diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Bus Simulator Ultimate - Explore Different Countries and Cities with Your Bus on iOS.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Bus Simulator Ultimate - Explore Different Countries and Cities with Your Bus on iOS.md
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Download Bus Simulator Ultimate iOS: The Best Bus Simulation Game
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Do you love driving buses and exploring different cities? Do you want to experience the most realistic and immersive bus simulation game on your iOS device? If yes, then you should download Bus Simulator Ultimate, the best bus simulation game developed by Zuuks Games. In this article, we will tell you everything you need to know about this amazing game, including its features, how to download it, and some tips and tricks to play it.
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What is Bus Simulator Ultimate?
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Bus Simulator Ultimate is a game that lets you drive official Mercedes-Benz Travego, Mercedes-Benz Tourismo and Setra licensed buses on realistic routes and bus stations. You can choose from over 30 countries and 250 cities to drive in, such as United States, United Kingdom, China, Canada, Russia, Germany, Italy, France, Spain, Netherlands, Turkey, South Korea, Japan, Brazil, Azerbaijan, Belgium, Bulgaria, Czech Republic, Dominican Republic, Indonesia, Philippines, South Africa, India, Hong Kong, Ireland, Israel, Qatar, Malaysia, Thailand, Taiwan and more. You can also establish your own bus company and become the largest bus corporation in the world by hiring employees and managing your finances.
Bus Simulator Ultimate features 32 amazing coach buses that are designed with detailed cockpits and realistic sound effects. You can also buy used buses from the market or customize your own buses with different colors and accessories. The game also offers realistic traffic system, weather conditions, highway toll roads, day and night cycle, and more. You can enjoy driving on different terrains and landscapes as you explore the world.
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Multiplayer mode and business management
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Bus Simulator Ultimate is not just a driving game. It is also a business simulation game that allows you to create your own bus company and compete with other players in the multiplayer mode. You can have offices in many places around the world and hire employees to work for you. You can also manage your income and expenses, set ticket prices, upgrade your buses, and more. You can also join the Ultimate League and rank up in the leaderboard by earning points from driving.
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Passenger system and social interactions
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Bus Simulator Ultimate has a unique passenger system that provides social and realistic reactions from your customers. You can see their faces, hear their voices, and read their reviews. You can also interact with them by using the horn or the microphone. You can also see their mood changes depending on how you drive. Some passengers may be happy, angry, sad, or bored. You have to make sure they are satisfied with your service.
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Customizable settings and controls
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Bus Simulator Ultimate gives you the option to customize your settings and controls according to your preferences. You can choose from three different control modes: tilt, buttons or steering wheel. You can also adjust the camera angle, the graphics quality, the sound volume, the language (more than 25 languages supported), and more. You can also enable or disable features such as speed limiters, traffic lights, indicators, mirrors, etc.
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How to download Bus Simulator Ultimate on iOS devices
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Requirements and compatibility
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Bus Simulator Ultimate is a free game that requires iOS 10.0 or later. It is compatible with iPhone 5S or newer models (including iPhone SE), iPad Air or newer models (including iPad mini 2), iPod touch (6th generation) or newer models
Steps to download and install
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To download Bus Simulator Ultimate on your iOS device, you need to follow these simple steps:
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Open the App Store on your device and search for "Bus Simulator Ultimate" or click on this link.
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Tap on the "Get" button and then on the "Install" button to start downloading the game.
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Wait for the download to finish and then tap on the "Open" button to launch the game.
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Enjoy playing Bus Simulator Ultimate on your iOS device!
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Subscription options and benefits
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Bus Simulator Ultimate is a free game that you can play without any limitations. However, if you want to enjoy some extra benefits and support the developers, you can subscribe to the Premium Membership. The Premium Membership offers you the following advantages:
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No ads
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Free bus skins
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Double XP and money
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10% discount on market and garage
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Premium badge and chat color
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The Premium Membership costs $4.99 per month or $49.99 per year. You can cancel your subscription at any time in your iTunes account settings.
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Tips and tricks to play Bus Simulator Ultimate
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Drive safely and follow traffic rules
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One of the most important tips to play Bus Simulator Ultimate is to drive safely and follow the traffic rules. You have to respect the speed limits, the traffic lights, the signs, and the other vehicles on the road. You also have to avoid accidents, collisions, and damages to your bus. If you drive recklessly, you will lose points, money, and reputation. You will also upset your passengers and get bad reviews.
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Expand your bus company and hire employees
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Another tip to play Bus Simulator Ultimate is to expand your bus company and hire employees. You can buy new buses, open new offices, and hire drivers from different countries. You can also assign routes and salaries to your employees and monitor their performance. By expanding your bus company, you will increase your income and reputation. You will also unlock new achievements and rewards.
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Listen to radio stations and enjoy the scenery
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A final tip to play Bus Simulator Ultimate is to listen to radio stations and enjoy the scenery. You can choose from over 250 radio stations from different countries and genres. You can also change the radio station or volume from the dashboard of your bus. Listening to radio stations will make your driving more enjoyable and relaxing. You can also admire the beautiful scenery of different cities and countries as you drive. You can see landmarks, buildings, bridges, mountains, forests, rivers, lakes, and more.
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Conclusion
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Bus Simulator Ultimate is a game that offers you the best bus simulation experience on your iOS device. You can drive realistic buses on realistic routes and bus stations in over 30 countries and 250 cities. You can also create your own bus company and compete with other players in the multiplayer mode. You can also interact with your passengers and listen to radio stations as you drive. Bus Simulator Ultimate is a game that you should download if you love driving buses and exploring different places.
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Frequently Asked Questions (FAQs)
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How do I update Bus Simulator Ultimate?
-
To update Bus Simulator Ultimate, you need to open the App Store on your device and go to the "Updates" tab. Then, you need to find Bus Simulator Ultimate in the list of apps that have updates available and tap on the "Update" button. Alternatively, you can enable automatic updates for Bus Simulator Ultimate in your device settings.
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How do I contact Bus Simulator Ultimate support?
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To contact Bus Simulator Ultimate support, you need to go to the settings menu of the game and tap on the "Support" button. Then, you need to fill out a form with your name, email address, subject, message, and attachments (optional). After that, you need to tap on the "Send" button and wait for a reply from the support team.
-
How do I change my name in Bus Simulator Ultimate?
-
To change your name in Bus Simulator Ultimate, you need to go to the profile menu of the game and tap on the "Edit Profile" button. Then, you need to type in your new name in the text box under "Name". After that, you need to tap on the "Save" button and confirm your changes.
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How do I get more money in Bus Simulator Ultimate?
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To get more money in Bus Simulator Ultimate, you have several options. You can drive more routes and complete more missions. You can also watch ads or complete offers to get free money. You can also subscribe to the Premium Membership to get double money. You can also use cheats or hacks to get unlimited money, but this is not recommended as it may ruin your game experience and get you banned.
-
How do I play Bus Simulator Ultimate with friends?
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To play Bus Simulator Ultimate with friends, you need to join the multiplayer mode of the game. You can either create your own room or join an existing room. You can also invite your friends to your room by sharing the room code or link. You can also chat with your friends and other players in the multiplayer mode.
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diff --git a/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Construct 2 The Ultimate 2D Game Engine for Beginners.md b/spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Construct 2 The Ultimate 2D Game Engine for Beginners.md
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What is Construct 2 and why should you use it?
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If you have ever dreamed of making your own games but don't know how to code, Construct 2 might be the perfect solution for you. Construct 2 is a game engine that lets you create HTML5 games without writing any code. You can use it to make games for web browsers, desktop computers, mobile devices, and even consoles. Construct 2 is easy to use, powerful, and flexible. You can make any kind of 2D game with it, from platformers to puzzles, from shooters to simulations. Whether you are a beginner or a professional, Construct 2 can help you turn your ideas into reality.
Construct 2 has many features that make it a great choice for game development. Here are some of them:
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Drag and drop interface
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Construct 2 has a user-friendly interface that lets you create your game visually. You can drag and drop objects, behaviors, effects, sounds, and more into your game layout. You can also edit the properties of your objects, such as their size, position, angle, opacity, etc. You don't need to worry about syntax errors or typos, as everything is done with the mouse.
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Event system
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Construct 2 uses an event system to control the logic of your game. Events are like sentences that tell your game what to do when something happens. For example, you can create an event that says "When the player presses the spacebar, make the character jump". Events are composed of conditions and actions. Conditions are the triggers that check if something is true or false. Actions are the commands that execute when the conditions are met. You can create events using a simple menu system that shows you all the available options.
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Preview and export options
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Construct 2 lets you preview your game in your browser with one click. You can also test your game on different devices using remote preview or local network preview. When you are ready to publish your game, you can export it to various platforms using different exporters. You can export your game as a HTML5 website, a Windows desktop app, an Android or iOS app, a Windows Store app, a Chrome Web Store app, a Facebook app, a Kongregate app, a Scirra Arcade app, or a NW.js app.
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How to get started with Construct 2
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If you want to learn how to use Construct 2, here are some steps you can follow:
-
Download and install Construct 2
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You can download Construct 2 from its official website. There are two versions available: the free edition and the paid edition. The free edition has some limitations on the number of events, layers, effects, etc. The paid edition has no limitations and also gives you access to more features and exporters. You can compare the editions here. To install Construct 2, just run the installer and follow the instructions.
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Create a new project
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To create a new project in Construct 2, click on File > New or press Ctrl+N. A dialog box will appear where you can choose a template or an example to start with. You can also choose a blank project if you want to start from scratch. You can then name your project and set some basic settings, such as the window size, the orientation, the scale mode, etc.
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Add objects and behaviors
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To add objects to your game layout, click on Insert > New Object or press I. A dialog box will appear where you can choose an object type, such as Sprite, Text, Button, Tilemap, etc. You can then name your object and place it on the layout. To edit your object, double-click on it or right-click and select Edit. You can also add behaviors to your object, such as Platform, Solid, Physics, etc. Behaviors are pre-made scripts that give your object certain abilities or characteristics. To add a behavior to your object, select it and click on Behaviors in the Properties panel. Then click on Add/Edit and choose a behavior from the list.
-
Add events and actions
-
To add events and actions to your game, click on Event Sheet in the Project panel. An event sheet is where you write the logic of your game using events. To add a new event, click on Add event or press E. A dialog box will appear where you can choose a condition from the list of objects, system expressions, keyboard inputs, mouse inputs, etc. You can also add sub-events, else events, or groups to organize your events. To add an action to your event, click on Add action or press A. A dialog box will appear where you can choose an action from the list of objects, system expressions, variables, functions, etc. You can also add comments to your events and actions to explain what they do.
-
Test and debug your game
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To test and debug your game, click on Preview or press F5. Your game will open in a new browser tab where you can play it and see how it works. You can also use the debugger tool to inspect the values of your objects, variables, expressions, etc. To use the debugger tool, click on Debug layout or press F6. A new browser tab will open with your game and a debugger panel on the right side. You can pause, resume, step, or restart your game using the buttons on the top of the panel. You can also expand the sections below to see the details of your game elements.
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Where to find resources and tutorials for Construct 2
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If you need more help or inspiration for using Construct 2, here are some places where you can find resources and tutorials:
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The official website and documentation
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The official website of Construct 2 is the best place to start if you want to learn more about the game engine and its features. You can find the official documentation that explains everything you need to know about Construct 2 in detail. You can also find tutorials that guide you through various aspects of game development with Construct 2, from beginner to advanced levels.
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The community forums and blogs
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The community forums are a great place to interact with other Construct 2 users and developers. You can ask questions, share tips, showcase your games, give feedback, and more. You can also find blogs that cover topics related to Construct 2 and game development in general.
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The online store and asset bundles
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The online store is where you can buy or sell assets for your games, such as graphics, sounds, music, templates, plugins, etc. You can also find asset bundles that offer a collection of assets for a discounted price.
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Conclusion and FAQs
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Construct 2 is a game engine that allows you to create HTML5 games without coding. It has a drag and drop interface, an event system, and various preview and export options. It is easy to use, powerful, and flexible. You can make any kind of 2D game with it for various platforms. You can also find resources and tutorials for Construct 2 on its official website, community forums, blogs, and online store. If you want to make your own games without coding, Construct 2 is a great option for you.
-
Here are some FAQs that you might have about Construct 2:
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-
-
Question
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Answer
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How much does Construct 2 cost?
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Construct 2 has a free edition and a paid edition. The free edition has some limitations on the number of events, layers, effects, etc. The paid edition has no limitations and also gives you access to more features and exporters. The paid edition costs $129.99 for a personal license and $429.99 for a business license.
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What are the system requirements for Construct 2?
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Construct 2 runs on Windows XP, Vista, 7, 8, or 10. It requires a DirectX 9 graphics card with at least 512 MB of memory. It also requires an internet connection for some features, such as previewing and exporting.
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Can I use Construct 2 offline?
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Yes, you can use Construct 2 offline, but you will need to activate it online first. You can activate Construct 2 by logging in with your Scirra account on the software. You can then use Construct 2 offline for up to 30 days before you need to activate it again.
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Can I use my own code in Construct 2?
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Yes, you can use your own code in Construct 2 by using plugins or behaviors. Plugins are extensions that add new object types or features to Construct 2. Behaviors are extensions that add new abilities or characteristics to existing objects. You can create your own plugins or behaviors using JavaScript or download them from the online store or the community forums.
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Can I monetize my games made with Construct 2?
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Yes, you can monetize your games made with Construct 2 by using various methods, such as ads, in-app purchases, sponsorships, donations, etc. You can also sell your games on various platforms, such as Steam, Google Play, App Store, etc. However, you will need to follow the terms and conditions of each platform and pay any fees or taxes that apply.
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diff --git a/spaces/1phancelerku/anime-remove-background/Dislyte APK Download the Game and Fight Alongside Heroes with God-like Powers on Your Android Device.md b/spaces/1phancelerku/anime-remove-background/Dislyte APK Download the Game and Fight Alongside Heroes with God-like Powers on Your Android Device.md
deleted file mode 100644
index ef94f1d2db3ff76e4963b9533dda4d7e60518b4d..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Dislyte APK Download the Game and Fight Alongside Heroes with God-like Powers on Your Android Device.md
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@@ -1,134 +0,0 @@
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If you are looking for a new and exciting role-playing game to play on your mobile device, you might want to check out Dislyte. This game is a visually stunning story of heroes with godlike powers who fight against monsters that emerge from mysterious portals. In this article, we will tell you what Dislyte is, what features it has, how to download and install it on your Android device, how to play it on your PC, what reviews and ratings it has, and what alternatives you can try if you want more games like Dislyte.
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What is Dislyte?
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Dislyte is an action role-playing game developed by FARLIGHT, a subsidiary of Lilith Games, one of the leading mobile game developers in China. The game is set in the near future, where the world is turned upside down by the appearance of portal-like sites called miracles. From these miracles, monsters emerge and wreak havoc on the cities. To fight back, ordinary people become awakened, god-like beings who gain divine powers through divine sound waves. These awakened ones are called Espers, and they are the main characters of the game.
Dislyte has a stylish, urban mythological theme that combines elements from various cultures and legends. The Espers are inspired by gods from Chinese, Egyptian, Greek, and Northern European mythologies, and they have diverse appearances and personalities. The game also features a unique gacha system that uses sound waves to summon new heroes. The game has a smooth gameplay experience that offers both story mode and PvP mode. The game also has a vibrant soundtrack that matches the mood of each scene.
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Features of Dislyte
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Dislyte has many features that make it an enjoyable and engaging game for RPG fans. Here are some of them:
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Urban adventure
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The game takes place in various urban settings that are beautifully rendered in 3D graphics. You can explore different locations such as streets, rooftops, subways, and more. You can also interact with various NPCs and objects in the environment. The game has a rich story that unfolds through dialogues, cutscenes, and quests. You can also choose different dialogue options that affect the outcome of some situations.
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Superheroic characters
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The game has over 50 Espers that you can collect and upgrade. Each Esper has a unique design, backstory, personality, voice, and skill set. The Espers are divided into five classes: Tank, Warrior, Mage, Support, and Assassin. Each class has its own strengths and weaknesses, and you need to balance your team composition accordingly. The Espers also have different affinities: Fire, Water, Wind, Earth, Light, and Dark. Each affinity has its own advantages and disadvantages against other affinities.
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Feel the beat
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The game has a distinctive feature that uses sound waves to summon new heroes. You can use different types of sound sources such as music players, microphones, or even your own voice to generate sound waves. The sound waves will then be converted into energy that can be used to activate the gacha machine. The gacha machine will randomly give you an Esper or other rewards such as coins or items. The quality of the sound source will affect the probability of getting a higher rarity Esper.
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Deep strategic gameplay
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The game has a turn-based combat system that requires strategy and tactics. You can control up to four Espers in each battle, and you can switch between them at any time. Each Esper has four skills: one basic skill, two active skills, and one ultimate skill. The skills have different effects such as damage, healing, buffing, debuffing, crowd control, etc. You need to use your skills wisely and effectively to defeat your enemies. You also need to pay attention to the energy bar, which determines how many skills you can use in each turn. The energy bar will replenish over time, but you can also use items or skills to speed up the process.
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How to download and install Dislyte APK on Android devices
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If you want to play Dislyte on your Android device, you need to download and install the APK file of the game. The APK file is a package that contains all the files and data needed to run the game. Here are the steps to download and install Dislyte APK on your Android device:
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Follow the instructions on the screen to complete the installation. You may need to grant some permissions to the app, such as access to your storage, microphone, or camera.
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After the installation is done, you can launch the game from your app drawer or home screen and enjoy playing Dislyte.
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How to play Dislyte on PC
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Download and install an Android emulator of your choice on your PC. Make sure that your PC meets the minimum system requirements of the emulator.
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Launch the emulator and sign in with your Google account. If you don't have one, you can create one for free.
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Conclusion
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Dislyte is a stylish urban mythological RPG that offers a thrilling adventure with god-like heroes who fight against monsters from mysterious portals. The game has stunning graphics, diverse characters, innovative sound wave gacha, smooth gameplay, and vibrant soundtrack. The game is available for Android devices, but you can also play it on PC using an Android emulator. The game has received mostly positive reviews and ratings, but it also has some drawbacks such as high battery consumption, long loading times, limited server capacity, lack of voice acting, and pay-to-win elements. If you are looking for more games like Dislyte, you can try some of the alternatives we have listed above.
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FAQs
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Q: What is the genre of Dislyte?
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A: Dislyte is an action role-playing game with a stylish urban mythological theme.
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Q: How can I download Dislyte APK on my Android device?
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A: You can download Dislyte APK from the official website or from a third-party website that provides the APK file. You need to enable the "Unknown sources" option in your device settings to install the APK file.
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Q: How can I play Dislyte on my PC?
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A: You can play Dislyte on your PC using an Android emulator such as BlueStacks, LDPlayer, or MEmu. You need to download and install the emulator on your PC, sign in with your Google account, and install Dislyte from the Google Play Store or from the APK file.
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Q: What are the classes and affinities of the Espers in Dislyte?
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A: The Espers are divided into five classes: Tank, Warrior, Mage, Support, and Assassin. Each class has its own strengths and weaknesses. The Espers also have different affinities: Fire, Water, Wind, Earth, Light, and Dark. Each affinity has its own advantages and disadvantages against other affinities.
-
Q: What is the sound wave gacha system in Dislyte?
-
A: The sound wave gacha system is a unique feature that uses sound waves to summon new heroes. You can use different types of sound sources such as music players, microphones, or even your own voice to generate sound waves. The sound waves will then be converted into energy that can be used to activate the gacha machine. The gacha machine will randomly give you an Esper or other rewards such as coins or items. The quality of the sound source will affect the probability of getting a higher rarity Esper.
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diff --git a/spaces/1phancelerku/anime-remove-background/Download 2 Chainz It 39s A Vibe [BETTER].md b/spaces/1phancelerku/anime-remove-background/Download 2 Chainz It 39s A Vibe [BETTER].md
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How to Download 2 Chainz's It's a Vibe
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It's a Vibe is a song by American rapper 2 Chainz, featuring American singers Ty Dolla Sign, Trey Songz, and Jhené Aiko. It was released on March 14, 2017 as the second single from his fourth studio album Pretty Girls Like Trap Music. The song is a smooth and laid-back track that showcases the chemistry and charisma of the four artists. It's a vibe that you don't want to miss.
If you love this song and want to download it to your device, you might be wondering how to do it. There are different ways to download It's a Vibe, depending on where you want to get it from. In this article, we will show you how to download It's a Vibe from YouTube, Spotify, and Apple Music. We will also tell you some benefits of downloading It's a Vibe and answer some frequently asked questions.
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What You Need to Download It's a Vibe
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Before we get into the details of how to download It's a Vibe, let's make sure you have everything you need. To download It's a Vibe, you will need:
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A device with internet access and enough storage space. This can be your computer, smartphone, tablet, or any other device that can play music.
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A source where you can find It's a Vibe. This can be YouTube, Spotify, Apple Music, or any other platform that has the song.
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A tool that can convert or download It's a Vibe. This can be a website, an app, or a software that can help you get the song in MP3 format.
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Once you have these things ready, you can proceed to download It's a Vibe from your preferred source.
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How to Download It's a Vibe from YouTube
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YouTube is one of the most popular places where you can watch and listen to It's a Vibe. The official music video has over 153 million views as of June 2023. If you want to download It's a Vibe from YouTube, here are the steps you need to follow:
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Find the official music video on YouTube. You can search for "2 Chain z It's a Vibe" or use this link: .
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Copy the video URL from the address bar or by right-clicking on the video and selecting "Copy video URL".
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Go to a YouTube to MP3 converter website, such as , , or . These are free and easy to use websites that can help you convert YouTube videos to MP3 files.
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Paste the URL into the input box and click "Convert" or "Download". The website will process the video and generate a download link for the MP3 file.
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Download the MP3 file by clicking on the download link or button. You can also choose to save the file to your cloud storage, such as Dropbox or Google Drive.
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Enjoy listening to It's a Vibe on your device. You can also transfer the file to other devices or share it with your friends.
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How to Download It's a Vibe from Spotify
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Spotify is another popular platform where you can stream and download It's a Vibe. The song is part of the album Pretty Girls Like Trap Music, which has over 1.5 billion streams on Spotify as of June 2023. If you want to download It's a Vibe from Spotify, here are the steps you need to follow:
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Sign up for a Spotify account or log in if you already have one. You will need a Spotify Premium subscription to download songs from Spotify. You can get a free trial for 30 days or pay $9.99 per month for unlimited access to millions of songs.
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Search for It's a Vibe on Spotify. You can use the search bar or browse through the genres and playlists. You can also use this link: .
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Add It's a Vibe to your library or playlist. You can do this by clicking on the heart icon next to the song title or by dragging and dropping the song to your desired playlist.
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Turn on the offline mode in the settings. This will allow you to download songs and listen to them without internet connection. You can find the offline mode option under "Playback" in the settings menu.
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Download It's a Vibe by toggling the download switch next to the song, album, or playlist. You will see a green arrow icon when the download is complete.
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Listen to It's a Vibe offline on your device. You can also sync your downloaded songs across different devices using the same Spotify account.
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How to Download It's a Vibe from Apple Music
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Apple Music is another great option for downloading It's a Vibe. The song is also part of the album Pretty Girls Like Trap Music, which has over 500 million streams on Apple Music as of June 2023. If you want to download It's a Vibe from Apple Music, here are the steps you need to follow:
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Subscribe to Apple Music or start a free trial if you are new. You will need an Apple Music subscription to download songs from Apple Music. You can get a free trial for 3 months or pay $9.99 per month for unlimited access to over 75 million songs.
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Search for It's a Vibe on Apple Music. You can use the search bar or browse through the categories and stations. You can also use this link: .
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Tap the plus icon (+) to add It's a Vibe to your library. This will make the song available for offline listening.
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Tap the cloud icon (☁️) to download It's a Vibe. You will see a checkmark icon when the download is complete.
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Play It's a Vibe from your library anytime. You can also access your downloaded songs from different devices using the same Apple ID.
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Benefits of Downloading It's a Vibe
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Now that you know how to download It's a Vibe from different sources, you might be wondering why you should do it. Here are some benefits of downloading It's a Vibe:
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You can listen to it anytime, anywhere, without internet connection or ads. This means you can enjoy the song without any interruptions or distractions.
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You can support the artist and his collaborators by streaming or buying the song. This means you can show your appreciation and respect for their work and talent.
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You can enjoy the high-quality sound and lyrics of the song. This means you can appreciate the production and performance of the song better.
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Conclusion and FAQs
In this article, we have shown you how to download 2 Chainz's It's a Vibe from YouTube, Spotify, and Apple Music. We have also told you some benefits of downloading It's a Vibe and why you should do it. We hope you found this article helpful and informative.
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If you have any questions or comments about downloading It's a Vibe, feel free to leave them below. We will try to answer them as soon as possible. Here are some FAQs that might interest you:
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FAQs
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Is It's a Vibe available on other platforms besides YouTube, Spotify, and Apple Music?
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Yes, It's a Vibe is also available on other platforms, such as Amazon Music, Tidal, Deezer, Pandora, and more. You can check the availability of the song on different platforms using this link: .
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Is It's a Vibe legal to download?
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It depends on the source and the method you use to download It's a Vibe. If you download It's a Vibe from an authorized platform, such as Spotify or Apple Music, and you have a valid subscription or purchase, then it is legal to download It's a Vibe. However, if you download It's a Vibe from an unauthorized platform, such as a YouTube to MP3 converter website, then it might be illegal to download It's a Vibe. You should always respect the intellectual property rights of the artist and his collaborators and follow the terms and conditions of the platform you use.
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How can I share It's a Vibe with my friends?
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You can share It's a Vibe with your friends by sending them the link to the song on your preferred platform or by using the share function on the platform. You can also create a playlist with It's a Vibe and other songs that you like and share it with your friends. However, you should not share the downloaded MP3 file of It's a Vibe with your friends, as this might violate the copyright laws and the platform rules.
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What are some other songs by 2 Chainz that I might like?
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If you like It's a Vibe, you might also like some other songs by 2 Chainz, such as No Lie (feat. Drake), Birthday Song (feat. Kanye West), I'm Different, Watch Out, Good Drank (feat. Gucci Mane and Quavo), 4 AM (feat. Travis Scott), Rule the World (feat. Ariana Grande), and Money Maker (feat. Lil Wayne). You can find these songs and more on 2 Chainz's albums and singles on various platforms.
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What are some other songs that feature Ty Dolla Sign, Trey Songz, or Jhené Aiko?
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If you like the features of Ty Dolla Sign, Trey Songz, or Jhené Aiko on It's a Vibe, you might also like some other songs that feature them, such as Paranoid (Ty Dolla Sign feat. B.o.B), Or Nah (Ty Dolla Sign feat. Wiz Khalifa and The Weeknd), Psycho (Post Malone feat. Ty Dolla Sign), Bottoms Up (Trey Songz feat. Nicki Minaj), Na Na (Trey Songz), Slow Motion (Trey Songz), The Worst (Jhené Aiko), Sativa (Jhené Aiko feat. Rae Sremmurd), B.S. (Jhené Aiko feat. H.E.R.), and None of Your Concern (Jhené Aiko feat. Big Sean). You can find these songs and more on their albums and singles on various platforms.
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diff --git a/spaces/1phancelerku/anime-remove-background/Download I Know You MP3 Song by Craig David with Bastille - JioSaavn.md b/spaces/1phancelerku/anime-remove-background/Download I Know You MP3 Song by Craig David with Bastille - JioSaavn.md
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Craig David - I Know You (feat. Bastille): A Review of the Hit Song
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Introduction
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If you are looking for a catchy, upbeat, and meaningful song to add to your playlist, you might want to check out Craig David's I Know You, featuring Bastille. This song is a collaboration between two of the most popular British artists in recent years, who have combined their talents and styles to create a hit that appeals to a wide range of listeners.
The song is about celebrating life, friendship, and love, despite all the challenges and struggles that we face. It is about finding paradise in our minds, even when we are stumbling through the night. It is about knowing each other, knowing ourselves, and knowing that we are not alone.
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Craig David is a singer, songwriter, rapper, DJ, and record producer who rose to fame in 1999 with his breakthrough single Re-Rewind, featuring Artful Dodger. He has since released seven studio albums, including his debut Born to Do It (2000), which is considered one of the best-selling albums in UK chart history. He has also won several awards, such as two BRIT Awards, three MOBO Awards, four Ivor Novello Awards, and two MTV Europe Music Awards.
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Bastille is an indie pop band that consists of four members: Dan Smith (lead vocals), Kyle Simmons (keyboards), Will Farquarson (guitar), and Chris Wood (drums). They formed in 2010 and released their debut album Bad Blood in 2013, which topped the UK Albums Chart and featured their signature hit Pompeii. They have also released two more albums, Wild World (2016) and Doom Days (2019), as well as several EPs, mixtapes, singles, and collaborations.
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The song was released on November 23, 2017, as the second single from Craig David's seventh album The Time Is Now (2018). It was written by Craig David, Dan Smith, Fraser T Smith, and Helen "Carmen Reece" Culver, with production handled by Fraser T Smith. It reached number five on the UK Singles Chart, as well as charting in several other countries.
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Background and Inspiration
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The song was born out of a mutual admiration between Craig David and Bastille. They first met when they were both appearing on BBC Radio 1's breakfast show in 201
6, and they performed a mash-up of Craig David's Fill Me In and Bastille's No Angels. They then decided to work together on a new song, and they spent a few days in the studio with Fraser T Smith, who had previously worked with both artists.
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Craig David said that he was inspired by Bastille's sound and energy, and that he wanted to create a song that would make people feel good and uplifted. He said, "I wanted to write a song that had an element of nostalgia, but also something that felt fresh and current. I wanted to capture that feeling of being with your friends and having a great time, no matter what's going on in the world."
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Dan Smith said that he was honored to collaborate with Craig David, who he considered one of his musical heroes. He said, "I grew up listening to his music and singing along to his songs. He has such an amazing voice and a knack for writing catchy hooks and melodies. I was really excited to work with him and see how our styles would blend together."
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The song is also influenced by the theme of The Time Is Now, which is about living in the present and enjoying the moment. Craig David said, "The album is about being grateful for what you have, and not worrying about what you don't have. It's about celebrating life and making the most of every opportunity. It's about knowing that you are enough, and that you have everything you need within yourself."
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Music and Lyrics
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The song is a pop song with elements of R&B, dance, and electronic music. It has a tempo of 120 beats per minute and is composed in the key of A minor. It has a simple structure of verse-chorus-verse-chorus-bridge-chorus-outro, with a duration of three minutes and 34 seconds.
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The song starts with a piano intro, followed by Craig David's smooth vocals over a pulsing beat and synth chords. He sings the first verse, which sets the scene of a night out with his friends. He then sings the pre-chorus, which builds up the anticipation for the chorus. He sings, "We're all stumbling through the night / But it's paradise in our minds / Falling together / Arms round each other / I know you / Know me too."
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The chorus is catchy and anthemic, with Craig David and Dan Smith singing in harmony over a soaring melody and an uplifting instrumental. They sing, "We're all stumbling through the night / It doesn't matter / We're all together / And there's paradise in our minds / Falling together / Arms round each other / I know you / Know me too."
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The second verse is sung by Dan Smith, who adds his distinctive tone and emotion to the song. He sings about feeling connected to someone on a deeper level, beyond the superficial aspects of appearance or status. He sings, "You don't care about the clothes I wear / You know I'm more than what meets the eye / You see my soul when you look in my eyes / And we don't need words to feel alive."
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The bridge is sung by both artists, who exchange lines and harmonize with each other. They sing about finding comfort and joy in each other's presence, even when they are facing difficulties or uncertainties. They sing, "When we're lost in the moment / And we can't see where we're going / We don't need to be afraid / 'Cause we've got each other / And we'll always find our way."
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The outro is a repetition of the chorus, with some ad-libs and vocalizations from both artists. The song ends with a fade-out of the instrumental and their voices.
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Reception and Impact
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The song received positive reviews from critics and fans alike, who praised its catchy hook, uplifting message, and seamless collaboration between Craig David and Bastille. Some of the comments include:
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"A brilliant pop song that showcases the best of both artists." - NME
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"A feel-good anthem that celebrates life, love, and friendship." - The Guardian
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"A catchy tune that will make you want to dance and sing along." - Billboard
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"A perfect blend of Craig David's smooth R&B vocals and Bastille's indie pop sensibilities." - The Independent
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"A refreshing and uplifting song that reminds us to enjoy the moment and appreciate each other." - Metro
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The song also performed well on the charts and streaming platforms, reaching number five on the UK Singles Chart, as well as charting in Australia, Belgium, Germany, Ireland, Netherlands , New Zealand, Scotland, and Sweden. It also accumulated over 100 million streams on Spotify and over 40 million views on YouTube. The song also had a positive impact on both artists' careers and fan bases, as it exposed them to new audiences and markets. Craig David said that the song helped him to reach a younger generation of listeners, who might not have been familiar with his previous work. He said, "It's amazing to see how the song has connected with people of different ages and backgrounds. I'm grateful for the opportunity to work with Bastille and share our music with their fans and vice versa." Bastille said that the song helped them to expand their musical horizons and experiment with different genres and sounds. They said, "It was a fun and challenging experience to work with Craig David and Fraser T Smith, who are both legends in their own right. We learned a lot from them and we enjoyed trying something new and different from our usual style." The song also inspired or influenced other artists or songs, such as: - Paradise by George Ezra, which has a similar theme and vibe to I Know You. - These Days by Rudimental, Jess Glynne, Macklemore, and Dan Caplen, which features Craig David's vocals in the chorus. - Happier by Marshmello and Bastille, which is another collaboration between Bastille and an electronic music producer. - Giant by Calvin Harris and Rag'n'Bone Man, which has a similar genre and style to I Know You. - Don't Leave Me Alone by David Guetta and Anne-Marie, which is another pop song that features piano chords and synth sounds.
Music Video and Live Performances
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The music video for the song was released on December 14, 2017, on Craig David's YouTube channel. It was directed by Alex Southam and produced by Odelay Films. It has over 40 million views as of June 2021.
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The concept and theme of the music video is based on the lyrics and mood of the song. It shows Craig David and Bastille hanging out with their friends at a house party, having fun and enjoying each other's company. It also shows them performing the song in different settings, such as a rooftop, a basement, a living room, and a garden. The video uses various lighting effects, camera angles, and editing techniques to create a dynamic and energetic atmosphere.
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The music video relates to the song's lyrics and mood by portraying the message of celebrating life, friendship, and love. It shows how the artists and their friends are stumbling through the night, but finding paradise in their minds. It shows how they are falling together, arms round each other, knowing each other and themselves. It also shows how they are not afraid of the dark or the unknown, as they have each other's support and guidance.
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Craig David and Bastille performed the song live several times, both together and separately. Some of the notable occasions include:
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The Graham Norton Show on December 1, 2017, where they performed the song for the first time on television.
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The BRIT Awards on February 21, 2018, where they performed the song as part of a medley with Dua Lipa's New Rules and Rag'n'Bone Man's Skin.
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The Voice UK on March 31, 2018, where they performed the song as part of the final show.
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The Biggest Weekend on May 26, 2018, where they performed the song at Swansea's Singleton Park.
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The Jingle Bell Ball on December 8, 2018, where they performed the song at London's O2 Arena.
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During their live performances, they interacted with each other and the audience in a friendly and enthusiastic manner. They sang with passion and emotion, while also adding some improvisations and variations to the song. They also engaged the crowd by encouraging them to sing along, clap along, or dance along to the song.
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Conclusion
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In conclusion, Craig David's I Know You, featuring Bastille, is a hit song that deserves your attention and download. It is a catchy, upbeat, and meaningful song that celebrates life, friendship , and love. It is a collaboration between two of the most popular British artists in recent years, who have combined their talents and styles to create a song that appeals to a wide range of listeners. It is a song that has a positive impact on both the artists' careers and fan bases, as well as on the music industry and culture. It is a song that has a catchy hook, uplifting message, and seamless collaboration.
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If you are looking for a song that will make you feel good and uplifted, you should definitely listen to and download I Know You. You can find it on various platforms, such as Spotify, Apple Music, YouTube, Amazon Music, and more. You can also check out the music video, which shows Craig David and Bastille having fun and enjoying each other's company at a house party. You can also watch their live performances, which show their passion and emotion, as well as their interaction with each other and the audience.
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Don't miss out on this amazing song that will make you want to dance and sing along. Download I Know You today and enjoy the paradise in your mind!
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FAQs
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Here are some of the frequently asked questions and answers about the song:
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What is the name of Craig David's seventh album?
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The name of Craig David's seventh album is The Time Is Now, which was released on January 26, 2018. It features 12 tracks, including I Know You, as well as collaborations with other artists, such as AJ Tracey, Ella Mai, JP Cooper, Kaytranada, and GoldLink.
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What is the name of Bastille's third album?
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The name of Bastille's third album is Doom Days, which was released on June 14, 2019. It features 11 tracks, including Happier, as well as collaborations with other artists, such as Alessia Cara, Rationale, Seeb, and James Arthur.
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Who are some of the other artists that Craig David and Bastille have collaborated with?
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Some of the other artists that Craig David and Bastille have collaborated with include:
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Craig David: Sting, Rita Ora, Tinchy Stryder, Kano, Sigala, Hardwell, Big Narstie, Blonde, and more.
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Bastille: Marshmello, Halsey, Craig David, Rag'n'Bone Man, Dua Lipa, Lizzo, Lewis Capaldi, Imagine Dragons, and more.
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Where can I find the lyrics of I Know You?
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You can find the lyrics of I Know You on various websites, such as Genius, AZLyrics, MetroLyrics, LyricsMode, and more.
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Where can I find more information about Craig David and Bastille?
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You can find more information about Craig David and Bastille on their official websites , social media accounts , Wikipedia pages , and more.
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diff --git a/spaces/1phancelerku/anime-remove-background/Download ludo by Johnny Drille - A Song from His Latest Album.md b/spaces/1phancelerku/anime-remove-background/Download ludo by Johnny Drille - A Song from His Latest Album.md
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Ludo by Johnny Drille: A Song Review
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Ludo is a board game that has been played for centuries by people of all ages. It is a game of chance, strategy, and luck, where players race their tokens from start to finish according to the rolls of a die. But Ludo is also a song by Johnny Drille, a Nigerian singer and songwriter who is known for his folk/alternative style of music. In this song, Johnny Drille uses Ludo as a metaphor for his love life, expressing his feelings of uncertainty, frustration, and hope. In this article, we will review the song Ludo by Johnny Drille, analyzing its lyrics, music, and video. We will also explore the history and meaning of Ludo, both as a game and as a song.
Johnny Drille is a Nigerian singer and songwriter who was born on July 5, 1990, in Edo State, Nigeria. He started singing in his father's church at an early age, and later taught himself music production techniques. He rose to fame after releasing a cover of Di'Ja's "Awww" in 2015, which caught the attention of Mavin Records CEO Don Jazzy. He signed with Mavin Records in 2017, becoming one of the few alternative artists on the label. He has since released several singles and collaborations, such as "Wait for Me", "Romeo & Juliet", "Halleluya" (featuring Simi), "Finding Efe", "Something Better", "Mystery Girl", "Bad Dancer", and "Loving Is Harder". He released his debut album, Before We Fall Asleep, on September 3, 2021.
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Ludo is one of the songs on Johnny Drille's debut album. It was released as a single on August 27, 2021, along with its official video. The song was produced by Johnny Drille himself, while the video was directed by Clarence Peters. The song is inspired by Johnny Drille's personal experiences with love and relationships, as he explained in an interview with Pulse Nigeria:
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"Ludo is about my love life...It's about how sometimes you feel like you're playing a game with someone you love or someone you're interested in...Sometimes you feel like they're not being honest with you or they're not being straightforward with you...Sometimes you feel like you're just rolling dice...You don't know what's going to happen next...You don't know if they're going to call you or not...You don't know if they're going to text you or not...You don't know if they're going to show up or not...You don't know if they're going to say yes or no...It's just like playing Ludo."
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The song also reflects Johnny Drille's musical influences and preferences, as he blends elements of folk, pop, rock, soul, R&B, and afrobeat in his unique style. The song showcases his vocal range and abilities, as well as his skills as a songwriter and producer. The song also conveys some universal themes and messages of love, trust, risk, and fate, that many listeners can relate to.
Analysis of the song
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Lyrics
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The song Ludo has a simple structure, consisting of two verses, a chorus, and a bridge. The lyrics are written in English, with some words and phrases in Pidgin English, a creole language that is widely spoken in Nigeria and other parts of West Africa. The lyrics use Ludo as a metaphor for the ups and downs of love, comparing the game's rules and outcomes to the dynamics and uncertainties of a relationship. The lyrics also use other metaphors, imagery, and repetition to convey the song's meaning and emotion.
-
For example, in the first verse, Johnny Drille sings:
-
"I don't know what you're doing to me
-But it feels like magic
-You got me feeling things I never felt before
-Like I'm walking on air"
-
Here, he uses the metaphor of magic to describe the attraction and excitement he feels for his lover. He also uses the imagery of walking on air to express his happiness and lightness. He repeats the phrase "I don't know" several times throughout the song, indicating his confusion and curiosity about his lover's intentions and actions.
-
In the chorus, Johnny Drille sings:
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"Are we playing Ludo?
-Are you rolling dice?
-Are you moving forward?
-Or are you going back?
-Are we playing Ludo?
-Are you rolling dice?
-Are you here to stay?
-Or are you going away?"
-
Here, he uses the metaphor of Ludo to question his lover's commitment and honesty. He compares the game's mechanics of rolling dice and moving tokens to his lover's behavior of being unpredictable and inconsistent. He repeats the phrase "Are we playing Ludo?" to emphasize his doubt and frustration. He also uses rhetorical questions to challenge his lover and seek clarity.
-
Music
-
The song Ludo has a moderate tempo and a catchy melody. The music is composed of various instruments, vocals, and sound effects that create its mood and tone. The music also reflects some of the musical influences and genres that Johnny Drille draws from in his style. The music also balances between simplicity and complexity in its composition and arrangement.
-
For example, some of the instruments that can be heard in the song are:
-
-
Guitar: The song features acoustic guitar strums and electric guitar riffs that add warmth and texture to the music. The guitar also creates a folk/rock vibe that matches Johnny Drille's genre.
-
Piano: The song features piano chords and notes that add depth and harmony to the music. The piano also creates a pop/soul vibe that matches Johnny Drille's genre.
-
Drums: The song features drum beats and fills that add rhythm and energy to the music. The drums also create an afrobeat vibe that matches Johnny Drille's origin.
-
Synth: The song features synth sounds and effects that add color and flair to the music. The synth also creates a modern/alternative vibe that matches Johnny Drille's style.
-
Some of the vocals and sound effects that can be heard in the song are:
-
-
Johnny Drille: The song features Johnny Drille's voice as the main vocal, singing the lyrics with emotion and expression. Johnny Drille has a distinctive voice that is smooth, soulful, and versatile. He can sing in different pitches and tones, ranging from low to high, soft to loud, and sweet to raspy. He also uses vocal techniques such as falsetto, vibrato, and harmony to add variation and richness to his singing.
-
Backing vocals: The song features backing vocals that support and complement Johnny Drille's voice. The backing vocals are mostly female voices that sing in harmony with Johnny Drille, creating a contrast and balance. The backing vocals also sing some ad-libs and hooks that add flavor and catchiness to the song.
-
Ludo sounds: The song features some sound effects that are related to Ludo, such as dice rolling, tokens moving, and board clicking. These sound effects are used to reinforce the metaphor of Ludo and create a playful and fun atmosphere. They also add some humor and irony to the song, as they contrast with the serious and emotional tone of the lyrics.
-
-
Video
-
The song Ludo has an official video that was released on August 27, 2021, along with the single. The video was directed by Clarence Peters, a Nigerian music video director who has worked with many popular artists such as Wizkid, Davido, Tiwa Savage, Burna Boy, and more. The video complements and enhances the song's message and aesthetics, using various visual elements and symbols that are related to Ludo, love, and Johnny Drille.
-
For example, some of the visual elements and symbols that can be seen in the video are:
-
-
Ludo board: The video features a giant Ludo board that serves as the main setting for the video. The Ludo board represents the game of love that Johnny Drille is playing with his lover. The board also has some twists and turns that make it more challenging and interesting.
-
Ludo tokens: The video features four Ludo tokens that are used by Johnny Drille and his lover to play the game. The tokens are colored red, yellow, green, and blue, representing the different emotions and moods that they experience during the game. The tokens also have some special features that make them more interactive and expressive.
-
Johnny Drille: The video features Johnny Drille as himself, singing and playing the game with his lover. Johnny Drille wears a casual outfit that matches his style and personality. He also shows his facial expressions and body language that convey his feelings and thoughts about the game.
-
Lover: The video features a female character who plays the role of Johnny Drille's lover. She wears a colorful dress that matches her beauty and charm. She also shows her facial expressions and body language that convey her feelings and thoughts about the game.
-
-
Conclusion
-
Ludo by Johnny Drille is a song that explores the complexities and uncertainties of love, using Ludo as a metaphor for the game of chance, strategy, and luck that lovers play with each other. The song combines lyrics, music, and video to create a captivating and meaningful piece of art that showcases Johnny Drille's talent and style as a singer, songwriter, producer, and performer. The song also resonates with many listeners who can relate to the themes and messages of the song.
-
The song's strengths include its originality, creativity, emotionality, catchiness, versatility, relatability, and quality. The song's weaknesses include its simplicity, repetitiveness, ambiguity, and predictability. However, these weaknesses can also be seen as strengths, depending on the listener's perspective and preference. Overall, the song is a well-crafted and enjoyable work of art that deserves recognition and appreciation.
-
If you are a fan of Johnny Drille or alternative music in general, you should definitely check out Ludo by Johnny Drille. You can listen to the song on various streaming platforms such as Spotify, Apple Music, YouTube Music, and more. You can also watch the video on YouTube or on Johnny Drille's official website. You can also follow Johnny Drille on his social media accounts such as Instagram, Twitter, Facebook, and TikTok to stay updated on his latest news and releases.
-
Ludo by Johnny Drille is a song that will make you feel, think, and play. It is a song that will challenge you, inspire you, and entertain you. It is a song that will make you love Ludo, both as a game and as a song.
-
FAQs
-
-
Q: What is the meaning of Ludo?
-A: Ludo is a board game that originated in India in the 6th century. The name Ludo comes from the Latin word ludus, which means "game". Ludo is also a song by Johnny Drille that uses Ludo as a metaphor for love.
-
Q: Who is Johnny Drille?
-A: Johnny Drille is a Nigerian singer and songwriter who is known for his folk/alternative style of music. He was born on July 5, 1990, in Edo State, Nigeria. He signed with Mavin Records in 2017 and released his debut album, Before We Fall Asleep, in 2021.
-
Q: When was Ludo by Johnny Drille released?
-A: Ludo by Johnny Drille was released as a single on August 27, 2021, along with its official video. It is one of the songs on Johnny Drille's debut album, Before We Fall Asleep.
-
Q: What are some of the musical influences and genres that can be heard in Ludo by Johnny Drille?
-A: Ludo by Johnny Drille blends elements of folk, pop, rock, soul, R&B, and afrobeat in its music. Some of the musical influences and genres that can be heard in the song are Ed Sheeran, Coldplay, John Mayer, Adele, Fela Kuti, Asa, and more.
-
Q: How can I download Ludo by Johnny Drille mp3?
-A: You can download Ludo by Johnny Drille mp3 from various sources online such as iTunes, Amazon Music, Google Play Music, and more. However, you should always respect the artist's rights and pay for the song if possible. Alternatively, you can stream the song on various platforms such as Spotify, Apple Music, YouTube Music, and more.
-
401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/1phancelerku/anime-remove-background/Enjoy the Latest Minecraft Update 1.18.2.03 on Android and Xbox with this APK File.md b/spaces/1phancelerku/anime-remove-background/Enjoy the Latest Minecraft Update 1.18.2.03 on Android and Xbox with this APK File.md
deleted file mode 100644
index 5b9c159f47d8bb8283cf79f325ce66e6bdc197ff..0000000000000000000000000000000000000000
--- a/spaces/1phancelerku/anime-remove-background/Enjoy the Latest Minecraft Update 1.18.2.03 on Android and Xbox with this APK File.md
+++ /dev/null
@@ -1,126 +0,0 @@
-
-
Minecraft 1.18.2.03 APK with Xbox Servers: What You Need to Know
-
Minecraft is one of the most popular sandbox video games in the world, with over 200 million copies sold and more than 130 million monthly active users. It allows players to create, explore, and survive in a procedurally generated world made of blocks, where they can build anything they can imagine, from simple houses to complex machines.
If you are an Android user who loves playing Minecraft, you might be interested in downloading the latest version of the game, which is Minecraft 1.18.2.03 APK with Xbox servers. This is a modified version of the official Minecraft app that lets you access online multiplayer features, such as joining servers hosted by other players or by official partners of Mojang Studios, the developer of Minecraft.
-
An APK file is an Android application package file that contains all the files and data needed to install an app on your device. You can download APK files from various sources on the internet, but you need to be careful about their safety and compatibility. Some APK files may contain malware or viruses that can harm your device or steal your personal information.
-
Xbox servers are online worlds created by Mojang Studios or by members of the Minecraft community that offer different types of gameplay, such as minigames, adventure maps, survival challenges, and more. You can join Xbox servers from any device that supports Minecraft: Bedrock Edition, such as Windows, mobile devices, tablets, Xbox, Nintendo Switch, or PlayStation 4.
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In this article, we will tell you everything you need to know about Minecraft 1.18.2.03 APK with Xbox servers, including its features, how to download and install it on your Android device, and some tips and warnings to keep in mind.
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Features of Minecraft 1.18.2.03 APK with Xbox Servers
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Minecraft 1.18.2.03 APK with Xbox servers is a stable update that contains several bug fixes and performance improvements, as well as some new features that enhance your gaming experience.
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Bug fixes and performance improvements
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Some of the bug fixes and performance improvements in this version are:
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Fixed an issue that affected some large world saves on PlayStation, resulting in corrupted textures and loss of data
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Fixed interacting with certain containers that did not properly open the inventory screen
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Fixed breaking blocks, opening chests, passing into portals when there are a lot of mobs around
-
Fixed errors when replacing root rock with deep shale
-
Optimized game loading time and reduced lag
-
-
New honeybee option and packs
-
This version also introduces a new honeybee option that saves you from getting poisoned and from hunger. Honeybees can also bring food for you and help you with construction. You can also find new packs in the store that offer different items and skins for your character.
-
Server/sign-in issue resolved
-
The most annoying server sign-in issue that prevented many players from joining online multiplayer games has been fixed in this version. Now you can sign in without any server timed out errors.
Compliance requirements for South Korean players
-
This version also contains some compliance updates for South Korean players, in accordance with the country's gaming laws. These updates include gameplay timers and notices that remind players to take occasional breaks from gameplay . If you are playing in South Korea, you will also need to verify your age and identity before purchasing and playing the Java edition of Minecraft.
-
How to Download and Install Minecraft 1.18.2.03 APK with Xbox Servers
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If you want to enjoy the latest features and improvements of Minecraft 1.18.2.03 APK with Xbox servers, you will need to download and install it on your Android device. Here is a step-by-step guide on how to do that:
-
-
Download the Minecraft 1.18.2.03 APK file from a trusted source, such as [this one](^5^). Make sure you have enough storage space on your device and a stable internet connection.
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Go to your device settings and enable the installation of apps from unknown sources. This will allow you to install the APK file that is not from the Google Play Store.
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Locate the downloaded APK file on your device using a file manager app or your browser's downloads folder. Tap on it to start the installation process.
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Follow the on-screen instructions and grant the necessary permissions to the app. Wait for the installation to finish.
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Launch the Minecraft app and sign in with your Microsoft account. If you don't have one, you can create one for free [here](^6^).
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Enjoy playing Minecraft 1.18.2.03 APK with Xbox servers!
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-
Tips and warnings
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Here are some tips and warnings to keep in mind when downloading and installing Minecraft 1.18.2.03 APK with Xbox servers:
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Make sure you download the APK file from a safe and reliable source, as some websites may contain malware or viruses that can harm your device or steal your personal information.
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Make a backup of your Minecraft worlds before installing the new version, as it may not be compatible with older versions or may cause data loss.
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Do not uninstall or update the official Minecraft app from the Google Play Store, as it may interfere with the APK version or cause errors.
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If you encounter any problems or bugs while playing, you can report them to Mojang Studios [here](^7^).
-
-
Conclusion
-
Minecraft 1.18.2.03 APK with Xbox servers is a great way to enjoy the latest features and improvements of the game on your Android device, as well as access online multiplayer features such as joining Xbox servers hosted by Mojang Studios or by other players. You can download and install it easily by following our guide above, but make sure you do it safely and responsibly.
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We hope you found this article helpful and informative. If you did, please share it with your friends and fellow Minecraft fans. Also, feel free to leave us a comment below if you have any questions or feedback about Minecraft 1.18.2.03 APK with Xbox servers. We would love to hear from you!
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FAQs
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What are the benefits of playing Minecraft on Xbox servers?
-
Xbox servers are online worlds created by Mojang Studios or by members of the Minecraft community that offer different types of gameplay, such as minigames, adventure maps, survival challenges, and more. You can join Xbox servers from any device that supports Minecraft: Bedrock Edition, such as Windows, mobile devices, tablets, Xbox, Nintendo Switch, or PlayStation 4. Some of the benefits of playing on Xbox servers are:
-
-
You can play with other players from around the world and make new friends.
-
You can experience new and exciting game modes and challenges that are not available in single-player mode.
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You can learn new skills and strategies from other players and improve your own gameplay.
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You can show off your creativity and achievements to other players and get inspired by their creations.
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You can have fun and enjoy yourself in a friendly and supportive community.
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-
How can I update my Minecraft to the latest version?
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If you have downloaded Minecraft 1.18.2.03 APK with Xbox servers, you will need to download and install a new APK file whenever there is a new update available for the game. You can check for updates by visiting [this website](^5^) or by following Mojang Studios on social media platforms such as [ Twitter] or [Facebook]. To update your Minecraft, you will need to follow the same steps as downloading and installing the APK file, as explained above. Make sure you backup your worlds before updating, as some updates may not be compatible with older versions or may cause data loss.
-
How can I customize my cave generation and add new structures with experimental data packs?
-
Minecraft 1.18.2.03 APK with Xbox servers also supports experimental data packs that allow you to customize your world generation and add new structures, such as caves, cliffs, biomes, dungeons, and more. Experimental data packs are not officially supported by Mojang Studios and may cause errors or crashes, so use them at your own risk. To use experimental data packs, you will need to download them from [this website] or from other sources on the internet. Then, you will need to follow these steps:
-
-
Create a new world or edit an existing one in Minecraft.
-
Go to the world settings and enable the "Use Experimental Gameplay" option.
-
Go to the "Data Packs" section and tap on the "Import" button.
-
Select the data pack file that you have downloaded and tap on "Apply".
-
Start or resume your world and enjoy the new features.
-
-
How can I change the music in the main menu of Minecraft?
-
Minecraft 1.18.2.03 APK with Xbox servers also allows you to change the music that plays in the main menu of the game, as well as in other screens such as settings, achievements, and credits. You can choose from different music tracks that are available in the game, or you can add your own custom music files. To change the music in the main menu of Minecraft, you will need to follow these steps:
-
-
Go to the settings menu and tap on the "Audio" option.
-
Tap on the "Music" option and select the music track that you want to play in the main menu.
-
If you want to add your own custom music files, you will need to copy them to the "music" folder inside the "com.mojang" folder on your device's internal storage. The music files must be in MP3 format and have a maximum size of 10 MB each.
-
Restart the game and enjoy your new music.
-
-
How can I avoid getting poisoned by honeybees in Minecraft?
-
Honeybees are a new feature in Minecraft 1.18.2.03 APK with Xbox servers that can help you with food and construction, but they can also sting you and poison you if you provoke them or disturb their nests. To avoid getting poisoned by honeybees in Minecraft, you will need to follow these tips:
-
-
Do not attack or hit honeybees or their nests, as they will become aggressive and chase you.
-
Do not break or move honeybee nests without using a tool with silk touch enchantment, as this will anger the honeybees inside.
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Do not stand too close to honeybee nests or hives when they are full of honey, as honeybees will come out and sting you.
-
Wear leather armor or a pumpkin on your head to reduce the damage from honeybee stings.
-
Use smoke from a campfire or a dispenser with a fire charge to calm down honeybees and make them stop attacking you.
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Eat honey bottles or honey blocks to cure poison effects from honeybee stings.
-
197e85843d
-
-
\ No newline at end of file
diff --git a/spaces/1toTree/lora_test/ppdiffusers/schedulers/__init__.py b/spaces/1toTree/lora_test/ppdiffusers/schedulers/__init__.py
deleted file mode 100644
index 1b29c1df0c3adce155a1c62fce8e78eb2e7402e0..0000000000000000000000000000000000000000
--- a/spaces/1toTree/lora_test/ppdiffusers/schedulers/__init__.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
-# Copyright 2022 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# flake8: noqa
-
-from ..utils import (
- OptionalDependencyNotAvailable,
- is_paddle_available,
- is_scipy_available,
-)
-
-try:
- if not is_paddle_available():
- raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
- from ..utils.dummy_paddle_objects import * # noqa F403
-else:
- from .scheduling_ddim import DDIMScheduler
- from .scheduling_ddpm import DDPMScheduler
- from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
- from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
- from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
- from .scheduling_euler_discrete import EulerDiscreteScheduler
- from .scheduling_heun_discrete import HeunDiscreteScheduler
- from .scheduling_ipndm import IPNDMScheduler
- from .scheduling_k_dpm_2_ancestral_discrete import KDPM2AncestralDiscreteScheduler
- from .scheduling_k_dpm_2_discrete import KDPM2DiscreteScheduler
- from .scheduling_karras_ve import KarrasVeScheduler
- from .scheduling_pndm import PNDMScheduler
- from .scheduling_repaint import RePaintScheduler
- from .scheduling_sde_ve import ScoreSdeVeScheduler
- from .scheduling_sde_vp import ScoreSdeVpScheduler
- from .scheduling_unclip import UnCLIPScheduler
- from .scheduling_utils import SchedulerMixin
- from .scheduling_vq_diffusion import VQDiffusionScheduler
-
-try:
- if not (is_paddle_available() and is_scipy_available()):
- raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
- from ..utils.dummy_paddle_and_scipy_objects import * # noqa F403
-else:
- from .scheduling_lms_discrete import LMSDiscreteScheduler
diff --git a/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/layers_new.py b/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/layers_new.py
deleted file mode 100644
index 44153b6a23399c6938affc61c71919eaa172bcee..0000000000000000000000000000000000000000
--- a/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/layers_new.py
+++ /dev/null
@@ -1,125 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
- super(Conv2DBNActiv, self).__init__()
- self.conv = nn.Sequential(
- nn.Conv2d(
- nin,
- nout,
- kernel_size=ksize,
- stride=stride,
- padding=pad,
- dilation=dilation,
- bias=False,
- ),
- nn.BatchNorm2d(nout),
- activ(),
- )
-
- def __call__(self, x):
- return self.conv(x)
-
-
-class Encoder(nn.Module):
- def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
- super(Encoder, self).__init__()
- self.conv1 = Conv2DBNActiv(nin, nout, ksize, stride, pad, activ=activ)
- self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ)
-
- def __call__(self, x):
- h = self.conv1(x)
- h = self.conv2(h)
-
- return h
-
-
-class Decoder(nn.Module):
- def __init__(
- self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
- ):
- super(Decoder, self).__init__()
- self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
- # self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ)
- self.dropout = nn.Dropout2d(0.1) if dropout else None
-
- def __call__(self, x, skip=None):
- x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
-
- if skip is not None:
- skip = spec_utils.crop_center(skip, x)
- x = torch.cat([x, skip], dim=1)
-
- h = self.conv1(x)
- # h = self.conv2(h)
-
- if self.dropout is not None:
- h = self.dropout(h)
-
- return h
-
-
-class ASPPModule(nn.Module):
- def __init__(self, nin, nout, dilations=(4, 8, 12), activ=nn.ReLU, dropout=False):
- super(ASPPModule, self).__init__()
- self.conv1 = nn.Sequential(
- nn.AdaptiveAvgPool2d((1, None)),
- Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ),
- )
- self.conv2 = Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ)
- self.conv3 = Conv2DBNActiv(
- nin, nout, 3, 1, dilations[0], dilations[0], activ=activ
- )
- self.conv4 = Conv2DBNActiv(
- nin, nout, 3, 1, dilations[1], dilations[1], activ=activ
- )
- self.conv5 = Conv2DBNActiv(
- nin, nout, 3, 1, dilations[2], dilations[2], activ=activ
- )
- self.bottleneck = Conv2DBNActiv(nout * 5, nout, 1, 1, 0, activ=activ)
- self.dropout = nn.Dropout2d(0.1) if dropout else None
-
- def forward(self, x):
- _, _, h, w = x.size()
- feat1 = F.interpolate(
- self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
- )
- feat2 = self.conv2(x)
- feat3 = self.conv3(x)
- feat4 = self.conv4(x)
- feat5 = self.conv5(x)
- out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
- out = self.bottleneck(out)
-
- if self.dropout is not None:
- out = self.dropout(out)
-
- return out
-
-
-class LSTMModule(nn.Module):
- def __init__(self, nin_conv, nin_lstm, nout_lstm):
- super(LSTMModule, self).__init__()
- self.conv = Conv2DBNActiv(nin_conv, 1, 1, 1, 0)
- self.lstm = nn.LSTM(
- input_size=nin_lstm, hidden_size=nout_lstm // 2, bidirectional=True
- )
- self.dense = nn.Sequential(
- nn.Linear(nout_lstm, nin_lstm), nn.BatchNorm1d(nin_lstm), nn.ReLU()
- )
-
- def forward(self, x):
- N, _, nbins, nframes = x.size()
- h = self.conv(x)[:, 0] # N, nbins, nframes
- h = h.permute(2, 0, 1) # nframes, N, nbins
- h, _ = self.lstm(h)
- h = self.dense(h.reshape(-1, h.size()[-1])) # nframes * N, nbins
- h = h.reshape(nframes, N, 1, nbins)
- h = h.permute(1, 2, 3, 0)
-
- return h
diff --git a/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/nets.py b/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/nets.py
deleted file mode 100644
index 5da3948c2f2e9edcc3cdac49bdf9f738e403de40..0000000000000000000000000000000000000000
--- a/spaces/801artistry/RVC801/infer/lib/uvr5_pack/lib_v5/nets.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import layers
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import spec_utils
-
-
-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, 16)
- self.stg1_high_band_net = BaseASPPNet(2, 16)
-
- self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0)
- self.stg2_full_band_net = BaseASPPNet(8, 16)
-
- self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
- self.stg3_full_band_net = BaseASPPNet(16, 32)
-
- self.out = nn.Conv2d(32, 2, 1, bias=False)
- self.aux1_out = nn.Conv2d(16, 2, 1, bias=False)
- self.aux2_out = nn.Conv2d(16, 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/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov5/yolov5_n-v61_syncbn_fast_8xb16-300e_coco.py b/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov5/yolov5_n-v61_syncbn_fast_8xb16-300e_coco.py
deleted file mode 100644
index b6f93428fc8d6dc1b94a8d447671ffc1a877dbb8..0000000000000000000000000000000000000000
--- a/spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_0_ClothesDetection/mmyolo/configs/yolov5/yolov5_n-v61_syncbn_fast_8xb16-300e_coco.py
+++ /dev/null
@@ -1,15 +0,0 @@
-_base_ = './yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
-
-deepen_factor = 0.33
-widen_factor = 0.25
-
-model = dict(
- backbone=dict(
- deepen_factor=deepen_factor,
- widen_factor=widen_factor,
- ),
- neck=dict(
- deepen_factor=deepen_factor,
- widen_factor=widen_factor,
- ),
- bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
diff --git a/spaces/Abhilashvj/planogram-compliance/hubconf.py b/spaces/Abhilashvj/planogram-compliance/hubconf.py
deleted file mode 100644
index 55169e9da867b33d0f928ad6012a0ec953210dac..0000000000000000000000000000000000000000
--- a/spaces/Abhilashvj/planogram-compliance/hubconf.py
+++ /dev/null
@@ -1,309 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5
-
-Usage:
- import torch
- model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # official model
- model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s') # from branch
- model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt') # custom/local model
- model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local') # local repo
-"""
-
-import torch
-
-
-def _create(
- name,
- pretrained=True,
- channels=3,
- classes=80,
- autoshape=True,
- verbose=True,
- device=None,
-):
- """Creates or loads a YOLOv5 model
-
- Arguments:
- name (str): model name 'yolov5s' or path 'path/to/best.pt'
- pretrained (bool): load pretrained weights into the model
- channels (int): number of input channels
- classes (int): number of model classes
- autoshape (bool): apply YOLOv5 .autoshape() wrapper to model
- verbose (bool): print all information to screen
- device (str, torch.device, None): device to use for model parameters
-
- Returns:
- YOLOv5 model
- """
- from pathlib import Path
-
- from models.common import AutoShape, DetectMultiBackend
- from models.experimental import attempt_load
- from models.yolo import ClassificationModel, DetectionModel, SegmentationModel
- from utils.downloads import attempt_download
- from utils.general import LOGGER, check_requirements, intersect_dicts, logging
- from utils.torch_utils import select_device
-
- if not verbose:
- LOGGER.setLevel(logging.WARNING)
- check_requirements(exclude=("opencv-python", "tensorboard", "thop"))
- name = Path(name)
- path = (
- name.with_suffix(".pt")
- if name.suffix == "" and not name.is_dir()
- else name
- ) # checkpoint path
- try:
- device = select_device(device)
- if pretrained and channels == 3 and classes == 80:
- try:
- model = DetectMultiBackend(
- path, device=device, fuse=autoshape
- ) # detection model
- if autoshape:
- if model.pt and isinstance(
- model.model, ClassificationModel
- ):
- LOGGER.warning(
- "WARNING ⚠️ YOLOv5 ClassificationModel is not yet AutoShape compatible. "
- "You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224)."
- )
- elif model.pt and isinstance(
- model.model, SegmentationModel
- ):
- LOGGER.warning(
- "WARNING ⚠️ YOLOv5 SegmentationModel is not yet AutoShape compatible. "
- "You will not be able to run inference with this model."
- )
- else:
- model = AutoShape(
- model
- ) # for file/URI/PIL/cv2/np inputs and NMS
- except Exception:
- model = attempt_load(
- path, device=device, fuse=False
- ) # arbitrary model
- else:
- cfg = list(
- (Path(__file__).parent / "models").rglob(f"{path.stem}.yaml")
- )[
- 0
- ] # model.yaml path
- model = DetectionModel(cfg, channels, classes) # create model
- if pretrained:
- ckpt = torch.load(
- attempt_download(path), map_location=device
- ) # load
- csd = (
- ckpt["model"].float().state_dict()
- ) # checkpoint state_dict as FP32
- csd = intersect_dicts(
- csd, model.state_dict(), exclude=["anchors"]
- ) # intersect
- model.load_state_dict(csd, strict=False) # load
- if len(ckpt["model"].names) == classes:
- model.names = ckpt[
- "model"
- ].names # set class names attribute
- if not verbose:
- LOGGER.setLevel(logging.INFO) # reset to default
- return model.to(device)
-
- except Exception as e:
- help_url = "https://github.com/ultralytics/yolov5/issues/36"
- s = f"{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help."
- raise Exception(s) from e
-
-
-def custom(
- path="path/to/model.pt", autoshape=True, _verbose=True, device=None
-):
- # YOLOv5 custom or local model
- return _create(path, autoshape=autoshape, verbose=_verbose, device=device)
-
-
-def yolov5n(
- pretrained=True,
- channels=3,
- classes=80,
- autoshape=True,
- _verbose=True,
- device=None,
-):
- # YOLOv5-nano model https://github.com/ultralytics/yolov5
- return _create(
- "yolov5n", pretrained, channels, classes, autoshape, _verbose, device
- )
-
-
-def yolov5s(
- pretrained=True,
- channels=3,
- classes=80,
- autoshape=True,
- _verbose=True,
- device=None,
-):
- # YOLOv5-small model https://github.com/ultralytics/yolov5
- return _create(
- "yolov5s", pretrained, channels, classes, autoshape, _verbose, device
- )
-
-
-def yolov5m(
- pretrained=True,
- channels=3,
- classes=80,
- autoshape=True,
- _verbose=True,
- device=None,
-):
- # YOLOv5-medium model https://github.com/ultralytics/yolov5
- return _create(
- "yolov5m", pretrained, channels, classes, autoshape, _verbose, device
- )
-
-
-def yolov5l(
- pretrained=True,
- channels=3,
- classes=80,
- autoshape=True,
- _verbose=True,
- device=None,
-):
- # YOLOv5-large model https://github.com/ultralytics/yolov5
- return _create(
- "yolov5l", pretrained, channels, classes, autoshape, _verbose, device
- )
-
-
-def yolov5x(
- pretrained=True,
- channels=3,
- classes=80,
- autoshape=True,
- _verbose=True,
- device=None,
-):
- # YOLOv5-xlarge model https://github.com/ultralytics/yolov5
- return _create(
- "yolov5x", pretrained, channels, classes, autoshape, _verbose, device
- )
-
-
-def yolov5n6(
- pretrained=True,
- channels=3,
- classes=80,
- autoshape=True,
- _verbose=True,
- device=None,
-):
- # YOLOv5-nano-P6 model https://github.com/ultralytics/yolov5
- return _create(
- "yolov5n6", pretrained, channels, classes, autoshape, _verbose, device
- )
-
-
-def yolov5s6(
- pretrained=True,
- channels=3,
- classes=80,
- autoshape=True,
- _verbose=True,
- device=None,
-):
- # YOLOv5-small-P6 model https://github.com/ultralytics/yolov5
- return _create(
- "yolov5s6", pretrained, channels, classes, autoshape, _verbose, device
- )
-
-
-def yolov5m6(
- pretrained=True,
- channels=3,
- classes=80,
- autoshape=True,
- _verbose=True,
- device=None,
-):
- # YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5
- return _create(
- "yolov5m6", pretrained, channels, classes, autoshape, _verbose, device
- )
-
-
-def yolov5l6(
- pretrained=True,
- channels=3,
- classes=80,
- autoshape=True,
- _verbose=True,
- device=None,
-):
- # YOLOv5-large-P6 model https://github.com/ultralytics/yolov5
- return _create(
- "yolov5l6", pretrained, channels, classes, autoshape, _verbose, device
- )
-
-
-def yolov5x6(
- pretrained=True,
- channels=3,
- classes=80,
- autoshape=True,
- _verbose=True,
- device=None,
-):
- # YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5
- return _create(
- "yolov5x6", pretrained, channels, classes, autoshape, _verbose, device
- )
-
-
-if __name__ == "__main__":
- import argparse
- from pathlib import Path
-
- import numpy as np
- from PIL import Image
-
- from utils.general import cv2, print_args
-
- # Argparser
- parser = argparse.ArgumentParser()
- parser.add_argument(
- "--model", type=str, default="yolov5s", help="model name"
- )
- opt = parser.parse_args()
- print_args(vars(opt))
-
- # Model
- model = _create(
- name=opt.model,
- pretrained=True,
- channels=3,
- classes=80,
- autoshape=True,
- verbose=True,
- )
- # model = custom(path='path/to/model.pt') # custom
-
- # Images
- imgs = [
- "data/images/zidane.jpg", # filename
- Path("data/images/zidane.jpg"), # Path
- "https://ultralytics.com/images/zidane.jpg", # URI
- cv2.imread("data/images/bus.jpg")[:, :, ::-1], # OpenCV
- Image.open("data/images/bus.jpg"), # PIL
- np.zeros((320, 640, 3)),
- ] # numpy
-
- # Inference
- results = model(imgs, size=320) # batched inference
-
- # Results
- results.print()
- results.save()
diff --git a/spaces/Adapter/CoAdapter/configs/mm/faster_rcnn_r50_fpn_coco.py b/spaces/Adapter/CoAdapter/configs/mm/faster_rcnn_r50_fpn_coco.py
deleted file mode 100644
index a9ad9528b22163ae7ce1390375b69227fd6eafd9..0000000000000000000000000000000000000000
--- a/spaces/Adapter/CoAdapter/configs/mm/faster_rcnn_r50_fpn_coco.py
+++ /dev/null
@@ -1,182 +0,0 @@
-checkpoint_config = dict(interval=1)
-# yapf:disable
-log_config = dict(
- interval=50,
- hooks=[
- dict(type='TextLoggerHook'),
- # dict(type='TensorboardLoggerHook')
- ])
-# yapf:enable
-dist_params = dict(backend='nccl')
-log_level = 'INFO'
-load_from = None
-resume_from = None
-workflow = [('train', 1)]
-# optimizer
-optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(
- policy='step',
- warmup='linear',
- warmup_iters=500,
- warmup_ratio=0.001,
- step=[8, 11])
-total_epochs = 12
-
-model = dict(
- type='FasterRCNN',
- pretrained='torchvision://resnet50',
- backbone=dict(
- type='ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- norm_cfg=dict(type='BN', requires_grad=True),
- norm_eval=True,
- style='pytorch'),
- neck=dict(
- type='FPN',
- in_channels=[256, 512, 1024, 2048],
- out_channels=256,
- num_outs=5),
- rpn_head=dict(
- type='RPNHead',
- in_channels=256,
- feat_channels=256,
- anchor_generator=dict(
- type='AnchorGenerator',
- scales=[8],
- ratios=[0.5, 1.0, 2.0],
- strides=[4, 8, 16, 32, 64]),
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[.0, .0, .0, .0],
- target_stds=[1.0, 1.0, 1.0, 1.0]),
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
- loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
- roi_head=dict(
- type='StandardRoIHead',
- bbox_roi_extractor=dict(
- type='SingleRoIExtractor',
- roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
- out_channels=256,
- featmap_strides=[4, 8, 16, 32]),
- bbox_head=dict(
- type='Shared2FCBBoxHead',
- in_channels=256,
- fc_out_channels=1024,
- roi_feat_size=7,
- num_classes=80,
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[0., 0., 0., 0.],
- target_stds=[0.1, 0.1, 0.2, 0.2]),
- reg_class_agnostic=False,
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
- loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
- # model training and testing settings
- train_cfg=dict(
- rpn=dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.7,
- neg_iou_thr=0.3,
- min_pos_iou=0.3,
- match_low_quality=True,
- ignore_iof_thr=-1),
- sampler=dict(
- type='RandomSampler',
- num=256,
- pos_fraction=0.5,
- neg_pos_ub=-1,
- add_gt_as_proposals=False),
- allowed_border=-1,
- pos_weight=-1,
- debug=False),
- rpn_proposal=dict(
- nms_pre=2000,
- max_per_img=1000,
- nms=dict(type='nms', iou_threshold=0.7),
- min_bbox_size=0),
- rcnn=dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.5,
- neg_iou_thr=0.5,
- min_pos_iou=0.5,
- match_low_quality=False,
- ignore_iof_thr=-1),
- sampler=dict(
- type='RandomSampler',
- num=512,
- pos_fraction=0.25,
- neg_pos_ub=-1,
- add_gt_as_proposals=True),
- pos_weight=-1,
- debug=False)),
- test_cfg=dict(
- rpn=dict(
- nms_pre=1000,
- max_per_img=1000,
- nms=dict(type='nms', iou_threshold=0.7),
- min_bbox_size=0),
- rcnn=dict(
- score_thr=0.05,
- nms=dict(type='nms', iou_threshold=0.5),
- max_per_img=100)
- # soft-nms is also supported for rcnn testing
- # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
- ))
-
-dataset_type = 'CocoDataset'
-data_root = 'data/coco'
-img_norm_cfg = dict(
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
- dict(type='RandomFlip', flip_ratio=0.5),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-test_pipeline = [
- dict(type='LoadImageFromFile'),
- dict(
- type='MultiScaleFlipAug',
- img_scale=(1333, 800),
- flip=False,
- transforms=[
- dict(type='Resize', keep_ratio=True),
- dict(type='RandomFlip'),
- dict(type='Normalize', **img_norm_cfg),
- dict(type='Pad', size_divisor=32),
- dict(type='DefaultFormatBundle'),
- dict(type='Collect', keys=['img']),
- ])
-]
-data = dict(
- samples_per_gpu=2,
- workers_per_gpu=2,
- train=dict(
- type=dataset_type,
- ann_file=f'{data_root}/annotations/instances_train2017.json',
- img_prefix=f'{data_root}/train2017/',
- pipeline=train_pipeline),
- val=dict(
- type=dataset_type,
- ann_file=f'{data_root}/annotations/instances_val2017.json',
- img_prefix=f'{data_root}/val2017/',
- pipeline=test_pipeline),
- test=dict(
- type=dataset_type,
- ann_file=f'{data_root}/annotations/instances_val2017.json',
- img_prefix=f'{data_root}/val2017/',
- pipeline=test_pipeline))
-evaluation = dict(interval=1, metric='bbox')
diff --git a/spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/updater/basic.py b/spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/updater/basic.py
deleted file mode 100644
index 122258f8d1359e4443ea7b72a5d19532bb6afb7b..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/agentverse/environments/simulation_env/rules/updater/basic.py
+++ /dev/null
@@ -1,75 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, List, Tuple
-
-from . import updater_registry as UpdaterRegistry
-from .base import BaseUpdater
-from agentverse.message import Message
-from agentverse.logging import get_logger
-
-if TYPE_CHECKING:
- from agentverse.environments import BaseEnvironment
- from agentverse.agents import BaseAgent
-
-logger = get_logger()
-
-
-@UpdaterRegistry.register("basic")
-class BasicUpdater(BaseUpdater):
- """
- The basic version of updater.
- The messages will be seen by all the receiver specified in the message.
- """
-
- def update_memory(self, environment: BaseEnvironment):
- added = False
- for message in environment.last_messages:
- if len(message.tool_response) > 0:
- self.add_tool_response(
- message.sender, environment.agents, message.tool_response
- )
- if message.content == "":
- continue
- added |= self.add_message_to_all_agents(environment.agents, message)
- # If no one speaks in this turn. Add an empty message to all agents
- if not added:
- for agent in environment.agents:
- agent.add_message_to_memory([Message(content="[Silence]")])
-
- def add_tool_response(
- self,
- name: str,
- agents: List[BaseAgent],
- tool_response: List[str],
- ):
- for agent in agents:
- if agent.name != name:
- continue
- if agent.tool_memory is not None:
- agent.tool_memory.add_message(tool_response)
- break
-
- def add_message_to_all_agents(
- self, agents: List[BaseAgent], message: Message
- ) -> bool:
- if "all" in message.receiver:
- # If receiver is all, then add the message to all agents
- for agent in agents:
- agent.add_message_to_memory([message])
- return True
- else:
- # If receiver is not all, then add the message to the specified agents
- receiver_set = message.receiver
- for agent in agents:
- if agent.name in receiver_set:
- agent.add_message_to_memory([message])
- receiver_set.remove(agent.name)
- if len(receiver_set) > 0:
- missing_receiver = ", ".join(list(receiver_set))
- # raise ValueError(
- # "Receiver {} not found. Message discarded".format(missing_receiver)
- # )
- logger.warn(
- "Receiver {} not found. Message discarded".format(missing_receiver)
- )
- return True
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/cube/Factory.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/cube/Factory.d.ts
deleted file mode 100644
index 906d09335354a4794f3b54763fc62bc74f3e8e8a..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/cube/Factory.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import Cube from './Cube';
-import Base from '../base/Base';
-
-export default function Factory(
- config?: Base.IConfig
-): Cube;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/container/Container.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/container/Container.js
deleted file mode 100644
index 239256c5649e185499e8c0ea258881330b30a548..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/container/Container.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import Container from '../../../plugins/containerlite.js';
-export default Container;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fade/Fade.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fade/Fade.ts
deleted file mode 100644
index 857a8cdc4d496b8654c585736eb788f2bdb5bbb5..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/fade/Fade.ts
+++ /dev/null
@@ -1,5 +0,0 @@
-import Fade from '../../../plugins/fade.js';
-import FadeIn from '../../../plugins/fade-in';
-import FadeOutDestroy from '../../../plugins/fade-out-destroy';
-
-export { Fade, FadeIn, FadeOutDestroy };
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/lineprogress/LineProgress.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/lineprogress/LineProgress.js
deleted file mode 100644
index f1c34031dda22bd1ee7281ed8d6797f1596930e9..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/lineprogress/LineProgress.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import LineProgress from '../../../plugins/lineprogress.js';
-export default LineProgress;
\ No newline at end of file
diff --git a/spaces/AlexWang/lama/saicinpainting/evaluation/data.py b/spaces/AlexWang/lama/saicinpainting/evaluation/data.py
deleted file mode 100644
index 69ddb8d3c12d0261e459f7c4f66a702d0c477df0..0000000000000000000000000000000000000000
--- a/spaces/AlexWang/lama/saicinpainting/evaluation/data.py
+++ /dev/null
@@ -1,167 +0,0 @@
-import glob
-import os
-
-import cv2
-import PIL.Image as Image
-import numpy as np
-
-from torch.utils.data import Dataset
-import torch.nn.functional as F
-
-
-def load_image(fname, mode='RGB', return_orig=False):
- img = np.array(Image.open(fname).convert(mode))
- if img.ndim == 3:
- img = np.transpose(img, (2, 0, 1))
- out_img = img.astype('float32') / 255
- if return_orig:
- return out_img, img
- else:
- return out_img
-
-
-def ceil_modulo(x, mod):
- if x % mod == 0:
- return x
- return (x // mod + 1) * mod
-
-
-def pad_img_to_modulo(img, mod):
- channels, height, width = img.shape
- out_height = ceil_modulo(height, mod)
- out_width = ceil_modulo(width, mod)
- return np.pad(img, ((0, 0), (0, out_height - height), (0, out_width - width)), mode='symmetric')
-
-
-def pad_tensor_to_modulo(img, mod):
- batch_size, channels, height, width = img.shape
- out_height = ceil_modulo(height, mod)
- out_width = ceil_modulo(width, mod)
- return F.pad(img, pad=(0, out_width - width, 0, out_height - height), mode='reflect')
-
-
-def scale_image(img, factor, interpolation=cv2.INTER_AREA):
- if img.shape[0] == 1:
- img = img[0]
- else:
- img = np.transpose(img, (1, 2, 0))
-
- img = cv2.resize(img, dsize=None, fx=factor, fy=factor, interpolation=interpolation)
-
- if img.ndim == 2:
- img = img[None, ...]
- else:
- img = np.transpose(img, (2, 0, 1))
- return img
-
-
-class InpaintingDataset(Dataset):
- def __init__(self, datadir, img_suffix='.jpg', pad_out_to_modulo=None, scale_factor=None):
- self.datadir = datadir
- self.mask_filenames = sorted(list(glob.glob(os.path.join(self.datadir, '**', '*mask*.png'), recursive=True)))
- self.img_filenames = [fname.rsplit('_mask', 1)[0] + img_suffix for fname in self.mask_filenames]
- self.pad_out_to_modulo = pad_out_to_modulo
- self.scale_factor = scale_factor
-
- def __len__(self):
- return len(self.mask_filenames)
-
- def __getitem__(self, i):
- image = load_image(self.img_filenames[i], mode='RGB')
- mask = load_image(self.mask_filenames[i], mode='L')
- result = dict(image=image, mask=mask[None, ...])
-
- if self.scale_factor is not None:
- result['image'] = scale_image(result['image'], self.scale_factor)
- result['mask'] = scale_image(result['mask'], self.scale_factor, interpolation=cv2.INTER_NEAREST)
-
- if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1:
- result['image'] = pad_img_to_modulo(result['image'], self.pad_out_to_modulo)
- result['mask'] = pad_img_to_modulo(result['mask'], self.pad_out_to_modulo)
-
- return result
-
-class OurInpaintingDataset(Dataset):
- def __init__(self, datadir, img_suffix='.jpg', pad_out_to_modulo=None, scale_factor=None):
- self.datadir = datadir
- self.mask_filenames = sorted(list(glob.glob(os.path.join(self.datadir, 'mask', '**', '*mask*.png'), recursive=True)))
- self.img_filenames = [os.path.join(self.datadir, 'img', os.path.basename(fname.rsplit('-', 1)[0].rsplit('_', 1)[0]) + '.png') for fname in self.mask_filenames]
- self.pad_out_to_modulo = pad_out_to_modulo
- self.scale_factor = scale_factor
-
- def __len__(self):
- return len(self.mask_filenames)
-
- def __getitem__(self, i):
- result = dict(image=load_image(self.img_filenames[i], mode='RGB'),
- mask=load_image(self.mask_filenames[i], mode='L')[None, ...])
-
- if self.scale_factor is not None:
- result['image'] = scale_image(result['image'], self.scale_factor)
- result['mask'] = scale_image(result['mask'], self.scale_factor)
-
- if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1:
- result['image'] = pad_img_to_modulo(result['image'], self.pad_out_to_modulo)
- result['mask'] = pad_img_to_modulo(result['mask'], self.pad_out_to_modulo)
-
- return result
-
-class PrecomputedInpaintingResultsDataset(InpaintingDataset):
- def __init__(self, datadir, predictdir, inpainted_suffix='_inpainted.jpg', **kwargs):
- super().__init__(datadir, **kwargs)
- if not datadir.endswith('/'):
- datadir += '/'
- self.predictdir = predictdir
- self.pred_filenames = [os.path.join(predictdir, os.path.splitext(fname[len(datadir):])[0] + inpainted_suffix)
- for fname in self.mask_filenames]
-
- def __getitem__(self, i):
- result = super().__getitem__(i)
- result['inpainted'] = load_image(self.pred_filenames[i])
- if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1:
- result['inpainted'] = pad_img_to_modulo(result['inpainted'], self.pad_out_to_modulo)
- return result
-
-class OurPrecomputedInpaintingResultsDataset(OurInpaintingDataset):
- def __init__(self, datadir, predictdir, inpainted_suffix="png", **kwargs):
- super().__init__(datadir, **kwargs)
- if not datadir.endswith('/'):
- datadir += '/'
- self.predictdir = predictdir
- self.pred_filenames = [os.path.join(predictdir, os.path.basename(os.path.splitext(fname)[0]) + f'_inpainted.{inpainted_suffix}')
- for fname in self.mask_filenames]
- # self.pred_filenames = [os.path.join(predictdir, os.path.splitext(fname[len(datadir):])[0] + inpainted_suffix)
- # for fname in self.mask_filenames]
-
- def __getitem__(self, i):
- result = super().__getitem__(i)
- result['inpainted'] = self.file_loader(self.pred_filenames[i])
-
- if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1:
- result['inpainted'] = pad_img_to_modulo(result['inpainted'], self.pad_out_to_modulo)
- return result
-
-class InpaintingEvalOnlineDataset(Dataset):
- def __init__(self, indir, mask_generator, img_suffix='.jpg', pad_out_to_modulo=None, scale_factor=None, **kwargs):
- self.indir = indir
- self.mask_generator = mask_generator
- self.img_filenames = sorted(list(glob.glob(os.path.join(self.indir, '**', f'*{img_suffix}' ), recursive=True)))
- self.pad_out_to_modulo = pad_out_to_modulo
- self.scale_factor = scale_factor
-
- def __len__(self):
- return len(self.img_filenames)
-
- def __getitem__(self, i):
- img, raw_image = load_image(self.img_filenames[i], mode='RGB', return_orig=True)
- mask = self.mask_generator(img, raw_image=raw_image)
- result = dict(image=img, mask=mask)
-
- if self.scale_factor is not None:
- result['image'] = scale_image(result['image'], self.scale_factor)
- result['mask'] = scale_image(result['mask'], self.scale_factor, interpolation=cv2.INTER_NEAREST)
-
- if self.pad_out_to_modulo is not None and self.pad_out_to_modulo > 1:
- result['image'] = pad_img_to_modulo(result['image'], self.pad_out_to_modulo)
- result['mask'] = pad_img_to_modulo(result['mask'], self.pad_out_to_modulo)
- return result
\ No newline at end of file
diff --git a/spaces/AlexWang/lama/saicinpainting/evaluation/evaluator.py b/spaces/AlexWang/lama/saicinpainting/evaluation/evaluator.py
deleted file mode 100644
index aa9e80402633c08a580929b38a5cb695cb7171d8..0000000000000000000000000000000000000000
--- a/spaces/AlexWang/lama/saicinpainting/evaluation/evaluator.py
+++ /dev/null
@@ -1,220 +0,0 @@
-import logging
-import math
-from typing import Dict
-
-import numpy as np
-import torch
-import torch.nn as nn
-import tqdm
-from torch.utils.data import DataLoader
-
-from saicinpainting.evaluation.utils import move_to_device
-
-LOGGER = logging.getLogger(__name__)
-
-
-class InpaintingEvaluator():
- def __init__(self, dataset, scores, area_grouping=True, bins=10, batch_size=32, device='cuda',
- integral_func=None, integral_title=None, clamp_image_range=None):
- """
- :param dataset: torch.utils.data.Dataset which contains images and masks
- :param scores: dict {score_name: EvaluatorScore object}
- :param area_grouping: in addition to the overall scores, allows to compute score for the groups of samples
- which are defined by share of area occluded by mask
- :param bins: number of groups, partition is generated by np.linspace(0., 1., bins + 1)
- :param batch_size: batch_size for the dataloader
- :param device: device to use
- """
- self.scores = scores
- self.dataset = dataset
-
- self.area_grouping = area_grouping
- self.bins = bins
-
- self.device = torch.device(device)
-
- self.dataloader = DataLoader(self.dataset, shuffle=False, batch_size=batch_size)
-
- self.integral_func = integral_func
- self.integral_title = integral_title
- self.clamp_image_range = clamp_image_range
-
- def _get_bin_edges(self):
- bin_edges = np.linspace(0, 1, self.bins + 1)
-
- num_digits = max(0, math.ceil(math.log10(self.bins)) - 1)
- interval_names = []
- for idx_bin in range(self.bins):
- start_percent, end_percent = round(100 * bin_edges[idx_bin], num_digits), \
- round(100 * bin_edges[idx_bin + 1], num_digits)
- start_percent = '{:.{n}f}'.format(start_percent, n=num_digits)
- end_percent = '{:.{n}f}'.format(end_percent, n=num_digits)
- interval_names.append("{0}-{1}%".format(start_percent, end_percent))
-
- groups = []
- for batch in self.dataloader:
- mask = batch['mask']
- batch_size = mask.shape[0]
- area = mask.to(self.device).reshape(batch_size, -1).mean(dim=-1)
- bin_indices = np.searchsorted(bin_edges, area.detach().cpu().numpy(), side='right') - 1
- # corner case: when area is equal to 1, bin_indices should return bins - 1, not bins for that element
- bin_indices[bin_indices == self.bins] = self.bins - 1
- groups.append(bin_indices)
- groups = np.hstack(groups)
-
- return groups, interval_names
-
- def evaluate(self, model=None):
- """
- :param model: callable with signature (image_batch, mask_batch); should return inpainted_batch
- :return: dict with (score_name, group_type) as keys, where group_type can be either 'overall' or
- name of the particular group arranged by area of mask (e.g. '10-20%')
- and score statistics for the group as values.
- """
- results = dict()
- if self.area_grouping:
- groups, interval_names = self._get_bin_edges()
- else:
- groups = None
-
- for score_name, score in tqdm.auto.tqdm(self.scores.items(), desc='scores'):
- score.to(self.device)
- with torch.no_grad():
- score.reset()
- for batch in tqdm.auto.tqdm(self.dataloader, desc=score_name, leave=False):
- batch = move_to_device(batch, self.device)
- image_batch, mask_batch = batch['image'], batch['mask']
- if self.clamp_image_range is not None:
- image_batch = torch.clamp(image_batch,
- min=self.clamp_image_range[0],
- max=self.clamp_image_range[1])
- if model is None:
- assert 'inpainted' in batch, \
- 'Model is None, so we expected precomputed inpainting results at key "inpainted"'
- inpainted_batch = batch['inpainted']
- else:
- inpainted_batch = model(image_batch, mask_batch)
- score(inpainted_batch, image_batch, mask_batch)
- total_results, group_results = score.get_value(groups=groups)
-
- results[(score_name, 'total')] = total_results
- if groups is not None:
- for group_index, group_values in group_results.items():
- group_name = interval_names[group_index]
- results[(score_name, group_name)] = group_values
-
- if self.integral_func is not None:
- results[(self.integral_title, 'total')] = dict(mean=self.integral_func(results))
-
- return results
-
-
-def ssim_fid100_f1(metrics, fid_scale=100):
- ssim = metrics[('ssim', 'total')]['mean']
- fid = metrics[('fid', 'total')]['mean']
- fid_rel = max(0, fid_scale - fid) / fid_scale
- f1 = 2 * ssim * fid_rel / (ssim + fid_rel + 1e-3)
- return f1
-
-
-def lpips_fid100_f1(metrics, fid_scale=100):
- neg_lpips = 1 - metrics[('lpips', 'total')]['mean'] # invert, so bigger is better
- fid = metrics[('fid', 'total')]['mean']
- fid_rel = max(0, fid_scale - fid) / fid_scale
- f1 = 2 * neg_lpips * fid_rel / (neg_lpips + fid_rel + 1e-3)
- return f1
-
-
-
-class InpaintingEvaluatorOnline(nn.Module):
- def __init__(self, scores, bins=10, image_key='image', inpainted_key='inpainted',
- integral_func=None, integral_title=None, clamp_image_range=None):
- """
- :param scores: dict {score_name: EvaluatorScore object}
- :param bins: number of groups, partition is generated by np.linspace(0., 1., bins + 1)
- :param device: device to use
- """
- super().__init__()
- LOGGER.info(f'{type(self)} init called')
- self.scores = nn.ModuleDict(scores)
- self.image_key = image_key
- self.inpainted_key = inpainted_key
- self.bins_num = bins
- self.bin_edges = np.linspace(0, 1, self.bins_num + 1)
-
- num_digits = max(0, math.ceil(math.log10(self.bins_num)) - 1)
- self.interval_names = []
- for idx_bin in range(self.bins_num):
- start_percent, end_percent = round(100 * self.bin_edges[idx_bin], num_digits), \
- round(100 * self.bin_edges[idx_bin + 1], num_digits)
- start_percent = '{:.{n}f}'.format(start_percent, n=num_digits)
- end_percent = '{:.{n}f}'.format(end_percent, n=num_digits)
- self.interval_names.append("{0}-{1}%".format(start_percent, end_percent))
-
- self.groups = []
-
- self.integral_func = integral_func
- self.integral_title = integral_title
- self.clamp_image_range = clamp_image_range
-
- LOGGER.info(f'{type(self)} init done')
-
- def _get_bins(self, mask_batch):
- batch_size = mask_batch.shape[0]
- area = mask_batch.view(batch_size, -1).mean(dim=-1).detach().cpu().numpy()
- bin_indices = np.clip(np.searchsorted(self.bin_edges, area) - 1, 0, self.bins_num - 1)
- return bin_indices
-
- def forward(self, batch: Dict[str, torch.Tensor]):
- """
- Calculate and accumulate metrics for batch. To finalize evaluation and obtain final metrics, call evaluation_end
- :param batch: batch dict with mandatory fields mask, image, inpainted (can be overriden by self.inpainted_key)
- """
- result = {}
- with torch.no_grad():
- image_batch, mask_batch, inpainted_batch = batch[self.image_key], batch['mask'], batch[self.inpainted_key]
- if self.clamp_image_range is not None:
- image_batch = torch.clamp(image_batch,
- min=self.clamp_image_range[0],
- max=self.clamp_image_range[1])
- self.groups.extend(self._get_bins(mask_batch))
-
- for score_name, score in self.scores.items():
- result[score_name] = score(inpainted_batch, image_batch, mask_batch)
- return result
-
- def process_batch(self, batch: Dict[str, torch.Tensor]):
- return self(batch)
-
- def evaluation_end(self, states=None):
- """:return: dict with (score_name, group_type) as keys, where group_type can be either 'overall' or
- name of the particular group arranged by area of mask (e.g. '10-20%')
- and score statistics for the group as values.
- """
- LOGGER.info(f'{type(self)}: evaluation_end called')
-
- self.groups = np.array(self.groups)
-
- results = {}
- for score_name, score in self.scores.items():
- LOGGER.info(f'Getting value of {score_name}')
- cur_states = [s[score_name] for s in states] if states is not None else None
- total_results, group_results = score.get_value(groups=self.groups, states=cur_states)
- LOGGER.info(f'Getting value of {score_name} done')
- results[(score_name, 'total')] = total_results
-
- for group_index, group_values in group_results.items():
- group_name = self.interval_names[group_index]
- results[(score_name, group_name)] = group_values
-
- if self.integral_func is not None:
- results[(self.integral_title, 'total')] = dict(mean=self.integral_func(results))
-
- LOGGER.info(f'{type(self)}: reset scores')
- self.groups = []
- for sc in self.scores.values():
- sc.reset()
- LOGGER.info(f'{type(self)}: reset scores done')
-
- LOGGER.info(f'{type(self)}: evaluation_end done')
- return results
diff --git a/spaces/Alycer/VITS-Umamusume-voice-synthesizer/README.md b/spaces/Alycer/VITS-Umamusume-voice-synthesizer/README.md
deleted file mode 100644
index ca5ce14e43d83960115b5ba1c4a87d7b46adea81..0000000000000000000000000000000000000000
--- a/spaces/Alycer/VITS-Umamusume-voice-synthesizer/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Multilingual Anime TTS
-emoji: 🎙🐴
-colorFrom: green
-colorTo: gray
-sdk: gradio
-sdk_version: 3.7
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py
deleted file mode 100644
index 54c605b94aa5fc8b1ddf2267ed349c2fcd08cc9e..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = './ms_rcnn_x101_64x4d_fpn_1x_coco.py'
-# learning policy
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/spaces/Andy1621/uniformer_image_detection/mmcv_custom/runner/__init__.py b/spaces/Andy1621/uniformer_image_detection/mmcv_custom/runner/__init__.py
deleted file mode 100644
index c701cb016abe470611830dc960999970738352bb..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/mmcv_custom/runner/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# Copyright (c) Open-MMLab. All rights reserved.
-from .checkpoint import save_checkpoint
-from .epoch_based_runner import EpochBasedRunnerAmp
-
-
-__all__ = [
- 'EpochBasedRunnerAmp', 'save_checkpoint'
-]
diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/encnet/encnet_r101-d8_512x512_20k_voc12aug.py b/spaces/Andy1621/uniformer_image_segmentation/configs/encnet/encnet_r101-d8_512x512_20k_voc12aug.py
deleted file mode 100644
index c264af998b5ef6a9e521db204205fb998cce68a9..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_segmentation/configs/encnet/encnet_r101-d8_512x512_20k_voc12aug.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './encnet_r50-d8_512x512_20k_voc12aug.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))
diff --git a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/LoRA.md b/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/LoRA.md
deleted file mode 100644
index f1504d1096c44227e8c510fce4bcaa6254849cb0..0000000000000000000000000000000000000000
--- a/spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/LoRA.md
+++ /dev/null
@@ -1,71 +0,0 @@
-# LoRA
-
-LoRA (Low-Rank Adaptation) is an extremely powerful method for customizing a base model by training only a small number of parameters. They can be attached to models at runtime.
-
-For instance, a 50mb LoRA can teach LLaMA an entire new language, a given writing style, or give it instruction-following or chat abilities.
-
-This is the current state of LoRA integration in the web UI:
-
-|Loader | Status |
-|--------|------|
-| Transformers | Full support in 16-bit, `--load-in-8bit`, `--load-in-4bit`, and CPU modes. |
-| ExLlama | Single LoRA support. Fast to remove the LoRA afterwards. |
-| AutoGPTQ | Single LoRA support. Removing the LoRA requires reloading the entire model.|
-| GPTQ-for-LLaMa | Full support with the [monkey patch](https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md#using-loras-with-gptq-for-llama). |
-
-## Downloading a LoRA
-
-The download script can be used. For instance:
-
-```
-python download-model.py tloen/alpaca-lora-7b
-```
-
-The files will be saved to `loras/tloen_alpaca-lora-7b`.
-
-## Using the LoRA
-
-The `--lora` command-line flag can be used. Examples:
-
-```
-python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b
-python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b --load-in-8bit
-python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b --load-in-4bit
-python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b --cpu
-```
-
-Instead of using the `--lora` command-line flag, you can also select the LoRA in the "Parameters" tab of the interface.
-
-## Prompt
-For the Alpaca LoRA in particular, the prompt must be formatted like this:
-
-```
-Below is an instruction that describes a task. Write a response that appropriately completes the request.
-### Instruction:
-Write a Python script that generates text using the transformers library.
-### Response:
-```
-
-Sample output:
-
-```
-Below is an instruction that describes a task. Write a response that appropriately completes the request.
-### Instruction:
-Write a Python script that generates text using the transformers library.
-### Response:
-
-import transformers
-from transformers import AutoTokenizer, AutoModelForCausalLM
-tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
-model = AutoModelForCausalLM.from_pretrained("bert-base-uncased")
-texts = ["Hello world", "How are you"]
-for sentence in texts:
-sentence = tokenizer(sentence)
-print(f"Generated {len(sentence)} tokens from '{sentence}'")
-output = model(sentences=sentence).predict()
-print(f"Predicted {len(output)} tokens for '{sentence}':\n{output}")
-```
-
-## Training a LoRA
-
-You can train your own LoRAs from the `Training` tab. See [Training LoRAs](Training-LoRAs.md) for details.
diff --git a/spaces/Arnx/MusicGenXvAKN/audiocraft/modules/activations.py b/spaces/Arnx/MusicGenXvAKN/audiocraft/modules/activations.py
deleted file mode 100644
index 8bd6f2917a56d72db56555d0ff54b2311bc21778..0000000000000000000000000000000000000000
--- a/spaces/Arnx/MusicGenXvAKN/audiocraft/modules/activations.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn as nn
-from torch import Tensor
-from typing import Union, Callable
-
-
-class CustomGLU(nn.Module):
- """Custom Gated Linear Unit activation.
- Applies a modified gated linear unit :math:`a * f(b)` where :math:`a` is the first half
- of the input matrices, :math:`b` is the second half, and :math:`f` is a provided activation
- function (i.e. sigmoid, swish, etc.).
-
- Args:
- activation (nn.Module): The custom activation to apply in the Gated Linear Unit
- dim (int): the dimension on which to split the input. Default: -1
-
- Shape:
- - Input: :math:`(\ast_1, N, \ast_2)` where `*` means, any number of additional
- dimensions
- - Output: :math:`(\ast_1, M, \ast_2)` where :math:`M=N/2`
-
- Examples::
- >>> m = CustomGLU(nn.Sigmoid())
- >>> input = torch.randn(4, 2)
- >>> output = m(input)
- """
- def __init__(self, activation: nn.Module, dim: int = -1):
- super(CustomGLU, self).__init__()
- self.dim = dim
- self.activation = activation
-
- def forward(self, x: Tensor):
- assert x.shape[self.dim] % 2 == 0 # M = N / 2
- a, b = torch.chunk(x, 2, dim=self.dim)
- return a * self.activation(b)
-
-
-class SwiGLU(CustomGLU):
- """SiLU Gated Linear Unit activation.
- Applies SiLU Gated Linear Unit :math:`a * SiLU(b)` where :math:`a` is
- the first half of the input matrices, :math:`b` is the second half.
-
- Args:
- dim (int): the dimension on which to split the input. Default: -1
- """
- def __init__(self, dim: int = -1):
- super(SwiGLU, self).__init__(nn.SiLU(), dim)
-
-
-class GeGLU(CustomGLU):
- """GeLU Gated Linear Unit activation.
- Applies GeLU Gated Linear Unit :math:`a * GELU(b)` where :math:`a` is
- the first half of the input matrices, :math:`b` is the second half.
-
- Args:
- dim (int): the dimension on which to split the input. Default: -1
- """
- def __init__(self, dim: int = -1):
- super(GeGLU, self).__init__(nn.GELU(), dim)
-
-
-class ReGLU(CustomGLU):
- """ReLU Gated Linear Unit activation.
- Applies ReLU Gated Linear Unit :math:`a * ReLU(b)` where :math:`a` is
- the first half of the input matrices, :math:`b` is the second half.
-
- Args:
- dim (int): the dimension on which to split the input. Default: -1
- """
- def __init__(self, dim: int = -1):
- super(ReGLU, self).__init__(nn.ReLU(), dim)
-
-
-def get_activation_fn(
- activation: Union[str, Callable[[Tensor], Tensor]]
-) -> Union[str, Callable[[Tensor], Tensor]]:
- """Helper function to map an activation string to the activation class.
- If the supplied activation is not a string that is recognized, the activation is passed back.
-
- Args:
- activation (Union[str, Callable[[Tensor], Tensor]]): Activation to check
- """
- if isinstance(activation, str):
- if activation == "reglu":
- return ReGLU()
- elif activation == "geglu":
- return GeGLU()
- elif activation == "swiglu":
- return SwiGLU()
- return activation
diff --git a/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/models/attention.py b/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/models/attention.py
deleted file mode 100644
index 6204a57dc8ce9fafb6c640f7b978adecb5ea95f2..0000000000000000000000000000000000000000
--- a/spaces/ArtGAN/Video-Diffusion-WebUI/video_diffusion/tuneavideo/models/attention.py
+++ /dev/null
@@ -1,322 +0,0 @@
-# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py
-
-from dataclasses import dataclass
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.models.attention import AdaLayerNorm, FeedForward
-from diffusers.models.cross_attention import CrossAttention
-from diffusers.models.modeling_utils import ModelMixin
-from diffusers.utils import BaseOutput
-from diffusers.utils.import_utils import is_xformers_available
-from einops import rearrange, repeat
-from torch import nn
-
-
-@dataclass
-class Transformer3DModelOutput(BaseOutput):
- sample: torch.FloatTensor
-
-
-if is_xformers_available():
- import xformers
- import xformers.ops
-else:
- xformers = None
-
-
-class Transformer3DModel(ModelMixin, ConfigMixin):
- @register_to_config
- def __init__(
- self,
- num_attention_heads: int = 16,
- attention_head_dim: int = 88,
- in_channels: Optional[int] = None,
- num_layers: int = 1,
- dropout: float = 0.0,
- norm_num_groups: int = 32,
- cross_attention_dim: Optional[int] = None,
- attention_bias: bool = False,
- activation_fn: str = "geglu",
- num_embeds_ada_norm: Optional[int] = None,
- use_linear_projection: bool = False,
- only_cross_attention: bool = False,
- upcast_attention: bool = False,
- ):
- super().__init__()
- self.use_linear_projection = use_linear_projection
- self.num_attention_heads = num_attention_heads
- self.attention_head_dim = attention_head_dim
- inner_dim = num_attention_heads * attention_head_dim
-
- # Define input layers
- self.in_channels = in_channels
-
- self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
- if use_linear_projection:
- self.proj_in = nn.Linear(in_channels, inner_dim)
- else:
- self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
-
- # Define transformers blocks
- self.transformer_blocks = nn.ModuleList(
- [
- BasicTransformerBlock(
- inner_dim,
- num_attention_heads,
- attention_head_dim,
- dropout=dropout,
- cross_attention_dim=cross_attention_dim,
- activation_fn=activation_fn,
- num_embeds_ada_norm=num_embeds_ada_norm,
- attention_bias=attention_bias,
- only_cross_attention=only_cross_attention,
- upcast_attention=upcast_attention,
- )
- for d in range(num_layers)
- ]
- )
-
- # 4. Define output layers
- if use_linear_projection:
- self.proj_out = nn.Linear(in_channels, inner_dim)
- else:
- self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
-
- def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, return_dict: bool = True):
- # Input
- assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}."
- video_length = hidden_states.shape[2]
- hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
- encoder_hidden_states = repeat(encoder_hidden_states, "b n c -> (b f) n c", f=video_length)
-
- batch, channel, height, weight = hidden_states.shape
- residual = hidden_states
-
- hidden_states = self.norm(hidden_states)
- if not self.use_linear_projection:
- hidden_states = self.proj_in(hidden_states)
- inner_dim = hidden_states.shape[1]
- hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
- else:
- inner_dim = hidden_states.shape[1]
- hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
- hidden_states = self.proj_in(hidden_states)
-
- # Blocks
- for block in self.transformer_blocks:
- hidden_states = block(
- hidden_states, encoder_hidden_states=encoder_hidden_states, timestep=timestep, video_length=video_length
- )
-
- # Output
- if not self.use_linear_projection:
- hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
- hidden_states = self.proj_out(hidden_states)
- else:
- hidden_states = self.proj_out(hidden_states)
- hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
-
- output = hidden_states + residual
-
- output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length)
- if not return_dict:
- return (output,)
-
- return Transformer3DModelOutput(sample=output)
-
-
-class BasicTransformerBlock(nn.Module):
- def __init__(
- self,
- dim: int,
- num_attention_heads: int,
- attention_head_dim: int,
- dropout=0.0,
- cross_attention_dim: Optional[int] = None,
- activation_fn: str = "geglu",
- num_embeds_ada_norm: Optional[int] = None,
- attention_bias: bool = False,
- only_cross_attention: bool = False,
- upcast_attention: bool = False,
- ):
- super().__init__()
- self.only_cross_attention = only_cross_attention
- self.use_ada_layer_norm = num_embeds_ada_norm is not None
-
- # SC-Attn
- self.attn1 = SparseCausalAttention(
- query_dim=dim,
- heads=num_attention_heads,
- dim_head=attention_head_dim,
- dropout=dropout,
- bias=attention_bias,
- cross_attention_dim=cross_attention_dim if only_cross_attention else None,
- upcast_attention=upcast_attention,
- )
- self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
-
- # Cross-Attn
- if cross_attention_dim is not None:
- self.attn2 = CrossAttention(
- query_dim=dim,
- cross_attention_dim=cross_attention_dim,
- heads=num_attention_heads,
- dim_head=attention_head_dim,
- dropout=dropout,
- bias=attention_bias,
- upcast_attention=upcast_attention,
- )
- else:
- self.attn2 = None
-
- if cross_attention_dim is not None:
- self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
- else:
- self.norm2 = None
-
- # Feed-forward
- self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
- self.norm3 = nn.LayerNorm(dim)
-
- # Temp-Attn
- self.attn_temp = CrossAttention(
- query_dim=dim,
- heads=num_attention_heads,
- dim_head=attention_head_dim,
- dropout=dropout,
- bias=attention_bias,
- upcast_attention=upcast_attention,
- )
- nn.init.zeros_(self.attn_temp.to_out[0].weight.data)
- self.norm_temp = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
-
- def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool):
- if not is_xformers_available():
- print("Here is how to install it")
- raise ModuleNotFoundError(
- "Refer to https://github.com/facebookresearch/xformers for more information on how to install"
- " xformers",
- name="xformers",
- )
- elif not torch.cuda.is_available():
- raise ValueError(
- "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only"
- " available for GPU "
- )
- else:
- try:
- # Make sure we can run the memory efficient attention
- _ = xformers.ops.memory_efficient_attention(
- torch.randn((1, 2, 40), device="cuda"),
- torch.randn((1, 2, 40), device="cuda"),
- torch.randn((1, 2, 40), device="cuda"),
- )
- except Exception as e:
- raise e
- self.attn1._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
- if self.attn2 is not None:
- self.attn2._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
- # self.attn_temp._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
-
- def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None, video_length=None):
- # SparseCausal-Attention
- norm_hidden_states = (
- self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states)
- )
-
- if self.only_cross_attention:
- hidden_states = (
- self.attn1(norm_hidden_states, encoder_hidden_states, attention_mask=attention_mask) + hidden_states
- )
- else:
- hidden_states = (
- self.attn1(norm_hidden_states, attention_mask=attention_mask, video_length=video_length) + hidden_states
- )
-
- if self.attn2 is not None:
- # Cross-Attention
- norm_hidden_states = (
- self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
- )
- hidden_states = (
- self.attn2(
- norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
- )
- + hidden_states
- )
-
- # Feed-forward
- hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
-
- # Temporal-Attention
- d = hidden_states.shape[1]
- hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length)
- norm_hidden_states = (
- self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states)
- )
- hidden_states = self.attn_temp(norm_hidden_states) + hidden_states
- hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
-
- return hidden_states
-
-
-class SparseCausalAttention(CrossAttention):
- def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None):
- batch_size, sequence_length, _ = hidden_states.shape
-
- encoder_hidden_states = encoder_hidden_states
-
- if self.group_norm is not None:
- hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
- query = self.to_q(hidden_states)
- dim = query.shape[-1]
- query = self.reshape_heads_to_batch_dim(query)
-
- if self.added_kv_proj_dim is not None:
- raise NotImplementedError
-
- encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
- key = self.to_k(encoder_hidden_states)
- value = self.to_v(encoder_hidden_states)
-
- former_frame_index = torch.arange(video_length) - 1
- former_frame_index[0] = 0
-
- key = rearrange(key, "(b f) d c -> b f d c", f=video_length)
- key = torch.cat([key[:, [0] * video_length], key[:, former_frame_index]], dim=2)
- key = rearrange(key, "b f d c -> (b f) d c")
-
- value = rearrange(value, "(b f) d c -> b f d c", f=video_length)
- value = torch.cat([value[:, [0] * video_length], value[:, former_frame_index]], dim=2)
- value = rearrange(value, "b f d c -> (b f) d c")
-
- key = self.reshape_heads_to_batch_dim(key)
- value = self.reshape_heads_to_batch_dim(value)
-
- if attention_mask is not None:
- if attention_mask.shape[-1] != query.shape[1]:
- target_length = query.shape[1]
- attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
- attention_mask = attention_mask.repeat_interleave(self.heads, dim=0)
-
- # attention, what we cannot get enough of
- if self._use_memory_efficient_attention_xformers:
- hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)
- # Some versions of xformers return output in fp32, cast it back to the dtype of the input
- hidden_states = hidden_states.to(query.dtype)
- else:
- if self._slice_size is None or query.shape[0] // self._slice_size == 1:
- hidden_states = self._attention(query, key, value, attention_mask)
- else:
- hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask)
-
- # linear proj
- hidden_states = self.to_out[0](hidden_states)
-
- # dropout
- hidden_states = self.to_out[1](hidden_states)
- return hidden_states
diff --git a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/requests/cookies.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/requests/cookies.py
deleted file mode 100644
index bf54ab237e410603061b8cec8fd195912d3cfb08..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/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/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/wheel.py b/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/wheel.py
deleted file mode 100644
index 527ed3b23306a3822388520115bafaf3eabb5024..0000000000000000000000000000000000000000
--- a/spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/setuptools/wheel.py
+++ /dev/null
@@ -1,222 +0,0 @@
-"""Wheels support."""
