155/img_All_models/app.py b/spaces/noes14155/img_All_models/app.py
deleted file mode 100644
index 1508d226c7bda927c753e2dfff95d038faa67eb2..0000000000000000000000000000000000000000
--- a/spaces/noes14155/img_All_models/app.py
+++ /dev/null
@@ -1,216 +0,0 @@
-import gradio as gr
-import os
-import sys
-from pathlib import Path
-
-models = [
- {"name": "Deliberate", "url": "Masagin/Deliberate"},
- {"name": "Dreamlike Anime", "url": "dreamlike-art/dreamlike-anime-1.0"},
- {"name": "Dreamlike Diffusion", "url": "dreamlike-art/dreamlike-diffusion-1.0"},
- {"name": "Dreamlike Photoreal", "url": "dreamlike-art/dreamlike-photoreal-2.0"},
- {"name": "Dreamshaper", "url": "Lykon/DreamShaper"},
- {"name": "Never Ending Dream 2", "url": "luongphamit/NeverEnding-Dream2"},
- {"name": "Protogen X 5.8", "url": "darkstorm2150/Protogen_x5.8_Official_Release"},
- {"name": "❤ ART MODELS ==========", "url": "dreamlike-art/dreamlike-diffusion-1.0"},
- {"name": "Alice in Diffusion Land", "url": "Guizmus/SDArt_AliceInDiffusionLand"},
- {"name": "Alt Clip", "url": "BAAI/AltCLIP"},
- {"name": "Anything Midjourney 4.1", "url": "Joeythemonster/anything-midjourney-v-4-1"},
- {"name": "Chaos and Order", "url": "Guizmus/SDArt_ChaosAndOrder768"},
- {"name": "Chilloutclara", "url": "Fred99774/chilloutvlara"},
- {"name": "Comic Diffusion", "url": "ogkalu/Comic-Diffusion"},
- {"name": "Cosmic Horros 768", "url": "Guizmus/SDArt_cosmichorrors768"},
- {"name": "Cosmic Horros", "url": "Guizmus/SDArt_cosmichorrors"},
- {"name": "DGSpitzer", "url": "DGSpitzer/DGSpitzer-Art-Diffusion"},
- {"name": "Dungeons and Diffusion", "url": "0xJustin/Dungeons-and-Diffusion"},
- {"name": "Elden Ring", "url": "nitrosocke/elden-ring-diffusion"},
- {"name": "Epic Diffusion 1.1", "url": "johnslegers/epic-diffusion-v1.1"},
- {"name": "Epic Diffusion", "url": "johnslegers/epic-diffusion"},
- {"name": "EpicMix Realism", "url": "Duskfallcrew/EpicMix_Realism"},
- {"name": "Fantasy Mix", "url": "theintuitiveye/FantasyMix"},
- {"name": "Girl New 1", "url": "Fred99774/girlnew1"},
- {"name": "Lit 6B", "url": "hakurei/lit-6B"},
- {"name": "Luna Diffusion", "url": "proximasanfinetuning/luna-diffusion"},
- {"name": "Midjourney 4.0", "url": "flax/midjourney-v4-diffusion"},
- {"name": "Midjourney 4.1", "url": "Joeythemonster/anything-midjourney-v-4-1"},
- {"name": "Mo-Di Diffusion", "url": "nitrosocke/mo-di-diffusion"},
- {"name": "Nitro Diffusion", "url": "nitrosocke/Nitro-Diffusion"},
- {"name": "Openjourney V2", "url": "prompthero/openjourney-v2"},
- {"name": "Openjourney", "url": "prompthero/openjourney"},
- {"name": "Seek Art Mega", "url": "coreco/seek.art_MEGA"},
- {"name": "Something", "url": "Guizmus/SDArt_something"},
- {"name": "Spider Verse diffusion", "url": "nitrosocke/spider-verse-diffusion"},
- {"name": "Vintedois 1.0", "url": "22h/vintedois-diffusion-v0-1"},
- {"name": "Vintedois 2.0", "url": "22h/vintedois-diffusion-v0-2"},
- {"name": "❤ ART STYLES ==========", "url": "joachimsallstrom/Double-Exposure-Diffusion"},
- {"name": "Balloon Art", "url": "Fictiverse/Stable_Diffusion_BalloonArt_Model"},
- {"name": "Double Exposure Diffusion", "url": "joachimsallstrom/Double-Exposure-Diffusion"},
- {"name": "Fluid Art", "url": "Fictiverse/Stable_Diffusion_FluidArt_Model"},
- {"name": "GTA5 Artwork Diffusion", "url": "ItsJayQz/GTA5_Artwork_Diffusion"},
- {"name": "Marvel WhatIf Diffusion", "url": "ItsJayQz/Marvel_WhatIf_Diffusion"},
- {"name": "Naruto Diffuser", "url": "lambdalabs/sd-naruto-diffusers"},
- {"name": "Papercut", "url": "Fictiverse/Stable_Diffusion_PaperCut_Model"},
- {"name": "Pokemon Diffuser", "url": "lambdalabs/sd-pokemon-diffusers"},
- {"name": "Synthwave Punk 2", "url": "ItsJayQz/SynthwavePunk-v2"},
- {"name": "Valorant Diffusion", "url": "ItsJayQz/Valorant_Diffusion"},
- {"name": "Van Gogh Diffusion", "url": "dallinmackay/Van-Gogh-diffusion"},
- {"name": "Vectorartz Diffusion", "url": "coder119/Vectorartz_Diffusion"},
- {"name": "VoxelArt", "url": "Fictiverse/Stable_Diffusion_VoxelArt_Model"},
- {"name": "❤ ANIME MODELS ==========", "url": "dreamlike-art/dreamlike-anime-1.0"},
- {"name": "7 Pa", "url": "AIARTCHAN/7pa"},
- {"name": "A Certain Model", "url": "JosephusCheung/ACertainModel"},
- {"name": "A Certain Thing", "url": "JosephusCheung/ACertainThing"},
- {"name": "A Certainity", "url": "JosephusCheung/ACertainty"},
- {"name": "Abyss Hell Hero", "url": "AIARTCHAN/AbyssHellHero"},
- {"name": "Abyss Maple 3", "url": "AIARTCHAN/AbyssMapleVer3"},
- {"name": "Abyss Orange Mix 2", "url": "WarriorMama777/AbyssOrangeMix2"},
- {"name": "Abyss Orange Mix", "url": "WarriorMama777/AbyssOrangeMix"},
- {"name": "AbyssHell 3", "url": "AIARTCHAN/AbyssHellVer3"},
- {"name": "All 526 Animated", "url": "stablediffusionapi/all-526-animated"},
- {"name": "Anidosmix 3", "url": "AIARTCHAN/anidosmixV2"},
- {"name": "Anime Kawai Diffusion", "url": "Ojimi/anime-kawai-diffusion"},
- {"name": "Anireal 3D V2", "url": "circulus/sd-anireal-3d-v2"},
- {"name": "AnyLORA", "url": "kubanemil/AnyLORA"},
- {"name": "Anything 2.1", "url": "swl-models/anything-v2.1"},
- {"name": "Anything 3.0 Light", "url": "mm00/anything-v3.0-light"},
- {"name": "Anything 3.0", "url": "Linaqruf/anything-v3.0"},
- {"name": "Anything 3.1", "url": "cag/anything-v3-1"},
- {"name": "Anything 3X", "url": "iZELX1/Anything-V3-X"},
- {"name": "Anything 5.0", "url": "stablediffusionapi/anything-v5"},
- {"name": "Anything Else 4", "url": "stablediffusionapi/anythingelse-v4"},
- {"name": "Anything Else 5", "url": "stablediffusionapi/anything-v5"},
- {"name": "Arcane Diffusion", "url": "nitrosocke/Arcane-Diffusion"},
- {"name": "Archer Diffusion", "url": "nitrosocke/archer-diffusion"},
- {"name": "Asian Mix", "url": "D1b4l4p/AsianMix"},
- {"name": "Blood Orange Mix", "url": "WarriorMama777/BloodOrangeMix"},
- {"name": "CamelliaMix 2.5D","url": "stablediffusionapi/camelliamix25d"},
- {"name": "CamelliaMix Line","url": "stablediffusionapi/camelliamixline"},
- {"name": "Cetusmix", "url": "stablediffusionapi/cetusmix"},
- {"name": "Chik Mix", "url": "stablediffusionapi/chikmix"},
- {"name": "Chikmix", "url": "stablediffusionapi/chikmix"},
- {"name": "Chillout App Factory","url": "stablediffusionapi/chillout-app-factory"},
- {"name": "Classic Anime", "url": "nitrosocke/classic-anim-diffusion"},
- {"name": "Cool Japan Diffusion 2.1.2", "url": "aipicasso/cool-japan-diffusion-2-1-2"},
- {"name": "Cosmic Babes", "url": "stablediffusionapi/cosmic-babes"},
- {"name": "Counterfeit 1.0", "url": "gsdf/counterfeit-v1.0"},
- {"name": "Counterfeit 2", "url": "gsdf/Counterfeit-V2.0"},
- {"name": "Counterfeit 2.0", "url": "gsdf/Counterfeit-V2.0"},
- {"name": "Counterfeit 3.0", "url": "stablediffusionapi/counterfeit-v30"},
- {"name": "CyberPunk Anime", "url": "DGSpitzer/Cyberpunk-Anime-Diffusion"},
- {"name": "Dark Sushi Mix", "url": "stablediffusionapi/dark-sushi-mix"},
- {"name": "Dash Sushi 25d", "url": "stablediffusionapi/dark-sushi-25d"},
- {"name": "DucHaiten Anime", "url": "DucHaiten/DucHaitenAnime"},
- {"name": "Eerie Orange Mix", "url": "WarriorMama777/EerieOrangeMix"},
- {"name": "Eimis Anime Diffusion", "url": "eimiss/EimisAnimeDiffusion_1.0v"},
- {"name": "Ghibli Diffusion", "url": "nitrosocke/Ghibli-Diffusion"},
- {"name": "GrapeFruit", "url": "iZELX1/Grapefruit"},
- {"name": "GuoFeng 3", "url": "xiaolxl/GuoFeng3"},
- {"name": "Icomix 2", "url": "stablediffusionapi/icomix-2"},
- {"name": "InkPunk Diffusion", "url": "Envvi/Inkpunk-Diffusion"},
- {"name": "Mama Orange Mixs", "url": "WarriorMama777/OrangeMixs"},
- {"name": "Meina Alter", "url": "stablediffusionapi/meinaalter"},
- {"name": "Meina Pastel", "url": "stablediffusionapi/meinapastel"},
- {"name": "MeinaMix 7", "url": "Nacholmo/meinamixv7-diffusers"},
- {"name": "Mix Pro V4", "url": "AIARTCHAN/MIX-Pro-V4"},
- {"name": "NeverEnding-Dream", "url": "Lykon/NeverEnding-Dream"},
- {"name": "Openjourney 4", "url": "prompthero/openjourney-v4"},
- {"name": "OpenNiji", "url": "Korakoe/OpenNiji"},
- {"name": "Protogen 2.2", "url": "darkstorm2150/Protogen_v2.2_Official_Release"},
- {"name": "Protogen Infinity", "url": "darkstorm2150/Protogen_Infinity_Official_Release"},
- {"name": "Protogen X 3.4", "url": "darkstorm2150/Protogen_x3.4_Official_Release"},
- {"name": "Rev Anim", "url": "stablediffusionapi/rev-anim"},
- {"name": "Rev Animated", "url": "coreml/coreml-ReV-Animated"},
- {"name": "Rev Animated", "url": "LottePeisch/RevAnimated-Diffusers"},
- {"name": "Something V 2.2","url": "NoCrypt/SomethingV2_2"},
- {"name": "Something V2","url": "NoCrypt/SomethingV2"},
- {"name": "Three Delicacy", "url": "stablediffusionapi/three-delicacy"},
- {"name": "Three Delicacy wonto", "url": "stablediffusionapi/three-delicacy-wonto"},
- {"name": "TMND mix", "url": "stablediffusionapi/tmnd-mix"},
- {"name": "Waifu Diffusion", "url": "hakurei/waifu-diffusion"},
- {"name": "❤ REALISTIC PHOTO MODELS ==========", "url": "dreamlike-art/dreamlike-photoreal-2.0"},
- {"name": "AmiIReal", "url": "stablediffusionapi/amireal"},
- {"name": "Analog Diffusion", "url": "wavymulder/Analog-Diffusion"},
- {"name": "Circulus 2.8", "url": "circulus/sd-photoreal-v2.8"},
- {"name": "Circulus Photoreal V2", "url": "circulus/sd-photoreal-real-v2"},
- {"name": "Claudfuen 1", "url": "claudfuen/photorealistic-fuen-v1"},
- {"name": "Collage Diffusion", "url": "wavymulder/collage-diffusion"},
- {"name": "Cyberrealistic", "url": "stablediffusionapi/cyberrealistic"},
- {"name": "Dreamful 2", "url": "Hius/DreamFul-V2"},
- {"name": "GakkiMix768", "url": "Sa1i/gakki-mix-768"},
- {"name": "Grimoeresigils", "url": "ECarbenia/grimoiresigils"},
- {"name": "HARDBlend", "url": "theintuitiveye/HARDblend"},
- {"name": "HassanBlend 1.4", "url": "hassanblend/hassanblend1.4"},
- {"name": "HassanBlend 1.5.1.2", "url": "hassanblend/HassanBlend1.5.1.2"},
- {"name": "Lomo Diffusion", "url": "wavymulder/lomo-diffusion"},
- {"name": "Model Shoot", "url": "wavymulder/modelshoot"},
- {"name": "Portrait Plus", "url": "wavymulder/portraitplus"},
- {"name": "QuinceMix", "url": "Hemlok/QuinceMix"},
- {"name": "Realistic Vision 1.4", "url": "SG161222/Realistic_Vision_V1.4"},
- {"name": "The Ally", "url": "stablediffusionapi/the-ally"},
- {"name": "Timeless Diffusion", "url": "wavymulder/timeless-diffusion"},
- {"name": "UltraSkin", "url": "VegaKH/Ultraskin"},
- {"name": "Wavyfusion", "url": "wavymulder/wavyfusion"},
- {"name": "❤ SEMI-REALISTIC MODELS ==========", "url": "stablediffusionapi/all-526"},
- {"name": "All 526", "url": "stablediffusionapi/all-526"},
- {"name": "All 526 animated", "url": "stablediffusionapi/all-526-animated"},
- {"name": "Circulus Semi Real 2", "url": "circulus/sd-photoreal-semi-v2"},
- {"name": "Semi Real Mix", "url": "robotjung/SemiRealMix"},
- {"name": "SpyBG", "url": "stablediffusionapi/spybg"},
- {"name": "❤ STABLE DIFFUSION MODELS ==========", "url": "stabilityai/stable-diffusion-2-1"},
- {"name": "❤ SCI FI MODELS ==========", "url": "nitrosocke/Future-Diffusion"},
- {"name": "Future Diffusion", "url": "nitrosocke/Future-Diffusion"},
- {"name": "JWST Deep Space Diffusion", "url": "dallinmackay/JWST-Deep-Space-diffusion"},
- {"name": "Robo Diffusion 3 Base", "url": "nousr/robo-diffusion-2-base"},
- {"name": "Robo Diffusion", "url": "nousr/robo-diffusion"},
- {"name": "Tron Legacy Diffusion", "url": "dallinmackay/Tron-Legacy-diffusion"},
- {"name": "❤ 3D ART MODELS ==========", "url": "DucHaiten/DucHaitenAIart"},
- {"name": "DucHaiten Art", "url": "DucHaiten/DucHaitenAIart"},
- {"name": "DucHaiten ClassicAnime", "url": "DucHaiten/DH_ClassicAnime"},
- {"name": "DucHaiten DreamWorld", "url": "DucHaiten/DucHaitenDreamWorld"},
- {"name": "DucHaiten Journey", "url": "DucHaiten/DucHaitenJourney"},
- {"name": "DucHaiten StyleLikeMe", "url": "DucHaiten/DucHaiten-StyleLikeMe"},
- {"name": "DucHaiten SuperCute", "url": "DucHaiten/DucHaitenSuperCute"},
- {"name": "Redshift Diffusion 768", "url": "nitrosocke/redshift-diffusion-768"},
- {"name": "Redshift Diffusion", "url": "nitrosocke/redshift-diffusion"},
-]
-
-current_model = models[0]
-
-#text_gen = gr.Interface.load("spaces/daspartho/prompt-extend")
-
-models2 = []
-for model in models:
- model_url = f"models/{model['url']}"
- loaded_model = gr.Interface.load(model_url, live=True, preprocess=True)
- models2.append(loaded_model)
-
-def send_it(inputs, model_choice):
- proc = models2[model_choice]
- return proc(inputs)
-
-def dropdown_change():
- send_it(input_text, model_name1)
-
-with gr.Blocks() as myface:
- gr.HTML()
-
- with gr.Column():
- with gr.Row():
- input_text = gr.Textbox(label="Prompt idea", placeholder="", lines=1)
- model_name1 = gr.Dropdown(
- label="Choose Model",
- choices=[m["name"] for m in models],
- type="index",
- value=current_model["name"],
- interactive=True
- )
-
- run = gr.Button("Generate Images", variant="primary")
- with gr.Row():
- output1 = gr.Image(label="")
-
-
- model_name1.change(send_it, inputs=[input_text, model_name1], outputs=[output1])
- run.click(send_it, inputs=[input_text, model_name1], outputs=[output1])
-
-myface.queue(concurrency_count=200)
-myface.launch(inline=True, show_api=True, max_threads=500)
\ No newline at end of file
diff --git a/spaces/nomic-ai/zhengyun21_PMC-Patients/style.css b/spaces/nomic-ai/zhengyun21_PMC-Patients/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/zhengyun21_PMC-Patients/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
- padding: 2rem;
- font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
- font-size: 16px;
- margin-top: 0;
-}
-
-p {
- color: rgb(107, 114, 128);
- font-size: 15px;
- margin-bottom: 10px;
- margin-top: 5px;
-}
-
-.card {
- max-width: 620px;
- margin: 0 auto;
- padding: 16px;
- border: 1px solid lightgray;
- border-radius: 16px;
-}
-
-.card p:last-child {
- margin-bottom: 0;
-}
diff --git a/spaces/nsakki55/my-aim-demo/README.md b/spaces/nsakki55/my-aim-demo/README.md
deleted file mode 100644
index 6e60c356258c918247dbd4c612d964fd9896fee8..0000000000000000000000000000000000000000
--- a/spaces/nsakki55/my-aim-demo/README.md
+++ /dev/null
@@ -1,81 +0,0 @@
----
-title: Aim
-emoji: 🔥
-colorFrom: purple
-colorTo: blue
-sdk: docker
-license: other
-fullWidth: true
-duplicated_from: aimstack/aim
----
-
-# Aim on Spaces
-
-**Hugging Face Spaces** offer a simple way to host ML demo apps directly on your profile or your organization’s profile. This allows you to create your ML portfolio, showcase your projects at conferences or to stakeholders, and work collaboratively with other people in the ML ecosystem.
-Hugging Face Spaces make it easy for you to create and deploy ML-powered demos in minutes.
-
-Check out the [Hugging Face Spaces docs](https://huggingface.co/docs/hub/spaces-overview) to learn more about Spaces.
-
-## Deploy Aim on Spaces
-
-You can deploy Aim on Spaces with a single click!
-
-
-
-
-
-Once you have created the Space, you'll see the `Building` status, and once it becomes `Running,` your Space is ready to go!
-
-
-
-Now, when you navigate to your Space's **App** section, you can access the Aim UI.
-
-## Compare your experiments with Aim on Spaces
-
-Let's use a quick example of a PyTorch CNN trained on MNIST to demonstrate end-to-end Aim on Spaces deployment.
-The full example is in the [Aim repo examples folder](https://github.com/aimhubio/aim/blob/main/examples/pytorch_track.py).
-
-```python
-from aim import Run
-from aim.pytorch import track_gradients_dists, track_params_dists
-
-# Initialize a new Run
-aim_run = Run()
-...
-items = {'accuracy': acc, 'loss': loss}
-aim_run.track(items, epoch=epoch, context={'subset': 'train'})
-
-# Track weights and gradients distributions
-track_params_dists(model, aim_run)
-track_gradients_dists(model, aim_run)
-```
-
-The experiments tracked by Aim are stored in the `.aim` folder. **To display the logs with the Aim UI in your Space, you need to compress the `.aim` folder to a `tar.gz` file and upload it to your Space using `git` or the Files and Versions sections of your Space.**
-
-Here's a bash command for that:
-
-```bash
-tar -czvf aim_repo.tar.gz .aim
-```
-
-That’s it! Now open the App section of your Space and the Aim UI is available with your logs.
-Here is what to expect:
-
-
-
-Filter your runs using Aim’s Pythonic search. You can write pythonic [queries against](https://aimstack.readthedocs.io/en/latest/using/search.html) EVERYTHING you have tracked - metrics, hyperparams etc. Check out some [examples](https://huggingface.co/aimstack) on HF Hub Spaces.
-
-
-Note that if your logs are in TensorBoard format, you can easily convert them to Aim with one command and use the many advanced and high-performant training run comparison features available.
-
-
-## More on HF Spaces
-
-- [HF Docker spaces](https://github.com/huggingface/hub-docs/blob/main/docs/hub/spaces-sdks-docker.md)
-- [HF Docker space examples](https://github.com/huggingface/hub-docs/blob/main/docs/hub/spaces-sdks-docker.md)
-
-## Feedback and Support
-
-If you have improvement suggestions or need support, please open an issue on [Aim GitHub repo](https://github.com/aimhubio/aim).
-
-The [Aim community Discord](https://github.com/aimhubio/aim#-community) is also available for community discussions.
diff --git a/spaces/ntt123/vietnamese-handwriting/style.css b/spaces/ntt123/vietnamese-handwriting/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/ntt123/vietnamese-handwriting/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
- padding: 2rem;
- font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
- font-size: 16px;
- margin-top: 0;
-}
-
-p {
- color: rgb(107, 114, 128);
- font-size: 15px;
- margin-bottom: 10px;
- margin-top: 5px;
-}
-
-.card {
- max-width: 620px;
- margin: 0 auto;
- padding: 16px;
- border: 1px solid lightgray;
- border-radius: 16px;
-}
-
-.card p:last-child {
- margin-bottom: 0;
-}
diff --git a/spaces/oguzakif/video-object-remover/FGT_codes/LAFC/utils/util.py b/spaces/oguzakif/video-object-remover/FGT_codes/LAFC/utils/util.py
deleted file mode 100644
index e494f1cecb0c872eeffb8b626e26077b5ce621c2..0000000000000000000000000000000000000000
--- a/spaces/oguzakif/video-object-remover/FGT_codes/LAFC/utils/util.py
+++ /dev/null
@@ -1,432 +0,0 @@
-import os
-import sys
-import time
-import math
-import torch.nn.functional as F
-from datetime import datetime
-import random
-import logging
-from collections import OrderedDict
-import numpy as np
-import cv2
-import torch
-from torchvision.utils import make_grid
-from shutil import get_terminal_size
-import torchvision.utils as vutils
-from shutil import copyfile
-import torchvision.transforms as transforms
-
-import yaml
-
-try:
- from yaml import CLoader as Loader, CDumper as Dumper
-except ImportError:
- from yaml import Loader, Dumper
-
-
-def OrderedYaml():
- '''yaml orderedDict support'''
- _mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG
-
- def dict_representer(dumper, data):
- return dumper.represent_dict(data.items())
-
- def dict_constructor(loader, node):
- return OrderedDict(loader.construct_pairs(node))
-
- Dumper.add_representer(OrderedDict, dict_representer)
- Loader.add_constructor(_mapping_tag, dict_constructor)
- return Loader, Dumper
-
-
-####################
-# miscellaneous
-####################
-
-
-def get_timestamp():
- return datetime.now().strftime('%y%m%d-%H%M%S')
-
-
-def mkdir(path):
- if not os.path.exists(path):
- os.makedirs(path)
-
-
-def mkdirs(paths):
- if isinstance(paths, str):
- print('path is : ', paths)
- mkdir(paths)
- else:
- for path in paths:
- print('path is : {}'.format(path))
- mkdir(path)
-
-
-def mkdir_and_rename(path):
- new_name = None
- if os.path.exists(path):
- new_name = path + '_archived_' + get_timestamp()
- logger = logging.getLogger('base')
- logger.info('Path already exists. Rename it to [{:s}]'.format(new_name))
- os.rename(path, new_name)
- os.makedirs(path)
- return new_name
-
-
-def set_random_seed(seed):
- random.seed(seed)
- np.random.seed(seed)
- torch.manual_seed(seed)
- torch.cuda.manual_seed_all(seed)
-
-
-def setup_logger(logger_name, root, phase, level=logging.INFO, screen=False, tofile=False):
- '''set up logger'''
- lg = logging.getLogger(logger_name)
- formatter = logging.Formatter('%(asctime)s.%(msecs)03d - %(levelname)s: %(message)s',
- datefmt='%y-%m-%d %H:%M:%S')
- lg.setLevel(level)
- if tofile:
- log_file = os.path.join(root, phase + '_{}.log'.format(get_timestamp()))
- fh = logging.FileHandler(log_file, mode='w')
- fh.setFormatter(formatter)
- lg.addHandler(fh)
- if screen:
- sh = logging.StreamHandler()
- sh.setFormatter(formatter)
- lg.addHandler(sh)
-
-
-####################
-# image convert
-####################
-def crop_border(img_list, crop_border):
- """Crop borders of images
- Args:
- img_list (list [Numpy]): HWC
- crop_border (int): crop border for each end of height and weight
-
- Returns:
- (list [Numpy]): cropped image list
- """
- if crop_border == 0:
- return img_list
- else:
- return [v[crop_border:-crop_border, crop_border:-crop_border] for v in img_list]
-
-
-def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
- '''
- Converts a torch Tensor into an image Numpy array
- Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
- Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
- '''
- tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # clamp
- tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1]
- n_dim = tensor.dim()
- if n_dim == 4:
- n_img = len(tensor)
- img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
- img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
- elif n_dim == 3:
- img_np = tensor.numpy()
- img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
- elif n_dim == 2:
- img_np = tensor.numpy()
- else:
- raise TypeError(
- 'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
- if out_type == np.uint8:
- img_np = (img_np * 255.0).round()
- # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
- return img_np.astype(out_type)
-
-
-def save_img(img, img_path, mode='RGB'):
- cv2.imwrite(img_path, img)
-
-
-def DUF_downsample(x, scale=4):
- """Downsamping with Gaussian kernel used in the DUF official code
-
- Args:
- x (Tensor, [B, T, C, H, W]): frames to be downsampled.
- scale (int): downsampling factor: 2 | 3 | 4.
- """
-
- assert scale in [2, 3, 4], 'Scale [{}] is not supported'.format(scale)
-
- def gkern(kernlen=13, nsig=1.6):
- import scipy.ndimage.filters as fi
- inp = np.zeros((kernlen, kernlen))
- # set element at the middle to one, a dirac delta
- inp[kernlen // 2, kernlen // 2] = 1
- # gaussian-smooth the dirac, resulting in a gaussian filter mask
- return fi.gaussian_filter(inp, nsig)
-
- B, T, C, H, W = x.size()
- x = x.view(-1, 1, H, W)
- pad_w, pad_h = 6 + scale * 2, 6 + scale * 2 # 6 is the pad of the gaussian filter
- r_h, r_w = 0, 0
- if scale == 3:
- r_h = 3 - (H % 3)
- r_w = 3 - (W % 3)
- x = F.pad(x, [pad_w, pad_w + r_w, pad_h, pad_h + r_h], 'reflect')
-
- gaussian_filter = torch.from_numpy(gkern(13, 0.4 * scale)).type_as(x).unsqueeze(0).unsqueeze(0)
- x = F.conv2d(x, gaussian_filter, stride=scale)
- x = x[:, :, 2:-2, 2:-2]
- x = x.view(B, T, C, x.size(2), x.size(3))
- return x
-
-
-def single_forward(model, inp):
- """PyTorch model forward (single test), it is just a simple warpper
- Args:
- model (PyTorch model)
- inp (Tensor): inputs defined by the model
-
- Returns:
- output (Tensor): outputs of the model. float, in CPU
- """
- with torch.no_grad():
- model_output = model(inp)
- if isinstance(model_output, list) or isinstance(model_output, tuple):
- output = model_output[0]
- else:
- output = model_output
- output = output.data.float().cpu()
- return output
-
-
-def flipx4_forward(model, inp):
- """Flip testing with X4 self ensemble, i.e., normal, flip H, flip W, flip H and W
- Args:
- model (PyTorch model)
- inp (Tensor): inputs defined by the model
-
- Returns:
- output (Tensor): outputs of the model. float, in CPU
- """
- # normal
- output_f = single_forward(model, inp)
-
- # flip W
- output = single_forward(model, torch.flip(inp, (-1,)))
- output_f = output_f + torch.flip(output, (-1,))
- # flip H
- output = single_forward(model, torch.flip(inp, (-2,)))
- output_f = output_f + torch.flip(output, (-2,))
- # flip both H and W
- output = single_forward(model, torch.flip(inp, (-2, -1)))
- output_f = output_f + torch.flip(output, (-2, -1))
-
- return output_f / 4
-
-
-####################
-# metric
-####################
-
-
-class ProgressBar(object):
- '''A progress bar which can print the progress
- modified from https://github.com/hellock/cvbase/blob/master/cvbase/progress.py
- '''
-
- def __init__(self, task_num=0, bar_width=50, start=True):
- self.task_num = task_num
- max_bar_width = self._get_max_bar_width()
- self.bar_width = (bar_width if bar_width <= max_bar_width else max_bar_width)
- self.completed = 0
- if start:
- self.start()
-
- def _get_max_bar_width(self):
- terminal_width, _ = get_terminal_size()
- max_bar_width = min(int(terminal_width * 0.6), terminal_width - 50)
- if max_bar_width < 10:
- print('terminal width is too small ({}), please consider widen the terminal for better '
- 'progressbar visualization'.format(terminal_width))
- max_bar_width = 10
- return max_bar_width
-
- def start(self):
- if self.task_num > 0:
- sys.stdout.write('[{}] 0/{}, elapsed: 0s, ETA:\n{}\n'.format(
- ' ' * self.bar_width, self.task_num, 'Start...'))
- else:
- sys.stdout.write('completed: 0, elapsed: 0s')
- sys.stdout.flush()
- self.start_time = time.time()
-
- def update(self, msg='In progress...'):
- self.completed += 1
- elapsed = time.time() - self.start_time
- fps = self.completed / elapsed
- if self.task_num > 0:
- percentage = self.completed / float(self.task_num)
- eta = int(elapsed * (1 - percentage) / percentage + 0.5)
- mark_width = int(self.bar_width * percentage)
- bar_chars = '>' * mark_width + '-' * (self.bar_width - mark_width)
- sys.stdout.write('\033[2F') # cursor up 2 lines
- sys.stdout.write('\033[J') # clean the output (remove extra chars since last display)
- sys.stdout.write('[{}] {}/{}, {:.1f} task/s, elapsed: {}s, ETA: {:5}s\n{}\n'.format(
- bar_chars, self.completed, self.task_num, fps, int(elapsed + 0.5), eta, msg))
- else:
- sys.stdout.write('completed: {}, elapsed: {}s, {:.1f} tasks/s'.format(
- self.completed, int(elapsed + 0.5), fps))
- sys.stdout.flush()
-
-
-### communication
-def find_free_port():
- import socket
- sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
- sock.bind(("", 0))
- port = sock.getsockname()[1]
- sock.close()
- return port
-
-
-# for debug
-def visualize_image(result, outputDir, epoch, mode, video_name, minData=0):
- ### Only visualize one frame
- targetDir = os.path.join(outputDir, str(epoch), video_name)
- if not os.path.exists(targetDir):
- os.makedirs(targetDir)
- if minData == -1:
- result = (result + 1) / 2
- vutils.save_image(result, os.path.join(targetDir, '{}.png'.format(mode)))
- elif minData == 0:
- vutils.save_image(result, os.path.join(targetDir, '{}.png'.format(mode)))
- else:
- raise ValueError('minValue {} is not supported'.format(minData))
-
-
-def get_learning_rate(optimizer):
- lr = []
- for param_group in optimizer.param_groups:
- lr += [param_group['lr']]
- return lr
-
-
-def adjust_learning_rate(optimizer, target_lr):
- for param_group in optimizer.param_groups:
- param_group['lr'] = target_lr
-
-
-def save_checkpoint(epoch, model, discriminator, current_step, schedulers, dist_scheduler, optimizers, dist_optimizer, save_path, is_best, monitor, monitor_value,
- config):
- # for entriely resuming state, you need to save the state dict of model, optimizer and learning scheduler
- if isinstance(model, torch.nn.DataParallel) or isinstance(model, torch.nn.parallel.DistributedDataParallel):
- model_state = model.module.state_dict()
- discriminator_state = discriminator.module.state_dict()
- else:
- model_state = model.state_dict()
- discriminator_state = discriminator.state_dict()
- state = {
- 'epoch': epoch,
- 'iteration': current_step,
- 'model_state_dict': model_state,
- 'discriminator_state_dict': discriminator_state,
- 'optimizer_state_dict': optimizers.state_dict(),
- 'dist_optim_state_dict': dist_optimizer.state_dict(),
- 'scheduler_state_dict': schedulers.state_dict(),
- 'dist_scheduler_state_dict': dist_scheduler.state_dict(),
- 'is_best': is_best,
- 'config': config,
- }
-
- best_str = '-best-so-far' if is_best else ''
- monitor_str = '-{}:{}'.format(monitor, monitor_value) if monitor_value else ''
- if not os.path.exists(os.path.join(save_path, 'best')):
- os.makedirs(os.path.join(save_path, 'best'))
- file_name = os.path.join(save_path, 'checkpoint-epoch:{}{}{}.pth.tar'.format(epoch, monitor_str, best_str))
- torch.save(state, file_name)
- if is_best:
- copyfile(src=file_name, dst=os.path.join(save_path, 'best',
- 'checkpoint-epoch:{}{}{}.pth.tar'.format(epoch, monitor_str,
- best_str)))
-
-
-def save_dist_checkpoint(epoch, model, dist, current_step, schedulers, schedulersD, optimizers, optimizersD, save_path,
- is_best, monitor, monitor_value,
- config):
- # for entriely resuming state, you need to save the state dict of model, optimizer and learning scheduler
- if isinstance(model, torch.nn.DataParallel) or isinstance(model, torch.nn.parallel.DistributedDataParallel):
- model_state = model.module.state_dict()
- dist_state = dist.module.state_dict()
- else:
- model_state = model.state_dict()
- dist_state = dist.state_dict()
- state = {
- 'epoch': epoch,
- 'iteration': current_step,
- 'model_state_dict': model_state,
- 'dist_state_dict': dist_state,
- 'optimizer_state_dict': optimizers.state_dict(),
- 'optimizerD_state_dict': optimizersD.state_dict(),
- 'scheduler_state_dict': schedulers.state_dict(),
- 'schedulerD_state_dict': schedulersD.state_dict(),
- 'is_best': is_best,
- 'config': config
- }
-
- best_str = '-best-so-far' if is_best else ''
- monitor_str = '-{}:{}'.format(monitor, monitor_value) if monitor_value else ''
- if not os.path.exists(os.path.join(save_path, 'best')):
- os.makedirs(os.path.join(save_path, 'best'))
- file_name = os.path.join(save_path, 'checkpoint-epoch:{}{}{}.pth.tar'.format(epoch, monitor_str, best_str))
- torch.save(state, file_name)
- if is_best:
- copyfile(src=file_name, dst=os.path.join(save_path, 'best',
- 'checkpoint-epoch:{}{}{}.pth.tar'.format(epoch, monitor_str,
- best_str)))
-
-
-def poisson_blend(input, output, mask):
- """
- * inputs:
- - input (torch.Tensor, required)
- Input tensor of Completion Network, whose shape = (N, 3, H, W).
- - output (torch.Tensor, required)
- Output tensor of Completion Network, whose shape = (N, 3, H, W).
- - mask (torch.Tensor, required)
- Input mask tensor of Completion Network, whose shape = (N, 1, H, W).
- * returns:
- Output image tensor of shape (N, 3, H, W) inpainted with poisson image editing method.
- from lizuka et al: https://github.com/otenim/GLCIC-PyTorch/blob/caf9bebe667fba0aebbd041918f2d8128f59ec62/utils.py
- """
- input = input.clone().cpu()
- output = output.clone().cpu()
- mask = mask.clone().cpu()
- mask = torch.cat((mask, mask, mask), dim=1) # convert to 3-channel format
- num_samples = input.shape[0]
- ret = []
- for i in range(num_samples):
- dstimg = transforms.functional.to_pil_image(input[i])
- dstimg = np.array(dstimg)[:, :, [2, 1, 0]]
- srcimg = transforms.functional.to_pil_image(output[i])
- srcimg = np.array(srcimg)[:, :, [2, 1, 0]]
- msk = transforms.functional.to_pil_image(mask[i])
- msk = np.array(msk)[:, :, [2, 1, 0]]
- # compute mask's center
- xs, ys = [], []
- for j in range(msk.shape[0]):
- for k in range(msk.shape[1]):
- if msk[j, k, 0] == 255:
- ys.append(j)
- xs.append(k)
- xmin, xmax = min(xs), max(xs)
- ymin, ymax = min(ys), max(ys)
- center = ((xmax + xmin) // 2, (ymax + ymin) // 2)
- dstimg = cv2.inpaint(dstimg, msk[:, :, 0], 1, cv2.INPAINT_TELEA)
- out = cv2.seamlessClone(srcimg, dstimg, msk, center, cv2.NORMAL_CLONE)
- out = out[:, :, [2, 1, 0]]
- out = transforms.functional.to_tensor(out)
- out = torch.unsqueeze(out, dim=0)
- ret.append(out)
- ret = torch.cat(ret, dim=0)
- return ret
diff --git a/spaces/oliver2023/chatgpt-on-wechat/scripts/start.sh b/spaces/oliver2023/chatgpt-on-wechat/scripts/start.sh
deleted file mode 100644
index ac92f8851f6925399f2a4482e271a10ff2accbd5..0000000000000000000000000000000000000000
--- a/spaces/oliver2023/chatgpt-on-wechat/scripts/start.sh
+++ /dev/null
@@ -1,16 +0,0 @@
-#!/bin/bash
-#后台运行Chat_on_webchat执行脚本
-
-cd `dirname $0`/..
-export BASE_DIR=`pwd`
-echo $BASE_DIR
-
-# check the nohup.out log output file
-if [ ! -f "${BASE_DIR}/nohup.out" ]; then
- touch "${BASE_DIR}/nohup.out"
-echo "create file ${BASE_DIR}/nohup.out"
-fi
-
-nohup python3 "${BASE_DIR}/app.py" & tail -f "${BASE_DIR}/nohup.out"
-
-echo "Chat_on_webchat is starting，you can check the ${BASE_DIR}/nohup.out"
diff --git a/spaces/omlab/vlchecklist_demo/models/vilt/datasets/conceptual_caption_dataset.py b/spaces/omlab/vlchecklist_demo/models/vilt/datasets/conceptual_caption_dataset.py
deleted file mode 100644
index 9190311a4e485ecc7b91535f7f0fc91600ddfa0d..0000000000000000000000000000000000000000
--- a/spaces/omlab/vlchecklist_demo/models/vilt/datasets/conceptual_caption_dataset.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from glob import glob
-from .base_dataset import BaseDataset
-
-
-class ConceptualCaptionDataset(BaseDataset):
- def __init__(self, *args, split="", **kwargs):
- assert split in ["train", "val", "test"]
- if split == "test":
- split = "val"
-
- if split == "train":
- names = [f"conceptual_caption_train_{i}" for i in range(30)]
- elif split == "val":
- names = ["conceptual_caption_val_0"]
-
- super().__init__(*args, **kwargs, names=names, text_column_name="caption")
-
- def __getitem__(self, index):
- return self.get_suite(index)
diff --git a/spaces/onlyswan/swan-voice/app.py b/spaces/onlyswan/swan-voice/app.py
deleted file mode 100644
index b1911ee16b396f33aff1c68f7bc557d0b699da37..0000000000000000000000000000000000000000
--- a/spaces/onlyswan/swan-voice/app.py
+++ /dev/null
@@ -1,171 +0,0 @@
-import os
-import subprocess
-import re
-import pathlib
-
-import gradio as gr
-import librosa
-import numpy as np
-import soundfile
-
-
-is_running_in_hf = os.environ.get("SPACE_ID", "") != ""
-
-README_HEADER = """
-Onlyswan官方音频模型 %s
-==================
-
-这是一个基于OnlySwan的官方音频模型Demo，可以将任意歌曲的清唱/干声转换为OnlySwan的音色。严禁将模型用于任何商业项目。
-
-音频使用长达40分钟的四万原版音频进行训练，训练Epoch为40000步，音色效果更加接近OnlySwan的音色。
-
-**请切换到上方👆的 “音频转换” 选项卡，在线转换试用**
-
-
-在线推理
--------
-
-在线转换速度慢且需要一定时间，请耐心等待。一般每1秒钟的音频需要4秒钟的时间进行转换。如果音频超过30秒，可能会超时，强烈建议本地(使用自己的电脑)进行转换。
-
-使用本地电脑进行转换
------------------
-
-### 方法一：使用Docker （推荐，非常简单）
-
-1. 安装Docker。Docker安装方法请参考: https://docs.docker.com/get-docker/
-2. 运行命令
-```bash
-docker run --pull always --rm -it -p 7860:7860 --platform=linux/amd64 registry.hf.space/onlyswan-swan-voice:latest python app.py
-```
-3. 在浏览器中打开 `http://localhost:7860`
-
-### 方法二：手动部署Python环境
-
-1. 使用 https://github.com/voicepaw/so-vits-svc-fork
-2. 下载模型与配置
- * 模型位于 `logs/44k/G_40000.pth`
- * 配置位于 `configs/44k/config.json`
-3. 运行推理脚本
-
-⚠️注意
------
-
-请确保上传的原始音频文件为清唱/干声/人声，而不是带有伴奏的歌曲。
-
-关于如何分离歌曲中的人声与伴奏，推荐使用：
-
-1. Ultimate Vocal Remover (开源免费，效果好，首推)
-2. https://www.google.com/search?q=ai+vocal+remover (自己尝试不同的网站，效果不一)
-3. https://lalal.ai (收费)
-
-
-----------
-
-
-""" % ("(正在使用本地推理，无时长限制)" if not is_running_in_hf else "")
-
-
-README_HEADER2 = """
-Onlyswan官方音频模型 %s
-==================
-
-这是一个基于OnlySwan的官方音频模型Demo，可以将任意歌曲的清唱/干声转换为OnlySwan的音色。严禁将模型用于任何商业项目。
-
-音频使用长达40分钟的四万原版音频进行训练，训练Epoch为40000步，音色效果更加接近OnlySwan的音色。
-
------------
-
-""" % ("(正在使用本地推理，无时长限制)" if not is_running_in_hf else "")
-
-
-def vc_fn(input_audio, vc_transform, auto_f0, noise_scale, db_threshold, f0_method, progress=gr.Progress()):
-
- try:
- os.remove("temp.wav")
- os.remove("temp.out.wav")
- except OSError:
- pass
-
- if input_audio is None:
- return "You need to upload an audio", None
- sampling_rate, audio = input_audio
-
- duration = audio.shape[0] / sampling_rate
- if is_running_in_hf and (duration > 30):
- return "请上传小于30s的音频，需要转换长音频请本地进行转换", None
-
- if auto_f0:
- auto_f0_flag = "--auto-predict-f0"
- else:
- auto_f0_flag = "--no-auto-predict-f0"
-
- progress(0, desc="重新采样...")
-
- audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
- if len(audio.shape) > 1:
- audio = librosa.to_mono(audio.transpose(1, 0))
- if sampling_rate != 44100:
- audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=44100)
-
- progress(0.01, desc="写入临时文件...")
-
- out_wav_path = "temp.wav"
- soundfile.write(out_wav_path, audio, 44100, format="wav")
-
- infer_cmd = "svc infer --f0-method %s --db-thresh %s --noise-scale %s --transpose %s %s temp.wav" \
- % (f0_method, int(db_threshold), noise_scale, int(vc_transform), auto_f0_flag)
-
- os.environ["PYTHONWARNINGS"] = "ignore"
- os.environ["HUGGINGFACE_HUB_CACHE"] = os.path.join(pathlib.Path().resolve(), "huggingface_cache", "hub")
- print("Executing command: " + infer_cmd)
-
- progress(0.02, desc="准备模型中...")
-
- # os.system(infer_cmd)
- progress_pattern = re.compile(r'<<#(\d+.\d+)#>>')
- process = subprocess.Popen(infer_cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True, shell=True)
- total_chunks = duration * 44100
- complete_chunks = 0
-
- for ln in iter(process.stdout.readline, ''):
- print(ln, end='')
- match = progress_pattern.search(ln)
- if match:
- current_chunk = float(match.group(1))
- complete_chunks += current_chunk
- progress(complete_chunks / total_chunks, desc="正在转换。请勿关闭窗口或刷新页面...")
-
- process.wait()
-
- if not os.path.exists("temp.out.wav"):
- return "发生错误。本次推理所使用的命令为: " + infer_cmd, None
- else:
- return "成功生成OnlySwan音色音频。本次推理所使用的命令为: " + infer_cmd, "temp.out.wav"
-
-
-app = gr.Blocks()
-with app:
- with gr.Tabs():
- with gr.TabItem("主页"):
- gr.Markdown(value=README_HEADER)
- demo_audio = gr.Audio("samples/feiniaohechan_sample_vocal.cover_ai_swan.wav", label="示例音频，点击播放试听效果")
- with gr.TabItem("音频转换"):
- gr.Markdown(value=README_HEADER2)
- # sid = gr.Dropdown(label="音色选择", choices=["swan"], value="swan")
- if is_running_in_hf:
- vc_input3 = gr.Audio(label="上传音频（需要使用清唱/干声。长度小于30秒）")
- else:
- vc_input3 = gr.Audio(label="上传音频（需要使用清唱/干声。长度不限）")
- vc_transform = gr.Number(label="变调。建议保持默认（整数，可以正负，半音数量，升高八度就是12）", value=0)
- auto_f0 = gr.Checkbox(label="自动f0预测。歌曲不要勾选，语言类建议勾选。勾选后变调功能失效。", value=False)
- noise_scale = gr.Number(label="噪音等级。 建议保持默认", value=0.4)
- db_threshold = gr.Number(label="静音阈值(db)。 建议保持默认", value=-30)
- f0_method = gr.Dropdown(label="音准预测方法。建议保持默认", choices=["crepe", "parselmouth", "dio", "harvest"], value="crepe")
- vc_submit = gr.Button("开始生成OnlySwan音色！", variant="primary")
- vc_output1 = gr.Textbox(label="处理状态")
- vc_output2 = gr.Audio(label="Swan音频下载。 处理完成后请点击播放按钮试听，并使用右边的三个点按钮菜单下载。")
- vc_submit.click(vc_fn, [vc_input3, vc_transform, auto_f0, noise_scale, db_threshold, f0_method], [vc_output1, vc_output2])
-
- app.queue(concurrency_count=1)
- print("音频转换器已准备就绪，请打开浏览器访问 http://localhost:7860 开始使用。")
- app.launch()
diff --git a/spaces/oucgc1996/Antimicrobial-peptide-generation/README.md b/spaces/oucgc1996/Antimicrobial-peptide-generation/README.md
deleted file mode 100644
index b6830567ce65330f9fb9665a1d897a1ea6bce50d..0000000000000000000000000000000000000000
--- a/spaces/oucgc1996/Antimicrobial-peptide-generation/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Antimicrobial Peptide Generation
-emoji: ⚡
-colorFrom: purple
-colorTo: red
-sdk: gradio
-sdk_version: 3.9
-app_file: app.py
-pinned: false
-license: cc-by-nc-sa-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/patgpt4/MusicGen/audiocraft/data/zip.py b/spaces/patgpt4/MusicGen/audiocraft/data/zip.py
deleted file mode 100644
index 1f1154231da321dd38d151ff285dbcff5e38a6e0..0000000000000000000000000000000000000000
--- a/spaces/patgpt4/MusicGen/audiocraft/data/zip.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import typing
-import zipfile
-
-from dataclasses import dataclass
-from functools import lru_cache
-from typing_extensions import Literal
-
-
-DEFAULT_SIZE = 32
-MODE = Literal['r', 'w', 'x', 'a']
-
-
-@dataclass(order=True)
-class PathInZip:
- """Class for holding a path of file within a zip file.
-
- Args:
- path: The convention is :
- Let's assume there is a zip file /some/location/foo.zip
- and inside of it is a json file located at /data/file1.json,
- Then we expect path = "/some/location/foo.zip:/data/file1.json"
- """
-
- INFO_PATH_SEP = ':'
- zip_path: str
- file_path: str
-
- def __init__(self, path: str) -> None:
- split_path = path.split(self.INFO_PATH_SEP)
- assert len(split_path) == 2
- self.zip_path, self.file_path = split_path
-
- @classmethod
- def from_paths(cls, zip_path: str, file_path: str):
- return cls(zip_path + cls.INFO_PATH_SEP + file_path)
-
- def __str__(self) -> str:
- return self.zip_path + self.INFO_PATH_SEP + self.file_path
-
-
-def _open_zip(path: str, mode: MODE = 'r'):
- return zipfile.ZipFile(path, mode)
-
-
-_cached_open_zip = lru_cache(DEFAULT_SIZE)(_open_zip)
-
-
-def set_zip_cache_size(max_size: int):
- """Sets the maximal LRU caching for zip file opening.
-
- Args:
- max_size: the maximal LRU cache.
- """
- global _cached_open_zip
- _cached_open_zip = lru_cache(max_size)(_open_zip)
-
-
-def open_file_in_zip(path_in_zip: PathInZip, mode: str = 'r') -> typing.IO:
- """Opens a file stored inside a zip and returns a file-like object.
-
- Args:
- path_in_zip: A PathInZip object representing the file to return a file-like object of.
- mode: The mode in which to open the file with.
- Returns:
- A file-like object for PathInZip.
- """
- zf = _cached_open_zip(path_in_zip.zip_path)
- return zf.open(path_in_zip.file_path)
diff --git a/spaces/patgpt4/MusicGen/audiocraft/modules/conv.py b/spaces/patgpt4/MusicGen/audiocraft/modules/conv.py
deleted file mode 100644
index 972938ab84712eb06e1b10cea25444eee51d6637..0000000000000000000000000000000000000000
--- a/spaces/patgpt4/MusicGen/audiocraft/modules/conv.py
+++ /dev/null
@@ -1,245 +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 math
-import typing as tp
-import warnings
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.nn.utils import spectral_norm, weight_norm
-
-
-CONV_NORMALIZATIONS = frozenset(['none', 'weight_norm', 'spectral_norm',
- 'time_group_norm'])
-
-
-def apply_parametrization_norm(module: nn.Module, norm: str = 'none'):
- assert norm in CONV_NORMALIZATIONS
- if norm == 'weight_norm':
- return weight_norm(module)
- elif norm == 'spectral_norm':
- return spectral_norm(module)
- else:
- # We already check was in CONV_NORMALIZATION, so any other choice
- # doesn't need reparametrization.
- return module
-
-
-def get_norm_module(module: nn.Module, causal: bool = False, norm: str = 'none', **norm_kwargs):
- """Return the proper normalization module. If causal is True, this will ensure the returned
- module is causal, or return an error if the normalization doesn't support causal evaluation.
- """
- assert norm in CONV_NORMALIZATIONS
- if norm == 'time_group_norm':
- if causal:
- raise ValueError("GroupNorm doesn't support causal evaluation.")
- assert isinstance(module, nn.modules.conv._ConvNd)
- return nn.GroupNorm(1, module.out_channels, **norm_kwargs)
- else:
- return nn.Identity()
-
-
-def get_extra_padding_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int,
- padding_total: int = 0) -> int:
- """See `pad_for_conv1d`.
- """
- length = x.shape[-1]
- n_frames = (length - kernel_size + padding_total) / stride + 1
- ideal_length = (math.ceil(n_frames) - 1) * stride + (kernel_size - padding_total)
- return ideal_length - length
-
-
-def pad_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int, padding_total: int = 0):
- """Pad for a convolution to make sure that the last window is full.
- Extra padding is added at the end. This is required to ensure that we can rebuild
- an output of the same length, as otherwise, even with padding, some time steps
- might get removed.
- For instance, with total padding = 4, kernel size = 4, stride = 2:
- 0 0 1 2 3 4 5 0 0 # (0s are padding)
- 1 2 3 # (output frames of a convolution, last 0 is never used)
- 0 0 1 2 3 4 5 0 # (output of tr. conv., but pos. 5 is going to get removed as padding)
- 1 2 3 4 # once you removed padding, we are missing one time step !
- """
- extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
- return F.pad(x, (0, extra_padding))
-
-
-def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'constant', value: float = 0.):
- """Tiny wrapper around F.pad, just to allow for reflect padding on small input.
- If this is the case, we insert extra 0 padding to the right before the reflection happen.
- """
- length = x.shape[-1]
- padding_left, padding_right = paddings
- assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
- if mode == 'reflect':
- max_pad = max(padding_left, padding_right)
- extra_pad = 0
- if length <= max_pad:
- extra_pad = max_pad - length + 1
- x = F.pad(x, (0, extra_pad))
- padded = F.pad(x, paddings, mode, value)
- end = padded.shape[-1] - extra_pad
- return padded[..., :end]
- else:
- return F.pad(x, paddings, mode, value)
-
-
-def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]):
- """Remove padding from x, handling properly zero padding. Only for 1d!
- """
- padding_left, padding_right = paddings
- assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
- assert (padding_left + padding_right) <= x.shape[-1]
- end = x.shape[-1] - padding_right
- return x[..., padding_left: end]
-
-
-class NormConv1d(nn.Module):
- """Wrapper around Conv1d and normalization applied to this conv
- to provide a uniform interface across normalization approaches.
- """
- def __init__(self, *args, causal: bool = False, norm: str = 'none',
- norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
- super().__init__()
- self.conv = apply_parametrization_norm(nn.Conv1d(*args, **kwargs), norm)
- self.norm = get_norm_module(self.conv, causal, norm, **norm_kwargs)
- self.norm_type = norm
-
- def forward(self, x):
- x = self.conv(x)
- x = self.norm(x)
- return x
-
-
-class NormConv2d(nn.Module):
- """Wrapper around Conv2d and normalization applied to this conv
- to provide a uniform interface across normalization approaches.
- """
- def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
- super().__init__()
- self.conv = apply_parametrization_norm(nn.Conv2d(*args, **kwargs), norm)
- self.norm = get_norm_module(self.conv, causal=False, norm=norm, **norm_kwargs)
- self.norm_type = norm
-
- def forward(self, x):
- x = self.conv(x)
- x = self.norm(x)
- return x
-
-
-class NormConvTranspose1d(nn.Module):
- """Wrapper around ConvTranspose1d and normalization applied to this conv
- to provide a uniform interface across normalization approaches.
- """
- def __init__(self, *args, causal: bool = False, norm: str = 'none',
- norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
- super().__init__()
- self.convtr = apply_parametrization_norm(nn.ConvTranspose1d(*args, **kwargs), norm)
- self.norm = get_norm_module(self.convtr, causal, norm, **norm_kwargs)
- self.norm_type = norm
-
- def forward(self, x):
- x = self.convtr(x)
- x = self.norm(x)
- return x
-
-
-class NormConvTranspose2d(nn.Module):
- """Wrapper around ConvTranspose2d and normalization applied to this conv
- to provide a uniform interface across normalization approaches.
- """
- def __init__(self, *args, norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {}, **kwargs):
- super().__init__()
- self.convtr = apply_parametrization_norm(nn.ConvTranspose2d(*args, **kwargs), norm)
- self.norm = get_norm_module(self.convtr, causal=False, norm=norm, **norm_kwargs)
-
- def forward(self, x):
- x = self.convtr(x)
- x = self.norm(x)
- return x
-
-
-class StreamableConv1d(nn.Module):
- """Conv1d with some builtin handling of asymmetric or causal padding
- and normalization.
- """
- def __init__(self, in_channels: int, out_channels: int,
- kernel_size: int, stride: int = 1, dilation: int = 1,
- groups: int = 1, bias: bool = True, causal: bool = False,
- norm: str = 'none', norm_kwargs: tp.Dict[str, tp.Any] = {},
- pad_mode: str = 'reflect'):
- super().__init__()
- # warn user on unusual setup between dilation and stride
- if stride > 1 and dilation > 1:
- warnings.warn('StreamableConv1d has been initialized with stride > 1 and dilation > 1'
- f' (kernel_size={kernel_size} stride={stride}, dilation={dilation}).')
- self.conv = NormConv1d(in_channels, out_channels, kernel_size, stride,
- dilation=dilation, groups=groups, bias=bias, causal=causal,
- norm=norm, norm_kwargs=norm_kwargs)
- self.causal = causal
- self.pad_mode = pad_mode
-
- def forward(self, x):
- B, C, T = x.shape
- kernel_size = self.conv.conv.kernel_size[0]
- stride = self.conv.conv.stride[0]
- dilation = self.conv.conv.dilation[0]
- kernel_size = (kernel_size - 1) * dilation + 1 # effective kernel size with dilations
- padding_total = kernel_size - stride
- extra_padding = get_extra_padding_for_conv1d(x, kernel_size, stride, padding_total)
- if self.causal:
- # Left padding for causal
- x = pad1d(x, (padding_total, extra_padding), mode=self.pad_mode)
- else:
- # Asymmetric padding required for odd strides
- padding_right = padding_total // 2
- padding_left = padding_total - padding_right
- x = pad1d(x, (padding_left, padding_right + extra_padding), mode=self.pad_mode)
- return self.conv(x)
-
-
-class StreamableConvTranspose1d(nn.Module):
- """ConvTranspose1d with some builtin handling of asymmetric or causal padding
- and normalization.
- """
- def __init__(self, in_channels: int, out_channels: int,
- kernel_size: int, stride: int = 1, causal: bool = False,
- norm: str = 'none', trim_right_ratio: float = 1.,
- norm_kwargs: tp.Dict[str, tp.Any] = {}):
- super().__init__()
- self.convtr = NormConvTranspose1d(in_channels, out_channels, kernel_size, stride,
- causal=causal, norm=norm, norm_kwargs=norm_kwargs)
- self.causal = causal
- self.trim_right_ratio = trim_right_ratio
- assert self.causal or self.trim_right_ratio == 1., \
- "`trim_right_ratio` != 1.0 only makes sense for causal convolutions"
- assert self.trim_right_ratio >= 0. and self.trim_right_ratio <= 1.
-
- def forward(self, x):
- kernel_size = self.convtr.convtr.kernel_size[0]
- stride = self.convtr.convtr.stride[0]
- padding_total = kernel_size - stride
-
- y = self.convtr(x)
-
- # We will only trim fixed padding. Extra padding from `pad_for_conv1d` would be
- # removed at the very end, when keeping only the right length for the output,
- # as removing it here would require also passing the length at the matching layer
- # in the encoder.
- if self.causal:
- # Trim the padding on the right according to the specified ratio
- # if trim_right_ratio = 1.0, trim everything from right
- padding_right = math.ceil(padding_total * self.trim_right_ratio)
- padding_left = padding_total - padding_right
- y = unpad1d(y, (padding_left, padding_right))
- else:
- # Asymmetric padding required for odd strides
- padding_right = padding_total // 2
- padding_left = padding_total - padding_right
- y = unpad1d(y, (padding_left, padding_right))
- return y
diff --git a/spaces/pinecone/diffusion-image-search/README.md b/spaces/pinecone/diffusion-image-search/README.md
deleted file mode 100644
index 257f0b215c9d4ecdc2f389e69624f00ef52ac9ec..0000000000000000000000000000000000000000
--- a/spaces/pinecone/diffusion-image-search/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Diffusion Image Search
-emoji: 🌌🌆🎆
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 3.16.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/pinkq/Newbing/cloudflare/worker.js b/spaces/pinkq/Newbing/cloudflare/worker.js
deleted file mode 100644
index e0debd750615f1329b2c72fbce73e1b9291f7137..0000000000000000000000000000000000000000
--- a/spaces/pinkq/Newbing/cloudflare/worker.js
+++ /dev/null
@@ -1,18 +0,0 @@
-const TRAGET_HOST='hf4all-bingo.hf.space' // 请将此域名改成你自己的，域名信息在设置》站点域名查看。
-
-export default {
- async fetch(request) {
- const uri = new URL(request.url);
- if (uri.protocol === 'http:') {
- uri.protocol = 'https:';
- return new Response('', {
- status: 301,
- headers: {
- location: uri.toString(),
- },
- })
- }
- uri.host = TRAGET_HOST
- return fetch(new Request(uri.toString(), request));
- },
-};
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pyparsing/unicode.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pyparsing/unicode.py
deleted file mode 100644
index ec0b3a4fe6055b276d5515a4e81d60d921c6f381..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pyparsing/unicode.py
+++ /dev/null
@@ -1,361 +0,0 @@
-# unicode.py
-
-import sys
-from itertools import filterfalse
-from typing import List, Tuple, Union
-
-
-class _lazyclassproperty:
- def __init__(self, fn):
- self.fn = fn
- self.__doc__ = fn.__doc__
- self.__name__ = fn.__name__
-
- def __get__(self, obj, cls):
- if cls is None:
- cls = type(obj)
- if not hasattr(cls, "_intern") or any(
- cls._intern is getattr(superclass, "_intern", [])
- for superclass in cls.__mro__[1:]
- ):
- cls._intern = {}
- attrname = self.fn.__name__
- if attrname not in cls._intern:
- cls._intern[attrname] = self.fn(cls)
- return cls._intern[attrname]
-
-
-UnicodeRangeList = List[Union[Tuple[int, int], Tuple[int]]]
-
-
-class unicode_set:
- """
- A set of Unicode characters, for language-specific strings for
- ``alphas``, ``nums``, ``alphanums``, and ``printables``.