-
-import email
-import itertools
-import os
-import posixpath
-import re
-import zipfile
-import contextlib
-
-from distutils.util import get_platform
-
-import pkg_resources
-import setuptools
-from pkg_resources import parse_version
-from setuptools.extern.packaging.tags import sys_tags
-from setuptools.extern.packaging.utils import canonicalize_name
-from setuptools.command.egg_info import write_requirements
-from setuptools.archive_util import _unpack_zipfile_obj
-
-
-WHEEL_NAME = re.compile(
- r"""^(?P.+?)-(?P\d.*?)
- ((-(?P\d.*?))?-(?P.+?)-(?P.+?)-(?P.+?)
- )\.whl$""",
- re.VERBOSE).match
-
-NAMESPACE_PACKAGE_INIT = \
- "__import__('pkg_resources').declare_namespace(__name__)\n"
-
-
-def unpack(src_dir, dst_dir):
- '''Move everything under `src_dir` to `dst_dir`, and delete the former.'''
- for dirpath, dirnames, filenames in os.walk(src_dir):
- subdir = os.path.relpath(dirpath, src_dir)
- for f in filenames:
- src = os.path.join(dirpath, f)
- dst = os.path.join(dst_dir, subdir, f)
- os.renames(src, dst)
- for n, d in reversed(list(enumerate(dirnames))):
- src = os.path.join(dirpath, d)
- dst = os.path.join(dst_dir, subdir, d)
- if not os.path.exists(dst):
- # Directory does not exist in destination,
- # rename it and prune it from os.walk list.
- os.renames(src, dst)
- del dirnames[n]
- # Cleanup.
- for dirpath, dirnames, filenames in os.walk(src_dir, topdown=True):
- assert not filenames
- os.rmdir(dirpath)
-
-
-@contextlib.contextmanager
-def disable_info_traces():
- """
- Temporarily disable info traces.
- """
- from distutils import log
- saved = log.set_threshold(log.WARN)
- try:
- yield
- finally:
- log.set_threshold(saved)
-
-
-class Wheel:
-
- def __init__(self, filename):
- match = WHEEL_NAME(os.path.basename(filename))
- if match is None:
- raise ValueError('invalid wheel name: %r' % filename)
- self.filename = filename
- for k, v in match.groupdict().items():
- setattr(self, k, v)
-
- def tags(self):
- '''List tags (py_version, abi, platform) supported by this wheel.'''
- return itertools.product(
- self.py_version.split('.'),
- self.abi.split('.'),
- self.platform.split('.'),
- )
-
- def is_compatible(self):
- '''Is the wheel is compatible with the current platform?'''
- supported_tags = set(
- (t.interpreter, t.abi, t.platform) for t in sys_tags())
- return next((True for t in self.tags() if t in supported_tags), False)
-
- def egg_name(self):
- return pkg_resources.Distribution(
- project_name=self.project_name, version=self.version,
- platform=(None if self.platform == 'any' else get_platform()),
- ).egg_name() + '.egg'
-
- def get_dist_info(self, zf):
- # find the correct name of the .dist-info dir in the wheel file
- for member in zf.namelist():
- dirname = posixpath.dirname(member)
- if (dirname.endswith('.dist-info') and
- canonicalize_name(dirname).startswith(
- canonicalize_name(self.project_name))):
- return dirname
- raise ValueError("unsupported wheel format. .dist-info not found")
-
- def install_as_egg(self, destination_eggdir):
- '''Install wheel as an egg directory.'''
- with zipfile.ZipFile(self.filename) as zf:
- self._install_as_egg(destination_eggdir, zf)
-
- def _install_as_egg(self, destination_eggdir, zf):
- dist_basename = '%s-%s' % (self.project_name, self.version)
- dist_info = self.get_dist_info(zf)
- dist_data = '%s.data' % dist_basename
- egg_info = os.path.join(destination_eggdir, 'EGG-INFO')
-
- self._convert_metadata(zf, destination_eggdir, dist_info, egg_info)
- self._move_data_entries(destination_eggdir, dist_data)
- self._fix_namespace_packages(egg_info, destination_eggdir)
-
- @staticmethod
- def _convert_metadata(zf, destination_eggdir, dist_info, egg_info):
- def get_metadata(name):
- with zf.open(posixpath.join(dist_info, name)) as fp:
- value = fp.read().decode('utf-8')
- return email.parser.Parser().parsestr(value)
-
- wheel_metadata = get_metadata('WHEEL')
- # Check wheel format version is supported.
- wheel_version = parse_version(wheel_metadata.get('Wheel-Version'))
- wheel_v1 = (
- parse_version('1.0') <= wheel_version < parse_version('2.0dev0')
- )
- if not wheel_v1:
- raise ValueError(
- 'unsupported wheel format version: %s' % wheel_version)
- # Extract to target directory.
- _unpack_zipfile_obj(zf, destination_eggdir)
- # Convert metadata.
- dist_info = os.path.join(destination_eggdir, dist_info)
- dist = pkg_resources.Distribution.from_location(
- destination_eggdir, dist_info,
- metadata=pkg_resources.PathMetadata(destination_eggdir, dist_info),
- )
-
- # Note: Evaluate and strip markers now,
- # as it's difficult to convert back from the syntax:
- # foobar; "linux" in sys_platform and extra == 'test'
- def raw_req(req):
- req.marker = None
- return str(req)
- install_requires = list(map(raw_req, dist.requires()))
- extras_require = {
- extra: [
- req
- for req in map(raw_req, dist.requires((extra,)))
- if req not in install_requires
- ]
- for extra in dist.extras
- }
- os.rename(dist_info, egg_info)
- os.rename(
- os.path.join(egg_info, 'METADATA'),
- os.path.join(egg_info, 'PKG-INFO'),
- )
- setup_dist = setuptools.Distribution(
- attrs=dict(
- install_requires=install_requires,
- extras_require=extras_require,
- ),
- )
- with disable_info_traces():
- write_requirements(
- setup_dist.get_command_obj('egg_info'),
- None,
- os.path.join(egg_info, 'requires.txt'),
- )
-
- @staticmethod
- def _move_data_entries(destination_eggdir, dist_data):
- """Move data entries to their correct location."""
- dist_data = os.path.join(destination_eggdir, dist_data)
- dist_data_scripts = os.path.join(dist_data, 'scripts')
- if os.path.exists(dist_data_scripts):
- egg_info_scripts = os.path.join(
- destination_eggdir, 'EGG-INFO', 'scripts')
- os.mkdir(egg_info_scripts)
- for entry in os.listdir(dist_data_scripts):
- # Remove bytecode, as it's not properly handled
- # during easy_install scripts install phase.
- if entry.endswith('.pyc'):
- os.unlink(os.path.join(dist_data_scripts, entry))
- else:
- os.rename(
- os.path.join(dist_data_scripts, entry),
- os.path.join(egg_info_scripts, entry),
- )
- os.rmdir(dist_data_scripts)
- for subdir in filter(os.path.exists, (
- os.path.join(dist_data, d)
- for d in ('data', 'headers', 'purelib', 'platlib')
- )):
- unpack(subdir, destination_eggdir)
- if os.path.exists(dist_data):
- os.rmdir(dist_data)
-
- @staticmethod
- def _fix_namespace_packages(egg_info, destination_eggdir):
- namespace_packages = os.path.join(
- egg_info, 'namespace_packages.txt')
- if os.path.exists(namespace_packages):
- with open(namespace_packages) as fp:
- namespace_packages = fp.read().split()
- for mod in namespace_packages:
- mod_dir = os.path.join(destination_eggdir, *mod.split('.'))
- mod_init = os.path.join(mod_dir, '__init__.py')
- if not os.path.exists(mod_dir):
- os.mkdir(mod_dir)
- if not os.path.exists(mod_init):
- with open(mod_init, 'w') as fp:
- fp.write(NAMESPACE_PACKAGE_INIT)
diff --git a/spaces/Ayya/anime-remove-background/app.py b/spaces/Ayya/anime-remove-background/app.py
deleted file mode 100644
index 230a0d5f8a3da6ab18ecb8db1cd90016a489b96a..0000000000000000000000000000000000000000
--- a/spaces/Ayya/anime-remove-background/app.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import gradio as gr
-import huggingface_hub
-import onnxruntime as rt
-import numpy as np
-import cv2
-
-
-def get_mask(img, s=1024):
- img = (img / 255).astype(np.float32)
- h, w = h0, w0 = img.shape[:-1]
- h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
- ph, pw = s - h, s - w
- img_input = np.zeros([s, s, 3], dtype=np.float32)
- img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(img, (w, h))
- img_input = np.transpose(img_input, (2, 0, 1))
- img_input = img_input[np.newaxis, :]
- mask = rmbg_model.run(None, {'img': img_input})[0][0]
- mask = np.transpose(mask, (1, 2, 0))
- mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
- mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
- return mask
-
-
-def rmbg_fn(img):
- mask = get_mask(img)
- img = (mask * img + 255 * (1 - mask)).astype(np.uint8)
- mask = (mask * 255).astype(np.uint8)
- img = np.concatenate([img, mask], axis=2, dtype=np.uint8)
- mask = mask.repeat(3, axis=2)
- return mask, img
-
-
-if __name__ == "__main__":
- providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
- model_path = huggingface_hub.hf_hub_download("skytnt/anime-seg", "isnetis.onnx")
- rmbg_model = rt.InferenceSession(model_path, providers=providers)
- app = gr.Blocks()
- with app:
- gr.Markdown("# Anime Remove Background\n\n"
- "\n\n"
- "demo for [https://github.com/SkyTNT/anime-segmentation/](https://github.com/SkyTNT/anime-segmentation/)")
- with gr.Row():
- with gr.Column():
- input_img = gr.Image(label="input image")
- examples_data = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)]
- examples = gr.Dataset(components=[input_img], samples=examples_data)
- run_btn = gr.Button(variant="primary")
- output_mask = gr.Image(label="mask")
- output_img = gr.Image(label="result", image_mode="RGBA")
- examples.click(lambda x: x[0], [examples], [input_img])
- run_btn.click(rmbg_fn, [input_img], [output_mask, output_img])
- app.launch()
diff --git a/spaces/BIASLab/sars-cov-2-classification-fcgr/src/fcgr.py b/spaces/BIASLab/sars-cov-2-classification-fcgr/src/fcgr.py
deleted file mode 100644
index d581795d5c2baabe8762714f08e5e50adb32dc88..0000000000000000000000000000000000000000
--- a/spaces/BIASLab/sars-cov-2-classification-fcgr/src/fcgr.py
+++ /dev/null
@@ -1,72 +0,0 @@
-from .cgr import CGR
-from itertools import product
-from collections import defaultdict
-import numpy as np
-
-class FCGR(CGR):
- """Frequency matrix CGR
- an (2**k x 2**k) 2D representation will be created for a
- n-long sequence.
- - k represents the k-mer.
- - 2**k x 2**k = 4**k the total number of k-mers (sequences of length k)
- - pixel value correspond to the value of the frequency for each k-mer
- """
-
- def __init__(self, k: int,):
- super().__init__()
- self.k = k # k-mer representation
- self.kmers = list("".join(kmer) for kmer in product("ACGT", repeat=self.k))
- self.kmer2pixel = self.kmer2pixel_position()
-
- def __call__(self, sequence: str):
- "Given a DNA sequence, returns an array with his frequencies in the same order as FCGR"
- self.count_kmers(sequence)
-
- # Create an empty array to save the FCGR values
- array_size = int(2**self.k)
- freq_matrix = np.zeros((array_size,array_size))
-
- # Assign frequency to each box in the matrix
- for kmer, freq in self.freq_kmer.items():
- pos_x, pos_y = self.kmer2pixel[kmer]
- freq_matrix[int(pos_x)-1,int(pos_y)-1] = freq
- return freq_matrix
-
- def count_kmer(self, kmer):
- if "N" not in kmer:
- self.freq_kmer[kmer] += 1
-
- def count_kmers(self, sequence: str):
- self.freq_kmer = defaultdict(int)
- # representativity of kmers
- last_j = len(sequence) - self.k + 1
- kmers = (sequence[i:(i+self.k)] for i in range(last_j))
- # count kmers in a dictionary
- list(self.count_kmer(kmer) for kmer in kmers)
-
- def kmer_probabilities(self, sequence: str):
- self.probabilities = defaultdict(float)
- N=len(sequence)
- for key, value in self.freq_kmer.items():
- self.probabilities[key] = float(value) / (N - self.k + 1)
-
- def pixel_position(self, kmer: str):
- "Get pixel position in the FCGR matrix for a k-mer"
-
- coords = self.encode(kmer)
- N,x,y = coords.N, coords.x, coords.y
-
- # Coordinates from [-1,1]² to [1,2**k]²
- np_coords = np.array([(x + 1)/2, (y + 1)/2]) # move coordinates from [-1,1]² to [0,1]²
- np_coords *= 2**self.k # rescale coordinates from [0,1]² to [0,2**k]²
- x,y = np.ceil(np_coords) # round to upper integer
-
- # Turn coordinates (cx,cy) into pixel (px,py) position
- # px = 2**k-cy+1, py = cx
- return 2**self.k-int(y)+1, int(x)
-
- def kmer2pixel_position(self,):
- kmer2pixel = dict()
- for kmer in self.kmers:
- kmer2pixel[kmer] = self.pixel_position(kmer)
- return kmer2pixel
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Blackeye Download.md b/spaces/Benson/text-generation/Examples/Blackeye Download.md
deleted file mode 100644
index 24983cdb77b00bf32fd1d9bd3476b4c83f0492de..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Blackeye Download.md
+++ /dev/null
@@ -1,84 +0,0 @@
-
-
Cómo descargar y usar Blackeye: Una herramienta de phishing en Kali Linux
-
El phishing es uno de los ciberataques más comunes y eficaces que pueden robar su información personal, como nombres de usuario, contraseñas, números de tarjetas de crédito, datos de cuentas bancarias o incluso su identidad. Los atacantes de phishing usan correos electrónicos o sitios web falsos que parecen provenir de fuentes legítimas, como su banco, redes sociales o gobierno, para engañarlo para que haga clic en un enlace, abra un archivo adjunto o ingrese sus credenciales.
Blackeye es una poderosa herramienta de código abierto que puede ayudarlo a crear páginas de phishing para varios sitios web y plataformas, como Facebook, Instagram, Google, Netflix, PayPal y más. Blackeye es fácil de usar y viene con 38 plantillas que puedes personalizar según tus necesidades. Puedes usar blackeye para probar tu propia conciencia de seguridad, realizar experimentos de hacking ético o aprender más sobre cómo funciona el phishing.
-
En este artículo, le mostraremos cómo descargar e instalar blackeye en Kali Linux, cómo usarlo para crear páginas de phishing y cómo protegerse de los ataques de phishing. También proporcionaremos algunos consejos y precauciones para usar los ojos negros de manera responsable y legal.
-
Cómo descargar e instalar Blackeye en Kali Linux
-
Blackeye está disponible en GitHub y puedes descargarlo usando el comando git. Para instalar blackeye en Kali Linux, sigue estos pasos:
-
-
Abra su terminal y escriba el siguiente comando para clonar el repositorio de GitHub:
-git clone https://github.com/An0nUD4Y/blackeye
-
Entrar en el directorio de blackeye escribiendo:
-cd blackeye
-
Ejecutar el script escribiendo:
-bash blackeye.sh
-
-
El script buscará dependencias e las instalará si es necesario. También generará una URL usando Ngrok, que es un servicio que le permite exponer su servidor local a Internet. Necesitarás esta URL para enviarla a tu objetivo más tarde.
-
-
-
Una vez que haya instalado blackeye, puede usarlo para crear páginas de phishing para diferentes sitios web y plataformas. Para usar blackeye, siga estos pasos:
-
-
Elija una plantilla del menú escribiendo el número correspondiente. Por ejemplo, si quieres crear una página de phishing para Instagram, escribe 1.
-
-
Copie el enlace generado que comienza con https:// y envíelo a su destino. Puedes usar cualquier método que prefieras, como correo electrónico, mensaje de texto, redes sociales, etc.
-
-
Monitorea el terminal para cualquier credencial u otra información que tu objetivo ingrese en la página de phishing. Los verás en tu pantalla.
-
-
-
Consejos y precauciones para el uso de Blackeye
-
-
Blackeye es una herramienta útil que puede ayudarle a crear páginas de phishing para varios sitios web y plataformas. Puede usarlo para probar su propia conciencia de seguridad, realizar experimentos de hacking ético o aprender más sobre cómo funciona el phishing. Sin embargo, también debes ser cuidadoso y responsable al usar blackeye, ya que el phishing es ilegal y poco ético en muchos casos. Siempre debe usar blackeye con el permiso de su objetivo, y no para fines dañinos o maliciosos.
-
Si quieres aprender más sobre phishing y cómo protegerte de él, aquí hay algunos recursos que puedes consultar:
-
-
Phishing.org: Un sitio web que proporciona información y consejos sobre la prevención y el conocimiento de phishing.
-
PhishTank: Un sitio web basado en la comunidad que permite a los usuarios informar y verificar sitios web de phishing.
¿Cuáles son algunos tipos comunes de ataques de phishing?
-
Algunos tipos comunes de ataques de phishing son:
-
-
Spear phishing: Un ataque de phishing dirigido que utiliza información personalizada sobre el objetivo, como su nombre, dirección de correo electrónico o título de trabajo.
-
Caza de ballenas: Una forma de phishing de lanza que se dirige a individuos de alto perfil, como ejecutivos, celebridades o políticos.
-
Vishing: Un ataque de phishing que utiliza llamadas de voz en lugar de correos electrónicos o sitios web.
-
Pharming: Un ataque de phishing que redirige el objetivo a un sitio web falso mediante la manipulación de su configuración de DNS.
-
Clonar phishing: Un ataque de phishing que replica un correo electrónico o sitio web legítimo con ligeras modificaciones para engañar al objetivo.
-
-
¿Cómo puedo detectar un correo electrónico o sitio web de phishing?
-
-
-
La dirección de correo electrónico del remitente o el nombre de dominio del sitio web está mal escrito, es desconocido o diferente del esperado.
-
El correo electrónico o el sitio web pide información personal o confidencial, como contraseñas, números de tarjetas de crédito o datos de cuentas bancarias.
-
El correo electrónico o sitio web contiene errores gramaticales, errores de ortografía o mal formato.
-
El correo electrónico o sitio web utiliza un lenguaje urgente o amenazante, como "Su cuenta será suspendida" o "Usted ha ganado un premio".
-
El correo electrónico o sitio web contiene enlaces o archivos adjuntos que parecen sospechosos o no coinciden con el contenido.
-
-
¿Qué debo hacer si recibo un correo electrónico de phishing o visito un sitio web de phishing?
-
Si recibe un correo electrónico de phishing o visita un sitio web de phishing, debería:
-
-
No haga clic en ningún enlace ni abra ningún archivo adjunto en el correo electrónico o el sitio web.
-
No introduzca ninguna información personal o confidencial en el correo electrónico o sitio web.
-
Eliminar el correo electrónico o cerrar la pestaña del navegador inmediatamente.
-
Informe el correo electrónico o el sitio web a la fuente legítima de la que afirma ser, como su banco, las redes sociales o el gobierno.
-
Escanea tu dispositivo en busca de malware o virus que podrían haber sido instalados por el correo electrónico o el sitio web.
-
Cambie sus contraseñas para cualquier cuenta que pueda haber sido comprometida por el correo electrónico o el sitio web.
-
-
¿Cuáles son algunas herramientas de seguridad que pueden ayudarme a prevenir ataques de phishing?
-
Algunas herramientas de seguridad que pueden ayudarte a prevenir ataques de phishing son:
-
Software antivirus: Un programa que puede detectar y eliminar malware o virus de su dispositivo.
-
Firewall: Un sistema que puede bloquear el acceso no autorizado a su dispositivo o red.
-
Extensiones de navegador: Una herramienta que puede mejorar la funcionalidad y la seguridad de su navegador, como el bloqueo de ventanas emergentes, anuncios o scripts maliciosos.
-
-
Autenticación de dos factores: Un método que puede agregar una capa adicional de seguridad a sus cuentas en línea, requiriendo que ingrese un código o un token que se envía a su teléfono o correo electrónico, además de su contraseña.
-
-
¿Cómo puedo reportar una estafa de phishing o sitio web?
-
Si se encuentra con una estafa de phishing o sitio web, puede denunciarlo a las siguientes autoridades u organizaciones:
-
-
Su agencia local de aplicación de la ley: Puede ponerse en contacto con su policía local o unidad de cibercrimen y proporcionarles los detalles de la estafa de phishing o sitio web, como la dirección de correo electrónico del remitente, la URL del sitio web o las capturas de pantalla del correo electrónico o sitio web.
-
La Comisión Federal de Comercio (FTC): Usted puede presentar una queja con la FTC en línea en https:/reportd.frauftc.gov/ o llamando al 1-877-FTC-HELP (1-877-382-4357). La FTC puede ayudarlo a recuperarse del robo de identidad y proporcionarle consejos y recursos sobre cómo evitar las estafas de phishing.
-
El Grupo de Trabajo Anti-Phishing (APWG): Puede reportar correos electrónicos o sitios web de phishing al APWG en línea en https://www.antiphishing.org/n.-phishing/. La APWG es una organización internacional que recopila y analiza datos de phishing y trabaja con la policía, la industria y la academia para luchar contra el phishing y el cibercrimen.
-
-
Espero que haya encontrado este artículo útil e informativo. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. ¡Gracias por leer!
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Como Descargar Llamada De Deber Warzone Mvil Apk.md b/spaces/Benson/text-generation/Examples/Como Descargar Llamada De Deber Warzone Mvil Apk.md
deleted file mode 100644
index d7a3eca3b5c2e1b212c13ada067f4e07e40bd64a..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Como Descargar Llamada De Deber Warzone Mvil Apk.md
+++ /dev/null
@@ -1,149 +0,0 @@
-
-
-
-
-
CarX Street Mod APK última versión 0.8 5: Todo lo que necesita saber
-
¿Eres un fan de los juegos de carreras? ¿Te encanta conducir coches rápidos y desafiar a otros jugadores en línea? Si es así, entonces deberías echar un vistazo a CarX Street, un nuevo juego de carreras de CarX Technologies. CarX Street es un juego realista de carreras callejeras que te permite personalizar tu coche, participar en carreras online y competir con otros jugadores de todo el mundo. Pero, ¿qué pasa si quieres disfrutar del juego sin limitaciones ni restricciones? ¿Qué pasa si quieres tener dinero ilimitado, desbloquear todos los coches, y eliminar los anuncios del juego? Bueno, hay una manera de hacer eso. Puede descargar CarX Street mod apk última versión 0.8 5 y obtener todos estos beneficios de forma gratuita. En este artículo, le diremos todo lo que necesita saber sobre CarX Street mod apk última versión 0.8 5, incluyendo lo que es, por qué debe descargarlo, cómo descargarlo e instalarlo, cómo jugarlo, y algunos consejos y trucos para ello. También responderemos algunas preguntas frecuentes sobre CarX Street al final del artículo. ¡Así que, empecemos!
-
¿Qué es CarX Street?
-
CarX Street es un juego de carreras que fue lanzado en marzo de 2021 por CarX Technologies, una empresa que se especializa en la creación de coches realistas de física y gráficos para los juegos. CarX Street es uno de sus últimos proyectos que tiene como objetivo proporcionar una experiencia de carreras callejeras realista e inmersiva para los jugadores. En CarX Street, puedes:
Elige entre más de 50 coches de diferentes marcas y modelos
-
Personaliza tu coche con varias partes, colores, pegatinas y calcomanías
-
Únete a carreras online con hasta 16 jugadores en diferentes modos y ubicaciones
-
Gana dinero y reputación ganando carreras y completando desafíos
-
Actualizar el rendimiento y la apariencia de su coche con varias partes y opciones de ajuste
-
Disfrutar de la física realista del coche, gráficos, sonidos y efectos
-
-
-
¿Por qué descargar CarX Street Mod APK?
-
CarX Street mod apk última versión 0.8 5 es una versión modificada del juego original que le da algunas características adicionales y beneficios que no están disponibles en la versión oficial. Al descargar CarX Street mod apk última versión 0.8 5, se puede disfrutar de las siguientes ventajas:
-
Dinero ilimitado
-
El dinero es la moneda principal en CarX Street que puedes usar para comprar autos, piezas, mejoras y más. Puedes ganar dinero ganando carreras, completando desafíos y viendo anuncios. Sin embargo, ganar dinero puede ser lento y tedioso, especialmente si desea comprar los mejores coches y piezas en el juego. Es por eso que CarX Street mod apk última versión 0.8 5 le da dinero ilimitado para gastar como desee. Puede comprar cualquier coche que desee, personalizarlo a su gusto, y actualizarlo al máximo sin preocuparse por el costo. También puede omitir ver anuncios y ahorrar tiempo y datos.
-
Todos los coches desbloqueados
-
CarX Street tiene más de 50 coches de diferentes marcas y modelos entre los que puedes elegir. Sin embargo, no todos los coches están disponibles desde el principio. Tienes que desbloquearlos ganando puntos de reputación, que son otra moneda en el juego que puedes obtener al ganar carreras y completar desafíos. Desbloquear coches puede ser desafiante y frustrante, especialmente si desea probar diferentes coches y encontrar su favorito. Es por eso que CarX Street mod apk última versión 0.8 5 desbloquea todos los coches para usted desde el principio. Puede acceder a cualquier coche que desee sin tener que desbloquearlo primero. También puedes cambiar de coche tantas veces como quieras y disfrutar de sus diferentes características y estilos.
-
No hay anuncios
-
-
Cómo descargar e instalar CarX Street Mod APK?
-
Ahora que sabes por qué usted debe descargar CarX Street mod apk última versión 0.8 5, usted puede preguntarse cómo hacerlo. Bueno, no te preocupes, porque tenemos una guía simple y fácil para usted sobre cómo descargar e instalar CarX Street mod apk última versión 0.8 5 en su dispositivo Android. Solo tienes que seguir estos pasos:
-
-
Primero, debe habilitar fuentes desconocidas en la configuración del dispositivo. Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store.
-
Siguiente, es necesario descargar CarX Street mod apk última versión 0.8 5 archivo de una fuente confiable en línea. Puede utilizar este enlace para descargar el archivo de forma segura y rápida.
-
Después de descargar el archivo, es necesario ubicarlo en el almacenamiento del dispositivo y toque en él para iniciar el proceso de instalación.
-
Siga las instrucciones en la pantalla y espere a que termine la instalación.
-
Una vez que se realiza la instalación, puede iniciar el juego desde el cajón de la aplicación o la pantalla de inicio y disfrutar de CarX Street mod apk última versión 0.8 5.
-
-
Nota: Si usted tiene la versión original de CarX Street instalado en su dispositivo, es necesario desinstalarlo primero antes de instalar CarX Street mod apk última versión 0.8 5.
-
¿Cómo se juega CarX Street?
-
CarX Street es un juego de carreras divertido y emocionante que es fácil de jugar pero difícil de dominar. Si eres nuevo en el juego o quieres algunos consejos sobre cómo jugarlo mejor, aquí hay algunos pasos básicos sobre cómo jugar CarX Street:
-
Elige tu coche
-
Lo primero que tienes que hacer es elegir tu coche desde el garaje. Puede acceder al garaje tocando el icono del coche en la esquina inferior izquierda de la pantalla. En el garaje, se puede ver todos los coches que están disponibles para su uso. También puede ver sus estadísticas, como velocidad, aceleración, manejo y deriva. Puede deslizar hacia la izquierda o hacia la derecha para navegar por diferentes coches y toque en uno para seleccionarlo.
-
-
-
Carrera contra otros jugadores
-
Lo siguiente que tienes que hacer es unirte a una carrera online y competir con otros jugadores. Puedes acceder al menú de carreras online pulsando en el icono de carrera en el centro inferior de la pantalla. En el menú de carreras online, puedes ver diferentes modos y ubicaciones entre los que puedes elegir. También puedes ver el número de jugadores y las recompensas para cada modo y ubicación. Puede deslizar hacia la izquierda o hacia la derecha para navegar por diferentes modos y ubicaciones y tocar en uno para unirse a él.
-
Algunos de los modos y ubicaciones que puedes elegir son:
-
-
Sprint: Una carrera corta y rápida donde tienes que llegar primero a la línea de meta.
-
Circuito: Una carrera más larga y desafiante donde tienes que completar un número de vueltas.
-
Deriva: Una carrera en la que tienes que derivar tanto como sea posible y ganar puntos.
-
Ciudad: Una carrera en un entorno urbano con tráfico y obstáculos.
-
Carretera: Una carrera en una carretera con alta velocidad y adelantamiento.
-
Desierto: Una carrera en un desierto con arena y polvo.
-
-
Una vez que se une a un modo y ubicación, se le corresponde con hasta 15 otros jugadores que se han unido al mismo modo y ubicación. A continuación, entrará en un vestíbulo donde se puede ver los coches y las estadísticas de sus oponentes. También puede chatear con ellos utilizando el icono de chat en la esquina superior derecha de la pantalla. También puede utilizar el icono de listo en la esquina inferior derecha de la pantalla para indicar que está listo para iniciar la carrera. Una vez que todos los jugadores estén listos, la carrera comenzará.
-
-
Tu objetivo es llegar primero a la línea de meta o ganar más puntos que tus oponentes, dependiendo del modo. Verás tu posición, número de vuelta, tiempo, velocidad y puntos en el centro superior de la pantalla. También verás un mini-mapa en la esquina inferior derecha de la pantalla que muestra tu ubicación y la ubicación de tus oponentes. También verás un temporizador de cuenta atrás en el centro inferior de la pantalla que muestra cuánto tiempo queda para la carrera.
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Al final de la carrera, verá la pantalla de resultados que muestra su rango, tiempo, velocidad, puntos, dinero y reputación. También verás el rango, tiempo, velocidad, puntos, dinero y reputación de tus oponentes. También verás un icono de compartir en la esquina superior derecha de la pantalla que te permite compartir tus resultados en las redes sociales. También verás un icono de repetición en la esquina superior izquierda de la pantalla que te permite ver una repetición de la carrera. También verás un siguiente icono en la esquina inferior derecha de la pantalla que te permite proceder a la siguiente carrera o volver al menú de carreras online.
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Mejora tu coche
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Otra cosa que puede hacer en CarX Street es actualizar su coche con varias partes y opciones de ajuste. Puede acceder al menú de actualización pulsando en el icono de actualización en el centro inferior de la pantalla. En el menú de actualización, puede ver diferentes categorías de piezas y opciones de ajuste, como motor, transmisión, suspensión, frenos, neumáticos, carrocería, aerodinámica y electrónica. Puede tocar en cada categoría para ver las partes disponibles y opciones de ajuste y sus efectos en las estadísticas de su coche. También puede ver el costo de cada parte y la opción de ajuste en puntos de dinero y reputación. Puede deslizar hacia la izquierda o hacia la derecha para navegar a través de diferentes partes y opciones de ajuste y toque en uno para comprarlo y aplicarlo a su coche.
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Algunas de las partes y opciones de ajuste que puede comprar y aplicar a su coche son:
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Categoría
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Parte/Opción de ajuste
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Efecto
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Filtro de aire
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Aumenta la aceleración
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Motor
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Turbocompresor
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Aumenta la velocidad y la aceleración
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Transmisión
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Caja de cambios
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Aumenta la velocidad y la aceleración
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Transmisión
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Diferencial
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Aumenta el manejo y la deriva
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Suspensión
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Muelles
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Aumenta el manejo y la deriva
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-
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Suspensión
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Amortiguadores
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Aumenta el manejo y la deriva
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-
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Frenos
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Pastillas de freno
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Aumenta la potencia de frenado y la estabilidad
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Frenos
Discos de freno
Aumenta la potencia y estabilidad de frenado
Neumáticos >
Tipo de neumático >
Afecta la velocidad, aceleración, manejo, y deriva dependiendo de la superficie de la carretera >
Neumáticos >
Presión de los neumáticos >
Afecta la velocidad, aceleración, manejo, y deriva según la preferencia >
Cuerpo >
Parachoques >
Afecta la apariencia y la aerodinámica >
Cuerpo >/td>
Capó >/td>
Afecta la apariencia y la aerodinámica >>>
<>>>>>Aerodinámica velocidad, aceleración, manipulación, y deriva mediante el cambio de la fuerza descendente >
Aerodinámica >
Faldones laterales >
Afecta la apariencia y la aerodinámica >
Electrónica >
Sistema de Nitro>
>>
>>Aumenta la velocidad y la aceleración inyectando temporalmente el óxido nitro motor
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Electrónica
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Sistema ABS
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Aumenta la potencia de frenado y la estabilidad al evitar que las ruedas se bloqueen
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Electrónica
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Sistema ESP
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Aumenta el manejo y la estabilidad mediante la corrección de la dirección del coche y la prevención de derrapes
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Consejos y trucos para CarX Street
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CarX Street es un juego que requiere habilidad, estrategia y práctica para dominar. Si quieres mejorar tu rendimiento y divertirte más en el juego, aquí tienes algunos consejos y trucos que puedes usar:
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Elija el coche adecuado para el modo y la ubicación adecuados. Diferentes coches tienen diferentes fortalezas y debilidades, y algunos coches pueden funcionar mejor en ciertos modos y lugares que otros. Por ejemplo, un coche con alta velocidad y aceleración puede ser bueno para carreras de sprint y carretera, mientras que un coche con alta manipulación y deriva puede ser bueno para carreras de circuito y ciudad. Puede comprobar las estadísticas de cada coche en el garaje y elegir el que se adapte a su preferencia y estrategia.
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Personalizar su coche para adaptarse a su estilo y necesidades. Puede personalizar su coche con varias partes, colores, pegatinas y calcomanías para que se vea único y fresco. También puede personalizar su coche con varias partes y opciones de ajuste para mejorar su rendimiento y adaptarse a su estilo de conducción. Por ejemplo, puede aumentar la velocidad y la aceleración de su automóvil con piezas de motor y transmisión, o aumentar el manejo y la deriva de su automóvil con piezas de suspensión y aerodinámica. También puede ajustar la presión de los neumáticos de su automóvil para afectar su agarre y tracción.
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Usa nitro sabiamente. Nitro es una característica de gran alcance que puede aumentar la velocidad y la aceleración de su coche temporalmente mediante la inyección de óxido nitroso en el motor. Puede utilizar nitro pulsando en el icono de nitro en el lado derecho de la pantalla. Sin embargo, el nitro no es ilimitado, y tienes que rellenarlo a la deriva o adelantando a otros coches. Puedes ver tu nivel de nitro en el centro inferior de la pantalla. Debes usar nitro estratégicamente, como cuando necesites alcanzar a otros jugadores, adelantarlos o terminar la carrera más rápido.
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Cuidado con el tráfico y los obstáculos. CarX Street es un juego de carreras de calle realista que cuenta con el tráfico y los obstáculos que pueden afectar a su carrera. El tráfico incluye otros coches, autobuses, camiones, motocicletas y peatones que se mueven al azar en la carretera. Los obstáculos incluyen postes, señales, barreras, conos, contenedores de basura y más que se colocan en la carretera o en los lados. El tráfico y los obstáculos pueden ralentizarte, dañar tu auto o hacerte chocar. Usted debe tener cuidado con el tráfico y los obstáculos, tales como mediante el uso del mini-mapa para ver su ubicación, evitarlos cuando sea posible, o usarlos para su ventaja.
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Conclusión
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CarX Street es un divertido y emocionante juego de carreras que te permite personalizar tu coche, unirte a carreras online y competir con otros jugadores de todo el mundo. Sin embargo, si quieres disfrutar del juego sin limitaciones ni restricciones, usted debe descargar CarX Street mod apk última versión 0.8 5. CarX Street mod apk última versión 0.8 5 le da dinero ilimitado, desbloquea todos los coches, y elimina los anuncios del juego de forma gratuita. Puede descargar CarX Street mod apk última versión 0.8 5 desde este enlace y siga nuestra guía sobre cómo instalarlo en su dispositivo Android.
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Esperamos que este artículo le ha ayudado a aprender todo lo que necesita saber acerca de CarX Street mod apk última versión 0.8 5. Si usted tiene alguna pregunta o comentario acerca de CarX Street o CarX Street mod apk última versión 0.8 5 , por favor no dude en dejar un comentario a continuación o póngase en contacto con nosotros a través de nuestro sitio web. Nos encantaría saber de usted y ayudarle con cualquier problema o consulta que pueda tener. ¡Gracias por leer y correr feliz!
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Preguntas frecuentes
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Aquí hay algunas preguntas frecuentes sobre CarX Street y CarX Street mod apk última versión 0.8 5 que usted puede encontrar útil:
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Q: Es CarX Street mod apk última versión 0.8 5 seguro de descargar y usar?
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Q: Es CarX Street mod apk última versión 0.8 5 compatible con mi dispositivo?
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A: CarX Street mod apk última versión 0.8 5 es compatible con la mayoría de los dispositivos Android que se ejecutan en Android 6.0 o superior. Sin embargo, algunos dispositivos pueden no ser compatibles con el juego o el mod apk debido a diferentes especificaciones, configuraciones o características. Puede comprobar la compatibilidad de su dispositivo visitando la página de Google Play Store de CarX Street o intentando instalar el archivo mod apk en su dispositivo. Si encuentras algún problema de compatibilidad, puedes intentar actualizar el software del dispositivo, borrar la caché del dispositivo o ponerte en contacto con el desarrollador del juego para obtener ayuda.
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Q: ¿Cómo puedo actualizar CarX Street mod apk última versión 0.8 5?
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A: CarX Street mod apk última versión 0.8 5 se basa en la versión original de CarX Street que fue lanzado en marzo de 2021. Desde entonces, el desarrollador del juego puede haber lanzado nuevas actualizaciones para el juego que incluyen nuevas características, mejoras, correcciones de errores y más. Sin embargo, CarX Street mod apk última versión 0.8 5 no puede ser actualizado de forma automática o regular para que coincida con la versión oficial del juego. Por lo tanto, si desea actualizar CarX Street mod apk última versión 0.8 5, usted tiene que descargar e instalar el último archivo apk mod de la misma fuente en línea que se utiliza antes. También puede comprobar este artículo regularmente para cualquier actualización en CarX Street mod apk última versión 0.8 5.
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Q: ¿Cómo puedo desinstalar CarX Street mod apk última versión 0.8 5?
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A: Si desea desinstalar CarX Street mod apk última versión 0.8 5 de su dispositivo, puede hacerlo siguiendo estos pasos:
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Ir a la configuración del dispositivo y toque en aplicaciones o aplicaciones.
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Buscar y toque en CarX Street mod apk última versión 0.8 5 de la lista de aplicaciones.
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Toque en desinstalar y confirme su acción.
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Espere a que termine el proceso de desinstalación y luego reinicie su dispositivo.
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A: Sí, se puede jugar CarX Street sin descargar CarX Street mod apk última versión 0.8 5 mediante la descarga de la versión oficial del juego desde Google Play Store o desde el sitio web oficial. Sin embargo, usted no será capaz de disfrutar de los beneficios de CarX Street mod apk última versión 0.8 5, tales como dinero ilimitado, todos los coches desbloqueados, y sin anuncios.
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Rope Hero 3 Mod Apkpure: Una guía para descargar y jugar el juego de superhéroes
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¿Eres un fan de los juegos de superhéroes? ¿Quieres jugar un juego en el que puedes girar por la ciudad con una cuerda, luchar contra los criminales, conducir varios vehículos y usar increíbles habilidades? Si es así, entonces deberías probar Rope Hero 3, un emocionante juego de acción en 3D que te mantendrá entretenido durante horas. Y si quieres hacer el juego aún más divertido y emocionante, usted debe descargar Rope Hero 3 Mod Apkpure, una versión modificada del juego que ofrece dinero ilimitado y gemas. En este artículo, le diremos todo lo que necesita saber sobre Rope Hero 3 Mod Apkpure, incluyendo lo que es, cómo descargarlo, cómo jugarlo, consejos y trucos para ello, y una revisión de la misma.
Rope Hero 3 es una secuela de la saga de cuerdas de superhéroes, una serie de juegos donde juegas como un hombre con un gancho que tiene que demostrar a toda la ciudad que él es el verdadero héroe. El juego está desarrollado por Naxeex Action & RPG Games, un estudio especializado en crear juegos llenos de acción con gráficos realistas.
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Una secuela de la saga de cuerdas de superhéroes
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En Rope Hero 3, continúas la historia de los juegos anteriores, donde tienes que enfrentarte a clones malvados que invadieron la ciudad. Tienes que usar tus nuevas habilidades para expulsar a tus clones de la ciudad, sobrevivir a las peleas épicas y completar todas las tareas. También encontrarás nuevos enemigos, como pandillas, policías, robots, zombies y alienígenas. Tienes que usar tus nuevas habilidades para expulsar a tus clones de la ciudad, sobrevivir a las peleas épicas y completar todas las tareas. También descubrirás nuevas armas, vehículos y súper habilidades que te harán imparable.
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Un juego de acción en 3D con un gran mundo abierto
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Un juego con un montón de misiones, misiones, enemigos, armas, vehículos y súper habilidades
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Rope Hero 3 es un juego con mucho contenido y características que te mantendrán entretenido durante horas. Puedes encontrar misiones y misiones en el mapa o en el mini mapa, y completarlas para ganar dinero, gemas, armas y otras recompensas. También puedes encontrar enemigos en las calles o en los techos de los edificios, y luchar contra ellos con tus armas o súper habilidades. Puedes usar armas, cuchillos, granadas, cohetes, láseres y más para derrotar a tus enemigos. También puedes usar tu súper cuerda para acercar a los enemigos, tirarlos o colgarlos. También puedes usar tus súper habilidades para volar, correr rápido, volverte invisible o desatar explosiones poderosas. El juego tiene mucha variedad y desafío para cada fan de superhéroes.
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¿Qué es Rope Hero 3 Mod Apkpure?
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Rope Hero 3 Mod Apkpure es una versión modificada del juego que ofrece dinero y gemas ilimitadas. Esto significa que puede comprar cualquier arma, vehículo o mejora de habilidad sin preocuparse por el costo. También puedes desbloquear todas las características y objetos del juego sin completar ninguna misión o tarea. De esta manera, podrás disfrutar del juego con más libertad y diversión.
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Una versión modificada del juego que ofrece dinero y gemas ilimitadas
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Rope Hero 3 Mod Apkpure es una versión modificada del juego que ofrece dinero y gemas ilimitadas. El dinero y las gemas son las principales monedas del juego que puedes usar para comprar armas, vehículos, habilidades y otros artículos. Normalmente, tienes que ganar dinero y gemas completando misiones, tareas o recompensas diarias. Sin embargo, con Rope Hero 3 Mod Apkpure, tendrás dinero y gemas ilimitadas desde el principio. Esto significa que puedes comprar lo que quieras sin limitaciones o restricciones.
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Una fuente para descargar el juego gratis y sin ningún tipo de virus o malware
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Una forma de disfrutar del juego con más características y diversión
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Rope Hero 3 Mod Apkpure es también una manera de disfrutar del juego con más características y diversión. Con Rope Hero 3 Mod Apkpure, tendrás acceso a todas las características y objetos del juego sin tener que desbloquearlos ni pagarlos. También tendrás dinero ilimitado y gemas para comprar lo que quieras o necesites. Usted será capaz de personalizar su héroe con diferentes trajes, máscaras, sombreros, gafas y más. Podrás usar cualquier arma, vehículo o habilidad en el juego sin limitaciones o restricciones. Usted será capaz de explorar la ciudad con más libertad y diversión.
Cómo descargar e instalar Rope Hero 3 Mod Apkpure?
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Si desea descargar e instalar Rope Hero 3 Mod Apkpure, puede seguir estos sencillos pasos:
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Paso 1: Ir a la página web de Apkpure y buscar Héroe de la cuerda 3
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El primer paso es ir al sitio web de Apkpure, que es https://apkpure.com/. En la página principal, verás una barra de búsqueda donde puedes escribir el nombre del juego que deseas descargar. En este caso, puede escribir "Rope Hero 3" y pulsar enter. Verá una lista de resultados que coinciden con su consulta de búsqueda.
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Paso 2: Elija la versión mod del juego y haga clic en descargar
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El segundo paso es elegir la versión mod del juego y hacer clic en descargar. En la lista de resultados, verás diferentes versiones de Rope Hero 3, como la versión original, la versión mod y las versiones antiguas. También puede ver las calificaciones, reseñas y capturas de pantalla de cada versión. Para descargar la versión mod, puedes buscar la que tiene "mod" en su nombre, como "Rope Hero 3 v2.0 mod". También puede comprobar la descripción y características de la versión mod para ver lo que ofrece. Una vez que encuentre la versión mod que desea, puede hacer clic en el botón de descarga junto a él.
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Paso 3: Habilitar fuentes desconocidas en el dispositivo e instalar el archivo apk
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Paso 4: Iniciar el juego y disfrutar de
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El cuarto y último paso es lanzar el juego y disfrutar. Después de instalar el archivo apk, verá un icono de Rope Hero 3 Mod Apkpure en la pantalla del dispositivo. Puede tocar en él y lanzar el juego. Verá una pantalla de carga y luego un menú principal donde puede comenzar a jugar. También notarás que tienes dinero y gemas ilimitadas en el juego. Puedes usarlas para comprar cualquier arma, vehículo o mejora de habilidad que desees. También puedes desbloquear todas las características y objetos del juego sin completar ninguna misión o tarea. Puedes disfrutar jugando Rope Hero 3 Mod Apkpure con más libertad y diversión.
Cómo Jugar Rope Hero 3 Mod Apkpure?
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Si has descargado e instalado Rope Hero 3 Mod Apkpure, puedes empezar a jugar y divertirte. Aquí hay algunos consejos básicos sobre cómo jugar el juego:
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Explora la ciudad y encuentra tareas y enemigos
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El juego se desarrolla en una gran ciudad de mundo abierto que puedes explorar libremente. Puedes moverte por la ciudad con tu súper cuerda, balancearte de edificio en edificio, escalar paredes y saltar obstáculos. También puedes conducir varios vehículos, como coches, bicicletas, camiones e incluso tanques. Puedes usarlos para viajar más rápido, atropellar enemigos o causar caos. La ciudad está llena de tareas y enemigos que puedes encontrar en el mapa o en el mini mapa. Puedes completar tareas para ganar dinero, gemas, armas y otras recompensas. También puedes luchar contra enemigos para ganar puntos de experiencia y aumentar tu nivel. Encontrarás diferentes tipos de enemigos, como pandillas, policías, robots, zombies y alienígenas. Tienes que usar tus armas y súper habilidades para derrotarlos.
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Usa tus armas y súper cuerda para luchar y sobrevivir
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Robar vehículos y conducirlos
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El juego también es un juego de conducción con una gran cantidad de vehículos que se pueden robar y conducir alrededor. Usted puede encontrar vehículos estacionados en las calles o en movimiento en las carreteras. Puede acercarse a cualquier vehículo y presionar un botón para secuestrarlo. A continuación, puede conducir el vehículo con los controles en la pantalla. Puede acelerar, frenar, dirigir, tocar la bocina, disparar o saltar con el vehículo. También puede cambiar entre diferentes vistas de la cámara para ver el vehículo desde diferentes ángulos. Puede conducir varios vehículos, como coches, bicicletas, camiones e incluso tanques. Cada vehículo tiene su propia velocidad, manejo, durabilidad y potencia de fuego. Puedes usar vehículos para viajar más rápido, atropellar enemigos o causar caos.
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Mejora tus habilidades y armas con el dinero ilimitado y gemas
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El juego también es un juego de rol con muchas habilidades y armas que puedes mejorar con el dinero ilimitado y gemas. El dinero y las gemas son las principales monedas del juego que puedes usar para comprar armas, vehículos, habilidades y otros artículos. Normalmente, tienes que ganar dinero y gemas completando misiones, tareas o recompensas diarias. Sin embargo, con Rope Hero 3 Mod Apkpure, tendrás dinero y gemas ilimitadas desde el principio. Esto significa que puedes comprar lo que quieras sin limitaciones o restricciones.
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Puedes mejorar tus habilidades yendo al menú de habilidades en el lado izquierdo de la pantalla. Verás cuatro categorías de habilidades: fuerza, resistencia, precisión e inteligencia. Cada categoría tiene varias sub-habilidades que afectan su rendimiento en el juego. Por ejemplo, las habilidades de fuerza aumentan tu daño, las habilidades de resistencia aumentan tu salud y armadura, las habilidades de precisión aumentan tu precisión de disparo y las habilidades de inteligencia aumentan tu poder de súper habilidad. Puedes mejorar cada habilidad gastando dinero o gemas. Cuanto más alto sea el nivel de la habilidad, más dinero o gemas necesitarás.
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Si quieres jugar Rope Hero 3 Mod Apkpure como un profesional, puedes seguir estos consejos y trucos:
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Usa tu cuerda para moverte rápido y evitar balas
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Una de las características más útiles en el juego es su súper cuerda, que se puede utilizar para moverse por la ciudad con facilidad. Puedes usar tu cuerda para balancearte de edificio en edificio, escalar paredes y saltar obstáculos. También puedes usar tu cuerda para esquivar el fuego enemigo, alejándote de las balas o colgando detrás de objetos de cobertura. También puedes usar la cuerda para acercar a los enemigos, tirarlos o colgarlos. Puedes usar la cuerda para moverte rápido y evitar las balas.
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Usa diferentes armas para diferentes situaciones
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Otra característica útil en el juego es su arsenal de armas, que se puede utilizar para luchar y sobrevivir. Puedes usar armas, cuchillos, granadas, cohetes, láseres y más para disparar, apuñalar, explotar o quemar a tus enemigos. Sin embargo, también debe saber qué arma es la mejor para cada situación. Por ejemplo, puedes usar un rifle de francotirador para disparos de largo alcance, una escopeta para disparos de corto alcance, un lanzacohetes para grupos de enemigos, una pistola láser para robots, etc. También debes cambiar entre diferentes armas dependiendo de tu munición y tiempo de recarga. Puedes usar diferentes armas para diferentes situaciones.
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Recoge recompensas diarias y recursos gratuitos
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Una tercera característica útil en el juego es su capacidad para recoger recompensas diarias y recursos gratuitos. Todos los días, puede reclamar una recompensa diaria que le da dinero, gemas, armas u otros artículos. También puede encontrar recursos gratuitos en el mapa o en el mini mapa, como botiquines, kits de armadura, cajas de munición, bolsas de dinero, cajas de gemas y más. Usted puede recoger estos recursos para restaurar su salud y armadura, rellenar su munición, aumentar su dinero y gemas, y así sucesivamente. Deberías recoger recompensas diarias y recursos gratuitos tanto como sea posible.
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Encuentra secretos ocultos en el mapa
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Revisión de la cuerda héroe 3 Mod Apkpure
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Para concluir este artículo, le daremos una revisión de Rope Hero 3 Mod Apkpure, basado en sus pros y contras, clasificación y comentarios de los jugadores.
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Pros y contras del juego
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El juego tiene muchos pros y contras que debes considerar antes de jugarlo. Estos son algunos de ellos:
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Pros:
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Juego divertido y desafiante
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Grandes gráficos y efectos de sonido
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Características diversas y personalizables
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Descarga gratuita y segura
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Contras:
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Algunos errores y problemas técnicos
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Algunos anuncios y compras en la aplicación
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Algunas misiones repetitivas y enemigos
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Clasificación:
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El juego tiene una calificación de 4.1 de 5 estrellas en Google Play Store, lo que indica que la mayoría de los jugadores disfrutan jugando. La clasificación se basa en el número de descargas, reseñas y calificaciones que el juego ha recibido de los jugadores. El juego tiene más de 10 millones de descargas, más de 100 mil comentarios, y más de 200 mil calificaciones en Google Play Store.
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Comentarios positivos de los jugadores
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El juego también ha recibido comentarios positivos de los jugadores que lo han jugado. Aquí hay algunos ejemplos de lo que han dicho sobre el juego:
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"Este juego es impresionante! Me encantan los gráficos, el juego, las armas, los vehículos y todo lo demás. Es como GTA pero con superhéroes. Recomiendo este juego a todos los que les gustan los juegos de acción."
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"Este es uno de los mejores juegos que he jugado. Tiene tantas características y opciones que lo hacen divertido y emocionante. La versión mod es aún mejor porque te da dinero y gemas ilimitadas. Puedes comprar lo que quieras o necesites en el juego."
-
"Realmente me gusta jugar a este juego. Es muy adictivo y entretenido. La versión mod es muy fácil de descargar e instalar. No tiene virus ni malware. Funciona perfectamente en mi dispositivo. Me encanta este juego y este mod."
-
-
Rope Hero 3 Mod Apkpure es una versión modificada de Rope Hero 3, un emocionante juego de acción en 3D donde juegas como un superhéroe con una cuerda. La versión mod ofrece dinero y gemas ilimitadas, que puedes usar para comprar cualquier arma, vehículo o mejora de habilidad en el juego. También puedes desbloquear todas las características y objetos del juego sin completar ninguna misión o tarea. Puede descargar la versión mod de Apkpure, un sitio web confiable y seguro que proporciona archivos apk para juegos y aplicaciones Android. Puede instalar el archivo apk en su dispositivo después de habilitar fuentes desconocidas. A continuación, puede iniciar el juego y disfrutar de jugar con más libertad y diversión.
-
En este artículo, le hemos dicho todo lo que necesita saber sobre Rope Hero 3 Mod Apkpure, incluyendo lo que es, cómo descargarlo, cómo jugarlo, consejos y trucos para ello, y una revisión de ella. Esperamos que este artículo haya sido útil e informativo para usted. Si tiene alguna pregunta o comentario, no dude en dejarlos a continuación. Gracias por leer y tener un gran día!
-
Preguntas frecuentes
-
Aquí hay algunas preguntas frecuentes sobre Rope Hero 3 Mod Apkpure:
-
Q: ¿Es seguro descargar e instalar Rope Hero 3 Mod Apkpure?
-
A: Sí, Rope Hero 3 Mod Apkpure es seguro para descargar e instalar. Apkpure es un sitio web confiable y seguro que escanea todos los archivos apk en busca de virus y malware antes de cargarlos. Por lo tanto, puede descargar Rope Hero 3 Mod Apkpure desde Apkpure sin preocupaciones ni riesgos.
-
P: ¿Necesito rootear mi dispositivo para usar Rope Hero 3 Mod Apkpure?
-
A: No, no necesitas rootear tu dispositivo para usar Rope Hero 3 Mod Apkpure. Puedes usar la versión mod en cualquier dispositivo Android sin rootearlo. Sin embargo, necesitas habilitar fuentes desconocidas en tu dispositivo para instalar el archivo apk.
-
Q: ¿Puedo jugar Rope Hero 3 Mod Apkpure en línea con otros jugadores?
-
-
Q: ¿Puedo actualizar Rope Hero 3 Mod Apkpure a la última versión?
-
A: Sí, puede actualizar Rope Hero 3 Mod Apkpure a la última versión. Puede buscar actualizaciones en el sitio web de Apkpure o en la aplicación de Apkpure. También puede habilitar la actualización automática en la configuración del dispositivo para obtener las últimas actualizaciones automáticamente.
-
Q: ¿Puedo desinstalar Rope Hero 3 Mod Apkpure si no me gusta?
-
A: Sí, puede desinstalar Rope Hero 3 Mod Apkpure si no le gusta. Puede desinstalar el juego como cualquier otra aplicación en su dispositivo. Puede ir a la configuración del dispositivo, luego aplicaciones, luego Rope Hero 3 Mod Apkpure, luego desinstalar. También puede eliminar el archivo apk de su carpeta de descargas si lo desea.
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Descargar Conseguir Sobre l Mod Men Apk.md b/spaces/Benson/text-generation/Examples/Descargar Conseguir Sobre l Mod Men Apk.md
deleted file mode 100644
index 3fe276edef5cdb10616fdba7a0f3a714bfb16844..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descargar Conseguir Sobre l Mod Men Apk.md
+++ /dev/null
@@ -1,67 +0,0 @@
-
-
Descargar Cómo superarlo Mod Menu Apk: Una guía para los usuarios de Android
-
Si usted está buscando una manera de mejorar su experiencia de juego con Getting Over It with Bennett Foddy, es posible que desee intentar descargar e instalar un archivo apk menú mod. Un archivo apk menú mod es una versión modificada del juego original que le permite acceder a varias características y opciones que no están disponibles en la versión oficial. En este artículo, vamos a explicar lo que Cómo Obtener Más Con Bennett Foddy es, lo que es un archivo apk menú mod es, y cómo descargar e instalar sobre él menú mod apk en su dispositivo Android.
Getting Over It with Bennett Foddy es un juego de escalada que fue lanzado en 2017 por Bennett Foddy, un desarrollador de juegos independiente y profesor de la Universidad de Nueva York. El juego está inspirado en un juego B de 2002 llamado Sexy Hiking, creado por Jazzuo. El juego te reta a subir una montaña usando solo un martillo y una olla, mientras escuchas los comentarios filosóficos del propio Foddy. El juego es conocido por su alto nivel de dificultad, ya que cualquier error puede causar que pierdas todo tu progreso y caigas de nuevo al fondo. El juego tampoco tiene puntos de control, ningún sistema de guardado, y ningún objetivo final, a excepción de llegar a la cima de la montaña.
-
El juego ha recibido críticas mixtas de críticos y jugadores por igual, pero también ha ganado un seguimiento de culto y una gran base de fans. El juego ha sido elogiado por su jugabilidad única, su narración humorística y su gratificante sensación de logro. Sin embargo, también ha sido criticado por su diseño frustrante, su falta de accesibilidad y su potencial para inducir el abandono de la rabia. El juego ha aparecido en muchos videos en línea, transmisiones, memes y parodias, así como en varios eventos de speedrunning. El juego también ha ganado varios premios, como el Nuovo Award en el Festival de Juegos Independientes en 2018.
-
¿Qué es un menú mod apk?
-
-
Un archivo apk menú mod puede ofrecer varios beneficios para los jugadores. Por ejemplo, puede permitirle disfrutar de más contenido, mejorar su rendimiento, personalizar su experiencia o superar desafíos. Sin embargo, un archivo apk menú mod también puede plantear algunos riesgos para su dispositivo y su cuenta. Por ejemplo, puede contener malware o virus que pueden dañar tu dispositivo o robar tus datos. También puede violar los términos de servicio o los derechos de propiedad intelectual de la aplicación o desarrollador de juegos. Por lo tanto, es importante ser cuidadoso y responsable cuando se utiliza un archivo apk menú mod. Siempre debe descargarlo de una fuente confiable, escanearlo en busca de virus y hacer una copia de seguridad de sus datos antes de instalarlo. También debe respetar los derechos y deseos de la aplicación o desarrollador de juegos, y evitar su uso con fines ilegales o poco éticos. Algunos ejemplos de archivos apk menú mod para juegos populares son GTA 5 Mod Menu Apk, PUBG Mobile Mod Menu Apk, y entre nosotros Mod Menu Apk.
Cómo descargar e instalar sobre él mod menú apk en Android?
-
Si desea tratar de conseguir más de él menú mod apk en su dispositivo Android, tendrá que seguir estos pasos:
-
-
Descargar el archivo apk de una fuente confiable. Usted puede buscar para conseguir más de él menú mod apk en Google o cualquier otro motor de búsqueda, o se puede utilizar un enlace proporcionado por un sitio web de confianza o un amigo. Asegúrese de que el archivo es compatible con su dispositivo y su versión de Android. El tamaño del archivo debe ser de alrededor de 100 MB.
-
Habilitar fuentes desconocidas en su dispositivo. Esto le permitirá instalar aplicaciones o juegos que no son de Google Play Store. Para hacer esto, vaya a Configuración > Seguridad > Fuentes desconocidas y conéctelo. También es posible que necesite conceder permiso a su navegador o administrador de archivos para instalar aplicaciones.
-
-
Iniciar y utilizar el menú mod en el juego. Abra el juego desde el cajón de la aplicación o la pantalla de inicio. Deberías ver un nuevo icono o botón que diga "Mod Menu" o algo similar. Toque en él y verá una lista de características u opciones que puede habilitar o deshabilitar. Por ejemplo, puedes activar la invencibilidad, teletransportación, hackeo de velocidad, control de gravedad o monedas ilimitadas. También puede ajustar la configuración del menú mod según su preferencia.
-
-
Felicidades! Usted ha descargado e instalado con éxito sobre él mod menú apk en su dispositivo Android. Ahora puedes disfrutar del juego con más diversión y menos frustración.
-
Conclusión
-
Superarlo con Bennett Foddy es un juego desafiante y gratificante que pone a prueba tu paciencia y habilidad. Sin embargo, si desea darle vida a su juego o superar algunos obstáculos, puede intentar usar un archivo apk menú mod. Un archivo apk menú mod es una versión modificada del juego que le da acceso a varias características y opciones que no están disponibles en la versión oficial. Sin embargo, también debe ser consciente de los riesgos y responsabilidades que vienen con el uso de un archivo apk menú mod. Solo debe descargarlo de una fuente confiable, escanearlo en busca de virus, realizar copias de seguridad de sus datos y respetar los derechos del desarrollador.
-
-
Si usted está interesado en la descarga y la instalación de conseguir sobre ella mod menú apk en su dispositivo Android, puede seguir los pasos que hemos descrito en este artículo. Esperamos que esta guía haya sido útil e informativa para usted. Si tiene alguna pregunta o comentario, no dude en dejar un comentario a continuación. ¡Gracias por leer!
-
Preguntas frecuentes
-
¿Cuáles son algunas de las características de conseguir sobre él mod menú apk?
-
Algunas características de conseguir sobre ella mod menú apk son:
-
-
Invencibilidad: Puedes evitar que te caigas o te lastimes por cualquier obstáculo.
-
Teletransportación: Puedes moverte a cualquier lugar del mapa al instante.
-
-
Control de gravedad: Puede cambiar el nivel de gravedad del juego.
-
Monedas ilimitadas: Puedes obtener monedas ilimitadas para comprar artículos o desbloquear logros.
-
-
¿Está consiguiendo sobre él menú mod apk seguro y legal de usar?
-
La seguridad y la legalidad de superarlo mod menú apk dependen de varios factores, tales como:
-
-
La fuente del archivo apk: Solo debe descargar el archivo apk de una fuente confiable que no contiene malware o virus.
-
El dispositivo y la versión de Android: Usted debe asegurarse de que el archivo apk es compatible con su dispositivo y su versión de Android.
-
La copia de seguridad y la seguridad: Usted debe copia de seguridad de sus datos antes de instalar el archivo apk y habilitar fuentes desconocidas a su propio riesgo.
-
Los términos de servicio y derechos de propiedad intelectual: Usted debe respetar los derechos y deseos de la aplicación o desarrollador de juegos y evitar el uso del archivo apk para fines ilegales o poco éticos.
-
-
Cómo actualizar sobre él mod menú apk?
-
Para actualizar Para obtener sobre ella mod menú apk, tendrá que seguir estos pasos:
-
-
Compruebe la última versión del archivo apk. Usted puede hacer esto visitando el sitio web o el enlace donde se descargó el archivo apk, o mediante la búsqueda de conseguir sobre ella mod menú apk en Google o cualquier otro motor de búsqueda. También puede comprobar el número de versión y la fecha del archivo apk en el almacenamiento de su dispositivo.
-
Descargar la última versión del archivo apk de una fuente de buena reputación. Asegúrese de que el archivo es compatible con su dispositivo y su versión de Android. El tamaño del archivo debe ser de alrededor de 100 MB.
-
Desinstalar la versión anterior del archivo apk en su dispositivo. Para hacer esto, vaya a Configuración > Aplicaciones > Superarlo > Desinstalar y confirmar su acción. También puede que necesite eliminar la caché y los datos de la aplicación.
-
-
Iniciar y utilizar el menú mod en el juego. Sigue los mismos pasos que antes para acceder y personalizar las características u opciones del menú mod del juego.
-
-
Eso es todo! Se ha actualizado con éxito conseguir sobre él menú mod apk en su dispositivo Android. Disfruta jugando el juego con las últimas características y opciones.
-
Cómo desinstalar conseguir sobre él mod menú apk?
-
Si desea desinstalar conseguir sobre él menú mod apk desde su dispositivo Android, tendrá que seguir estos pasos:
-
-
Ir a Configuración > Aplicaciones > Superarlo > Desinstalar y confirmar su acción. Esto eliminará la aplicación y el menú mod de su dispositivo.
-
Eliminar el archivo apk de almacenamiento de su dispositivo. Busque el archivo descargado en el almacenamiento de su dispositivo y toque en él. Puede ver un mensaje de advertencia que dice "Este tipo de archivo puede dañar su dispositivo". Ignórelo y toque en "Eliminar" o "Aceptar".
-
Restaurar los datos si usted ha hecho una copia de seguridad antes de instalar el archivo apk. Puede usar un servicio en la nube, un cable USB o una aplicación de terceros para transferir sus datos a su dispositivo.
-
-
Eso es todo! Usted ha desinstalado con éxito conseguir sobre él menú mod apk desde su dispositivo Android. Ahora puedes jugar el juego sin modificaciones o mejoras.
-
¿Dónde puedo encontrar más información y soporte para superarlo mod menú apk?
-
Si necesita más información o soporte para superarlo mod menú apk, puede probar estas fuentes:
-
-
El sitio web o el enlace donde descargó el archivo apk. Puede encontrar algunas preguntas frecuentes, tutoriales, comentarios o detalles de contacto allí.
-
El sitio web oficial o cuentas de redes sociales de Bennett Foddy, el desarrollador de Getting Over It with Bennett Foddy. Puede encontrar algunas noticias, actualizaciones, consejos o comentarios allí.
-
Los foros o comunidades en línea de Cómo superarlo con los fans o jugadores de Bennett Foddy. Usted puede encontrar algunas discusiones, sugerencias, experiencias, o ayuda allí.
-
64aa2da5cf
-
-
\ No newline at end of file
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/configprovider.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/configprovider.py
deleted file mode 100644
index 6f1d6cf0e71f4d3d4204b4256451c9de4f9bb966..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/configprovider.py
+++ /dev/null
@@ -1,838 +0,0 @@
-# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-"""This module contains the inteface for controlling how configuration
-is loaded.
-"""
-import copy
-import logging
-import os
-
-from botocore import utils
-
-logger = logging.getLogger(__name__)
-
-
-#: A default dictionary that maps the logical names for session variables
-#: to the specific environment variables and configuration file names
-#: that contain the values for these variables.
-#: When creating a new Session object, you can pass in your own dictionary
-#: to remap the logical names or to add new logical names. You can then
-#: get the current value for these variables by using the
-#: ``get_config_variable`` method of the :class:`botocore.session.Session`
-#: class.
-#: These form the keys of the dictionary. The values in the dictionary
-#: are tuples of (, , ,
-#: ).
-#: The conversion func is a function that takes the configuration value
-#: as an argument and returns the converted value. If this value is
-#: None, then the configuration value is returned unmodified. This
-#: conversion function can be used to type convert config values to
-#: values other than the default values of strings.
-#: The ``profile`` and ``config_file`` variables should always have a
-#: None value for the first entry in the tuple because it doesn't make
-#: sense to look inside the config file for the location of the config
-#: file or for the default profile to use.
-#: The ``config_name`` is the name to look for in the configuration file,
-#: the ``env var`` is the OS environment variable (``os.environ``) to
-#: use, and ``default_value`` is the value to use if no value is otherwise
-#: found.
-BOTOCORE_DEFAUT_SESSION_VARIABLES = {
- # logical: config_file, env_var, default_value, conversion_func
- 'profile': (None, ['AWS_DEFAULT_PROFILE', 'AWS_PROFILE'], None, None),
- 'region': ('region', 'AWS_DEFAULT_REGION', None, None),
- 'data_path': ('data_path', 'AWS_DATA_PATH', None, None),
- 'config_file': (None, 'AWS_CONFIG_FILE', '~/.aws/config', None),
- 'ca_bundle': ('ca_bundle', 'AWS_CA_BUNDLE', None, None),
- 'api_versions': ('api_versions', None, {}, None),
- # This is the shared credentials file amongst sdks.
- 'credentials_file': (
- None,
- 'AWS_SHARED_CREDENTIALS_FILE',
- '~/.aws/credentials',
- None,
- ),
- # These variables only exist in the config file.
- # This is the number of seconds until we time out a request to
- # the instance metadata service.
- 'metadata_service_timeout': (
- 'metadata_service_timeout',
- 'AWS_METADATA_SERVICE_TIMEOUT',
- 1,
- int,
- ),
- # This is the number of request attempts we make until we give
- # up trying to retrieve data from the instance metadata service.
- 'metadata_service_num_attempts': (
- 'metadata_service_num_attempts',
- 'AWS_METADATA_SERVICE_NUM_ATTEMPTS',
- 1,
- int,
- ),
- 'ec2_metadata_service_endpoint': (
- 'ec2_metadata_service_endpoint',
- 'AWS_EC2_METADATA_SERVICE_ENDPOINT',
- None,
- None,
- ),
- 'ec2_metadata_service_endpoint_mode': (
- 'ec2_metadata_service_endpoint_mode',
- 'AWS_EC2_METADATA_SERVICE_ENDPOINT_MODE',
- None,
- None,
- ),
- 'imds_use_ipv6': (
- 'imds_use_ipv6',
- 'AWS_IMDS_USE_IPV6',
- False,
- utils.ensure_boolean,
- ),
- 'use_dualstack_endpoint': (
- 'use_dualstack_endpoint',
- 'AWS_USE_DUALSTACK_ENDPOINT',
- None,
- utils.ensure_boolean,
- ),
- 'use_fips_endpoint': (
- 'use_fips_endpoint',
- 'AWS_USE_FIPS_ENDPOINT',
- None,
- utils.ensure_boolean,
- ),
- 'parameter_validation': ('parameter_validation', None, True, None),
- # Client side monitoring configurations.
- # Note: These configurations are considered internal to botocore.
- # Do not use them until publicly documented.
- 'csm_enabled': (
- 'csm_enabled',
- 'AWS_CSM_ENABLED',
- False,
- utils.ensure_boolean,
- ),
- 'csm_host': ('csm_host', 'AWS_CSM_HOST', '127.0.0.1', None),
- 'csm_port': ('csm_port', 'AWS_CSM_PORT', 31000, int),
- 'csm_client_id': ('csm_client_id', 'AWS_CSM_CLIENT_ID', '', None),
- # Endpoint discovery configuration
- 'endpoint_discovery_enabled': (
- 'endpoint_discovery_enabled',
- 'AWS_ENDPOINT_DISCOVERY_ENABLED',
- 'auto',
- None,
- ),
- 'sts_regional_endpoints': (
- 'sts_regional_endpoints',
- 'AWS_STS_REGIONAL_ENDPOINTS',
- 'legacy',
- None,
- ),
- 'retry_mode': ('retry_mode', 'AWS_RETRY_MODE', 'legacy', None),
- 'defaults_mode': ('defaults_mode', 'AWS_DEFAULTS_MODE', 'legacy', None),
- # We can't have a default here for v1 because we need to defer to
- # whatever the defaults are in _retry.json.
- 'max_attempts': ('max_attempts', 'AWS_MAX_ATTEMPTS', None, int),
-}
-# A mapping for the s3 specific configuration vars. These are the configuration
-# vars that typically go in the s3 section of the config file. This mapping
-# follows the same schema as the previous session variable mapping.
-DEFAULT_S3_CONFIG_VARS = {
- 'addressing_style': (('s3', 'addressing_style'), None, None, None),
- 'use_accelerate_endpoint': (
- ('s3', 'use_accelerate_endpoint'),
- None,
- None,
- utils.ensure_boolean,
- ),
- 'use_dualstack_endpoint': (
- ('s3', 'use_dualstack_endpoint'),
- None,
- None,
- utils.ensure_boolean,
- ),
- 'payload_signing_enabled': (
- ('s3', 'payload_signing_enabled'),
- None,
- None,
- utils.ensure_boolean,
- ),
- 'use_arn_region': (
- ['s3_use_arn_region', ('s3', 'use_arn_region')],
- 'AWS_S3_USE_ARN_REGION',
- None,
- utils.ensure_boolean,
- ),
- 'us_east_1_regional_endpoint': (
- [
- 's3_us_east_1_regional_endpoint',
- ('s3', 'us_east_1_regional_endpoint'),
- ],
- 'AWS_S3_US_EAST_1_REGIONAL_ENDPOINT',
- None,
- None,
- ),
- 's3_disable_multiregion_access_points': (
- ('s3', 's3_disable_multiregion_access_points'),
- 'AWS_S3_DISABLE_MULTIREGION_ACCESS_POINTS',
- None,
- utils.ensure_boolean,
- ),
-}
-# A mapping for the proxy specific configuration vars. These are
-# used to configure how botocore interacts with proxy setups while
-# sending requests.
-DEFAULT_PROXIES_CONFIG_VARS = {
- 'proxy_ca_bundle': ('proxy_ca_bundle', None, None, None),
- 'proxy_client_cert': ('proxy_client_cert', None, None, None),
- 'proxy_use_forwarding_for_https': (
- 'proxy_use_forwarding_for_https',
- None,
- None,
- utils.normalize_boolean,
- ),
-}
-
-
-def create_botocore_default_config_mapping(session):
- chain_builder = ConfigChainFactory(session=session)
- config_mapping = _create_config_chain_mapping(
- chain_builder, BOTOCORE_DEFAUT_SESSION_VARIABLES
- )
- config_mapping['s3'] = SectionConfigProvider(
- 's3',
- session,
- _create_config_chain_mapping(chain_builder, DEFAULT_S3_CONFIG_VARS),
- )
- config_mapping['proxies_config'] = SectionConfigProvider(
- 'proxies_config',
- session,
- _create_config_chain_mapping(
- chain_builder, DEFAULT_PROXIES_CONFIG_VARS
- ),
- )
- return config_mapping
-
-
-def _create_config_chain_mapping(chain_builder, config_variables):
- mapping = {}
- for logical_name, config in config_variables.items():
- mapping[logical_name] = chain_builder.create_config_chain(
- instance_name=logical_name,
- env_var_names=config[1],
- config_property_names=config[0],
- default=config[2],
- conversion_func=config[3],
- )
- return mapping
-
-
-class DefaultConfigResolver:
- def __init__(self, default_config_data):
- self._base_default_config = default_config_data['base']
- self._modes = default_config_data['modes']
- self._resolved_default_configurations = {}
-
- def _resolve_default_values_by_mode(self, mode):
- default_config = self._base_default_config.copy()
- modifications = self._modes.get(mode)
-
- for config_var in modifications:
- default_value = default_config[config_var]
- modification_dict = modifications[config_var]
- modification = list(modification_dict.keys())[0]
- modification_value = modification_dict[modification]
- if modification == 'multiply':
- default_value *= modification_value
- elif modification == 'add':
- default_value += modification_value
- elif modification == 'override':
- default_value = modification_value
- default_config[config_var] = default_value
- return default_config
-
- def get_default_modes(self):
- default_modes = ['legacy', 'auto']
- default_modes.extend(self._modes.keys())
- return default_modes
-
- def get_default_config_values(self, mode):
- if mode not in self._resolved_default_configurations:
- defaults = self._resolve_default_values_by_mode(mode)
- self._resolved_default_configurations[mode] = defaults
- return self._resolved_default_configurations[mode]
-
-
-class ConfigChainFactory:
- """Factory class to create our most common configuration chain case.
-
- This is a convenience class to construct configuration chains that follow
- our most common pattern. This is to prevent ordering them incorrectly,
- and to make the config chain construction more readable.
- """
-
- def __init__(self, session, environ=None):
- """Initialize a ConfigChainFactory.
-
- :type session: :class:`botocore.session.Session`
- :param session: This is the session that should be used to look up
- values from the config file.
-
- :type environ: dict
- :param environ: A mapping to use for environment variables. If this
- is not provided it will default to use os.environ.
- """
- self._session = session
- if environ is None:
- environ = os.environ
- self._environ = environ
-
- def create_config_chain(
- self,
- instance_name=None,
- env_var_names=None,
- config_property_names=None,
- default=None,
- conversion_func=None,
- ):
- """Build a config chain following the standard botocore pattern.
-
- In botocore most of our config chains follow the the precendence:
- session_instance_variables, environment, config_file, default_value.
-
- This is a convenience function for creating a chain that follow
- that precendence.
-
- :type instance_name: str
- :param instance_name: This indicates what session instance variable
- corresponds to this config value. If it is None it will not be
- added to the chain.
-
- :type env_var_names: str or list of str or None
- :param env_var_names: One or more environment variable names to
- search for this value. They are searched in order. If it is None
- it will not be added to the chain.
-
- :type config_property_names: str/tuple or list of str/tuple or None
- :param config_property_names: One of more strings or tuples
- representing the name of the key in the config file for this
- config option. They are searched in order. If it is None it will
- not be added to the chain.
-
- :type default: Any
- :param default: Any constant value to be returned.
-
- :type conversion_func: None or callable
- :param conversion_func: If this value is None then it has no effect on
- the return type. Otherwise, it is treated as a function that will
- conversion_func our provided type.
-
- :rvalue: ConfigChain
- :returns: A ConfigChain that resolves in the order env_var_names ->
- config_property_name -> default. Any values that were none are
- omitted form the chain.
- """
- providers = []
- if instance_name is not None:
- providers.append(
- InstanceVarProvider(
- instance_var=instance_name, session=self._session
- )
- )
- if env_var_names is not None:
- providers.extend(self._get_env_providers(env_var_names))
- if config_property_names is not None:
- providers.extend(
- self._get_scoped_config_providers(config_property_names)
- )
- if default is not None:
- providers.append(ConstantProvider(value=default))
-
- return ChainProvider(
- providers=providers,
- conversion_func=conversion_func,
- )
-
- def _get_env_providers(self, env_var_names):
- env_var_providers = []
- if not isinstance(env_var_names, list):
- env_var_names = [env_var_names]
- for env_var_name in env_var_names:
- env_var_providers.append(
- EnvironmentProvider(name=env_var_name, env=self._environ)
- )
- return env_var_providers
-
- def _get_scoped_config_providers(self, config_property_names):
- scoped_config_providers = []
- if not isinstance(config_property_names, list):
- config_property_names = [config_property_names]
- for config_property_name in config_property_names:
- scoped_config_providers.append(
- ScopedConfigProvider(
- config_var_name=config_property_name,
- session=self._session,
- )
- )
- return scoped_config_providers
-
-
-class ConfigValueStore:
- """The ConfigValueStore object stores configuration values."""
-
- def __init__(self, mapping=None):
- """Initialize a ConfigValueStore.
-
- :type mapping: dict
- :param mapping: The mapping parameter is a map of string to a subclass
- of BaseProvider. When a config variable is asked for via the
- get_config_variable method, the corresponding provider will be
- invoked to load the value.
- """
- self._overrides = {}
- self._mapping = {}
- if mapping is not None:
- for logical_name, provider in mapping.items():
- self.set_config_provider(logical_name, provider)
-
- def __deepcopy__(self, memo):
- return ConfigValueStore(copy.deepcopy(self._mapping, memo))
-
- def get_config_variable(self, logical_name):
- """
- Retrieve the value associeated with the specified logical_name
- from the corresponding provider. If no value is found None will
- be returned.
-
- :type logical_name: str
- :param logical_name: The logical name of the session variable
- you want to retrieve. This name will be mapped to the
- appropriate environment variable name for this session as
- well as the appropriate config file entry.
-
- :returns: value of variable or None if not defined.
- """
- if logical_name in self._overrides:
- return self._overrides[logical_name]
- if logical_name not in self._mapping:
- return None
- provider = self._mapping[logical_name]
- return provider.provide()
-
- def get_config_provider(self, logical_name):
- """
- Retrieve the provider associated with the specified logical_name.
- If no provider is found None will be returned.
-
- :type logical_name: str
- :param logical_name: The logical name of the session variable
- you want to retrieve. This name will be mapped to the
- appropriate environment variable name for this session as
- well as the appropriate config file entry.
-
- :returns: configuration provider or None if not defined.
- """
- if (
- logical_name in self._overrides
- or logical_name not in self._mapping
- ):
- return None
- provider = self._mapping[logical_name]
- return provider
-
- def set_config_variable(self, logical_name, value):
- """Set a configuration variable to a specific value.
-
- By using this method, you can override the normal lookup
- process used in ``get_config_variable`` by explicitly setting
- a value. Subsequent calls to ``get_config_variable`` will
- use the ``value``. This gives you per-session specific
- configuration values.
-
- ::
- >>> # Assume logical name 'foo' maps to env var 'FOO'
- >>> os.environ['FOO'] = 'myvalue'
- >>> s.get_config_variable('foo')
- 'myvalue'
- >>> s.set_config_variable('foo', 'othervalue')
- >>> s.get_config_variable('foo')
- 'othervalue'
-
- :type logical_name: str
- :param logical_name: The logical name of the session variable
- you want to set. These are the keys in ``SESSION_VARIABLES``.
-
- :param value: The value to associate with the config variable.
- """
- self._overrides[logical_name] = value
-
- def clear_config_variable(self, logical_name):
- """Remove an override config variable from the session.
-
- :type logical_name: str
- :param logical_name: The name of the parameter to clear the override
- value from.
- """
- self._overrides.pop(logical_name, None)
-
- def set_config_provider(self, logical_name, provider):
- """Set the provider for a config value.
-
- This provides control over how a particular configuration value is
- loaded. This replaces the provider for ``logical_name`` with the new
- ``provider``.
-
- :type logical_name: str
- :param logical_name: The name of the config value to change the config
- provider for.
-
- :type provider: :class:`botocore.configprovider.BaseProvider`
- :param provider: The new provider that should be responsible for
- providing a value for the config named ``logical_name``.
- """
- self._mapping[logical_name] = provider
-
-
-class SmartDefaultsConfigStoreFactory:
- def __init__(self, default_config_resolver, imds_region_provider):
- self._default_config_resolver = default_config_resolver
- self._imds_region_provider = imds_region_provider
- # Initializing _instance_metadata_region as None so we
- # can fetch region in a lazy fashion only when needed.
- self._instance_metadata_region = None
-
- def merge_smart_defaults(self, config_store, mode, region_name):
- if mode == 'auto':
- mode = self.resolve_auto_mode(region_name)
- default_configs = (
- self._default_config_resolver.get_default_config_values(mode)
- )
- for config_var in default_configs:
- config_value = default_configs[config_var]
- method = getattr(self, f'_set_{config_var}', None)
- if method:
- method(config_store, config_value)
-
- def resolve_auto_mode(self, region_name):
- current_region = None
- if os.environ.get('AWS_EXECUTION_ENV'):
- default_region = os.environ.get('AWS_DEFAULT_REGION')
- current_region = os.environ.get('AWS_REGION', default_region)
- if not current_region:
- if self._instance_metadata_region:
- current_region = self._instance_metadata_region
- else:
- try:
- current_region = self._imds_region_provider.provide()
- self._instance_metadata_region = current_region
- except Exception:
- pass
-
- if current_region:
- if region_name == current_region:
- return 'in-region'
- else:
- return 'cross-region'
- return 'standard'
-
- def _update_provider(self, config_store, variable, value):
- provider = config_store.get_config_provider(variable)
- default_provider = ConstantProvider(value)
- if isinstance(provider, ChainProvider):
- provider.set_default_provider(default_provider)
- return
- elif isinstance(provider, BaseProvider):
- default_provider = ChainProvider(
- providers=[provider, default_provider]
- )
- config_store.set_config_provider(variable, default_provider)
-
- def _update_section_provider(
- self, config_store, section_name, variable, value
- ):
- section_provider = config_store.get_config_provider(section_name)
- section_provider.set_default_provider(
- variable, ConstantProvider(value)
- )
-
- def _set_retryMode(self, config_store, value):
- self._update_provider(config_store, 'retry_mode', value)
-
- def _set_stsRegionalEndpoints(self, config_store, value):
- self._update_provider(config_store, 'sts_regional_endpoints', value)
-
- def _set_s3UsEast1RegionalEndpoints(self, config_store, value):
- self._update_section_provider(
- config_store, 's3', 'us_east_1_regional_endpoint', value
- )
-
- def _set_connectTimeoutInMillis(self, config_store, value):
- self._update_provider(config_store, 'connect_timeout', value / 1000)
-
-
-class BaseProvider:
- """Base class for configuration value providers.
-
- A configuration provider has some method of providing a configuration
- value.
- """
-
- def provide(self):
- """Provide a config value."""
- raise NotImplementedError('provide')
-
-
-class ChainProvider(BaseProvider):
- """This provider wraps one or more other providers.
-
- Each provider in the chain is called, the first one returning a non-None
- value is then returned.
- """
-
- def __init__(self, providers=None, conversion_func=None):
- """Initalize a ChainProvider.
-
- :type providers: list
- :param providers: The initial list of providers to check for values
- when invoked.
-
- :type conversion_func: None or callable
- :param conversion_func: If this value is None then it has no affect on
- the return type. Otherwise, it is treated as a function that will
- transform provided value.
- """
- if providers is None:
- providers = []
- self._providers = providers
- self._conversion_func = conversion_func
-
- def __deepcopy__(self, memo):
- return ChainProvider(
- copy.deepcopy(self._providers, memo), self._conversion_func
- )
-
- def provide(self):
- """Provide the value from the first provider to return non-None.
-
- Each provider in the chain has its provide method called. The first
- one in the chain to return a non-None value is the returned from the
- ChainProvider. When no non-None value is found, None is returned.
- """
- for provider in self._providers:
- value = provider.provide()
- if value is not None:
- return self._convert_type(value)
- return None
-
- def set_default_provider(self, default_provider):
- if self._providers and isinstance(
- self._providers[-1], ConstantProvider
- ):
- self._providers[-1] = default_provider
- else:
- self._providers.append(default_provider)
-
- num_of_constants = sum(
- isinstance(provider, ConstantProvider)
- for provider in self._providers
- )
- if num_of_constants > 1:
- logger.info(
- 'ChainProvider object contains multiple '
- 'instances of ConstantProvider objects'
- )
-
- def _convert_type(self, value):
- if self._conversion_func is not None:
- return self._conversion_func(value)
- return value
-
- def __repr__(self):
- return '[%s]' % ', '.join([str(p) for p in self._providers])
-
-
-class InstanceVarProvider(BaseProvider):
- """This class loads config values from the session instance vars."""
-
- def __init__(self, instance_var, session):
- """Initialize InstanceVarProvider.
-
- :type instance_var: str
- :param instance_var: The instance variable to load from the session.
-
- :type session: :class:`botocore.session.Session`
- :param session: The botocore session to get the loaded configuration
- file variables from.
- """
- self._instance_var = instance_var
- self._session = session
-
- def __deepcopy__(self, memo):
- return InstanceVarProvider(
- copy.deepcopy(self._instance_var, memo), self._session
- )
-
- def provide(self):
- """Provide a config value from the session instance vars."""
- instance_vars = self._session.instance_variables()
- value = instance_vars.get(self._instance_var)
- return value
-
- def __repr__(self):
- return 'InstanceVarProvider(instance_var={}, session={})'.format(
- self._instance_var,
- self._session,
- )
-
-
-class ScopedConfigProvider(BaseProvider):
- def __init__(self, config_var_name, session):
- """Initialize ScopedConfigProvider.
-
- :type config_var_name: str or tuple
- :param config_var_name: The name of the config variable to load from
- the configuration file. If the value is a tuple, it must only
- consist of two items, where the first item represents the section
- and the second item represents the config var name in the section.
-
- :type session: :class:`botocore.session.Session`
- :param session: The botocore session to get the loaded configuration
- file variables from.
- """
- self._config_var_name = config_var_name
- self._session = session
-
- def __deepcopy__(self, memo):
- return ScopedConfigProvider(
- copy.deepcopy(self._config_var_name, memo), self._session
- )
-
- def provide(self):
- """Provide a value from a config file property."""
- scoped_config = self._session.get_scoped_config()
- if isinstance(self._config_var_name, tuple):
- section_config = scoped_config.get(self._config_var_name[0])
- if not isinstance(section_config, dict):
- return None
- return section_config.get(self._config_var_name[1])
- return scoped_config.get(self._config_var_name)
-
- def __repr__(self):
- return 'ScopedConfigProvider(config_var_name={}, session={})'.format(
- self._config_var_name,
- self._session,
- )
-
-
-class EnvironmentProvider(BaseProvider):
- """This class loads config values from environment variables."""
-
- def __init__(self, name, env):
- """Initialize with the keys in the dictionary to check.
-
- :type name: str
- :param name: The key with that name will be loaded and returned.
-
- :type env: dict
- :param env: Environment variables dictionary to get variables from.
- """
- self._name = name
- self._env = env
-
- def __deepcopy__(self, memo):
- return EnvironmentProvider(
- copy.deepcopy(self._name, memo), copy.deepcopy(self._env, memo)
- )
-
- def provide(self):
- """Provide a config value from a source dictionary."""
- if self._name in self._env:
- return self._env[self._name]
- return None
-
- def __repr__(self):
- return f'EnvironmentProvider(name={self._name}, env={self._env})'
-
-
-class SectionConfigProvider(BaseProvider):
- """Provides a dictionary from a section in the scoped config
-
- This is useful for retrieving scoped config variables (i.e. s3) that have
- their own set of config variables and resolving logic.
- """
-
- def __init__(self, section_name, session, override_providers=None):
- self._section_name = section_name
- self._session = session
- self._scoped_config_provider = ScopedConfigProvider(
- self._section_name, self._session
- )
- self._override_providers = override_providers
- if self._override_providers is None:
- self._override_providers = {}
-
- def __deepcopy__(self, memo):
- return SectionConfigProvider(
- copy.deepcopy(self._section_name, memo),
- self._session,
- copy.deepcopy(self._override_providers, memo),
- )
-
- def provide(self):
- section_config = self._scoped_config_provider.provide()
- if section_config and not isinstance(section_config, dict):
- logger.debug(
- "The %s config key is not a dictionary type, "
- "ignoring its value of: %s",
- self._section_name,
- section_config,
- )
- return None
- for section_config_var, provider in self._override_providers.items():
- provider_val = provider.provide()
- if provider_val is not None:
- if section_config is None:
- section_config = {}
- section_config[section_config_var] = provider_val
- return section_config
-
- def set_default_provider(self, key, default_provider):
- provider = self._override_providers.get(key)
- if isinstance(provider, ChainProvider):
- provider.set_default_provider(default_provider)
- return
- elif isinstance(provider, BaseProvider):
- default_provider = ChainProvider(
- providers=[provider, default_provider]
- )
- self._override_providers[key] = default_provider
-
- def __repr__(self):
- return (
- f'SectionConfigProvider(section_name={self._section_name}, '
- f'session={self._session}, '
- f'override_providers={self._override_providers})'
- )
-
-
-class ConstantProvider(BaseProvider):
- """This provider provides a constant value."""
-
- def __init__(self, value):
- self._value = value
-
- def __deepcopy__(self, memo):
- return ConstantProvider(copy.deepcopy(self._value, memo))
-
- def provide(self):
- """Provide the constant value given during initialization."""
- return self._value
-
- def __repr__(self):
- return 'ConstantProvider(value=%s)' % self._value
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/charsetprober.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/charsetprober.py
deleted file mode 100644
index a103ca11356606402c03b320a4fcdb8635051623..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/chardet/charsetprober.py
+++ /dev/null
@@ -1,147 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is Mozilla Universal charset detector code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 2001
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-# Mark Pilgrim - port to Python
-# Shy Shalom - original C code
-#
-# 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 #########################
-
-import logging
-import re
-from typing import Optional, Union
-
-from .enums import LanguageFilter, ProbingState
-
-INTERNATIONAL_WORDS_PATTERN = re.compile(
- b"[a-zA-Z]*[\x80-\xFF]+[a-zA-Z]*[^a-zA-Z\x80-\xFF]?"
-)
-
-
-class CharSetProber:
-
- SHORTCUT_THRESHOLD = 0.95
-
- def __init__(self, lang_filter: LanguageFilter = LanguageFilter.NONE) -> None:
- self._state = ProbingState.DETECTING
- self.active = True
- self.lang_filter = lang_filter
- self.logger = logging.getLogger(__name__)
-
- def reset(self) -> None:
- self._state = ProbingState.DETECTING
-
- @property
- def charset_name(self) -> Optional[str]:
- return None
-
- @property
- def language(self) -> Optional[str]:
- raise NotImplementedError
-
- def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState:
- raise NotImplementedError
-
- @property
- def state(self) -> ProbingState:
- return self._state
-
- def get_confidence(self) -> float:
- return 0.0
-
- @staticmethod
- def filter_high_byte_only(buf: Union[bytes, bytearray]) -> bytes:
- buf = re.sub(b"([\x00-\x7F])+", b" ", buf)
- return buf
-
- @staticmethod
- def filter_international_words(buf: Union[bytes, bytearray]) -> bytearray:
- """
- We define three types of bytes:
- alphabet: english alphabets [a-zA-Z]
- international: international characters [\x80-\xFF]
- marker: everything else [^a-zA-Z\x80-\xFF]
- The input buffer can be thought to contain a series of words delimited
- by markers. This function works to filter all words that contain at
- least one international character. All contiguous sequences of markers
- are replaced by a single space ascii character.
- This filter applies to all scripts which do not use English characters.
- """
- filtered = bytearray()
-
- # This regex expression filters out only words that have at-least one
- # international character. The word may include one marker character at
- # the end.
- words = INTERNATIONAL_WORDS_PATTERN.findall(buf)
-
- for word in words:
- filtered.extend(word[:-1])
-
- # If the last character in the word is a marker, replace it with a
- # space as markers shouldn't affect our analysis (they are used
- # similarly across all languages and may thus have similar
- # frequencies).
- last_char = word[-1:]
- if not last_char.isalpha() and last_char < b"\x80":
- last_char = b" "
- filtered.extend(last_char)
-
- return filtered
-
- @staticmethod
- def remove_xml_tags(buf: Union[bytes, bytearray]) -> bytes:
- """
- Returns a copy of ``buf`` that retains only the sequences of English
- alphabet and high byte characters that are not between <> characters.
- This filter can be applied to all scripts which contain both English
- characters and extended ASCII characters, but is currently only used by
- ``Latin1Prober``.
- """
- filtered = bytearray()
- in_tag = False
- prev = 0
- buf = memoryview(buf).cast("c")
-
- for curr, buf_char in enumerate(buf):
- # Check if we're coming out of or entering an XML tag
-
- # https://github.com/python/typeshed/issues/8182
- if buf_char == b">": # type: ignore[comparison-overlap]
- prev = curr + 1
- in_tag = False
- # https://github.com/python/typeshed/issues/8182
- elif buf_char == b"<": # type: ignore[comparison-overlap]
- if curr > prev and not in_tag:
- # Keep everything after last non-extended-ASCII,
- # non-alphabetic character
- filtered.extend(buf[prev:curr])
- # Output a space to delimit stretch we kept
- filtered.extend(b" ")
- in_tag = True
-
- # If we're not in a tag...
- if not in_tag:
- # Keep everything after last non-extended-ASCII, non-alphabetic
- # character
- filtered.extend(buf[prev:])
-
- return filtered
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/ordered_set.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/ordered_set.py
deleted file mode 100644
index 14876000de895a609d5b9f3de39c3c8fc44ef1fc..0000000000000000000000000000000000000000
--- a/spaces/Big-Web/MMSD/env/Lib/site-packages/setuptools/_vendor/ordered_set.py
+++ /dev/null
@@ -1,488 +0,0 @@
-"""
-An OrderedSet is a custom MutableSet that remembers its order, so that every
-entry has an index that can be looked up.
-
-Based on a recipe originally posted to ActiveState Recipes by Raymond Hettiger,
-and released under the MIT license.
-"""
-import itertools as it
-from collections import deque
-
-try:
- # Python 3
- from collections.abc import MutableSet, Sequence
-except ImportError:
- # Python 2.7
- from collections import MutableSet, Sequence
-
-SLICE_ALL = slice(None)
-__version__ = "3.1"
-
-
-def is_iterable(obj):
- """
- Are we being asked to look up a list of things, instead of a single thing?
- We check for the `__iter__` attribute so that this can cover types that
- don't have to be known by this module, such as NumPy arrays.
-
- Strings, however, should be considered as atomic values to look up, not
- iterables. The same goes for tuples, since they are immutable and therefore
- valid entries.
-
- We don't need to check for the Python 2 `unicode` type, because it doesn't
- have an `__iter__` attribute anyway.
- """
- return (
- hasattr(obj, "__iter__")
- and not isinstance(obj, str)
- and not isinstance(obj, tuple)
- )
-
-
-class OrderedSet(MutableSet, Sequence):
- """
- An OrderedSet is a custom MutableSet that remembers its order, so that
- every entry has an index that can be looked up.
-
- Example:
- >>> OrderedSet([1, 1, 2, 3, 2])
- OrderedSet([1, 2, 3])
- """
-
- def __init__(self, iterable=None):
- self.items = []
- self.map = {}
- if iterable is not None:
- self |= iterable
-
- def __len__(self):
- """
- Returns the number of unique elements in the ordered set
-
- Example:
- >>> len(OrderedSet([]))
- 0
- >>> len(OrderedSet([1, 2]))
- 2
- """
- return len(self.items)
-
- def __getitem__(self, index):
- """
- Get the item at a given index.
-
- If `index` is a slice, you will get back that slice of items, as a
- new OrderedSet.
-
- If `index` is a list or a similar iterable, you'll get a list of
- items corresponding to those indices. This is similar to NumPy's
- "fancy indexing". The result is not an OrderedSet because you may ask
- for duplicate indices, and the number of elements returned should be
- the number of elements asked for.
-
- Example:
- >>> oset = OrderedSet([1, 2, 3])
- >>> oset[1]
- 2
- """
- if isinstance(index, slice) and index == SLICE_ALL:
- return self.copy()
- elif is_iterable(index):
- return [self.items[i] for i in index]
- elif hasattr(index, "__index__") or isinstance(index, slice):
- result = self.items[index]
- if isinstance(result, list):
- return self.__class__(result)
- else:
- return result
- else:
- raise TypeError("Don't know how to index an OrderedSet by %r" % index)
-
- def copy(self):
- """
- Return a shallow copy of this object.
-
- Example:
- >>> this = OrderedSet([1, 2, 3])
- >>> other = this.copy()
- >>> this == other
- True
- >>> this is other
- False
- """
- return self.__class__(self)
-
- def __getstate__(self):
- if len(self) == 0:
- # The state can't be an empty list.
- # We need to return a truthy value, or else __setstate__ won't be run.
- #
- # This could have been done more gracefully by always putting the state
- # in a tuple, but this way is backwards- and forwards- compatible with
- # previous versions of OrderedSet.
- return (None,)
- else:
- return list(self)
-
- def __setstate__(self, state):
- if state == (None,):
- self.__init__([])
- else:
- self.__init__(state)
-
- def __contains__(self, key):
- """
- Test if the item is in this ordered set
-
- Example:
- >>> 1 in OrderedSet([1, 3, 2])
- True
- >>> 5 in OrderedSet([1, 3, 2])
- False
- """
- return key in self.map
-
- def add(self, key):
- """
- Add `key` as an item to this OrderedSet, then return its index.
-
- If `key` is already in the OrderedSet, return the index it already
- had.
-
- Example:
- >>> oset = OrderedSet()
- >>> oset.append(3)
- 0
- >>> print(oset)
- OrderedSet([3])
- """
- if key not in self.map:
- self.map[key] = len(self.items)
- self.items.append(key)
- return self.map[key]
-
- append = add
-
- def update(self, sequence):
- """
- Update the set with the given iterable sequence, then return the index
- of the last element inserted.
-
- Example:
- >>> oset = OrderedSet([1, 2, 3])
- >>> oset.update([3, 1, 5, 1, 4])
- 4
- >>> print(oset)
- OrderedSet([1, 2, 3, 5, 4])
- """
- item_index = None
- try:
- for item in sequence:
- item_index = self.add(item)
- except TypeError:
- raise ValueError(
- "Argument needs to be an iterable, got %s" % type(sequence)
- )
- return item_index
-
- def index(self, key):
- """
- Get the index of a given entry, raising an IndexError if it's not
- present.
-
- `key` can be an iterable of entries that is not a string, in which case
- this returns a list of indices.
-
- Example:
- >>> oset = OrderedSet([1, 2, 3])
- >>> oset.index(2)
- 1
- """
- if is_iterable(key):
- return [self.index(subkey) for subkey in key]
- return self.map[key]
-
- # Provide some compatibility with pd.Index
- get_loc = index
- get_indexer = index
-
- def pop(self):
- """
- Remove and return the last element from the set.
-
- Raises KeyError if the set is empty.
-
- Example:
- >>> oset = OrderedSet([1, 2, 3])
- >>> oset.pop()
- 3
- """
- if not self.items:
- raise KeyError("Set is empty")
-
- elem = self.items[-1]
- del self.items[-1]
- del self.map[elem]
- return elem
-
- def discard(self, key):
- """
- Remove an element. Do not raise an exception if absent.
-
- The MutableSet mixin uses this to implement the .remove() method, which
- *does* raise an error when asked to remove a non-existent item.
-
- Example:
- >>> oset = OrderedSet([1, 2, 3])
- >>> oset.discard(2)
- >>> print(oset)
- OrderedSet([1, 3])
- >>> oset.discard(2)
- >>> print(oset)
- OrderedSet([1, 3])
- """
- if key in self:
- i = self.map[key]
- del self.items[i]
- del self.map[key]
- for k, v in self.map.items():
- if v >= i:
- self.map[k] = v - 1
-
- def clear(self):
- """
- Remove all items from this OrderedSet.
- """
- del self.items[:]
- self.map.clear()
-
- def __iter__(self):
- """
- Example:
- >>> list(iter(OrderedSet([1, 2, 3])))
- [1, 2, 3]
- """
- return iter(self.items)
-
- def __reversed__(self):
- """
- Example:
- >>> list(reversed(OrderedSet([1, 2, 3])))
- [3, 2, 1]
- """
- return reversed(self.items)
-
- def __repr__(self):
- if not self:
- return "%s()" % (self.__class__.__name__,)
- return "%s(%r)" % (self.__class__.__name__, list(self))
-
- def __eq__(self, other):
- """
- Returns true if the containers have the same items. If `other` is a
- Sequence, then order is checked, otherwise it is ignored.
-
- Example:
- >>> oset = OrderedSet([1, 3, 2])
- >>> oset == [1, 3, 2]
- True
- >>> oset == [1, 2, 3]
- False
- >>> oset == [2, 3]
- False
- >>> oset == OrderedSet([3, 2, 1])
- False
- """
- # In Python 2 deque is not a Sequence, so treat it as one for
- # consistent behavior with Python 3.
- if isinstance(other, (Sequence, deque)):
- # Check that this OrderedSet contains the same elements, in the
- # same order, as the other object.
- return list(self) == list(other)
- try:
- other_as_set = set(other)
- except TypeError:
- # If `other` can't be converted into a set, it's not equal.
- return False
- else:
- return set(self) == other_as_set
-
- def union(self, *sets):
- """
- Combines all unique items.
- Each items order is defined by its first appearance.
-
- Example:
- >>> oset = OrderedSet.union(OrderedSet([3, 1, 4, 1, 5]), [1, 3], [2, 0])
- >>> print(oset)
- OrderedSet([3, 1, 4, 5, 2, 0])
- >>> oset.union([8, 9])
- OrderedSet([3, 1, 4, 5, 2, 0, 8, 9])
- >>> oset | {10}
- OrderedSet([3, 1, 4, 5, 2, 0, 10])
- """
- cls = self.__class__ if isinstance(self, OrderedSet) else OrderedSet
- containers = map(list, it.chain([self], sets))
- items = it.chain.from_iterable(containers)
- return cls(items)
-
- def __and__(self, other):
- # the parent implementation of this is backwards
- return self.intersection(other)
-
- def intersection(self, *sets):
- """
- Returns elements in common between all sets. Order is defined only
- by the first set.
-
- Example:
- >>> oset = OrderedSet.intersection(OrderedSet([0, 1, 2, 3]), [1, 2, 3])
- >>> print(oset)
- OrderedSet([1, 2, 3])
- >>> oset.intersection([2, 4, 5], [1, 2, 3, 4])
- OrderedSet([2])
- >>> oset.intersection()
- OrderedSet([1, 2, 3])
- """
- cls = self.__class__ if isinstance(self, OrderedSet) else OrderedSet
- if sets:
- common = set.intersection(*map(set, sets))
- items = (item for item in self if item in common)
- else:
- items = self
- return cls(items)
-
- def difference(self, *sets):
- """
- Returns all elements that are in this set but not the others.
-
- Example:
- >>> OrderedSet([1, 2, 3]).difference(OrderedSet([2]))
- OrderedSet([1, 3])
- >>> OrderedSet([1, 2, 3]).difference(OrderedSet([2]), OrderedSet([3]))
- OrderedSet([1])
- >>> OrderedSet([1, 2, 3]) - OrderedSet([2])
- OrderedSet([1, 3])
- >>> OrderedSet([1, 2, 3]).difference()
- OrderedSet([1, 2, 3])
- """
- cls = self.__class__
- if sets:
- other = set.union(*map(set, sets))
- items = (item for item in self if item not in other)
- else:
- items = self
- return cls(items)
-
- def issubset(self, other):
- """
- Report whether another set contains this set.
-
- Example:
- >>> OrderedSet([1, 2, 3]).issubset({1, 2})
- False
- >>> OrderedSet([1, 2, 3]).issubset({1, 2, 3, 4})
- True
- >>> OrderedSet([1, 2, 3]).issubset({1, 4, 3, 5})
- False
- """
- if len(self) > len(other): # Fast check for obvious cases
- return False
- return all(item in other for item in self)
-
- def issuperset(self, other):
- """
- Report whether this set contains another set.
-
- Example:
- >>> OrderedSet([1, 2]).issuperset([1, 2, 3])
- False
- >>> OrderedSet([1, 2, 3, 4]).issuperset({1, 2, 3})
- True
- >>> OrderedSet([1, 4, 3, 5]).issuperset({1, 2, 3})
- False
- """
- if len(self) < len(other): # Fast check for obvious cases
- return False
- return all(item in self for item in other)
-
- def symmetric_difference(self, other):
- """
- Return the symmetric difference of two OrderedSets as a new set.
- That is, the new set will contain all elements that are in exactly
- one of the sets.
-
- Their order will be preserved, with elements from `self` preceding
- elements from `other`.
-
- Example:
- >>> this = OrderedSet([1, 4, 3, 5, 7])
- >>> other = OrderedSet([9, 7, 1, 3, 2])
- >>> this.symmetric_difference(other)
- OrderedSet([4, 5, 9, 2])
- """
- cls = self.__class__ if isinstance(self, OrderedSet) else OrderedSet
- diff1 = cls(self).difference(other)
- diff2 = cls(other).difference(self)
- return diff1.union(diff2)
-
- def _update_items(self, items):
- """
- Replace the 'items' list of this OrderedSet with a new one, updating
- self.map accordingly.
- """
- self.items = items
- self.map = {item: idx for (idx, item) in enumerate(items)}
-
- def difference_update(self, *sets):
- """
- Update this OrderedSet to remove items from one or more other sets.
-
- Example:
- >>> this = OrderedSet([1, 2, 3])
- >>> this.difference_update(OrderedSet([2, 4]))
- >>> print(this)
- OrderedSet([1, 3])
-
- >>> this = OrderedSet([1, 2, 3, 4, 5])
- >>> this.difference_update(OrderedSet([2, 4]), OrderedSet([1, 4, 6]))
- >>> print(this)
- OrderedSet([3, 5])
- """
- items_to_remove = set()
- for other in sets:
- items_to_remove |= set(other)
- self._update_items([item for item in self.items if item not in items_to_remove])
-
- def intersection_update(self, other):
- """
- Update this OrderedSet to keep only items in another set, preserving
- their order in this set.
-
- Example:
- >>> this = OrderedSet([1, 4, 3, 5, 7])
- >>> other = OrderedSet([9, 7, 1, 3, 2])
- >>> this.intersection_update(other)
- >>> print(this)
- OrderedSet([1, 3, 7])
- """
- other = set(other)
- self._update_items([item for item in self.items if item in other])
-
- def symmetric_difference_update(self, other):
- """
- Update this OrderedSet to remove items from another set, then
- add items from the other set that were not present in this set.
-
- Example:
- >>> this = OrderedSet([1, 4, 3, 5, 7])
- >>> other = OrderedSet([9, 7, 1, 3, 2])
- >>> this.symmetric_difference_update(other)
- >>> print(this)
- OrderedSet([4, 5, 9, 2])
- """
- items_to_add = [item for item in other if item not in self]
- items_to_remove = set(other)
- self._update_items(
- [item for item in self.items if item not in items_to_remove] + items_to_add
- )
diff --git a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/models/ban/model_cfgs.py b/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/models/ban/model_cfgs.py
deleted file mode 100644
index e0b8abcbb0963cb73a2c4bdaf953387a6a2c8521..0000000000000000000000000000000000000000
--- a/spaces/CVPR/Dual-Key_Backdoor_Attacks/openvqa/openvqa/models/ban/model_cfgs.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# --------------------------------------------------------
-# OpenVQA
-# Written by Zhenwei Shao https://github.com/ParadoxZW
-# --------------------------------------------------------
-
-from openvqa.core.base_cfgs import BaseCfgs
-
-
-class Cfgs(BaseCfgs):
- def __init__(self):
- super(Cfgs, self).__init__()
-
- self.IMG_FEAT_SIZE = 2048
- self.GLIMPSE = 8
- self.HIDDEN_SIZE = 1024
- self.K_TIMES = 3
- self.BA_HIDDEN_SIZE = self.K_TIMES * self.HIDDEN_SIZE
- self.DROPOUT_R = 0.2
- self.CLASSIFER_DROPOUT_R = 0.5
- self.FLAT_OUT_SIZE = 2048
diff --git a/spaces/CVPR/LIVE/thrust/thrust/detail/allocator/temporary_allocator.h b/spaces/CVPR/LIVE/thrust/thrust/detail/allocator/temporary_allocator.h
deleted file mode 100644
index 4d2ac429c9b32a05e6470e5e38def2c1ada43efa..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/detail/allocator/temporary_allocator.h
+++ /dev/null
@@ -1,85 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-#pragma once
-
-#include
-#include
-#include
-#include
-#include
-#include
-
-namespace thrust
-{
-namespace detail
-{
-
-
-// XXX the pointer parameter given to tagged_allocator should be related to
-// the type of the expression get_temporary_buffer(system, n).first
-// without decltype, compromise on pointer
-template
- class temporary_allocator
- : public thrust::detail::tagged_allocator<
- T, System, thrust::pointer
- >
-{
- private:
- typedef thrust::detail::tagged_allocator<
- T, System, thrust::pointer
- > super_t;
-
- System &m_system;
-
- public:
- typedef typename super_t::pointer pointer;
- typedef typename super_t::size_type size_type;
-
- inline __host__ __device__
- temporary_allocator(const temporary_allocator &other) :
- super_t(),
- m_system(other.m_system)
- {}
-
- inline __host__ __device__
- explicit temporary_allocator(thrust::execution_policy &system) :
- super_t(),
- m_system(thrust::detail::derived_cast(system))
- {}
-
- __host__ __device__
- pointer allocate(size_type cnt);
-
- __host__ __device__
- void deallocate(pointer p, size_type n);
-
- __host__ __device__
- inline System &system()
- {
- return m_system;
- } // end system()
-
- private:
- typedef thrust::pair pointer_and_size;
-}; // end temporary_allocator
-
-
-} // end detail
-} // end thrust
-
-#include
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/iterator/permutation_iterator.h b/spaces/CVPR/LIVE/thrust/thrust/iterator/permutation_iterator.h
deleted file mode 100644
index 73827040abd1000ccb616c18a6fdb0d7d8484ccd..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/iterator/permutation_iterator.h
+++ /dev/null
@@ -1,217 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-
-/*! \file thrust/iterator/permutation_iterator.h
- * \brief An iterator which performs a gather or scatter operation when dereferenced
- */
-
-/*
- * (C) Copyright Toon Knapen 2001.
- * (C) Copyright David Abrahams 2003.
- * (C) Copyright Roland Richter 2003.
- *
- * Distributed under the Boost Software License, Version 1.0.
- * (See accompanying NOTICE file for the complete license)
- *
- * For more information, see http://www.boost.org
- */
-
-#pragma once
-
-#include
-#include
-#include
-#include
-#include
-
-namespace thrust
-{
-
-
-/*! \addtogroup iterators
- * \{
- */
-
-/*! \addtogroup fancyiterator Fancy Iterators
- * \ingroup iterators
- * \{
- */
-
-/*! \p permutation_iterator is an iterator which represents a pointer into a
- * reordered view of a given range. \p permutation_iterator is an imprecise name;
- * the reordered view need not be a strict permutation. This iterator is useful
- * for fusing a scatter or gather operation with other algorithms.
- *
- * This iterator takes two arguments:
- *
- * - an iterator to the range \c V on which the "permutation" will be applied
- * - the reindexing scheme that defines how the elements of \c V will be permuted.
- *
- * Note that \p permutation_iterator is not limited to strict permutations of the
- * given range \c V. The distance between begin and end of the reindexing iterators
- * is allowed to be smaller compared to the size of the range \c V, in which case
- * the \p permutation_iterator only provides a "permutation" of a subrange of \c V.
- * The indices neither need to be unique. In this same context, it must be noted
- * that the past-the-end \p permutation_iterator is completely defined by means of
- * the past-the-end iterator to the indices.
- *
- * The following code snippet demonstrates how to create a \p permutation_iterator
- * which represents a reordering of the contents of a \p device_vector.
- *
- * \code
- * #include
- * #include
- * ...
- * thrust::device_vector values(4);
- * values[0] = 10.0f;
- * values[1] = 20.0f;
- * values[2] = 30.0f;
- * values[3] = 40.0f;
- * values[4] = 50.0f;
- * values[5] = 60.0f;
- * values[6] = 70.0f;
- * values[7] = 80.0f;
- *
- * thrust::device_vector indices(4);
- * indices[0] = 2;
- * indices[1] = 6;
- * indices[2] = 1;
- * indices[3] = 3;
- *
- * typedef thrust::device_vector::iterator ElementIterator;
- * typedef thrust::device_vector::iterator IndexIterator;
- *
- * thrust::permutation_iterator iter(values.begin(), indices.begin());
- *
- * *iter; // returns 30.0f;
- * iter[0]; // returns 30.0f;
- * iter[1]; // returns 70.0f;
- * iter[2]; // returns 20.0f;
- * iter[3]; // returns 40.0f;
- *
- * // iter[4] is an out-of-bounds error
- *
- * *iter = -1.0f; // sets values[2] to -1.0f;
- * iter[0] = -1.0f; // sets values[2] to -1.0f;
- * iter[1] = -1.0f; // sets values[6] to -1.0f;
- * iter[2] = -1.0f; // sets values[1] to -1.0f;
- * iter[3] = -1.0f; // sets values[3] to -1.0f;
- *
- * // values is now {10, -1, -1, -1, 50, 60, -1, 80}
- * \endcode
- *
- * \see make_permutation_iterator
- */
-template
- class permutation_iterator
- : public thrust::detail::permutation_iterator_base<
- ElementIterator,
- IndexIterator
- >::type
-{
- /*! \cond
- */
- private:
- typedef typename detail::permutation_iterator_base::type super_t;
-
- friend class thrust::iterator_core_access;
- /*! \endcond
- */
-
- public:
- /*! Null constructor calls the null constructor of this \p permutation_iterator's
- * element iterator.
- */
- __host__ __device__
- permutation_iterator()
- : m_element_iterator() {}
-
- /*! Constructor accepts an \c ElementIterator into a range of values and an
- * \c IndexIterator into a range of indices defining the indexing scheme on the
- * values.
- *
- * \param x An \c ElementIterator pointing this \p permutation_iterator's range of values.
- * \param y An \c IndexIterator pointing to an indexing scheme to use on \p x.
- */
- __host__ __device__
- explicit permutation_iterator(ElementIterator x, IndexIterator y)
- : super_t(y), m_element_iterator(x) {}
-
- /*! Copy constructor accepts a related \p permutation_iterator.
- * \param r A compatible \p permutation_iterator to copy from.
- */
- template
- __host__ __device__
- permutation_iterator(permutation_iterator const &r
- // XXX remove these guards when we have static_assert
- , typename detail::enable_if_convertible::type* = 0
- , typename detail::enable_if_convertible::type* = 0
- )
- : super_t(r.base()), m_element_iterator(r.m_element_iterator)
- {}
-
- /*! \cond
- */
- private:
- // MSVC 2013 and 2015 incorrectly warning about returning a reference to
- // a local/temporary here.
- // See goo.gl/LELTNp
- THRUST_DISABLE_MSVC_WARNING_BEGIN(4172)
-
- __thrust_exec_check_disable__
- __host__ __device__
- typename super_t::reference dereference() const
- {
- return *(m_element_iterator + *this->base());
- }
-
- THRUST_DISABLE_MSVC_WARNING_END(4172)
-
- // make friends for the copy constructor
- template friend class permutation_iterator;
-
- ElementIterator m_element_iterator;
- /*! \endcond
- */
-}; // end permutation_iterator
-
-
-/*! \p make_permutation_iterator creates a \p permutation_iterator
- * from an \c ElementIterator pointing to a range of elements to "permute"
- * and an \c IndexIterator pointing to a range of indices defining an indexing
- * scheme on the values.
- *
- * \param e An \c ElementIterator pointing to a range of values.
- * \param i An \c IndexIterator pointing to an indexing scheme to use on \p e.
- * \return A new \p permutation_iterator which permutes the range \p e by \p i.
- * \see permutation_iterator
- */
-template
-__host__ __device__
-permutation_iterator make_permutation_iterator(ElementIterator e, IndexIterator i)
-{
- return permutation_iterator(e,i);
-}
-
-/*! \} // end fancyiterators
- */
-
-/*! \} // end iterators
- */
-
-} // end thrust
-
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/equal.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/equal.h
deleted file mode 100644
index 6b02e33b857eb9d7efae5747cc9bcbde6b8c0b17..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/equal.h
+++ /dev/null
@@ -1,44 +0,0 @@
-/*
- * Copyright 2008-2013 NVIDIA Corporation
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a equal 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.
- */
-
-#pragma once
-
-#include
-
-// the purpose of this header is to #include the equal.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch equal
-
-#include
-
-// SCons can't see through the #defines below to figure out what this header
-// includes, so we fake it out by specifying all possible files we might end up
-// including inside an #if 0.
-#if 0
-#include
-#include
-#include
-#include
-#endif
-
-#define __THRUST_HOST_SYSTEM_EQUAL_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/equal.h>
-#include __THRUST_HOST_SYSTEM_EQUAL_HEADER
-#undef __THRUST_HOST_SYSTEM_EQUAL_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_EQUAL_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/equal.h>
-#include __THRUST_DEVICE_SYSTEM_EQUAL_HEADER
-#undef __THRUST_DEVICE_SYSTEM_EQUAL_HEADER
-
diff --git a/spaces/CVPR/WALT/mmdet/models/detectors/rpn.py b/spaces/CVPR/WALT/mmdet/models/detectors/rpn.py
deleted file mode 100644
index 1a77294549d1c3dc7821063c3f3d08bb331fbe59..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/models/detectors/rpn.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import mmcv
-from mmcv.image import tensor2imgs
-
-from mmdet.core import bbox_mapping
-from ..builder import DETECTORS, build_backbone, build_head, build_neck
-from .base import BaseDetector
-
-
-@DETECTORS.register_module()
-class RPN(BaseDetector):
- """Implementation of Region Proposal Network."""
-
- def __init__(self,
- backbone,
- neck,
- rpn_head,
- train_cfg,
- test_cfg,
- pretrained=None):
- super(RPN, self).__init__()
- self.backbone = build_backbone(backbone)
- self.neck = build_neck(neck) if neck is not None else None
- rpn_train_cfg = train_cfg.rpn if train_cfg is not None else None
- rpn_head.update(train_cfg=rpn_train_cfg)
- rpn_head.update(test_cfg=test_cfg.rpn)
- self.rpn_head = build_head(rpn_head)
- self.train_cfg = train_cfg
- self.test_cfg = test_cfg
- self.init_weights(pretrained=pretrained)
-
- def init_weights(self, pretrained=None):
- """Initialize the weights in detector.
-
- Args:
- pretrained (str, optional): Path to pre-trained weights.
- Defaults to None.
- """
- super(RPN, self).init_weights(pretrained)
- self.backbone.init_weights(pretrained=pretrained)
- if self.with_neck:
- self.neck.init_weights()
- self.rpn_head.init_weights()
-
- def extract_feat(self, img):
- """Extract features.
-
- Args:
- img (torch.Tensor): Image tensor with shape (n, c, h ,w).
-
- Returns:
- list[torch.Tensor]: Multi-level features that may have
- different resolutions.
- """
- x = self.backbone(img)
- if self.with_neck:
- x = self.neck(x)
- return x
-
- def forward_dummy(self, img):
- """Dummy forward function."""
- x = self.extract_feat(img)
- rpn_outs = self.rpn_head(x)
- return rpn_outs
-
- def forward_train(self,
- img,
- img_metas,
- gt_bboxes=None,
- gt_bboxes_ignore=None):
- """
- Args:
- img (Tensor): Input images of shape (N, C, H, W).
- Typically these should be mean centered and std scaled.
- img_metas (list[dict]): A List of image info dict where each dict
- has: 'img_shape', 'scale_factor', 'flip', and may also contain
- 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
- For details on the values of these keys see
- :class:`mmdet.datasets.pipelines.Collect`.
- gt_bboxes (list[Tensor]): Each item are the truth boxes for each
- image in [tl_x, tl_y, br_x, br_y] format.
- gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
- boxes can be ignored when computing the loss.
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
- if (isinstance(self.train_cfg.rpn, dict)
- and self.train_cfg.rpn.get('debug', False)):
- self.rpn_head.debug_imgs = tensor2imgs(img)
-
- x = self.extract_feat(img)
- losses = self.rpn_head.forward_train(x, img_metas, gt_bboxes, None,
- gt_bboxes_ignore)
- return losses
-
- def simple_test(self, img, img_metas, rescale=False):
- """Test function without test time augmentation.
-
- Args:
- imgs (list[torch.Tensor]): List of multiple images
- img_metas (list[dict]): List of image information.
- rescale (bool, optional): Whether to rescale the results.
- Defaults to False.
-
- Returns:
- list[np.ndarray]: proposals
- """
- x = self.extract_feat(img)
- proposal_list = self.rpn_head.simple_test_rpn(x, img_metas)
- if rescale:
- for proposals, meta in zip(proposal_list, img_metas):
- proposals[:, :4] /= proposals.new_tensor(meta['scale_factor'])
-
- return [proposal.cpu().numpy() for proposal in proposal_list]
-
- def aug_test(self, imgs, img_metas, rescale=False):
- """Test function with test time augmentation.
-
- Args:
- imgs (list[torch.Tensor]): List of multiple images
- img_metas (list[dict]): List of image information.
- rescale (bool, optional): Whether to rescale the results.
- Defaults to False.
-
- Returns:
- list[np.ndarray]: proposals
- """
- proposal_list = self.rpn_head.aug_test_rpn(
- self.extract_feats(imgs), img_metas)
- if not rescale:
- for proposals, img_meta in zip(proposal_list, img_metas[0]):
- img_shape = img_meta['img_shape']
- scale_factor = img_meta['scale_factor']
- flip = img_meta['flip']
- flip_direction = img_meta['flip_direction']
- proposals[:, :4] = bbox_mapping(proposals[:, :4], img_shape,
- scale_factor, flip,
- flip_direction)
- return [proposal.cpu().numpy() for proposal in proposal_list]
-
- def show_result(self, data, result, top_k=20, **kwargs):
- """Show RPN proposals on the image.
-
- Args:
- data (str or np.ndarray): Image filename or loaded image.
- result (Tensor or tuple): The results to draw over `img`
- bbox_result or (bbox_result, segm_result).
- top_k (int): Plot the first k bboxes only
- if set positive. Default: 20
-
- Returns:
- np.ndarray: The image with bboxes drawn on it.
- """
- mmcv.imshow_bboxes(data, result, top_k=top_k)
diff --git a/spaces/CVPR/regionclip-demo/detectron2/structures/image_list.py b/spaces/CVPR/regionclip-demo/detectron2/structures/image_list.py
deleted file mode 100644
index 26e6e49c55e27120ab26b6107cebb6c885f81c38..0000000000000000000000000000000000000000
--- a/spaces/CVPR/regionclip-demo/detectron2/structures/image_list.py
+++ /dev/null
@@ -1,124 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from __future__ import division
-from typing import Any, List, Tuple
-import torch
-from torch import device
-from torch.nn import functional as F
-
-from detectron2.utils.env import TORCH_VERSION
-
-
-def _as_tensor(x: Tuple[int, int]) -> torch.Tensor:
- """
- An equivalent of `torch.as_tensor`, but works under tracing if input
- is a list of tensor. `torch.as_tensor` will record a constant in tracing,
- but this function will use `torch.stack` instead.
- """
- if torch.jit.is_scripting():
- return torch.as_tensor(x)
- if isinstance(x, (list, tuple)) and all([isinstance(t, torch.Tensor) for t in x]):
- return torch.stack(x)
- return torch.as_tensor(x)
-
-
-class ImageList(object):
- """
- Structure that holds a list of images (of possibly
- varying sizes) as a single tensor.
- This works by padding the images to the same size,
- and storing in a field the original sizes of each image
-
- Attributes:
- image_sizes (list[tuple[int, int]]): each tuple is (h, w).
- During tracing, it becomes list[Tensor] instead.
- """
-
- def __init__(self, tensor: torch.Tensor, image_sizes: List[Tuple[int, int]]):
- """
- Arguments:
- tensor (Tensor): of shape (N, H, W) or (N, C_1, ..., C_K, H, W) where K >= 1
- image_sizes (list[tuple[int, int]]): Each tuple is (h, w). It can
- be smaller than (H, W) due to padding.
- """
- self.tensor = tensor
- self.image_sizes = image_sizes
-
- def __len__(self) -> int:
- return len(self.image_sizes)
-
- def __getitem__(self, idx) -> torch.Tensor:
- """
- Access the individual image in its original size.
-
- Args:
- idx: int or slice
-
- Returns:
- Tensor: an image of shape (H, W) or (C_1, ..., C_K, H, W) where K >= 1
- """
- size = self.image_sizes[idx]
- return self.tensor[idx, ..., : size[0], : size[1]]
-
- @torch.jit.unused
- def to(self, *args: Any, **kwargs: Any) -> "ImageList":
- cast_tensor = self.tensor.to(*args, **kwargs)
- return ImageList(cast_tensor, self.image_sizes)
-
- @property
- def device(self) -> device:
- return self.tensor.device
-
- @staticmethod
- def from_tensors(
- tensors: List[torch.Tensor], size_divisibility: int = 0, pad_value: float = 0.0
- ) -> "ImageList":
- """
- Args:
- tensors: a tuple or list of `torch.Tensor`, each of shape (Hi, Wi) or
- (C_1, ..., C_K, Hi, Wi) where K >= 1. The Tensors will be padded
- to the same shape with `pad_value`.
- size_divisibility (int): If `size_divisibility > 0`, add padding to ensure
- the common height and width is divisible by `size_divisibility`.
- This depends on the model and many models need a divisibility of 32.
- pad_value (float): value to pad
-
- Returns:
- an `ImageList`.
- """
- assert len(tensors) > 0
- assert isinstance(tensors, (tuple, list))
- for t in tensors:
- assert isinstance(t, torch.Tensor), type(t)
- assert t.shape[:-2] == tensors[0].shape[:-2], t.shape
-
- image_sizes = [(im.shape[-2], im.shape[-1]) for im in tensors]
- image_sizes_tensor = [_as_tensor(x) for x in image_sizes]
- max_size = torch.stack(image_sizes_tensor).max(0).values
-
- if size_divisibility > 1:
- stride = size_divisibility
- # the last two dims are H,W, both subject to divisibility requirement
- max_size = (max_size + (stride - 1)) // stride * stride
-
- # handle weirdness of scripting and tracing ...
- if torch.jit.is_scripting():
- max_size: List[int] = max_size.to(dtype=torch.long).tolist()
- else:
- # https://github.com/pytorch/pytorch/issues/42448
- if TORCH_VERSION >= (1, 7) and torch.jit.is_tracing():
- image_sizes = image_sizes_tensor
-
- if len(tensors) == 1:
- # This seems slightly (2%) faster.
- # TODO: check whether it's faster for multiple images as well
- image_size = image_sizes[0]
- padding_size = [0, max_size[-1] - image_size[1], 0, max_size[-2] - image_size[0]]
- batched_imgs = F.pad(tensors[0], padding_size, value=pad_value).unsqueeze_(0)
- else:
- # max_size can be a tensor in tracing mode, therefore convert to list
- batch_shape = [len(tensors)] + list(tensors[0].shape[:-2]) + list(max_size)
- batched_imgs = tensors[0].new_full(batch_shape, pad_value)
- for img, pad_img in zip(tensors, batched_imgs):
- pad_img[..., : img.shape[-2], : img.shape[-1]].copy_(img)
-
- return ImageList(batched_imgs.contiguous(), image_sizes)
diff --git a/spaces/Carlosito16/HXM-summarization/helper_function.py b/spaces/Carlosito16/HXM-summarization/helper_function.py
deleted file mode 100644
index 33158987a284c9f14619a5d7bb30bac9e3bdeadb..0000000000000000000000000000000000000000
--- a/spaces/Carlosito16/HXM-summarization/helper_function.py
+++ /dev/null
@@ -1,140 +0,0 @@
-def get_all_models():
- with open("requirements.txt") as f:
- content = f.readlines()
- models = []
- for line in content:
- if "huggingface.co" in line:
- models.append(line.split("/")[4])
- return models
-
-
-def clear_input():
- return ("", "")
-
-
-def camembert_generate_summary(article_text):
- inputs = cmb_tokenizer([article_text], padding="max_length", truncation=True,
- max_length=512,
- return_tensors="pt")
- input_ids = inputs.input_ids.to(device)
- attention_mask = inputs.attention_mask.to(device)
- output = cmb_model.generate(input_ids, attention_mask=attention_mask)
- return cmb_tokenizer.decode(output[0], skip_special_tokens=True)
-
-
-def t5_generate_summary(article_text):
- input_ids = t5_tokenizer(
- [WHITESPACE_HANDLER(article_text)],
- return_tensors="pt",
- padding="max_length",
- truncation=True,
- max_length=512)["input_ids"]
-
- output_ids = t5_model.generate(
- input_ids=input_ids,
- max_length=84,
- no_repeat_ngram_size=2,
- num_beams=4
- )[0]
-
- output = t5_tokenizer.decode(
- output_ids,
- skip_special_tokens=True,
- clean_up_tokenization_spaces=False
- )
-
- return output
-
-def summarizer(dropdown_model, article_text):
- """
- Ruturs a summarized version from the full article based on the selected pretrained-model
- """
-
- if dropdown_model == 'camembert':
- summary = camembert_generate_summary(article_text)
-
- elif dropdown_model == 'T5':
- summary = t5_generate_summary(article_text)
-
- return summary
-
-
-class keyWordExtractor():
-
- def __init__(self,
- article_text,
- similarity_model,
- n_gram = 1,
- top_n = 3,
- french_stopwords = None,
- ner= None,
- ):
- self.article_text = article_text
- self.french_stopwords = french_stopwords
- self.candidates = self.count_vectorizer(n_gram)
- self.noun_candidates, self.proper_noun_candidates = self.slice_only_noun_token(ner, self.candidates)
- self.top_n_keywords = self.top_n_extractor(similarity_model, top_n)
-
- def count_vectorizer(self, n_gram):
- n_gram_range = (n_gram, n_gram)
- # Extract candidate words/phrases
- count = CountVectorizer(ngram_range=n_gram_range,
- stop_words = self.french_stopwords).fit([self.article_text]) #Main change
- candidates = count.get_feature_names_out()
-
- return candidates
-
- def slice_only_noun_token(self, ner, token_list):
- """
- Given the tokenized list, this function returns only the "NOUN" token
- Args:
- ner (spacy): The NER class to detect the `token.pos_`
- token_list (list): List of token from the full article
-
- Returns:
- slice_list (list): List of token containing only "NOUN" part of speech
- """
-
- noun_slice_list = []
- proper_noun_slice_list = []
- for word_idx in range(len(token_list)):
- doc = ner(token_list[word_idx])
-
- for token in doc:
- if token.pos_ == 'NOUN':
- noun_slice_list.append(token.text)
- elif token.pos_ == 'PROPN':
- proper_noun_slice_list.append(token.text)
-
- return noun_slice_list, proper_noun_slice_list
-
- def top_n_extractor(self, model, top_n):
- doc_embedding = model.encode([self.article_text])
- candidate_embeddings = model.encode(self.noun_candidates)
- distances = cosine_similarity(doc_embedding, candidate_embeddings)
- keywords = [self.noun_candidates[index] for index in distances.argsort()[0][-top_n:]]
-
- return keywords
-
-
-
-def extract_top_3(article):
- nlp = spacy.load("fr_core_news_md")
- # model = SentenceTransformer("dangvantuan/sentence-camembert-large") #
-
- a= keyWordExtractor(article,
- n_gram = 1,
- top_n = 3,
- ner = nlp,
- similarity_model = model)
- keyword = ", ".join(a.top_n_keywords) #to return ['a' , 'b'] >> "a, b"
- proper_nonuns = ", ".join(a.proper_noun_candidates)
-
- return keyword, proper_nonuns
-
-
-def runall(dropdown_model, article_text):
- summary = summarizer(dropdown_model, article_text)
- keywords, proper_n = extract_top_3(article_text)
-
- return summary, keywords, proper_n
\ No newline at end of file
diff --git a/spaces/CelesteChen/GPT-token/app.py b/spaces/CelesteChen/GPT-token/app.py
deleted file mode 100644
index 81b1756762c7e287ad088d538bb9104e34122337..0000000000000000000000000000000000000000
--- a/spaces/CelesteChen/GPT-token/app.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import os
-
-import gradio as gr
-import tiktoken
-
-
-os.environ["TIKTOKEN_CACHE_DIR"] = ""
-
-encoding = tiktoken.get_encoding("cl100k_base")
-
-enc_mapping = {
- "gpt-4": "cl100k_base", "gpt-3.5-turbo(chatgpt)": "cl100k_base", "text-embedding-ada-002": "cl100k_base", "Codex": "p50k_base", "text-davinci-002": "p50k_base", "text-davinci-003": "p50k_base", "gpt3": "r50k_base", "gpt2": "r50k_base"
-}
-
-
-def tokenize(text, model):
- encoding = tiktoken.get_encoding(enc_mapping[model])
- enc = encoding.encode(text)
- return len(enc), enc
-
-
-title = "GPT Token"
-description = "This demo uses tiktoken to calculate the token number needed for GPT models."
-
-iface = gr.Interface(fn=tokenize,
- inputs=[
- gr.Textbox(label="input sequence"),
- gr.Radio(choices=["gpt-4", "gpt-3.5-turbo(chatgpt)", "text-embedding-ada-002", "Codex", "text-davinci-002", "text-davinci-003", "gpt3", "gpt2"], value="gpt-3.5-turbo(chatgpt)", label="model")],
- outputs=[gr.Textbox(label="token number"), gr.Textbox(
- label="token sequence")],
- title=title,
- description=description,
- allow_flagging='never')
-iface.launch(share=False, debug=True)
diff --git a/spaces/Clebersla/RVC_V2_Huggingface_Version/run.sh b/spaces/Clebersla/RVC_V2_Huggingface_Version/run.sh
deleted file mode 100644
index 31d0be013006e9130e7b3b24d479272dd01c8acd..0000000000000000000000000000000000000000
--- a/spaces/Clebersla/RVC_V2_Huggingface_Version/run.sh
+++ /dev/null
@@ -1,16 +0,0 @@
-# Install Debian packages
-sudo apt-get update
-sudo apt-get install -qq -y build-essential ffmpeg aria2
-
-# Upgrade pip and setuptools
-pip install --upgrade pip
-pip install --upgrade setuptools
-
-# Install wheel package (built-package format for Python)
-pip install wheel
-
-# Install Python packages using pip
-pip install -r requirements.txt
-
-# Run application locally at http://127.0.0.1:7860
-python app.py
diff --git a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/utils.py b/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/utils.py
deleted file mode 100644
index 5b1d79a812ab3db034cf817583281c006b11b90a..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/ContourNet-ABI/maskrcnn_benchmark/modeling/utils.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-"""
-Miscellaneous utility functions
-"""
-
-import torch
-
-
-def cat(tensors, dim=0):
- """
- Efficient version of torch.cat that avoids a copy if there is only a single element in a list
- """
- assert isinstance(tensors, (list, tuple))
- if len(tensors) == 1:
- return tensors[0]
- return torch.cat(tensors, dim)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/feaLib/__init__.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/feaLib/__init__.py
deleted file mode 100644
index ae532cd31b6eb54bdd5778c13989c1475b643db3..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/feaLib/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-"""fontTools.feaLib -- a package for dealing with OpenType feature files."""
-
-# The structure of OpenType feature files is defined here:
-# http://www.adobe.com/devnet/opentype/afdko/topic_feature_file_syntax.html
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I_B_.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I_B_.py
deleted file mode 100644
index 8a6c14c444595508c35bdc6ebace60b4bbbbdaba..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/T_S_I_B_.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from .T_S_I_V_ import table_T_S_I_V_
-
-
-class table_T_S_I_B_(table_T_S_I_V_):
- pass
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_m_o_r_t.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_m_o_r_t.py
deleted file mode 100644
index 261e593e27ffc7fe065b964eea533dc2591fcb1e..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/ttLib/tables/_m_o_r_t.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-# https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6mort.html
-class table__m_o_r_t(BaseTTXConverter):
- pass
diff --git a/spaces/DaleChen/AutoGPT/autogpt/json_utils/json_fix_llm.py b/spaces/DaleChen/AutoGPT/autogpt/json_utils/json_fix_llm.py
deleted file mode 100644
index 869aed125cfb8cd7a69ed02eeb389cc72a3e296b..0000000000000000000000000000000000000000
--- a/spaces/DaleChen/AutoGPT/autogpt/json_utils/json_fix_llm.py
+++ /dev/null
@@ -1,220 +0,0 @@
-"""This module contains functions to fix JSON strings generated by LLM models, such as ChatGPT, using the assistance
-of the ChatGPT API or LLM models."""
-from __future__ import annotations
-
-import contextlib
-import json
-from typing import Any, Dict
-
-from colorama import Fore
-from regex import regex
-
-from autogpt.config import Config
-from autogpt.json_utils.json_fix_general import correct_json
-from autogpt.llm_utils import call_ai_function
-from autogpt.logs import logger
-from autogpt.speech import say_text
-
-JSON_SCHEMA = """
-{
- "command": {
- "name": "command name",
- "args": {
- "arg name": "value"
- }
- },
- "thoughts":
- {
- "text": "thought",
- "reasoning": "reasoning",
- "plan": "- short bulleted\n- list that conveys\n- long-term plan",
- "criticism": "constructive self-criticism",
- "speak": "thoughts summary to say to user"
- }
-}
-"""
-
-CFG = Config()
-
-
-def auto_fix_json(json_string: str, schema: str) -> str:
- """Fix the given JSON string to make it parseable and fully compliant with
- the provided schema using GPT-3.
-
- Args:
- json_string (str): The JSON string to fix.
- schema (str): The schema to use to fix the JSON.
- Returns:
- str: The fixed JSON string.
- """
- # Try to fix the JSON using GPT:
- function_string = "def fix_json(json_string: str, schema:str=None) -> str:"
- args = [f"'''{json_string}'''", f"'''{schema}'''"]
- description_string = (
- "This function takes a JSON string and ensures that it"
- " is parseable and fully compliant with the provided schema. If an object"
- " or field specified in the schema isn't contained within the correct JSON,"
- " it is omitted. The function also escapes any double quotes within JSON"
- " string values to ensure that they are valid. If the JSON string contains"
- " any None or NaN values, they are replaced with null before being parsed."
- )
-
- # If it doesn't already start with a "`", add one:
- if not json_string.startswith("`"):
- json_string = "```json\n" + json_string + "\n```"
- result_string = call_ai_function(
- function_string, args, description_string, model=CFG.fast_llm_model
- )
- logger.debug("------------ JSON FIX ATTEMPT ---------------")
- logger.debug(f"Original JSON: {json_string}")
- logger.debug("-----------")
- logger.debug(f"Fixed JSON: {result_string}")
- logger.debug("----------- END OF FIX ATTEMPT ----------------")
-
- try:
- json.loads(result_string) # just check the validity
- return result_string
- except json.JSONDecodeError: # noqa: E722
- # Get the call stack:
- # import traceback
- # call_stack = traceback.format_exc()
- # print(f"Failed to fix JSON: '{json_string}' "+call_stack)
- return "failed"
-
-
-def fix_json_using_multiple_techniques(assistant_reply: str) -> Dict[Any, Any]:
- """Fix the given JSON string to make it parseable and fully compliant with two techniques.
-
- Args:
- json_string (str): The JSON string to fix.
-
- Returns:
- str: The fixed JSON string.
- """
-
- # Parse and print Assistant response
- assistant_reply_json = fix_and_parse_json(assistant_reply)
- if assistant_reply_json == {}:
- assistant_reply_json = attempt_to_fix_json_by_finding_outermost_brackets(
- assistant_reply
- )
-
- if assistant_reply_json != {}:
- return assistant_reply_json
-
- logger.error(
- "Error: The following AI output couldn't be converted to a JSON:\n",
- assistant_reply,
- )
- if CFG.speak_mode:
- say_text("I have received an invalid JSON response from the OpenAI API.")
-
- return {}
-
-
-def fix_and_parse_json(
- json_to_load: str, try_to_fix_with_gpt: bool = True
-) -> Dict[Any, Any]:
- """Fix and parse JSON string
-
- Args:
- json_to_load (str): The JSON string.
- try_to_fix_with_gpt (bool, optional): Try to fix the JSON with GPT.
- Defaults to True.
-
- Returns:
- str or dict[Any, Any]: The parsed JSON.
- """
-
- with contextlib.suppress(json.JSONDecodeError):
- json_to_load = json_to_load.replace("\t", "")
- return json.loads(json_to_load)
-
- with contextlib.suppress(json.JSONDecodeError):
- json_to_load = correct_json(json_to_load)
- return json.loads(json_to_load)
- # Let's do something manually:
- # sometimes GPT responds with something BEFORE the braces:
- # "I'm sorry, I don't understand. Please try again."
- # {"text": "I'm sorry, I don't understand. Please try again.",
- # "confidence": 0.0}
- # So let's try to find the first brace and then parse the rest
- # of the string
- try:
- brace_index = json_to_load.index("{")
- maybe_fixed_json = json_to_load[brace_index:]
- last_brace_index = maybe_fixed_json.rindex("}")
- maybe_fixed_json = maybe_fixed_json[: last_brace_index + 1]
- return json.loads(maybe_fixed_json)
- except (json.JSONDecodeError, ValueError) as e:
- return try_ai_fix(try_to_fix_with_gpt, e, json_to_load)
-
-
-def try_ai_fix(
- try_to_fix_with_gpt: bool, exception: Exception, json_to_load: str
-) -> Dict[Any, Any]:
- """Try to fix the JSON with the AI
-
- Args:
- try_to_fix_with_gpt (bool): Whether to try to fix the JSON with the AI.
- exception (Exception): The exception that was raised.
- json_to_load (str): The JSON string to load.
-
- Raises:
- exception: If try_to_fix_with_gpt is False.
-
- Returns:
- str or dict[Any, Any]: The JSON string or dictionary.
- """
- if not try_to_fix_with_gpt:
- raise exception
- if CFG.debug_mode:
- logger.warn(
- "Warning: Failed to parse AI output, attempting to fix."
- "\n If you see this warning frequently, it's likely that"
- " your prompt is confusing the AI. Try changing it up"
- " slightly."
- )
- # Now try to fix this up using the ai_functions
- ai_fixed_json = auto_fix_json(json_to_load, JSON_SCHEMA)
-
- if ai_fixed_json != "failed":
- return json.loads(ai_fixed_json)
- # This allows the AI to react to the error message,
- # which usually results in it correcting its ways.
- # logger.error("Failed to fix AI output, telling the AI.")
- return {}
-
-
-def attempt_to_fix_json_by_finding_outermost_brackets(json_string: str):
- if CFG.speak_mode and CFG.debug_mode:
- say_text(
- "I have received an invalid JSON response from the OpenAI API. "
- "Trying to fix it now."
- )
- logger.error("Attempting to fix JSON by finding outermost brackets\n")
-
- try:
- json_pattern = regex.compile(r"\{(?:[^{}]|(?R))*\}")
- json_match = json_pattern.search(json_string)
-
- if json_match:
- # Extract the valid JSON object from the string
- json_string = json_match.group(0)
- logger.typewriter_log(
- title="Apparently json was fixed.", title_color=Fore.GREEN
- )
- if CFG.speak_mode and CFG.debug_mode:
- say_text("Apparently json was fixed.")
- else:
- return {}
-
- except (json.JSONDecodeError, ValueError):
- if CFG.debug_mode:
- logger.error(f"Error: Invalid JSON: {json_string}\n")
- if CFG.speak_mode:
- say_text("Didn't work. I will have to ignore this response then.")
- logger.error("Error: Invalid JSON, setting it to empty JSON now.\n")
- json_string = {}
-
- return fix_and_parse_json(json_string)
diff --git a/spaces/Datasculptor/LoRA-DreamBooth-Training-UI/constants.py b/spaces/Datasculptor/LoRA-DreamBooth-Training-UI/constants.py
deleted file mode 100644
index baaebbae71058fbb4faed35fd00e7559305dc409..0000000000000000000000000000000000000000
--- a/spaces/Datasculptor/LoRA-DreamBooth-Training-UI/constants.py
+++ /dev/null
@@ -1,6 +0,0 @@
-import enum
-
-
-class UploadTarget(enum.Enum):
- PERSONAL_PROFILE = 'Personal Profile'
- LORA_LIBRARY = 'LoRA Library'
diff --git a/spaces/DeepLearning101/Speech-Quality-Inspection_Meta-Denoiser/denoiser/executor.py b/spaces/DeepLearning101/Speech-Quality-Inspection_Meta-Denoiser/denoiser/executor.py
deleted file mode 100644
index 491a6ef924a12e9eeec852bf956f34d8f36f1e4e..0000000000000000000000000000000000000000
--- a/spaces/DeepLearning101/Speech-Quality-Inspection_Meta-Denoiser/denoiser/executor.py
+++ /dev/null
@@ -1,79 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-# author: adefossez
-"""
-Start multiple process locally for DDP.
-"""
-
-import logging
-import subprocess as sp
-import sys
-
-from hydra import utils
-
-logger = logging.getLogger(__name__)
-
-
-class ChildrenManager:
- def __init__(self):
- self.children = []
- self.failed = False
-
- def add(self, child):
- child.rank = len(self.children)
- self.children.append(child)
-
- def __enter__(self):
- return self
-
- def __exit__(self, exc_type, exc_value, traceback):
- if exc_value is not None:
- logger.error("An exception happened while starting workers %r", exc_value)
- self.failed = True
- try:
- while self.children and not self.failed:
- for child in list(self.children):
- try:
- exitcode = child.wait(0.1)
- except sp.TimeoutExpired:
- continue
- else:
- self.children.remove(child)
- if exitcode:
- logger.error(f"Worker {child.rank} died, killing all workers")
- self.failed = True
- except KeyboardInterrupt:
- logger.error("Received keyboard interrupt, trying to kill all workers.")
- self.failed = True
- for child in self.children:
- child.terminate()
- if not self.failed:
- logger.info("All workers completed successfully")
-
-
-def start_ddp_workers():
- import torch as th
-
- world_size = th.cuda.device_count()
- if not world_size:
- logger.error(
- "DDP is only available on GPU. Make sure GPUs are properly configured with cuda.")
- sys.exit(1)
- logger.info(f"Starting {world_size} worker processes for DDP.")
- with ChildrenManager() as manager:
- for rank in range(world_size):
- kwargs = {}
- argv = list(sys.argv)
- argv += [f"world_size={world_size}", f"rank={rank}"]
- if rank > 0:
- kwargs['stdin'] = sp.DEVNULL
- kwargs['stdout'] = sp.DEVNULL
- kwargs['stderr'] = sp.DEVNULL
- log = utils.HydraConfig().hydra.job_logging.handlers.file.filename
- log += f".{rank}"
- argv.append("hydra.job_logging.handlers.file.filename=" + log)
- manager.add(sp.Popen([sys.executable] + argv, cwd=utils.get_original_cwd(), **kwargs))
- sys.exit(int(manager.failed))
diff --git a/spaces/Demosthene-OR/avr23-cds-translation/tabs/template_save/intro.py b/spaces/Demosthene-OR/avr23-cds-translation/tabs/template_save/intro.py
deleted file mode 100644
index a7b06c1173d14afd39bce4ca7ee887d1daec41bd..0000000000000000000000000000000000000000
--- a/spaces/Demosthene-OR/avr23-cds-translation/tabs/template_save/intro.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import streamlit as st
-
-
-title = "My Awesome DataScientest project."
-sidebar_name = "Introduction"
-
-
-def run():
-
- # TODO: choose between one of these GIFs
- # st.image("https://dst-studio-template.s3.eu-west-3.amazonaws.com/1.gif")
- st.image("https://dst-studio-template.s3.eu-west-3.amazonaws.com/2.gif")
- # st.image("https://dst-studio-template.s3.eu-west-3.amazonaws.com/3.gif")
-
- st.title(title)
-
- st.markdown("---")
-
- st.markdown(
- """
- Here is a bootsrap template for your DataScientest project, built with [Streamlit](https://streamlit.io).
-
- You can browse streamlit documentation and demos to get some inspiration:
- - Check out [streamlit.io](https://streamlit.io)
- - Jump into streamlit [documentation](https://docs.streamlit.io)
- - Use a neural net to [analyze the Udacity Self-driving Car Image
- Dataset] (https://github.com/streamlit/demo-self-driving)
- - Explore a [New York City rideshare dataset]
- (https://github.com/streamlit/demo-uber-nyc-pickups)
- """
- )
diff --git a/spaces/Destinycy/Destiny_LOL/app.py b/spaces/Destinycy/Destiny_LOL/app.py
deleted file mode 100644
index 3eef48016f1becc29c3b30b95c7bf67d7f4f9d77..0000000000000000000000000000000000000000
--- a/spaces/Destinycy/Destiny_LOL/app.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import gradio as gr
-from gradio.mix import Parallel
-
-title="my First Text Generator"
-description="input text."
-
-model11=gr.Interface.load("huggingface/EleutherAI/gpt-j-6B")
-model12=gr.Interface.load("huggingface/gpt2")
-model13=gr.Interface.load("huggingface/EleutherAI/gpt-neo-125M")
-
-gr.Parallel(model11, model12, model13, title=title, description=description).launch()
\ No newline at end of file
diff --git a/spaces/DragGan/DragGan-Inversion/torch_utils/ops/conv2d_gradfix.py b/spaces/DragGan/DragGan-Inversion/torch_utils/ops/conv2d_gradfix.py
deleted file mode 100644
index 563543d23df5ae0432461a2c637aec71a4bee9ca..0000000000000000000000000000000000000000
--- a/spaces/DragGan/DragGan-Inversion/torch_utils/ops/conv2d_gradfix.py
+++ /dev/null
@@ -1,225 +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.
-
-"""Custom replacement for `torch.nn.functional.conv2d` that supports
-arbitrarily high order gradients with zero performance penalty."""
-
-import contextlib
-import torch
-
-# pylint: disable=redefined-builtin
-# pylint: disable=arguments-differ
-# pylint: disable=protected-access
-
-# ----------------------------------------------------------------------------
-
-# Enable the custom op by setting this to true.
-enabled = False
-# Forcefully disable computation of gradients with respect to the weights.
-weight_gradients_disabled = False
-
-
-@contextlib.contextmanager
-def no_weight_gradients(disable=True):
- global weight_gradients_disabled
- old = weight_gradients_disabled
- if disable:
- weight_gradients_disabled = True
- yield
- weight_gradients_disabled = old
-
-# ----------------------------------------------------------------------------
-
-
-def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
- if _should_use_custom_op(input):
- return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias)
- return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-
-
-def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1):
- if _should_use_custom_op(input):
- return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias)
- return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation)
-
-# ----------------------------------------------------------------------------
-
-
-def _should_use_custom_op(input):
- assert isinstance(input, torch.Tensor)
- if (not enabled) or (not torch.backends.cudnn.enabled):
- return False
- if input.device.type != 'cuda':
- return False
- return True
-
-
-def _tuple_of_ints(xs, ndim):
- xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
- assert len(xs) == ndim
- assert all(isinstance(x, int) for x in xs)
- return xs
-
-# ----------------------------------------------------------------------------
-
-
-_conv2d_gradfix_cache = dict()
-_null_tensor = torch.empty([0])
-
-
-def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups):
- # Parse arguments.
- ndim = 2
- weight_shape = tuple(weight_shape)
- stride = _tuple_of_ints(stride, ndim)
- padding = _tuple_of_ints(padding, ndim)
- output_padding = _tuple_of_ints(output_padding, ndim)
- dilation = _tuple_of_ints(dilation, ndim)
-
- # Lookup from cache.
- key = (transpose, weight_shape, stride, padding,
- output_padding, dilation, groups)
- if key in _conv2d_gradfix_cache:
- return _conv2d_gradfix_cache[key]
-
- # Validate arguments.
- assert groups >= 1
- assert len(weight_shape) == ndim + 2
- assert all(stride[i] >= 1 for i in range(ndim))
- assert all(padding[i] >= 0 for i in range(ndim))
- assert all(dilation[i] >= 0 for i in range(ndim))
- if not transpose:
- assert all(output_padding[i] == 0 for i in range(ndim))
- else: # transpose
- assert all(0 <= output_padding[i] < max(
- stride[i], dilation[i]) for i in range(ndim))
-
- # Helpers.
- common_kwargs = dict(stride=stride, padding=padding,
- dilation=dilation, groups=groups)
-
- def calc_output_padding(input_shape, output_shape):
- if transpose:
- return [0, 0]
- return [
- input_shape[i + 2]
- - (output_shape[i + 2] - 1) * stride[i]
- - (1 - 2 * padding[i])
- - dilation[i] * (weight_shape[i + 2] - 1)
- for i in range(ndim)
- ]
-
- # Forward & backward.
- class Conv2d(torch.autograd.Function):
- @staticmethod
- def forward(ctx, input, weight, bias):
- assert weight.shape == weight_shape
- ctx.save_for_backward(
- input if weight.requires_grad else _null_tensor,
- weight if input.requires_grad else _null_tensor,
- )
- ctx.input_shape = input.shape
-
- # Simple 1x1 convolution => cuBLAS (only on Volta, not on Ampere).
- if weight_shape[2:] == stride == dilation == (1, 1) and padding == (0, 0) and torch.cuda.get_device_capability(input.device) < (8, 0):
- a = weight.reshape(
- groups, weight_shape[0] // groups, weight_shape[1])
- b = input.reshape(
- input.shape[0], groups, input.shape[1] // groups, -1)
- c = (a.transpose(1, 2) if transpose else a) @ b.permute(1,
- 2, 0, 3).flatten(2)
- c = c.reshape(-1, input.shape[0],
- *input.shape[2:]).transpose(0, 1)
- c = c if bias is None else c + \
- bias.unsqueeze(0).unsqueeze(2).unsqueeze(3)
- return c.contiguous(memory_format=(torch.channels_last if input.stride(1) == 1 else torch.contiguous_format))
-
- # General case => cuDNN.
- if transpose:
- return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs)
- return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)
-
- @staticmethod
- def backward(ctx, grad_output):
- input, weight = ctx.saved_tensors
- input_shape = ctx.input_shape
- grad_input = None
- grad_weight = None
- grad_bias = None
-
- if ctx.needs_input_grad[0]:
- p = calc_output_padding(
- input_shape=input_shape, output_shape=grad_output.shape)
- op = _conv2d_gradfix(transpose=(
- not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs)
- grad_input = op.apply(grad_output, weight, None)
- assert grad_input.shape == input_shape
-
- if ctx.needs_input_grad[1] and not weight_gradients_disabled:
- grad_weight = Conv2dGradWeight.apply(grad_output, input)
- assert grad_weight.shape == weight_shape
-
- if ctx.needs_input_grad[2]:
- grad_bias = grad_output.sum([0, 2, 3])
-
- return grad_input, grad_weight, grad_bias
-
- # Gradient with respect to the weights.
- class Conv2dGradWeight(torch.autograd.Function):
- @staticmethod
- def forward(ctx, grad_output, input):
- ctx.save_for_backward(
- grad_output if input.requires_grad else _null_tensor,
- input if grad_output.requires_grad else _null_tensor,
- )
- ctx.grad_output_shape = grad_output.shape
- ctx.input_shape = input.shape
-
- # Simple 1x1 convolution => cuBLAS (on both Volta and Ampere).
- if weight_shape[2:] == stride == dilation == (1, 1) and padding == (0, 0):
- a = grad_output.reshape(
- grad_output.shape[0], groups, grad_output.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2)
- b = input.reshape(
- input.shape[0], groups, input.shape[1] // groups, -1).permute(1, 2, 0, 3).flatten(2)
- c = (b @ a.transpose(1, 2) if transpose else a @
- b.transpose(1, 2)).reshape(weight_shape)
- return c.contiguous(memory_format=(torch.channels_last if input.stride(1) == 1 else torch.contiguous_format))
-
- # General case => cuDNN.
- name = 'aten::cudnn_convolution_transpose_backward_weight' if transpose else 'aten::cudnn_convolution_backward_weight'
- flags = [torch.backends.cudnn.benchmark,
- torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32]
- return torch._C._jit_get_operation(name)(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags)
-
- @staticmethod
- def backward(ctx, grad2_grad_weight):
- grad_output, input = ctx.saved_tensors
- grad_output_shape = ctx.grad_output_shape
- input_shape = ctx.input_shape
- grad2_grad_output = None
- grad2_input = None
-
- if ctx.needs_input_grad[0]:
- grad2_grad_output = Conv2d.apply(
- input, grad2_grad_weight, None)
- assert grad2_grad_output.shape == grad_output_shape
-
- if ctx.needs_input_grad[1]:
- p = calc_output_padding(
- input_shape=input_shape, output_shape=grad_output_shape)
- op = _conv2d_gradfix(transpose=(
- not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs)
- grad2_input = op.apply(grad_output, grad2_grad_weight, None)
- assert grad2_input.shape == input_shape
-
- return grad2_grad_output, grad2_input
-
- _conv2d_gradfix_cache[key] = Conv2d
- return Conv2d
-
-# ----------------------------------------------------------------------------
diff --git a/spaces/Duskfallcrew/Duskfallcrew-duskfallai/app.py b/spaces/Duskfallcrew/Duskfallcrew-duskfallai/app.py
deleted file mode 100644
index d2cd7015c0b87875aa83dec2b3da631f5e69fd1c..0000000000000000000000000000000000000000
--- a/spaces/Duskfallcrew/Duskfallcrew-duskfallai/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/Duskfallcrew/duskfallai").launch()
\ No newline at end of file
diff --git a/spaces/Eddycrack864/Applio-Inference/infer/modules/vc/__init__.py b/spaces/Eddycrack864/Applio-Inference/infer/modules/vc/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Egrt/LicenseGAN/utils/utils_fit.py b/spaces/Egrt/LicenseGAN/utils/utils_fit.py
deleted file mode 100644
index 4273dae2da982fa056ae89d50c924b2ee28c36a8..0000000000000000000000000000000000000000
--- a/spaces/Egrt/LicenseGAN/utils/utils_fit.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import torch
-from tqdm import tqdm
-
-from .utils import get_lr, show_result
-from .utils_metrics import PSNR, SSIM
-
-
-def fit_one_epoch(G_model_train, D_model_train, G_model, D_model, VGG_feature_model, G_optimizer, D_optimizer, BCEWithLogits_loss, L1_loss, epoch, epoch_size, gen, Epoch, cuda, batch_size, save_interval):
- G_total_loss = 0
- D_total_loss = 0
- G_total_PSNR = 0
- G_total_SSIM = 0
-
- with tqdm(total=epoch_size,desc=f'Epoch {epoch + 1}/{Epoch}',postfix=dict,mininterval=0.3) as pbar:
- for iteration, batch in enumerate(gen):
- if iteration >= epoch_size:
- break
-
- with torch.no_grad():
- lr_images, hr_images = batch
- lr_images, hr_images = torch.from_numpy(lr_images).type(torch.FloatTensor), torch.from_numpy(hr_images).type(torch.FloatTensor)
- y_real, y_fake = torch.ones(batch_size), torch.zeros(batch_size)
- if cuda:
- lr_images, hr_images, y_real, y_fake = lr_images.cuda(), hr_images.cuda(), y_real.cuda(), y_fake.cuda()
-
- #-------------------------------------------------#
- # 训练判别器
- #-------------------------------------------------#
- D_optimizer.zero_grad()
-
- D_result_r = D_model_train(hr_images)
-
- G_result = G_model_train(lr_images)
- D_result_f = D_model_train(G_result).squeeze()
- D_result_rf = D_result_r - D_result_f.mean()
- D_result_fr = D_result_f - D_result_r.mean()
- D_train_loss_rf = BCEWithLogits_loss(D_result_rf, y_real)
- D_train_loss_fr = BCEWithLogits_loss(D_result_fr, y_fake)
- D_train_loss = (D_train_loss_rf + D_train_loss_fr) / 2
- D_train_loss.backward()
-
- D_optimizer.step()
-
- #-------------------------------------------------#
- # 训练生成器
- #-------------------------------------------------#
- G_optimizer.zero_grad()
-
- G_result = G_model_train(lr_images)
- image_loss = L1_loss(G_result, hr_images)
-
- D_result_r = D_model_train(hr_images)
- D_result_f = D_model_train(G_result).squeeze()
- D_result_rf = D_result_r - D_result_f.mean()
- D_result_fr = D_result_f - D_result_r.mean()
- D_train_loss_rf = BCEWithLogits_loss(D_result_rf, y_fake)
- D_train_loss_fr = BCEWithLogits_loss(D_result_fr, y_real)
- adversarial_loss = (D_train_loss_rf + D_train_loss_fr) / 2
-
- perception_loss = L1_loss(VGG_feature_model(G_result), VGG_feature_model(hr_images))
-
- G_train_loss = image_loss + 1e-1 * adversarial_loss + 1e-1 * perception_loss
-
- G_train_loss.backward()
- G_optimizer.step()
-
- G_total_loss += G_train_loss.item()
- D_total_loss += D_train_loss.item()
-
- with torch.no_grad():
- G_total_PSNR += PSNR(G_result, hr_images).item()
- G_total_SSIM += SSIM(G_result, hr_images).item()
-
- pbar.set_postfix(**{'G_loss' : G_total_loss / (iteration + 1),
- 'D_loss' : D_total_loss / (iteration + 1),
- 'G_PSNR' : G_total_PSNR / (iteration + 1),
- 'G_SSIM' : G_total_SSIM / (iteration + 1),
- 'lr' : get_lr(G_optimizer)})
- pbar.update(1)
-
- if iteration % save_interval == 0:
- show_result(epoch + 1, G_model_train, lr_images, hr_images)
-
- print('Epoch:'+ str(epoch + 1) + '/' + str(Epoch))
- print('G Loss: %.4f || D Loss: %.4f ' % (G_total_loss / epoch_size, D_total_loss / epoch_size))
- print('Saving state, iter:', str(epoch+1))
-
- if (epoch + 1) % 10==0:
- torch.save(G_model.state_dict(), 'logs/G_Epoch%d-GLoss%.4f-DLoss%.4f.pth'%((epoch + 1), G_total_loss / epoch_size, D_total_loss / epoch_size))
- torch.save(D_model.state_dict(), 'logs/D_Epoch%d-GLoss%.4f-DLoss%.4f.pth'%((epoch + 1), G_total_loss / epoch_size, D_total_loss / epoch_size))
diff --git a/spaces/Epoching/3D_Photo_Inpainting/bilateral_filtering.py b/spaces/Epoching/3D_Photo_Inpainting/bilateral_filtering.py
deleted file mode 100644
index 28cc7dc79cc2f3c0b9065d6a1eb290b9554af879..0000000000000000000000000000000000000000
--- a/spaces/Epoching/3D_Photo_Inpainting/bilateral_filtering.py
+++ /dev/null
@@ -1,215 +0,0 @@
-import numpy as np
-from functools import reduce
-
-def sparse_bilateral_filtering(
- depth, image, config, HR=False, mask=None, gsHR=True, edge_id=None, num_iter=None, num_gs_iter=None, spdb=False
-):
- """
- config:
- - filter_size
- """
- import time
-
- save_images = []
- save_depths = []
- save_discontinuities = []
- vis_depth = depth.copy()
- backup_vis_depth = vis_depth.copy()
-
- depth_max = vis_depth.max()
- depth_min = vis_depth.min()
- vis_image = image.copy()
- for i in range(num_iter):
- if isinstance(config["filter_size"], list):
- window_size = config["filter_size"][i]
- else:
- window_size = config["filter_size"]
- vis_image = image.copy()
- save_images.append(vis_image)
- save_depths.append(vis_depth)
- u_over, b_over, l_over, r_over = vis_depth_discontinuity(vis_depth, config, mask=mask)
- vis_image[u_over > 0] = np.array([0, 0, 0])
- vis_image[b_over > 0] = np.array([0, 0, 0])
- vis_image[l_over > 0] = np.array([0, 0, 0])
- vis_image[r_over > 0] = np.array([0, 0, 0])
-
- discontinuity_map = (u_over + b_over + l_over + r_over).clip(0.0, 1.0)
- discontinuity_map[depth == 0] = 1
- save_discontinuities.append(discontinuity_map)
- if mask is not None:
- discontinuity_map[mask == 0] = 0
- vis_depth = bilateral_filter(
- vis_depth, config, discontinuity_map=discontinuity_map, HR=HR, mask=mask, window_size=window_size
- )
-
- return save_images, save_depths
-
-
-def vis_depth_discontinuity(depth, config, vis_diff=False, label=False, mask=None):
- """
- config:
- -
- """
- if label == False:
- disp = 1./depth
- u_diff = (disp[1:, :] - disp[:-1, :])[:-1, 1:-1]
- b_diff = (disp[:-1, :] - disp[1:, :])[1:, 1:-1]
- l_diff = (disp[:, 1:] - disp[:, :-1])[1:-1, :-1]
- r_diff = (disp[:, :-1] - disp[:, 1:])[1:-1, 1:]
- if mask is not None:
- u_mask = (mask[1:, :] * mask[:-1, :])[:-1, 1:-1]
- b_mask = (mask[:-1, :] * mask[1:, :])[1:, 1:-1]
- l_mask = (mask[:, 1:] * mask[:, :-1])[1:-1, :-1]
- r_mask = (mask[:, :-1] * mask[:, 1:])[1:-1, 1:]
- u_diff = u_diff * u_mask
- b_diff = b_diff * b_mask
- l_diff = l_diff * l_mask
- r_diff = r_diff * r_mask
- u_over = (np.abs(u_diff) > config['depth_threshold']).astype(np.float32)
- b_over = (np.abs(b_diff) > config['depth_threshold']).astype(np.float32)
- l_over = (np.abs(l_diff) > config['depth_threshold']).astype(np.float32)
- r_over = (np.abs(r_diff) > config['depth_threshold']).astype(np.float32)
- else:
- disp = depth
- u_diff = (disp[1:, :] * disp[:-1, :])[:-1, 1:-1]
- b_diff = (disp[:-1, :] * disp[1:, :])[1:, 1:-1]
- l_diff = (disp[:, 1:] * disp[:, :-1])[1:-1, :-1]
- r_diff = (disp[:, :-1] * disp[:, 1:])[1:-1, 1:]
- if mask is not None:
- u_mask = (mask[1:, :] * mask[:-1, :])[:-1, 1:-1]
- b_mask = (mask[:-1, :] * mask[1:, :])[1:, 1:-1]
- l_mask = (mask[:, 1:] * mask[:, :-1])[1:-1, :-1]
- r_mask = (mask[:, :-1] * mask[:, 1:])[1:-1, 1:]
- u_diff = u_diff * u_mask
- b_diff = b_diff * b_mask
- l_diff = l_diff * l_mask
- r_diff = r_diff * r_mask
- u_over = (np.abs(u_diff) > 0).astype(np.float32)
- b_over = (np.abs(b_diff) > 0).astype(np.float32)
- l_over = (np.abs(l_diff) > 0).astype(np.float32)
- r_over = (np.abs(r_diff) > 0).astype(np.float32)
- u_over = np.pad(u_over, 1, mode='constant')
- b_over = np.pad(b_over, 1, mode='constant')
- l_over = np.pad(l_over, 1, mode='constant')
- r_over = np.pad(r_over, 1, mode='constant')
- u_diff = np.pad(u_diff, 1, mode='constant')
- b_diff = np.pad(b_diff, 1, mode='constant')
- l_diff = np.pad(l_diff, 1, mode='constant')
- r_diff = np.pad(r_diff, 1, mode='constant')
-
- if vis_diff:
- return [u_over, b_over, l_over, r_over], [u_diff, b_diff, l_diff, r_diff]
- else:
- return [u_over, b_over, l_over, r_over]
-
-def bilateral_filter(depth, config, discontinuity_map=None, HR=False, mask=None, window_size=False):
- sort_time = 0
- replace_time = 0
- filter_time = 0
- init_time = 0
- filtering_time = 0
- sigma_s = config['sigma_s']
- sigma_r = config['sigma_r']
- if window_size == False:
- window_size = config['filter_size']
- midpt = window_size//2
- ax = np.arange(-midpt, midpt+1.)
- xx, yy = np.meshgrid(ax, ax)
- if discontinuity_map is not None:
- spatial_term = np.exp(-(xx**2 + yy**2) / (2. * sigma_s**2))
-
- # padding
- depth = depth[1:-1, 1:-1]
- depth = np.pad(depth, ((1,1), (1,1)), 'edge')
- pad_depth = np.pad(depth, (midpt,midpt), 'edge')
- if discontinuity_map is not None:
- discontinuity_map = discontinuity_map[1:-1, 1:-1]
- discontinuity_map = np.pad(discontinuity_map, ((1,1), (1,1)), 'edge')
- pad_discontinuity_map = np.pad(discontinuity_map, (midpt,midpt), 'edge')
- pad_discontinuity_hole = 1 - pad_discontinuity_map
- # filtering
- output = depth.copy()
- pad_depth_patches = rolling_window(pad_depth, [window_size, window_size], [1,1])
- if discontinuity_map is not None:
- pad_discontinuity_patches = rolling_window(pad_discontinuity_map, [window_size, window_size], [1,1])
- pad_discontinuity_hole_patches = rolling_window(pad_discontinuity_hole, [window_size, window_size], [1,1])
-
- if mask is not None:
- pad_mask = np.pad(mask, (midpt,midpt), 'constant')
- pad_mask_patches = rolling_window(pad_mask, [window_size, window_size], [1,1])
- from itertools import product
- if discontinuity_map is not None:
- pH, pW = pad_depth_patches.shape[:2]
- for pi in range(pH):
- for pj in range(pW):
- if mask is not None and mask[pi, pj] == 0:
- continue
- if discontinuity_map is not None:
- if bool(pad_discontinuity_patches[pi, pj].any()) is False:
- continue
- discontinuity_patch = pad_discontinuity_patches[pi, pj]
- discontinuity_holes = pad_discontinuity_hole_patches[pi, pj]
- depth_patch = pad_depth_patches[pi, pj]
- depth_order = depth_patch.ravel().argsort()
- patch_midpt = depth_patch[window_size//2, window_size//2]
- if discontinuity_map is not None:
- coef = discontinuity_holes.astype(np.float32)
- if mask is not None:
- coef = coef * pad_mask_patches[pi, pj]
- else:
- range_term = np.exp(-(depth_patch-patch_midpt)**2 / (2. * sigma_r**2))
- coef = spatial_term * range_term
- if coef.max() == 0:
- output[pi, pj] = patch_midpt
- continue
- if discontinuity_map is not None and (coef.max() == 0):
- output[pi, pj] = patch_midpt
- else:
- coef = coef/(coef.sum())
- coef_order = coef.ravel()[depth_order]
- cum_coef = np.cumsum(coef_order)
- ind = np.digitize(0.5, cum_coef)
- output[pi, pj] = depth_patch.ravel()[depth_order][ind]
- else:
- pH, pW = pad_depth_patches.shape[:2]
- for pi in range(pH):
- for pj in range(pW):
- if discontinuity_map is not None:
- if pad_discontinuity_patches[pi, pj][window_size//2, window_size//2] == 1:
- continue
- discontinuity_patch = pad_discontinuity_patches[pi, pj]
- discontinuity_holes = (1. - discontinuity_patch)
- depth_patch = pad_depth_patches[pi, pj]
- depth_order = depth_patch.ravel().argsort()
- patch_midpt = depth_patch[window_size//2, window_size//2]
- range_term = np.exp(-(depth_patch-patch_midpt)**2 / (2. * sigma_r**2))
- if discontinuity_map is not None:
- coef = spatial_term * range_term * discontinuity_holes
- else:
- coef = spatial_term * range_term
- if coef.sum() == 0:
- output[pi, pj] = patch_midpt
- continue
- if discontinuity_map is not None and (coef.sum() == 0):
- output[pi, pj] = patch_midpt
- else:
- coef = coef/(coef.sum())
- coef_order = coef.ravel()[depth_order]
- cum_coef = np.cumsum(coef_order)
- ind = np.digitize(0.5, cum_coef)
- output[pi, pj] = depth_patch.ravel()[depth_order][ind]
-
- return output
-
-def rolling_window(a, window, strides):
- assert len(a.shape)==len(window)==len(strides), "\'a\', \'window\', \'strides\' dimension mismatch"
- shape_fn = lambda i,w,s: (a.shape[i]-w)//s + 1
- shape = [shape_fn(i,w,s) for i,(w,s) in enumerate(zip(window, strides))] + list(window)
- def acc_shape(i):
- if i+1>=len(a.shape):
- return 1
- else:
- return reduce(lambda x,y:x*y, a.shape[i+1:])
- _strides = [acc_shape(i)*s*a.itemsize for i,s in enumerate(strides)] + list(a.strides)
-
- return np.lib.stride_tricks.as_strided(a, shape=shape, strides=_strides)
diff --git a/spaces/FKBaffour/Expresso_Customer_Churn_Prediction/app.py b/spaces/FKBaffour/Expresso_Customer_Churn_Prediction/app.py
deleted file mode 100644
index 4c76a566a553fef2f7971a7a5ce2e936e9d0c7e9..0000000000000000000000000000000000000000
--- a/spaces/FKBaffour/Expresso_Customer_Churn_Prediction/app.py
+++ /dev/null
@@ -1,205 +0,0 @@
-# Importing required Libraries
-import streamlit as st
-import pandas as pd
-import numpy as np
-import os, pickle
-
-# Setting up page configuration and directory path
-st.set_page_config(page_title= "Customer Churn Prediction", page_icon="🛳️", layout="centered")
-DIRPATH = os.path.dirname(os.path.realpath(__file__))
-
-# Setting background image
-import base64
-def add_bg_from_local(image_file):
- with open(image_file, "rb") as image_file:
- encoded_string = base64.b64encode(image_file.read())
- st.markdown(
- f"""
-
- """,
- unsafe_allow_html=True
- )
-add_bg_from_local('images/background.jpg')
-
-# Setting up logo
-left, mid, mid1, right = st.columns(4)
-with mid:
- st.image("images/logo.jpeg", use_column_width=True)
-
-# Setting up Sidebar
-social_acc = ['Data Field Description', 'EDA', 'About App']
-social_acc_nav = st.sidebar.radio('**INFORMATION SECTION**', social_acc)
-
-if social_acc_nav == 'Data Field Description':
- st.sidebar.markdown("
Data Field Description
", unsafe_allow_html=True)
- st.sidebar.markdown("""
- The table below gives a description on the variables required to make predictions.
- | Variable | Definition: |
- | :------------ |:--------------- |
- | FREQUENCE | number of times the client has made an income |
- | TENURE | duration in the network |
- | FREQUENCE_RECH| number of times the customer refilled |
- | MONTANT | top-up amount |
- | DATA_VOLUME | number of connections|
- | ORANGE | call to orange |
- | ARPU_SEGMENT | income over 90 days / 3 |
- | ON_NET | inter expresso call |
- | REGULARITY | number of times the client is active for 90 days |
- | FREQ_TOP_PACK | number of times client has activated the top pack packages|
- | REVENUE | monthly income of each client |
- """)
-
-elif social_acc_nav == 'EDA':
- st.sidebar.markdown("
Exploratory Data Analysis
", unsafe_allow_html=True)
- st.sidebar.markdown('''---''')
- st.sidebar.markdown("""
- | About EDA|
- | :------------ |
- The exploratory data analysis of this project can be found in a Notebook in a github repository from the link below""" )
- st.sidebar.markdown("[Open Repository ](https://github.com/Kyei-frank/Customer-Churn-Prediction--Expresso)")
-
-elif social_acc_nav == 'About App':
- st.sidebar.markdown("
Titanic Survival Prediction App
", unsafe_allow_html=True)
- st.sidebar.markdown('''---''')
- st.sidebar.markdown("""
- | Brief Introduction|
- | :------------ |
- This projet is based on a Zindi challenge for an African telecommunications company (Expresso)
- that provides customers with airtime and mobile data bundles. The objective of this challenge
- is to develop a machine learning model to predict the likelihood of each customer “churning,”
- i.e. becoming inactive and not making any transactions for 90 days. This solution will help
- this telecom company to better serve their customers by understanding which customers are at risk of leaving""")
- st.sidebar.markdown("")
- st.sidebar.markdown("[ Visit Github Repository for more information](https://github.com/Kyei-frank/Customer-Churn-Prediction--Expresso)")
-
-# Loading Machine Learning Objects
-@st.experimental_memo
-def load_saved_objects(file_path = 'ML_items'):
- # Function to load saved objects
- with open('ML_items', 'rb') as file:
- loaded_object = pickle.load(file)
-
- return loaded_object
-
-# Instantiating ML_items
-Loaded_object = load_saved_objects(file_path = 'ML_items')
-pipeline_of_my_app = Loaded_object["pipeline"]
-
-
-# Setting up variables for input data
-@st.experimental_memo
-def setup(tmp_df_file):
- "Setup the required elements like files, models, global variables, etc"
- pd.DataFrame(
- dict(
- FREQUENCE= [],
- TENURE= [],
- FREQUENCE_RECH= [],
- MONTANT= [],
- DATA_VOLUME= [],
- ORANGE= [],
- ARPU_SEGMENT= [],
- ON_NET= [],
- REGULARITY= [],
- FREQ_TOP_PACK= [],
- REVENUE= [],
- )
- ).to_csv(tmp_df_file, index=False)
-
-# Setting up a file to save our input data
-tmp_df_file = os.path.join(DIRPATH, "tmp", "data.csv")
-setup(tmp_df_file)
-
-# setting Title for forms
-st.markdown("
....... Customer Churn Prediction ......
", unsafe_allow_html=True)
-st.markdown("
Fill in the details below and click on SUBMIT button to make a prediction for a Client.
", unsafe_allow_html=True)
-
-# Creating columns for input data(forms)
-left_col, middle_col, right_col = st.columns(3)
-
-# Developing forms to collect input data
-with st.form(key="information", clear_on_submit=True):
-
- # Setting up input data for 1st column
- left_col.markdown(":blue[**CALLS & ACTIVITY DETAILS**]")
- ORANGE = left_col.number_input("Insert Number of calls to ORANGE")
- ON_NET = left_col.number_input("Insert Number of inter expresso calls")
- DATA_VOLUME = left_col.number_input("Insert Number of connections")
- REGULARITY = left_col.number_input("Insert number of times the client is active for 90 days")
- FREQ_TOP_PACK = left_col.number_input("Insert number of times client has activated the top pack packages")
-
- # Setting up input data for 2nd column
- middle_col.markdown(":blue[**TOP-UP & INCOME DETAILS**]")
- MONTANT = middle_col.number_input("Insert top-up amount")
- FREQUENCE_RECH = middle_col.number_input("Insert Number of times the customer refilled")
- REVENUE = middle_col.number_input("Insert monthly income of client")
- ARPU_SEGMENT = middle_col.number_input("Insert income over 90 days / 3")
- FREQUENCE = middle_col.number_input("Insert number of times client has made an income")
-
- # Setting up input data for 2nd column
- right_col.markdown(":blue[**TENURE DETAILS**]")
- TENURE = right_col.radio("What is Client's duration in the network?", ('D 3-6 month',
- 'E 6-9 month', 'F 9-12 month', 'G 12-15 month', 'H 15-18 month',
- 'I 18-21 month', 'J 21-24 month', 'K > 24 month',))
-
- submitted = st.form_submit_button(label="Submit")
-
-# Setting up background operations after submitting forms
-if submitted:
- # Saving input data as csv after submission
- pd.read_csv(tmp_df_file).append(
- dict(
- FREQUENCE= FREQUENCE,
- TENURE= TENURE,
- FREQUENCE_RECH= FREQUENCE_RECH,
- MONTANT= MONTANT,
- DATA_VOLUME= DATA_VOLUME,
- ORANGE= ORANGE,
- ARPU_SEGMENT= ARPU_SEGMENT,
- ON_NET= ON_NET,
- REGULARITY= REGULARITY,
- FREQ_TOP_PACK= FREQ_TOP_PACK,
- REVENUE= REVENUE,
- ),
- ignore_index=True,
- ).to_csv(tmp_df_file, index=False)
- st.balloons()
-
- # Converting input data to a dataframe for predictions
- df = pd.read_csv(tmp_df_file)
- df= df.copy()
-
- # Making Predictions
- # Passing data to pipeline to make prediction
- pred_output = pipeline_of_my_app.predict(df)
- prob_output = np.max(pipeline_of_my_app.predict_proba(df))
-
- # Interpleting prediction output for display
- X= pred_output[-1]
- if X == 1:
- explanation = 'Client will CHURN'
- else:
- explanation = 'Client will NOT CHURN'
- output = explanation
-
- # Displaying prediction results
- st.markdown('''---''')
- st.markdown("
Prediction Results
", unsafe_allow_html=True)
- st.success(f"Prediction: {output}")
- st.success(f"Confidence Probability: {prob_output}")
- st.markdown('''---''')
-
- # Making expander to view all records
- expander = st.expander("See all records")
- with expander:
- df = pd.read_csv(tmp_df_file)
- df['Survival']= pred_output
- st.dataframe(df)
-
-
-
\ No newline at end of file
diff --git a/spaces/FluxWaveCorp/Ghostwriter-Bloom/flask_app.py b/spaces/FluxWaveCorp/Ghostwriter-Bloom/flask_app.py
deleted file mode 100644
index 5adf8b384d4860147a3be09d1eeaa29bacaad872..0000000000000000000000000000000000000000
--- a/spaces/FluxWaveCorp/Ghostwriter-Bloom/flask_app.py
+++ /dev/null
@@ -1,54 +0,0 @@
-from flask import Flask, request
-from generators.title_to_abstract import title_to_abstract_generator
-from generators.topic_to_abstract import topic_to_abstract_generator
-app = Flask(__name__)
-max_length_char = 360
-
-
-@app.route('/')
-def rewriter():
- data = request.get_json(silent=True)
- if data is None:
- return 'No data received'
- else:
- if data.get('title') is None and data.get('topic') is not None:
- return generate(data, 'topic')
- elif data.get('title') is not None:
- return generate(data, 'title')
- else:
- return 'No data received'
-
-
-def generate(data, value):
- obj = abstract_generator_obj(value)
- result = obj(template_generator(value, data[value]))
- if len(result['result']) > 40:
- return {"result": result['result']}
- else:
- result_re = obj(template_generator(value, data[value]))
- while len(result_re['result']) < 50:
- print(f"getting results again, {result_re}")
- result_re = obj(template_generator(value, data[value]))
- if len(result_re['result']) > 40:
- return {"result": result_re['result']}
-
-
-def abstract_generator_obj(value):
- if value == 'title':
- return title_to_abstract_generator
- else:
- return topic_to_abstract_generator
-
-
-def template_generator(type, value):
- if type == 'title':
- return "title: " + value + "\n" + 'abstract: '
- else:
- keyword_str = "\n"
- for k in value:
- keyword_str = keyword_str + k + "\n"
- return "topic: " + keyword_str + "\n" + 'abstract: '
-
-
-if __name__ == '__main__':
- app.run()
diff --git a/spaces/GeorgeOrville/bingo/src/components/chat-scroll-anchor.tsx b/spaces/GeorgeOrville/bingo/src/components/chat-scroll-anchor.tsx
deleted file mode 100644
index ac809f4486a48e134cb69314c3d0dae5e68d614e..0000000000000000000000000000000000000000
--- a/spaces/GeorgeOrville/bingo/src/components/chat-scroll-anchor.tsx
+++ /dev/null
@@ -1,29 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import { useInView } from 'react-intersection-observer'
-
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-
-interface ChatScrollAnchorProps {
- trackVisibility?: boolean
-}
-
-export function ChatScrollAnchor({ trackVisibility }: ChatScrollAnchorProps) {
- const isAtBottom = useAtBottom()
- const { ref, entry, inView } = useInView({
- trackVisibility,
- delay: 100,
- rootMargin: '0px 0px -150px 0px'
- })
-
- React.useEffect(() => {
- if (isAtBottom && trackVisibility && !inView) {
- entry?.target.scrollIntoView({
- block: 'start'
- })
- }
- }, [inView, entry, isAtBottom, trackVisibility])
-
- return
-}
diff --git a/spaces/Gladiator/Text-Summarizer/app.py b/spaces/Gladiator/Text-Summarizer/app.py
deleted file mode 100644
index ad0348d97c7517c40be7c7836facc36448148d2e..0000000000000000000000000000000000000000
--- a/spaces/Gladiator/Text-Summarizer/app.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import nltk
-import validators
-import streamlit as st
-from transformers import AutoTokenizer, pipeline
-
-# local modules
-from extractive_summarizer.model_processors import Summarizer
-from utils import (
- clean_text,
- fetch_article_text,
- preprocess_text_for_abstractive_summarization,
- read_text_from_file,
-)
-
-from rouge import Rouge
-
-if __name__ == "__main__":
- # ---------------------------------
- # Main Application
- # ---------------------------------
- st.title("Text Summarizer 📝")
-
- st.markdown("Creator: [Atharva Ingle](https://github.com/Gladiator07)")
- st.markdown(
- "Source code: [GitHub Repository](https://github.com/Gladiator07/Text-Summarizer)"
- )
- summarize_type = st.sidebar.selectbox(
- "Summarization type", options=["Extractive", "Abstractive"]
- )
-
- st.markdown(
- "Enter a text or a url to get a concise summary of the article while conserving the overall meaning. This app supports text in the following formats:"
- )
- st.markdown(
- """- Raw text in text box
-- URL of article/news to be summarized
-- .txt, .pdf, .docx file formats"""
- )
- st.markdown(
- """This app supports two type of summarization:
-
-1. **Extractive Summarization**: The extractive approach involves picking up the most important phrases and lines from the documents. It then combines all the important lines to create the summary. So, in this case, every line and word of the summary actually belongs to the original document which is summarized.
-2. **Abstractive Summarization**: The abstractive approach involves rephrasing the complete document while capturing the complete meaning of the document. This type of summarization provides more human-like summary"""
- )
- st.markdown("---")
- # ---------------------------
- # SETUP & Constants
- nltk.download("punkt")
- abs_tokenizer_name = "facebook/bart-large-cnn"
- abs_model_name = "facebook/bart-large-cnn"
- abs_tokenizer = AutoTokenizer.from_pretrained(abs_tokenizer_name)
- abs_max_length = 90
- abs_min_length = 30
- # ---------------------------
-
- inp_text = st.text_input("Enter text or a url here")
- st.markdown(
- "
OR
",
- unsafe_allow_html=True,
- )
- uploaded_file = st.file_uploader(
- "Upload a .txt, .pdf, .docx file for summarization"
- )
-
- is_url = validators.url(inp_text)
- if is_url:
- # complete text, chunks to summarize (list of sentences for long docs)
- text, cleaned_txt = fetch_article_text(url=inp_text)
- elif uploaded_file:
- cleaned_txt = read_text_from_file(uploaded_file)
- cleaned_txt = clean_text(cleaned_txt)
- else:
- cleaned_txt = clean_text(inp_text)
-
- # view summarized text (expander)
- with st.expander("View input text"):
- if is_url:
- st.write(cleaned_txt[0])
- else:
- st.write(cleaned_txt)
- summarize = st.button("Summarize")
-
- # called on toggle button [summarize]
- if summarize:
- if summarize_type == "Extractive":
- if is_url:
- text_to_summarize = " ".join([txt for txt in cleaned_txt])
- else:
- text_to_summarize = cleaned_txt
- # extractive summarizer
-
- with st.spinner(
- text="Creating extractive summary. This might take a few seconds ..."
- ):
- ext_model = Summarizer()
- summarized_text = ext_model(text_to_summarize, num_sentences=5)
-
- elif summarize_type == "Abstractive":
- with st.spinner(
- text="Creating abstractive summary. This might take a few seconds ..."
- ):
- text_to_summarize = cleaned_txt
- abs_summarizer = pipeline(
- "summarization", model=abs_model_name, tokenizer=abs_tokenizer_name
- )
-
- if is_url is False:
- # list of chunks
- text_to_summarize = preprocess_text_for_abstractive_summarization(
- tokenizer=abs_tokenizer, text=cleaned_txt
- )
-
- tmp_sum = abs_summarizer(
- text_to_summarize,
- max_length=abs_max_length,
- min_length=abs_min_length,
- do_sample=False,
- )
-
- summarized_text = " ".join([summ["summary_text"] for summ in tmp_sum])
-
- # final summarized output
- st.subheader("Summarized text")
- st.info(summarized_text)
-
- st.subheader("Rogue Scores")
- rouge_sc = Rouge()
- ground_truth = cleaned_txt[0] if is_url else cleaned_txt
- score = rouge_sc.get_scores(summarized_text, ground_truth, avg=True)
- st.code(score)
diff --git a/spaces/GolDNenex/Super-Resolution-Anime-Diffusion/RealESRGANv030/docs/anime_comparisons_CN.md b/spaces/GolDNenex/Super-Resolution-Anime-Diffusion/RealESRGANv030/docs/anime_comparisons_CN.md
deleted file mode 100644
index 43ba58344ed9554d5b30e2815d1b7d4ab8bc503f..0000000000000000000000000000000000000000
--- a/spaces/GolDNenex/Super-Resolution-Anime-Diffusion/RealESRGANv030/docs/anime_comparisons_CN.md
+++ /dev/null
@@ -1,68 +0,0 @@
-# 动漫视频模型比较
-
-[English](anime_comparisons.md) **|** [简体中文](anime_comparisons_CN.md)
-
-## 更新
-
-- 2022/04/24: 发布 **AnimeVideo-v3**. 主要做了以下更新:
- - **更自然**
- - **更少瑕疵**
- - **颜色保持得更好**
- - **更好的纹理恢复**
- - **虚化背景处理**
-
-## 比较
-
-我们将 RealESRGAN-AnimeVideo-v3 与以下方法进行了比较。我们的 RealESRGAN-AnimeVideo-v3 可以以更快的推理速度获得更好的结果。
-
-- [waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan). 超参数: `tile=0`, `noiselevel=2`
-- [Real-CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN): 我们使用了[20220227](https://github.com/bilibili/ailab/releases/tag/Real-CUGAN-add-faster-low-memory-mode)版本, 超参: `cache_mode=0`, `tile=0`, `alpha=1`.
-- 我们的 RealESRGAN-AnimeVideo-v3
-
-## 结果
-
-您可能需要**放大**以比较详细信息, 或者**单击图像**以查看完整尺寸。 请注意下面表格的图片是从原图里裁剪patch并且resize后的结果,您可以从
-[Google Drive](https://drive.google.com/drive/folders/1bc_Hje1Nqop9NDkUvci2VACSjL7HZMRp?usp=sharing) 里下载原始的输入和输出。
-
-**更自然的结果,更好的虚化背景恢复**
-
-| 输入 | waifu2x | Real-CUGAN | RealESRGAN AnimeVideo-v3 |
-| :---: | :---: | :---: | :---: |
-| |  |  |  |
-| |  |  |  |
-| |  |  |  |
-
-**更少瑕疵,更好的细节纹理**
-
-| 输入 | waifu2x | Real-CUGAN | RealESRGAN AnimeVideo-v3 |
-| :---: | :---: | :---: | :---: |
-| |  |  |  |
-| |  |  |  |
-| |  |  |  |
-| |  |  |  |
-
-**其他更好的结果**
-
-| 输入 | waifu2x | Real-CUGAN | RealESRGAN AnimeVideo-v3 |
-| :---: | :---: | :---: | :---: |
-| |  |  |  |
-| |  |  |  |
-|  |   |   |   |
-| |  |  |  |
-| |  |  |  |
-
-## 推理速度比较
-
-### PyTorch
-
-请注意,我们只报告了**模型推理**的时间, 而忽略了读写硬盘的时间.
-
-| GPU | 输入尺寸 | waifu2x | Real-CUGAN | RealESRGAN-AnimeVideo-v3
-| :---: | :---: | :---: | :---: | :---: |
-| V100 | 1921 x 1080 | - | 3.4 fps | **10.0** fps |
-| V100 | 1280 x 720 | - | 7.2 fps | **22.6** fps |
-| V100 | 640 x 480 | - | 24.4 fps | **65.9** fps |
-
-### ncnn
-
-- [ ] TODO
diff --git a/spaces/Gradio-Blocks/EmojiGAN/torch_utils/training_stats.py b/spaces/Gradio-Blocks/EmojiGAN/torch_utils/training_stats.py
deleted file mode 100644
index 26f467f9eaa074ee13de1cf2625cd7da44880847..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/EmojiGAN/torch_utils/training_stats.py
+++ /dev/null
@@ -1,268 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto. Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Facilities for reporting and collecting training statistics across
-multiple processes and devices. The interface is designed to minimize
-synchronization overhead as well as the amount of boilerplate in user
-code."""
-
-import re
-import numpy as np
-import torch
-import dnnlib
-
-from . import misc
-
-#----------------------------------------------------------------------------
-
-_num_moments = 3 # [num_scalars, sum_of_scalars, sum_of_squares]
-_reduce_dtype = torch.float32 # Data type to use for initial per-tensor reduction.
-_counter_dtype = torch.float64 # Data type to use for the internal counters.
-_rank = 0 # Rank of the current process.
-_sync_device = None # Device to use for multiprocess communication. None = single-process.
-_sync_called = False # Has _sync() been called yet?
-_counters = dict() # Running counters on each device, updated by report(): name => device => torch.Tensor
-_cumulative = dict() # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor
-
-#----------------------------------------------------------------------------
-
-def init_multiprocessing(rank, sync_device):
- r"""Initializes `torch_utils.training_stats` for collecting statistics
- across multiple processes.
-
- This function must be called after
- `torch.distributed.init_process_group()` and before `Collector.update()`.
- The call is not necessary if multi-process collection is not needed.
-
- Args:
- rank: Rank of the current process.
- sync_device: PyTorch device to use for inter-process
- communication, or None to disable multi-process
- collection. Typically `torch.device('cuda', rank)`.
- """
- global _rank, _sync_device
- assert not _sync_called
- _rank = rank
- _sync_device = sync_device
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def report(name, value):
- r"""Broadcasts the given set of scalars to all interested instances of
- `Collector`, across device and process boundaries.
-
- This function is expected to be extremely cheap and can be safely
- called from anywhere in the training loop, loss function, or inside a
- `torch.nn.Module`.
-
- Warning: The current implementation expects the set of unique names to
- be consistent across processes. Please make sure that `report()` is
- called at least once for each unique name by each process, and in the
- same order. If a given process has no scalars to broadcast, it can do
- `report(name, [])` (empty list).
-
- Args:
- name: Arbitrary string specifying the name of the statistic.
- Averages are accumulated separately for each unique name.
- value: Arbitrary set of scalars. Can be a list, tuple,
- NumPy array, PyTorch tensor, or Python scalar.
-
- Returns:
- The same `value` that was passed in.
- """
- if name not in _counters:
- _counters[name] = dict()
-
- elems = torch.as_tensor(value)
- if elems.numel() == 0:
- return value
-
- elems = elems.detach().flatten().to(_reduce_dtype)
- moments = torch.stack([
- torch.ones_like(elems).sum(),
- elems.sum(),
- elems.square().sum(),
- ])
- assert moments.ndim == 1 and moments.shape[0] == _num_moments
- moments = moments.to(_counter_dtype)
-
- device = moments.device
- if device not in _counters[name]:
- _counters[name][device] = torch.zeros_like(moments)
- _counters[name][device].add_(moments)
- return value
-
-#----------------------------------------------------------------------------
-
-def report0(name, value):
- r"""Broadcasts the given set of scalars by the first process (`rank = 0`),
- but ignores any scalars provided by the other processes.
- See `report()` for further details.
- """
- report(name, value if _rank == 0 else [])
- return value
-
-#----------------------------------------------------------------------------
-
-class Collector:
- r"""Collects the scalars broadcasted by `report()` and `report0()` and
- computes their long-term averages (mean and standard deviation) over
- user-defined periods of time.
-
- The averages are first collected into internal counters that are not
- directly visible to the user. They are then copied to the user-visible
- state as a result of calling `update()` and can then be queried using
- `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the
- internal counters for the next round, so that the user-visible state
- effectively reflects averages collected between the last two calls to
- `update()`.
-
- Args:
- regex: Regular expression defining which statistics to
- collect. The default is to collect everything.
- keep_previous: Whether to retain the previous averages if no
- scalars were collected on a given round
- (default: True).
- """
- def __init__(self, regex='.*', keep_previous=True):
- self._regex = re.compile(regex)
- self._keep_previous = keep_previous
- self._cumulative = dict()
- self._moments = dict()
- self.update()
- self._moments.clear()
-
- def names(self):
- r"""Returns the names of all statistics broadcasted so far that
- match the regular expression specified at construction time.
- """
- return [name for name in _counters if self._regex.fullmatch(name)]
-
- def update(self):
- r"""Copies current values of the internal counters to the
- user-visible state and resets them for the next round.
-
- If `keep_previous=True` was specified at construction time, the
- operation is skipped for statistics that have received no scalars
- since the last update, retaining their previous averages.
-
- This method performs a number of GPU-to-CPU transfers and one
- `torch.distributed.all_reduce()`. It is intended to be called
- periodically in the main training loop, typically once every
- N training steps.
- """
- if not self._keep_previous:
- self._moments.clear()
- for name, cumulative in _sync(self.names()):
- if name not in self._cumulative:
- self._cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
- delta = cumulative - self._cumulative[name]
- self._cumulative[name].copy_(cumulative)
- if float(delta[0]) != 0:
- self._moments[name] = delta
-
- def _get_delta(self, name):
- r"""Returns the raw moments that were accumulated for the given
- statistic between the last two calls to `update()`, or zero if
- no scalars were collected.
- """
- assert self._regex.fullmatch(name)
- if name not in self._moments:
- self._moments[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
- return self._moments[name]
-
- def num(self, name):
- r"""Returns the number of scalars that were accumulated for the given
- statistic between the last two calls to `update()`, or zero if
- no scalars were collected.
- """
- delta = self._get_delta(name)
- return int(delta[0])
-
- def mean(self, name):
- r"""Returns the mean of the scalars that were accumulated for the
- given statistic between the last two calls to `update()`, or NaN if
- no scalars were collected.
- """
- delta = self._get_delta(name)
- if int(delta[0]) == 0:
- return float('nan')
- return float(delta[1] / delta[0])
-
- def std(self, name):
- r"""Returns the standard deviation of the scalars that were
- accumulated for the given statistic between the last two calls to
- `update()`, or NaN if no scalars were collected.
- """
- delta = self._get_delta(name)
- if int(delta[0]) == 0 or not np.isfinite(float(delta[1])):
- return float('nan')
- if int(delta[0]) == 1:
- return float(0)
- mean = float(delta[1] / delta[0])
- raw_var = float(delta[2] / delta[0])
- return np.sqrt(max(raw_var - np.square(mean), 0))
-
- def as_dict(self):
- r"""Returns the averages accumulated between the last two calls to
- `update()` as an `dnnlib.EasyDict`. The contents are as follows:
-
- dnnlib.EasyDict(
- NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT),
- ...
- )
- """
- stats = dnnlib.EasyDict()
- for name in self.names():
- stats[name] = dnnlib.EasyDict(num=self.num(name), mean=self.mean(name), std=self.std(name))
- return stats
-
- def __getitem__(self, name):
- r"""Convenience getter.
- `collector[name]` is a synonym for `collector.mean(name)`.
- """
- return self.mean(name)
-
-#----------------------------------------------------------------------------
-
-def _sync(names):
- r"""Synchronize the global cumulative counters across devices and
- processes. Called internally by `Collector.update()`.
- """
- if len(names) == 0:
- return []
- global _sync_called
- _sync_called = True
-
- # Collect deltas within current rank.
- deltas = []
- device = _sync_device if _sync_device is not None else torch.device('cpu')
- for name in names:
- delta = torch.zeros([_num_moments], dtype=_counter_dtype, device=device)
- for counter in _counters[name].values():
- delta.add_(counter.to(device))
- counter.copy_(torch.zeros_like(counter))
- deltas.append(delta)
- deltas = torch.stack(deltas)
-
- # Sum deltas across ranks.
- if _sync_device is not None:
- torch.distributed.all_reduce(deltas)
-
- # Update cumulative values.
- deltas = deltas.cpu()
- for idx, name in enumerate(names):
- if name not in _cumulative:
- _cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
- _cumulative[name].add_(deltas[idx])
-
- # Return name-value pairs.
- return [(name, _cumulative[name]) for name in names]
-
-#----------------------------------------------------------------------------
diff --git a/spaces/Gradio-Blocks/Gradio_YOLOv5_Det/model_download/yolov5_model_p6_all.sh b/spaces/Gradio-Blocks/Gradio_YOLOv5_Det/model_download/yolov5_model_p6_all.sh
deleted file mode 100644
index 514a04785d2b939261a8f03770aacf1df9d0b61b..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/Gradio_YOLOv5_Det/model_download/yolov5_model_p6_all.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-cd ./yolov5
-
-# 下载YOLOv5模型
-wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n6.pt
-wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s6.pt
-wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m6.pt
-wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l6.pt
-wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x6.pt
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py
deleted file mode 100644
index 5118895f00345a42fdbc6d2edba084ccd3f1a3c8..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py'
-model = dict(
- backbone=dict(
- norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py
deleted file mode 100644
index 1b9bf60fc13364ca1b7b3842664950f653426e67..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/fcn/fcn_r101b-d8_512x1024_80k_cityscapes.py
+++ /dev/null
@@ -1,4 +0,0 @@
-_base_ = './fcn_r50-d8_512x1024_80k_cityscapes.py'
-model = dict(
- pretrained='torchvision://resnet101',
- backbone=dict(type='ResNet', depth=101))
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/backtranslation/README.md b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/backtranslation/README.md
deleted file mode 100644
index 73675f1125d80f58aa824db67d8970504d4d6b2a..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/backtranslation/README.md
+++ /dev/null
@@ -1,297 +0,0 @@
-# Understanding Back-Translation at Scale (Edunov et al., 2018)
-
-This page includes pre-trained models from the paper [Understanding Back-Translation at Scale (Edunov et al., 2018)](https://arxiv.org/abs/1808.09381).
-
-## Pre-trained models
-
-Model | Description | Dataset | Download
----|---|---|---
-`transformer.wmt18.en-de` | Transformer ([Edunov et al., 2018](https://arxiv.org/abs/1808.09381)) WMT'18 winner | [WMT'18 English-German](http://www.statmt.org/wmt18/translation-task.html) | [download (.tar.gz)](https://dl.fbaipublicfiles.com/fairseq/models/wmt18.en-de.ensemble.tar.gz) See NOTE in the archive
-
-## Example usage (torch.hub)
-
-We require a few additional Python dependencies for preprocessing:
-```bash
-pip install subword_nmt sacremoses
-```
-
-Then to generate translations from the full model ensemble:
-```python
-import torch
-
-# List available models
-torch.hub.list('pytorch/fairseq') # [..., 'transformer.wmt18.en-de', ... ]
-
-# Load the WMT'18 En-De ensemble
-en2de_ensemble = torch.hub.load(
- 'pytorch/fairseq', 'transformer.wmt18.en-de',
- checkpoint_file='wmt18.model1.pt:wmt18.model2.pt:wmt18.model3.pt:wmt18.model4.pt:wmt18.model5.pt',
- tokenizer='moses', bpe='subword_nmt')
-
-# The ensemble contains 5 models
-len(en2de_ensemble.models)
-# 5
-
-# Translate
-en2de_ensemble.translate('Hello world!')
-# 'Hallo Welt!'
-```
-
-## Training your own model (WMT'18 English-German)
-
-The following instructions can be adapted to reproduce the models from the paper.
-
-
-#### Step 1. Prepare parallel data and optionally train a baseline (English-German) model
-
-First download and preprocess the data:
-```bash
-# Download and prepare the data
-cd examples/backtranslation/
-bash prepare-wmt18en2de.sh
-cd ../..
-
-# Binarize the data
-TEXT=examples/backtranslation/wmt18_en_de
-fairseq-preprocess \
- --joined-dictionary \
- --source-lang en --target-lang de \
- --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \
- --destdir data-bin/wmt18_en_de --thresholdtgt 0 --thresholdsrc 0 \
- --workers 20
-
-# Copy the BPE code into the data-bin directory for future use
-cp examples/backtranslation/wmt18_en_de/code data-bin/wmt18_en_de/code
-```
-
-(Optionally) Train a baseline model (English-German) using just the parallel data:
-```bash
-CHECKPOINT_DIR=checkpoints_en_de_parallel
-fairseq-train --fp16 \
- data-bin/wmt18_en_de \
- --source-lang en --target-lang de \
- --arch transformer_wmt_en_de_big --share-all-embeddings \
- --dropout 0.3 --weight-decay 0.0 \
- --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
- --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \
- --lr 0.001 --lr-scheduler inverse_sqrt --warmup-updates 4000 \
- --max-tokens 3584 --update-freq 16 \
- --max-update 30000 \
- --save-dir $CHECKPOINT_DIR
-# Note: the above command assumes 8 GPUs. Adjust `--update-freq` if you have a
-# different number of GPUs.
-```
-
-Average the last 10 checkpoints:
-```bash
-python scripts/average_checkpoints.py \
- --inputs $CHECKPOINT_DIR \
- --num-epoch-checkpoints 10 \
- --output $CHECKPOINT_DIR/checkpoint.avg10.pt
-```
-
-Evaluate BLEU:
-```bash
-# tokenized BLEU on newstest2017:
-bash examples/backtranslation/tokenized_bleu.sh \
- wmt17 \
- en-de \
- data-bin/wmt18_en_de \
- data-bin/wmt18_en_de/code \
- $CHECKPOINT_DIR/checkpoint.avg10.pt
-# BLEU4 = 29.57, 60.9/35.4/22.9/15.5 (BP=1.000, ratio=1.014, syslen=63049, reflen=62152)
-# compare to 29.46 in Table 1, which is also for tokenized BLEU
-
-# generally it's better to report (detokenized) sacrebleu though:
-bash examples/backtranslation/sacrebleu.sh \
- wmt17 \
- en-de \
- data-bin/wmt18_en_de \
- data-bin/wmt18_en_de/code \
- $CHECKPOINT_DIR/checkpoint.avg10.pt
-# BLEU+case.mixed+lang.en-de+numrefs.1+smooth.exp+test.wmt17+tok.13a+version.1.4.3 = 29.0 60.6/34.7/22.4/14.9 (BP = 1.000 ratio = 1.013 hyp_len = 62099 ref_len = 61287)
-```
-
-
-#### Step 2. Back-translate monolingual German data
-
-Train a reverse model (German-English) to do the back-translation:
-```bash
-CHECKPOINT_DIR=checkpoints_de_en_parallel
-fairseq-train --fp16 \
- data-bin/wmt18_en_de \
- --source-lang de --target-lang en \
- --arch transformer_wmt_en_de_big --share-all-embeddings \
- --dropout 0.3 --weight-decay 0.0 \
- --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
- --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \
- --lr 0.001 --lr-scheduler inverse_sqrt --warmup-updates 4000 \
- --max-tokens 3584 --update-freq 16 \
- --max-update 30000 \
- --save-dir $CHECKPOINT_DIR
-# Note: the above command assumes 8 GPUs. Adjust `--update-freq` if you have a
-# different number of GPUs.
-```
-
-Let's evaluate the back-translation (BT) model to make sure it is well trained:
-```bash
-bash examples/backtranslation/sacrebleu.sh \
- wmt17 \
- de-en \
- data-bin/wmt18_en_de \
- data-bin/wmt18_en_de/code \
- $CHECKPOINT_DIR/checkpoint_best.py
-# BLEU+case.mixed+lang.de-en+numrefs.1+smooth.exp+test.wmt17+tok.13a+version.1.4.3 = 34.9 66.9/41.8/28.5/19.9 (BP = 0.983 ratio = 0.984 hyp_len = 63342 ref_len = 64399)
-# compare to the best system from WMT'17 which scored 35.1: http://matrix.statmt.org/matrix/systems_list/1868
-```
-
-Next prepare the monolingual data:
-```bash
-# Download and prepare the monolingual data
-# By default the script samples 25M monolingual sentences, which after
-# deduplication should be just over 24M sentences. These are split into 25
-# shards, each with 1M sentences (except for the last shard).
-cd examples/backtranslation/
-bash prepare-de-monolingual.sh
-cd ../..
-
-# Binarize each shard of the monolingual data
-TEXT=examples/backtranslation/wmt18_de_mono
-for SHARD in $(seq -f "%02g" 0 24); do \
- fairseq-preprocess \
- --only-source \
- --source-lang de --target-lang en \
- --joined-dictionary \
- --srcdict data-bin/wmt18_en_de/dict.de.txt \
- --testpref $TEXT/bpe.monolingual.dedup.${SHARD} \
- --destdir data-bin/wmt18_de_mono/shard${SHARD} \
- --workers 20; \
- cp data-bin/wmt18_en_de/dict.en.txt data-bin/wmt18_de_mono/shard${SHARD}/; \
-done
-```
-
-Now we're ready to perform back-translation over the monolingual data. The
-following command generates via sampling, but it's possible to use greedy
-decoding (`--beam 1`), beam search (`--beam 5`),
-top-k sampling (`--sampling --beam 1 --sampling-topk 10`), etc.:
-```bash
-mkdir backtranslation_output
-for SHARD in $(seq -f "%02g" 0 24); do \
- fairseq-generate --fp16 \
- data-bin/wmt18_de_mono/shard${SHARD} \
- --path $CHECKPOINT_DIR/checkpoint_best.pt \
- --skip-invalid-size-inputs-valid-test \
- --max-tokens 4096 \
- --sampling --beam 1 \
- > backtranslation_output/sampling.shard${SHARD}.out; \
-done
-```
-
-After BT, use the `extract_bt_data.py` script to re-combine the shards, extract
-the back-translations and apply length ratio filters:
-```bash
-python examples/backtranslation/extract_bt_data.py \
- --minlen 1 --maxlen 250 --ratio 1.5 \
- --output backtranslation_output/bt_data --srclang en --tgtlang de \
- backtranslation_output/sampling.shard*.out
-
-# Ensure lengths are the same:
-# wc -l backtranslation_output/bt_data.{en,de}
-# 21795614 backtranslation_output/bt_data.en
-# 21795614 backtranslation_output/bt_data.de
-# 43591228 total
-```
-
-Binarize the filtered BT data and combine it with the parallel data:
-```bash
-TEXT=backtranslation_output
-fairseq-preprocess \
- --source-lang en --target-lang de \
- --joined-dictionary \
- --srcdict data-bin/wmt18_en_de/dict.en.txt \
- --trainpref $TEXT/bt_data \
- --destdir data-bin/wmt18_en_de_bt \
- --workers 20
-
-# We want to train on the combined data, so we'll symlink the parallel + BT data
-# in the wmt18_en_de_para_plus_bt directory. We link the parallel data as "train"
-# and the BT data as "train1", so that fairseq will combine them automatically
-# and so that we can use the `--upsample-primary` option to upsample the
-# parallel data (if desired).
-PARA_DATA=$(readlink -f data-bin/wmt18_en_de)
-BT_DATA=$(readlink -f data-bin/wmt18_en_de_bt)
-COMB_DATA=data-bin/wmt18_en_de_para_plus_bt
-mkdir -p $COMB_DATA
-for LANG in en de; do \
- ln -s ${PARA_DATA}/dict.$LANG.txt ${COMB_DATA}/dict.$LANG.txt; \
- for EXT in bin idx; do \
- ln -s ${PARA_DATA}/train.en-de.$LANG.$EXT ${COMB_DATA}/train.en-de.$LANG.$EXT; \
- ln -s ${BT_DATA}/train.en-de.$LANG.$EXT ${COMB_DATA}/train1.en-de.$LANG.$EXT; \
- ln -s ${PARA_DATA}/valid.en-de.$LANG.$EXT ${COMB_DATA}/valid.en-de.$LANG.$EXT; \
- ln -s ${PARA_DATA}/test.en-de.$LANG.$EXT ${COMB_DATA}/test.en-de.$LANG.$EXT; \
- done; \
-done
-```
-
-
-#### 3. Train an English-German model over the combined parallel + BT data
-
-Finally we can train a model over the parallel + BT data:
-```bash
-CHECKPOINT_DIR=checkpoints_en_de_parallel_plus_bt
-fairseq-train --fp16 \
- data-bin/wmt18_en_de_para_plus_bt \
- --upsample-primary 16 \
- --source-lang en --target-lang de \
- --arch transformer_wmt_en_de_big --share-all-embeddings \
- --dropout 0.3 --weight-decay 0.0 \
- --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
- --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \
- --lr 0.0007 --lr-scheduler inverse_sqrt --warmup-updates 4000 \
- --max-tokens 3584 --update-freq 16 \
- --max-update 100000 \
- --save-dir $CHECKPOINT_DIR
-# Note: the above command assumes 8 GPUs. Adjust `--update-freq` if you have a
-# different number of GPUs.
-```
-
-Average the last 10 checkpoints:
-```bash
-python scripts/average_checkpoints.py \
- --inputs $CHECKPOINT_DIR \
- --num-epoch-checkpoints 10 \
- --output $CHECKPOINT_DIR/checkpoint.avg10.pt
-```
-
-Evaluate BLEU:
-```bash
-# tokenized BLEU on newstest2017:
-bash examples/backtranslation/tokenized_bleu.sh \
- wmt17 \
- en-de \
- data-bin/wmt18_en_de \
- data-bin/wmt18_en_de/code \
- $CHECKPOINT_DIR/checkpoint.avg10.pt
-# BLEU4 = 32.35, 64.4/38.9/26.2/18.3 (BP=0.977, ratio=0.977, syslen=60729, reflen=62152)
-# compare to 32.35 in Table 1, which is also for tokenized BLEU
-
-# generally it's better to report (detokenized) sacrebleu:
-bash examples/backtranslation/sacrebleu.sh \
- wmt17 \
- en-de \
- data-bin/wmt18_en_de \
- data-bin/wmt18_en_de/code \
- $CHECKPOINT_DIR/checkpoint.avg10.pt
-# BLEU+case.mixed+lang.en-de+numrefs.1+smooth.exp+test.wmt17+tok.13a+version.1.4.3 = 31.5 64.3/38.2/25.6/17.6 (BP = 0.971 ratio = 0.971 hyp_len = 59515 ref_len = 61287)
-```
-
-
-## Citation
-```bibtex
-@inproceedings{edunov2018backtranslation,
- title = {Understanding Back-Translation at Scale},
- author = {Edunov, Sergey and Ott, Myle and Auli, Michael and Grangier, David},
- booktitle = {Conference of the Association for Computational Linguistics (ACL)},
- year = 2018,
-}
-```
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/audio/multi_modality_dataset.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/audio/multi_modality_dataset.py
deleted file mode 100644
index 69d23d31c1eb66803fa5062b5991a7c34ab07dc7..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/data/audio/multi_modality_dataset.py
+++ /dev/null
@@ -1,263 +0,0 @@
-# Copyright (c) 2021-present, Facebook, Inc.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the LICENSE file in
-# the root directory of this source tree. An additional grant of patent rights
-# can be found in the PATENTS file in the same directory.
-
-import logging
-import math
-from typing import List, Optional, NamedTuple
-
-import numpy as np
-import torch
-from fairseq.data import (
- ConcatDataset,
- LanguagePairDataset,
- FileAudioDataset,
- data_utils,
-)
-from fairseq.data import FairseqDataset
-
-logger = logging.getLogger(__name__)
-
-
-class ModalityDatasetItem(NamedTuple):
- datasetname: str
- dataset: any
- max_positions: List[int]
- max_tokens: Optional[int] = None
- max_sentences: Optional[int] = None
-
-# MultiModalityDataset: it concate multiple datasets with different modalities.
-# Compared with ConcatDataset it can 1) sample data given the ratios for different datasets
-# 2) it adds mode to indicate what type of the data samples come from.
-# It will be used with GroupedEpochBatchIterator together to generate mini-batch with samples
-# from the same type of dataset
-# If only one dataset is used, it will perform like the original dataset with mode added
-class MultiModalityDataset(ConcatDataset):
- def __init__(self, datasets: List[ModalityDatasetItem]):
- id_to_mode = []
- dsets = []
- max_tokens = []
- max_sentences = []
- max_positions = []
- for dset in datasets:
- id_to_mode.append(dset.datasetname)
- dsets.append(dset.dataset)
- max_tokens.append(dset.max_tokens)
- max_positions.append(dset.max_positions)
- max_sentences.append(dset.max_sentences)
- weights = [1.0 for s in dsets]
- super().__init__(dsets, weights)
- self.max_tokens = max_tokens
- self.max_positions = max_positions
- self.max_sentences = max_sentences
- self.id_to_mode = id_to_mode
- self.raw_sub_batch_samplers = []
- self._cur_epoch = 0
-
- def set_epoch(self, epoch):
- super().set_epoch(epoch)
- self._cur_epoch = epoch
-
- def __getitem__(self, idx):
- dataset_idx, sample_idx = self._get_dataset_and_sample_index(idx)
- sample = self.datasets[dataset_idx][sample_idx]
- return (dataset_idx, sample)
-
- def collater(self, samples):
- if len(samples) == 0:
- return {}
- dataset_idx = samples[0][0]
- # make sure all samples in samples are from same dataset
- assert sum([0 if dataset_idx == s[0] else 1 for s in samples]) == 0
- samples = self.datasets[dataset_idx].collater([x[1] for x in samples])
- # add mode
- samples["net_input"]["mode"] = self.id_to_mode[dataset_idx]
-
- return samples
-
- def size(self, index: int):
- if len(self.datasets) == 1:
- return self.datasets[0].size(index)
- return super().size(index)
-
- @property
- def sizes(self):
- if len(self.datasets) == 1:
- return self.datasets[0].sizes
- super().sizes
-
- def ordered_indices(self):
- """
- Returns indices sorted by length. So less padding is needed.