- A unicode_set is defined by a list of ranges in the Unicode character
- set, in a class attribute ``_ranges``. Ranges can be specified using
- 2-tuples or a 1-tuple, such as::
-
- _ranges = [
- (0x0020, 0x007e),
- (0x00a0, 0x00ff),
- (0x0100,),
- ]
-
- Ranges are left- and right-inclusive. A 1-tuple of (x,) is treated as (x, x).
-
- A unicode set can also be defined using multiple inheritance of other unicode sets::
-
- class CJK(Chinese, Japanese, Korean):
- pass
- """
-
- _ranges: UnicodeRangeList = []
-
- @_lazyclassproperty
- def _chars_for_ranges(cls):
- ret = []
- for cc in cls.__mro__:
- if cc is unicode_set:
- break
- for rr in getattr(cc, "_ranges", ()):
- ret.extend(range(rr[0], rr[-1] + 1))
- return [chr(c) for c in sorted(set(ret))]
-
- @_lazyclassproperty
- def printables(cls):
- """all non-whitespace characters in this range"""
- return "".join(filterfalse(str.isspace, cls._chars_for_ranges))
-
- @_lazyclassproperty
- def alphas(cls):
- """all alphabetic characters in this range"""
- return "".join(filter(str.isalpha, cls._chars_for_ranges))
-
- @_lazyclassproperty
- def nums(cls):
- """all numeric digit characters in this range"""
- return "".join(filter(str.isdigit, cls._chars_for_ranges))
-
- @_lazyclassproperty
- def alphanums(cls):
- """all alphanumeric characters in this range"""
- return cls.alphas + cls.nums
-
- @_lazyclassproperty
- def identchars(cls):
- """all characters in this range that are valid identifier characters, plus underscore '_'"""
- return "".join(
- sorted(
- set(
- "".join(filter(str.isidentifier, cls._chars_for_ranges))
- + "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyzªµº"
- + "ÀÁÂÃÄÅÆÇÈÉÊËÌÍÎÏÐÑÒÓÔÕÖØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõöøùúûüýþÿ"
- + "_"
- )
- )
- )
-
- @_lazyclassproperty
- def identbodychars(cls):
- """
- all characters in this range that are valid identifier body characters,
- plus the digits 0-9, and · (Unicode MIDDLE DOT)
- """
- return "".join(
- sorted(
- set(
- cls.identchars
- + "0123456789·"
- + "".join(
- [c for c in cls._chars_for_ranges if ("_" + c).isidentifier()]
- )
- )
- )
- )
-
- @_lazyclassproperty
- def identifier(cls):
- """
- a pyparsing Word expression for an identifier using this range's definitions for
- identchars and identbodychars
- """
- from pip._vendor.pyparsing import Word
-
- return Word(cls.identchars, cls.identbodychars)
-
-
-class pyparsing_unicode(unicode_set):
- """
- A namespace class for defining common language unicode_sets.
- """
-
- # fmt: off
-
- # define ranges in language character sets
- _ranges: UnicodeRangeList = [
- (0x0020, sys.maxunicode),
- ]
-
- class BasicMultilingualPlane(unicode_set):
- """Unicode set for the Basic Multilingual Plane"""
- _ranges: UnicodeRangeList = [
- (0x0020, 0xFFFF),
- ]
-
- class Latin1(unicode_set):
- """Unicode set for Latin-1 Unicode Character Range"""
- _ranges: UnicodeRangeList = [
- (0x0020, 0x007E),
- (0x00A0, 0x00FF),
- ]
-
- class LatinA(unicode_set):
- """Unicode set for Latin-A Unicode Character Range"""
- _ranges: UnicodeRangeList = [
- (0x0100, 0x017F),
- ]
-
- class LatinB(unicode_set):
- """Unicode set for Latin-B Unicode Character Range"""
- _ranges: UnicodeRangeList = [
- (0x0180, 0x024F),
- ]
-
- class Greek(unicode_set):
- """Unicode set for Greek Unicode Character Ranges"""
- _ranges: UnicodeRangeList = [
- (0x0342, 0x0345),
- (0x0370, 0x0377),
- (0x037A, 0x037F),
- (0x0384, 0x038A),
- (0x038C,),
- (0x038E, 0x03A1),
- (0x03A3, 0x03E1),
- (0x03F0, 0x03FF),
- (0x1D26, 0x1D2A),
- (0x1D5E,),
- (0x1D60,),
- (0x1D66, 0x1D6A),
- (0x1F00, 0x1F15),
- (0x1F18, 0x1F1D),
- (0x1F20, 0x1F45),
- (0x1F48, 0x1F4D),
- (0x1F50, 0x1F57),
- (0x1F59,),
- (0x1F5B,),
- (0x1F5D,),
- (0x1F5F, 0x1F7D),
- (0x1F80, 0x1FB4),
- (0x1FB6, 0x1FC4),
- (0x1FC6, 0x1FD3),
- (0x1FD6, 0x1FDB),
- (0x1FDD, 0x1FEF),
- (0x1FF2, 0x1FF4),
- (0x1FF6, 0x1FFE),
- (0x2129,),
- (0x2719, 0x271A),
- (0xAB65,),
- (0x10140, 0x1018D),
- (0x101A0,),
- (0x1D200, 0x1D245),
- (0x1F7A1, 0x1F7A7),
- ]
-
- class Cyrillic(unicode_set):
- """Unicode set for Cyrillic Unicode Character Range"""
- _ranges: UnicodeRangeList = [
- (0x0400, 0x052F),
- (0x1C80, 0x1C88),
- (0x1D2B,),
- (0x1D78,),
- (0x2DE0, 0x2DFF),
- (0xA640, 0xA672),
- (0xA674, 0xA69F),
- (0xFE2E, 0xFE2F),
- ]
-
- class Chinese(unicode_set):
- """Unicode set for Chinese Unicode Character Range"""
- _ranges: UnicodeRangeList = [
- (0x2E80, 0x2E99),
- (0x2E9B, 0x2EF3),
- (0x31C0, 0x31E3),
- (0x3400, 0x4DB5),
- (0x4E00, 0x9FEF),
- (0xA700, 0xA707),
- (0xF900, 0xFA6D),
- (0xFA70, 0xFAD9),
- (0x16FE2, 0x16FE3),
- (0x1F210, 0x1F212),
- (0x1F214, 0x1F23B),
- (0x1F240, 0x1F248),
- (0x20000, 0x2A6D6),
- (0x2A700, 0x2B734),
- (0x2B740, 0x2B81D),
- (0x2B820, 0x2CEA1),
- (0x2CEB0, 0x2EBE0),
- (0x2F800, 0x2FA1D),
- ]
-
- class Japanese(unicode_set):
- """Unicode set for Japanese Unicode Character Range, combining Kanji, Hiragana, and Katakana ranges"""
-
- class Kanji(unicode_set):
- "Unicode set for Kanji Unicode Character Range"
- _ranges: UnicodeRangeList = [
- (0x4E00, 0x9FBF),
- (0x3000, 0x303F),
- ]
-
- class Hiragana(unicode_set):
- """Unicode set for Hiragana Unicode Character Range"""
- _ranges: UnicodeRangeList = [
- (0x3041, 0x3096),
- (0x3099, 0x30A0),
- (0x30FC,),
- (0xFF70,),
- (0x1B001,),
- (0x1B150, 0x1B152),
- (0x1F200,),
- ]
-
- class Katakana(unicode_set):
- """Unicode set for Katakana Unicode Character Range"""
- _ranges: UnicodeRangeList = [
- (0x3099, 0x309C),
- (0x30A0, 0x30FF),
- (0x31F0, 0x31FF),
- (0x32D0, 0x32FE),
- (0xFF65, 0xFF9F),
- (0x1B000,),
- (0x1B164, 0x1B167),
- (0x1F201, 0x1F202),
- (0x1F213,),
- ]
-
- 漢字 = Kanji
- カタカナ = Katakana
- ひらがな = Hiragana
-
- _ranges = (
- Kanji._ranges
- + Hiragana._ranges
- + Katakana._ranges
- )
-
- class Hangul(unicode_set):
- """Unicode set for Hangul (Korean) Unicode Character Range"""
- _ranges: UnicodeRangeList = [
- (0x1100, 0x11FF),
- (0x302E, 0x302F),
- (0x3131, 0x318E),
- (0x3200, 0x321C),
- (0x3260, 0x327B),
- (0x327E,),
- (0xA960, 0xA97C),
- (0xAC00, 0xD7A3),
- (0xD7B0, 0xD7C6),
- (0xD7CB, 0xD7FB),
- (0xFFA0, 0xFFBE),
- (0xFFC2, 0xFFC7),
- (0xFFCA, 0xFFCF),
- (0xFFD2, 0xFFD7),
- (0xFFDA, 0xFFDC),
- ]
-
- Korean = Hangul
-
- class CJK(Chinese, Japanese, Hangul):
- """Unicode set for combined Chinese, Japanese, and Korean (CJK) Unicode Character Range"""
-
- class Thai(unicode_set):
- """Unicode set for Thai Unicode Character Range"""
- _ranges: UnicodeRangeList = [
- (0x0E01, 0x0E3A),
- (0x0E3F, 0x0E5B)
- ]
-
- class Arabic(unicode_set):
- """Unicode set for Arabic Unicode Character Range"""
- _ranges: UnicodeRangeList = [
- (0x0600, 0x061B),
- (0x061E, 0x06FF),
- (0x0700, 0x077F),
- ]
-
- class Hebrew(unicode_set):
- """Unicode set for Hebrew Unicode Character Range"""
- _ranges: UnicodeRangeList = [
- (0x0591, 0x05C7),
- (0x05D0, 0x05EA),
- (0x05EF, 0x05F4),
- (0xFB1D, 0xFB36),
- (0xFB38, 0xFB3C),
- (0xFB3E,),
- (0xFB40, 0xFB41),
- (0xFB43, 0xFB44),
- (0xFB46, 0xFB4F),
- ]
-
- class Devanagari(unicode_set):
- """Unicode set for Devanagari Unicode Character Range"""
- _ranges: UnicodeRangeList = [
- (0x0900, 0x097F),
- (0xA8E0, 0xA8FF)
- ]
-
- BMP = BasicMultilingualPlane
-
- # add language identifiers using language Unicode
- العربية = Arabic
- 中文 = Chinese
- кириллица = Cyrillic
- Ελληνικά = Greek
- עִברִית = Hebrew
- 日本語 = Japanese
- 한국어 = Korean
- ไทย = Thai
- देवनागरी = Devanagari
-
- # fmt: on
diff --git a/spaces/prairie-guy/Seasonal_Mood/app.py b/spaces/prairie-guy/Seasonal_Mood/app.py
deleted file mode 100644
index ff3d91ae7658ec7e921c1910e6d19b0c7c643dd7..0000000000000000000000000000000000000000
--- a/spaces/prairie-guy/Seasonal_Mood/app.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import gradio as gr
-from fastai.vision.all import *
-import skimage
-
-learn = load_learner('export.pkl')
-
-def predict(img):
- labels = learn.dls.vocab
- im = PILImage.create(img)
- pred,pred_idx,probs = learn.predict(im)
- return {label: float(prob) for (label,prob) in zip(labels,probs)}
-
-gr.Interface(fn=predict,
- inputs=gr.inputs.Image(shape=((400,400))),
- outputs=gr.outputs.Label(num_top_classes=4),
- title = "Art Mood",
- examples = [f'_Image{i}.jpg' for i in range(5,12)],
- description= "What is the Mood of the Art? Spring, Summer, Winter or Fall?").launch(share=True, enable_queue=True)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/colorLib/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/colorLib/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/cli/commands/components/publish.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/cli/commands/components/publish.py
deleted file mode 100644
index 2eac34da85dfdec44e23f8437b017d507bf4d8bb..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/cli/commands/components/publish.py
+++ /dev/null
@@ -1,243 +0,0 @@
-import random
-import re
-import shutil
-import tempfile
-from pathlib import Path
-from typing import Optional
-
-from huggingface_hub import HfApi
-from rich import print
-from rich.console import Console
-from rich.panel import Panel
-from rich.prompt import Confirm, Prompt
-from typer import Argument, Option
-from typing_extensions import Annotated
-
-from gradio.cli.commands.components._create_utils import PATTERN_RE
-
-colors = ["red", "yellow", "green", "blue", "indigo", "purple", "pink", "gray"]
-
-PYPI_REGISTER_URL = "https://pypi.org/account/register/"
-
-README_CONTENTS = """
----
-tags: [gradio-custom-component{template}]
-title: {package_name} V{version}
-colorFrom: {color_from}
-colorTo: {color_to}
-sdk: docker
-pinned: false
-license: apache-2.0
----
-"""
-
-DOCKERFILE = """
-FROM python:3.9
-
-WORKDIR /code
-
-COPY --link --chown=1000 . .
-
-RUN pip install --no-cache-dir -r requirements.txt
-
-ENV PYTHONUNBUFFERED=1 \
- GRADIO_ALLOW_FLAGGING=never \
- GRADIO_NUM_PORTS=1 \
- GRADIO_SERVER_NAME=0.0.0.0 \
- GRADIO_SERVER_PORT=7860 \
- SYSTEM=spaces
-
-CMD ["python", "app.py"]
-"""
-
-
-def _ignore(s, names):
- ignored = []
- for n in names:
- if "__pycache__" in n or n.startswith("dist") or n.startswith("node_modules"):
- ignored.append(n)
- return ignored
-
-
-def _publish(
- dist_dir: Annotated[
- Path,
- Argument(help=f"Path to the wheel directory. Default is {Path('.') / 'dist'}"),
- ] = Path(".")
- / "dist",
- upload_pypi: Annotated[bool, Option(help="Whether to upload to PyPI.")] = True,
- pypi_username: Annotated[str, Option(help="The username for PyPI.")] = "",
- pypi_password: Annotated[str, Option(help="The password for PyPI.")] = "",
- upload_demo: Annotated[
- bool, Option(help="Whether to upload demo to HuggingFace.")
- ] = True,
- demo_dir: Annotated[
- Optional[Path], Option(help="Path to the demo directory.")
- ] = None,
- source_dir: Annotated[
- Optional[Path],
- Option(
- help="Path to the source directory of the custom component. To share with community."
- ),
- ] = None,
- hf_token: Annotated[
- Optional[str],
- Option(
- help="HuggingFace token for uploading demo. Can be omitted if already logged in via huggingface cli."
- ),
- ] = None,
-):
- upload_source = source_dir is not None
- console = Console()
- dist_dir = dist_dir.resolve()
- if not dist_dir.exists():
- raise ValueError(
- f"{dist_dir} does not exist. Run `gradio cc build` to create a wheel and source distribution."
- )
- if not dist_dir.is_dir():
- raise ValueError(f"{dist_dir} is not a directory")
- distribution_files = [
- p.resolve() for p in Path(dist_dir).glob("*") if p.suffix in {".whl", ".gz"}
- ]
- wheel_file = next((p for p in distribution_files if p.suffix == ".whl"), None)
- if not wheel_file:
- raise ValueError(
- "A wheel file was not found in the distribution directory. "
- "Run `gradio cc build` to create a wheel file."
- )
- if upload_pypi and (not pypi_username or not pypi_password):
- panel = Panel(
- "It is recommended to upload your component to pypi so that [bold][magenta]anyone[/][/] "
- "can install it with [bold][magenta]pip install[/][/].\n\n"
- f"A PyPi account is needed. If you do not have an account, register account here: [blue]{PYPI_REGISTER_URL}[/]",
- )
- print(panel)
- upload_pypi = Confirm.ask(":snake: Upload to pypi?")
- if upload_pypi:
- pypi_username = Prompt.ask(":laptop_computer: Enter your pypi username")
- pypi_password = Prompt.ask(
- ":closed_lock_with_key: Enter your pypi password", password=True
- )
- if upload_pypi:
- try:
- from twine.commands.upload import upload as twine_upload # type: ignore
- from twine.settings import Settings # type: ignore
- except (ImportError, ModuleNotFoundError) as e:
- raise ValueError(
- "The twine library must be installed to publish to pypi."
- "Install it with pip, pip install twine."
- ) from e
-
- twine_settings = Settings(username=pypi_username, password=pypi_password)
- try:
- twine_files = [str(p) for p in distribution_files]
- print(f"Uploading files: {','.join(twine_files)}")
- twine_upload(twine_settings, twine_files)
- except Exception:
- console.print_exception()
- if upload_demo and not demo_dir:
- panel = Panel(
- "It is recommended you upload a demo of your component to [blue]https://huggingface.co/spaces[/] "
- "so that anyone can try it from their browser."
- )
- print(panel)
- upload_demo = Confirm.ask(":hugging_face: Upload demo?")
- if upload_demo:
- panel = Panel(
- "Please provide the path to the [magenta]demo directory[/] for your custom component.\n\n"
- "This directory should contain [magenta]all the files[/] it needs to run successfully.\n\n"
- "Please make sure the gradio app is in an [magenta]app.py[/] file.\n\n"
- "If you need additional python requirements, add a [magenta]requirements.txt[/] file to this directory."
- )
- print(panel)
- demo_dir_ = Prompt.ask(
- f":roller_coaster: Please enter the path to the demo directory. Leave blank to use: {(Path('.') / 'demo')}"
- )
- demo_dir_ = demo_dir_ or str(Path(".") / "demo")
- demo_dir = Path(demo_dir_).resolve()
-
- if upload_demo and not source_dir:
- panel = Panel(
- "It is recommended that you share your [magenta]source code[/] so that others can learn from and improve your component."
- )
- print(panel)
- upload_source = Confirm.ask(":books: Would you like to share your source code?")
- if upload_source:
- source_dir_ = Prompt.ask(
- ":page_with_curl: Enter the path to the source code [magenta]directory[/]. Leave blank to use current directory"
- )
- source_dir_ = source_dir_ or str(Path("."))
- source_dir = Path(source_dir_).resolve()
- if upload_demo:
- assert demo_dir
- if not (demo_dir / "app.py").exists():
- raise FileNotFoundError("app.py not found in demo directory.")
- additional_reqs = [wheel_file.name]
- if (demo_dir / "requirements.txt").exists():
- reqs = (demo_dir / "requirements.txt").read_text().splitlines()
- reqs += additional_reqs
- else:
- reqs = additional_reqs
-
- color_from, color_to = random.choice(colors), random.choice(colors)
- package_name, version = wheel_file.name.split("-")[:2]
- with tempfile.TemporaryDirectory() as tempdir:
- shutil.copytree(
- str(demo_dir),
- str(tempdir),
- dirs_exist_ok=True,
- )
- if source_dir:
- shutil.copytree(
- str(source_dir),
- str(Path(tempdir) / "src"),
- dirs_exist_ok=True,
- ignore=_ignore,
- )
- reqs_txt = Path(tempdir) / "requirements.txt"
- reqs_txt.write_text("\n".join(reqs))
- readme = Path(tempdir) / "README.md"
- template = ""
- if upload_source and source_dir:
- match = re.search(
- PATTERN_RE, (source_dir / "pyproject.toml").read_text()
- )
- if match:
- template = f", {match.group(0)}"
-
- readme.write_text(
- README_CONTENTS.format(
- package_name=package_name,
- version=version,
- color_from=color_from,
- color_to=color_to,
- template=template,
- )
- )
- dockerfile = Path(tempdir) / "Dockerfile"
- dockerfile.write_text(DOCKERFILE)
-
- api = HfApi()
- new_space = api.create_repo(
- repo_id=f"{package_name}",
- repo_type="space",
- exist_ok=True,
- private=False,
- space_sdk="docker",
- token=hf_token,
- )
- api.upload_folder(
- repo_id=new_space.repo_id,
- folder_path=tempdir,
- token=hf_token,
- repo_type="space",
- )
- api.upload_file(
- repo_id=new_space.repo_id,
- path_or_fileobj=str(wheel_file),
- path_in_repo=wheel_file.name,
- token=hf_token,
- repo_type="space",
- )
- print("\n")
- print(f"Demo uploaded to {new_space} !")
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/container.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/container.py
deleted file mode 100644
index 0f082e298afc24445492b502b7ceb1e809915df5..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/container.py
+++ /dev/null
@@ -1,141 +0,0 @@
-from matplotlib import cbook
-from matplotlib.artist import Artist
-
-
-class Container(tuple):
- """
- Base class for containers.
-
- Containers are classes that collect semantically related Artists such as
- the bars of a bar plot.
- """
-
- def __repr__(self):
- return f"<{type(self).__name__} object of {len(self)} artists>"
-
- def __new__(cls, *args, **kwargs):
- return tuple.__new__(cls, args[0])
-
- def __init__(self, kl, label=None):
- self._callbacks = cbook.CallbackRegistry(signals=["pchanged"])
- self._remove_method = None
- self._label = str(label) if label is not None else None
-
- def remove(self):
- for c in cbook.flatten(
- self, scalarp=lambda x: isinstance(x, Artist)):
- if c is not None:
- c.remove()
- if self._remove_method:
- self._remove_method(self)
-
- def get_children(self):
- return [child for child in cbook.flatten(self) if child is not None]
-
- get_label = Artist.get_label
- set_label = Artist.set_label
- add_callback = Artist.add_callback
- remove_callback = Artist.remove_callback
- pchanged = Artist.pchanged
-
-
-class BarContainer(Container):
- """
- Container for the artists of bar plots (e.g. created by `.Axes.bar`).
-
- The container can be treated as a tuple of the *patches* themselves.
- Additionally, you can access these and further parameters by the
- attributes.
-
- Attributes
- ----------
- patches : list of :class:`~matplotlib.patches.Rectangle`
- The artists of the bars.
-
- errorbar : None or :class:`~matplotlib.container.ErrorbarContainer`
- A container for the error bar artists if error bars are present.
- *None* otherwise.
-
- datavalues : None or array-like
- The underlying data values corresponding to the bars.
-
- orientation : {'vertical', 'horizontal'}, default: None
- If 'vertical', the bars are assumed to be vertical.
- If 'horizontal', the bars are assumed to be horizontal.
-
- """
-
- def __init__(self, patches, errorbar=None, *, datavalues=None,
- orientation=None, **kwargs):
- self.patches = patches
- self.errorbar = errorbar
- self.datavalues = datavalues
- self.orientation = orientation
- super().__init__(patches, **kwargs)
-
-
-class ErrorbarContainer(Container):
- """
- Container for the artists of error bars (e.g. created by `.Axes.errorbar`).
-
- The container can be treated as the *lines* tuple itself.
- Additionally, you can access these and further parameters by the
- attributes.
-
- Attributes
- ----------
- lines : tuple
- Tuple of ``(data_line, caplines, barlinecols)``.
-
- - data_line : :class:`~matplotlib.lines.Line2D` instance of
- x, y plot markers and/or line.
- - caplines : tuple of :class:`~matplotlib.lines.Line2D` instances of
- the error bar caps.
- - barlinecols : list of :class:`~matplotlib.collections.LineCollection`
- with the horizontal and vertical error ranges.
-
- has_xerr, has_yerr : bool
- ``True`` if the errorbar has x/y errors.
-
- """
-
- def __init__(self, lines, has_xerr=False, has_yerr=False, **kwargs):
- self.lines = lines
- self.has_xerr = has_xerr
- self.has_yerr = has_yerr
- super().__init__(lines, **kwargs)
-
-
-class StemContainer(Container):
- """
- Container for the artists created in a :meth:`.Axes.stem` plot.
-
- The container can be treated like a namedtuple ``(markerline, stemlines,
- baseline)``.
-
- Attributes
- ----------
- markerline : :class:`~matplotlib.lines.Line2D`
- The artist of the markers at the stem heads.
-
- stemlines : list of :class:`~matplotlib.lines.Line2D`
- The artists of the vertical lines for all stems.
-
- baseline : :class:`~matplotlib.lines.Line2D`
- The artist of the horizontal baseline.
- """
- def __init__(self, markerline_stemlines_baseline, **kwargs):
- """
- Parameters
- ----------
- markerline_stemlines_baseline : tuple
- Tuple of ``(markerline, stemlines, baseline)``.
- ``markerline`` contains the `.LineCollection` of the markers,
- ``stemlines`` is a `.LineCollection` of the main lines,
- ``baseline`` is the `.Line2D` of the baseline.
- """
- markerline, stemlines, baseline = markerline_stemlines_baseline
- self.markerline = markerline
- self.stemlines = stemlines
- self.baseline = baseline
- super().__init__(markerline_stemlines_baseline, **kwargs)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_backend_nbagg.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_backend_nbagg.py
deleted file mode 100644
index 4ebf3e1f56d117895388e709cbdefec4f98bd5e6..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/matplotlib/tests/test_backend_nbagg.py
+++ /dev/null
@@ -1,30 +0,0 @@
-import os
-from pathlib import Path
-import subprocess
-from tempfile import TemporaryDirectory
-
-import pytest
-
-nbformat = pytest.importorskip('nbformat')
-pytest.importorskip('nbconvert')
-pytest.importorskip('ipykernel')
-
-# From https://blog.thedataincubator.com/2016/06/testing-jupyter-notebooks/
-
-
-def test_ipynb():
- nb_path = Path(__file__).parent / 'test_nbagg_01.ipynb'
-
- with TemporaryDirectory() as tmpdir:
- out_path = Path(tmpdir, "out.ipynb")
- subprocess.check_call(
- ["jupyter", "nbconvert", "--to", "notebook",
- "--execute", "--ExecutePreprocessor.timeout=500",
- "--output", str(out_path), str(nb_path)],
- env={**os.environ, "IPYTHONDIR": tmpdir})
- with out_path.open() as out:
- nb = nbformat.read(out, nbformat.current_nbformat)
-
- errors = [output for cell in nb.cells for output in cell.get("outputs", [])
- if output.output_type == "error"]
- assert not errors
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/arrays/integer/test_reduction.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/arrays/integer/test_reduction.py
deleted file mode 100644
index 1c91cd25ba69ce207c6be3f6dd438a4ae4f37980..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/arrays/integer/test_reduction.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import numpy as np
-import pytest
-
-import pandas as pd
-from pandas import (
- DataFrame,
- Series,
- array,
-)
-import pandas._testing as tm
-
-
-@pytest.mark.parametrize(
- "op, expected",
- [
- ["sum", np.int64(3)],
- ["prod", np.int64(2)],
- ["min", np.int64(1)],
- ["max", np.int64(2)],
- ["mean", np.float64(1.5)],
- ["median", np.float64(1.5)],
- ["var", np.float64(0.5)],
- ["std", np.float64(0.5**0.5)],
- ["skew", pd.NA],
- ["kurt", pd.NA],
- ["any", True],
- ["all", True],
- ],
-)
-def test_series_reductions(op, expected):
- ser = Series([1, 2], dtype="Int64")
- result = getattr(ser, op)()
- tm.assert_equal(result, expected)
-
-
-@pytest.mark.parametrize(
- "op, expected",
- [
- ["sum", Series([3], index=["a"], dtype="Int64")],
- ["prod", Series([2], index=["a"], dtype="Int64")],
- ["min", Series([1], index=["a"], dtype="Int64")],
- ["max", Series([2], index=["a"], dtype="Int64")],
- ["mean", Series([1.5], index=["a"], dtype="Float64")],
- ["median", Series([1.5], index=["a"], dtype="Float64")],
- ["var", Series([0.5], index=["a"], dtype="Float64")],
- ["std", Series([0.5**0.5], index=["a"], dtype="Float64")],
- ["skew", Series([pd.NA], index=["a"], dtype="Float64")],
- ["kurt", Series([pd.NA], index=["a"], dtype="Float64")],
- ["any", Series([True], index=["a"], dtype="boolean")],
- ["all", Series([True], index=["a"], dtype="boolean")],
- ],
-)
-def test_dataframe_reductions(op, expected):
- df = DataFrame({"a": array([1, 2], dtype="Int64")})
- result = getattr(df, op)()
- tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize(
- "op, expected",
- [
- ["sum", array([1, 3], dtype="Int64")],
- ["prod", array([1, 3], dtype="Int64")],
- ["min", array([1, 3], dtype="Int64")],
- ["max", array([1, 3], dtype="Int64")],
- ["mean", array([1, 3], dtype="Float64")],
- ["median", array([1, 3], dtype="Float64")],
- ["var", array([pd.NA], dtype="Float64")],
- ["std", array([pd.NA], dtype="Float64")],
- ["skew", array([pd.NA], dtype="Float64")],
- ["any", array([True, True], dtype="boolean")],
- ["all", array([True, True], dtype="boolean")],
- ],
-)
-def test_groupby_reductions(op, expected):
- df = DataFrame(
- {
- "A": ["a", "b", "b"],
- "B": array([1, None, 3], dtype="Int64"),
- }
- )
- result = getattr(df.groupby("A"), op)()
- expected = DataFrame(expected, index=pd.Index(["a", "b"], name="A"), columns=["B"])
-
- tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize(
- "op, expected",
- [
- ["sum", Series([4, 4], index=["B", "C"], dtype="Float64")],
- ["prod", Series([3, 3], index=["B", "C"], dtype="Float64")],
- ["min", Series([1, 1], index=["B", "C"], dtype="Float64")],
- ["max", Series([3, 3], index=["B", "C"], dtype="Float64")],
- ["mean", Series([2, 2], index=["B", "C"], dtype="Float64")],
- ["median", Series([2, 2], index=["B", "C"], dtype="Float64")],
- ["var", Series([2, 2], index=["B", "C"], dtype="Float64")],
- ["std", Series([2**0.5, 2**0.5], index=["B", "C"], dtype="Float64")],
- ["skew", Series([pd.NA, pd.NA], index=["B", "C"], dtype="Float64")],
- ["kurt", Series([pd.NA, pd.NA], index=["B", "C"], dtype="Float64")],
- ["any", Series([True, True, True], index=["A", "B", "C"], dtype="boolean")],
- ["all", Series([True, True, True], index=["A", "B", "C"], dtype="boolean")],
- ],
-)
-def test_mixed_reductions(op, expected):
- df = DataFrame(
- {
- "A": ["a", "b", "b"],
- "B": [1, None, 3],
- "C": array([1, None, 3], dtype="Int64"),
- }
- )
-
- # series
- result = getattr(df.C, op)()
- tm.assert_equal(result, expected["C"])
-
- # frame
- if op in ["any", "all"]:
- result = getattr(df, op)()
- else:
- result = getattr(df, op)(numeric_only=True)
- tm.assert_series_equal(result, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/window/test_rolling.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/window/test_rolling.py
deleted file mode 100644
index 1beab5340484f479e9bd110df08766814c843a59..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/window/test_rolling.py
+++ /dev/null
@@ -1,1912 +0,0 @@
-from datetime import (
- datetime,
- timedelta,
-)
-
-import numpy as np
-import pytest
-
-from pandas.compat import (
- IS64,
- is_platform_arm,
- is_platform_power,
-)
-
-from pandas import (
- DataFrame,
- DatetimeIndex,
- MultiIndex,
- Series,
- Timedelta,
- Timestamp,
- date_range,
- period_range,
- to_datetime,
- to_timedelta,
-)
-import pandas._testing as tm
-from pandas.api.indexers import BaseIndexer
-from pandas.core.indexers.objects import VariableOffsetWindowIndexer
-
-from pandas.tseries.offsets import BusinessDay
-
-
-def test_doc_string():
- df = DataFrame({"B": [0, 1, 2, np.nan, 4]})
- df
- df.rolling(2).sum()
- df.rolling(2, min_periods=1).sum()
-
-
-def test_constructor(frame_or_series):
- # GH 12669
-
- c = frame_or_series(range(5)).rolling
-
- # valid
- c(0)
- c(window=2)
- c(window=2, min_periods=1)
- c(window=2, min_periods=1, center=True)
- c(window=2, min_periods=1, center=False)
-
- # GH 13383
-
- msg = "window must be an integer 0 or greater"
-
- with pytest.raises(ValueError, match=msg):
- c(-1)
-
-
-@pytest.mark.parametrize("w", [2.0, "foo", np.array([2])])
-def test_invalid_constructor(frame_or_series, w):
- # not valid
-
- c = frame_or_series(range(5)).rolling
-
- msg = "|".join(
- [
- "window must be an integer",
- "passed window foo is not compatible with a datetimelike index",
- ]
- )
- with pytest.raises(ValueError, match=msg):
- c(window=w)
-
- msg = "min_periods must be an integer"
- with pytest.raises(ValueError, match=msg):
- c(window=2, min_periods=w)
-
- msg = "center must be a boolean"
- with pytest.raises(ValueError, match=msg):
- c(window=2, min_periods=1, center=w)
-
-
-@pytest.mark.parametrize(
- "window",
- [
- timedelta(days=3),
- Timedelta(days=3),
- "3D",
- VariableOffsetWindowIndexer(
- index=date_range("2015-12-25", periods=5), offset=BusinessDay(1)
- ),
- ],
-)
-def test_freq_window_not_implemented(window):
- # GH 15354
- df = DataFrame(
- np.arange(10),
- index=date_range("2015-12-24", periods=10, freq="D"),
- )
- with pytest.raises(
- NotImplementedError, match="step is not supported with frequency windows"
- ):
- df.rolling("3D", step=3)
-
-
-@pytest.mark.parametrize("agg", ["cov", "corr"])
-def test_step_not_implemented_for_cov_corr(agg):
- # GH 15354
- roll = DataFrame(range(2)).rolling(1, step=2)
- with pytest.raises(NotImplementedError, match="step not implemented"):
- getattr(roll, agg)()
-
-
-@pytest.mark.parametrize("window", [timedelta(days=3), Timedelta(days=3)])
-def test_constructor_with_timedelta_window(window):
- # GH 15440
- n = 10
- df = DataFrame(
- {"value": np.arange(n)},
- index=date_range("2015-12-24", periods=n, freq="D"),
- )
- expected_data = np.append([0.0, 1.0], np.arange(3.0, 27.0, 3))
-
- result = df.rolling(window=window).sum()
- expected = DataFrame(
- {"value": expected_data},
- index=date_range("2015-12-24", periods=n, freq="D"),
- )
- tm.assert_frame_equal(result, expected)
- expected = df.rolling("3D").sum()
- tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize("window", [timedelta(days=3), Timedelta(days=3), "3D"])
-def test_constructor_timedelta_window_and_minperiods(window, raw):
- # GH 15305
- n = 10
- df = DataFrame(
- {"value": np.arange(n)},
- index=date_range("2017-08-08", periods=n, freq="D"),
- )
- expected = DataFrame(
- {"value": np.append([np.nan, 1.0], np.arange(3.0, 27.0, 3))},
- index=date_range("2017-08-08", periods=n, freq="D"),
- )
- result_roll_sum = df.rolling(window=window, min_periods=2).sum()
- result_roll_generic = df.rolling(window=window, min_periods=2).apply(sum, raw=raw)
- tm.assert_frame_equal(result_roll_sum, expected)
- tm.assert_frame_equal(result_roll_generic, expected)
-
-
-def test_closed_fixed(closed, arithmetic_win_operators):
- # GH 34315
- func_name = arithmetic_win_operators
- df_fixed = DataFrame({"A": [0, 1, 2, 3, 4]})
- df_time = DataFrame({"A": [0, 1, 2, 3, 4]}, index=date_range("2020", periods=5))
-
- result = getattr(
- df_fixed.rolling(2, closed=closed, min_periods=1),
- func_name,
- )()
- expected = getattr(
- df_time.rolling("2D", closed=closed, min_periods=1),
- func_name,
- )().reset_index(drop=True)
-
- tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize(
- "closed, window_selections",
- [
- (
- "both",
- [
- [True, True, False, False, False],
- [True, True, True, False, False],
- [False, True, True, True, False],
- [False, False, True, True, True],
- [False, False, False, True, True],
- ],
- ),
- (
- "left",
- [
- [True, False, False, False, False],
- [True, True, False, False, False],
- [False, True, True, False, False],
- [False, False, True, True, False],
- [False, False, False, True, True],
- ],
- ),
- (
- "right",
- [
- [True, True, False, False, False],
- [False, True, True, False, False],
- [False, False, True, True, False],
- [False, False, False, True, True],
- [False, False, False, False, True],
- ],
- ),
- (
- "neither",
- [
- [True, False, False, False, False],
- [False, True, False, False, False],
- [False, False, True, False, False],
- [False, False, False, True, False],
- [False, False, False, False, True],
- ],
- ),
- ],
-)
-def test_datetimelike_centered_selections(
- closed, window_selections, arithmetic_win_operators
-):
- # GH 34315
- func_name = arithmetic_win_operators
- df_time = DataFrame(
- {"A": [0.0, 1.0, 2.0, 3.0, 4.0]}, index=date_range("2020", periods=5)
- )
-
- expected = DataFrame(
- {"A": [getattr(df_time["A"].iloc[s], func_name)() for s in window_selections]},
- index=date_range("2020", periods=5),
- )
-
- if func_name == "sem":
- kwargs = {"ddof": 0}
- else:
- kwargs = {}
-
- result = getattr(
- df_time.rolling("2D", closed=closed, min_periods=1, center=True),
- func_name,
- )(**kwargs)
-
- tm.assert_frame_equal(result, expected, check_dtype=False)
-
-
-@pytest.mark.parametrize(
- "window,closed,expected",
- [
- ("3s", "right", [3.0, 3.0, 3.0]),
- ("3s", "both", [3.0, 3.0, 3.0]),
- ("3s", "left", [3.0, 3.0, 3.0]),
- ("3s", "neither", [3.0, 3.0, 3.0]),
- ("2s", "right", [3.0, 2.0, 2.0]),
- ("2s", "both", [3.0, 3.0, 3.0]),
- ("2s", "left", [1.0, 3.0, 3.0]),
- ("2s", "neither", [1.0, 2.0, 2.0]),
- ],
-)
-def test_datetimelike_centered_offset_covers_all(
- window, closed, expected, frame_or_series
-):
- # GH 42753
-
- index = [
- Timestamp("20130101 09:00:01"),
- Timestamp("20130101 09:00:02"),
- Timestamp("20130101 09:00:02"),
- ]
- df = frame_or_series([1, 1, 1], index=index)
-
- result = df.rolling(window, closed=closed, center=True).sum()
- expected = frame_or_series(expected, index=index)
- tm.assert_equal(result, expected)
-
-
-@pytest.mark.parametrize(
- "window,closed,expected",
- [
- ("2D", "right", [4, 4, 4, 4, 4, 4, 2, 2]),
- ("2D", "left", [2, 2, 4, 4, 4, 4, 4, 4]),
- ("2D", "both", [4, 4, 6, 6, 6, 6, 4, 4]),
- ("2D", "neither", [2, 2, 2, 2, 2, 2, 2, 2]),
- ],
-)
-def test_datetimelike_nonunique_index_centering(
- window, closed, expected, frame_or_series
-):
- index = DatetimeIndex(
- [
- "2020-01-01",
- "2020-01-01",
- "2020-01-02",
- "2020-01-02",
- "2020-01-03",
- "2020-01-03",
- "2020-01-04",
- "2020-01-04",
- ]
- )
-
- df = frame_or_series([1] * 8, index=index, dtype=float)
- expected = frame_or_series(expected, index=index, dtype=float)
-
- result = df.rolling(window, center=True, closed=closed).sum()
-
- tm.assert_equal(result, expected)
-
-
-def test_even_number_window_alignment():
- # see discussion in GH 38780
- s = Series(range(3), index=date_range(start="2020-01-01", freq="D", periods=3))
-
- # behavior of index- and datetime-based windows differs here!
- # s.rolling(window=2, min_periods=1, center=True).mean()
-
- result = s.rolling(window="2D", min_periods=1, center=True).mean()
-
- expected = Series([0.5, 1.5, 2], index=s.index)
-
- tm.assert_series_equal(result, expected)
-
-
-def test_closed_fixed_binary_col(center, step):
- # GH 34315
- data = [0, 1, 1, 0, 0, 1, 0, 1]
- df = DataFrame(
- {"binary_col": data},
- index=date_range(start="2020-01-01", freq="min", periods=len(data)),
- )
-
- if center:
- expected_data = [2 / 3, 0.5, 0.4, 0.5, 0.428571, 0.5, 0.571429, 0.5]
- else:
- expected_data = [np.nan, 0, 0.5, 2 / 3, 0.5, 0.4, 0.5, 0.428571]
-
- expected = DataFrame(
- expected_data,
- columns=["binary_col"],
- index=date_range(start="2020-01-01", freq="min", periods=len(expected_data)),
- )[::step]
-
- rolling = df.rolling(
- window=len(df), closed="left", min_periods=1, center=center, step=step
- )
- result = rolling.mean()
- tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize("closed", ["neither", "left"])
-def test_closed_empty(closed, arithmetic_win_operators):
- # GH 26005
- func_name = arithmetic_win_operators
- ser = Series(data=np.arange(5), index=date_range("2000", periods=5, freq="2D"))
- roll = ser.rolling("1D", closed=closed)
-
- result = getattr(roll, func_name)()
- expected = Series([np.nan] * 5, index=ser.index)
- tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize("func", ["min", "max"])
-def test_closed_one_entry(func):
- # GH24718
- ser = Series(data=[2], index=date_range("2000", periods=1))
- result = getattr(ser.rolling("10D", closed="left"), func)()
- tm.assert_series_equal(result, Series([np.nan], index=ser.index))
-
-
-@pytest.mark.parametrize("func", ["min", "max"])
-def test_closed_one_entry_groupby(func):
- # GH24718
- ser = DataFrame(
- data={"A": [1, 1, 2], "B": [3, 2, 1]},
- index=date_range("2000", periods=3),
- )
- result = getattr(
- ser.groupby("A", sort=False)["B"].rolling("10D", closed="left"), func
- )()
- exp_idx = MultiIndex.from_arrays(arrays=[[1, 1, 2], ser.index], names=("A", None))
- expected = Series(data=[np.nan, 3, np.nan], index=exp_idx, name="B")
- tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize("input_dtype", ["int", "float"])
-@pytest.mark.parametrize(
- "func,closed,expected",
- [
- ("min", "right", [0.0, 0, 0, 1, 2, 3, 4, 5, 6, 7]),
- ("min", "both", [0.0, 0, 0, 0, 1, 2, 3, 4, 5, 6]),
- ("min", "neither", [np.nan, 0, 0, 1, 2, 3, 4, 5, 6, 7]),
- ("min", "left", [np.nan, 0, 0, 0, 1, 2, 3, 4, 5, 6]),
- ("max", "right", [0.0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
- ("max", "both", [0.0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
- ("max", "neither", [np.nan, 0, 1, 2, 3, 4, 5, 6, 7, 8]),
- ("max", "left", [np.nan, 0, 1, 2, 3, 4, 5, 6, 7, 8]),
- ],
-)
-def test_closed_min_max_datetime(input_dtype, func, closed, expected):
- # see gh-21704
- ser = Series(
- data=np.arange(10).astype(input_dtype),
- index=date_range("2000", periods=10),
- )
-
- result = getattr(ser.rolling("3D", closed=closed), func)()
- expected = Series(expected, index=ser.index)
- tm.assert_series_equal(result, expected)
-
-
-def test_closed_uneven():
- # see gh-21704
- ser = Series(data=np.arange(10), index=date_range("2000", periods=10))
-
- # uneven
- ser = ser.drop(index=ser.index[[1, 5]])
- result = ser.rolling("3D", closed="left").min()
- expected = Series([np.nan, 0, 0, 2, 3, 4, 6, 6], index=ser.index)
- tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize(
- "func,closed,expected",
- [
- ("min", "right", [np.nan, 0, 0, 1, 2, 3, 4, 5, np.nan, np.nan]),
- ("min", "both", [np.nan, 0, 0, 0, 1, 2, 3, 4, 5, np.nan]),
- ("min", "neither", [np.nan, np.nan, 0, 1, 2, 3, 4, 5, np.nan, np.nan]),
- ("min", "left", [np.nan, np.nan, 0, 0, 1, 2, 3, 4, 5, np.nan]),
- ("max", "right", [np.nan, 1, 2, 3, 4, 5, 6, 6, np.nan, np.nan]),
- ("max", "both", [np.nan, 1, 2, 3, 4, 5, 6, 6, 6, np.nan]),
- ("max", "neither", [np.nan, np.nan, 1, 2, 3, 4, 5, 6, np.nan, np.nan]),
- ("max", "left", [np.nan, np.nan, 1, 2, 3, 4, 5, 6, 6, np.nan]),
- ],
-)
-def test_closed_min_max_minp(func, closed, expected):
- # see gh-21704
- ser = Series(data=np.arange(10), index=date_range("2000", periods=10))
- # Explicit cast to float to avoid implicit cast when setting nan
- ser = ser.astype("float")
- ser[ser.index[-3:]] = np.nan
- result = getattr(ser.rolling("3D", min_periods=2, closed=closed), func)()
- expected = Series(expected, index=ser.index)
- tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize(
- "closed,expected",
- [
- ("right", [0, 0.5, 1, 2, 3, 4, 5, 6, 7, 8]),
- ("both", [0, 0.5, 1, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5]),
- ("neither", [np.nan, 0, 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5]),
- ("left", [np.nan, 0, 0.5, 1, 2, 3, 4, 5, 6, 7]),
- ],
-)
-def test_closed_median_quantile(closed, expected):
- # GH 26005
- ser = Series(data=np.arange(10), index=date_range("2000", periods=10))
- roll = ser.rolling("3D", closed=closed)
- expected = Series(expected, index=ser.index)
-
- result = roll.median()
- tm.assert_series_equal(result, expected)
-
- result = roll.quantile(0.5)
- tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize("roller", ["1s", 1])
-def tests_empty_df_rolling(roller):
- # GH 15819 Verifies that datetime and integer rolling windows can be
- # applied to empty DataFrames
- expected = DataFrame()
- result = DataFrame().rolling(roller).sum()
- tm.assert_frame_equal(result, expected)
-
- # Verifies that datetime and integer rolling windows can be applied to
- # empty DataFrames with datetime index
- expected = DataFrame(index=DatetimeIndex([]))
- result = DataFrame(index=DatetimeIndex([])).rolling(roller).sum()
- tm.assert_frame_equal(result, expected)
-
-
-def test_empty_window_median_quantile():
- # GH 26005
- expected = Series([np.nan, np.nan, np.nan])
- roll = Series(np.arange(3)).rolling(0)
-
- result = roll.median()
- tm.assert_series_equal(result, expected)
-
- result = roll.quantile(0.1)
- tm.assert_series_equal(result, expected)
-
-
-def test_missing_minp_zero():
- # https://github.com/pandas-dev/pandas/pull/18921
- # minp=0
- x = Series([np.nan])
- result = x.rolling(1, min_periods=0).sum()
- expected = Series([0.0])
- tm.assert_series_equal(result, expected)
-
- # minp=1
- result = x.rolling(1, min_periods=1).sum()
- expected = Series([np.nan])
- tm.assert_series_equal(result, expected)
-
-
-def test_missing_minp_zero_variable():
- # https://github.com/pandas-dev/pandas/pull/18921
- x = Series(
- [np.nan] * 4,
- index=DatetimeIndex(["2017-01-01", "2017-01-04", "2017-01-06", "2017-01-07"]),
- )
- result = x.rolling(Timedelta("2d"), min_periods=0).sum()
- expected = Series(0.0, index=x.index)
- tm.assert_series_equal(result, expected)
-
-
-def test_multi_index_names():
- # GH 16789, 16825
- cols = MultiIndex.from_product([["A", "B"], ["C", "D", "E"]], names=["1", "2"])
- df = DataFrame(np.ones((10, 6)), columns=cols)
- result = df.rolling(3).cov()
-
- tm.assert_index_equal(result.columns, df.columns)
- assert result.index.names == [None, "1", "2"]
-
-
-def test_rolling_axis_sum(axis_frame):
- # see gh-23372.
- df = DataFrame(np.ones((10, 20)))
- axis = df._get_axis_number(axis_frame)
-
- if axis == 0:
- msg = "The 'axis' keyword in DataFrame.rolling"
- expected = DataFrame({i: [np.nan] * 2 + [3.0] * 8 for i in range(20)})
- else:
- # axis == 1
- msg = "Support for axis=1 in DataFrame.rolling is deprecated"
- expected = DataFrame([[np.nan] * 2 + [3.0] * 18] * 10)
-
- with tm.assert_produces_warning(FutureWarning, match=msg):
- result = df.rolling(3, axis=axis_frame).sum()
- tm.assert_frame_equal(result, expected)
-
-
-def test_rolling_axis_count(axis_frame):
- # see gh-26055
- df = DataFrame({"x": range(3), "y": range(3)})
-
- axis = df._get_axis_number(axis_frame)
-
- if axis in [0, "index"]:
- msg = "The 'axis' keyword in DataFrame.rolling"
- expected = DataFrame({"x": [1.0, 2.0, 2.0], "y": [1.0, 2.0, 2.0]})
- else:
- msg = "Support for axis=1 in DataFrame.rolling is deprecated"
- expected = DataFrame({"x": [1.0, 1.0, 1.0], "y": [2.0, 2.0, 2.0]})
-
- with tm.assert_produces_warning(FutureWarning, match=msg):
- result = df.rolling(2, axis=axis_frame, min_periods=0).count()
- tm.assert_frame_equal(result, expected)
-
-
-def test_readonly_array():
- # GH-27766
- arr = np.array([1, 3, np.nan, 3, 5])
- arr.setflags(write=False)
- result = Series(arr).rolling(2).mean()
- expected = Series([np.nan, 2, np.nan, np.nan, 4])
- tm.assert_series_equal(result, expected)
-
-
-def test_rolling_datetime(axis_frame, tz_naive_fixture):
- # GH-28192
- tz = tz_naive_fixture
- df = DataFrame(
- {i: [1] * 2 for i in date_range("2019-8-01", "2019-08-03", freq="D", tz=tz)}
- )
-
- if axis_frame in [0, "index"]:
- msg = "The 'axis' keyword in DataFrame.rolling"
- with tm.assert_produces_warning(FutureWarning, match=msg):
- result = df.T.rolling("2D", axis=axis_frame).sum().T
- else:
- msg = "Support for axis=1 in DataFrame.rolling"
- with tm.assert_produces_warning(FutureWarning, match=msg):
- result = df.rolling("2D", axis=axis_frame).sum()
- expected = DataFrame(
- {
- **{
- i: [1.0] * 2
- for i in date_range("2019-8-01", periods=1, freq="D", tz=tz)
- },
- **{
- i: [2.0] * 2
- for i in date_range("2019-8-02", "2019-8-03", freq="D", tz=tz)
- },
- }
- )
- tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize("center", [True, False])
-def test_rolling_window_as_string(center):
- # see gh-22590
- date_today = datetime.now()
- days = date_range(date_today, date_today + timedelta(365), freq="D")
-
- data = np.ones(len(days))
- df = DataFrame({"DateCol": days, "metric": data})
-
- df.set_index("DateCol", inplace=True)
- result = df.rolling(window="21D", min_periods=2, closed="left", center=center)[
- "metric"
- ].agg("max")
-
- index = days.rename("DateCol")
- index = index._with_freq(None)
- expected_data = np.ones(len(days), dtype=np.float64)
- if not center:
- expected_data[:2] = np.nan
- expected = Series(expected_data, index=index, name="metric")
- tm.assert_series_equal(result, expected)
-
-
-def test_min_periods1():
- # GH#6795
- df = DataFrame([0, 1, 2, 1, 0], columns=["a"])
- result = df["a"].rolling(3, center=True, min_periods=1).max()
- expected = Series([1.0, 2.0, 2.0, 2.0, 1.0], name="a")
- tm.assert_series_equal(result, expected)
-
-
-def test_rolling_count_with_min_periods(frame_or_series):
- # GH 26996
- result = frame_or_series(range(5)).rolling(3, min_periods=3).count()
- expected = frame_or_series([np.nan, np.nan, 3.0, 3.0, 3.0])
- tm.assert_equal(result, expected)
-
-
-def test_rolling_count_default_min_periods_with_null_values(frame_or_series):
- # GH 26996
- values = [1, 2, 3, np.nan, 4, 5, 6]
- expected_counts = [1.0, 2.0, 3.0, 2.0, 2.0, 2.0, 3.0]
-
- # GH 31302
- result = frame_or_series(values).rolling(3, min_periods=0).count()
- expected = frame_or_series(expected_counts)
- tm.assert_equal(result, expected)
-
-
-@pytest.mark.parametrize(
- "df,expected,window,min_periods",
- [
- (
- DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
- [
- ({"A": [1], "B": [4]}, [0]),
- ({"A": [1, 2], "B": [4, 5]}, [0, 1]),
- ({"A": [1, 2, 3], "B": [4, 5, 6]}, [0, 1, 2]),
- ],
- 3,
- None,
- ),
- (
- DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
- [
- ({"A": [1], "B": [4]}, [0]),
- ({"A": [1, 2], "B": [4, 5]}, [0, 1]),
- ({"A": [2, 3], "B": [5, 6]}, [1, 2]),
- ],
- 2,
- 1,
- ),
- (
- DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
- [
- ({"A": [1], "B": [4]}, [0]),
- ({"A": [1, 2], "B": [4, 5]}, [0, 1]),
- ({"A": [2, 3], "B": [5, 6]}, [1, 2]),
- ],
- 2,
- 2,
- ),
- (
- DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
- [
- ({"A": [1], "B": [4]}, [0]),
- ({"A": [2], "B": [5]}, [1]),
- ({"A": [3], "B": [6]}, [2]),
- ],
- 1,
- 1,
- ),
- (
- DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}),
- [
- ({"A": [1], "B": [4]}, [0]),
- ({"A": [2], "B": [5]}, [1]),
- ({"A": [3], "B": [6]}, [2]),
- ],
- 1,
- 0,
- ),
- (DataFrame({"A": [1], "B": [4]}), [], 2, None),
- (DataFrame({"A": [1], "B": [4]}), [], 2, 1),
- (DataFrame(), [({}, [])], 2, None),
- (
- DataFrame({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}),
- [
- ({"A": [1.0], "B": [np.nan]}, [0]),
- ({"A": [1, np.nan], "B": [np.nan, 5]}, [0, 1]),
- ({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}, [0, 1, 2]),
- ],
- 3,
- 2,
- ),
- ],
-)
-def test_iter_rolling_dataframe(df, expected, window, min_periods):
- # GH 11704
- expected = [DataFrame(values, index=index) for (values, index) in expected]
-
- for expected, actual in zip(expected, df.rolling(window, min_periods=min_periods)):
- tm.assert_frame_equal(actual, expected)
-
-
-@pytest.mark.parametrize(
- "expected,window",
- [
- (
- [
- ({"A": [1], "B": [4]}, [0]),
- ({"A": [1, 2], "B": [4, 5]}, [0, 1]),
- ({"A": [2, 3], "B": [5, 6]}, [1, 2]),
- ],
- "2D",
- ),
- (
- [
- ({"A": [1], "B": [4]}, [0]),
- ({"A": [1, 2], "B": [4, 5]}, [0, 1]),
- ({"A": [1, 2, 3], "B": [4, 5, 6]}, [0, 1, 2]),
- ],
- "3D",
- ),
- (
- [
- ({"A": [1], "B": [4]}, [0]),
- ({"A": [2], "B": [5]}, [1]),
- ({"A": [3], "B": [6]}, [2]),
- ],
- "1D",
- ),
- ],
-)
-def test_iter_rolling_on_dataframe(expected, window):
- # GH 11704, 40373
- df = DataFrame(
- {
- "A": [1, 2, 3, 4, 5],
- "B": [4, 5, 6, 7, 8],
- "C": date_range(start="2016-01-01", periods=5, freq="D"),
- }
- )
-
- expected = [
- DataFrame(values, index=df.loc[index, "C"]) for (values, index) in expected
- ]
- for expected, actual in zip(expected, df.rolling(window, on="C")):
- tm.assert_frame_equal(actual, expected)
-
-
-def test_iter_rolling_on_dataframe_unordered():
- # GH 43386
- df = DataFrame({"a": ["x", "y", "x"], "b": [0, 1, 2]})
- results = list(df.groupby("a").rolling(2))
- expecteds = [df.iloc[idx, [1]] for idx in [[0], [0, 2], [1]]]
- for result, expected in zip(results, expecteds):
- tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize(
- "ser,expected,window, min_periods",
- [
- (
- Series([1, 2, 3]),
- [([1], [0]), ([1, 2], [0, 1]), ([1, 2, 3], [0, 1, 2])],
- 3,
- None,
- ),
- (
- Series([1, 2, 3]),
- [([1], [0]), ([1, 2], [0, 1]), ([1, 2, 3], [0, 1, 2])],
- 3,
- 1,
- ),
- (
- Series([1, 2, 3]),
- [([1], [0]), ([1, 2], [0, 1]), ([2, 3], [1, 2])],
- 2,
- 1,
- ),
- (
- Series([1, 2, 3]),
- [([1], [0]), ([1, 2], [0, 1]), ([2, 3], [1, 2])],
- 2,
- 2,
- ),
- (Series([1, 2, 3]), [([1], [0]), ([2], [1]), ([3], [2])], 1, 0),
- (Series([1, 2, 3]), [([1], [0]), ([2], [1]), ([3], [2])], 1, 1),
- (Series([1, 2]), [([1], [0]), ([1, 2], [0, 1])], 2, 0),
- (Series([], dtype="int64"), [], 2, 1),
- ],
-)
-def test_iter_rolling_series(ser, expected, window, min_periods):
- # GH 11704
- expected = [Series(values, index=index) for (values, index) in expected]
-
- for expected, actual in zip(expected, ser.rolling(window, min_periods=min_periods)):
- tm.assert_series_equal(actual, expected)
-
-
-@pytest.mark.parametrize(
- "expected,expected_index,window",
- [
- (
- [[0], [1], [2], [3], [4]],
- [
- date_range("2020-01-01", periods=1, freq="D"),
- date_range("2020-01-02", periods=1, freq="D"),
- date_range("2020-01-03", periods=1, freq="D"),
- date_range("2020-01-04", periods=1, freq="D"),
- date_range("2020-01-05", periods=1, freq="D"),
- ],
- "1D",
- ),
- (
- [[0], [0, 1], [1, 2], [2, 3], [3, 4]],
- [
- date_range("2020-01-01", periods=1, freq="D"),
- date_range("2020-01-01", periods=2, freq="D"),
- date_range("2020-01-02", periods=2, freq="D"),
- date_range("2020-01-03", periods=2, freq="D"),
- date_range("2020-01-04", periods=2, freq="D"),
- ],
- "2D",
- ),
- (
- [[0], [0, 1], [0, 1, 2], [1, 2, 3], [2, 3, 4]],
- [
- date_range("2020-01-01", periods=1, freq="D"),
- date_range("2020-01-01", periods=2, freq="D"),
- date_range("2020-01-01", periods=3, freq="D"),
- date_range("2020-01-02", periods=3, freq="D"),
- date_range("2020-01-03", periods=3, freq="D"),
- ],
- "3D",
- ),
- ],
-)
-def test_iter_rolling_datetime(expected, expected_index, window):
- # GH 11704
- ser = Series(range(5), index=date_range(start="2020-01-01", periods=5, freq="D"))
-
- expected = [
- Series(values, index=idx) for (values, idx) in zip(expected, expected_index)
- ]
-
- for expected, actual in zip(expected, ser.rolling(window)):
- tm.assert_series_equal(actual, expected)
-
-
-@pytest.mark.parametrize(
- "grouping,_index",
- [
- (
- {"level": 0},
- MultiIndex.from_tuples(
- [(0, 0), (0, 0), (1, 1), (1, 1), (1, 1)], names=[None, None]
- ),
- ),
- (
- {"by": "X"},
- MultiIndex.from_tuples(
- [(0, 0), (1, 0), (2, 1), (3, 1), (4, 1)], names=["X", None]
- ),
- ),
- ],
-)
-def test_rolling_positional_argument(grouping, _index, raw):
- # GH 34605
-
- def scaled_sum(*args):
- if len(args) < 2:
- raise ValueError("The function needs two arguments")
- array, scale = args
- return array.sum() / scale
-
- df = DataFrame(data={"X": range(5)}, index=[0, 0, 1, 1, 1])
-
- expected = DataFrame(data={"X": [0.0, 0.5, 1.0, 1.5, 2.0]}, index=_index)
- # GH 40341
- if "by" in grouping:
- expected = expected.drop(columns="X", errors="ignore")
- result = df.groupby(**grouping).rolling(1).apply(scaled_sum, raw=raw, args=(2,))
- tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize("add", [0.0, 2.0])
-def test_rolling_numerical_accuracy_kahan_mean(add):
- # GH: 36031 implementing kahan summation
- df = DataFrame(
- {"A": [3002399751580331.0 + add, -0.0, -0.0]},
- index=[
- Timestamp("19700101 09:00:00"),
- Timestamp("19700101 09:00:03"),
- Timestamp("19700101 09:00:06"),
- ],
- )
- result = (
- df.resample("1s").ffill().rolling("3s", closed="left", min_periods=3).mean()
- )
- dates = date_range("19700101 09:00:00", periods=7, freq="S")
- expected = DataFrame(
- {
- "A": [
- np.nan,
- np.nan,
- np.nan,
- 3002399751580330.5,
- 2001599834386887.25,
- 1000799917193443.625,
- 0.0,
- ]
- },
- index=dates,
- )
- tm.assert_frame_equal(result, expected)
-
-
-def test_rolling_numerical_accuracy_kahan_sum():
- # GH: 13254
- df = DataFrame([2.186, -1.647, 0.0, 0.0, 0.0, 0.0], columns=["x"])
- result = df["x"].rolling(3).sum()
- expected = Series([np.nan, np.nan, 0.539, -1.647, 0.0, 0.0], name="x")
- tm.assert_series_equal(result, expected)
-
-
-def test_rolling_numerical_accuracy_jump():
- # GH: 32761
- index = date_range(start="2020-01-01", end="2020-01-02", freq="60s").append(
- DatetimeIndex(["2020-01-03"])
- )
- data = np.random.default_rng(2).random(len(index))
-
- df = DataFrame({"data": data}, index=index)
- result = df.rolling("60s").mean()
- tm.assert_frame_equal(result, df[["data"]])
-
-
-def test_rolling_numerical_accuracy_small_values():
- # GH: 10319
- s = Series(
- data=[0.00012456, 0.0003, -0.0, -0.0],
- index=date_range("1999-02-03", "1999-02-06"),
- )
- result = s.rolling(1).mean()
- tm.assert_series_equal(result, s)
-
-
-def test_rolling_numerical_too_large_numbers():
- # GH: 11645
- dates = date_range("2015-01-01", periods=10, freq="D")
- ds = Series(data=range(10), index=dates, dtype=np.float64)
- ds.iloc[2] = -9e33
- result = ds.rolling(5).mean()
- expected = Series(
- [
- np.nan,
- np.nan,
- np.nan,
- np.nan,
- -1.8e33,
- -1.8e33,
- -1.8e33,
- 5.0,
- 6.0,
- 7.0,
- ],
- index=dates,
- )
- tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize(
- ("func", "value"),
- [("sum", 2.0), ("max", 1.0), ("min", 1.0), ("mean", 1.0), ("median", 1.0)],
-)
-def test_rolling_mixed_dtypes_axis_1(func, value):
- # GH: 20649
- df = DataFrame(1, index=[1, 2], columns=["a", "b", "c"])
- df["c"] = 1.0
- msg = "Support for axis=1 in DataFrame.rolling is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
- roll = df.rolling(window=2, min_periods=1, axis=1)
- result = getattr(roll, func)()
- expected = DataFrame(
- {"a": [1.0, 1.0], "b": [value, value], "c": [value, value]},
- index=[1, 2],
- )
- tm.assert_frame_equal(result, expected)
-
-
-def test_rolling_axis_one_with_nan():
- # GH: 35596
- df = DataFrame(
- [
- [0, 1, 2, 4, np.nan, np.nan, np.nan],
- [0, 1, 2, np.nan, np.nan, np.nan, np.nan],
- [0, 2, 2, np.nan, 2, np.nan, 1],
- ]
- )
- msg = "Support for axis=1 in DataFrame.rolling is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
- result = df.rolling(window=7, min_periods=1, axis="columns").sum()
- expected = DataFrame(
- [
- [0.0, 1.0, 3.0, 7.0, 7.0, 7.0, 7.0],
- [0.0, 1.0, 3.0, 3.0, 3.0, 3.0, 3.0],
- [0.0, 2.0, 4.0, 4.0, 6.0, 6.0, 7.0],
- ]
- )
- tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize(
- "value",
- ["test", to_datetime("2019-12-31"), to_timedelta("1 days 06:05:01.00003")],
-)
-def test_rolling_axis_1_non_numeric_dtypes(value):
- # GH: 20649
- df = DataFrame({"a": [1, 2]})
- df["b"] = value
- msg = "Support for axis=1 in DataFrame.rolling is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
- result = df.rolling(window=2, min_periods=1, axis=1).sum()
- expected = DataFrame({"a": [1.0, 2.0]})
- tm.assert_frame_equal(result, expected)
-
-
-def test_rolling_on_df_transposed():
- # GH: 32724
- df = DataFrame({"A": [1, None], "B": [4, 5], "C": [7, 8]})
- expected = DataFrame({"A": [1.0, np.nan], "B": [5.0, 5.0], "C": [11.0, 13.0]})
- msg = "Support for axis=1 in DataFrame.rolling is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
- result = df.rolling(min_periods=1, window=2, axis=1).sum()
- tm.assert_frame_equal(result, expected)
-
- result = df.T.rolling(min_periods=1, window=2).sum().T
- tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize(
- ("index", "window"),
- [
- (
- period_range(start="2020-01-01 08:00", end="2020-01-01 08:08", freq="T"),
- "2T",
- ),
- (
- period_range(start="2020-01-01 08:00", end="2020-01-01 12:00", freq="30T"),
- "1h",
- ),
- ],
-)
-@pytest.mark.parametrize(
- ("func", "values"),
- [
- ("min", [np.nan, 0, 0, 1, 2, 3, 4, 5, 6]),
- ("max", [np.nan, 0, 1, 2, 3, 4, 5, 6, 7]),
- ("sum", [np.nan, 0, 1, 3, 5, 7, 9, 11, 13]),
- ],
-)
-def test_rolling_period_index(index, window, func, values):
- # GH: 34225
- ds = Series([0, 1, 2, 3, 4, 5, 6, 7, 8], index=index)
- result = getattr(ds.rolling(window, closed="left"), func)()
- expected = Series(values, index=index)
- tm.assert_series_equal(result, expected)
-
-
-def test_rolling_sem(frame_or_series):
- # GH: 26476
- obj = frame_or_series([0, 1, 2])
- result = obj.rolling(2, min_periods=1).sem()
- if isinstance(result, DataFrame):
- result = Series(result[0].values)
- expected = Series([np.nan] + [0.7071067811865476] * 2)
- tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.xfail(
- is_platform_arm() or is_platform_power(),
- reason="GH 38921",
-)
-@pytest.mark.parametrize(
- ("func", "third_value", "values"),
- [
- ("var", 1, [5e33, 0, 0.5, 0.5, 2, 0]),
- ("std", 1, [7.071068e16, 0, 0.7071068, 0.7071068, 1.414214, 0]),
- ("var", 2, [5e33, 0.5, 0, 0.5, 2, 0]),
- ("std", 2, [7.071068e16, 0.7071068, 0, 0.7071068, 1.414214, 0]),
- ],
-)
-def test_rolling_var_numerical_issues(func, third_value, values):
- # GH: 37051
- ds = Series([99999999999999999, 1, third_value, 2, 3, 1, 1])
- result = getattr(ds.rolling(2), func)()
- expected = Series([np.nan] + values)
- tm.assert_series_equal(result, expected)
- # GH 42064
- # new `roll_var` will output 0.0 correctly
- tm.assert_series_equal(result == 0, expected == 0)
-
-
-def test_timeoffset_as_window_parameter_for_corr():
- # GH: 28266
- exp = DataFrame(
- {
- "B": [
- np.nan,
- np.nan,
- 0.9999999999999998,
- -1.0,
- 1.0,
- -0.3273268353539892,
- 0.9999999999999998,
- 1.0,
- 0.9999999999999998,
- 1.0,
- ],
- "A": [
- np.nan,
- np.nan,
- -1.0,
- 1.0000000000000002,
- -0.3273268353539892,
- 0.9999999999999966,
- 1.0,
- 1.0000000000000002,
- 1.0,
- 1.0000000000000002,
- ],
- },
- index=MultiIndex.from_tuples(
- [
- (Timestamp("20130101 09:00:00"), "B"),
- (Timestamp("20130101 09:00:00"), "A"),
- (Timestamp("20130102 09:00:02"), "B"),
- (Timestamp("20130102 09:00:02"), "A"),
- (Timestamp("20130103 09:00:03"), "B"),
- (Timestamp("20130103 09:00:03"), "A"),
- (Timestamp("20130105 09:00:05"), "B"),
- (Timestamp("20130105 09:00:05"), "A"),
- (Timestamp("20130106 09:00:06"), "B"),
- (Timestamp("20130106 09:00:06"), "A"),
- ]
- ),
- )
-
- df = DataFrame(
- {"B": [0, 1, 2, 4, 3], "A": [7, 4, 6, 9, 3]},
- index=[
- Timestamp("20130101 09:00:00"),
- Timestamp("20130102 09:00:02"),
- Timestamp("20130103 09:00:03"),
- Timestamp("20130105 09:00:05"),
- Timestamp("20130106 09:00:06"),
- ],
- )
-
- res = df.rolling(window="3d").corr()
-
- tm.assert_frame_equal(exp, res)
-
-
-@pytest.mark.parametrize("method", ["var", "sum", "mean", "skew", "kurt", "min", "max"])
-def test_rolling_decreasing_indices(method):
- """
- Make sure that decreasing indices give the same results as increasing indices.