- """
- if len(self.datasets) == 1:
- return self.datasets[0].ordered_indices()
- indices_group = []
- for d_idx, ds in enumerate(self.datasets):
- sample_num = self.cumulative_sizes[d_idx]
- if d_idx > 0:
- sample_num = sample_num - self.cumulative_sizes[d_idx - 1]
- assert sample_num == len(ds)
- indices_group.append(ds.ordered_indices())
- return indices_group
-
- def get_raw_batch_samplers(self, required_batch_size_multiple, seed):
- if len(self.raw_sub_batch_samplers) > 0:
- logger.info(" raw_sub_batch_samplers exists. No action is taken")
- return
- with data_utils.numpy_seed(seed):
- indices = self.ordered_indices()
- for i, ds in enumerate(self.datasets):
- indices[i] = ds.filter_indices_by_size(
- indices[i],
- self.max_positions[i],
- )[0]
- sub_batch_sampler = ds.batch_by_size(
- indices[i],
- max_tokens=self.max_tokens[i],
- max_sentences=self.max_sentences[i],
- required_batch_size_multiple=required_batch_size_multiple,
- )
- self.raw_sub_batch_samplers.append(sub_batch_sampler)
-
- def get_batch_samplers(self, mult_ratios, required_batch_size_multiple, seed):
- self.get_raw_batch_samplers(required_batch_size_multiple, seed)
- batch_samplers = []
- for i, _ in enumerate(self.datasets):
- if i > 0:
- sub_batch_sampler = [
- [y + self.cumulative_sizes[i - 1] for y in x]
- for x in self.raw_sub_batch_samplers[i]
- ]
- else:
- sub_batch_sampler = list(self.raw_sub_batch_samplers[i])
- smp_r = mult_ratios[i]
- if smp_r != 1:
- is_increase = "increased" if smp_r > 1 else "decreased"
- logger.info(
- "number of batch for the dataset {} is {} from {} to {}".format(
- self.id_to_mode[i],
- is_increase,
- len(sub_batch_sampler),
- int(len(sub_batch_sampler) * smp_r),
- )
- )
- mul_samplers = []
- for _ in range(math.floor(smp_r)):
- mul_samplers = mul_samplers + sub_batch_sampler
- if math.floor(smp_r) != smp_r:
- with data_utils.numpy_seed(seed + self._cur_epoch):
- np.random.shuffle(sub_batch_sampler)
- smp_num = int(
- (smp_r - math.floor(smp_r)) * len(sub_batch_sampler)
- )
- mul_samplers = mul_samplers + sub_batch_sampler[:smp_num]
- sub_batch_sampler = mul_samplers
- else:
- logger.info(
- "dataset {} batch number is {} ".format(
- self.id_to_mode[i], len(sub_batch_sampler)
- )
- )
- batch_samplers.append(sub_batch_sampler)
-
- return batch_samplers
-
-
-class LangPairMaskDataset(FairseqDataset):
- def __init__(
- self,
- dataset: LanguagePairDataset,
- src_eos: int,
- src_bos: Optional[int] = None,
- noise_id: Optional[int] = -1,
- mask_ratio: Optional[float] = 0,
- mask_type: Optional[str] = "random",
- ):
- self.dataset = dataset
- self.src_eos = src_eos
- self.src_bos = src_bos
- self.noise_id = noise_id
- self.mask_ratio = mask_ratio
- self.mask_type = mask_type
- assert mask_type in ("random", "tail")
-
- @property
- def src_sizes(self):
- return self.dataset.src_sizes
-
- @property
- def tgt_sizes(self):
- return self.dataset.tgt_sizes
-
- @property
- def sizes(self):
- # dataset.sizes can be a dynamically computed sizes:
- return self.dataset.sizes
-
- def get_batch_shapes(self):
- return self.dataset.buckets
-
- def num_tokens_vec(self, indices):
- return self.dataset.num_tokens_vec(indices)
-
- def __len__(self):
- return len(self.dataset)
-
- def num_tokens(self, index):
- return self.dataset.num_tokens(index)
-
- def size(self, index):
- return self.dataset.size(index)
-
- def ordered_indices(self):
- return self.dataset.ordered_indices()
-
- @property
- def supports_prefetch(self):
- return getattr(self.dataset, "supports_prefetch", False)
-
- def prefetch(self, indices):
- return self.dataset.prefetch(indices)
-
- def mask_src_tokens(self, sample):
- src_item = sample["source"]
- mask = None
- if self.mask_type == "random":
- mask = torch.rand(len(src_item)).le(self.mask_ratio)
- else:
- mask = torch.ones(len(src_item))
- mask[: int(len(src_item) * (1 - self.mask_ratio))] = 0
- mask = mask.eq(1)
- if src_item[0] == self.src_bos:
- mask[0] = False
- if src_item[-1] == self.src_eos:
- mask[-1] = False
- mask_src_item = src_item.masked_fill(mask, self.noise_id)
- smp = {"id": sample["id"], "source": mask_src_item, "target": sample["target"]}
- return smp
-
- def __getitem__(self, index):
- sample = self.dataset[index]
- if self.mask_ratio > 0:
- sample = self.mask_src_tokens(sample)
- return sample
-
- def collater(self, samples, pad_to_length=None):
- return self.dataset.collater(samples, pad_to_length)
-
-
-class FileAudioDatasetWrapper(FileAudioDataset):
- def collater(self, samples):
- samples = super().collater(samples)
- if len(samples) == 0:
- return {}
- samples["net_input"]["src_tokens"] = samples["net_input"]["source"]
- samples["net_input"]["prev_output_tokens"] = None
- del samples["net_input"]["source"]
- samples["net_input"]["src_lengths"] = None
- samples["net_input"]["alignment"] = None
- return samples
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/dataclass/utils.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/dataclass/utils.py
deleted file mode 100644
index 1320ec473756c78ec949f72f9260420c19caff0f..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/dataclass/utils.py
+++ /dev/null
@@ -1,493 +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 ast
-import inspect
-import logging
-import os
-import re
-from argparse import ArgumentError, ArgumentParser, Namespace
-from dataclasses import _MISSING_TYPE, MISSING, is_dataclass
-from enum import Enum
-from typing import Any, Dict, List, Optional, Tuple, Type
-
-from fairseq.dataclass import FairseqDataclass
-from fairseq.dataclass.configs import FairseqConfig
-from hydra.core.global_hydra import GlobalHydra
-from hydra.experimental import compose, initialize
-from omegaconf import DictConfig, OmegaConf, open_dict, _utils
-
-logger = logging.getLogger(__name__)
-
-
-def eval_str_list(x, x_type=float):
- if x is None:
- return None
- if isinstance(x, str):
- if len(x) == 0:
- return []
- x = ast.literal_eval(x)
- try:
- return list(map(x_type, x))
- except TypeError:
- return [x_type(x)]
-
-
-def interpret_dc_type(field_type):
- if isinstance(field_type, str):
- raise RuntimeError("field should be a type")
-
- if field_type == Any:
- return str
-
- typestring = str(field_type)
- if re.match(
- r"(typing.|^)Union\[(.*), NoneType\]$", typestring
- ) or typestring.startswith("typing.Optional"):
- return field_type.__args__[0]
- return field_type
-
-
-def gen_parser_from_dataclass(
- parser: ArgumentParser,
- dataclass_instance: FairseqDataclass,
- delete_default: bool = False,
- with_prefix: Optional[str] = None,
-) -> None:
- """
- convert a dataclass instance to tailing parser arguments.
-
- If `with_prefix` is provided, prefix all the keys in the resulting parser with it. It means that we are
- building a flat namespace from a structured dataclass (see transformer_config.py for example).
- """
-
- def argparse_name(name: str):
- if name == "data" and (with_prefix is None or with_prefix == ''):
- # normally data is positional args, so we don't add the -- nor the prefix
- return name
- if name == "_name":
- # private member, skip
- return None
- full_name = "--" + name.replace("_", "-")
- if with_prefix is not None and with_prefix != '':
- # if a prefix is specified, construct the prefixed arg name
- full_name = with_prefix + "-" + full_name[2:] # strip -- when composing
- return full_name
-
- def get_kwargs_from_dc(
- dataclass_instance: FairseqDataclass, k: str
- ) -> Dict[str, Any]:
- """k: dataclass attributes"""
-
- kwargs = {}
-
- field_type = dataclass_instance._get_type(k)
- inter_type = interpret_dc_type(field_type)
-
- field_default = dataclass_instance._get_default(k)
-
- if isinstance(inter_type, type) and issubclass(inter_type, Enum):
- field_choices = [t.value for t in list(inter_type)]
- else:
- field_choices = None
-
- field_help = dataclass_instance._get_help(k)
- field_const = dataclass_instance._get_argparse_const(k)
-
- if isinstance(field_default, str) and field_default.startswith("${"):
- kwargs["default"] = field_default
- else:
- if field_default is MISSING:
- kwargs["required"] = True
- if field_choices is not None:
- kwargs["choices"] = field_choices
- if (
- isinstance(inter_type, type)
- and (issubclass(inter_type, List) or issubclass(inter_type, Tuple))
- ) or ("List" in str(inter_type) or "Tuple" in str(inter_type)):
- if "int" in str(inter_type):
- kwargs["type"] = lambda x: eval_str_list(x, int)
- elif "float" in str(inter_type):
- kwargs["type"] = lambda x: eval_str_list(x, float)
- elif "str" in str(inter_type):
- kwargs["type"] = lambda x: eval_str_list(x, str)
- else:
- raise NotImplementedError(
- "parsing of type " + str(inter_type) + " is not implemented"
- )
- if field_default is not MISSING:
- kwargs["default"] = (
- ",".join(map(str, field_default))
- if field_default is not None
- else None
- )
- elif (
- isinstance(inter_type, type) and issubclass(inter_type, Enum)
- ) or "Enum" in str(inter_type):
- kwargs["type"] = str
- if field_default is not MISSING:
- if isinstance(field_default, Enum):
- kwargs["default"] = field_default.value
- else:
- kwargs["default"] = field_default
- elif inter_type is bool:
- kwargs["action"] = (
- "store_false" if field_default is True else "store_true"
- )
- kwargs["default"] = field_default
- else:
- kwargs["type"] = inter_type
- if field_default is not MISSING:
- kwargs["default"] = field_default
-
- # build the help with the hierarchical prefix
- if with_prefix is not None and with_prefix != '' and field_help is not None:
- field_help = with_prefix[2:] + ': ' + field_help
-
- kwargs["help"] = field_help
- if field_const is not None:
- kwargs["const"] = field_const
- kwargs["nargs"] = "?"
-
- return kwargs
-
- for k in dataclass_instance._get_all_attributes():
- field_name = argparse_name(dataclass_instance._get_name(k))
- field_type = dataclass_instance._get_type(k)
- if field_name is None:
- continue
- elif inspect.isclass(field_type) and issubclass(field_type, FairseqDataclass):
- # for fields that are of type FairseqDataclass, we can recursively
- # add their fields to the namespace (so we add the args from model, task, etc. to the root namespace)
- prefix = None
- if with_prefix is not None:
- # if a prefix is specified, then we don't want to copy the subfields directly to the root namespace
- # but we prefix them with the name of the current field.
- prefix = field_name
- gen_parser_from_dataclass(parser, field_type(), delete_default, prefix)
- continue
-
- kwargs = get_kwargs_from_dc(dataclass_instance, k)
-
- field_args = [field_name]
- alias = dataclass_instance._get_argparse_alias(k)
- if alias is not None:
- field_args.append(alias)
-
- if "default" in kwargs:
- if isinstance(kwargs["default"], str) and kwargs["default"].startswith(
- "${"
- ):
- if kwargs["help"] is None:
- # this is a field with a name that will be added elsewhere
- continue
- else:
- del kwargs["default"]
- if delete_default and "default" in kwargs:
- del kwargs["default"]
- try:
- parser.add_argument(*field_args, **kwargs)
- except ArgumentError:
- pass
-
-
-def _set_legacy_defaults(args, cls):
- """Helper to set default arguments based on *add_args*."""
- if not hasattr(cls, "add_args"):
- return
-
- import argparse
-
- parser = argparse.ArgumentParser(
- argument_default=argparse.SUPPRESS, allow_abbrev=False
- )
- cls.add_args(parser)
- # copied from argparse.py:
- defaults = argparse.Namespace()
- for action in parser._actions:
- if action.dest is not argparse.SUPPRESS:
- if not hasattr(defaults, action.dest):
- if action.default is not argparse.SUPPRESS:
- setattr(defaults, action.dest, action.default)
- for key, default_value in vars(defaults).items():
- if not hasattr(args, key):
- setattr(args, key, default_value)
-
-
-def _override_attr(
- sub_node: str, data_class: Type[FairseqDataclass], args: Namespace
-) -> List[str]:
- overrides = []
-
- if not inspect.isclass(data_class) or not issubclass(data_class, FairseqDataclass):
- return overrides
-
- def get_default(f):
- if not isinstance(f.default_factory, _MISSING_TYPE):
- return f.default_factory()
- return f.default
-
- for k, v in data_class.__dataclass_fields__.items():
- if k.startswith("_"):
- # private member, skip
- continue
-
- val = get_default(v) if not hasattr(args, k) else getattr(args, k)
-
- field_type = interpret_dc_type(v.type)
- if (
- isinstance(val, str)
- and not val.startswith("${") # not interpolation
- and field_type != str
- and (
- not inspect.isclass(field_type) or not issubclass(field_type, Enum)
- ) # not choices enum
- ):
- # upgrade old models that stored complex parameters as string
- val = ast.literal_eval(val)
-
- if isinstance(val, tuple):
- val = list(val)
-
- v_type = getattr(v.type, "__origin__", None)
- if (
- (v_type is List or v_type is list or v_type is Optional)
- # skip interpolation
- and not (isinstance(val, str) and val.startswith("${"))
- ):
- # if type is int but val is float, then we will crash later - try to convert here
- if hasattr(v.type, "__args__"):
- t_args = v.type.__args__
- if len(t_args) == 1 and (t_args[0] is float or t_args[0] is int):
- val = list(map(t_args[0], val))
- elif val is not None and (
- field_type is int or field_type is bool or field_type is float
- ):
- try:
- val = field_type(val)
- except:
- pass # ignore errors here, they are often from interpolation args
-
- if val is None:
- overrides.append("{}.{}=null".format(sub_node, k))
- elif val == "":
- overrides.append("{}.{}=''".format(sub_node, k))
- elif isinstance(val, str):
- val = val.replace("'", r"\'")
- overrides.append("{}.{}='{}'".format(sub_node, k, val))
- elif isinstance(val, FairseqDataclass):
- overrides += _override_attr(f"{sub_node}.{k}", type(val), args)
- elif isinstance(val, Namespace):
- sub_overrides, _ = override_module_args(val)
- for so in sub_overrides:
- overrides.append(f"{sub_node}.{k}.{so}")
- else:
- overrides.append("{}.{}={}".format(sub_node, k, val))
-
- return overrides
-
-
-def migrate_registry(
- name, value, registry, args, overrides, deletes, use_name_as_val=False
-):
- if value in registry:
- overrides.append("{}={}".format(name, value))
- overrides.append("{}._name={}".format(name, value))
- overrides.extend(_override_attr(name, registry[value], args))
- elif use_name_as_val and value is not None:
- overrides.append("{}={}".format(name, value))
- else:
- deletes.append(name)
-
-
-def override_module_args(args: Namespace) -> Tuple[List[str], List[str]]:
- """use the field in args to overrides those in cfg"""
- overrides = []
- deletes = []
-
- for k in FairseqConfig.__dataclass_fields__.keys():
- overrides.extend(
- _override_attr(k, FairseqConfig.__dataclass_fields__[k].type, args)
- )
-
- if args is not None:
- if hasattr(args, "task"):
- from fairseq.tasks import TASK_DATACLASS_REGISTRY
-
- migrate_registry(
- "task", args.task, TASK_DATACLASS_REGISTRY, args, overrides, deletes
- )
- else:
- deletes.append("task")
-
- # these options will be set to "None" if they have not yet been migrated
- # so we can populate them with the entire flat args
- CORE_REGISTRIES = {"criterion", "optimizer", "lr_scheduler"}
-
- from fairseq.registry import REGISTRIES
-
- for k, v in REGISTRIES.items():
- if hasattr(args, k):
- migrate_registry(
- k,
- getattr(args, k),
- v["dataclass_registry"],
- args,
- overrides,
- deletes,
- use_name_as_val=k not in CORE_REGISTRIES,
- )
- else:
- deletes.append(k)
-
- no_dc = True
- if hasattr(args, "arch"):
- from fairseq.models import ARCH_MODEL_REGISTRY, ARCH_MODEL_NAME_REGISTRY
-
- if args.arch in ARCH_MODEL_REGISTRY:
- m_cls = ARCH_MODEL_REGISTRY[args.arch]
- dc = getattr(m_cls, "__dataclass", None)
- if dc is not None:
- m_name = ARCH_MODEL_NAME_REGISTRY[args.arch]
- overrides.append("model={}".format(m_name))
- overrides.append("model._name={}".format(args.arch))
- # override model params with those exist in args
- overrides.extend(_override_attr("model", dc, args))
- no_dc = False
- if no_dc:
- deletes.append("model")
-
- return overrides, deletes
-
-
-class omegaconf_no_object_check:
- def __init__(self):
- self.old_is_primitive = _utils.is_primitive_type
-
- def __enter__(self):
- _utils.is_primitive_type = lambda _: True
-
- def __exit__(self, type, value, traceback):
- _utils.is_primitive_type = self.old_is_primitive
-
-
-def convert_namespace_to_omegaconf(args: Namespace) -> DictConfig:
- """Convert a flat argparse.Namespace to a structured DictConfig."""
-
- # Here we are using field values provided in args to override counterparts inside config object
- overrides, deletes = override_module_args(args)
-
- # configs will be in fairseq/config after installation
- config_path = os.path.join("..", "config")
-
- GlobalHydra.instance().clear()
-
- with initialize(config_path=config_path):
- try:
- composed_cfg = compose("config", overrides=overrides, strict=False)
- except:
- logger.error("Error when composing. Overrides: " + str(overrides))
- raise
-
- for k in deletes:
- composed_cfg[k] = None
-
- cfg = OmegaConf.create(
- OmegaConf.to_container(composed_cfg, resolve=True, enum_to_str=True)
- )
-
- # hack to be able to set Namespace in dict config. this should be removed when we update to newer
- # omegaconf version that supports object flags, or when we migrate all existing models
- from omegaconf import _utils
-
- with omegaconf_no_object_check():
- if cfg.task is None and getattr(args, "task", None):
- cfg.task = Namespace(**vars(args))
- from fairseq.tasks import TASK_REGISTRY
-
- _set_legacy_defaults(cfg.task, TASK_REGISTRY[args.task])
- cfg.task._name = args.task
- if cfg.model is None and getattr(args, "arch", None):
- cfg.model = Namespace(**vars(args))
- from fairseq.models import ARCH_MODEL_REGISTRY
-
- _set_legacy_defaults(cfg.model, ARCH_MODEL_REGISTRY[args.arch])
- cfg.model._name = args.arch
- if cfg.optimizer is None and getattr(args, "optimizer", None):
- cfg.optimizer = Namespace(**vars(args))
- from fairseq.optim import OPTIMIZER_REGISTRY
-
- _set_legacy_defaults(cfg.optimizer, OPTIMIZER_REGISTRY[args.optimizer])
- cfg.optimizer._name = args.optimizer
- if cfg.lr_scheduler is None and getattr(args, "lr_scheduler", None):
- cfg.lr_scheduler = Namespace(**vars(args))
- from fairseq.optim.lr_scheduler import LR_SCHEDULER_REGISTRY
-
- _set_legacy_defaults(
- cfg.lr_scheduler, LR_SCHEDULER_REGISTRY[args.lr_scheduler]
- )
- cfg.lr_scheduler._name = args.lr_scheduler
- if cfg.criterion is None and getattr(args, "criterion", None):
- cfg.criterion = Namespace(**vars(args))
- from fairseq.criterions import CRITERION_REGISTRY
-
- _set_legacy_defaults(cfg.criterion, CRITERION_REGISTRY[args.criterion])
- cfg.criterion._name = args.criterion
-
- OmegaConf.set_struct(cfg, True)
- return cfg
-
-
-def overwrite_args_by_name(cfg: DictConfig, overrides: Dict[str, any]):
- # this will be deprecated when we get rid of argparse and model_overrides logic
-
- from fairseq.registry import REGISTRIES
-
- with open_dict(cfg):
- for k in cfg.keys():
- # "k in cfg" will return false if its a "mandatory value (e.g. ???)"
- if k in cfg and isinstance(cfg[k], DictConfig):
- if k in overrides and isinstance(overrides[k], dict):
- for ok, ov in overrides[k].items():
- if isinstance(ov, dict) and cfg[k][ok] is not None:
- overwrite_args_by_name(cfg[k][ok], ov)
- else:
- cfg[k][ok] = ov
- else:
- overwrite_args_by_name(cfg[k], overrides)
- elif k in cfg and isinstance(cfg[k], Namespace):
- for override_key, val in overrides.items():
- setattr(cfg[k], override_key, val)
- elif k in overrides:
- if (
- k in REGISTRIES
- and overrides[k] in REGISTRIES[k]["dataclass_registry"]
- ):
- cfg[k] = DictConfig(
- REGISTRIES[k]["dataclass_registry"][overrides[k]]
- )
- overwrite_args_by_name(cfg[k], overrides)
- cfg[k]._name = overrides[k]
- else:
- cfg[k] = overrides[k]
-
-
-def merge_with_parent(dc: FairseqDataclass, cfg: DictConfig, remove_missing=True):
- if remove_missing:
-
- if is_dataclass(dc):
- target_keys = set(dc.__dataclass_fields__.keys())
- else:
- target_keys = set(dc.keys())
-
- with open_dict(cfg):
- for k in list(cfg.keys()):
- if k not in target_keys:
- del cfg[k]
-
- merged_cfg = OmegaConf.merge(dc, cfg)
- merged_cfg.__dict__["_parent"] = cfg.__dict__["_parent"]
- OmegaConf.set_struct(merged_cfg, True)
- return merged_cfg
diff --git a/spaces/HemanthSai7/IntelligentQuestionGenerator/src/Pipeline/Reader.py b/spaces/HemanthSai7/IntelligentQuestionGenerator/src/Pipeline/Reader.py
deleted file mode 100644
index f3d5e5056605fbdca7f9ec49474689aada31cb88..0000000000000000000000000000000000000000
--- a/spaces/HemanthSai7/IntelligentQuestionGenerator/src/Pipeline/Reader.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import PyPDF2
-import fitz
-
-class PdfReader:
- def __init__(self, filename):
- self.filename = filename
-
- def total_pages(self):
- with open(self.filename, 'rb') as f:
- pdf_reader = PyPDF2.PdfFileReader(f)
- return pdf_reader.numPages
-
- def read(self):
- with open(self.filename, 'rb') as f:
- pdf_reader = PyPDF2.PdfFileReader(f)
- num_pages = pdf_reader.numPages
- count = 0
- text = ''
- while count < num_pages:
- text += pdf_reader.getPage(count).extractText()
- count += 1
- return text
-
- def read_pages(self, start_page, end_page):
- with open(self.filename, 'rb') as f:
- pdf_reader = PyPDF2.PdfFileReader(f)
- text = ''
- for page in range(start_page, end_page):
- text += pdf_reader.getPage(page).extractText()
- return text
-
- def extract_images(self):
- doc = fitz.open(self.filename)
- for page_index in range(len(doc)):
- for img in doc.get_page_images(page_index):
- xref = img[0]
- pix = fitz.Pixmap(doc, xref)
- if pix.n < 5: # GRAY or RGB
- pix.save(f"{xref}.png")
- else: # convert to RGB
- pix1 = fitz.Pixmap(fitz.csRGB, pix)
- pix1.save(f"{xref}.png")
- pix1 = None
- pix = None
-
-class ExtractedText(PdfReader):
- def __init__(self, filename, output_filename):
- super().__init__(filename)
- self.output_filename = output_filename
-
- def save(self,start_page, end_page):
- with open(self.filename,'rb') as f:
- pdf_reader = PyPDF2.PdfFileReader(f)
- text = ''
- for page in range(start_page, end_page):
- text += pdf_reader.getPage(page).extractText()
- with open(self.output_filename, 'w',encoding='utf-8') as f:
- f.write(text)
diff --git a/spaces/ICML2022/OFA/fairseq/fairseq/modules/quant_noise.py b/spaces/ICML2022/OFA/fairseq/fairseq/modules/quant_noise.py
deleted file mode 100644
index d777dfbb6c1bf6a9b769dfdaec35d5ef084c8a8b..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/fairseq/modules/quant_noise.py
+++ /dev/null
@@ -1,107 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn as nn
-
-
-def quant_noise(module, p, block_size):
- """
- Wraps modules and applies quantization noise to the weights for
- subsequent quantization with Iterative Product Quantization as
- described in "Training with Quantization Noise for Extreme Model Compression"
-
- Args:
- - module: nn.Module
- - p: amount of Quantization Noise
- - block_size: size of the blocks for subsequent quantization with iPQ
-
- Remarks:
- - Module weights must have the right sizes wrt the block size
- - Only Linear, Embedding and Conv2d modules are supported for the moment
- - For more detail on how to quantize by blocks with convolutional weights,
- see "And the Bit Goes Down: Revisiting the Quantization of Neural Networks"
- - We implement the simplest form of noise here as stated in the paper
- which consists in randomly dropping blocks
- """
-
- # if no quantization noise, don't register hook
- if p <= 0:
- return module
-
- # supported modules
- assert isinstance(module, (nn.Linear, nn.Embedding, nn.Conv2d))
-
- # test whether module.weight has the right sizes wrt block_size
- is_conv = module.weight.ndim == 4
-
- # 2D matrix
- if not is_conv:
- assert (
- module.weight.size(1) % block_size == 0
- ), "Input features must be a multiple of block sizes"
-
- # 4D matrix
- else:
- # 1x1 convolutions
- if module.kernel_size == (1, 1):
- assert (
- module.in_channels % block_size == 0
- ), "Input channels must be a multiple of block sizes"
- # regular convolutions
- else:
- k = module.kernel_size[0] * module.kernel_size[1]
- assert k % block_size == 0, "Kernel size must be a multiple of block size"
-
- def _forward_pre_hook(mod, input):
- # no noise for evaluation
- if mod.training:
- if not is_conv:
- # gather weight and sizes
- weight = mod.weight
- in_features = weight.size(1)
- out_features = weight.size(0)
-
- # split weight matrix into blocks and randomly drop selected blocks
- mask = torch.zeros(
- in_features // block_size * out_features, device=weight.device
- )
- mask.bernoulli_(p)
- mask = mask.repeat_interleave(block_size, -1).view(-1, in_features)
-
- else:
- # gather weight and sizes
- weight = mod.weight
- in_channels = mod.in_channels
- out_channels = mod.out_channels
-
- # split weight matrix into blocks and randomly drop selected blocks
- if mod.kernel_size == (1, 1):
- mask = torch.zeros(
- int(in_channels // block_size * out_channels),
- device=weight.device,
- )
- mask.bernoulli_(p)
- mask = mask.repeat_interleave(block_size, -1).view(-1, in_channels)
- else:
- mask = torch.zeros(
- weight.size(0), weight.size(1), device=weight.device
- )
- mask.bernoulli_(p)
- mask = (
- mask.unsqueeze(2)
- .unsqueeze(3)
- .repeat(1, 1, mod.kernel_size[0], mod.kernel_size[1])
- )
-
- # scale weights and apply mask
- mask = mask.to(
- torch.bool
- ) # x.bool() is not currently supported in TorchScript
- s = 1 / (1 - p)
- mod.weight.data = s * weight.masked_fill(mask, 0)
-
- module.register_forward_pre_hook(_forward_pre_hook)
- return module
diff --git a/spaces/ICML2022/resefa/utils/loggers/dummy_logger.py b/spaces/ICML2022/resefa/utils/loggers/dummy_logger.py
deleted file mode 100644
index fb6220e6757c6ce4516834f5102cd0957f8669df..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/resefa/utils/loggers/dummy_logger.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# python3.7
-"""Contains the class of dummy logger.
-
-This logger has all expected logging functions but behaves silently, which is
-very useful in multi-processing mode. Only the chief process can have the logger
-with normal behavior.
-"""
-
-from .base_logger import BaseLogger
-
-__all__ = ['DummyLogger']
-
-
-class DummyLogger(BaseLogger):
- """Implements a dummy logger which logs nothing."""
-
- def __init__(self,
- logger_name='logger',
- logfile=None,
- screen_level=None,
- file_level=None,
- indent_space=4,
- verbose_log=False):
- super().__init__(logger_name=logger_name,
- logfile=logfile,
- screen_level=screen_level,
- file_level=file_level,
- indent_space=indent_space,
- verbose_log=verbose_log)
-
- def _log(self, message, **kwargs):
- return
-
- def _debug(self, message, **kwargs):
- return
-
- def _info(self, message, **kwargs):
- return
-
- def _warning(self, message, **kwargs):
- return
-
- def _error(self, message, **kwargs):
- return
-
- def _exception(self, message, **kwargs):
- return
-
- def _critical(self, message, **kwargs):
- return
-
- def _print(self, *messages, **kwargs):
- return
-
- def init_pbar(self, leave=False):
- return
-
- def add_pbar_task(self, name, total, **kwargs):
- return -1
-
- def update_pbar(self, task_id, advance=1):
- return
-
- def close_pbar(self):
- return
diff --git a/spaces/Ibtehaj10/cheating-detection/centroidtracker.py b/spaces/Ibtehaj10/cheating-detection/centroidtracker.py
deleted file mode 100644
index 39332a797c0f5ab3c2022fa9b9cdfaa16e40dfdd..0000000000000000000000000000000000000000
--- a/spaces/Ibtehaj10/cheating-detection/centroidtracker.py
+++ /dev/null
@@ -1,172 +0,0 @@
-# import the necessary packages
-from scipy.spatial import distance as dist
-from collections import OrderedDict
-import numpy as np
-
-
-class CentroidTracker:
- def __init__(self, maxDisappeared=50, maxDistance=50):
- # initialize the next unique object ID along with two ordered
- # dictionaries used to keep track of mapping a given object
- # ID to its centroid and number of consecutive frames it has
- # been marked as "disappeared", respectively
- self.nextObjectID = 0
- self.objects = OrderedDict()
- self.disappeared = OrderedDict()
- self.bbox = OrderedDict() # CHANGE
-
- # store the number of maximum consecutive frames a given
- # object is allowed to be marked as "disappeared" until we
- # need to deregister the object from tracking
- self.maxDisappeared = maxDisappeared
-
- # store the maximum distance between centroids to associate
- # an object -- if the distance is larger than this maximum
- # distance we'll start to mark the object as "disappeared"
- self.maxDistance = maxDistance
-
- def register(self, centroid, inputRect):
- # when registering an object we use the next available object
- # ID to store the centroid
- self.objects[self.nextObjectID] = centroid
- self.bbox[self.nextObjectID] = inputRect # CHANGE
- self.disappeared[self.nextObjectID] = 0
- self.nextObjectID += 1
-
- def deregister(self, objectID):
- # to deregister an object ID we delete the object ID from
- # both of our respective dictionaries
- del self.objects[objectID]
- del self.disappeared[objectID]
- del self.bbox[objectID] # CHANGE
-
- def update(self, rects):
- # check to see if the list of input bounding box rectangles
- # is empty
- if len(rects) == 0:
- # loop over any existing tracked objects and mark them
- # as disappeared
- for objectID in list(self.disappeared.keys()):
- self.disappeared[objectID] += 1
-
- # if we have reached a maximum number of consecutive
- # frames where a given object has been marked as
- # missing, deregister it
- if self.disappeared[objectID] > self.maxDisappeared:
- self.deregister(objectID)
-
- # return early as there are no centroids or tracking info
- # to update
- # return self.objects
- return self.bbox
-
- # initialize an array of input centroids for the current frame
- inputCentroids = np.zeros((len(rects), 2), dtype="int")
- inputRects = []
- # loop over the bounding box rectangles
- for (i, (startX, startY, endX, endY)) in enumerate(rects):
- # use the bounding box coordinates to derive the centroid
- cX = int((startX + endX) / 2.0)
- cY = int((startY + endY) / 2.0)
- inputCentroids[i] = (cX, cY)
- inputRects.append(rects[i]) # CHANGE
-
- # if we are currently not tracking any objects take the input
- # centroids and register each of them
- if len(self.objects) == 0:
- for i in range(0, len(inputCentroids)):
- self.register(inputCentroids[i], inputRects[i]) # CHANGE
-
- # otherwise, are are currently tracking objects so we need to
- # try to match the input centroids to existing object
- # centroids
- else:
- # grab the set of object IDs and corresponding centroids
- objectIDs = list(self.objects.keys())
- objectCentroids = list(self.objects.values())
-
- # compute the distance between each pair of object
- # centroids and input centroids, respectively -- our
- # goal will be to match an input centroid to an existing
- # object centroid
- D = dist.cdist(np.array(objectCentroids), inputCentroids)
-
- # in order to perform this matching we must (1) find the
- # smallest value in each row and then (2) sort the row
- # indexes based on their minimum values so that the row
- # with the smallest value as at the *front* of the index
- # list
- rows = D.min(axis=1).argsort()
-
- # next, we perform a similar process on the columns by
- # finding the smallest value in each column and then
- # sorting using the previously computed row index list
- cols = D.argmin(axis=1)[rows]
-
- # in order to determine if we need to update, register,
- # or deregister an object we need to keep track of which
- # of the rows and column indexes we have already examined
- usedRows = set()
- usedCols = set()
-
- # loop over the combination of the (row, column) index
- # tuples
- for (row, col) in zip(rows, cols):
- # if we have already examined either the row or
- # column value before, ignore it
- if row in usedRows or col in usedCols:
- continue
-
- # if the distance between centroids is greater than
- # the maximum distance, do not associate the two
- # centroids to the same object
- if D[row, col] > self.maxDistance:
- continue
-
- # otherwise, grab the object ID for the current row,
- # set its new centroid, and reset the disappeared
- # counter
- objectID = objectIDs[row]
- self.objects[objectID] = inputCentroids[col]
- self.bbox[objectID] = inputRects[col] # CHANGE
- self.disappeared[objectID] = 0
-
- # indicate that we have examined each of the row and
- # column indexes, respectively
- usedRows.add(row)
- usedCols.add(col)
-
- # compute both the row and column index we have NOT yet
- # examined
- unusedRows = set(range(0, D.shape[0])).difference(usedRows)
- unusedCols = set(range(0, D.shape[1])).difference(usedCols)
-
- # in the event that the number of object centroids is
- # equal or greater than the number of input centroids
- # we need to check and see if some of these objects have
- # potentially disappeared
- if D.shape[0] >= D.shape[1]:
- # loop over the unused row indexes
- for row in unusedRows:
- # grab the object ID for the corresponding row
- # index and increment the disappeared counter
- objectID = objectIDs[row]
- self.disappeared[objectID] += 1
-
- # check to see if the number of consecutive
- # frames the object has been marked "disappeared"
- # for warrants deregistering the object
- if self.disappeared[objectID] > self.maxDisappeared:
- self.deregister(objectID)
-
- # otherwise, if the number of input centroids is greater
- # than the number of existing object centroids we need to
- # register each new input centroid as a trackable object
- else:
- for col in unusedCols:
- self.register(inputCentroids[col], inputRects[col])
-
- # return the set of trackable objects
- # return self.objects
- return self.bbox
-
diff --git a/spaces/Ibtehaj10/cheating-detection/pages/signup.py b/spaces/Ibtehaj10/cheating-detection/pages/signup.py
deleted file mode 100644
index ef614763a8094efd62c0ea99c2ddc51a0e7a70ee..0000000000000000000000000000000000000000
--- a/spaces/Ibtehaj10/cheating-detection/pages/signup.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import pickle
-from pathlib import Path
-import streamlit as st
-import os
-import pandas as pd
-import csv
-data = ['Id','Password']
-
-# with open('LoginStatus.csv', 'w') as file:
-# writer = csv.writer(file)
-# writer.writerow(data)
-db = {}
-
-l1 = []
-l2 = []
-ids = st.text_input("Email Address")
-password = st.text_input("Password",type="password",key="password")
-# l1.append(ids)
-# l2.append(password)
-
-# l1.append(ids)
-# l2.append(password)
-key1 = "Id"
-db.setdefault(key1, [])
-db[key1].append(ids)
-
-key2 = "password"
-db.setdefault(key2, [])
-db[key2].append(password)
-
-# print(db)
-# db['Id'] = l1
-# db['Password'] = l2
-# for i in db:
-df = pd.DataFrame(db)
-# st.write(db)
-# df
-if st.button("Add Data"):
- df.to_csv('LoginStatus.csv', mode='a', header=False, index=False)
-
-
-
-# import streamlit as st
-# def check_password():
-# """Returns `True` if the user had a correct password."""
-
-# def password_entered():
-# """Checks whether a password entered by the user is correct."""
-# if (
-# st.session_state["username"] in st.secrets["passwords"]
-# and st.session_state["password"]
-# == st.secrets["passwords"][st.session_state["username"]]
-# ):
-# st.session_state["password_correct"] = True
-# del st.session_state["password"] # don't store username + password
-# del st.session_state["username"]
-# else:
-# st.session_state["password_correct"] = False
-
-# if "password_correct" not in st.session_state:
-# # First run, show inputs for username + password.
-# st.text_input("Username", on_change=password_entered, key="username")
-# st.text_input(
-# "Password", type="password", on_change=password_entered, key="password"
-# )
-# return False
-# elif not st.session_state["password_correct"]:
-# # Password not correct, show input + error.
-# st.text_input("Username", on_change=password_entered, key="username")
-# st.text_input(
-# "Password", type="password", on_change=password_entered, key="password"
-# )
-# st.error("😕 User not known or password incorrect")
-# return False
-# else:
-# # Password correct.
-# return True
-
-# if check_password():
-# st.write("Here goes your normal Streamlit app...")
-# st.button("Click me")
\ No newline at end of file
diff --git a/spaces/Iceclear/StableSR/StableSR/taming/data/conditional_builder/objects_center_points.py b/spaces/Iceclear/StableSR/StableSR/taming/data/conditional_builder/objects_center_points.py
deleted file mode 100644
index 9a480329cc47fb38a7b8729d424e092b77d40749..0000000000000000000000000000000000000000
--- a/spaces/Iceclear/StableSR/StableSR/taming/data/conditional_builder/objects_center_points.py
+++ /dev/null
@@ -1,168 +0,0 @@
-import math
-import random
-import warnings
-from itertools import cycle
-from typing import List, Optional, Tuple, Callable
-
-from PIL import Image as pil_image, ImageDraw as pil_img_draw, ImageFont
-from more_itertools.recipes import grouper
-from taming.data.conditional_builder.utils import COLOR_PALETTE, WHITE, GRAY_75, BLACK, FULL_CROP, filter_annotations, \
- additional_parameters_string, horizontally_flip_bbox, pad_list, get_circle_size, get_plot_font_size, \
- absolute_bbox, rescale_annotations
-from taming.data.helper_types import BoundingBox, Annotation
-from taming.data.image_transforms import convert_pil_to_tensor
-from torch import LongTensor, Tensor
-
-
-class ObjectsCenterPointsConditionalBuilder:
- def __init__(self, no_object_classes: int, no_max_objects: int, no_tokens: int, encode_crop: bool,
- use_group_parameter: bool, use_additional_parameters: bool):
- self.no_object_classes = no_object_classes
- self.no_max_objects = no_max_objects
- self.no_tokens = no_tokens
- self.encode_crop = encode_crop
- self.no_sections = int(math.sqrt(self.no_tokens))
- self.use_group_parameter = use_group_parameter
- self.use_additional_parameters = use_additional_parameters
-
- @property
- def none(self) -> int:
- return self.no_tokens - 1
-
- @property
- def object_descriptor_length(self) -> int:
- return 2
-
- @property
- def embedding_dim(self) -> int:
- extra_length = 2 if self.encode_crop else 0
- return self.no_max_objects * self.object_descriptor_length + extra_length
-
- def tokenize_coordinates(self, x: float, y: float) -> int:
- """
- Express 2d coordinates with one number.
- Example: assume self.no_tokens = 16, then no_sections = 4:
- 0 0 0 0
- 0 0 # 0
- 0 0 0 0
- 0 0 0 x
- Then the # position corresponds to token 6, the x position to token 15.
- @param x: float in [0, 1]
- @param y: float in [0, 1]
- @return: discrete tokenized coordinate
- """
- x_discrete = int(round(x * (self.no_sections - 1)))
- y_discrete = int(round(y * (self.no_sections - 1)))
- return y_discrete * self.no_sections + x_discrete
-
- def coordinates_from_token(self, token: int) -> (float, float):
- x = token % self.no_sections
- y = token // self.no_sections
- return x / (self.no_sections - 1), y / (self.no_sections - 1)
-
- def bbox_from_token_pair(self, token1: int, token2: int) -> BoundingBox:
- x0, y0 = self.coordinates_from_token(token1)
- x1, y1 = self.coordinates_from_token(token2)
- return x0, y0, x1 - x0, y1 - y0
-
- def token_pair_from_bbox(self, bbox: BoundingBox) -> Tuple[int, int]:
- return self.tokenize_coordinates(bbox[0], bbox[1]), \
- self.tokenize_coordinates(bbox[0] + bbox[2], bbox[1] + bbox[3])
-
- def inverse_build(self, conditional: LongTensor) \
- -> Tuple[List[Tuple[int, Tuple[float, float]]], Optional[BoundingBox]]:
- conditional_list = conditional.tolist()
- crop_coordinates = None
- if self.encode_crop:
- crop_coordinates = self.bbox_from_token_pair(conditional_list[-2], conditional_list[-1])
- conditional_list = conditional_list[:-2]
- table_of_content = grouper(conditional_list, self.object_descriptor_length)
- assert conditional.shape[0] == self.embedding_dim
- return [
- (object_tuple[0], self.coordinates_from_token(object_tuple[1]))
- for object_tuple in table_of_content if object_tuple[0] != self.none
- ], crop_coordinates
-
- def plot(self, conditional: LongTensor, label_for_category_no: Callable[[int], str], figure_size: Tuple[int, int],
- line_width: int = 3, font_size: Optional[int] = None) -> Tensor:
- plot = pil_image.new('RGB', figure_size, WHITE)
- draw = pil_img_draw.Draw(plot)
- circle_size = get_circle_size(figure_size)
- font = ImageFont.truetype('/usr/share/fonts/truetype/lato/Lato-Regular.ttf',
- size=get_plot_font_size(font_size, figure_size))
- width, height = plot.size
- description, crop_coordinates = self.inverse_build(conditional)
- for (representation, (x, y)), color in zip(description, cycle(COLOR_PALETTE)):
- x_abs, y_abs = x * width, y * height
- ann = self.representation_to_annotation(representation)
- label = label_for_category_no(ann.category_no) + ' ' + additional_parameters_string(ann)
- ellipse_bbox = [x_abs - circle_size, y_abs - circle_size, x_abs + circle_size, y_abs + circle_size]
- draw.ellipse(ellipse_bbox, fill=color, width=0)
- draw.text((x_abs, y_abs), label, anchor='md', fill=BLACK, font=font)
- if crop_coordinates is not None:
- draw.rectangle(absolute_bbox(crop_coordinates, width, height), outline=GRAY_75, width=line_width)
- return convert_pil_to_tensor(plot) / 127.5 - 1.
-
- def object_representation(self, annotation: Annotation) -> int:
- modifier = 0
- if self.use_group_parameter:
- modifier |= 1 * (annotation.is_group_of is True)
- if self.use_additional_parameters:
- modifier |= 2 * (annotation.is_occluded is True)
- modifier |= 4 * (annotation.is_depiction is True)
- modifier |= 8 * (annotation.is_inside is True)
- return annotation.category_no + self.no_object_classes * modifier
-
- def representation_to_annotation(self, representation: int) -> Annotation:
- category_no = representation % self.no_object_classes
- modifier = representation // self.no_object_classes
- # noinspection PyTypeChecker
- return Annotation(
- area=None, image_id=None, bbox=None, category_id=None, id=None, source=None, confidence=None,
- category_no=category_no,
- is_group_of=bool((modifier & 1) * self.use_group_parameter),
- is_occluded=bool((modifier & 2) * self.use_additional_parameters),
- is_depiction=bool((modifier & 4) * self.use_additional_parameters),
- is_inside=bool((modifier & 8) * self.use_additional_parameters)
- )
-
- def _crop_encoder(self, crop_coordinates: BoundingBox) -> List[int]:
- return list(self.token_pair_from_bbox(crop_coordinates))
-
- def _make_object_descriptors(self, annotations: List[Annotation]) -> List[Tuple[int, ...]]:
- object_tuples = [
- (self.object_representation(a),
- self.tokenize_coordinates(a.bbox[0] + a.bbox[2] / 2, a.bbox[1] + a.bbox[3] / 2))
- for a in annotations
- ]
- empty_tuple = (self.none, self.none)
- object_tuples = pad_list(object_tuples, empty_tuple, self.no_max_objects)
- return object_tuples
-
- def build(self, annotations: List, crop_coordinates: Optional[BoundingBox] = None, horizontal_flip: bool = False) \
- -> LongTensor:
- if len(annotations) == 0:
- warnings.warn('Did not receive any annotations.')
- if len(annotations) > self.no_max_objects:
- warnings.warn('Received more annotations than allowed.')
- annotations = annotations[:self.no_max_objects]
-
- if not crop_coordinates:
- crop_coordinates = FULL_CROP
-
- random.shuffle(annotations)
- annotations = filter_annotations(annotations, crop_coordinates)
- if self.encode_crop:
- annotations = rescale_annotations(annotations, FULL_CROP, horizontal_flip)
- if horizontal_flip:
- crop_coordinates = horizontally_flip_bbox(crop_coordinates)
- extra = self._crop_encoder(crop_coordinates)
- else:
- annotations = rescale_annotations(annotations, crop_coordinates, horizontal_flip)
- extra = []
-
- object_tuples = self._make_object_descriptors(annotations)
- flattened = [token for tuple_ in object_tuples for token in tuple_] + extra
- assert len(flattened) == self.embedding_dim
- assert all(0 <= value < self.no_tokens for value in flattened)
- return LongTensor(flattened)
diff --git a/spaces/InpaintAI/Inpaint-Anything/replace_anything.py b/spaces/InpaintAI/Inpaint-Anything/replace_anything.py
deleted file mode 100644
index e08cb0497bdee5ad5e9cc4689d6c9cd02a64f28f..0000000000000000000000000000000000000000
--- a/spaces/InpaintAI/Inpaint-Anything/replace_anything.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import cv2
-import sys
-import argparse
-import numpy as np
-import torch
-from pathlib import Path
-from matplotlib import pyplot as plt
-from typing import Any, Dict, List
-from sam_segment import predict_masks_with_sam
-from stable_diffusion_inpaint import replace_img_with_sd
-from utils import load_img_to_array, save_array_to_img, dilate_mask, \
- show_mask, show_points
-
-
-def setup_args(parser):
- parser.add_argument(
- "--input_img", type=str, required=True,
- help="Path to a single input img",
- )
- parser.add_argument(
- "--point_coords", type=float, nargs='+', required=True,
- help="The coordinate of the point prompt, [coord_W coord_H].",
- )
- parser.add_argument(
- "--point_labels", type=int, nargs='+', required=True,
- help="The labels of the point prompt, 1 or 0.",
- )
- parser.add_argument(
- "--text_prompt", type=str, required=True,
- help="Text prompt",
- )
- parser.add_argument(
- "--dilate_kernel_size", type=int, default=None,
- help="Dilate kernel size. Default: None",
- )
- parser.add_argument(
- "--output_dir", type=str, required=True,
- help="Output path to the directory with results.",
- )
- parser.add_argument(
- "--sam_model_type", type=str,
- default="vit_h", choices=['vit_h', 'vit_l', 'vit_b'],
- help="The type of sam model to load. Default: 'vit_h"
- )
- parser.add_argument(
- "--sam_ckpt", type=str, required=True,
- help="The path to the SAM checkpoint to use for mask generation.",
- )
- parser.add_argument(
- "--seed", type=int,
- help="Specify seed for reproducibility.",
- )
- parser.add_argument(
- "--deterministic", action="store_true",
- help="Use deterministic algorithms for reproducibility.",
- )
-
-
-
-if __name__ == "__main__":
- """Example usage:
- python replace_anything.py \
- --input_img FA_demo/FA1_dog.png \
- --point_coords 750 500 \
- --point_labels 1 \
- --text_prompt "sit on the swing" \
- --output_dir ./results \
- --sam_model_type "vit_h" \
- --sam_ckpt sam_vit_h_4b8939.pth
- """
- parser = argparse.ArgumentParser()
- setup_args(parser)
- args = parser.parse_args(sys.argv[1:])
- device = "cuda" if torch.cuda.is_available() else "cpu"
-
- img = load_img_to_array(args.input_img)
-
- masks, _, _ = predict_masks_with_sam(
- img,
- [args.point_coords],
- args.point_labels,
- model_type=args.sam_model_type,
- ckpt_p=args.sam_ckpt,
- device=device,
- )
- masks = masks.astype(np.uint8) * 255
-
- # dilate mask to avoid unmasked edge effect
- if args.dilate_kernel_size is not None:
- masks = [dilate_mask(mask, args.dilate_kernel_size) for mask in masks]
-
- # visualize the segmentation results
- img_stem = Path(args.input_img).stem
- out_dir = Path(args.output_dir) / img_stem
- out_dir.mkdir(parents=True, exist_ok=True)
- for idx, mask in enumerate(masks):
- # path to the results
- mask_p = out_dir / f"mask_{idx}.png"
- img_points_p = out_dir / f"with_points.png"
- img_mask_p = out_dir / f"with_{Path(mask_p).name}"
-
- # save the mask
- save_array_to_img(mask, mask_p)
-
- # save the pointed and masked image
- dpi = plt.rcParams['figure.dpi']
- height, width = img.shape[:2]
- plt.figure(figsize=(width/dpi/0.77, height/dpi/0.77))
- plt.imshow(img)
- plt.axis('off')
- show_points(plt.gca(), [args.point_coords], args.point_labels,
- size=(width*0.04)**2)
- plt.savefig(img_points_p, bbox_inches='tight', pad_inches=0)
- show_mask(plt.gca(), mask, random_color=False)
- plt.savefig(img_mask_p, bbox_inches='tight', pad_inches=0)
- plt.close()
-
- # fill the masked image
- for idx, mask in enumerate(masks):
- if args.seed is not None:
- torch.manual_seed(args.seed)
- mask_p = out_dir / f"mask_{idx}.png"
- img_replaced_p = out_dir / f"replaced_with_{Path(mask_p).name}"
- img_replaced = replace_img_with_sd(
- img, mask, args.text_prompt, device=device)
- save_array_to_img(img_replaced, img_replaced_p)
diff --git a/spaces/JMalott/ai_architecture/README.md b/spaces/JMalott/ai_architecture/README.md
deleted file mode 100644
index 43d3b1161c68fb873b732745984e25169c71adac..0000000000000000000000000000000000000000
--- a/spaces/JMalott/ai_architecture/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Ai Architecture
-emoji: 😻
-colorFrom: gray
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Janardhan2003/MyGenAIChatBot/README.md b/spaces/Janardhan2003/MyGenAIChatBot/README.md
deleted file mode 100644
index becf036dbd33d2df0cbb581faf0dd7575810080f..0000000000000000000000000000000000000000
--- a/spaces/Janardhan2003/MyGenAIChatBot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MyGenAIChatBot
-emoji: 👀
-colorFrom: pink
-colorTo: yellow
-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/Jipski/MegStuart_gpt-2/app.py b/spaces/Jipski/MegStuart_gpt-2/app.py
deleted file mode 100644
index 9379e4120182aeb7cd55fcb758cb5e67806bc6f5..0000000000000000000000000000000000000000
--- a/spaces/Jipski/MegStuart_gpt-2/app.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import transformers
-import streamlit as st
-from transformers import AutoTokenizer, AutoModelWithLMHead
-tokenizer = AutoTokenizer.from_pretrained("anonymous-german-nlp/german-gpt2")
-@st.cache
-def load_model(model_name):
- model = AutoModelWithLMHead.from_pretrained("Jipski/MegStuart_gpt-2")
- return model
-model = load_model("Jipski/MegStuart_gpt-2")
-def infer(input_ids, max_length, temperature, top_k, top_p, num_return_sequences):
- output_sequences = model.generate(
- input_ids=input_ids,
- max_length=max_length,
- temperature=temperature,
- top_k=top_k,
- top_p=top_p,
- do_sample=True,
- num_return_sequences=num_return_sequences,
- )
- return output_sequences
-def update_showing():
- st.session_state.showing = st.session_state.gen
-
-default_value = "Jetzt tippen!"
-#prompts
-st.title("Meg Stuart gpt-2")
-#st.write("The almighty king of text generation, GPT-2 comes in four available sizes, only three of which have been publicly made available. Feared for its fake news generation capabilities, it currently stands as the most syntactically coherent model. A direct successor to the original GPT, it reinforces the already established pre-training/fine-tuning killer duo. From the paper: Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever.")
-sent = st.text_area("Text", default_value, key='showing', height = 275)
-max_length = st.sidebar.slider("Max Length", min_value = 50, max_value=500)
-temperature = st.sidebar.slider("Temperature", value = 1.0, min_value = 0.0, max_value=1.0, step=0.05)
-top_k = st.sidebar.slider("Top-k", min_value = 0, max_value=5, value = 0)
-top_p = st.sidebar.slider("Top-p", min_value = 0.0, max_value=1.0, step = 0.05, value = 0.9)
-num_return_sequences = st.sidebar.number_input('Number of Return Sequences', min_value=1, max_value=5, value=1, step=1)
-encoded_prompt = tokenizer.encode(sent, add_special_tokens=False, return_tensors="pt")
-if encoded_prompt.size()[-1] == 0:
- input_ids = None
-else:
- input_ids = encoded_prompt
-output_sequences = infer(input_ids, max_length, temperature, top_k, top_p, num_return_sequences)
-for generated_sequence_idx, generated_sequence in enumerate(output_sequences):
-
- print(f"=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===")
- generated_sequences = generated_sequence.tolist()
- # Decode text
- text = tokenizer.decode(generated_sequence, clean_up_tokenization_spaces=True)
- # Remove all text after the stop token
- #text = text[: text.find(args.stop_token) if args.stop_token else None]
- # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
- total_sequence = (
- sent + text[len(tokenizer.decode(encoded_prompt[0], clean_up_tokenization_spaces=True)) :]
- )
- generated_sequences.append(total_sequence)
- print(total_sequence)
-
-st.write(generated_sequences[-1])
\ No newline at end of file
diff --git a/spaces/Joeythemonster/flax-midjourney-v4-diffusion/app.py b/spaces/Joeythemonster/flax-midjourney-v4-diffusion/app.py
deleted file mode 100644
index 364c6a6661f1bc587f1f388fb6378a9798d3f94f..0000000000000000000000000000000000000000
--- a/spaces/Joeythemonster/flax-midjourney-v4-diffusion/app.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import gradio as gr
-
-import os
-
-os.system('pip install --upgrade pip')
-
-gr.Interface.load("models/flax/midjourney-v4-diffusion").launch()
\ No newline at end of file
diff --git a/spaces/JohnSmith9982/ChuanhuChatGPT/chatgpt - macOS.command b/spaces/JohnSmith9982/ChuanhuChatGPT/chatgpt - macOS.command
deleted file mode 100644
index fa015edca9e6916f24394813ce8ba77d2072e296..0000000000000000000000000000000000000000
--- a/spaces/JohnSmith9982/ChuanhuChatGPT/chatgpt - macOS.command
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/bin/bash
-echo Opening ChuanhuChatGPT...
-cd "$(dirname "${BASH_SOURCE[0]}")"
-nohup python3 ChuanhuChatbot.py >/dev/null 2>&1 &
-sleep 5
-open http://127.0.0.1:7860
-echo Finished opening ChuanhuChatGPT (http://127.0.0.1:7860/). If you kill ChuanhuChatbot, Use "pkill -f 'ChuanhuChatbot'" command in terminal.
\ No newline at end of file
diff --git a/spaces/KAIST-Geometric-AI-Lab/salad-demo/salad/spaghetti/utils/files_utils.py b/spaces/KAIST-Geometric-AI-Lab/salad-demo/salad/spaghetti/utils/files_utils.py
deleted file mode 100644
index bfe0e48febbd94dcb1ea9ce702e9a2e25043c07c..0000000000000000000000000000000000000000
--- a/spaces/KAIST-Geometric-AI-Lab/salad-demo/salad/spaghetti/utils/files_utils.py
+++ /dev/null
@@ -1,558 +0,0 @@
-import os
-from .. import constants as const
-import pickle
-import pickle5
-from shutil import copyfile, move
-from ..custom_types import *
-from PIL import Image
-import time
-import json
-import matplotlib.pyplot as plt
-import sys
-from ..constants import PROJECT_ROOT
-if PROJECT_ROOT not in sys.path:
- sys.path.append(PROJECT_ROOT)
-
-# sys.path.append("/home/juil/projects/3D_CRISPR/spaghetti_github")
-def image_to_display(img) -> ARRAY:
- if type(img) is str:
- img = Image.open(str(img))
- if type(img) is not V:
- img = V(img)
- return img
-
-
-def imshow(img, title: Optional[str] = None):
- img = image_to_display(img)
- plt.imshow(img)
- plt.axis("off")
- if title is not None:
- plt.title(title)
- plt.show()
- plt.close('all')
-
-
-def load_image(path: str, color_type: str = 'RGB') -> ARRAY:
- for suffix in ('.png', '.jpg'):
- path_ = add_suffix(path, suffix)
- if os.path.isfile(path_):
- path = path_
- break
- image = Image.open(path).convert(color_type)
- return V(image)
-
-
-def save_image(image: Union[ARRAY, Image.Image], path: str):
- if type(image) is ARRAY:
- if image.shape[-1] == 1:
- image = image[:, :, 0]
- image = Image.fromarray(image)
- init_folders(path)
- image.save(path)
-
-
-def split_path(path: str) -> List[str]:
- extension = os.path.splitext(path)[1]
- dir_name, name = os.path.split(path)
- name = name[: len(name) - len(extension)]
- return [dir_name, name, extension]
-
-
-def init_folders(*folders):
- if const.DEBUG:
- return
- for f in folders:
- dir_name = os.path.dirname(f)
- if dir_name and not os.path.exists(dir_name):
- os.makedirs(dir_name)
-
-
-def is_file(path: str):
- return os.path.isfile(path)
-
-
-def add_suffix(path: str, suffix: str) -> str:
- if len(path) < len(suffix) or path[-len(suffix):] != suffix:
- path = f'{path}{suffix}'
- return path
-
-
-def remove_suffix(path: str, suffix: str) -> str:
- if len(path) > len(suffix) and path[-len(suffix):] == suffix:
- path = path[:-len(suffix)]
- return path
-
-
-def path_init(suffix: str, path_arg_ind: int, is_save: bool):
-
- def wrapper(func):
-
- def do(*args, **kwargs):
- path = add_suffix(args[path_arg_ind], suffix)
- if is_save:
- init_folders(path)
- args = [args[i] if i != path_arg_ind else path for i in range(len(args))]
- return func(*args, **kwargs)
-
- return do
-
- return wrapper
-
-
-def copy_file(src: str, dest: str, force=False):
- if const.DEBUG:
- return
- if os.path.isfile(src):
- if force or not os.path.isfile(dest):
- copyfile(src, dest)
- return True
- else:
- print("Destination file already exist. To override, set force=True")
- return False
-
-
-def load_image(path: str, color_type: str = 'RGB') -> ARRAY:
- for suffix in ('.png', '.jpg'):
- path_ = add_suffix(path, suffix)
- if os.path.isfile(path_):
- path = path_
- break
- image = Image.open(path).convert(color_type)
- return V(image)
-
-
-@path_init('.png', 1, True)
-def save_image(image: ARRAY, path: str):
- if type(image) is ARRAY:
- if image.shape[-1] == 1:
- image = image[:, :, 0]
- image = Image.fromarray(image)
- image.save(path)
-
-
-def save_np(arr_or_dict: Union[ARRAY, T, dict], path: str):
- if const.DEBUG:
- return
- init_folders(path)
- if type(arr_or_dict) is dict:
- path = add_suffix(path, '.npz')
- np.savez_compressed(path, **arr_or_dict)
- else:
- if type(arr_or_dict) is T:
- arr_or_dict = arr_or_dict.detach().cpu().numpy()
- path = remove_suffix(path, '.npy')
- np.save(path, arr_or_dict)
-
-
-@path_init('.npy', 0, False)
-def load_np(path: str):
- return np.load(path)
-
-
-@path_init('.pkl', 0, False)
-def load_pickle(path: str):
- data = None
- if os.path.isfile(path):
- try:
- with open(path, 'rb') as f:
- data = pickle.load(f)
- except ValueError:
- with open(path, 'rb') as f:
- data = pickle5.load(f)
- return data
-
-
-@path_init('.pkl', 1, True)
-def save_pickle(obj, path: str):
- if const.DEBUG:
- return
- with open(path, 'wb') as f:
- pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)
-
-
-def load_txt_labels(path: str) -> VN:
- for suffix in ('.txt', '.seg'):
- path_ = add_suffix(path, suffix)
- if os.path.isfile(path_):
- return np.loadtxt(path_, dtype=np.int64) - 1
- return None
-
-
-@path_init('.txt', 0, False)
-def load_txt(path: str) -> List[str]:
- data = []
- if os.path.isfile(path):
- with open(path, 'r') as f:
- for line in f:
- data.append(line.strip())
- return data
-
-
-# def load_points(path: str) -> T:
-# path = add_suffix(path, '.pts')
-# points = [int_b(num) for num in load_txt(path)]
-# return torch.tensor(points, dtype=torch.int64)
-
-
-def save_txt(array, path: str):
- if const.DEBUG:
- return
- path_ = add_suffix(path, '.txt')
- with open(path_, 'w') as f:
- for i, num in enumerate(array):
- f.write(f'{num}{" " if i < len(array) - 1 else ""}')
-
-
-def move_file(src: str, dest: str):
- if const.DEBUG:
- return
- if os.path.isfile(src):
- move(src, dest)
- return True
- return False
-
-
-@path_init('.json', 1, True)
-def save_json(obj, path: str):
- with open(path, 'w') as f:
- json.dump(obj, f, indent=4)
-
-
-def collect(root: str, *suffix, prefix='') -> List[List[str]]:
- if os.path.isfile(root):
- folder = os.path.split(root)[0] + '/'
- extension = os.path.splitext(root)[-1]
- name = root[len(folder): -len(extension)]
- paths = [[folder, name, extension]]
- else:
- paths = []
- root = add_suffix(root, '/')
- if not os.path.isdir(root):
- print(f'Warning: trying to collect from {root} but dir isn\'t exist')
- else:
- p_len = len(prefix)
- for path, _, files in os.walk(root):
- for file in files:
- file_name, file_extension = os.path.splitext(file)
- p_len_ = min(p_len, len(file_name))
- if file_extension in suffix and file_name[:p_len_] == prefix:
- paths.append((f'{add_suffix(path, "/")}', file_name, file_extension))
- paths.sort(key=lambda x: os.path.join(x[1], x[2]))
- return paths
-
-
-def delete_all(root:str, *suffix: str):
- if const.DEBUG:
- return
- paths = collect(root, *suffix)
- for path in paths:
- os.remove(''.join(path))
-
-
-def delete_single(path: str) -> bool:
- if os.path.isfile(path):
- os.remove(path)
- return True
- return False
-
-
-def colors_to_colors(colors: COLORS, mesh: T_Mesh) -> T:
- if type(colors) is not T:
- if type(colors) is V:
- colors = torch.from_numpy(colors).long()
- else:
- colors = torch.tensor(colors, dtype=torch.int64)
- if colors.max() > 1:
- colors = colors.float() / 255
- if colors.dim() == 1:
- colors = colors.unsqueeze(int(colors.shape[0] != 3)).expand_as(mesh[0])
- return colors
-
-
-def load_mesh(file_name: str, dtype: Union[type(T), type(V)] = T,
- device: D = CPU) -> Union[T_Mesh, V_Mesh, T, Tuple[T, List[List[int]]]]:
-
- def off_parser():
- header = None
-
- def parser_(clean_line: list):
- nonlocal header
- if not clean_line:
- return False
- if len(clean_line) == 3 and not header:
- header = True
- elif len(clean_line) == 3:
- return 0, 0, float
- elif len(clean_line) > 3:
- return 1, -int(clean_line[0]), int
-
- return parser_
-
- def obj_parser(clean_line: list):
- nonlocal is_quad
- if not clean_line:
- return False
- elif clean_line[0] == 'v':
- return 0, 1, float
- elif clean_line[0] == 'f':
- is_quad = is_quad or len(clean_line) != 4
- return 1, 1, int
- return False
-
- def fetch(lst: list, idx: int, dtype: type):
- uv_vs_ids = None
- if '/' in lst[idx]:
- lst = [item.split('/') for item in lst[idx:]]
- lst = [item[0] for item in lst]
- idx = 0
- face_vs_ids = [dtype(c.split('/')[0]) for c in lst[idx:]]
- if dtype is float and len(face_vs_ids) > 3:
- face_vs_ids = face_vs_ids[:3]
- return face_vs_ids, uv_vs_ids
-
- def load_from_txt(parser) -> TS:
- mesh_ = [[], []]
- with open(file_name, 'r') as f:
- for line in f:
- clean_line = line.strip().split()
- info = parser(clean_line)
- if not info:
- continue
- data = fetch(clean_line, info[1], info[2])
- mesh_[info[0]].append(data[0])
- if is_quad:
- faces = mesh_[1]
- for face in faces:
- for i in range(len(face)):
- face[i] -= 1
- else:
- faces = torch.tensor(mesh_[1], dtype=torch.int64)
- if len(faces) > 0 and faces.min() != 0:
- faces -= 1
- mesh_ = torch.tensor(mesh_[0], dtype=torch.float32), faces
- return mesh_
-
- for suffix in ['.obj', '.off', '.ply']:
- file_name_tmp = add_suffix(file_name, suffix)
- if os.path.isfile(file_name_tmp):
- file_name = file_name_tmp
- break
-
- is_quad = False
- name, extension = os.path.splitext(file_name)
- if extension == '.obj':
- mesh = load_from_txt(obj_parser)
- elif extension == '.off':
- mesh = load_from_txt(off_parser())
- elif extension == '.ply':
- mesh = load_ply(file_name)
- else:
- raise ValueError(f'mesh file {file_name} is not exist or not supported')
- if type(mesh[1]) is T and not ((mesh[1] >= 0) * (mesh[1] < mesh[0].shape[0])).all():
- print(f"err: {file_name}")
- assert type(mesh[1]) is not T or ((mesh[1] >= 0) * (mesh[1] < mesh[0].shape[0])).all()
- if dtype is V:
- mesh = mesh[0].numpy(), mesh[1].numpy()
- elif device != CPU:
- mesh = mesh[0].to(device), mesh[1].to(device)
- if len(mesh[1]) == 0 and len(mesh[0]) > 0:
- return mesh[0]
- return mesh
-
-
-@path_init('.xyz', 1, True)
-def export_xyz(pc: T, path: str, normals: Optional[T] = None):
- pc = pc.tolist()
- if normals is not None:
- normals = normals.tolist()
- with open(path, 'w') as f:
- for i in range(len(pc)):
- x, y, z = pc[i]
- f.write(f'{x} {y} {z}')
- if normals is not None:
- x, y, z = normals[i]
- f.write(f' {x} {y} {z}')
- if i < len(pc) - 1:
- f.write('\n')
-
-
-@path_init('.txt', 2, True)
-def export_gmm(gmm: TS, item: int, file_name: str, included: Optional[List[int]] = None):
- if included is None:
- included = [1] * gmm[0].shape[2]
- mu, p, phi, eigen = [tensor[item, 0].flatten().cpu() for tensor in gmm]
- # phi = phi.softmax(0)
- with open(file_name, 'w') as f:
- for tensor in (phi, mu, eigen, p):
- tensor_str = [f'{number:.5f}' for number in tensor.tolist()]
- f.write(f"{' '.join(tensor_str)}\n")
- list_str = [f'{number:d}' for number in included]
- f.write(f"{' '.join(list_str)}\n")
-
-
-@path_init('.txt', 0, False)
-def load_gmm(path, as_np: bool = False, device: D = CPU):
- parsed = []
- with open(path, 'r') as f:
- lines = [line.strip() for line in f]
- for i, line in enumerate(lines):
- line = line.split(" ")
- arr = [float(item) for item in line]
- if as_np:
- arr = V(arr)
- else:
- arr = torch.tensor(arr, device=device)
- if 0 < i < 3:
- arr = arr.reshape((-1, 3))
- # swap = arr[:, 2].copy()
- # arr[:, 2] = arr[:, 1]
- # arr[:, 1] = swap
- elif i == 3:
- arr = arr.reshape((-1, 3, 3))
- # arr = arr.transpose(0, 2, 1)
- elif i == 4:
- if as_np:
- arr = arr.astype(np.bool_)
- else:
- arr = arr.bool()
- parsed.append(arr)
- return parsed
-
-
-@path_init('.txt', 1, True)
-def export_list(lst: List[Any], path: str):
- with open(path, "w") as f:
- for i in range(len(lst)):
- f.write(f'{lst[i]}\n')
-
-
-@path_init('.obj', 1, True)
-def export_mesh(mesh: Union[V_Mesh, T_Mesh, T, Tuple[T, List[List[int]]]], file_name: str,
- colors: Optional[COLORS] = None, normals: TN = None, edges=None, spheres=None):
- # return
- if type(mesh) is not tuple and type(mesh) is not list:
- mesh = mesh, None
- vs, faces = mesh
- if vs.shape[1] < 3:
- vs = torch.cat((vs, torch.zeros(len(vs), 3 - vs.shape[1], device=vs.device)), dim=1)
- if colors is not None:
- colors = colors_to_colors(colors, mesh)
- if not os.path.isdir(os.path.dirname(file_name)):
- return
- if faces is not None:
- if type(faces) is T:
- faces: T = faces + 1
- faces_lst = faces.tolist()
- else:
- faces_lst_: List[List[int]] = faces
- faces_lst = []
- for face in faces_lst_:
- faces_lst.append([face[i] + 1 for i in range(len(face))])
- with open(file_name, 'w') as f:
- for vi, v in enumerate(vs):
- if colors is None or colors[vi, 0] < 0:
- v_color = ''
- else:
- v_color = ' %f %f %f' % (colors[vi, 0].item(), colors[vi, 1].item(), colors[vi, 2].item())
- f.write("v %f %f %f%s\n" % (v[0], v[1], v[2], v_color))
- if normals is not None:
- for n in normals:
- f.write("vn %f %f %f\n" % (n[0], n[1], n[2]))
- if faces is not None:
- for face in faces_lst:
- face = [str(f) for f in face]
- f.write(f'f {" ".join(face)}\n')
- if edges is not None:
- for edges_id in range(edges.shape[0]):
- f.write(f'\ne {edges[edges_id][0].item():d} {edges[edges_id][1].item():d}')
- if spheres is not None:
- for sphere_id in range(spheres.shape[0]):
- f.write(f'\nsp {spheres[sphere_id].item():d}')
-
-@path_init('.ply', 1, True)
-def export_ply(mesh: T_Mesh, path: str, colors: T):
- colors = colors_to_colors(colors, mesh)
- colors = (colors * 255).long()
- vs, faces = mesh
- vs = vs.clone()
- swap = vs[:, 1].clone()
- vs[:, 1] = vs[:, 2]
- vs[:, 2] = swap
- min_cor, max_cor= vs.min(0)[0], vs.max(0)[0]
- vs = vs - ((min_cor + max_cor) / 2)[None, :]
- vs = vs / vs.max()
- vs[:, 2] = vs[:, 2] - vs[:, 2].min()
- num_vs = vs.shape[0]
- num_faces = faces.shape[0]
- with open(path, 'w') as f:
- f.write(f'ply\nformat ascii 1.0\n'
- f'element vertex {num_vs:d}\nproperty float x\nproperty float y\nproperty float z\n'
- f'property uchar red\nproperty uchar green\nproperty uchar blue\n'
- f'element face {num_faces:d}\nproperty list uchar int vertex_indices\nend_header\n')
- for vi, v in enumerate(vs):
- color = f'{colors[vi, 0].item():d} {colors[vi, 1].item():d} {colors[vi, 2].item():d}'
- f.write(f'{v[0].item():f} {v[1].item():f} {v[2].item():f} {color}\n')
- for face in faces:
- f.write(f'3 {face[0].item():d} {face[1].item():d} {face[2].item():d}\n')
-
-
-@path_init('.ply', 0, False)
-def load_ply(path: str):
- import plyfile
- plydata = plyfile.PlyData.read(path)
- vertices = plydata.elements[0].data
- vertices = [[float(item[0]), float(item[1]), float(item[2])] for item in vertices]
- vertices = torch.tensor(vertices)
- faces = plydata.elements[1].data
- faces = [[int(item[0][0]), int(item[0][1]), int(item[0][2])] for item in faces]
- faces = torch.tensor(faces)
- return vertices, faces
-
-
-@path_init('', 1, True)
-def save_model(model: Union[Optimizer, nn.Module], model_path: str):
- if const.DEBUG:
- return
- init_folders(model_path)
- torch.save(model.state_dict(), model_path)
-
-
-def load_model(model: Union[Optimizer, nn.Module], model_path: str, device: D, verbose: bool = False):
- if os.path.isfile(model_path):
- model.load_state_dict(torch.load(model_path, map_location=device))
- if verbose:
- print(f'loading {type(model).__name__} from {model_path}')
- elif verbose:
- print(f'init {type(model).__name__}')
- return model
-
-
-def measure_time(func, num_iters: int, *args):
- start_time = time.time()
- for i in range(num_iters):
- func(*args)
- total_time = time.time() - start_time
- avg_time = total_time / num_iters
- print(f"{str(func).split()[1].split('.')[-1]} total time: {total_time}, average time: {avg_time}")
-
-
-def get_time_name(name: str, format_="%m_%d-%H_%M") -> str:
- return f'{name}_{time.strftime(format_)}'
-
-
-@path_init('.txt', 0, False)
-def load_shapenet_seg(path: str) -> TS:
- labels, vs = [], []
- with open(path, 'r') as f:
- for line in f:
- data = line.strip().split()
- vs.append([float(item) for item in data[:3]])
- labels.append(int(data[-1].split('.')[0]))
- return torch.tensor(vs, dtype=torch.float32), torch.tensor(labels, dtype=torch.int64)
-
-
-@path_init('.json', 0, False)
-def load_json(path: str):
- with open(path, 'r') as f:
- data = json.load(f)
- return data
diff --git a/spaces/KyanChen/RSPrompter/mmdet/datasets/api_wrappers/__init__.py b/spaces/KyanChen/RSPrompter/mmdet/datasets/api_wrappers/__init__.py
deleted file mode 100644
index a27afc46028ae184cb121caad6b320c5acd50790..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/datasets/api_wrappers/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from .coco_api import COCO, COCOeval, COCOPanoptic
-
-__all__ = ['COCO', 'COCOeval', 'COCOPanoptic']
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/detectors/queryinst.py b/spaces/KyanChen/RSPrompter/mmdet/models/detectors/queryinst.py
deleted file mode 100644
index 400ce20c01f5c3825e343f2d32accf740c5dd55c..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/detectors/queryinst.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from mmdet.registry import MODELS
-from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
-from .sparse_rcnn import SparseRCNN
-
-
-@MODELS.register_module()
-class QueryInst(SparseRCNN):
- r"""Implementation of
- `Instances as Queries `_"""
-
- def __init__(self,
- backbone: ConfigType,
- rpn_head: ConfigType,
- roi_head: ConfigType,
- train_cfg: ConfigType,
- test_cfg: ConfigType,
- neck: OptConfigType = None,
- data_preprocessor: OptConfigType = None,
- init_cfg: OptMultiConfig = None) -> None:
- super().__init__(
- backbone=backbone,
- neck=neck,
- rpn_head=rpn_head,
- roi_head=roi_head,
- train_cfg=train_cfg,
- test_cfg=test_cfg,
- data_preprocessor=data_preprocessor,
- init_cfg=init_cfg)
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py
deleted file mode 100644
index 3a089dfafcb69784f2fc266f0945e6d56b0466d3..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py
+++ /dev/null
@@ -1,474 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from typing import List, Tuple
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, build_conv_layer, build_upsample_layer
-from mmcv.ops.carafe import CARAFEPack
-from mmengine.config import ConfigDict
-from mmengine.model import BaseModule, ModuleList
-from mmengine.structures import InstanceData
-from torch import Tensor
-from torch.nn.modules.utils import _pair
-
-from mmdet.models.task_modules.samplers import SamplingResult
-from mmdet.models.utils import empty_instances
-from mmdet.registry import MODELS
-from mmdet.structures.mask import mask_target
-from mmdet.utils import ConfigType, InstanceList, OptConfigType, OptMultiConfig
-
-BYTES_PER_FLOAT = 4
-# TODO: This memory limit may be too much or too little. It would be better to
-# determine it based on available resources.
-GPU_MEM_LIMIT = 1024**3 # 1 GB memory limit
-
-
-@MODELS.register_module()
-class FCNMaskHead(BaseModule):
-
- def __init__(self,
- num_convs: int = 4,
- roi_feat_size: int = 14,
- in_channels: int = 256,
- conv_kernel_size: int = 3,
- conv_out_channels: int = 256,
- num_classes: int = 80,
- class_agnostic: int = False,
- upsample_cfg: ConfigType = dict(
- type='deconv', scale_factor=2),
- conv_cfg: OptConfigType = None,
- norm_cfg: OptConfigType = None,
- predictor_cfg: ConfigType = dict(type='Conv'),
- loss_mask: ConfigType = dict(
- type='CrossEntropyLoss', use_mask=True, loss_weight=1.0),
- init_cfg: OptMultiConfig = None) -> None:
- assert init_cfg is None, 'To prevent abnormal initialization ' \
- 'behavior, init_cfg is not allowed to be set'
- super().__init__(init_cfg=init_cfg)
- self.upsample_cfg = upsample_cfg.copy()
- if self.upsample_cfg['type'] not in [
- None, 'deconv', 'nearest', 'bilinear', 'carafe'
- ]:
- raise ValueError(
- f'Invalid upsample method {self.upsample_cfg["type"]}, '
- 'accepted methods are "deconv", "nearest", "bilinear", '
- '"carafe"')
- self.num_convs = num_convs
- # WARN: roi_feat_size is reserved and not used
- self.roi_feat_size = _pair(roi_feat_size)
- self.in_channels = in_channels
- self.conv_kernel_size = conv_kernel_size
- self.conv_out_channels = conv_out_channels
- self.upsample_method = self.upsample_cfg.get('type')
- self.scale_factor = self.upsample_cfg.pop('scale_factor', None)
- self.num_classes = num_classes
- self.class_agnostic = class_agnostic
- self.conv_cfg = conv_cfg
- self.norm_cfg = norm_cfg
- self.predictor_cfg = predictor_cfg
- self.loss_mask = MODELS.build(loss_mask)
-
- self.convs = ModuleList()
- for i in range(self.num_convs):
- in_channels = (
- self.in_channels if i == 0 else self.conv_out_channels)
- padding = (self.conv_kernel_size - 1) // 2
- self.convs.append(
- ConvModule(
- in_channels,
- self.conv_out_channels,
- self.conv_kernel_size,
- padding=padding,
- conv_cfg=conv_cfg,
- norm_cfg=norm_cfg))
- upsample_in_channels = (
- self.conv_out_channels if self.num_convs > 0 else in_channels)
- upsample_cfg_ = self.upsample_cfg.copy()
- if self.upsample_method is None:
- self.upsample = None
- elif self.upsample_method == 'deconv':
- upsample_cfg_.update(
- in_channels=upsample_in_channels,
- out_channels=self.conv_out_channels,
- kernel_size=self.scale_factor,
- stride=self.scale_factor)
- self.upsample = build_upsample_layer(upsample_cfg_)
- elif self.upsample_method == 'carafe':
- upsample_cfg_.update(
- channels=upsample_in_channels, scale_factor=self.scale_factor)
- self.upsample = build_upsample_layer(upsample_cfg_)
- else:
- # suppress warnings
- align_corners = (None
- if self.upsample_method == 'nearest' else False)
- upsample_cfg_.update(
- scale_factor=self.scale_factor,
- mode=self.upsample_method,
- align_corners=align_corners)
- self.upsample = build_upsample_layer(upsample_cfg_)
-
- out_channels = 1 if self.class_agnostic else self.num_classes
- logits_in_channel = (
- self.conv_out_channels
- if self.upsample_method == 'deconv' else upsample_in_channels)
- self.conv_logits = build_conv_layer(self.predictor_cfg,
- logits_in_channel, out_channels, 1)
- self.relu = nn.ReLU(inplace=True)
- self.debug_imgs = None
-
- def init_weights(self) -> None:
- """Initialize the weights."""
- super().init_weights()
- for m in [self.upsample, self.conv_logits]:
- if m is None:
- continue
- elif isinstance(m, CARAFEPack):
- m.init_weights()
- elif hasattr(m, 'weight') and hasattr(m, 'bias'):
- nn.init.kaiming_normal_(
- m.weight, mode='fan_out', nonlinearity='relu')
- nn.init.constant_(m.bias, 0)
-
- def forward(self, x: Tensor) -> Tensor:
- """Forward features from the upstream network.
-
- Args:
- x (Tensor): Extract mask RoI features.
-
- Returns:
- Tensor: Predicted foreground masks.
- """
- for conv in self.convs:
- x = conv(x)
- if self.upsample is not None:
- x = self.upsample(x)
- if self.upsample_method == 'deconv':
- x = self.relu(x)
- mask_preds = self.conv_logits(x)
- return mask_preds
-
- def get_targets(self, sampling_results: List[SamplingResult],
- batch_gt_instances: InstanceList,
- rcnn_train_cfg: ConfigDict) -> Tensor:
- """Calculate the ground truth for all samples in a batch according to
- the sampling_results.
-
- Args:
- sampling_results (List[obj:SamplingResult]): Assign results of
- all images in a batch after sampling.
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes``, ``labels``, and
- ``masks`` attributes.
- rcnn_train_cfg (obj:ConfigDict): `train_cfg` of RCNN.
-
- Returns:
- Tensor: Mask target of each positive proposals in the image.
- """
- pos_proposals = [res.pos_priors for res in sampling_results]
- pos_assigned_gt_inds = [
- res.pos_assigned_gt_inds for res in sampling_results
- ]
- gt_masks = [res.masks for res in batch_gt_instances]
- mask_targets = mask_target(pos_proposals, pos_assigned_gt_inds,
- gt_masks, rcnn_train_cfg)
- return mask_targets
-
- def loss_and_target(self, mask_preds: Tensor,
- sampling_results: List[SamplingResult],
- batch_gt_instances: InstanceList,
- rcnn_train_cfg: ConfigDict) -> dict:
- """Calculate the loss based on the features extracted by the mask head.
-
- Args:
- mask_preds (Tensor): Predicted foreground masks, has shape
- (num_pos, num_classes, h, w).
- sampling_results (List[obj:SamplingResult]): Assign results of
- all images in a batch after sampling.
- batch_gt_instances (list[:obj:`InstanceData`]): Batch of
- gt_instance. It usually includes ``bboxes``, ``labels``, and
- ``masks`` attributes.
- rcnn_train_cfg (obj:ConfigDict): `train_cfg` of RCNN.
-
- Returns:
- dict: A dictionary of loss and targets components.
- """
- mask_targets = self.get_targets(
- sampling_results=sampling_results,
- batch_gt_instances=batch_gt_instances,
- rcnn_train_cfg=rcnn_train_cfg)
-
- pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
-
- loss = dict()
- if mask_preds.size(0) == 0:
- loss_mask = mask_preds.sum()
- else:
- if self.class_agnostic:
- loss_mask = self.loss_mask(mask_preds, mask_targets,
- torch.zeros_like(pos_labels))
- else:
- loss_mask = self.loss_mask(mask_preds, mask_targets,
- pos_labels)
- loss['loss_mask'] = loss_mask
- # TODO: which algorithm requires mask_targets?
- return dict(loss_mask=loss, mask_targets=mask_targets)
-
- def predict_by_feat(self,
- mask_preds: Tuple[Tensor],
- results_list: List[InstanceData],
- batch_img_metas: List[dict],
- rcnn_test_cfg: ConfigDict,
- rescale: bool = False,
- activate_map: bool = False) -> InstanceList:
- """Transform a batch of output features extracted from the head into
- mask results.
-
- Args:
- mask_preds (tuple[Tensor]): Tuple of predicted foreground masks,
- each has shape (n, num_classes, h, w).
- results_list (list[:obj:`InstanceData`]): Detection results of
- each image.
- batch_img_metas (list[dict]): List of image information.
- rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of Bbox Head.
- rescale (bool): If True, return boxes in original image space.
- Defaults to False.
- activate_map (book): Whether get results with augmentations test.
- If True, the `mask_preds` will not process with sigmoid.
- Defaults to False.
-
- Returns:
- list[:obj:`InstanceData`]: Detection results of each image
- after the post process. Each item usually contains following keys.
-
- - scores (Tensor): Classification scores, has a shape
- (num_instance, )
- - labels (Tensor): Labels of bboxes, has a shape
- (num_instances, ).
- - bboxes (Tensor): Has a shape (num_instances, 4),
- the last dimension 4 arrange as (x1, y1, x2, y2).
- - masks (Tensor): Has a shape (num_instances, H, W).
- """
- assert len(mask_preds) == len(results_list) == len(batch_img_metas)
-
- for img_id in range(len(batch_img_metas)):
- img_meta = batch_img_metas[img_id]
- results = results_list[img_id]
- bboxes = results.bboxes
- if bboxes.shape[0] == 0:
- results_list[img_id] = empty_instances(
- [img_meta],
- bboxes.device,
- task_type='mask',
- instance_results=[results],
- mask_thr_binary=rcnn_test_cfg.mask_thr_binary)[0]
- else:
- im_mask = self._predict_by_feat_single(
- mask_preds=mask_preds[img_id],
- bboxes=bboxes,
- labels=results.labels,
- img_meta=img_meta,
- rcnn_test_cfg=rcnn_test_cfg,
- rescale=rescale,
- activate_map=activate_map)
- results.masks = im_mask
- return results_list
-
- def _predict_by_feat_single(self,
- mask_preds: Tensor,
- bboxes: Tensor,
- labels: Tensor,
- img_meta: dict,
- rcnn_test_cfg: ConfigDict,
- rescale: bool = False,
- activate_map: bool = False) -> Tensor:
- """Get segmentation masks from mask_preds and bboxes.
-
- Args:
- mask_preds (Tensor): Predicted foreground masks, has shape
- (n, num_classes, h, w).
- bboxes (Tensor): Predicted bboxes, has shape (n, 4)
- labels (Tensor): Labels of bboxes, has shape (n, )
- img_meta (dict): image information.
- rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of Bbox Head.
- Defaults to None.
- rescale (bool): If True, return boxes in original image space.
- Defaults to False.
- activate_map (book): Whether get results with augmentations test.
- If True, the `mask_preds` will not process with sigmoid.
- Defaults to False.
-
- Returns:
- Tensor: Encoded masks, has shape (n, img_w, img_h)
-
- Example:
- >>> from mmengine.config import Config
- >>> from mmdet.models.roi_heads.mask_heads.fcn_mask_head import * # NOQA
- >>> N = 7 # N = number of extracted ROIs
- >>> C, H, W = 11, 32, 32
- >>> # Create example instance of FCN Mask Head.
- >>> self = FCNMaskHead(num_classes=C, num_convs=0)
- >>> inputs = torch.rand(N, self.in_channels, H, W)
- >>> mask_preds = self.forward(inputs)
- >>> # Each input is associated with some bounding box
- >>> bboxes = torch.Tensor([[1, 1, 42, 42 ]] * N)
- >>> labels = torch.randint(0, C, size=(N,))
- >>> rcnn_test_cfg = Config({'mask_thr_binary': 0, })
- >>> ori_shape = (H * 4, W * 4)
- >>> scale_factor = (1, 1)
- >>> rescale = False
- >>> img_meta = {'scale_factor': scale_factor,
- ... 'ori_shape': ori_shape}
- >>> # Encoded masks are a list for each category.
- >>> encoded_masks = self._get_seg_masks_single(
- ... mask_preds, bboxes, labels,
- ... img_meta, rcnn_test_cfg, rescale)
- >>> assert encoded_masks.size()[0] == N
- >>> assert encoded_masks.size()[1:] == ori_shape
- """
- scale_factor = bboxes.new_tensor(img_meta['scale_factor']).repeat(
- (1, 2))
- img_h, img_w = img_meta['ori_shape'][:2]
- device = bboxes.device
-
- if not activate_map:
- mask_preds = mask_preds.sigmoid()
- else:
- # In AugTest, has been activated before
- mask_preds = bboxes.new_tensor(mask_preds)
-
- if rescale: # in-placed rescale the bboxes
- bboxes /= scale_factor
- else:
- w_scale, h_scale = scale_factor[0, 0], scale_factor[0, 1]
- img_h = np.round(img_h * h_scale.item()).astype(np.int32)
- img_w = np.round(img_w * w_scale.item()).astype(np.int32)
-
- N = len(mask_preds)
- # The actual implementation split the input into chunks,
- # and paste them chunk by chunk.
- if device.type == 'cpu':
- # CPU is most efficient when they are pasted one by one with
- # skip_empty=True, so that it performs minimal number of
- # operations.
- num_chunks = N
- else:
- # GPU benefits from parallelism for larger chunks,
- # but may have memory issue
- # the types of img_w and img_h are np.int32,
- # when the image resolution is large,
- # the calculation of num_chunks will overflow.
- # so we need to change the types of img_w and img_h to int.
- # See https://github.com/open-mmlab/mmdetection/pull/5191
- num_chunks = int(
- np.ceil(N * int(img_h) * int(img_w) * BYTES_PER_FLOAT /
- GPU_MEM_LIMIT))
- assert (num_chunks <=
- N), 'Default GPU_MEM_LIMIT is too small; try increasing it'
- chunks = torch.chunk(torch.arange(N, device=device), num_chunks)
-
- threshold = rcnn_test_cfg.mask_thr_binary
- im_mask = torch.zeros(
- N,
- img_h,
- img_w,
- device=device,
- dtype=torch.bool if threshold >= 0 else torch.uint8)
-
- if not self.class_agnostic:
- mask_preds = mask_preds[range(N), labels][:, None]
-
- for inds in chunks:
- masks_chunk, spatial_inds = _do_paste_mask(
- mask_preds[inds],
- bboxes[inds],
- img_h,
- img_w,
- skip_empty=device.type == 'cpu')
-
- if threshold >= 0:
- masks_chunk = (masks_chunk >= threshold).to(dtype=torch.bool)
- else:
- # for visualization and debugging
- masks_chunk = (masks_chunk * 255).to(dtype=torch.uint8)
-
- im_mask[(inds, ) + spatial_inds] = masks_chunk
- return im_mask
-
-
-def _do_paste_mask(masks: Tensor,
- boxes: Tensor,
- img_h: int,
- img_w: int,
- skip_empty: bool = True) -> tuple:
- """Paste instance masks according to boxes.
-
- This implementation is modified from
- https://github.com/facebookresearch/detectron2/
-
- Args:
- masks (Tensor): N, 1, H, W
- boxes (Tensor): N, 4
- img_h (int): Height of the image to be pasted.
- img_w (int): Width of the image to be pasted.
- skip_empty (bool): Only paste masks within the region that
- tightly bound all boxes, and returns the results this region only.
- An important optimization for CPU.
-
- Returns:
- tuple: (Tensor, tuple). The first item is mask tensor, the second one
- is the slice object.
-
- If skip_empty == False, the whole image will be pasted. It will
- return a mask of shape (N, img_h, img_w) and an empty tuple.
-
- If skip_empty == True, only area around the mask will be pasted.
- A mask of shape (N, h', w') and its start and end coordinates
- in the original image will be returned.
- """
- # On GPU, paste all masks together (up to chunk size)
- # by using the entire image to sample the masks
- # Compared to pasting them one by one,
- # this has more operations but is faster on COCO-scale dataset.
- device = masks.device
- if skip_empty:
- x0_int, y0_int = torch.clamp(
- boxes.min(dim=0).values.floor()[:2] - 1,
- min=0).to(dtype=torch.int32)
- x1_int = torch.clamp(
- boxes[:, 2].max().ceil() + 1, max=img_w).to(dtype=torch.int32)
- y1_int = torch.clamp(
- boxes[:, 3].max().ceil() + 1, max=img_h).to(dtype=torch.int32)
- else:
- x0_int, y0_int = 0, 0
- x1_int, y1_int = img_w, img_h
- x0, y0, x1, y1 = torch.split(boxes, 1, dim=1) # each is Nx1
-
- N = masks.shape[0]
-
- img_y = torch.arange(y0_int, y1_int, device=device).to(torch.float32) + 0.5
- img_x = torch.arange(x0_int, x1_int, device=device).to(torch.float32) + 0.5
- img_y = (img_y - y0) / (y1 - y0) * 2 - 1
- img_x = (img_x - x0) / (x1 - x0) * 2 - 1
- # img_x, img_y have shapes (N, w), (N, h)
- # IsInf op is not supported with ONNX<=1.7.0
- if not torch.onnx.is_in_onnx_export():
- if torch.isinf(img_x).any():
- inds = torch.where(torch.isinf(img_x))
- img_x[inds] = 0
- if torch.isinf(img_y).any():
- inds = torch.where(torch.isinf(img_y))
- img_y[inds] = 0
-
- gx = img_x[:, None, :].expand(N, img_y.size(1), img_x.size(1))
- gy = img_y[:, :, None].expand(N, img_y.size(1), img_x.size(1))
- grid = torch.stack([gx, gy], dim=3)
-
- img_masks = F.grid_sample(
- masks.to(dtype=torch.float32), grid, align_corners=False)
-
- if skip_empty:
- return img_masks[:, 0], (slice(y0_int, y1_int), slice(x0_int, x1_int))
- else:
- return img_masks[:, 0], ()
diff --git a/spaces/Lianjd/stock_dashboard/backtrader/indicators/hurst.py b/spaces/Lianjd/stock_dashboard/backtrader/indicators/hurst.py
deleted file mode 100644
index 8decd1c085bf83488b51e0d08399a3a7721470ea..0000000000000000000000000000000000000000
--- a/spaces/Lianjd/stock_dashboard/backtrader/indicators/hurst.py
+++ /dev/null
@@ -1,96 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8; py-indent-offset:4 -*-
-###############################################################################
-#
-# Copyright (C) 2015-2020 Daniel Rodriguez
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program. If not, see .
-#
-###############################################################################
-from __future__ import (absolute_import, division, print_function,
- unicode_literals)
-
-from . import PeriodN
-
-
-__all__ = ['HurstExponent', 'Hurst']
-
-
-class HurstExponent(PeriodN):
- '''
- References:
-
- - https://www.quantopian.com/posts/hurst-exponent
- - https://www.quantopian.com/posts/some-code-from-ernie-chans-new-book-implemented-in-python
-
- Interpretation of the results
-
- 1. Geometric random walk (H=0.5)
- 2. Mean-reverting series (H<0.5)
- 3. Trending Series (H>0.5)
-
- Important notes:
-
- - The default period is ``40``, but experimentation by users has shown
- that it would be advisable to have at least 2000 samples (i.e.: a
- period of at least 2000) to have stable values.
-
- - The `lag_start` and `lag_end` values will default to be ``2`` and
- ``self.p.period / 2`` unless the parameters are specified.
-
- Experimentation by users has also shown that values of around ``10``
- and ``500`` produce good results
-
- The original values (40, 2, self.p.period / 2) are kept for backwards
- compatibility
-
- '''
- frompackages = (
- ('numpy', ('asarray', 'log10', 'polyfit', 'sqrt', 'std', 'subtract')),
- )
-
- alias = ('Hurst',)
- lines = ('hurst',)
- params = (
- ('period', 40), # 2000 was proposed
- ('lag_start', None), # 10 was proposed
- ('lag_end', None), # 500 was proposed
- )
-
- def _plotlabel(self):
- plabels = [self.p.period]
- plabels += [self._lag_start]
- plabels += [self._lag_end]
- return plabels
-
- def __init__(self):
- super(HurstExponent, self).__init__()
- # Prepare the lags array
- self._lag_start = lag_start = self.p.lag_start or 2
- self._lag_end = lag_end = self.p.lag_end or (self.p.period // 2)
- self.lags = asarray(range(lag_start, lag_end))
- self.log10lags = log10(self.lags)
-
- def next(self):
- # Fetch the data
- ts = asarray(self.data.get(size=self.p.period))
-
- # Calculate the array of the variances of the lagged differences
- tau = [sqrt(std(subtract(ts[lag:], ts[:-lag]))) for lag in self.lags]
-
- # Use a linear fit to estimate the Hurst Exponent
- poly = polyfit(self.log10lags, log10(tau), 1)
-
- # Return the Hurst exponent from the polyfit output
- self.lines.hurst[0] = poly[0] * 2.0
diff --git a/spaces/Loren/Streamlit_OCR_comparator/configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py b/spaces/Loren/Streamlit_OCR_comparator/configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py
deleted file mode 100644
index 045e89a3bb1fa44ff33da1d2b8b32b42e396c58b..0000000000000000000000000000000000000000
--- a/spaces/Loren/Streamlit_OCR_comparator/configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py
+++ /dev/null
@@ -1,33 +0,0 @@
-_base_ = [
- '../../_base_/default_runtime.py',
- '../../_base_/schedules/schedule_sgd_1200e.py',
- '../../_base_/det_models/textsnake_r50_fpn_unet.py',
- '../../_base_/det_datasets/ctw1500.py',
- '../../_base_/det_pipelines/textsnake_pipeline.py'
-]
-
-train_list = {{_base_.train_list}}
-test_list = {{_base_.test_list}}
-
-train_pipeline = {{_base_.train_pipeline}}
-test_pipeline = {{_base_.test_pipeline}}
-
-data = dict(
- samples_per_gpu=4,
- workers_per_gpu=4,
- val_dataloader=dict(samples_per_gpu=1),
- test_dataloader=dict(samples_per_gpu=1),
- train=dict(
- type='UniformConcatDataset',
- datasets=train_list,
- pipeline=train_pipeline),
- val=dict(
- type='UniformConcatDataset',
- datasets=test_list,
- pipeline=test_pipeline),
- test=dict(
- type='UniformConcatDataset',
- datasets=test_list,
- pipeline=test_pipeline))
-
-evaluation = dict(interval=10, metric='hmean-iou')
diff --git a/spaces/Loren/Streamlit_OCR_comparator/configs/textrecog/seg/seg_r31_1by16_fpnocr_toy_dataset.py b/spaces/Loren/Streamlit_OCR_comparator/configs/textrecog/seg/seg_r31_1by16_fpnocr_toy_dataset.py
deleted file mode 100644
index 893bebba496c04e9364bdcea3caef651e3d426d0..0000000000000000000000000000000000000000
--- a/spaces/Loren/Streamlit_OCR_comparator/configs/textrecog/seg/seg_r31_1by16_fpnocr_toy_dataset.py
+++ /dev/null
@@ -1,39 +0,0 @@
-_base_ = [
- '../../_base_/default_runtime.py',
- '../../_base_/recog_datasets/seg_toy_data.py',
- '../../_base_/recog_models/seg.py',
- '../../_base_/recog_pipelines/seg_pipeline.py',
-]
-
-train_list = {{_base_.train_list}}
-test_list = {{_base_.test_list}}
-
-train_pipeline = {{_base_.train_pipeline}}
-test_pipeline = {{_base_.test_pipeline}}
-
-# optimizer
-optimizer = dict(type='Adam', lr=1e-4)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(policy='step', step=[3, 4])
-total_epochs = 5
-
-data = dict(
- samples_per_gpu=8,
- workers_per_gpu=1,
- train=dict(
- type='UniformConcatDataset',
- datasets=train_list,
- pipeline=train_pipeline),
- val=dict(
- type='UniformConcatDataset',
- datasets=test_list,
- pipeline=test_pipeline),
- test=dict(
- type='UniformConcatDataset',
- datasets=test_list,
- pipeline=test_pipeline))
-
-evaluation = dict(interval=1, metric='acc')
-
-find_unused_parameters = True
diff --git a/spaces/MBZ/LoRA-DreamBooth-Training-UI/app_inference.py b/spaces/MBZ/LoRA-DreamBooth-Training-UI/app_inference.py
deleted file mode 100644
index a9969e649ca321a5246130d7d560ac3c431a12f2..0000000000000000000000000000000000000000
--- a/spaces/MBZ/LoRA-DreamBooth-Training-UI/app_inference.py
+++ /dev/null
@@ -1,176 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import enum
-
-import gradio as gr
-from huggingface_hub import HfApi
-
-from inference import InferencePipeline
-from utils import find_exp_dirs
-
-SAMPLE_MODEL_IDS = [
- 'patrickvonplaten/lora_dreambooth_dog_example',
- 'sayakpaul/sd-model-finetuned-lora-t4',
-]
-
-
-class ModelSource(enum.Enum):
- SAMPLE = 'Sample'
- HUB_LIB = 'Hub (lora-library)'
- LOCAL = 'Local'
-
-
-class InferenceUtil:
- def __init__(self, hf_token: str | None):
- self.hf_token = hf_token
-
- @staticmethod
- def load_sample_lora_model_list():
- return gr.update(choices=SAMPLE_MODEL_IDS, value=SAMPLE_MODEL_IDS[0])
-
- def load_hub_lora_model_list(self) -> dict:
- api = HfApi(token=self.hf_token)
- choices = [
- info.modelId for info in api.list_models(author='lora-library')
- ]
- return gr.update(choices=choices,
- value=choices[0] if choices else None)
-
- @staticmethod
- def load_local_lora_model_list() -> dict:
- choices = find_exp_dirs()
- return gr.update(choices=choices,
- value=choices[0] if choices else None)
-
- def reload_lora_model_list(self, model_source: str) -> dict:
- if model_source == ModelSource.SAMPLE.value:
- return self.load_sample_lora_model_list()
- elif model_source == ModelSource.HUB_LIB.value:
- return self.load_hub_lora_model_list()
- elif model_source == ModelSource.LOCAL.value:
- return self.load_local_lora_model_list()
- else:
- raise ValueError
-
- def load_model_info(self, lora_model_id: str) -> tuple[str, str]:
- try:
- card = InferencePipeline.get_model_card(lora_model_id,
- self.hf_token)
- except Exception:
- return '', ''
- base_model = getattr(card.data, 'base_model', '')
- instance_prompt = getattr(card.data, 'instance_prompt', '')
- return base_model, instance_prompt
-
- def reload_lora_model_list_and_update_model_info(
- self, model_source: str) -> tuple[dict, str, str]:
- model_list_update = self.reload_lora_model_list(model_source)
- model_list = model_list_update['choices']
- model_info = self.load_model_info(model_list[0] if model_list else '')
- return model_list_update, *model_info
-
-
-def create_inference_demo(pipe: InferencePipeline,
- hf_token: str | None = None) -> gr.Blocks:
- app = InferenceUtil(hf_token)
-
- with gr.Blocks() as demo:
- with gr.Row():
- with gr.Column():
- with gr.Box():
- model_source = gr.Radio(
- label='Model Source',
- choices=[_.value for _ in ModelSource],
- value=ModelSource.SAMPLE.value)
- reload_button = gr.Button('Reload Model List')
- lora_model_id = gr.Dropdown(label='LoRA Model ID',
- choices=SAMPLE_MODEL_IDS,
- value=SAMPLE_MODEL_IDS[0])
- with gr.Accordion(
- label=
- 'Model info (Base model and instance prompt used for training)',
- open=False):
- with gr.Row():
- base_model_used_for_training = gr.Text(
- label='Base model', interactive=False)
- instance_prompt_used_for_training = gr.Text(
- label='Instance prompt', interactive=False)
- prompt = gr.Textbox(
- label='Prompt',
- max_lines=1,
- placeholder='Example: "A picture of a sks dog in a bucket"'
- )
- alpha = gr.Slider(label='LoRA alpha',
- minimum=0,
- maximum=2,
- step=0.05,
- value=1)
- seed = gr.Slider(label='Seed',
- minimum=0,
- maximum=100000,
- step=1,
- value=0)
- with gr.Accordion('Other Parameters', open=False):
- num_steps = gr.Slider(label='Number of Steps',
- minimum=0,
- maximum=100,
- step=1,
- value=25)
- guidance_scale = gr.Slider(label='CFG Scale',
- minimum=0,
- maximum=50,
- step=0.1,
- value=7.5)
-
- run_button = gr.Button('Generate')
-
- gr.Markdown('''
- - After training, you can press "Reload Model List" button to load your trained model names.