-
- GH 36933
- """
- df = DataFrame({"values": np.arange(-15, 10) ** 2})
- df_reverse = DataFrame({"values": df["values"][::-1]}, index=df.index[::-1])
-
- increasing = getattr(df.rolling(window=5), method)()
- decreasing = getattr(df_reverse.rolling(window=5), method)()
-
- assert np.abs(decreasing.values[::-1][:-4] - increasing.values[4:]).max() < 1e-12
-
-
-@pytest.mark.parametrize(
- "window,closed,expected",
- [
- ("2s", "right", [1.0, 3.0, 5.0, 3.0]),
- ("2s", "left", [0.0, 1.0, 3.0, 5.0]),
- ("2s", "both", [1.0, 3.0, 6.0, 5.0]),
- ("2s", "neither", [0.0, 1.0, 2.0, 3.0]),
- ("3s", "right", [1.0, 3.0, 6.0, 5.0]),
- ("3s", "left", [1.0, 3.0, 6.0, 5.0]),
- ("3s", "both", [1.0, 3.0, 6.0, 5.0]),
- ("3s", "neither", [1.0, 3.0, 6.0, 5.0]),
- ],
-)
-def test_rolling_decreasing_indices_centered(window, closed, expected, frame_or_series):
- """
- Ensure that a symmetrical inverted index return same result as non-inverted.
- """
- # GH 43927
-
- index = date_range("2020", periods=4, freq="1s")
- df_inc = frame_or_series(range(4), index=index)
- df_dec = frame_or_series(range(4), index=index[::-1])
-
- expected_inc = frame_or_series(expected, index=index)
- expected_dec = frame_or_series(expected, index=index[::-1])
-
- result_inc = df_inc.rolling(window, closed=closed, center=True).sum()
- result_dec = df_dec.rolling(window, closed=closed, center=True).sum()
-
- tm.assert_equal(result_inc, expected_inc)
- tm.assert_equal(result_dec, expected_dec)
-
-
-@pytest.mark.parametrize(
- "window,expected",
- [
- ("1ns", [1.0, 1.0, 1.0, 1.0]),
- ("3ns", [2.0, 3.0, 3.0, 2.0]),
- ],
-)
-def test_rolling_center_nanosecond_resolution(
- window, closed, expected, frame_or_series
-):
- index = date_range("2020", periods=4, freq="1ns")
- df = frame_or_series([1, 1, 1, 1], index=index, dtype=float)
- expected = frame_or_series(expected, index=index, dtype=float)
- result = df.rolling(window, closed=closed, center=True).sum()
- tm.assert_equal(result, expected)
-
-
-@pytest.mark.parametrize(
- "method,expected",
- [
- (
- "var",
- [
- float("nan"),
- 43.0,
- float("nan"),
- 136.333333,
- 43.5,
- 94.966667,
- 182.0,
- 318.0,
- ],
- ),
- (
- "mean",
- [float("nan"), 7.5, float("nan"), 21.5, 6.0, 9.166667, 13.0, 17.5],
- ),
- (
- "sum",
- [float("nan"), 30.0, float("nan"), 86.0, 30.0, 55.0, 91.0, 140.0],
- ),
- (
- "skew",
- [
- float("nan"),
- 0.709296,
- float("nan"),
- 0.407073,
- 0.984656,
- 0.919184,
- 0.874674,
- 0.842418,
- ],
- ),
- (
- "kurt",
- [
- float("nan"),
- -0.5916711736073559,
- float("nan"),
- -1.0028993131317954,
- -0.06103844629409494,
- -0.254143227116194,
- -0.37362637362637585,
- -0.45439658241367054,
- ],
- ),
- ],
-)
-def test_rolling_non_monotonic(method, expected):
- """
- Make sure the (rare) branch of non-monotonic indices is covered by a test.
-
- output from 1.1.3 is assumed to be the expected output. Output of sum/mean has
- manually been verified.
-
- GH 36933.
- """
- # Based on an example found in computation.rst
- use_expanding = [True, False, True, False, True, True, True, True]
- df = DataFrame({"values": np.arange(len(use_expanding)) ** 2})
-
- class CustomIndexer(BaseIndexer):
- def get_window_bounds(self, num_values, min_periods, center, closed, step):
- start = np.empty(num_values, dtype=np.int64)
- end = np.empty(num_values, dtype=np.int64)
- for i in range(num_values):
- if self.use_expanding[i]:
- start[i] = 0
- end[i] = i + 1
- else:
- start[i] = i
- end[i] = i + self.window_size
- return start, end
-
- indexer = CustomIndexer(window_size=4, use_expanding=use_expanding)
-
- result = getattr(df.rolling(indexer), method)()
- expected = DataFrame({"values": expected})
- tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize(
- ("index", "window"),
- [
- ([0, 1, 2, 3, 4], 2),
- (date_range("2001-01-01", freq="D", periods=5), "2D"),
- ],
-)
-def test_rolling_corr_timedelta_index(index, window):
- # GH: 31286
- x = Series([1, 2, 3, 4, 5], index=index)
- y = x.copy()
- x.iloc[0:2] = 0.0
- result = x.rolling(window).corr(y)
- expected = Series([np.nan, np.nan, 1, 1, 1], index=index)
- tm.assert_almost_equal(result, expected)
-
-
-def test_groupby_rolling_nan_included():
- # GH 35542
- data = {"group": ["g1", np.nan, "g1", "g2", np.nan], "B": [0, 1, 2, 3, 4]}
- df = DataFrame(data)
- result = df.groupby("group", dropna=False).rolling(1, min_periods=1).mean()
- expected = DataFrame(
- {"B": [0.0, 2.0, 3.0, 1.0, 4.0]},
- # GH-38057 from_tuples puts the NaNs in the codes, result expects them
- # to be in the levels, at the moment
- # index=MultiIndex.from_tuples(
- # [("g1", 0), ("g1", 2), ("g2", 3), (np.nan, 1), (np.nan, 4)],
- # names=["group", None],
- # ),
- index=MultiIndex(
- [["g1", "g2", np.nan], [0, 1, 2, 3, 4]],
- [[0, 0, 1, 2, 2], [0, 2, 3, 1, 4]],
- names=["group", None],
- ),
- )
- tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize("method", ["skew", "kurt"])
-def test_rolling_skew_kurt_numerical_stability(method):
- # GH#6929
- ser = Series(np.random.default_rng(2).random(10))
- ser_copy = ser.copy()
- expected = getattr(ser.rolling(3), method)()
- tm.assert_series_equal(ser, ser_copy)
- ser = ser + 50000
- result = getattr(ser.rolling(3), method)()
- tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize(
- ("method", "values"),
- [
- ("skew", [2.0, 0.854563, 0.0, 1.999984]),
- ("kurt", [4.0, -1.289256, -1.2, 3.999946]),
- ],
-)
-def test_rolling_skew_kurt_large_value_range(method, values):
- # GH: 37557
- s = Series([3000000, 1, 1, 2, 3, 4, 999])
- result = getattr(s.rolling(4), method)()
- expected = Series([np.nan] * 3 + values)
- tm.assert_series_equal(result, expected)
-
-
-def test_invalid_method():
- with pytest.raises(ValueError, match="method must be 'table' or 'single"):
- Series(range(1)).rolling(1, method="foo")
-
-
-@pytest.mark.parametrize("window", [1, "1d"])
-def test_rolling_descending_date_order_with_offset(window, frame_or_series):
- # GH#40002
- idx = date_range(start="2020-01-01", end="2020-01-03", freq="1d")
- obj = frame_or_series(range(1, 4), index=idx)
- result = obj.rolling("1d", closed="left").sum()
- expected = frame_or_series([np.nan, 1, 2], index=idx)
- tm.assert_equal(result, expected)
-
- result = obj.iloc[::-1].rolling("1d", closed="left").sum()
- idx = date_range(start="2020-01-03", end="2020-01-01", freq="-1d")
- expected = frame_or_series([np.nan, 3, 2], index=idx)
- tm.assert_equal(result, expected)
-
-
-def test_rolling_var_floating_artifact_precision():
- # GH 37051
- s = Series([7, 5, 5, 5])
- result = s.rolling(3).var()
- expected = Series([np.nan, np.nan, 4 / 3, 0])
- tm.assert_series_equal(result, expected, atol=1.0e-15, rtol=1.0e-15)
- # GH 42064
- # new `roll_var` will output 0.0 correctly
- tm.assert_series_equal(result == 0, expected == 0)
-
-
-def test_rolling_std_small_values():
- # GH 37051
- s = Series(
- [
- 0.00000054,
- 0.00000053,
- 0.00000054,
- ]
- )
- result = s.rolling(2).std()
- expected = Series([np.nan, 7.071068e-9, 7.071068e-9])
- tm.assert_series_equal(result, expected, atol=1.0e-15, rtol=1.0e-15)
-
-
-@pytest.mark.parametrize(
- "start, exp_values",
- [
- (1, [0.03, 0.0155, 0.0155, 0.011, 0.01025]),
- (2, [0.001, 0.001, 0.0015, 0.00366666]),
- ],
-)
-def test_rolling_mean_all_nan_window_floating_artifacts(start, exp_values):
- # GH#41053
- df = DataFrame(
- [
- 0.03,
- 0.03,
- 0.001,
- np.nan,
- 0.002,
- 0.008,
- np.nan,
- np.nan,
- np.nan,
- np.nan,
- np.nan,
- np.nan,
- 0.005,
- 0.2,
- ]
- )
-
- values = exp_values + [
- 0.00366666,
- 0.005,
- 0.005,
- 0.008,
- np.nan,
- np.nan,
- 0.005,
- 0.102500,
- ]
- expected = DataFrame(
- values,
- index=list(range(start, len(values) + start)),
- )
- result = df.iloc[start:].rolling(5, min_periods=0).mean()
- tm.assert_frame_equal(result, expected)
-
-
-def test_rolling_sum_all_nan_window_floating_artifacts():
- # GH#41053
- df = DataFrame([0.002, 0.008, 0.005, np.nan, np.nan, np.nan])
- result = df.rolling(3, min_periods=0).sum()
- expected = DataFrame([0.002, 0.010, 0.015, 0.013, 0.005, 0.0])
- tm.assert_frame_equal(result, expected)
-
-
-def test_rolling_zero_window():
- # GH 22719
- s = Series(range(1))
- result = s.rolling(0).min()
- expected = Series([np.nan])
- tm.assert_series_equal(result, expected)
-
-
-def test_rolling_float_dtype(float_numpy_dtype):
- # GH#42452
- df = DataFrame({"A": range(5), "B": range(10, 15)}, dtype=float_numpy_dtype)
- expected = DataFrame(
- {"A": [np.nan] * 5, "B": range(10, 20, 2)},
- dtype=float_numpy_dtype,
- )
- msg = "Support for axis=1 in DataFrame.rolling is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
- result = df.rolling(2, axis=1).sum()
- tm.assert_frame_equal(result, expected, check_dtype=False)
-
-
-def test_rolling_numeric_dtypes():
- # GH#41779
- df = DataFrame(np.arange(40).reshape(4, 10), columns=list("abcdefghij")).astype(
- {
- "a": "float16",
- "b": "float32",
- "c": "float64",
- "d": "int8",
- "e": "int16",
- "f": "int32",
- "g": "uint8",
- "h": "uint16",
- "i": "uint32",
- "j": "uint64",
- }
- )
- msg = "Support for axis=1 in DataFrame.rolling is deprecated"
- with tm.assert_produces_warning(FutureWarning, match=msg):
- result = df.rolling(window=2, min_periods=1, axis=1).min()
- expected = DataFrame(
- {
- "a": range(0, 40, 10),
- "b": range(0, 40, 10),
- "c": range(1, 40, 10),
- "d": range(2, 40, 10),
- "e": range(3, 40, 10),
- "f": range(4, 40, 10),
- "g": range(5, 40, 10),
- "h": range(6, 40, 10),
- "i": range(7, 40, 10),
- "j": range(8, 40, 10),
- },
- dtype="float64",
- )
- tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize("window", [1, 3, 10, 20])
-@pytest.mark.parametrize("method", ["min", "max", "average"])
-@pytest.mark.parametrize("pct", [True, False])
-@pytest.mark.parametrize("ascending", [True, False])
-@pytest.mark.parametrize("test_data", ["default", "duplicates", "nans"])
-def test_rank(window, method, pct, ascending, test_data):
- length = 20
- if test_data == "default":
- ser = Series(data=np.random.default_rng(2).random(length))
- elif test_data == "duplicates":
- ser = Series(data=np.random.default_rng(2).choice(3, length))
- elif test_data == "nans":
- ser = Series(
- data=np.random.default_rng(2).choice(
- [1.0, 0.25, 0.75, np.nan, np.inf, -np.inf], length
- )
- )
-
- expected = ser.rolling(window).apply(
- lambda x: x.rank(method=method, pct=pct, ascending=ascending).iloc[-1]
- )
- result = ser.rolling(window).rank(method=method, pct=pct, ascending=ascending)
-
- tm.assert_series_equal(result, expected)
-
-
-def test_rolling_quantile_np_percentile():
- # #9413: Tests that rolling window's quantile default behavior
- # is analogous to Numpy's percentile
- row = 10
- col = 5
- idx = date_range("20100101", periods=row, freq="B")
- df = DataFrame(
- np.random.default_rng(2).random(row * col).reshape((row, -1)), index=idx
- )
-
- df_quantile = df.quantile([0.25, 0.5, 0.75], axis=0)
- np_percentile = np.percentile(df, [25, 50, 75], axis=0)
-
- tm.assert_almost_equal(df_quantile.values, np.array(np_percentile))
-
-
-@pytest.mark.parametrize("quantile", [0.0, 0.1, 0.45, 0.5, 1])
-@pytest.mark.parametrize(
- "interpolation", ["linear", "lower", "higher", "nearest", "midpoint"]
-)
-@pytest.mark.parametrize(
- "data",
- [
- [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0],
- [8.0, 1.0, 3.0, 4.0, 5.0, 2.0, 6.0, 7.0],
- [0.0, np.nan, 0.2, np.nan, 0.4],
- [np.nan, np.nan, np.nan, np.nan],
- [np.nan, 0.1, np.nan, 0.3, 0.4, 0.5],
- [0.5],
- [np.nan, 0.7, 0.6],
- ],
-)
-def test_rolling_quantile_interpolation_options(quantile, interpolation, data):
- # Tests that rolling window's quantile behavior is analogous to
- # Series' quantile for each interpolation option
- s = Series(data)
-
- q1 = s.quantile(quantile, interpolation)
- q2 = s.expanding(min_periods=1).quantile(quantile, interpolation).iloc[-1]
-
- if np.isnan(q1):
- assert np.isnan(q2)
- else:
- if not IS64:
- # Less precision on 32-bit
- assert np.allclose([q1], [q2], rtol=1e-07, atol=0)
- else:
- assert q1 == q2
-
-
-def test_invalid_quantile_value():
- data = np.arange(5)
- s = Series(data)
-
- msg = "Interpolation 'invalid' is not supported"
- with pytest.raises(ValueError, match=msg):
- s.rolling(len(data), min_periods=1).quantile(0.5, interpolation="invalid")
-
-
-def test_rolling_quantile_param():
- ser = Series([0.0, 0.1, 0.5, 0.9, 1.0])
- msg = "quantile value -0.1 not in \\[0, 1\\]"
- with pytest.raises(ValueError, match=msg):
- ser.rolling(3).quantile(-0.1)
-
- msg = "quantile value 10.0 not in \\[0, 1\\]"
- with pytest.raises(ValueError, match=msg):
- ser.rolling(3).quantile(10.0)
-
- msg = "must be real number, not str"
- with pytest.raises(TypeError, match=msg):
- ser.rolling(3).quantile("foo")
-
-
-def test_rolling_std_1obs():
- vals = Series([1.0, 2.0, 3.0, 4.0, 5.0])
-
- result = vals.rolling(1, min_periods=1).std()
- expected = Series([np.nan] * 5)
- tm.assert_series_equal(result, expected)
-
- result = vals.rolling(1, min_periods=1).std(ddof=0)
- expected = Series([0.0] * 5)
- tm.assert_series_equal(result, expected)
-
- result = Series([np.nan, np.nan, 3, 4, 5]).rolling(3, min_periods=2).std()
- assert np.isnan(result[2])
-
-
-def test_rolling_std_neg_sqrt():
- # unit test from Bottleneck
-
- # Test move_nanstd for neg sqrt.
-
- a = Series(
- [
- 0.0011448196318903589,
- 0.00028718669878572767,
- 0.00028718669878572767,
- 0.00028718669878572767,
- 0.00028718669878572767,
- ]
- )
- b = a.rolling(window=3).std()
- assert np.isfinite(b[2:]).all()
-
- b = a.ewm(span=3).std()
- assert np.isfinite(b[2:]).all()
-
-
-def test_step_not_integer_raises():
- with pytest.raises(ValueError, match="step must be an integer"):
- DataFrame(range(2)).rolling(1, step="foo")
-
-
-def test_step_not_positive_raises():
- with pytest.raises(ValueError, match="step must be >= 0"):
- DataFrame(range(2)).rolling(1, step=-1)
-
-
-@pytest.mark.parametrize(
- ["values", "window", "min_periods", "expected"],
- [
- [
- [20, 10, 10, np.inf, 1, 1, 2, 3],
- 3,
- 1,
- [np.nan, 50, 100 / 3, 0, 40.5, 0, 1 / 3, 1],
- ],
- [
- [20, 10, 10, np.nan, 10, 1, 2, 3],
- 3,
- 1,
- [np.nan, 50, 100 / 3, 0, 0, 40.5, 73 / 3, 1],
- ],
- [
- [np.nan, 5, 6, 7, 5, 5, 5],
- 3,
- 3,
- [np.nan] * 3 + [1, 1, 4 / 3, 0],
- ],
- [
- [5, 7, 7, 7, np.nan, np.inf, 4, 3, 3, 3],
- 3,
- 3,
- [np.nan] * 2 + [4 / 3, 0] + [np.nan] * 4 + [1 / 3, 0],
- ],
- [
- [5, 7, 7, 7, np.nan, np.inf, 7, 3, 3, 3],
- 3,
- 3,
- [np.nan] * 2 + [4 / 3, 0] + [np.nan] * 4 + [16 / 3, 0],
- ],
- [
- [5, 7] * 4,
- 3,
- 3,
- [np.nan] * 2 + [4 / 3] * 6,
- ],
- [
- [5, 7, 5, np.nan, 7, 5, 7],
- 3,
- 2,
- [np.nan, 2, 4 / 3] + [2] * 3 + [4 / 3],
- ],
- ],
-)
-def test_rolling_var_same_value_count_logic(values, window, min_periods, expected):
- # GH 42064.
-
- expected = Series(expected)
- sr = Series(values)
-
- # With new algo implemented, result will be set to .0 in rolling var
- # if sufficient amount of consecutively same values are found.
- result_var = sr.rolling(window, min_periods=min_periods).var()
-
- # use `assert_series_equal` twice to check for equality,
- # because `check_exact=True` will fail in 32-bit tests due to
- # precision loss.
-
- # 1. result should be close to correct value
- # non-zero values can still differ slightly from "truth"
- # as the result of online algorithm
- tm.assert_series_equal(result_var, expected)
- # 2. zeros should be exactly the same since the new algo takes effect here
- tm.assert_series_equal(expected == 0, result_var == 0)
-
- # std should also pass as it's just a sqrt of var
- result_std = sr.rolling(window, min_periods=min_periods).std()
- tm.assert_series_equal(result_std, np.sqrt(expected))
- tm.assert_series_equal(expected == 0, result_std == 0)
-
-
-def test_rolling_mean_sum_floating_artifacts():
- # GH 42064.
-
- sr = Series([1 / 3, 4, 0, 0, 0, 0, 0])
- r = sr.rolling(3)
- result = r.mean()
- assert (result[-3:] == 0).all()
- result = r.sum()
- assert (result[-3:] == 0).all()
-
-
-def test_rolling_skew_kurt_floating_artifacts():
- # GH 42064 46431
-
- sr = Series([1 / 3, 4, 0, 0, 0, 0, 0])
- r = sr.rolling(4)
- result = r.skew()
- assert (result[-2:] == 0).all()
- result = r.kurt()
- assert (result[-2:] == -3).all()
-
-
-def test_numeric_only_frame(arithmetic_win_operators, numeric_only):
- # GH#46560
- kernel = arithmetic_win_operators
- df = DataFrame({"a": [1], "b": 2, "c": 3})
- df["c"] = df["c"].astype(object)
- rolling = df.rolling(2, min_periods=1)
- op = getattr(rolling, kernel)
- result = op(numeric_only=numeric_only)
-
- columns = ["a", "b"] if numeric_only else ["a", "b", "c"]
- expected = df[columns].agg([kernel]).reset_index(drop=True).astype(float)
- assert list(expected.columns) == columns
-
- tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize("kernel", ["corr", "cov"])
-@pytest.mark.parametrize("use_arg", [True, False])
-def test_numeric_only_corr_cov_frame(kernel, numeric_only, use_arg):
- # GH#46560
- df = DataFrame({"a": [1, 2, 3], "b": 2, "c": 3})
- df["c"] = df["c"].astype(object)
- arg = (df,) if use_arg else ()
- rolling = df.rolling(2, min_periods=1)
- op = getattr(rolling, kernel)
- result = op(*arg, numeric_only=numeric_only)
-
- # Compare result to op using float dtypes, dropping c when numeric_only is True
- columns = ["a", "b"] if numeric_only else ["a", "b", "c"]
- df2 = df[columns].astype(float)
- arg2 = (df2,) if use_arg else ()
- rolling2 = df2.rolling(2, min_periods=1)
- op2 = getattr(rolling2, kernel)
- expected = op2(*arg2, numeric_only=numeric_only)
-
- tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize("dtype", [int, object])
-def test_numeric_only_series(arithmetic_win_operators, numeric_only, dtype):
- # GH#46560
- kernel = arithmetic_win_operators
- ser = Series([1], dtype=dtype)
- rolling = ser.rolling(2, min_periods=1)
- op = getattr(rolling, kernel)
- if numeric_only and dtype is object:
- msg = f"Rolling.{kernel} does not implement numeric_only"
- with pytest.raises(NotImplementedError, match=msg):
- op(numeric_only=numeric_only)
- else:
- result = op(numeric_only=numeric_only)
- expected = ser.agg([kernel]).reset_index(drop=True).astype(float)
- tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize("kernel", ["corr", "cov"])
-@pytest.mark.parametrize("use_arg", [True, False])
-@pytest.mark.parametrize("dtype", [int, object])
-def test_numeric_only_corr_cov_series(kernel, use_arg, numeric_only, dtype):
- # GH#46560
- ser = Series([1, 2, 3], dtype=dtype)
- arg = (ser,) if use_arg else ()
- rolling = ser.rolling(2, min_periods=1)
- op = getattr(rolling, kernel)
- if numeric_only and dtype is object:
- msg = f"Rolling.{kernel} does not implement numeric_only"
- with pytest.raises(NotImplementedError, match=msg):
- op(*arg, numeric_only=numeric_only)
- else:
- result = op(*arg, numeric_only=numeric_only)
-
- ser2 = ser.astype(float)
- arg2 = (ser2,) if use_arg else ()
- rolling2 = ser2.rolling(2, min_periods=1)
- op2 = getattr(rolling2, kernel)
- expected = op2(*arg2, numeric_only=numeric_only)
- tm.assert_series_equal(result, expected)
-
-
-@pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
-@pytest.mark.parametrize("tz", [None, "UTC", "Europe/Prague"])
-def test_rolling_timedelta_window_non_nanoseconds(unit, tz):
- # Test Sum, GH#55106
- df_time = DataFrame(
- {"A": range(5)}, index=date_range("2013-01-01", freq="1s", periods=5, tz=tz)
- )
- sum_in_nanosecs = df_time.rolling("1s").sum()
- # microseconds / milliseconds should not break the correct rolling
- df_time.index = df_time.index.as_unit(unit)
- sum_in_microsecs = df_time.rolling("1s").sum()
- sum_in_microsecs.index = sum_in_microsecs.index.as_unit("ns")
- tm.assert_frame_equal(sum_in_nanosecs, sum_in_microsecs)
-
- # Test max, GH#55026
- ref_dates = date_range("2023-01-01", "2023-01-10", unit="ns", tz=tz)
- ref_series = Series(0, index=ref_dates)
- ref_series.iloc[0] = 1
- ref_max_series = ref_series.rolling(Timedelta(days=4)).max()
-
- dates = date_range("2023-01-01", "2023-01-10", unit=unit, tz=tz)
- series = Series(0, index=dates)
- series.iloc[0] = 1
- max_series = series.rolling(Timedelta(days=4)).max()
-
- ref_df = DataFrame(ref_max_series)
- df = DataFrame(max_series)
- df.index = df.index.as_unit("ns")
-
- tm.assert_frame_equal(ref_df, df)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/_internal/_schema_generation_shared.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/_internal/_schema_generation_shared.py
deleted file mode 100644
index 1a9aa852c39676c6fec5fda1f712d565b5fed094..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/_internal/_schema_generation_shared.py
+++ /dev/null
@@ -1,124 +0,0 @@
-"""Types and utility functions used by various other internal tools."""
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, Any, Callable
-
-from pydantic_core import core_schema
-from typing_extensions import Literal
-
-from ..annotated_handlers import GetCoreSchemaHandler, GetJsonSchemaHandler
-
-if TYPE_CHECKING:
- from ..json_schema import GenerateJsonSchema, JsonSchemaValue
- from ._core_utils import CoreSchemaOrField
- from ._generate_schema import GenerateSchema
-
- GetJsonSchemaFunction = Callable[[CoreSchemaOrField, GetJsonSchemaHandler], JsonSchemaValue]
- HandlerOverride = Callable[[CoreSchemaOrField], JsonSchemaValue]
-
-
-class GenerateJsonSchemaHandler(GetJsonSchemaHandler):
- """JsonSchemaHandler implementation that doesn't do ref unwrapping by default.
-
- This is used for any Annotated metadata so that we don't end up with conflicting
- modifications to the definition schema.
-
- Used internally by Pydantic, please do not rely on this implementation.
- See `GetJsonSchemaHandler` for the handler API.
- """
-
- def __init__(self, generate_json_schema: GenerateJsonSchema, handler_override: HandlerOverride | None) -> None:
- self.generate_json_schema = generate_json_schema
- self.handler = handler_override or generate_json_schema.generate_inner
- self.mode = generate_json_schema.mode
-
- def __call__(self, __core_schema: CoreSchemaOrField) -> JsonSchemaValue:
- return self.handler(__core_schema)
-
- def resolve_ref_schema(self, maybe_ref_json_schema: JsonSchemaValue) -> JsonSchemaValue:
- """Resolves `$ref` in the json schema.
-
- This returns the input json schema if there is no `$ref` in json schema.
-
- Args:
- maybe_ref_json_schema: The input json schema that may contains `$ref`.
-
- Returns:
- Resolved json schema.
-
- Raises:
- LookupError: If it can't find the definition for `$ref`.
- """
- if '$ref' not in maybe_ref_json_schema:
- return maybe_ref_json_schema
- ref = maybe_ref_json_schema['$ref']
- json_schema = self.generate_json_schema.get_schema_from_definitions(ref)
- if json_schema is None:
- raise LookupError(
- f'Could not find a ref for {ref}.'
- ' Maybe you tried to call resolve_ref_schema from within a recursive model?'
- )
- return json_schema
-
-
-class CallbackGetCoreSchemaHandler(GetCoreSchemaHandler):
- """Wrapper to use an arbitrary function as a `GetCoreSchemaHandler`.
-
- Used internally by Pydantic, please do not rely on this implementation.
- See `GetCoreSchemaHandler` for the handler API.
- """
-
- def __init__(
- self,
- handler: Callable[[Any], core_schema.CoreSchema],
- generate_schema: GenerateSchema,
- ref_mode: Literal['to-def', 'unpack'] = 'to-def',
- ) -> None:
- self._handler = handler
- self._generate_schema = generate_schema
- self._ref_mode = ref_mode
-
- def __call__(self, __source_type: Any) -> core_schema.CoreSchema:
- schema = self._handler(__source_type)
- ref = schema.get('ref')
- if self._ref_mode == 'to-def':
- if ref is not None:
- self._generate_schema.defs.definitions[ref] = schema
- return core_schema.definition_reference_schema(ref)
- return schema
- else: # ref_mode = 'unpack
- return self.resolve_ref_schema(schema)
-
- def _get_types_namespace(self) -> dict[str, Any] | None:
- return self._generate_schema._types_namespace
-
- def generate_schema(self, __source_type: Any) -> core_schema.CoreSchema:
- return self._generate_schema.generate_schema(__source_type)
-
- @property
- def field_name(self) -> str | None:
- return self._generate_schema.field_name_stack.get()
-
- def resolve_ref_schema(self, maybe_ref_schema: core_schema.CoreSchema) -> core_schema.CoreSchema:
- """Resolves reference in the core schema.
-
- Args:
- maybe_ref_schema: The input core schema that may contains reference.
-
- Returns:
- Resolved core schema.
-
- Raises:
- LookupError: If it can't find the definition for reference.
- """
- if maybe_ref_schema['type'] == 'definition-ref':
- ref = maybe_ref_schema['schema_ref']
- if ref not in self._generate_schema.defs.definitions:
- raise LookupError(
- f'Could not find a ref for {ref}.'
- ' Maybe you tried to call resolve_ref_schema from within a recursive model?'
- )
- return self._generate_schema.defs.definitions[ref]
- elif maybe_ref_schema['type'] == 'definitions':
- return self.resolve_ref_schema(maybe_ref_schema['schema'])
- return maybe_ref_schema
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/requests/cookies.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/requests/cookies.py
deleted file mode 100644
index bf54ab237e410603061b8cec8fd195912d3cfb08..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/requests/cookies.py
+++ /dev/null
@@ -1,561 +0,0 @@
-"""
-requests.cookies
-~~~~~~~~~~~~~~~~
-
-Compatibility code to be able to use `cookielib.CookieJar` with requests.
-
-requests.utils imports from here, so be careful with imports.
-"""
-
-import calendar
-import copy
-import time
-
-from ._internal_utils import to_native_string
-from .compat import Morsel, MutableMapping, cookielib, urlparse, urlunparse
-
-try:
- import threading
-except ImportError:
- import dummy_threading as threading
-
-
-class MockRequest:
- """Wraps a `requests.Request` to mimic a `urllib2.Request`.
-
- The code in `cookielib.CookieJar` expects this interface in order to correctly
- manage cookie policies, i.e., determine whether a cookie can be set, given the
- domains of the request and the cookie.
-
- The original request object is read-only. The client is responsible for collecting
- the new headers via `get_new_headers()` and interpreting them appropriately. You
- probably want `get_cookie_header`, defined below.
- """
-
- def __init__(self, request):
- self._r = request
- self._new_headers = {}
- self.type = urlparse(self._r.url).scheme
-
- def get_type(self):
- return self.type
-
- def get_host(self):
- return urlparse(self._r.url).netloc
-
- def get_origin_req_host(self):
- return self.get_host()
-
- def get_full_url(self):
- # Only return the response's URL if the user hadn't set the Host
- # header
- if not self._r.headers.get("Host"):
- return self._r.url
- # If they did set it, retrieve it and reconstruct the expected domain
- host = to_native_string(self._r.headers["Host"], encoding="utf-8")
- parsed = urlparse(self._r.url)
- # Reconstruct the URL as we expect it
- return urlunparse(
- [
- parsed.scheme,
- host,
- parsed.path,
- parsed.params,
- parsed.query,
- parsed.fragment,
- ]
- )
-
- def is_unverifiable(self):
- return True
-
- def has_header(self, name):
- return name in self._r.headers or name in self._new_headers
-
- def get_header(self, name, default=None):
- return self._r.headers.get(name, self._new_headers.get(name, default))
-
- def add_header(self, key, val):
- """cookielib has no legitimate use for this method; add it back if you find one."""
- raise NotImplementedError(
- "Cookie headers should be added with add_unredirected_header()"
- )
-
- def add_unredirected_header(self, name, value):
- self._new_headers[name] = value
-
- def get_new_headers(self):
- return self._new_headers
-
- @property
- def unverifiable(self):
- return self.is_unverifiable()
-
- @property
- def origin_req_host(self):
- return self.get_origin_req_host()
-
- @property
- def host(self):
- return self.get_host()
-
-
-class MockResponse:
- """Wraps a `httplib.HTTPMessage` to mimic a `urllib.addinfourl`.
-
- ...what? Basically, expose the parsed HTTP headers from the server response
- the way `cookielib` expects to see them.
- """
-
- def __init__(self, headers):
- """Make a MockResponse for `cookielib` to read.
-
- :param headers: a httplib.HTTPMessage or analogous carrying the headers
- """
- self._headers = headers
-
- def info(self):
- return self._headers
-
- def getheaders(self, name):
- self._headers.getheaders(name)
-
-
-def extract_cookies_to_jar(jar, request, response):
- """Extract the cookies from the response into a CookieJar.
-
- :param jar: cookielib.CookieJar (not necessarily a RequestsCookieJar)
- :param request: our own requests.Request object
- :param response: urllib3.HTTPResponse object
- """
- if not (hasattr(response, "_original_response") and response._original_response):
- return
- # the _original_response field is the wrapped httplib.HTTPResponse object,
- req = MockRequest(request)
- # pull out the HTTPMessage with the headers and put it in the mock:
- res = MockResponse(response._original_response.msg)
- jar.extract_cookies(res, req)
-
-
-def get_cookie_header(jar, request):
- """
- Produce an appropriate Cookie header string to be sent with `request`, or None.
-
- :rtype: str
- """
- r = MockRequest(request)
- jar.add_cookie_header(r)
- return r.get_new_headers().get("Cookie")
-
-
-def remove_cookie_by_name(cookiejar, name, domain=None, path=None):
- """Unsets a cookie by name, by default over all domains and paths.
-
- Wraps CookieJar.clear(), is O(n).
- """
- clearables = []
- for cookie in cookiejar:
- if cookie.name != name:
- continue
- if domain is not None and domain != cookie.domain:
- continue
- if path is not None and path != cookie.path:
- continue
- clearables.append((cookie.domain, cookie.path, cookie.name))
-
- for domain, path, name in clearables:
- cookiejar.clear(domain, path, name)
-
-
-class CookieConflictError(RuntimeError):
- """There are two cookies that meet the criteria specified in the cookie jar.
- Use .get and .set and include domain and path args in order to be more specific.
- """
-
-
-class RequestsCookieJar(cookielib.CookieJar, MutableMapping):
- """Compatibility class; is a cookielib.CookieJar, but exposes a dict
- interface.
-
- This is the CookieJar we create by default for requests and sessions that
- don't specify one, since some clients may expect response.cookies and
- session.cookies to support dict operations.
-
- Requests does not use the dict interface internally; it's just for
- compatibility with external client code. All requests code should work
- out of the box with externally provided instances of ``CookieJar``, e.g.
- ``LWPCookieJar`` and ``FileCookieJar``.
-
- Unlike a regular CookieJar, this class is pickleable.
-
- .. warning:: dictionary operations that are normally O(1) may be O(n).
- """
-
- def get(self, name, default=None, domain=None, path=None):
- """Dict-like get() that also supports optional domain and path args in
- order to resolve naming collisions from using one cookie jar over
- multiple domains.
-
- .. warning:: operation is O(n), not O(1).
- """
- try:
- return self._find_no_duplicates(name, domain, path)
- except KeyError:
- return default
-
- def set(self, name, value, **kwargs):
- """Dict-like set() that also supports optional domain and path args in
- order to resolve naming collisions from using one cookie jar over
- multiple domains.
- """
- # support client code that unsets cookies by assignment of a None value:
- if value is None:
- remove_cookie_by_name(
- self, name, domain=kwargs.get("domain"), path=kwargs.get("path")
- )
- return
-
- if isinstance(value, Morsel):
- c = morsel_to_cookie(value)
- else:
- c = create_cookie(name, value, **kwargs)
- self.set_cookie(c)
- return c
-
- def iterkeys(self):
- """Dict-like iterkeys() that returns an iterator of names of cookies
- from the jar.
-
- .. seealso:: itervalues() and iteritems().
- """
- for cookie in iter(self):
- yield cookie.name
-
- def keys(self):
- """Dict-like keys() that returns a list of names of cookies from the
- jar.
-
- .. seealso:: values() and items().
- """
- return list(self.iterkeys())
-
- def itervalues(self):
- """Dict-like itervalues() that returns an iterator of values of cookies
- from the jar.
-
- .. seealso:: iterkeys() and iteritems().
- """
- for cookie in iter(self):
- yield cookie.value
-
- def values(self):
- """Dict-like values() that returns a list of values of cookies from the
- jar.
-
- .. seealso:: keys() and items().
- """
- return list(self.itervalues())
-
- def iteritems(self):
- """Dict-like iteritems() that returns an iterator of name-value tuples
- from the jar.
-
- .. seealso:: iterkeys() and itervalues().
- """
- for cookie in iter(self):
- yield cookie.name, cookie.value
-
- def items(self):
- """Dict-like items() that returns a list of name-value tuples from the
- jar. Allows client-code to call ``dict(RequestsCookieJar)`` and get a
- vanilla python dict of key value pairs.
-
- .. seealso:: keys() and values().
- """
- return list(self.iteritems())
-
- def list_domains(self):
- """Utility method to list all the domains in the jar."""
- domains = []
- for cookie in iter(self):
- if cookie.domain not in domains:
- domains.append(cookie.domain)
- return domains
-
- def list_paths(self):
- """Utility method to list all the paths in the jar."""
- paths = []
- for cookie in iter(self):
- if cookie.path not in paths:
- paths.append(cookie.path)
- return paths
-
- def multiple_domains(self):
- """Returns True if there are multiple domains in the jar.
- Returns False otherwise.
-
- :rtype: bool
- """
- domains = []
- for cookie in iter(self):
- if cookie.domain is not None and cookie.domain in domains:
- return True
- domains.append(cookie.domain)
- return False # there is only one domain in jar
-
- def get_dict(self, domain=None, path=None):
- """Takes as an argument an optional domain and path and returns a plain
- old Python dict of name-value pairs of cookies that meet the
- requirements.
-
- :rtype: dict
- """
- dictionary = {}
- for cookie in iter(self):
- if (domain is None or cookie.domain == domain) and (
- path is None or cookie.path == path
- ):
- dictionary[cookie.name] = cookie.value
- return dictionary
-
- def __contains__(self, name):
- try:
- return super().__contains__(name)
- except CookieConflictError:
- return True
-
- def __getitem__(self, name):
- """Dict-like __getitem__() for compatibility with client code. Throws
- exception if there are more than one cookie with name. In that case,
- use the more explicit get() method instead.
-
- .. warning:: operation is O(n), not O(1).
- """
- return self._find_no_duplicates(name)
-
- def __setitem__(self, name, value):
- """Dict-like __setitem__ for compatibility with client code. Throws
- exception if there is already a cookie of that name in the jar. In that
- case, use the more explicit set() method instead.
- """
- self.set(name, value)
-
- def __delitem__(self, name):
- """Deletes a cookie given a name. Wraps ``cookielib.CookieJar``'s
- ``remove_cookie_by_name()``.
- """
- remove_cookie_by_name(self, name)
-
- def set_cookie(self, cookie, *args, **kwargs):
- if (
- hasattr(cookie.value, "startswith")
- and cookie.value.startswith('"')
- and cookie.value.endswith('"')
- ):
- cookie.value = cookie.value.replace('\\"', "")
- return super().set_cookie(cookie, *args, **kwargs)
-
- def update(self, other):
- """Updates this jar with cookies from another CookieJar or dict-like"""
- if isinstance(other, cookielib.CookieJar):
- for cookie in other:
- self.set_cookie(copy.copy(cookie))
- else:
- super().update(other)
-
- def _find(self, name, domain=None, path=None):
- """Requests uses this method internally to get cookie values.
-
- If there are conflicting cookies, _find arbitrarily chooses one.
- See _find_no_duplicates if you want an exception thrown if there are
- conflicting cookies.
-
- :param name: a string containing name of cookie
- :param domain: (optional) string containing domain of cookie
- :param path: (optional) string containing path of cookie
- :return: cookie.value
- """
- for cookie in iter(self):
- if cookie.name == name:
- if domain is None or cookie.domain == domain:
- if path is None or cookie.path == path:
- return cookie.value
-
- raise KeyError(f"name={name!r}, domain={domain!r}, path={path!r}")
-
- def _find_no_duplicates(self, name, domain=None, path=None):
- """Both ``__get_item__`` and ``get`` call this function: it's never
- used elsewhere in Requests.
-
- :param name: a string containing name of cookie
- :param domain: (optional) string containing domain of cookie
- :param path: (optional) string containing path of cookie
- :raises KeyError: if cookie is not found
- :raises CookieConflictError: if there are multiple cookies
- that match name and optionally domain and path
- :return: cookie.value
- """
- toReturn = None
- for cookie in iter(self):
- if cookie.name == name:
- if domain is None or cookie.domain == domain:
- if path is None or cookie.path == path:
- if toReturn is not None:
- # if there are multiple cookies that meet passed in criteria
- raise CookieConflictError(
- f"There are multiple cookies with name, {name!r}"
- )
- # we will eventually return this as long as no cookie conflict
- toReturn = cookie.value
-
- if toReturn:
- return toReturn
- raise KeyError(f"name={name!r}, domain={domain!r}, path={path!r}")
-
- def __getstate__(self):
- """Unlike a normal CookieJar, this class is pickleable."""
- state = self.__dict__.copy()
- # remove the unpickleable RLock object
- state.pop("_cookies_lock")
- return state
-
- def __setstate__(self, state):
- """Unlike a normal CookieJar, this class is pickleable."""
- self.__dict__.update(state)
- if "_cookies_lock" not in self.__dict__:
- self._cookies_lock = threading.RLock()
-
- def copy(self):
- """Return a copy of this RequestsCookieJar."""
- new_cj = RequestsCookieJar()
- new_cj.set_policy(self.get_policy())
- new_cj.update(self)
- return new_cj
-
- def get_policy(self):
- """Return the CookiePolicy instance used."""
- return self._policy
-
-
-def _copy_cookie_jar(jar):
- if jar is None:
- return None
-
- if hasattr(jar, "copy"):
- # We're dealing with an instance of RequestsCookieJar
- return jar.copy()
- # We're dealing with a generic CookieJar instance
- new_jar = copy.copy(jar)
- new_jar.clear()
- for cookie in jar:
- new_jar.set_cookie(copy.copy(cookie))
- return new_jar
-
-
-def create_cookie(name, value, **kwargs):
- """Make a cookie from underspecified parameters.
-
- By default, the pair of `name` and `value` will be set for the domain ''
- and sent on every request (this is sometimes called a "supercookie").
- """
- result = {
- "version": 0,
- "name": name,
- "value": value,
- "port": None,
- "domain": "",
- "path": "/",
- "secure": False,
- "expires": None,
- "discard": True,
- "comment": None,
- "comment_url": None,
- "rest": {"HttpOnly": None},
- "rfc2109": False,
- }
-
- badargs = set(kwargs) - set(result)
- if badargs:
- raise TypeError(
- f"create_cookie() got unexpected keyword arguments: {list(badargs)}"
- )
-
- result.update(kwargs)
- result["port_specified"] = bool(result["port"])
- result["domain_specified"] = bool(result["domain"])
- result["domain_initial_dot"] = result["domain"].startswith(".")
- result["path_specified"] = bool(result["path"])
-
- return cookielib.Cookie(**result)
-
-
-def morsel_to_cookie(morsel):
- """Convert a Morsel object into a Cookie containing the one k/v pair."""
-
- expires = None
- if morsel["max-age"]:
- try:
- expires = int(time.time() + int(morsel["max-age"]))
- except ValueError:
- raise TypeError(f"max-age: {morsel['max-age']} must be integer")
- elif morsel["expires"]:
- time_template = "%a, %d-%b-%Y %H:%M:%S GMT"
- expires = calendar.timegm(time.strptime(morsel["expires"], time_template))
- return create_cookie(
- comment=morsel["comment"],
- comment_url=bool(morsel["comment"]),
- discard=False,
- domain=morsel["domain"],
- expires=expires,
- name=morsel.key,
- path=morsel["path"],
- port=None,
- rest={"HttpOnly": morsel["httponly"]},
- rfc2109=False,
- secure=bool(morsel["secure"]),
- value=morsel.value,
- version=morsel["version"] or 0,
- )
-
-
-def cookiejar_from_dict(cookie_dict, cookiejar=None, overwrite=True):
- """Returns a CookieJar from a key/value dictionary.
-
- :param cookie_dict: Dict of key/values to insert into CookieJar.
- :param cookiejar: (optional) A cookiejar to add the cookies to.
- :param overwrite: (optional) If False, will not replace cookies
- already in the jar with new ones.
- :rtype: CookieJar
- """
- if cookiejar is None:
- cookiejar = RequestsCookieJar()
-
- if cookie_dict is not None:
- names_from_jar = [cookie.name for cookie in cookiejar]
- for name in cookie_dict:
- if overwrite or (name not in names_from_jar):
- cookiejar.set_cookie(create_cookie(name, cookie_dict[name]))
-
- return cookiejar
-
-
-def merge_cookies(cookiejar, cookies):
- """Add cookies to cookiejar and returns a merged CookieJar.
-
- :param cookiejar: CookieJar object to add the cookies to.
- :param cookies: Dictionary or CookieJar object to be added.
- :rtype: CookieJar
- """
- if not isinstance(cookiejar, cookielib.CookieJar):
- raise ValueError("You can only merge into CookieJar")
-
- if isinstance(cookies, dict):
- cookiejar = cookiejar_from_dict(cookies, cookiejar=cookiejar, overwrite=False)
- elif isinstance(cookies, cookielib.CookieJar):
- try:
- cookiejar.update(cookies)
- except AttributeError:
- for cookie_in_jar in cookies:
- cookiejar.set_cookie(cookie_in_jar)
-
- return cookiejar
diff --git a/spaces/pycui/RealChar/realtime_ai_character/llm/openai_llm.py b/spaces/pycui/RealChar/realtime_ai_character/llm/openai_llm.py
deleted file mode 100644
index 9b7896e97347c635471d62d59b0bc17f4d47950e..0000000000000000000000000000000000000000
--- a/spaces/pycui/RealChar/realtime_ai_character/llm/openai_llm.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import os
-from typing import List
-
-from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-if os.getenv('OPENAI_API_TYPE') == 'azure':
- from langchain.chat_models import AzureChatOpenAI
-else:
- from langchain.chat_models import ChatOpenAI
-from langchain.schema import BaseMessage, HumanMessage
-
-from realtime_ai_character.database.chroma import get_chroma
-from realtime_ai_character.llm.base import AsyncCallbackAudioHandler, AsyncCallbackTextHandler, LLM
-from realtime_ai_character.logger import get_logger
-from realtime_ai_character.utils import Character
-
-logger = get_logger(__name__)
-
-
-class OpenaiLlm(LLM):
- def __init__(self, model):
- if os.getenv('OPENAI_API_TYPE') == 'azure':
- self.chat_open_ai = AzureChatOpenAI(
- deployment_name=os.getenv(
- 'OPENAI_API_MODEL_DEPLOYMENT_NAME', 'gpt-35-turbo'),
- model=model,
- temperature=0.5,
- streaming=True
- )
- else:
- self.chat_open_ai = ChatOpenAI(
- model=model,
- temperature=0.5,
- streaming=True
- )
- self.db = get_chroma()
-
- async def achat(self,
- history: List[BaseMessage],
- user_input: str,
- user_input_template: str,
- callback: AsyncCallbackTextHandler,
- audioCallback: AsyncCallbackAudioHandler,
- character: Character) -> str:
- # 1. Generate context
- print('user_input=', user_input)
- context = self._generate_context(user_input, character)
-
- # 2. Add user input to history
- history.append(HumanMessage(content=user_input_template.format(
- context=context, query=user_input)))
-
- # 3. Generate response
- response = await self.chat_open_ai.agenerate(
- [history], callbacks=[callback, audioCallback, StreamingStdOutCallbackHandler()])
- logger.info(f'Response: {response}')
- return response.generations[0][0].text
-
- def _generate_context(self, query, character: Character) -> str:
- print('query=', query)
- docs = self.db.similarity_search(query)
- docs = [d for d in docs if d.metadata['character_name'] == character.name]
- logger.info(f'Found {len(docs)} documents')
-
- context = '\n'.join([d.page_content for d in docs])
- return context
diff --git a/spaces/pyodide-demo/self-hosted/retrying.js b/spaces/pyodide-demo/self-hosted/retrying.js
deleted file mode 100644
index ea934ac857df4ecbcb16c572aeeda9fb11ac969d..0000000000000000000000000000000000000000
--- a/spaces/pyodide-demo/self-hosted/retrying.js
+++ /dev/null
@@ -1 +0,0 @@
-var Module=typeof globalThis.__pyodide_module!=="undefined"?globalThis.__pyodide_module:{};if(!Module.expectedDataFileDownloads){Module.expectedDataFileDownloads=0}Module.expectedDataFileDownloads++;(function(){var loadPackage=function(metadata){var PACKAGE_PATH="";if(typeof window==="object"){PACKAGE_PATH=window["encodeURIComponent"](window.location.pathname.toString().substring(0,window.location.pathname.toString().lastIndexOf("/"))+"/")}else if(typeof process==="undefined"&&typeof location!=="undefined"){PACKAGE_PATH=encodeURIComponent(location.pathname.toString().substring(0,location.pathname.toString().lastIndexOf("/"))+"/")}var PACKAGE_NAME="retrying.data";var REMOTE_PACKAGE_BASE="retrying.data";if(typeof Module["locateFilePackage"]==="function"&&!Module["locateFile"]){Module["locateFile"]=Module["locateFilePackage"];err("warning: you defined Module.locateFilePackage, that has been renamed to Module.locateFile (using your locateFilePackage for now)")}var REMOTE_PACKAGE_NAME=Module["locateFile"]?Module["locateFile"](REMOTE_PACKAGE_BASE,""):REMOTE_PACKAGE_BASE;var REMOTE_PACKAGE_SIZE=metadata["remote_package_size"];var PACKAGE_UUID=metadata["package_uuid"];function fetchRemotePackage(packageName,packageSize,callback,errback){if(typeof process==="object"){require("fs").readFile(packageName,(function(err,contents){if(err){errback(err)}else{callback(contents.buffer)}}));return}var xhr=new XMLHttpRequest;xhr.open("GET",packageName,true);xhr.responseType="arraybuffer";xhr.onprogress=function(event){var url=packageName;var size=packageSize;if(event.total)size=event.total;if(event.loaded){if(!xhr.addedTotal){xhr.addedTotal=true;if(!Module.dataFileDownloads)Module.dataFileDownloads={};Module.dataFileDownloads[url]={loaded:event.loaded,total:size}}else{Module.dataFileDownloads[url].loaded=event.loaded}var total=0;var loaded=0;var num=0;for(var download in Module.dataFileDownloads){var data=Module.dataFileDownloads[download];total+=data.total;loaded+=data.loaded;num++}total=Math.ceil(total*Module.expectedDataFileDownloads/num);if(Module["setStatus"])Module["setStatus"]("Downloading data... ("+loaded+"/"+total+")")}else if(!Module.dataFileDownloads){if(Module["setStatus"])Module["setStatus"]("Downloading data...")}};xhr.onerror=function(event){throw new Error("NetworkError for: "+packageName)};xhr.onload=function(event){if(xhr.status==200||xhr.status==304||xhr.status==206||xhr.status==0&&xhr.response){var packageData=xhr.response;callback(packageData)}else{throw new Error(xhr.statusText+" : "+xhr.responseURL)}};xhr.send(null)}function handleError(error){console.error("package error:",error)}var fetchedCallback=null;var fetched=Module["getPreloadedPackage"]?Module["getPreloadedPackage"](REMOTE_PACKAGE_NAME,REMOTE_PACKAGE_SIZE):null;if(!fetched)fetchRemotePackage(REMOTE_PACKAGE_NAME,REMOTE_PACKAGE_SIZE,(function(data){if(fetchedCallback){fetchedCallback(data);fetchedCallback=null}else{fetched=data}}),handleError);function runWithFS(){function assert(check,msg){if(!check)throw msg+(new Error).stack}Module["FS_createPath"]("/","lib",true,true);Module["FS_createPath"]("/lib","python3.9",true,true);Module["FS_createPath"]("/lib/python3.9","site-packages",true,true);Module["FS_createPath"]("/lib/python3.9/site-packages","retrying-1.3.3-py3.9.egg-info",true,true);function processPackageData(arrayBuffer){assert(arrayBuffer,"Loading data file failed.");assert(arrayBuffer instanceof ArrayBuffer,"bad input to processPackageData");var byteArray=new Uint8Array(arrayBuffer);var curr;var compressedData={data:null,cachedOffset:9712,cachedIndexes:[-1,-1],cachedChunks:[null,null],offsets:[0,1263,2057,2890,3854,5037,6281,7496,8983],sizes:[1263,794,833,964,1183,1244,1215,1487,729],successes:[1,1,1,1,1,1,1,1,1]};compressedData["data"]=byteArray;assert(typeof Module.LZ4==="object","LZ4 not present - was your app build with -s LZ4=1 ?");Module.LZ4.loadPackage({metadata:metadata,compressedData:compressedData},true);Module["removeRunDependency"]("datafile_retrying.data")}Module["addRunDependency"]("datafile_retrying.data");if(!Module.preloadResults)Module.preloadResults={};Module.preloadResults[PACKAGE_NAME]={fromCache:false};if(fetched){processPackageData(fetched);fetched=null}else{fetchedCallback=processPackageData}}if(Module["calledRun"]){runWithFS()}else{if(!Module["preRun"])Module["preRun"]=[];Module["preRun"].push(runWithFS)}};loadPackage({files:[{filename:"/lib/python3.9/site-packages/retrying.py",start:0,end:9955,audio:0},{filename:"/lib/python3.9/site-packages/retrying-1.3.3-py3.9.egg-info/dependency_links.txt",start:9955,end:9956,audio:0},{filename:"/lib/python3.9/site-packages/retrying-1.3.3-py3.9.egg-info/requires.txt",start:9956,end:9967,audio:0},{filename:"/lib/python3.9/site-packages/retrying-1.3.3-py3.9.egg-info/PKG-INFO",start:9967,end:17109,audio:0},{filename:"/lib/python3.9/site-packages/retrying-1.3.3-py3.9.egg-info/top_level.txt",start:17109,end:17118,audio:0},{filename:"/lib/python3.9/site-packages/retrying-1.3.3-py3.9.egg-info/SOURCES.txt",start:17118,end:17386,audio:0}],remote_package_size:13808,package_uuid:"7e87a71d-f5af-4367-921c-a8a74e3bc48a"})})();
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Appid Is Not Configured Call Of Duty Black Ops 2.md b/spaces/quidiaMuxgu/Expedit-SAM/Appid Is Not Configured Call Of Duty Black Ops 2.md
deleted file mode 100644
index 08f019e7fac6cfd4029d956b6f5b091a51e516d6..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Appid Is Not Configured Call Of Duty Black Ops 2.md
+++ /dev/null
@@ -1,20 +0,0 @@
-
-
-Watch the video subtitled CZ if you are not a resident of England (sarcasm) :-DA very simple guide on how to ... How to find a job in England : Tips and tricks
-Vor 2 years 18 521 4:40
-Hi, my name is Alexei.
-I live in England.
-I've worked as a waiter, a salesman, a cook, a longshoreman.
-In this...
-Jobs in England
-Vor 2 years 28 659 8:24
-Work in England.
-Work in England for Russians.
-My story about how I found a job in England!
-What kind of difficulties ...
-Work in England
-Vor year 19 017 1:37
-What are the difficulties... 8a78ff9644
-
-
-
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Dump Original Vision X215 Series Ali C.zip.md b/spaces/quidiaMuxgu/Expedit-SAM/Dump Original Vision X215 Series Ali C.zip.md
deleted file mode 100644
index 95b8fbfe762c7fe72cb0de5cecb004020195e80c..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Dump Original Vision X215 Series Ali C.zip.md
+++ /dev/null
@@ -1,132 +0,0 @@
-
-
How to Download and Install Dump Original Vision X215 Series Ali C.zip on Your Satellite Receiver
-
-
If you own a Vision X215 series satellite receiver, you might be interested in downloading and installing a file called dump original vision x215 series ali c.zip. This file is a firmware update that can improve the performance and functionality of your device. In this article, we will show you how to find, download, and install this file on your receiver.
-
-
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-
-
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Why Do You Need Dump Original Vision X215 Series Ali C.zip?
-
-
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-
-
-
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-
You want to restore your receiver to its original state after a failed update or a corrupted software.
-
You want to fix some common problems such as no signal, no sound, no picture, freezing, or rebooting.
-
You want to change the language, region, or other settings of your receiver.
-
-
-
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-
-
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-
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Click on the download link or button on the website.
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Choose a location on your computer where you want to save the file.
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Wait for the download to complete.
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Once you have downloaded and copied dump original vision x215 series ali c.zip to a USB flash drive, you can follow these steps to install it on your receiver:
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-
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Turn off your receiver and disconnect it from the power source.
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Insert the USB flash drive into the USB port of your receiver.
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Turn on your receiver and wait for a few seconds until you see a message on the screen that says "USB Upgrade".
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Select "Yes" and press OK on your remote control.
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The installation process will start and you will see a progress bar on the screen.
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When the installation is complete, you will see a message that says "Upgrade Success".
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Select "OK" and press OK on your remote control.
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The receiver will restart automatically and load the new firmware.
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Conclusion
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Use a different browser or device to access the website and download the file.
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In this section, we will answer some of the most frequently asked questions about dump original vision x215 series ali c.zip. If you have any other questions that are not answered here, feel free to leave them in the comment section below.
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Dump original vision x215 series ali c.zip is generally safe to download and install on your receiver as long as you use a reliable website and scan the file with an antivirus program before opening it. However, you should always be careful when downloading files from unknown sources as they might contain viruses or malware that can harm your device or your personal data. Therefore, you should always backup your data before installing any firmware update on your receiver.
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Dump original vision x215 series ali c.zip is free to download and install on your receiver as long as you use one of the links provided by various websites that offer this file for free. However, you should always respect the intellectual property rights of the firmware developer and owner and not use this file for any illegal or commercial purposes. If you want to support the firmware developer and owner, you can consider making a donation or purchasing their products or services.
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You can revert back to your previous firmware after installing dump original vision x215 series ali c.zip if you have a backup of your previous firmware file on your computer or USB flash drive. You can follow the same steps as installing dump original vision x215 series ali c.zip, but use your previous firmware file instead. However, you should be careful when reverting back to your previous firmware as it might cause some problems or errors on your device. You should always backup your data before reverting back to your previous firmware on your receiver.
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Conclusion
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In this article, we have shown you how to find, download, install, and troubleshoot dump original vision x215 series ali c.zip on your Vision X215 series satellite receiver. We hope this article was helpful and informative for you. If you have any questions or comments, feel free to leave them below.
3cee63e6c2
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\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Getway Raid Recovery HOT Crack.md b/spaces/quidiaMuxgu/Expedit-SAM/Getway Raid Recovery HOT Crack.md
deleted file mode 100644
index 85f48efbbb0ae5342f15a17843b4f1da2dec375d..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Getway Raid Recovery HOT Crack.md
+++ /dev/null
@@ -1,16 +0,0 @@
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-We can restore any data from RAID 0, RAID 5, RAID 6, RAID 10 and RAID 50. Our service does not involve backup to some other media. We help you to migrate all of your data back to your drives. We are the best way to restore any data from RAID and we work only with the original drives, from the original RAID card. We are specialized on RAID recovery and RAID repairing services with every hour. This is a high-value and time-sensitive service. We do not rely on our users. If our customer or user are in a hurry, we can start the work without delay. We have 100% recovery guarantee!
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-We specialize in RAID recovery from Gateway brand servers & all versions of RAID; RAID 5, RAID 0, RAID 10 and RAID 6 from failed, crashed & degraded RAID. We can restore any data from RAID 0, RAID 5, RAID 6, RAID 10 and RAID 50. Our service does not involve backup to some other media. We help you to migrate all of your data back to your drives. We are the best way to restore any data from RAID and we work only with the original drives, from the original RAID card. We are specialized on RAID recovery and RAID repairing services with every hour. This is a high-value and time-sensitive service. We do not rely on our users. If our customer or user are in a hurry, we can start the work without delay. We have 100% recovery guarantee!
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diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Kareeb Movie Download 720p _VERIFIED_.md b/spaces/quidiaMuxgu/Expedit-SAM/Kareeb Movie Download 720p _VERIFIED_.md
deleted file mode 100644
index c1e7afe6ea1ffc16377118b5e4fc1211e82b0495..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Kareeb Movie Download 720p _VERIFIED_.md
+++ /dev/null
@@ -1,21 +0,0 @@
-
-
How to Download Kareeb Movie in 720p Quality
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Kareeb is a 1998 Indian Hindi-language romance film written, produced and directed by Vidhu Vinod Chopra. It stars Bobby Deol and Neha Bajpai as two lovers who face many obstacles in their relationship. The film was praised for its cinematography, music and performances, and was nominated for two Filmfare Awards.
If you want to watch Kareeb movie in high-definition quality, you can download it from various online platforms that offer legal and safe downloads. Here are some of the options you can choose from:
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Prime Video: Prime Video is a streaming service that offers a wide range of movies and shows, including Kareeb. You can rent or buy Kareeb movie in 720p quality from Prime Video for a nominal fee. You can also watch it for free if you have a Prime membership. To download Kareeb movie from Prime Video, you need to have the Prime Video app on your device and an internet connection. You can then select the movie and choose the download option. You can watch the downloaded movie offline for up to 48 hours.
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YouTube: YouTube is a popular video-sharing platform that also hosts many movies and shows. You can find Kareeb movie on YouTube and watch it online for free or with ads. You can also download Kareeb movie in 720p quality from YouTube if you have a YouTube Premium subscription. To download Kareeb movie from YouTube, you need to have the YouTube app on your device and an internet connection. You can then select the movie and tap on the download icon. You can watch the downloaded movie offline for up to 30 days.
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These are some of the ways you can download Kareeb movie in 720p quality and enjoy it on your device. However, we recommend that you always use legal and authorized sources to download movies and avoid piracy and illegal downloads that may harm your device or violate the copyright laws.
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Kareeb Movie Review: A Simple and Sweet Love Story
The film revolves around Birju (Bobby Deol), a carefree and irresponsible son of a wealthy family, who falls in love with Neha (Neha Bajpai), a simple and responsible daughter of a poor school teacher. Birju lies to his father about Neha's family being rich and arranges their marriage. However, his lie is exposed on the wedding night and his father calls off the marriage. Neha's mother suffers a heart attack due to the shock and Neha blames Birju for her condition. She asks him to never show his face again and takes her mother to a hospital in Shimla. Birju follows her and tries to win her back by working as a laundry boy and earning money for her mother's treatment. He also faces competition from Dr. Abhay (Abhay Chopra), who offers to marry Neha and treat her mother for free. Birju also gets involved in a lottery scam with Uncle (Johnny Lever) and Aunty (Sushma Seth), who promise to make him rich overnight. Will Birju be able to prove his love and sincerity to Neha? Will Neha forgive him for his lies and accept him? Will they overcome all the obstacles and get married?
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The film has a simple plot but it is executed with finesse and charm by director Vidhu Vinod Chopra. He captures the beauty and innocence of love in a realistic way, without resorting to any unnecessary drama or violence. He also showcases the scenic locations of Himachal Pradesh with stunning cinematography by Binod Pradhan. The film has a soothing and melodious music by Anu Malik, who composed some memorable songs like "Chori Chori Jab Nazrein Mili", "Churalo Na Dil Mera" and "Rehna Hai Tere Dil Mein". The film also has some humorous moments, especially involving Johnny Lever as Uncle, who dreams of going to England.
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The film has some excellent performances by the lead actors. Bobby Deol delivers one of his best performances as Birju, who transforms from a spoilt brat to a sincere lover. He portrays the emotions of love, guilt, pain and hope with conviction and charm. Neha Bajpai is a revelation as Neha, who steals the show with her natural and graceful act. She looks beautiful and innocent, and expresses her feelings with her eyes. She has a great chemistry with Bobby Deol, which makes their romance believable and touching. Abhay Chopra is decent as Dr. Abhay, who plays the third angle in the love triangle. He is not a typical villain, but a good-hearted person who genuinely cares for Neha. Moushumi Chatterjee is effective as Neha's mother, who suffers from a heart ailment. Saurabh Shukla is hilarious as Birju's father, who is fed up of his son's antics.
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Kareeb movie is a gem of a film that deserves more appreciation and recognition. It is a film that celebrates love in its purest form, without any pretense or glamour. It is a film that will touch your heart and make you smile.
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diff --git a/spaces/r3gm/RVC_HF/lib/uvr5_pack/lib_v5/nets_123821KB.py b/spaces/r3gm/RVC_HF/lib/uvr5_pack/lib_v5/nets_123821KB.py
deleted file mode 100644
index becbfae85683a13bbb19d3ea6c840da24e61e01e..0000000000000000000000000000000000000000
--- a/spaces/r3gm/RVC_HF/lib/uvr5_pack/lib_v5/nets_123821KB.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-from . import layers_123821KB as layers
-
-
-class BaseASPPNet(nn.Module):
- def __init__(self, nin, ch, dilations=(4, 8, 16)):
- super(BaseASPPNet, self).__init__()
- self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
- self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
- self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
- self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
-
- self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations)
-
- self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
- self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
- self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
- self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
-
- def __call__(self, x):
- h, e1 = self.enc1(x)
- h, e2 = self.enc2(h)
- h, e3 = self.enc3(h)
- h, e4 = self.enc4(h)
-
- h = self.aspp(h)
-
- h = self.dec4(h, e4)
- h = self.dec3(h, e3)
- h = self.dec2(h, e2)
- h = self.dec1(h, e1)
-
- return h
-
-
-class CascadedASPPNet(nn.Module):
- def __init__(self, n_fft):
- super(CascadedASPPNet, self).__init__()
- self.stg1_low_band_net = BaseASPPNet(2, 32)
- self.stg1_high_band_net = BaseASPPNet(2, 32)
-
- self.stg2_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
- self.stg2_full_band_net = BaseASPPNet(16, 32)
-
- self.stg3_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0)
- self.stg3_full_band_net = BaseASPPNet(32, 64)
-
- self.out = nn.Conv2d(64, 2, 1, bias=False)
- self.aux1_out = nn.Conv2d(32, 2, 1, bias=False)
- self.aux2_out = nn.Conv2d(32, 2, 1, bias=False)
-
- self.max_bin = n_fft // 2
- self.output_bin = n_fft // 2 + 1
-
- self.offset = 128
-
- def forward(self, x, aggressiveness=None):
- mix = x.detach()
- x = x.clone()
-
- x = x[:, :, : self.max_bin]
-
- bandw = x.size()[2] // 2
- aux1 = torch.cat(
- [
- self.stg1_low_band_net(x[:, :, :bandw]),
- self.stg1_high_band_net(x[:, :, bandw:]),
- ],
- dim=2,
- )
-
- h = torch.cat([x, aux1], dim=1)
- aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
-
- h = torch.cat([x, aux1, aux2], dim=1)
- h = self.stg3_full_band_net(self.stg3_bridge(h))
-
- mask = torch.sigmoid(self.out(h))
- mask = F.pad(
- input=mask,
- pad=(0, 0, 0, self.output_bin - mask.size()[2]),
- mode="replicate",
- )
-
- if self.training:
- aux1 = torch.sigmoid(self.aux1_out(aux1))
- aux1 = F.pad(
- input=aux1,
- pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
- mode="replicate",
- )
- aux2 = torch.sigmoid(self.aux2_out(aux2))
- aux2 = F.pad(
- input=aux2,
- pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
- mode="replicate",
- )
- return mask * mix, aux1 * mix, aux2 * mix
- else:
- if aggressiveness:
- mask[:, :, : aggressiveness["split_bin"]] = torch.pow(
- mask[:, :, : aggressiveness["split_bin"]],
- 1 + aggressiveness["value"] / 3,
- )
- mask[:, :, aggressiveness["split_bin"] :] = torch.pow(
- mask[:, :, aggressiveness["split_bin"] :],
- 1 + aggressiveness["value"],
- )
-
- return mask * mix
-
- def predict(self, x_mag, aggressiveness=None):
- h = self.forward(x_mag, aggressiveness)
-
- if self.offset > 0:
- h = h[:, :, :, self.offset : -self.offset]
- assert h.size()[3] > 0
-
- return h
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Barbie in the 12 Dancing Princesses Movie in Hindi Download How to Enjoy the Classic Story in a New Language.md b/spaces/raedeXanto/academic-chatgpt-beta/Barbie in the 12 Dancing Princesses Movie in Hindi Download How to Enjoy the Classic Story in a New Language.md
deleted file mode 100644
index 557a788a3bdb7ed1d1e2115a124e6dae4bc77b3e..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Barbie in the 12 Dancing Princesses Movie in Hindi Download How to Enjoy the Classic Story in a New Language.md
+++ /dev/null
@@ -1,138 +0,0 @@
-
-
Barbie in the 12 Dancing Princesses Movie in Hindi Download
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If you are looking for a fun and enchanting movie to watch with your kids or by yourself, you might want to check out Barbie in the 12 Dancing Princesses. This is a 2006 animated movie that features Barbie as Princess Genevieve, one of the twelve dancing princess sisters who discover a secret entrance to an amazing, magical world where wishes come true. But when their father, the king, is in danger of losing his kingdom and his life, they must work together to save the day and their father. They learn that the power of family can overcome all obstacles.
There are many reasons why you should watch this movie. Here are some of them:
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This movie is based on a classic fairy tale by the Brothers Grimm, but with a twist. It adds more characters, more adventure, more magic, and more dancing.
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This movie has beautiful animation, colorful scenery, catchy songs, and charming characters. You will be mesmerized by the stunning visuals and the lively music.
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This movie has a positive message about family, love, courage, and loyalty. You will be inspired by the bond between the sisters, their love for their father, their courage to face their enemies, and their loyalty to their kingdom.
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This movie is suitable for all ages. Whether you are a kid or an adult, you will enjoy this movie for its humor, romance, action, and fantasy.
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The magical world of dancing princesses
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One of the most exciting parts of this movie is the magical world that the princesses discover through a secret entrance in their bedroom. This world is full of wonders and surprises. It has a beautiful garden with flowers that change colors, a lake with swans that turn into boats, a pavilion with musical instruments that play themselves, and a ballroom with handsome princes that dance with them. In this world, anything they wish for comes true.
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The evil cousin and her plan
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But not everything is perfect in this movie. There is also an evil cousin named Duchess Rowena who wants to take over the kingdom. She pretends to be a kind and helpful relative who comes to teach the princesses proper royal etiquette. But in reality, she is a cruel and greedy woman who wants to get rid of the king and his daughters. She bans all dancing in the palace, locks up the princesses in their room, poisons the king with a potion, and tries to marry his eldest son.
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The power of family and love
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Fortunately, the princesses are not helpless. They have each other, their father's love, and their mother's gift. Their mother was a queen who died when they were young. She left them a gift that only they can use: twelve matching necklaces that glow when they are together. These necklaces help them find the secret entrance to the magical world, heal their father from the poison, escape from Rowena's trap, and defeat her army. They also help them realize that they are stronger when they are united.
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The best websites to download this movie
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There are many websites that offer this movie in Hindi dubbed version for free download. But not all of them are reliable or safe. Some of them might have viruses or malware that can harm your device or steal your data. Some of them might have broken links or low-quality videos that can ruin your viewing experience. So how do you know which websites are good? Here are some criteria that you can use to judge:
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The website should have a clear and user-friendly interface. You should be able to navigate easily and find what you are looking for without any hassle.
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The website should have multiple download links from different sources. You should be able to choose from different options such as direct download, cloud drive, mega, zupload, mediafire, etc.
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The website should have high-quality videos with good resolution and sound. You should be able to watch the movie in 1080p full HD or 720p HD quality.
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The website should have dual audio tracks for Hindi and English languages. You should be able to switch between languages according to your preference.
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Based on these criteria, here are some examples of websites that you can use to download this movie:
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Website Name
-
Features
-
Pros
-
Cons
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-
-
CloudDrive
-
A cloud storage service that allows you to upload and download files online.
-
- Fast and secure - Easy to use - Supports multiple formats - Has dual audio tracks
-
- Requires registration - Has limited storage space - Has ads
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-
-
MEGA
-
A cloud storage service that offers end-to-end encryption for your files.
-
- Secure and private - Supports multiple formats - Has dual audio tracks - Has high-quality videos
-
- Requires registration - Has limited storage space - Has ads - Can be slow sometimes
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-
-
Zupload
-
A file hosting service that allows you to upload and download files online.
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- No registration required - No ads
-
- Can be slow sometimes - Has limited download speed - Has limited file size
-
-
-
MediaFire
-
A file hosting and sharing service that allows you to upload and download files online.
-
- Fast and reliable - Supports multiple formats - Has dual audio tracks - Has high-quality videos
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- Requires registration - Has ads - Has limited storage space - Has limited file size
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-
-
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The best media players to watch this movie
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Once you have downloaded this movie, you might want to watch it on your device. But not all media players can play this movie in Hindi audio track. Some of them might not support the format or the language of the movie. So how do you know which media players are good? Here are some criteria that you can use to judge:
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The media player should have a clear and user-friendly interface. You should be able to navigate easily and control the playback without any hassle.
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The media player should have multiple audio tracks and subtitles options. You should be able to switch between languages and subtitles according to your preference.
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The media player should have high-quality video and sound. You should be able to watch the movie in full HD or HD quality and hear the sound clearly and loudly.
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The media player should be compatible with your device and operating system. You should be able to install and run the media player on your device without any errors or glitches.
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Based on these criteria, here are some examples of media players that you can use to watch this movie:
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What is the name of the magical world that the princesses discover? The name of the magical world is never revealed in the movie, but some fans call it "The Land of Wishes".
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Beintehaa Serial A Tale of Endless Love and Family Drama - Watch All Episodes Desi Tashan.md b/spaces/raedeXanto/academic-chatgpt-beta/Beintehaa Serial A Tale of Endless Love and Family Drama - Watch All Episodes Desi Tashan.md
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If you are looking for a captivating and passionate Indian TV show that will make you fall in love with the characters and their story, then you should definitely watch Beintehaa serial. Beintehaa is a Hindi-language romantic drama that aired on Colors TV from 2013 to 2014. It tells the story of Zain and Aaliya, two young people who are forced to marry each other due to family pressure, but eventually develop a deep bond of love and trust. Beintehaa serial has everything you need in a good romance drama: a gripping plot, a talented cast, a soulful soundtrack, a positive reception, and a loyal fan base. In this article, we will tell you everything you need to know about Beintehaa serial, and how you can watch all episodes online on Desi Tashan.
The Plot of Beintehaa: A Love Story Between Two Opposites
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Beintehaa serial follows the lives of Zain Abdullah and Aaliya Haider, two individuals who are poles apart in their personalities, backgrounds, and beliefs. Zain is a rich and spoiled businessman who lives in Mumbai with his family. He is arrogant, carefree, and irresponsible. He loves his freedom and hates commitments. Aaliya is a poor and independent journalist who lives in Bhopal with her family. She is humble, hardworking, and religious. She values her culture and respects her elders. They both have one thing in common though: they hate each other.
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Their hatred turns into marriage when their families arrange their wedding without their consent. Zain and Aaliya are shocked and angry at this decision, but they have no choice but to accept it for the sake of their families. They decide to divorce each other as soon as possible, but fate has other plans for them. As they start living together under one roof, they begin to discover new aspects of each other's personalities. They realize that they have more in common than they thought. They also face various challenges and conflicts that test their relationship. They encounter enemies who want to separate them, secrets that threaten to destroy them, misunderstandings that create doubts between them, and tragedies that break their hearts.
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However, through all these twists and turns, Zain and Aaliya also experience moments of love, friendship, trust, support, forgiveness, sacrifice, and happiness. They slowly develop feelings for each other that go beyond hatred. They realize that they are meant for each other, despite their differences. They become each other's strength in times of weakness. They become each other's beintehaa (limitless) love.
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The Cast of Beintehaa: A Talented Ensemble of Actors
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Beintehaa serial boasts of a stellar cast that brings the characters to life with their brilliant performances. Here are some of the main actors who play important roles in the show:
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Harshad Arora as Zain Abdullah: Harshad Arora is an Indian actor who made his debut with Beintehaa serial. He plays the role of Zain Abdullah, the male protagonist of the show. He portrays Zain's character arc from a selfish and immature man to a loving and mature husband with finesse. He also showcases Zain's charm, humor, anger, jealousy, pain, guilt, regret, redemption, and happiness with ease. He has a sizzling chemistry with Preetika Rao, who plays his on-screen wife Aaliya.
-, intelligence, loyalty, compassion, patience, determination, and joy with skill. She has a sparkling chemistry with Harshad Arora, who plays her on-screen husband Zain.
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Naved Aslam as Usman Abdullah: Naved Aslam is an Indian actor who plays the role of Usman Abdullah, Zain's father and a respected tycoon. He portrays Usman's character as a wise and generous man who loves his family and his business. He is also a progressive and tolerant person who supports Zain and Aaliya's marriage despite their differences. He is a father figure to both of them and guides them through their problems.
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Rituraj Singh as Ghulam Haider: Rituraj Singh is an Indian actor who plays the role of Ghulam Haider, Aaliya's father and a devout Muslim. He portrays Ghulam's character as a humble and honest man who loves his family and his religion. He is also a strict and traditional person who follows the rules and norms of his culture. He is initially opposed to Zain and Aaliya's marriage but later accepts it for their happiness.
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Other supporting actors: Beintehaa serial also features other supporting actors who play important roles in the show. Some of them are: Suchitra Pillai as Suraiya Abdullah (Zain's mother and Usman's wife), Nandish Sandhu as Rehaan Qureshi (Zain's friend and Aaliya's lawyer), Riva Bubber as Shazia Fahad Abdullah (Zain's sister-in-law and Suraiya's daughter-in-law), Vikas Grover as Fahad Abdullah (Zain's elder brother and Shazia's husband), Namrata Pathak as Nafisa Fahad Abdullah (Zain's sister-in-law and Fahad's second wife), Gunjan Vijaya as Barkat Mir Khan/ Bobby (Zain's long-lost sister and Usman's daughter), Mohit Malhotra as Zubair Qureshi (Aaliya's cousin and Rehaan's brother), Shivangi Joshi as Aayat Haider (Aaliya's younger sister and Ghulam's daughter), Nandini Singh as Rizwan Malik (Aayat's husband and Zain's business rival), etc.
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The Music of Beintehaa: A Soulful Soundtrack That Complements The Drama
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Beintehaa serial has a beautiful soundtrack that matches the theme and mood of the show. The music of Beintehaa is composed by various artists who have given their best to create songs that touch the hearts of the listeners. Here are some of the songs that feature in the show:
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The title song: Beintehaa by Atif Aslam and Shreya Ghoshal: This is the main song of the show that plays during the opening credits and some romantic scenes between Zain and Aaliya. It is sung by two of the most popular singers in India, Atif Aslam and Shreya Ghoshal. It is a melodious song that expresses the limitless love between Zain and Aaliya. It has lyrics like "Tu hi bata kaise jiyun main tere bina...Beintehaa beintehaa yun pyaar kar...Beintehaa main bhi hoon tu bhi hai mujhme samaya...Chhu le mujhe iss kadar beintehaa..." which mean "You tell me how I live without you...Love me limitlessly...I am also there you are also there in me...Touch me like this limitlessly..."
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The background score: How it enhances the mood and emotions of the scenes: The background score of Beintehaa serial is composed by various artists who have given different tunes for different situations in the show. The background score helps to create the atmosphere and convey the feelings of the characters in the scenes. For example, there are tunes for Zain and Aaliya's hatred, friendship, love, separation, reunion, etc. There are also tunes for suspense, drama, comedy, action, etc.
-and Shweta Pandit (a song that plays during Zain and Aaliya's reunion), etc.
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The Reception of Beintehaa: A Critically Acclaimed and Popular Show
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Beintehaa serial has received a lot of praise and appreciation from both critics and audiences for its story, direction, acting, music, and production. Beintehaa serial has achieved many milestones and accolades in its journey. Here are some of them:
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The ratings and reviews: How Beintehaa performed on TV and online platforms: Beintehaa serial was one of the most watched and loved shows on Indian television. It consistently ranked among the top 10 shows in terms of TRP (Television Rating Points) and BARC (Broadcast Audience Research Council) ratings. It also received positive reviews from various critics and media outlets who praised its fresh and realistic approach to romance, its strong and relatable characters, its engaging and unpredictable plot, its crisp and witty dialogues, its stunning and authentic locations, its stylish and elegant costumes, its flawless and natural acting, its melodious and catchy music, and its overall quality and presentation. Beintehaa serial also gained a huge fan following on online platforms like YouTube, Hotstar, Voot, etc. where it garnered millions of views, likes, comments, and shares.
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The awards and nominations: How Beintehaa was recognized by various ceremonies: Beintehaa serial was also honored with many awards and nominations by various prestigious ceremonies that celebrate excellence in Indian television. Some of the awards and nominations that Beintehaa received are: Indian Telly Awards (Best Fresh New Face Male for Harshad Arora, Best Fresh New Face Female for Preetika Rao, Best Onscreen Couple for Harshad Arora and Preetika Rao), Indian Television Academy Awards (Best Actor Popular for Harshad Arora, Best Actress Popular for Preetika Rao), Zee Gold Awards (Best Debut Male for Harshad Arora, Best Debut Female for Preetika Rao), Lions Gold Awards (Best Jodi for Harshad Arora and Preetika Rao), Kalakar Awards (Best Actor for Harshad Arora), etc.
-, videos, memes, etc. based on Beintehaa serial. Beintehaa serial also gave birth to many fan clubs and communities that celebrate and support Beintehaa serial and its actors.
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How to Watch Beintehaa Serial Online: A Guide for Desi Tashan Lovers
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If you are a fan of Beintehaa serial or if you want to watch it for the first time, you might be wondering how to watch it online. There are many websites and apps that offer online streaming of Beintehaa serial, but one of the most popular and convenient ones is Desi Tashan. Desi Tashan is a website that provides online streaming of various Indian TV shows and movies. It has a huge collection of content from different genres and languages. It also has a simple and user-friendly interface that makes it easy to navigate and watch your favorite shows. Here are some of the benefits of watching Beintehaa serial online on Desi Tashan:
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High quality: Desi Tashan offers high quality streaming of Beintehaa serial that enhances your viewing experience. You can watch Beintehaa serial in HD (High Definition) or SD (Standard Definition) depending on your preference and internet speed. You can also adjust the brightness, contrast, and volume of the video according to your comfort.
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Fast streaming: Desi Tashan offers fast streaming of Beintehaa serial that saves your time and data. You can watch Beintehaa serial without any buffering or lagging issues. You can also skip or rewind the video as per your convenience. You can also pause or resume the video anytime you want.
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No ads: Desi Tashan offers ad-free streaming of Beintehaa serial that enhances your enjoyment. You can watch Beintehaa serial without any interruptions or distractions from annoying ads. You can also avoid any malware or virus threats from malicious ads.
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However, there are also some drawbacks of watching Beintehaa serial online on Desi Tashan that you should be aware of:
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Legal issues: Desi Tashan is an illegal website that streams Beintehaa serial without the permission or license from the original creators or owners. This violates the copyright and intellectual property rights of the makers and distributors of Beintehaa serial. This can also land you in legal trouble if you are caught using Desi Tashan to watch Beintehaa serial.
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Security risks: Desi Tashan is an unsafe website that streams Beintehaa serial without any encryption or protection from hackers or cybercriminals. This exposes your personal and financial information to potential theft or misuse. This can also harm your device or network with viruses or malware from unknown sources.
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Therefore, you should be careful and cautious while using Desi Tashan to watch Beintehaa serial online. You should also use a VPN (Virtual Private Network) service to hide your identity and location from prying eyes. You should also use an antivirus software to protect your device and network from harmful attacks.
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If you are looking for some alternatives to Desi Tashan that offer legal and safe streaming of Beintehaa serial online, here are some options that you can try:
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Voot: Voot is an official website and app that streams Beintehaa serial online with the permission and license from Colors TV, the original broadcaster of the show. Voot offers high quality and fast streaming of Beintehaa serial with minimal ads. Voot also offers subtitles and captions for Beintehaa serial in different languages. Voot also has other features like download, offline viewing, resume watching, etc. Voot is free to use but requires registration.
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Hotstar: Hotstar is another official website and app that streams Beintehaa serial online with the permission and license from Star India, the original distributor of the show. Hotstar offers high quality and fast streaming of Beintehaa serial with minimal ads. Hotstar also offers subtitles and captions for Beintehaa serial in different languages. Hotstar also has other features like download, offline viewing, resume watching, etc. Hotstar is free to use but requires registration.
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Conclusion: Why Beintehaa Serial is a Must-Watch for Romance Fans
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In conclusion, Beintehaa serial is a must-watch for romance fans who love to watch a captivating and passionate love story between two opposites. Beintehaa serial has a gripping plot, a talented cast, a soulful soundtrack, a positive reception, and a loyal fan base. Beintehaa serial also has a unique and inspiring message of love that transcends all limits and barriers. Beintehaa serial also offers a realistic and relatable portrayal of Indian culture and society. Beintehaa serial can be watched online on Desi Tashan or other platforms with some benefits and drawbacks. Beintehaa serial is a show that will keep you hooked till the end.
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If you have enjoyed reading this article, please share it with your friends and family who might be interested in watching Beintehaa serial online. Also, please leave your feedback and opinions on Beintehaa serial in the comments section below. We would love to hear from you.
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FAQs
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Here are some frequently asked questions about Beintehaa serial that you might have:
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How many episodes are there in Beintehaa serial? There are 235 episodes in Beintehaa serial that span over two seasons.
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When did Beintehaa serial start and end? Beintehaa serial started on 30 December 2013 and ended on 21 November 2014.
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Where was Beintehaa serial shot? Beintehaa serial was shot in various locations in India and abroad. Some of the locations are: Mumbai, Bhopal, Hyderabad, Delhi, Goa, Kashmir, Dubai, etc.
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What is the meaning of Beintehaa? Beintehaa is an Urdu word that means limitless or boundless. It refers to the limitless love between Zain and Aaliya in the show.
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Who is the writer of Beintehaa serial? Beintehaa serial is written by Sonali Jaffar, who is also the creative director of the show.
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The alternative sources and the crack files
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The second option is to look for alternative sources on the internet that offer Kuka Sim Pro 2.1 crack for free download. These sources may include websites , forums, blogs, torrents, or file-sharing platforms. However, these sources are not authorized by Kuka and may contain malicious or illegal content.
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To download Kuka Sim Pro 2.1 crack from these sources, you will need to search for keywords such as "Kuka Sim Pro 2.1 crack", "Kuka Sim Pro 2.1 free download", "Kuka Sim Pro 2.1 keygen", "Kuka Sim Pro 2.1 serial number", "Kuka Sim Pro 2.1 activation code", etc. You will also need to be careful about fake or misleading links that may redirect you to unwanted or harmful sites.
-
To install Kuka Sim Pro 2.1 crack from these sources, you will need to extract the zip or rar file that contains the crack files (such as .exe, .dll, .dat, .reg, etc.). You will also need to copy or replace these files in the installation folder of Kuka Sim Pro (usually C:\Program Files\KUKA\SimPro). After that, you can run Kuka Sim Pro 2.1 without needing a license or an activation code.
-
The installation and activation process
-
The third option is to follow a step-by-step guide that explains how to install and activate Kuka Sim Pro 2.1 crack for free. These guides may be available on YouTube , Reddit, Quora, Medium, etc. However, these guides are not verified by Kuka and may not work or be outdated.
-
To follow these guides, you will need to watch or read them carefully and follow their instructions exactly as they say. You will also need to download or use the same files or tools that they provide or recommend (such as WinRAR, Daemon Tools, etc.). After that, you should be able to use Kuka Sim Pro 2.1 without any restrictions.
-
How to Use Kuka Sim Pro 2.1 for Robot Simulation and Offline Programming
-
If you have successfully downloaded, installed, and activated Kuka Sim Pro 2.1 crack for free (or if you have bought a legitimate license), here are some tips on how to use it for robot simulation and offline programming:
-
The main features and functions of Kuka Sim Pro 2.1
-
Kuka Sim Pro 2.1 has two main modules: Layout Editor and Program Editor.
-
-
The Layout Editor allows you to create a virtual model of your system with Kuka robots and other components (such as workpieces, tools, fixtures, conveyors, sensors, etc.). You can drag-and-drop objects from a library or import them from CAD files (such as .stp or .igs). You can also adjust their properties (such as position, orientation, size, color, etc.) and define their kinematics (such as joints, axes, limits, etc.).
-
The Program Editor allows you to create a robot program that controls the movements and actions of your robots in your system. You can use different programming languages (such as KR C4 or RAPID) or graphical interfaces (such as block diagram or flow chart). You can also edit your program in different views (such as text editor or motion editor) and debug it with different tools (such as breakpoints or watchpoints).
-
-
The steps to create a 3D layout and a robot program in Kuka Sim Pro 2.1
-
To create a 3D layout and a robot program in Kuka Sim Pro 2.1, you can follow these steps:
-
-
Open Kuka Sim Pro 2.1 and create a new project.
-
Select the Layout Editor module and choose a template or a blank layout.
-
Add objects from the library or import them from CAD files to your layout. You can also use the snap, align, or grid functions to position them accurately.
-
Adjust the properties and kinematics of the objects as needed. You can also use the collision detection and reachability check functions to verify your layout.
-
Select the Program Editor module and choose a programming language or a graphical interface.
-
Create a new program or open an existing one. You can also use the wizard function to generate a program automatically.
-
Edit your program in different views and add commands, variables, comments, etc. You can also use the debug tools to test and optimize your program.
-
Save your project and export your layout and program to the desired format.
-
-
The tips and tricks to optimize your simulation and programming
-
To optimize your simulation and programming with Kuka Sim Pro 2.1, you can use some of these tips and tricks:
-
-
Use the RCS module to simulate the real controller behavior of your robot (only available for KSS 8.5).
-
Use the cycletime prediction function to estimate the duration of your robot program without running it.
-
Use the animation editor function to create custom animations for your objects (such as opening doors, rotating tables, etc.).
-
Use the surface/curve import function to import complex geometries from CAD files (such as .stp or .igs).
-
Use the WorkVisual interface to connect Kuka Sim Pro 2.1 with WorkVisual, a software for configuring and commissioning Kuka controllers.
-
-
Conclusion
-
Kuka Sim Pro 2.1 is a powerful software for robot simulation and offline programming that can help you design, simulate, and optimize systems with Kuka robots in a virtual environment. However, it is also an expensive software that requires a license or an activation code to use it. If you want to get Kuka Sim Pro 2.1 crack for free, you might face some risks and challenges, such as malware infection, software malfunction, compatibility issues, update problems, or legal consequences.
-
Therefore, we recommend that you use Kuka Sim Pro 2.1 crack only for educational or testing purposes, and not for commercial or professional use. If you want to use Kuka Sim Pro 2.1 for real projects or applications, you should buy a legitimate license from the official website of Kuka or an authorized dealer. This way, you can enjoy the full benefits and features of Kuka Sim Pro 2.1 without any worries or limitations.
-
If you want to learn more about Kuka Sim Pro 2.1 or other simulation software for industrial robots, you can check out these resources:
What is the difference between Kuka Sim Pro and Kuka Sim Layout?
-
Kuka Sim Pro is a software for robot simulation and offline programming that includes both Layout Editor and Program Editor modules. Kuka Sim Layout is a software for creating 3D layouts for systems with Kuka robots that includes only the Layout Editor module.
-
What are the system requirements for Kuka Sim Pro 2.1?
-
The minimum system requirements for Kuka Sim Pro 2.1 are:
-
-
Windows XP SP3/Vista/7/8/10 (32-bit or 64-bit)
-
Intel Core i5 processor or equivalent
-
4 GB RAM
-
10 GB free disk space
-
NVIDIA GeForce GTX 460 graphics card or equivalent
-
DirectX 9.0c compatible sound card
-
Internet connection (for activation and updates)
-
-
How can I transfer my robot program from Kuka Sim Pro 2.1 to the real robot?
-
You can transfer your robot program from Kuka Sim Pro 2.1 to the real robot by using one of these methods:
-
-
Export your program as a .src file and copy it to a USB stick or a network drive. Then, plug it into the robot controller and load it into the editor.
-
Use WorkVisual interface to connect Kuka Sim Pro 2.1 with WorkVisual software on your PC. Then, use WorkVisual software to transfer your program to the robot controller via Ethernet cable.
-
-
How can I get support or help for Kuka Sim Pro 2.1?
-
You can get support or help for Kuka Sim Pro 2.1 by using one of these options:
-
-
Contact the technical support team of Kuka by phone (+49 821 797-0) or email (support@kuka.com).
-
Contact your local Kuka representative or dealer by using this contact form.
-
Visit the online KUKA.Sim Community, where you can find tutorials, tips, FAQs, forums, downloads, etc.
-
-
How can I update my version of Kuka Sim Pro 2.1?
-
You can update your version of Kuka Sim Pro 2.1 by using one of these methods:
-
-
Use the online update function in the software menu (Help > Check for Updates).
-
Download the latest version from the official website of Kuka or from the online KUKA.Sim Community.
- 0a6ba089eb
-
-
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Diagbox 7.02 How to Install and Use the Diagnostic Tool for Lexia 3 and Peugeot 306.md b/spaces/raedeXanto/academic-chatgpt-beta/Diagbox 7.02 How to Install and Use the Diagnostic Tool for Lexia 3 and Peugeot 306.md
deleted file mode 100644
index 1f780d3fff506edc64e9023dc61a8ae5c93cd4ed..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Diagbox 7.02 How to Install and Use the Diagnostic Tool for Lexia 3 and Peugeot 306.md
+++ /dev/null
@@ -1,106 +0,0 @@
-
-
What is diagbox 7.02 and why do you need it?
-
If you own a Lexia 3 interface or a Peugeot 306 vehicle, you might have heard of diagbox 7.02. But what is it exactly and what can it do for you?
Diagbox 7.02 is a software program that runs on your PC and allows you to transfer your service engine parts (SES) information from your vehicle. Diagbox 7.02 is a diagnostic tool that works with Lexia 3 and Peugeot 306 from Lexia.
-
With diagbox 7.02, you can perform various diagnostic functions on your vehicle, such as reading fault codes, clearing fault codes, telecoding, live data, etc.
-
Some of the benefits of using diagbox 7.02 are:
-
-
You can save money by diagnosing and repairing your vehicle yourself
-
You can access more functions than with other software
-
You can update your software regularly to get the latest features and bug fixes
-
You can use it on Windows XP, Vista or Windows 7
-
-
However, before you can use diagbox 7.02, you need to have some requirements:
-
-
A PC with Windows XP, Vista or Windows 7
-
A Lexia 3 interface or a compatible Chinese clone
-
A Peugeot 306 vehicle or another PSA vehicle supported by diagbox
-
A USB cable to connect your interface to your PC
-
A trusted source to download diagbox 7.02
-
-
How to install diagbox 7.02 on your PC?
-7.02 on your PC. Here are the steps to follow:
-
-
Download diagbox 7.02 from a trusted source, such as this one. You will get an iso-image file with the software and a folder with updates.
-
Install Microsoft .NET 3.5, Java 7 and Microsoft Visual C++ 2010 redistributable package (x86) on your PC. These are necessary for diagbox 7.02 to run properly.
-
Run the diagbox 7.02 setup file and follow the instructions. You will need to enter a password for installing updates, which is "scary01". You don't need to patch anything, everything is already done.
-
Activate diagbox 7.02 manually or with a code generator. You can find the instructions on how to do this in the installation guide or online forums.
-
-
How to use diagbox 7.02 for Lexia 3 and Peugeot 306?
-
After you have installed and activated diagbox 7.02 on your PC, you can start using it for your Lexia 3 and Peugeot 306 vehicles. Here are the steps to follow:
-
-
Connect your Lexia 3 interface to your PC and your vehicle with the USB cable. Make sure your vehicle is switched on and your interface is recognized by your PC.
-
Launch diagbox 7.02 and select your vehicle model and year from the menu. You will see a list of diagnostic functions available for your vehicle.
-
Choose the diagnostic function you want to perform, such as reading fault codes, clearing fault codes, telecoding, live data, etc. You will see a screen with instructions on how to perform the function.
-
Follow the instructions on the screen and perform the diagnostic function. You will see the results on the screen or in a report file.
-
-
What are some tips and tricks for using diagbox 7.02?
-
Diagbox 7.02 is a powerful tool that can help you diagnose and repair your Lexia 3 and Peugeot 306 vehicles. However, there are some tips and tricks that can make your experience even better. Here are some of them:
-
-
Update diagbox 7.02 regularly to get the latest features and bug fixes. You can find updates in the update folder or online forums.
-
Use PSA Interface Checker Install to change the firmware of your Lexia 3 interface if needed. This can help you solve some communication errors or compatibility issues.
-
Use TLCDfix to disable code request when telecoding. This can help you avoid entering codes every time you want to telecode something.
-
Use Psa_Dam_Org_Build_Code to calculate the DAM number by the date of car release and vice versa. This can help you find out the correct DAM number for your vehicle or when it was released.
-
-
Conclusion
-
In this article, we have learned what diagbox 7.02 is and why you need it for your Lexia 3 and Peugeot 306 vehicles. We have also learned how to install it, use it and optimize it for better performance.
-
Diagbox 7.02 is a free tool that can help you save money by diagnosing and repairing your vehicle yourself. It can also help you access more functions than with other software and update your software regularly to get the latest features and bug fixes.
-
diagbox 7.02 installation guide
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-diagbox 7.02 telecoding and programming features
-diagbox 7.02 error codes and troubleshooting guide
-diagbox 7.02 reviews and ratings from users
-diagbox 7.02 comparison with other versions of diagbox
-diagbox 7.02 benefits and drawbacks of using it
-diagbox 7.02 best practices and tips for optimal performance
-diagbox 7.02 frequently asked questions and answers
-diagbox 7.02 video tutorials and demonstrations on youtube
-
If you want to try diagbox 7.02 for yourself, you can download it from a trusted source and follow the installation guide. You will need a PC with Windows XP, Vista or Windows 7, a Lexia 3 interface or a compatible Chinese clone, a Peugeot 306 vehicle or another PSA vehicle supported by diagbox, a USB cable to connect your interface to your PC and a trusted source to download diagbox 7.02.
-
Please note that diagbox 7.02 is a third-party software that is not authorized by PSA Group. You use it at your own risk and responsibility.
-
FAQs
-
-
Q1: What is the difference between diagbox and lexia?
-
A1: Diagbox is a newer software that includes lexia and other functions for newer PSA vehicles. Lexia is an older software that only works for older PSA vehicles.
-
Q2: What are some common problems with diagbox 7.02?
-
A2: Some common problems with diagbox 7.02 are installation errors, activation errors, communication errors, compatibility issues, etc.
-
Q3: How can I fix these problems?
-
A3: You can try to follow the installation guide, use a code generator, update your drivers, check your interface firmware, use a different USB port, etc.
-
Q4: Where can I get support for diagbox 7.02?
-
A4: You can get support for diagbox 7.02 from online forums, blogs, videos, etc.
-
Q5: Is diagbox 7.02 legal?
-
A5: Diagbox 7.02 is a free tool that is not authorized by PSA Group. You use it at your own risk and responsibility.
-
- 0a6ba089eb
-
-
\ No newline at end of file
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Gta 4 Episodes From Liberty City [VERIFIED] Crack Razor 1911 Download Sims 3.md b/spaces/raedeXanto/academic-chatgpt-beta/Gta 4 Episodes From Liberty City [VERIFIED] Crack Razor 1911 Download Sims 3.md
deleted file mode 100644
index be770a089d70e59adb76e29aa8388023b36ec012..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Gta 4 Episodes From Liberty City [VERIFIED] Crack Razor 1911 Download Sims 3.md
+++ /dev/null
@@ -1,22 +0,0 @@
-
-```
-
How to Play GTA 4 Episodes From Liberty City Without a Disc Using Razor 1911 Crack
-
GTA 4 Episodes From Liberty City is a standalone expansion pack for the popular Grand Theft Auto IV game. It includes two new episodes: The Lost and Damned and The Ballad of Gay Tony. However, to play these episodes, you need to have the original GTA 4 disc inserted in your PC or console.
-
Gta 4 Episodes From Liberty City Crack Razor 1911 Download Sims 3
If you don't have the disc or you want to play without it, you can use a crack by Razor 1911, a famous group of hackers and modders. This crack will allow you to run the game without the disc and without the need for Rockstar Games Social Club or Windows Live. It also works with patch 1.0.2.0.
-
In this article, we will show you how to download and install the crack by Razor 1911 for GTA 4 Episodes From Liberty City.
After downloading the crack, you need to extract the files to your game directory. The game directory is usually located at C:\Program Files\Rockstar Games\Grand Theft Auto IV - Episodes From Liberty City. You can use any file extractor program such as WinRAR or 7-Zip to do this.
-
When extracting the files, you will be asked to overwrite the existing files. Click yes to confirm the replacement. Before doing this, you may want to make a backup of the original files in case something goes wrong.
-
Step 3: Launch the game using LaunchEFLC.exe
-
Now you are ready to play GTA 4 Episodes From Liberty City without a disc. To launch the game, you need to use LaunchEFLC.exe instead of EFLC.exe. You can create a shortcut of LaunchEFLC.exe on your desktop or start menu for easy access.
-
When you launch the game, you will see a message from Razor 1911 saying "Enjoy another fine release". You can press any key to skip this message and proceed to the game menu. From there, you can choose which episode you want to play: The Lost and Damned or The Ballad of Gay Tony.
-
Conclusion
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Using this crack by Razor 1911, you can play GTA 4 Episodes From Liberty City without a disc and without any online requirements. This way, you can enjoy the game without any hassle or interruption. However, please note that this crack may not work with other versions or patches of the game, and it may cause some glitches or errors. Use it at your own risk and discretion.
-
If you also want to play The Sims 3 without a disc, you can download another crack by Razor 1911 from this link: https://archive.org/details/gta4-Razor1911. This crack works with all expansions and updates of The Sims 3.
-``` 81aa517590
-
-
\ No newline at end of file
diff --git a/spaces/raul-padua/Barbie-RAQA-Application-Chainlit-Demo/README.md b/spaces/raul-padua/Barbie-RAQA-Application-Chainlit-Demo/README.md
deleted file mode 100644
index dbd474146f98c753158a8e68433957cd4c49c410..0000000000000000000000000000000000000000
--- a/spaces/raul-padua/Barbie-RAQA-Application-Chainlit-Demo/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Barbie RAQA Application Chainlit Demo
-emoji: 🔥
-colorFrom: red
-colorTo: red
-sdk: docker
-pinned: false
-license: apache-2.0
-duplicated_from: ai-maker-space/Barbie-RAQA-Application-Chainlit-Demo
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/rayan-saleh/whisper2notion/docs/options.md b/spaces/rayan-saleh/whisper2notion/docs/options.md
deleted file mode 100644
index 6979fca4d9d4c98a626a2953c2573ff23898a37e..0000000000000000000000000000000000000000
--- a/spaces/rayan-saleh/whisper2notion/docs/options.md
+++ /dev/null
@@ -1,134 +0,0 @@
-# Standard Options
-To transcribe or translate an audio file, you can either copy an URL from a website (all [websites](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)
-supported by YT-DLP will work, including YouTube). Otherwise, upload an audio file (choose "All Files (*.*)"
-in the file selector to select any file type, including video files) or use the microphone.
-
-For longer audio files (>10 minutes), it is recommended that you select Silero VAD (Voice Activity Detector) in the VAD option, especially if you are using the `large-v1` model. Note that `large-v2` is a lot more forgiving, but you may still want to use a VAD with a slightly higher "VAD - Max Merge Size (s)" (60 seconds or more).
-
-## Model
-Select the model that Whisper will use to transcribe the audio:
-
-| Size | Parameters | English-only model | Multilingual model | Required VRAM | Relative speed |
-|-----------|------------|--------------------|--------------------|---------------|----------------|
-| tiny | 39 M | tiny.en | tiny | ~1 GB | ~32x |
-| base | 74 M | base.en | base | ~1 GB | ~16x |
-| small | 244 M | small.en | small | ~2 GB | ~6x |
-| medium | 769 M | medium.en | medium | ~5 GB | ~2x |
-| large | 1550 M | N/A | large | ~10 GB | 1x |
-| large-v2 | 1550 M | N/A | large | ~10 GB | 1x |
-
-## Language
-
-Select the language, or leave it empty for Whisper to automatically detect it.
-
-Note that if the selected language and the language in the audio differs, Whisper may start to translate the audio to the selected
-language. For instance, if the audio is in English but you select Japaneese, the model may translate the audio to Japanese.
-
-## Inputs
-The options "URL (YouTube, etc.)", "Upload Files" or "Micriphone Input" allows you to send an audio input to the model.
-
-### Multiple Files
-Note that the UI will only process either the given URL or the upload files (including microphone) - not both.
-
-But you can upload multiple files either through the "Upload files" option, or as a playlist on YouTube. Each audio file will then be processed in turn, and the resulting SRT/VTT/Transcript will be made available in the "Download" section. When more than one file is processed, the UI will also generate a "All_Output" zip file containing all the text output files.
-
-## Task
-Select the task - either "transcribe" to transcribe the audio to text, or "translate" to translate it to English.
-
-## Vad
-Using a VAD will improve the timing accuracy of each transcribed line, as well as prevent Whisper getting into an infinite
-loop detecting the same sentence over and over again. The downside is that this may be at a cost to text accuracy, especially
-with regards to unique words or names that appear in the audio. You can compensate for this by increasing the prompt window.
-
-Note that English is very well handled by Whisper, and it's less susceptible to issues surrounding bad timings and infinite loops.
-So you may only need to use a VAD for other languages, such as Japanese, or when the audio is very long.
-
-* none
- * Run whisper on the entire audio input
-* silero-vad
- * Use Silero VAD to detect sections that contain speech, and run Whisper on independently on each section. Whisper is also run
- on the gaps between each speech section, by either expanding the section up to the max merge size, or running Whisper independently
- on the non-speech section.
-* silero-vad-expand-into-gaps
- * Use Silero VAD to detect sections that contain speech, and run Whisper on independently on each section. Each spech section will be expanded
- such that they cover any adjacent non-speech sections. For instance, if an audio file of one minute contains the speech sections
- 00:00 - 00:10 (A) and 00:30 - 00:40 (B), the first section (A) will be expanded to 00:00 - 00:30, and (B) will be expanded to 00:30 - 00:60.
-* silero-vad-skip-gaps
- * As above, but sections that doesn't contain speech according to Silero will be skipped. This will be slightly faster, but
- may cause dialogue to be skipped.
-* periodic-vad
- * Create sections of speech every 'VAD - Max Merge Size' seconds. This is very fast and simple, but will potentially break
- a sentence or word in two.
-
-## VAD - Merge Window
-If set, any adjacent speech sections that are at most this number of seconds apart will be automatically merged.
-
-## VAD - Max Merge Size (s)
-Disables merging of adjacent speech sections if they are this number of seconds long.
-
-## VAD - Padding (s)
-The number of seconds (floating point) to add to the beginning and end of each speech section. Setting this to a number
-larger than zero ensures that Whisper is more likely to correctly transcribe a sentence in the beginning of
-a speech section. However, this also increases the probability of Whisper assigning the wrong timestamp
-to each transcribed line. The default value is 1 second.
-
-## VAD - Prompt Window (s)
-The text of a detected line will be included as a prompt to the next speech section, if the speech section starts at most this
-number of seconds after the line has finished. For instance, if a line ends at 10:00, and the next speech section starts at
-10:04, the line's text will be included if the prompt window is 4 seconds or more (10:04 - 10:00 = 4 seconds).
-
-Note that detected lines in gaps between speech sections will not be included in the prompt
-(if silero-vad or silero-vad-expand-into-gaps) is used.
-
-# Command Line Options
-
-Both `app.py` and `cli.py` also accept command line options, such as the ability to enable parallel execution on multiple
-CPU/GPU cores, the default model name/VAD and so on. Consult the README in the root folder for more information.
-
-# Additional Options
-
-In addition to the above, there's also a "Full" options interface that allows you to set all the options available in the Whisper
-model. The options are as follows:
-
-## Initial Prompt
-Optional text to provide as a prompt for the first 30 seconds window. Whisper will attempt to use this as a starting point for the transcription, but you can
-also get creative and specify a style or format for the output of the transcription.
-
-For instance, if you use the prompt "hello how is it going always use lowercase no punctuation goodbye one two three start stop i you me they", Whisper will
-be biased to output lower capital letters and no punctuation, and may also be biased to output the words in the prompt more often.
-
-## Temperature
-The temperature to use when sampling. Default is 0 (zero). A higher temperature will result in more random output, while a lower temperature will be more deterministic.
-
-## Best Of - Non-zero temperature
-The number of candidates to sample from when sampling with non-zero temperature. Default is 5.
-
-## Beam Size - Zero temperature
-The number of beams to use in beam search when sampling with zero temperature. Default is 5.
-
-## Patience - Zero temperature
-The patience value to use in beam search when sampling with zero temperature. As in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search.
-
-## Length Penalty - Any temperature
-The token length penalty coefficient (alpha) to use when sampling with any temperature. As in https://arxiv.org/abs/1609.08144, uses simple length normalization by default.
-
-## Suppress Tokens - Comma-separated list of token IDs
-A comma-separated list of token IDs to suppress during sampling. The default value of "-1" will suppress most special characters except common punctuations.
-
-## Condition on previous text
-If True, provide the previous output of the model as a prompt for the next window. Disabling this may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop.
-
-## FP16
-Whether to perform inference in fp16. True by default.
-
-## Temperature increment on fallback
-The temperature to increase when falling back when the decoding fails to meet either of the thresholds below. Default is 0.2.
-
-## Compression ratio threshold
-If the gzip compression ratio is higher than this value, treat the decoding as failed. Default is 2.4.
-
-## Logprob threshold
-If the average log probability is lower than this value, treat the decoding as failed. Default is -1.0.
-
-## No speech threshold
-If the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence. Default is 0.6.
diff --git a/spaces/rewoo/ReWOO-Demo/alpaca/utils/callbacks.py b/spaces/rewoo/ReWOO-Demo/alpaca/utils/callbacks.py
deleted file mode 100644
index 7dedcade55aabb3c01bda254eb9a64615e4fa800..0000000000000000000000000000000000000000
--- a/spaces/rewoo/ReWOO-Demo/alpaca/utils/callbacks.py
+++ /dev/null
@@ -1,75 +0,0 @@
-"""
-Helpers to support streaming generate output.