- ''')
- with gr.Column():
- result = gr.Image(label='Result')
-
- model_source.change(
- fn=app.reload_lora_model_list_and_update_model_info,
- inputs=model_source,
- outputs=[
- lora_model_id,
- base_model_used_for_training,
- instance_prompt_used_for_training,
- ])
- reload_button.click(
- fn=app.reload_lora_model_list_and_update_model_info,
- inputs=model_source,
- outputs=[
- lora_model_id,
- base_model_used_for_training,
- instance_prompt_used_for_training,
- ])
- lora_model_id.change(fn=app.load_model_info,
- inputs=lora_model_id,
- outputs=[
- base_model_used_for_training,
- instance_prompt_used_for_training,
- ])
- inputs = [
- lora_model_id,
- prompt,
- alpha,
- seed,
- num_steps,
- guidance_scale,
- ]
- prompt.submit(fn=pipe.run, inputs=inputs, outputs=result)
- run_button.click(fn=pipe.run, inputs=inputs, outputs=result)
- return demo
-
-
-if __name__ == '__main__':
- import os
-
- hf_token = os.getenv('HF_TOKEN')
- pipe = InferencePipeline(hf_token)
- demo = create_inference_demo(pipe, hf_token)
- demo.queue(max_size=10).launch(share=False)
diff --git a/spaces/Mahit/DDoS_Attack_Classifier/README.md b/spaces/Mahit/DDoS_Attack_Classifier/README.md
deleted file mode 100644
index b35aa6d1c0462bcbb2a8df8d3057a22aadcceab3..0000000000000000000000000000000000000000
--- a/spaces/Mahit/DDoS_Attack_Classifier/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: DDoS Attack Classifier
-emoji: 🏆
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 4.1.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/inference/transforms/zoom_in.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/inference/transforms/zoom_in.py
deleted file mode 100644
index 6c11ecc241570fe2429e85bdccbb713a70d9ffd6..0000000000000000000000000000000000000000
--- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/inference/transforms/zoom_in.py
+++ /dev/null
@@ -1,171 +0,0 @@
-import torch
-
-from ..clicker import Click
-from ...utils.misc import get_bbox_iou, get_bbox_from_mask, expand_bbox, clamp_bbox
-from .base import BaseTransform
-
-
-class ZoomIn(BaseTransform):
- def __init__(self,
- target_size=400,
- skip_clicks=1,
- expansion_ratio=1.4,
- min_crop_size=200,
- recompute_thresh_iou=0.5,
- prob_thresh=0.50):
- super().__init__()
- self.target_size = target_size
- self.min_crop_size = min_crop_size
- self.skip_clicks = skip_clicks
- self.expansion_ratio = expansion_ratio
- self.recompute_thresh_iou = recompute_thresh_iou
- self.prob_thresh = prob_thresh
-
- self._input_image_shape = None
- self._prev_probs = None
- self._object_roi = None
- self._roi_image = None
-
- def transform(self, image_nd, clicks_lists):
- assert image_nd.shape[0] == 1 and len(clicks_lists) == 1
- self.image_changed = False
-
- clicks_list = clicks_lists[0]
- if len(clicks_list) <= self.skip_clicks:
- return image_nd, clicks_lists
-
- self._input_image_shape = image_nd.shape
-
- current_object_roi = None
- if self._prev_probs is not None:
- current_pred_mask = (self._prev_probs > self.prob_thresh)[0, 0]
- if current_pred_mask.sum() > 0:
- current_object_roi = get_object_roi(current_pred_mask, clicks_list,
- self.expansion_ratio, self.min_crop_size)
-
- if current_object_roi is None:
- return image_nd, clicks_lists
-
- update_object_roi = False
- if self._object_roi is None:
- update_object_roi = True
- elif not check_object_roi(self._object_roi, clicks_list):
- update_object_roi = True
- elif get_bbox_iou(current_object_roi, self._object_roi) < self.recompute_thresh_iou:
- update_object_roi = True
-
- if update_object_roi:
- self._object_roi = current_object_roi
- self._roi_image = get_roi_image_nd(image_nd, self._object_roi, self.target_size)
- self.image_changed = True
-
- tclicks_lists = [self._transform_clicks(clicks_list)]
- return self._roi_image.to(image_nd.device), tclicks_lists
-
- def inv_transform(self, prob_map):
- if self._object_roi is None:
- self._prev_probs = prob_map.cpu().numpy()
- return prob_map
-
- assert prob_map.shape[0] == 1
- rmin, rmax, cmin, cmax = self._object_roi
- prob_map = torch.nn.functional.interpolate(prob_map, size=(rmax - rmin + 1, cmax - cmin + 1),
- mode='bilinear', align_corners=True)
-
- if self._prev_probs is not None:
- new_prob_map = torch.zeros(*self._prev_probs.shape, device=prob_map.device, dtype=prob_map.dtype)
- new_prob_map[:, :, rmin:rmax + 1, cmin:cmax + 1] = prob_map
- else:
- new_prob_map = prob_map
-
- self._prev_probs = new_prob_map.cpu().numpy()
-
- return new_prob_map
-
- def check_possible_recalculation(self):
- if self._prev_probs is None or self._object_roi is not None or self.skip_clicks > 0:
- return False
-
- pred_mask = (self._prev_probs > self.prob_thresh)[0, 0]
- if pred_mask.sum() > 0:
- possible_object_roi = get_object_roi(pred_mask, [],
- self.expansion_ratio, self.min_crop_size)
- image_roi = (0, self._input_image_shape[2] - 1, 0, self._input_image_shape[3] - 1)
- if get_bbox_iou(possible_object_roi, image_roi) < 0.50:
- return True
- return False
-
- def get_state(self):
- roi_image = self._roi_image.cpu() if self._roi_image is not None else None
- return self._input_image_shape, self._object_roi, self._prev_probs, roi_image, self.image_changed
-
- def set_state(self, state):
- self._input_image_shape, self._object_roi, self._prev_probs, self._roi_image, self.image_changed = state
-
- def reset(self):
- self._input_image_shape = None
- self._object_roi = None
- self._prev_probs = None
- self._roi_image = None
- self.image_changed = False
-
- def _transform_clicks(self, clicks_list):
- if self._object_roi is None:
- return clicks_list
-
- rmin, rmax, cmin, cmax = self._object_roi
- crop_height, crop_width = self._roi_image.shape[2:]
-
- transformed_clicks = []
- for click in clicks_list:
- new_r = crop_height * (click.coords[0] - rmin) / (rmax - rmin + 1)
- new_c = crop_width * (click.coords[1] - cmin) / (cmax - cmin + 1)
- transformed_clicks.append(Click(is_positive=click.is_positive, coords=(new_r, new_c)))
- return transformed_clicks
-
-
-def get_object_roi(pred_mask, clicks_list, expansion_ratio, min_crop_size):
- pred_mask = pred_mask.copy()
-
- for click in clicks_list:
- if click.is_positive:
- pred_mask[int(click.coords[0]), int(click.coords[1])] = 1
-
- bbox = get_bbox_from_mask(pred_mask)
- bbox = expand_bbox(bbox, expansion_ratio, min_crop_size)
- h, w = pred_mask.shape[0], pred_mask.shape[1]
- bbox = clamp_bbox(bbox, 0, h - 1, 0, w - 1)
-
- return bbox
-
-
-def get_roi_image_nd(image_nd, object_roi, target_size):
- rmin, rmax, cmin, cmax = object_roi
-
- height = rmax - rmin + 1
- width = cmax - cmin + 1
-
- if isinstance(target_size, tuple):
- new_height, new_width = target_size
- else:
- scale = target_size / max(height, width)
- new_height = int(round(height * scale))
- new_width = int(round(width * scale))
-
- with torch.no_grad():
- roi_image_nd = image_nd[:, :, rmin:rmax + 1, cmin:cmax + 1]
- roi_image_nd = torch.nn.functional.interpolate(roi_image_nd, size=(new_height, new_width),
- mode='bilinear', align_corners=True)
-
- return roi_image_nd
-
-
-def check_object_roi(object_roi, clicks_list):
- for click in clicks_list:
- if click.is_positive:
- if click.coords[0] < object_roi[0] or click.coords[0] >= object_roi[1]:
- return False
- if click.coords[1] < object_roi[2] or click.coords[1] >= object_roi[3]:
- return False
-
- return True
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/utils/cython/dist_maps.py b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/utils/cython/dist_maps.py
deleted file mode 100644
index 8ffa1e3f25231cd7c48b66ef8ef5167235c3ea4e..0000000000000000000000000000000000000000
--- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/XMem/inference/interact/fbrs/utils/cython/dist_maps.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import pyximport; pyximport.install(pyximport=True, language_level=3)
-# noinspection PyUnresolvedReferences
-from ._get_dist_maps import get_dist_maps
\ No newline at end of file
diff --git a/spaces/Makiing/coolb-in-gtest/tests/parse.ts b/spaces/Makiing/coolb-in-gtest/tests/parse.ts
deleted file mode 100644
index 92940fe6315f1d7cb2b267ba5e5a7e26460a1de3..0000000000000000000000000000000000000000
--- a/spaces/Makiing/coolb-in-gtest/tests/parse.ts
+++ /dev/null
@@ -1,13 +0,0 @@
-import { promises as fs } from 'fs'
-import { join } from 'path'
-import { parseHeadersFromCurl } from '@/lib/utils'
-
-(async () => {
- const content = await fs.readFile(join(__dirname, './fixtures/curl.txt'), 'utf-8')
- const headers = parseHeadersFromCurl(content)
- console.log(headers)
-
- const cmdContent = await fs.readFile(join(__dirname, './fixtures/cmd.txt'), 'utf-8')
- const cmdHeaders = parseHeadersFromCurl(cmdContent)
- console.log(cmdHeaders)
-})()
diff --git a/spaces/Marshalls/testmtd/analysis/shift_bvh.py b/spaces/Marshalls/testmtd/analysis/shift_bvh.py
deleted file mode 100644
index 25d3b59354a98675805236bc475cc27ab5059b58..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/analysis/shift_bvh.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from analysis.pymo.parsers import BVHParser
-from analysis.pymo.data import Joint, MocapData
-from analysis.pymo.preprocessing import *
-from analysis.pymo.viz_tools import *
-from analysis.pymo.writers import *
-from sklearn.pipeline import Pipeline
-from pathlib import Path
-import sys
-from feature_extraction.utils import distribute_tasks
-
-p = BVHParser()
-datas = []
-filename = sys.argv[1]
-shift_amount = float(sys.argv[2])
-data = p.parse(filename)
-
-data.values["Hips_Yposition"] += shift_amount
-
-writer = BVHWriter()
-
-with open(filename,'w') as f:
- writer.write(data, f)
diff --git a/spaces/Matthijs/mms-tts-demo/uroman/lib/NLP/Romanizer.pm b/spaces/Matthijs/mms-tts-demo/uroman/lib/NLP/Romanizer.pm
deleted file mode 100644
index b504ec6eefcf1b6b28e216c9fe3d69d2735b2b25..0000000000000000000000000000000000000000
--- a/spaces/Matthijs/mms-tts-demo/uroman/lib/NLP/Romanizer.pm
+++ /dev/null
@@ -1,2020 +0,0 @@
-################################################################
-# #
-# Romanizer #
-# #
-################################################################
-
-package NLP::Romanizer;
-
-use NLP::Chinese;
-use NLP::UTF8;
-use NLP::utilities;
-use JSON;
-$utf8 = NLP::UTF8;
-$util = NLP::utilities;
-$chinesePM = NLP::Chinese;
-
-my $verbosePM = 0;
-%empty_ht = ();
-
-my $braille_capital_letter_indicator = "\xE2\xA0\xA0";
-my $braille_number_indicator = "\xE2\xA0\xBC";
-my $braille_decimal_point = "\xE2\xA0\xA8";
-my $braille_comma = "\xE2\xA0\x82";
-my $braille_solidus = "\xE2\xA0\x8C";
-my $braille_numeric_space = "\xE2\xA0\x90";
-my $braille_letter_indicator = "\xE2\xA0\xB0";
-my $braille_period = "\xE2\xA0\xB2";
-
-sub new {
- local($caller) = @_;
-
- my $object = {};
- my $class = ref( $caller ) || $caller;
- bless($object, $class);
- return $object;
-}
-
-sub load_unicode_data {
- local($this, *ht, $filename) = @_;
- # ../../data/UnicodeData.txt
-
- $n = 0;
- if (open(IN, $filename)) {
- while () {
- if (($unicode_value, $char_name, $general_category, $canon_comb_classes, $bidir_category, $char_decomp_mapping, $decimal_digit_value, $digit_value, $numeric_value, $mirrored, $unicode_1_0_name, $comment_field, $uc_mapping, $lc_mapping, $title_case_mapping) = split(";", $_)) {
- $utf8_code = $utf8->unicode_hex_string2string($unicode_value);
- $ht{UTF_TO_CHAR_NAME}->{$utf8_code} = $char_name;
- $ht{UTF_NAME_TO_UNICODE}->{$char_name} = $unicode_value;
- $ht{UTF_NAME_TO_CODE}->{$char_name} = $utf8_code;
- $ht{UTF_TO_CAT}->{$utf8_code} = $general_category;
- $ht{UTF_TO_NUMERIC}->{$utf8_code} = $numeric_value unless $numeric_value eq "";
- $n++;
- }
- }
- close(IN);
- # print STDERR "Loaded $n entries from $filename\n";
- } else {
- print STDERR "Can't open $filename\n";
- }
-}
-
-sub load_unicode_overwrite_romanization {
- local($this, *ht, $filename) = @_;
- # ../../data/UnicodeDataOverwrite.txt
-
- $n = 0;
- if (open(IN, $filename)) {
- while () {
- next if /^#/;
- $unicode_value = $util->slot_value_in_double_colon_del_list($_, "u");
- $romanization = $util->slot_value_in_double_colon_del_list($_, "r");
- $numeric = $util->slot_value_in_double_colon_del_list($_, "num");
- $picture = $util->slot_value_in_double_colon_del_list($_, "pic");
- $syllable_info = $util->slot_value_in_double_colon_del_list($_, "syllable-info");
- $tone_mark = $util->slot_value_in_double_colon_del_list($_, "tone-mark");
- $char_name = $util->slot_value_in_double_colon_del_list($_, "name");
- $entry_processed_p = 0;
- $utf8_code = $utf8->unicode_hex_string2string($unicode_value);
- if ($unicode_value) {
- $ht{UTF_TO_CHAR_ROMANIZATION}->{$utf8_code} = $romanization if $romanization;
- $ht{UTF_TO_NUMERIC}->{$utf8_code} = $numeric if defined($numeric) && ($numeric ne "");
- $ht{UTF_TO_PICTURE_DESCR}->{$utf8_code} = $picture if $picture;
- $ht{UTF_TO_SYLLABLE_INFO}->{$utf8_code} = $syllable_info if $syllable_info;
- $ht{UTF_TO_TONE_MARK}->{$utf8_code} = $tone_mark if $tone_mark;
- $ht{UTF_TO_CHAR_NAME}->{$utf8_code} = $char_name if $char_name;
- $entry_processed_p = 1 if $romanization || $numeric || $picture || $syllable_info || $tone_mark;
- }
- $n++ if $entry_processed_p;
- }
- close(IN);
- } else {
- print STDERR "Can't open $filename\n";
- }
-}
-
-sub load_script_data {
- local($this, *ht, $filename) = @_;
- # ../../data/Scripts.txt
-
- $n = 0;
- if (open(IN, $filename)) {
- while () {
- next unless $script_name = $util->slot_value_in_double_colon_del_list($_, "script-name");
- $abugida_default_vowel_s = $util->slot_value_in_double_colon_del_list($_, "abugida-default-vowel");
- $alt_script_name_s = $util->slot_value_in_double_colon_del_list($_, "alt-script-name");
- $language_s = $util->slot_value_in_double_colon_del_list($_, "language");
- $direction = $util->slot_value_in_double_colon_del_list($_, "direction"); # right-to-left
- $font_family_s = $util->slot_value_in_double_colon_del_list($_, "font-family");
- $ht{SCRIPT_P}->{$script_name} = 1;
- $ht{SCRIPT_NORM}->{(uc $script_name)} = $script_name;
- $ht{DIRECTION}->{$script_name} = $direction if $direction;
- foreach $language (split(/,\s*/, $language_s)) {
- $ht{SCRIPT_LANGUAGE}->{$script_name}->{$language} = 1;
- $ht{LANGUAGE_SCRIPT}->{$language}->{$script_name} = 1;
- }
- foreach $alt_script_name (split(/,\s*/, $alt_script_name_s)) {
- $ht{SCRIPT_NORM}->{$alt_script_name} = $script_name;
- $ht{SCRIPT_NORM}->{(uc $alt_script_name)} = $script_name;
- }
- foreach $abugida_default_vowel (split(/,\s*/, $abugida_default_vowel_s)) {
- $ht{SCRIPT_ABUDIGA_DEFAULT_VOWEL}->{$script_name}->{$abugida_default_vowel} = 1 if $abugida_default_vowel;
- }
- foreach $font_family (split(/,\s*/, $font_family_s)) {
- $ht{SCRIPT_FONT}->{$script_name}->{$font_family} = 1 if $font_family;
- }
- $n++;
- }
- close(IN);
- # print STDERR "Loaded $n entries from $filename\n";
- } else {
- print STDERR "Can't open $filename\n";
- }
-}
-
-sub unicode_hangul_romanization {
- local($this, $s, $pass_through_p) = @_;
-
- $pass_through_p = 0 unless defined($pass_through_p);
- @leads = split(/\s+/, "g gg n d dd r m b bb s ss - j jj c k t p h");
- # @vowels = split(/\s+/, "a ae ya yai e ei ye yei o oa oai oi yo u ue uei ui yu w wi i");
- @vowels = split(/\s+/, "a ae ya yae eo e yeo ye o wa wai oe yo u weo we wi yu eu yi i");
- @tails = split(/\s+/, "- g gg gs n nj nh d l lg lm lb ls lt lp lh m b bs s ss ng j c k t p h");
- $result = "";
- @chars = $utf8->split_into_utf8_characters($s, "return only chars", *empty_ht);
- foreach $char (@chars) {
- $unicode = $utf8->utf8_to_unicode($char);
- if (($unicode >= 0xAC00) && ($unicode <= 0xD7A3)) {
- $code = $unicode - 0xAC00;
- $lead_index = int($code / (28*21));
- $vowel_index = int($code/28) % 21;
- $tail_index = $code % 28;
- $rom = $leads[$lead_index] . $vowels[$vowel_index] . $tails[$tail_index];
- $rom =~ s/-//g;
- $result .= $rom;
- } elsif ($pass_through_p) {
- $result .= $char;
- }
- }
- return $result;
-}
-
-sub listify_comma_sep_string {
- local($this, $s) = @_;
-
- @result_list = ();
- return @result_list unless $s =~ /\S/;
- $s = $util->trim2($s);
- my $elem;
-
- while (($elem, $rest) = ($s =~ /^("(?:\\"|[^"])*"|'(?:\\'|[^'])*'|[^"', ]+),\s*(.*)$/)) {
- push(@result_list, $util->dequote_string($elem));
- $s = $rest;
- }
- push(@result_list, $util->dequote_string($s)) if $s =~ /\S/;
-
- return @result_list;
-}
-
-sub braille_string_p {
- local($this, $s) = @_;
-
- return ($s =~ /^(\xE2[\xA0-\xA3][\x80-\xBF])+$/);
-}
-
-sub register_word_boundary_info {
- local($this, *ht, $lang_code, $utf8_source_string, $utf8_target_string, $use_only_for_whole_word_p,
- $use_only_at_start_of_word_p, $use_only_at_end_of_word_p,
- $dont_use_at_start_of_word_p, $dont_use_at_end_of_word_p) = @_;
-
- if ($use_only_for_whole_word_p) {
- if ($lang_code) {
- $ht{USE_ONLY_FOR_WHOLE_WORD_LANG_SPEC}->{$lang_code}->{$utf8_source_string}->{$utf8_target_string} = 1;
- } else {
- $ht{USE_ONLY_FOR_WHOLE_WORD}->{$utf8_source_string}->{$utf8_target_string} = 1;
- }
- }
- if ($use_only_at_start_of_word_p) {
- if ($lang_code) {
- $ht{USE_ONLY_AT_START_OF_WORD_LANG_SPEC}->{$lang_code}->{$utf8_source_string}->{$utf8_target_string} = 1;
- } else {
- $ht{USE_ONLY_AT_START_OF_WORD}->{$utf8_source_string}->{$utf8_target_string} = 1;
- }
- }
- if ($use_only_at_end_of_word_p) {
- if ($lang_code) {
- $ht{USE_ONLY_AT_END_OF_WORD_LANG_SPEC}->{$lang_code}->{$utf8_source_string}->{$utf8_target_string} = 1;
- } else {
- $ht{USE_ONLY_AT_END_OF_WORD}->{$utf8_source_string}->{$utf8_target_string} = 1;
- }
- }
- if ($dont_use_at_start_of_word_p) {
- if ($lang_code) {
- $ht{DONT_USE_AT_START_OF_WORD_LANG_SPEC}->{$lang_code}->{$utf8_source_string}->{$utf8_target_string} = 1;
- } else {
- $ht{DONT_USE_AT_START_OF_WORD}->{$utf8_source_string}->{$utf8_target_string} = 1;
- }
- }
- if ($dont_use_at_end_of_word_p) {
- if ($lang_code) {
- $ht{DONT_USE_AT_END_OF_WORD_LANG_SPEC}->{$lang_code}->{$utf8_source_string}->{$utf8_target_string} = 1;
- } else {
- $ht{DONT_USE_AT_END_OF_WORD}->{$utf8_source_string}->{$utf8_target_string} = 1;
- }
- }
-}
-
-sub load_romanization_table {
- local($this, *ht, $filename) = @_;
- # ../../data/romanization-table.txt
-
- $n = 0;
- $line_number = 0;
- if (open(IN, $filename)) {
- while () {
- $line_number++;
- next if /^#/;
- if ($_ =~ /^::preserve\s/) {
- $from_unicode = $util->slot_value_in_double_colon_del_list($_, "from");
- $to_unicode = $util->slot_value_in_double_colon_del_list($_, "to");
- if ($from_unicode =~ /^(?:U\+|\\u)[0-9A-F]{4,}$/i) {
- $from_unicode =~ s/^(?:U\+|\\u)//;
- $from_code_point = hex($from_unicode);
- } else {
- $from_code_point = "";
- }
- if ($to_unicode =~ /^(?:U\+|\\u)[0-9A-F]{4,}$/i) {
- $to_unicode =~ s/^(?:U\+|\\u)//;
- $to_code_point = hex($to_unicode);
- } else {
- $to_code_point = $from_code_point;
- }
- if ($from_code_point ne "") {
- # print STDERR "Preserve code-points $from_unicode--$to_unicode = $from_code_point--$to_code_point\n";
- foreach $code_point (($from_code_point .. $to_code_point)) {
- $utf8_string = $utf8->unicode2string($code_point);
- $ht{UTF_CHAR_MAPPING}->{$utf8_string}->{$utf8_string} = 1;
- }
- $n++;
- }
- next;
- }
- $utf8_source_string = $util->slot_value_in_double_colon_del_list($_, "s");
- $utf8_target_string = $util->slot_value_in_double_colon_del_list($_, "t");
- $utf8_alt_target_string_s = $util->slot_value_in_double_colon_del_list($_, "t-alt");
- $use_alt_in_pointed_p = ($_ =~ /::use-alt-in-pointed\b/);
- $use_only_for_whole_word_p = ($_ =~ /::use-only-for-whole-word\b/);
- $use_only_at_start_of_word_p = ($_ =~ /::use-only-at-start-of-word\b/);
- $use_only_at_end_of_word_p = ($_ =~ /::use-only-at-end-of-word\b/);
- $dont_use_at_start_of_word_p = ($_ =~ /::dont-use-at-start-of-word\b/);
- $dont_use_at_end_of_word_p = ($_ =~ /::dont-use-at-end-of-word\b/);
- $use_only_in_lower_case_enviroment_p = ($_ =~ /::use-only-in-lower-case-enviroment\b/);
- $word_external_punctuation_p = ($_ =~ /::word-external-punctuation\b/);
- $utf8_source_string =~ s/\s*$//;
- $utf8_target_string =~ s/\s*$//;
- $utf8_alt_target_string_s =~ s/\s*$//;
- $utf8_target_string =~ s/^"(.*)"$/$1/;
- $utf8_target_string =~ s/^'(.*)'$/$1/;
- @utf8_alt_targets = $this->listify_comma_sep_string($utf8_alt_target_string_s);
- $numeric = $util->slot_value_in_double_colon_del_list($_, "num");
- $numeric =~ s/\s*$//;
- $annotation = $util->slot_value_in_double_colon_del_list($_, "annotation");
- $annotation =~ s/\s*$//;
- $lang_code = $util->slot_value_in_double_colon_del_list($_, "lcode");
- $prob = $util->slot_value_in_double_colon_del_list($_, "p") || 1;
- unless (($utf8_target_string eq "") && ($numeric =~ /\d/)) {
- if ($lang_code) {
- $ht{UTF_CHAR_MAPPING_LANG_SPEC}->{$lang_code}->{$utf8_source_string}->{$utf8_target_string} = $prob;
- } else {
- $ht{UTF_CHAR_MAPPING}->{$utf8_source_string}->{$utf8_target_string} = $prob;
- }
- if ($word_external_punctuation_p) {
- if ($lang_code) {
- $ht{WORD_EXTERNAL_PUNCTUATION_LANG_SPEC}->{$lang_code}->{$utf8_source_string}->{$utf8_target_string} = $prob;
- } else {
- $ht{WORD_EXTERNAL_PUNCTUATION}->{$utf8_source_string}->{$utf8_target_string} = $prob;
- }
- }
- if ($this->braille_string_p($utf8_source_string)) {
- if (($utf8_target_string =~ /^[a-z]+$/)
- && (! ($utf8_source_string =~ /^$braille_capital_letter_indicator/))) {
- my $uc_utf8_source_string = "$braille_capital_letter_indicator$utf8_source_string";
- my $uc_utf8_target_string = ucfirst $utf8_target_string;
- if ($lang_code) {
- $ht{UTF_CHAR_MAPPING_LANG_SPEC}->{$lang_code}->{$uc_utf8_source_string}->{$uc_utf8_target_string} = $prob;
- } else {
- $ht{UTF_CHAR_MAPPING}->{$uc_utf8_source_string}->{$uc_utf8_target_string} = $prob;
- }
- $this->register_word_boundary_info(*ht, $lang_code, $uc_utf8_source_string, $uc_utf8_target_string,
- $use_only_for_whole_word_p, $use_only_at_start_of_word_p, $use_only_at_end_of_word_p,
- $dont_use_at_start_of_word_p, $dont_use_at_end_of_word_p);
- }
- if (($utf8_target_string =~ /^[0-9]$/)
- && ($utf8_source_string =~ /^$braille_number_indicator./)) {
- my $core_number_char = $utf8_source_string;
- $core_number_char =~ s/$braille_number_indicator//;
- $ht{BRAILLE_TO_DIGIT}->{$core_number_char} = $utf8_target_string;
- }
- }
- }
- if ($use_only_in_lower_case_enviroment_p) {
- if ($lang_code) {
- $ht{USE_ONLY_IN_LOWER_CASE_ENVIROMENT_LANG_SPEC}->{$lang_code}->{$utf8_source_string}->{$utf8_target_string} = 1;
- } else {
- $ht{USE_ONLY_IN_LOWER_CASE_ENVIROMENT}->{$utf8_source_string}->{$utf8_target_string} = 1;
- }
- }
- $this->register_word_boundary_info(*ht, $lang_code, $utf8_source_string, $utf8_target_string,
- $use_only_for_whole_word_p, $use_only_at_start_of_word_p, $use_only_at_end_of_word_p,
- $dont_use_at_start_of_word_p, $dont_use_at_end_of_word_p);
- foreach $utf8_alt_target (@utf8_alt_targets) {
- if ($lang_code) {
- $ht{UTF_CHAR_ALT_MAPPING_LANG_SPEC}->{$lang_code}->{$utf8_source_string}->{$utf8_alt_target} = $prob;
- $ht{USE_ALT_IN_POINTED_LANG_SPEC}->{$lang_code}->{$utf8_source_string}->{$utf8_alt_target} = 1 if $use_alt_in_pointed_p;
- } else {
- $ht{UTF_CHAR_ALT_MAPPING}->{$utf8_source_string}->{$utf8_alt_target} = $prob;
- $ht{USE_ALT_IN_POINTED}->{$utf8_source_string}->{$utf8_alt_target} = 1 if $use_alt_in_pointed_p;
- }
- if ($use_only_for_whole_word_p) {
- if ($lang_code) {
- $ht{USE_ALT_ONLY_FOR_WHOLE_WORD_LANG_SPEC}->{$lang_code}->{$utf8_source_string}->{$utf8_alt_target} = 1;
- } else {
- $ht{USE_ALT_ONLY_FOR_WHOLE_WORD}->{$utf8_source_string}->{$utf8_alt_target} = 1;
- }
- }
- if ($use_only_at_start_of_word_p) {
- if ($lang_code) {
- $ht{USE_ALT_ONLY_AT_START_OF_WORD_LANG_SPEC}->{$lang_code}->{$utf8_source_string}->{$utf8_alt_target} = 1;
- } else {
- $ht{USE_ALT_ONLY_AT_START_OF_WORD}->{$utf8_source_string}->{$utf8_alt_target} = 1;
- }
- }
- if ($use_only_at_end_of_word_p) {
- if ($lang_code) {
- $ht{USE_ALT_ONLY_AT_END_OF_WORD_LANG_SPEC}->{$lang_code}->{$utf8_source_string}->{$utf8_alt_target} = 1;
- } else {
- $ht{USE_ALT_ONLY_AT_END_OF_WORD}->{$utf8_source_string}->{$utf8_alt_target} = 1;
- }
- }
- }
- if ($numeric =~ /\d/) {
- $ht{UTF_TO_NUMERIC}->{$utf8_source_string} = $numeric;
- }
- if ($annotation =~ /\S/) {
- $ht{UTF_ANNOTATION}->{$utf8_source_string} = $annotation;
- }
- $n++;
- }
- close(IN);
- # print STDERR "Loaded $n entries from $filename\n";
- } else {
- print STDERR "Can't open $filename\n";
- }
-}
-
-sub char_name_to_script {
- local($this, $char_name, *ht) = @_;
-
- return $cached_result if $cached_result = $ht{CHAR_NAME_TO_SCRIPT}->{$char_name};
- $orig_char_name = $char_name;
- $char_name =~ s/\s+(CONSONANT|LETTER|LIGATURE|SIGN|SYLLABLE|SYLLABICS|VOWEL)\b.*$//;
- my $script_name;
- while ($char_name) {
- last if $script_name = $ht{SCRIPT_NORM}->{(uc $char_name)};
- $char_name =~ s/\s*\S+\s*$//;
- }
- $script_name = "" unless defined($script_name);
- $ht{CHAR_NAME_TO_SCRIPT}->{$char_name} = $script_name;
- return $script_name;
-}
-
-sub letter_plus_char_p {
- local($this, $char_name) = @_;
-
- return $cached_result if $cached_result = $ht{CHAR_NAME_LETTER_PLUS}->{$char_name};
- my $letter_plus_p = ($char_name =~ /\b(?:LETTER|VOWEL SIGN|AU LENGTH MARK|CONSONANT SIGN|SIGN VIRAMA|SIGN PAMAAEH|SIGN COENG|SIGN AL-LAKUNA|SIGN ASAT|SIGN ANUSVARA|SIGN ANUSVARAYA|SIGN BINDI|TIPPI|SIGN NIKAHIT|SIGN CANDRABINDU|SIGN VISARGA|SIGN REAHMUK|SIGN NUKTA|SIGN DOT BELOW|HEBREW POINT)\b/) ? 1 : 0;
- $ht{CHAR_NAME_LETTER_PLUS}->{$char_name} = $letter_plus_p;
- return $letter_plus_p;
-}
-
-sub subjoined_char_p {
- local($this, $char_name) = @_;
-
- return $cached_result if $cached_result = $ht{CHAR_NAME_SUBJOINED}->{$char_name};
- my $subjoined_p = (($char_name =~ /\b(?:SUBJOINED LETTER|VOWEL SIGN|AU LENGTH MARK|EMPHASIS MARK|CONSONANT SIGN|SIGN VIRAMA|SIGN PAMAAEH|SIGN COENG|SIGN ASAT|SIGN ANUSVARA|SIGN ANUSVARAYA|SIGN BINDI|TIPPI|SIGN NIKAHIT|SIGN CANDRABINDU|SIGN VISARGA|SIGN REAHMUK|SIGN DOT BELOW|HEBREW (POINT|PUNCTUATION GERESH)|ARABIC (?:DAMMA|DAMMATAN|FATHA|FATHATAN|HAMZA|KASRA|KASRATAN|MADDAH|SHADDA|SUKUN))\b/)) ? 1 : 0;
- $ht{CHAR_NAME_SUBJOINED}->{$char_name} = $subjoined_p;
- return $subjoined_p;
-}
-
-sub new_node_id {
- local($this, *chart_ht) = @_;
-
- my $n_nodes = $chart_ht{N_NODES};
- $n_nodes++;
- $chart_ht{N_NODES} = $n_nodes;
- return $n_nodes;
-}
-
-sub add_node {
- local($this, $s, $start, $end, *chart_ht, $type, $comment) = @_;
-
- my $node_id = $this->new_node_id(*chart_ht);
- # print STDERR "add_node($node_id, $start-$end): $s [$comment]\n" if $comment =~ /number/;
- # print STDERR "add_node($node_id, $start-$end): $s [$comment]\n" if ($start >= 0) && ($start < 50);
- $chart_ht{NODE_START}->{$node_id} = $start;
- $chart_ht{NODE_END}->{$node_id} = $end;
- $chart_ht{NODES_STARTING_AT}->{$start}->{$node_id} = 1;
- $chart_ht{NODES_ENDING_AT}->{$end}->{$node_id} = 1;
- $chart_ht{NODES_STARTING_AND_ENDING_AT}->{$start}->{$end}->{$node_id} = 1;
- $chart_ht{NODE_TYPE}->{$node_id} = $type;
- $chart_ht{NODE_COMMENT}->{$node_id} = $comment;
- $chart_ht{NODE_ROMAN}->{$node_id} = $s;
- return $node_id;
-}
-
-sub get_node_for_span {
- local($this, $start, $end, *chart_ht) = @_;
-
- return "" unless defined($chart_ht{NODES_STARTING_AND_ENDING_AT}->{$start}->{$end});
- my @node_ids = sort { $a <=> $b } keys %{$chart_ht{NODES_STARTING_AND_ENDING_AT}->{$start}->{$end}};
-
- return (@node_ids) ? $node_ids[0] : "";
-}
-
-sub get_node_for_span_and_type {
- local($this, $start, $end, *chart_ht, $type) = @_;
-
- return "" unless defined($chart_ht{NODES_STARTING_AND_ENDING_AT}->{$start}->{$end});
- my @node_ids = sort { $a <=> $b } keys %{$chart_ht{NODES_STARTING_AND_ENDING_AT}->{$start}->{$end}};
-
- foreach $node_id (@node_ids) {
- return $node_id if $chart_ht{NODE_TYPE}->{$node_id} eq $type;
- }
- return "";
-}
-
-sub get_node_roman {
- local($this, $node_id, *chart_id, $default) = @_;
-
- $default = "" unless defined($default);
- my $roman = $chart_ht{NODE_ROMAN}->{$node_id};
- return (defined($roman)) ? $roman : $default;
-}
-
-sub set_node_id_slot_value {
- local($this, $node_id, $slot, $value, *chart_id) = @_;
-
- $chart_ht{NODE_SLOT}->{$node_id}->{$slot} = $value;
-}
-
-sub copy_slot_values {
- local($this, $old_node_id, $new_node_id, *chart_id, @slots) = @_;
-
- if (@slots) {
- foreach $slot (keys %{$chart_ht{NODE_SLOT}->{$old_node_id}}) {
- if (($slots[0] eq "all") || $util->member($slot, @slots)) {
- my $value = $chart_ht{NODE_SLOT}->{$old_node_id}->{$slot};
- $chart_ht{NODE_SLOT}->{$new_node_id}->{$slot} = $value if defined($value);
- }
- }
- }
-}
-
-sub get_node_id_slot_value {
- local($this, $node_id, $slot, *chart_id, $default) = @_;
-
- $default = "" unless defined($default);
- my $value = $chart_ht{NODE_SLOT}->{$node_id}->{$slot};
- return (defined($value)) ? $value : $default;
-}
-
-sub get_node_for_span_with_slot_value {
- local($this, $start, $end, $slot, *chart_id, $default) = @_;
-
- $default = "" unless defined($default);
- return $default unless defined($chart_ht{NODES_STARTING_AND_ENDING_AT}->{$start}->{$end});
- my @node_ids = sort { $a <=> $b } keys %{$chart_ht{NODES_STARTING_AND_ENDING_AT}->{$start}->{$end}};
- foreach $node_id (@node_ids) {
- my $value = $chart_ht{NODE_SLOT}->{$node_id}->{$slot};
- return $value if defined($value);
- }
- return $default;
-}
-
-sub get_node_for_span_with_slot {
- local($this, $start, $end, $slot, *chart_id, $default) = @_;
-
- $default = "" unless defined($default);
- return $default unless defined($chart_ht{NODES_STARTING_AND_ENDING_AT}->{$start}->{$end});
- my @node_ids = sort { $a <=> $b } keys %{$chart_ht{NODES_STARTING_AND_ENDING_AT}->{$start}->{$end}};
- foreach $node_id (@node_ids) {
- my $value = $chart_ht{NODE_SLOT}->{$node_id}->{$slot};
- return $node_id if defined($value);
- }
- return $default;
-}
-
-sub register_new_complex_number_span_segment {
- local($this, $start, $mid, $end, *chart_id, $line_number) = @_;
- # e.g. 4 10 (= 40); 20 5 (= 25)
- # might become part of larger complex number span, e.g. 4 1000 3 100 20 1
-
- # print STDERR "register_new_complex_number_span_segment $start-$mid-$end\n" if $line_number == 43;
- if (defined($old_start = $chart_ht{COMPLEX_NUMERIC_END_START}->{$mid})) {
- undef($chart_ht{COMPLEX_NUMERIC_END_START}->{$mid});
- $chart_ht{COMPLEX_NUMERIC_START_END}->{$old_start} = $end;
- $chart_ht{COMPLEX_NUMERIC_END_START}->{$end} = $old_start;
- } else {
- $chart_ht{COMPLEX_NUMERIC_START_END}->{$start} = $end;
- $chart_ht{COMPLEX_NUMERIC_END_START}->{$end} = $start;
- }
-}
-
-sub romanize_by_token_with_caching {
- local($this, $s, $lang_code, $output_style, *ht, *pinyin_ht, $initial_char_offset, $control, $line_number) = @_;
-
- $control = "" unless defined($control);
- my $return_chart_p = ($control =~ /return chart/i);
- my $return_offset_mappings_p = ($control =~ /return offset mappings/i);
- return $this->romanize($s, $lang_code, $output_style, *ht, *pinyin_ht, $initial_char_offset, $control, $line_number)
- if $return_chart_p || $return_offset_mappings_p;
- my $result = "";
- my @separators = ();
- my @tokens = ();
- $s =~ s/\n$//; # Added May 2, 2019 as bug-fix (duplicate empty lines)
- while (($sep, $token, $rest) = ($s =~ /^(\s*)(\S+)(.*)$/)) {
- push(@separators, $sep);
- push(@tokens, $token);
- $s = $rest;
- }
- push(@separators, $s);
- while (@tokens) {
- my $sep = shift @separators;
- my $token = shift @tokens;
- $result .= $sep;
- if ($token =~ /^[\x00-\x7F]*$/) { # all ASCII
- $result .= $token;
- } else {
- my $rom_token = $ht{CACHED_ROMANIZATION}->{$lang_code}->{$token};
- unless (defined($rom_token)) {
- $rom_token = $this->romanize($token, $lang_code, $output_style, *ht, *pinyin_ht, $initial_char_offset, $control, $line_number);
- $ht{CACHED_ROMANIZATION}->{$lang_code}->{$token} = $rom_token if defined($rom_token);
- }
- $result .= $rom_token;
- }
- }
- my $sep = shift @separators;
- $result .= $sep if defined($sep);
-
- return $result;
-}
-
-sub romanize {
- local($this, $s, $lang_code, $output_style, *ht, *pinyin_ht, $initial_char_offset, $control, $line_number, $initial_rom_char_offset) = @_;
-
- my $orig_lang_code = $lang_code;
- # Check whether the text (to be romanized) starts with a language code directive.
- if (($line_lang_code) = ($s =~ /^::lcode\s+([a-z][a-z][a-z])\s/)) {
- $lang_code = $line_lang_code;
- }
- $initial_char_offset = 0 unless defined($initial_char_offset);
- $initial_rom_char_offset = 0 unless defined($initial_rom_char_offset);
- $control = "" unless defined($control);
- my $return_chart_p = ($control =~ /return chart/i);
- my $return_offset_mappings_p = ($control =~ /return offset mappings/i);
- $line_number = "" unless defined($line_number);
- my @chars = $utf8->split_into_utf8_characters($s, "return only chars", *empty_ht);
- my $n_characters = $#chars + 1;
- %chart_ht = ();
- $chart_ht{N_CHARS} = $n_characters;
- $chart_ht{N_NODES} = 0;
- my $char = "";
- my $char_name = "";
- my $prev_script = "";
- my $current_script = "";
- my $script_start = 0;
- my $script_end = 0;
- my $prev_letter_plus_script = "";
- my $current_letter_plus_script = "";
- my $letter_plus_script_start = 0;
- my $letter_plus_script_end = 0;
- my $log ="";
- my $n_right_to_left_chars = 0;
- my $n_left_to_right_chars = 0;
- my $hebrew_word_start = ""; # used to identify Hebrew words with points
- my $hebrew_word_contains_point = 0;
- my $current_word_start = "";
- my $current_word_script = "";
- my $braille_all_caps_p = 0;
-
- # prep
- foreach $i ((0 .. ($#chars + 1))) {
- if ($i <= $#chars) {
- $char = $chars[$i];
- $chart_ht{ORIG_CHAR}->{$i} = $char;
- $char_name = $ht{UTF_TO_CHAR_NAME}->{$char} || "";
- $chart_ht{CHAR_NAME}->{$i} = $char_name;
- $current_script = $this->char_name_to_script($char_name, *ht);
- $current_script_direction = $ht{DIRECTION}->{$current_script} || '';
- if ($current_script_direction eq 'right-to-left') {
- $n_right_to_left_chars++;
- } elsif (($char =~ /^[a-z]$/i) || ! ($char =~ /^[\x00-\x7F]$/)) {
- $n_left_to_right_chars++;
- }
- $chart_ht{CHAR_SCRIPT}->{$i} = $current_script;
- $chart_ht{SCRIPT_SEGMENT_START}->{$i} = ""; # default value, to be updated later
- $chart_ht{SCRIPT_SEGMENT_END}->{$i} = ""; # default value, to be updated later
- $chart_ht{LETTER_TOKEN_SEGMENT_START}->{$i} = ""; # default value, to be updated later
- $chart_ht{LETTER_TOKEN_SEGMENT_END}->{$i} = ""; # default value, to be updated later
- $subjoined_char_p = $this->subjoined_char_p($char_name);
- $chart_ht{CHAR_SUBJOINED}->{$i} = $subjoined_char_p;
- $letter_plus_char_p = $this->letter_plus_char_p($char_name);
- $chart_ht{CHAR_LETTER_PLUS}->{$i} = $letter_plus_char_p;
- $current_letter_plus_script = ($letter_plus_char_p) ? $current_script : "";
- $numeric_value = $ht{UTF_TO_NUMERIC}->{$char};
- $numeric_value = "" unless defined($numeric_value);
- $annotation = $ht{UTF_ANNOTATION}->{$char};
- $annotation = "" unless defined($annotation);
- $chart_ht{CHAR_NUMERIC_VALUE}->{$i} = $numeric_value;
- $chart_ht{CHAR_ANNOTATION}->{$i} = $annotation;
- $syllable_info = $ht{UTF_TO_SYLLABLE_INFO}->{$char} || "";
- $chart_ht{CHAR_SYLLABLE_INFO}->{$i} = $syllable_info;
- $tone_mark = $ht{UTF_TO_TONE_MARK}->{$char} || "";
- $chart_ht{CHAR_TONE_MARK}->{$i} = $tone_mark;
- } else {
- $char = "";
- $char_name = "";
- $current_script = "";
- $current_letter_plus_script = "";
- }
- if ($char_name =~ /^HEBREW (LETTER|POINT|PUNCTUATION GERESH) /) {
- $hebrew_word_start = $i if $hebrew_word_start eq "";
- $hebrew_word_contains_point = 1 if $char_name =~ /^HEBREW POINT /;
- } elsif ($hebrew_word_start ne "") {
- if ($hebrew_word_contains_point) {
- foreach $j (($hebrew_word_start .. ($i-1))) {
- $chart_ht{CHAR_PART_OF_POINTED_HEBREW_WORD}->{$j} = 1;
- }
- $chart_ht{CHAR_START_OF_WORD}->{$hebrew_word_start} = 1;
- $chart_ht{CHAR_END_OF_WORD}->{($i-1)} = 1;
- }
- $hebrew_word_start = "";
- $hebrew_word_contains_point = 0;
- }
- my $part_of_word_p = $current_script
- && ($this->letter_plus_char_p($char_name)
- || $this->subjoined_char_p($char_name)
- || ($char_name =~ /\b(LETTER|SYLLABLE|SYLLABICS|LIGATURE)\b/));
-
- # Braille punctuation
- my $end_offset = 0;
- if ($char_name =~ /^Braille\b/i) {
- if (($char =~ /^\s*$/) || ($char_name =~ /BLANK/)) {
- $part_of_word_p = 0;
- $braille_all_caps_p = 0;
- } elsif ($chart_ht{NOT_PART_OF_WORD_P}->{$i}) {
- $part_of_word_p = 0;
- $braille_all_caps_p = 0;
- } elsif ((keys %{$ht{WORD_EXTERNAL_PUNCTUATION_LANG_SPEC}->{$lang_code}->{$char}})
- || (keys %{$ht{WORD_EXTERNAL_PUNCTUATION}->{$char}})) {
- $part_of_word_p = 0;
- $braille_all_caps_p = 0;
- } elsif (($i+1 <= $#chars)
- && ($s1 = $char . $chars[$i+1])
- && ((keys %{$ht{WORD_EXTERNAL_PUNCTUATION_LANG_SPEC}->{$lang_code}->{$s1}})
- || (keys %{$ht{WORD_EXTERNAL_PUNCTUATION}->{$s1}}))) {
- $part_of_word_p = 0;
- $braille_all_caps_p = 0;
- $chart_ht{NOT_PART_OF_WORD_P}->{($i+1)} = 1;
- } elsif (($i+2 <= $#chars)
- && ($s2 = $char . $chars[$i+1] . $chars[$i+2])
- && ((keys %{$ht{WORD_EXTERNAL_PUNCTUATION_LANG_SPEC}->{$lang_code}->{$s2}})
- || (keys %{$ht{WORD_EXTERNAL_PUNCTUATION}->{$s2}}))) {
- $part_of_word_p = 0;
- $braille_all_caps_p = 0;
- $chart_ht{NOT_PART_OF_WORD_P}->{($i+1)} = 1;
- $chart_ht{NOT_PART_OF_WORD_P}->{($i+2)} = 1;
- } elsif (($i+1 <= $#chars)
- && ($char eq $braille_capital_letter_indicator)
- && ($chars[$i+1] eq $braille_capital_letter_indicator)) {
- $braille_all_caps_p = 1;
- } else {
- $part_of_word_p = 1;
- }
- # last period in Braille text is also not part_of_word_p
- if (($char eq $braille_period)
- && (($i == $#chars)
- || (($i < $#chars)
- && (! $this->braille_string_p($chars[$i+1]))))) {
- $part_of_word_p = 0;
- }
- # period before other word-external punctuation is also not part_of_word_p
- if (($i > 0)
- && ($chars[$i-1] eq $braille_period)
- && (! $part_of_word_p)
- && ($current_word_start ne "")) {
- $end_offset = -1;
- }
- } else {
- $braille_all_caps_p = 0;
- }
- $chart_ht{BRAILLE_ALL_CAPS_P}->{$i} = $braille_all_caps_p;
-
- if (($current_word_start ne "")
- && ((! $part_of_word_p)
- || ($current_script ne $current_word_script))) {
- # END OF WORD
- $chart_ht{CHAR_START_OF_WORD}->{$current_word_start} = 1;
- $chart_ht{CHAR_END_OF_WORD}->{($i-1+$end_offset)} = 1;
- my $word = join("", @chars[$current_word_start .. ($i-1+$end_offset)]);
- $chart_ht{WORD_START_END}->{$current_word_start}->{$i} = $word;
- $chart_ht{WORD_END_START}->{$i+$end_offset}->{$current_word_start} = $word;
- # print STDERR "Word ($current_word_start-$i+$end_offset): $word ($current_word_script)\n";
- $current_word_start = "";
- $current_word_script = "";
- }
- if ($part_of_word_p && ($current_word_start eq "")) {
- # START OF WORD
- $current_word_start = $i;
- $current_word_script = $current_script;
- }
- # print STDERR "$i char: $char ($current_script)\n";
- unless ($current_script eq $prev_script) {
- if ($prev_script && ($i-1 >= $script_start)) {
- my $script_end = $i;
- $chart_ht{SCRIPT_SEGMENT_START_TO_END}->{$script_start} = $script_end;
- $chart_ht{SCRIPT_SEGMENT_END_TO_START}->{$script_end} = $script_start;
- foreach $i (($script_start .. $script_end)) {
- $chart_ht{SCRIPT_SEGMENT_START}->{$i} = $script_start;
- $chart_ht{SCRIPT_SEGMENT_END}->{$i} = $script_end;
- }
- # print STDERR "Script segment $script_start-$script_end: $prev_script\n";
- }
- $script_start = $i;
- }
- unless ($current_letter_plus_script eq $prev_letter_plus_script) {
- if ($prev_letter_plus_script && ($i-1 >= $letter_plus_script_start)) {
- my $letter_plus_script_end = $i;
- $chart_ht{LETTER_TOKEN_SEGMENT_START_TO_END}->{$letter_plus_script_start} = $letter_plus_script_end;
- $chart_ht{LETTER_TOKEN_SEGMENT_END_TO_START}->{$letter_plus_script_end} = $letter_plus_script_start;
- foreach $i (($letter_plus_script_start .. $letter_plus_script_end)) {
- $chart_ht{LETTER_TOKEN_SEGMENT_START}->{$i} = $letter_plus_script_start;
- $chart_ht{LETTER_TOKEN_SEGMENT_END}->{$i} = $letter_plus_script_end;
- }
- # print STDERR "Script token segment $letter_plus_script_start-$letter_plus_script_end: $prev_letter_plus_script\n";
- }
- $letter_plus_script_start = $i;
- }
- $prev_script = $current_script;
- $prev_letter_plus_script = $current_letter_plus_script;
- }
- $ht{STRING_IS_DOMINANTLY_RIGHT_TO_LEFT}->{$s} = 1 if $n_right_to_left_chars > $n_left_to_right_chars;
-
- # main
- my $i = 0;
- while ($i <= $#chars) {
- my $char = $chart_ht{ORIG_CHAR}->{$i};
- my $current_script = $chart_ht{CHAR_SCRIPT}->{$i};
- $chart_ht{CHART_CONTAINS_SCRIPT}->{$current_script} = 1;
- my $script_segment_start = $chart_ht{SCRIPT_SEGMENT_START}->{$i};
- my $script_segment_end = $chart_ht{SCRIPT_SEGMENT_END}->{$i};
- my $char_name = $chart_ht{CHAR_NAME}->{$i};
- my $subjoined_char_p = $chart_ht{CHAR_SUBJOINED}->{$i};
- my $letter_plus_char_p = $chart_ht{CHAR_LETTER_PLUS}->{$i};
- my $numeric_value = $chart_ht{CHAR_NUMERIC_VALUE}->{$i};
- my $annotation = $chart_ht{CHAR_ANNOTATION}->{$i};
- # print STDERR " $char_name annotation: $annotation\n" if $annotation;
- my $tone_mark = $chart_ht{CHAR_TONE_MARK}->{$i};
- my $found_char_mapping_p = 0;
- my $prev_char_name = ($i >= 1) ? $chart_ht{CHAR_NAME}->{($i-1)} : "";
- my $prev2_script = ($i >= 2) ? $chart_ht{CHAR_SCRIPT}->{($i-2)} : "";
- my $prev_script = ($i >= 1) ? $chart_ht{CHAR_SCRIPT}->{($i-1)} : "";
- my $next_script = ($i < $#chars) ? $chart_ht{CHAR_SCRIPT}->{($i+1)} : "";
- my $next_char = ($i < $#chars) ? $chart_ht{ORIG_CHAR}->{($i+1)} : "";
- my $next_char_name = $ht{UTF_TO_CHAR_NAME}->{$next_char} || "";
- my $prev2_letter_plus_char_p = ($i >= 2) ? $chart_ht{CHAR_LETTER_PLUS}->{($i-2)} : 0;
- my $prev_letter_plus_char_p = ($i >= 1) ? $chart_ht{CHAR_LETTER_PLUS}->{($i-1)} : 0;
- my $next_letter_plus_char_p = ($i < $#chars) ? $chart_ht{CHAR_LETTER_PLUS}->{($i+1)} : 0;
- my $next_index = $i + 1;
-
- # Braille numeric mode
- if ($char eq $braille_number_indicator) {
- my $offset = 0;
- my $numeric_value = "";
- my $digit;
- while ($i+$offset < $#chars) {
- $offset++;
- my $offset_char = $chart_ht{ORIG_CHAR}->{$i+$offset};
- if (defined($digit = $ht{BRAILLE_TO_DIGIT}->{$offset_char})) {
- $numeric_value .= $digit;
- } elsif (($offset_char eq $braille_decimal_point)
- || ($ht{UTF_CHAR_MAPPING}->{$offset_char}->{"."})) {
- $numeric_value .= ".";
- } elsif ($offset_char eq $braille_comma) {
- $numeric_value .= ",";
- } elsif ($offset_char eq $braille_numeric_space) {
- $numeric_value .= " ";
- } elsif ($offset_char eq $braille_solidus) {
- $numeric_value .= "/";
- } elsif ($offset_char eq $braille_number_indicator) {
- # stay in Braille numeric mode
- } elsif ($offset_char eq $braille_letter_indicator) {
- # consider as part of number, but without contributing to numeric_value
- last;
- } else {
- $offset--;
- last;
- }
- }
- if ($offset) {
- $next_index = $i + $offset + 1;
- $node_id = $this->add_node($numeric_value, $i, $next_index, *chart_ht, "", "braille number");
- $found_char_mapping_p = 1;
- }
- }
-
- unless ($found_char_mapping_p) {
- foreach $string_length (reverse(1 .. 6)) {
- next if ($i + $string_length-1) > $#chars;
- my $start_of_word_p = $chart_ht{CHAR_START_OF_WORD}->{$i} || 0;
- my $end_of_word_p = $chart_ht{CHAR_END_OF_WORD}->{($i+$string_length-1)} || 0;
- my $multi_char_substring = join("", @chars[$i..($i+$string_length-1)]);
- my @mappings = keys %{$ht{UTF_CHAR_MAPPING_LANG_SPEC}->{$lang_code}->{$multi_char_substring}};
- @mappings = keys %{$ht{UTF_CHAR_MAPPING}->{$multi_char_substring}} unless @mappings;
- my @mappings_whole = ();
- my @mappings_start_or_end = ();
- my @mappings_other = ();
- foreach $mapping (@mappings) {
- next if $mapping =~ /\(__.*__\)/;
- if ($ht{USE_ONLY_FOR_WHOLE_WORD_LANG_SPEC}->{$lang_code}->{$multi_char_substring}->{$mapping}
- || $ht{USE_ONLY_FOR_WHOLE_WORD}->{$multi_char_substring}->{$mapping}) {
- push(@mappings_whole, $mapping) if $start_of_word_p && $end_of_word_p;
- } elsif ($ht{USE_ONLY_AT_START_OF_WORD_LANG_SPEC}->{$lang_code}->{$multi_char_substring}->{$mapping}
- || $ht{USE_ONLY_AT_START_OF_WORD}->{$multi_char_substring}->{$mapping}) {
- push(@mappings_start_or_end, $mapping) if $start_of_word_p;
- } elsif ($ht{USE_ONLY_AT_END_OF_WORD_LANG_SPEC}->{$lang_code}->{$multi_char_substring}->{$mapping}
- || $ht{USE_ONLY_AT_END_OF_WORD}->{$multi_char_substring}->{$mapping}) {
- push(@mappings_start_or_end, $mapping) if $end_of_word_p;
- } else {
- push(@mappings_other, $mapping);
- }
- }
- @mappings = @mappings_whole;
- @mappings = @mappings_start_or_end unless @mappings;
- @mappings = @mappings_other unless @mappings;
- foreach $mapping (@mappings) {
- next if $mapping =~ /\(__.*__\)/;
- if ($ht{DONT_USE_AT_START_OF_WORD_LANG_SPEC}->{$lang_code}->{$multi_char_substring}->{$mapping}
- || $ht{DONT_USE_AT_START_OF_WORD}->{$multi_char_substring}->{$mapping}) {
- next if $start_of_word_p;
- }
- if ($ht{DONT_USE_AT_END_OF_WORD_LANG_SPEC}->{$lang_code}->{$multi_char_substring}->{$mapping}
- || $ht{DONT_USE_AT_END_OF_WORD}->{$multi_char_substring}->{$mapping}) {
- next if $end_of_word_p;
- }
- my $mapping2 = ($chart_ht{BRAILLE_ALL_CAPS_P}->{$i}) ? (uc $mapping) : $mapping;
- $node_id = $this->add_node($mapping2, $i, $i+$string_length, *chart_ht, "", "multi-char-mapping");
- $next_index = $i + $string_length;
- $found_char_mapping_p = 1;
- if ($annotation) {
- @annotation_elems = split(/,\s*/, $annotation);
- foreach $annotation_elem (@annotation_elems) {
- if (($a_slot, $a_value) = ($annotation_elem =~ /^(\S+?):(\S+)\s*$/)) {
- $this->set_node_id_slot_value($node_id, $a_slot, $a_value, *chart_ht);
- } else {
- $this->set_node_id_slot_value($node_id, $annotation_elem, 1, *chart_ht);
- }
- }
- }
- }
- my @alt_mappings = keys %{$ht{UTF_CHAR_ALT_MAPPING_LANG_SPEC}->{$lang_code}->{$multi_char_substring}};
- @alt_mappings = keys %{$ht{UTF_CHAR_ALT_MAPPING}->{$multi_char_substring}} unless @alt_mappings;
- @alt_mappings = () if ($#alt_mappings == 0) && ($alt_mappings[0] eq "_NONE_");
- foreach $alt_mapping (@alt_mappings) {
- if ($chart_ht{CHAR_PART_OF_POINTED_HEBREW_WORD}->{$i}) {
- next unless
- $ht{USE_ALT_IN_POINTED_LANG_SPEC}->{$lang_code}->{$multi_char_substring}->{$alt_mapping}
- || $ht{USE_ALT_IN_POINTED}->{$multi_char_substring}->{$alt_mapping};
- }
- if ($ht{USE_ALT_ONLY_FOR_WHOLE_WORD_LANG_SPEC}->{$lang_code}->{$multi_char_substring}->{$alt_mapping}
- || $ht{USE_ALT_ONLY_FOR_WHOLE_WORD}->{$multi_char_substring}->{$alt_mapping}) {
- next unless $start_of_word_p && $end_of_word_p;
- }
- if ($ht{USE_ALT_ONLY_AT_START_OF_WORD_LANG_SPEC}->{$lang_code}->{$multi_char_substring}->{$alt_mapping}
- || $ht{USE_ALT_ONLY_AT_START_OF_WORD}->{$multi_char_substring}->{$alt_mapping}) {
- next unless $start_of_word_p;
- }
- if ($ht{USE_ALT_ONLY_AT_END_OF_WORD_LANG_SPEC}->{$lang_code}->{$multi_char_substring}->{$alt_mapping}
- || $ht{USE_ALT_ONLY_AT_END_OF_WORD}->{$multi_char_substring}->{$alt_mapping}) {
- next unless $end_of_word_p;
- }
- my $alt_mapping2 = ($chart_ht{BRAILLE_ALL_CAPS_P}->{$i}) ? (uc $alt_mapping) : $alt_mapping;
- $node_id = $this->add_node($alt_mapping2, $i, $i+$string_length, *chart_ht, "alt", "multi-char-mapping");
- if ($annotation) {
- @annotation_elems = split(/,\s*/, $annotation);
- foreach $annotation_elem (@annotation_elems) {
- if (($a_slot, $a_value) = ($annotation_elem =~ /^(\S+?):(\S+)\s*$/)) {
- $this->set_node_id_slot_value($node_id, $a_slot, $a_value, *chart_ht);
- } else {
- $this->set_node_id_slot_value($node_id, $annotation_elem, 1, *chart_ht);
- }
- }
- }
- }
- }
- }
- unless ($found_char_mapping_p) {
- my $prev_node_id = $this->get_node_for_span($i-4, $i, *chart_ht)
- || $this->get_node_for_span($i-3, $i, *chart_ht)
- || $this->get_node_for_span($i-2, $i, *chart_ht)
- || $this->get_node_for_span($i-1, $i, *chart_ht);
- my $prev_char_roman = ($prev_node_id) ? $this->get_node_roman($prev_node_id, *chart_id) : "";
- my $prev_node_start = ($prev_node_id) ? $chart_ht{NODE_START}->{$prev_node_id} : "";
-
- # Number
- if (($numeric_value =~ /\d/)
- && (! ($char_name =~ /SUPERSCRIPT/))) {
- my $prev_numeric_value = $this->get_node_for_span_with_slot_value($i-1, $i, "numeric-value", *chart_id);
- my $sep = "";
- $sep = " " if ($char_name =~ /^vulgar fraction /i) && ($prev_numeric_value =~ /\d/);
- $node_id = $this->add_node("$sep$numeric_value", $i, $i+1, *chart_ht, "", "number");
- $this->set_node_id_slot_value($node_id, "numeric-value", $numeric_value, *chart_ht);
- if ((($prev_numeric_value =~ /\d/) && ($numeric_value =~ /\d\d/))
- || (($prev_numeric_value =~ /\d\d/) && ($numeric_value =~ /\d/))) {
- # pull in any other parts of single digits
- my $j = 1;
- # pull in any single digits adjoining on left
- if ($prev_numeric_value =~ /^\d$/) {
- while (1) {
- if (($i-$j-1 >= 0)
- && defined($digit_value = $this->get_node_for_span_with_slot_value($i-$j-1, $i-$j, "numeric-value", *chart_id))
- && ($digit_value =~ /^\d$/)) {
- $j++;
- } elsif (($i-$j-2 >= 0)
- && ($chart_ht{ORIG_CHAR}->{($i-$j-1)} =~ /^[.,]$/)
- && defined($digit_value = $this->get_node_for_span_with_slot_value($i-$j-2, $i-$j-1, "numeric-value", *chart_id))
- && ($digit_value =~ /^\d$/)) {
- $j += 2;
- } else {
- last;
- }
- }
- }
- # pull in any single digits adjoining on right
- my $k = 0;
- if ($numeric_value =~ /^\d$/) {
- while (1) {
- if (defined($next_numeric_value = $chart_ht{CHAR_NUMERIC_VALUE}->{($i+$k+1)})
- && ($next_numeric_value =~ /^\d$/)) {
- $k++;
- } else {
- last;
- }
- }
- }
- $this->register_new_complex_number_span_segment($i-$j, $i, $i+$k+1, *chart_ht, $line_number);
- }
- if ($chinesePM->string_contains_utf8_cjk_unified_ideograph_p($char)
- && ($tonal_translit = $chinesePM->tonal_pinyin($char, *pinyin_ht, ""))) {
- $de_accented_translit = $util->de_accent_string($tonal_translit);
- if ($numeric_value =~ /^(10000|1000000000000|10000000000000000)$/) {
- $chart_ht{NODE_TYPE}->{$node_id} = "alt"; # keep, but demote
- $alt_node_id = $this->add_node($de_accented_translit, $i, $i+1, *chart_ht, "", "CJK");
- } else {
- $alt_node_id = $this->add_node($de_accented_translit, $i, $i+1, *chart_ht, "alt", "CJK");
- }
- }
-
- # ASCII
- } elsif ($char =~ /^[\x00-\x7F]$/) {
- $this->add_node($char, $i, $i+1, *chart_ht, "", "ASCII"); # ASCII character, incl. control characters
-
- # Emoji, dingbats, pictographs
- } elsif ($char =~ /^(\xE2[\x98-\x9E]|\xF0\x9F[\x8C-\xA7])/) {
- $this->add_node($char, $i, $i+1, *chart_ht, "", "pictograph");
-
- # Hangul (Korean)
- } elsif (($char =~ /^[\xEA-\xED]/)
- && ($romanized_char = $this->unicode_hangul_romanization($char))) {
- $this->add_node($romanized_char, $i, $i+1, *chart_ht, "", "Hangul");
-
- # CJK (Chinese, Japanese, Korean)
- } elsif ($chinesePM->string_contains_utf8_cjk_unified_ideograph_p($char)
- && ($tonal_translit = $chinesePM->tonal_pinyin($char, *pinyin_ht, ""))) {
- $de_accented_translit = $util->de_accent_string($tonal_translit);
- $this->add_node($de_accented_translit, $i, $i+1, *chart_ht, "", "CJK");
-
- # Virama (cancel preceding vowel in Abudiga scripts)
- } elsif ($char_name =~ /\bSIGN (?:VIRAMA|AL-LAKUNA|ASAT|COENG|PAMAAEH)\b/) {
- # VIRAMA: cancel preceding default vowel (in Abudiga scripts)
- if (($prev_script eq $current_script)
- && (($prev_char_roman_consonant, $prev_char_roman_vowel) = ($prev_char_roman =~ /^(.*[bcdfghjklmnpqrstvwxyz])([aeiou]+)$/i))
- && ($ht{SCRIPT_ABUDIGA_DEFAULT_VOWEL}->{$current_script}->{(lc $prev_char_roman_vowel)})) {
- $this->add_node($prev_char_roman_consonant, $prev_node_start, $i+1, *chart_ht, "", "virama");
- } else {
- $this->add_node("", $i, $i+1, *chart_ht, "", "unexpected-virama");
- }
-
- # Nukta (special (typically foreign) variant)
- } elsif ($char_name =~ /\bSIGN (?:NUKTA)\b/) {
- # NUKTA (dot): indicates special (typically foreign) variant; normally covered by multi-mappings
- if ($prev_script eq $current_script) {
- my $node_id = $this->add_node($prev_char_roman, $prev_node_start, $i+1, *chart_ht, "", "nukta");
- $this->copy_slot_values($prev_node_id, $node_id, *chart_id, "all");
- $this->set_node_id_slot_value($node_id, "nukta", 1, *chart_ht);
- } else {
- $this->add_node("", $i, $i+1, *chart_ht, "", "unexpected-nukta");
- }
-
- # Zero-width character, incl. zero width space/non-joiner/joiner, left-to-right/right-to-left mark
- } elsif ($char =~ /^\xE2\x80[\x8B-\x8F\xAA-\xAE]$/) {
- if ($prev_node_id) {
- my $node_id = $this->add_node($prev_char_roman, $prev_node_start, $i+1, *chart_ht, "", "zero-width-char");
- $this->copy_slot_values($prev_node_id, $node_id, *chart_id, "all");
- } else {
- $this->add_node("", $i, $i+1, *chart_ht, "", "zero-width-char");
- }
- } elsif (($char =~ /^\xEF\xBB\xBF$/) && $prev_node_id) { # OK to leave byte-order-mark at beginning of line
- my $node_id = $this->add_node($prev_char_roman, $prev_node_start, $i+1, *chart_ht, "", "zero-width-char");
- $this->copy_slot_values($prev_node_id, $node_id, *chart_id, "all");
-
- # Tone mark
- } elsif ($tone_mark) {
- if ($prev_script eq $current_script) {
- my $node_id = $this->add_node($prev_char_roman, $prev_node_start, $i+1, *chart_ht, "", "tone-mark");
- $this->copy_slot_values($prev_node_id, $node_id, *chart_id, "all");
- $this->set_node_id_slot_value($node_id, "tone-mark", $tone_mark, *chart_ht);
- } else {
- $this->add_node("", $i, $i+1, *chart_ht, "", "unexpected-tone-mark");
- }
-
- # Diacritic
- } elsif (($char_name =~ /\b(ACCENT|TONE|COMBINING DIAERESIS|COMBINING DIAERESIS BELOW|COMBINING MACRON|COMBINING VERTICAL LINE ABOVE|COMBINING DOT ABOVE RIGHT|COMBINING TILDE|COMBINING CYRILLIC|MUUSIKATOAN|TRIISAP)\b/) && ($ht{UTF_TO_CAT}->{$char} =~ /^Mn/)) {
- if ($prev_script eq $current_script) {
- my $node_id = $this->add_node($prev_char_roman, $prev_node_start, $i+1, *chart_ht, "", "diacritic");
- $this->copy_slot_values($prev_node_id, $node_id, *chart_id, "all");
- $diacritic = lc $char_name;
- $diacritic =~ s/^.*(?:COMBINING CYRILLIC|COMBINING|SIGN)\s+//i;
- $diacritic =~ s/^.*(ACCENT|TONE)/$1/i;
- $diacritic =~ s/^\s*//;
- $this->set_node_id_slot_value($node_id, "diacritic", $diacritic, *chart_ht);
- # print STDERR "diacritic: $diacritic\n";
- } else {
- $this->add_node("", $i, $i+1, *chart_ht, "", "unexpected-diacritic");
- }
-
- # Romanize to find out more
- } elsif ($char_name) {
- if (defined($romanized_char = $this->romanize_char_at_position($i, $lang_code, $output_style, *ht, *chart_ht))) {
- # print STDERR "ROM l.$line_number/$i: $romanized_char\n" if $line_number =~ /^[12]$/;
- print STDOUT "ROM l.$line_number/$i: $romanized_char\n" if $verbosePM;
-
- # Empty string mapping
- if ($romanized_char eq "\"\"") {
- $this->add_node("", $i, $i+1, *chart_ht, "", "empty-string-mapping");
- # consider adding something for implausible romanizations of length 6+
-
- # keep original character (instead of romanized_char lengthener, character-18b00 etc.)
- } elsif (($romanized_char =~ /^(character|lengthener|modifier)/)) {
- $this->add_node($char, $i, $i+1, *chart_ht, "", "nevermind-keep-original");
-
- # Syllabic suffix in Abudiga languages, e.g. -m, -ng
- } elsif (($romanized_char =~ /^\+(H|M|N|NG)$/i)
- && ($prev_script eq $current_script)
- && ($ht{SCRIPT_ABUDIGA_DEFAULT_VOWEL}->{$current_script}->{"a"})) {
- my $core_suffix = $romanized_char;
- $core_suffix =~ s/^\+//;
- if ($prev_char_roman =~ /[aeiou]$/i) {
- $this->add_node($core_suffix, $i, $i+1, *chart_ht, "", "syllable-end-consonant");
- } else {
- $this->add_node(join("", $prev_char_roman, "a", $core_suffix), $prev_node_start, $i+1, *chart_ht, "", "syllable-end-consonant-with-added-a");
- $this->add_node(join("", "a", $core_suffix), $i, $i+1, *chart_ht, "backup", "syllable-end-consonant");
- }
-
- # Japanese special cases
- } elsif ($char_name =~ /(?:HIRAGANA|KATAKANA) LETTER SMALL Y/) {
- if (($prev_script eq $current_script)
- && (($prev_char_roman_consonant) = ($prev_char_roman =~ /^(.*[bcdfghjklmnpqrstvwxyz])i$/i))) {
- unless ($this->get_node_for_span_and_type($prev_node_start, $i+1, *chart_ht, "")) {
- $this->add_node("$prev_char_roman_consonant$romanized_char", $prev_node_start, $i+1, *chart_ht, "", "japanese-contraction");
- }
- } else {
- $this->add_node($romanized_char, $i, $i+1, *chart_ht, "", "unexpected-japanese-contraction-character");
- }
- } elsif (($prev_script =~ /^(HIRAGANA|KATAKANA)$/i)
- && ($char_name eq "KATAKANA-HIRAGANA PROLONGED SOUND MARK") # Choonpu
- && (($prev_char_roman_vowel) = ($prev_char_roman =~ /([aeiou])$/i))) {
- $this->add_node("$prev_char_roman$prev_char_roman_vowel", $prev_node_start, $i+1, *chart_ht, "", "japanese-vowel-lengthening");
- } elsif (($current_script =~ /^(Hiragana|Katakana)$/i)
- && ($char_name =~ /^(HIRAGANA|KATAKANA) LETTER SMALL TU$/i) # Sokuon/Sukun
- && ($next_script eq $current_script)
- && ($romanized_next_char = $this->romanize_char_at_position_incl_multi($i+1, $lang_code, $output_style, *ht, *chart_ht))
- && (($doubled_consonant) = ($romanized_next_char =~ /^(ch|[bcdfghjklmnpqrstwz])/i))) {
- # Note: $romanized_next_char could be part of a multi-character mapping
- # print STDERR "current_script: $current_script char_name: $char_name next_script: $next_script romanized_next_char: $romanized_next_char doubled_consonant: $doubled_consonant\n";
- $doubled_consonant = "t" if $doubled_consonant eq "ch";
- $this->add_node($doubled_consonant, $i, $i+1, *chart_ht, "", "japanese-consonant-doubling");
-
- # Greek small letter mu to micro-sign (instead of to "m") as used in abbreviations for microgram/micrometer/microliter/microsecond/micromolar/microfarad etc.
- } elsif (($char_name eq "GREEK SMALL LETTER MU")
- && (! ($prev_script =~ /^GREEK$/))
- && ($i < $#chars)
- && ($chart_ht{ORIG_CHAR}->{($i+1)} =~ /^[cfgjlmstv]$/i)) {
- $this->add_node("\xC2\xB5", $i, $i+1, *chart_ht, "", "greek-mu-to-micro-sign");
-
- # Gurmukhi addak (doubles following consonant)
- } elsif (($current_script eq "Gurmukhi")
- && ($char_name eq "GURMUKHI ADDAK")) {
- if (($next_script eq $current_script)
- && ($romanized_next_char = $this->romanize_char_at_position_incl_multi($i+1, $lang_code, $output_style, *ht, *chart_ht))
- && (($doubled_consonant) = ($romanized_next_char =~ /^([bcdfghjklmnpqrstvwxz])/i))) {
- $this->add_node($doubled_consonant, $i, $i+1, *chart_ht, "", "gurmukhi-consonant-doubling");
- } else {
- $this->add_node("'", $i, $i+1, *chart_ht, "", "gurmukhi-unexpected-addak");
- }
-
- # Subjoined character
- } elsif ($subjoined_char_p
- && ($prev_script eq $current_script)
- && (($prev_char_roman_consonant, $prev_char_roman_vowel) = ($prev_char_roman =~ /^(.*[bcdfghjklmnpqrstvwxyz])([aeiou]+)$/i))
- && ($ht{SCRIPT_ABUDIGA_DEFAULT_VOWEL}->{$current_script}->{(lc $prev_char_roman_vowel)})) {
- my $new_roman = "$prev_char_roman_consonant$romanized_char";
- $this->add_node($new_roman, $prev_node_start, $i+1, *chart_ht, "", "subjoined-character");
- # print STDERR " Subjoin l.$line_number/$i: $new_roman\n" if $line_number =~ /^[12]$/;
-
- # Thai special case: written-pre-consonant-spoken-post-consonant
- } elsif (($char_name =~ /THAI CHARACTER/)
- && ($prev_script eq $current_script)
- && ($chart_ht{CHAR_SYLLABLE_INFO}->{($i-1)} =~ /written-pre-consonant-spoken-post-consonant/i)
- && ($prev_char_roman =~ /^[aeiou]+$/i)
- && ($romanized_char =~ /^[bcdfghjklmnpqrstvwxyz]/)) {
- $this->add_node("$romanized_char$prev_char_roman", $prev_node_start, $i+1, *chart_ht, "", "thai-vowel-consonant-swap");
-
- # Thai special case: THAI CHARACTER O ANG (U+0E2D "\xE0\xB8\xAD")
- } elsif ($char_name eq "THAI CHARACTER O ANG") {
- if ($prev_script ne $current_script) {
- $this->add_node("", $i, $i+1, *chart_ht, "", "thai-initial-o-ang-drop");
- } elsif ($next_script ne $current_script) {
- $this->add_node("", $i, $i+1, *chart_ht, "", "thai-final-o-ang-drop");
- } else {
- my $romanized_next_char = $this->romanize_char_at_position($i+1, $lang_code, $output_style, *ht, *chart_ht);
- my $romanized_prev2_char = $this->romanize_char_at_position($i-2, $lang_code, $output_style, *ht, *chart_ht);
- if (($prev_char_roman =~ /^[bcdfghjklmnpqrstvwxz]+$/i)
- && ($romanized_next_char =~ /^[bcdfghjklmnpqrstvwxz]+$/i)) {
- $this->add_node("o", $i, $i+1, *chart_ht, "", "thai-middle-o-ang"); # keep between consonants
- } elsif (($prev2_script eq $current_script)
- && 0
- && ($prev_char_name =~ /^THAI CHARACTER MAI [A-Z]+$/) # Thai tone
- && ($romanized_prev2_char =~ /^[bcdfghjklmnpqrstvwxz]+$/i)
- && ($romanized_next_char =~ /^[bcdfghjklmnpqrstvwxz]+$/i)) {
- $this->add_node("o", $i, $i+1, *chart_ht, "", "thai-middle-o-ang"); # keep between consonant+tone-mark and consonant
- } else {
- $this->add_node("", $i, $i+1, *chart_ht, "", "thai-middle-o-ang-drop"); # drop next to vowel
- }
- }
-
- # Romanization with space
- } elsif ($romanized_char =~ /\s/) {
- $this->add_node($char, $i, $i+1, *chart_ht, "", "space");
-
- # Tibetan special cases
- } elsif ($current_script eq "Tibetan") {
-
- if ($subjoined_char_p
- && ($prev_script eq $current_script)
- && $prev_letter_plus_char_p
- && ($prev_char_roman =~ /^[bcdfghjklmnpqrstvwxyz]+$/i)) {
- $this->add_node("$prev_char_roman$romanized_char", $prev_node_start, $i+1, *chart_ht, "", "subjoined-tibetan-character");
- } elsif ($romanized_char =~ /^-A$/i) {
- my $romanized_next_char = $this->romanize_char_at_position($i+1, $lang_code, $output_style, *ht, *chart_ht);
- if (! $prev_letter_plus_char_p) {
- $this->add_node("'", $i, $i+1, *chart_ht, "", "tibetan-frontal-dash-a");
- } elsif (($prev_script eq $current_script)
- && ($next_script eq $current_script)
- && ($prev_char_roman =~ /[bcdfghjklmnpqrstvwxyz]$/)
- && ($romanized_next_char =~ /^[aeiou]/)) {
- $this->add_node("a'", $i, $i+1, *chart_ht, "", "tibetan-medial-dash-a");
- } elsif (($prev_script eq $current_script)
- && ($next_script eq $current_script)
- && ($prev_char_roman =~ /[aeiou]$/)
- && ($romanized_next_char =~ /[aeiou]/)) {
- $this->add_node("'", $i, $i+1, *chart_ht, "", "tibetan-reduced-medial-dash-a");
- } elsif (($prev_script eq $current_script)
- && (! ($prev_char_roman =~ /[aeiou]/))
- && (! $next_letter_plus_char_p)) {
- $this->add_node("a", $i, $i+1, *chart_ht, "", "tibetan-final-dash-a");
- } else {
- $this->add_node("a", $i, $i+1, *chart_ht, "", "unexpected-tibetan-dash-a");
- }
- } elsif (($romanized_char =~ /^[AEIOU]/i)
- && ($prev_script eq $current_script)
- && ($prev_char_roman =~ /^A$/i)
- && (! $prev2_letter_plus_char_p)) {
- $this->add_node($romanized_char, $prev_node_start, $i+1, *chart_ht, "", "tibetan-dropped-word-initial-a");
- } else {
- $this->add_node($romanized_char, $i, $i+1, *chart_ht, "", "standard-unicode-based-romanization");
- }
-
- # Khmer (for MUUSIKATOAN etc. see under "Diacritic" above)
- } elsif (($current_script eq "Khmer")
- && (($char_roman_consonant, $char_roman_vowel) = ($romanized_char =~ /^(.*[bcdfghjklmnpqrstvwxyz])([ao]+)-$/i))) {
- my $romanized_next_char = $this->romanize_char_at_position($i+1, $lang_code, $output_style, *ht, *chart_ht);
- if (($next_script eq $current_script)
- && ($romanized_next_char =~ /^[aeiouy]/i)) {
- $this->add_node($char_roman_consonant, $i, $i+1, *chart_ht, "", "khmer-vowel-drop");
- } else {
- $this->add_node("$char_roman_consonant$char_roman_vowel", $i, $i+1, *chart_ht, "", "khmer-standard-unicode-based-romanization");
- }
-
- # Abudiga add default vowel
- } elsif ((@abudiga_default_vowels = sort keys %{$ht{SCRIPT_ABUDIGA_DEFAULT_VOWEL}->{$current_script}})
- && ($abudiga_default_vowel = $abudiga_default_vowels[0])
- && ($romanized_char =~ /^[bcdfghjklmnpqrstvwxyz]+$/i)) {
- my $new_roman = join("", $romanized_char, $abudiga_default_vowel);
- $this->add_node($new_roman, $i, $i+1, *chart_ht, "", "standard-unicode-based-romanization-plus-abudiga-default-vowel");
- # print STDERR " Abudiga add default vowel l.$line_number/$i: $new_roman\n" if $line_number =~ /^[12]$/;
-
- # Standard romanization
- } else {
- $node_id = $this->add_node($romanized_char, $i, $i+1, *chart_ht, "", "standard-unicode-based-romanization");
- }
- } else {
- $this->add_node($char, $i, $i+1, *chart_ht, "", "unexpected-original");
- }
- } elsif (defined($romanized_char = $this->romanize_char_at_position($i, $lang_code, $output_style, *ht, *chart_ht))
- && ((length($romanized_char) <= 2)
- || ($ht{UTF_TO_CHAR_ROMANIZATION}->{$char}))) { # or from unicode_overwrite_romanization table
- $romanized_char =~ s/^""$//;
- $this->add_node($romanized_char, $i, $i+1, *chart_ht, "", "romanized-without-character-name");
- } else {
- $this->add_node($char, $i, $i+1, *chart_ht, "", "unexpected-original-without-character-name");
- }
- }
- $i = $next_index;
- }
-
- $this->schwa_deletion(0, $n_characters, *chart_ht, $lang_code);
- $this->default_vowelize_tibetan(0, $n_characters, *chart_ht, $lang_code, $line_number) if $chart_ht{CHART_CONTAINS_SCRIPT}->{"Tibetan"};
- $this->assemble_numbers_in_chart(*chart_ht, $line_number);
-
- if ($return_chart_p) {
- } elsif ($return_offset_mappings_p) {
- ($result, $offset_mappings, $new_char_offset, $new_rom_char_offset) = $this->best_romanized_string(0, $n_characters, *chart_ht, $control, $initial_char_offset, $initial_rom_char_offset);
- } else {
- $result = $this->best_romanized_string(0, $n_characters, *chart_ht) unless $return_chart_p;
- }
-
- if ($verbosePM) {
- my $logfile = "/nfs/isd/ulf/cgi-mt/amr-tmp/uroman-log.txt";
- $util->append_to_file($logfile, $log) if $log && (-r $logfile);
- }
-
- return ($result, $offset_mappings) if $return_offset_mappings_p;
- return *chart_ht if $return_chart_p;
- return $result;
-}
-
-sub string_to_json_string {
- local($this, $s) = @_;
-
- utf8::decode($s);
- my $j = JSON->new->utf8->encode([$s]);
- $j =~ s/^\[(.*)\]$/$1/;
- return $j;
-}
-
-sub chart_to_json_romanization_elements {
- local($this, $chart_start, $chart_end, *chart_ht, $line_number) = @_;
-
- my $result = "";
- my $start = $chart_start;
- my $end;
- while ($start < $chart_end) {
- $end = $this->find_end_of_rom_segment($start, $chart_end, *chart_ht);
- my @best_romanizations;
- if (($end && ($start < $end))
- && (@best_romanizations = $this->best_romanizations($start, $end, *chart_ht))) {
- $orig_segment = $this->orig_string_at_span($start, $end, *chart_ht);
- $next_start = $end;
- } else {
- $orig_segment = $chart_ht{ORIG_CHAR}->{$start};
- @best_romanizations = ($orig);
- $next_start = $start + 1;
- }
- $exclusive_end = $end - 1;
- # $guarded_orig = $util->string_guard($orig_segment);
- $guarded_orig = $this->string_to_json_string($orig_segment);
- $result .= " { \"line\": $line_number, \"start\": $start, \"end\": $exclusive_end, \"orig\": $guarded_orig, \"roms\": [";
- foreach $i ((0 .. $#best_romanizations)) {
- my $rom = $best_romanizations[$i];
- # my $guarded_rom = $util->string_guard($rom);
- my $guarded_rom = $this->string_to_json_string($rom);
- $result .= " { \"rom\": $guarded_rom";
- # $result .= ", \"alt\": true" if $i >= 1;
- $result .= " }";
- $result .= "," if $i < $#best_romanizations;
- }
- $result .= " ] },\n";
- $start = $next_start;
- }
- return $result;
-}
-
-sub default_vowelize_tibetan {
- local($this, $chart_start, $chart_end, *chart_ht, $lang_code, $line_number) = @_;
-
- # my $verbose = ($line_number == 103);
- # print STDERR "\nStart default_vowelize_tibetan l.$line_number $chart_start-$chart_end\n" if $verbose;
- my $token_start = $chart_start;
- my $next_token_start = $chart_start;
- while (($token_start = $next_token_start) < $chart_end) {
- $next_token_start = $token_start + 1;
-
- next unless $chart_ht{CHAR_LETTER_PLUS}->{$token_start};
- my $current_script = $chart_ht{CHAR_SCRIPT}->{$token_start};
- next unless ($current_script eq "Tibetan");
- my $token_end = $chart_ht{LETTER_TOKEN_SEGMENT_START_TO_END}->{$token_start};
- next unless $token_end;
- next unless $token_end > $token_start;
- $next_token_start = $token_end;
-
- my $start = $token_start;
- my $end;
- my @node_ids = ();
- while ($start < $token_end) {
- $end = $this->find_end_of_rom_segment($start, $chart_end, *chart_ht);
- last unless $end && ($end > $start);
- my @alt_node_ids = sort { $a <=> $b } keys %{$chart_ht{NODES_STARTING_AND_ENDING_AT}->{$start}->{$end}};
- last unless @alt_node_ids;
- push(@node_ids, $alt_node_ids[0]);
- $start = $end;
- }
- my $contains_vowel_p = 0;
- my @romanizations = ();
- foreach $node_id (@node_ids) {
- my $roman = $chart_ht{NODE_ROMAN}->{$node_id};
- $roman = "" unless defined($roman);
- push(@romanizations, $roman);
- $contains_vowel_p = 1 if $roman =~ /[aeiou]/i;
- }
- # print STDERR " old: $token_start-$token_end @romanizations\n" if $verbose;
- unless ($contains_vowel_p) {
- my $default_vowel_target_index;
- if ($#node_ids <= 1) {
- $default_vowel_target_index = 0;
- } elsif ($romanizations[$#romanizations] eq "s") {
- if ($romanizations[($#romanizations-1)] eq "y") {
- $default_vowel_target_index = $#romanizations-1;
- } else {
- $default_vowel_target_index = $#romanizations-2;
- }
- } else {
- $default_vowel_target_index = $#romanizations-1;
- }
- $romanizations[$default_vowel_target_index] .= "a";
- my $old_node_id = $node_ids[$default_vowel_target_index];
- my $old_start = $chart_ht{NODE_START}->{$old_node_id};
- my $old_end = $chart_ht{NODE_END}->{$old_node_id};
- my $old_roman = $chart_ht{NODE_ROMAN}->{$old_node_id};
- my $new_roman = $old_roman . "a";
- my $new_node_id = $this->add_node($new_roman, $old_start, $old_end, *chart_ht, "", "tibetan-default-vowel");
- $this->copy_slot_values($old_node_id, $new_node_id, *chart_id, "all");
- $chart_ht{NODE_TYPE}->{$old_node_id} = "backup"; # keep, but demote
- }
- if (($romanizations[0] eq "'")
- && ($#romanizations >= 1)
- && ($romanizations[1] =~ /^[o]$/)) {
- my $old_node_id = $node_ids[0];
- my $old_start = $chart_ht{NODE_START}->{$old_node_id};
- my $old_end = $chart_ht{NODE_END}->{$old_node_id};
- my $new_node_id = $this->add_node("", $old_start, $old_end, *chart_ht, "", "tibetan-delete-apostrophe");
- $this->copy_slot_values($old_node_id, $new_node_id, *chart_id, "all");
- $chart_ht{NODE_TYPE}->{$old_node_id} = "alt"; # keep, but demote
- }
- if (($#node_ids >= 1)
- && ($romanizations[$#romanizations] =~ /^[bcdfghjklmnpqrstvwxz]+y$/)) {
- my $old_node_id = $node_ids[$#romanizations];
- my $old_start = $chart_ht{NODE_START}->{$old_node_id};
- my $old_end = $chart_ht{NODE_END}->{$old_node_id};
- my $old_roman = $chart_ht{NODE_ROMAN}->{$old_node_id};
- my $new_roman = $old_roman . "a";
- my $new_node_id = $this->add_node($new_roman, $old_start, $old_end, *chart_ht, "", "tibetan-syllable-final-vowel");
- $this->copy_slot_values($old_node_id, $new_node_id, *chart_id, "all");
- $chart_ht{NODE_TYPE}->{$old_node_id} = "alt"; # keep, but demote
- }
- foreach $old_node_id (@node_ids) {
- my $old_roman = $chart_ht{NODE_ROMAN}->{$old_node_id};
- next unless $old_roman =~ /-a/;
- my $old_start = $chart_ht{NODE_START}->{$old_node_id};
- my $old_end = $chart_ht{NODE_END}->{$old_node_id};
- my $new_roman = $old_roman;
- $new_roman =~ s/-a/a/;
- my $new_node_id = $this->add_node($new_roman, $old_start, $old_end, *chart_ht, "", "tibetan-syllable-delete-dash");
- $this->copy_slot_values($old_node_id, $new_node_id, *chart_id, "all");
- $chart_ht{NODE_TYPE}->{$old_node_id} = "alt"; # keep, but demote
- }
- }
-}
-
-sub schwa_deletion {
- local($this, $chart_start, $chart_end, *chart_ht, $lang_code) = @_;
- # delete word-final simple "a" in Devanagari (e.g. nepaala -> nepaal)
- # see Wikipedia article "Schwa deletion in Indo-Aryan languages"
-
- if ($chart_ht{CHART_CONTAINS_SCRIPT}->{"Devanagari"}) {
- my $script_start = $chart_start;
- my $next_script_start = $chart_start;
- while (($script_start = $next_script_start) < $chart_end) {
- $next_script_start = $script_start + 1;
-
- my $current_script = $chart_ht{CHAR_SCRIPT}->{$script_start};
- next unless ($current_script eq "Devanagari");
- my $script_end = $chart_ht{SCRIPT_SEGMENT_START_TO_END}->{$script_start};
- next unless $script_end;
- next unless $script_end - $script_start >= 2;
- $next_script_start = $script_end;
- my $end_node_id = $this->get_node_for_span($script_end-1, $script_end, *chart_ht);
- next unless $end_node_id;
- my $end_roman = $chart_ht{NODE_ROMAN}->{$end_node_id};
- next unless ($end_consonant) = ($end_roman =~ /^([bcdfghjklmnpqrstvwxz]+)a$/i);
- my $prev_node_id = $this->get_node_for_span($script_end-4, $script_end-1, *chart_ht)
- || $this->get_node_for_span($script_end-3, $script_end-1, *chart_ht)
- || $this->get_node_for_span($script_end-2, $script_end-1, *chart_ht);
- next unless $prev_node_id;
- my $prev_roman = $chart_ht{NODE_ROMAN}->{$prev_node_id};
- next unless $prev_roman =~ /[aeiou]/i;
- # TO DO: check further back for vowel (e.g. if $prev_roman eq "r" due to vowel cancelation)
-
- $chart_ht{NODE_TYPE}->{$end_node_id} = "alt"; # keep, but demote
- # print STDERR "* Schwa deletion " . ($script_end-1) . "-$script_end $end_roman->$end_consonant\n";
- $this->add_node($end_consonant, $script_end-1, $script_end, *chart_ht, "", "devanagari-with-deleted-final-schwa");
- }
- }
-}
-
-sub best_romanized_string {
- local($this, $chart_start, $chart_end, *chart_ht, $control, $orig_char_offset, $rom_char_offset) = @_;
-
- $control = "" unless defined($control);
- my $current_orig_char_offset = $orig_char_offset || 0;
- my $current_rom_char_offset = $rom_char_offset || 0;
- my $return_offset_mappings_p = ($control =~ /\breturn offset mappings\b/);
- my $result = "";
- my $start = $chart_start;
- my $end;
- my @char_offsets = ("$current_orig_char_offset:$current_rom_char_offset");
- while ($start < $chart_end) {
- $end = $this->find_end_of_rom_segment($start, $chart_end, *chart_ht);
- my $n_orig_chars_in_segment = 0;
- my $n_rom_chars_in_segment = 0;
- if ($end && ($start < $end)) {
- my @best_romanizations = $this->best_romanizations($start, $end, *chart_ht);
- my $best_romanization = (@best_romanizations) ? $best_romanizations[0] : undef;
- if (defined($best_romanization)) {
- $result .= $best_romanization;
- if ($return_offset_mappings_p) {
- $n_orig_chars_in_segment = $end-$start;
- $n_rom_chars_in_segment = $utf8->length_in_utf8_chars($best_romanization);
- }
- $start = $end;
- } else {
- my $best_romanization = $chart_ht{ORIG_CHAR}->{$start};
- $result .= $best_romanization;
- $start++;
- if ($return_offset_mappings_p) {
- $n_orig_chars_in_segment = 1;
- $n_rom_chars_in_segment = $utf8->length_in_utf8_chars($best_romanization);
- }
- }
- } else {
- my $best_romanization = $chart_ht{ORIG_CHAR}->{$start};
- $result .= $best_romanization;
- $start++;
- if ($return_offset_mappings_p) {
- $n_orig_chars_in_segment = 1;
- $n_rom_chars_in_segment = $utf8->length_in_utf8_chars($best_romanization);
- }
- }
- if ($return_offset_mappings_p) {
- my $new_orig_char_offset = $current_orig_char_offset + $n_orig_chars_in_segment;
- my $new_rom_char_offset = $current_rom_char_offset + $n_rom_chars_in_segment;
- my $offset_mapping = "$new_orig_char_offset:$new_rom_char_offset";
- push(@char_offsets, $offset_mapping);
- $current_orig_char_offset = $new_orig_char_offset;
- $current_rom_char_offset = $new_rom_char_offset;
- }
- }
- return ($result, join(",", @char_offsets), $current_orig_char_offset, $current_rom_char_offset) if $return_offset_mappings_p;
- return $result;
-}
-
-sub orig_string_at_span {
- local($this, $start, $end, *chart_ht) = @_;
-
- my $result = "";
- foreach $i (($start .. ($end-1))) {
- $result .= $chart_ht{ORIG_CHAR}->{$i};
- }
- return $result;
-}
-
-sub find_end_of_rom_segment {
- local($this, $start, $chart_end, *chart_ht) = @_;
-
- my @ends = sort { $a <=> $b } keys %{$chart_ht{NODES_STARTING_AND_ENDING_AT}->{$start}};
- my $end_index = $#ends;
- while (($end_index >= 0) && ($ends[$end_index] > $chart_end)) {
- $end_index--;
- }
- if (($end_index >= 0)
- && defined($end = $ends[$end_index])
- && ($start < $end)) {
- return $end;
- } else {
- return "";
- }
-}
-
-sub best_romanizations {
- local($this, $start, $end, *chart_ht) = @_;
-
- @regular_romanizations = ();
- @alt_romanizations = ();
- @backup_romanizations = ();
-
- foreach $node_id (sort { $a <=> $b } keys %{$chart_ht{NODES_STARTING_AND_ENDING_AT}->{$start}->{$end}}) {
- my $type = $chart_ht{NODE_TYPE}->{$node_id};
- my $roman = $chart_ht{NODE_ROMAN}->{$node_id};
- if (! defined($roman)) {
- # ignore
- } elsif (($type eq "backup") && ! defined($backup_romanization)) {
- push(@backup_romanizations, $roman) unless $util->member($roman, @backup_romanizations);
- } elsif (($type eq "alt") && ! defined($alt_romanization)) {
- push(@alt_romanizations, $roman) unless $util->member($roman, @alt_romanizations);
- } else {
- push(@regular_romanizations, $roman) unless $util->member($roman, @regular_romanizations);
- }
- }
- @regular_alt_romanizations = sort @regular_romanizations;
- foreach $alt_romanization (sort @alt_romanizations) {
- push(@regular_alt_romanizations, $alt_romanization) unless $util->member($alt_romanization, @regular_alt_romanizations);
- }
- return @regular_alt_romanizations if @regular_alt_romanizations;
- return sort @backup_romanizations;
-}
-
-sub join_alt_romanizations_for_viz {
- local($this, @list) = @_;
-
- my @viz_romanizations = ();
-
- foreach $alt_rom (@list) {
- if ($alt_rom eq "") {
- push(@viz_romanizations, "-");
- } else {
- push(@viz_romanizations, $alt_rom);
- }
- }
- return join(", ", @viz_romanizations);
-}
-
-sub markup_orig_rom_strings {
- local($this, $chart_start, $chart_end, *ht, *chart_ht, *pinyin_ht, $last_group_id_index) = @_;
-
- my $marked_up_rom = "";
- my $marked_up_orig = "";
- my $start = $chart_start;
- my $end;
- while ($start < $chart_end) {
- my $segment_start = $start;
- my $segment_end = $start+1;
- my $end = $this->find_end_of_rom_segment($start, $chart_end, *chart_ht);
- my $rom_segment = "";
- my $orig_segment = "";
- my $rom_title = "";
- my $orig_title = "";
- my $contains_alt_romanizations = 0;
- if ($end) {
- $segment_end = $end;
- my @best_romanizations = $this->best_romanizations($start, $end, *chart_ht);
- my $best_romanization = (@best_romanizations) ? $best_romanizations[0] : undef;
- if (defined($best_romanization)) {
- $rom_segment .= $best_romanization;
- $orig_segment .= $this->orig_string_at_span($start, $end, *chart_ht);
- $segment_end = $end;
- if ($#best_romanizations >= 1) {
- $rom_title .= $util->guard_html("Alternative romanizations: " . $this->join_alt_romanizations_for_viz(@best_romanizations) . "\n");
- $contains_alt_romanizations = 1;
- }
- } else {
- my $segment = $this->orig_string_at_span($start, $start+1, *chart_ht);
- $rom_segment .= $segment;
- $orig_segment .= $segment;
- $segment_end = $start+1;
- }
- $start = $segment_end;
- } else {
- $rom_segment .= $chart_ht{ORIG_CHAR}->{$start};
- $orig_segment .= $this->orig_string_at_span($start, $start+1, *chart_ht);
- $segment_end = $start+1;
- $start = $segment_end;
- }
- my $next_char = $chart_ht{ORIG_CHAR}->{$segment_end};
- my $next_char_is_combining_p = $this->char_is_combining_char($next_char, *ht);
- while ($next_char_is_combining_p
- && ($segment_end < $chart_end)
- && ($end = $this->find_end_of_rom_segment($segment_end, $chart_end, *chart_ht))
- && ($end > $segment_end)
- && (@best_romanizations = $this->best_romanizations($segment_end, $end, *chart_ht))
- && defined($best_romanization = $best_romanizations[0])) {
- $orig_segment .= $this->orig_string_at_span($segment_end, $end, *chart_ht);
- $rom_segment .= $best_romanization;
- if ($#best_romanizations >= 1) {
- $rom_title .= $util->guard_html("Alternative romanizations: " . $this->join_alt_romanizations_for_viz(@best_romanizations) . "\n");
- $contains_alt_romanizations = 1;
- }
- $segment_end = $end;
- $start = $segment_end;
- $next_char = $chart_ht{ORIG_CHAR}->{$segment_end};
- $next_char_is_combining_p = $this->char_is_combining_char($next_char, *ht);
- }
- foreach $i (($segment_start .. ($segment_end-1))) {
- $orig_title .= "+ " unless $orig_title eq "";
- my $char = $chart_ht{ORIG_CHAR}->{$i};
- my $numeric = $ht{UTF_TO_NUMERIC}->{$char};
- $numeric = "" unless defined($numeric);
- my $pic_descr = $ht{UTF_TO_PICTURE_DESCR}->{$char};
- $pic_descr = "" unless defined($pic_descr);
- if ($char =~ /^\xE4\xB7[\x80-\xBF]$/) {
- $orig_title .= "$char_name\n";
- } elsif (($char =~ /^[\xE3-\xE9][\x80-\xBF]{2,2}$/) && $chinesePM->string_contains_utf8_cjk_unified_ideograph_p($char)) {
- my $unicode = $utf8->utf8_to_unicode($char);
- $orig_title .= "CJK Unified Ideograph U+" . (uc sprintf("%04x", $unicode)) . "\n";
- $orig_title .= "Chinese: $tonal_translit\n" if $tonal_translit = $chinesePM->tonal_pinyin($char, *pinyin_ht, "");
- $orig_title .= "Number: $numeric\n" if $numeric =~ /\d/;
- } elsif ($char_name = $ht{UTF_TO_CHAR_NAME}->{$char}) {
- $orig_title .= "$char_name\n";
- $orig_title .= "Number: $numeric\n" if $numeric =~ /\d/;
- $orig_title .= "Picture: $pic_descr\n" if $pic_descr =~ /\S/;
- } else {
- my $unicode = $utf8->utf8_to_unicode($char);
- if (($unicode >= 0xAC00) && ($unicode <= 0xD7A3)) {
- $orig_title .= "Hangul syllable U+" . (uc sprintf("%04x", $unicode)) . "\n";
- } else {
- $orig_title .= "Unicode character U+" . (uc sprintf("%04x", $unicode)) . "\n";
- }
- }
- }
- (@non_ascii_roms) = ($rom_segment =~ /([\xC0-\xFF][\x80-\xBF]*)/g);
- foreach $char (@non_ascii_roms) {
- my $char_name = $ht{UTF_TO_CHAR_NAME}->{$char};
- my $unicode = $utf8->utf8_to_unicode($char);
- my $unicode_s = "U+" . (uc sprintf("%04x", $unicode));
- if ($char_name) {
- $rom_title .= "$char_name\n";
- } else {
- $rom_title .= "$unicode_s\n";
- }
- }
- $last_group_id_index++;
- $rom_title =~ s/\s*$//;
- $rom_title =~ s/\n/
/g;
- $orig_title =~ s/\s*$//;
- $orig_title =~ s/\n/
/g;
- $orig_title = "" . $orig_title . "";
- my $rom_title_clause = ($rom_title eq "") ? "" : " title=\"$rom_title\"";
- my $orig_title_clause = ($orig_title eq "") ? "" : " title=\"$orig_title\"";
- my $alt_rom_clause = ($contains_alt_romanizations) ? "border-bottom:1px dotted;" : "";
- $marked_up_rom .= "" . $util->guard_html($rom_segment) . "<\/span>";
- $marked_up_orig .= "" . $util->guard_html($orig_segment) . "<\/span>";
- if (($last_char = $chart_ht{ORIG_CHAR}->{($segment_end-1)})
- && ($last_char_name = $ht{UTF_TO_CHAR_NAME}->{$last_char})
- && ($last_char_name =~ /^(FULLWIDTH COLON|FULLWIDTH COMMA|FULLWIDTH RIGHT PARENTHESIS|IDEOGRAPHIC COMMA|IDEOGRAPHIC FULL STOP|RIGHT CORNER BRACKET|BRAILLE PATTERN BLANK|TIBETAN MARK .*)$/)) {
- $marked_up_orig .= "";
- $marked_up_rom .= "";
- }
- }
- return ($marked_up_rom, $marked_up_orig, $last_group_id_index);
-}
-
-sub romanizations_with_alternatives {
- local($this, *ht, *chart_ht, *pinyin_ht, $chart_start, $chart_end) = @_;
-
- $chart_start = 0 unless defined($chart_start);
- $chart_end = $chart_ht{N_CHARS} unless defined($chart_end);
- my $result = "";
- my $start = $chart_start;
- my $end;
- # print STDOUT "romanizations_with_alternatives $chart_start-$chart_end\n";
- while ($start < $chart_end) {
- my $segment_start = $start;
- my $segment_end = $start+1;
- my $end = $this->find_end_of_rom_segment($start, $chart_end, *chart_ht);
- my $rom_segment = "";
- # print STDOUT " $start-$end\n";
- if ($end) {
- $segment_end = $end;
- my @best_romanizations = $this->best_romanizations($start, $end, *chart_ht);
- # print STDOUT " $start-$end @best_romanizations\n";
- if (@best_romanizations) {
- if ($#best_romanizations == 0) {
- $rom_segment .= $best_romanizations[0];
- } else {
- $rom_segment .= "{" . join("|", @best_romanizations) . "}";
- }
- $segment_end = $end;
- } else {
- my $segment = $this->orig_string_at_span($start, $start+1, *chart_ht);
- $rom_segment .= $segment;
- $segment_end = $start+1;
- }
- $start = $segment_end;
- } else {
- $rom_segment .= $chart_ht{ORIG_CHAR}->{$start};
- $segment_end = $start+1;
- $start = $segment_end;
- }
- # print STDOUT " $start-$end ** $rom_segment\n";
- $result .= $rom_segment;
- }
- return $result;
-}
-
-sub quick_romanize {
- local($this, $s, $lang_code, *ht) = @_;
-
- my $result = "";
- my @chars = $utf8->split_into_utf8_characters($s, "return only chars", *empty_ht);
- while (@chars) {
- my $found_match_in_table_p = 0;
- foreach $string_length (reverse(1..4)) {
- next if ($string_length-1) > $#chars;
- $multi_char_substring = join("", @chars[0..($string_length-1)]);
- my @mappings = keys %{$ht{UTF_CHAR_MAPPING_LANG_SPEC}->{$lang_code}->{$multi_char_substring}};
- @mappings = keys %{$ht{UTF_CHAR_MAPPING}->{$multi_char_substring}} unless @mappings;
- if (@mappings) {
- my $mapping = $mappings[0];
- $result .= $mapping;
- foreach $_ ((1 .. $string_length)) {
- shift @chars;
- }
- $found_match_in_table_p = 1;
- last;
- }
- }
- unless ($found_match_in_table_p) {
- $result .= $chars[0];
- shift @chars;
- }
- }
- return $result;
-}
-
-sub char_is_combining_char {
- local($this, $c, *ht) = @_;
-
- return 0 unless $c;
- my $category = $ht{UTF_TO_CAT}->{$c};
- return 0 unless $category;
- return $category =~ /^M/;
-}
-
-sub mark_up_string_for_mouse_over {
- local($this, $s, *ht, $control, *pinyin_ht) = @_;
-
- $control = "" unless defined($control);
- $no_ascii_p = ($control =~ /NO-ASCII/);
- my $result = "";
- @chars = $utf8->split_into_utf8_characters($s, "return only chars", *empty_ht);
- while (@chars) {
- $char = shift @chars;
- $numeric = $ht{UTF_TO_NUMERIC}->{$char};
- $numeric = "" unless defined($numeric);
- $pic_descr = $ht{UTF_TO_PICTURE_DESCR}->{$char};
- $pic_descr = "" unless defined($pic_descr);
- $next_char = ($#chars >= 0) ? $chars[0] : "";
- $next_char_is_combining_p = $this->char_is_combining_char($next_char, *ht);
- if ($no_ascii_p
- && ($char =~ /^[\x00-\x7F]*$/)
- && ! $next_char_is_combining_p) {
- $result .= $util->guard_html($char);
- } elsif (($char =~ /^[\xE3-\xE9][\x80-\xBF]{2,2}$/) && $chinesePM->string_contains_utf8_cjk_unified_ideograph_p($char)) {
- $unicode = $utf8->utf8_to_unicode($char);
- $title = "CJK Unified Ideograph U+" . (uc sprintf("%04x", $unicode));
- $title .= "
Chinese: $tonal_translit" if $tonal_translit = $chinesePM->tonal_pinyin($char, *pinyin_ht, "");
- $title .= "
Number: $numeric" if $numeric =~ /\d/;
- $result .= "" . $util->guard_html($char) . "<\/span>";
- } elsif ($char_name = $ht{UTF_TO_CHAR_NAME}->{$char}) {
- $title = $char_name;
- $title .= "
Number: $numeric" if $numeric =~ /\d/;
- $title .= "
Picture: $pic_descr" if $pic_descr =~ /\S/;
- $char_plus = $char;
- while ($next_char_is_combining_p) {
- # combining marks (Mc:non-spacing, Mc:spacing combining, Me: enclosing)
- $next_char_name = $ht{UTF_TO_CHAR_NAME}->{$next_char};
- $title .= "
+ $next_char_name";
- $char = shift @chars;
- $char_plus .= $char;
- $next_char = ($#chars >= 0) ? $chars[0] : "";
- $next_char_is_combining_p = $this->char_is_combining_char($next_char, *ht);
- }
- $result .= "" . $util->guard_html($char_plus) . "<\/span>";
- $result .= "" if $char_name =~ /^(FULLWIDTH COLON|FULLWIDTH COMMA|FULLWIDTH RIGHT PARENTHESIS|IDEOGRAPHIC COMMA|IDEOGRAPHIC FULL STOP|RIGHT CORNER BRACKET)$/;
- } elsif (($unicode = $utf8->utf8_to_unicode($char))
- && ($unicode >= 0xAC00) && ($unicode <= 0xD7A3)) {
- $title = "Hangul syllable U+" . (uc sprintf("%04x", $unicode));
- $result .= "" . $util->guard_html($char) . "<\/span>";
- } else {
- $result .= $util->guard_html($char);
- }
- }
- return $result;
-}
-
-sub romanize_char_at_position_incl_multi {
- local($this, $i, $lang_code, $output_style, *ht, *chart_ht) = @_;
-
- my $char = $chart_ht{ORIG_CHAR}->{$i};
- return "" unless defined($char);
- my @mappings = keys %{$ht{UTF_CHAR_MAPPING_LANG_SPEC}->{$lang_code}->{$char}};
- return $mappings[0] if @mappings;
- @mappings = keys %{$ht{UTF_CHAR_MAPPING}->{$char}};
- return $mappings[0] if @mappings;
- return $this->romanize_char_at_position($i, $lang_code, $output_style, *ht, *chart_ht);
-}
-
-sub romanize_char_at_position {
- local($this, $i, $lang_code, $output_style, *ht, *chart_ht) = @_;
-
- my $char = $chart_ht{ORIG_CHAR}->{$i};
- return "" unless defined($char);
- return $char if $char =~ /^[\x00-\x7F]$/; # ASCII
- my $romanization = $ht{UTF_TO_CHAR_ROMANIZATION}->{$char};
- return $romanization if $romanization;
- my $char_name = $chart_ht{CHAR_NAME}->{$i};
- $romanization = $this->romanize_charname($char_name, $lang_code, $output_style, *ht, $char);
- $ht{SUSPICIOUS_ROMANIZATION}->{$char_name}->{$romanization}
- = ($ht{SUSPICIOUS_ROMANIZATION}->{$char_name}->{$romanization} || 0) + 1
- unless (length($romanization) < 4)
- || ($romanization =~ /\s/)
- || ($romanization =~ /^[bcdfghjklmnpqrstvwxyz]{2,3}[aeiou]-$/) # Khmer ngo-/nyo-/pho- OK
- || ($romanization =~ /^[bcdfghjklmnpqrstvwxyz]{2,2}[aeiougw][aeiou]{1,2}$/) # Canadian, Ethiopic syllable OK
- || ($romanization =~ /^(allah|bbux|nyaa|nnya|quuv|rrep|shch|shur|syrx)$/i) # Arabic; Yi; Ethiopic syllable nyaa; Cyrillic letter shcha
- || (($char_name =~ /^(YI SYLLABLE|VAI SYLLABLE|ETHIOPIC SYLLABLE|CANADIAN SYLLABICS|CANADIAN SYLLABICS CARRIER)\s+(\S+)$/) && (length($romanization) <= 5));
- # print STDERR "romanize_char_at_position $i $char_name :: $romanization\n" if $char_name =~ /middle/i;
- return $romanization;
-}
-
-sub romanize_charname {
- local($this, $char_name, $lang_code, $output_style, *ht, $char) = @_;
-
- my $cached_result = $ht{ROMANIZE_CHARNAME}->{$char_name}->{$lang_code}->{$output_style};
- # print STDERR "(C) romanize_charname($char_name): $cached_result\n" if $cached_result && ($char_name =~ /middle/i);
- return $cached_result if defined($cashed_result);
- $orig_char_name = $char_name;
- $char_name =~ s/^.* LETTER\s+([A-Z]+)-\d+$/$1/; # HENTAIGANA LETTER A-3
- $char_name =~ s/^.* LETTER\s+//;
- $char_name =~ s/^.* SYLLABLE\s+B\d\d\d\s+//; # Linear B syllables
- $char_name =~ s/^.* SYLLABLE\s+//;
- $char_name =~ s/^.* SYLLABICS\s+//;
- $char_name =~ s/^.* LIGATURE\s+//;
- $char_name =~ s/^.* VOWEL SIGN\s+//;
- $char_name =~ s/^.* CONSONANT SIGN\s+//;
- $char_name =~ s/^.* CONSONANT\s+//;
- $char_name =~ s/^.* VOWEL\s+//;
- $char_name =~ s/ WITH .*$//;
- $char_name =~ s/ WITHOUT .*$//;
- $char_name =~ s/\s+(ABOVE|AGUNG|BAR|BARREE|BELOW|CEDILLA|CEREK|DIGRAPH|DOACHASHMEE|FINAL FORM|GHUNNA|GOAL|INITIAL FORM|ISOLATED FORM|KAWI|LELET|LELET RASWADI|LONSUM|MAHAPRANA|MEDIAL FORM|MURDA|MURDA MAHAPRANA|REVERSED|ROTUNDA|SASAK|SUNG|TAM|TEDUNG|TYPE ONE|TYPE TWO|WOLOSO)\s*$//;
- $char_name =~ s/^([A-Z]+)\d+$/$1/; # Linear B syllables etc.
- foreach $_ ((1 .. 3)) {
- $char_name =~ s/^.*\b(?:ABKHASIAN|ACADEMY|AFRICAN|AIVILIK|AITON|AKHMIMIC|ALEUT|ALI GALI|ALPAPRAANA|ALTERNATE|ALTERNATIVE|AMBA|ARABIC|ARCHAIC|ASPIRATED|ATHAPASCAN|BASELINE|BLACKLETTER|BARRED|BASHKIR|BERBER|BHATTIPROLU|BIBLE-CREE|BIG|BINOCULAR|BLACKFOOT|BLENDED|BOTTOM|BROAD|BROKEN|CANDRA|CAPITAL|CARRIER|CHILLU|CLOSE|CLOSED|COPTIC|CROSSED|CRYPTOGRAMMIC|CURLED|CURLY|CYRILLIC|DANTAJA|DENTAL|DIALECT-P|DIAERESIZED|DOTLESS|DOUBLE|DOUBLE-STRUCK|EASTERN PWO KAREN|EGYPTOLOGICAL|FARSI|FINAL|FLATTENED|GLOTTAL|GREAT|GREEK|HALF|HIGH|INITIAL|INSULAR|INVERTED|IOTIFIED|JONA|KANTAJA|KASHMIRI|KHAKASSIAN|KHAMTI|KHANDA|KINNA|KIRGHIZ|KOMI|L-SHAPED|LATINATE|LITTLE|LONG|LONG-LEGGED|LOOPED|LOW|MAHAAPRAANA|MALAYALAM|MANCHU|MANDAILING|MATHEMATICAL|MEDIAL|MIDDLE-WELSH|MON|MONOCULAR|MOOSE-CREE|MULTIOCULAR|MUURDHAJA|N-CREE|NARROW|NASKAPI|NDOLE|NEUTRAL|NIKOLSBURG|NORTHERN|NUBIAN|NUNAVIK|NUNAVUT|OJIBWAY|OLD|OPEN|ORKHON|OVERLONG|PALI|PERSIAN|PHARYNGEAL|PRISHTHAMATRA|R-CREE|REDUPLICATION|REVERSED|ROMANIAN|ROUND|ROUNDED|RUDIMENTA|RUMAI PALAUNG|SANSKRIT|SANYAKA|SARA|SAYISI|SCRIPT|SEBATBEIT|SEMISOFT|SGAW KAREN|SHAN|SHARP|SHWE PALAUNG|SHORT|SIBE|SIDEWAYS|SIMALUNGUN|SMALL|SOGDIAN|SOFT|SOUTH-SLAVEY|SOUTHERN|SPIDERY|STIRRUP|STRAIGHT|STRETCHED|SUBSCRIPT|SWASH|TAI LAING|TAILED|TAILLESS|TAALUJA|TH-CREE|TALL|THREE-LEGGED|TURNED|TODO|TOP|TROKUTASTI|TUAREG|UKRAINIAN|UNBLENDED|VISIGOTHIC|VOCALIC|VOICED|VOICELESS|VOLAPUK|WAVY|WESTERN PWO KAREN|WEST-CREE|WESTERN|WIDE|WOODS-CREE|Y-CREE|YENISEI|YIDDISH)\s+//;
- }
- $char_name =~ s/\s+(ABOVE|AGUNG|BAR|BARREE|BELOW|CEDILLA|CEREK|DIGRAPH|DOACHASHMEE|FINAL FORM|GHUNNA|GOAL|INITIAL FORM|ISOLATED FORM|KAWI|LELET|LELET RASWADI|LONSUM|MAHAPRANA|MEDIAL FORM|MURDA|MURDA MAHAPRANA|REVERSED|ROTUNDA|SASAK|SUNG|TAM|TEDUNG|TYPE ONE|TYPE TWO|WOLOSO)\s*$//;
- if ($char_name =~ /THAI CHARACTER/) {
- $char_name =~ s/^THAI CHARACTER\s+//;
- if ($char =~ /^\xE0\xB8[\x81-\xAE]/) {
- # Thai consonants
- $char_name =~ s/^([^AEIOU]*).*/$1/i;
- } elsif ($char_name =~ /^SARA [AEIOU]/) {
- # Thai vowels
- $char_name =~ s/^SARA\s+//;
- } else {
- $char_name = $char;
- }
- }
- if ($orig_char_name =~ /(HIRAGANA LETTER|KATAKANA LETTER|SYLLABLE|LIGATURE)/) {
- $char_name = lc $char_name;
- } elsif ($char_name =~ /\b(ANUSVARA|ANUSVARAYA|NIKAHIT|SIGN BINDI|TIPPI)\b/) {
- $char_name = "+m";
- } elsif ($char_name =~ /\bSCHWA\b/) {
- $char_name = "e";
- } elsif ($char_name =~ /\bIOTA\b/) {
- $char_name = "i";
- } elsif ($char_name =~ /\s/) {
- } elsif ($orig_char_name =~ /KHMER LETTER/) {
- $char_name .= "-";
- } elsif ($orig_char_name =~ /CHEROKEE LETTER/) {
- # use whole letter as is
- } elsif ($orig_char_name =~ /KHMER INDEPENDENT VOWEL/) {
- $char_name =~ s/q//;
- } elsif ($orig_char_name =~ /LETTER/) {
- $char_name =~ s/^[AEIOU]+([^AEIOU]+)$/$1/i;
- $char_name =~ s/^([^-AEIOUY]+)[AEIOU].*/$1/i;
- $char_name =~ s/^(Y)[AEIOU].*/$1/i if $orig_char_name =~ /\b(?:BENGALI|DEVANAGARI|GURMUKHI|GUJARATI|KANNADA|MALAYALAM|MODI|MYANMAR|ORIYA|TAMIL|TELUGU|TIBETAN)\b.*\bLETTER YA\b/;
- $char_name =~ s/^(Y[AEIOU]+)[^AEIOU].*$/$1/i;
- $char_name =~ s/^([AEIOU]+)[^AEIOU]+[AEIOU].*/$1/i;
- }
-
- my $result = ($orig_char_name =~ /\bCAPITAL\b/) ? (uc $char_name) : (lc $char_name);
- # print STDERR "(R) romanize_charname($orig_char_name): $result\n" if $orig_char_name =~ /middle/i;
- $ht{ROMANIZE_CHARNAME}->{$char_name}->{$lang_code}->{$output_style} = $result;
- return $result;
-}
-
-sub assemble_numbers_in_chart {
- local($this, *chart_ht, $line_number) = @_;
-
- foreach $start (sort { $a <=> $b } keys %{$chart_ht{COMPLEX_NUMERIC_START_END}}) {
- my $end = $chart_ht{COMPLEX_NUMERIC_START_END}->{$start};
- my @numbers = ();
- foreach $i (($start .. ($end-1))) {
- my $orig_char = $chart_ht{ORIG_CHAR}->{$i};
- my $node_id = $this->get_node_for_span_with_slot($i, $i+1, "numeric-value", *chart_id);
- if (defined($node_id)) {
- my $number = $chart_ht{NODE_ROMAN}->{$node_id};
- if (defined($number)) {
- push(@numbers, $number);
- } elsif ($orig_char =~ /^[.,]$/) { # decimal point, comma separator
- push(@numbers, $orig_char);
- } else {
- print STDERR "Found no romanization for node_id $node_id ($i-" . ($i+1) . ") in assemble_numbers_in_chart\n" if $verbosePM;
- }
- } else {
- print STDERR "Found no node_id for span $i-" . ($i+1) . " in assemble_numbers_in_chart\n" if $verbosePM;
- }
- }
- my $complex_number = $this->assemble_number(join("\xC2\xB7", @numbers), $line_number);
- # print STDERR "assemble_numbers_in_chart l.$line_number $start-$end $complex_number (@numbers)\n";
- $this->add_node($complex_number, $start, $end, *chart_ht, "", "complex-number");
- }
-}
-
-sub assemble_number {
- local($this, $s, $line_number) = @_;
- # e.g. 10 9 100 7 10 8 = 1978
-
- my $middot = "\xC2\xB7";
- my @tokens = split(/$middot/, $s); # middle dot U+00B7
- my $i = 0;
- my @orig_tokens = @tokens;
-
- # assemble single digit numbers, e.g. 1 7 5 -> 175
- while ($i < $#tokens) {
- if ($tokens[$i] =~ /^\d$/) {
- my $j = $i+1;
- while (($j <= $#tokens) && ($tokens[$j] =~ /^[0-9.,]$/)) {
- $j++;
- }
- $j--;
- if ($j>$i) {
- my $new_token = join("", @tokens[$i .. $j]);
- $new_token =~ s/,//g;
- splice(@tokens, $i, $j-$i+1, $new_token);
- }
- }
- $i++;
- }
-
- foreach $power ((10, 100, 1000, 10000, 100000, 1000000, 100000000, 1000000000, 1000000000000)) {
- for (my $i=0; $i <= $#tokens; $i++) {
- if ($tokens[$i] == $power) {
- if (($i > 0) && ($tokens[($i-1)] < $power)) {
- splice(@tokens, $i-1, 2, ($tokens[($i-1)] * $tokens[$i]));
- $i--;
- if (($i < $#tokens) && ($tokens[($i+1)] < $power)) {
- splice(@tokens, $i, 2, ($tokens[$i] + $tokens[($i+1)]));
- $i--;
- }
- }
- }
- # 400 30 (e.g. Egyptian)
- my $gen_pattern = $power;
- $gen_pattern =~ s/^1/\[1-9\]/;
- if (($tokens[$i] =~ /^$gen_pattern$/) && ($i < $#tokens) && ($tokens[($i+1)] < $power)) {
- splice(@tokens, $i, 2, ($tokens[$i] + $tokens[($i+1)]));
- $i--;
- }
- }
- last if $#tokens == 0;
- }
- my $result = join($middot, @tokens);
- if ($verbosePM) {
- my $logfile = "/nfs/isd/ulf/cgi-mt/amr-tmp/uroman-number-log.txt";
- $util->append_to_file($logfile, "$s -> $result\n") if -r $logfile;
- # print STDERR " assemble number l.$line_number @orig_tokens -> $result\n" if $line_number == 43;
- }
- return $result;
-}
-
-1;
-
diff --git a/spaces/MihaiPopa2/ChatGPT-Prompt-Generator/README.md b/spaces/MihaiPopa2/ChatGPT-Prompt-Generator/README.md
deleted file mode 100644
index f90b53becc93b3e3b95b4c620a7ab9b83586f7eb..0000000000000000000000000000000000000000
--- a/spaces/MihaiPopa2/ChatGPT-Prompt-Generator/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: ChatGPT Prompt Generator
-emoji: 👨🏻🎤
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.16.2
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: EinfachOlder/ChatGPT-prompt-generator
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Mountchicken/MAERec-Gradio/configs/textdet/dbnetpp/_base_dbnetpp_resnet50-dcnv2_fpnc.py b/spaces/Mountchicken/MAERec-Gradio/configs/textdet/dbnetpp/_base_dbnetpp_resnet50-dcnv2_fpnc.py
deleted file mode 100644
index ec4d1bcc5624d32db8bcf7ba96015d4780118925..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/configs/textdet/dbnetpp/_base_dbnetpp_resnet50-dcnv2_fpnc.py
+++ /dev/null
@@ -1,72 +0,0 @@
-model = dict(
- type='DBNet',
- backbone=dict(
- type='mmdet.ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=-1,
- norm_cfg=dict(type='BN', requires_grad=True),
- norm_eval=False,
- style='pytorch',
- dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
- init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
- stage_with_dcn=(False, True, True, True)),
- neck=dict(
- type='FPNC',
- in_channels=[256, 512, 1024, 2048],
- lateral_channels=256,
- asf_cfg=dict(attention_type='ScaleChannelSpatial')),
- det_head=dict(
- type='DBHead',
- in_channels=256,
- module_loss=dict(type='DBModuleLoss'),
- postprocessor=dict(
- type='DBPostprocessor', text_repr_type='quad',
- epsilon_ratio=0.002)),
- data_preprocessor=dict(
- type='TextDetDataPreprocessor',
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- bgr_to_rgb=True,
- pad_size_divisor=32))
-
-train_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- dict(
- type='LoadOCRAnnotations',
- with_bbox=True,
- with_polygon=True,
- with_label=True,
- ),
- dict(
- type='TorchVisionWrapper',
- op='ColorJitter',
- brightness=32.0 / 255,
- saturation=0.5),
- dict(
- type='ImgAugWrapper',
- args=[['Fliplr', 0.5],
- dict(cls='Affine', rotate=[-10, 10]), ['Resize', [0.5, 3.0]]]),
- dict(type='RandomCrop', min_side_ratio=0.1),
- dict(type='Resize', scale=(640, 640), keep_ratio=True),
- dict(type='Pad', size=(640, 640)),
- dict(
- type='PackTextDetInputs',
- meta_keys=('img_path', 'ori_shape', 'img_shape'))
-]
-
-test_pipeline = [
- dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
- dict(type='Resize', scale=(4068, 1024), keep_ratio=True),
- dict(
- type='LoadOCRAnnotations',
- with_polygon=True,
- with_bbox=True,
- with_label=True,
- ),
- dict(
- type='PackTextDetInputs',
- meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor',
- 'instances'))
-]
diff --git a/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textdet/rctw_converter.py b/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textdet/rctw_converter.py
deleted file mode 100644
index cc46dd85999c616a89167a56de27ccf2f306ec4a..0000000000000000000000000000000000000000
--- a/spaces/Mountchicken/MAERec-Gradio/tools/dataset_converters/textdet/rctw_converter.py
+++ /dev/null
@@ -1,193 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import argparse
-import math
-import os
-import os.path as osp
-
-import mmcv
-import mmengine
-
-from mmocr.utils import dump_ocr_data
-
-
-def collect_files(img_dir, gt_dir, ratio):
- """Collect all images and their corresponding groundtruth files.