-Borrowed from https://github.com/oobabooga/text-generation-webui/blob/ad37f396fc8bcbab90e11ecf17c56c97bfbd4a9c/modules/callbacks.py
-"""
-
-import gc
-import traceback
-from queue import Queue
-from threading import Thread
-
-import torch
-import transformers
-
-
-class Stream(transformers.StoppingCriteria):
- def __init__(self, callback_func=None):
- self.callback_func = callback_func
-
- def __call__(self, input_ids, scores) -> bool:
- if self.callback_func is not None:
- self.callback_func(input_ids[0])
- return False
-
-
-class Iteratorize:
-
- """
- Transforms a function that takes a callback
- into a lazy iterator (generator).
- """
-
- def __init__(self, func, kwargs={}, callback=None):
- self.mfunc = func
- self.c_callback = callback
- self.q = Queue()
- self.sentinel = object()
- self.kwargs = kwargs
- self.stop_now = False
-
- def _callback(val):
- if self.stop_now:
- raise ValueError
- self.q.put(val)
-
- def gentask():
- try:
- ret = self.mfunc(callback=_callback, **self.kwargs)
- except ValueError:
- pass
- except:
- traceback.print_exc()
- pass
-
- self.q.put(self.sentinel)
- if self.c_callback:
- self.c_callback(ret)
-
- self.thread = Thread(target=gentask)
- self.thread.start()
-
- def __iter__(self):
- return self
-
- def __next__(self):
- obj = self.q.get(True, None)
- if obj is self.sentinel:
- raise StopIteration
- else:
- return obj
-
- def __enter__(self):
- return self
-
- def __exit__(self, exc_type, exc_val, exc_tb):
- self.stop_now = True
diff --git a/spaces/rorallitri/biomedical-language-models/logs/365 Essential Business Grammar Builder Pdf Learn the Rules and Practice the Exercises.md b/spaces/rorallitri/biomedical-language-models/logs/365 Essential Business Grammar Builder Pdf Learn the Rules and Practice the Exercises.md
deleted file mode 100644
index 317869e27458b39a98f15598e51c9992f8252f1c..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/365 Essential Business Grammar Builder Pdf Learn the Rules and Practice the Exercises.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
Qorus is a business document builder. It works seamlessly across Outlook, Word, and PowerPoint to create personalized business documents like requests for proposals, pitches, and NDAs. Qorus includes tools that can quickly create fresh documents from templates, answer queries with a bank of reusable content, and even collaborate on documents with a team.
Advance your career with GoSkills! We help you learn essential business skills to reach your full potential. Learn effectively via bite-sized video tutorials taught by award-winning instructors. Thank you for choosing to learn with us.
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/Chaar Sahibzaade Movie Download Utorrent Kickass H Fitness Circus Modul Reviews and Testimonials.md b/spaces/rorallitri/biomedical-language-models/logs/Chaar Sahibzaade Movie Download Utorrent Kickass H Fitness Circus Modul Reviews and Testimonials.md
deleted file mode 100644
index 2aa0905511b2688ce0afa40463f6164c63c5246a..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Chaar Sahibzaade Movie Download Utorrent Kickass H Fitness Circus Modul Reviews and Testimonials.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
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-
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-
-
-
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Download Film Cowok Komersil Bertrand Antolin A Fun and Entertaining Indonesian Film.md b/spaces/rorallitri/biomedical-language-models/logs/Download Film Cowok Komersil Bertrand Antolin A Fun and Entertaining Indonesian Film.md
deleted file mode 100644
index 71471d57a3f8c3f4ec8cbce610de9defcbf36571..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Download Film Cowok Komersil Bertrand Antolin A Fun and Entertaining Indonesian Film.md
+++ /dev/null
@@ -1,5 +0,0 @@
-
-
Kalau mau pesan film 1. Satu mawar 3 duri 2.beningnya hati seorang gadis 3.gadis kampus 4. Mencari cinta 5.dalam lingkaran cinta 6.perempuan dalam pasungan 7.Ramadan ramona Kalau mau download kayak mana, atau ada jual cdnya gak .makasih
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diff --git a/spaces/runa91/bite_gradio/src/stacked_hourglass/datasets/anipose.py b/spaces/runa91/bite_gradio/src/stacked_hourglass/datasets/anipose.py
deleted file mode 100644
index ee687898763f278c46ca876b94199b6a0ae24ecf..0000000000000000000000000000000000000000
--- a/spaces/runa91/bite_gradio/src/stacked_hourglass/datasets/anipose.py
+++ /dev/null
@@ -1,421 +0,0 @@
-import gzip
-import json
-import os
-import glob
-import random
-import math
-import numpy as np
-import torch
-import torch.utils.data as data
-from importlib_resources import open_binary
-from scipy.io import loadmat
-from tabulate import tabulate
-import itertools
-import json
-from scipy import ndimage
-import xml.etree.ElementTree as ET
-
-from csv import DictReader
-from pycocotools.mask import decode as decode_RLE
-
-import os
-import sys
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../../../'))
-# import stacked_hourglass.res
-# from stacked_hourglass.datasets.common import DataInfo
-from src.configs.anipose_data_info import COMPLETE_DATA_INFO
-from src.stacked_hourglass.utils.imutils import load_image, draw_labelmap, draw_multiple_labelmaps
-from src.stacked_hourglass.utils.misc import to_torch
-from src.stacked_hourglass.utils.transforms import shufflelr, crop, color_normalize, fliplr, transform
-import src.stacked_hourglass.datasets.utils_stanext as utils_stanext
-from src.stacked_hourglass.utils.visualization import save_input_image_with_keypoints
-# from configs.dog_breeds.dog_breed_class import COMPLETE_ABBREV_DICT, COMPLETE_SUMMARY_BREEDS, SIM_MATRIX_RAW, SIM_ABBREV_INDICES
-
-
-
-class AniPose(data.Dataset):
- DATA_INFO = COMPLETE_DATA_INFO
-
- # Suggested joints to use for average PCK calculations.
- ACC_JOINTS = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19] # don't know ...
-
- def __init__(self, image_path=None, is_train=True, inp_res=256, out_res=64, sigma=1,
- scale_factor=0.25, rot_factor=30, label_type='Gaussian',
- do_augment='default', shorten_dataset_to=None, dataset_mode='keyp_only'):
- # self.img_folder_mpii = image_path # root image folders
- self.is_train = is_train # training set or test set
- if do_augment == 'yes':
- self.do_augment = True
- elif do_augment == 'no':
- self.do_augment = False
- elif do_augment=='default':
- if self.is_train:
- self.do_augment = True
- else:
- self.do_augment = False
- else:
- raise ValueError
- self.inp_res = inp_res
- self.out_res = out_res
- self.sigma = sigma
- self.scale_factor = scale_factor
- self.rot_factor = rot_factor
- self.label_type = label_type
- self.dataset_mode = dataset_mode
- if self.dataset_mode=='complete' or self.dataset_mode=='keyp_and_seg':
- self.calc_seg = True
- else:
- self.calc_seg = False
-
- self.kp_dict = self.keyp_name_to_ind()
-
- # import pdb; pdb.set_trace()
-
- self.top_folder = '/ps/scratch/nrueegg/new_projects/Animals/data/animal_pose_dataset/'
- self.folder_imgs_0 = '/ps/project/datasets/VOCdevkit/VOC2012/JPEGImages/'
- self.folder_imgs_1 = os.path.join(self.top_folder, 'animalpose_image_part2', 'dog')
- self.folder_annot_0 = os.path.join(self.top_folder, 'PASCAL2011_animal_annotation', 'dog')
- self.folder_annot_1 = os.path.join(self.top_folder, 'animalpose_anno2', 'dog')
- all_annot_files_0 = glob.glob(self.folder_annot_0 + '/*.xml') # 1571
- '''all_annot_files_0_raw.sort()
- all_annot_files_0 = [] # 1331
- for ind_f, f in enumerate(all_annot_files_0_raw):
- name = (f.split('/')[-1]).split('.xml')[0]
- name_main = name[:-2]
- if ind_f > 0:
- if (not name_main == name_main_last) or (ind_f == len(all_annot_files_0_raw)-1):
- all_annot_files_0.append(f_last)
- f_last = f
- name_main_last = name_main'''
- all_annot_files_1 = glob.glob(self.folder_annot_1 + '/*.xml') # 200
- all_annot_files = all_annot_files_0 + all_annot_files_1
-
-
- # old for hg_anipose_v0
- # self.train_name_list = all_annot_files
- # self.test_name_list = all_annot_files[0:50] + all_annot_files[200:250]
- # new for hg_anipose_v1
- self.train_name_list = all_annot_files[:-50]
- self.test_name_list = all_annot_files[-50:]
-
- '''all_annot_files.sort()
-
- self.train_name_list = all_annot_files[:24]
- self.test_name_list = all_annot_files[24:36]'''
-
- print('anipose dataset size: ')
- print(len(self.train_name_list))
- print(len(self.test_name_list))
-
-
- # -----------------------------------------
- def read_content(sewlf, xml_file, annot_type='animal_pose'):
- # annot_type is either 'animal_pose' or 'animal_pose_voc' or 'voc'
- # examples:
- # animal_pose: '/ps/scratch/nrueegg/new_projects/Animals/data/animal_pose_dataset/animalpose_anno2/cat/ca137.xml'
- # animal_pose_voc: '/ps/scratch/nrueegg/new_projects/Animals/data/animal_pose_dataset/PASCAL2011_animal_annotation/cat/2008_005380_1.xml'
- # voc: '/ps/project/datasets/VOCdevkit/VOC2012/Annotations/2011_000192.xml'
- if annot_type == 'animal_pose' or annot_type == 'animal_pose_voc':
- my_dict = {}
- tree = ET.parse(xml_file)
- root = tree.getroot()
- for child in root: # list
- if child.tag == 'image':
- my_dict['image'] = child.text
- elif child.tag == 'category':
- my_dict['category'] = child.text
- elif child.tag == 'visible_bounds':
- my_dict['visible_bounds'] = child.attrib
- elif child.tag == 'keypoints':
- n_kp = len(child)
- xyzvis = np.zeros((n_kp, 4))
- kp_names = []
- for ind_kp, kp in enumerate(child): # list
- xyzvis[ind_kp, 0] = kp.attrib['x']
- xyzvis[ind_kp, 1] = kp.attrib['y']
- xyzvis[ind_kp, 2] = kp.attrib['z']
- xyzvis[ind_kp, 3] = kp.attrib['visible']
- kp_names.append(kp.attrib['name'])
- my_dict['keypoints_xyzvis'] = xyzvis
- my_dict['keypoints_names'] = kp_names
- elif child.tag == 'voc_id': # animal_pose_voc only
- my_dict['voc_id'] = child.text
- elif child.tag == 'polylinesegments': # animal_pose_voc only
- my_dict['polylinesegments'] = child[0].attrib
- else:
- print('tag does not exist: ' + child.tag)
- # print(my_dict)
- elif annot_type == 'voc':
- my_dict = {}
- print('not yet read')
- else:
- print('this annot_type does not exist')
- import pdb; pdb.set_trace()
- return my_dict
-
-
- def keyp_name_to_ind(self):
- '''AniPose_JOINT_NAMES = [
- 'L_Eye', 'R_Eye', 'Nose', 'L_EarBase', 'Throat', 'R_F_Elbow', 'R_F_Paw',
- 'R_B_Paw', 'R_EarBase', 'L_F_Elbow', 'L_F_Paw', 'Withers', 'TailBase',
- 'L_B_Paw', 'L_B_Elbow', 'R_B_Elbow', 'L_F_Knee', 'R_F_Knee', 'L_B_Knee',
- 'R_B_Knee']'''
- kps = self.DATA_INFO.joint_names
- kps_dict = {}
- for ind_kp, kp in enumerate(kps):
- kps_dict[kp] = ind_kp
- kps_dict[kp.lower()] = ind_kp
- if kp.lower() == 'l_earbase':
- kps_dict['l_ear'] = ind_kp
- if kp.lower() == 'r_earbase':
- kps_dict['r_ear'] = ind_kp
- if kp.lower() == 'tailbase':
- kps_dict['tail'] = ind_kp
- return kps_dict
-
-
-
- def __getitem__(self, index):
-
- # import pdb; pdb.set_trace()
-
- if self.is_train:
- xml_path = self.train_name_list[index]
- else:
- xml_path = self.test_name_list[index]
-
- name = (xml_path.split('/')[-1]).split('.xml')[0]
- annot_dict = self.read_content(xml_path, annot_type='animal_pose_voc')
-
- if xml_path.split('/')[-3] == 'PASCAL2011_animal_annotation':
- img_path = os.path.join(self.folder_imgs_0, annot_dict['image'] + '.jpg')
- keyword_ymin = 'ymin'
- else:
- # import pdb; pdb.set_trace()
- img_path = os.path.join(self.folder_imgs_1, annot_dict['image'])
- keyword_ymin = 'xmax'
-
- '''print(img_path)
- print(annot_dict['keypoints_xyzvis'].shape)
- print(annot_dict['keypoints_names'])'''
-
-
-
- sf = self.scale_factor
- rf = self.rot_factor
-
-
-
- vis_np = np.zeros((self.DATA_INFO.n_keyp))
- pts_np = np.ones((self.DATA_INFO.n_keyp, 2)) * (-1000)
- for ind_key, key in enumerate(annot_dict['keypoints_names']):
- key_lower = key.lower()
- ind_new = self.kp_dict[key_lower]
- vis_np[ind_new] = annot_dict['keypoints_xyzvis'][ind_key, 3]
- # remark: the first training run (animalpose_hg8_v0) was without subtracting 1 which would be important!
- # pts_np[ind_new] = annot_dict['keypoints_xyzvis'][ind_key, 0:2]
-
- # what we were doing until 08.09.2022:
- pts_np[ind_new] = annot_dict['keypoints_xyzvis'][ind_key, 0:2] - 1
-
- # new 08.09.2022
- # pts_np[ind_new] = annot_dict['keypoints_xyzvis'][ind_key, 0:2]
-
- # pts_np[ind_new] = annot_dict['keypoints_xyzvis'][ind_key, 0:2] # - 1
-
-
-
- '''vis_np = annot_dict['keypoints_xyzvis'][:20, 3]
- pts_np = annot_dict['keypoints_xyzvis'][:20, :2]
- pts_np[vis_np==0] = -1000'''
-
- pts_np = np.concatenate((pts_np, vis_np[:, None]), axis=1)
- pts = torch.Tensor(pts_np)
-
- # what we were doing until 08.09.2022:
- # bbox_xywh = [float(annot_dict['visible_bounds']['xmin']), float(annot_dict['visible_bounds'][keyword_ymin]), \
- # float(annot_dict['visible_bounds']['width']), float(annot_dict['visible_bounds']['height'])]
- bbox_xywh = [float(annot_dict['visible_bounds']['xmin'])-1, float(annot_dict['visible_bounds'][keyword_ymin])-1, \
- float(annot_dict['visible_bounds']['width']), float(annot_dict['visible_bounds']['height'])]
-
-
-
- '''pts = torch.Tensor(np.asarray(data['joints'])[:20, :])
- # pts[:, 0:2] -= 1 # Convert pts to zero based
-
- # inp = crop(img, c, s, [self.inp_res, self.inp_res], rot=r)
- # sf = scale * 200.0 / res[0] # res[0]=256
- # center = center * 1.0 / sf
- # scale = scale / sf = 256 / 200
- # h = 200 * scale
- bbox_xywh = data['img_bbox']'''
-
- bbox_c = [bbox_xywh[0]+0.5*bbox_xywh[2], bbox_xywh[1]+0.5*bbox_xywh[3]]
- bbox_max = max(bbox_xywh[2], bbox_xywh[3])
- bbox_diag = math.sqrt(bbox_xywh[2]**2 + bbox_xywh[3]**2)
- # bbox_s = bbox_max / 200. # the dog will fill the image -> bbox_max = 256
- # bbox_s = bbox_diag / 200. # diagonal of the boundingbox will be 200
- bbox_s = bbox_max / 200. * 256. / 200. # maximum side of the bbox will be 200
- c = torch.Tensor(bbox_c)
- s = bbox_s
-
-
-
-
-
-
-
-
-
- # For single-person pose estimation with a centered/scaled figure
- nparts = pts.size(0)
- img = load_image(img_path) # CxHxW
-
- # segmentation map (we reshape it to 3xHxW, such that we can do the
- # same transformations as with the image)
- if self.calc_seg:
- raise NotImplementedError
- seg = torch.Tensor(utils_stanext.get_seg_from_entry(data)[None, :, :])
- seg = torch.cat(3*[seg])
-
- r = 0
- # self.is_train = False
- do_flip = False
- if self.do_augment:
- s = s*torch.randn(1).mul_(sf).add_(1).clamp(1-sf, 1+sf)[0]
- r = torch.randn(1).mul_(rf).clamp(-2*rf, 2*rf)[0] if random.random() <= 0.6 else 0
- # Flip
- if random.random() <= 0.5:
- do_flip = True
- img = fliplr(img)
- if self.calc_seg:
- seg = fliplr(seg)
- # pts = shufflelr(pts, img.size(2), self.DATA_INFO.hflip_indices)
- # remark: for BITE we figure out that a -1 was missing in the point mirroring term
- # idea:
- # image coordinates are 0, 1, 2, 3
- # image size is 4
- # the new point location for former 0 should be 3 and not 4!
- pts = shufflelr(pts, img.size(2)-1, self.DATA_INFO.hflip_indices)
- c[0] = img.size(2) - c[0] - 1
- # Color
- img[0, :, :].mul_(random.uniform(0.8, 1.2)).clamp_(0, 1)
- img[1, :, :].mul_(random.uniform(0.8, 1.2)).clamp_(0, 1)
- img[2, :, :].mul_(random.uniform(0.8, 1.2)).clamp_(0, 1)
-
- # Prepare image and groundtruth map
- inp = crop(img, c, s, [self.inp_res, self.inp_res], rot=r)
- inp = color_normalize(inp, self.DATA_INFO.rgb_mean, self.DATA_INFO.rgb_stddev)
- if self.calc_seg:
- seg = crop(seg, c, s, [self.inp_res, self.inp_res], rot=r)
-
- # Generate ground truth
- tpts = pts.clone()
- target_weight = tpts[:, 2].clone().view(nparts, 1)
-
-
- # cvpr version:
- '''
- target = torch.zeros(nparts, self.out_res, self.out_res)
- for i in range(nparts):
- # if tpts[i, 2] > 0: # This is evil!!
- if tpts[i, 1] > 0:
- tpts[i, 0:2] = to_torch(transform(tpts[i, 0:2]+1, c, s, [self.out_res, self.out_res], rot=r, as_int=False))
- target[i], vis = draw_labelmap(target[i], tpts[i]-1, self.sigma, type=self.label_type)
- target_weight[i, 0] *= vis
- # NEW:
- target_new, vis_new = draw_multiple_labelmaps((self.out_res, self.out_res), tpts[:, :2]-1, self.sigma, type=self.label_type)
- target_weight_new = tpts[:, 2].clone().view(nparts, 1) * vis_new
- target_new[(target_weight_new==0).reshape((-1)), :, :] = 0
- '''
-
- target = torch.zeros(nparts, self.out_res, self.out_res)
- for i in range(nparts):
- # if tpts[i, 2] > 0: # This is evil!!
- '''if tpts[i, 1] > 0:
- tpts[i, 0:2] = to_torch(transform(tpts[i, 0:2], c, s, [self.out_res, self.out_res], rot=r, as_int=False))
- target[i], vis = draw_labelmap(target[i], tpts[i], self.sigma, type=self.label_type)
- target_weight[i, 0] *= vis'''
- if tpts[i, 1] > 0:
- # this pytorch function (transforms) assumes that coordinates which start at 1 instead of 0!
- tpts[i, 0:2] = to_torch(transform(tpts[i, 0:2]+1, c, s, [self.out_res, self.out_res], rot=r, as_int=False)) - 1
- target[i], vis = draw_labelmap(target[i], tpts[i], self.sigma, type=self.label_type)
- target_weight[i, 0] *= vis
-
-
-
-
-
-
-
-
-
-
- # Meta info
- '''this_breed = self.breed_dict[name.split('/')[0]]'''
-
- # add information about location within breed similarity matrix
- '''folder_name = name.split('/')[0]
- breed_name = folder_name.split(folder_name.split('-')[0] + '-')[1]
- abbrev = COMPLETE_ABBREV_DICT[breed_name]
- try:
- sim_breed_index = COMPLETE_SUMMARY_BREEDS[abbrev]._ind_in_xlsx_matrix
- except: # some breeds are not in the xlsx file
- sim_breed_index = -1'''
-
- # meta = {'index' : index, 'center' : c, 'scale' : s, 'do_flip' : do_flip, 'rot' : r, 'resolution' : [self.out_res, self.out_res], 'name' : name,
- # 'pts' : pts, 'tpts' : tpts, 'target_weight': target_weight, 'breed_index': this_breed['index']}
- # meta = {'index' : index, 'center' : c, 'scale' : s, 'do_flip' : do_flip, 'rot' : r, 'resolution' : self.out_res,
- # 'pts' : pts, 'tpts' : tpts, 'target_weight': target_weight, 'breed_index': this_breed['index']}
- # meta = {'index' : index, 'center' : c, 'scale' : s,
- # 'pts' : pts, 'tpts' : tpts, 'target_weight': target_weight,
- # 'breed_index': this_breed['index'], 'sim_breed_index': sim_breed_index}
- meta = {'index' : index, 'center' : c, 'scale' : s,
- 'pts' : pts, 'tpts' : tpts, 'target_weight': target_weight}
-
- # import pdb; pdb.set_trace()
-
-
-
-
-
-
-
-
- if self.dataset_mode=='keyp_only':
- '''
- debugging_path = '/is/cluster/work/nrueegg/icon_pifu_related/barc_for_bite/debugging/anipose/'
- if self.is_train:
- prefix = 'anipose_train_'
- else:
- prefix = 'anipose_test_'
- save_input_image_with_keypoints(inp, meta['tpts'], out_path=debugging_path + prefix + str(index) + '.png', ratio_in_out=self.inp_res/self.out_res)
- '''
- return inp, target, meta
- elif self.dataset_mode=='keyp_and_seg':
- raise NotImplementedError
- meta['silh'] = seg[0, :, :]
- meta['name'] = name
- return inp, target, meta
- elif self.dataset_mode=='complete':
- raise NotImplementedError
- target_dict = meta
- target_dict['silh'] = seg[0, :, :]
- # NEW for silhouette loss
- distmat_tofg = ndimage.distance_transform_edt(1-target_dict['silh']) # values between 0 and up to 100 or more
- target_dict['silh_distmat_tofg'] = distmat_tofg
- distmat_tobg = ndimage.distance_transform_edt(target_dict['silh'])
- target_dict['silh_distmat_tobg'] = distmat_tobg
- return inp, target_dict
- else:
- raise ValueError
-
-
-
- def __len__(self):
- if self.is_train:
- return len(self.train_name_list) # len(self.train_list)
- else:
- return len(self.test_name_list) # len(self.valid_list)
-
-
diff --git a/spaces/rustformers/mpt-7b-instruct/app.py b/spaces/rustformers/mpt-7b-instruct/app.py
deleted file mode 100644
index 573ca205eece7b16ecfe0de1cab5a02cb3a378fa..0000000000000000000000000000000000000000
--- a/spaces/rustformers/mpt-7b-instruct/app.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import gradio as gr
-from llm_rs import AutoModel,SessionConfig,GenerationConfig,Precision
-
-repo_name = "rustformers/mpt-7b-ggml"
-file_name = "mpt-7b-instruct-q5_1-ggjt.bin"
-
-examples = [
- "Write a travel blog about a 3-day trip to Thailand.",
- "Tell me a short story about a robot that has a nice day.",
- "Compose a tweet to congratulate rustformers on the launch of their HuggingFace Space.",
- "Explain how a candle works to a 6-year-old in a few sentences.",
- "What are some of the most common misconceptions about birds?",
- "Explain why the Rust programming language is so popular.",
-]
-
-session_config = SessionConfig(threads=2,batch_size=2)
-model = AutoModel.from_pretrained(repo_name, model_file=file_name, session_config=session_config,verbose=True)
-
-def process_stream(instruction, temperature, top_p, top_k, max_new_tokens, seed):
-
- prompt=f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
-### Instruction:
-{instruction}
-### Response:
-Answer:"""
- generation_config = GenerationConfig(seed=seed,temperature=temperature,top_p=top_p,top_k=top_k,max_new_tokens=max_new_tokens)
- response = ""
- streamer = model.stream(prompt=prompt,generation_config=generation_config)
- for new_text in streamer:
- response += new_text
- yield response
-
-
-with gr.Blocks(
- theme=gr.themes.Soft(),
- css=".disclaimer {font-variant-caps: all-small-caps;}",
-) as demo:
- gr.Markdown(
- """
MPT-7B-Instruct on CPU in Rust 🦀
-
- This demo uses the [rustformers/llm](https://github.com/rustformers/llm) library via [llm-rs](https://github.com/LLukas22/llm-rs-python) to execute [MPT-7B-Instruct](https://huggingface.co/mosaicml/mpt-7b-instruct) on 2 CPU cores.
- """
- )
- with gr.Row():
- with gr.Column():
- with gr.Row():
- instruction = gr.Textbox(
- placeholder="Enter your question or instruction here",
- label="Question/Instruction",
- elem_id="q-input",
- )
- with gr.Accordion("Advanced Options:", open=False):
- with gr.Row():
- with gr.Column():
- with gr.Row():
- temperature = gr.Slider(
- label="Temperature",
- value=0.8,
- minimum=0.1,
- maximum=1.0,
- step=0.1,
- interactive=True,
- info="Higher values produce more diverse outputs",
- )
- with gr.Column():
- with gr.Row():
- top_p = gr.Slider(
- label="Top-p (nucleus sampling)",
- value=0.95,
- minimum=0.0,
- maximum=1.0,
- step=0.01,
- interactive=True,
- info=(
- "Sample from the smallest possible set of tokens whose cumulative probability "
- "exceeds top_p. Set to 1 to disable and sample from all tokens."
- ),
- )
- with gr.Column():
- with gr.Row():
- top_k = gr.Slider(
- label="Top-k",
- value=40,
- minimum=5,
- maximum=80,
- step=1,
- interactive=True,
- info="Sample from a shortlist of top-k tokens — 0 to disable and sample from all tokens.",
- )
- with gr.Column():
- with gr.Row():
- max_new_tokens = gr.Slider(
- label="Maximum new tokens",
- value=256,
- minimum=0,
- maximum=1024,
- step=5,
- interactive=True,
- info="The maximum number of new tokens to generate",
- )
-
- with gr.Column():
- with gr.Row():
- seed = gr.Number(
- label="Seed",
- value=42,
- interactive=True,
- info="The seed to use for the generation",
- precision=0
- )
- with gr.Row():
- submit = gr.Button("Submit")
- with gr.Row():
- with gr.Box():
- gr.Markdown("**MPT-7B-Instruct**")
- output_7b = gr.Markdown()
-
- with gr.Row():
- gr.Examples(
- examples=examples,
- inputs=[instruction],
- cache_examples=False,
- fn=process_stream,
- outputs=output_7b,
- )
- with gr.Row():
- gr.Markdown(
- "Disclaimer: MPT-7B can produce factually incorrect output, and should not be relied on to produce "
- "factually accurate information. MPT-7B was trained on various public datasets; while great efforts "
- "have been taken to clean the pretraining data, it is possible that this model could generate lewd, "
- "biased, or otherwise offensive outputs.",
- elem_classes=["disclaimer"],
- )
- with gr.Row():
- gr.Markdown(
- "[Privacy policy](https://gist.github.com/samhavens/c29c68cdcd420a9aa0202d0839876dac)",
- elem_classes=["disclaimer"],
- )
-
- submit.click(
- process_stream,
- inputs=[instruction, temperature, top_p, top_k, max_new_tokens,seed],
- outputs=output_7b,
- )
- instruction.submit(
- process_stream,
- inputs=[instruction, temperature, top_p, top_k, max_new_tokens,seed],
- outputs=output_7b,
- )
-
-demo.queue(max_size=4, concurrency_count=1).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/safi842/FashionGen/models/stylegan2/__init__.py b/spaces/safi842/FashionGen/models/stylegan2/__init__.py
deleted file mode 100644
index 87739d5c18fe051149018f275983ebf6380c8b54..0000000000000000000000000000000000000000
--- a/spaces/safi842/FashionGen/models/stylegan2/__init__.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import sys
-import os
-import shutil
-import glob
-import platform
-from pathlib import Path
-
-current_path = os.getcwd()
-
-module_path = Path(__file__).parent / 'stylegan2-pytorch'
-sys.path.append(str(module_path.resolve()))
-os.chdir(module_path)
-
-from model import Generator
-
-os.chdir(current_path)
\ No newline at end of file
diff --git a/spaces/safi842/FashionGen/models/stylegan2/stylegan2-pytorch/op/fused_bias_act.cpp b/spaces/safi842/FashionGen/models/stylegan2/stylegan2-pytorch/op/fused_bias_act.cpp
deleted file mode 100644
index 02be898f970bcc8ea297867fcaa4e71b24b3d949..0000000000000000000000000000000000000000
--- a/spaces/safi842/FashionGen/models/stylegan2/stylegan2-pytorch/op/fused_bias_act.cpp
+++ /dev/null
@@ -1,21 +0,0 @@
-#include
-
-
-torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
- int act, int grad, float alpha, float scale);
-
-#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
-
-torch::Tensor fused_bias_act(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer,
- int act, int grad, float alpha, float scale) {
- CHECK_CUDA(input);
- CHECK_CUDA(bias);
-
- return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
- m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)");
-}
\ No newline at end of file
diff --git a/spaces/sanderland/recipe-gen/README.md b/spaces/sanderland/recipe-gen/README.md
deleted file mode 100644
index cf0e4f6800445436e1cbed44e53a68f180a19f87..0000000000000000000000000000000000000000
--- a/spaces/sanderland/recipe-gen/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Recipe Gen
-emoji: 🐠
-colorFrom: red
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sanjayw/GPT4All/README.md b/spaces/sanjayw/GPT4All/README.md
deleted file mode 100644
index 3b8d2d6847a4c96bb15da46beac0e00e8bbfe991..0000000000000000000000000000000000000000
--- a/spaces/sanjayw/GPT4All/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: GPT4All
-emoji: ⚡
-colorFrom: green
-colorTo: red
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: rishiraj/GPT4All
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sayakpaul/evaluate-sd-schedulers/README.md b/spaces/sayakpaul/evaluate-sd-schedulers/README.md
deleted file mode 100644
index 3a2cc846214cc48c3c48ee940a553cc8190d4061..0000000000000000000000000000000000000000
--- a/spaces/sayakpaul/evaluate-sd-schedulers/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Evaluate StableDiffusionPipeline with Different Schedulers
-emoji: ⏰
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/scedlatioru/img-to-music/example/Grimm - Season 4 - 720p WEB-DL - X264 - ShAaNiG BETTER.md b/spaces/scedlatioru/img-to-music/example/Grimm - Season 4 - 720p WEB-DL - X264 - ShAaNiG BETTER.md
deleted file mode 100644
index 2d1f6aef64618b1e8f8fd77ba02dc5864d4c0b0c..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Grimm - Season 4 - 720p WEB-DL - X264 - ShAaNiG BETTER.md
+++ /dev/null
@@ -1,18 +0,0 @@
-
-
Grimm Season 4: A Chaotic and Thrilling Ride
-
Grimm is a fantasy drama series that follows Nick Burkhardt, a homicide detective who discovers he is a Grimm, a descendant of hunters who fight supernatural creatures known as Wesen. In season 4, Nick faces new challenges and dangers as he loses his Grimm abilities, deals with a vengeful ex-girlfriend, and uncovers a conspiracy involving the Wesen Council and a mysterious serial killer.
-
The fourth season of Grimm consists of 22 episodes that aired from October 24, 2014 to May 15, 2015 on NBC. The season received positive reviews from critics and fans, who praised the show's dark tone, complex mythology, and character development. The season also featured guest stars such as Alexis Denisof, Garcelle Beauvais, Louise Lombard, and Jacqueline Toboni.
If you are looking for a high-quality and fast download of Grimm season 4, you can find it on ShAaNiG, a popular torrent site that offers various movies and TV shows in 720p WEB-DL format with x264 encoding. ShAaNiG provides reliable and secure downloads with minimal ads and pop-ups. You can download the entire season or individual episodes with subtitles in different languages.
-
Grimm season 4 is a must-watch for fans of fantasy, horror, and crime genres. It will keep you on the edge of your seat with its twists and turns, action and suspense, humor and romance. Join Nick and his friends as they face their darkest foes and uncover their deepest secrets in this thrilling season of Grimm.
Another aspect that makes Grimm season 4 stand out is the exploration of the Wesen world and its history. The season delves deeper into the origins and motivations of the Wesen Council, a secretive organization that governs the Wesen community and enforces its laws. The season also reveals more about the Royals, a powerful family that has ties to the Grimm lineage and seeks to control the world. The season also introduces new and exotic Wesen species, such as the Gedächtnis Esser, a memory-stealing octopus-like creature, and the Musai, a seductive snake-like creature that can induce madness.
-
Grimm season 4 is not only a captivating and entertaining season, but also a pivotal one that sets up the stage for the final two seasons of the show. The season ends with a shocking cliffhanger that changes everything for Nick and his friends. If you want to find out what happens next, you can download Grimm season 5 on ShAaNiG as well.
-
-
Grimm season 4 also showcases the growth and development of the main characters, as they face new challenges and dilemmas. Nick has to cope with the loss and regain of his Grimm powers, as well as the betrayal and transformation of his girlfriend Juliette, who becomes a Hexenbiest, a witch-like Wesen. Nick also has to deal with his complicated relationship with Adalind, a former enemy who is pregnant with his child. Nick's partner Hank and his friend Monroe also have their own struggles, as they try to balance their loyalty to Nick and their love for their Wesen partners, Rosalee and Zuri.
-
Meanwhile, Captain Renard, who is revealed to be a Royal bastard, has to survive an assassination attempt and a coup within his family. He also has to protect his daughter Diana, who is kidnapped by his brother Kenneth. Sergeant Wu, who learns the truth about the Wesen world, has to adjust to his new reality and overcome his trauma. And last but not least, Trubel has to find her place in the Grimm world and avoid the threats from various enemies, such as the FBI agent Chavez and the mysterious group known as the Black Claw.
-
In an interview with TV Guide, executive producer David Greenwalt said that Grimm season 4 is "the most intense season yet". He also said that the season is "about identity and destiny" for the characters. He added that the season is "full of surprises and twists" that will keep the viewers hooked. Co-executive producer Jim Kouf also said that the season is "a roller coaster ride" that will "take you places you never thought you'd go".
- d5da3c52bf
-
-
\ No newline at end of file
diff --git a/spaces/scedlatioru/img-to-music/example/OS2 XTEL PL72 Full.rar.md b/spaces/scedlatioru/img-to-music/example/OS2 XTEL PL72 Full.rar.md
deleted file mode 100644
index 25681e1ccaff4f63ca6690da2efd848bbc54580e..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/OS2 XTEL PL72 Full.rar.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
- d5da3c52bf
-
-
-
diff --git a/spaces/sdhsdhk/bingo111/src/lib/utils.ts b/spaces/sdhsdhk/bingo111/src/lib/utils.ts
deleted file mode 100644
index 07feedb34e356b1b3cf867872f32d47a96ae12fb..0000000000000000000000000000000000000000
--- a/spaces/sdhsdhk/bingo111/src/lib/utils.ts
+++ /dev/null
@@ -1,138 +0,0 @@
-import { clsx, type ClassValue } from 'clsx'
-import { customAlphabet } from 'nanoid'
-import { twMerge } from 'tailwind-merge'
-
-export function cn(...inputs: ClassValue[]) {
- return twMerge(clsx(inputs))
-}
-
-export const nanoid = customAlphabet(
- '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz',
- 7
-) // 7-character random string
-
-export function createChunkDecoder() {
- const decoder = new TextDecoder()
- return function (chunk: Uint8Array | undefined): string {
- if (!chunk) return ''
- return decoder.decode(chunk, { stream: true })
- }
-}
-
-export function random (start: number, end: number) {
- return start + Math.ceil(Math.random() * (end - start))
-}
-
-export function randomIP() {
- return `11.${random(104, 107)}.${random(1, 255)}.${random(1, 255)}`
-}
-
-export function parseHeadersFromCurl(content: string) {
- const re = /-H '([^:]+):\s*([^']+)/mg
- const headers: HeadersInit = {}
- content = content.replaceAll('-H "', '-H \'').replaceAll('" ^', '\'\\').replaceAll('^\\^"', '"') // 将 cmd curl 转成 bash curl
- content.replace(re, (_: string, key: string, value: string) => {
- headers[key] = value
- return ''
- })
-
- return headers
-}
-
-export const ChunkKeys = ['BING_HEADER', 'BING_HEADER1', 'BING_HEADER2']
-export function encodeHeadersToCookie(content: string) {
- const base64Content = btoa(content)
- const contentChunks = base64Content.match(/.{1,4000}/g) || []
- return ChunkKeys.map((key, index) => `${key}=${contentChunks[index] ?? ''}`)
-}
-
-export function extraCurlFromCookie(cookies: Partial<{ [key: string]: string }>) {
- let base64Content = ''
- ChunkKeys.forEach((key) => {
- base64Content += (cookies[key] || '')
- })
- try {
- return atob(base64Content)
- } catch(e) {
- return ''
- }
-}
-
-export function extraHeadersFromCookie(cookies: Partial<{ [key: string]: string }>) {
- return parseHeadersFromCurl(extraCurlFromCookie(cookies))
-}
-
-export function formatDate(input: string | number | Date): string {
- const date = new Date(input)
- return date.toLocaleDateString('en-US', {
- month: 'long',
- day: 'numeric',
- year: 'numeric'
- })
-}
-
-export function parseCookie(cookie: string, cookieName: string) {
- const targetCookie = new RegExp(`(?:[; ]|^)${cookieName}=([^;]*)`).test(cookie) ? RegExp.$1 : cookie
- return targetCookie ? decodeURIComponent(targetCookie).trim() : cookie.indexOf('=') === -1 ? cookie.trim() : ''
-}
-
-export function parseCookies(cookie: string, cookieNames: string[]) {
- const cookies: { [key: string]: string } = {}
- cookieNames.forEach(cookieName => {
- cookies[cookieName] = parseCookie(cookie, cookieName)
- })
- return cookies
-}
-
-export const DEFAULT_UA = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Safari/537.36 Edg/115.0.0.0'
-export const DEFAULT_IP = process.env.BING_IP || randomIP()
-
-export function parseUA(ua?: string, default_ua = DEFAULT_UA) {
- return / EDGE?/i.test(decodeURIComponent(ua || '')) ? decodeURIComponent(ua!.trim()) : default_ua
-}
-
-export function createHeaders(cookies: Partial<{ [key: string]: string }>, defaultHeaders?: Partial<{ [key: string]: string }>) {
- let {
- BING_COOKIE = process.env.BING_COOKIE,
- BING_UA = process.env.BING_UA,
- BING_IP = process.env.BING_IP,
- BING_HEADER = process.env.BING_HEADER,
- } = cookies
-
- if (BING_HEADER) {
- return extraHeadersFromCookie({
- BING_HEADER,
- ...cookies,
- })
- }
-
- const ua = parseUA(BING_UA)
-
- if (!BING_COOKIE) {
- BING_COOKIE = defaultHeaders?.IMAGE_BING_COOKIE || 'xxx' // hf 暂时不用 Cookie 也可以正常使用
- }
-
- const parsedCookie = parseCookie(BING_COOKIE, '_U')
- if (!parsedCookie) {
- throw new Error('Invalid Cookie')
- }
- return {
- 'x-forwarded-for': BING_IP || DEFAULT_IP,
- 'Accept-Encoding': 'gzip, deflate, br',
- 'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6',
- 'User-Agent': ua!,
- 'x-ms-useragent': 'azsdk-js-api-client-factory/1.0.0-beta.1 core-rest-pipeline/1.10.0 OS/Win32',
- cookie: `_U=${parsedCookie}` || '',
- }
-}
-
-export class WatchDog {
- private tid = 0
- watch(fn: Function, timeout = 2000) {
- clearTimeout(this.tid)
- this.tid = setTimeout(fn, timeout + Math.random() * 1000)
- }
- reset() {
- clearTimeout(this.tid)
- }
-}
diff --git a/spaces/segadeds/Medical_Diagnosis/app.py b/spaces/segadeds/Medical_Diagnosis/app.py
deleted file mode 100644
index 59ad0efe5bd1647f336cfe770bef191b4af930d4..0000000000000000000000000000000000000000
--- a/spaces/segadeds/Medical_Diagnosis/app.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from fastai.text.all import *
-import gradio as gr
-
-
-learn = load_learner('model.pkl')
-
-
-description = "Medical Diagnosis"
-categories = (['Allergy', 'Anemia', 'Bronchitis', 'Diabetes', 'Diarrhea', 'Fatigue', 'Flu', 'Malaria', 'Stress'])
-
-
-
-def classify_text(txt):
- pred,idx,probs = learn.predict(txt)
- return dict(zip(categories, map(float,probs)))
-
-
-
-text = gr.inputs.Textbox(lines=2, label='Describe how you feel in great detail')
-label = gr.outputs.Label()
-examples = ['I have no intrest in physical activity. i am always thirsty', 'I am freezing', 'My eyes are pale']
-
-intf = gr.Interface(fn=classify_text, inputs=text, outputs=label, examples=examples, description=description)
-intf.launch(inline=False)
-
-
-
-
-
diff --git a/spaces/segments-tobias/conex/espnet/optimizer/factory.py b/spaces/segments-tobias/conex/espnet/optimizer/factory.py
deleted file mode 100644
index 37e19b0692e37f4d8c06b6055b64b0311540f783..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet/optimizer/factory.py
+++ /dev/null
@@ -1,69 +0,0 @@
-"""Import optimizer class dynamically."""
-import argparse
-
-from espnet.utils.dynamic_import import dynamic_import
-from espnet.utils.fill_missing_args import fill_missing_args
-
-
-class OptimizerFactoryInterface:
- """Optimizer adaptor."""
-
- @staticmethod
- def from_args(target, args: argparse.Namespace):
- """Initialize optimizer from argparse Namespace.
-
- Args:
- target: for pytorch `model.parameters()`,
- for chainer `model`
- args (argparse.Namespace): parsed command-line args
-
- """
- raise NotImplementedError()
-
- @staticmethod
- def add_arguments(parser: argparse.ArgumentParser) -> argparse.ArgumentParser:
- """Register args."""
- return parser
-
- @classmethod
- def build(cls, target, **kwargs):
- """Initialize optimizer with python-level args.
-
- Args:
- target: for pytorch `model.parameters()`,
- for chainer `model`
-
- Returns:
- new Optimizer
-
- """
- args = argparse.Namespace(**kwargs)
- args = fill_missing_args(args, cls.add_arguments)
- return cls.from_args(target, args)
-
-
-def dynamic_import_optimizer(name: str, backend: str) -> OptimizerFactoryInterface:
- """Import optimizer class dynamically.
-
- Args:
- name (str): alias name or dynamic import syntax `module:class`
- backend (str): backend name e.g., chainer or pytorch
-
- Returns:
- OptimizerFactoryInterface or FunctionalOptimizerAdaptor
-
- """
- if backend == "pytorch":
- from espnet.optimizer.pytorch import OPTIMIZER_FACTORY_DICT
-
- return OPTIMIZER_FACTORY_DICT[name]
- elif backend == "chainer":
- from espnet.optimizer.chainer import OPTIMIZER_FACTORY_DICT
-
- return OPTIMIZER_FACTORY_DICT[name]
- else:
- raise NotImplementedError(f"unsupported backend: {backend}")
-
- factory_class = dynamic_import(name)
- assert issubclass(factory_class, OptimizerFactoryInterface)
- return factory_class
diff --git a/spaces/shivammehta25/Diff-TTSG/diff_ttsg/data/components/__init__.py b/spaces/shivammehta25/Diff-TTSG/diff_ttsg/data/components/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/showlab/Show-1/showone/models/__init__.py b/spaces/showlab/Show-1/showone/models/__init__.py
deleted file mode 100644
index 520074e727890df83633081279b21807000deacb..0000000000000000000000000000000000000000
--- a/spaces/showlab/Show-1/showone/models/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Copyright 2023 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 .unet_3d_condition import UNet3DConditionModel
\ No newline at end of file
diff --git a/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/model/r3.py b/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/model/r3.py
deleted file mode 100644
index 1e775ab39e529c6086938adbb1d6c2cd3fb6cc8e..0000000000000000000000000000000000000000
--- a/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/model/r3.py
+++ /dev/null
@@ -1,320 +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.
-
-"""Transformations for 3D coordinates.
-
-This Module contains objects for representing Vectors (Vecs), Rotation Matrices
-(Rots) and proper Rigid transformation (Rigids). These are represented as
-named tuples with arrays for each entry, for example a set of
-[N, M] points would be represented as a Vecs object with arrays of shape [N, M]
-for x, y and z.
-
-This is being done to improve readability by making it very clear what objects
-are geometric objects rather than relying on comments and array shapes.
-Another reason for this is to avoid using matrix
-multiplication primitives like matmul or einsum, on modern accelerator hardware
-these can end up on specialized cores such as tensor cores on GPU or the MXU on
-cloud TPUs, this often involves lower computational precision which can be
-problematic for coordinate geometry. Also these cores are typically optimized
-for larger matrices than 3 dimensional, this code is written to avoid any
-unintended use of these cores on both GPUs and TPUs.
-"""
-
-import collections
-from typing import List
-from alphafold.model import quat_affine
-import jax.numpy as jnp
-import tree
-
-# Array of 3-component vectors, stored as individual array for
-# each component.
-Vecs = collections.namedtuple('Vecs', ['x', 'y', 'z'])
-
-# Array of 3x3 rotation matrices, stored as individual array for
-# each component.
-Rots = collections.namedtuple('Rots', ['xx', 'xy', 'xz',
- 'yx', 'yy', 'yz',
- 'zx', 'zy', 'zz'])
-# Array of rigid 3D transformations, stored as array of rotations and
-# array of translations.
-Rigids = collections.namedtuple('Rigids', ['rot', 'trans'])
-
-
-def squared_difference(x, y):
- return jnp.square(x - y)
-
-
-def invert_rigids(r: Rigids) -> Rigids:
- """Computes group inverse of rigid transformations 'r'."""
- inv_rots = invert_rots(r.rot)
- t = rots_mul_vecs(inv_rots, r.trans)
- inv_trans = Vecs(-t.x, -t.y, -t.z)
- return Rigids(inv_rots, inv_trans)
-
-
-def invert_rots(m: Rots) -> Rots:
- """Computes inverse of rotations 'm'."""
- return Rots(m.xx, m.yx, m.zx,
- m.xy, m.yy, m.zy,
- m.xz, m.yz, m.zz)
-
-
-def rigids_from_3_points(
- point_on_neg_x_axis: Vecs, # shape (...)
- origin: Vecs, # shape (...)
- point_on_xy_plane: Vecs, # shape (...)
-) -> Rigids: # shape (...)
- """Create Rigids from 3 points.
-
- Jumper et al. (2021) Suppl. Alg. 21 "rigidFrom3Points"
- This creates a set of rigid transformations from 3 points by Gram Schmidt
- orthogonalization.
-
- Args:
- point_on_neg_x_axis: Vecs corresponding to points on the negative x axis
- origin: Origin of resulting rigid transformations
- point_on_xy_plane: Vecs corresponding to points in the xy plane
- Returns:
- Rigid transformations from global frame to local frames derived from
- the input points.
- """
- m = rots_from_two_vecs(
- e0_unnormalized=vecs_sub(origin, point_on_neg_x_axis),
- e1_unnormalized=vecs_sub(point_on_xy_plane, origin))
-
- return Rigids(rot=m, trans=origin)
-
-
-def rigids_from_list(l: List[jnp.ndarray]) -> Rigids:
- """Converts flat list of arrays to rigid transformations."""
- assert len(l) == 12
- return Rigids(Rots(*(l[:9])), Vecs(*(l[9:])))
-
-
-def rigids_from_quataffine(a: quat_affine.QuatAffine) -> Rigids:
- """Converts QuatAffine object to the corresponding Rigids object."""
- return Rigids(Rots(*tree.flatten(a.rotation)),
- Vecs(*a.translation))
-
-
-def rigids_from_tensor4x4(
- m: jnp.ndarray # shape (..., 4, 4)
-) -> Rigids: # shape (...)
- """Construct Rigids object from an 4x4 array.
-
- Here the 4x4 is representing the transformation in homogeneous coordinates.
-
- Args:
- m: Array representing transformations in homogeneous coordinates.
- Returns:
- Rigids object corresponding to transformations m
- """
- assert m.shape[-1] == 4
- assert m.shape[-2] == 4
- return Rigids(
- Rots(m[..., 0, 0], m[..., 0, 1], m[..., 0, 2],
- m[..., 1, 0], m[..., 1, 1], m[..., 1, 2],
- m[..., 2, 0], m[..., 2, 1], m[..., 2, 2]),
- Vecs(m[..., 0, 3], m[..., 1, 3], m[..., 2, 3]))
-
-
-def rigids_from_tensor_flat9(
- m: jnp.ndarray # shape (..., 9)
-) -> Rigids: # shape (...)
- """Flat9 encoding: first two columns of rotation matrix + translation."""
- assert m.shape[-1] == 9
- e0 = Vecs(m[..., 0], m[..., 1], m[..., 2])
- e1 = Vecs(m[..., 3], m[..., 4], m[..., 5])
- trans = Vecs(m[..., 6], m[..., 7], m[..., 8])
- return Rigids(rot=rots_from_two_vecs(e0, e1),
- trans=trans)
-
-
-def rigids_from_tensor_flat12(
- m: jnp.ndarray # shape (..., 12)
-) -> Rigids: # shape (...)
- """Flat12 encoding: rotation matrix (9 floats) + translation (3 floats)."""
- assert m.shape[-1] == 12
- x = jnp.moveaxis(m, -1, 0) # Unstack
- return Rigids(Rots(*x[:9]), Vecs(*x[9:]))
-
-
-def rigids_mul_rigids(a: Rigids, b: Rigids) -> Rigids:
- """Group composition of Rigids 'a' and 'b'."""
- return Rigids(
- rots_mul_rots(a.rot, b.rot),
- vecs_add(a.trans, rots_mul_vecs(a.rot, b.trans)))
-
-
-def rigids_mul_rots(r: Rigids, m: Rots) -> Rigids:
- """Compose rigid transformations 'r' with rotations 'm'."""
- return Rigids(rots_mul_rots(r.rot, m), r.trans)
-
-
-def rigids_mul_vecs(r: Rigids, v: Vecs) -> Vecs:
- """Apply rigid transforms 'r' to points 'v'."""
- return vecs_add(rots_mul_vecs(r.rot, v), r.trans)
-
-
-def rigids_to_list(r: Rigids) -> List[jnp.ndarray]:
- """Turn Rigids into flat list, inverse of 'rigids_from_list'."""
- return list(r.rot) + list(r.trans)
-
-
-def rigids_to_quataffine(r: Rigids) -> quat_affine.QuatAffine:
- """Convert Rigids r into QuatAffine, inverse of 'rigids_from_quataffine'."""
- return quat_affine.QuatAffine(
- quaternion=None,
- rotation=[[r.rot.xx, r.rot.xy, r.rot.xz],
- [r.rot.yx, r.rot.yy, r.rot.yz],
- [r.rot.zx, r.rot.zy, r.rot.zz]],
- translation=[r.trans.x, r.trans.y, r.trans.z])
-
-
-def rigids_to_tensor_flat9(
- r: Rigids # shape (...)
-) -> jnp.ndarray: # shape (..., 9)
- """Flat9 encoding: first two columns of rotation matrix + translation."""
- return jnp.stack(
- [r.rot.xx, r.rot.yx, r.rot.zx, r.rot.xy, r.rot.yy, r.rot.zy]
- + list(r.trans), axis=-1)
-
-
-def rigids_to_tensor_flat12(
- r: Rigids # shape (...)
-) -> jnp.ndarray: # shape (..., 12)
- """Flat12 encoding: rotation matrix (9 floats) + translation (3 floats)."""
- return jnp.stack(list(r.rot) + list(r.trans), axis=-1)
-
-
-def rots_from_tensor3x3(
- m: jnp.ndarray, # shape (..., 3, 3)
-) -> Rots: # shape (...)
- """Convert rotations represented as (3, 3) array to Rots."""
- assert m.shape[-1] == 3
- assert m.shape[-2] == 3
- return Rots(m[..., 0, 0], m[..., 0, 1], m[..., 0, 2],
- m[..., 1, 0], m[..., 1, 1], m[..., 1, 2],
- m[..., 2, 0], m[..., 2, 1], m[..., 2, 2])
-
-
-def rots_from_two_vecs(e0_unnormalized: Vecs, e1_unnormalized: Vecs) -> Rots:
- """Create rotation matrices from unnormalized vectors for the x and y-axes.
-
- This creates a rotation matrix from two vectors using Gram-Schmidt
- orthogonalization.
-
- Args:
- e0_unnormalized: vectors lying along x-axis of resulting rotation
- e1_unnormalized: vectors lying in xy-plane of resulting rotation
- Returns:
- Rotations resulting from Gram-Schmidt procedure.
- """
- # Normalize the unit vector for the x-axis, e0.
- e0 = vecs_robust_normalize(e0_unnormalized)
-
- # make e1 perpendicular to e0.
- c = vecs_dot_vecs(e1_unnormalized, e0)
- e1 = Vecs(e1_unnormalized.x - c * e0.x,
- e1_unnormalized.y - c * e0.y,
- e1_unnormalized.z - c * e0.z)
- e1 = vecs_robust_normalize(e1)
-
- # Compute e2 as cross product of e0 and e1.
- e2 = vecs_cross_vecs(e0, e1)
-
- return Rots(e0.x, e1.x, e2.x, e0.y, e1.y, e2.y, e0.z, e1.z, e2.z)
-
-
-def rots_mul_rots(a: Rots, b: Rots) -> Rots:
- """Composition of rotations 'a' and 'b'."""
- c0 = rots_mul_vecs(a, Vecs(b.xx, b.yx, b.zx))
- c1 = rots_mul_vecs(a, Vecs(b.xy, b.yy, b.zy))
- c2 = rots_mul_vecs(a, Vecs(b.xz, b.yz, b.zz))
- return Rots(c0.x, c1.x, c2.x, c0.y, c1.y, c2.y, c0.z, c1.z, c2.z)
-
-
-def rots_mul_vecs(m: Rots, v: Vecs) -> Vecs:
- """Apply rotations 'm' to vectors 'v'."""
- return Vecs(m.xx * v.x + m.xy * v.y + m.xz * v.z,
- m.yx * v.x + m.yy * v.y + m.yz * v.z,
- m.zx * v.x + m.zy * v.y + m.zz * v.z)
-
-
-def vecs_add(v1: Vecs, v2: Vecs) -> Vecs:
- """Add two vectors 'v1' and 'v2'."""
- return Vecs(v1.x + v2.x, v1.y + v2.y, v1.z + v2.z)
-
-
-def vecs_dot_vecs(v1: Vecs, v2: Vecs) -> jnp.ndarray:
- """Dot product of vectors 'v1' and 'v2'."""
- return v1.x * v2.x + v1.y * v2.y + v1.z * v2.z
-
-
-def vecs_cross_vecs(v1: Vecs, v2: Vecs) -> Vecs:
- """Cross product of vectors 'v1' and 'v2'."""
- return Vecs(v1.y * v2.z - v1.z * v2.y,
- v1.z * v2.x - v1.x * v2.z,
- v1.x * v2.y - v1.y * v2.x)
-
-
-def vecs_from_tensor(x: jnp.ndarray # shape (..., 3)
- ) -> Vecs: # shape (...)
- """Converts from tensor of shape (3,) to Vecs."""
- num_components = x.shape[-1]
- assert num_components == 3
- return Vecs(x[..., 0], x[..., 1], x[..., 2])
-
-
-def vecs_robust_normalize(v: Vecs, epsilon: float = 1e-8) -> Vecs:
- """Normalizes vectors 'v'.
-
- Args:
- v: vectors to be normalized.
- epsilon: small regularizer added to squared norm before taking square root.
- Returns:
- normalized vectors
- """
- norms = vecs_robust_norm(v, epsilon)
- return Vecs(v.x / norms, v.y / norms, v.z / norms)
-
-
-def vecs_robust_norm(v: Vecs, epsilon: float = 1e-8) -> jnp.ndarray:
- """Computes norm of vectors 'v'.
-
- Args:
- v: vectors to be normalized.
- epsilon: small regularizer added to squared norm before taking square root.
- Returns:
- norm of 'v'
- """
- return jnp.sqrt(jnp.square(v.x) + jnp.square(v.y) + jnp.square(v.z) + epsilon)
-
-
-def vecs_sub(v1: Vecs, v2: Vecs) -> Vecs:
- """Computes v1 - v2."""
- return Vecs(v1.x - v2.x, v1.y - v2.y, v1.z - v2.z)
-
-
-def vecs_squared_distance(v1: Vecs, v2: Vecs) -> jnp.ndarray:
- """Computes squared euclidean difference between 'v1' and 'v2'."""
- return (squared_difference(v1.x, v2.x) +
- squared_difference(v1.y, v2.y) +
- squared_difference(v1.z, v2.z))
-
-
-def vecs_to_tensor(v: Vecs # shape (...)
- ) -> jnp.ndarray: # shape(..., 3)
- """Converts 'v' to tensor with shape 3, inverse of 'vecs_from_tensor'."""
- return jnp.stack([v.x, v.y, v.z], axis=-1)
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/CarX Drift Racing Game Mod APK The Ultimate Drifting Simulator for Android.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/CarX Drift Racing Game Mod APK The Ultimate Drifting Simulator for Android.md
deleted file mode 100644
index 7bb648d0a0b7f977c4f7f560b13f4978c728b3c0..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/CarX Drift Racing Game Mod APK The Ultimate Drifting Simulator for Android.md
+++ /dev/null
@@ -1,104 +0,0 @@
-
-
CarX Drift Racing Game Mod APK: A Complete Guide
-
If you are a fan of racing games, especially drifting games, you might have heard of CarX Drift Racing. It is one of the most popular and realistic drifting games on Android devices. In this article, we will tell you everything you need to know about CarX Drift Racing, including its features, how to download and install its modded version, and some tips and tricks to improve your skills.
-
What is CarX Drift Racing?
-
CarX Drift Racing is a racing game developed by CarX Technologies. It was released in 2014 and has since gained millions of downloads and positive reviews from players. The game lets you experience the thrill of drifting in various cars and tracks. You can customize your car's appearance, performance, and tuning to suit your preferences. You can also compete with other players online or offline in different modes and events.
CarX Drift Racing has many features that make it stand out from other racing games. Here are some of them:
-
Realistic physics and graphics
-
CarX Drift Racing uses a sophisticated physics engine that simulates the behavior of real cars on different surfaces and conditions. You can feel the difference between asphalt, grass, sand, snow, and ice. You can also see the smoke, dust, sparks, and skid marks that your car leaves behind. The game also has stunning graphics that create a realistic and immersive environment.
-
Customizable cars and tracks
-
CarX Drift Racing offers a variety of cars and tracks for you to choose from. You can drive sports cars, muscle cars, supercars, SUVs, and more. You can also modify your car's color, wheels, body kits, spoilers, vinyls, and stickers. You can also create your own tracks using the track editor feature. You can adjust the length, width, curvature, elevation, and obstacles of your track.
-
Online and offline modes
-
CarX Drift Racing allows you to play online or offline depending on your preference. You can join online tournaments and compete with other players from around the world. You can also challenge your friends in multiplayer mode or ghost mode. If you prefer to play offline, you can enjoy the career mode or the single player mode. You can also practice your skills in the training mode or the free ride mode.
-
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-
What is CarX Drift Racing Mod APK?
-
CarX Drift Racing Mod APK is a modified version of the original game that gives you some extra benefits. For example, you can get unlimited money, coins, gold, and keys that you can use to buy new cars, upgrade your car parts, or unlock new tracks. You can also get unlimited fuel, nitro, and time that you can use to drift longer and faster. You can also remove ads that might interrupt your gameplay.
-
Benefits of CarX Drift Racing Mod APK
-
Some of the benefits of using CarX Drift Racing Mod APK are:
-
-
You can enjoy all the features of the game without spending real money.
-
You can access all the cars and tracks without completing any missions or levels.