- Args:
- img_dir (str): The image directory
- gt_dir (str): The groundtruth directory
- ratio (float): Split ratio for val set
-
- Returns:
- files (list): The list of tuples (img_file, groundtruth_file)
- """
- assert isinstance(img_dir, str)
- assert img_dir
- assert isinstance(gt_dir, str)
- assert gt_dir
- assert isinstance(ratio, float)
- assert ratio < 1.0, 'val_ratio should be a float between 0.0 to 1.0'
-
- ann_list, imgs_list = [], []
- for ann_file in os.listdir(gt_dir):
- ann_list.append(osp.join(gt_dir, ann_file))
- imgs_list.append(osp.join(img_dir, ann_file.replace('txt', 'jpg')))
-
- all_files = list(zip(imgs_list, ann_list))
- assert len(all_files), f'No images found in {img_dir}'
- print(f'Loaded {len(all_files)} images from {img_dir}')
-
- trn_files, val_files = [], []
- if ratio > 0:
- for i, file in enumerate(all_files):
- if i % math.floor(1 / ratio):
- trn_files.append(file)
- else:
- val_files.append(file)
- else:
- trn_files, val_files = all_files, []
-
- print(f'training #{len(trn_files)}, val #{len(val_files)}')
-
- return trn_files, val_files
-
-
-def collect_annotations(files, nproc=1):
- """Collect the annotation information.
- Args:
- files (list): The list of tuples (image_file, groundtruth_file)
- nproc (int): The number of process to collect annotations
-
- Returns:
- images (list): The list of image information dicts
- """
- assert isinstance(files, list)
- assert isinstance(nproc, int)
-
- if nproc > 1:
- images = mmengine.track_parallel_progress(
- load_img_info, files, nproc=nproc)
- else:
- images = mmengine.track_progress(load_img_info, files)
-
- return images
-
-
-def load_img_info(files):
- """Load the information of one image.
- Args:
- files (tuple): The tuple of (img_file, groundtruth_file)
-
- Returns:
- img_info (dict): The dict of the img and annotation information
- """
- assert isinstance(files, tuple)
-
- img_file, gt_file = files
- assert osp.basename(gt_file).split('.')[0] == osp.basename(img_file).split(
- '.')[0]
- # read imgs while ignoring orientations
- img = mmcv.imread(img_file)
-
- img_info = dict(
- file_name=osp.join(osp.basename(img_file)),
- height=img.shape[0],
- width=img.shape[1],
- segm_file=osp.join(osp.basename(gt_file)))
-
- if osp.splitext(gt_file)[1] == '.txt':
- img_info = load_txt_info(gt_file, img_info)
- else:
- raise NotImplementedError
-
- return img_info
-
-
-def load_txt_info(gt_file, img_info):
- """Collect the annotation information.
-
- The annotation format is as the following:
- x1, y1, x2, y2, x3, y3, x4, y4, difficult, text
-
- 390,902,1856,902,1856,1225,390,1225,0,"金氏眼镜"
- 1875,1170,2149,1170,2149,1245,1875,1245,0,"创于1989"
- 2054,1277,2190,1277,2190,1323,2054,1323,0,"城建店"
-
- Args:
- gt_file (str): The path to ground-truth
- img_info (dict): The dict of the img and annotation information
-
- Returns:
- img_info (dict): The dict of the img and annotation information
- """
-
- anno_info = []
- with open(gt_file, encoding='utf-8-sig') as f:
- lines = f.readlines()
- for line in lines:
- points = line.split(',')[0:8]
- word = line.split(',')[9].rstrip('\n').strip('"')
- difficult = 1 if line.split(',')[8] != '0' else 0
- segmentation = [int(pt) for pt in points]
- x = max(0, min(segmentation[0::2]))
- y = max(0, min(segmentation[1::2]))
- w = abs(max(segmentation[0::2]) - x)
- h = abs(max(segmentation[1::2]) - y)
- bbox = [x, y, w, h]
-
- if word == '###' or difficult == 1:
- iscrowd = 1
- else:
- iscrowd = 0
-
- anno = dict(
- iscrowd=iscrowd,
- category_id=1,
- bbox=bbox,
- area=w * h,
- segmentation=[segmentation])
- anno_info.append(anno)
-
- img_info.update(anno_info=anno_info)
-
- return img_info
-
-
-def parse_args():
- parser = argparse.ArgumentParser(
- description='Generate training and val set of RCTW.')
- parser.add_argument('root_path', help='Root dir path of RCTW')
- parser.add_argument(
- '--val-ratio', help='Split ratio for val set', default=0.0, type=float)
- parser.add_argument(
- '--nproc', default=1, type=int, help='Number of process')
- args = parser.parse_args()
- return args
-
-
-def main():
- args = parse_args()
- root_path = args.root_path
- ratio = args.val_ratio
-
- trn_files, val_files = collect_files(
- osp.join(root_path, 'imgs'), osp.join(root_path, 'annotations'), ratio)
-
- # Train set
- with mmengine.Timer(
- print_tmpl='It takes {}s to convert RCTW Training annotation'):
- trn_infos = collect_annotations(trn_files, nproc=args.nproc)
- dump_ocr_data(trn_infos, osp.join(root_path,
- 'instances_training.json'),
- 'textdet')
-
- # Val set
- if len(val_files) > 0:
- with mmengine.Timer(
- print_tmpl='It takes {}s to convert RCTW Val annotation'):
- val_infos = collect_annotations(val_files, nproc=args.nproc)
- dump_ocr_data(val_infos, osp.join(root_path, 'instances_val.json'),
- 'textdet')
-
-
-if __name__ == '__main__':
- main()
diff --git a/spaces/NCTCMumbai/NCTC/models/official/modeling/activations/__init__.py b/spaces/NCTCMumbai/NCTC/models/official/modeling/activations/__init__.py
deleted file mode 100644
index 2b558fef3cb276c61e58d93c219db6a899c107ef..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/modeling/activations/__init__.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Activations package definition."""
-from official.modeling.activations.gelu import gelu
-from official.modeling.activations.swish import hard_swish
-from official.modeling.activations.swish import identity
-from official.modeling.activations.swish import simple_swish
diff --git a/spaces/NCTCMumbai/NCTC/models/official/utils/misc/keras_utils.py b/spaces/NCTCMumbai/NCTC/models/official/utils/misc/keras_utils.py
deleted file mode 100644
index 2cca51f1d24701802b0fd7cfc62a84306eedded2..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/utils/misc/keras_utils.py
+++ /dev/null
@@ -1,199 +0,0 @@
-# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Helper functions for the Keras implementations of models."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import multiprocessing
-import os
-import time
-
-from absl import logging
-import tensorflow as tf
-
-
-class BatchTimestamp(object):
- """A structure to store batch time stamp."""
-
- def __init__(self, batch_index, timestamp):
- self.batch_index = batch_index
- self.timestamp = timestamp
-
- def __repr__(self):
- return "'BatchTimestamp'".format(
- self.batch_index, self.timestamp)
-
-
-class TimeHistory(tf.keras.callbacks.Callback):
- """Callback for Keras models."""
-
- def __init__(self, batch_size, log_steps, initial_step=0, logdir=None):
- """Callback for logging performance.
-
- Args:
- batch_size: Total batch size.
- log_steps: Interval of steps between logging of batch level stats.
- initial_step: Optional, initial step.
- logdir: Optional directory to write TensorBoard summaries.
- """
- # TODO(wcromar): remove this parameter and rely on `logs` parameter of
- # on_train_batch_end()
- self.batch_size = batch_size
- super(TimeHistory, self).__init__()
- self.log_steps = log_steps
- self.last_log_step = initial_step
- self.steps_before_epoch = initial_step
- self.steps_in_epoch = 0
- self.start_time = None
-
- if logdir:
- self.summary_writer = tf.summary.create_file_writer(logdir)
- else:
- self.summary_writer = None
-
- # Logs start of step 1 then end of each step based on log_steps interval.
- self.timestamp_log = []
-
- # Records the time each epoch takes to run from start to finish of epoch.
- self.epoch_runtime_log = []
-
- @property
- def global_steps(self):
- """The current 1-indexed global step."""
- return self.steps_before_epoch + self.steps_in_epoch
-
- @property
- def average_steps_per_second(self):
- """The average training steps per second across all epochs."""
- return self.global_steps / sum(self.epoch_runtime_log)
-
- @property
- def average_examples_per_second(self):
- """The average number of training examples per second across all epochs."""
- return self.average_steps_per_second * self.batch_size
-
- def get_examples_per_sec(self, warmup=1):
- """Calculates examples/sec through timestamp_log and skip warmup period."""
- # First entry in timestamp_log is the start of the step 1. The rest of the
- # entries are the end of each step recorded.
- time_log = self.timestamp_log
- seconds = time_log[-1].timestamp - time_log[warmup].timestamp
- steps = time_log[-1].batch_index - time_log[warmup].batch_index
- return self.batch_size * steps / seconds
-
- def get_startup_time(self, start_time_sec):
- return self.timestamp_log[0].timestamp - start_time_sec
-
- def on_train_end(self, logs=None):
- self.train_finish_time = time.time()
-
- if self.summary_writer:
- self.summary_writer.flush()
-
- def on_epoch_begin(self, epoch, logs=None):
- self.epoch_start = time.time()
-
- def on_batch_begin(self, batch, logs=None):
- if not self.start_time:
- self.start_time = time.time()
-
- # Record the timestamp of the first global step
- if not self.timestamp_log:
- self.timestamp_log.append(BatchTimestamp(self.global_steps,
- self.start_time))
-
- def on_batch_end(self, batch, logs=None):
- """Records elapse time of the batch and calculates examples per second."""
- self.steps_in_epoch = batch + 1
- steps_since_last_log = self.global_steps - self.last_log_step
- if steps_since_last_log >= self.log_steps:
- now = time.time()
- elapsed_time = now - self.start_time
- steps_per_second = steps_since_last_log / elapsed_time
- examples_per_second = steps_per_second * self.batch_size
-
- self.timestamp_log.append(BatchTimestamp(self.global_steps, now))
- logging.info(
- 'TimeHistory: %.2f seconds, %.2f examples/second between steps %d '
- 'and %d', elapsed_time, examples_per_second, self.last_log_step,
- self.global_steps)
-
- if self.summary_writer:
- with self.summary_writer.as_default():
- tf.summary.scalar('steps_per_second', steps_per_second,
- self.global_steps)
- tf.summary.scalar('examples_per_second', examples_per_second,
- self.global_steps)
-
- self.last_log_step = self.global_steps
- self.start_time = None
-
- def on_epoch_end(self, epoch, logs=None):
- epoch_run_time = time.time() - self.epoch_start
- self.epoch_runtime_log.append(epoch_run_time)
-
- self.steps_before_epoch += self.steps_in_epoch
- self.steps_in_epoch = 0
-
-
-class SimpleCheckpoint(tf.keras.callbacks.Callback):
- """Keras callback to save tf.train.Checkpoints."""
-
- def __init__(self, checkpoint_manager):
- super(SimpleCheckpoint, self).__init__()
- self.checkpoint_manager = checkpoint_manager
-
- def on_epoch_end(self, epoch, logs=None):
- step_counter = self.checkpoint_manager._step_counter.numpy() # pylint: disable=protected-access
- self.checkpoint_manager.save(checkpoint_number=step_counter)
-
-
-def set_session_config(enable_xla=False):
- """Sets the session config."""
- if enable_xla:
- tf.config.optimizer.set_jit(True)
-
-# TODO(hongkuny): remove set_config_v2 globally.
-set_config_v2 = set_session_config
-
-
-def set_gpu_thread_mode_and_count(gpu_thread_mode,
- datasets_num_private_threads,
- num_gpus, per_gpu_thread_count):
- """Set GPU thread mode and count, and adjust dataset threads count."""
- cpu_count = multiprocessing.cpu_count()
- logging.info('Logical CPU cores: %s', cpu_count)
-
- # Allocate private thread pool for each GPU to schedule and launch kernels
- per_gpu_thread_count = per_gpu_thread_count or 2
- os.environ['TF_GPU_THREAD_MODE'] = gpu_thread_mode
- os.environ['TF_GPU_THREAD_COUNT'] = str(per_gpu_thread_count)
- logging.info('TF_GPU_THREAD_COUNT: %s',
- os.environ['TF_GPU_THREAD_COUNT'])
- logging.info('TF_GPU_THREAD_MODE: %s',
- os.environ['TF_GPU_THREAD_MODE'])
-
- # Limit data preprocessing threadpool to CPU cores minus number of total GPU
- # private threads and memory copy threads.
- total_gpu_thread_count = per_gpu_thread_count * num_gpus
- num_runtime_threads = num_gpus
- if not datasets_num_private_threads:
- datasets_num_private_threads = min(
- cpu_count - total_gpu_thread_count - num_runtime_threads,
- num_gpus * 8)
- logging.info('Set datasets_num_private_threads to %s',
- datasets_num_private_threads)
diff --git a/spaces/NCTCMumbai/NCTC/models/research/cognitive_planning/policies.py b/spaces/NCTCMumbai/NCTC/models/research/cognitive_planning/policies.py
deleted file mode 100644
index 5c7e2207db1302c3fd1d8bff3e30eaba022480fd..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/research/cognitive_planning/policies.py
+++ /dev/null
@@ -1,474 +0,0 @@
-# Copyright 2018 The TensorFlow Authors All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""Interface for the policy of the agents use for navigation."""
-
-import abc
-import tensorflow as tf
-from absl import logging
-import embedders
-from envs import task_env
-
-slim = tf.contrib.slim
-
-def _print_debug_ios(history, goal, output):
- """Prints sizes of history, goal and outputs."""
- if history is not None:
- shape = history.get_shape().as_list()
- # logging.info('history embedding shape ')
- # logging.info(shape)
- if len(shape) != 3:
- raise ValueError('history Tensor must have rank=3')
- if goal is not None:
- logging.info('goal embedding shape ')
- logging.info(goal.get_shape().as_list())
- if output is not None:
- logging.info('targets shape ')
- logging.info(output.get_shape().as_list())
-
-
-class Policy(object):
- """Represents the policy of the agent for navigation tasks.
-
- Instantiates a policy that takes embedders for each modality and builds a
- model to infer the actions.
- """
- __metaclass__ = abc.ABCMeta
-
- def __init__(self, embedders_dict, action_size):
- """Instantiates the policy.
-
- Args:
- embedders_dict: Dictionary of embedders for different modalities. Keys
- should be identical to keys of observation modality.
- action_size: Number of possible actions.
- """
- self._embedders = embedders_dict
- self._action_size = action_size
-
- @abc.abstractmethod
- def build(self, observations, prev_state):
- """Builds the model that represents the policy of the agent.
-
- Args:
- observations: Dictionary of observations from different modalities. Keys
- are the name of the modalities.
- prev_state: The tensor of the previous state of the model. Should be set
- to None if the policy is stateless
- Returns:
- Tuple of (action, state) where action is the action logits and state is
- the state of the model after taking new observation.
- """
- raise NotImplementedError(
- 'Needs implementation as part of Policy interface')
-
-
-class LSTMPolicy(Policy):
- """Represents the implementation of the LSTM based policy.
-
- The architecture of the model is as follows. It embeds all the observations
- using the embedders, concatenates the embeddings of all the modalities. Feed
- them through two fully connected layers. The lstm takes the features from
- fully connected layer and the previous action and success of previous action
- and feed them to LSTM. The value for each action is predicted afterwards.
- Although the class name has the word LSTM in it, it also supports a mode that
- builds the network without LSTM just for comparison purposes.
- """
-
- def __init__(self,
- modality_names,
- embedders_dict,
- action_size,
- params,
- max_episode_length,
- feedforward_mode=False):
- """Instantiates the LSTM policy.
-
- Args:
- modality_names: List of modality names. Makes sure the ordering in
- concatenation remains the same as modality_names list. Each modality
- needs to be in the embedders_dict.
- embedders_dict: Dictionary of embedders for different modalities. Keys
- should be identical to keys of observation modality. Values should be
- instance of Embedder class. All the observations except PREV_ACTION
- requires embedder.
- action_size: Number of possible actions.
- params: is instance of tf.hparams and contains the hyperparameters for the
- policy network.
- max_episode_length: integer, specifying the maximum length of each
- episode.
- feedforward_mode: If True, it does not add LSTM to the model. It should
- only be set True for comparison between LSTM and feedforward models.
- """
- super(LSTMPolicy, self).__init__(embedders_dict, action_size)
-
- self._modality_names = modality_names
-
- self._lstm_state_size = params.lstm_state_size
- self._fc_channels = params.fc_channels
- self._weight_decay = params.weight_decay
- self._target_embedding_size = params.target_embedding_size
- self._max_episode_length = max_episode_length
- self._feedforward_mode = feedforward_mode
-
- def _build_lstm(self, encoded_inputs, prev_state, episode_length,
- prev_action=None):
- """Builds an LSTM on top of the encoded inputs.
-
- If prev_action is not None then it concatenates them to the input of LSTM.
-
- Args:
- encoded_inputs: The embedding of the observations and goal.
- prev_state: previous state of LSTM.
- episode_length: The tensor that contains the length of the sequence for
- each element of the batch.
- prev_action: tensor to previous chosen action and additional bit for
- indicating whether the previous action was successful or not.
-
- Returns:
- a tuple of (lstm output, lstm state).
- """
-
- # Adding prev action and success in addition to the embeddings of the
- # modalities.
- if prev_action is not None:
- encoded_inputs = tf.concat([encoded_inputs, prev_action], axis=-1)
-
- with tf.variable_scope('LSTM'):
- lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(self._lstm_state_size)
- if prev_state is None:
- # If prev state is set to None, a state of all zeros will be
- # passed as a previous value for the cell. Should be used for the
- # first step of each episode.
- tf_prev_state = lstm_cell.zero_state(
- encoded_inputs.get_shape().as_list()[0], dtype=tf.float32)
- else:
- tf_prev_state = tf.nn.rnn_cell.LSTMStateTuple(prev_state[0],
- prev_state[1])
-
- lstm_outputs, lstm_state = tf.nn.dynamic_rnn(
- cell=lstm_cell,
- inputs=encoded_inputs,
- sequence_length=episode_length,
- initial_state=tf_prev_state,
- dtype=tf.float32,
- )
- lstm_outputs = tf.reshape(lstm_outputs, [-1, lstm_cell.output_size])
- return lstm_outputs, lstm_state
-
- def build(
- self,
- observations,
- prev_state,
- ):
- """Builds the model that represents the policy of the agent.
-
- Args:
- observations: Dictionary of observations from different modalities. Keys
- are the name of the modalities. Observation should have the following
- key-values.
- observations['goal']: One-hot tensor that indicates the semantic
- category of the goal. The shape should be
- (batch_size x max_sequence_length x goals).
- observations[task_env.ModalityTypes.PREV_ACTION]: has action_size + 1
- elements where the first action_size numbers are the one hot vector
- of the previous action and the last element indicates whether the
- previous action was successful or not. If
- task_env.ModalityTypes.PREV_ACTION is not in the observation, it
- will not be used in the policy.
- prev_state: Previous state of the model. It should be a tuple of (c,h)
- where c and h are the previous cell value and hidden state of the lstm.
- Each element of tuple has shape of (batch_size x lstm_cell_size).
- If it is set to None, then it initializes the state of the lstm with all
- zeros.
-
- Returns:
- Tuple of (action, state) where action is the action logits and state is
- the state of the model after taking new observation.
- Raises:
- ValueError: If any of the modality names is not in observations or
- embedders_dict.
- ValueError: If 'goal' is not in the observations.
- """
-
- for modality_name in self._modality_names:
- if modality_name not in observations:
- raise ValueError('modality name does not exist in observations: {} not '
- 'in {}'.format(modality_name, observations.keys()))
- if modality_name not in self._embedders:
- if modality_name == task_env.ModalityTypes.PREV_ACTION:
- continue
- raise ValueError('modality name does not have corresponding embedder'
- ' {} not in {}'.format(modality_name,
- self._embedders.keys()))
-
- if task_env.ModalityTypes.GOAL not in observations:
- raise ValueError('goal should be provided in the observations')
-
- goal = observations[task_env.ModalityTypes.GOAL]
- prev_action = None
- if task_env.ModalityTypes.PREV_ACTION in observations:
- prev_action = observations[task_env.ModalityTypes.PREV_ACTION]
-
- with tf.variable_scope('policy'):
- with slim.arg_scope(
- [slim.fully_connected],
- activation_fn=tf.nn.relu,
- weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
- weights_regularizer=slim.l2_regularizer(self._weight_decay)):
- all_inputs = []
-
- # Concatenating the embedding of each modality by applying the embedders
- # to corresponding observations.
- def embed(name):
- with tf.variable_scope('embed_{}'.format(name)):
- # logging.info('Policy uses embedding %s', name)
- return self._embedders[name].build(observations[name])
-
- all_inputs = map(embed, [
- x for x in self._modality_names
- if x != task_env.ModalityTypes.PREV_ACTION
- ])
-
- # Computing goal embedding.
- shape = goal.get_shape().as_list()
- with tf.variable_scope('embed_goal'):
- encoded_goal = tf.reshape(goal, [shape[0] * shape[1], -1])
- encoded_goal = slim.fully_connected(encoded_goal,
- self._target_embedding_size)
- encoded_goal = tf.reshape(encoded_goal, [shape[0], shape[1], -1])
- all_inputs.append(encoded_goal)
-
- # Concatenating all the modalities and goal.
- all_inputs = tf.concat(all_inputs, axis=-1, name='concat_embeddings')
-
- shape = all_inputs.get_shape().as_list()
- all_inputs = tf.reshape(all_inputs, [shape[0] * shape[1], shape[2]])
-
- # Applying fully connected layers.
- encoded_inputs = slim.fully_connected(all_inputs, self._fc_channels)
- encoded_inputs = slim.fully_connected(encoded_inputs, self._fc_channels)
-
- if not self._feedforward_mode:
- encoded_inputs = tf.reshape(encoded_inputs,
- [shape[0], shape[1], self._fc_channels])
- lstm_outputs, lstm_state = self._build_lstm(
- encoded_inputs=encoded_inputs,
- prev_state=prev_state,
- episode_length=tf.ones((shape[0],), dtype=tf.float32) *
- self._max_episode_length,
- prev_action=prev_action,
- )
- else:
- # If feedforward_mode=True, directly compute bypass the whole LSTM
- # computations.
- lstm_outputs = encoded_inputs
-
- lstm_outputs = slim.fully_connected(lstm_outputs, self._fc_channels)
- action_values = slim.fully_connected(
- lstm_outputs, self._action_size, activation_fn=None)
- action_values = tf.reshape(action_values, [shape[0], shape[1], -1])
- if not self._feedforward_mode:
- return action_values, lstm_state
- else:
- return action_values, None
-
-
-class TaskPolicy(Policy):
- """A covenience abstract class providing functionality to deal with Tasks."""
-
- def __init__(self,
- task_config,
- model_hparams=None,
- embedder_hparams=None,
- train_hparams=None):
- """Constructs a policy which knows how to work with tasks (see tasks.py).
-
- It allows to read task history, goal and outputs in consistency with the
- task config.
-
- Args:
- task_config: an object of type tasks.TaskIOConfig (see tasks.py)
- model_hparams: a tf.HParams object containing parameter pertaining to
- model (these are implementation specific)
- embedder_hparams: a tf.HParams object containing parameter pertaining to
- history, goal embedders (these are implementation specific)
- train_hparams: a tf.HParams object containing parameter pertaining to
- trainin (these are implementation specific)`
- """
- super(TaskPolicy, self).__init__(None, None)
- self._model_hparams = model_hparams
- self._embedder_hparams = embedder_hparams
- self._train_hparams = train_hparams
- self._task_config = task_config
- self._extra_train_ops = []
-
- @property
- def extra_train_ops(self):
- """Training ops in addition to the loss, e.g. batch norm updates.
-
- Returns:
- A list of tf ops.
- """
- return self._extra_train_ops
-
- def _embed_task_ios(self, streams):
- """Embeds a list of heterogenous streams.
-
- These streams correspond to task history, goal and output. The number of
- streams is equal to the total number of history, plus one for the goal if
- present, plus one for the output. If the number of history is k, then the
- first k streams are the history.
-
- The used embedders depend on the input (or goal) types. If an input is an
- image, then a ResNet embedder is used, otherwise
- MLPEmbedder (see embedders.py).
-
- Args:
- streams: a list of Tensors.
- Returns:
- Three float Tensors history, goal, output. If there are no history, or no
- goal, then the corresponding returned values are None. The shape of the
- embedded history is batch_size x sequence_length x sum of all embedding
- dimensions for all history. The shape of the goal is embedding dimension.
- """
- # EMBED history.
- index = 0
- inps = []
- scopes = []
- for c in self._task_config.inputs:
- if c == task_env.ModalityTypes.IMAGE:
- scope_name = 'image_embedder/image'
- reuse = scope_name in scopes
- scopes.append(scope_name)
- with tf.variable_scope(scope_name, reuse=reuse):
- resnet_embedder = embedders.ResNet(self._embedder_hparams.image)
- image_embeddings = resnet_embedder.build(streams[index])
- # Uncover batch norm ops.
- if self._embedder_hparams.image.is_train:
- self._extra_train_ops += resnet_embedder.extra_train_ops
- inps.append(image_embeddings)
- index += 1
- else:
- scope_name = 'input_embedder/vector'
- reuse = scope_name in scopes
- scopes.append(scope_name)
- with tf.variable_scope(scope_name, reuse=reuse):
- input_vector_embedder = embedders.MLPEmbedder(
- layers=self._embedder_hparams.vector)
- vector_embedder = input_vector_embedder.build(streams[index])
- inps.append(vector_embedder)
- index += 1
- history = tf.concat(inps, axis=2) if inps else None
-
- # EMBED goal.
- goal = None
- if self._task_config.query is not None:
- scope_name = 'image_embedder/query'
- reuse = scope_name in scopes
- scopes.append(scope_name)
- with tf.variable_scope(scope_name, reuse=reuse):
- resnet_goal_embedder = embedders.ResNet(self._embedder_hparams.goal)
- goal = resnet_goal_embedder.build(streams[index])
- if self._embedder_hparams.goal.is_train:
- self._extra_train_ops += resnet_goal_embedder.extra_train_ops
- index += 1
-
- # Embed true targets if needed (tbd).
- true_target = streams[index]
-
- return history, goal, true_target
-
- @abc.abstractmethod
- def build(self, feeds, prev_state):
- pass
-
-
-class ReactivePolicy(TaskPolicy):
- """A policy which ignores history.
-
- It processes only the current observation (last element in history) and the
- goal to output a prediction.
- """
-
- def __init__(self, *args, **kwargs):
- super(ReactivePolicy, self).__init__(*args, **kwargs)
-
- # The current implementation ignores the prev_state as it is purely reactive.
- # It returns None for the current state.
- def build(self, feeds, prev_state):
- history, goal, _ = self._embed_task_ios(feeds)
- _print_debug_ios(history, goal, None)
-
- with tf.variable_scope('output_decoder'):
- # Concatenate the embeddings of the current observation and the goal.
- reactive_input = tf.concat([tf.squeeze(history[:, -1, :]), goal], axis=1)
- oconfig = self._task_config.output.shape
- assert len(oconfig) == 1
- decoder = embedders.MLPEmbedder(
- layers=self._embedder_hparams.predictions.layer_sizes + oconfig)
- predictions = decoder.build(reactive_input)
-
- return predictions, None
-
-
-class RNNPolicy(TaskPolicy):
- """A policy which takes into account the full history via RNN.
-
- The implementation might and will change.
- The history, together with the goal, is processed using a stacked LSTM. The
- output of the last LSTM step is used to produce a prediction. Currently, only
- a single step output is supported.
- """
-
- def __init__(self, lstm_hparams, *args, **kwargs):
- super(RNNPolicy, self).__init__(*args, **kwargs)
- self._lstm_hparams = lstm_hparams
-
- # The prev_state is ignored as for now the full history is specified as first
- # element of the feeds. It might turn out to be beneficial to keep the state
- # as part of the policy object.
- def build(self, feeds, state):
- history, goal, _ = self._embed_task_ios(feeds)
- _print_debug_ios(history, goal, None)
-
- params = self._lstm_hparams
- cell = lambda: tf.contrib.rnn.BasicLSTMCell(params.cell_size)
- stacked_lstm = tf.contrib.rnn.MultiRNNCell(
- [cell() for _ in range(params.num_layers)])
- # history is of shape batch_size x seq_len x embedding_dimension
- batch_size, seq_len, _ = tuple(history.get_shape().as_list())
-
- if state is None:
- state = stacked_lstm.zero_state(batch_size, tf.float32)
- for t in range(seq_len):
- if params.concat_goal_everywhere:
- lstm_input = tf.concat([tf.squeeze(history[:, t, :]), goal], axis=1)
- else:
- lstm_input = tf.squeeze(history[:, t, :])
- output, state = stacked_lstm(lstm_input, state)
-
- with tf.variable_scope('output_decoder'):
- oconfig = self._task_config.output.shape
- assert len(oconfig) == 1
- features = tf.concat([output, goal], axis=1)
- assert len(output.get_shape().as_list()) == 2
- assert len(goal.get_shape().as_list()) == 2
- decoder = embedders.MLPEmbedder(
- layers=self._embedder_hparams.predictions.layer_sizes + oconfig)
- # Prediction is done off the last step lstm output and the goal.
- predictions = decoder.build(features)
-
- return predictions, state
diff --git a/spaces/NSect/VALL-E-X/modules/__init__.py b/spaces/NSect/VALL-E-X/modules/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Naveen618/mygenAIAvatharSpeech/README.md b/spaces/Naveen618/mygenAIAvatharSpeech/README.md
deleted file mode 100644
index 0c2236ed3e6dfcf8b624766bc2a7631d76bab71b..0000000000000000000000000000000000000000
--- a/spaces/Naveen618/mygenAIAvatharSpeech/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MygenAIAvatharSpeech
-emoji: 📉
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Nunchakuka/FrenchAnonymizer/speaker_encoder/model.py b/spaces/Nunchakuka/FrenchAnonymizer/speaker_encoder/model.py
deleted file mode 100644
index c022b663ee5c344c52041026bc88dc02734afa33..0000000000000000000000000000000000000000
--- a/spaces/Nunchakuka/FrenchAnonymizer/speaker_encoder/model.py
+++ /dev/null
@@ -1,135 +0,0 @@
-from speaker_encoder.params_model import *
-from speaker_encoder.params_data import *
-from scipy.interpolate import interp1d
-from sklearn.metrics import roc_curve
-from torch.nn.utils import clip_grad_norm_
-from scipy.optimize import brentq
-from torch import nn
-import numpy as np
-import torch
-
-
-class SpeakerEncoder(nn.Module):
- def __init__(self, device, loss_device):
- super().__init__()
- self.loss_device = loss_device
-
- # Network defition
- self.lstm = nn.LSTM(input_size=mel_n_channels, # 40
- hidden_size=model_hidden_size, # 256
- num_layers=model_num_layers, # 3
- batch_first=True).to(device)
- self.linear = nn.Linear(in_features=model_hidden_size,
- out_features=model_embedding_size).to(device)
- self.relu = torch.nn.ReLU().to(device)
-
- # Cosine similarity scaling (with fixed initial parameter values)
- self.similarity_weight = nn.Parameter(torch.tensor([10.])).to(loss_device)
- self.similarity_bias = nn.Parameter(torch.tensor([-5.])).to(loss_device)
-
- # Loss
- self.loss_fn = nn.CrossEntropyLoss().to(loss_device)
-
- def do_gradient_ops(self):
- # Gradient scale
- self.similarity_weight.grad *= 0.01
- self.similarity_bias.grad *= 0.01
-
- # Gradient clipping
- clip_grad_norm_(self.parameters(), 3, norm_type=2)
-
- def forward(self, utterances, hidden_init=None):
- """
- Computes the embeddings of a batch of utterance spectrograms.
-
- :param utterances: batch of mel-scale filterbanks of same duration as a tensor of shape
- (batch_size, n_frames, n_channels)
- :param hidden_init: initial hidden state of the LSTM as a tensor of shape (num_layers,
- batch_size, hidden_size). Will default to a tensor of zeros if None.
- :return: the embeddings as a tensor of shape (batch_size, embedding_size)
- """
- # Pass the input through the LSTM layers and retrieve all outputs, the final hidden state
- # and the final cell state.
- out, (hidden, cell) = self.lstm(utterances, hidden_init)
-
- # We take only the hidden state of the last layer
- embeds_raw = self.relu(self.linear(hidden[-1]))
-
- # L2-normalize it
- embeds = embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
-
- return embeds
-
- def similarity_matrix(self, embeds):
- """
- Computes the similarity matrix according the section 2.1 of GE2E.
-
- :param embeds: the embeddings as a tensor of shape (speakers_per_batch,
- utterances_per_speaker, embedding_size)
- :return: the similarity matrix as a tensor of shape (speakers_per_batch,
- utterances_per_speaker, speakers_per_batch)
- """
- speakers_per_batch, utterances_per_speaker = embeds.shape[:2]
-
- # Inclusive centroids (1 per speaker). Cloning is needed for reverse differentiation
- centroids_incl = torch.mean(embeds, dim=1, keepdim=True)
- centroids_incl = centroids_incl.clone() / torch.norm(centroids_incl, dim=2, keepdim=True)
-
- # Exclusive centroids (1 per utterance)
- centroids_excl = (torch.sum(embeds, dim=1, keepdim=True) - embeds)
- centroids_excl /= (utterances_per_speaker - 1)
- centroids_excl = centroids_excl.clone() / torch.norm(centroids_excl, dim=2, keepdim=True)
-
- # Similarity matrix. The cosine similarity of already 2-normed vectors is simply the dot
- # product of these vectors (which is just an element-wise multiplication reduced by a sum).
- # We vectorize the computation for efficiency.
- sim_matrix = torch.zeros(speakers_per_batch, utterances_per_speaker,
- speakers_per_batch).to(self.loss_device)
- mask_matrix = 1 - np.eye(speakers_per_batch, dtype=np.int)
- for j in range(speakers_per_batch):
- mask = np.where(mask_matrix[j])[0]
- sim_matrix[mask, :, j] = (embeds[mask] * centroids_incl[j]).sum(dim=2)
- sim_matrix[j, :, j] = (embeds[j] * centroids_excl[j]).sum(dim=1)
-
- ## Even more vectorized version (slower maybe because of transpose)
- # sim_matrix2 = torch.zeros(speakers_per_batch, speakers_per_batch, utterances_per_speaker
- # ).to(self.loss_device)
- # eye = np.eye(speakers_per_batch, dtype=np.int)
- # mask = np.where(1 - eye)
- # sim_matrix2[mask] = (embeds[mask[0]] * centroids_incl[mask[1]]).sum(dim=2)
- # mask = np.where(eye)
- # sim_matrix2[mask] = (embeds * centroids_excl).sum(dim=2)
- # sim_matrix2 = sim_matrix2.transpose(1, 2)
-
- sim_matrix = sim_matrix * self.similarity_weight + self.similarity_bias
- return sim_matrix
-
- def loss(self, embeds):
- """
- Computes the softmax loss according the section 2.1 of GE2E.
-
- :param embeds: the embeddings as a tensor of shape (speakers_per_batch,
- utterances_per_speaker, embedding_size)
- :return: the loss and the EER for this batch of embeddings.
- """
- speakers_per_batch, utterances_per_speaker = embeds.shape[:2]
-
- # Loss
- sim_matrix = self.similarity_matrix(embeds)
- sim_matrix = sim_matrix.reshape((speakers_per_batch * utterances_per_speaker,
- speakers_per_batch))
- ground_truth = np.repeat(np.arange(speakers_per_batch), utterances_per_speaker)
- target = torch.from_numpy(ground_truth).long().to(self.loss_device)
- loss = self.loss_fn(sim_matrix, target)
-
- # EER (not backpropagated)
- with torch.no_grad():
- inv_argmax = lambda i: np.eye(1, speakers_per_batch, i, dtype=np.int)[0]
- labels = np.array([inv_argmax(i) for i in ground_truth])
- preds = sim_matrix.detach().cpu().numpy()
-
- # Snippet from https://yangcha.github.io/EER-ROC/
- fpr, tpr, thresholds = roc_curve(labels.flatten(), preds.flatten())
- eer = brentq(lambda x: 1. - x - interp1d(fpr, tpr)(x), 0., 1.)
-
- return loss, eer
\ No newline at end of file
diff --git a/spaces/OAOA/DifFace/facelib/detection/__init__.py b/spaces/OAOA/DifFace/facelib/detection/__init__.py
deleted file mode 100644
index 296262d4e2e29eaa2afba7bda1f0399d77da24f6..0000000000000000000000000000000000000000
--- a/spaces/OAOA/DifFace/facelib/detection/__init__.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import os
-import torch
-from torch import nn
-from copy import deepcopy
-
-from facelib.utils import load_file_from_url
-from facelib.utils import download_pretrained_models
-from facelib.detection.yolov5face.models.common import Conv
-
-from .retinaface.retinaface import RetinaFace
-from .yolov5face.face_detector import YoloDetector
-
-
-def init_detection_model(model_name, half=False, device='cuda'):
- if 'retinaface' in model_name:
- model = init_retinaface_model(model_name, half, device)
- elif 'YOLOv5' in model_name:
- model = init_yolov5face_model(model_name, device)
- else:
- raise NotImplementedError(f'{model_name} is not implemented.')
-
- return model
-
-
-def init_retinaface_model(model_name, half=False, device='cuda'):
- if model_name == 'retinaface_resnet50':
- model = RetinaFace(network_name='resnet50', half=half)
- model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth'
- elif model_name == 'retinaface_mobile0.25':
- model = RetinaFace(network_name='mobile0.25', half=half)
- model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_mobilenet0.25_Final.pth'
- else:
- raise NotImplementedError(f'{model_name} is not implemented.')
-
- model_path = load_file_from_url(url=model_url, model_dir='weights/facelib', progress=True, file_name=None)
- load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
- # remove unnecessary 'module.'
- for k, v in deepcopy(load_net).items():
- if k.startswith('module.'):
- load_net[k[7:]] = v
- load_net.pop(k)
- model.load_state_dict(load_net, strict=True)
- model.eval()
- model = model.to(device)
-
- return model
-
-
-def init_yolov5face_model(model_name, device='cuda'):
- if model_name == 'YOLOv5l':
- model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5l.yaml', device=device)
- model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5l-face.pth'
- elif model_name == 'YOLOv5n':
- model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5n.yaml', device=device)
- model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/yolov5n-face.pth'
- else:
- raise NotImplementedError(f'{model_name} is not implemented.')
-
- model_path = load_file_from_url(url=model_url, model_dir='weights/facelib', progress=True, file_name=None)
- load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
- model.detector.load_state_dict(load_net, strict=True)
- model.detector.eval()
- model.detector = model.detector.to(device).float()
-
- for m in model.detector.modules():
- if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
- m.inplace = True # pytorch 1.7.0 compatibility
- elif isinstance(m, Conv):
- m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
-
- return model
-
-
-# Download from Google Drive
-# def init_yolov5face_model(model_name, device='cuda'):
-# if model_name == 'YOLOv5l':
-# model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5l.yaml', device=device)
-# f_id = {'yolov5l-face.pth': '131578zMA6B2x8VQHyHfa6GEPtulMCNzV'}
-# elif model_name == 'YOLOv5n':
-# model = YoloDetector(config_name='facelib/detection/yolov5face/models/yolov5n.yaml', device=device)
-# f_id = {'yolov5n-face.pth': '1fhcpFvWZqghpGXjYPIne2sw1Fy4yhw6o'}
-# else:
-# raise NotImplementedError(f'{model_name} is not implemented.')
-
-# model_path = os.path.join('weights/facelib', list(f_id.keys())[0])
-# if not os.path.exists(model_path):
-# download_pretrained_models(file_ids=f_id, save_path_root='weights/facelib')
-
-# load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
-# model.detector.load_state_dict(load_net, strict=True)
-# model.detector.eval()
-# model.detector = model.detector.to(device).float()
-
-# for m in model.detector.modules():
-# if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
-# m.inplace = True # pytorch 1.7.0 compatibility
-# elif isinstance(m, Conv):
-# m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
-
-# return model
\ No newline at end of file
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/character_token_embedder.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/character_token_embedder.py
deleted file mode 100644
index 181221b61b9f76453b67e3b848b198620dce912c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/modules/character_token_embedder.py
+++ /dev/null
@@ -1,214 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-from typing import List, Tuple
-
-import torch
-import torch.nn.functional as F
-from fairseq.data import Dictionary
-from torch import nn
-
-
-CHAR_PAD_IDX = 0
-CHAR_EOS_IDX = 257
-
-
-logger = logging.getLogger(__name__)
-
-
-class CharacterTokenEmbedder(torch.nn.Module):
- def __init__(
- self,
- vocab: Dictionary,
- filters: List[Tuple[int, int]],
- char_embed_dim: int,
- word_embed_dim: int,
- highway_layers: int,
- max_char_len: int = 50,
- char_inputs: bool = False,
- ):
- super(CharacterTokenEmbedder, self).__init__()
-
- self.onnx_trace = False
- self.embedding_dim = word_embed_dim
- self.max_char_len = max_char_len
- self.char_embeddings = nn.Embedding(257, char_embed_dim, padding_idx=0)
- self.symbol_embeddings = nn.Parameter(torch.FloatTensor(2, word_embed_dim))
- self.eos_idx, self.unk_idx = 0, 1
- self.char_inputs = char_inputs
-
- self.convolutions = nn.ModuleList()
- for width, out_c in filters:
- self.convolutions.append(
- nn.Conv1d(char_embed_dim, out_c, kernel_size=width)
- )
-
- last_dim = sum(f[1] for f in filters)
-
- self.highway = Highway(last_dim, highway_layers) if highway_layers > 0 else None
-
- self.projection = nn.Linear(last_dim, word_embed_dim)
-
- assert (
- vocab is not None or char_inputs
- ), "vocab must be set if not using char inputs"
- self.vocab = None
- if vocab is not None:
- self.set_vocab(vocab, max_char_len)
-
- self.reset_parameters()
-
- def prepare_for_onnx_export_(self):
- self.onnx_trace = True
-
- def set_vocab(self, vocab, max_char_len):
- word_to_char = torch.LongTensor(len(vocab), max_char_len)
-
- truncated = 0
- for i in range(len(vocab)):
- if i < vocab.nspecial:
- char_idxs = [0] * max_char_len
- else:
- chars = vocab[i].encode()
- # +1 for padding
- char_idxs = [c + 1 for c in chars] + [0] * (max_char_len - len(chars))
- if len(char_idxs) > max_char_len:
- truncated += 1
- char_idxs = char_idxs[:max_char_len]
- word_to_char[i] = torch.LongTensor(char_idxs)
-
- if truncated > 0:
- logger.info(
- "truncated {} words longer than {} characters".format(
- truncated, max_char_len
- )
- )
-
- self.vocab = vocab
- self.word_to_char = word_to_char
-
- @property
- def padding_idx(self):
- return Dictionary().pad() if self.vocab is None else self.vocab.pad()
-
- def reset_parameters(self):
- nn.init.xavier_normal_(self.char_embeddings.weight)
- nn.init.xavier_normal_(self.symbol_embeddings)
- nn.init.xavier_uniform_(self.projection.weight)
-
- nn.init.constant_(
- self.char_embeddings.weight[self.char_embeddings.padding_idx], 0.0
- )
- nn.init.constant_(self.projection.bias, 0.0)
-
- def forward(
- self,
- input: torch.Tensor,
- ):
- if self.char_inputs:
- chars = input.view(-1, self.max_char_len)
- pads = chars[:, 0].eq(CHAR_PAD_IDX)
- eos = chars[:, 0].eq(CHAR_EOS_IDX)
- if eos.any():
- if self.onnx_trace:
- chars = torch.where(eos.unsqueeze(1), chars.new_zeros(1), chars)
- else:
- chars[eos] = 0
-
- unk = None
- else:
- flat_words = input.view(-1)
- chars = self.word_to_char[flat_words.type_as(self.word_to_char)].type_as(
- input
- )
- pads = flat_words.eq(self.vocab.pad())
- eos = flat_words.eq(self.vocab.eos())
- unk = flat_words.eq(self.vocab.unk())
-
- word_embs = self._convolve(chars)
- if self.onnx_trace:
- if pads.any():
- word_embs = torch.where(
- pads.unsqueeze(1), word_embs.new_zeros(1), word_embs
- )
- if eos.any():
- word_embs = torch.where(
- eos.unsqueeze(1), self.symbol_embeddings[self.eos_idx], word_embs
- )
- if unk is not None and unk.any():
- word_embs = torch.where(
- unk.unsqueeze(1), self.symbol_embeddings[self.unk_idx], word_embs
- )
- else:
- if pads.any():
- word_embs[pads] = 0
- if eos.any():
- word_embs[eos] = self.symbol_embeddings[self.eos_idx]
- if unk is not None and unk.any():
- word_embs[unk] = self.symbol_embeddings[self.unk_idx]
-
- return word_embs.view(input.size()[:2] + (-1,))
-
- def _convolve(
- self,
- char_idxs: torch.Tensor,
- ):
- char_embs = self.char_embeddings(char_idxs)
- char_embs = char_embs.transpose(1, 2) # BTC -> BCT
-
- conv_result = []
-
- for conv in self.convolutions:
- x = conv(char_embs)
- x, _ = torch.max(x, -1)
- x = F.relu(x)
- conv_result.append(x)
-
- x = torch.cat(conv_result, dim=-1)
-
- if self.highway is not None:
- x = self.highway(x)
- x = self.projection(x)
-
- return x
-
-
-class Highway(torch.nn.Module):
- """
- A `Highway layer `_.
- Adopted from the AllenNLP implementation.
- """
-
- def __init__(self, input_dim: int, num_layers: int = 1):
- super(Highway, self).__init__()
- self.input_dim = input_dim
- self.layers = nn.ModuleList(
- [nn.Linear(input_dim, input_dim * 2) for _ in range(num_layers)]
- )
- self.activation = nn.ReLU()
-
- self.reset_parameters()
-
- def reset_parameters(self):
- for layer in self.layers:
- # As per comment in AllenNLP:
- # We should bias the highway layer to just carry its input forward. We do that by
- # setting the bias on `B(x)` to be positive, because that means `g` will be biased to
- # be high, so we will carry the input forward. The bias on `B(x)` is the second half
- # of the bias vector in each Linear layer.
- nn.init.constant_(layer.bias[self.input_dim :], 1)
-
- nn.init.constant_(layer.bias[: self.input_dim], 0)
- nn.init.xavier_normal_(layer.weight)
-
- def forward(self, x: torch.Tensor):
- for layer in self.layers:
- projection = layer(x)
- proj_x, gate = projection.chunk(2, dim=-1)
- proj_x = self.activation(proj_x)
- gate = torch.sigmoid(gate)
- x = gate * x + (gate.new_tensor([1]) - gate) * proj_x
- return x
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/scripts/rm_pt.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/scripts/rm_pt.py
deleted file mode 100644
index 6cd063d21f0610fa7c42c2cfb2ee8af7c9c78677..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/scripts/rm_pt.py
+++ /dev/null
@@ -1,141 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import os
-import re
-import shutil
-import sys
-
-
-pt_regexp = re.compile(r"checkpoint(\d+|_\d+_\d+|_[a-z]+)\.pt")
-pt_regexp_epoch_based = re.compile(r"checkpoint(\d+)\.pt")
-pt_regexp_update_based = re.compile(r"checkpoint_\d+_(\d+)\.pt")
-
-
-def parse_checkpoints(files):
- entries = []
- for f in files:
- m = pt_regexp_epoch_based.fullmatch(f)
- if m is not None:
- entries.append((int(m.group(1)), m.group(0)))
- else:
- m = pt_regexp_update_based.fullmatch(f)
- if m is not None:
- entries.append((int(m.group(1)), m.group(0)))
- return entries
-
-
-def last_n_checkpoints(files, n):
- entries = parse_checkpoints(files)
- return [x[1] for x in sorted(entries, reverse=True)[:n]]
-
-
-def every_n_checkpoints(files, n):
- entries = parse_checkpoints(files)
- return [x[1] for x in sorted(sorted(entries)[::-n])]
-
-
-def main():
- parser = argparse.ArgumentParser(
- description=(
- "Recursively delete checkpoint files from `root_dir`, "
- "but preserve checkpoint_best.pt and checkpoint_last.pt"
- )
- )
- parser.add_argument("root_dirs", nargs="*")
- parser.add_argument(
- "--save-last", type=int, default=0, help="number of last checkpoints to save"
- )
- parser.add_argument(
- "--save-every", type=int, default=0, help="interval of checkpoints to save"
- )
- parser.add_argument(
- "--preserve-test",
- action="store_true",
- help="preserve checkpoints in dirs that start with test_ prefix (default: delete them)",
- )
- parser.add_argument(
- "--delete-best", action="store_true", help="delete checkpoint_best.pt"
- )
- parser.add_argument(
- "--delete-last", action="store_true", help="delete checkpoint_last.pt"
- )
- parser.add_argument(
- "--no-dereference", action="store_true", help="don't dereference symlinks"
- )
- args = parser.parse_args()
-
- files_to_desymlink = []
- files_to_preserve = []
- files_to_delete = []
- for root_dir in args.root_dirs:
- for root, _subdirs, files in os.walk(root_dir):
- if args.save_last > 0:
- to_save = last_n_checkpoints(files, args.save_last)
- else:
- to_save = []
- if args.save_every > 0:
- to_save += every_n_checkpoints(files, args.save_every)
- for file in files:
- if not pt_regexp.fullmatch(file):
- continue
- full_path = os.path.join(root, file)
- if (
- not os.path.basename(root).startswith("test_") or args.preserve_test
- ) and (
- (file == "checkpoint_last.pt" and not args.delete_last)
- or (file == "checkpoint_best.pt" and not args.delete_best)
- or file in to_save
- ):
- if os.path.islink(full_path) and not args.no_dereference:
- files_to_desymlink.append(full_path)
- else:
- files_to_preserve.append(full_path)
- else:
- files_to_delete.append(full_path)
-
- if len(files_to_desymlink) == 0 and len(files_to_delete) == 0:
- print("Nothing to do.")
- sys.exit(0)
-
- files_to_desymlink = sorted(files_to_desymlink)
- files_to_preserve = sorted(files_to_preserve)
- files_to_delete = sorted(files_to_delete)
-
- print("Operations to perform (in order):")
- if len(files_to_desymlink) > 0:
- for file in files_to_desymlink:
- print(" - preserve (and dereference symlink): " + file)
- if len(files_to_preserve) > 0:
- for file in files_to_preserve:
- print(" - preserve: " + file)
- if len(files_to_delete) > 0:
- for file in files_to_delete:
- print(" - delete: " + file)
- while True:
- resp = input("Continue? (Y/N): ")
- if resp.strip().lower() == "y":
- break
- elif resp.strip().lower() == "n":
- sys.exit(0)
-
- print("Executing...")
- if len(files_to_desymlink) > 0:
- for file in files_to_desymlink:
- realpath = os.path.realpath(file)
- print("rm " + file)
- os.remove(file)
- print("cp {} {}".format(realpath, file))
- shutil.copyfile(realpath, file)
- if len(files_to_delete) > 0:
- for file in files_to_delete:
- print("rm " + file)
- os.remove(file)
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/discriminative_reranking_nmt/criterions/__init__.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/discriminative_reranking_nmt/criterions/__init__.py
deleted file mode 100644
index 7c257c2700f015cb123a976584aef72f0429eb0c..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/discriminative_reranking_nmt/criterions/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from .discriminative_reranking_criterion import KLDivergenceRerankingCriterion
-
-
-__all__ = [
- "KLDivergenceRerankingCriterion",
-]
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/scripts/prepare_audio.sh b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/scripts/prepare_audio.sh
deleted file mode 100644
index 013f7a9b055a7693a29f9c5ba1e4003a9a25850e..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/scripts/prepare_audio.sh
+++ /dev/null
@@ -1,78 +0,0 @@
-#!/usr/bin/env zsh
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-source_dir=$1
-tgt_dir=$2
-model=$3
-
-if [ -z "$4" ]
- then
- dim=512
- else
- dim=$4
-fi
-
-echo "using $dim dim for PCA"
-
-if [ -z "$5" ]
- then
- layer=14
- else
- layer=$5
-fi
-
-echo "extracting from layer $layer"
-
-train_split=train
-valid_split=valid
-test_split=test
-
-all_splits=($train_split)
-
-if [[ -f "$source_dir/valid.tsv" ]]; then
- all_splits+=('valid')
-fi
-
-if [[ -f "$source_dir/test.tsv" ]]; then
- all_splits+=('test')
-fi
-
-echo "processing splits: $all_splits"
-
-mkdir -p $tgt_dir
-
-cp $source_dir/*.tsv $tgt_dir
-cp $source_dir/*.wrd $tgt_dir
-cp $source_dir/*.ltr $tgt_dir
-cp $source_dir/*.phn $tgt_dir
-cp $source_dir/dict* $tgt_dir
-
-setopt shwordsplit
-
-for split in $all_splits; do
- python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/wav2vec_extract_features.py $source_dir --split $split \
- --save-dir $tgt_dir --checkpoint $model --layer $layer
-done
-
-python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/wav2vec_cluster_faiss.py $tgt_dir/${train_split}.tsv \
---checkpoint $model --save-dir $tgt_dir -f "CLUS128" --sample-pct 1.0
-
-for split in $all_splits; do
- python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/wav2vec_apply_cluster_faiss.py $tgt_dir \
- --checkpoint $model --path $tgt_dir/CLUS128 --split $split
-done
-
-python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/pca.py $tgt_dir/${train_split}.npy --output $tgt_dir/pca --dim $dim
-
-for split in $all_splits; do
- python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/apply_pca.py $tgt_dir --split $split --save-dir $tgt_dir/precompute_pca$dim --pca-path $tgt_dir/pca/${dim}_pca --batch-size 1048000
-
- python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/merge_clusters.py $tgt_dir/precompute_pca$dim --cluster-dir $tgt_dir/CLUS128 \
- --split $split --save-dir $tgt_dir/precompute_pca${dim}_cls128_mean --pooling mean
-
- python $FAIRSEQ_ROOT/examples/wav2vec/unsupervised/scripts/mean_pool.py $tgt_dir/precompute_pca${dim}_cls128_mean \
- --save-dir $tgt_dir/precompute_pca${dim}_cls128_mean_pooled --split $split
-done
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/vq-wav2vec_featurize.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/vq-wav2vec_featurize.py
deleted file mode 100644
index 627072ee174c22831209e00984b945eb9dc2c279..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/vq-wav2vec_featurize.py
+++ /dev/null
@@ -1,250 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Helper script to pre-compute embeddings for a flashlight (previously called wav2letter++) dataset
-"""
-
-import argparse
-import glob
-import os
-import os.path as osp
-import pprint
-
-import soundfile as sf
-import torch
-import fairseq
-from torch import nn
-from torch.utils.data import DataLoader
-
-
-try:
- import tqdm
-except:
- print("Install tqdm to use --log-format=tqdm")
-
-
-class FilesDataset:
- def __init__(self, files, labels):
- self.files = files
- if labels and osp.exists(labels):
- with open(labels, "r") as lbl_f:
- self.labels = [line.rstrip() for line in lbl_f]
- else:
- self.labels = labels
-
- def __len__(self):
- return len(self.files)
-
- def __getitem__(self, index):
- fname = self.files[index]
-
- wav, sr = sf.read(fname)
- assert sr == 16000
-
- wav = torch.from_numpy(wav).float()
- lbls = None
- if self.labels:
- if isinstance(self.labels, str):
- lbl_file = osp.splitext(fname)[0] + "." + self.labels
- with open(lbl_file, "r") as lblf:
- lbls = lblf.readline()
- assert lbls is not None
- else:
- lbls = self.labels[index]
- return wav, lbls
-
- def collate(self, batch):
- return batch
-
-
-class ArgTypes:
- @staticmethod
- def existing_path(arg):
- arg = str(arg)
- assert osp.exists(arg), f"File {arg} does not exist"
- return arg
-
- @staticmethod
- def mkdir(arg):
- arg = str(arg)
- os.makedirs(arg, exist_ok=True)
- return arg
-
-
-class DatasetWriter:
- def __init__(self):
-
- self.args = self.load_config()
- pprint.pprint(self.args.__dict__)
-
- self.model = self.load_model()
-
- def __getattr__(self, attr):
- return getattr(self.args, attr)
-
- def read_manifest(self, fname):
-
- with open(fname, "r") as fp:
- lines = fp.read().split("\n")
- root = lines.pop(0).strip()
- fnames = [
- osp.join(root, line.split("\t")[0]) for line in lines if len(line) > 0
- ]
-
- return fnames
-
- def process_splits(self):
-
- if self.args.shard is not None or self.args.num_shards is not None:
- assert self.args.shard is not None and self.args.num_shards is not None
-
- for split in self.splits:
- print(split)
-
- if self.extension == "tsv":
- datadir = osp.join(self.data_dir, f"{split}.{self.extension}")
- print("Reading manifest file: ", datadir)
- files = self.read_manifest(datadir)
- else:
- datadir = osp.join(self.data_dir, split, f"**/*.{self.extension}")
- files = glob.glob(datadir, recursive=True)
-
- assert len(files) > 0
-
- if self.args.shard is not None:
- files = files[self.args.shard :: self.args.num_shards]
-
- lbls = []
- with open(self.data_file(split), "w") as srcf:
- for line, lbl in self.iterate(files):
- print(line, file=srcf)
- if self.args.labels:
- lbls.append(lbl + "\n")
-
- if self.args.labels:
- assert all(a is not None for a in lbls)
- with open(self.lbl_file(split), "w") as lblf:
- lblf.writelines(lbls)
-
- def iterate(self, files):
-
- data = self.load_data(files)
- for samples in tqdm.tqdm(data, total=len(files) // 32):
-
- for wav, lbl in samples:
- x = wav.unsqueeze(0).float().cuda()
-
- div = 1
- while x.size(-1) // div > self.args.max_size:
- div += 1
-
- xs = x.chunk(div, dim=-1)
-
- result = []
- for x in xs:
- torch.cuda.empty_cache()
- x = self.model.feature_extractor(x)
- if self.quantize_location == "encoder":
- with torch.no_grad():
- _, idx = self.model.vector_quantizer.forward_idx(x)
- idx = idx.squeeze(0).cpu()
- else:
- with torch.no_grad():
- z = self.model.feature_aggregator(x)
- _, idx = self.model.vector_quantizer.forward_idx(z)
- idx = idx.squeeze(0).cpu()
- result.append(idx)
-
- idx = torch.cat(result, dim=0)
- yield " ".join("-".join(map(str, a.tolist())) for a in idx), lbl
-
- def lbl_file(self, name):
- shard_part = "" if self.args.shard is None else f".{self.args.shard}"
- return osp.join(self.output_dir, f"{name}.lbl{shard_part}")
-
- def data_file(self, name):
- shard_part = "" if self.args.shard is None else f".{self.args.shard}"
- return osp.join(self.output_dir, f"{name}.src{shard_part}")
-
- def var_file(self):
- return osp.join(self.output_dir, f"vars.pt")
-
- def load_config(self):
-
- parser = argparse.ArgumentParser("Vector Quantized wav2vec features")
-
- # Model Arguments
- parser.add_argument("--checkpoint", type=ArgTypes.existing_path, required=True)
- parser.add_argument("--data-parallel", action="store_true")
-
- # Output Arguments
- parser.add_argument("--output-dir", type=ArgTypes.mkdir, required=True)
-
- # Data Arguments
- parser.add_argument("--data-dir", type=ArgTypes.existing_path, required=True)
- parser.add_argument("--splits", type=str, nargs="+", required=True)
- parser.add_argument("--extension", type=str, required=True)
- parser.add_argument("--labels", type=str, required=False)
-
- parser.add_argument("--shard", type=int, default=None)
- parser.add_argument("--num-shards", type=int, default=None)
- parser.add_argument("--max-size", type=int, default=1300000)
-
- # Logger Arguments
- parser.add_argument(
- "--log-format", type=str, choices=["none", "simple", "tqdm"]
- )
-
- return parser.parse_args()
-
- def load_data(self, fnames):
-
- dataset = FilesDataset(fnames, self.args.labels)
- loader = DataLoader(
- dataset, batch_size=32, collate_fn=dataset.collate, num_workers=8
- )
- return loader
-
- def load_model(self):
- model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([self.checkpoint])
- model = model[0]
-
- self.quantize_location = getattr(cfg.model, "vq", "encoder")
-
- model.eval().float()
- model.cuda()
-
- if self.data_parallel:
- model = nn.DataParallel(model)
-
- return model
-
- def __call__(self):
-
- self.process_splits()
-
- if hasattr(self.model.feature_extractor, "vars") and (
- self.args.shard is None or self.args.shard == 0
- ):
- vars = (
- self.model.feature_extractor.vars.view(
- self.model.feature_extractor.banks,
- self.model.feature_extractor.num_vars,
- -1,
- )
- .cpu()
- .detach()
- )
- print("writing learned latent variable embeddings: ", vars.shape)
- torch.save(vars, self.var_file())
-
-
-if __name__ == "__main__":
- write_data = DatasetWriter()
-
- write_data()
- print("Done.")
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/hub_utils.py b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/hub_utils.py
deleted file mode 100644
index d74470d2ecba2825221a2efa2ce21a9b698340df..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/fairseq/hub_utils.py
+++ /dev/null
@@ -1,303 +0,0 @@
-#!/usr/bin/env python3 -u
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import copy
-import logging
-import os
-from typing import Any, Dict, Iterator, List
-
-import torch
-from fairseq import utils
-from fairseq.data import encoders
-from omegaconf import open_dict
-from torch import nn
-
-
-logger = logging.getLogger(__name__)
-
-
-def from_pretrained(
- model_name_or_path,
- checkpoint_file="model.pt",
- data_name_or_path=".",
- archive_map=None,
- **kwargs
-):
- from fairseq import checkpoint_utils, file_utils
-
- if archive_map is not None:
- if model_name_or_path in archive_map:
- model_name_or_path = archive_map[model_name_or_path]
- if data_name_or_path is not None and data_name_or_path in archive_map:
- data_name_or_path = archive_map[data_name_or_path]
-
- # allow archive_map to set default arg_overrides (e.g., tokenizer, bpe)
- # for each model
- if isinstance(model_name_or_path, dict):
- for k, v in model_name_or_path.items():
- if k == "checkpoint_file":
- checkpoint_file = v
- elif (
- k != "path"
- # only set kwargs that don't already have overrides
- and k not in kwargs
- ):
- kwargs[k] = v
- model_name_or_path = model_name_or_path["path"]
-
- model_path = file_utils.load_archive_file(model_name_or_path)
-
- # convenience hack for loading data and BPE codes from model archive
- if data_name_or_path.startswith("."):
- kwargs["data"] = os.path.abspath(os.path.join(model_path, data_name_or_path))
- else:
- kwargs["data"] = file_utils.load_archive_file(data_name_or_path)
- for file, arg in {
- "code": "bpe_codes",
- "bpecodes": "bpe_codes",
- "sentencepiece.bpe.model": "sentencepiece_model",
- "merges.txt": "bpe_merges",
- "vocab.json": "bpe_vocab",
- }.items():
- path = os.path.join(model_path, file)
- if os.path.exists(path):
- kwargs[arg] = path
-
- if "user_dir" in kwargs:
- utils.import_user_module(argparse.Namespace(user_dir=kwargs["user_dir"]))
-
- models, args, task = checkpoint_utils.load_model_ensemble_and_task(
- [os.path.join(model_path, cpt) for cpt in checkpoint_file.split(os.pathsep)],
- arg_overrides=kwargs,
- )
-
- return {
- "args": args,
- "task": task,
- "models": models,
- }
-
-
-class GeneratorHubInterface(nn.Module):
- """
- PyTorch Hub interface for generating sequences from a pre-trained
- translation or language model.
- """
-
- def __init__(self, cfg, task, models):
- super().__init__()
- self.cfg = cfg
- self.task = task
- self.models = nn.ModuleList(models)
- self.src_dict = task.source_dictionary
- self.tgt_dict = task.target_dictionary
-
- # optimize model for generation
- for model in self.models:
- model.prepare_for_inference_(cfg)
-
- # Load alignment dictionary for unknown word replacement
- # (None if no unknown word replacement, empty if no path to align dictionary)
- self.align_dict = utils.load_align_dict(cfg.generation.replace_unk)
-
- self.tokenizer = encoders.build_tokenizer(cfg.tokenizer)
- self.bpe = encoders.build_bpe(cfg.bpe)
-
- self.max_positions = utils.resolve_max_positions(
- self.task.max_positions(), *[model.max_positions() for model in models]
- )
-
- # this is useful for determining the device
- self.register_buffer("_float_tensor", torch.tensor([0], dtype=torch.float))
-
- @property
- def device(self):
- return self._float_tensor.device
-
- def translate(
- self, sentences: List[str], beam: int = 5, verbose: bool = False, **kwargs
- ) -> List[str]:
- return self.sample(sentences, beam, verbose, **kwargs)
-
- def sample(
- self, sentences: List[str], beam: int = 1, verbose: bool = False, **kwargs
- ) -> List[str]:
- if isinstance(sentences, str):
- return self.sample([sentences], beam=beam, verbose=verbose, **kwargs)[0]
- tokenized_sentences = [self.encode(sentence) for sentence in sentences]
- batched_hypos = self.generate(tokenized_sentences, beam, verbose, **kwargs)
- return [self.decode(hypos[0]["tokens"]) for hypos in batched_hypos]
-
- def score(self, sentences: List[str], **kwargs):
- if isinstance(sentences, str):
- return self.score([sentences], **kwargs)[0]
- # NOTE: this doesn't support translation tasks currently
- tokenized_sentences = [self.encode(sentence) for sentence in sentences]
- return [
- hypos[0]
- for hypos in self.generate(
- tokenized_sentences, score_reference=True, **kwargs
- )
- ]
-
- def generate(
- self,
- tokenized_sentences: List[torch.LongTensor],
- beam: int = 5,
- verbose: bool = False,
- skip_invalid_size_inputs=False,
- inference_step_args=None,
- prefix_allowed_tokens_fn=None,
- **kwargs
- ) -> List[List[Dict[str, torch.Tensor]]]:
- if torch.is_tensor(tokenized_sentences) and tokenized_sentences.dim() == 1:
- return self.generate(
- tokenized_sentences.unsqueeze(0), beam=beam, verbose=verbose, **kwargs
- )[0]
-
- # build generator using current args as well as any kwargs
- gen_args = copy.deepcopy(self.cfg.generation)
- with open_dict(gen_args):
- gen_args.beam = beam
- for k, v in kwargs.items():
- setattr(gen_args, k, v)
- generator = self.task.build_generator(
- self.models,
- gen_args,
- prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
- )
-
- inference_step_args = inference_step_args or {}
- results = []
- for batch in self._build_batches(tokenized_sentences, skip_invalid_size_inputs):
- batch = utils.apply_to_sample(lambda t: t.to(self.device), batch)
- translations = self.task.inference_step(
- generator, self.models, batch, **inference_step_args
- )
- for id, hypos in zip(batch["id"].tolist(), translations):
- results.append((id, hypos))
-
- # sort output to match input order
- outputs = [hypos for _, hypos in sorted(results, key=lambda x: x[0])]
-
- if verbose:
-
- def getarg(name, default):
- return getattr(gen_args, name, getattr(self.cfg, name, default))
-
- for source_tokens, target_hypotheses in zip(tokenized_sentences, outputs):
- src_str_with_unk = self.string(source_tokens)
- logger.info("S\t{}".format(src_str_with_unk))
- for hypo in target_hypotheses:
- hypo_str = self.decode(hypo["tokens"])
- logger.info("H\t{}\t{}".format(hypo["score"], hypo_str))
- logger.info(
- "P\t{}".format(
- " ".join(
- map(
- lambda x: "{:.4f}".format(x),
- hypo["positional_scores"].tolist(),
- )
- )
- )
- )
- if hypo["alignment"] is not None and getarg(
- "print_alignment", False
- ):
- logger.info(
- "A\t{}".format(
- " ".join(
- [
- "{}-{}".format(src_idx, tgt_idx)
- for src_idx, tgt_idx in hypo["alignment"]
- ]
- )
- )
- )
- return outputs
-
- def encode(self, sentence: str) -> torch.LongTensor:
- sentence = self.tokenize(sentence)
- sentence = self.apply_bpe(sentence)
- return self.binarize(sentence)
-
- def decode(self, tokens: torch.LongTensor) -> str:
- sentence = self.string(tokens)
- sentence = self.remove_bpe(sentence)
- return self.detokenize(sentence)
-
- def tokenize(self, sentence: str) -> str:
- if self.tokenizer is not None:
- sentence = self.tokenizer.encode(sentence)
- return sentence
-
- def detokenize(self, sentence: str) -> str:
- if self.tokenizer is not None:
- sentence = self.tokenizer.decode(sentence)
- return sentence
-
- def apply_bpe(self, sentence: str) -> str:
- if self.bpe is not None:
- sentence = self.bpe.encode(sentence)
- return sentence
-
- def remove_bpe(self, sentence: str) -> str:
- if self.bpe is not None:
- sentence = self.bpe.decode(sentence)
- return sentence
-
- def binarize(self, sentence: str) -> torch.LongTensor:
- return self.src_dict.encode_line(sentence, add_if_not_exist=False).long()
-
- def string(self, tokens: torch.LongTensor) -> str:
- return self.tgt_dict.string(tokens)
-
- def _build_batches(
- self, tokens: List[List[int]], skip_invalid_size_inputs: bool
- ) -> Iterator[Dict[str, Any]]:
- lengths = torch.LongTensor([t.numel() for t in tokens])
- batch_iterator = self.task.get_batch_iterator(
- dataset=self.task.build_dataset_for_inference(tokens, lengths),
- max_tokens=self.cfg.dataset.max_tokens,
- max_sentences=self.cfg.dataset.batch_size,
- max_positions=self.max_positions,
- ignore_invalid_inputs=skip_invalid_size_inputs,
- disable_iterator_cache=True,
- ).next_epoch_itr(shuffle=False)
- return batch_iterator
-
-
-class BPEHubInterface(object):
- """PyTorch Hub interface for Byte-Pair Encoding (BPE)."""
-
- def __init__(self, bpe, **kwargs):
- super().__init__()
- args = argparse.Namespace(bpe=bpe, **kwargs)
- self.bpe = encoders.build_bpe(args)
- assert self.bpe is not None
-
- def encode(self, sentence: str) -> str:
- return self.bpe.encode(sentence)
-
- def decode(self, sentence: str) -> str:
- return self.bpe.decode(sentence)
-
-
-class TokenizerHubInterface(object):
- """PyTorch Hub interface for tokenization."""
-
- def __init__(self, tokenizer, **kwargs):
- super().__init__()
- args = argparse.Namespace(tokenizer=tokenizer, **kwargs)
- self.tokenizer = encoders.build_tokenizer(args)
- assert self.tokenizer is not None
-
- def encode(self, sentence: str) -> str:
- return self.tokenizer.encode(sentence)
-
- def decode(self, sentence: str) -> str:
- return self.tokenizer.decode(sentence)
diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/laser/README.md b/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/laser/README.md
deleted file mode 100644
index 66acada04f58fa235cd312753f144f6f1e5f4a33..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/laser/README.md
+++ /dev/null
@@ -1,144 +0,0 @@
-# LASER Language-Agnostic SEntence Representations
-
-LASER is a library to calculate and use multilingual sentence embeddings.
-
-You can find more information about LASER and how to use it on the official [LASER repository](https://github.com/facebookresearch/LASER).
-
-This folder contains source code for training LASER embeddings.
-
-
-## Prepare data and configuration file
-
-Binarize your data with fairseq, as described [here](https://fairseq.readthedocs.io/en/latest/getting_started.html#data-pre-processing).
-
-Create a json config file with this format:
-```
-{
- "src_vocab": "/path/to/spm.src.cvocab",
- "tgt_vocab": "/path/to/spm.tgt.cvocab",
- "train": [
- {
- "type": "translation",
- "id": 0,
- "src": "/path/to/srclang1-tgtlang0/train.srclang1",
- "tgt": "/path/to/srclang1-tgtlang0/train.tgtlang0"
- },
- {
- "type": "translation",
- "id": 1,
- "src": "/path/to/srclang1-tgtlang1/train.srclang1",
- "tgt": "/path/to/srclang1-tgtlang1/train.tgtlang1"
- },
- {
- "type": "translation",
- "id": 0,
- "src": "/path/to/srclang2-tgtlang0/train.srclang2",
- "tgt": "/path/to/srclang2-tgtlang0/train.tgtlang0"
- },
- {
- "type": "translation",
- "id": 1,
- "src": "/path/to/srclang2-tgtlang1/train.srclang2",
- "tgt": "/path/to/srclang2-tgtlang1/train.tgtlang1"
- },
- ...
- ],
- "valid": [
- {
- "type": "translation",
- "id": 0,
- "src": "/unused",
- "tgt": "/unused"
- }
- ]
-}
-```
-where paths are paths to binarized indexed fairseq dataset files.
-`id` represents the target language id.
-
-
-## Training Command Line Example
-
-```
-fairseq-train \
- /path/to/configfile_described_above.json \
- --user-dir examples/laser/laser_src \
- --log-interval 100 --log-format simple \
- --task laser --arch laser_lstm \
- --save-dir . \
- --optimizer adam \
- --lr 0.001 \
- --lr-scheduler inverse_sqrt \
- --clip-norm 5 \
- --warmup-updates 90000 \
- --update-freq 2 \
- --dropout 0.0 \
- --encoder-dropout-out 0.1 \
- --max-tokens 2000 \
- --max-epoch 50 \
- --encoder-bidirectional \
- --encoder-layers 5 \
- --encoder-hidden-size 512 \
- --decoder-layers 1 \
- --decoder-hidden-size 2048 \
- --encoder-embed-dim 320 \
- --decoder-embed-dim 320 \
- --decoder-lang-embed-dim 32 \
- --warmup-init-lr 0.001 \
- --disable-validation
-```
-
-
-## Applications
-
-We showcase several applications of multilingual sentence embeddings
-with code to reproduce our results (in the directory "tasks").
-
-* [**Cross-lingual document classification**](https://github.com/facebookresearch/LASER/tree/master/tasks/mldoc) using the
- [*MLDoc*](https://github.com/facebookresearch/MLDoc) corpus [2,6]
-* [**WikiMatrix**](https://github.com/facebookresearch/LASER/tree/master/tasks/WikiMatrix)
- Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia [7]
-* [**Bitext mining**](https://github.com/facebookresearch/LASER/tree/master/tasks/bucc) using the
- [*BUCC*](https://comparable.limsi.fr/bucc2018/bucc2018-task.html) corpus [3,5]
-* [**Cross-lingual NLI**](https://github.com/facebookresearch/LASER/tree/master/tasks/xnli)
- using the [*XNLI*](https://www.nyu.edu/projects/bowman/xnli/) corpus [4,5,6]
-* [**Multilingual similarity search**](https://github.com/facebookresearch/LASER/tree/master/tasks/similarity) [1,6]
-* [**Sentence embedding of text files**](https://github.com/facebookresearch/LASER/tree/master/tasks/embed)
- example how to calculate sentence embeddings for arbitrary text files in any of the supported language.
-
-**For all tasks, we use exactly the same multilingual encoder, without any task specific optimization or fine-tuning.**
-
-
-
-## References
-
-[1] Holger Schwenk and Matthijs Douze,
- [*Learning Joint Multilingual Sentence Representations with Neural Machine Translation*](https://aclanthology.info/papers/W17-2619/w17-2619),
- ACL workshop on Representation Learning for NLP, 2017
-
-[2] Holger Schwenk and Xian Li,
- [*A Corpus for Multilingual Document Classification in Eight Languages*](http://www.lrec-conf.org/proceedings/lrec2018/pdf/658.pdf),
- LREC, pages 3548-3551, 2018.
-
-[3] Holger Schwenk,
- [*Filtering and Mining Parallel Data in a Joint Multilingual Space*](http://aclweb.org/anthology/P18-2037)
- ACL, July 2018
-
-[4] Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R. Bowman, Holger Schwenk and Veselin Stoyanov,
- [*XNLI: Cross-lingual Sentence Understanding through Inference*](https://aclweb.org/anthology/D18-1269),
- EMNLP, 2018.
-
-[5] Mikel Artetxe and Holger Schwenk,
- [*Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings*](https://arxiv.org/abs/1811.01136)
- arXiv, Nov 3 2018.
-
-[6] Mikel Artetxe and Holger Schwenk,
- [*Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond*](https://arxiv.org/abs/1812.10464)
- arXiv, Dec 26 2018.
-
-[7] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman,
- [*WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia*](https://arxiv.org/abs/1907.05791)
- arXiv, July 11 2019.
-
-[8] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin
- [*CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB*](https://arxiv.org/abs/1911.04944)
diff --git a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/laser/laser_src/multitask_data_utils.py b/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/laser/laser_src/multitask_data_utils.py
deleted file mode 100644
index b05caea26793bf5112a7abc29d76225f578f3ebe..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Visual_Grounding/fairseq/examples/laser/laser_src/multitask_data_utils.py
+++ /dev/null
@@ -1,143 +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 collections import OrderedDict
-
-import numpy as np
-
-from fairseq.data import BaseWrapperDataset, FairseqDataset, iterators
-
-
-class MultiItr(object):
- def __init__(self, itr):
- self.itr = itr
- self._counts = [0 for x in itr]
-
- def __len__(self):
- return sum(len(itr) for itr in self.itr)
-
- def __iter__(self):
- return self
-
- def __next__(self):
- ratios = [count / len(itr) for count, itr in zip(self._counts, self.itr)]
- idx = ratios.index(min(ratios))
- self._counts[idx] += 1
- return next(self.itr[idx])
-
-
-class MultidatasetEpochBatchIterator(iterators.EpochBatchIterating):
- """A wrapper around multiple epoch batch iterators."""
-
- def __init__(
- self,
- dataset,
- batch_sampler,
- seed=1,
- num_shards=1,
- shard_id=0,
- num_workers=0,
- epoch=1,
- ):
-
- assert isinstance(dataset, OrderedDict)
- assert len(dataset)
- assert isinstance(dataset[next(iter(dataset))], FairseqDataset)
-
- self.iterators = []
-
- self.epoch = epoch
- for key, dt in dataset.items():
- epoch_iter = iterators.EpochBatchIterator(
- dataset=dt,
- collate_fn=dt.collater,
- batch_sampler=batch_sampler[key],
- seed=seed,
- num_shards=num_shards,
- shard_id=shard_id,
- num_workers=0,
- epoch=epoch,
- )
- self.iterators.append(epoch_iter)
-
- def __len__(self):
- return sum(len(itr) for itr in self.iterators)
-
- def next_epoch_itr(self, shuffle=True, fix_batches_to_gpus=False):
- # `self.epoch += 1` should be handled by underlying `EpochBatchIterator`s.
- return MultiItr(
- [
- itr.next_epoch_itr(
- shuffle=shuffle, fix_batches_to_gpus=fix_batches_to_gpus
- )
- for itr in self.iterators
- ]
- )
-
- def end_of_epoch(self):
- return all(itr.end_of_epoch() for itr in self.iterators)
-
- @property
- def next_epoch_idx(self):
- """Return the epoch index after *next_epoch_itr* is called."""
-
- epochs = [itr.next_epoch_idx for itr in self.iterators]
- self.epoch = epochs[0]
- assert all(epoch == self.epoch for epoch in epochs)
-
- return self.epoch
-
- @property
- def iterations_in_epoch(self):
- return sum(itr.iterations_in_epoch for itr in self.iterators)
-
- def state_dict(self):
- return {
- "iterators": [it.state_dict() for it in self.iterators],
- "epoch": self.epoch,
- }
-
- def load_state_dict(self, state_dict):
- self.epoch = state_dict["epoch"]
- for it, d in zip(self.iterators, state_dict["iterators"]):
- it.load_state_dict(d)
-
-
-class MultitaskDatasetWrapper(BaseWrapperDataset):
- """A wrapper for a multitask dataset."""
-
- def __init__(self, dataset, target_language_id, sample=1.0, name=""):
- super().__init__(dataset)
- self.target_language_id = target_language_id
- self.sample = sample
- self.name = name
-
- def collater(self, *args, **kwargs):
- ans = self.dataset.collater(*args, **kwargs)
- if "net_input" in ans:
- ans["net_input"]["target_language_id"] = self.target_language_id
- ans["net_input"]["dataset_name"] = self.name
- return ans
-
- def num_tokens(self, *args, **kwargs):
- return self.dataset.num_tokens(*args, **kwargs)
-
- def ordered_indices(self, *args, **kwargs):
- indices = self.dataset.ordered_indices(*args, **kwargs)
- # Hacky solution for sampling
- size = int(self.sample * indices.shape[0])
-
- return indices.take(np.sort(np.random.permutation(indices.shape[0])[:size]))
-
- def size(self, index: int):
- return self.dataset.size(index)
-
- @property
- def supports_prefetch(self):
- """Whether this dataset supports prefetching."""
- return getattr(self.dataset, "supports_prefetch", False)
-
- def prefetch(self, indices):
- return self.dataset.prefetch(indices)
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/beamable_mm.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/beamable_mm.py
deleted file mode 100644
index eff1a4607f600c71210e6b914985dc48731aae86..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/modules/beamable_mm.py
+++ /dev/null
@@ -1,49 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn as nn
-
-
-class BeamableMM(nn.Module):
- """This module provides an optimized MM for beam decoding with attention.
-
- It leverage the fact that the source-side of the input is replicated beam
- times and the target-side of the input is of width one. This layer speeds up
- inference by replacing the inputs {(bsz x 1 x nhu), (bsz x sz2 x nhu)}
- with smaller inputs {(bsz/beam x beam x nhu), (bsz/beam x sz2 x nhu)}.
- """
-
- def __init__(self, beam_size=None):
- super(BeamableMM, self).__init__()
- self.beam_size = beam_size
-
- def forward(self, input1, input2):
- if (
- not self.training
- and self.beam_size is not None # test mode
- and input1.dim() == 3 # beam size is set
- and input1.size(1) # only support batched input
- == 1 # single time step update
- ):
- bsz, beam = input1.size(0), self.beam_size
-
- # bsz x 1 x nhu --> bsz/beam x beam x nhu
- input1 = input1[:, 0, :].unfold(0, beam, beam).transpose(2, 1)
-
- # bsz x sz2 x nhu --> bsz/beam x sz2 x nhu
- input2 = input2.unfold(0, beam, beam)[:, :, :, 0]
-
- # use non batched operation if bsz = beam
- if input1.size(0) == 1:
- output = torch.mm(input1[0, :, :], input2[0, :, :])
- else:
- output = input1.bmm(input2)
- return output.view(bsz, 1, -1)
- else:
- return input1.bmm(input2)
-
- def set_beam_size(self, beam_size):
- self.beam_size = beam_size
diff --git a/spaces/ORI-Muchim/PowerTTS/modules.py b/spaces/ORI-Muchim/PowerTTS/modules.py
deleted file mode 100644
index 9c7fd9cd6eb8b7e0ec0e08957e970744a374a924..0000000000000000000000000000000000000000
--- a/spaces/ORI-Muchim/PowerTTS/modules.py
+++ /dev/null
@@ -1,390 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
- def __init__(self, channels, eps=1e-5):
- super().__init__()
- self.channels = channels
- self.eps = eps
-
- self.gamma = nn.Parameter(torch.ones(channels))
- self.beta = nn.Parameter(torch.zeros(channels))
-
- def forward(self, x):
- x = x.transpose(1, -1)
- x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
- return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
- def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
- super().__init__()
- self.in_channels = in_channels
- self.hidden_channels = hidden_channels
- self.out_channels = out_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
- assert n_layers > 1, "Number of layers should be larger than 0."
-
- self.conv_layers = nn.ModuleList()
- self.norm_layers = nn.ModuleList()
- self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.relu_drop = nn.Sequential(
- nn.ReLU(),
- nn.Dropout(p_dropout))
- for _ in range(n_layers-1):
- self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
- self.norm_layers.append(LayerNorm(hidden_channels))
- self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask):
- x_org = x
- for i in range(self.n_layers):
- x = self.conv_layers[i](x * x_mask)
- x = self.norm_layers[i](x)
- x = self.relu_drop(x)
- x = x_org + self.proj(x)
- return x * x_mask
-
-
-class DDSConv(nn.Module):
- """
- Dialted and Depth-Separable Convolution
- """
- def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
- super().__init__()
- self.channels = channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.p_dropout = p_dropout
-
- self.drop = nn.Dropout(p_dropout)
- self.convs_sep = nn.ModuleList()
- self.convs_1x1 = nn.ModuleList()
- self.norms_1 = nn.ModuleList()
- self.norms_2 = nn.ModuleList()
- for i in range(n_layers):
- dilation = kernel_size ** i
- padding = (kernel_size * dilation - dilation) // 2
- self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
- groups=channels, dilation=dilation, padding=padding
- ))
- self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
- self.norms_1.append(LayerNorm(channels))
- self.norms_2.append(LayerNorm(channels))
-
- def forward(self, x, x_mask, g=None):
- if g is not None:
- x = x + g
- for i in range(self.n_layers):
- y = self.convs_sep[i](x * x_mask)
- y = self.norms_1[i](y)
- y = F.gelu(y)
- y = self.convs_1x1[i](y)
- y = self.norms_2[i](y)
- y = F.gelu(y)
- y = self.drop(y)
- x = x + y
- return x * x_mask
-
-
-class WN(torch.nn.Module):
- def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
- super(WN, self).__init__()
- assert(kernel_size % 2 == 1)
- self.hidden_channels =hidden_channels
- self.kernel_size = kernel_size,
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.gin_channels = gin_channels
- self.p_dropout = p_dropout
-
- self.in_layers = torch.nn.ModuleList()
- self.res_skip_layers = torch.nn.ModuleList()
- self.drop = nn.Dropout(p_dropout)
-
- if gin_channels != 0:
- cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
- self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
- for i in range(n_layers):
- dilation = dilation_rate ** i
- padding = int((kernel_size * dilation - dilation) / 2)
- in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
- dilation=dilation, padding=padding)
- in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
- self.in_layers.append(in_layer)
-
- # last one is not necessary
- if i < n_layers - 1:
- res_skip_channels = 2 * hidden_channels
- else:
- res_skip_channels = hidden_channels
-
- res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
- res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
- self.res_skip_layers.append(res_skip_layer)
-
- def forward(self, x, x_mask, g=None, **kwargs):
- output = torch.zeros_like(x)
- n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
- if g is not None:
- g = self.cond_layer(g)
-
- for i in range(self.n_layers):
- x_in = self.in_layers[i](x)
- if g is not None:
- cond_offset = i * 2 * self.hidden_channels
- g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
- else:
- g_l = torch.zeros_like(x_in)
-
- acts = commons.fused_add_tanh_sigmoid_multiply(
- x_in,
- g_l,
- n_channels_tensor)
- acts = self.drop(acts)
-
- res_skip_acts = self.res_skip_layers[i](acts)
- if i < self.n_layers - 1:
- res_acts = res_skip_acts[:,:self.hidden_channels,:]
- x = (x + res_acts) * x_mask
- output = output + res_skip_acts[:,self.hidden_channels:,:]
- else:
- output = output + res_skip_acts
- return output * x_mask
-
- def remove_weight_norm(self):
- if self.gin_channels != 0:
- torch.nn.utils.remove_weight_norm(self.cond_layer)
- for l in self.in_layers:
- torch.nn.utils.remove_weight_norm(l)
- for l in self.res_skip_layers:
- torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
- super(ResBlock1, self).__init__()
- self.convs1 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
- padding=get_padding(kernel_size, dilation[2])))
- ])
- self.convs1.apply(init_weights)
-
- self.convs2 = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
- padding=get_padding(kernel_size, 1)))
- ])
- self.convs2.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c1, c2 in zip(self.convs1, self.convs2):
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c1(xt)
- xt = F.leaky_relu(xt, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c2(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs1:
- remove_weight_norm(l)
- for l in self.convs2:
- remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
- def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
- super(ResBlock2, self).__init__()
- self.convs = nn.ModuleList([
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
- padding=get_padding(kernel_size, dilation[0]))),
- weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
- padding=get_padding(kernel_size, dilation[1])))
- ])
- self.convs.apply(init_weights)
-
- def forward(self, x, x_mask=None):
- for c in self.convs:
- xt = F.leaky_relu(x, LRELU_SLOPE)
- if x_mask is not None:
- xt = xt * x_mask
- xt = c(xt)
- x = xt + x
- if x_mask is not None:
- x = x * x_mask
- return x
-
- def remove_weight_norm(self):
- for l in self.convs:
- remove_weight_norm(l)
-
-
-class Log(nn.Module):
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
- logdet = torch.sum(-y, [1, 2])
- return y, logdet
- else:
- x = torch.exp(x) * x_mask
- return x
-
-
-class Flip(nn.Module):
- def forward(self, x, *args, reverse=False, **kwargs):
- x = torch.flip(x, [1])
- if not reverse:
- logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
- return x, logdet
- else:
- return x
-
-
-class ElementwiseAffine(nn.Module):
- def __init__(self, channels):
- super().__init__()
- self.channels = channels
- self.m = nn.Parameter(torch.zeros(channels,1))
- self.logs = nn.Parameter(torch.zeros(channels,1))
-
- def forward(self, x, x_mask, reverse=False, **kwargs):
- if not reverse:
- y = self.m + torch.exp(self.logs) * x
- y = y * x_mask
- logdet = torch.sum(self.logs * x_mask, [1,2])
- return y, logdet
- else:
- x = (x - self.m) * torch.exp(-self.logs) * x_mask
- return x
-
-
-class ResidualCouplingLayer(nn.Module):
- def __init__(self,
- channels,
- hidden_channels,
- kernel_size,
- dilation_rate,
- n_layers,
- p_dropout=0,
- gin_channels=0,
- mean_only=False):
- assert channels % 2 == 0, "channels should be divisible by 2"
- super().__init__()
- self.channels = channels
- self.hidden_channels = hidden_channels
- self.kernel_size = kernel_size
- self.dilation_rate = dilation_rate
- self.n_layers = n_layers
- self.half_channels = channels // 2
- self.mean_only = mean_only
-
- self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
- self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
- self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
- self.post.weight.data.zero_()
- self.post.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0) * x_mask
- h = self.enc(h, x_mask, g=g)
- stats = self.post(h) * x_mask
- if not self.mean_only:
- m, logs = torch.split(stats, [self.half_channels]*2, 1)
- else:
- m = stats
- logs = torch.zeros_like(m)
-
- if not reverse:
- x1 = m + x1 * torch.exp(logs) * x_mask
- x = torch.cat([x0, x1], 1)
- logdet = torch.sum(logs, [1,2])
- return x, logdet
- else:
- x1 = (x1 - m) * torch.exp(-logs) * x_mask
- x = torch.cat([x0, x1], 1)
- return x
-
-
-class ConvFlow(nn.Module):
- def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
- super().__init__()
- self.in_channels = in_channels
- self.filter_channels = filter_channels
- self.kernel_size = kernel_size
- self.n_layers = n_layers
- self.num_bins = num_bins
- self.tail_bound = tail_bound
- self.half_channels = in_channels // 2
-
- self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
- self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
- self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
- self.proj.weight.data.zero_()
- self.proj.bias.data.zero_()
-
- def forward(self, x, x_mask, g=None, reverse=False):
- x0, x1 = torch.split(x, [self.half_channels]*2, 1)
- h = self.pre(x0)
- h = self.convs(h, x_mask, g=g)
- h = self.proj(h) * x_mask
-
- b, c, t = x0.shape
- h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
- unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
- unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
- x1, logabsdet = piecewise_rational_quadratic_transform(x1,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=reverse,
- tails='linear',
- tail_bound=self.tail_bound
- )
-
- x = torch.cat([x0, x1], 1) * x_mask
- logdet = torch.sum(logabsdet * x_mask, [1,2])
- if not reverse:
- return x, logdet
- else:
- return x
diff --git a/spaces/Olivier-Truong/faster-whisper-webui-v2/LICENSE.md b/spaces/Olivier-Truong/faster-whisper-webui-v2/LICENSE.md
deleted file mode 100644
index f5f4b8b5ecd27c09e4ef16e9662bcb7bb2bfc76f..0000000000000000000000000000000000000000
--- a/spaces/Olivier-Truong/faster-whisper-webui-v2/LICENSE.md
+++ /dev/null
@@ -1,195 +0,0 @@
-Apache License
-==============
-
-_Version 2.0, January 2004_
-_<>_
-
-### Terms and Conditions for use, reproduction, and distribution
-
-#### 1. Definitions
-
-“License” shall mean the terms and conditions for use, reproduction, and
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-owner that is granting the License.
-
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-that control, are controlled by, or are under common control with that entity.
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-#### 7. Disclaimer of Warranty
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-Unless required by applicable law or agreed to in writing, Licensor provides the
-Work (and each Contributor provides its Contributions) on an “AS IS” BASIS,
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-### APPENDIX: How to apply the Apache License to your work
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-
diff --git a/spaces/Omnibus/MusicGen/tests/common_utils/__init__.py b/spaces/Omnibus/MusicGen/tests/common_utils/__init__.py
deleted file mode 100644
index 74ffcfef96fec35c99b2a1a053a61f44f7a8bbe9..0000000000000000000000000000000000000000
--- a/spaces/Omnibus/MusicGen/tests/common_utils/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# flake8: noqa
-from .temp_utils import TempDirMixin
-from .wav_utils import get_batch_white_noise, get_white_noise, save_wav
diff --git a/spaces/OpenDILabCommunity/LLMRiddlesChatGLMCN/llmriddles/llms/llm_server.py b/spaces/OpenDILabCommunity/LLMRiddlesChatGLMCN/llmriddles/llms/llm_server.py
deleted file mode 100644
index ddf7a21f27eac955e831e5e596d0066a2333b248..0000000000000000000000000000000000000000
--- a/spaces/OpenDILabCommunity/LLMRiddlesChatGLMCN/llmriddles/llms/llm_server.py
+++ /dev/null
@@ -1,72 +0,0 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from flask import Flask, request
-import argparse
-import logging
-
-
-class LLMInstance:
-
- def __init__(self, model_path: str, device: str = "cuda"):
-
- self.model = AutoModelForCausalLM.from_pretrained(model_path)
- self.tokenizer = AutoTokenizer.from_pretrained(model_path)
- self.model.to(device)
- self.device = device
-
- def query(self, message):
- try:
- messages = [
- {"role": "user", "content": message},
- ]
- encodeds = self.tokenizer.apply_chat_template(messages, return_tensors="pt")
- model_inputs = encodeds.to(self.device)
-
- generated_ids = self.model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
- decoded = self.tokenizer.batch_decode(generated_ids)
-
- # output is the string decoded[0] after "[/INST]". There may exist "", delete it.
- output = decoded[0].split("[/INST]")[1].split("")[0]
- return {
- 'code': 0,
- 'ret': True,
- 'error_msg': None,
- 'output': output
- }
- except Exception as e:
- return {
- 'code': 1,
- 'ret': False,
- 'error_msg': str(e),
- 'output': None
- }
-
-
-def create_app(core):
- app = Flask(__name__)
-
- @app.route('/ask_llm_for_answer', methods=['POST'])
- def ask_llm_for_answer():
- user_text = request.json['user_text']
- return core.query(user_text)
-
- return app
-
-
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument('-m', '--model_path', required=True, default='Mistral-7B-Instruct-v0.1', help='the model path of reward model')
- parser.add_argument('--ip', default='0.0.0.0')
- parser.add_argument('-p', '--port', default=8001)
- parser.add_argument('--debug', action='store_true')
- args = parser.parse_args()
-
- if args.debug:
- logging.getLogger().setLevel(logging.DEBUG)
- else:
- logging.getLogger().setLevel(logging.INFO)
- logging.getLogger().addHandler(logging.StreamHandler())
- logging.getLogger().handlers[0].setFormatter(logging.Formatter("%(message)s"))
-
- core = LLMInstance(args.model_path)
- app = create_app(core)
- app.run(host=args.ip, port=args.port)
diff --git a/spaces/PKUWilliamYang/VToonify/vtoonify/LICENSE.md b/spaces/PKUWilliamYang/VToonify/vtoonify/LICENSE.md
deleted file mode 100644
index a7e5837d44361b7aa1d633b9d36783ac838a45bc..0000000000000000000000000000000000000000
--- a/spaces/PKUWilliamYang/VToonify/vtoonify/LICENSE.md
+++ /dev/null
@@ -1,12 +0,0 @@
-# S-Lab License 1.0
-
-Copyright 2022 S-Lab
-
-Redistribution and use for non-commercial purpose in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
-1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
-2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
-3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.\
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-4. In the event that redistribution and/or use for commercial purpose in source or binary forms, with or without modification is required, please contact the contributor(s) of the work.
-
-
diff --git a/spaces/PKUWilliamYang/VToonify/vtoonify/model/raft/core/__init__.py b/spaces/PKUWilliamYang/VToonify/vtoonify/model/raft/core/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-17.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-17.go
deleted file mode 100644
index 9ee82cfb524166a9822b4a39183fcafe3051c8c9..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/srfi/srfi-17.go and /dev/null differ
diff --git a/spaces/PeerChristensen/TrumpTweetsDevice/app.py b/spaces/PeerChristensen/TrumpTweetsDevice/app.py
deleted file mode 100644
index 242b7385a04ce9a2f15840a5f55761cfd4df2723..0000000000000000000000000000000000000000
--- a/spaces/PeerChristensen/TrumpTweetsDevice/app.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import gradio as gr
-import pandas as pd
-import joblib
-import json
-from json import encoder
-
-encoder.FLOAT_REPR = lambda o: format(o, '.2f')
-
-model = joblib.load("nb_model.pickle")
-
-'''
-def return_output(text):
- """Alternative function to output predictions as simple text"""
- output = predict(text)
- output_string = f"Device: {str(output[0])}\n\nProbability: {int(output[1])}"
- return output_string
- '''
-
-
-def predict(text):
-
- data = pd.DataFrame({"text": [text]})
- #prediction_class = model.predict(data)[0]
- #prediction_prob = round(model.predict_proba(data).max(), 3)
- #return prediction_class, prediction_prob
- pred = model.predict_proba(data)[0]
- return {'Android': json.dumps(pred[0]), 'iPhone': json.dumps(pred[1])}
-
-
-description = "According to the dataset used for this model, Trump mainly uses two devices for tweeting" \
- " - an Android and an iPhone device.\nIt seems likely that members of his staff are tweeting on his" \
- " behalf using iPhone.\nTry and see if you can write an 'iPhone' and an 'Android' tweet."
-
-iface = gr.Interface(fn=predict,
- #fn=return_output,
- inputs="text",
- #outputs="text",
- outputs="label",
- allow_flagging='auto', title="iPhone or Android?",
- interpretation="default", description=description)
-
-iface.launch(enable_queue=True)
\ No newline at end of file
diff --git a/spaces/PhilPome/seo-analysis-tool/app.py b/spaces/PhilPome/seo-analysis-tool/app.py
deleted file mode 100644
index 4933c3aa70947d5f6d67ac62cf00108a569db6bf..0000000000000000000000000000000000000000
--- a/spaces/PhilPome/seo-analysis-tool/app.py
+++ /dev/null
@@ -1,524 +0,0 @@
-import os
-import requests
-from bs4 import BeautifulSoup, Tag
-from collections import Counter
-import re
-import string
-import nltk
-from nltk.corpus import stopwords
-from nltk.corpus import words
-from nltk.tokenize import word_tokenize
-from gensim.models import Word2Vec
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-import tempfile
-import gradio as gr
-import openai
-from googlesearch import search
-from pytrends.request import TrendReq
-from sklearn.manifold import MDS, TSNE
-from sklearn.metrics.pairwise import cosine_similarity
-from sklearn.cluster import KMeans
-from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
-from IPython.display import HTML
-import numpy as np
-import matplotlib.cm as cm
-from urllib.parse import urlparse, urljoin
-import os
-
-
-
-nltk.download('stopwords')
-nltk.download('punkt')
-nltk.download('words')
-
-# Set your OpenAI API key here
-openai.api_key = os.environ['OPENAI_API_KEY']
-
-
-#@title Define functions
-
-def get_image_html(fig):
- buf = io.BytesIO()
- fig.savefig(buf, format='png')
- buf.seek(0)
- return ''.format(base64.b64encode(buf.getvalue()).decode('ascii'))
-
-
-def search_top_competitors(keywords, num_results=10):
- competitors = set()
- for keyword in keywords:
- for url in search(keyword, num_results=num_results):
- competitors.add(url)
- return list(competitors)
-
-
-
-def get_page_content(url):
- response = requests.get(url)
- return BeautifulSoup(response.text, 'html.parser')
-
-def get_meta_tags(soup):
- meta_tags = soup.find_all('meta')
- return {tag.get('name'): tag.get('content') for tag in meta_tags if tag.get('name')}
-
-def get_heading_tags(soup):
- headings = {}
- for tag in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6']:
- headings[tag] = [heading.text for heading in soup.find_all(tag)]
- return headings
-
-def analyze_keywords(keywords_counter, top_n=10):
- return keywords_counter.most_common(top_n)
-
-def visualize_keywords(keywords_counter, top_n=10):
- common_keywords = analyze_keywords(keywords_counter, top_n)
- df = pd.DataFrame(common_keywords, columns=['Keyword', 'Count'])
- df.set_index('Keyword', inplace=True)
- df.plot(kind='bar', figsize=(12, 6))
- plt.title('Top Keywords')
- plt.xlabel('Keywords')
- plt.ylabel('Frequency')
-
- fig = plt.gcf() # Get the current figure
-
- plt.tight_layout()
- temp_image_file = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
- plt.savefig(temp_image_file.name, format='png')
- plt.close()
- return temp_image_file.name
-
-
-def plot_trends(keywords):
- pytrends = TrendReq(hl='en-US', tz=360, retries=3)
- pytrends.build_payload(keywords, cat=0, timeframe='today 12-m', geo='', gprop='')
- trends_data = pytrends.interest_over_time()
- return trends_data
-
-
-
-def preprocess_text(text, min_word_length=3):
- stop_words = set(stopwords.words('english'))
- words = word_tokenize(text.lower())
- words = [word for word in words if word.isalnum()]
- words = [word for word in words if len(word) >= min_word_length and word not in stop_words]
- return words
-
-def visualize_clusters(words, model):
- matrix = np.zeros((len(words), model.vector_size))
-
- for i, word in enumerate(words):
- matrix[i, :] = model.wv[word]
-
- mds = MDS(n_components=2, dissimilarity='precomputed', random_state=42)
- distance_matrix = 1 - cosine_similarity(matrix)
- coords = mds.fit_transform(distance_matrix)
-
- x, y = coords[:, 0], coords[:, 1]
-
- for i, word in enumerate(words):
- plt.scatter(x[i], y[i], alpha=0.5)
- plt.text(x[i], y[i], word, fontsize=10)
-
- plt.title('Word Clusters based on Thematic Relatedness')
- plt.show()
-
-
-
-def create_cluster_table(words, model, clusters):
- matrix = np.zeros((len(words), model.vector_size))
-
- for i, word in enumerate(words):
- matrix[i, :] = model.wv[word]
-
- # Create a dictionary to store words per cluster
- cluster_dict = {}
- for i, word in enumerate(words):
- cluster_id = clusters[i]
- if cluster_id not in cluster_dict:
- cluster_dict[cluster_id] = []
- cluster_dict[cluster_id].append(word)
-
- # Create a DataFrame from the dictionary
- max_words = max(len(cluster_words) for cluster_words in cluster_dict.values())
- num_clusters = len(cluster_dict)
- data = {f"Cluster {i}": cluster_dict.get(i, []) + [None] * (max_words - len(cluster_dict.get(i, [])))
- for i in range(num_clusters)}
-
- df = pd.DataFrame(data)
- return df
-
-
-def clean_text(text):
- # Separate words that are meant to be separated
- text = re.sub(r'([a-z])([A-Z])', r'\1 \2', text)
-
- # Tokenize the text
- tokens = nltk.word_tokenize(text)
-
- # Remove nonsensical words
- try:
- english_words = set(words)
- except:
- english_words = set(words.words())
- clean_tokens = [token for token in tokens if token.lower() in english_words or token.istitle()]
-
- # Join tokens back into a string
- clean_text = ' '.join(clean_tokens)
-
- return clean_text
-
-def visualize_clusters_og(words, model):
- matrix = np.zeros((len(words), model.vector_size))
-
- for i, word in enumerate(words):
- matrix[i, :] = model.wv[word]
-
- n_clusters = 5
- kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
- clusters = kmeans.fit_predict(matrix)
-
- tsne = TSNE(n_components=2, random_state=42)
- coords = tsne.fit_transform(matrix)
-
- x, y = coords[:, 0], coords[:, 1]
-
- colors = cm.rainbow(np.linspace(0, 1, n_clusters))
-
- plt.figure(figsize=(8, 8))
- for i, word in enumerate(words):
- plt.scatter(x[i], y[i], c=[colors[clusters[i]]], alpha=0.7)
- plt.text(x[i], y[i], word, fontsize=10)
-
- plt.xticks([])
- plt.yticks([])
- plt.title('Word Clusters based on Thematic Relatedness')
- plt.show()
-
-
-def visualize_clusters_plot(words, model):
- matrix = np.zeros((len(words), model.vector_size))
-
- for i, word in enumerate(words):
- matrix[i, :] = model.wv[word]
-
- n_clusters = 4
- kmeans = KMeans(n_clusters=n_clusters, random_state=42, n_init=10)
- clusters = kmeans.fit_predict(matrix)
-
- try:
- tsne = TSNE(n_components=2, random_state=42)
- coords = tsne.fit_transform(matrix)
- except ValueError:
- max_perplexity = len(words) - 1
- tsne = TSNE(n_components=2, random_state=42, perplexity=max_perplexity)
- coords = tsne.fit_transform(matrix)
-
-
- x, y = coords[:, 0], coords[:, 1]
-
- colors = cm.rainbow(np.linspace(0, 1, n_clusters))
-
- fig, axs = plt.subplots(2, 2, figsize=(8, 8), gridspec_kw={'width_ratios': [sum(clusters == 0) + sum(clusters == 1), sum(clusters == 2) + sum(clusters == 3)], 'height_ratios': [sum(clusters == 0) + sum(clusters == 2), sum(clusters == 1) + sum(clusters == 3)]})
- fig.subplots_adjust(wspace=0, hspace=0)
-
- for ax in axs.ravel():
- ax.axis('off')
-
- for i, word in enumerate(words):
- cluster_idx = clusters[i]
- ax = axs[cluster_idx // 2, cluster_idx % 2]
- ax.scatter(x[i], y[i], c=[colors[cluster_idx]], alpha=0.7)
- ax.text(x[i], y[i], word, fontsize=10)
-
- plt.legend(loc="best", fontsize=13)
- plt.tight_layout()
- temp_image_file = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
- plt.savefig(temp_image_file.name, format='png')
- plt.close()
- return temp_image_file.name, clusters
-
-
-def sanitize_url(url):
- if not re.match('^(http|https)://', url):
- url = 'http://' + url
-
- if not re.match('^(http|https)://www\.', url):
- url = re.sub('^(http|https)://', r'\g<0>www.', url)
-
- return url
-
-
-
-
-# Define the inputs and outputs
-competitor_url_input = gr.inputs.Textbox(label="Competitor URL", placeholder="Enter a competitor URL")
-
-full_site_scrape_checkbox = gr.inputs.Checkbox(label="Tick for full site scrape (otherwise landing page only)")
-
-
-meta_tags_output = gr.outputs.Textbox(label="Meta Tags")
-heading_tags_output = gr.outputs.Textbox(label="Heading Tags")
-top10keywords_output = gr.outputs.Textbox(label="Top 10 Keywords")
-cluster_table_output = gr.outputs.HTML(label="Cluster Table")
-cluster_plot_output = gr.outputs.Image(type='filepath', label="Cluster Plot")
-keyword_plot_output = gr.outputs.Image(type='filepath', label="Keyword Plot")
-seo_analysis_output = gr.outputs.Textbox(label="SEO Analysis")
-
-def append_unique_elements(source, target):
- for element in source:
- if isinstance(element, Tag) and element not in target:
- target.append(element)
-
-def get_internal_links(url: str):
- response = requests.get(url)
- soup = BeautifulSoup(response.content, "html.parser")
- internal_links = set()
-
- for link in soup.find_all("a"):
- href = link.get("href")
-
- if href:
- joined_url = urljoin(url, href)
- parsed_url = urlparse(joined_url)
-
- if parsed_url.netloc == urlparse(url).netloc:
- internal_links.add(joined_url)
-
- return internal_links
-
-def analyze_single_page(competitor_url: str):
- sanitized_url = sanitize_url(competitor_url)
- soup = get_page_content(sanitized_url)
-
- # Scrape and analyze meta tags
- meta_tags = get_meta_tags(soup)
- topmetatags = ""
- for name, content in meta_tags.items():
- if "description" in name.lower():
- topmetatags += (f"{name}: {content}\n")
-
- # Scrape and analyze heading tags
- heading_tags = get_heading_tags(soup)
- topheadingtags = ""
- for tag, headings in heading_tags.items():
- filtered_headings = [heading for heading in headings if len(heading) > 2]
- if filtered_headings:
- topheadingtags += (f"{tag}: {', '.join(filtered_headings)}\n")
-
- # Scrape, analyze, and visualize keywords from page content
- page_text = soup.get_text()
- page_text_cleaned = clean_text(page_text)
- preprocessed_text = preprocess_text(page_text_cleaned)
-
- keywords_counter = Counter(preprocessed_text)
- top10keywords = ""
-
- for keyword, count in analyze_keywords(keywords_counter, top_n=10):
- top10keywords += (f"{keyword}: {count}\n")
-
- # Semantic clustering and visualization
- sentences = [preprocessed_text[i:i+10] for i in range(0, len(preprocessed_text), 10)]
- model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, workers=4)
-
- words = [word for word, _ in analyze_keywords(keywords_counter, top_n=50)]
- clusters = [model.wv.doesnt_match(words)] * len(words)
-
-
- cluster_plot,clusters = visualize_clusters_plot(words, model)
- cluster_table = create_cluster_table(words, model, clusters)
- keyword_plot = visualize_keywords(keywords_counter, top_n=10)
-
- table_string = cluster_table.to_string(index=False)
- SEO_prompt = f"""The following information is given about a company's website:
- Meta Tags:
- {{meta_tags}}
- Heading Tags:
- {{heading_tags}}
- Top 10 Keywords:
- {{top10keywords}}
- The following table represents clusters of thematically related words identified using NLP and clustering techniques. Each column represents a different cluster, and the words in each column are thematically related.