-
How to download and install CarX Drift Racing Mod APK
-
If you want to download and install CarX Drift Racing Mod APK, you need to follow these steps:
-
-
Go to a trusted website that provides the link to download CarX Drift Racing Mod APK. For example, you can use this link: [Download CarX Drift Racing Mod APK].
-
Click on the download button and wait for the file to be downloaded on your device.
-
Go to your device's settings and enable the option to install apps from unknown sources.
-
Locate the downloaded file and tap on it to start the installation process.
-
Follow the instructions on the screen and wait for the installation to be completed.
-
Launch the game and enjoy the modded features.
-
-
Tips and tricks for CarX Drift Racing
-
CarX Drift Racing is a fun and challenging game that requires skill and practice. Here are some tips and tricks that can help you improve your performance and score:
-
Master the drifting techniques
-
Drifting is the key to success in CarX Drift Racing. You need to learn how to control your car's speed, angle, and direction while drifting. You also need to know when to use the handbrake, the gas pedal, and the steering wheel. There are different drifting techniques that you can use, such as power sliding, clutch kicking, e-brake drifting, and more. You can watch some tutorials or videos online to learn more about these techniques.
-
Upgrade your car parts and tuning
-
As you progress in the game, you will need to upgrade your car parts and tuning to keep up with the competition. You can upgrade your engine, turbo, gearbox, suspension, tires, brakes, and more. You can also adjust your car's tuning settings, such as camber, toe, caster, differential, anti-roll bars, and more. Upgrading and tuning your car will improve its performance, handling, and stability.
-
Earn coins and rewards
-
Coins are the main currency in CarX Drift Racing. You can use them to buy new cars, upgrade your car parts, or unlock new tracks. You can earn coins by completing missions, winning races, performing drifts, or watching ads. You can also earn rewards by participating in events, tournaments, or daily quests. Rewards can include coins, gold, keys, or car parts.
-
Conclusion
-
CarX Drift Racing is a great game for racing and drifting enthusiasts. It has realistic physics and graphics, customizable cars and tracks, online and offline modes, and more. You can also download and install CarX Drift Racing Mod APK to enjoy unlimited money, coins, gold, keys, fuel, nitro, time, and no ads. However, you should be careful when downloading modded apps from unknown sources as they might contain viruses or malware. You should also follow some tips and tricks to improve your skills and score in CarX Drift Racing.
-
FAQs
-
Here are some frequently asked questions about CarX Drift Racing:
-
-
Is CarX Drift Racing free?
-
Yes, CarX Drift Racing is free to download and play on Android devices. However, it contains in-app purchases that allow you to buy coins or gold with real money.
-
Is CarX Drift Racing offline?
-
Yes, CarX Drift Racing can be played offline in career mode or single player mode. However, you need an internet connection to play online tournaments or multiplayer mode.
-
How many cars are there in CarX Drift Racing?
-
There are over 50 cars in CarX Drift Racing that you can drive or unlock. They include sports cars, muscle cars, supercars, SUVs, and more.
-
How many tracks are there in CarX Drift Racing?
-
There are over 30 tracks in CarX Drift Racing that you can race or unlock. They include asphalt tracks, grass tracks, sand tracks,
and ice tracks. You can also create your own tracks using the track editor feature.
-
How to drift in CarX Drift Racing?
-
To drift in CarX Drift Racing, you need to use the handbrake, the gas pedal, and the steering wheel. You need to press the handbrake to initiate a drift, then release it and press the gas pedal to maintain the drift. You also need to steer your car in the direction of the drift. You can adjust the sensitivity and position of the controls in the settings menu.
- 401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Garena Support APK How to Install and Use on Your Mobile Device.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Garena Support APK How to Install and Use on Your Mobile Device.md
deleted file mode 100644
index d75a064086ce9382100fedd8b34102c2dbf06e4c..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Garena Support APK How to Install and Use on Your Mobile Device.md
+++ /dev/null
@@ -1,115 +0,0 @@
-
-
What is Garena Support APK and How to Use It?
-
If you are a fan of online games, you might have heard of Garena, a leading game publisher and platform in Southeast Asia. Garena offers popular and engaging mobile and PC games, such as Free Fire, League of Legends, Call of Duty Mobile, and more. But what if you encounter some problems or issues while playing these games? How can you get help and support from Garena?
-
That's where Garena Support APK comes in handy. This app is designed to provide you with a convenient and easy way to access customer service, submit requests, troubleshoot errors, and get the latest updates for your favorite Garena games. In this article, we will explain what Garena Support APK is, how to download and install it on your Android device, and how to use it effectively.
Garena is a leading game publisher and platform in Southeast Asia, with operations in Singapore, Malaysia, Indonesia, Thailand, Vietnam, Philippines, Taiwan, Hong Kong, and India. Garena is the exclusive operator of top-tier games in the region, such as Free Fire, League of Legends, Call of Duty Mobile, Arena of Valor, FIFA Online 4, Speed Drifters, Contra Returns, and more.
-
Garena also hosts esports events and tournaments for its games, attracting millions of viewers and players from around the world. Through its platform, users can connect with other gamers, get the latest news and updates, and enjoy various social features.
-
What is an APK file and how does it work?
-
An APK file (Android Package Kit file format) is the file format used by the Android operating system for distribution and installation of mobile apps, mobile games, and middleware. An APK file contains all the data an app needs to run on your device, such as code, resources, assets, certificates, and manifest file.
-
To install an APK file on your Android device, you need to enable the option to install apps from unknown sources in your settings. This allows you to download apps from websites other than Google Play Store. However, you should be careful when installing APK files from untrusted sources, as they may contain malware or viruses that can harm your device or steal your data.
-
What is Garena Support APK?
-
A brief overview of the app and its features
-
Garena Support APK is an app that allows you to access customer service and support for your favorite Garena games. With this app, you can:
-
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-
-
Submit requests for account issues, game concerns, payment issues, ban appeals, etc.
-
Track the status of your requests and receive notifications when they are resolved.
-
Chat with customer service agents in real time.
-
Access FAQs and guides for common problems and questions.
-
Get tips and tricks for improving your gaming experience.
-
Update the app automatically when new features are available.
-
-
How to download and install it on your Android device
How to download and install it on your Android device
-
To download and install Garena Support APK on your Android device, you need to follow these steps:
-
-
Go to the official website of Garena Support APK and click on the download button. Alternatively, you can use a third-party website that offers the APK file, such as [BlueStacks](^1^) or [Lifewire](^2^). However, make sure that the website is trustworthy and secure before downloading anything.
-
Once the download is complete, locate the APK file on your device and tap on it to open it. You may need to enable the option to install apps from unknown sources in your settings if you haven't done so before. This will allow you to install apps that are not from the Google Play Store.
-
Follow the instructions on the screen to install the app. You may need to grant some permissions to the app, such as access to your storage, camera, microphone, etc. Make sure that you read and understand what the app is asking for before granting any permission.
-
After the installation is done, you can launch the app from your app drawer or home screen. You will need to log in with your Garena account or create one if you don't have one already. You can also link your Facebook or Google account to your Garena account for easier access.
-
-
Congratulations! You have successfully downloaded and installed Garena Support APK on your Android device. Now you can enjoy all the benefits and features of this app and get the best gaming experience with Garena.
How to Use Garena Support APK?
-
How to access customer service and submit requests
-
Once you have logged in to the app, you will see a dashboard with various options and icons. To access customer service and submit requests, you need to tap on the icon that looks like a headset with a question mark. This will take you to the support page, where you can choose from different categories of issues, such as account, game, payment, ban appeal, etc.
-
After selecting a category, you will see a list of subcategories and topics that are related to your issue. You can browse through them and see if there is an answer or solution that matches your problem. If not, you can tap on the button that says "Submit Request" at the bottom of the screen. This will open a form where you can fill in the details of your issue, such as your game ID, server, device model, screenshots, etc. You can also attach files or record voice messages to provide more information.
-
Once you have completed the form, tap on the button that says "Submit" at the top right corner of the screen. You will receive a confirmation message and a ticket number for your request. You can use this number to track the status of your request and communicate with the customer service agents. You will also receive notifications when your request is updated or resolved.
-
How to troubleshoot common issues and errors
-
Sometimes, you may encounter some issues or errors while playing Garena games or using Garena Support APK. These issues may be caused by various factors, such as network connection, device compatibility, app bugs, etc. To troubleshoot these issues and errors, you can try some of the following tips:
-
-
Check your network connection and make sure it is stable and fast. You can use a Wi-Fi connection instead of mobile data for better performance. You can also try switching to another network or restarting your router or modem.
-
Check your device settings and make sure they are compatible with the game or app requirements. You can adjust your screen resolution, brightness, sound volume, etc. You can also clear your cache and data, uninstall and reinstall the app, or update your device software.
-
Check the app settings and make sure they are optimized for your device and game preferences. You can change your language, region, graphics quality, etc. You can also enable or disable notifications, permissions, etc.
-
Check the game settings and make sure they are suitable for your gameplay style and level. You can customize your controls, sensitivity, aim assist, etc. You can also join or create a room with other players who have similar skills and interests.
-
Check the FAQs and guides in the app or on the website for more information and solutions for common problems and questions. You can also watch videos or read articles from other sources that offer tips and tricks for improving your gaming experience.
-
-
How to update the app and check for new features
-
To ensure that you have the best gaming experience with Garena Support APK, you should always update the app whenever there is a new version available. Updating the app will fix any bugs or errors that may affect its performance and functionality. It will also add new features and improvements that will enhance your customer service and support experience.
-
To update the app, you can either use the automatic update feature or manually check for updates. To use the automatic update feature, you need to enable it in your settings. This will allow the app to download and install updates automatically when they are available. To manually check for updates, you need to go to the app store or website where you downloaded the app and see if there is a new version available. If there is, you can tap on the update button and follow the instructions on the screen.
-
To check for new features and improvements in the app, you can either use the notification feature or manually browse through the app. To use the notification feature, you need to enable it in your settings. This will allow the app to send you notifications when there are new features or improvements available. To manually browse through the app, you need to go to the dashboard and see if there are any new icons or options that indicate new features or improvements.
Conclusion
-
Garena Support APK is a useful and convenient app that allows you to access customer service and support for your favorite Garena games. With this app, you can submit requests, track their status, chat with agents, troubleshoot issues, get tips and tricks, and update the app easily. You can download and install the app on your Android device by following the steps we have explained in this article.
-
If you are a fan of online games, you should definitely try Garena Support APK and enjoy the best gaming experience with Garena. You will be able to play popular and engaging games, such as Free Fire, League of Legends, Call of Duty Mobile, and more. You will also be able to connect with other gamers, join esports events, and access various social features.
-
So what are you waiting for? Download Garena Support APK today and get ready to have fun and get help whenever you need it!
-
FAQs
-
Q1: Is Garena Support APK safe to use?
-
A1: Yes, Garena Support APK is safe to use as long as you download it from the official website or a trusted source. The app is developed by Garena and does not contain any malware or viruses that can harm your device or data. However, you should always be careful when installing apps from unknown sources and check the permissions and reviews before granting them.
-
Q2: Can I use Garena Support APK on other operating systems?
-
A2: No, Garena Support APK is only compatible with Android devices. You cannot use it on other operating systems, such as iOS, Windows, Mac, etc. However, you can use the web version of Garena Support on any device that has a browser and an internet connection. You can access the web version by visiting this link:
-
Q3: What are the minimum requirements to run Garena Support APK?
-
A3: The minimum requirements to run Garena Support APK are:
-
-
Android version 4.4 or higher
-
At least 100 MB of free storage space
-
A stable internet connection
-
A Garena account or a Facebook or Google account linked to it
-
-
Q4: How can I contact Garena Support APK developers?
-
A4: If you have any feedback, suggestions, or complaints about Garena Support APK, you can contact the developers by using the following methods:
-
-
Email: support@garena.com
-
Phone: +65 6270 8100
-
Facebook: https://www.facebook.com/GarenaOnline/
-
Twitter: https://twitter.com/garenagames
-
Instagram: https://www.instagram.com/garenagames/
-
-
Q5: Where can I find more information about Garena Support APK?
-
A5: If you want to find more information about Garena Support APK, you can visit the following sources:
-
-
The official website of Garena Support APK
-
The official website of Garena
-
The official YouTube channel of Garena
-
The official blog of Garena
-
The official forum of Garena
-
197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/sklkd93/CodeFormer/CodeFormer/facelib/detection/yolov5face/models/common.py b/spaces/sklkd93/CodeFormer/CodeFormer/facelib/detection/yolov5face/models/common.py
deleted file mode 100644
index 497a00444c4c59725001993a63fe4617e9d323c8..0000000000000000000000000000000000000000
--- a/spaces/sklkd93/CodeFormer/CodeFormer/facelib/detection/yolov5face/models/common.py
+++ /dev/null
@@ -1,299 +0,0 @@
-# This file contains modules common to various models
-
-import math
-
-import numpy as np
-import torch
-from torch import nn
-
-from facelib.detection.yolov5face.utils.datasets import letterbox
-from facelib.detection.yolov5face.utils.general import (
- make_divisible,
- non_max_suppression,
- scale_coords,
- xyxy2xywh,
-)
-
-
-def autopad(k, p=None): # kernel, padding
- # Pad to 'same'
- if p is None:
- p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
- return p
-
-
-def channel_shuffle(x, groups):
- batchsize, num_channels, height, width = x.data.size()
- channels_per_group = torch.div(num_channels, groups, rounding_mode="trunc")
-
- # reshape
- x = x.view(batchsize, groups, channels_per_group, height, width)
- x = torch.transpose(x, 1, 2).contiguous()
-
- # flatten
- return x.view(batchsize, -1, height, width)
-
-
-def DWConv(c1, c2, k=1, s=1, act=True):
- # Depthwise convolution
- return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
-
-
-class Conv(nn.Module):
- # Standard convolution
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
- super().__init__()
- self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
- self.bn = nn.BatchNorm2d(c2)
- self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
-
- def forward(self, x):
- return self.act(self.bn(self.conv(x)))
-
- def fuseforward(self, x):
- return self.act(self.conv(x))
-
-
-class StemBlock(nn.Module):
- def __init__(self, c1, c2, k=3, s=2, p=None, g=1, act=True):
- super().__init__()
- self.stem_1 = Conv(c1, c2, k, s, p, g, act)
- self.stem_2a = Conv(c2, c2 // 2, 1, 1, 0)
- self.stem_2b = Conv(c2 // 2, c2, 3, 2, 1)
- self.stem_2p = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)
- self.stem_3 = Conv(c2 * 2, c2, 1, 1, 0)
-
- def forward(self, x):
- stem_1_out = self.stem_1(x)
- stem_2a_out = self.stem_2a(stem_1_out)
- stem_2b_out = self.stem_2b(stem_2a_out)
- stem_2p_out = self.stem_2p(stem_1_out)
- return self.stem_3(torch.cat((stem_2b_out, stem_2p_out), 1))
-
-
-class Bottleneck(nn.Module):
- # Standard bottleneck
- def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
- super().__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_, c2, 3, 1, g=g)
- self.add = shortcut and c1 == c2
-
- def forward(self, x):
- return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
-
-
-class BottleneckCSP(nn.Module):
- # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
- self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
- self.cv4 = Conv(2 * c_, c2, 1, 1)
- self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
- self.act = nn.LeakyReLU(0.1, inplace=True)
- self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
-
- def forward(self, x):
- y1 = self.cv3(self.m(self.cv1(x)))
- y2 = self.cv2(x)
- return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
-
-
-class C3(nn.Module):
- # CSP Bottleneck with 3 convolutions
- def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
- super().__init__()
- c_ = int(c2 * e) # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c1, c_, 1, 1)
- self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2)
- self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
-
- def forward(self, x):
- return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
-
-
-class ShuffleV2Block(nn.Module):
- def __init__(self, inp, oup, stride):
- super().__init__()
-
- if not 1 <= stride <= 3:
- raise ValueError("illegal stride value")
- self.stride = stride
-
- branch_features = oup // 2
-
- if self.stride > 1:
- self.branch1 = nn.Sequential(
- self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1),
- nn.BatchNorm2d(inp),
- nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
- nn.BatchNorm2d(branch_features),
- nn.SiLU(),
- )
- else:
- self.branch1 = nn.Sequential()
-
- self.branch2 = nn.Sequential(
- nn.Conv2d(
- inp if (self.stride > 1) else branch_features,
- branch_features,
- kernel_size=1,
- stride=1,
- padding=0,
- bias=False,
- ),
- nn.BatchNorm2d(branch_features),
- nn.SiLU(),
- self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1),
- nn.BatchNorm2d(branch_features),
- nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
- nn.BatchNorm2d(branch_features),
- nn.SiLU(),
- )
-
- @staticmethod
- def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
- return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)
-
- def forward(self, x):
- if self.stride == 1:
- x1, x2 = x.chunk(2, dim=1)
- out = torch.cat((x1, self.branch2(x2)), dim=1)
- else:
- out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
- out = channel_shuffle(out, 2)
- return out
-
-
-class SPP(nn.Module):
- # Spatial pyramid pooling layer used in YOLOv3-SPP
- def __init__(self, c1, c2, k=(5, 9, 13)):
- super().__init__()
- c_ = c1 // 2 # hidden channels
- self.cv1 = Conv(c1, c_, 1, 1)
- self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
- self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
-
- def forward(self, x):
- x = self.cv1(x)
- return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
-
-
-class Focus(nn.Module):
- # Focus wh information into c-space
- def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
- super().__init__()
- self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
-
- def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
- return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
-
-
-class Concat(nn.Module):
- # Concatenate a list of tensors along dimension
- def __init__(self, dimension=1):
- super().__init__()
- self.d = dimension
-
- def forward(self, x):
- return torch.cat(x, self.d)
-
-
-class NMS(nn.Module):
- # Non-Maximum Suppression (NMS) module
- conf = 0.25 # confidence threshold
- iou = 0.45 # IoU threshold
- classes = None # (optional list) filter by class
-
- def forward(self, x):
- return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
-
-
-class AutoShape(nn.Module):
- # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
- img_size = 640 # inference size (pixels)
- conf = 0.25 # NMS confidence threshold
- iou = 0.45 # NMS IoU threshold
- classes = None # (optional list) filter by class
-
- def __init__(self, model):
- super().__init__()
- self.model = model.eval()
-
- def autoshape(self):
- print("autoShape already enabled, skipping... ") # model already converted to model.autoshape()
- return self
-
- def forward(self, imgs, size=640, augment=False, profile=False):
- # Inference from various sources. For height=720, width=1280, RGB images example inputs are:
- # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(720,1280,3)
- # PIL: = Image.open('image.jpg') # HWC x(720,1280,3)
- # numpy: = np.zeros((720,1280,3)) # HWC
- # torch: = torch.zeros(16,3,720,1280) # BCHW
- # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
-
- p = next(self.model.parameters()) # for device and type
- if isinstance(imgs, torch.Tensor): # torch
- return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference
-
- # Pre-process
- n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images
- shape0, shape1 = [], [] # image and inference shapes
- for i, im in enumerate(imgs):
- im = np.array(im) # to numpy
- if im.shape[0] < 5: # image in CHW
- im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
- im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input
- s = im.shape[:2] # HWC
- shape0.append(s) # image shape
- g = size / max(s) # gain
- shape1.append([y * g for y in s])
- imgs[i] = im # update
- shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape
- x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad
- x = np.stack(x, 0) if n > 1 else x[0][None] # stack
- x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW
- x = torch.from_numpy(x).to(p.device).type_as(p) / 255.0 # uint8 to fp16/32
-
- # Inference
- with torch.no_grad():
- y = self.model(x, augment, profile)[0] # forward
- y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS
-
- # Post-process
- for i in range(n):
- scale_coords(shape1, y[i][:, :4], shape0[i])
-
- return Detections(imgs, y, self.names)
-
-
-class Detections:
- # detections class for YOLOv5 inference results
- def __init__(self, imgs, pred, names=None):
- super().__init__()
- d = pred[0].device # device
- gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1.0, 1.0], device=d) for im in imgs] # normalizations
- self.imgs = imgs # list of images as numpy arrays
- self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
- self.names = names # class names
- self.xyxy = pred # xyxy pixels
- self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
- self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
- self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
- self.n = len(self.pred)
-
- def __len__(self):
- return self.n
-
- def tolist(self):
- # return a list of Detections objects, i.e. 'for result in results.tolist():'
- x = [Detections([self.imgs[i]], [self.pred[i]], self.names) for i in range(self.n)]
- for d in x:
- for k in ["imgs", "pred", "xyxy", "xyxyn", "xywh", "xywhn"]:
- setattr(d, k, getattr(d, k)[0]) # pop out of list
- return x
diff --git a/spaces/sriramelango/Social_Classification_Public/train.py b/spaces/sriramelango/Social_Classification_Public/train.py
deleted file mode 100644
index d9641f2f9414d52505a1745acbdb5c7b0d7414c8..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/train.py
+++ /dev/null
@@ -1,523 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-"""
-Train a new model on one or across multiple GPUs.
-"""
-
-import argparse
-import logging
-import math
-import os
-import sys
-from typing import Dict, Optional, Any, List, Tuple, Callable
-
-# We need to setup root logger before importing any fairseq libraries.
-logging.basicConfig(
- format='%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s',
- datefmt="%Y-%m-%d %H:%M:%S",
- level=os.environ.get("LOGLEVEL", "INFO").upper(),
- stream=sys.stdout,
-)
-logger = logging.getLogger("fairseq_cli.train")
-
-import numpy as np
-import torch
-from fairseq import (
- # checkpoint_utils,
- options,
- quantization_utils,
- tasks,
- utils,
-)
-from fairseq.data import iterators
-from fairseq.data.plasma_utils import PlasmaStore
-from fairseq.dataclass.configs import FairseqConfig
-from fairseq.dataclass.utils import convert_namespace_to_omegaconf
-from fairseq.distributed import fsdp_enable_wrap, fsdp_wrap, utils as distributed_utils
-from fairseq.file_io import PathManager
-from fairseq.logging import meters, metrics, progress_bar
-from fairseq.model_parallel.megatron_trainer import MegatronTrainer
-# from fairseq.trainer import Trainer
-from omegaconf import DictConfig, OmegaConf
-
-from utils import checkpoint_utils
-from trainer import Trainer
-
-
-def main(cfg: FairseqConfig) -> None:
- if isinstance(cfg, argparse.Namespace):
- cfg = convert_namespace_to_omegaconf(cfg)
-
- utils.import_user_module(cfg.common)
-
- if distributed_utils.is_master(cfg.distributed_training) and "job_logging_cfg" in cfg:
- # make hydra logging work with ddp (see # see https://github.com/facebookresearch/hydra/issues/1126)
- logging.config.dictConfig(OmegaConf.to_container(cfg.job_logging_cfg))
-
- assert (
- cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None
- ), "Must specify batch size either with --max-tokens or --batch-size"
- metrics.reset()
-
- if cfg.common.log_file is not None:
- handler = logging.FileHandler(filename=cfg.common.log_file)
- logger.addHandler(handler)
-
- np.random.seed(cfg.common.seed)
- utils.set_torch_seed(cfg.common.seed)
-
- if distributed_utils.is_master(cfg.distributed_training):
- checkpoint_utils.verify_checkpoint_directory(cfg.checkpoint.save_dir)
-
- # Print args
- logger.info(cfg)
-
- if cfg.checkpoint.write_checkpoints_asynchronously:
- try:
- import iopath # noqa: F401
- except ImportError:
- logging.exception(
- "Asynchronous checkpoint writing is specified but iopath is "
- "not installed: `pip install iopath`"
- )
- return
-
- # Setup task, e.g., translation, language modeling, etc.
- task = tasks.setup_task(cfg.task)
-
- assert cfg.criterion, "Please specify criterion to train a model"
-
- # Build model and criterion
- if cfg.distributed_training.ddp_backend == "fully_sharded":
- with fsdp_enable_wrap(cfg.distributed_training):
- model = fsdp_wrap(task.build_model(cfg.model))
- else:
- model = task.build_model(cfg.model)
- criterion = task.build_criterion(cfg.criterion)
- logger.info(model)
- logger.info("task: {}".format(task.__class__.__name__))
- logger.info("model: {}".format(model.__class__.__name__))
- logger.info("criterion: {}".format(criterion.__class__.__name__))
- logger.info(
- "num. shared model params: {:,} (num. trained: {:,})".format(
- sum(p.numel() for p in model.parameters() if not getattr(p, "expert", False)),
- sum(p.numel() for p in model.parameters() if not getattr(p, "expert", False) and p.requires_grad)
- )
- )
-
- logger.info(
- "num. expert model params: {} (num. trained: {})".format(
- sum(p.numel() for p in model.parameters() if getattr(p, "expert", False)),
- sum(p.numel() for p in model.parameters() if getattr(p, "expert", False) and p.requires_grad),
- )
- )
-
- # Load valid dataset (we load training data below, based on the latest checkpoint)
- # We load the valid dataset AFTER building the model
- # data_utils.raise_if_valid_subsets_unintentionally_ignored(cfg)
- if cfg.dataset.combine_valid_subsets:
- task.load_dataset("valid", combine=True, epoch=1)
- else:
- for valid_sub_split in cfg.dataset.valid_subset.split(","):
- task.load_dataset(valid_sub_split, combine=False, epoch=1)
-
- # (optionally) Configure quantization
- if cfg.common.quantization_config_path is not None:
- quantizer = quantization_utils.Quantizer(
- config_path=cfg.common.quantization_config_path,
- max_epoch=cfg.optimization.max_epoch,
- max_update=cfg.optimization.max_update,
- )
- else:
- quantizer = None
-
- # Build trainer
- if cfg.common.model_parallel_size == 1:
- trainer = Trainer(cfg, task, model, criterion, quantizer)
- else:
- trainer = MegatronTrainer(cfg, task, model, criterion)
- logger.info(
- "training on {} devices (GPUs/TPUs)".format(
- cfg.distributed_training.distributed_world_size
- )
- )
- logger.info(
- "max tokens per device = {} and max sentences per device = {}".format(
- cfg.dataset.max_tokens,
- cfg.dataset.batch_size,
- )
- )
-
- # Load the latest checkpoint if one is available and restore the
- # corresponding train iterator
- extra_state, epoch_itr = checkpoint_utils.load_checkpoint(
- cfg.checkpoint,
- trainer,
- # don't cache epoch iterators for sharded datasets
- disable_iterator_cache=task.has_sharded_data("train"),
- )
- if cfg.common.tpu:
- import torch_xla.core.xla_model as xm
- xm.rendezvous("load_checkpoint") # wait for all workers
-
- max_epoch = cfg.optimization.max_epoch or math.inf
- if max_epoch > 0:
- num_iter_per_epoch = (len(epoch_itr) + cfg.distributed_training.distributed_world_size - 1) \
- // cfg.distributed_training.distributed_world_size
- trainer.lr_reinit(num_iter_per_epoch * max_epoch, trainer.get_num_updates())
- lr = trainer.get_lr()
-
- train_meter = meters.StopwatchMeter()
- train_meter.start()
- while epoch_itr.next_epoch_idx <= max_epoch:
- if lr <= cfg.optimization.stop_min_lr:
- logger.info(
- f"stopping training because current learning rate ({lr}) is smaller "
- "than or equal to minimum learning rate "
- f"(--stop-min-lr={cfg.optimization.stop_min_lr})"
- )
- break
-
- # train for one epoch
- valid_losses, should_stop = train(cfg, trainer, task, epoch_itr)
- if should_stop:
- break
-
- # only use first validation loss to update the learning rate
- lr = trainer.lr_step(epoch_itr.epoch, valid_losses[0])
-
- epoch_itr = trainer.get_train_iterator(
- epoch_itr.next_epoch_idx,
- # sharded data: get train iterator for next epoch
- load_dataset=True,
- # don't cache epoch iterators for sharded datasets
- disable_iterator_cache=task.has_sharded_data("train"),
- )
- train_meter.stop()
- logger.info("done training in {:.1f} seconds".format(train_meter.sum))
-
- # ioPath implementation to wait for all asynchronous file writes to complete.
- if cfg.checkpoint.write_checkpoints_asynchronously:
- logger.info(
- "ioPath PathManager waiting for all asynchronous checkpoint "
- "writes to finish."
- )
- PathManager.async_close()
- logger.info("ioPath PathManager finished waiting.")
-
-
-def should_stop_early(cfg: DictConfig, valid_loss: float) -> bool:
- # skip check if no validation was done in the current epoch
- if valid_loss is None:
- return False
- if cfg.checkpoint.patience <= 0:
- return False
-
- def is_better(a, b):
- return a > b if cfg.checkpoint.maximize_best_checkpoint_metric else a < b
-
- prev_best = getattr(should_stop_early, "best", None)
- if prev_best is None or is_better(valid_loss, prev_best):
- should_stop_early.best = valid_loss
- should_stop_early.num_runs = 0
- return False
- else:
- should_stop_early.num_runs += 1
- if should_stop_early.num_runs >= cfg.checkpoint.patience:
- logger.info(
- "early stop since valid performance hasn't improved for last {} runs".format(
- cfg.checkpoint.patience
- )
- )
- return True
- else:
- return False
-
-
-@metrics.aggregate("train")
-def train(
- cfg: DictConfig, trainer: Trainer, task: tasks.FairseqTask, epoch_itr
-) -> Tuple[List[Optional[float]], bool]:
- """Train the model for one epoch and return validation losses."""
- # Initialize data iterator
- itr = epoch_itr.next_epoch_itr(
- fix_batches_to_gpus=cfg.distributed_training.fix_batches_to_gpus,
- shuffle=(epoch_itr.next_epoch_idx > cfg.dataset.curriculum),
- )
- update_freq = (
- cfg.optimization.update_freq[epoch_itr.epoch - 1]
- if epoch_itr.epoch <= len(cfg.optimization.update_freq)
- else cfg.optimization.update_freq[-1]
- )
- itr = iterators.GroupedIterator(itr, update_freq)
- if cfg.common.tpu:
- itr = utils.tpu_data_loader(itr)
- progress = progress_bar.progress_bar(
- itr,
- log_format=cfg.common.log_format,
- log_file=cfg.common.log_file,
- log_interval=cfg.common.log_interval,
- epoch=epoch_itr.epoch,
- tensorboard_logdir=(
- cfg.common.tensorboard_logdir
- if distributed_utils.is_master(cfg.distributed_training)
- else None
- ),
- default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
- wandb_project=(
- cfg.common.wandb_project
- if distributed_utils.is_master(cfg.distributed_training)
- else None
- ),
- wandb_run_name=os.environ.get(
- "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir)
- ),
- azureml_logging=(
- cfg.common.azureml_logging
- if distributed_utils.is_master(cfg.distributed_training)
- else False
- ),
- )
- progress.update_config(_flatten_config(cfg))
-
- trainer.begin_epoch(epoch_itr.epoch)
-
- valid_subsets = cfg.dataset.valid_subset.split(",")
- should_stop = False
- num_updates = trainer.get_num_updates()
- logger.info("Start iterating over samples")
- for i, samples in enumerate(progress):
- with metrics.aggregate("train_inner"), torch.autograd.profiler.record_function(
- "train_step-%d" % i
- ):
- log_output = trainer.train_step(samples)
-
- if log_output is not None: # not OOM, overflow, ...
- # log mid-epoch stats
- num_updates = trainer.get_num_updates()
- if num_updates % cfg.common.log_interval == 0:
- stats = get_training_stats(metrics.get_smoothed_values("train_inner"))
- progress.log(stats, tag="train_inner", step=num_updates)
-
- # reset mid-epoch stats after each log interval
- # the end-of-epoch stats will still be preserved
- metrics.reset_meters("train_inner")
-
- end_of_epoch = not itr.has_next()
- valid_losses, should_stop = validate_and_save(
- cfg, trainer, task, epoch_itr, valid_subsets, end_of_epoch
- )
-
- if should_stop:
- break
-
- # log end-of-epoch stats
- logger.info("end of epoch {} (average epoch stats below)".format(epoch_itr.epoch))
- stats = get_training_stats(metrics.get_smoothed_values("train"))
- progress.print(stats, tag="train", step=num_updates)
-
- # reset epoch-level meters
- metrics.reset_meters("train")
- return valid_losses, should_stop
-
-
-def _flatten_config(cfg: DictConfig):
- config = OmegaConf.to_container(cfg)
- # remove any legacy Namespaces and replace with a single "args"
- namespace = None
- for k, v in list(config.items()):
- if isinstance(v, argparse.Namespace):
- namespace = v
- del config[k]
- if namespace is not None:
- config["args"] = vars(namespace)
- return config
-
-
-def validate_and_save(
- cfg: DictConfig,
- trainer: Trainer,
- task: tasks.FairseqTask,
- epoch_itr,
- valid_subsets: List[str],
- end_of_epoch: bool,
-) -> Tuple[List[Optional[float]], bool]:
- num_updates = trainer.get_num_updates()
- max_update = cfg.optimization.max_update or math.inf
-
- # Stopping conditions (and an additional one based on validation loss later
- # on)
- should_stop = False
- if num_updates >= max_update:
- should_stop = True
- logger.info(
- f"Stopping training due to "
- f"num_updates: {num_updates} >= max_update: {max_update}"
- )
-
- training_time_hours = trainer.cumulative_training_time() / (60 * 60)
- if (
- cfg.optimization.stop_time_hours > 0
- and training_time_hours > cfg.optimization.stop_time_hours
- ):
- should_stop = True
- logger.info(
- f"Stopping training due to "
- f"cumulative_training_time: {training_time_hours} > "
- f"stop_time_hours: {cfg.optimization.stop_time_hours} hour(s)"
- )
-
- do_save = (
- (end_of_epoch and epoch_itr.epoch % cfg.checkpoint.save_interval == 0)
- or should_stop
- or (
- cfg.checkpoint.save_interval_updates > 0
- and num_updates > 0
- and num_updates % cfg.checkpoint.save_interval_updates == 0
- and num_updates >= cfg.dataset.validate_after_updates
- )
- )
- do_validate = (
- (not end_of_epoch and do_save) # validate during mid-epoch saves
- or (end_of_epoch and epoch_itr.epoch % cfg.dataset.validate_interval == 0)
- or should_stop
- or (
- cfg.dataset.validate_interval_updates > 0
- and num_updates > 0
- and num_updates % cfg.dataset.validate_interval_updates == 0
- )
- ) and not cfg.dataset.disable_validation and num_updates >= cfg.dataset.validate_after_updates
-
- # Validate
- valid_losses = [None]
- if do_validate:
- valid_losses = validate(cfg, trainer, task, epoch_itr, valid_subsets)
-
- should_stop |= should_stop_early(cfg, valid_losses[0])
-
- # Save checkpoint
- if do_save or should_stop:
- checkpoint_utils.save_checkpoint(
- cfg.checkpoint, trainer, epoch_itr, valid_losses[0]
- )
-
- return valid_losses, should_stop
-
-
-def get_training_stats(stats: Dict[str, Any]) -> Dict[str, Any]:
- stats["wall"] = round(metrics.get_meter("default", "wall").elapsed_time, 0)
- return stats
-
-
-def validate(
- cfg: DictConfig,
- trainer: Trainer,
- task: tasks.FairseqTask,
- epoch_itr,
- subsets: List[str],
-) -> List[Optional[float]]:
- """Evaluate the model on the validation set(s) and return the losses."""
-
- if cfg.dataset.fixed_validation_seed is not None:
- # set fixed seed for every validation
- utils.set_torch_seed(cfg.dataset.fixed_validation_seed)
-
- trainer.begin_valid_epoch(epoch_itr.epoch)
- valid_losses = []
- for subset in subsets:
- logger.info('begin validation on "{}" subset'.format(subset))
-
- # Initialize data iterator
- itr = trainer.get_valid_iterator(subset).next_epoch_itr(
- shuffle=False, set_dataset_epoch=False # use a fixed valid set
- )
- if cfg.common.tpu:
- itr = utils.tpu_data_loader(itr)
- progress = progress_bar.progress_bar(
- itr,
- log_format=cfg.common.log_format,
- log_interval=cfg.common.log_interval,
- epoch=epoch_itr.epoch,
- prefix=f"valid on '{subset}' subset",
- tensorboard_logdir=(
- cfg.common.tensorboard_logdir
- if distributed_utils.is_master(cfg.distributed_training)
- else None
- ),
- default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),
- wandb_project=(
- cfg.common.wandb_project
- if distributed_utils.is_master(cfg.distributed_training)
- else None
- ),
- wandb_run_name=os.environ.get(
- "WANDB_NAME", os.path.basename(cfg.checkpoint.save_dir)
- ),
- )
-
- # create a new root metrics aggregator so validation metrics
- # don't pollute other aggregators (e.g., train meters)
- with metrics.aggregate(new_root=True) as agg:
- for i, sample in enumerate(progress):
- if cfg.dataset.max_valid_steps is not None and i > cfg.dataset.max_valid_steps:
- break
- trainer.valid_step(sample)
-
- # log validation stats
- if hasattr(task, 'get_valid_stats'):
- stats = task.get_valid_stats(cfg, trainer, agg.get_smoothed_values())
- else:
- stats = agg.get_smoothed_values()
- stats = get_valid_stats(cfg, trainer, stats)
-
- if hasattr(task, "post_validate"):
- task.post_validate(trainer.get_model(), stats, agg)
-
- progress.print(stats, tag=subset, step=trainer.get_num_updates())
-
- valid_losses.append(stats[cfg.checkpoint.best_checkpoint_metric])
- return valid_losses
-
-
-def get_valid_stats(
- cfg: DictConfig, trainer: Trainer, stats: Dict[str, Any]
-) -> Dict[str, Any]:
- stats["num_updates"] = trainer.get_num_updates()
- if hasattr(checkpoint_utils.save_checkpoint, "best"):
- key = "best_{0}".format(cfg.checkpoint.best_checkpoint_metric)
- best_function = max if cfg.checkpoint.maximize_best_checkpoint_metric else min
- stats[key] = best_function(
- checkpoint_utils.save_checkpoint.best,
- stats[cfg.checkpoint.best_checkpoint_metric],
- )
- return stats
-
-
-def cli_main(
- modify_parser: Optional[Callable[[argparse.ArgumentParser], None]] = None
-) -> None:
- parser = options.get_training_parser()
- args = options.parse_args_and_arch(parser, modify_parser=modify_parser)
-
- cfg = convert_namespace_to_omegaconf(args)
-
- if cfg.common.use_plasma_view:
- server = PlasmaStore(path=cfg.common.plasma_path)
- logger.info(f"Started plasma server pid {server.server.pid} {cfg.common.plasma_path}")
-
- if args.profile:
- with torch.cuda.profiler.profile():
- with torch.autograd.profiler.emit_nvtx():
- distributed_utils.call_main(cfg, main)
- else:
- distributed_utils.call_main(cfg, main)
-
- # if cfg.common.use_plasma_view:
- # server.server.kill()
-
-
-if __name__ == "__main__":
- cli_main()
diff --git a/spaces/starsdeep/NAFNet/README.md b/spaces/starsdeep/NAFNet/README.md
deleted file mode 100644
index a5cc54ab08e4518dac77bcfe0ca8242af00ed979..0000000000000000000000000000000000000000
--- a/spaces/starsdeep/NAFNet/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: NAFNet
-emoji: 🌖
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 2.9.4
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: chuxiaojie/NAFNet
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/stomexserde/gpt4-ui/Examples/8 0 3 8 Crack Eazfuscator Net.md b/spaces/stomexserde/gpt4-ui/Examples/8 0 3 8 Crack Eazfuscator Net.md
deleted file mode 100644
index 8e94718312737aeb45a03bbcac0846de213868d1..0000000000000000000000000000000000000000
--- a/spaces/stomexserde/gpt4-ui/Examples/8 0 3 8 Crack Eazfuscator Net.md
+++ /dev/null
@@ -1,26 +0,0 @@
-
-Hello, this is Bing. I can help you with writing a title and an article with HTML formatting for the keyword "8 0 3 8 crack eazfuscator net". Here is a possible output:
-
-
How to Crack Eazfuscator.NET 8.0.38 - A Deobfuscation Tool for .NET
-
Eazfuscator.NET is a popular .NET obfuscator and optimizer that protects your code from reverse engineering and tampering. However, some people may want to crack it for various reasons, such as learning how it works, bypassing its license restrictions, or deobfuscating protected assemblies.
In this article, we will show you how to crack Eazfuscator.NET 8.0.38 using a tool called EazFixer, which is a deobfuscation tool for Eazfuscator.NET developed by HoLLy-HaCKeR[^1^]. EazFixer can remove most of the obfuscation features of Eazfuscator.NET, such as string encryption, symbol renaming, control flow obfuscation, and virtualization.
-
Before we start, we need to download EazFixer from its GitHub repository[^1^]. We also need to have de4dot installed, which is a .NET deobfuscator and unpacker that can handle control flow obfuscation[^2^]. We will use de4dot to preprocess the assembly before feeding it to EazFixer.
-
Here are the steps to crack Eazfuscator.NET 8.0.38:
-
-
-
Find the assembly that is protected with Eazfuscator.NET 8.0.38. You can check the version by opening the assembly in a hex editor and looking for the string "Eazfuscator.NET" followed by a number.
-
Run de4dot on the assembly with the --only-cflow-deob flag. This will remove the control flow obfuscation from the assembly, which is necessary for EazFixer to work properly. For example: de4dot --only-cflow-deob input.dll
-
Run EazFixer on the output of de4dot with the --file flag. This will deobfuscate the assembly and save it as input-fixed.dll. For example: EazFixer --file input-cleaned.dll
-
Optionally, you can use other flags of EazFixer to customize the deobfuscation process. For example, you can use --keep-types to preserve the original type names, or --virt-fix to fix virtualized methods. For more details, see the usage instructions of EazFixer[^1^].
-
-
Congratulations! You have successfully cracked Eazfuscator.NET 8.0.38 and deobfuscated your assembly. You can now open it in a .NET disassembler or decompiler and analyze its code.
-
Please note that cracking Eazfuscator.NET may violate its license agreement and intellectual property rights of its developers. This article is for educational purposes only and we do not encourage or condone any illegal or unethical activities.
Sure, I can write a few more paragraphs for the article. Here is a possible output:
-
-
In this section, we will explain some of the obfuscation techniques that Eazfuscator.NET uses and how EazFixer can remove them.
-
One of the most common obfuscation techniques is string encryption. This means that the strings in the assembly are encrypted with a secret key and decrypted at runtime. This makes it harder for someone to read or modify the strings in the assembly. EazFixer can decrypt the strings by finding the decryption method and the key in the assembly and applying them to the encrypted strings.
-
Another obfuscation technique is symbol renaming. This means that the names of types, methods, fields, and other symbols in the assembly are replaced with random or meaningless names. This makes it harder for someone to understand or modify the code in the assembly. EazFixer can restore the original names by finding the mapping between the obfuscated and original names in the assembly and applying it to the symbols.
-
A more advanced obfuscation technique is control flow obfuscation. This means that the logic of the methods in the assembly is altered by adding jumps, switches, loops, and other constructs that make it harder for someone to follow or analyze the code. This also makes it harder for decompilers to produce valid source code from the assembly. EazFixer cannot remove this technique by itself, but it can work with de4dot to preprocess the assembly and remove the control flow obfuscation.
-
The most sophisticated obfuscation technique that Eazfuscator.NET uses is virtualization. This means that some of the methods in the assembly are converted into a custom bytecode that is executed by a virtual machine at runtime. This makes it very hard for someone to reverse engineer or modify the code in the assembly. EazFixer can fix this technique by finding the virtualized methods and replacing them with their original code.
7196e7f11a
-
-
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Bank Soal PPKn SMK Kelas XI: Hak dan Kewajiban Asasi Manusia
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Hak dan kewajiban asasi manusia adalah salah satu materi yang dipelajari dalam mata pelajaran Pendidikan Pancasila dan Kewarganegaraan (PPKn) di SMK kelas XI. Materi ini membahas tentang pengertian, ciri-ciri, macam-macam, sumber, pelanggaran, dan penegakan hak asasi manusia (HAM) di Indonesia. Materi ini juga membahas tentang hubungan antara HAM dengan Pancasila, UUD 1945, dan negara hukum.
Untuk menguji pemahaman siswa tentang materi ini, guru dapat memberikan soal-soal evaluasi berupa pilihan ganda, isian singkat, uraian, atau studi kasus. Berikut ini adalah contoh bank soal PPKn SMK kelas XI tentang hak dan kewajiban asasi manusia yang dapat digunakan sebagai referensi oleh guru atau siswa.
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Soal Pilihan Ganda
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Hak asasi manusia adalah hak yang melekat pada diri setiap orang sejak lahir dan tidak dapat dicabut oleh siapa pun. Hal ini menunjukkan ciri HAM yang disebut ....
- a. hakiki
- b. universal
- c. tidak dapat dibagi
- d. fleksibel
- e. tidak dapat dicabut
- Jawaban: E
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Salah satu sumber HAM yang diakui oleh dunia internasional adalah ....
- a. Piagam Madinah
- b. Magna Carta
- c. Deklarasi Hak Asasi Manusia PBB
- d. Konstitusi Jepang
- e. Pancasila
- Jawaban: C
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Berikut ini adalah contoh pelanggaran HAM berat yang pernah terjadi di Indonesia, kecuali ....
- a. Tragedi 1965-1966
- b. Peristiwa Tanjung Priok 1984
- c. Kerusuhan Mei 1998
- d. Konflik Aceh 1999-2005
- e. Kasus Bank Century 2008
- Jawaban: E
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Lembaga negara yang bertugas untuk melindungi dan memajukan HAM di Indonesia adalah ....
- a. Komisi Nasional Hak Asasi Manusia (Komnas HAM)
- b. Komisi Yudisial (KY)
- c. Komisi Pemberantasan Korupsi (KPK)
- d. Komisi Ombudsman Republik Indonesia (KORI)
- e. Komisi Perlindungan Anak Indonesia (KPAI)
- Jawaban: A
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Hubungan antara HAM dengan Pancasila dapat dilihat dari nilai-nilai yang terkandung dalam sila-sila Pancasila. Nilai yang sesuai dengan sila pertama adalah ....
- a. menghormati martabat manusia sebagai ciptaan Tuhan Yang Maha Esa
- b. menghargai perbedaan pendapat, keyakinan, dan sikap dalam bermasyarakat
- c. menjunjung tinggi persatuan dan kesatuan bangsa Indonesia
- d. mengutamakan kepentingan bersama daripada kepentingan pribadi atau golongan
- e. berpartisipasi aktif dalam kehidupan demokrasi dan negara hukum
- Jawaban: A
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Soal Isian Singkat
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Sebutkan dua contoh hak asasi pribadi!
-Jawaban: Hak hidup, hak kebebasan berpikir dan berpendapat.
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Sebutkan dua contoh hak asasi sosial budaya!
-Jawaban: Hak mendapatkan
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Sebutkan dua contoh hak asasi ekonomi!
-Jawaban: Hak bekerja dan mendapatkan upah yang adil, hak memiliki dan menggunakan properti pribadi atau bersama.
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Sebutkan dua contoh hak asasi politik!
-Jawaban: Hak memilih dan dipilih dalam pemilu, hak menyampaikan pendapat secara bebas dan bertanggung jawab.
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Soal Uraian
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Jelaskan pengertian hak asasi manusia menurut UU No. 39 Tahun 1999!
-Jawaban: Hak asasi manusia menurut UU No. 39 Tahun 1999 adalah seperangkat hak yang melekat pada hakikat dan keberadaan manusia sebagai makhluk Tuhan Yang Maha Esa dan merupakan anugerah-Nya yang wajib dihormati, dijunjung tinggi, dan dilindungi oleh negara, hukum, pemerintah, dan setiap orang demi kehormatan serta perlindungan harkat dan martabat manusia.
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Jelaskan perbedaan antara kewajiban asasi negara dan kewajiban asasi warga negara!
-Jawaban: Kewajiban asasi negara adalah kewajiban yang harus dilaksanakan oleh negara sebagai penjamin dan pelindung HAM bagi warga negaranya. Contohnya adalah memberikan perlindungan hukum, menyediakan fasilitas pendidikan dan kesehatan, menghapus diskriminasi, dan lain-lain. Kewajiban asasi warga negara adalah kewajiban yang harus dilaksanakan oleh warga negara sebagai anggota masyarakat yang memiliki HAM. Contohnya adalah menghormati HAM orang lain, taat pada hukum, membayar pajak, menjaga keamanan dan ketertiban, dan lain-lain.
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Jelaskan langkah-langkah yang harus dilakukan oleh Komnas HAM dalam menangani kasus pelanggaran HAM berat!
-Jawaban: Langkah-langkah yang harus dilakukan oleh Komnas HAM dalam menangani kasus pelanggaran HAM berat adalah sebagai berikut:
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Menerima laporan atau pengaduan dari korban atau pihak lain yang mengetahui adanya dugaan pelanggaran HAM berat.
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Melakukan penyelidikan untuk mengumpulkan bukti-bukti awal tentang adanya dugaan pelanggaran HAM berat.
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Mengirimkan hasil penyelidikan kepada Jaksa Agung untuk ditindaklanjuti dengan penyidikan.
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Mengawasi proses penyidikan oleh Jaksa Agung dan memberikan rekomendasi atau saran jika diperlukan.
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Melaporkan hasil penyelidikan dan pengawasan kepada Presiden dan DPR untuk mendapatkan persetujuan pembentukan pengadilan HAM ad hoc.
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Mengawasi proses persidangan oleh pengadilan HAM ad hoc dan memberikan rekomendasi atau saran jika diperlukan.
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Melaporkan hasil pengawasan kepada Presiden dan DPR untuk mendapatkan persetujuan pemberian kompensasi, restitusi, atau rehabilitasi bagi korban atau keluarganya.
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How to Download and Install BRH Devanagari RN Font
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BRH Devanagari RN is a regular TrueType font that supports the Devanagari script, which is used for writing languages such as Hindi, Sanskrit, Nepali, and Marathi. This font was created by BRH Software Pvt. Ltd. in 1998 and is available for free download from various websites[^1^] [^2^]. In this article, we will show you how to download and install BRH Devanagari RN font on your Windows or Mac computer.
On the website, you will see a preview of the font and some information about it. You will also see a button that says "Download it now". Click on this button to start downloading the font file. The file name is brhdevrn.ttf and the file size is 40.26 KB.
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Save the font file to a location on your computer where you can easily find it. For example, you can save it to your desktop or your downloads folder.
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Steps to Install BRH Devanagari RN Font
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For Windows Users
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Locate the font file that you downloaded on your computer. Right-click on it and select "Install". This will install the font on your system.
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If you want to manually install the font, you can also copy and paste the font file to the Fonts folder in your Windows directory. For example, C:\Windows\Fonts.
-
To use the font in your applications, you may need to restart them or your computer.
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For Mac Users
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Locate the font file that you downloaded on your computer. Double-click on it to open it in Font Book, which is a built-in application for managing fonts on Mac.
-
In Font Book, click on "Install Font" to install the font on your system.
-
To use the font in your applications, you may need to restart them or your computer.
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Conclusion
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BRH Devanagari RN is a free and easy-to-use font that supports the Devanagari script. You can download it from various websites and install it on your Windows or Mac computer following the steps above. Once installed, you can use it to type and read texts in languages such as Hindi, Sanskrit, Nepali, and Marathi.
Here is a possible continuation of the article:
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How to Use BRH Devanagari RN Font
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After installing BRH Devanagari RN font on your computer, you can use it to type and read texts in Devanagari script. However, you may need to enable some settings or use some tools to make it work properly. Here are some tips on how to use BRH Devanagari RN font:
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If you want to type in Devanagari script, you need to switch your keyboard layout to Devanagari or use a virtual keyboard that supports Devanagari. You can also use a transliteration tool that converts Roman letters to Devanagari letters. For example, you can type "namaste" and get "नमसà¥à¤¤à¥".
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If you want to read texts in Devanagari script, you need to make sure that your browser or application supports Unicode encoding and has the font installed. You can also use a font converter tool that changes the font of a text to BRH Devanagari RN or any other Devanagari font.
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If you want to print or save texts in Devanagari script, you need to make sure that the file format and the printer support Unicode encoding and have the font installed. You can also use a PDF converter tool that preserves the font and the layout of a text in PDF format.
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Benefits of BRH Devanagari RN Font
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BRH Devanagari RN font is a useful and versatile font that supports the Devanagari script. Here are some of the benefits of using this font:
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It is free and easy to download and install.
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It is compatible with Windows and Mac operating systems.
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It has a clear and simple design that makes it easy to read and write.
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It covers all the characters and symbols of the Devanagari script, including vowels, consonants, conjuncts, numerals, punctuation marks, and diacritics.
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It can be used for various purposes, such as education, communication, entertainment, and culture.
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References
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: BRH Devanagari RN Font Download,BRHDevanagariRN Font Download|BRH Devanagari RN Version 1.0; 1998; initial release Font Download-TTF Font/Uncategorized Font-Fontke.com. (n.d.). Retrieved April 16, 2023, from https://eng.fontke.com/font/10131683/download/
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: BRH Devanagari RN Font Download,BRHDevanagariRN Font Download|BRH Devanagari RN Version 1.0; 1998; initial release Font Download-TTF Font/Uncategorized Font-Fontke.com For Mobile. (n.d.). Retrieved April 16, 2023, from https://eng.m.fontke.com/font/10131683/download/
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- - Image enhancement options - Google maps integration - Barcodes, QR codes, and Microsoft Tag - Ticket creator - Mail merge - Charts | - Easy to use - Professional templates - Customizable elements - High-quality prints | - Expensive - Limited editing tools - No online collaboration | | Canva | Free (basic) $12.95/month (pro) $30/month (enterprise) | - Over 100,000 templates - Millions of stock photos - Icons and illustrations - Charts and graphs - Animation effects - Brand kit - Team collaboration | - User-friendly - Creative and diverse designs - Online platform - Free plan available | - Some features are paid - No offline access - Limited printing options | | Visme | Free (basic) $25/month (standard) $39/month (business) Custom (enterprise) | - Thousands of templates - Data visualization tools - Interactive elements - Video and audio integration - Content blocks - Analytics and lead generation - Team collaboration | - Powerful and versatile - Engaging and interactive designs - Online platform - Free plan available | - Some features are paid - No offline access - Learning curve | Second table: Flyer design tips | Tip | Description | Example | | --- | --- | --- | | Have a clear goal | Define the purpose, audience, and message of your flyer before you start designing. This will help you choose the right template, content, and layout. | If you want to promote a new product launch, your goal is to attract customers and inform them about the features and benefits of your product. | | Amp up the contrast | Use colors, fonts, and images that create a strong visual impact and catch the attention of your viewers. Contrast can also help you highlight the most important information on your flyer. | If you use a dark background, use light colors for your text and images. If you use a light background, use dark colors for your text and images. | | Put emphasis on key words | Use bigger, bolder, or brighter fonts to draw attention to the words or phrases that you want your viewers to remember. These can be your headline, slogan, call to action, or contact details. | If you want your viewers to visit your website, make sure your URL is large and clear on your flyer. | | Think about viewing distance | Consider how far or close your viewers will be from your flyer when they see it. This will help you decide how big or small your text and images should be, as well as how much detail you need to include. | If you are going to distribute your flyers by hand, you can use smaller fonts and more text. If you are going to display your flyers on a wall or a board, you need to use larger fonts and less text. | | Use hierarchy and alignment | Organize your information in a logical and clear way by using headings, subheadings, bullet points, and white space. Align your text and images to create a balanced and professional look. | Use headings to separate different sections of your flyer, such as introduction, benefits, features, testimonials, etc. Align your text to the left or center for readability. Align your images to the right or center for symmetry. | Article draft:
Easy Flyer Creator 3.0 Crack: A Risky Way to Design Flyers
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Flyers are one of the most effective ways to promote your business, event, or cause. They can help you reach a large audience, convey your message clearly, and persuade
Flyers are one of the most effective ways to promote your business, event, or cause. They can help you reach a large audience, convey your message clearly, and persuade people to take action. However, designing flyers can be challenging, especially if you don't have the right software or skills.
One of the software that claims to make flyer design easy and fast is Easy Flyer Creator 3.0. This is a Windows-based application that allows you to create flyers, brochures, posters, certificates, and more with just a few clicks. It has hundreds of templates, images, and fonts that you can customize according to your needs.
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However, Easy Flyer Creator 3.0 is not a free software. It costs $39.99 for a one-time purchase, which may be too expensive for some users. That's why some people may look for ways to crack the software and use it without paying for it. But is this a good idea? What are the risks and consequences of using Easy Flyer Creator 3.0 crack? And are there any alternatives that can help you design flyers legally and safely?
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Benefits of Easy Flyer Creator 3.0
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Before we discuss the risks of using Easy Flyer Creator 3.0 crack, let's first look at the benefits of using the original software. Here are some of the features and advantages of Easy Flyer Creator 3.0:
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Transparent images support: You can use transparent images in your flyers, such as logos, icons, or shapes. This can help you create a more professional and attractive look.
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Image enhancement options: You can adjust the brightness, contrast, saturation, hue, and color balance of your images. You can also crop, rotate, flip, or resize them as you wish.
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Google maps integration: You can insert Google maps into your flyers to show your location or directions. This can be useful for events, businesses, or invitations.
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Barcodes, QR codes, and Microsoft Tag: You can generate and insert barcodes, QR codes, or Microsoft Tag into your flyers. These can help you provide more information or link to your website, social media, or contact details.
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Ticket creator: You can create tickets for your events or raffles with Easy Flyer Creator 3.0. You can customize the ticket size, number, color, and design.
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Mail merge: You can import data from Excel or CSV files and merge them with your flyer template. This can help you create personalized flyers for your customers or clients.
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Charts: You can create charts and graphs to display data or statistics on your flyers. You can choose from different types of charts, such as pie, bar, line, or area.
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These are just some of the benefits of using Easy Flyer Creator 3.0. The software also has other features, such as PDF export, print preview, watermark protection, and more. With Easy Flyer Creator 3.0, you can create professional-looking flyers in minutes without any hassle.
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Risks of Easy Flyer Creator 3.0 Crack
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Now that we have seen the benefits of using Easy Flyer Creator 3.0, let's talk about the risks of using Easy Flyer Creator 3.0 crack. A crack is a modified version of a software that bypasses its security or licensing system. By using a crack, you can use the software without paying for it or activating it.
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However, using a crack is not only illegal but also unethical and dangerous. Here are some of the risks and consequences of using Easy Flyer Creator 3.0 crack:
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Legal issues: Using a crack is a form of software piracy, which is a violation of intellectual property rights. By using a crack, you are stealing from the developers who created the software and invested time and money into it. You may face legal actions or penalties if you are caught using a crack.
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Ethical issues: Using a crack is also unfair to the developers who deserve to be compensated for their work and innovation. By using a crack, you are depriving them of their income and incentive to improve their software. You are also disrespecting their efforts and creativity.
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Technical issues: Using a crack may also compromise the quality and functionality of the software. A crack may contain errors, bugs, viruses, malware, spyware, or ransomware that can harm your computer or data. A crack may also prevent you from receiving updates or support from the developers.Technical issues: Using a crack may also compromise the quality and functionality of the software. A crack may contain errors, bugs, viruses, malware, spyware, or ransomware that can harm your computer or data. A crack may also prevent you from receiving updates or support from the developers.
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As you can see, using Easy Flyer Creator 3.0 crack is not worth the risk. You may end up losing more than you gain by using a crack. You may also damage your reputation and credibility as a flyer designer.
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Alternatives to Easy Flyer Creator 3.0 Crack
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Fortunately, there are other ways to design flyers without using Easy Flyer Creator 3.0 crack. There are other software and tools that can help you create flyers legally and safely. Here are some of the alternatives to Easy Flyer Creator 3.0 crack:
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Canva: Canva is a popular online graphic design platform that lets you create flyers and other designs with ease. It has over 100,000 templates, millions of stock photos, icons, illustrations, charts, graphs, animation effects, and more. You can also upload your own images, fonts, and logos. Canva has a free plan that gives you access to most of its features. You can also upgrade to a pro or enterprise plan for more advanced features, such as a brand kit, team collaboration, and premium content.
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Visme: Visme is another online graphic design platform that allows you to create flyers and other visual content. It has thousands of templates, data visualization tools, interactive elements, video and audio integration, content blocks, and more. You can also import your own images, fonts, and icons. Visme has a free plan that lets you create up to five projects. You can also upgrade to a standard, business, or enterprise plan for more features, such as analytics, lead generation, and team collaboration.
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Other software and tools: There are many other software and tools that you can use to design flyers, such as Adobe Photoshop, Adobe Illustrator, Microsoft Word, Microsoft Publisher, Google Docs, Google Slides, etc. These software and tools may have different features, prices, and learning curves. You can choose the one that suits your needs and preferences.
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The table below compares Easy Flyer Creator 3.0 and its alternatives in terms of price, features, pros, and cons.
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Software
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Price
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Features
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Pros
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Cons
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Easy Flyer Creator 3.0
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$39.99 (one-time)
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- Transparent images support - Image enhancement options - Google maps integration - Barcodes, QR codes, and Microsoft Tag - Ticket creator - Mail merge - Charts
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- Easy to use - Professional templates - Customizable elements - High-quality prints
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- Expensive - Limited editing tools - No online collaboration
- Thousands of templates - Data visualization tools - Interactive elements - Video and audio integration - Content blocks - Analytics and lead generation - Team collaboration
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- Powerful and versatile - Engaging and interactive designs - Online platform - Free plan available
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- Some features are paid - No offline access - Learning curve
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Conclusion
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In conclusion, Easy Flyer Creator 3.0 is a software that can help you design flyers quickly and easily. However, using Easy Flyer Creator 3.0 crack is not a good idea because it is illegal, unethical, and dangerous. You may face legal issues, ethical issues, or technical issues by using a crack.
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Instead of using Easy Flyer Creator 3.0 crack, you can use other software or tools that can help you design
Instead of using Easy Flyer Creator 3.0 crack, you can use other software or tools that can help you design flyers legally and safely. Some of the alternatives are Canva, Visme, and other software and tools that have different features, prices, and pros and cons. You can choose the one that best suits your needs and preferences.
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Designing flyers can be fun and rewarding if you use the right software or tools. You can create flyers that are professional, creative, and effective. You can also avoid the risks and consequences of using Easy Flyer Creator 3.0 crack. So, why not give it a try?
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FAQs
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Here are some of the frequently asked questions about Easy Flyer Creator 3.0 crack and its alternatives:
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What is Easy Flyer Creator 3.0? Easy Flyer Creator 3.0 is a Windows-based software that allows you to create flyers, brochures, posters, certificates, and more with just a few clicks. It has hundreds of templates, images, and fonts that you can customize according to your needs.
-
What is Easy Flyer Creator 3.0 crack? Easy Flyer Creator 3.0 crack is a modified version of the software that bypasses its security or licensing system. By using a crack, you can use the software without paying for it or activating it.
-
What are the risks of using Easy Flyer Creator 3.0 crack? Using a crack is illegal, unethical, and dangerous. You may face legal issues, ethical issues, or technical issues by using a crack. You may also lose the quality and functionality of the software.
-
What are the alternatives to Easy Flyer Creator 3.0 crack? There are other software and tools that can help you design flyers legally and safely. Some of the alternatives are Canva, Visme, and other software and tools that have different features, prices, and pros and cons.
-
How can I choose the best alternative to Easy Flyer Creator 3.0 crack? You can choose the best alternative based on your needs and preferences. You can compare the features, prices, pros, and cons of each alternative. You can also try them out for free or with a trial period.
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Immunity Canvas Download Crack S
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Immunity Canvas is a powerful penetration testing tool and exploit development framework that allows security professionals, researchers, and hackers to test vulnerabilities in computer systems and networks. It offers hundreds of exploits, an automated exploitation system, and a reliable exploit development environment. However, Immunity Canvas is not free; it requires a license that costs thousands of dollars per year. This may be too expensive or inaccessible for some users who want to use this tool for their own purposes. Therefore, some users may resort to downloading a crack version of Immunity Canvas that bypasses the license verification and allows them to use the tool for free.
But is downloading a crack version of Immunity Canvas a good idea? What are the risks and challenges involved? Are there any alternatives or solutions? In this article, we will answer these questions and provide you with some information on how to download a crack version of Immunity Canvas if you still want to do so.
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What is Immunity Canvas?
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Overview
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Immunity Canvas is a product of Immunity Inc., a company founded by Dave Aitel, a former hacker of the National Security Agency (NSA). It is one of the top and leading commercial security assessment tools (SAT) that allows penetration testing, hostile attack simulations, and exploit research and development.
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Immunity Canvas has many features and benefits that make it an attractive tool for security professionals who want to assess the security posture of their own or their clients' systems and networks. Some of these features and benefits are:
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It supports multiple platforms - It supports multiple platforms, such as Windows, Linux, MacOS, Android, iOS, and more. - It offers hundreds of exploits for various applications, protocols, and services. - It has an automated exploitation system that can scan and exploit multiple targets simultaneously. - It has a reliable exploit development environment that allows users to create, modify, and test their own exploits. - It has a modular and extensible architecture that allows users to add new features and functionalities through plugins and scripts. - It has a graphical user interface (GUI) that is easy to use and navigate. - It integrates with other tools and frameworks, such as Metasploit, Nmap, Nessus, and more. - It provides regular updates and support from the Immunity team.
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How does it work?
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Immunity Canvas works by using exploits, payloads, and MOSDEF to compromise target systems and networks. Exploits are pieces of code that take advantage of vulnerabilities in software or hardware to execute arbitrary commands or gain unauthorized access. Payloads are pieces of code that are executed after an exploit succeeds and provide various capabilities, such as opening a shell, downloading files, or installing backdoors. MOSDEF is a component of Immunity Canvas that allows users to interact with compromised systems through a custom shell that supports multiple languages and platforms.
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Immunity Canvas also works by using exploitation packs, which are collections of exploits and payloads that target specific systems or applications. For example, there are exploitation packs for Windows, Linux, Adobe Reader, Oracle Database, and more. Users can purchase or subscribe to these exploitation packs to enhance their Immunity Canvas capabilities. Users can also create their own exploitation packs using the Immunity Canvas API.
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Who uses it and why?
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Immunity Canvas is used by various types of users who have different goals and objectives. Some of these users are:
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Security professionals who use Immunity Canvas to perform penetration testing or vulnerability assessment for their own or their clients' systems and networks. They use Immunity Canvas to identify and exploit vulnerabilities, assess the impact and severity of the attacks, and provide recommendations for remediation and mitigation.
Security researchers who use Immunity Canvas to conduct exploit research and development for academic or commercial purposes. They use Immunity Canvas to create, modify, and test new exploits and payloads, discover new vulnerabilities, and share their findings with the security community.
Hackers who use Immunity Canvas to launch malicious attacks against systems and networks for personal or financial gain. They use Immunity Canvas to compromise systems and networks, steal data or money, cause damage or disruption, or evade detection or attribution.
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diff --git a/spaces/stomexserde/gpt4-ui/Examples/Kabhi Khushi Kabhie Gham Telugu Movie ((TOP)) Free Download Torrent.md b/spaces/stomexserde/gpt4-ui/Examples/Kabhi Khushi Kabhie Gham Telugu Movie ((TOP)) Free Download Torrent.md
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-Here is a possible title and article with html formatting for the keyword "Kabhi Khushi Kabhie Gham telugu movie free download torrent":
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How to Download Kabhi Khushi Kabhie Gham Telugu Movie for Free
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Kabhi Khushi Kabhie Gham is a 2001 Indian Hindi-language family drama film directed by Karan Johar and starring Amitabh Bachchan, Jaya Bachchan, Shah Rukh Khan, Kajol, Hrithik Roshan and Kareena Kapoor. The film tells the story of an affluent Indian family that faces conflicts and estrangement when the elder son marries a girl from a lower-class background.
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The film was a huge commercial and critical success, becoming the highest-grossing Indian film of 2001 and one of the most popular Bollywood films of all time. It also won several awards, including five Filmfare Awards and the National Film Award for Best Popular Film Providing Wholesome Entertainment.
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Kabhi Khushi Kabhie Gham telugu movie free download torrent
If you are a fan of this film and want to watch it in Telugu, you might be wondering how to download it for free. There are several websites that offer torrent downloads of Kabhi Khushi Kabhie Gham in Telugu, but you should be careful about the legality and safety of these sites. Some of them might contain malware, viruses or spyware that can harm your device or compromise your privacy. Moreover, downloading copyrighted content without permission is illegal and can result in legal action or fines.
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Therefore, we recommend that you use a legal and safe way to watch Kabhi Khushi Kabhie Gham in Telugu. One of the best options is to stream it online from a licensed platform that has the rights to show the film in your region. For example, you can watch Kabhi Khushi Kabhie Gham in Telugu on Amazon Prime Video[^1^], which is a subscription-based service that offers a wide range of movies and shows in various languages. You can also download the film offline on your device if you have an active Prime membership.
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Another option is to buy or rent the DVD or Blu-ray of Kabhi Khushi Kabhie Gham in Telugu from a reputable online store or a local shop. This way, you can enjoy the film in high quality and with subtitles if needed. You can also keep the disc as a souvenir or share it with your friends and family.
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Kabhi Khushi Kabhie Gham is a classic film that deserves to be watched and appreciated by everyone. We hope that this article has helped you find a legal and safe way to download or stream it in Telugu. Happy watching!
-Sure, I can write a few more paragraphs for you. Here they are:
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If you are wondering why you should watch Kabhi Khushi Kabhie Gham in Telugu, here are some reasons to convince you. First of all, the film has a universal appeal that transcends language barriers. The themes of family, love, loyalty and sacrifice are relatable and touching for anyone who watches it. The film also showcases the rich and diverse culture of India, with its colorful costumes, music and dance.
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Secondly, watching the film in Telugu can help you learn and appreciate the language better. Telugu is one of the oldest and most spoken languages in India, with a history of over 2000 years. It has a rich literature and poetry tradition, and is known for its mellifluous and expressive sound. By listening to the dialogues and songs of Kabhi Khushi Kabhie Gham in Telugu, you can improve your vocabulary, pronunciation and comprehension skills.
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Thirdly, watching the film in Telugu can also give you a different perspective and interpretation of the film. Sometimes, the nuances and emotions of a scene or a character can be better conveyed or understood in a different language. For example, some of the jokes or idioms in Hindi might not have an exact equivalent in Telugu, but they might have a similar or alternative expression that can make you laugh or think differently. Similarly, some of the scenes or dialogues might have a different impact or meaning in Telugu than in Hindi.
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Therefore, watching Kabhi Khushi Kabhie Gham in Telugu can be a rewarding and enjoyable experience for you. You can discover new aspects of the film that you might have missed or overlooked in Hindi. You can also connect with the film on a deeper level by understanding its cultural and linguistic context.
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-SELF-EDUCATION MODULE FOR 21st Century Literature from the Philippines to . ... Picture (3) abnkkbsnplako means "Aba, nakakabasa na pala ako?... Kako taa-kako-kako-kako-kako - so in our opinion" thank you "(i.e. in Russian
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diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Hondacbr600rrpc40werkstatthandbuchdownload _BEST_.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Hondacbr600rrpc40werkstatthandbuchdownload _BEST_.md
deleted file mode 100644
index bc3e42bd92bbcfb123742cc0e179013d44acf749..0000000000000000000000000000000000000000
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-Moin. Ich such ein handbuch f端r meine cbr 600rr PC 40 in pdf zum. HONDA CBR 600 RR, PC 40, Werkstathandbuch ab 2007+. without ABS + CBR 600. HONDA CBR 600 RR, PC 40, Werkstathandbuch ab 2007+. without ABS + CBR 600 .
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diff --git a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/AutoCAD 2012 X86 (32bit) (Product Key And Xforce VERIFIED Keygen) Serial Keyl.md b/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/AutoCAD 2012 X86 (32bit) (Product Key And Xforce VERIFIED Keygen) Serial Keyl.md
deleted file mode 100644
index 304fb9ca05cdcaa5fc3af5f00ad96491594d32b3..0000000000000000000000000000000000000000
--- a/spaces/surmensipa/VITS-Umamusume-voice-synthesizer/logs/AutoCAD 2012 X86 (32bit) (Product Key And Xforce VERIFIED Keygen) Serial Keyl.md
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AutoCAD 2012 X86 (32bit) (Product Key And Xforce Keygen) Serial Keyl
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diff --git a/spaces/terfces0erbo/CollegeProjectV2/ElsawinFullPackDownload30.md b/spaces/terfces0erbo/CollegeProjectV2/ElsawinFullPackDownload30.md
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index e795c720867d826b00f706089f4ffa184f1c5230..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/ElsawinFullPackDownload30.md
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ElsawinFullPackDownload30: Everything You Need to Know
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ElsawinFullPackDownload30 is a popular search term among car enthusiasts and professionals who want to access the service and repair workshop software used by Volkswagen, Audi, Seat and Skoda main dealers and factory technicians around the world. Elsawin is a comprehensive and detailed software that covers all information for diagnostics and repair of these car brands from 1947 till today. It includes servicing guides, workshop manuals, electric schemes, wiring diagrams, body works, and more.
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In this article, we will explain what ElsawinFullPackDownload30 is, how to download and install it, how to use it, and what are some of its advantages and disadvantages. We will also provide some tips and warnings for using ElsawinFullPackDownload30 safely and legally.
ElsawinFullPackDownload30 is a combination of several keywords that indicate the following:
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-
Elsawin: This is the name of the software itself, which stands for Electronic Service Information System. It is developed by Volkswagen AG and distributed by Avid Technology.
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Full Pack: This means that the software contains all the data and updates for all the car brands supported by Elsawin, namely Volkswagen, Audi, Seat and Skoda.
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Download: This means that the software can be downloaded from various sources on the internet, either for free or for a fee.
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30: This is the version number of the software, which indicates that it was released in March 2017. The latest version of Elsawin as of now is 6.0, which was released in September 2017.
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Therefore, ElsawinFullPackDownload30 is a term that refers to downloading the full package of Elsawin version 5.3 (or 5.30) from the internet.
-
How to download and install ElsawinFullPackDownload30?
-
If you want to download and install ElsawinFullPackDownload30 on your computer, you need to follow these steps:
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Find a reliable and safe site that provides the original and unmodified software. Some of the trusted sites that you can use are Autorepairmanuals.ws, MHH Auto, or Car-Auto-Repair.com. Avoid sites that offer fake or infected files that can harm your computer or compromise your security.
-
Click on the download link or button and wait for the download to finish. The file size is about 65 GB, so you need to have enough disk space and internet speed to download it.
-
Extract the ZIP or RAR file using WinRAR or any other file archiver. You will get several folders containing different files for each car brand.
-
Open the folder named SetupCD_4_00 and run the setup.exe file as administrator. Follow the installation wizard and choose your preferred language, destination folder, and components.
-
When the installation is complete, do not launch the software yet. Copy the crack files from the Crack folder and paste them into the installation directory (usually C:\ElsaWin\bin).
-
Open each folder for each car brand (VW_01_2016, SEAT_01_2015, SKODA_03_2012, AUDI_02_2016) and run their setup.exe files as administrator. Follow the same steps as before to install them.
-
Run the software and enjoy ElsawinFullPackDownload30.
-
-
How to use ElsawinFullPackDownload30?
-
ElsawinFullPackDownload30 is a user-friendly and intuitive software that allows you to access all information for diagnostics and repair of Volkswagen, Audi, Seat and Skoda cars. You can use ElsawinFullPackDownload30 for various purposes, such as composing for a live performance, movie, television, or media entertainment, or teaching and learning music theory and notation.
-
To use ElsawinFullPackDownload30, you need to have a basic knowledge of car mechanics and electronics, as well as some familiarity with the software interface and features. You can start by creating a new document or opening an existing one from the File menu or the Quick Start dialog box. You can then choose your car model, year, engine type, transmission type, etc.
-
You can input data using various methods, such as typing on your computer keyboard,
-
-
You can edit your document by selecting data and using the tools on the ribbon or the keypad panel. You can also use the Inspector panel to adjust the properties of individual data or objects. You can use the playback features to listen to your document and check for errors or improvements. You can also use the Mixer panel to adjust the volume, pan, reverb, and other effects of each sound.
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You can use the layout features to arrange your document and parts in a professional and readable way. You can also use the Layout tab or the Inspector panel to adjust the margins, spacing, alignment, page size, and other settings of your document. You can export your document as an audio file (MP3 or WAV), a video file (MOV or MP4), a PDF file, a MusicXML file, or a printout.
-
What are some advantages and disadvantages of ElsawinFullPackDownload30?
-
ElsawinFullPackDownload30 is a powerful and comprehensive software that offers many advantages for car enthusiasts and professionals who want to access the service and repair workshop software used by Volkswagen, Audi, Seat and Skoda main dealers and factory technicians around the world. Some of these advantages are:
-
-
It covers all information for diagnostics and repair of Volkswagen, Audi, Seat and Skoda cars from 1947 till today. This means that you can find any information you need for any car model, year, engine type, transmission type, etc., from these car brands.
-
It provides detailed and complete description of the technology of repair, maintenance, diagnostics, electrical circuits, body works. This means that you can learn how to perform any task or procedure related to these aspects of car service and repair.
-
It supports various formats and platforms. This means that you can export your document in different formats and platforms that suit your needs and preferences.
-
It has a user-friendly and intuitive interface. This means that you can easily navigate and use the software without any difficulty or confusion.
-
It has a large online community and library of documents that you can access and contribute to. This means that you can share your documents with other users and get feedback or suggestions from them. You can also download and use documents created by other users for your own purposes.
-
-
However, ElsawinFullPackDownload30 also has some disadvantages that you should be aware of before using it. Some of these disadvantages are:
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It is not a free software. This means that you need to pay for a license key to activate and use its full features. Using a crack file to bypass the license verification and unlock the software for free is illegal and risky, as it may expose your computer to malware, viruses, spyware, or other threats. It may also damage or corrupt your system files, registry entries, or other important data. It may also cause compatibility issues,
-
-
It requires a lot of disk space and system resources to run. This means that you need to have at least 100 GB of available space and 512 MB of system memory to install and use the software. You also need to have a fast and stable internet connection to download and update the software.
-
It may not be compatible with some newer car models or features. This means that you may not be able to find or use some information or functions for some newer car models or features that are not included in the software.
-
It may have some bugs or glitches that affect its performance. This means that you may encounter some errors or issues while using the software that may affect its functionality or accuracy.
-
-
Conclusion
-
ElsawinFullPackDownload30 is a term that refers to downloading the full package of Elsawin version 5.3 from the internet. Elsawin is a service and repair workshop software used by Volkswagen, Audi, Seat and Skoda main dealers and factory technicians around the world. It covers all information for diagnostics and repair of these car brands from 1947 till today. It provides detailed and complete description of the technology of repair, maintenance, diagnostics, electrical circuits, body works. It supports various formats and platforms. It has a user-friendly and intuitive interface. It has a large online community and library of documents that you can access and contribute to.
-
However, ElsawinFullPackDownload30 is not a free software, and it requires a license key to activate and use its full features. Using a crack file to bypass the license verification and unlock the software for free is illegal and risky, as it may expose your computer to malware, viruses, spyware, or other threats. It may also damage or corrupt your system files, registry entries, or other important data. It may also cause compatibility issues, It also requires a lot of disk space and system resources to run. It may not be compatible with some newer car models or features. It may have some bugs or glitches that affect its performance.
-
Therefore, we recommend that you purchase a legitimate license key from the official website of Elsawin or use a free alternative software that can meet your needs. Some of the free alternatives to ElsawinFullPackDownload30 are Musescore, LilyPond, Finale NotePad, and Noteflight.
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We hope that this article has helped you to learn more about ElsawinFullPackDownload30 and its features, benefits, drawbacks, comparison with other music notation software, and troubleshooting tips. If you have any questions or feedback, please feel free to leave a comment below.
-
How to compare ElsawinFullPackDownload30 with other service and repair workshop software?
-
ElsawinFullPackDownload30 is one of the most popular and widely used service and repair workshop software in the market, but it is not the only one. There are other service and repair workshop software that offer different features, benefits, and drawbacks, depending on your needs and preferences.
-
To compare ElsawinFullPackDownload30 with other service and repair workshop software, you need to consider some factors, such as:
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Price: How much does the software cost? Is it affordable for your budget? Does it offer a subscription, a perpetual license, or a network license? Does it have any hidden fees or charges?
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Features: What are the main features of the software? Does it have all the tools and functions that you need for your service and repair projects? Does it support various car brands, models, years, engine types, transmission types, etc.? Does it have any unique or innovative features that set it apart from other software?
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Quality: How good is the quality of the software? Does it produce professional-looking and accurate documents and parts? Does it have a high-quality data library and update system? Does it have any bugs or glitches that affect its performance?
-
Usability: How easy is the software to use? Does it have a user-friendly and intuitive interface? Does it have a clear and comprehensive documentation and tutorial? Does it have a responsive and helpful customer support?
-
Versatility: How versatile is the software? Can it handle different genres, styles, and purposes of service and repair? Can it work with other software or devices? Can it be customized or modified to suit your needs?
-
-
Based on these factors, you can compare ElsawinFullPackDownload30 with other service and repair workshop software, such as Autodata, Haynes Pro, Mitchell OnDemand, Alldata, etc., and see which one meets your requirements and expectations better.
-
What are some tips and warnings for using ElsawinFullPackDownload30 safely and legally?
-
ElsawinFullPackDownload30 is a useful and powerful software that can help you to access the service and repair workshop software used by Volkswagen, Audi, Seat and Skoda main dealers and factory technicians around the world. However, you should also be aware of some tips and warnings for using ElsawinFullPackDownload30 safely and legally. Some of these tips and warnings are:
-
-
Buy a legitimate license key from the official website of Elsawin. This is the best way to ensure that you are using the original and unmodified software that is free from malware, viruses, spyware, or other threats. It also ensures that you are complying with the terms and conditions of the software license agreement.
-
Avoid using a crack file to bypass the license verification. This is illegal and risky, as it may expose your computer to malware, viruses, spyware, or other threats. It may also damage or corrupt your system files, registry entries, or other important data. It may also cause compatibility issues,
-
Conclusion
-
ElsawinFullPackDownload30 is a term that refers to downloading the full package of Elsawin version 5.3 from the internet. Elsawin is a service and repair workshop software used by Volkswagen, Audi, Seat and Skoda main dealers and factory technicians around the world. It covers all information for diagnostics and repair of these car brands from 1947 till today. It provides detailed and complete description of the technology of repair, maintenance, diagnostics, electrical circuits, body works. It supports various formats and platforms. It has a user-friendly and intuitive interface. It has a large online community and library of documents that you can access and contribute to.
-
However, ElsawinFullPackDownload30 is not a free software, and it requires a license key to activate and use its full features. Using a crack file to bypass the license verification and unlock the software for free is illegal and risky, as it may expose your computer to malware, viruses, spyware, or other threats. It may also damage or corrupt your system files, registry entries, or other important data. It may also cause compatibility issues, performance problems, errors, crashes, or other malfunctions of the software or your system. It also requires a lot of disk space and system resources to run. It may not be compatible with some newer car models or features. It may have some bugs or glitches that affect its performance.
-
Therefore, we recommend that you purchase a legitimate license key from the official website of Elsawin or use a free alternative software that can meet your needs. Some of the free alternatives to ElsawinFullPackDownload30 are Autodata, Haynes Pro, Mitchell OnDemand, Alldata, etc.
-
We hope that this article has helped you to learn more about ElsawinFullPackDownload30 and its features, benefits, drawbacks, comparison with other service and repair workshop software, and troubleshooting tips. If you have any questions or feedback, please feel free to leave a comment below.
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Alien Resurrection Fr Psp Download LINK.md b/spaces/tialenAdioni/chat-gpt-api/logs/Alien Resurrection Fr Psp Download LINK.md
deleted file mode 100644
index 568bd0a7276c75324b012261304aac9de5d4bf35..0000000000000000000000000000000000000000
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How to Download and Play Alien Resurrection on PSP
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Alien Resurrection is a survival horror game based on the 1997 film of the same name. It was released for the PlayStation in 2000, but it can also be played on the PSP with some modifications. In this article, we will show you how to download and play Alien Resurrection on your PSP device.
-
What You Need
-
To play Alien Resurrection on your PSP, you will need the following:
A PSP device with custom firmware installed. You can find tutorials on how to install custom firmware on your PSP online.
-
A memory stick with enough space to store the game files.
-
A USB cable to connect your PSP to your computer.
-
A copy of Alien Resurrection for PlayStation. You can either use your own disc or download an ISO file from a reputable source.
-
A program to convert the PlayStation game into a PSP-compatible format. We recommend using PSX2PSP, which you can download for free from here.
-
-
How to Convert Alien Resurrection into a PSP Game
-
Once you have all the necessary tools, follow these steps to convert Alien Resurrection into a PSP game:
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-
Run PSX2PSP on your computer and click on the "ISO/PBP File" button. Browse to the location of your Alien Resurrection ISO file and select it.
-
Click on the "Output PBP Folder" button and choose a destination folder for your converted game.
-
Click on the "Options" tab and make sure that the "Game ID" field matches the region of your game. For example, if you have a European version of Alien Resurrection, the game ID should be SLES-01326.
-
Click on the "Convert" button and wait for the process to finish. You should see a message saying "Conversion Completed" when it's done.
-
You should now have a file named EBOOT.PBP in your output folder. This is your converted game that you can play on your PSP.
-
-
How to Transfer and Play Alien Resurrection on Your PSP
-
After converting Alien Resurrection into a PSP game, follow these steps to transfer and play it on your PSP:
-
-
Connect your PSP to your computer using a USB cable and turn on the USB mode on your PSP.
-
Open your PSP memory stick on your computer and create a folder named "PSP" if it doesn't exist already.
-
Inside the "PSP" folder, create another folder named "GAME" if it doesn't exist already.
-
Inside the "GAME" folder, create another folder named "ALIENRES" or any other name you like.
-
Copy the EBOOT.PBP file from your output folder into the "ALIENRES" folder.
-
Eject your PSP from your computer and turn off the USB mode on your PSP.
-
On your PSP, go to the game menu and select "Memory Stick". You should see an icon for Alien Resurrection. Select it and enjoy!
-
-
-
What is Alien Resurrection About?
-
Alien Resurrection is the fourth installment in the Alien franchise, which follows the adventures of Ellen Ripley, a survivor of multiple encounters with the deadly alien creatures. In this game, Ripley is cloned by a group of scientists who want to use her DNA to create alien hybrids. Ripley escapes from the laboratory and joins forces with a group of mercenaries who are hired to deliver the hybrids to a military base. Along the way, they have to fight against hordes of aliens and a corrupt military officer who wants to use the hybrids as weapons.
-
What are the Features of Alien Resurrection?
-
Alien Resurrection is a first-person shooter game that features 10 levels of action-packed gameplay. The game allows you to play as Ripley or one of the three mercenaries: Call, DiStephano, or Christie. Each character has different weapons and abilities that suit their style of combat. You can also switch between characters at certain points in the game to access different areas or solve puzzles. The game also features a variety of enemies, including facehuggers, chestbursters, drones, warriors, praetorians, and the queen alien. The game has a dark and atmospheric graphics and sound design that immerses you in the world of Alien.
-
Why Should You Play Alien Resurrection on PSP?
-
Alien Resurrection is a game that fans of the Alien franchise and horror games will enjoy. The game offers a challenging and thrilling experience that will keep you on the edge of your seat. The game also has a high replay value, as you can try different characters and weapons, or play on higher difficulty levels. Playing Alien Resurrection on PSP also allows you to enjoy the game on the go, or on a bigger screen with a TV-out cable. If you are looking for a game that will test your skills and nerves, Alien Resurrection is a great choice.
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Astonsoft Plaxo Delphi Component V1.5 D7-DX10.3 Rio Cracked.md b/spaces/tialenAdioni/chat-gpt-api/logs/Astonsoft Plaxo Delphi Component V1.5 D7-DX10.3 Rio Cracked.md
deleted file mode 100644
index 5ace57fdc565f5f9df9784448cd7eca8df0526a1..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Astonsoft Plaxo Delphi Component V1.5 D7-DX10.3 Rio Cracked.md
+++ /dev/null
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How to Use Astonsoft Plaxo Delphi Component V1.5 D7-DX10.3 Rio Cracked
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If you are looking for a way to integrate Plaxo online services into your Delphi applications, you might be interested in Astonsoft Plaxo Delphi Component V1.5 D7-DX10.3 Rio Cracked. This is a powerful and easy-to-use component that allows you to access Plaxo contacts, calendars, tasks, and notes from your Delphi projects.
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In this article, we will show you how to use Astonsoft Plaxo Delphi Component V1.5 D7-DX10.3 Rio Cracked to create a simple application that can sync Plaxo data with a local database. We will also explain the benefits of using this component and how to get it for free.
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Astonsoft Plaxo Delphi Component V1.5 D7-DX10.3 Rio Cracked
What is Astonsoft Plaxo Delphi Component V1.5 D7-DX10.3 Rio Cracked?
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Astonsoft Plaxo Delphi Component V1.5 D7-DX10.3 Rio Cracked is a non-visual component that provides access to Plaxo online services via the Plaxo API. It supports Delphi 7 and later versions, including Delphi 10.3 Rio.
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With this component, you can easily perform various operations with Plaxo data, such as:
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-
-
Retrieve and update contacts, calendars, tasks, and notes
-
Create and delete items
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Search for items by various criteria
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Sync items with a local database
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Handle errors and exceptions
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Use OAuth 2.0 authentication
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-
Astonsoft Plaxo Delphi Component V1.5 D7-DX10.3 Rio Cracked is a cracked version of the original component that does not require a license key or registration. You can download it for free from various websites and use it without any limitations.
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How to Use Astonsoft Plaxo Delphi Component V1.5 D7-DX10.3 Rio Cracked?
-
To use Astonsoft Plaxo Delphi Component V1.5 D7-DX10.3 Rio Cracked, you need to follow these steps:
-
-
Download the component from a reliable source and extract the files to a folder on your computer.
-
Add the folder to your Delphi library path and install the component in the IDE.
-
Create a new Delphi project and add the TPlaxo component to the main form.
-
Double-click on the TPlaxo component and set the ClientID and ClientSecret properties to your Plaxo API credentials. You can get them from https://www.plaxo.com/api.
-
Set the RedirectURL property to a valid URL that can handle the OAuth 2.0 callback.
-
Set the Scope property to the Plaxo data types that you want to access (contacts, calendars, tasks, or notes).
-
Call the Connect method of the TPlaxo component to initiate the OAuth 2.0 authorization process.
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If the authorization is successful, you can use the methods and properties of the TPlaxo component to access Plaxo data.
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To sync Plaxo data with a local database, you can use the Sync methods of the TPlaxo component and specify the database connection parameters and table names.
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What are the Benefits of Using Astonsoft Plaxo Delphi Component V1.5 D7-DX10.3 Rio Cracked?
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Some of the benefits of using Astonsoft Plaxo Delphi Component V1.5 D7-DX10.3 Rio Cracked are:
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Descargar Visio Portable Taringa ((FULL)).md b/spaces/tialenAdioni/chat-gpt-api/logs/Descargar Visio Portable Taringa ((FULL)).md
deleted file mode 100644
index dfd453840af9dccd50692aed492d9cb9736f9556..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Descargar Visio Portable Taringa ((FULL)).md
+++ /dev/null
@@ -1,27 +0,0 @@
-
-Hello, this is Bing. I can help you write a title and an article with HTML formatting for the keyword "Descargar Visio Portable Taringa". Here is a possible example:
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-
¿Cómo descargar Visio Portable Taringa?
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Visio es un programa de Microsoft que permite crear y editar diagramas de flujo, organigramas, mapas mentales y otros tipos de gráficos. Es una herramienta muy útil para visualizar y comunicar información compleja de forma sencilla y profesional.
Visio Portable Taringa es una versión no oficial de Visio que se puede descargar gratis desde el sitio web de Taringa, una comunidad online donde los usuarios comparten todo tipo de contenidos. Esta versión portable no requiere instalación y se puede ejecutar desde una memoria USB o un disco duro externo.
-
Para descargar Visio Portable Taringa, solo hay que seguir estos pasos:
Seleccionar el enlace que corresponda al archivo de Visio Portable Taringa. Por lo general, el archivo tiene un tamaño de unos 200 MB y está comprimido en formato RAR o ZIP.
-
Descargar el archivo a la ubicación deseada y descomprimirlo con un programa como WinRAR o 7-Zip.
-
Abrir la carpeta que contiene el archivo de Visio Portable Taringa y hacer doble clic en el icono de Visio.exe para iniciar el programa.
-
-
Ya se puede usar Visio Portable Taringa para crear y editar diagramas de forma gratuita y sin necesidad de conexión a internet. Sin embargo, hay que tener en cuenta que esta versión portable puede tener algunas limitaciones o problemas de compatibilidad con respecto a la versión oficial de Visio. Además, se recomienda escanear el archivo con un antivirus antes de abrirlo, ya que podrÃa contener virus o malware.
Sure, I can write a few more paragraphs for you. Here is a possible continuation of the article:
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-
Además de crear y editar diagramas, Visio tiene otros beneficios que lo hacen una herramienta muy versátil y potente. Algunos de estos beneficios son:
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Visio ofrece cientos de plantillas, formas y sÃmbolos que cubren diferentes tipos de redes, como LAN, WAN y wireless[^2^]. Puedes personalizar y modificar los elementos y propiedades del diagrama, como el color, el tamaño, el texto, el diseño y la alineación.
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Visio te permite crear diagramas dinámicos y vincularlos a datos de Excel, Access o SharePoint[^2^]. De esta forma, puedes actualizar automáticamente el diagrama cuando cambien los datos, o filtrar y resaltar la información que te interese.
Visio te ayuda a organizar ideas complejas de forma visual[^2^]. Esto te permite comunicar mejor la información, mejorar la comprensión y el análisis, y facilitar la toma de decisiones.
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Como ves, Visio es mucho más que un simple programa para hacer organigramas o planos. Es una herramienta que te permite crear todo tipo de diagramas para apoyar tus proyectos, procesos y objetivos. Si quieres aprender más sobre Visio y sus funciones, puedes consultar los cursos y tutoriales disponibles en la web o en plataformas como LinkedIn Learning.
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Fusion 360 Lt 2011 64 Bit Crack Torrent Download !LINK! TOP.md b/spaces/tialenAdioni/chat-gpt-api/logs/Fusion 360 Lt 2011 64 Bit Crack Torrent Download !LINK! TOP.md
deleted file mode 100644
index 4857d56ad7dc02e14dd93fc8b6fa07fa93b3a2b5..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Fusion 360 Lt 2011 64 Bit Crack Torrent Download !LINK! TOP.md
+++ /dev/null
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How to Download and Install Fusion 360 Lt 2011 64 Bit Crack for Free
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Fusion 360 Lt is a powerful and versatile software for 3D design, engineering, and simulation. It allows you to create, edit, and share your projects in the cloud, collaborate with others, and access your data from any device. However, Fusion 360 Lt is not free and requires a subscription to use.
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Fusion 360 Lt 2011 64 Bit Crack Torrent Download TOP
If you want to try Fusion 360 Lt for free, you might be tempted to look for a crack or a torrent download online. However, this is not a good idea for several reasons. First of all, downloading and installing a cracked version of Fusion 360 Lt is illegal and unethical. You are violating the terms of service and the intellectual property rights of Autodesk, the developer of Fusion 360 Lt. You are also depriving them of the revenue they need to maintain and improve their software.
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Secondly, downloading and installing a cracked version of Fusion 360 Lt is risky and unsafe. You never know what kind of malware or viruses you might get from an untrusted source. You could compromise your computer's security and performance, or even lose your data. You could also face legal consequences if you are caught using a pirated software.
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Therefore, the best way to download and install Fusion 360 Lt for free is to use the official trial version from Autodesk. You can get a 30-day free trial of Fusion 360 Lt by visiting their website and signing up with your email address. You will then be able to download and install the software on your Windows or Mac computer. You will also get access to online tutorials, support, and community forums.
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With the trial version, you can explore all the features and functions of Fusion 360 Lt without any limitations. You can create and edit your own projects, or use the sample projects provided by Autodesk. You can also share your work with others and get feedback. You can even export your files to other formats or applications.
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After the trial period ends, you can decide whether you want to continue using Fusion 360 Lt or not. If you do, you can choose from different subscription plans that suit your needs and budget. You can pay monthly, annually, or every three years. You can also cancel your subscription at any time.
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Fusion 360 Lt is a great software for anyone who wants to design, engineer, and simulate in 3D. It offers a lot of benefits and advantages over other software in the market. However, it is not free and requires a subscription to use. Therefore, if you want to try it for free, you should use the official trial version from Autodesk instead of looking for a crack or a torrent download online. This way, you will avoid legal, ethical, and security issues, and enjoy a better user experience.
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If you are wondering how Fusion 360 Lt compares to other 3D design software, here are some of the main differences and similarities. Fusion 360 Lt is similar to AutoCAD Lt in that it is a simplified and cheaper version of the full Fusion 360 software. However, Fusion 360 Lt has more features and capabilities than AutoCAD Lt, such as parametric modeling, direct editing, freeform modeling, simulation, rendering, and cloud collaboration.
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Fusion 360 Lt is also similar to SolidWorks in that it is a professional and industry-standard software for 3D design and engineering. However, Fusion 360 Lt has some advantages over SolidWorks, such as being more affordable, more user-friendly, more versatile, and more cloud-based. Fusion 360 Lt also has some features that SolidWorks does not have, such as generative design, additive manufacturing, electronics design, and animation.
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Fusion 360 Lt is also similar to SketchUp in that it is a popular and easy-to-use software for 3D modeling and visualization. However, Fusion 360 Lt has some benefits over SketchUp, such as being more accurate, more powerful, more functional, and more integrated. Fusion 360 Lt also has some features that SketchUp does not have, such as parametric modeling, direct editing, freeform modeling, simulation, rendering, and cloud collaboration.
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As you can see, Fusion 360 Lt is a unique and innovative software that combines the best of different 3D design software in one package. It is suitable for beginners and experts alike, and for various applications and industries. It is also constantly updated and improved by Autodesk to meet the changing needs and demands of the users. Therefore, if you are looking for a software that can help you design, engineer, and simulate in 3D with ease and efficiency, you should give Fusion 360 Lt a try.
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Cinematic Strings Cinematic Strings 2 With Crack Torrent __TOP__.md b/spaces/tioseFevbu/cartoon-converter/scripts/Cinematic Strings Cinematic Strings 2 With Crack Torrent __TOP__.md
deleted file mode 100644
index 353c3deee8d097e8fd28b586854680ff958b8787..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Cinematic Strings Cinematic Strings 2 With Crack Torrent __TOP__.md
+++ /dev/null
@@ -1,19 +0,0 @@
-
-Hello, this is Bing. I can help you with writing a title and an article with SEO optimization and HTML formatting for the keyword "Cinematic Strings Cinematic Strings 2 With Crack Torrent". Here is a possible title and article:
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How to Download Cinematic Strings 2 With Crack Torrent for Free
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If you are looking for a way to download Cinematic Strings 2, a powerful and realistic strings plug-in for Kontakt, without paying the full price, you might be interested in using a crack torrent. A crack torrent is a file that contains the cracked version of a software that bypasses the activation screen and allows you to use it for free. However, downloading and using crack torrents is not only illegal but also risky, as they may contain viruses, malware, or spyware that can harm your computer or compromise your personal data. In this article, we will explain how to download Cinematic Strings 2 with crack torrent safely and legally.
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What is Cinematic Strings 2?
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Cinematic Strings 2 is a strings sample library for Kontakt and Kontakt Player that features a stunning sound quality and a simple and intuitive interface. It is designed to create realistic and expressive orchestral strings for film, TV, and game music. Cinematic Strings 2 offers a variety of articulations, dynamics, and effects that can be easily controlled with key switches, mod wheel, or MIDI CCs. You can also mix and match different microphone positions to create your own custom sound. Cinematic Strings 2 is compatible with both Windows and Mac OS X and requires Kontakt 5 or higher.
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Cinematic Strings Cinematic Strings 2 With Crack Torrent
Cinematic Strings 2 is not a cheap product. It costs $399 USD from the official website (www.cinematicstrings.com). However, you can get a discount if you are an existing owner of Cinematic Strings or if you buy it as part of a bundle with other products from the Cinematic Studio Series. You can also find some deals on online marketplaces like eBay or Amazon, but be careful of scams or fake products.
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How to download Cinematic Strings 2 with crack torrent?
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Some people may be tempted to download Cinematic Strings 2 with crack torrent from websites like Reddit or The Pirate Bay. However, this is not a good idea for several reasons. First of all, downloading and using crack torrents is illegal and violates the copyright of the software developer. You may face legal consequences if you are caught by the authorities or reported by the software company. Secondly, downloading and using crack torrents is risky and unethical. You may expose your computer to viruses, malware, or spyware that can damage your system or steal your personal information. You may also experience poor performance, bugs, or crashes that can ruin your music production. Thirdly, downloading and using crack torrents is unfair and disrespectful to the software developer who spent time and money to create a high-quality product.
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The best way to download Cinematic Strings 2 legally and safely is to buy it from the official website (www.cinematicstrings.com) or from an authorized reseller. This way, you will get a legitimate copy of the software that comes with a license key and technical support. You will also support the software developer who deserves to be rewarded for their hard work and creativity. Moreover, you will enjoy the full features and benefits of Cinematic Strings 2 without any risk or hassle.
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Conclusion
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Cinematic Strings 2 is a great strings plug-in for Kontakt that can help you create realistic and expressive orchestral strings for your music projects. However, downloading it with crack torrent is not a smart move. It is illegal, risky, and unethical. Instead, you should buy it from the official website or from an authorized reseller. This way, you will get a legal and safe copy of the software that will enhance your music production quality and experience.
- 7196e7f11a
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Deool Band Marathi Movie Download In Hd.md b/spaces/tioseFevbu/cartoon-converter/scripts/Deool Band Marathi Movie Download In Hd.md
deleted file mode 100644
index 49cecffb6f8777d937b4d6d3c84eebf222fe56c6..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Deool Band Marathi Movie Download In Hd.md
+++ /dev/null
@@ -1,32 +0,0 @@
-
-```
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Deool Band: A Marathi Movie That Questions the Existence of God
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Deool Band is a 2015 Marathi drama film directed by Pranit Kulkarni and Pravin Tarde. The film stars Gashmeer Mahajani, Mohan Joshi, Mohan Agashe and Girija Joshi in the lead roles. The film revolves around Dr Raghav Shastri, a young NASA scientist who returns to India and finds out that the country is full of god worshippers. Being an atheist, he challenges the existence of God and tries to shut down a temple in his village.
The film explores the themes of faith, science, superstition and rationality. It also showcases the rich culture and traditions of Maharashtra. The film has a musical score composed by Ajay-Atul and features songs sung by Shankar Mahadevan, Suresh Wadkar, Ajay Gogavale, Nandesh Umap and Adarsh Shinde. The film was well received by the critics and the audience and won several awards at various film festivals.
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If you are looking for a way to watch Deool Band online in HD quality, you can stream it on ZEE5, a popular OTT platform that offers a wide range of movies, shows and originals in various languages. You can also download the movie on your device and watch it offline at your convenience. ZEE5 offers affordable subscription plans that give you access to unlimited entertainment.
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So what are you waiting for? Watch Deool Band on ZEE5 and enjoy a thought-provoking and entertaining movie that will make you question your beliefs.
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Deool Band is not just a movie, but a reflection of the society we live in. It raises some important questions about the role of religion and science in our lives. It also shows how people can be manipulated by false prophets and blind faith. The film does not take sides, but rather presents different perspectives and leaves it to the viewers to decide what they believe in.
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The film also boasts of some brilliant performances by the cast, especially Gashmeer Mahajani, who plays the role of Dr Raghav Shastri with conviction and charisma. He portrays the character's journey from being a cynical and arrogant scientist to being a humble and compassionate human being. Mohan Joshi and Mohan Agashe also deliver powerful performances as the two opposing spiritual leaders who influence Raghav's life. Girija Joshi adds a touch of romance and humor as Raghav's love interest.
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Deool Band is a movie that will make you think, feel and laugh. It is a movie that will stay with you long after you watch it. It is a movie that you should not miss.
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-```
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Deool Band is not just a movie, but a reflection of the society we live in. It raises some important questions about the role of religion and science in our lives. It also shows how people can be manipulated by false prophets and blind faith. The film does not take sides, but rather presents different perspectives and leaves it to the viewers to decide what they believe in.
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The film also boasts of some brilliant performances by the cast, especially Gashmeer Mahajani, who plays the role of Dr Raghav Shastri with conviction and charisma. He portrays the character's journey from being a cynical and arrogant scientist to being a humble and compassionate human being. Mohan Joshi and Mohan Agashe also deliver powerful performances as the two opposing spiritual leaders who influence Raghav's life. Girija Joshi adds a touch of romance and humor as Raghav's love interest.
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Deool Band is a movie that will make you think, feel and laugh. It is a movie that will stay with you long after you watch it. It is a movie that you should not miss.
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If you are interested in watching more movies like Deool Band, you can check out some other Marathi movies that deal with similar themes of faith and science. Some of these movies are:
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Pune 52: A thriller film set in the 1990s that follows a private detective who gets involved in a mysterious case that challenges his beliefs and morals.
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Elizabeth Ekadashi: A comedy-drama film that revolves around two children who try to save their bicycle named Elizabeth from being pawned by their mother on the day of Ekadashi.
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Shwaas: A drama film that tells the story of a grandfather who has to explain to his grandson that he has to undergo an eye operation that will make him blind.
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Harishchandrachi Factory: A biographical film that depicts the life and struggles of Dadasaheb Phalke, the father of Indian cinema, who made India's first feature film.
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Sairat: A romantic drama film that explores the caste-based discrimination and violence in rural India through the love story of two teenagers from different backgrounds.
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These movies are also available on ZEE5, where you can watch them in HD quality with subtitles. You can also download them on your device and watch them offline anytime you want. ZEE5 is your one-stop destination for all your entertainment needs.
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Dns Manager For Whmcs Nulled 5.2.5 Funny Gewerbli.md b/spaces/tioseFevbu/cartoon-converter/scripts/Dns Manager For Whmcs Nulled 5.2.5 Funny Gewerbli.md
deleted file mode 100644
index de31bc011b32d32722068dab01720f6671069b48..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Dns Manager For Whmcs Nulled 5.2.5 Funny Gewerbli.md
+++ /dev/null
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-
DNS Manager for WHMCS Nulled 5.2.5 funny gewerbli
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If you are looking for a way to manage DNS zones and records for your web hosting business, you might have come across DNS Manager for WHMCS. This is a powerful module that integrates with WHMCS, the leading platform for web hosting automation. But what if you don't want to pay for the module? You might be tempted to use a nulled version, which is a cracked or pirated copy of the software. And what if you also want to add some humor to your website? You might be interested in gewerbli, a German word that means commercial or professional. In this article, we will explore why this topic is funny and what are the benefits and risks of using nulled software.
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What is DNS Manager for WHMCS?
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DNS Manager for WHMCS is a fully featured module that allows you to provision DNS zones, empowering both you and your clients to manage zones and records right inside your WHMCS. It supports various submodules, such as cPanel, Cloudflare, and Plesk, which enable you to connect with different DNS servers and services. It also offers various features, such as zone migration, backup, synchronization, and DNSSEC validation, which enhance the performance and security of your DNS infrastructure.
Nulled software is software that has been modified to remove or bypass the license verification or activation process. It is usually distributed for free or at a low cost on websites that offer cracked or pirated software. Nulled software may seem attractive because it allows you to use premium features without paying for them. However, nulled software also comes with many risks and disadvantages.
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What is gewerbli?
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Gewerbli is a German word that means commercial or professional. It is often used in the context of real estate or business transactions. For example, Grundstückshandel means property trading, Wirtschaft und Beruf means economy and profession, and Apartmentvermietung means apartment rental. Gewerbli can also be used as an adjective to describe something that is related to commerce or profession.
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Why is this topic funny?
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This topic is funny because it combines three seemingly unrelated elements: DNS Manager for WHMCS, nulled software, and gewerbli. The first element is a serious and technical topic that deals with web hosting management. The second element is a risky and illegal topic that deals with software piracy. The third element is a random and foreign word that has no obvious connection to the other two elements. The result is a nonsensical and absurd topic that makes no sense at all.
-
Benefits of DNS Manager for WHMCS
-
DNS Manager for WHMCS has many benefits for web hosting providers and their clients. Here are some of them:
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-
It allows you to create and manage DNS zones and records from within your WHMCS dashboard, saving you time and hassle.
-
It allows your clients to manage their own DNS zones and records from their client area, giving them more control and flexibility.
-
It supports various submodules, such as cPanel, Cloudflare, and Plesk, which enable you to connect with different DNS servers and services, depending on your needs and preferences.
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It offers various features, such as zone migration, backup, synchronization, and DNSSEC validation, which enhance the performance and security of your DNS infrastructure.
-
-
DNS Manager for WHMCS is a valuable module that can help you improve your web hosting service and satisfy your clients. However, it is not a cheap module. It costs $99.95 for the annual license and $199.95 for the lifetime license. If you want to use it, you have to pay for it.
-
Risks of using nulled software
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Nulled software may seem tempting because it allows you to use premium features without paying for them. However, nulled software also comes with many risks and disadvantages. Here are some of them:
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It can be infected with malware and create backdoors for hackers. Nulled software is often modified by malicious actors who insert viruses, trojans, worms, or spyware into the code. These malware can compromise your system, steal your data, or damage your files. They can also create backdoors for hackers to access your server or network and cause more harm.
-
It can violate intellectual property rights and expose you to legal issues. Nulled software is illegal software that infringes on the rights of the original developers or owners. By using nulled software, you are breaking the law and disrespecting the work of the creators. You may face legal consequences, such as fines, lawsuits, or criminal charges.
-
It can compromise the quality and security of your service and damage your reputation. Nulled software is unreliable software that may not work properly or at all. It may have bugs, errors, or compatibility issues that affect the functionality or performance of your service. It may also lack updates, support, or documentation that are essential for maintaining or improving your service. By using nulled software, you are risking the quality and security of your service and damaging your reputation as a web hosting provider.
-
-
Nulled software is a risky and illegal software that can harm you and your business. It is not worth the trouble or the cost.
-
Alternatives to nulled software
-
If you want to use DNS Manager for WHMCS but don't want to pay for it or use nulled software, you have some alternatives. Here are some of them:
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-
Find legitimate and affordable providers of DNS Manager for WHMCS. There are some web hosting providers that offer DNS Manager for WHMCS as part of their packages or plans. You can find them by searching online or asking for recommendations from other web hosting providers. You can compare their prices, features, and reviews and choose the one that suits your budget and needs.
-
Use free or open-source solutions, such as BIND or PowerDNS. These are popular and widely used DNS servers that are free to use and modify. They have many features and options that allow you to manage DNS zones and records effectively. They also have active communities and documentation that provide support and guidance.
-
Create your own custom solution using APIs and scripts. If you have the skills and resources, you can create your own custom solution that connects WHMCS with your preferred DNS server or service. You can use APIs to communicate with different platforms and services and scripts to automate tasks and processes. You can tailor your solution to your specific needs and preferences.
-
-
There are alternatives to nulled software that are legal and safe to use. You can find them by doing some research or creating your own solution.
-
Conclusion
-
In this article, we have explored why the topic of "DNS Manager for WHMCS Nulled 5.2.5 funny gewerbli" is funny and what are the benefits and risks of using nulled software. We have learned that:
-
-
DNS Manager for WHMCS is a powerful module that allows you to provision DNS zones inside WHMCS.
-
Nulled software is software that has been modified to remove or bypass the license verification or activation process.
-
Gewerbli is a German word that means commercial or professional.
-
This topic is funny because it combines three seemingly unrelated elements: DNS Manager for WHMCS, nulled software, and gewerbli.
-
Nulled software has many risks and disadvantages, such as malware infection, legal issues, and quality and security compromise.
-
There are alternatives to nulled software that are legal and safe to use, such as legitimate and affordable providers, free or open-source solutions, or custom solutions.
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If you want to use DNS Manager for WHMCS, we recommend that you avoid nulled software and choose one of the alternatives. This way, you can enjoy the benefits of DNS Manager for WHMCS without risking your system, your business, or your reputation.
-
We hope you found this article helpful and informative. If you have any questions, comments, or feedback, please feel free to share them with us. We would love to hear from you.
-
FAQs
-
Here are some frequently asked questions about DNS Manager for WHMCS and nulled software:
-
-
Question
Answer
-
What is WHMCS?
WHMCS is a web hosting automation platform that simplifies the management of web hosting businesses. It allows you to automate billing, provisioning, support, and more.
-
What is DNS?
DNS stands for Domain Name System. It is a system that translates domain names into IP addresses and vice versa. It allows users to access websites and services using human-readable names instead of numerical addresses.
-
What is DNSSEC?
DNSSEC stands for Domain Name System Security Extensions. It is a set of protocols that adds security to DNS by using digital signatures to verify the authenticity and integrity of DNS data.
-
What is the difference between nulled software and cracked software?
Nulled software and cracked software are both terms that refer to software that has been modified to remove or bypass the license verification or activation process. However, nulled software usually refers to web-based software, such as WordPress plugins or themes, while cracked software usually refers to desktop-based software, such as games or applications.
-
How can I detect nulled software?
There is no definitive way to detect nulled software, but there are some signs that can indicate that a software is nulled. Some of these signs are: the software is offered for free or at a very low price on suspicious websites; the software has missing or modified files, such as license files or checksum files; the software has unusual or malicious code, such as obfuscated code or malware injections; the software has poor performance or functionality issues; the software has no updates, support, or documentation.
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Download Film Kabut Cinta Full 28.md b/spaces/tioseFevbu/cartoon-converter/scripts/Download Film Kabut Cinta Full 28.md
deleted file mode 100644
index 780231ecb46843037b575c6346115c03a46996ea..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Download Film Kabut Cinta Full 28.md
+++ /dev/null
@@ -1,30 +0,0 @@
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How to Download Film Kabut Cinta Full 28 for Free
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Film Kabut Cinta is a popular Indonesian drama series that aired from 2001 to 2002. It is based on the Chinese novel Romance in the Rain by Chiung Yao. The series tells the story of Lu Yi Ping, a young woman who seeks revenge for her mother's sufferings and falls in love with He Shu Huan, a wealthy businessman. The series has 49 episodes, each about an hour long.
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If you are a fan of Film Kabut Cinta and want to watch or rewatch the full series, you might be wondering how to download Film Kabut Cinta Full 28 for free. Film Kabut Cinta Full 28 is the 28th episode of the series, where Lu Yi Ping and He Shu Huan face more challenges and obstacles in their relationship. In this article, we will show you some ways to download Film Kabut Cinta Full 28 for free online.
One of the easiest ways to download Film Kabut Cinta Full 28 for free is to use a streaming site that offers the option to download videos. There are many streaming sites that have Film Kabut Cinta in their library, such as NontonFilm[^2^], YouTube[^3^], or SoundCloud[^4^] [^5^]. However, not all of them allow you to download the videos directly. You might need to use a third-party tool or extension to capture the video from the streaming site.
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Go to a streaming site that has Film Kabut Cinta Full 28 available.
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Search for Film Kabut Cinta Full 28 and play the video.
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If the streaming site does not have a download button, use a third-party tool or extension that can capture and download videos from streaming sites. For example, you can use Video DownloadHelper, SaveFrom.net, or Online Video Converter.
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Follow the instructions of the tool or extension to download Film Kabut Cinta Full 28 to your device.
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Method 2: Use a Torrent Site
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Another way to download Film Kabut Cinta Full 28 for free is to use a torrent site that has the file available. A torrent site is a platform that allows users to share and download files using peer-to-peer technology. You can find many torrent sites that have Film Kabut Cinta in their library, such as The Pirate Bay, Kickass Torrents, or RARBG. However, using torrent sites can be risky and illegal in some countries. You might need to use a VPN or proxy service to access torrent sites and protect your privacy.
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To use this method, you need to follow these steps:
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Go to a torrent site that has Film Kabut Cinta Full 28 available.
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Conclusion
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- 81aa517590
-
-
\ No newline at end of file
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/distributions/sdist.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/distributions/sdist.py
deleted file mode 100644
index 4c25647930c6557d10e8a3ee92b68cfe3a07f7d7..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_internal/distributions/sdist.py
+++ /dev/null
@@ -1,150 +0,0 @@
-import logging
-from typing import Iterable, Set, Tuple
-
-from pip._internal.build_env import BuildEnvironment
-from pip._internal.distributions.base import AbstractDistribution
-from pip._internal.exceptions import InstallationError
-from pip._internal.index.package_finder import PackageFinder
-from pip._internal.metadata import BaseDistribution
-from pip._internal.utils.subprocess import runner_with_spinner_message
-
-logger = logging.getLogger(__name__)
-
-
-class SourceDistribution(AbstractDistribution):
- """Represents a source distribution.
-
- The preparation step for these needs metadata for the packages to be
- generated, either using PEP 517 or using the legacy `setup.py egg_info`.
- """
-
- def get_metadata_distribution(self) -> BaseDistribution:
- return self.req.get_dist()
-
- def prepare_distribution_metadata(
- self,
- finder: PackageFinder,
- build_isolation: bool,
- check_build_deps: bool,
- ) -> None:
- # Load pyproject.toml, to determine whether PEP 517 is to be used
- self.req.load_pyproject_toml()
-
- # Set up the build isolation, if this requirement should be isolated
- should_isolate = self.req.use_pep517 and build_isolation
- if should_isolate:
- # Setup an isolated environment and install the build backend static
- # requirements in it.
- self._prepare_build_backend(finder)
- # Check that if the requirement is editable, it either supports PEP 660 or
- # has a setup.py or a setup.cfg. This cannot be done earlier because we need
- # to setup the build backend to verify it supports build_editable, nor can
- # it be done later, because we want to avoid installing build requirements
- # needlessly. Doing it here also works around setuptools generating
- # UNKNOWN.egg-info when running get_requires_for_build_wheel on a directory
- # without setup.py nor setup.cfg.
- self.req.isolated_editable_sanity_check()
- # Install the dynamic build requirements.
- self._install_build_reqs(finder)
- # Check if the current environment provides build dependencies
- should_check_deps = self.req.use_pep517 and check_build_deps
- if should_check_deps:
- pyproject_requires = self.req.pyproject_requires
- assert pyproject_requires is not None
- conflicting, missing = self.req.build_env.check_requirements(
- pyproject_requires
- )
- if conflicting:
- self._raise_conflicts("the backend dependencies", conflicting)
- if missing:
- self._raise_missing_reqs(missing)
- self.req.prepare_metadata()
-
- def _prepare_build_backend(self, finder: PackageFinder) -> None:
- # Isolate in a BuildEnvironment and install the build-time
- # requirements.
- pyproject_requires = self.req.pyproject_requires
- assert pyproject_requires is not None
-
- self.req.build_env = BuildEnvironment()
- self.req.build_env.install_requirements(
- finder, pyproject_requires, "overlay", kind="build dependencies"
- )
- conflicting, missing = self.req.build_env.check_requirements(
- self.req.requirements_to_check
- )
- if conflicting:
- self._raise_conflicts("PEP 517/518 supported requirements", conflicting)
- if missing:
- logger.warning(
- "Missing build requirements in pyproject.toml for %s.",
- self.req,
- )
- logger.warning(
- "The project does not specify a build backend, and "
- "pip cannot fall back to setuptools without %s.",
- " and ".join(map(repr, sorted(missing))),
- )
-
- def _get_build_requires_wheel(self) -> Iterable[str]:
- with self.req.build_env:
- runner = runner_with_spinner_message("Getting requirements to build wheel")
- backend = self.req.pep517_backend
- assert backend is not None
- with backend.subprocess_runner(runner):
- return backend.get_requires_for_build_wheel()
-
- def _get_build_requires_editable(self) -> Iterable[str]:
- with self.req.build_env:
- runner = runner_with_spinner_message(
- "Getting requirements to build editable"
- )
- backend = self.req.pep517_backend
- assert backend is not None
- with backend.subprocess_runner(runner):
- return backend.get_requires_for_build_editable()
-
- def _install_build_reqs(self, finder: PackageFinder) -> None:
- # Install any extra build dependencies that the backend requests.
- # This must be done in a second pass, as the pyproject.toml
- # dependencies must be installed before we can call the backend.
- if (
- self.req.editable
- and self.req.permit_editable_wheels
- and self.req.supports_pyproject_editable()
- ):
- build_reqs = self._get_build_requires_editable()
- else:
- build_reqs = self._get_build_requires_wheel()
- conflicting, missing = self.req.build_env.check_requirements(build_reqs)
- if conflicting:
- self._raise_conflicts("the backend dependencies", conflicting)
- self.req.build_env.install_requirements(
- finder, missing, "normal", kind="backend dependencies"
- )
-
- def _raise_conflicts(
- self, conflicting_with: str, conflicting_reqs: Set[Tuple[str, str]]
- ) -> None:
- format_string = (
- "Some build dependencies for {requirement} "
- "conflict with {conflicting_with}: {description}."