- {table_string}
- Please analyze the provided information and perform the following tasks:
- 1. Predict what the website is all about (the market sector).
- 2. Based on the market sector of the company, give a name to each cluster based on the theme it represents. The name needs to be the best summary of all the words in the cluster.
- 3. Perform a SWOT analysis (Strengths, Weaknesses, Opportunities, and Threats) from an SEO perspective for the company as a whole, taking into account the meta tags, heading tags, top 10 keywords, and the clusters.
- Please provide your analysis in a clear and concise manner.
- 4. Lastly, suggest a list of 5 single words and 5 phrases (no longer than 3 words each) that the company should be using to improve their SEO
- """.format(meta_tags=meta_tags, heading_tags=heading_tags, top10keywords=top10keywords, table_string=table_string)
-
-
-
- def analyse_SEO(SEO_prompt):
- response = openai.Completion.create(
- model="text-davinci-003",
- prompt = SEO_prompt,
- temperature=0.7,
- max_tokens=1000,
- top_p=1,
- frequency_penalty=0,
- presence_penalty=0
- )
- gpt3_response = response.get('choices')[0].text
- return gpt3_response,response
-
-
- seo_analysis = analyse_SEO(SEO_prompt)
-
- return topmetatags, topheadingtags, top10keywords, cluster_table.to_html(), cluster_plot, keyword_plot, seo_analysis[0]
-
-
-
-
-def analyze_website(competitor_url: str, full_site_scrape: bool = False):
-
- if not full_site_scrape:
- topmetatags, topheadingtags, top10keywords, cluster_table, cluster_plot, keyword_plot, seo_analysis = analyze_single_page(competitor_url)
- return topmetatags, topheadingtags, top10keywords, cluster_table, cluster_plot, keyword_plot, seo_analysis
-
- sanitized_url = sanitize_url(competitor_url)
- internal_links = get_internal_links(sanitized_url)
- soup_collection = BeautifulSoup("", "html.parser")
-
- for link in internal_links:
- try:
- soup = get_page_content(link)
- append_unique_elements(soup.head, soup_collection.head)
- append_unique_elements(soup.body, soup_collection.body)
- except Exception as e:
- print(f"Failed to analyze link: {link}. Error: {e}")
-
- print('got all the links')
-
- # Scrape and analyze meta tags
- meta_tags = get_meta_tags(soup_collection)
- topmetatags = ""
- for name, content in meta_tags.items():
- if "description" in name.lower():
- topmetatags += (f"{name}: {content}\n")
-
- print('fetched metatags')
-
- # Scrape and analyze heading tags
- heading_tags = get_heading_tags(soup_collection)
- topheadingtags = ""
- for tag, headings in heading_tags.items():
- filtered_headings = [heading for heading in headings if len(heading) > 2]
- if filtered_headings:
- topheadingtags += (f"{tag}: {', '.join(filtered_headings)}\n")
-
- print("fetched heading tags")
-
- # Scrape, analyze, and visualize keywords from page content
- page_text = soup_collection.get_text()
- page_text_cleaned = clean_text(page_text)
- preprocessed_text = preprocess_text(page_text_cleaned)
-
- keywords_counter = Counter(preprocessed_text)
- top10keywords = ""
-
- for keyword, count in analyze_keywords(keywords_counter, top_n=10):
- top10keywords += (f"{keyword}: {count}\n")
-
- print("fetched keywords")
-
- # Semantic clustering and visualization
- sentences = [preprocessed_text[i:i+10] for i in range(0, len(preprocessed_text), 10)]
- model = Word2Vec(sentences, vector_size=100, window=5, min_count=1, workers=4)
-
- words = [word for word, _ in analyze_keywords(keywords_counter, top_n=50)]
- clusters = [model.wv.doesnt_match(words)] * len(words)
-
- print("calculated clusters")
-
- cluster_plot,clusters = visualize_clusters_plot(words, model)
- cluster_table = create_cluster_table(words, model, clusters)
- keyword_plot = visualize_keywords(keywords_counter, top_n=10)
-
-
- print("plotted figures")
-
- table_string = cluster_table.to_string(index=False)
-
- print("created table string")
-
- heading_tags_compressed = {}
-
- for key, values in heading_tags.items():
- count = Counter(values)
- sorted_values = sorted(count.keys(), key=lambda x: count[x], reverse=True)
- filtered_values = [value for value in sorted_values if value.strip() != ""]
- heading_tags_compressed[key] = filtered_values[:10]
-
-
- heading_tags_clean = {}
-
- for key, values in heading_tags.items():
- count = Counter(values)
- sorted_values_clean = sorted(count.keys(), key=lambda x: count[x], reverse=True)
- heading_tags_clean = [value for value in sorted_values_clean if value.strip() != ""]
-
- print("cleaned up heading tags")
-
-
- SEO_prompt = f"""The following information is given about a company's website:
- Meta Tags:
- {{meta_tags}}
- Heading Tags:
- {{heading_tags_compressed}}
- Top 10 Keywords:
- {{top10keywords}}
- The following table represents clusters of thematically related words identified using NLP and clustering techniques. Each column represents a different cluster, and the words in each column are thematically related.
- {table_string}
- Please analyze the provided information and perform the following tasks:
- 1. Predict what the website is all about (the market sector).
- 2. Based on the market sector of the company, give a name to each cluster based on the theme it represents. The name needs to be the best summary of all the words in the cluster.
- 3. Perform a SWOT analysis (Strengths, Weaknesses, Opportunities, and Threats) from an SEO perspective for the company as a whole, taking into account the meta tags, heading tags, top 10 keywords, and the clusters.
- Please provide your analysis in a clear and concise manner.
- 4. Lastly, suggest a list of 10 words and 10 phrases that the company should be using to improve their SEO
- """.format(meta_tags=meta_tags, heading_tags_compressed=heading_tags_compressed, top10keywords=top10keywords, table_string=table_string)
-
- print("defined SEO prompt")
-
- def analyse_SEO(SEO_prompt):
- response = openai.Completion.create(
- model="text-davinci-003",
- prompt = SEO_prompt,
- temperature=0.7,
- max_tokens=1000,
- top_p=1,
- frequency_penalty=0,
- presence_penalty=0
- )
- gpt3_response = response.get('choices')[0].text
- return gpt3_response,response
-
-
- seo_analysis = analyse_SEO(SEO_prompt)
-
- print("ran seo analysis")
-
- print(topmetatags, heading_tags_clean,top10keywords,cluster_table.to_html(), cluster_plot, keyword_plot,seo_analysis[0])
-
-
- return topmetatags, heading_tags_clean, top10keywords, cluster_table.to_html(), cluster_plot, keyword_plot, seo_analysis[0]
-
-
-
-gr.Interface(
- fn=analyze_website,
- inputs=[competitor_url_input, full_site_scrape_checkbox],
- outputs=[
- meta_tags_output,
- heading_tags_output,
- top10keywords_output,
- cluster_table_output,
- cluster_plot_output,
- keyword_plot_output,
- seo_analysis_output,
- ],
- title="SEO Analysis Tool",
- description="Enter a competitor URL to perform a SEO analysis (some javascript pages will deny full scrape).",
- layout="vertical"
-).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/Pie31415/control-animation/annotator/midas/midas/midas_net.py b/spaces/Pie31415/control-animation/annotator/midas/midas/midas_net.py
deleted file mode 100644
index 8a954977800b0a0f48807e80fa63041910e33c1f..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/midas/midas/midas_net.py
+++ /dev/null
@@ -1,76 +0,0 @@
-"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
-This file contains code that is adapted from
-https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
-"""
-import torch
-import torch.nn as nn
-
-from .base_model import BaseModel
-from .blocks import FeatureFusionBlock, Interpolate, _make_encoder
-
-
-class MidasNet(BaseModel):
- """Network for monocular depth estimation.
- """
-
- def __init__(self, path=None, features=256, non_negative=True):
- """Init.
-
- Args:
- path (str, optional): Path to saved model. Defaults to None.
- features (int, optional): Number of features. Defaults to 256.
- backbone (str, optional): Backbone network for encoder. Defaults to resnet50
- """
- print("Loading weights: ", path)
-
- super(MidasNet, self).__init__()
-
- use_pretrained = False if path is None else True
-
- self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained)
-
- self.scratch.refinenet4 = FeatureFusionBlock(features)
- self.scratch.refinenet3 = FeatureFusionBlock(features)
- self.scratch.refinenet2 = FeatureFusionBlock(features)
- self.scratch.refinenet1 = FeatureFusionBlock(features)
-
- self.scratch.output_conv = nn.Sequential(
- nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
- Interpolate(scale_factor=2, mode="bilinear"),
- nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
- nn.ReLU(True),
- nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
- nn.ReLU(True) if non_negative else nn.Identity(),
- )
-
- if path:
- self.load(path)
-
- def forward(self, x):
- """Forward pass.
-
- Args:
- x (tensor): input data (image)
-
- Returns:
- tensor: depth
- """
-
- layer_1 = self.pretrained.layer1(x)
- layer_2 = self.pretrained.layer2(layer_1)
- layer_3 = self.pretrained.layer3(layer_2)
- layer_4 = self.pretrained.layer4(layer_3)
-
- layer_1_rn = self.scratch.layer1_rn(layer_1)
- layer_2_rn = self.scratch.layer2_rn(layer_2)
- layer_3_rn = self.scratch.layer3_rn(layer_3)
- layer_4_rn = self.scratch.layer4_rn(layer_4)
-
- path_4 = self.scratch.refinenet4(layer_4_rn)
- path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
- path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
- path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
-
- out = self.scratch.output_conv(path_1)
-
- return torch.squeeze(out, dim=1)
diff --git a/spaces/Pie31415/control-animation/annotator/uniformer/configs/_base_/models/ann_r50-d8.py b/spaces/Pie31415/control-animation/annotator/uniformer/configs/_base_/models/ann_r50-d8.py
deleted file mode 100644
index a2cb653827e44e6015b3b83bc578003e614a6aa1..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/annotator/uniformer/configs/_base_/models/ann_r50-d8.py
+++ /dev/null
@@ -1,46 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
- type='EncoderDecoder',
- pretrained='open-mmlab://resnet50_v1c',
- backbone=dict(
- type='ResNetV1c',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- dilations=(1, 1, 2, 4),
- strides=(1, 2, 1, 1),
- norm_cfg=norm_cfg,
- norm_eval=False,
- style='pytorch',
- contract_dilation=True),
- decode_head=dict(
- type='ANNHead',
- in_channels=[1024, 2048],
- in_index=[2, 3],
- channels=512,
- project_channels=256,
- query_scales=(1, ),
- key_pool_scales=(1, 3, 6, 8),
- dropout_ratio=0.1,
- num_classes=19,
- norm_cfg=norm_cfg,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
- auxiliary_head=dict(
- type='FCNHead',
- in_channels=1024,
- in_index=2,
- channels=256,
- num_convs=1,
- concat_input=False,
- dropout_ratio=0.1,
- num_classes=19,
- norm_cfg=norm_cfg,
- align_corners=False,
- loss_decode=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
- # model training and testing settings
- train_cfg=dict(),
- test_cfg=dict(mode='whole'))
diff --git a/spaces/Purple11/Grounded-Diffusion/ldm/models/diffusion/ddim.py b/spaces/Purple11/Grounded-Diffusion/ldm/models/diffusion/ddim.py
deleted file mode 100644
index 2fcda95846b04944ae870b8b9a87efa1bd25140d..0000000000000000000000000000000000000000
--- a/spaces/Purple11/Grounded-Diffusion/ldm/models/diffusion/ddim.py
+++ /dev/null
@@ -1,254 +0,0 @@
-"""SAMPLING ONLY."""
-
-import torch
-import numpy as np
-from tqdm import tqdm
-from functools import partial
-from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, \
- extract_into_tensor
-
-def clear_start_noise():
- global start_noise
- start_noise=0
-def get_start_noise():
- global start_noise
- return start_noise
-class DDIMSampler(object):
- def __init__(self, model, schedule="linear", **kwargs):
- super().__init__()
- self.model = model
- self.ddpm_num_timesteps = model.num_timesteps
- self.schedule = schedule
-
- def register_buffer(self, name, attr):
- if type(attr) == torch.Tensor:
- if attr.device != torch.device("cuda"):
- attr = attr.to(torch.device("cuda")) if torch.cuda.is_available() else attr.to(torch.device("cpu"))
- setattr(self, name, attr)
-
- def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
- self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
- num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
- alphas_cumprod = self.model.alphas_cumprod
- assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
- to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
-
- self.register_buffer('betas', to_torch(self.model.betas))
- self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
- self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
-
- # calculations for diffusion q(x_t | x_{t-1}) and others
- self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
- self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
- self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
- self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
- self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
-
- # ddim sampling parameters
- ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
- ddim_timesteps=self.ddim_timesteps,
- eta=ddim_eta,verbose=verbose)
- self.register_buffer('ddim_sigmas', ddim_sigmas)
- self.register_buffer('ddim_alphas', ddim_alphas)
- self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
- self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
- sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
- (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
- 1 - self.alphas_cumprod / self.alphas_cumprod_prev))
- self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
-
-
- def sample(self,
- S,
- batch_size,
- shape,
- conditioning=None,
- callback=None,
- normals_sequence=None,
- img_callback=None,
- quantize_x0=False,
- eta=0.,
- mask=None,
- x0=None,
- temperature=1.,
- noise_dropout=0.,
- score_corrector=None,
- corrector_kwargs=None,
- verbose=True,
- x_T=None,
- log_every_t=100,
- unconditional_guidance_scale=1.,
- unconditional_conditioning=None,
- class_token_index=[],
- # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
- **kwargs
- ):
- if conditioning is not None:
- if isinstance(conditioning, dict):
- cbs = conditioning[list(conditioning.keys())[0]].shape[0]
- if cbs != batch_size:
- print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
- else:
- if conditioning.shape[0] != batch_size:
- print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
- self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
- # sampling
- C, H, W = shape
- size = (batch_size, C, H, W)
- print(f'Data shape for DDIM sampling is {size}, eta {eta}')
-
- samples, intermediates,seg = self.ddim_sampling(conditioning, size,
- callback=callback,
- img_callback=img_callback,
- quantize_denoised=quantize_x0,
- mask=mask, x0=x0,
- ddim_use_original_steps=False,
- noise_dropout=noise_dropout,
- temperature=temperature,
- score_corrector=score_corrector,
- corrector_kwargs=corrector_kwargs,
- x_T=x_T,
- log_every_t=log_every_t,
- unconditional_guidance_scale=unconditional_guidance_scale,
- unconditional_conditioning=unconditional_conditioning,
- class_token_index=class_token_index,
- )
- return samples, intermediates,seg
-
-
- def ddim_sampling(self, cond, shape,
- x_T=None, ddim_use_original_steps=False,
- callback=None, timesteps=None, quantize_denoised=False,
- mask=None, x0=None, img_callback=None, log_every_t=100,
- temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
- unconditional_guidance_scale=1., unconditional_conditioning=None,class_token_index=[],):
- device = self.model.betas.device
- b = shape[0]
- if x_T is None:
- img = torch.randn(shape, device=device)
- global start_noise
- start_noise=img
-
- else:
-
- img = x_T
-
- if timesteps is None:
- timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
- elif timesteps is not None and not ddim_use_original_steps:
- subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
- timesteps = self.ddim_timesteps[:subset_end]
-
- intermediates = {'x_inter': [img], 'pred_x0': [img]}
- time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
- total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
- print(f"Running DDIM Sampling with {total_steps} timesteps")
-
- iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
-
- for i, step in enumerate(iterator):
-
- index = total_steps - i - 1
- ts = torch.full((b,), step, device=device, dtype=torch.long)
-
- if mask is not None:
- assert x0 is not None
- img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
- img = img_orig * mask + (1. - mask) * img
-
- outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
- quantize_denoised=quantize_denoised, temperature=temperature,
- noise_dropout=noise_dropout, score_corrector=score_corrector,
- corrector_kwargs=corrector_kwargs,
- unconditional_guidance_scale=unconditional_guidance_scale,
- unconditional_conditioning=unconditional_conditioning,class_token_index=class_token_index,)
- img, pred_x0,seg = outs
-
- if callback: callback(i)
- if img_callback: img_callback(pred_x0, i)
-
- if index % log_every_t == 0 or index == total_steps - 1:
- intermediates['x_inter'].append(img)
- intermediates['pred_x0'].append(pred_x0)
-
- return img, intermediates,seg
-
-
- def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
- temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
- unconditional_guidance_scale=1., unconditional_conditioning=None,class_token_index=[],):
- b, *_, device = *x.shape, x.device
-
- if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
- e_t, seg = self.model.apply_model(x, t, c,class_token_index=class_token_index)
- else:
- x_in = torch.cat([x] * 2)
- t_in = torch.cat([t] * 2)
- c_in = torch.cat([unconditional_conditioning, c])
-
- e_init, seg = self.model.apply_model(x_in, t_in, c_in,class_token_index=class_token_index)
- e_t_uncond, e_t = e_init.chunk(2)
- e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-
- if score_corrector is not None:
- assert self.model.parameterization == "eps"
- e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
- alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
- alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
- sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
- sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
- # select parameters corresponding to the currently considered timestep
- a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
- a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
- sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
- sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
- # current prediction for x_0
- pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
- if quantize_denoised:
- pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
- # direction pointing to x_t
- dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
- noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
- if noise_dropout > 0.:
- noise = torch.nn.functional.dropout(noise, p=noise_dropout)
- x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
- return x_prev, pred_x0, seg
-
- # @torch.no_grad()
- def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
- # fast, but does not allow for exact reconstruction
- # t serves as an index to gather the correct alphas
- if use_original_steps:
- sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
- sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
- else:
- sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
- sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
-
- if noise is None:
- noise = torch.randn_like(x0)
- return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
- extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
-
- # @torch.no_grad()
- def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
- use_original_steps=False):
-
- timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
- timesteps = timesteps[:t_start]
-
- time_range = np.flip(timesteps)
- total_steps = timesteps.shape[0]
-
- x_dec = x_latent
- for i in range(total_steps):
- step = time_range[i]
- index = total_steps - i - 1
- ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
- x_dec, _, seg = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
- unconditional_guidance_scale=unconditional_guidance_scale,
- unconditional_conditioning=unconditional_conditioning)
- return x_dec, seg
\ No newline at end of file
diff --git a/spaces/Puyush/MultiLabel-TextClassification/app.py b/spaces/Puyush/MultiLabel-TextClassification/app.py
deleted file mode 100644
index 2198184b9381ec63ddad8d2f456ac96bcc7f5c0f..0000000000000000000000000000000000000000
--- a/spaces/Puyush/MultiLabel-TextClassification/app.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import re
-import nltk
-import keras
-import spacy
-import string
-import pickle
-import tempfile
-import numpy as np
-import gradio as gr
-import contractions
-import tensorflow as tf
-from nltk.stem import WordNetLemmatizer
-from nltk.tokenize import word_tokenize
-from nltk.corpus import stopwords, wordnet
-from tensorflow.keras.layers import Layer
-from tensorflow.keras import backend as K
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-from transformers import TFBertModel
-
-# Load the BERT model architecture (without weights)
-# bert_model = TFBertModel.from_pretrained('bert-base-uncased')
-
-# Load the custom layers and the saved weights
-# sentiment_model = tf.keras.models.load_model('sentiment_model.h5')
-
-# The custom layers and weights will be automatically loaded.
-
-# Load text tokenizer
-def load_tokenizer(path):
- with open(path, 'rb') as f:
- tokenizer = pickle.load(f)
- return tokenizer
-
-# text_tokenizer = load_tokenizer('tokenizer.pkl')
-
-def prepare_data(input_text, tokenizer):
- token = tokenizer.encode_plus(
- input_text,
- max_length=128,
- truncation=True,
- padding='max_length',
- add_special_tokens=True,
- return_tensors='tf'
- )
- return {
- 'input_ids': tf.cast(token.input_ids, tf.float64),
- 'attention_mask': tf.cast(token.attention_mask, tf.float64)
- }
-
-def make_prediction(model, processed_data, classes=['Anger/ Intermittent Explosive Disorder', 'Anxiety Disorder', 'Depression', 'Narcissistic Disorder', 'Panic Disorder']):
- probs = model.predict(processed_data)[0]
- return classes[np.argmax(probs)]
-
-def transcribe_audio(text):
- processed_data = prepare_data(text, text_tokenizer)
- # Load sentiment analysis model
- result = make_prediction(sentiment_model, processed_data)
- return result
-
-text_tokenizer = load_tokenizer('tokenizer.pkl')
-# Load the BERT model architecture (without weights)
-custom_objects = {'TFBertModel': TFBertModel}
-sentiment_model = tf.keras.models.load_model('sentiment_model.h5', custom_objects=custom_objects)
-# bert_model = TFBertModel.from_pretrained('bert-base-uncased')
-# sentiment_model = tf.keras.models.load_model("sentiment_model.keras")
-# Set the starting state to an empty string
-interface = gr.Interface(fn=transcribe_audio, inputs = gr.inputs.Textbox(lines=5, placeholder='Enter a positive or negative tweet here...'),
- outputs='text',title='Twitter Sentimental Analysis', theme='darkhuggingface')
-interface.launch(inline = False)
diff --git a/spaces/RMXK/RVC_HFF/infer/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py b/spaces/RMXK/RVC_HFF/infer/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
deleted file mode 100644
index 55abcfdb87636a9ee85b8df5cdc1bec64098b5da..0000000000000000000000000000000000000000
--- a/spaces/RMXK/RVC_HFF/infer/lib/infer_pack/modules/F0Predictor/DioF0Predictor.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import numpy as np
-import pyworld
-
-from infer.lib.infer_pack.modules.F0Predictor.F0Predictor import F0Predictor
-
-
-class DioF0Predictor(F0Predictor):
- def __init__(self, hop_length=512, f0_min=50, f0_max=1100, sampling_rate=44100):
- self.hop_length = hop_length
- self.f0_min = f0_min
- self.f0_max = f0_max
- self.sampling_rate = sampling_rate
-
- def interpolate_f0(self, f0):
- """
- 对F0进行插值处理
- """
-
- data = np.reshape(f0, (f0.size, 1))
-
- vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
- vuv_vector[data > 0.0] = 1.0
- vuv_vector[data <= 0.0] = 0.0
-
- ip_data = data
-
- frame_number = data.size
- last_value = 0.0
- for i in range(frame_number):
- if data[i] <= 0.0:
- j = i + 1
- for j in range(i + 1, frame_number):
- if data[j] > 0.0:
- break
- if j < frame_number - 1:
- if last_value > 0.0:
- step = (data[j] - data[i - 1]) / float(j - i)
- for k in range(i, j):
- ip_data[k] = data[i - 1] + step * (k - i + 1)
- else:
- for k in range(i, j):
- ip_data[k] = data[j]
- else:
- for k in range(i, frame_number):
- ip_data[k] = last_value
- else:
- ip_data[i] = data[i] # 这里可能存在一个没有必要的拷贝
- last_value = data[i]
-
- return ip_data[:, 0], vuv_vector[:, 0]
-
- def resize_f0(self, x, target_len):
- source = np.array(x)
- source[source < 0.001] = np.nan
- target = np.interp(
- np.arange(0, len(source) * target_len, len(source)) / target_len,
- np.arange(0, len(source)),
- source,
- )
- res = np.nan_to_num(target)
- return res
-
- def compute_f0(self, wav, p_len=None):
- if p_len is None:
- p_len = wav.shape[0] // self.hop_length
- f0, t = pyworld.dio(
- wav.astype(np.double),
- fs=self.sampling_rate,
- f0_floor=self.f0_min,
- f0_ceil=self.f0_max,
- frame_period=1000 * self.hop_length / self.sampling_rate,
- )
- f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
- for index, pitch in enumerate(f0):
- f0[index] = round(pitch, 1)
- return self.interpolate_f0(self.resize_f0(f0, p_len))[0]
-
- def compute_f0_uv(self, wav, p_len=None):
- if p_len is None:
- p_len = wav.shape[0] // self.hop_length
- f0, t = pyworld.dio(
- wav.astype(np.double),
- fs=self.sampling_rate,
- f0_floor=self.f0_min,
- f0_ceil=self.f0_max,
- frame_period=1000 * self.hop_length / self.sampling_rate,
- )
- f0 = pyworld.stonemask(wav.astype(np.double), f0, t, self.sampling_rate)
- for index, pitch in enumerate(f0):
- f0[index] = round(pitch, 1)
- return self.interpolate_f0(self.resize_f0(f0, p_len))
diff --git a/spaces/RamAnanth1/videocrafter/extralibs/midas/midas/transforms.py b/spaces/RamAnanth1/videocrafter/extralibs/midas/midas/transforms.py
deleted file mode 100644
index 350cbc11662633ad7f8968eb10be2e7de6e384e9..0000000000000000000000000000000000000000
--- a/spaces/RamAnanth1/videocrafter/extralibs/midas/midas/transforms.py
+++ /dev/null
@@ -1,234 +0,0 @@
-import numpy as np
-import cv2
-import math
-
-
-def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
- """Rezise the sample to ensure the given size. Keeps aspect ratio.
-
- Args:
- sample (dict): sample
- size (tuple): image size
-
- Returns:
- tuple: new size
- """
- shape = list(sample["disparity"].shape)
-
- if shape[0] >= size[0] and shape[1] >= size[1]:
- return sample
-
- scale = [0, 0]
- scale[0] = size[0] / shape[0]
- scale[1] = size[1] / shape[1]
-
- scale = max(scale)
-
- shape[0] = math.ceil(scale * shape[0])
- shape[1] = math.ceil(scale * shape[1])
-
- # resize
- sample["image"] = cv2.resize(
- sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
- )
-
- sample["disparity"] = cv2.resize(
- sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
- )
- sample["mask"] = cv2.resize(
- sample["mask"].astype(np.float32),
- tuple(shape[::-1]),
- interpolation=cv2.INTER_NEAREST,
- )
- sample["mask"] = sample["mask"].astype(bool)
-
- return tuple(shape)
-
-
-class Resize(object):
- """Resize sample to given size (width, height).
- """
-
- def __init__(
- self,
- width,
- height,
- resize_target=True,
- keep_aspect_ratio=False,
- ensure_multiple_of=1,
- resize_method="lower_bound",
- image_interpolation_method=cv2.INTER_AREA,
- ):
- """Init.
-
- Args:
- width (int): desired output width
- height (int): desired output height
- resize_target (bool, optional):
- True: Resize the full sample (image, mask, target).
- False: Resize image only.
- Defaults to True.
- keep_aspect_ratio (bool, optional):
- True: Keep the aspect ratio of the input sample.
- Output sample might not have the given width and height, and
- resize behaviour depends on the parameter 'resize_method'.
- Defaults to False.
- ensure_multiple_of (int, optional):
- Output width and height is constrained to be multiple of this parameter.
- Defaults to 1.
- resize_method (str, optional):
- "lower_bound": Output will be at least as large as the given size.
- "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
- "minimal": Scale as least as possible. (Output size might be smaller than given size.)
- Defaults to "lower_bound".
- """
- self.__width = width
- self.__height = height
-
- self.__resize_target = resize_target
- self.__keep_aspect_ratio = keep_aspect_ratio
- self.__multiple_of = ensure_multiple_of
- self.__resize_method = resize_method
- self.__image_interpolation_method = image_interpolation_method
-
- def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
- y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
- if max_val is not None and y > max_val:
- y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
- if y < min_val:
- y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
- return y
-
- def get_size(self, width, height):
- # determine new height and width
- scale_height = self.__height / height
- scale_width = self.__width / width
-
- if self.__keep_aspect_ratio:
- if self.__resize_method == "lower_bound":
- # scale such that output size is lower bound
- if scale_width > scale_height:
- # fit width
- scale_height = scale_width
- else:
- # fit height
- scale_width = scale_height
- elif self.__resize_method == "upper_bound":
- # scale such that output size is upper bound
- if scale_width < scale_height:
- # fit width
- scale_height = scale_width
- else:
- # fit height
- scale_width = scale_height
- elif self.__resize_method == "minimal":
- # scale as least as possbile
- if abs(1 - scale_width) < abs(1 - scale_height):
- # fit width
- scale_height = scale_width
- else:
- # fit height
- scale_width = scale_height
- else:
- raise ValueError(
- f"resize_method {self.__resize_method} not implemented"
- )
-
- if self.__resize_method == "lower_bound":
- new_height = self.constrain_to_multiple_of(
- scale_height * height, min_val=self.__height
- )
- new_width = self.constrain_to_multiple_of(
- scale_width * width, min_val=self.__width
- )
- elif self.__resize_method == "upper_bound":
- new_height = self.constrain_to_multiple_of(
- scale_height * height, max_val=self.__height
- )
- new_width = self.constrain_to_multiple_of(
- scale_width * width, max_val=self.__width
- )
- elif self.__resize_method == "minimal":
- new_height = self.constrain_to_multiple_of(scale_height * height)
- new_width = self.constrain_to_multiple_of(scale_width * width)
- else:
- raise ValueError(f"resize_method {self.__resize_method} not implemented")
-
- return (new_width, new_height)
-
- def __call__(self, sample):
- width, height = self.get_size(
- sample["image"].shape[1], sample["image"].shape[0]
- )
-
- # resize sample
- sample["image"] = cv2.resize(
- sample["image"],
- (width, height),
- interpolation=self.__image_interpolation_method,
- )
-
- if self.__resize_target:
- if "disparity" in sample:
- sample["disparity"] = cv2.resize(
- sample["disparity"],
- (width, height),
- interpolation=cv2.INTER_NEAREST,
- )
-
- if "depth" in sample:
- sample["depth"] = cv2.resize(
- sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
- )
-
- sample["mask"] = cv2.resize(
- sample["mask"].astype(np.float32),
- (width, height),
- interpolation=cv2.INTER_NEAREST,
- )
- sample["mask"] = sample["mask"].astype(bool)
-
- return sample
-
-
-class NormalizeImage(object):
- """Normlize image by given mean and std.
- """
-
- def __init__(self, mean, std):
- self.__mean = mean
- self.__std = std
-
- def __call__(self, sample):
- sample["image"] = (sample["image"] - self.__mean) / self.__std
-
- return sample
-
-
-class PrepareForNet(object):
- """Prepare sample for usage as network input.
- """
-
- def __init__(self):
- pass
-
- def __call__(self, sample):
- image = np.transpose(sample["image"], (2, 0, 1))
- sample["image"] = np.ascontiguousarray(image).astype(np.float32)
-
- if "mask" in sample:
- sample["mask"] = sample["mask"].astype(np.float32)
- sample["mask"] = np.ascontiguousarray(sample["mask"])
-
- if "disparity" in sample:
- disparity = sample["disparity"].astype(np.float32)
- sample["disparity"] = np.ascontiguousarray(disparity)
-
- if "depth" in sample:
- depth = sample["depth"].astype(np.float32)
- sample["depth"] = np.ascontiguousarray(depth)
-
- return sample
diff --git a/spaces/Realcat/image-matching-webui/third_party/TopicFM/flop_counter.py b/spaces/Realcat/image-matching-webui/third_party/TopicFM/flop_counter.py
deleted file mode 100644
index 915f703bd76146e54a3f2f9e819a7b1b85f2d700..0000000000000000000000000000000000000000
--- a/spaces/Realcat/image-matching-webui/third_party/TopicFM/flop_counter.py
+++ /dev/null
@@ -1,82 +0,0 @@
-import torch
-from fvcore.nn import FlopCountAnalysis
-from einops.einops import rearrange
-
-from src import get_model_cfg
-from src.models.backbone import FPN as topicfm_featnet
-from src.models.modules import TopicFormer
-from src.utils.dataset import read_scannet_gray
-
-from third_party.loftr.src.loftr.utils.cvpr_ds_config import default_cfg
-from third_party.loftr.src.loftr.backbone import ResNetFPN_8_2 as loftr_featnet
-from third_party.loftr.src.loftr.loftr_module import LocalFeatureTransformer
-
-
-def feat_net_flops(feat_net, config, input):
- model = feat_net(config)
- model.eval()
- flops = FlopCountAnalysis(model, input)
- feat_c, _ = model(input)
- return feat_c, flops.total() / 1e9
-
-
-def coarse_model_flops(coarse_model, config, inputs):
- model = coarse_model(config)
- model.eval()
- flops = FlopCountAnalysis(model, inputs)
- return flops.total() / 1e9
-
-
-if __name__ == "__main__":
- path_img0 = "assets/scannet_sample_images/scene0711_00_frame-001680.jpg"
- path_img1 = "assets/scannet_sample_images/scene0711_00_frame-001995.jpg"
- img0, img1 = read_scannet_gray(path_img0), read_scannet_gray(path_img1)
- img0, img1 = img0.unsqueeze(0), img1.unsqueeze(0)
-
- # LoFTR
- loftr_conf = dict(default_cfg)
- feat_c0, loftr_featnet_flops0 = feat_net_flops(
- loftr_featnet, loftr_conf["resnetfpn"], img0
- )
- feat_c1, loftr_featnet_flops1 = feat_net_flops(
- loftr_featnet, loftr_conf["resnetfpn"], img1
- )
- print(
- "FLOPs of feature extraction in LoFTR: {} GFLOPs".format(
- (loftr_featnet_flops0 + loftr_featnet_flops1) / 2
- )
- )
- feat_c0 = rearrange(feat_c0, "n c h w -> n (h w) c")
- feat_c1 = rearrange(feat_c1, "n c h w -> n (h w) c")
- loftr_coarse_model_flops = coarse_model_flops(
- LocalFeatureTransformer, loftr_conf["coarse"], (feat_c0, feat_c1)
- )
- print(
- "FLOPs of coarse matching model in LoFTR: {} GFLOPs".format(
- loftr_coarse_model_flops
- )
- )
-
- # TopicFM
- topicfm_conf = get_model_cfg()
- feat_c0, topicfm_featnet_flops0 = feat_net_flops(
- topicfm_featnet, topicfm_conf["fpn"], img0
- )
- feat_c1, topicfm_featnet_flops1 = feat_net_flops(
- topicfm_featnet, topicfm_conf["fpn"], img1
- )
- print(
- "FLOPs of feature extraction in TopicFM: {} GFLOPs".format(
- (topicfm_featnet_flops0 + topicfm_featnet_flops1) / 2
- )
- )
- feat_c0 = rearrange(feat_c0, "n c h w -> n (h w) c")
- feat_c1 = rearrange(feat_c1, "n c h w -> n (h w) c")
- topicfm_coarse_model_flops = coarse_model_flops(
- TopicFormer, topicfm_conf["coarse"], (feat_c0, feat_c1)
- )
- print(
- "FLOPs of coarse matching model in TopicFM: {} GFLOPs".format(
- topicfm_coarse_model_flops
- )
- )
diff --git a/spaces/Ricecake123/RVC-demo/train/mel_processing.py b/spaces/Ricecake123/RVC-demo/train/mel_processing.py
deleted file mode 100644
index 1c871ab6b838b174407d163c201df899cc3e2b14..0000000000000000000000000000000000000000
--- a/spaces/Ricecake123/RVC-demo/train/mel_processing.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import torch
-import torch.utils.data
-from librosa.filters import mel as librosa_mel_fn
-
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
- """
- PARAMS
- ------
- C: compression factor
- """
- return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
- """
- PARAMS
- ------
- C: compression factor used to compress
- """
- return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
- return dynamic_range_compression_torch(magnitudes)
-
-
-def spectral_de_normalize_torch(magnitudes):
- return dynamic_range_decompression_torch(magnitudes)
-
-
-# Reusable banks
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
- """Convert waveform into Linear-frequency Linear-amplitude spectrogram.
-
- Args:
- y :: (B, T) - Audio waveforms
- n_fft
- sampling_rate
- hop_size
- win_size
- center
- Returns:
- :: (B, Freq, Frame) - Linear-frequency Linear-amplitude spectrogram
- """
- # Validation
- if torch.min(y) < -1.07:
- print("min value is ", torch.min(y))
- if torch.max(y) > 1.07:
- print("max value is ", torch.max(y))
-
- # Window - Cache if needed
- global hann_window
- dtype_device = str(y.dtype) + "_" + str(y.device)
- wnsize_dtype_device = str(win_size) + "_" + dtype_device
- if wnsize_dtype_device not in hann_window:
- hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
- dtype=y.dtype, device=y.device
- )
-
- # Padding
- y = torch.nn.functional.pad(
- y.unsqueeze(1),
- (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
- mode="reflect",
- )
- y = y.squeeze(1)
-
- # Complex Spectrogram :: (B, T) -> (B, Freq, Frame, RealComplex=2)
- spec = torch.stft(
- y,
- n_fft,
- hop_length=hop_size,
- win_length=win_size,
- window=hann_window[wnsize_dtype_device],
- center=center,
- pad_mode="reflect",
- normalized=False,
- onesided=True,
- return_complex=False,
- )
-
- # Linear-frequency Linear-amplitude spectrogram :: (B, Freq, Frame, RealComplex=2) -> (B, Freq, Frame)
- spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
- return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
- # MelBasis - Cache if needed
- global mel_basis
- dtype_device = str(spec.dtype) + "_" + str(spec.device)
- fmax_dtype_device = str(fmax) + "_" + dtype_device
- if fmax_dtype_device not in mel_basis:
- mel = librosa_mel_fn(
- sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
- )
- mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
- dtype=spec.dtype, device=spec.device
- )
-
- # Mel-frequency Log-amplitude spectrogram :: (B, Freq=num_mels, Frame)
- melspec = torch.matmul(mel_basis[fmax_dtype_device], spec)
- melspec = spectral_normalize_torch(melspec)
- return melspec
-
-
-def mel_spectrogram_torch(
- y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False
-):
- """Convert waveform into Mel-frequency Log-amplitude spectrogram.
-
- Args:
- y :: (B, T) - Waveforms
- Returns:
- melspec :: (B, Freq, Frame) - Mel-frequency Log-amplitude spectrogram
- """
- # Linear-frequency Linear-amplitude spectrogram :: (B, T) -> (B, Freq, Frame)
- spec = spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center)
-
- # Mel-frequency Log-amplitude spectrogram :: (B, Freq, Frame) -> (B, Freq=num_mels, Frame)
- melspec = spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax)
-
- return melspec
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/detectors/mask_scoring_rcnn.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/detectors/mask_scoring_rcnn.py
deleted file mode 100644
index b6252b6e1d234a201725342a5780fade7e21957c..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/detectors/mask_scoring_rcnn.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from ..builder import DETECTORS
-from .two_stage import TwoStageDetector
-
-
-@DETECTORS.register_module()
-class MaskScoringRCNN(TwoStageDetector):
- """Mask Scoring RCNN.
-
- https://arxiv.org/abs/1903.00241
- """
-
- def __init__(self,
- backbone,
- rpn_head,
- roi_head,
- train_cfg,
- test_cfg,
- neck=None,
- pretrained=None):
- super(MaskScoringRCNN, self).__init__(
- backbone=backbone,
- neck=neck,
- rpn_head=rpn_head,
- roi_head=roi_head,
- train_cfg=train_cfg,
- test_cfg=test_cfg,
- pretrained=pretrained)
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/dynamic_roi_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/dynamic_roi_head.py
deleted file mode 100644
index 89427a931f45f5a920c0e66fd88058bf9fa05f5c..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet/models/roi_heads/dynamic_roi_head.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import numpy as np
-import torch
-
-from mmdet.core import bbox2roi
-from mmdet.models.losses import SmoothL1Loss
-from ..builder import HEADS
-from .standard_roi_head import StandardRoIHead
-
-EPS = 1e-15
-
-
-@HEADS.register_module()
-class DynamicRoIHead(StandardRoIHead):
- """RoI head for `Dynamic R-CNN `_."""
-
- def __init__(self, **kwargs):
- super(DynamicRoIHead, self).__init__(**kwargs)
- assert isinstance(self.bbox_head.loss_bbox, SmoothL1Loss)
- # the IoU history of the past `update_iter_interval` iterations
- self.iou_history = []
- # the beta history of the past `update_iter_interval` iterations
- self.beta_history = []
-
- def forward_train(self,
- x,
- img_metas,
- proposal_list,
- gt_bboxes,
- gt_labels,
- gt_bboxes_ignore=None,
- gt_masks=None):
- """Forward function for training.
-
- Args:
- x (list[Tensor]): list of multi-level img features.
-
- img_metas (list[dict]): list of image info dict where each dict
- has: 'img_shape', 'scale_factor', 'flip', and may also contain
- 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
- For details on the values of these keys see
- `mmdet/datasets/pipelines/formatting.py:Collect`.
-
- proposals (list[Tensors]): list of region proposals.
-
- gt_bboxes (list[Tensor]): each item are the truth boxes for each
- image in [tl_x, tl_y, br_x, br_y] format.
-
- gt_labels (list[Tensor]): class indices corresponding to each box
-
- gt_bboxes_ignore (None | list[Tensor]): specify which bounding
- boxes can be ignored when computing the loss.
-
- gt_masks (None | Tensor) : true segmentation masks for each box
- used if the architecture supports a segmentation task.
-
- Returns:
- dict[str, Tensor]: a dictionary of loss components
- """
- # assign gts and sample proposals
- if self.with_bbox or self.with_mask:
- num_imgs = len(img_metas)
- if gt_bboxes_ignore is None:
- gt_bboxes_ignore = [None for _ in range(num_imgs)]
- sampling_results = []
- cur_iou = []
- for i in range(num_imgs):
- assign_result = self.bbox_assigner.assign(
- proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i],
- gt_labels[i])
- sampling_result = self.bbox_sampler.sample(
- assign_result,
- proposal_list[i],
- gt_bboxes[i],
- gt_labels[i],
- feats=[lvl_feat[i][None] for lvl_feat in x])
- # record the `iou_topk`-th largest IoU in an image
- iou_topk = min(self.train_cfg.dynamic_rcnn.iou_topk,
- len(assign_result.max_overlaps))
- ious, _ = torch.topk(assign_result.max_overlaps, iou_topk)
- cur_iou.append(ious[-1].item())
- sampling_results.append(sampling_result)
- # average the current IoUs over images
- cur_iou = np.mean(cur_iou)
- self.iou_history.append(cur_iou)
-
- losses = dict()
- # bbox head forward and loss
- if self.with_bbox:
- bbox_results = self._bbox_forward_train(x, sampling_results,
- gt_bboxes, gt_labels,
- img_metas)
- losses.update(bbox_results['loss_bbox'])
-
- # mask head forward and loss
- if self.with_mask:
- mask_results = self._mask_forward_train(x, sampling_results,
- bbox_results['bbox_feats'],
- gt_masks, img_metas)
- losses.update(mask_results['loss_mask'])
-
- # update IoU threshold and SmoothL1 beta
- update_iter_interval = self.train_cfg.dynamic_rcnn.update_iter_interval
- if len(self.iou_history) % update_iter_interval == 0:
- new_iou_thr, new_beta = self.update_hyperparameters()
-
- return losses
-
- def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels,
- img_metas):
- num_imgs = len(img_metas)
- rois = bbox2roi([res.bboxes for res in sampling_results])
- bbox_results = self._bbox_forward(x, rois)
-
- bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes,
- gt_labels, self.train_cfg)
- # record the `beta_topk`-th smallest target
- # `bbox_targets[2]` and `bbox_targets[3]` stand for bbox_targets
- # and bbox_weights, respectively
- pos_inds = bbox_targets[3][:, 0].nonzero().squeeze(1)
- num_pos = len(pos_inds)
- cur_target = bbox_targets[2][pos_inds, :2].abs().mean(dim=1)
- beta_topk = min(self.train_cfg.dynamic_rcnn.beta_topk * num_imgs,
- num_pos)
- cur_target = torch.kthvalue(cur_target, beta_topk)[0].item()
- self.beta_history.append(cur_target)
- loss_bbox = self.bbox_head.loss(bbox_results['cls_score'],
- bbox_results['bbox_pred'], rois,
- *bbox_targets)
-
- bbox_results.update(loss_bbox=loss_bbox)
- return bbox_results
-
- def update_hyperparameters(self):
- """Update hyperparameters like IoU thresholds for assigner and beta for
- SmoothL1 loss based on the training statistics.
-
- Returns:
- tuple[float]: the updated ``iou_thr`` and ``beta``.
- """
- new_iou_thr = max(self.train_cfg.dynamic_rcnn.initial_iou,
- np.mean(self.iou_history))
- self.iou_history = []
- self.bbox_assigner.pos_iou_thr = new_iou_thr
- self.bbox_assigner.neg_iou_thr = new_iou_thr
- self.bbox_assigner.min_pos_iou = new_iou_thr
- if (np.median(self.beta_history) < EPS):
- # avoid 0 or too small value for new_beta
- new_beta = self.bbox_head.loss_bbox.beta
- else:
- new_beta = min(self.train_cfg.dynamic_rcnn.initial_beta,
- np.median(self.beta_history))
- self.beta_history = []
- self.bbox_head.loss_bbox.beta = new_beta
- return new_iou_thr, new_beta
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/yolact_head.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/yolact_head.py
deleted file mode 100644
index 10d311f94ee99e1bf65ee3e5827f1699c28a23e3..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmdet_null/models/dense_heads/yolact_head.py
+++ /dev/null
@@ -1,943 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, xavier_init
-from mmcv.runner import force_fp32
-
-from mmdet.core import build_sampler, fast_nms, images_to_levels, multi_apply
-from ..builder import HEADS, build_loss
-from .anchor_head import AnchorHead
-
-
-@HEADS.register_module()
-class YOLACTHead(AnchorHead):
- """YOLACT box head used in https://arxiv.org/abs/1904.02689.
-
- Note that YOLACT head is a light version of RetinaNet head.
- Four differences are described as follows:
-
- 1. YOLACT box head has three-times fewer anchors.
- 2. YOLACT box head shares the convs for box and cls branches.
- 3. YOLACT box head uses OHEM instead of Focal loss.
- 4. YOLACT box head predicts a set of mask coefficients for each box.
-
- Args:
- num_classes (int): Number of categories excluding the background
- category.
- in_channels (int): Number of channels in the input feature map.
- anchor_generator (dict): Config dict for anchor generator
- loss_cls (dict): Config of classification loss.
- loss_bbox (dict): Config of localization loss.
- num_head_convs (int): Number of the conv layers shared by
- box and cls branches.
- num_protos (int): Number of the mask coefficients.
- use_ohem (bool): If true, ``loss_single_OHEM`` will be used for
- cls loss calculation. If false, ``loss_single`` will be used.
- conv_cfg (dict): Dictionary to construct and config conv layer.
- norm_cfg (dict): Dictionary to construct and config norm layer.
- """
-
- def __init__(self,
- num_classes,
- in_channels,
- anchor_generator=dict(
- type='AnchorGenerator',
- octave_base_scale=3,
- scales_per_octave=1,
- ratios=[0.5, 1.0, 2.0],
- strides=[8, 16, 32, 64, 128]),
- loss_cls=dict(
- type='CrossEntropyLoss',
- use_sigmoid=False,
- reduction='none',
- loss_weight=1.0),
- loss_bbox=dict(
- type='SmoothL1Loss', beta=1.0, loss_weight=1.5),
- num_head_convs=1,
- num_protos=32,
- use_ohem=True,
- conv_cfg=None,
- norm_cfg=None,
- **kwargs):
- self.num_head_convs = num_head_convs
- self.num_protos = num_protos
- self.use_ohem = use_ohem
- self.conv_cfg = conv_cfg
- self.norm_cfg = norm_cfg
- super(YOLACTHead, self).__init__(
- num_classes,
- in_channels,
- loss_cls=loss_cls,
- loss_bbox=loss_bbox,
- anchor_generator=anchor_generator,
- **kwargs)
- if self.use_ohem:
- sampler_cfg = dict(type='PseudoSampler')
- self.sampler = build_sampler(sampler_cfg, context=self)
- self.sampling = False
-
- def _init_layers(self):
- """Initialize layers of the head."""
- self.relu = nn.ReLU(inplace=True)
- self.head_convs = nn.ModuleList()
- for i in range(self.num_head_convs):
- chn = self.in_channels if i == 0 else self.feat_channels
- self.head_convs.append(
- ConvModule(
- chn,
- self.feat_channels,
- 3,
- stride=1,
- padding=1,
- conv_cfg=self.conv_cfg,
- norm_cfg=self.norm_cfg))
- self.conv_cls = nn.Conv2d(
- self.feat_channels,
- self.num_anchors * self.cls_out_channels,
- 3,
- padding=1)
- self.conv_reg = nn.Conv2d(
- self.feat_channels, self.num_anchors * 4, 3, padding=1)
- self.conv_coeff = nn.Conv2d(
- self.feat_channels,
- self.num_anchors * self.num_protos,
- 3,
- padding=1)
-
- def init_weights(self):
- """Initialize weights of the head."""
- for m in self.head_convs:
- xavier_init(m.conv, distribution='uniform', bias=0)
- xavier_init(self.conv_cls, distribution='uniform', bias=0)
- xavier_init(self.conv_reg, distribution='uniform', bias=0)
- xavier_init(self.conv_coeff, distribution='uniform', bias=0)
-
- def forward_single(self, x):
- """Forward feature of a single scale level.
-
- Args:
- x (Tensor): Features of a single scale level.
-
- Returns:
- tuple:
- cls_score (Tensor): Cls scores for a single scale level \
- the channels number is num_anchors * num_classes.
- bbox_pred (Tensor): Box energies / deltas for a single scale \
- level, the channels number is num_anchors * 4.
- coeff_pred (Tensor): Mask coefficients for a single scale \
- level, the channels number is num_anchors * num_protos.
- """
- for head_conv in self.head_convs:
- x = head_conv(x)
- cls_score = self.conv_cls(x)
- bbox_pred = self.conv_reg(x)
- coeff_pred = self.conv_coeff(x).tanh()
- return cls_score, bbox_pred, coeff_pred
-
- @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
- def loss(self,
- cls_scores,
- bbox_preds,
- gt_bboxes,
- gt_labels,
- img_metas,
- gt_bboxes_ignore=None):
- """A combination of the func:``AnchorHead.loss`` and
- func:``SSDHead.loss``.
-
- When ``self.use_ohem == True``, it functions like ``SSDHead.loss``,
- otherwise, it follows ``AnchorHead.loss``. Besides, it additionally
- returns ``sampling_results``.
-
- Args:
- cls_scores (list[Tensor]): Box scores for each scale level
- Has shape (N, num_anchors * num_classes, H, W)
- bbox_preds (list[Tensor]): Box energies / deltas for each scale
- level with shape (N, num_anchors * 4, H, W)
- gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
- shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
- gt_labels (list[Tensor]): Class indices corresponding to each box
- img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
- boxes can be ignored when computing the loss. Default: None
-
- Returns:
- tuple:
- dict[str, Tensor]: A dictionary of loss components.
- List[:obj:``SamplingResult``]: Sampler results for each image.
- """
- featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
- assert len(featmap_sizes) == self.anchor_generator.num_levels
-
- device = cls_scores[0].device
-
- anchor_list, valid_flag_list = self.get_anchors(
- featmap_sizes, img_metas, device=device)
- label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
- cls_reg_targets = self.get_targets(
- anchor_list,
- valid_flag_list,
- gt_bboxes,
- img_metas,
- gt_bboxes_ignore_list=gt_bboxes_ignore,
- gt_labels_list=gt_labels,
- label_channels=label_channels,
- unmap_outputs=not self.use_ohem,
- return_sampling_results=True)
- if cls_reg_targets is None:
- return None
- (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
- num_total_pos, num_total_neg, sampling_results) = cls_reg_targets
-
- if self.use_ohem:
- num_images = len(img_metas)
- all_cls_scores = torch.cat([
- s.permute(0, 2, 3, 1).reshape(
- num_images, -1, self.cls_out_channels) for s in cls_scores
- ], 1)
- all_labels = torch.cat(labels_list, -1).view(num_images, -1)
- all_label_weights = torch.cat(label_weights_list,
- -1).view(num_images, -1)
- all_bbox_preds = torch.cat([
- b.permute(0, 2, 3, 1).reshape(num_images, -1, 4)
- for b in bbox_preds
- ], -2)
- all_bbox_targets = torch.cat(bbox_targets_list,
- -2).view(num_images, -1, 4)
- all_bbox_weights = torch.cat(bbox_weights_list,
- -2).view(num_images, -1, 4)
-
- # concat all level anchors to a single tensor
- all_anchors = []
- for i in range(num_images):
- all_anchors.append(torch.cat(anchor_list[i]))
-
- # check NaN and Inf
- assert torch.isfinite(all_cls_scores).all().item(), \
- 'classification scores become infinite or NaN!'
- assert torch.isfinite(all_bbox_preds).all().item(), \
- 'bbox predications become infinite or NaN!'
-
- losses_cls, losses_bbox = multi_apply(
- self.loss_single_OHEM,
- all_cls_scores,
- all_bbox_preds,
- all_anchors,
- all_labels,
- all_label_weights,
- all_bbox_targets,
- all_bbox_weights,
- num_total_samples=num_total_pos)
- else:
- num_total_samples = (
- num_total_pos +
- num_total_neg if self.sampling else num_total_pos)
-
- # anchor number of multi levels
- num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
- # concat all level anchors and flags to a single tensor
- concat_anchor_list = []
- for i in range(len(anchor_list)):
- concat_anchor_list.append(torch.cat(anchor_list[i]))
- all_anchor_list = images_to_levels(concat_anchor_list,
- num_level_anchors)
- losses_cls, losses_bbox = multi_apply(
- self.loss_single,
- cls_scores,
- bbox_preds,
- all_anchor_list,
- labels_list,
- label_weights_list,
- bbox_targets_list,
- bbox_weights_list,
- num_total_samples=num_total_samples)
-
- return dict(
- loss_cls=losses_cls, loss_bbox=losses_bbox), sampling_results
-
- def loss_single_OHEM(self, cls_score, bbox_pred, anchors, labels,
- label_weights, bbox_targets, bbox_weights,
- num_total_samples):
- """"See func:``SSDHead.loss``."""
- loss_cls_all = self.loss_cls(cls_score, labels, label_weights)
-
- # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
- pos_inds = ((labels >= 0) & (labels < self.num_classes)).nonzero(
- as_tuple=False).reshape(-1)
- neg_inds = (labels == self.num_classes).nonzero(
- as_tuple=False).view(-1)
-
- num_pos_samples = pos_inds.size(0)
- if num_pos_samples == 0:
- num_neg_samples = neg_inds.size(0)
- else:
- num_neg_samples = self.train_cfg.neg_pos_ratio * num_pos_samples
- if num_neg_samples > neg_inds.size(0):
- num_neg_samples = neg_inds.size(0)
- topk_loss_cls_neg, _ = loss_cls_all[neg_inds].topk(num_neg_samples)
- loss_cls_pos = loss_cls_all[pos_inds].sum()
- loss_cls_neg = topk_loss_cls_neg.sum()
- loss_cls = (loss_cls_pos + loss_cls_neg) / num_total_samples
- if self.reg_decoded_bbox:
- # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
- # is applied directly on the decoded bounding boxes, it
- # decodes the already encoded coordinates to absolute format.
- bbox_pred = self.bbox_coder.decode(anchors, bbox_pred)
- loss_bbox = self.loss_bbox(
- bbox_pred,
- bbox_targets,
- bbox_weights,
- avg_factor=num_total_samples)
- return loss_cls[None], loss_bbox
-
- @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'coeff_preds'))
- def get_bboxes(self,
- cls_scores,
- bbox_preds,
- coeff_preds,
- img_metas,
- cfg=None,
- rescale=False):
- """"Similiar to func:``AnchorHead.get_bboxes``, but additionally
- processes coeff_preds.
-
- Args:
- cls_scores (list[Tensor]): Box scores for each scale level
- with shape (N, num_anchors * num_classes, H, W)
- bbox_preds (list[Tensor]): Box energies / deltas for each scale
- level with shape (N, num_anchors * 4, H, W)
- coeff_preds (list[Tensor]): Mask coefficients for each scale
- level with shape (N, num_anchors * num_protos, H, W)
- img_metas (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- cfg (mmcv.Config | None): Test / postprocessing configuration,
- if None, test_cfg would be used
- rescale (bool): If True, return boxes in original image space.
- Default: False.
-
- Returns:
- list[tuple[Tensor, Tensor, Tensor]]: Each item in result_list is
- a 3-tuple. The first item is an (n, 5) tensor, where the
- first 4 columns are bounding box positions
- (tl_x, tl_y, br_x, br_y) and the 5-th column is a score
- between 0 and 1. The second item is an (n,) tensor where each
- item is the predicted class label of the corresponding box.
- The third item is an (n, num_protos) tensor where each item
- is the predicted mask coefficients of instance inside the
- corresponding box.
- """
- assert len(cls_scores) == len(bbox_preds)
- num_levels = len(cls_scores)
-
- device = cls_scores[0].device
- featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)]
- mlvl_anchors = self.anchor_generator.grid_anchors(
- featmap_sizes, device=device)
-
- det_bboxes = []
- det_labels = []
- det_coeffs = []
- for img_id in range(len(img_metas)):
- cls_score_list = [
- cls_scores[i][img_id].detach() for i in range(num_levels)
- ]
- bbox_pred_list = [
- bbox_preds[i][img_id].detach() for i in range(num_levels)
- ]
- coeff_pred_list = [
- coeff_preds[i][img_id].detach() for i in range(num_levels)
- ]
- img_shape = img_metas[img_id]['img_shape']
- scale_factor = img_metas[img_id]['scale_factor']
- bbox_res = self._get_bboxes_single(cls_score_list, bbox_pred_list,
- coeff_pred_list, mlvl_anchors,
- img_shape, scale_factor, cfg,
- rescale)
- det_bboxes.append(bbox_res[0])
- det_labels.append(bbox_res[1])
- det_coeffs.append(bbox_res[2])
- return det_bboxes, det_labels, det_coeffs
-
- def _get_bboxes_single(self,
- cls_score_list,
- bbox_pred_list,
- coeff_preds_list,
- mlvl_anchors,
- img_shape,
- scale_factor,
- cfg,
- rescale=False):
- """"Similiar to func:``AnchorHead._get_bboxes_single``, but
- additionally processes coeff_preds_list and uses fast NMS instead of
- traditional NMS.
-
- Args:
- cls_score_list (list[Tensor]): Box scores for a single scale level
- Has shape (num_anchors * num_classes, H, W).
- bbox_pred_list (list[Tensor]): Box energies / deltas for a single
- scale level with shape (num_anchors * 4, H, W).
- coeff_preds_list (list[Tensor]): Mask coefficients for a single
- scale level with shape (num_anchors * num_protos, H, W).
- mlvl_anchors (list[Tensor]): Box reference for a single scale level
- with shape (num_total_anchors, 4).
- img_shape (tuple[int]): Shape of the input image,
- (height, width, 3).
- scale_factor (ndarray): Scale factor of the image arange as
- (w_scale, h_scale, w_scale, h_scale).
- cfg (mmcv.Config): Test / postprocessing configuration,
- if None, test_cfg would be used.
- rescale (bool): If True, return boxes in original image space.
-
- Returns:
- tuple[Tensor, Tensor, Tensor]: The first item is an (n, 5) tensor,
- where the first 4 columns are bounding box positions
- (tl_x, tl_y, br_x, br_y) and the 5-th column is a score between
- 0 and 1. The second item is an (n,) tensor where each item is
- the predicted class label of the corresponding box. The third
- item is an (n, num_protos) tensor where each item is the
- predicted mask coefficients of instance inside the
- corresponding box.
- """
- cfg = self.test_cfg if cfg is None else cfg
- assert len(cls_score_list) == len(bbox_pred_list) == len(mlvl_anchors)
- mlvl_bboxes = []
- mlvl_scores = []
- mlvl_coeffs = []
- for cls_score, bbox_pred, coeff_pred, anchors in \
- zip(cls_score_list, bbox_pred_list,
- coeff_preds_list, mlvl_anchors):
- assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
- cls_score = cls_score.permute(1, 2,
- 0).reshape(-1, self.cls_out_channels)
- if self.use_sigmoid_cls:
- scores = cls_score.sigmoid()
- else:
- scores = cls_score.softmax(-1)
- bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
- coeff_pred = coeff_pred.permute(1, 2,
- 0).reshape(-1, self.num_protos)
- nms_pre = cfg.get('nms_pre', -1)
- if nms_pre > 0 and scores.shape[0] > nms_pre:
- # Get maximum scores for foreground classes.
- if self.use_sigmoid_cls:
- max_scores, _ = scores.max(dim=1)
- else:
- # remind that we set FG labels to [0, num_class-1]
- # since mmdet v2.0
- # BG cat_id: num_class
- max_scores, _ = scores[:, :-1].max(dim=1)
- _, topk_inds = max_scores.topk(nms_pre)
- anchors = anchors[topk_inds, :]
- bbox_pred = bbox_pred[topk_inds, :]
- scores = scores[topk_inds, :]
- coeff_pred = coeff_pred[topk_inds, :]
- bboxes = self.bbox_coder.decode(
- anchors, bbox_pred, max_shape=img_shape)
- mlvl_bboxes.append(bboxes)
- mlvl_scores.append(scores)
- mlvl_coeffs.append(coeff_pred)
- mlvl_bboxes = torch.cat(mlvl_bboxes)
- if rescale:
- mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
- mlvl_scores = torch.cat(mlvl_scores)
- mlvl_coeffs = torch.cat(mlvl_coeffs)
- if self.use_sigmoid_cls:
- # Add a dummy background class to the backend when using sigmoid
- # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
- # BG cat_id: num_class
- padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
- mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
- det_bboxes, det_labels, det_coeffs = fast_nms(mlvl_bboxes, mlvl_scores,
- mlvl_coeffs,
- cfg.score_thr,
- cfg.iou_thr, cfg.top_k,
- cfg.max_per_img)
- return det_bboxes, det_labels, det_coeffs
-
-
-@HEADS.register_module()
-class YOLACTSegmHead(nn.Module):
- """YOLACT segmentation head used in https://arxiv.org/abs/1904.02689.
-
- Apply a semantic segmentation loss on feature space using layers that are
- only evaluated during training to increase performance with no speed
- penalty.
-
- Args:
- in_channels (int): Number of channels in the input feature map.
- num_classes (int): Number of categories excluding the background
- category.
- loss_segm (dict): Config of semantic segmentation loss.
- """
-
- def __init__(self,
- num_classes,
- in_channels=256,
- loss_segm=dict(
- type='CrossEntropyLoss',
- use_sigmoid=True,
- loss_weight=1.0)):
- super(YOLACTSegmHead, self).__init__()
- self.in_channels = in_channels
- self.num_classes = num_classes
- self.loss_segm = build_loss(loss_segm)
- self._init_layers()
- self.fp16_enabled = False
-
- def _init_layers(self):
- """Initialize layers of the head."""
- self.segm_conv = nn.Conv2d(
- self.in_channels, self.num_classes, kernel_size=1)
-
- def init_weights(self):
- """Initialize weights of the head."""
- xavier_init(self.segm_conv, distribution='uniform')
-
- def forward(self, x):
- """Forward feature from the upstream network.
-
- Args:
- x (Tensor): Feature from the upstream network, which is
- a 4D-tensor.
-
- Returns:
- Tensor: Predicted semantic segmentation map with shape
- (N, num_classes, H, W).
- """
- return self.segm_conv(x)
-
- @force_fp32(apply_to=('segm_pred', ))
- def loss(self, segm_pred, gt_masks, gt_labels):
- """Compute loss of the head.
-
- Args:
- segm_pred (list[Tensor]): Predicted semantic segmentation map
- with shape (N, num_classes, H, W).
- gt_masks (list[Tensor]): Ground truth masks for each image with
- the same shape of the input image.
- gt_labels (list[Tensor]): Class indices corresponding to each box.
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
- loss_segm = []
- num_imgs, num_classes, mask_h, mask_w = segm_pred.size()
- for idx in range(num_imgs):
- cur_segm_pred = segm_pred[idx]
- cur_gt_masks = gt_masks[idx].float()
- cur_gt_labels = gt_labels[idx]
- segm_targets = self.get_targets(cur_segm_pred, cur_gt_masks,
- cur_gt_labels)
- if segm_targets is None:
- loss = self.loss_segm(cur_segm_pred,
- torch.zeros_like(cur_segm_pred),
- torch.zeros_like(cur_segm_pred))
- else:
- loss = self.loss_segm(
- cur_segm_pred,
- segm_targets,
- avg_factor=num_imgs * mask_h * mask_w)
- loss_segm.append(loss)
- return dict(loss_segm=loss_segm)
-
- def get_targets(self, segm_pred, gt_masks, gt_labels):
- """Compute semantic segmentation targets for each image.
-
- Args:
- segm_pred (Tensor): Predicted semantic segmentation map
- with shape (num_classes, H, W).
- gt_masks (Tensor): Ground truth masks for each image with
- the same shape of the input image.
- gt_labels (Tensor): Class indices corresponding to each box.
-
- Returns:
- Tensor: Semantic segmentation targets with shape
- (num_classes, H, W).
- """
- if gt_masks.size(0) == 0:
- return None
- num_classes, mask_h, mask_w = segm_pred.size()
- with torch.no_grad():
- downsampled_masks = F.interpolate(
- gt_masks.unsqueeze(0), (mask_h, mask_w),
- mode='bilinear',
- align_corners=False).squeeze(0)
- downsampled_masks = downsampled_masks.gt(0.5).float()
- segm_targets = torch.zeros_like(segm_pred, requires_grad=False)
- for obj_idx in range(downsampled_masks.size(0)):
- segm_targets[gt_labels[obj_idx] - 1] = torch.max(
- segm_targets[gt_labels[obj_idx] - 1],
- downsampled_masks[obj_idx])
- return segm_targets
-
-
-@HEADS.register_module()
-class YOLACTProtonet(nn.Module):
- """YOLACT mask head used in https://arxiv.org/abs/1904.02689.
-
- This head outputs the mask prototypes for YOLACT.
-
- Args:
- in_channels (int): Number of channels in the input feature map.
- proto_channels (tuple[int]): Output channels of protonet convs.
- proto_kernel_sizes (tuple[int]): Kernel sizes of protonet convs.
- include_last_relu (Bool): If keep the last relu of protonet.
- num_protos (int): Number of prototypes.
- num_classes (int): Number of categories excluding the background
- category.
- loss_mask_weight (float): Reweight the mask loss by this factor.
- max_masks_to_train (int): Maximum number of masks to train for
- each image.
- """
-
- def __init__(self,
- num_classes,
- in_channels=256,
- proto_channels=(256, 256, 256, None, 256, 32),
- proto_kernel_sizes=(3, 3, 3, -2, 3, 1),
- include_last_relu=True,
- num_protos=32,
- loss_mask_weight=1.0,
- max_masks_to_train=100):
- super(YOLACTProtonet, self).__init__()
- self.in_channels = in_channels
- self.proto_channels = proto_channels
- self.proto_kernel_sizes = proto_kernel_sizes
- self.include_last_relu = include_last_relu
- self.protonet = self._init_layers()
-
- self.loss_mask_weight = loss_mask_weight
- self.num_protos = num_protos
- self.num_classes = num_classes
- self.max_masks_to_train = max_masks_to_train
- self.fp16_enabled = False
-
- def _init_layers(self):
- """A helper function to take a config setting and turn it into a
- network."""
- # Possible patterns:
- # ( 256, 3) -> conv
- # ( 256,-2) -> deconv
- # (None,-2) -> bilinear interpolate
- in_channels = self.in_channels
- protonets = nn.ModuleList()
- for num_channels, kernel_size in zip(self.proto_channels,
- self.proto_kernel_sizes):
- if kernel_size > 0:
- layer = nn.Conv2d(
- in_channels,
- num_channels,
- kernel_size,
- padding=kernel_size // 2)
- else:
- if num_channels is None:
- layer = InterpolateModule(
- scale_factor=-kernel_size,
- mode='bilinear',
- align_corners=False)
- else:
- layer = nn.ConvTranspose2d(
- in_channels,
- num_channels,
- -kernel_size,
- padding=kernel_size // 2)
- protonets.append(layer)
- protonets.append(nn.ReLU(inplace=True))
- in_channels = num_channels if num_channels is not None \
- else in_channels
- if not self.include_last_relu:
- protonets = protonets[:-1]
- return nn.Sequential(*protonets)
-
- def init_weights(self):
- """Initialize weights of the head."""
- for m in self.protonet:
- if isinstance(m, nn.Conv2d):
- xavier_init(m, distribution='uniform')
-
- def forward(self, x, coeff_pred, bboxes, img_meta, sampling_results=None):
- """Forward feature from the upstream network to get prototypes and
- linearly combine the prototypes, using masks coefficients, into
- instance masks. Finally, crop the instance masks with given bboxes.
-
- Args:
- x (Tensor): Feature from the upstream network, which is
- a 4D-tensor.
- coeff_pred (list[Tensor]): Mask coefficients for each scale
- level with shape (N, num_anchors * num_protos, H, W).
- bboxes (list[Tensor]): Box used for cropping with shape
- (N, num_anchors * 4, H, W). During training, they are
- ground truth boxes. During testing, they are predicted
- boxes.
- img_meta (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- sampling_results (List[:obj:``SamplingResult``]): Sampler results
- for each image.
-
- Returns:
- list[Tensor]: Predicted instance segmentation masks.
- """
- prototypes = self.protonet(x)
- prototypes = prototypes.permute(0, 2, 3, 1).contiguous()
-
- num_imgs = x.size(0)
- # Training state
- if self.training:
- coeff_pred_list = []
- for coeff_pred_per_level in coeff_pred:
- coeff_pred_per_level = \
- coeff_pred_per_level.permute(0, 2, 3, 1)\
- .reshape(num_imgs, -1, self.num_protos)
- coeff_pred_list.append(coeff_pred_per_level)
- coeff_pred = torch.cat(coeff_pred_list, dim=1)
-
- mask_pred_list = []
- for idx in range(num_imgs):
- cur_prototypes = prototypes[idx]
- cur_coeff_pred = coeff_pred[idx]
- cur_bboxes = bboxes[idx]
- cur_img_meta = img_meta[idx]
-
- # Testing state
- if not self.training:
- bboxes_for_cropping = cur_bboxes
- else:
- cur_sampling_results = sampling_results[idx]
- pos_assigned_gt_inds = \
- cur_sampling_results.pos_assigned_gt_inds
- bboxes_for_cropping = cur_bboxes[pos_assigned_gt_inds].clone()
- pos_inds = cur_sampling_results.pos_inds
- cur_coeff_pred = cur_coeff_pred[pos_inds]
-
- # Linearly combine the prototypes with the mask coefficients
- mask_pred = cur_prototypes @ cur_coeff_pred.t()
- mask_pred = torch.sigmoid(mask_pred)
-
- h, w = cur_img_meta['img_shape'][:2]
- bboxes_for_cropping[:, 0] /= w
- bboxes_for_cropping[:, 1] /= h
- bboxes_for_cropping[:, 2] /= w
- bboxes_for_cropping[:, 3] /= h
-
- mask_pred = self.crop(mask_pred, bboxes_for_cropping)
- mask_pred = mask_pred.permute(2, 0, 1).contiguous()
- mask_pred_list.append(mask_pred)
- return mask_pred_list
-
- @force_fp32(apply_to=('mask_pred', ))
- def loss(self, mask_pred, gt_masks, gt_bboxes, img_meta, sampling_results):
- """Compute loss of the head.
-
- Args:
- mask_pred (list[Tensor]): Predicted prototypes with shape
- (num_classes, H, W).
- gt_masks (list[Tensor]): Ground truth masks for each image with
- the same shape of the input image.
- gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
- shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
- img_meta (list[dict]): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- sampling_results (List[:obj:``SamplingResult``]): Sampler results
- for each image.
-
- Returns:
- dict[str, Tensor]: A dictionary of loss components.
- """
- loss_mask = []
- num_imgs = len(mask_pred)
- total_pos = 0
- for idx in range(num_imgs):
- cur_mask_pred = mask_pred[idx]
- cur_gt_masks = gt_masks[idx].float()
- cur_gt_bboxes = gt_bboxes[idx]
- cur_img_meta = img_meta[idx]
- cur_sampling_results = sampling_results[idx]
-
- pos_assigned_gt_inds = cur_sampling_results.pos_assigned_gt_inds
- num_pos = pos_assigned_gt_inds.size(0)
- # Since we're producing (near) full image masks,
- # it'd take too much vram to backprop on every single mask.
- # Thus we select only a subset.
- if num_pos > self.max_masks_to_train:
- perm = torch.randperm(num_pos)
- select = perm[:self.max_masks_to_train]
- cur_mask_pred = cur_mask_pred[select]
- pos_assigned_gt_inds = pos_assigned_gt_inds[select]
- num_pos = self.max_masks_to_train
- total_pos += num_pos
-
- gt_bboxes_for_reweight = cur_gt_bboxes[pos_assigned_gt_inds]
-
- mask_targets = self.get_targets(cur_mask_pred, cur_gt_masks,
- pos_assigned_gt_inds)
- if num_pos == 0:
- loss = cur_mask_pred.sum() * 0.
- elif mask_targets is None:
- loss = F.binary_cross_entropy(cur_mask_pred,
- torch.zeros_like(cur_mask_pred),
- torch.zeros_like(cur_mask_pred))
- else:
- cur_mask_pred = torch.clamp(cur_mask_pred, 0, 1)
- loss = F.binary_cross_entropy(
- cur_mask_pred, mask_targets,
- reduction='none') * self.loss_mask_weight
-
- h, w = cur_img_meta['img_shape'][:2]
- gt_bboxes_width = (gt_bboxes_for_reweight[:, 2] -
- gt_bboxes_for_reweight[:, 0]) / w
- gt_bboxes_height = (gt_bboxes_for_reweight[:, 3] -
- gt_bboxes_for_reweight[:, 1]) / h
- loss = loss.mean(dim=(1,
- 2)) / gt_bboxes_width / gt_bboxes_height
- loss = torch.sum(loss)
- loss_mask.append(loss)
-
- if total_pos == 0:
- total_pos += 1 # avoid nan
- loss_mask = [x / total_pos for x in loss_mask]
-
- return dict(loss_mask=loss_mask)
-
- def get_targets(self, mask_pred, gt_masks, pos_assigned_gt_inds):
- """Compute instance segmentation targets for each image.
-
- Args:
- mask_pred (Tensor): Predicted prototypes with shape
- (num_classes, H, W).
- gt_masks (Tensor): Ground truth masks for each image with
- the same shape of the input image.
- pos_assigned_gt_inds (Tensor): GT indices of the corresponding
- positive samples.
- Returns:
- Tensor: Instance segmentation targets with shape
- (num_instances, H, W).
- """
- if gt_masks.size(0) == 0:
- return None
- mask_h, mask_w = mask_pred.shape[-2:]
- gt_masks = F.interpolate(
- gt_masks.unsqueeze(0), (mask_h, mask_w),
- mode='bilinear',
- align_corners=False).squeeze(0)
- gt_masks = gt_masks.gt(0.5).float()
- mask_targets = gt_masks[pos_assigned_gt_inds]
- return mask_targets
-
- def get_seg_masks(self, mask_pred, label_pred, img_meta, rescale):
- """Resize, binarize, and format the instance mask predictions.
-
- Args:
- mask_pred (Tensor): shape (N, H, W).
- label_pred (Tensor): shape (N, ).
- img_meta (dict): Meta information of each image, e.g.,
- image size, scaling factor, etc.
- rescale (bool): If rescale is False, then returned masks will
- fit the scale of imgs[0].
- Returns:
- list[ndarray]: Mask predictions grouped by their predicted classes.
- """
- ori_shape = img_meta['ori_shape']
- scale_factor = img_meta['scale_factor']
- if rescale:
- img_h, img_w = ori_shape[:2]
- else:
- img_h = np.round(ori_shape[0] * scale_factor[1]).astype(np.int32)
- img_w = np.round(ori_shape[1] * scale_factor[0]).astype(np.int32)
-
- cls_segms = [[] for _ in range(self.num_classes)]
- if mask_pred.size(0) == 0:
- return cls_segms
-
- mask_pred = F.interpolate(
- mask_pred.unsqueeze(0), (img_h, img_w),
- mode='bilinear',
- align_corners=False).squeeze(0) > 0.5
- mask_pred = mask_pred.cpu().numpy().astype(np.uint8)
-
- for m, l in zip(mask_pred, label_pred):
- cls_segms[l].append(m)
- return cls_segms
-
- def crop(self, masks, boxes, padding=1):
- """Crop predicted masks by zeroing out everything not in the predicted
- bbox.
-
- Args:
- masks (Tensor): shape [H, W, N].
- boxes (Tensor): bbox coords in relative point form with
- shape [N, 4].
-
- Return:
- Tensor: The cropped masks.
- """
- h, w, n = masks.size()
- x1, x2 = self.sanitize_coordinates(
- boxes[:, 0], boxes[:, 2], w, padding, cast=False)
- y1, y2 = self.sanitize_coordinates(
- boxes[:, 1], boxes[:, 3], h, padding, cast=False)
-
- rows = torch.arange(
- w, device=masks.device, dtype=x1.dtype).view(1, -1,
- 1).expand(h, w, n)
- cols = torch.arange(
- h, device=masks.device, dtype=x1.dtype).view(-1, 1,
- 1).expand(h, w, n)
-
- masks_left = rows >= x1.view(1, 1, -1)
- masks_right = rows < x2.view(1, 1, -1)
- masks_up = cols >= y1.view(1, 1, -1)
- masks_down = cols < y2.view(1, 1, -1)
-
- crop_mask = masks_left * masks_right * masks_up * masks_down
-
- return masks * crop_mask.float()
-
- def sanitize_coordinates(self, x1, x2, img_size, padding=0, cast=True):
- """Sanitizes the input coordinates so that x1 < x2, x1 != x2, x1 >= 0,
- and x2 <= image_size. Also converts from relative to absolute
- coordinates and casts the results to long tensors.
-
- Warning: this does things in-place behind the scenes so
- copy if necessary.
-
- Args:
- _x1 (Tensor): shape (N, ).
- _x2 (Tensor): shape (N, ).
- img_size (int): Size of the input image.
- padding (int): x1 >= padding, x2 <= image_size-padding.
- cast (bool): If cast is false, the result won't be cast to longs.
-
- Returns:
- tuple:
- x1 (Tensor): Sanitized _x1.
- x2 (Tensor): Sanitized _x2.
- """
- x1 = x1 * img_size
- x2 = x2 * img_size
- if cast:
- x1 = x1.long()
- x2 = x2.long()
- x1 = torch.min(x1, x2)
- x2 = torch.max(x1, x2)
- x1 = torch.clamp(x1 - padding, min=0)
- x2 = torch.clamp(x2 + padding, max=img_size)
- return x1, x2
-
-
-class InterpolateModule(nn.Module):
- """This is a module version of F.interpolate.
-
- Any arguments you give it just get passed along for the ride.
- """
-
- def __init__(self, *args, **kwargs):
- super().__init__()
-
- self.args = args
- self.kwargs = kwargs
-
- def forward(self, x):
- """Forward features from the upstream network."""
- return F.interpolate(x, *self.args, **self.kwargs)
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/image/io.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/image/io.py
deleted file mode 100644
index d3fa2e8cc06b1a7b0b69de6406980b15d61a1e5d..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/mmcv/image/io.py
+++ /dev/null
@@ -1,258 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import io
-import os.path as osp
-from pathlib import Path
-
-import cv2
-import numpy as np
-from cv2 import (IMREAD_COLOR, IMREAD_GRAYSCALE, IMREAD_IGNORE_ORIENTATION,
- IMREAD_UNCHANGED)
-
-from annotator.uniformer.mmcv.utils import check_file_exist, is_str, mkdir_or_exist
-
-try:
- from turbojpeg import TJCS_RGB, TJPF_BGR, TJPF_GRAY, TurboJPEG
-except ImportError:
- TJCS_RGB = TJPF_GRAY = TJPF_BGR = TurboJPEG = None
-
-try:
- from PIL import Image, ImageOps
-except ImportError:
- Image = None
-
-try:
- import tifffile
-except ImportError:
- tifffile = None
-
-jpeg = None
-supported_backends = ['cv2', 'turbojpeg', 'pillow', 'tifffile']
-
-imread_flags = {
- 'color': IMREAD_COLOR,
- 'grayscale': IMREAD_GRAYSCALE,
- 'unchanged': IMREAD_UNCHANGED,
- 'color_ignore_orientation': IMREAD_IGNORE_ORIENTATION | IMREAD_COLOR,
- 'grayscale_ignore_orientation':
- IMREAD_IGNORE_ORIENTATION | IMREAD_GRAYSCALE
-}
-
-imread_backend = 'cv2'
-
-
-def use_backend(backend):
- """Select a backend for image decoding.
-
- Args:
- backend (str): The image decoding backend type. Options are `cv2`,
- `pillow`, `turbojpeg` (see https://github.com/lilohuang/PyTurboJPEG)
- and `tifffile`. `turbojpeg` is faster but it only supports `.jpeg`
- file format.
- """
- assert backend in supported_backends
- global imread_backend
- imread_backend = backend
- if imread_backend == 'turbojpeg':
- if TurboJPEG is None:
- raise ImportError('`PyTurboJPEG` is not installed')
- global jpeg
- if jpeg is None:
- jpeg = TurboJPEG()
- elif imread_backend == 'pillow':
- if Image is None:
- raise ImportError('`Pillow` is not installed')
- elif imread_backend == 'tifffile':
- if tifffile is None:
- raise ImportError('`tifffile` is not installed')
-
-
-def _jpegflag(flag='color', channel_order='bgr'):
- channel_order = channel_order.lower()
- if channel_order not in ['rgb', 'bgr']:
- raise ValueError('channel order must be either "rgb" or "bgr"')
-
- if flag == 'color':
- if channel_order == 'bgr':
- return TJPF_BGR
- elif channel_order == 'rgb':
- return TJCS_RGB
- elif flag == 'grayscale':
- return TJPF_GRAY
- else:
- raise ValueError('flag must be "color" or "grayscale"')
-
-
-def _pillow2array(img, flag='color', channel_order='bgr'):
- """Convert a pillow image to numpy array.
-
- Args:
- img (:obj:`PIL.Image.Image`): The image loaded using PIL
- flag (str): Flags specifying the color type of a loaded image,
- candidates are 'color', 'grayscale' and 'unchanged'.
- Default to 'color'.
- channel_order (str): The channel order of the output image array,
- candidates are 'bgr' and 'rgb'. Default to 'bgr'.
-
- Returns:
- np.ndarray: The converted numpy array
- """
- channel_order = channel_order.lower()
- if channel_order not in ['rgb', 'bgr']:
- raise ValueError('channel order must be either "rgb" or "bgr"')
-
- if flag == 'unchanged':
- array = np.array(img)
- if array.ndim >= 3 and array.shape[2] >= 3: # color image
- array[:, :, :3] = array[:, :, (2, 1, 0)] # RGB to BGR
- else:
- # Handle exif orientation tag
- if flag in ['color', 'grayscale']:
- img = ImageOps.exif_transpose(img)
- # If the image mode is not 'RGB', convert it to 'RGB' first.
- if img.mode != 'RGB':
- if img.mode != 'LA':
- # Most formats except 'LA' can be directly converted to RGB
- img = img.convert('RGB')
- else:
- # When the mode is 'LA', the default conversion will fill in
- # the canvas with black, which sometimes shadows black objects
- # in the foreground.
- #
- # Therefore, a random color (124, 117, 104) is used for canvas
- img_rgba = img.convert('RGBA')
- img = Image.new('RGB', img_rgba.size, (124, 117, 104))
- img.paste(img_rgba, mask=img_rgba.split()[3]) # 3 is alpha
- if flag in ['color', 'color_ignore_orientation']:
- array = np.array(img)
- if channel_order != 'rgb':
- array = array[:, :, ::-1] # RGB to BGR
- elif flag in ['grayscale', 'grayscale_ignore_orientation']:
- img = img.convert('L')
- array = np.array(img)
- else:
- raise ValueError(
- 'flag must be "color", "grayscale", "unchanged", '
- f'"color_ignore_orientation" or "grayscale_ignore_orientation"'
- f' but got {flag}')
- return array
-
-
-def imread(img_or_path, flag='color', channel_order='bgr', backend=None):
- """Read an image.
-
- Args:
- img_or_path (ndarray or str or Path): Either a numpy array or str or
- pathlib.Path. If it is a numpy array (loaded image), then
- it will be returned as is.
- flag (str): Flags specifying the color type of a loaded image,
- candidates are `color`, `grayscale`, `unchanged`,
- `color_ignore_orientation` and `grayscale_ignore_orientation`.
- By default, `cv2` and `pillow` backend would rotate the image
- according to its EXIF info unless called with `unchanged` or
- `*_ignore_orientation` flags. `turbojpeg` and `tifffile` backend
- always ignore image's EXIF info regardless of the flag.
- The `turbojpeg` backend only supports `color` and `grayscale`.
- channel_order (str): Order of channel, candidates are `bgr` and `rgb`.
- backend (str | None): The image decoding backend type. Options are
- `cv2`, `pillow`, `turbojpeg`, `tifffile`, `None`.
- If backend is None, the global imread_backend specified by
- ``mmcv.use_backend()`` will be used. Default: None.
-
- Returns:
- ndarray: Loaded image array.
- """
-
- if backend is None:
- backend = imread_backend
- if backend not in supported_backends:
- raise ValueError(f'backend: {backend} is not supported. Supported '
- "backends are 'cv2', 'turbojpeg', 'pillow'")
- if isinstance(img_or_path, Path):
- img_or_path = str(img_or_path)
-
- if isinstance(img_or_path, np.ndarray):
- return img_or_path
- elif is_str(img_or_path):
- check_file_exist(img_or_path,
- f'img file does not exist: {img_or_path}')
- if backend == 'turbojpeg':
- with open(img_or_path, 'rb') as in_file:
- img = jpeg.decode(in_file.read(),
- _jpegflag(flag, channel_order))
- if img.shape[-1] == 1:
- img = img[:, :, 0]
- return img
- elif backend == 'pillow':
- img = Image.open(img_or_path)
- img = _pillow2array(img, flag, channel_order)
- return img
- elif backend == 'tifffile':
- img = tifffile.imread(img_or_path)
- return img
- else:
- flag = imread_flags[flag] if is_str(flag) else flag
- img = cv2.imread(img_or_path, flag)
- if flag == IMREAD_COLOR and channel_order == 'rgb':
- cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img)
- return img
- else:
- raise TypeError('"img" must be a numpy array or a str or '
- 'a pathlib.Path object')
-
-
-def imfrombytes(content, flag='color', channel_order='bgr', backend=None):
- """Read an image from bytes.
-
- Args:
- content (bytes): Image bytes got from files or other streams.
- flag (str): Same as :func:`imread`.
- backend (str | None): The image decoding backend type. Options are
- `cv2`, `pillow`, `turbojpeg`, `None`. If backend is None, the
- global imread_backend specified by ``mmcv.use_backend()`` will be
- used. Default: None.
-
- Returns:
- ndarray: Loaded image array.
- """
-
- if backend is None:
- backend = imread_backend
- if backend not in supported_backends:
- raise ValueError(f'backend: {backend} is not supported. Supported '
- "backends are 'cv2', 'turbojpeg', 'pillow'")
- if backend == 'turbojpeg':
- img = jpeg.decode(content, _jpegflag(flag, channel_order))
- if img.shape[-1] == 1:
- img = img[:, :, 0]
- return img
- elif backend == 'pillow':
- buff = io.BytesIO(content)
- img = Image.open(buff)
- img = _pillow2array(img, flag, channel_order)
- return img
- else:
- img_np = np.frombuffer(content, np.uint8)
- flag = imread_flags[flag] if is_str(flag) else flag
- img = cv2.imdecode(img_np, flag)
- if flag == IMREAD_COLOR and channel_order == 'rgb':
- cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img)
- return img
-
-
-def imwrite(img, file_path, params=None, auto_mkdir=True):
- """Write image to file.
-
- Args:
- img (ndarray): Image array to be written.
- file_path (str): Image file path.
- params (None or list): Same as opencv :func:`imwrite` interface.
- auto_mkdir (bool): If the parent folder of `file_path` does not exist,
- whether to create it automatically.
-
- Returns:
- bool: Successful or not.
- """
- if auto_mkdir:
- dir_name = osp.abspath(osp.dirname(file_path))
- mkdir_or_exist(dir_name)
- return cv2.imwrite(file_path, img, params)
diff --git a/spaces/Rongjiehuang/ProDiff/tasks/vocoder/vocoder_base.py b/spaces/Rongjiehuang/ProDiff/tasks/vocoder/vocoder_base.py
deleted file mode 100644
index 04f45af60c8ac1c1f8303d091f8c6031ec8451bf..0000000000000000000000000000000000000000
--- a/spaces/Rongjiehuang/ProDiff/tasks/vocoder/vocoder_base.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import os
-
-import torch
-import torch.distributed as dist
-from torch.utils.data import DistributedSampler
-
-from tasks.base_task import BaseTask
-from tasks.base_task import data_loader
-from tasks.vocoder.dataset_utils import VocoderDataset, EndlessDistributedSampler
-from utils.hparams import hparams
-
-
-class VocoderBaseTask(BaseTask):
- def __init__(self):
- super(VocoderBaseTask, self).__init__()
- self.max_sentences = hparams['max_sentences']
- self.max_valid_sentences = hparams['max_valid_sentences']
- if self.max_valid_sentences == -1:
- hparams['max_valid_sentences'] = self.max_valid_sentences = self.max_sentences
- self.dataset_cls = VocoderDataset
-
- @data_loader
- def train_dataloader(self):
- train_dataset = self.dataset_cls('train', shuffle=True)
- return self.build_dataloader(train_dataset, True, self.max_sentences, hparams['endless_ds'])
-
- @data_loader
- def val_dataloader(self):
- valid_dataset = self.dataset_cls('valid', shuffle=False)
- return self.build_dataloader(valid_dataset, False, self.max_valid_sentences)
-
- @data_loader
- def test_dataloader(self):
- test_dataset = self.dataset_cls('test', shuffle=False)
- return self.build_dataloader(test_dataset, False, self.max_valid_sentences)
-
- def build_dataloader(self, dataset, shuffle, max_sentences, endless=False):
- world_size = 1
- rank = 0
- if dist.is_initialized():
- world_size = dist.get_world_size()
- rank = dist.get_rank()
- sampler_cls = DistributedSampler if not endless else EndlessDistributedSampler
- train_sampler = sampler_cls(
- dataset=dataset,
- num_replicas=world_size,
- rank=rank,
- shuffle=shuffle,
- )
- return torch.utils.data.DataLoader(
- dataset=dataset,
- shuffle=False,
- collate_fn=dataset.collater,
- batch_size=max_sentences,
- num_workers=dataset.num_workers,
- sampler=train_sampler,
- pin_memory=True,
- )
-
- def test_start(self):
- self.gen_dir = os.path.join(hparams['work_dir'],
- f'generated_{self.trainer.global_step}_{hparams["gen_dir_name"]}')
- os.makedirs(self.gen_dir, exist_ok=True)
-
- def test_end(self, outputs):
- return {}
diff --git a/spaces/SankarSrin/image-matting-app/ppmatting/models/layers/__init__.py b/spaces/SankarSrin/image-matting-app/ppmatting/models/layers/__init__.py
deleted file mode 100644
index 31eba2cacd64eddaf0734495b5a992a86b7bad37..0000000000000000000000000000000000000000
--- a/spaces/SankarSrin/image-matting-app/ppmatting/models/layers/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
-#
-# 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 .gca_module import GuidedCxtAtten
diff --git a/spaces/ServerX/PorcoDiaz/lib/infer_pack/modules/F0Predictor/F0Predictor.py b/spaces/ServerX/PorcoDiaz/lib/infer_pack/modules/F0Predictor/F0Predictor.py
deleted file mode 100644
index f56e49e7f0e6eab3babf0711cae2933371b9f9cc..0000000000000000000000000000000000000000
--- a/spaces/ServerX/PorcoDiaz/lib/infer_pack/modules/F0Predictor/F0Predictor.py
+++ /dev/null
@@ -1,16 +0,0 @@
-class F0Predictor(object):
- def compute_f0(self, wav, p_len):
- """
- input: wav:[signal_length]
- p_len:int
- output: f0:[signal_length//hop_length]
- """
- pass
-
- def compute_f0_uv(self, wav, p_len):
- """
- input: wav:[signal_length]
- p_len:int
- output: f0:[signal_length//hop_length],uv:[signal_length//hop_length]
- """
- pass
diff --git a/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/cpp/longcode/jpge.cpp b/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/cpp/longcode/jpge.cpp
deleted file mode 100644
index 2e26b71ed5aad0d46478fdbcd3a880be1401f946..0000000000000000000000000000000000000000
--- a/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/cpp/longcode/jpge.cpp
+++ /dev/null
@@ -1,1049 +0,0 @@
-// jpge.cpp - C++ class for JPEG compression.
-// Public domain, Rich Geldreich
-// v1.01, Dec. 18, 2010 - Initial release
-// v1.02, Apr. 6, 2011 - Removed 2x2 ordered dither in H2V1 chroma subsampling method load_block_16_8_8(). (The rounding factor was 2, when it should have been 1. Either way, it wasn't helping.)
-// v1.03, Apr. 16, 2011 - Added support for optimized Huffman code tables, optimized dynamic memory allocation down to only 1 alloc.
-// Also from Alex Evans: Added RGBA support, linear memory allocator (no longer needed in v1.03).
-// v1.04, May. 19, 2012: Forgot to set m_pFile ptr to NULL in cfile_stream::close(). Thanks to Owen Kaluza for reporting this bug.
-// Code tweaks to fix VS2008 static code analysis warnings (all looked harmless).
-// Code review revealed method load_block_16_8_8() (used for the non-default H2V1 sampling mode to downsample chroma) somehow didn't get the rounding factor fix from v1.02.
-
-#include "jpge.h"
-
-#include
-#include
-#if PLATFORM_WINDOWS
-#include
-#endif
-
-#define JPGE_MAX(a,b) (((a)>(b))?(a):(b))
-#define JPGE_MIN(a,b) (((a)<(b))?(a):(b))
-
-namespace jpge {
-
-static inline void *jpge_malloc(size_t nSize) { return FMemory::Malloc(nSize); }
-static inline void jpge_free(void *p) { FMemory::Free(p);; }
-
-// Various JPEG enums and tables.
-enum { M_SOF0 = 0xC0, M_DHT = 0xC4, M_SOI = 0xD8, M_EOI = 0xD9, M_SOS = 0xDA, M_DQT = 0xDB, M_APP0 = 0xE0 };
-enum { DC_LUM_CODES = 12, AC_LUM_CODES = 256, DC_CHROMA_CODES = 12, AC_CHROMA_CODES = 256, MAX_HUFF_SYMBOLS = 257, MAX_HUFF_CODESIZE = 32 };
-
-static uint8 s_zag[64] = { 0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };
-static int16 s_std_lum_quant[64] = { 16,11,12,14,12,10,16,14,13,14,18,17,16,19,24,40,26,24,22,22,24,49,35,37,29,40,58,51,61,60,57,51,56,55,64,72,92,78,64,68,87,69,55,56,80,109,81,87,95,98,103,104,103,62,77,113,121,112,100,120,92,101,103,99 };
-static int16 s_std_croma_quant[64] = { 17,18,18,24,21,24,47,26,26,47,99,66,56,66,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99,99 };
-static uint8 s_dc_lum_bits[17] = { 0,0,1,5,1,1,1,1,1,1,0,0,0,0,0,0,0 };
-static uint8 s_dc_lum_val[DC_LUM_CODES] = { 0,1,2,3,4,5,6,7,8,9,10,11 };
-static uint8 s_ac_lum_bits[17] = { 0,0,2,1,3,3,2,4,3,5,5,4,4,0,0,1,0x7d };
-static uint8 s_ac_lum_val[AC_LUM_CODES] =
-{
- 0x01,0x02,0x03,0x00,0x04,0x11,0x05,0x12,0x21,0x31,0x41,0x06,0x13,0x51,0x61,0x07,0x22,0x71,0x14,0x32,0x81,0x91,0xa1,0x08,0x23,0x42,0xb1,0xc1,0x15,0x52,0xd1,0xf0,
- 0x24,0x33,0x62,0x72,0x82,0x09,0x0a,0x16,0x17,0x18,0x19,0x1a,0x25,0x26,0x27,0x28,0x29,0x2a,0x34,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,0x49,
- 0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x83,0x84,0x85,0x86,0x87,0x88,0x89,
- 0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,0xc4,0xc5,
- 0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe1,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf1,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,
- 0xf9,0xfa
-};
-static uint8 s_dc_chroma_bits[17] = { 0,0,3,1,1,1,1,1,1,1,1,1,0,0,0,0,0 };
-static uint8 s_dc_chroma_val[DC_CHROMA_CODES] = { 0,1,2,3,4,5,6,7,8,9,10,11 };
-static uint8 s_ac_chroma_bits[17] = { 0,0,2,1,2,4,4,3,4,7,5,4,4,0,1,2,0x77 };
-static uint8 s_ac_chroma_val[AC_CHROMA_CODES] =
-{
- 0x00,0x01,0x02,0x03,0x11,0x04,0x05,0x21,0x31,0x06,0x12,0x41,0x51,0x07,0x61,0x71,0x13,0x22,0x32,0x81,0x08,0x14,0x42,0x91,0xa1,0xb1,0xc1,0x09,0x23,0x33,0x52,0xf0,
- 0x15,0x62,0x72,0xd1,0x0a,0x16,0x24,0x34,0xe1,0x25,0xf1,0x17,0x18,0x19,0x1a,0x26,0x27,0x28,0x29,0x2a,0x35,0x36,0x37,0x38,0x39,0x3a,0x43,0x44,0x45,0x46,0x47,0x48,
- 0x49,0x4a,0x53,0x54,0x55,0x56,0x57,0x58,0x59,0x5a,0x63,0x64,0x65,0x66,0x67,0x68,0x69,0x6a,0x73,0x74,0x75,0x76,0x77,0x78,0x79,0x7a,0x82,0x83,0x84,0x85,0x86,0x87,
- 0x88,0x89,0x8a,0x92,0x93,0x94,0x95,0x96,0x97,0x98,0x99,0x9a,0xa2,0xa3,0xa4,0xa5,0xa6,0xa7,0xa8,0xa9,0xaa,0xb2,0xb3,0xb4,0xb5,0xb6,0xb7,0xb8,0xb9,0xba,0xc2,0xc3,
- 0xc4,0xc5,0xc6,0xc7,0xc8,0xc9,0xca,0xd2,0xd3,0xd4,0xd5,0xd6,0xd7,0xd8,0xd9,0xda,0xe2,0xe3,0xe4,0xe5,0xe6,0xe7,0xe8,0xe9,0xea,0xf2,0xf3,0xf4,0xf5,0xf6,0xf7,0xf8,
- 0xf9,0xfa
-};
-
-// Low-level helper functions.
-template inline void clear_obj(T &obj) { memset(&obj, 0, sizeof(obj)); }
-
-const int YR = 19595, YG = 38470, YB = 7471, CB_R = -11059, CB_G = -21709, CB_B = 32768, CR_R = 32768, CR_G = -27439, CR_B = -5329;
-static inline uint8 clamp(int i) { if (static_cast(i) > 255U) { if (i < 0) i = 0; else if (i > 255) i = 255; } return static_cast(i); }
-
-static void RGB_to_YCC(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
- for ( ; num_pixels; pDst += 3, pSrc += 3, num_pixels--)
- {
- const int r = pSrc[0], g = pSrc[1], b = pSrc[2];
- pDst[0] = static_cast((r * YR + g * YG + b * YB + 32768) >> 16);
- pDst[1] = clamp(128 + ((r * CB_R + g * CB_G + b * CB_B + 32768) >> 16));
- pDst[2] = clamp(128 + ((r * CR_R + g * CR_G + b * CR_B + 32768) >> 16));
- }
-}
-
-static void RGB_to_Y(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
- for ( ; num_pixels; pDst++, pSrc += 3, num_pixels--)
- pDst[0] = static_cast((pSrc[0] * YR + pSrc[1] * YG + pSrc[2] * YB + 32768) >> 16);
-}
-
-static void RGBA_to_YCC(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
- for ( ; num_pixels; pDst += 3, pSrc += 4, num_pixels--)
- {
- const int r = pSrc[0], g = pSrc[1], b = pSrc[2];
- pDst[0] = static_cast((r * YR + g * YG + b * YB + 32768) >> 16);
- pDst[1] = clamp(128 + ((r * CB_R + g * CB_G + b * CB_B + 32768) >> 16));
- pDst[2] = clamp(128 + ((r * CR_R + g * CR_G + b * CR_B + 32768) >> 16));
- }
-}
-
-static void RGBA_to_Y(uint8* pDst, const uint8 *pSrc, int num_pixels)
-{
- for ( ; num_pixels; pDst++, pSrc += 4, num_pixels--)
- pDst[0] = static_cast((pSrc[0] * YR + pSrc[1] * YG + pSrc[2] * YB + 32768) >> 16);
-}
-
-static void Y_to_YCC(uint8* pDst, const uint8* pSrc, int num_pixels)
-{
- for( ; num_pixels; pDst += 3, pSrc++, num_pixels--) { pDst[0] = pSrc[0]; pDst[1] = 128; pDst[2] = 128; }
-}
-
-// Forward DCT - DCT derived from jfdctint.
-#define CONST_BITS 13
-#define ROW_BITS 2
-#define DCT_DESCALE(x, n) (((x) + (((int32)1) << ((n) - 1))) >> (n))
-#define DCT_MUL(var, c) (static_cast(var) * static_cast(c))
-#define DCT1D(s0, s1, s2, s3, s4, s5, s6, s7) \
- int32 t0 = s0 + s7, t7 = s0 - s7, t1 = s1 + s6, t6 = s1 - s6, t2 = s2 + s5, t5 = s2 - s5, t3 = s3 + s4, t4 = s3 - s4; \
- int32 t10 = t0 + t3, t13 = t0 - t3, t11 = t1 + t2, t12 = t1 - t2; \
- int32 u1 = DCT_MUL(t12 + t13, 4433); \
- s2 = u1 + DCT_MUL(t13, 6270); \
- s6 = u1 + DCT_MUL(t12, -15137); \
- u1 = t4 + t7; \
- int32 u2 = t5 + t6, u3 = t4 + t6, u4 = t5 + t7; \
- int32 z5 = DCT_MUL(u3 + u4, 9633); \
- t4 = DCT_MUL(t4, 2446); t5 = DCT_MUL(t5, 16819); \
- t6 = DCT_MUL(t6, 25172); t7 = DCT_MUL(t7, 12299); \
- u1 = DCT_MUL(u1, -7373); u2 = DCT_MUL(u2, -20995); \
- u3 = DCT_MUL(u3, -16069); u4 = DCT_MUL(u4, -3196); \
- u3 += z5; u4 += z5; \
- s0 = t10 + t11; s1 = t7 + u1 + u4; s3 = t6 + u2 + u3; s4 = t10 - t11; s5 = t5 + u2 + u4; s7 = t4 + u1 + u3;
-
-static void DCT2D(int32 *p)
-{
- int32 c, *q = p;
- for (c = 7; c >= 0; c--, q += 8)
- {
- int32 s0 = q[0], s1 = q[1], s2 = q[2], s3 = q[3], s4 = q[4], s5 = q[5], s6 = q[6], s7 = q[7];
- DCT1D(s0, s1, s2, s3, s4, s5, s6, s7);
- q[0] = s0 << ROW_BITS; q[1] = DCT_DESCALE(s1, CONST_BITS-ROW_BITS); q[2] = DCT_DESCALE(s2, CONST_BITS-ROW_BITS); q[3] = DCT_DESCALE(s3, CONST_BITS-ROW_BITS);
- q[4] = s4 << ROW_BITS; q[5] = DCT_DESCALE(s5, CONST_BITS-ROW_BITS); q[6] = DCT_DESCALE(s6, CONST_BITS-ROW_BITS); q[7] = DCT_DESCALE(s7, CONST_BITS-ROW_BITS);
- }
- for (q = p, c = 7; c >= 0; c--, q++)
- {
- int32 s0 = q[0*8], s1 = q[1*8], s2 = q[2*8], s3 = q[3*8], s4 = q[4*8], s5 = q[5*8], s6 = q[6*8], s7 = q[7*8];
- DCT1D(s0, s1, s2, s3, s4, s5, s6, s7);
- q[0*8] = DCT_DESCALE(s0, ROW_BITS+3); q[1*8] = DCT_DESCALE(s1, CONST_BITS+ROW_BITS+3); q[2*8] = DCT_DESCALE(s2, CONST_BITS+ROW_BITS+3); q[3*8] = DCT_DESCALE(s3, CONST_BITS+ROW_BITS+3);
- q[4*8] = DCT_DESCALE(s4, ROW_BITS+3); q[5*8] = DCT_DESCALE(s5, CONST_BITS+ROW_BITS+3); q[6*8] = DCT_DESCALE(s6, CONST_BITS+ROW_BITS+3); q[7*8] = DCT_DESCALE(s7, CONST_BITS+ROW_BITS+3);
- }
-}
-
-struct sym_freq { uint m_key, m_sym_index; };
-
-// Radix sorts sym_freq[] array by 32-bit key m_key. Returns ptr to sorted values.
-static inline sym_freq* radix_sort_syms(uint num_syms, sym_freq* pSyms0, sym_freq* pSyms1)
-{
- const uint cMaxPasses = 4;
- uint32 hist[256 * cMaxPasses]; clear_obj(hist);
- for (uint i = 0; i < num_syms; i++) { uint freq = pSyms0[i].m_key; hist[freq & 0xFF]++; hist[256 + ((freq >> 8) & 0xFF)]++; hist[256*2 + ((freq >> 16) & 0xFF)]++; hist[256*3 + ((freq >> 24) & 0xFF)]++; }
- sym_freq* pCur_syms = pSyms0, *pNew_syms = pSyms1;
- uint total_passes = cMaxPasses; while ((total_passes > 1) && (num_syms == hist[(total_passes - 1) * 256])) total_passes--;
- for (uint pass_shift = 0, pass = 0; pass < total_passes; pass++, pass_shift += 8)
- {
- const uint32* pHist = &hist[pass << 8];
- uint offsets[256], cur_ofs = 0;
- for (uint i = 0; i < 256; i++) { offsets[i] = cur_ofs; cur_ofs += pHist[i]; }
- for (uint i = 0; i < num_syms; i++)
- pNew_syms[offsets[(pCur_syms[i].m_key >> pass_shift) & 0xFF]++] = pCur_syms[i];
- sym_freq* t = pCur_syms; pCur_syms = pNew_syms; pNew_syms = t;
- }
- return pCur_syms;
-}
-
-// calculate_minimum_redundancy() originally written by: Alistair Moffat, alistair@cs.mu.oz.au, Jyrki Katajainen, jyrki@diku.dk, November 1996.
-static void calculate_minimum_redundancy(sym_freq *A, int n)
-{
- int root, leaf, next, avbl, used, dpth;
- if (n==0) return; else if (n==1) { A[0].m_key = 1; return; }
- A[0].m_key += A[1].m_key; root = 0; leaf = 2;
- for (next=1; next < n-1; next++)
- {
- if (leaf>=n || A[root].m_key=n || (root=0; next--) A[next].m_key = A[A[next].m_key].m_key+1;
- avbl = 1; used = dpth = 0; root = n-2; next = n-1;
- while (avbl>0)
- {
- while (root>=0 && (int)A[root].m_key==dpth) { used++; root--; }
- while (avbl>used) { A[next--].m_key = dpth; avbl--; }
- avbl = 2*used; dpth++; used = 0;
- }
-}
-
-// Limits canonical Huffman code table's max code size to max_code_size.
-static void huffman_enforce_max_code_size(int *pNum_codes, int code_list_len, int max_code_size)
-{
- if (code_list_len <= 1) return;
-
- for (int i = max_code_size + 1; i <= MAX_HUFF_CODESIZE; i++) pNum_codes[max_code_size] += pNum_codes[i];
-
- uint32 total = 0;
- for (int i = max_code_size; i > 0; i--)
- total += (((uint32)pNum_codes[i]) << (max_code_size - i));
-
- while (total != (1UL << max_code_size))
- {
- pNum_codes[max_code_size]--;
- for (int i = max_code_size - 1; i > 0; i--)
- {
- if (pNum_codes[i]) { pNum_codes[i]--; pNum_codes[i + 1] += 2; break; }
- }
- total--;
- }
-}
-
-// Generates an optimized offman table.