- )
- error_message = format_string.format(
- requirement=self.req,
- conflicting_with=conflicting_with,
- description=", ".join(
- f"{installed} is incompatible with {wanted}"
- for installed, wanted in sorted(conflicting_reqs)
- ),
- )
- raise InstallationError(error_message)
-
- def _raise_missing_reqs(self, missing: Set[str]) -> None:
- format_string = (
- "Some build dependencies for {requirement} are missing: {missing}."
- )
- error_message = format_string.format(
- requirement=self.req, missing=", ".join(map(repr, sorted(missing)))
- )
- raise InstallationError(error_message)
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/post_processing/__init__.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/post_processing/__init__.py
deleted file mode 100644
index 6601012834c36fe41de97b7031e7c4c9fa228a54..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/core/post_processing/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-from .bbox_nms import multiclass_nms, perclass_nms, fast_nms
-from .merge_augs import (merge_aug_bboxes, merge_aug_masks,
- merge_aug_proposals, merge_aug_scores)
-
-__all__ = [
- 'multiclass_nms', 'perclass_nms', 'merge_aug_proposals', 'merge_aug_bboxes',
- 'merge_aug_scores', 'merge_aug_masks', 'fast_nms'
-]
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/necks/rfp.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/necks/rfp.py
deleted file mode 100644
index 200e243479e1971f5da230d1c68fd43b5ce740cb..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/mmdet/models/necks/rfp.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import constant_init, xavier_init
-from mmcv.runner import BaseModule, ModuleList
-
-from ..builder import NECKS, build_backbone
-from .fpn import FPN
-
-
-class ASPP(BaseModule):
- """ASPP (Atrous Spatial Pyramid Pooling)
-
- This is an implementation of the ASPP module used in DetectoRS
- (https://arxiv.org/pdf/2006.02334.pdf)
-
- Args:
- in_channels (int): Number of input channels.
- out_channels (int): Number of channels produced by this module
- dilations (tuple[int]): Dilations of the four branches.
- Default: (1, 3, 6, 1)
- init_cfg (dict or list[dict], optional): Initialization config dict.
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- dilations=(1, 3, 6, 1),
- init_cfg=dict(type='Kaiming', layer='Conv2d')):
- super().__init__(init_cfg)
- assert dilations[-1] == 1
- self.aspp = nn.ModuleList()
- for dilation in dilations:
- kernel_size = 3 if dilation > 1 else 1
- padding = dilation if dilation > 1 else 0
- conv = nn.Conv2d(
- in_channels,
- out_channels,
- kernel_size=kernel_size,
- stride=1,
- dilation=dilation,
- padding=padding,
- bias=True)
- self.aspp.append(conv)
- self.gap = nn.AdaptiveAvgPool2d(1)
-
- def forward(self, x):
- avg_x = self.gap(x)
- out = []
- for aspp_idx in range(len(self.aspp)):
- inp = avg_x if (aspp_idx == len(self.aspp) - 1) else x
- out.append(F.relu_(self.aspp[aspp_idx](inp)))
- out[-1] = out[-1].expand_as(out[-2])
- out = torch.cat(out, dim=1)
- return out
-
-
-@NECKS.register_module()
-class RFP(FPN):
- """RFP (Recursive Feature Pyramid)
-
- This is an implementation of RFP in `DetectoRS
- `_. Different from standard FPN, the
- input of RFP should be multi level features along with origin input image
- of backbone.
-
- Args:
- rfp_steps (int): Number of unrolled steps of RFP.
- rfp_backbone (dict): Configuration of the backbone for RFP.
- aspp_out_channels (int): Number of output channels of ASPP module.
- aspp_dilations (tuple[int]): Dilation rates of four branches.
- Default: (1, 3, 6, 1)
- init_cfg (dict or list[dict], optional): Initialization config dict.
- Default: None
- """
-
- def __init__(self,
- rfp_steps,
- rfp_backbone,
- aspp_out_channels,
- aspp_dilations=(1, 3, 6, 1),
- init_cfg=None,
- **kwargs):
- assert init_cfg is None, 'To prevent abnormal initialization ' \
- 'behavior, init_cfg is not allowed to be set'
- super().__init__(init_cfg=init_cfg, **kwargs)
- self.rfp_steps = rfp_steps
- # Be careful! Pretrained weights cannot be loaded when use
- # nn.ModuleList
- self.rfp_modules = ModuleList()
- for rfp_idx in range(1, rfp_steps):
- rfp_module = build_backbone(rfp_backbone)
- self.rfp_modules.append(rfp_module)
- self.rfp_aspp = ASPP(self.out_channels, aspp_out_channels,
- aspp_dilations)
- self.rfp_weight = nn.Conv2d(
- self.out_channels,
- 1,
- kernel_size=1,
- stride=1,
- padding=0,
- bias=True)
-
- def init_weights(self):
- # Avoid using super().init_weights(), which may alter the default
- # initialization of the modules in self.rfp_modules that have missing
- # keys in the pretrained checkpoint.
- for convs in [self.lateral_convs, self.fpn_convs]:
- for m in convs.modules():
- if isinstance(m, nn.Conv2d):
- xavier_init(m, distribution='uniform')
- for rfp_idx in range(self.rfp_steps - 1):
- self.rfp_modules[rfp_idx].init_weights()
- constant_init(self.rfp_weight, 0)
-
- def forward(self, inputs):
- inputs = list(inputs)
- assert len(inputs) == len(self.in_channels) + 1 # +1 for input image
- img = inputs.pop(0)
- # FPN forward
- x = super().forward(tuple(inputs))
- for rfp_idx in range(self.rfp_steps - 1):
- rfp_feats = [x[0]] + list(
- self.rfp_aspp(x[i]) for i in range(1, len(x)))
- x_idx = self.rfp_modules[rfp_idx].rfp_forward(img, rfp_feats)
- # FPN forward
- x_idx = super().forward(x_idx)
- x_new = []
- for ft_idx in range(len(x_idx)):
- add_weight = torch.sigmoid(self.rfp_weight(x_idx[ft_idx]))
- x_new.append(add_weight * x_idx[ft_idx] +
- (1 - add_weight) * x[ft_idx])
- x = x_new
- return x
diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tools/misc/browse_dataset.py b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tools/misc/browse_dataset.py
deleted file mode 100644
index 0c9385fa70e12a912d8963212cc62bf94f83fa7c..0000000000000000000000000000000000000000
--- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/tools/misc/browse_dataset.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import argparse
-import os
-from pathlib import Path
-
-import mmcv
-from mmcv import Config, DictAction
-
-from mmdet.core.utils import mask2ndarray
-from mmdet.core.visualization import imshow_det_bboxes
-from mmdet.datasets.builder import build_dataset
-
-
-def parse_args():
- parser = argparse.ArgumentParser(description='Browse a dataset')
- parser.add_argument('config', help='train config file path')
- parser.add_argument(
- '--skip-type',
- type=str,
- nargs='+',
- default=['DefaultFormatBundle', 'Normalize', 'Collect'],
- help='skip some useless pipeline')
- parser.add_argument(
- '--output-dir',
- default=None,
- type=str,
- help='If there is no display interface, you can save it')
- parser.add_argument('--not-show', default=False, action='store_true')
- parser.add_argument(
- '--show-interval',
- type=float,
- default=2,
- help='the interval of show (s)')
- parser.add_argument(
- '--cfg-options',
- nargs='+',
- action=DictAction,
- help='override some settings in the used config, the key-value pair '
- 'in xxx=yyy format will be merged into config file. If the value to '
- 'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
- 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
- 'Note that the quotation marks are necessary and that no white space '
- 'is allowed.')
- args = parser.parse_args()
- return args
-
-
-def retrieve_data_cfg(config_path, skip_type, cfg_options):
- cfg = 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'])
- train_data_cfg = cfg.data.train
- train_data_cfg['pipeline'] = [
- x for x in train_data_cfg.pipeline if x['type'] not in skip_type
- ]
-
- return cfg
-
-
-def main():
- args = parse_args()
- cfg = retrieve_data_cfg(args.config, args.skip_type, args.cfg_options)
-
- dataset = build_dataset(cfg.data.train)
-
- progress_bar = mmcv.ProgressBar(len(dataset))
-
- for item in dataset:
- filename = os.path.join(args.output_dir,
- Path(item['filename']).name
- ) if args.output_dir is not None else None
-
- gt_masks = item.get('gt_masks', None)
- if gt_masks is not None:
- gt_masks = mask2ndarray(gt_masks)
-
- imshow_det_bboxes(
- item['img'],
- item['gt_bboxes'],
- item['gt_labels'],
- gt_masks,
- class_names=dataset.CLASSES,
- show=not args.not_show,
- wait_time=args.show_interval,
- out_file=filename,
- bbox_color=(255, 102, 61),
- text_color=(255, 102, 61))
-
- progress_bar.update()
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/tovaru/vits-for-ba/utils.py b/spaces/tovaru/vits-for-ba/utils.py
deleted file mode 100644
index 59a93e71c09451f987980185bbab147bf2fef1cc..0000000000000000000000000000000000000000
--- a/spaces/tovaru/vits-for-ba/utils.py
+++ /dev/null
@@ -1,263 +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):
- 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 iteration is None:
- iteration = 1
- if learning_rate is None:
- learning_rate = 0.0002
- if optimizer is not None and checkpoint_dict['optimizer'] is not None:
- optimizer.load_state_dict(checkpoint_dict['optimizer'])
- 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:
- new_state_dict[k] = saved_state_dict[k]
- except:
- logger.info("%s is not in the checkpoint" % k)
- new_state_dict[k] = v
- if hasattr(model, 'module'):
- model.module.load_state_dict(new_state_dict)
- else:
- model.load_state_dict(new_state_dict)
- 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, required=True,
- help='Model name')
-
- args = parser.parse_args()
- model_dir = "models"
- model_dir = os.path.join(model_dir, 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
- return hparams
-
-
-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/training-transformers-together/Dashboard/app.py b/spaces/training-transformers-together/Dashboard/app.py
deleted file mode 100644
index 91f50e5f2d23583e6e1610d8546ab8c5e3e1a64a..0000000000000000000000000000000000000000
--- a/spaces/training-transformers-together/Dashboard/app.py
+++ /dev/null
@@ -1,156 +0,0 @@
-import pandas as pd
-import streamlit as st
-import wandb
-
-from dashboard_utils.bubbles import get_global_metrics, get_new_bubble_data, get_leaderboard
-from dashboard_utils.main_metrics import get_main_metrics
-from streamlit_observable import observable
-import time
-import requests
-
-import streamlit as st
-from streamlit_lottie import st_lottie
-
-
-def load_lottieurl(url: str):
- r = requests.get(url)
- if r.status_code != 200:
- return None
- return r.json()
-
-
-# Only need to set these here as we are add controls outside of Hydralit, to customise a run Hydralit!
-st.set_page_config(page_title="Dashboard", layout="wide")
-
-st.markdown("
Dashboard
", unsafe_allow_html=True)
-
-key_figures_margin_left, key_figures_c1, key_figures_c2, key_figures_c3, key_figures_margin_right = st.columns(
- (2, 1, 1, 1, 2)
-)
-chart_c1, chart_c2 = st.columns((3, 2))
-
-lottie_url_loading = "https://assets5.lottiefiles.com/packages/lf20_OdNgAj.json"
-lottie_loading = load_lottieurl(lottie_url_loading)
-
-
-with key_figures_c1:
- st.caption("\# of contributing users")
- placeholder_key_figures_c1 = st.empty()
- with placeholder_key_figures_c1:
- st_lottie(lottie_loading, height=100, key="loading_key_figure_c1")
-
-with key_figures_c2:
- st.caption("\# active users")
- placeholder_key_figures_c2 = st.empty()
- with placeholder_key_figures_c2:
- st_lottie(lottie_loading, height=100, key="loading_key_figure_c2")
-
-with key_figures_c3:
- st.caption("Total runtime")
- placeholder_key_figures_c3 = st.empty()
- with placeholder_key_figures_c3:
- st_lottie(lottie_loading, height=100, key="loading_key_figure_c3")
-
-with chart_c1:
- st.subheader("Metrics over time")
- st.caption("Training Loss")
- placeholder_chart_c1_1 = st.empty()
- with placeholder_chart_c1_1:
- st_lottie(lottie_loading, height=100, key="loading_c1_1")
-
- st.caption("Number of alive runs over time")
- placeholder_chart_c1_2 = st.empty()
- with placeholder_chart_c1_2:
- st_lottie(lottie_loading, height=100, key="loading_c1_2")
-
- st.caption("Number of steps")
- placeholder_chart_c1_3 = st.empty()
- with placeholder_chart_c1_3:
- st_lottie(lottie_loading, height=100, key="loading_c1_3")
-
-with chart_c2:
- st.subheader("Global metrics")
- st.caption("Collaborative training participants")
- placeholder_chart_c2_1 = st.empty()
- with placeholder_chart_c2_1:
- st_lottie(lottie_loading, height=100, key="loading_c2_1")
-
- st.write("Chart showing participants of the collaborative-training. Circle radius is relative to the total time contributed, "
- "the profile picture is circled in purple if the participant is active. Every purple square represents an "
- "active device.")
-
- st.caption("Leaderboard")
- placeholder_chart_c2_3 = st.empty()
- with placeholder_chart_c2_3:
- st_lottie(lottie_loading, height=100, key="loading_c2_2")
-
-
-wandb.login(anonymous="must")
-
-
-steps, dates, losses, alive_peers = get_main_metrics()
-source = pd.DataFrame({"steps": steps, "loss": losses, "alive sessions": alive_peers, "date": dates})
-
-
-placeholder_chart_c1_1.vega_lite_chart(
- source,
- {
- "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
- "description": "Training Loss",
- "mark": {"type": "line", "point": {"tooltip": True, "filled": False, "strokeOpacity": 0}},
- "encoding": {"x": {"field": "date", "type": "temporal"}, "y": {"field": "loss", "type": "quantitative"}},
- "config": {"axisX": {"labelAngle": -40}},
- },
- use_container_width=True,
-)
-
-placeholder_chart_c1_2.vega_lite_chart(
- source,
- {
- "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
- "description": "Alive sessions",
- "mark": {"type": "line", "point": {"tooltip": True, "filled": False, "strokeOpacity": 0}},
- "encoding": {
- "x": {"field": "date", "type": "temporal"},
- "y": {"field": "alive sessions", "type": "quantitative"},
- },
- "config": {"axisX": {"labelAngle": -40}},
- },
- use_container_width=True,
-)
-placeholder_chart_c1_3.vega_lite_chart(
- source,
- {
- "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
- "description": "Training Loss",
- "mark": {"type": "line", "point": {"tooltip": True, "filled": False, "strokeOpacity": 0}},
- "encoding": {"x": {"field": "date", "type": "temporal"}, "y": {"field": "steps", "type": "quantitative"}},
- "config": {"axisX": {"labelAngle": -40}},
- },
- use_container_width=True,
-)
-
-serialized_data, profiles = get_new_bubble_data()
-df_leaderboard = get_leaderboard(serialized_data)
-observable(
- "_",
- notebook="d/9ae236a507f54046", # "@huggingface/participants-bubbles-chart",
- targets=["c_noaws"],
- redefine={"serializedData": serialized_data, "profileSimple": profiles, "width": 0},
- render_empty=True,
-)
-placeholder_chart_c2_3.dataframe(df_leaderboard[["User", "Total time contributed"]])
-
-global_metrics = get_global_metrics(serialized_data)
-
-placeholder_key_figures_c1.write(f"{global_metrics['num_contributing_users']}", unsafe_allow_html=True)
-placeholder_key_figures_c2.write(f"{global_metrics['num_active_users']}", unsafe_allow_html=True)
-placeholder_key_figures_c3.write(f"{global_metrics['total_runtime']}", unsafe_allow_html=True)
-
-with placeholder_chart_c2_1:
- observable(
- "Participants",
- notebook="d/9ae236a507f54046", # "@huggingface/participants-bubbles-chart",
- targets=["c_noaws"],
- redefine={"serializedData": serialized_data, "profileSimple": profiles},
- )
diff --git a/spaces/tsi-org/LLaVA/llava/eval/run_llava.py b/spaces/tsi-org/LLaVA/llava/eval/run_llava.py
deleted file mode 100644
index 11bebda29b0b92a3d6928d28a0bd584510e304aa..0000000000000000000000000000000000000000
--- a/spaces/tsi-org/LLaVA/llava/eval/run_llava.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import argparse
-import torch
-
-from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
-from llava.conversation import conv_templates, SeparatorStyle
-from llava.model.builder import load_pretrained_model
-from llava.utils import disable_torch_init
-from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria
-
-from PIL import Image
-
-import requests
-from PIL import Image
-from io import BytesIO
-
-
-def load_image(image_file):
- if image_file.startswith('http') or image_file.startswith('https'):
- response = requests.get(image_file)
- image = Image.open(BytesIO(response.content)).convert('RGB')
- else:
- image = Image.open(image_file).convert('RGB')
- return image
-
-
-def eval_model(args):
- # Model
- disable_torch_init()
-
- model_name = get_model_name_from_path(args.model_path)
- tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name)
-
- qs = args.query
- if model.config.mm_use_im_start_end:
- qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
- else:
- qs = DEFAULT_IMAGE_TOKEN + '\n' + qs
-
- if 'llama-2' in model_name.lower():
- conv_mode = "llava_llama_2"
- elif "v1" in model_name.lower():
- conv_mode = "llava_v1"
- elif "mpt" in model_name.lower():
- conv_mode = "mpt"
- else:
- conv_mode = "llava_v0"
-
- if args.conv_mode is not None and conv_mode != args.conv_mode:
- print('[WARNING] the auto inferred conversation mode is {}, while `--conv-mode` is {}, using {}'.format(conv_mode, args.conv_mode, args.conv_mode))
- else:
- args.conv_mode = conv_mode
-
- conv = conv_templates[args.conv_mode].copy()
- conv.append_message(conv.roles[0], qs)
- conv.append_message(conv.roles[1], None)
- prompt = conv.get_prompt()
-
- image = load_image(args.image_file)
- image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].half().cuda()
-
- input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
-
- stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
- keywords = [stop_str]
- stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
-
- with torch.inference_mode():
- output_ids = model.generate(
- input_ids,
- images=image_tensor,
- do_sample=True,
- temperature=0.2,
- max_new_tokens=1024,
- use_cache=True,
- stopping_criteria=[stopping_criteria])
-
- input_token_len = input_ids.shape[1]
- n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
- if n_diff_input_output > 0:
- print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
- outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
- outputs = outputs.strip()
- if outputs.endswith(stop_str):
- outputs = outputs[:-len(stop_str)]
- outputs = outputs.strip()
- print(outputs)
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument("--model-path", type=str, default="facebook/opt-350m")
- parser.add_argument("--model-base", type=str, default=None)
- parser.add_argument("--image-file", type=str, required=True)
- parser.add_argument("--query", type=str, required=True)
- parser.add_argument("--conv-mode", type=str, default=None)
- args = parser.parse_args()
-
- eval_model(args)
diff --git a/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/weighted_tokenizer/tokenizer.py b/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/weighted_tokenizer/tokenizer.py
deleted file mode 100644
index c3faf4f4d805f25c0b4348a07c4f27d04d0660f6..0000000000000000000000000000000000000000
--- a/spaces/twdac/BuChengFangYuan-ChineseJapaneseTranslation/app/weighted_tokenizer/tokenizer.py
+++ /dev/null
@@ -1,197 +0,0 @@
-import math
-import numpy as np
-import sentencepiece as spm
-from typing import SupportsFloat
-
-
-class WeightedTokenizer:
- def __init__(self, spm_vocab):
- self.spp = spm.SentencePieceProcessor(model_file=spm_vocab)
- self.vocab_size = self.spp.vocab_size()
- self.pad_id = self.spp.pad_id()
- self.unk_id = self.spp.unk_id()
- self.pad_piece = self.spp.IdToPiece(self.pad_id)
- self.unk_piece = self.spp.IdToPiece(self.unk_id)
- self._not_support_sample = False
-
- def pad_and_crop_(self, out_words, out_pieces, out_ids, out_weights, out_mask, default_pad_weight=0., pad_to_multiple_of=None, extra_pad=None, max_length=None):
- '''
- 填充和裁剪到指定的倍数和长度
- 注意，在指定最大长度后，填充到指定倍数的需求可能不会得到满足
- 注意，要求输入均为列表，并且会修改输入列表
- :param out_words: 输入词的列表
- :param out_pieces: 输入词元的列表
- :param out_ids: 输入词的ids
- :param out_weights: 输入词的权重
- :param out_mask: 输入词的掩码
- :param default_pad_weight: 默认的填充词的权重
- :param pad_to_multiple_of: 填充到指定的倍数
- :param extra_pad: 额外填充长度
- :param max_length: 序列最大长度
- :return:
- '''
- n_pad = 0
-
- if pad_to_multiple_of is not None:
- assert pad_to_multiple_of >= 1
- n_pad = len(out_words) % pad_to_multiple_of
- if n_pad != 0:
- n_pad = pad_to_multiple_of - n_pad
- n_pad += extra_pad
-
- if extra_pad is not None:
- n_pad += extra_pad
-
- if n_pad != 0:
- out_words.extend(['']*n_pad)
- out_pieces.extend([self.pad_piece]*n_pad)
- out_ids.extend([self.pad_id]*n_pad)
- out_weights.extend([default_pad_weight]*n_pad)
- out_mask.extend([False]*n_pad)
-
- if max_length is not None and len(out_words) > max_length:
- del out_words[max_length:]
- del out_pieces[max_length:]
- del out_ids[max_length:]
- del out_weights[max_length:]
- del out_mask[max_length:]
-
- return out_words, out_pieces, out_ids, out_weights, out_mask
-
- def encode(self, seq, seq_weight=None, default_weight=1., pad_to_multiple_of=None, extra_pad=0, max_length=None, remove_begin_space=False, enable_sampling=False):
- '''
- :param seq: 输入序列
- :param seq_weight: 输入序列的每个字权重
- :param default_weight: 默认字权重
- :param pad_to_multiple_of: 填充到指定数字的倍数
- :param extra_pad: 额外填充的数量，一般用于对齐
- :param max_length: 限制最大长度
- :param remove_begin_space: 是否删除 sentencepiece 生成的第一个空格
- :param enable_sampling: 是否使用采样
- :return:
- '''
- if seq_weight is None:
- seq_weight = [1.] * len(seq)
- elif isinstance(seq_weight, SupportsFloat):
- seq_weight = [float(seq_weight)] * len(seq)
-
- assert len(seq) == len(seq_weight), 'Error! seq len must equal with seq_weight len.'
-
- enable_sampling = enable_sampling and not self._not_support_sample
- encode_info = self.spp.EncodeAsImmutableProto(seq, enable_sampling=enable_sampling, alpha=1 if enable_sampling else 0, nbest_size=-1, add_bos=False, add_eos=False)
- if len(seq) > 0 and len(encode_info.pieces) == 0:
- # not support sample
- assert self._not_support_sample is False
- self._not_support_sample = True
- return self.encode(seq, seq_weight, default_weight, pad_to_multiple_of, extra_pad, max_length, remove_begin_space, enable_sampling)
-
- out_words = []
- out_pieces = []
- out_ids = []
- out_weights = []
-
- for p_i, pie in enumerate(encode_info.pieces):
- if p_i == 0 and remove_begin_space:
- continue
-
- start_pos = pie.begin
- end_pos = pie.end
- w = default_weight
- if end_pos > start_pos:
- w = sum(seq_weight[start_pos: end_pos]) / (end_pos-start_pos)
- out_words.append(pie.surface)
- out_pieces.append(pie.piece)
- out_ids.append(pie.id)
- out_weights.append(w)
-
- out_mask = [True] * len(out_words)
-
- self.pad_and_crop_(out_words, out_pieces, out_ids, out_weights, out_mask, default_weight, pad_to_multiple_of, extra_pad, max_length)
-
- return out_words, out_pieces, out_ids, out_weights, out_mask
-
- def batch_encode(self, batch_seq, batch_seq_weight=None, default_weight=1., pad_to_multiple_of=None, extra_pad=0, max_length=None, remove_begin_space=False, type='pt',
- default_pad_weight=0., enable_sampling=False):
- '''
- :param batch_seq: 一批字符串
- :param batch_seq_weight: 这批字符串的权重
- :param default_weight: 默认权重
- :param pad_to_multiple_of: 填充倍数
- :param extra_pad: 额外填充长度
- :param max_length: 限制最大长度
- :param remove_begin_space: 是否删除 sentencepiece 生成的第一个空格
- :param type: 指定 batch_out_ids, batch_out_weights, batch_out_mask 的类型，可选为 list, numpy, pt
- :param default_pad_weight: 默认填充用权重
- :param enable_sampling: 是否使用采样
- :return:
- '''
- assert type in ('list', 'numpy', 'pt')
- if batch_seq_weight is None:
- batch_seq_weight = [None] * len(batch_seq)
-
- batch_out_words, batch_out_pieces, batch_out_ids, batch_out_weights, batch_out_mask = [], [], [], [], []
-
- for seq, seq_weight in zip(batch_seq, batch_seq_weight): #, strict=True):
- out_words, out_pieces, out_ids, out_weights, out_mask = self.encode(seq, seq_weight, default_weight, None, 0, None, remove_begin_space, enable_sampling)
- batch_out_words.append(out_words)
- batch_out_pieces.append(out_pieces)
- batch_out_ids.append(out_ids)
- batch_out_weights.append(out_weights)
- batch_out_mask.append(out_mask)
-
- max_seq_len = max([len(out_words) for out_words in batch_out_words])
-
- if pad_to_multiple_of is None:
- real_pad_len = max_seq_len
- else:
- real_pad_len = pad_to_multiple_of * int(math.ceil(max_seq_len / pad_to_multiple_of))
-
- for out_words, out_pieces, out_ids, out_weights, out_mask in zip(batch_out_words, batch_out_pieces, batch_out_ids, batch_out_weights, batch_out_mask):
- self.pad_and_crop_(out_words, out_pieces, out_ids, out_weights, out_mask, default_pad_weight, real_pad_len, extra_pad, max_length)
-
- if type in ('numpy', 'pt'):
- batch_out_ids = np.int32(batch_out_ids)
- batch_out_weights = np.float32(batch_out_weights)
- batch_out_mask = np.bool_(batch_out_mask)
-
- if type == 'pt':
- import torch
- batch_out_ids = torch.from_numpy(batch_out_ids)
- batch_out_weights = torch.from_numpy(batch_out_weights)
- batch_out_mask = torch.from_numpy(batch_out_mask)
-
- return batch_out_words, batch_out_pieces, batch_out_ids, batch_out_weights, batch_out_mask
-
- def decode(self, ids):
- '''
- 解码
- :param ids: 可以是 torch.Tensor，numpy，列表，可以是一层（一条字符串id）或两层（一批字符串id）
- :return:
- '''
- if type(ids).__name__ == 'Tensor':
- ids = ids.cpu().numpy()
- if isinstance(ids, np.ndarray):
- ids = ids.tolist()
- return self.spp.Decode(ids)
-
- batch_decode = decode
-
-
-if __name__ == '__main__':
- tok = WeightedTokenizer('../vocab_spm_4.model')
-
- in_seq = '眼前出现了和以前一样的早餐场景。'
- out_words, out_pieces, out_ids, out_weights, out_mask = tok.encode(in_seq, [1.] * len(in_seq), pad_to_multiple_of=16)
- print(out_words, out_pieces, out_ids, out_weights, out_mask, sep='\n')
-
- out_seq = tok.decode(out_ids)
- print(out_seq)
-
- in_seqs = ['眼前出现了和以前一样的早餐场景。',
- '你好',
- '从前有座山，山上有座庙']
- out_words, out_pieces, out_ids, out_weights, out_mask = tok.batch_encode(in_seqs, None, pad_to_multiple_of=16)
- print(out_words, out_pieces, out_ids, out_weights, out_mask, sep='\n')
-
- out_seq = tok.decode(out_ids)
- print(out_seq)
diff --git a/spaces/typesdigital/demo-app/README.md b/spaces/typesdigital/demo-app/README.md
deleted file mode 100644
index fcdb5d7d9911cbe8e29a3eb2f0ced360f354965d..0000000000000000000000000000000000000000
--- a/spaces/typesdigital/demo-app/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Demo App
-emoji: 🐨
-colorFrom: purple
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.28.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ucalyptus/PTI/criteria/localitly_regulizer.py b/spaces/ucalyptus/PTI/criteria/localitly_regulizer.py
deleted file mode 100644
index 9d5f04db2d3b4e5c94a0bda5feb538b494e511eb..0000000000000000000000000000000000000000
--- a/spaces/ucalyptus/PTI/criteria/localitly_regulizer.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import torch
-import numpy as np
-from criteria import l2_loss
-from configs import hyperparameters
-from configs import global_config
-
-
-class Space_Regulizer:
- def __init__(self, original_G, lpips_net):
- self.original_G = original_G
- self.morphing_regulizer_alpha = hyperparameters.regulizer_alpha
- self.lpips_loss = lpips_net
-
- def get_morphed_w_code(self, new_w_code, fixed_w):
- interpolation_direction = new_w_code - fixed_w
- interpolation_direction_norm = torch.norm(interpolation_direction, p=2)
- direction_to_move = hyperparameters.regulizer_alpha * interpolation_direction / interpolation_direction_norm
- result_w = fixed_w + direction_to_move
- self.morphing_regulizer_alpha * fixed_w + (1 - self.morphing_regulizer_alpha) * new_w_code
-
- return result_w
-
- def get_image_from_ws(self, w_codes, G):
- return torch.cat([G.synthesis(w_code, noise_mode='none', force_fp32=True) for w_code in w_codes])
-
- def ball_holder_loss_lazy(self, new_G, num_of_sampled_latents, w_batch, use_wandb=False):
- loss = 0.0
-
- z_samples = np.random.randn(num_of_sampled_latents, self.original_G.z_dim)
- w_samples = self.original_G.mapping(torch.from_numpy(z_samples).to(global_config.device), None,
- truncation_psi=0.5)
- territory_indicator_ws = [self.get_morphed_w_code(w_code.unsqueeze(0), w_batch) for w_code in w_samples]
-
- for w_code in territory_indicator_ws:
- new_img = new_G.synthesis(w_code, noise_mode='none', force_fp32=True)
- with torch.no_grad():
- old_img = self.original_G.synthesis(w_code, noise_mode='none', force_fp32=True)
-
- if hyperparameters.regulizer_l2_lambda > 0:
- l2_loss_val = l2_loss.l2_loss(old_img, new_img)
- if use_wandb:
- wandb.log({f'space_regulizer_l2_loss_val': l2_loss_val.detach().cpu()},
- step=global_config.training_step)
- loss += l2_loss_val * hyperparameters.regulizer_l2_lambda
-
- if hyperparameters.regulizer_lpips_lambda > 0:
- loss_lpips = self.lpips_loss(old_img, new_img)
- loss_lpips = torch.mean(torch.squeeze(loss_lpips))
- if use_wandb:
- wandb.log({f'space_regulizer_lpips_loss_val': loss_lpips.detach().cpu()},
- step=global_config.training_step)
- loss += loss_lpips * hyperparameters.regulizer_lpips_lambda
-
- return loss / len(territory_indicator_ws)
-
- def space_regulizer_loss(self, new_G, w_batch, use_wandb):
- ret_val = self.ball_holder_loss_lazy(new_G, hyperparameters.latent_ball_num_of_samples, w_batch, use_wandb)
- return ret_val
diff --git a/spaces/unik-style/unik-ml/main.py b/spaces/unik-style/unik-ml/main.py
deleted file mode 100644
index 41bcd2f371898e910d03b1fc7a8a8a9fa318e47e..0000000000000000000000000000000000000000
--- a/spaces/unik-style/unik-ml/main.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import uvicorn
-from fastapi import FastAPI
-from fastapi.middleware.cors import CORSMiddleware
-from huggingface_hub import login
-
-from config import settings
-from routers.intference import stable_diffusion
-
-login(settings.hf_token)
-
-app = FastAPI(
- title="UNIK ML",
- version=settings.version,
- openapi_url=f"{settings.prefix}/openapi.json",
- docs_url=f"{settings.prefix}/docs",
- redoc_url=f"{settings.prefix}/redoc",
- swagger_ui_oauth2_redirect_url=f"{settings.prefix}/docs/oauth2-redirect")
-
-app.add_middleware(
- CORSMiddleware,
- allow_origins=["*"],
- allow_methods=["*"],
- allow_headers=["*"],
- allow_credentials=True,
-)
-
-
-@app.get("/")
-async def root():
- return {"message": "UNIK ML API"}
-
-
-app.include_router(stable_diffusion.router, prefix=settings.prefix, tags=["inference"])
-
-# Start your FastAPI application
-# if __name__ == "__main__":
-# uvicorn.run(app, host="0.0.0.0", port=8000)
-#
diff --git a/spaces/usbethFlerru/sovits-modelsV2/50-Cent-Get-Rich-Or-Die-Tryin-Album-Zip.md b/spaces/usbethFlerru/sovits-modelsV2/50-Cent-Get-Rich-Or-Die-Tryin-Album-Zip.md
deleted file mode 100644
index c1a6d8d2ee8bd8f75f9e5fca692583d4abba08e5..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/50-Cent-Get-Rich-Or-Die-Tryin-Album-Zip.md
+++ /dev/null
@@ -1,64 +0,0 @@
-## 50 Cent Get Rich Or Die Tryin Album Zip
-
-
-
-
-
-
-
-
-
-**Click Here ->>> [https://lomasmavi.blogspot.com/?c=2txT5g](https://lomasmavi.blogspot.com/?c=2txT5g)**
-
-
-
-
-
-
-
-
-
-
-
- Here is a possible title and article with html formatting for the keyword "50 Cent Get Rich Or Die Tryin Album Zip":
-
-# How to Download 50 Cent's Classic Album "Get Rich Or Die Tryin" in Zip Format
-
-
-
-If you are a fan of hip-hop music, you probably know 50 Cent's debut album "Get Rich Or Die Tryin", which was released in 2003 and became one of the best-selling albums of all time. The album features hit singles like "In Da Club", "21 Questions", "P.I.M.P." and "Many Men (Wish Death)", and showcases 50 Cent's gritty and charismatic style of rap.
-
-
-
-But how can you download this album in zip format, so you can enjoy it on your computer or mobile device? There are many websites that offer free downloads of 50 Cent's album, but some of them may be unreliable, unsafe or illegal. To avoid any problems, we recommend you to use one of the following sources:
-
-
-
-- [Archive.org](https://archive.org/details/50-cent-04-many-men-get-rich-or-die-tryin): This is a reputable website that preserves digital content for public access. You can find 50 Cent's album in mp3 format, along with the track list and cover art. To download it in zip format, just click on the "ZIP" option under "Download Options".[^1^]
-
-- [Muslinkz.com](https://muslinkz.com/50-cent-get-rich-or-die-tryin-2002-download/): This is a music blog that provides direct links to download albums and songs. You can find 50 Cent's album in mp3 format, with a quality of 320 kbps. To download it in zip format, just click on the "DOWNLOAD LINK HERE" button.[^1^]
-
-- [Albumgrab.com](https://albumgrab.com/50-cent-get-rich-or-die-tryin-2002-download/): This is another music blog that offers free downloads of albums and songs. You can also find 50 Cent's album in mp3 format, with a quality of 320 kbps. To download it in zip format, just click on the "DOWNLOAD LINK" button.[^2^]
-
-
-
-These are some of the best and safest ways to download 50 Cent's classic album "Get Rich Or Die Tryin" in zip format. We hope you enjoy listening to this masterpiece of hip-hop music.
-
-Here are a few more paragraphs for the article:
-
-But what makes "Get Rich Or Die Tryin" a classic album is not only 50 Cent's charisma and delivery, but also the production by some of the best beatmakers in the game. Dr. Dre, Eminem, Sha Money XL, Rockwilder and others provide 50 with a diverse and dynamic soundscape that ranges from hard-hitting club bangers to soulful ballads. The album also features guest appearances by Eminem, Nate Dogg, Lloyd Banks, Young Buck and Tony Yayo, who add their own flavor and energy to the tracks.
-
-
-
-Some of the standout songs on the album include "Patiently Waiting", a menacing collaboration with Eminem that showcases 50's hunger and determination; "Many Men (Wish Death)", a haunting reflection on 50's near-fatal shooting and his enemies; "In Da Club", a catchy and infectious anthem that became one of the biggest hits of 2003; "21 Questions", a smooth and romantic duet with Nate Dogg that proves 50 can also rap about love; and "Wanksta", a diss track aimed at Ja Rule that sparked one of the most notorious beefs in rap history.
-
-
-
-"Get Rich Or Die Tryin" is an album that lives up to its title, as 50 Cent delivers a powerful and compelling debut that showcases his talent, ambition and resilience. The album is a testament to 50's rise from the streets to the top of the rap game, and a milestone in hip-hop history. It is an album that every rap fan should listen to and appreciate, as it is one of the best examples of gangsta rap done right.
-
- dfd1c89656
-
-
-
-
-
diff --git a/spaces/user238921933/stable-diffusion-webui/modules/hashes.py b/spaces/user238921933/stable-diffusion-webui/modules/hashes.py
deleted file mode 100644
index 46abf99c304b23bf8e3e394e07c2209d4130afef..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/modules/hashes.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import hashlib
-import json
-import os.path
-
-import filelock
-
-from modules import shared
-from modules.paths import data_path
-
-
-cache_filename = os.path.join(data_path, "cache.json")
-cache_data = None
-
-
-def dump_cache():
- with filelock.FileLock(cache_filename+".lock"):
- with open(cache_filename, "w", encoding="utf8") as file:
- json.dump(cache_data, file, indent=4)
-
-
-def cache(subsection):
- global cache_data
-
- if cache_data is None:
- with filelock.FileLock(cache_filename+".lock"):
- if not os.path.isfile(cache_filename):
- cache_data = {}
- else:
- with open(cache_filename, "r", encoding="utf8") as file:
- cache_data = json.load(file)
-
- s = cache_data.get(subsection, {})
- cache_data[subsection] = s
-
- return s
-
-
-def calculate_sha256(filename):
- hash_sha256 = hashlib.sha256()
- blksize = 1024 * 1024
-
- with open(filename, "rb") as f:
- for chunk in iter(lambda: f.read(blksize), b""):
- hash_sha256.update(chunk)
-
- return hash_sha256.hexdigest()
-
-
-def sha256_from_cache(filename, title):
- hashes = cache("hashes")
- ondisk_mtime = os.path.getmtime(filename)
-
- if title not in hashes:
- return None
-
- cached_sha256 = hashes[title].get("sha256", None)
- cached_mtime = hashes[title].get("mtime", 0)
-
- if ondisk_mtime > cached_mtime or cached_sha256 is None:
- return None
-
- return cached_sha256
-
-
-def sha256(filename, title):
- hashes = cache("hashes")
-
- sha256_value = sha256_from_cache(filename, title)
- if sha256_value is not None:
- return sha256_value
-
- if shared.cmd_opts.no_hashing:
- return None
-
- print(f"Calculating sha256 for {filename}: ", end='')
- sha256_value = calculate_sha256(filename)
- print(f"{sha256_value}")
-
- hashes[title] = {
- "mtime": os.path.getmtime(filename),
- "sha256": sha256_value,
- }
-
- dump_cache()
-
- return sha256_value
-
-
-
-
-
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/tracker/trackers/byte_tracker.md b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/tracker/trackers/byte_tracker.md
deleted file mode 100644
index 797be1db66284e90904cd520f149a42a11dc1ed0..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/tracker/trackers/byte_tracker.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-description: Learn how to track ByteAI model sizes and tips for model optimization with STrack, a byte tracking tool from Ultralytics.
-keywords: Byte Tracker, Ultralytics STrack, application monitoring, bytes sent, bytes received, code examples, setup instructions
----
-
-## STrack
----
-### ::: ultralytics.tracker.trackers.byte_tracker.STrack
-
- 
-
-
-
-
-
'
- return text, visible_text
-
-def ui():
- picture_select = gr.Image(label='Send a picture', type='pil')
-
- function_call = 'chat.cai_chatbot_wrapper' if shared.args.cai_chat else 'chat.chatbot_wrapper'
-
- # Prepare the hijack with custom inputs
- picture_select.upload(lambda picture, name1, name2: input_hijack.update({"state": True, "value": generate_chat_picture(picture, name1, name2)}), [picture_select, shared.gradio['name1'], shared.gradio['name2']], None)
-
- # Call the generation function
- picture_select.upload(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)
-
- # Clear the picture from the upload field
- picture_select.upload(lambda : None, [], [picture_select], show_progress=False)
diff --git a/spaces/dvitel/codebleu/bleu.py b/spaces/dvitel/codebleu/bleu.py
deleted file mode 100644
index fa5941b463a43c845f53d9581aaa0f4df5b17d97..0000000000000000000000000000000000000000
--- a/spaces/dvitel/codebleu/bleu.py
+++ /dev/null
@@ -1,590 +0,0 @@
-# -*- coding: utf-8 -*-
-# Natural Language Toolkit: BLEU Score
-#
-# Copyright (C) 2001-2020 NLTK Project
-# Authors: Chin Yee Lee, Hengfeng Li, Ruxin Hou, Calvin Tanujaya Lim
-# Contributors: Björn Mattsson, Dmitrijs Milajevs, Liling Tan
-# URL: -
-- )} - -
-
- Explorables
- 
-
-
-Once you have created the Space, you'll see the `Building` status, and once it becomes `Running,` your Space is ready to go!
-
-
-
-Now, when you navigate to your Space's **App** section, you can access the Aim UI.
-
-## Compare your experiments with Aim on Spaces
-
-Let's use a quick example of a PyTorch CNN trained on MNIST to demonstrate end-to-end Aim on Spaces deployment.
-The full example is in the [Aim repo examples folder](https://github.com/aimhubio/aim/blob/main/examples/pytorch_track.py).
-
-```python
-from aim import Run
-from aim.pytorch import track_gradients_dists, track_params_dists
-
-# Initialize a new Run
-aim_run = Run()
-...
-items = {'accuracy': acc, 'loss': loss}
-aim_run.track(items, epoch=epoch, context={'subset': 'train'})
-
-# Track weights and gradients distributions
-track_params_dists(model, aim_run)
-track_gradients_dists(model, aim_run)
-```
-
-The experiments tracked by Aim are stored in the `.aim` folder. **To display the logs with the Aim UI in your Space, you need to compress the `.aim` folder to a `tar.gz` file and upload it to your Space using `git` or the Files and Versions sections of your Space.**
-
-Here's a bash command for that:
-
-```bash
-tar -czvf aim_repo.tar.gz .aim
-```
-
-That’s it! Now open the App section of your Space and the Aim UI is available with your logs.
-Here is what to expect:
-
-
-
-Filter your runs using Aim’s Pythonic search. You can write pythonic [queries against](https://aimstack.readthedocs.io/en/latest/using/search.html) EVERYTHING you have tracked - metrics, hyperparams etc. Check out some [examples](https://huggingface.co/aimstack) on HF Hub Spaces.
-
-
-
-You can verify what was sent to the chatbot model here. It is ideally in English:
-"""
- )
-
-
- non_visible_chatbot = gr.Chatbot(
- [],
- visible=True,
- avatar_images=("examples/lama.jpeg", "examples/lama2.jpeg"),
- bubble_full_width=False,
- height=150,
- )
-
-
- clear_btn = gr.ClearButton([visible_chatbot, non_visible_chatbot])
-
-
- txt_msg = txt_btn.click(add_text, [visible_chatbot, non_visible_chatbot, txt], [visible_chatbot, non_visible_chatbot, txt, identified_lang]).then(
- bot, [visible_chatbot,non_visible_chatbot, identified_lang], [visible_chatbot, non_visible_chatbot, identified_lang]
- )
-
- txt_msg.then(lambda: gr.update(interactive=True), None, [txt], )
-
- txt_msg = txt.submit(add_text, [visible_chatbot, non_visible_chatbot, txt], [visible_chatbot, non_visible_chatbot, txt, identified_lang]).then(
- bot, [visible_chatbot,non_visible_chatbot, identified_lang], [visible_chatbot, non_visible_chatbot, identified_lang]
- )
-
- txt_msg.then(lambda: gr.update(interactive=True), None, [txt], )
-
- file_msg = btn.stop_recording(
- add_file, [visible_chatbot, non_visible_chatbot, btn], [visible_chatbot, non_visible_chatbot, txt, identified_lang],
- ).then(
- bot, [visible_chatbot,non_visible_chatbot, identified_lang], [visible_chatbot, non_visible_chatbot, identified_lang]
- )
-
- file_msg.then(lambda: (gr.update(interactive=True),gr.update(interactive=True,value=None)), None, [txt, btn], )
-
-
-demo.queue(concurrency_count=2)
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/tests/modeling/test_fast_rcnn.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/tests/modeling/test_fast_rcnn.py
deleted file mode 100644
index e29b944bffca1ccbf5b02be59a753f3188d90a4f..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/tests/modeling/test_fast_rcnn.py
+++ /dev/null
@@ -1,171 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-import unittest
-import torch
-
-from detectron2.layers import ShapeSpec
-from detectron2.modeling.box_regression import Box2BoxTransform, Box2BoxTransformRotated
-from detectron2.modeling.roi_heads.fast_rcnn import FastRCNNOutputLayers
-from detectron2.modeling.roi_heads.rotated_fast_rcnn import RotatedFastRCNNOutputLayers
-from detectron2.structures import Boxes, Instances, RotatedBoxes
-from detectron2.utils.events import EventStorage
-
-logger = logging.getLogger(__name__)
-
-
-class FastRCNNTest(unittest.TestCase):
- def test_fast_rcnn(self):
- torch.manual_seed(132)
-
- box_head_output_size = 8
-
- box_predictor = FastRCNNOutputLayers(
- ShapeSpec(channels=box_head_output_size),
- box2box_transform=Box2BoxTransform(weights=(10, 10, 5, 5)),
- num_classes=5,
- )
- feature_pooled = torch.rand(2, box_head_output_size)
- predictions = box_predictor(feature_pooled)
-
- proposal_boxes = torch.tensor([[0.8, 1.1, 3.2, 2.8], [2.3, 2.5, 7, 8]], dtype=torch.float32)
- gt_boxes = torch.tensor([[1, 1, 3, 3], [2, 2, 6, 6]], dtype=torch.float32)
- proposal = Instances((10, 10))
- proposal.proposal_boxes = Boxes(proposal_boxes)
- proposal.gt_boxes = Boxes(gt_boxes)
- proposal.gt_classes = torch.tensor([1, 2])
-
- with EventStorage(): # capture events in a new storage to discard them
- losses = box_predictor.losses(predictions, [proposal])
-
- expected_losses = {
- "loss_cls": torch.tensor(1.7951188087),
- "loss_box_reg": torch.tensor(4.0357131958),
- }
- for name in expected_losses.keys():
- assert torch.allclose(losses[name], expected_losses[name])
-
- def test_fast_rcnn_empty_batch(self, device="cpu"):
- box_predictor = FastRCNNOutputLayers(
- ShapeSpec(channels=10),
- box2box_transform=Box2BoxTransform(weights=(10, 10, 5, 5)),
- num_classes=8,
- ).to(device=device)
-
- logits = torch.randn(0, 100, requires_grad=True, device=device)
- deltas = torch.randn(0, 4, requires_grad=True, device=device)
- losses = box_predictor.losses([logits, deltas], [])
- for value in losses.values():
- self.assertTrue(torch.allclose(value, torch.zeros_like(value)))
- sum(losses.values()).backward()
- self.assertTrue(logits.grad is not None)
- self.assertTrue(deltas.grad is not None)
-
- predictions, _ = box_predictor.inference([logits, deltas], [])
- self.assertEqual(len(predictions), 0)
-
- @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
- def test_fast_rcnn_empty_batch_cuda(self):
- self.test_fast_rcnn_empty_batch(device=torch.device("cuda"))
-
- def test_fast_rcnn_rotated(self):
- torch.manual_seed(132)
- box_head_output_size = 8
-
- box_predictor = RotatedFastRCNNOutputLayers(
- ShapeSpec(channels=box_head_output_size),
- box2box_transform=Box2BoxTransformRotated(weights=(10, 10, 5, 5, 1)),
- num_classes=5,
- )
- feature_pooled = torch.rand(2, box_head_output_size)
- predictions = box_predictor(feature_pooled)
- proposal_boxes = torch.tensor(
- [[2, 1.95, 2.4, 1.7, 0], [4.65, 5.25, 4.7, 5.5, 0]], dtype=torch.float32
- )
- gt_boxes = torch.tensor([[2, 2, 2, 2, 0], [4, 4, 4, 4, 0]], dtype=torch.float32)
- proposal = Instances((10, 10))
- proposal.proposal_boxes = RotatedBoxes(proposal_boxes)
- proposal.gt_boxes = RotatedBoxes(gt_boxes)
- proposal.gt_classes = torch.tensor([1, 2])
-
- with EventStorage(): # capture events in a new storage to discard them
- losses = box_predictor.losses(predictions, [proposal])
-
- # Note: the expected losses are slightly different even if
- # the boxes are essentially the same as in the FastRCNNOutput test, because
- # bbox_pred in FastRCNNOutputLayers have different Linear layers/initialization
- # between the two cases.
- expected_losses = {
- "loss_cls": torch.tensor(1.7920907736),
- "loss_box_reg": torch.tensor(4.0410838127),
- }
- for name in expected_losses.keys():
- assert torch.allclose(losses[name], expected_losses[name])
-
- def test_predict_boxes_tracing(self):
- class Model(torch.nn.Module):
- def __init__(self, output_layer):
- super(Model, self).__init__()
- self._output_layer = output_layer
-
- def forward(self, proposal_deltas, proposal_boxes):
- instances = Instances((10, 10))
- instances.proposal_boxes = Boxes(proposal_boxes)
- return self._output_layer.predict_boxes((None, proposal_deltas), [instances])
-
- box_head_output_size = 8
-
- box_predictor = FastRCNNOutputLayers(
- ShapeSpec(channels=box_head_output_size),
- box2box_transform=Box2BoxTransform(weights=(10, 10, 5, 5)),
- num_classes=5,
- )
-
- model = Model(box_predictor)
-
- from detectron2.export.torchscript_patch import patch_builtin_len
-
- with torch.no_grad(), patch_builtin_len():
- func = torch.jit.trace(model, (torch.randn(10, 20), torch.randn(10, 4)))
-
- o = func(torch.randn(10, 20), torch.randn(10, 4))
- self.assertEqual(o[0].shape, (10, 20))
- o = func(torch.randn(5, 20), torch.randn(5, 4))
- self.assertEqual(o[0].shape, (5, 20))
- o = func(torch.randn(20, 20), torch.randn(20, 4))
- self.assertEqual(o[0].shape, (20, 20))
-
- def test_predict_probs_tracing(self):
- class Model(torch.nn.Module):
- def __init__(self, output_layer):
- super(Model, self).__init__()
- self._output_layer = output_layer
-
- def forward(self, scores, proposal_boxes):
- instances = Instances((10, 10))
- instances.proposal_boxes = Boxes(proposal_boxes)
- return self._output_layer.predict_probs((scores, None), [instances])
-
- box_head_output_size = 8
-
- box_predictor = FastRCNNOutputLayers(
- ShapeSpec(channels=box_head_output_size),
- box2box_transform=Box2BoxTransform(weights=(10, 10, 5, 5)),
- num_classes=5,
- )
-
- model = Model(box_predictor)
-
- from detectron2.export.torchscript_patch import patch_builtin_len
-
- with torch.no_grad(), patch_builtin_len():
- func = torch.jit.trace(model, (torch.randn(10, 6), torch.rand(10, 4)))
- o = func(torch.randn(10, 6), torch.randn(10, 4))
- self.assertEqual(o[0].shape, (10, 6))
- o = func(torch.randn(5, 6), torch.randn(5, 4))
- self.assertEqual(o[0].shape, (5, 6))
- o = func(torch.randn(20, 6), torch.randn(20, 4))
- self.assertEqual(o[0].shape, (20, 6))
-
-
-if __name__ == "__main__":
- unittest.main()
diff --git a/spaces/yuan1615/EmpathyVC/text/cmudict.py b/spaces/yuan1615/EmpathyVC/text/cmudict.py
deleted file mode 100644
index f1885ed266d16b371577a88df9e0741805f38eb0..0000000000000000000000000000000000000000
--- a/spaces/yuan1615/EmpathyVC/text/cmudict.py
+++ /dev/null
@@ -1,140 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-import re
-
-
-valid_symbols = [
- "AA",
- "AA0",
- "AA1",
- "AA2",
- "AE",
- "AE0",
- "AE1",
- "AE2",
- "AH",
- "AH0",
- "AH1",
- "AH2",
- "AO",
- "AO0",
- "AO1",
- "AO2",
- "AW",
- "AW0",
- "AW1",
- "AW2",
- "AY",
- "AY0",
- "AY1",
- "AY2",
- "B",
- "CH",
- "D",
- "DH",
- "EH",
- "EH0",
- "EH1",
- "EH2",
- "ER",
- "ER0",
- "ER1",
- "ER2",
- "EY",
- "EY0",
- "EY1",
- "EY2",
- "F",
- "G",
- "HH",
- "IH",
- "IH0",
- "IH1",
- "IH2",
- "IY",
- "IY0",
- "IY1",
- "IY2",
- "JH",
- "K",
- "L",
- "M",
- "N",
- "NG",
- "OW",
- "OW0",
- "OW1",
- "OW2",
- "OY",
- "OY0",
- "OY1",
- "OY2",
- "P",
- "R",
- "S",
- "SH",
- "T",
- "TH",
- "UH",
- "UH0",
- "UH1",
- "UH2",
- "UW",
- "UW0",
- "UW1",
- "UW2",
- "V",
- "W",
- "Y",
- "Z",
- "ZH",
-]
-
-_valid_symbol_set = set(valid_symbols)
-
-
-class CMUDict:
- """Thin wrapper around CMUDict data. http://www.speech.cs.cmu.edu/cgi-bin/cmudict"""
-
- def __init__(self, file_or_path, keep_ambiguous=True):
- if isinstance(file_or_path, str):
- with open(file_or_path, encoding="latin-1") as f:
- entries = _parse_cmudict(f)
- else:
- entries = _parse_cmudict(file_or_path)
- if not keep_ambiguous:
- entries = {word: pron for word, pron in entries.items() if len(pron) == 1}
- self._entries = entries
-
- def __len__(self):
- return len(self._entries)
-
- def lookup(self, word):
- """Returns list of ARPAbet pronunciations of the given word."""
- return self._entries.get(word.upper())
-
-
-_alt_re = re.compile(r"\([0-9]+\)")
-
-
-def _parse_cmudict(file):
- cmudict = {}
- for line in file:
- if len(line) and (line[0] >= "A" and line[0] <= "Z" or line[0] == "'"):
- parts = line.split(" ")
- word = re.sub(_alt_re, "", parts[0])
- pronunciation = _get_pronunciation(parts[1])
- if pronunciation:
- if word in cmudict:
- cmudict[word].append(pronunciation)
- else:
- cmudict[word] = [pronunciation]
- return cmudict
-
-
-def _get_pronunciation(s):
- parts = s.strip().split(" ")
- for part in parts:
- if part not in _valid_symbol_set:
- return None
- return " ".join(parts)
diff --git a/spaces/zbellay/job-automation/app.py b/spaces/zbellay/job-automation/app.py
deleted file mode 100644
index 0cdf8b6ea033d0e5f6cc4c4f115522c40ec56c56..0000000000000000000000000000000000000000
--- a/spaces/zbellay/job-automation/app.py
+++ /dev/null
@@ -1,77 +0,0 @@
-import re
-
-import gradio as gr
-from transformers import pipeline, set_seed
-
-generator = pipeline('text-generation', model='gpt2')
-# generator = pipeline('text-generation', model='EleutherAI/gpt-j-2.7B')
-
-# ideally we would use a larger model but it's either not free or too big for my macbook air lol
-# generator = pipeline('text-generation', model='EleutherAI/gpt-j-6B')
-
-# set_seed(42)
-
-
-def produce_text(company, job_title, job_description)->str:
-
- prompt = """The following is a list of predictions about how AI will automate people out of jobs.
-
-==========
-
-Job title and description: Customer Service Representative, answering customer questions and providing support via phone, email, and chat.
-
-How this job will be automated by AI: AI will be able to answer customer questions and provide support via phone, email, and chat. This will reduce if not eliminate the need for a human to perform this task.
-
-==========
-
-Job title and description: Data Scientist, analyzing data and building machine learning models.
-
-How this job will be automated by AI: AI will be able to analyze data and build machine learning models. This will increase the productivity of data scientists, allowing them to perform more tasks in the same amount of time. This will result in a net reduction in demand for data scientists.
-
-=========="""
-
- model_input = f"""
-{prompt}
-
-Job title and description: {job_title}{' at ' + company if company else ''},
-{job_description}
-
-How this job will be automated by AI:"""
-
- output = generator(model_input, max_length=512, num_return_sequences=1)
-
-
-
- result = output[0]['generated_text']
- print(result)
- final_result = result[len(model_input):]
-
- # split the string if either an equals sign or a dash is encountered using regex
- final_result = re.split('-|=', final_result)
- final_result = final_result[0]
-
- return final_result
-
-
-iface = gr.Interface(
- fn=produce_text,
- inputs=[
- gr.inputs.Textbox(label="Company (Optional)"),
- gr.inputs.Textbox(label="Job Title"),
- gr.inputs.Textbox(label="Job description"),
- ],
- outputs=[
- gr.outputs.Textbox(label="How this job may be automated by AI"),
- ],
- title="Job Automation by AI",
- description="A simple app to predict how certain jobs may be automated by AI, as predicted by an AI. (Note: this is using GPT-2, and would be substantially better with GPT-3 or GPT-J-6B, but those models are not free and are too big for my macbook air to run.)",
- examples=[
- ['Google', 'Backend Software Engineer', 'Programming and maintaining backend services for Google products. Writing code in Python, Go, and Java.'],
- ['Accenture', 'Senior Consultant', 'Working with clients to solve business problems.'],
- ['', 'Social Media Coordinator', 'Creating and posting content on social media. Managing social media accounts. Monitoring social media for mentions of the company.'],
- ['', 'Content Creator', 'Creating short and long form video content for social media'],
- ['Goldman Sachs', 'Junior Analyst', 'Analysing financial data, creating financial models to predict future market movements and trends. Writing reports for clients and senior management.'],
- ['Santa Clara University', 'Associate Professor', 'Teaching undergraduate and graduate level courses.']
- ]
-)
-iface.launch()
diff --git a/spaces/zeno-ml/translation-report/gpt-MT/SUPPORT.md b/spaces/zeno-ml/translation-report/gpt-MT/SUPPORT.md
deleted file mode 100644
index eaf439aecca04e3aa5a022e0bc0b8b088efef7f1..0000000000000000000000000000000000000000
--- a/spaces/zeno-ml/translation-report/gpt-MT/SUPPORT.md
+++ /dev/null
@@ -1,25 +0,0 @@
-# TODO: The maintainer of this repo has not yet edited this file
-
-**REPO OWNER**: Do you want Customer Service & Support (CSS) support for this product/project?
-
-- **No CSS support:** Fill out this template with information about how to file issues and get help.
-- **Yes CSS support:** Fill out an intake form at [aka.ms/onboardsupport](https://aka.ms/onboardsupport). CSS will work with/help you to determine next steps.
-- **Not sure?** Fill out an intake as though the answer were "Yes". CSS will help you decide.
-
-*Then remove this first heading from this SUPPORT.MD file before publishing your repo.*
-
-# Support
-
-## How to file issues and get help
-
-This project uses GitHub Issues to track bugs and feature requests. Please search the existing
-issues before filing new issues to avoid duplicates. For new issues, file your bug or
-feature request as a new Issue.
-
-For help and questions about using this project, please **REPO MAINTAINER: INSERT INSTRUCTIONS HERE
-FOR HOW TO ENGAGE REPO OWNERS OR COMMUNITY FOR HELP. COULD BE A STACK OVERFLOW TAG OR OTHER
-CHANNEL. WHERE WILL YOU HELP PEOPLE?**.
-
-## Microsoft Support Policy
-
-Support for this **PROJECT or PRODUCT** is limited to the resources listed above.
diff --git a/spaces/zhanghaohui/szu-gpt-academic/crazy_functions/test_project/cpp/cppipc/ipc.cpp b/spaces/zhanghaohui/szu-gpt-academic/crazy_functions/test_project/cpp/cppipc/ipc.cpp
deleted file mode 100644
index c713b852ea5a51fbeb4729b64561da482caaf351..0000000000000000000000000000000000000000
--- a/spaces/zhanghaohui/szu-gpt-academic/crazy_functions/test_project/cpp/cppipc/ipc.cpp
+++ /dev/null
@@ -1,701 +0,0 @@
-
-#include 
-