-void jpeg_encoder::optimize_huffman_table(int table_num, int table_len)
-{
- sym_freq syms0[MAX_HUFF_SYMBOLS], syms1[MAX_HUFF_SYMBOLS];
- syms0[0].m_key = 1; syms0[0].m_sym_index = 0; // dummy symbol, assures that no valid code contains all 1's
- int num_used_syms = 1;
- const uint32 *pSym_count = &m_huff_count[table_num][0];
- for (int i = 0; i < table_len; i++)
- if (pSym_count[i]) { syms0[num_used_syms].m_key = pSym_count[i]; syms0[num_used_syms++].m_sym_index = i + 1; }
- sym_freq* pSyms = radix_sort_syms(num_used_syms, syms0, syms1);
- calculate_minimum_redundancy(pSyms, num_used_syms);
-
- // Count the # of symbols of each code size.
- int num_codes[1 + MAX_HUFF_CODESIZE]; clear_obj(num_codes);
- for (int i = 0; i < num_used_syms; i++)
- num_codes[pSyms[i].m_key]++;
-
- const uint JPGE_CODE_SIZE_LIMIT = 16; // the maximum possible size of a JPEG Huffman code (valid range is [9,16] - 9 vs. 8 because of the dummy symbol)
- huffman_enforce_max_code_size(num_codes, num_used_syms, JPGE_CODE_SIZE_LIMIT);
-
- // Compute m_huff_bits array, which contains the # of symbols per code size.
- clear_obj(m_huff_bits[table_num]);
- for (int i = 1; i <= (int)JPGE_CODE_SIZE_LIMIT; i++)
- m_huff_bits[table_num][i] = static_cast(num_codes[i]);
-
- // Remove the dummy symbol added above, which must be in largest bucket.
- for (int i = JPGE_CODE_SIZE_LIMIT; i >= 1; i--)
- {
- if (m_huff_bits[table_num][i]) { m_huff_bits[table_num][i]--; break; }
- }
-
- // Compute the m_huff_val array, which contains the symbol indices sorted by code size (smallest to largest).
- for (int i = num_used_syms - 1; i >= 1; i--)
- m_huff_val[table_num][num_used_syms - 1 - i] = static_cast(pSyms[i].m_sym_index - 1);
-}
-
-// JPEG marker generation.
-void jpeg_encoder::emit_byte(uint8 i)
-{
- m_all_stream_writes_succeeded = m_all_stream_writes_succeeded && m_pStream->put_obj(i);
-}
-
-void jpeg_encoder::emit_word(uint i)
-{
- emit_byte(uint8(i >> 8)); emit_byte(uint8(i & 0xFF));
-}
-
-void jpeg_encoder::emit_marker(int marker)
-{
- emit_byte(uint8(0xFF)); emit_byte(uint8(marker));
-}
-
-// Emit JFIF marker
-void jpeg_encoder::emit_jfif_app0()
-{
- emit_marker(M_APP0);
- emit_word(2 + 4 + 1 + 2 + 1 + 2 + 2 + 1 + 1);
- emit_byte(0x4A); emit_byte(0x46); emit_byte(0x49); emit_byte(0x46); /* Identifier: ASCII "JFIF" */
- emit_byte(0);
- emit_byte(1); /* Major version */
- emit_byte(1); /* Minor version */
- emit_byte(0); /* Density unit */
- emit_word(1);
- emit_word(1);
- emit_byte(0); /* No thumbnail image */
- emit_byte(0);
-}
-
-// Emit quantization tables
-void jpeg_encoder::emit_dqt()
-{
- for (int i = 0; i < ((m_num_components == 3) ? 2 : 1); i++)
- {
- emit_marker(M_DQT);
- emit_word(64 + 1 + 2);
- emit_byte(static_cast(i));
- for (int j = 0; j < 64; j++)
- emit_byte(static_cast(m_quantization_tables[i][j]));
- }
-}
-
-// Emit start of frame marker
-void jpeg_encoder::emit_sof()
-{
- emit_marker(M_SOF0); /* baseline */
- emit_word(3 * m_num_components + 2 + 5 + 1);
- emit_byte(8); /* precision */
- emit_word(m_image_y);
- emit_word(m_image_x);
- emit_byte(m_num_components);
- for (int i = 0; i < m_num_components; i++)
- {
- emit_byte(static_cast(i + 1)); /* component ID */
- emit_byte((m_comp_h_samp[i] << 4) + m_comp_v_samp[i]); /* h and v sampling */
- emit_byte(i > 0); /* quant. table num */
- }
-}
-
-// Emit Huffman table.
-void jpeg_encoder::emit_dht(uint8 *bits, uint8 *val, int index, bool ac_flag)
-{
- emit_marker(M_DHT);
-
- int length = 0;
- for (int i = 1; i <= 16; i++)
- length += bits[i];
-
- emit_word(length + 2 + 1 + 16);
- emit_byte(static_cast(index + (ac_flag << 4)));
-
- for (int i = 1; i <= 16; i++)
- emit_byte(bits[i]);
-
- for (int i = 0; i < length; i++)
- emit_byte(val[i]);
-}
-
-// Emit all Huffman tables.
-void jpeg_encoder::emit_dhts()
-{
- emit_dht(m_huff_bits[0+0], m_huff_val[0+0], 0, false);
- emit_dht(m_huff_bits[2+0], m_huff_val[2+0], 0, true);
- if (m_num_components == 3)
- {
- emit_dht(m_huff_bits[0+1], m_huff_val[0+1], 1, false);
- emit_dht(m_huff_bits[2+1], m_huff_val[2+1], 1, true);
- }
-}
-
-// emit start of scan
-void jpeg_encoder::emit_sos()
-{
- emit_marker(M_SOS);
- emit_word(2 * m_num_components + 2 + 1 + 3);
- emit_byte(m_num_components);
- for (int i = 0; i < m_num_components; i++)
- {
- emit_byte(static_cast(i + 1));
- if (i == 0)
- emit_byte((0 << 4) + 0);
- else
- emit_byte((1 << 4) + 1);
- }
- emit_byte(0); /* spectral selection */
- emit_byte(63);
- emit_byte(0);
-}
-
-// Emit all markers at beginning of image file.
-void jpeg_encoder::emit_markers()
-{
- emit_marker(M_SOI);
- emit_jfif_app0();
- emit_dqt();
- emit_sof();
- emit_dhts();
- emit_sos();
-}
-
-// Compute the actual canonical Huffman codes/code sizes given the JPEG huff bits and val arrays.
-void jpeg_encoder::compute_huffman_table(uint *codes, uint8 *code_sizes, uint8 *bits, uint8 *val)
-{
- int i, l, last_p, si;
- uint8 huff_size[257];
- uint huff_code[257];
- uint code;
-
- int p = 0;
- for (l = 1; l <= 16; l++)
- for (i = 1; i <= bits[l]; i++)
- huff_size[p++] = (char)l;
-
- huff_size[p] = 0; last_p = p; // write sentinel
-
- code = 0; si = huff_size[0]; p = 0;
-
- while (huff_size[p])
- {
- while (huff_size[p] == si)
- huff_code[p++] = code++;
- code <<= 1;
- si++;
- }
-
- memset(codes, 0, sizeof(codes[0])*256);
- memset(code_sizes, 0, sizeof(code_sizes[0])*256);
- for (p = 0; p < last_p; p++)
- {
- codes[val[p]] = huff_code[p];
- code_sizes[val[p]] = huff_size[p];
- }
-}
-
-// Quantization table generation.
-void jpeg_encoder::compute_quant_table(int32 *pDst, int16 *pSrc)
-{
- int32 q;
- if (m_params.m_quality < 50)
- q = 5000 / m_params.m_quality;
- else
- q = 200 - m_params.m_quality * 2;
- for (int i = 0; i < 64; i++)
- {
- int32 j = *pSrc++; j = (j * q + 50L) / 100L;
- *pDst++ = JPGE_MIN(JPGE_MAX(j, 1), 255);
- }
-}
-
-// Higher-level methods.
-void jpeg_encoder::first_pass_init()
-{
- m_bit_buffer = 0; m_bits_in = 0;
- memset(m_last_dc_val, 0, 3 * sizeof(m_last_dc_val[0]));
- m_mcu_y_ofs = 0;
- m_pass_num = 1;
-}
-
-bool jpeg_encoder::second_pass_init()
-{
- compute_huffman_table(&m_huff_codes[0+0][0], &m_huff_code_sizes[0+0][0], m_huff_bits[0+0], m_huff_val[0+0]);
- compute_huffman_table(&m_huff_codes[2+0][0], &m_huff_code_sizes[2+0][0], m_huff_bits[2+0], m_huff_val[2+0]);
- if (m_num_components > 1)
- {
- compute_huffman_table(&m_huff_codes[0+1][0], &m_huff_code_sizes[0+1][0], m_huff_bits[0+1], m_huff_val[0+1]);
- compute_huffman_table(&m_huff_codes[2+1][0], &m_huff_code_sizes[2+1][0], m_huff_bits[2+1], m_huff_val[2+1]);
- }
- first_pass_init();
- emit_markers();
- m_pass_num = 2;
- return true;
-}
-
-bool jpeg_encoder::jpg_open(int p_x_res, int p_y_res, int src_channels)
-{
- m_num_components = 3;
- switch (m_params.m_subsampling)
- {
- case Y_ONLY:
- {
- m_num_components = 1;
- m_comp_h_samp[0] = 1; m_comp_v_samp[0] = 1;
- m_mcu_x = 8; m_mcu_y = 8;
- break;
- }
- case H1V1:
- {
- m_comp_h_samp[0] = 1; m_comp_v_samp[0] = 1;
- m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
- m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
- m_mcu_x = 8; m_mcu_y = 8;
- break;
- }
- case H2V1:
- {
- m_comp_h_samp[0] = 2; m_comp_v_samp[0] = 1;
- m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
- m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
- m_mcu_x = 16; m_mcu_y = 8;
- break;
- }
- case H2V2:
- {
- m_comp_h_samp[0] = 2; m_comp_v_samp[0] = 2;
- m_comp_h_samp[1] = 1; m_comp_v_samp[1] = 1;
- m_comp_h_samp[2] = 1; m_comp_v_samp[2] = 1;
- m_mcu_x = 16; m_mcu_y = 16;
- }
- }
-
- m_image_x = p_x_res; m_image_y = p_y_res;
- m_image_bpp = src_channels;
- m_image_bpl = m_image_x * src_channels;
- m_image_x_mcu = (m_image_x + m_mcu_x - 1) & (~(m_mcu_x - 1));
- m_image_y_mcu = (m_image_y + m_mcu_y - 1) & (~(m_mcu_y - 1));
- m_image_bpl_xlt = m_image_x * m_num_components;
- m_image_bpl_mcu = m_image_x_mcu * m_num_components;
- m_mcus_per_row = m_image_x_mcu / m_mcu_x;
-
- if ((m_mcu_lines[0] = static_cast(jpge_malloc(m_image_bpl_mcu * m_mcu_y))) == NULL) return false;
- for (int i = 1; i < m_mcu_y; i++)
- m_mcu_lines[i] = m_mcu_lines[i-1] + m_image_bpl_mcu;
-
- compute_quant_table(m_quantization_tables[0], s_std_lum_quant);
- compute_quant_table(m_quantization_tables[1], m_params.m_no_chroma_discrim_flag ? s_std_lum_quant : s_std_croma_quant);
-
- m_out_buf_left = JPGE_OUT_BUF_SIZE;
- m_pOut_buf = m_out_buf;
-
- if (m_params.m_two_pass_flag)
- {
- clear_obj(m_huff_count);
- first_pass_init();
- }
- else
- {
- memcpy(m_huff_bits[0+0], s_dc_lum_bits, 17); memcpy(m_huff_val [0+0], s_dc_lum_val, DC_LUM_CODES);
- memcpy(m_huff_bits[2+0], s_ac_lum_bits, 17); memcpy(m_huff_val [2+0], s_ac_lum_val, AC_LUM_CODES);
- memcpy(m_huff_bits[0+1], s_dc_chroma_bits, 17); memcpy(m_huff_val [0+1], s_dc_chroma_val, DC_CHROMA_CODES);
- memcpy(m_huff_bits[2+1], s_ac_chroma_bits, 17); memcpy(m_huff_val [2+1], s_ac_chroma_val, AC_CHROMA_CODES);
- if (!second_pass_init()) return false; // in effect, skip over the first pass
- }
- return m_all_stream_writes_succeeded;
-}
-
-void jpeg_encoder::load_block_8_8_grey(int x)
-{
- uint8 *pSrc;
- sample_array_t *pDst = m_sample_array;
- x <<= 3;
- for (int i = 0; i < 8; i++, pDst += 8)
- {
- pSrc = m_mcu_lines[i] + x;
- pDst[0] = pSrc[0] - 128; pDst[1] = pSrc[1] - 128; pDst[2] = pSrc[2] - 128; pDst[3] = pSrc[3] - 128;
- pDst[4] = pSrc[4] - 128; pDst[5] = pSrc[5] - 128; pDst[6] = pSrc[6] - 128; pDst[7] = pSrc[7] - 128;
- }
-}
-
-void jpeg_encoder::load_block_8_8(int x, int y, int c)
-{
- uint8 *pSrc;
- sample_array_t *pDst = m_sample_array;
- x = (x * (8 * 3)) + c;
- y <<= 3;
- for (int i = 0; i < 8; i++, pDst += 8)
- {
- pSrc = m_mcu_lines[y + i] + x;
- pDst[0] = pSrc[0 * 3] - 128; pDst[1] = pSrc[1 * 3] - 128; pDst[2] = pSrc[2 * 3] - 128; pDst[3] = pSrc[3 * 3] - 128;
- pDst[4] = pSrc[4 * 3] - 128; pDst[5] = pSrc[5 * 3] - 128; pDst[6] = pSrc[6 * 3] - 128; pDst[7] = pSrc[7 * 3] - 128;
- }
-}
-
-void jpeg_encoder::load_block_16_8(int x, int c)
-{
- uint8 *pSrc1, *pSrc2;
- sample_array_t *pDst = m_sample_array;
- x = (x * (16 * 3)) + c;
- int a = 0, b = 2;
- for (int i = 0; i < 16; i += 2, pDst += 8)
- {
- pSrc1 = m_mcu_lines[i + 0] + x;
- pSrc2 = m_mcu_lines[i + 1] + x;
- pDst[0] = ((pSrc1[ 0 * 3] + pSrc1[ 1 * 3] + pSrc2[ 0 * 3] + pSrc2[ 1 * 3] + a) >> 2) - 128; pDst[1] = ((pSrc1[ 2 * 3] + pSrc1[ 3 * 3] + pSrc2[ 2 * 3] + pSrc2[ 3 * 3] + b) >> 2) - 128;
- pDst[2] = ((pSrc1[ 4 * 3] + pSrc1[ 5 * 3] + pSrc2[ 4 * 3] + pSrc2[ 5 * 3] + a) >> 2) - 128; pDst[3] = ((pSrc1[ 6 * 3] + pSrc1[ 7 * 3] + pSrc2[ 6 * 3] + pSrc2[ 7 * 3] + b) >> 2) - 128;
- pDst[4] = ((pSrc1[ 8 * 3] + pSrc1[ 9 * 3] + pSrc2[ 8 * 3] + pSrc2[ 9 * 3] + a) >> 2) - 128; pDst[5] = ((pSrc1[10 * 3] + pSrc1[11 * 3] + pSrc2[10 * 3] + pSrc2[11 * 3] + b) >> 2) - 128;
- pDst[6] = ((pSrc1[12 * 3] + pSrc1[13 * 3] + pSrc2[12 * 3] + pSrc2[13 * 3] + a) >> 2) - 128; pDst[7] = ((pSrc1[14 * 3] + pSrc1[15 * 3] + pSrc2[14 * 3] + pSrc2[15 * 3] + b) >> 2) - 128;
- int temp = a; a = b; b = temp;
- }
-}
-
-void jpeg_encoder::load_block_16_8_8(int x, int c)
-{
- uint8 *pSrc1;
- sample_array_t *pDst = m_sample_array;
- x = (x * (16 * 3)) + c;
- for (int i = 0; i < 8; i++, pDst += 8)
- {
- pSrc1 = m_mcu_lines[i + 0] + x;
- pDst[0] = ((pSrc1[ 0 * 3] + pSrc1[ 1 * 3]) >> 1) - 128; pDst[1] = ((pSrc1[ 2 * 3] + pSrc1[ 3 * 3]) >> 1) - 128;
- pDst[2] = ((pSrc1[ 4 * 3] + pSrc1[ 5 * 3]) >> 1) - 128; pDst[3] = ((pSrc1[ 6 * 3] + pSrc1[ 7 * 3]) >> 1) - 128;
- pDst[4] = ((pSrc1[ 8 * 3] + pSrc1[ 9 * 3]) >> 1) - 128; pDst[5] = ((pSrc1[10 * 3] + pSrc1[11 * 3]) >> 1) - 128;
- pDst[6] = ((pSrc1[12 * 3] + pSrc1[13 * 3]) >> 1) - 128; pDst[7] = ((pSrc1[14 * 3] + pSrc1[15 * 3]) >> 1) - 128;
- }
-}
-
-void jpeg_encoder::load_quantized_coefficients(int component_num)
-{
- int32 *q = m_quantization_tables[component_num > 0];
- int16 *pDst = m_coefficient_array;
- for (int i = 0; i < 64; i++)
- {
- sample_array_t j = m_sample_array[s_zag[i]];
- if (j < 0)
- {
- if ((j = -j + (*q >> 1)) < *q)
- *pDst++ = 0;
- else
- *pDst++ = static_cast(-(j / *q));
- }
- else
- {
- if ((j = j + (*q >> 1)) < *q)
- *pDst++ = 0;
- else
- *pDst++ = static_cast((j / *q));
- }
- q++;
- }
-}
-
-void jpeg_encoder::flush_output_buffer()
-{
- if (m_out_buf_left != JPGE_OUT_BUF_SIZE)
- m_all_stream_writes_succeeded = m_all_stream_writes_succeeded && m_pStream->put_buf(m_out_buf, JPGE_OUT_BUF_SIZE - m_out_buf_left);
- m_pOut_buf = m_out_buf;
- m_out_buf_left = JPGE_OUT_BUF_SIZE;
-}
-
-void jpeg_encoder::put_bits(uint bits, uint len)
-{
- m_bit_buffer |= ((uint32)bits << (24 - (m_bits_in += len)));
- while (m_bits_in >= 8)
- {
- uint8 c;
- #define JPGE_PUT_BYTE(c) { *m_pOut_buf++ = (c); if (--m_out_buf_left == 0) flush_output_buffer(); }
- JPGE_PUT_BYTE(c = (uint8)((m_bit_buffer >> 16) & 0xFF));
- if (c == 0xFF) JPGE_PUT_BYTE(0);
- m_bit_buffer <<= 8;
- m_bits_in -= 8;
- }
-}
-
-void jpeg_encoder::code_coefficients_pass_one(int component_num)
-{
- if (component_num >= 3) return; // just to shut up static analysis
- int i, run_len, nbits, temp1;
- int16 *src = m_coefficient_array;
- uint32 *dc_count = component_num ? m_huff_count[0 + 1] : m_huff_count[0 + 0], *ac_count = component_num ? m_huff_count[2 + 1] : m_huff_count[2 + 0];
-
- temp1 = src[0] - m_last_dc_val[component_num];
- m_last_dc_val[component_num] = src[0];
- if (temp1 < 0) temp1 = -temp1;
-
- nbits = 0;
- while (temp1)
- {
- nbits++; temp1 >>= 1;
- }
-
- dc_count[nbits]++;
- for (run_len = 0, i = 1; i < 64; i++)
- {
- if ((temp1 = m_coefficient_array[i]) == 0)
- run_len++;
- else
- {
- while (run_len >= 16)
- {
- ac_count[0xF0]++;
- run_len -= 16;
- }
- if (temp1 < 0) temp1 = -temp1;
- nbits = 1;
- while (temp1 >>= 1) nbits++;
- ac_count[(run_len << 4) + nbits]++;
- run_len = 0;
- }
- }
- if (run_len) ac_count[0]++;
-}
-
-void jpeg_encoder::code_coefficients_pass_two(int component_num)
-{
- int i, j, run_len, nbits, temp1, temp2;
- int16 *pSrc = m_coefficient_array;
- uint *codes[2];
- uint8 *code_sizes[2];
-
- if (component_num == 0)
- {
- codes[0] = m_huff_codes[0 + 0]; codes[1] = m_huff_codes[2 + 0];
- code_sizes[0] = m_huff_code_sizes[0 + 0]; code_sizes[1] = m_huff_code_sizes[2 + 0];
- }
- else
- {
- codes[0] = m_huff_codes[0 + 1]; codes[1] = m_huff_codes[2 + 1];
- code_sizes[0] = m_huff_code_sizes[0 + 1]; code_sizes[1] = m_huff_code_sizes[2 + 1];
- }
-
- temp1 = temp2 = pSrc[0] - m_last_dc_val[component_num];
- m_last_dc_val[component_num] = pSrc[0];
-
- if (temp1 < 0)
- {
- temp1 = -temp1; temp2--;
- }
-
- nbits = 0;
- while (temp1)
- {
- nbits++; temp1 >>= 1;
- }
-
- put_bits(codes[0][nbits], code_sizes[0][nbits]);
- if (nbits) put_bits(temp2 & ((1 << nbits) - 1), nbits);
-
- for (run_len = 0, i = 1; i < 64; i++)
- {
- if ((temp1 = m_coefficient_array[i]) == 0)
- run_len++;
- else
- {
- while (run_len >= 16)
- {
- put_bits(codes[1][0xF0], code_sizes[1][0xF0]);
- run_len -= 16;
- }
- if ((temp2 = temp1) < 0)
- {
- temp1 = -temp1;
- temp2--;
- }
- nbits = 1;
- while (temp1 >>= 1)
- nbits++;
- j = (run_len << 4) + nbits;
- put_bits(codes[1][j], code_sizes[1][j]);
- put_bits(temp2 & ((1 << nbits) - 1), nbits);
- run_len = 0;
- }
- }
- if (run_len)
- put_bits(codes[1][0], code_sizes[1][0]);
-}
-
-void jpeg_encoder::code_block(int component_num)
-{
- DCT2D(m_sample_array);
- load_quantized_coefficients(component_num);
- if (m_pass_num == 1)
- code_coefficients_pass_one(component_num);
- else
- code_coefficients_pass_two(component_num);
-}
-
-void jpeg_encoder::process_mcu_row()
-{
- if (m_num_components == 1)
- {
- for (int i = 0; i < m_mcus_per_row; i++)
- {
- load_block_8_8_grey(i); code_block(0);
- }
- }
- else if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1))
- {
- for (int i = 0; i < m_mcus_per_row; i++)
- {
- load_block_8_8(i, 0, 0); code_block(0); load_block_8_8(i, 0, 1); code_block(1); load_block_8_8(i, 0, 2); code_block(2);
- }
- }
- else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1))
- {
- for (int i = 0; i < m_mcus_per_row; i++)
- {
- load_block_8_8(i * 2 + 0, 0, 0); code_block(0); load_block_8_8(i * 2 + 1, 0, 0); code_block(0);
- load_block_16_8_8(i, 1); code_block(1); load_block_16_8_8(i, 2); code_block(2);
- }
- }
- else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2))
- {
- for (int i = 0; i < m_mcus_per_row; i++)
- {
- load_block_8_8(i * 2 + 0, 0, 0); code_block(0); load_block_8_8(i * 2 + 1, 0, 0); code_block(0);
- load_block_8_8(i * 2 + 0, 1, 0); code_block(0); load_block_8_8(i * 2 + 1, 1, 0); code_block(0);
- load_block_16_8(i, 1); code_block(1); load_block_16_8(i, 2); code_block(2);
- }
- }
-}
-
-bool jpeg_encoder::terminate_pass_one()
-{
- optimize_huffman_table(0+0, DC_LUM_CODES); optimize_huffman_table(2+0, AC_LUM_CODES);
- if (m_num_components > 1)
- {
- optimize_huffman_table(0+1, DC_CHROMA_CODES); optimize_huffman_table(2+1, AC_CHROMA_CODES);
- }
- return second_pass_init();
-}
-
-bool jpeg_encoder::terminate_pass_two()
-{
- put_bits(0x7F, 7);
- flush_output_buffer();
- emit_marker(M_EOI);
- m_pass_num++; // purposely bump up m_pass_num, for debugging
- return true;
-}
-
-bool jpeg_encoder::process_end_of_image()
-{
- if (m_mcu_y_ofs)
- {
- if (m_mcu_y_ofs < 16) // check here just to shut up static analysis
- {
- for (int i = m_mcu_y_ofs; i < m_mcu_y; i++)
- memcpy(m_mcu_lines[i], m_mcu_lines[m_mcu_y_ofs - 1], m_image_bpl_mcu);
- }
-
- process_mcu_row();
- }
-
- if (m_pass_num == 1)
- return terminate_pass_one();
- else
- return terminate_pass_two();
-}
-
-void jpeg_encoder::load_mcu(const void *pSrc)
-{
- const uint8* Psrc = reinterpret_cast(pSrc);
-
- uint8* pDst = m_mcu_lines[m_mcu_y_ofs]; // OK to write up to m_image_bpl_xlt bytes to pDst
-
- if (m_num_components == 1)
- {
- if (m_image_bpp == 4)
- RGBA_to_Y(pDst, Psrc, m_image_x);
- else if (m_image_bpp == 3)
- RGB_to_Y(pDst, Psrc, m_image_x);
- else
- memcpy(pDst, Psrc, m_image_x);
- }
- else
- {
- if (m_image_bpp == 4)
- RGBA_to_YCC(pDst, Psrc, m_image_x);
- else if (m_image_bpp == 3)
- RGB_to_YCC(pDst, Psrc, m_image_x);
- else
- Y_to_YCC(pDst, Psrc, m_image_x);
- }
-
- // Possibly duplicate pixels at end of scanline if not a multiple of 8 or 16
- if (m_num_components == 1)
- memset(m_mcu_lines[m_mcu_y_ofs] + m_image_bpl_xlt, pDst[m_image_bpl_xlt - 1], m_image_x_mcu - m_image_x);
- else
- {
- const uint8 y = pDst[m_image_bpl_xlt - 3 + 0], cb = pDst[m_image_bpl_xlt - 3 + 1], cr = pDst[m_image_bpl_xlt - 3 + 2];
- uint8 *q = m_mcu_lines[m_mcu_y_ofs] + m_image_bpl_xlt;
- for (int i = m_image_x; i < m_image_x_mcu; i++)
- {
- *q++ = y; *q++ = cb; *q++ = cr;
- }
- }
-
- if (++m_mcu_y_ofs == m_mcu_y)
- {
- process_mcu_row();
- m_mcu_y_ofs = 0;
- }
-}
-
-void jpeg_encoder::clear()
-{
- m_mcu_lines[0] = NULL;
- m_pass_num = 0;
- m_all_stream_writes_succeeded = true;
-}
-
-jpeg_encoder::jpeg_encoder()
-{
- clear();
-}
-
-jpeg_encoder::~jpeg_encoder()
-{
- deinit();
-}
-
-bool jpeg_encoder::init(output_stream *pStream, int64_t width, int64_t height, int64_t src_channels, const params &comp_params)
-{
- deinit();
- if (((!pStream) || (width < 1) || (height < 1)) || ((src_channels != 1) && (src_channels != 3) && (src_channels != 4)) || (!comp_params.check_valid())) return false;
- m_pStream = pStream;
- m_params = comp_params;
- return jpg_open(width, height, src_channels);
-}
-
-void jpeg_encoder::deinit()
-{
- jpge_free(m_mcu_lines[0]);
- clear();
-}
-
-bool jpeg_encoder::process_scanline(const void* pScanline)
-{
- if ((m_pass_num < 1) || (m_pass_num > 2)) return false;
- if (m_all_stream_writes_succeeded)
- {
- if (!pScanline)
- {
- if (!process_end_of_image()) return false;
- }
- else
- {
- load_mcu(pScanline);
- }
- }
- return m_all_stream_writes_succeeded;
-}
-
-// Higher level wrappers/examples (optional).
-#include
-
-class cfile_stream : public output_stream
-{
- cfile_stream(const cfile_stream &);
- cfile_stream &operator= (const cfile_stream &);
-
- FILE* m_pFile;
- bool m_bStatus;
-
-public:
- cfile_stream() : m_pFile(NULL), m_bStatus(false) { }
-
- virtual ~cfile_stream()
- {
- close();
- }
-
- bool open(const char *pFilename)
- {
- close();
-#if defined(_MSC_VER)
- if (fopen_s(&m_pFile, pFilename, "wb") != 0)
- {
- return false;
- }
-#else
- m_pFile = fopen(pFilename, "wb");
-#endif
- m_bStatus = (m_pFile != NULL);
- return m_bStatus;
- }
-
- bool close()
- {
- if (m_pFile)
- {
- if (fclose(m_pFile) == EOF)
- {
- m_bStatus = false;
- }
- m_pFile = NULL;
- }
- return m_bStatus;
- }
-
- virtual bool put_buf(const void* pBuf, int64_t len)
- {
- m_bStatus = m_bStatus && (fwrite(pBuf, len, 1, m_pFile) == 1);
- return m_bStatus;
- }
-
- uint get_size() const
- {
- return m_pFile ? ftell(m_pFile) : 0;
- }
-};
-
-// Writes JPEG image to file.
-bool compress_image_to_jpeg_file(const char *pFilename, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params)
-{
- cfile_stream dst_stream;
- if (!dst_stream.open(pFilename))
- return false;
-
- jpge::jpeg_encoder dst_image;
- if (!dst_image.init(&dst_stream, width, height, num_channels, comp_params))
- return false;
-
- for (uint pass_index = 0; pass_index < dst_image.get_total_passes(); pass_index++)
- {
- for (int64_t i = 0; i < height; i++)
- {
- // i, width, and num_channels are all 64bit
- const uint8* pBuf = pImage_data + i * width * num_channels;
- if (!dst_image.process_scanline(pBuf))
- return false;
- }
- if (!dst_image.process_scanline(NULL))
- return false;
- }
-
- dst_image.deinit();
-
- return dst_stream.close();
-}
-
-class memory_stream : public output_stream
-{
- memory_stream(const memory_stream &);
- memory_stream &operator= (const memory_stream &);
-
- uint8 *m_pBuf;
- uint64_t m_buf_size, m_buf_ofs;
-
-public:
- memory_stream(void *pBuf, uint64_t buf_size) : m_pBuf(static_cast(pBuf)), m_buf_size(buf_size), m_buf_ofs(0) { }
-
- virtual ~memory_stream() { }
-
- virtual bool put_buf(const void* pBuf, int64_t len)
- {
- uint64_t buf_remaining = m_buf_size - m_buf_ofs;
- if ((uint64_t)len > buf_remaining)
- return false;
- memcpy(m_pBuf + m_buf_ofs, pBuf, len);
- m_buf_ofs += len;
- return true;
- }
-
- uint64_t get_size() const
- {
- return m_buf_ofs;
- }
-};
-
-bool compress_image_to_jpeg_file_in_memory(void *pDstBuf, int64_t &buf_size, int64_t width, int64_t height, int64_t num_channels, const uint8 *pImage_data, const params &comp_params)
-{
- if ((!pDstBuf) || (!buf_size))
- return false;
-
- memory_stream dst_stream(pDstBuf, buf_size);
-
- buf_size = 0;
-
- jpge::jpeg_encoder dst_image;
- if (!dst_image.init(&dst_stream, width, height, num_channels, comp_params))
- return false;
-
- for (uint pass_index = 0; pass_index < dst_image.get_total_passes(); pass_index++)
- {
- for (int64_t i = 0; i < height; i++)
- {
- const uint8* pScanline = pImage_data + i * width * num_channels;
- if (!dst_image.process_scanline(pScanline))
- return false;
- }
- if (!dst_image.process_scanline(NULL))
- return false;
- }
-
- dst_image.deinit();
-
- buf_size = dst_stream.get_size();
- return true;
-}
-
-} // namespace jpge
\ No newline at end of file
diff --git a/spaces/St4arsp0laris/PPolar/Dockerfile b/spaces/St4arsp0laris/PPolar/Dockerfile
deleted file mode 100644
index 0e92a5e8827bf231288924e9c549282e3540b205..0000000000000000000000000000000000000000
--- a/spaces/St4arsp0laris/PPolar/Dockerfile
+++ /dev/null
@@ -1,11 +0,0 @@
-DE nó: 18-bullseye-slim
-RUN apt-get update && \
-apt-get install -y git
-RUN git clone https://gitgud.io/khanon/oai-reverse-proxy.git /app
-WORKDIR /app
-RUN npm install
-COPY Dockerfile greeting.md* .env* ./
-RUN npm run build
-EXPOSE 7860
-ENV NODE_ENV=produção
-CMD [ "npm", "start" ]
\ No newline at end of file
diff --git "a/spaces/Sudhanshu976/NLP_FULL_APP/pages/3_\360\237\231\216_RESUME.py" "b/spaces/Sudhanshu976/NLP_FULL_APP/pages/3_\360\237\231\216_RESUME.py"
deleted file mode 100644
index c5763e74a71fabcd01c54c66bf59d843896ea6ef..0000000000000000000000000000000000000000
--- "a/spaces/Sudhanshu976/NLP_FULL_APP/pages/3_\360\237\231\216_RESUME.py"
+++ /dev/null
@@ -1,120 +0,0 @@
-from pathlib import Path
-
-import streamlit as st
-from PIL import Image
-st.set_page_config(
- page_title="NLP WEB APP"
-)
-
-
-# --- PATH SETTINGS ---
-current_dir = Path(__file__).parent if "__file__" in locals() else Path.cwd()
-css_file = current_dir / "styles" / "main.css"
-resume_file = current_dir / "assets" / "my_resume.pdf"
-profile_pic = current_dir / "assets" / "profile-pic.png"
-
-
-# --- GENERAL SETTINGS ---
-PAGE_TITLE = "Digital CV | John Doe"
-PAGE_ICON = ":wave:"
-NAME = "SUDHANSHU"
-DESCRIPTION = """
-Aspiring Data Scientist | 18-Year-Old Data Enthusiast | 1 Year of Hands-On Experience | Passionate about Solving Real-World Problems"
-"""
-EMAIL = "gusainsudhanshu43@gmail.com"
-SOCIAL_MEDIA = {
- "YouTube": "https://youtube.com/",
- "LinkedIn": "https://www.linkedin.com/in/sudhanshu-gusain-34271028a/",
- "GitHub": "https://github.com/sudhanshu976",
- "Website": "https://nlpappbysudhanshu.streamlit.app/",
-}
-PROJECTS = {
- "🏆 POWER-BI Dashboards - Making interactive and dynamic dashboards": "https://github.com/sudhanshu976/POWER-BI-PROJECTS",
- "🏆 Potato Disease Classifier using CNN - Checks whether a given potato leaf is healthy , early-blight or late-blight": "https://github.com/sudhanshu976/POTATO-DISEASE-CLASSIFIER-WITH-DEPLOYMENT",
- "🏆 Combined NLP WEB APP - This web app contains all NLP Projects I have made till date ": "https://github.com/sudhanshu976/NLP_FULL",
-}
-
-
-
-
-# --- LOAD CSS, PDF & PROFIL PIC ---
-with open(css_file) as f:
- st.markdown("".format(f.read()), unsafe_allow_html=True)
-with open(resume_file, "rb") as pdf_file:
- PDFbyte = pdf_file.read()
-profile_pic = Image.open(profile_pic)
-
-
-# --- HERO SECTION ---
-col1, col2 = st.columns(2, gap="small")
-with col1:
- st.image(profile_pic, width=230)
-
-with col2:
- st.title(NAME)
- st.write(DESCRIPTION)
- st.download_button(
- label=" 📄 Download Resume",
- data=PDFbyte,
- file_name=resume_file.name,
- mime="application/octet-stream",
- )
- st.write("📫", EMAIL)
-
-
-# --- SOCIAL LINKS ---
-st.write('\n')
-cols = st.columns(len(SOCIAL_MEDIA))
-for index, (platform, link) in enumerate(SOCIAL_MEDIA.items()):
- cols[index].write(f"[{platform}]({link})")
-
-
-# --- EXPERIENCE & QUALIFICATIONS ---
-st.write('\n')
-st.subheader("Experience & Qulifications")
-st.write(
- """
-- ✔️ 1 Year expereince of performing various Data Science and NLP tasks
-- ✔️ Strong hands on experience and knowledge in Python , ML , DL and NLP
-- ✔️ Good understanding of statistical principles and their respective applications
-- ✔️ Excellent team-player and displaying strong sense of initiative on tasks
-"""
-)
-
-
-# --- SKILLS ---
-st.write('\n')
-st.subheader("Hard Skills")
-st.write(
- """
-- 👩💻 Programming: Python (Scikit-learn, Pandas , Numpy , Pytorch , Tensorflow)
-- 📊 Data Visulization: PowerBi, Matplotlib , Seaborn
-- 📚 Modeling: Supervised and Unsupervised ML algorithms , ANN , RNN , CNN
-- 🗄️ Databases: MySQL
-- 🗄️ WEB DEPLOYMENT: FLASK , Streamlit , Heroku
-"""
-)
-
-
-# --- WORK HISTORY ---
-st.write('\n')
-st.subheader("Work History")
-st.write("---")
-
-# --- JOB 1
-st.write("🚧", "**Freelancer Data Scientist and NLP Engineer**")
-st.write("05/2023 - Present")
-st.write(
- """
-- ► Used PowerBI for creating interactive dashboards
-- ► Solved many ML , DL and NLP problems in various fields like medical , agriculture , etc
-- ► Well versed in solving real life problems especially using NLP
-"""
-)
-
-# --- Projects & Accomplishments ---
-st.write('\n')
-st.subheader("Projects & Accomplishments")
-st.write("---")
-for project, link in PROJECTS.items():
- st.write(f"[{project}]({link})")
\ No newline at end of file
diff --git a/spaces/SudharsanSundar/token_edit_distance/README.md b/spaces/SudharsanSundar/token_edit_distance/README.md
deleted file mode 100644
index 23566c70ca2ca12523912f97d6e4af394956c575..0000000000000000000000000000000000000000
--- a/spaces/SudharsanSundar/token_edit_distance/README.md
+++ /dev/null
@@ -1,33 +0,0 @@
----
-title: Token Edit Distance
-emoji: 🐠
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.42.0
-app_file: app.py
-pinned: true
----
-
-# Token Edit Distance
-This is an NLP evaluation metric that records the minimum number of token edits (insertions, deletions, and replacements, all weighted equally) to the prediction string in order to make it exactly match the reference string. Uses identical logic to Levenshtein Edit Distance, except applied to tokens (i.e. individual ints in a list) as opposed to individual characters in a string.
-
-## Args:
-* predictions: ```List[List[Int]]```, list of predictions to score.
- * Each prediction should be tokenized into a list of tokens.
-* references: ```List[List[Int]]```, list of references/ground truth output to score against.
- * Each reference should be tokenized into a list of tokens.
-
-## Returns:
-* "avg_token_edit_distance": ```Float```, average Token Edit Distance for all inputted predictions and references
-* "token_edit_distances": ```List[Int]```, the Token Edit Distance for each inputted prediction and reference
-
-## Examples:
-```
->>> token_edit_distance_metric = datasets.load_metric('Token Edit Distance')
->>> references = [[15, 4243], [100, 10008]]
->>> predictions = [[15, 4243], [100, 10009]]
->>> results = token_edit_distance_metric.compute(predictions=predictions, references=references)
->>> print(results)
-{'avg_token_edit_distance': 0.5, 'token_edit_distances': array([0. 1.])}
-```
\ No newline at end of file
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/display_trap.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/display_trap.py
deleted file mode 100644
index 9931dfe2dfc62031f87418dc206101f237dc2a26..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/display_trap.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# encoding: utf-8
-"""
-A context manager for handling sys.displayhook.
-
-Authors:
-
-* Robert Kern
-* Brian Granger
-"""
-
-#-----------------------------------------------------------------------------
-# Copyright (C) 2008-2011 The IPython Development Team
-#
-# Distributed under the terms of the BSD License. The full license is in
-# the file COPYING, distributed as part of this software.
-#-----------------------------------------------------------------------------
-
-#-----------------------------------------------------------------------------
-# Imports
-#-----------------------------------------------------------------------------
-
-import sys
-
-from traitlets.config.configurable import Configurable
-from traitlets import Any
-
-#-----------------------------------------------------------------------------
-# Classes and functions
-#-----------------------------------------------------------------------------
-
-
-class DisplayTrap(Configurable):
- """Object to manage sys.displayhook.
-
- This came from IPython.core.kernel.display_hook, but is simplified
- (no callbacks or formatters) until more of the core is refactored.
- """
-
- hook = Any()
-
- def __init__(self, hook=None):
- super(DisplayTrap, self).__init__(hook=hook, config=None)
- self.old_hook = None
- # We define this to track if a single BuiltinTrap is nested.
- # Only turn off the trap when the outermost call to __exit__ is made.
- self._nested_level = 0
-
- def __enter__(self):
- if self._nested_level == 0:
- self.set()
- self._nested_level += 1
- return self
-
- def __exit__(self, type, value, traceback):
- if self._nested_level == 1:
- self.unset()
- self._nested_level -= 1
- # Returning False will cause exceptions to propagate
- return False
-
- def set(self):
- """Set the hook."""
- if sys.displayhook is not self.hook:
- self.old_hook = sys.displayhook
- sys.displayhook = self.hook
-
- def unset(self):
- """Unset the hook."""
- sys.displayhook = self.old_hook
-
diff --git a/spaces/Suniilkumaar/MusicGen-updated/audiocraft/__init__.py b/spaces/Suniilkumaar/MusicGen-updated/audiocraft/__init__.py
deleted file mode 100644
index 6b8594f470200ff5c000542ef115375ed69b749c..0000000000000000000000000000000000000000
--- a/spaces/Suniilkumaar/MusicGen-updated/audiocraft/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# flake8: noqa
-from . import data, modules, models
-
-__version__ = '0.0.2a2'
diff --git a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/layers/__init__.py b/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/layers/__init__.py
deleted file mode 100644
index 761a3d1c7afa049e9779ee9fc4d299e9aae38cad..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/layers/__init__.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from .batch_norm import FrozenBatchNorm2d, get_norm, NaiveSyncBatchNorm, CycleBatchNormList
-from .deform_conv import DeformConv, ModulatedDeformConv
-from .mask_ops import paste_masks_in_image
-from .nms import batched_nms, batched_nms_rotated, nms, nms_rotated
-from .roi_align import ROIAlign, roi_align
-from .roi_align_rotated import ROIAlignRotated, roi_align_rotated
-from .shape_spec import ShapeSpec
-from .wrappers import (
- BatchNorm2d,
- Conv2d,
- ConvTranspose2d,
- cat,
- interpolate,
- Linear,
- nonzero_tuple,
- cross_entropy,
- empty_input_loss_func_wrapper,
- shapes_to_tensor,
- move_device_like,
-)
-from .blocks import CNNBlockBase, DepthwiseSeparableConv2d
-from .aspp import ASPP
-from .losses import ciou_loss, diou_loss
-
-__all__ = [k for k in globals().keys() if not k.startswith("_")]
diff --git a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/layers/roi_align.py b/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/layers/roi_align.py
deleted file mode 100644
index 163462e1f194e1e4100da92d76d9516f7cc22e35..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/oneformer/detectron2/layers/roi_align.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-from torch import nn
-from torchvision.ops import roi_align
-
-
-# NOTE: torchvision's RoIAlign has a different default aligned=False
-class ROIAlign(nn.Module):
- def __init__(self, output_size, spatial_scale, sampling_ratio, aligned=True):
- """
- Args:
- output_size (tuple): h, w
- spatial_scale (float): scale the input boxes by this number
- sampling_ratio (int): number of inputs samples to take for each output
- sample. 0 to take samples densely.
- aligned (bool): if False, use the legacy implementation in
- Detectron. If True, align the results more perfectly.
-
- Note:
- The meaning of aligned=True:
-
- Given a continuous coordinate c, its two neighboring pixel indices (in our
- pixel model) are computed by floor(c - 0.5) and ceil(c - 0.5). For example,
- c=1.3 has pixel neighbors with discrete indices [0] and [1] (which are sampled
- from the underlying signal at continuous coordinates 0.5 and 1.5). But the original
- roi_align (aligned=False) does not subtract the 0.5 when computing neighboring
- pixel indices and therefore it uses pixels with a slightly incorrect alignment
- (relative to our pixel model) when performing bilinear interpolation.
-
- With `aligned=True`,
- we first appropriately scale the ROI and then shift it by -0.5
- prior to calling roi_align. This produces the correct neighbors; see
- detectron2/tests/test_roi_align.py for verification.
-
- The difference does not make a difference to the model's performance if
- ROIAlign is used together with conv layers.
- """
- super().__init__()
- self.output_size = output_size
- self.spatial_scale = spatial_scale
- self.sampling_ratio = sampling_ratio
- self.aligned = aligned
-
- from torchvision import __version__
-
- version = tuple(int(x) for x in __version__.split(".")[:2])
- # https://github.com/pytorch/vision/pull/2438
- assert version >= (0, 7), "Require torchvision >= 0.7"
-
- def forward(self, input, rois):
- """
- Args:
- input: NCHW images
- rois: Bx5 boxes. First column is the index into N. The other 4 columns are xyxy.
- """
- assert rois.dim() == 2 and rois.size(1) == 5
- if input.is_quantized:
- input = input.dequantize()
- return roi_align(
- input,
- rois.to(dtype=input.dtype),
- self.output_size,
- self.spatial_scale,
- self.sampling_ratio,
- self.aligned,
- )
-
- def __repr__(self):
- tmpstr = self.__class__.__name__ + "("
- tmpstr += "output_size=" + str(self.output_size)
- tmpstr += ", spatial_scale=" + str(self.spatial_scale)
- tmpstr += ", sampling_ratio=" + str(self.sampling_ratio)
- tmpstr += ", aligned=" + str(self.aligned)
- tmpstr += ")"
- return tmpstr
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/image/io.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/image/io.py
deleted file mode 100644
index d3fa2e8cc06b1a7b0b69de6406980b15d61a1e5d..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/image/io.py
+++ /dev/null
@@ -1,258 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import io
-import os.path as osp
-from pathlib import Path
-
-import cv2
-import numpy as np
-from cv2 import (IMREAD_COLOR, IMREAD_GRAYSCALE, IMREAD_IGNORE_ORIENTATION,
- IMREAD_UNCHANGED)
-
-from annotator.uniformer.mmcv.utils import check_file_exist, is_str, mkdir_or_exist
-
-try:
- from turbojpeg import TJCS_RGB, TJPF_BGR, TJPF_GRAY, TurboJPEG
-except ImportError:
- TJCS_RGB = TJPF_GRAY = TJPF_BGR = TurboJPEG = None
-
-try:
- from PIL import Image, ImageOps
-except ImportError:
- Image = None
-
-try:
- import tifffile
-except ImportError:
- tifffile = None
-
-jpeg = None
-supported_backends = ['cv2', 'turbojpeg', 'pillow', 'tifffile']
-
-imread_flags = {
- 'color': IMREAD_COLOR,
- 'grayscale': IMREAD_GRAYSCALE,
- 'unchanged': IMREAD_UNCHANGED,
- 'color_ignore_orientation': IMREAD_IGNORE_ORIENTATION | IMREAD_COLOR,
- 'grayscale_ignore_orientation':
- IMREAD_IGNORE_ORIENTATION | IMREAD_GRAYSCALE
-}
-
-imread_backend = 'cv2'
-
-
-def use_backend(backend):
- """Select a backend for image decoding.
-
- Args:
- backend (str): The image decoding backend type. Options are `cv2`,
- `pillow`, `turbojpeg` (see https://github.com/lilohuang/PyTurboJPEG)
- and `tifffile`. `turbojpeg` is faster but it only supports `.jpeg`
- file format.
- """
- assert backend in supported_backends
- global imread_backend
- imread_backend = backend
- if imread_backend == 'turbojpeg':
- if TurboJPEG is None:
- raise ImportError('`PyTurboJPEG` is not installed')
- global jpeg
- if jpeg is None:
- jpeg = TurboJPEG()
- elif imread_backend == 'pillow':
- if Image is None:
- raise ImportError('`Pillow` is not installed')
- elif imread_backend == 'tifffile':
- if tifffile is None:
- raise ImportError('`tifffile` is not installed')
-
-
-def _jpegflag(flag='color', channel_order='bgr'):
- channel_order = channel_order.lower()
- if channel_order not in ['rgb', 'bgr']:
- raise ValueError('channel order must be either "rgb" or "bgr"')
-
- if flag == 'color':
- if channel_order == 'bgr':
- return TJPF_BGR
- elif channel_order == 'rgb':
- return TJCS_RGB
- elif flag == 'grayscale':
- return TJPF_GRAY
- else:
- raise ValueError('flag must be "color" or "grayscale"')
-
-
-def _pillow2array(img, flag='color', channel_order='bgr'):
- """Convert a pillow image to numpy array.
-
- Args:
- img (:obj:`PIL.Image.Image`): The image loaded using PIL
- flag (str): Flags specifying the color type of a loaded image,
- candidates are 'color', 'grayscale' and 'unchanged'.
- Default to 'color'.
- channel_order (str): The channel order of the output image array,
- candidates are 'bgr' and 'rgb'. Default to 'bgr'.
-
- Returns:
- np.ndarray: The converted numpy array
- """
- channel_order = channel_order.lower()
- if channel_order not in ['rgb', 'bgr']:
- raise ValueError('channel order must be either "rgb" or "bgr"')
-
- if flag == 'unchanged':
- array = np.array(img)
- if array.ndim >= 3 and array.shape[2] >= 3: # color image
- array[:, :, :3] = array[:, :, (2, 1, 0)] # RGB to BGR
- else:
- # Handle exif orientation tag
- if flag in ['color', 'grayscale']:
- img = ImageOps.exif_transpose(img)
- # If the image mode is not 'RGB', convert it to 'RGB' first.
- if img.mode != 'RGB':
- if img.mode != 'LA':
- # Most formats except 'LA' can be directly converted to RGB
- img = img.convert('RGB')
- else:
- # When the mode is 'LA', the default conversion will fill in
- # the canvas with black, which sometimes shadows black objects
- # in the foreground.
- #
- # Therefore, a random color (124, 117, 104) is used for canvas
- img_rgba = img.convert('RGBA')
- img = Image.new('RGB', img_rgba.size, (124, 117, 104))
- img.paste(img_rgba, mask=img_rgba.split()[3]) # 3 is alpha
- if flag in ['color', 'color_ignore_orientation']:
- array = np.array(img)
- if channel_order != 'rgb':
- array = array[:, :, ::-1] # RGB to BGR
- elif flag in ['grayscale', 'grayscale_ignore_orientation']:
- img = img.convert('L')
- array = np.array(img)
- else:
- raise ValueError(
- 'flag must be "color", "grayscale", "unchanged", '
- f'"color_ignore_orientation" or "grayscale_ignore_orientation"'
- f' but got {flag}')
- return array
-
-
-def imread(img_or_path, flag='color', channel_order='bgr', backend=None):
- """Read an image.
-
- Args:
- img_or_path (ndarray or str or Path): Either a numpy array or str or
- pathlib.Path. If it is a numpy array (loaded image), then
- it will be returned as is.
- flag (str): Flags specifying the color type of a loaded image,
- candidates are `color`, `grayscale`, `unchanged`,
- `color_ignore_orientation` and `grayscale_ignore_orientation`.
- By default, `cv2` and `pillow` backend would rotate the image
- according to its EXIF info unless called with `unchanged` or
- `*_ignore_orientation` flags. `turbojpeg` and `tifffile` backend
- always ignore image's EXIF info regardless of the flag.
- The `turbojpeg` backend only supports `color` and `grayscale`.
- channel_order (str): Order of channel, candidates are `bgr` and `rgb`.
- backend (str | None): The image decoding backend type. Options are
- `cv2`, `pillow`, `turbojpeg`, `tifffile`, `None`.
- If backend is None, the global imread_backend specified by
- ``mmcv.use_backend()`` will be used. Default: None.
-
- Returns:
- ndarray: Loaded image array.
- """
-
- if backend is None:
- backend = imread_backend
- if backend not in supported_backends:
- raise ValueError(f'backend: {backend} is not supported. Supported '
- "backends are 'cv2', 'turbojpeg', 'pillow'")
- if isinstance(img_or_path, Path):
- img_or_path = str(img_or_path)
-
- if isinstance(img_or_path, np.ndarray):
- return img_or_path
- elif is_str(img_or_path):
- check_file_exist(img_or_path,
- f'img file does not exist: {img_or_path}')
- if backend == 'turbojpeg':
- with open(img_or_path, 'rb') as in_file:
- img = jpeg.decode(in_file.read(),
- _jpegflag(flag, channel_order))
- if img.shape[-1] == 1:
- img = img[:, :, 0]
- return img
- elif backend == 'pillow':
- img = Image.open(img_or_path)
- img = _pillow2array(img, flag, channel_order)
- return img
- elif backend == 'tifffile':
- img = tifffile.imread(img_or_path)
- return img
- else:
- flag = imread_flags[flag] if is_str(flag) else flag
- img = cv2.imread(img_or_path, flag)
- if flag == IMREAD_COLOR and channel_order == 'rgb':
- cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img)
- return img
- else:
- raise TypeError('"img" must be a numpy array or a str or '
- 'a pathlib.Path object')
-
-
-def imfrombytes(content, flag='color', channel_order='bgr', backend=None):
- """Read an image from bytes.
-
- Args:
- content (bytes): Image bytes got from files or other streams.
- flag (str): Same as :func:`imread`.
- backend (str | None): The image decoding backend type. Options are
- `cv2`, `pillow`, `turbojpeg`, `None`. If backend is None, the
- global imread_backend specified by ``mmcv.use_backend()`` will be
- used. Default: None.
-
- Returns:
- ndarray: Loaded image array.
- """
-
- if backend is None:
- backend = imread_backend
- if backend not in supported_backends:
- raise ValueError(f'backend: {backend} is not supported. Supported '
- "backends are 'cv2', 'turbojpeg', 'pillow'")
- if backend == 'turbojpeg':
- img = jpeg.decode(content, _jpegflag(flag, channel_order))
- if img.shape[-1] == 1:
- img = img[:, :, 0]
- return img
- elif backend == 'pillow':
- buff = io.BytesIO(content)
- img = Image.open(buff)
- img = _pillow2array(img, flag, channel_order)
- return img
- else:
- img_np = np.frombuffer(content, np.uint8)
- flag = imread_flags[flag] if is_str(flag) else flag
- img = cv2.imdecode(img_np, flag)
- if flag == IMREAD_COLOR and channel_order == 'rgb':
- cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img)
- return img
-
-
-def imwrite(img, file_path, params=None, auto_mkdir=True):
- """Write image to file.
-
- Args:
- img (ndarray): Image array to be written.
- file_path (str): Image file path.
- params (None or list): Same as opencv :func:`imwrite` interface.
- auto_mkdir (bool): If the parent folder of `file_path` does not exist,
- whether to create it automatically.
-
- Returns:
- bool: Successful or not.
- """
- if auto_mkdir:
- dir_name = osp.abspath(osp.dirname(file_path))
- mkdir_or_exist(dir_name)
- return cv2.imwrite(file_path, img, params)
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/runner/hooks/profiler.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/runner/hooks/profiler.py
deleted file mode 100644
index b70236997eec59c2209ef351ae38863b4112d0ec..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmcv/runner/hooks/profiler.py
+++ /dev/null
@@ -1,180 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import warnings
-from typing import Callable, List, Optional, Union
-
-import torch
-
-from ..dist_utils import master_only
-from .hook import HOOKS, Hook
-
-
-@HOOKS.register_module()
-class ProfilerHook(Hook):
- """Profiler to analyze performance during training.
-
- PyTorch Profiler is a tool that allows the collection of the performance
- metrics during the training. More details on Profiler can be found at
- https://pytorch.org/docs/1.8.1/profiler.html#torch.profiler.profile
-
- Args:
- by_epoch (bool): Profile performance by epoch or by iteration.
- Default: True.
- profile_iters (int): Number of iterations for profiling.
- If ``by_epoch=True``, profile_iters indicates that they are the
- first profile_iters epochs at the beginning of the
- training, otherwise it indicates the first profile_iters
- iterations. Default: 1.
- activities (list[str]): List of activity groups (CPU, CUDA) to use in
- profiling. Default: ['cpu', 'cuda'].
- schedule (dict, optional): Config of generating the callable schedule.
- if schedule is None, profiler will not add step markers into the
- trace and table view. Default: None.
- on_trace_ready (callable, dict): Either a handler or a dict of generate
- handler. Default: None.
- record_shapes (bool): Save information about operator's input shapes.
- Default: False.
- profile_memory (bool): Track tensor memory allocation/deallocation.
- Default: False.
- with_stack (bool): Record source information (file and line number)
- for the ops. Default: False.
- with_flops (bool): Use formula to estimate the FLOPS of specific
- operators (matrix multiplication and 2D convolution).
- Default: False.
- json_trace_path (str, optional): Exports the collected trace in Chrome
- JSON format. Default: None.
-
- Example:
- >>> runner = ... # instantiate a Runner
- >>> # tensorboard trace
- >>> trace_config = dict(type='tb_trace', dir_name='work_dir')
- >>> profiler_config = dict(on_trace_ready=trace_config)
- >>> runner.register_profiler_hook(profiler_config)
- >>> runner.run(data_loaders=[trainloader], workflow=[('train', 1)])
- """
-
- def __init__(self,
- by_epoch: bool = True,
- profile_iters: int = 1,
- activities: List[str] = ['cpu', 'cuda'],
- schedule: Optional[dict] = None,
- on_trace_ready: Optional[Union[Callable, dict]] = None,
- record_shapes: bool = False,
- profile_memory: bool = False,
- with_stack: bool = False,
- with_flops: bool = False,
- json_trace_path: Optional[str] = None) -> None:
- try:
- from torch import profiler # torch version >= 1.8.1
- except ImportError:
- raise ImportError('profiler is the new feature of torch1.8.1, '
- f'but your version is {torch.__version__}')
-
- assert isinstance(by_epoch, bool), '``by_epoch`` should be a boolean.'
- self.by_epoch = by_epoch
-
- if profile_iters < 1:
- raise ValueError('profile_iters should be greater than 0, but got '
- f'{profile_iters}')
- self.profile_iters = profile_iters
-
- if not isinstance(activities, list):
- raise ValueError(
- f'activities should be list, but got {type(activities)}')
- self.activities = []
- for activity in activities:
- activity = activity.lower()
- if activity == 'cpu':
- self.activities.append(profiler.ProfilerActivity.CPU)
- elif activity == 'cuda':
- self.activities.append(profiler.ProfilerActivity.CUDA)
- else:
- raise ValueError(
- f'activity should be "cpu" or "cuda", but got {activity}')
-
- if schedule is not None:
- self.schedule = profiler.schedule(**schedule)
- else:
- self.schedule = None
-
- self.on_trace_ready = on_trace_ready
- self.record_shapes = record_shapes
- self.profile_memory = profile_memory
- self.with_stack = with_stack
- self.with_flops = with_flops
- self.json_trace_path = json_trace_path
-
- @master_only
- def before_run(self, runner):
- if self.by_epoch and runner.max_epochs < self.profile_iters:
- raise ValueError('self.profile_iters should not be greater than '
- f'{runner.max_epochs}')
-
- if not self.by_epoch and runner.max_iters < self.profile_iters:
- raise ValueError('self.profile_iters should not be greater than '
- f'{runner.max_iters}')
-
- if callable(self.on_trace_ready): # handler
- _on_trace_ready = self.on_trace_ready
- elif isinstance(self.on_trace_ready, dict): # config of handler
- trace_cfg = self.on_trace_ready.copy()
- trace_type = trace_cfg.pop('type') # log_trace handler
- if trace_type == 'log_trace':
-
- def _log_handler(prof):
- print(prof.key_averages().table(**trace_cfg))
-
- _on_trace_ready = _log_handler
- elif trace_type == 'tb_trace': # tensorboard_trace handler
- try:
- import torch_tb_profiler # noqa: F401
- except ImportError:
- raise ImportError('please run "pip install '
- 'torch-tb-profiler" to install '
- 'torch_tb_profiler')
- _on_trace_ready = torch.profiler.tensorboard_trace_handler(
- **trace_cfg)
- else:
- raise ValueError('trace_type should be "log_trace" or '
- f'"tb_trace", but got {trace_type}')
- elif self.on_trace_ready is None:
- _on_trace_ready = None # type: ignore
- else:
- raise ValueError('on_trace_ready should be handler, dict or None, '
- f'but got {type(self.on_trace_ready)}')
-
- if runner.max_epochs > 1:
- warnings.warn(f'profiler will profile {runner.max_epochs} epochs '
- 'instead of 1 epoch. Since profiler will slow down '
- 'the training, it is recommended to train 1 epoch '
- 'with ProfilerHook and adjust your setting according'
- ' to the profiler summary. During normal training '
- '(epoch > 1), you may disable the ProfilerHook.')
-
- self.profiler = torch.profiler.profile(
- activities=self.activities,
- schedule=self.schedule,
- on_trace_ready=_on_trace_ready,
- record_shapes=self.record_shapes,
- profile_memory=self.profile_memory,
- with_stack=self.with_stack,
- with_flops=self.with_flops)
-
- self.profiler.__enter__()
- runner.logger.info('profiler is profiling...')
-
- @master_only
- def after_train_epoch(self, runner):
- if self.by_epoch and runner.epoch == self.profile_iters - 1:
- runner.logger.info('profiler may take a few minutes...')
- self.profiler.__exit__(None, None, None)
- if self.json_trace_path is not None:
- self.profiler.export_chrome_trace(self.json_trace_path)
-
- @master_only
- def after_train_iter(self, runner):
- self.profiler.step()
- if not self.by_epoch and runner.iter == self.profile_iters - 1:
- runner.logger.info('profiler may take a few minutes...')
- self.profiler.__exit__(None, None, None)
- if self.json_trace_path is not None:
- self.profiler.export_chrome_trace(self.json_trace_path)
diff --git a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/models/losses/lovasz_loss.py b/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/models/losses/lovasz_loss.py
deleted file mode 100644
index 6badb67f6d987b59fb07aa97caaaf89896e27a8d..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/uniformer/mmseg/models/losses/lovasz_loss.py
+++ /dev/null
@@ -1,303 +0,0 @@
-"""Modified from https://github.com/bermanmaxim/LovaszSoftmax/blob/master/pytor
-ch/lovasz_losses.py Lovasz-Softmax and Jaccard hinge loss in PyTorch Maxim
-Berman 2018 ESAT-PSI KU Leuven (MIT License)"""
-
-import annotator.uniformer.mmcv as mmcv
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from ..builder import LOSSES
-from .utils import get_class_weight, weight_reduce_loss
-
-
-def lovasz_grad(gt_sorted):
- """Computes gradient of the Lovasz extension w.r.t sorted errors.
-
- See Alg. 1 in paper.
- """
- p = len(gt_sorted)
- gts = gt_sorted.sum()
- intersection = gts - gt_sorted.float().cumsum(0)
- union = gts + (1 - gt_sorted).float().cumsum(0)
- jaccard = 1. - intersection / union
- if p > 1: # cover 1-pixel case
- jaccard[1:p] = jaccard[1:p] - jaccard[0:-1]
- return jaccard
-
-
-def flatten_binary_logits(logits, labels, ignore_index=None):
- """Flattens predictions in the batch (binary case) Remove labels equal to
- 'ignore_index'."""
- logits = logits.view(-1)
- labels = labels.view(-1)
- if ignore_index is None:
- return logits, labels
- valid = (labels != ignore_index)
- vlogits = logits[valid]
- vlabels = labels[valid]
- return vlogits, vlabels
-
-
-def flatten_probs(probs, labels, ignore_index=None):
- """Flattens predictions in the batch."""
- if probs.dim() == 3:
- # assumes output of a sigmoid layer
- B, H, W = probs.size()
- probs = probs.view(B, 1, H, W)
- B, C, H, W = probs.size()
- probs = probs.permute(0, 2, 3, 1).contiguous().view(-1, C) # B*H*W, C=P,C
- labels = labels.view(-1)
- if ignore_index is None:
- return probs, labels
- valid = (labels != ignore_index)
- vprobs = probs[valid.nonzero().squeeze()]
- vlabels = labels[valid]
- return vprobs, vlabels
-
-
-def lovasz_hinge_flat(logits, labels):
- """Binary Lovasz hinge loss.
-
- Args:
- logits (torch.Tensor): [P], logits at each prediction
- (between -infty and +infty).
- labels (torch.Tensor): [P], binary ground truth labels (0 or 1).
-
- Returns:
- torch.Tensor: The calculated loss.
- """
- if len(labels) == 0:
- # only void pixels, the gradients should be 0
- return logits.sum() * 0.
- signs = 2. * labels.float() - 1.
- errors = (1. - logits * signs)
- errors_sorted, perm = torch.sort(errors, dim=0, descending=True)
- perm = perm.data
- gt_sorted = labels[perm]
- grad = lovasz_grad(gt_sorted)
- loss = torch.dot(F.relu(errors_sorted), grad)
- return loss
-
-
-def lovasz_hinge(logits,
- labels,
- classes='present',
- per_image=False,
- class_weight=None,
- reduction='mean',
- avg_factor=None,
- ignore_index=255):
- """Binary Lovasz hinge loss.
-
- Args:
- logits (torch.Tensor): [B, H, W], logits at each pixel
- (between -infty and +infty).
- labels (torch.Tensor): [B, H, W], binary ground truth masks (0 or 1).
- classes (str | list[int], optional): Placeholder, to be consistent with
- other loss. Default: None.
- per_image (bool, optional): If per_image is True, compute the loss per
- image instead of per batch. Default: False.
- class_weight (list[float], optional): Placeholder, to be consistent
- with other loss. Default: None.
- reduction (str, optional): The method used to reduce the loss. Options
- are "none", "mean" and "sum". This parameter only works when
- per_image is True. Default: 'mean'.
- avg_factor (int, optional): Average factor that is used to average
- the loss. This parameter only works when per_image is True.
- Default: None.
- ignore_index (int | None): The label index to be ignored. Default: 255.
-
- Returns:
- torch.Tensor: The calculated loss.
- """
- if per_image:
- loss = [
- lovasz_hinge_flat(*flatten_binary_logits(
- logit.unsqueeze(0), label.unsqueeze(0), ignore_index))
- for logit, label in zip(logits, labels)
- ]
- loss = weight_reduce_loss(
- torch.stack(loss), None, reduction, avg_factor)
- else:
- loss = lovasz_hinge_flat(
- *flatten_binary_logits(logits, labels, ignore_index))
- return loss
-
-
-def lovasz_softmax_flat(probs, labels, classes='present', class_weight=None):
- """Multi-class Lovasz-Softmax loss.
-
- Args:
- probs (torch.Tensor): [P, C], class probabilities at each prediction
- (between 0 and 1).
- labels (torch.Tensor): [P], ground truth labels (between 0 and C - 1).
- classes (str | list[int], optional): Classes chosen to calculate loss.
- 'all' for all classes, 'present' for classes present in labels, or
- a list of classes to average. Default: 'present'.
- class_weight (list[float], optional): The weight for each class.
- Default: None.
-
- Returns:
- torch.Tensor: The calculated loss.
- """
- if probs.numel() == 0:
- # only void pixels, the gradients should be 0
- return probs * 0.
- C = probs.size(1)
- losses = []
- class_to_sum = list(range(C)) if classes in ['all', 'present'] else classes
- for c in class_to_sum:
- fg = (labels == c).float() # foreground for class c
- if (classes == 'present' and fg.sum() == 0):
- continue
- if C == 1:
- if len(classes) > 1:
- raise ValueError('Sigmoid output possible only with 1 class')
- class_pred = probs[:, 0]
- else:
- class_pred = probs[:, c]
- errors = (fg - class_pred).abs()
- errors_sorted, perm = torch.sort(errors, 0, descending=True)
- perm = perm.data
- fg_sorted = fg[perm]
- loss = torch.dot(errors_sorted, lovasz_grad(fg_sorted))
- if class_weight is not None:
- loss *= class_weight[c]
- losses.append(loss)
- return torch.stack(losses).mean()
-
-
-def lovasz_softmax(probs,
- labels,
- classes='present',
- per_image=False,
- class_weight=None,
- reduction='mean',
- avg_factor=None,
- ignore_index=255):
- """Multi-class Lovasz-Softmax loss.
-
- Args:
- probs (torch.Tensor): [B, C, H, W], class probabilities at each
- prediction (between 0 and 1).
- labels (torch.Tensor): [B, H, W], ground truth labels (between 0 and
- C - 1).
- classes (str | list[int], optional): Classes chosen to calculate loss.
- 'all' for all classes, 'present' for classes present in labels, or
- a list of classes to average. Default: 'present'.
- per_image (bool, optional): If per_image is True, compute the loss per
- image instead of per batch. Default: False.
- class_weight (list[float], optional): The weight for each class.
- Default: None.
- reduction (str, optional): The method used to reduce the loss. Options
- are "none", "mean" and "sum". This parameter only works when
- per_image is True. Default: 'mean'.
- avg_factor (int, optional): Average factor that is used to average
- the loss. This parameter only works when per_image is True.
- Default: None.
- ignore_index (int | None): The label index to be ignored. Default: 255.
-
- Returns:
- torch.Tensor: The calculated loss.
- """
-
- if per_image:
- loss = [
- lovasz_softmax_flat(
- *flatten_probs(
- prob.unsqueeze(0), label.unsqueeze(0), ignore_index),
- classes=classes,
- class_weight=class_weight)
- for prob, label in zip(probs, labels)
- ]
- loss = weight_reduce_loss(
- torch.stack(loss), None, reduction, avg_factor)
- else:
- loss = lovasz_softmax_flat(
- *flatten_probs(probs, labels, ignore_index),
- classes=classes,
- class_weight=class_weight)
- return loss
-
-
-@LOSSES.register_module()
-class LovaszLoss(nn.Module):
- """LovaszLoss.
-
- This loss is proposed in `The Lovasz-Softmax loss: A tractable surrogate
- for the optimization of the intersection-over-union measure in neural
- networks `_.
-
- Args:
- loss_type (str, optional): Binary or multi-class loss.
- Default: 'multi_class'. Options are "binary" and "multi_class".
- classes (str | list[int], optional): Classes chosen to calculate loss.
- 'all' for all classes, 'present' for classes present in labels, or
- a list of classes to average. Default: 'present'.
- per_image (bool, optional): If per_image is True, compute the loss per
- image instead of per batch. Default: False.
- reduction (str, optional): The method used to reduce the loss. Options
- are "none", "mean" and "sum". This parameter only works when
- per_image is True. Default: 'mean'.
- class_weight (list[float] | str, optional): Weight of each class. If in
- str format, read them from a file. Defaults to None.
- loss_weight (float, optional): Weight of the loss. Defaults to 1.0.
- """
-
- def __init__(self,
- loss_type='multi_class',
- classes='present',
- per_image=False,
- reduction='mean',
- class_weight=None,
- loss_weight=1.0):
- super(LovaszLoss, self).__init__()
- assert loss_type in ('binary', 'multi_class'), "loss_type should be \
- 'binary' or 'multi_class'."
-
- if loss_type == 'binary':
- self.cls_criterion = lovasz_hinge
- else:
- self.cls_criterion = lovasz_softmax
- assert classes in ('all', 'present') or mmcv.is_list_of(classes, int)
- if not per_image:
- assert reduction == 'none', "reduction should be 'none' when \
- per_image is False."
-
- self.classes = classes
- self.per_image = per_image
- self.reduction = reduction
- self.loss_weight = loss_weight
- self.class_weight = get_class_weight(class_weight)
-
- def forward(self,
- cls_score,
- label,
- weight=None,
- avg_factor=None,
- reduction_override=None,
- **kwargs):
- """Forward function."""
- assert reduction_override in (None, 'none', 'mean', 'sum')
- reduction = (
- reduction_override if reduction_override else self.reduction)
- if self.class_weight is not None:
- class_weight = cls_score.new_tensor(self.class_weight)
- else:
- class_weight = None
-
- # if multi-class loss, transform logits to probs
- if self.cls_criterion == lovasz_softmax:
- cls_score = F.softmax(cls_score, dim=1)
-
- loss_cls = self.loss_weight * self.cls_criterion(
- cls_score,
- label,
- self.classes,
- self.per_image,
- class_weight=class_weight,
- reduction=reduction,
- avg_factor=avg_factor,
- **kwargs)
- return loss_cls
diff --git a/spaces/TEnngal/bingo/src/components/button-scroll-to-bottom.tsx b/spaces/TEnngal/bingo/src/components/button-scroll-to-bottom.tsx
deleted file mode 100644
index b68ab9c0e48320c356e51a52d11b9ca63909e6c5..0000000000000000000000000000000000000000
--- a/spaces/TEnngal/bingo/src/components/button-scroll-to-bottom.tsx
+++ /dev/null
@@ -1,34 +0,0 @@
-'use client'
-
-import * as React from 'react'
-
-import { cn } from '@/lib/utils'
-import { useAtBottom } from '@/lib/hooks/use-at-bottom'
-import { Button, type ButtonProps } from '@/components/ui/button'
-import { IconArrowDown } from '@/components/ui/icons'
-
-export function ButtonScrollToBottom({ className, ...props }: ButtonProps) {
- const isAtBottom = useAtBottom()
-
- return (
-
- )
-}
diff --git a/spaces/TNR-5/lib/app.py b/spaces/TNR-5/lib/app.py
deleted file mode 100644
index e9c8e095f4aa2a6d08a2195da22dde58fef3eef7..0000000000000000000000000000000000000000
--- a/spaces/TNR-5/lib/app.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import streamlit as st
-from st_utils import bm25_search, semantic_search, hf_api, paginator
-from huggingface_hub import ModelSearchArguments
-import webbrowser
-from numerize.numerize import numerize
-import math
-
-st.set_page_config(
- page_title="HF Search Engine",
- page_icon="🔎",
- layout="wide",
- initial_sidebar_state="auto",
-)
-
-### SIDEBAR
-search_backend = st.sidebar.selectbox(
- "Search method",
- ["semantic", "bm25", "hfapi"],
- format_func=lambda x: {"hfapi": "Keyword search", "bm25": "BM25 search", "semantic": "Semantic Search"}[x],
-)
-limit_results = int(st.sidebar.number_input("Limit results", min_value=0, value=10))
-sort_by = st.sidebar.selectbox(
- "Sort by",
- [None, "downloads", "likes", "lastModified"],
- format_func=lambda x: {None: "Relevance", "downloads": "Most downloads", "likes": "Most likes", "lastModified": "Recently updated"}[x],
-)
-
-st.sidebar.markdown("# Filters")
-args = ModelSearchArguments()
-library = st.sidebar.multiselect(
- "Library", args.library.values(), format_func=lambda x: {v: k for k, v in args.library.items()}[x]
-)
-task = st.sidebar.multiselect(
- "Task", args.pipeline_tag.values(), format_func=lambda x: {v: k for k, v in args.pipeline_tag.items()}[x]
-)
-
-### MAIN PAGE
-st.markdown(
- "
",
- unsafe_allow_html=True,
-)
\ No newline at end of file
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/configs/common/README.md b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/configs/common/README.md
deleted file mode 100644
index 912cc29927542bfe4258d3208cf52d73cb0ea477..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/configs/common/README.md
+++ /dev/null
@@ -1,6 +0,0 @@
-This directory provides definitions for a few common models, dataloaders, scheduler,
-and optimizers that are often used in training.
-The definition of these objects are provided in the form of lazy instantiation:
-their arguments can be edited by users before constructing the objects.
-
-They can be imported, or loaded by `model_zoo.get_config` API in users' own configs.
diff --git a/spaces/Tetel/secondbing/SydneyGPT/__init__.py b/spaces/Tetel/secondbing/SydneyGPT/__init__.py
deleted file mode 100644
index 0895f233f1ef5a7384ab8ac1a9f2c6d0f39b4b96..0000000000000000000000000000000000000000
--- a/spaces/Tetel/secondbing/SydneyGPT/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-import os, sys
-sys.path.append(os.path.dirname(os.path.realpath(__file__)))
diff --git a/spaces/ThirdEyeData/Entity-Extraction/app.py b/spaces/ThirdEyeData/Entity-Extraction/app.py
deleted file mode 100644
index 2a3589958b6bd7b594dbbd01ee02d06ea5de81b0..0000000000000000000000000000000000000000
--- a/spaces/ThirdEyeData/Entity-Extraction/app.py
+++ /dev/null
@@ -1,92 +0,0 @@
-#Core Packages
-import streamlit as st
-
-#nlp pkgs
-import spacy
-from spacy import displacy
-import spacy_streamlit
-from spacy_streamlit import visualize_ner
-import en_core_web_sm
-
-nlp = spacy.load("en_core_web_sm")
-
-def main():
- """A simple NLP app with spacy-streamlit"""
-
- spacy_model = "en_core_web_sm"
- st.title('Entity Extraction')
-
- #menu = ["Home","NER"]
- #choice = st.sidebar.selectbox("Menu", menu)
- with st.sidebar:
- st.write("Sample Text")
- st.write("""Ai-Khanoum (/aɪ ˈhɑːnjuːm/, meaning Lady Moon; Uzbek: Oyxonim) is the archaeological site of a Hellenistic city in Takhar Province, Afghanistan.
-The city, whose original name is unknown,[a] was probably founded by an early ruler of the Seleucid Empire and served as a military and economic centre for
-the rulers of the Greco-Bactrian Kingdom until its destruction c. 145 BC. Rediscovered in 1961, the ruins of the city were excavated by a French team of
-archaeologists until the outbreak of conflict in Afghanistan in the late 1970s. """)
-
-
- st.subheader("Tokenization")
- raw_text = st.text_area("Your Text","Enter the Text Here")
- docx = nlp(raw_text)
- if st.button("Tokenize"):
- spacy_streamlit.visualize_tokens(docx,attrs = ['text','pos_','dep_','ent_type_'])
-
- #st.subheader("Name Entity Recognition")
- if st.button("Entity Extraction"):
-
- spacy_streamlit.visualize_ner(docx,labels = nlp.get_pipe('ner').labels,show_table = False)
-
-
-
-
-if __name__ == '__main__':
- main()
-
-
-st.write("""
-For a detailed information on Entity Label please look through our the file
-""")
-url = 'https://huggingface.co/spaces/ThirdEyeData/Entity-Extraction/blob/main/entity.md'
-
-st.markdown(f'''
-
-''',
-unsafe_allow_html=True)
-
-st.write("""
-For a detailed description please look through our Documentation
-""")
-
-url = 'https://huggingface.co/spaces/ThirdEyeData/Entity-Extraction/blob/main/README.md'
-
-st.markdown(f'''
-
-''',
-unsafe_allow_html=True)
-
-
-
-#def prediction(raw_text):
- #text1= NER(raw_text)
- #st.write("List wise NERs:")
- #st.write("------------------")
- #st.write(f"{'Text' : <10}{'NER' : >10}")
-
- #for word in text1.ents:
- # st.write(word.text,"\t\t",word.label_)
- #print()
- #st.write("------------------")
- #st.write("NERs in the sentence:")
- #spacy_streamlit.visualize(displacy.render(text1,style="ent"))
-
- #models = ["en_core_web_sm"]
- #spacy_streamlit.visualize(text1,models = models)
- #visualize_ner(text1, labels=nlp.get_pipe("ner").labels)
-
-#raw_text = """Ai-Khanoum (/aɪ ˈhɑːnjuːm/, meaning Lady Moon; Uzbek: Oyxonim) is the archaeological site of a Hellenistic city in Takhar Province, Afghanistan.
-#The city, whose original name is unknown,[a] was probably founded by an early ruler of the Seleucid Empire and served as a military and economic centre for the rulers of the Greco-Bactrian Kingdom until its destruction c. 145 BC.
-#Rediscovered in 1961, the ruins of the city were excavated by a French team of archaeologists until the outbreak of conflict in Afghanistan in the late 1970s. """
-#prediction(raw_text)
-
-
diff --git a/spaces/VIPLab/Caption-Anything/caption_anything/utils/utils.py b/spaces/VIPLab/Caption-Anything/caption_anything/utils/utils.py
deleted file mode 100644
index 560d6f03d0f34f1f843b9bd3121a69f0fc4a6387..0000000000000000000000000000000000000000
--- a/spaces/VIPLab/Caption-Anything/caption_anything/utils/utils.py
+++ /dev/null
@@ -1,496 +0,0 @@
-import os
-import time
-import sys
-
-import cv2
-import hashlib
-import requests
-import numpy as np
-
-from typing import Union
-
-from PIL import Image
-from tqdm import tqdm
-
-
-def load_image(image: Union[np.ndarray, Image.Image, str], return_type='numpy'):
- """
- Load image from path or PIL.Image or numpy.ndarray to required format.
- """
-
- # Check if image is already in return_type
- if isinstance(image, Image.Image) and return_type == 'pil' or \
- isinstance(image, np.ndarray) and return_type == 'numpy':
- return image
-
- # PIL.Image as intermediate format
- if isinstance(image, str):
- image = Image.open(image)
- elif isinstance(image, np.ndarray):
- image = Image.fromarray(image)
-
- if image.mode == "RGBA":
- image = image.convert("RGB")
-
- if return_type == 'pil':
- return image
- elif return_type == 'numpy':
- return np.asarray(image)
- else:
- raise NotImplementedError()
-
-
-def image_resize(image: Image.Image, res=1024):
- width, height = org_size = image.size
- ratio = min(1.0 * res / max(width, height), 1.0)
- if ratio < 1.0:
- image = image.resize((int(width * ratio), int(height * ratio)))
- print('Scaling image from {} to {}'.format(org_size, image.size))
- return image
-
-def xywh_to_x1y1x2y2(bbox):
- x, y, w, h = bbox
- return x,y,x+w,y+h
-
-
-def x1y1x2y2_to_xywh(bbox):
- x1, y1, x2, y2 = bbox
- return x1,y1,x2-x1,y2-y1
-
-
-def get_image_shape(image):
- if isinstance(image, str):
- return Image.open(image).size
- elif isinstance(image, np.ndarray):
- return image.shape
- elif isinstance(image, Image.Image):
- return image.size
- else:
- raise NotImplementedError
-
-def is_platform_win():
- return sys.platform == "win32"
-
-
-def colormap(rgb=True):
- color_list = np.array(
- [
- 0.000, 0.000, 0.000,
- 1.000, 1.000, 1.000,
- 1.000, 0.498, 0.313,
- 0.392, 0.581, 0.929,
- 0.000, 0.447, 0.741,
- 0.850, 0.325, 0.098,
- 0.929, 0.694, 0.125,
- 0.494, 0.184, 0.556,
- 0.466, 0.674, 0.188,
- 0.301, 0.745, 0.933,
- 0.635, 0.078, 0.184,
- 0.300, 0.300, 0.300,
- 0.600, 0.600, 0.600,
- 1.000, 0.000, 0.000,
- 1.000, 0.500, 0.000,
- 0.749, 0.749, 0.000,
- 0.000, 1.000, 0.000,
- 0.000, 0.000, 1.000,
- 0.667, 0.000, 1.000,
- 0.333, 0.333, 0.000,
- 0.333, 0.667, 0.000,
- 0.333, 1.000, 0.000,
- 0.667, 0.333, 0.000,
- 0.667, 0.667, 0.000,
- 0.667, 1.000, 0.000,
- 1.000, 0.333, 0.000,
- 1.000, 0.667, 0.000,
- 1.000, 1.000, 0.000,
- 0.000, 0.333, 0.500,
- 0.000, 0.667, 0.500,
- 0.000, 1.000, 0.500,
- 0.333, 0.000, 0.500,
- 0.333, 0.333, 0.500,
- 0.333, 0.667, 0.500,
- 0.333, 1.000, 0.500,
- 0.667, 0.000, 0.500,
- 0.667, 0.333, 0.500,
- 0.667, 0.667, 0.500,
- 0.667, 1.000, 0.500,
- 1.000, 0.000, 0.500,
- 1.000, 0.333, 0.500,
- 1.000, 0.667, 0.500,
- 1.000, 1.000, 0.500,
- 0.000, 0.333, 1.000,
- 0.000, 0.667, 1.000,
- 0.000, 1.000, 1.000,
- 0.333, 0.000, 1.000,
- 0.333, 0.333, 1.000,
- 0.333, 0.667, 1.000,
- 0.333, 1.000, 1.000,
- 0.667, 0.000, 1.000,
- 0.667, 0.333, 1.000,
- 0.667, 0.667, 1.000,
- 0.667, 1.000, 1.000,
- 1.000, 0.000, 1.000,
- 1.000, 0.333, 1.000,
- 1.000, 0.667, 1.000,
- 0.167, 0.000, 0.000,
- 0.333, 0.000, 0.000,
- 0.500, 0.000, 0.000,
- 0.667, 0.000, 0.000,
- 0.833, 0.000, 0.000,
- 1.000, 0.000, 0.000,
- 0.000, 0.167, 0.000,
- 0.000, 0.333, 0.000,
- 0.000, 0.500, 0.000,
- 0.000, 0.667, 0.000,
- 0.000, 0.833, 0.000,
- 0.000, 1.000, 0.000,
- 0.000, 0.000, 0.167,
- 0.000, 0.000, 0.333,
- 0.000, 0.000, 0.500,
- 0.000, 0.000, 0.667,
- 0.000, 0.000, 0.833,
- 0.000, 0.000, 1.000,
- 0.143, 0.143, 0.143,
- 0.286, 0.286, 0.286,
- 0.429, 0.429, 0.429,
- 0.571, 0.571, 0.571,
- 0.714, 0.714, 0.714,
- 0.857, 0.857, 0.857
- ]
- ).astype(np.float32)
- color_list = color_list.reshape((-1, 3)) * 255
- if not rgb:
- color_list = color_list[:, ::-1]
- return color_list
-
-
-color_list = colormap()
-color_list = color_list.astype('uint8').tolist()
-
-
-def vis_add_mask(image, mask, color, alpha, kernel_size):
- color = np.array(color)
- mask = mask.astype('float').copy()
- mask = (cv2.GaussianBlur(mask, (kernel_size, kernel_size), kernel_size) / 255.) * (alpha)
- for i in range(3):
- image[:, :, i] = image[:, :, i] * (1 - alpha + mask) + color[i] * (alpha - mask)
- return image
-
-
-def vis_add_mask_wo_blur(image, mask, color, alpha):
- color = np.array(color)
- mask = mask.astype('float').copy()
- for i in range(3):
- image[:, :, i] = image[:, :, i] * (1 - alpha + mask) + color[i] * (alpha - mask)
- return image
-
-
-def vis_add_mask_wo_gaussian(image, background_mask, contour_mask, background_color, contour_color, background_alpha,
- contour_alpha):
- background_color = np.array(background_color)
- contour_color = np.array(contour_color)
-
- # background_mask = 1 - background_mask
- # contour_mask = 1 - contour_mask
-
- for i in range(3):
- image[:, :, i] = image[:, :, i] * (1 - background_alpha + background_mask * background_alpha) \
- + background_color[i] * (background_alpha - background_mask * background_alpha)
-
- image[:, :, i] = image[:, :, i] * (1 - contour_alpha + contour_mask * contour_alpha) \
- + contour_color[i] * (contour_alpha - contour_mask * contour_alpha)
-
- return image.astype('uint8')
-
-
-def mask_painter(input_image, input_mask, background_alpha=0.7, background_blur_radius=7, contour_width=3,
- contour_color=3, contour_alpha=1, background_color=0, paint_foreground=False):
- """
- add color mask to the background/foreground area
- input_image: numpy array (w, h, C)
- input_mask: numpy array (w, h)
- background_alpha: transparency of background, [0, 1], 1: all black, 0: do nothing
- background_blur_radius: radius of background blur, must be odd number
- contour_width: width of mask contour, must be odd number
- contour_color: color index (in color map) of mask contour, 0: black, 1: white, >1: others
- background_color: color index of the background (area with input_mask == False)
- contour_alpha: transparency of mask contour, [0, 1], if 0: no contour highlighted
- paint_foreground: True for paint on foreground, False for background. Default: Flase
-
- Output:
- painted_image: numpy array
- """
- assert input_image.shape[:2] == input_mask.shape, 'different shape'
- assert background_blur_radius % 2 * contour_width % 2 > 0, 'background_blur_radius and contour_width must be ODD'
-
- # 0: background, 1: foreground
- input_mask[input_mask > 0] = 255
- if paint_foreground:
- painted_image = vis_add_mask(input_image, 255 - input_mask, color_list[background_color], background_alpha,
- background_blur_radius) # black for background
- else:
- # mask background
- painted_image = vis_add_mask(input_image, input_mask, color_list[background_color], background_alpha,
- background_blur_radius) # black for background
- # mask contour
- contour_mask = input_mask.copy()
- contour_mask = cv2.Canny(contour_mask, 100, 200) # contour extraction
- # widden contour
- kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (contour_width, contour_width))
- contour_mask = cv2.dilate(contour_mask, kernel)
- painted_image = vis_add_mask(painted_image, 255 - contour_mask, color_list[contour_color], contour_alpha,
- contour_width)
- return painted_image
-
-
-def mask_painter_foreground_all(input_image, input_masks, background_alpha=0.7, background_blur_radius=7,
- contour_width=3, contour_color=3, contour_alpha=1):
- """
- paint color mask on the all foreground area
- input_image: numpy array with shape (w, h, C)
- input_mask: list of masks, each mask is a numpy array with shape (w,h)
- background_alpha: transparency of background, [0, 1], 1: all black, 0: do nothing
- background_blur_radius: radius of background blur, must be odd number
- contour_width: width of mask contour, must be odd number
- contour_color: color index (in color map) of mask contour, 0: black, 1: white, >1: others
- background_color: color index of the background (area with input_mask == False)
- contour_alpha: transparency of mask contour, [0, 1], if 0: no contour highlighted
-
- Output:
- painted_image: numpy array
- """
-
- for i, input_mask in enumerate(input_masks):
- input_image = mask_painter(input_image, input_mask, background_alpha, background_blur_radius, contour_width,
- contour_color, contour_alpha, background_color=i + 2, paint_foreground=True)
- return input_image
-
-
-def mask_generator_00(mask, background_radius, contour_radius):
- # no background width when '00'
- # distance map
- dist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)
- dist_transform_back = cv2.distanceTransform(1 - mask, cv2.DIST_L2, 3)
- dist_map = dist_transform_fore - dist_transform_back
- # ...:::!!!:::...
- contour_radius += 2
- contour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))
- contour_mask = contour_mask / np.max(contour_mask)
- contour_mask[contour_mask > 0.5] = 1.
-
- return mask, contour_mask
-
-
-def mask_generator_01(mask, background_radius, contour_radius):
- # no background width when '00'
- # distance map
- dist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)
- dist_transform_back = cv2.distanceTransform(1 - mask, cv2.DIST_L2, 3)
- dist_map = dist_transform_fore - dist_transform_back
- # ...:::!!!:::...
- contour_radius += 2
- contour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))
- contour_mask = contour_mask / np.max(contour_mask)
- return mask, contour_mask
-
-
-def mask_generator_10(mask, background_radius, contour_radius):
- # distance map
- dist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)
- dist_transform_back = cv2.distanceTransform(1 - mask, cv2.DIST_L2, 3)
- dist_map = dist_transform_fore - dist_transform_back
- # .....:::::!!!!!
- background_mask = np.clip(dist_map, -background_radius, background_radius)
- background_mask = (background_mask - np.min(background_mask))
- background_mask = background_mask / np.max(background_mask)
- # ...:::!!!:::...
- contour_radius += 2
- contour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))
- contour_mask = contour_mask / np.max(contour_mask)
- contour_mask[contour_mask > 0.5] = 1.
- return background_mask, contour_mask
-
-
-def mask_generator_11(mask, background_radius, contour_radius):
- # distance map
- dist_transform_fore = cv2.distanceTransform(mask, cv2.DIST_L2, 3)
- dist_transform_back = cv2.distanceTransform(1 - mask, cv2.DIST_L2, 3)
- dist_map = dist_transform_fore - dist_transform_back
- # .....:::::!!!!!
- background_mask = np.clip(dist_map, -background_radius, background_radius)
- background_mask = (background_mask - np.min(background_mask))
- background_mask = background_mask / np.max(background_mask)
- # ...:::!!!:::...
- contour_radius += 2
- contour_mask = np.abs(np.clip(dist_map, -contour_radius, contour_radius))
- contour_mask = contour_mask / np.max(contour_mask)
- return background_mask, contour_mask
-
-
-def mask_painter_wo_gaussian(input_image, input_mask, background_alpha=0.5, background_blur_radius=7, contour_width=3,
- contour_color=3, contour_alpha=1, mode='11'):
- """
- Input:
- input_image: numpy array
- input_mask: numpy array
- background_alpha: transparency of background, [0, 1], 1: all black, 0: do nothing
- background_blur_radius: radius of background blur, must be odd number
- contour_width: width of mask contour, must be odd number
- contour_color: color index (in color map) of mask contour, 0: black, 1: white, >1: others
- contour_alpha: transparency of mask contour, [0, 1], if 0: no contour highlighted
- mode: painting mode, '00', no blur, '01' only blur contour, '10' only blur background, '11' blur both
-
- Output:
- painted_image: numpy array
- """
- assert input_image.shape[:2] == input_mask.shape, 'different shape'
- assert background_blur_radius % 2 * contour_width % 2 > 0, 'background_blur_radius and contour_width must be ODD'
- assert mode in ['00', '01', '10', '11'], 'mode should be 00, 01, 10, or 11'
-
- # downsample input image and mask
- width, height = input_image.shape[0], input_image.shape[1]
- res = 1024
- ratio = min(1.0 * res / max(width, height), 1.0)
- input_image = cv2.resize(input_image, (int(height * ratio), int(width * ratio)))
- input_mask = cv2.resize(input_mask, (int(height * ratio), int(width * ratio)))
-
- # 0: background, 1: foreground
- msk = np.clip(input_mask, 0, 1)
-
- # generate masks for background and contour pixels
- background_radius = (background_blur_radius - 1) // 2
- contour_radius = (contour_width - 1) // 2
- generator_dict = {'00': mask_generator_00, '01': mask_generator_01, '10': mask_generator_10,
- '11': mask_generator_11}
- background_mask, contour_mask = generator_dict[mode](msk, background_radius, contour_radius)
-
- # paint
- painted_image = vis_add_mask_wo_gaussian \
- (input_image, background_mask, contour_mask, color_list[0], color_list[contour_color], background_alpha,
- contour_alpha) # black for background
-
- return painted_image
-
-
-seg_model_map = {
- 'base': 'vit_b',
- 'large': 'vit_l',
- 'huge': 'vit_h'
-}
-ckpt_url_map = {
- 'vit_b': 'https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth',
- 'vit_l': 'https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth',
- 'vit_h': 'https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth'
-}
-expected_sha256_map = {
- 'vit_b': 'ec2df62732614e57411cdcf32a23ffdf28910380d03139ee0f4fcbe91eb8c912',
- 'vit_l': '3adcc4315b642a4d2101128f611684e8734c41232a17c648ed1693702a49a622',
- 'vit_h': 'a7bf3b02f3ebf1267aba913ff637d9a2d5c33d3173bb679e46d9f338c26f262e'
-}
-
-
-def prepare_segmenter(segmenter="huge", download_root: str = None):
- """
- Prepare segmenter model and download checkpoint if necessary.
-
- Returns: segmenter model name from 'vit_b', 'vit_l', 'vit_h'.
-
- """
-
- os.makedirs('result', exist_ok=True)
- seg_model_name = seg_model_map[segmenter]
- checkpoint_url = ckpt_url_map[seg_model_name]
- folder = download_root or os.path.expanduser("~/.cache/SAM")
- filename = os.path.basename(checkpoint_url)
- segmenter_checkpoint = download_checkpoint(checkpoint_url, folder, filename, expected_sha256_map[seg_model_name])
-
- return seg_model_name, segmenter_checkpoint
-
-
-def download_checkpoint(url, folder, filename, expected_sha256):
- os.makedirs(folder, exist_ok=True)
- download_target = os.path.join(folder, filename)
- if os.path.isfile(download_target):
- if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256:
- return download_target
-
- print(f'Download SAM checkpoint {url}, saving to {download_target} ...')
- with requests.get(url, stream=True) as response, open(download_target, "wb") as output:
- progress = tqdm(total=int(response.headers.get('content-length', 0)), unit='B', unit_scale=True)
- for data in response.iter_content(chunk_size=1024):
- size = output.write(data)
- progress.update(size)
- if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256:
- raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match")
- return download_target
-
-
-if __name__ == '__main__':
-
- background_alpha = 0.7 # transparency of background 1: all black, 0: do nothing
- background_blur_radius = 31 # radius of background blur, must be odd number
- contour_width = 11 # contour width, must be odd number
- contour_color = 3 # id in color map, 0: black, 1: white, >1: others
- contour_alpha = 1 # transparency of background, 0: no contour highlighted
-
- # load input image and mask
- input_image = np.array(Image.open('./test_images/painter_input_image.jpg').convert('RGB'))
- input_mask = np.array(Image.open('./test_images/painter_input_mask.jpg').convert('P'))
-
- # paint
- overall_time_1 = 0
- overall_time_2 = 0
- overall_time_3 = 0
- overall_time_4 = 0
- overall_time_5 = 0
-
- for i in range(50):
- t2 = time.time()
- painted_image_00 = mask_painter_wo_gaussian(input_image, input_mask, background_alpha, background_blur_radius,
- contour_width, contour_color, contour_alpha, mode='00')
- e2 = time.time()
-
- t3 = time.time()
- painted_image_10 = mask_painter_wo_gaussian(input_image, input_mask, background_alpha, background_blur_radius,
- contour_width, contour_color, contour_alpha, mode='10')
- e3 = time.time()
-
- t1 = time.time()
- painted_image = mask_painter(input_image, input_mask, background_alpha, background_blur_radius, contour_width,
- contour_color, contour_alpha)
- e1 = time.time()
-
- t4 = time.time()
- painted_image_01 = mask_painter_wo_gaussian(input_image, input_mask, background_alpha, background_blur_radius,
- contour_width, contour_color, contour_alpha, mode='01')
- e4 = time.time()
-
- t5 = time.time()
- painted_image_11 = mask_painter_wo_gaussian(input_image, input_mask, background_alpha, background_blur_radius,
- contour_width, contour_color, contour_alpha, mode='11')
- e5 = time.time()
-
- overall_time_1 += (e1 - t1)
- overall_time_2 += (e2 - t2)
- overall_time_3 += (e3 - t3)
- overall_time_4 += (e4 - t4)
- overall_time_5 += (e5 - t5)
-
- print(f'average time w gaussian: {overall_time_1 / 50}')
- print(f'average time w/o gaussian00: {overall_time_2 / 50}')
- print(f'average time w/o gaussian10: {overall_time_3 / 50}')
- print(f'average time w/o gaussian01: {overall_time_4 / 50}')
- print(f'average time w/o gaussian11: {overall_time_5 / 50}')
-
- # save
- painted_image_00 = Image.fromarray(painted_image_00)
- painted_image_00.save('./test_images/painter_output_image_00.png')
-
- painted_image_10 = Image.fromarray(painted_image_10)
- painted_image_10.save('./test_images/painter_output_image_10.png')
-
- painted_image_01 = Image.fromarray(painted_image_01)
- painted_image_01.save('./test_images/painter_output_image_01.png')
-
- painted_image_11 = Image.fromarray(painted_image_11)
- painted_image_11.save('./test_images/painter_output_image_11.png')
diff --git a/spaces/VincentZB/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_seg.py b/spaces/VincentZB/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_seg.py
deleted file mode 100644
index dfbda0744cc674a5b3195e47d7a507192f999eb2..0000000000000000000000000000000000000000
--- a/spaces/VincentZB/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_seg.py
+++ /dev/null
@@ -1,353 +0,0 @@
-import gradio as gr
-import numpy as np
-import torch
-from diffusers import ControlNetModel, StableDiffusionControlNetPipeline
-from PIL import Image
-from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
-
-from diffusion_webui.utils.model_list import stable_model_list
-from diffusion_webui.utils.scheduler_list import (
- SCHEDULER_LIST,
- get_scheduler_list,
-)
-
-
-def ade_palette():
- """ADE20K palette that maps each class to RGB values."""
- return [
- [120, 120, 120],
- [180, 120, 120],
- [6, 230, 230],
- [80, 50, 50],
- [4, 200, 3],
- [120, 120, 80],
- [140, 140, 140],
- [204, 5, 255],
- [230, 230, 230],
- [4, 250, 7],
- [224, 5, 255],
- [235, 255, 7],
- [150, 5, 61],
- [120, 120, 70],
- [8, 255, 51],
- [255, 6, 82],
- [143, 255, 140],
- [204, 255, 4],
- [255, 51, 7],
- [204, 70, 3],
- [0, 102, 200],
- [61, 230, 250],
- [255, 6, 51],
- [11, 102, 255],
- [255, 7, 71],
- [255, 9, 224],
- [9, 7, 230],
- [220, 220, 220],
- [255, 9, 92],
- [112, 9, 255],
- [8, 255, 214],
- [7, 255, 224],
- [255, 184, 6],
- [10, 255, 71],
- [255, 41, 10],
- [7, 255, 255],
- [224, 255, 8],
- [102, 8, 255],
- [255, 61, 6],
- [255, 194, 7],
- [255, 122, 8],
- [0, 255, 20],
- [255, 8, 41],
- [255, 5, 153],
- [6, 51, 255],
- [235, 12, 255],
- [160, 150, 20],
- [0, 163, 255],
- [140, 140, 140],
- [250, 10, 15],
- [20, 255, 0],
- [31, 255, 0],
- [255, 31, 0],
- [255, 224, 0],
- [153, 255, 0],
- [0, 0, 255],
- [255, 71, 0],
- [0, 235, 255],
- [0, 173, 255],
- [31, 0, 255],
- [11, 200, 200],
- [255, 82, 0],
- [0, 255, 245],
- [0, 61, 255],
- [0, 255, 112],
- [0, 255, 133],
- [255, 0, 0],
- [255, 163, 0],
- [255, 102, 0],
- [194, 255, 0],
- [0, 143, 255],
- [51, 255, 0],
- [0, 82, 255],
- [0, 255, 41],
- [0, 255, 173],
- [10, 0, 255],
- [173, 255, 0],
- [0, 255, 153],
- [255, 92, 0],
- [255, 0, 255],
- [255, 0, 245],
- [255, 0, 102],
- [255, 173, 0],
- [255, 0, 20],
- [255, 184, 184],
- [0, 31, 255],
- [0, 255, 61],
- [0, 71, 255],
- [255, 0, 204],
- [0, 255, 194],
- [0, 255, 82],
- [0, 10, 255],
- [0, 112, 255],
- [51, 0, 255],
- [0, 194, 255],
- [0, 122, 255],
- [0, 255, 163],
- [255, 153, 0],
- [0, 255, 10],
- [255, 112, 0],
- [143, 255, 0],
- [82, 0, 255],
- [163, 255, 0],
- [255, 235, 0],
- [8, 184, 170],
- [133, 0, 255],
- [0, 255, 92],
- [184, 0, 255],
- [255, 0, 31],
- [0, 184, 255],
- [0, 214, 255],
- [255, 0, 112],
- [92, 255, 0],
- [0, 224, 255],
- [112, 224, 255],
- [70, 184, 160],
- [163, 0, 255],
- [153, 0, 255],
- [71, 255, 0],
- [255, 0, 163],
- [255, 204, 0],
- [255, 0, 143],
- [0, 255, 235],
- [133, 255, 0],
- [255, 0, 235],
- [245, 0, 255],
- [255, 0, 122],
- [255, 245, 0],
- [10, 190, 212],
- [214, 255, 0],
- [0, 204, 255],
- [20, 0, 255],
- [255, 255, 0],
- [0, 153, 255],
- [0, 41, 255],
- [0, 255, 204],
- [41, 0, 255],
- [41, 255, 0],
- [173, 0, 255],
- [0, 245, 255],
- [71, 0, 255],
- [122, 0, 255],
- [0, 255, 184],
- [0, 92, 255],
- [184, 255, 0],
- [0, 133, 255],
- [255, 214, 0],
- [25, 194, 194],
- [102, 255, 0],
- [92, 0, 255],
- ]
-
-
-class StableDiffusionControlNetSegGenerator:
- def __init__(self):
- self.pipe = None
-
- def load_model(
- self,
- stable_model_path,
- scheduler,
- ):
-
- if self.pipe is None:
- controlnet = ControlNetModel.from_pretrained(
- "lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16
- )
- self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
- pretrained_model_name_or_path=stable_model_path,
- controlnet=controlnet,
- safety_checker=None,
- torch_dtype=torch.float16,
- )
-
- self.pipe = get_scheduler_list(pipe=self.pipe, scheduler=scheduler)
- self.pipe.to("cuda")
- self.pipe.enable_xformers_memory_efficient_attention()
-
- return self.pipe
-
- def controlnet_seg(self, image_path: str):
- image_processor = AutoImageProcessor.from_pretrained(
- "openmmlab/upernet-convnext-small"
- )
- image_segmentor = UperNetForSemanticSegmentation.from_pretrained(
- "openmmlab/upernet-convnext-small"
- )
-
- image = Image.open(image_path).convert("RGB")
- pixel_values = image_processor(image, return_tensors="pt").pixel_values
-
- with torch.no_grad():
- outputs = image_segmentor(pixel_values)
-
- seg = image_processor.post_process_semantic_segmentation(
- outputs, target_sizes=[image.size[::-1]]
- )[0]
-
- color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
- palette = np.array(ade_palette())
-
- for label, color in enumerate(palette):
- color_seg[seg == label, :] = color
-
- color_seg = color_seg.astype(np.uint8)
- image = Image.fromarray(color_seg)
-
- return image
-
- def generate_image(
- self,
- image_path: str,
- model_path: str,
- prompt: str,
- negative_prompt: str,
- num_images_per_prompt: int,
- guidance_scale: int,
- num_inference_step: int,
- scheduler: str,
- seed_generator: int,
- ):
-
- image = self.controlnet_seg(image_path=image_path)
- pipe = self.load_model(
- stable_model_path=model_path,
- scheduler=scheduler,
- )
- if seed_generator == 0:
- random_seed = torch.randint(0, 1000000, (1,))
- generator = torch.manual_seed(random_seed)
- else:
- generator = torch.manual_seed(seed_generator)
-
- output = pipe(
- prompt=prompt,
- image=image,
- negative_prompt=negative_prompt,
- num_images_per_prompt=num_images_per_prompt,
- num_inference_steps=num_inference_step,
- guidance_scale=guidance_scale,
- generator=generator,
- ).images
-
- return output
-
- def app():
- with gr.Blocks():
- with gr.Row():
- with gr.Column():
- controlnet_seg_image_file = gr.Image(
- type="filepath", label="Image"
- )
-
- controlnet_seg_prompt = gr.Textbox(
- lines=1,
- show_label=False,
- placeholder="Prompt",
- )
-
- controlnet_seg_negative_prompt = gr.Textbox(
- lines=1,
- show_label=False,
- placeholder="Negative Prompt",
- )
-
- with gr.Row():
- with gr.Column():
- controlnet_seg_model_id = gr.Dropdown(
- choices=stable_model_list,
- value=stable_model_list[0],
- label="Stable Model Id",
- )
- controlnet_seg_guidance_scale = gr.Slider(
- minimum=0.1,
- maximum=15,
- step=0.1,
- value=7.5,
- label="Guidance Scale",
- )
-
- controlnet_seg_num_inference_step = gr.Slider(
- minimum=1,
- maximum=100,
- step=1,
- value=50,
- label="Num Inference Step",
- )
-
- with gr.Row():
- with gr.Column():
- controlnet_seg_scheduler = gr.Dropdown(
- choices=SCHEDULER_LIST,
- value=SCHEDULER_LIST[0],
- label="Scheduler",
- )
- controlnet_seg_num_images_per_prompt = (
- gr.Slider(
- minimum=1,
- maximum=10,
- step=1,
- value=1,
- label="Number Of Images",
- )
- )
- controlnet_seg_seed_generator = gr.Slider(
- minimum=0,
- maximum=1000000,
- step=1,
- value=0,
- label="Seed Generator",
- )
-
- controlnet_seg_predict = gr.Button(value="Generator")
-
- with gr.Column():
- output_image = gr.Gallery(
- label="Generated images",
- show_label=False,
- elem_id="gallery",
- ).style(grid=(1, 2))
-
- controlnet_seg_predict.click(
- fn=StableDiffusionControlNetSegGenerator().generate_image,
- inputs=[
- controlnet_seg_image_file,
- controlnet_seg_model_id,
- controlnet_seg_prompt,
- controlnet_seg_negative_prompt,
- controlnet_seg_num_images_per_prompt,
- controlnet_seg_guidance_scale,
- controlnet_seg_num_inference_step,
- controlnet_seg_scheduler,
- controlnet_seg_seed_generator,
- ],
- outputs=[output_image],
- )
diff --git a/spaces/WAT-ai-AA/stable-diffused-adversarial-attacks/app.py b/spaces/WAT-ai-AA/stable-diffused-adversarial-attacks/app.py
deleted file mode 100644
index 5c3bd4e144ee37232e923a6b7ac4deba37876d69..0000000000000000000000000000000000000000
--- a/spaces/WAT-ai-AA/stable-diffused-adversarial-attacks/app.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import torch
-import gradio as gr
-from torchvision import transforms
-from diffusers import StableDiffusionPipeline
-from model import ResNet, ResidualBlock
-from attack import Attack
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-pipe = StableDiffusionPipeline.from_pretrained(
- "stabilityai/stable-diffusion-2-1-base"
-)
-pipe = pipe.to(device)
-
-CLASSES = (
- "plane",
- "car",
- "bird",
- "cat",
- "deer",
- "dog",
- "frog",
- "horse",
- "ship",
- "truck",
-)
-
-
-def load_classifer(model_path):
- # load resnet model
- model = ResNet(ResidualBlock, [2, 2, 2])
- model.load_state_dict(torch.load(model_path, map_location=device))
- model.eval()
- return model
-
-
-classifer = load_classifer("./models/resnet.ckpt")
-attack = Attack(pipe, classifer, device)
-
-
-def classifer_pred(image):
- to_pil = transforms.ToPILImage()
- input = attack.transform(to_pil(image[0]))
- outputs = classifer(input)
- _, predicted = torch.max(outputs, 1)
- return CLASSES[predicted[0]]
-
-
-def run_attack(prompt, epsilon):
- image, perturbed_image = attack(prompt, epsilon=epsilon)
- pred = classifer_pred(perturbed_image)
- return image, pred
-
-
-demo = gr.Interface(
- run_attack,
- [gr.Text(), gr.Slider(minimum=0.0, maximum=0.3, value=float)],
- [gr.Image(), gr.Text()],
- title="Stable Diffused Adversarial Attacks",
-)
-demo.launch()
diff --git a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/numpytovideo.py b/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/numpytovideo.py
deleted file mode 100644
index 5a064e26f6c85d956d13254490749dd32d02d20f..0000000000000000000000000000000000000000
--- a/spaces/XS-1/BW_IMAGE_VIDEO_COLORIZER/numpytovideo.py
+++ /dev/null
@@ -1,25 +0,0 @@
-# out numpy array of images is in the charile temp directory
-#
-import numpy
-from numpy import load
-import cv2
-colorImagesNumpyArray = load('video/np/video.npy')
-print(colorImagesNumpyArray.shape)
-
-# we can see the Deoldify conda environment is not compitable
-# I will a diffrent enviroment that has numpy and open cv. You can do the same
-
-#(2499, 226, 400, 3)
-
-# so we have 2499 images . each image is has a resoltion of 226X400 with 3 color channles
-
-# extract the dimentions of the first images (all of them are the same)
-height , width , channels = colorImagesNumpyArray[0].shape
-size = (width, height)
-newVideoOut = cv2.VideoWriter('video/np/video.avi',cv2.VideoWriter_fourcc(*'DIVX'),15,size)
-
-for image in colorImagesNumpyArray:
- newVideoOut.write(image)
-
-newVideoOut.release()
-cv2.destroyAllWindows()
diff --git a/spaces/Xenova/llama2.c/Dockerfile b/spaces/Xenova/llama2.c/Dockerfile
deleted file mode 100644
index a1304930455d2a41ad716a431996985ca7191e70..0000000000000000000000000000000000000000
--- a/spaces/Xenova/llama2.c/Dockerfile
+++ /dev/null
@@ -1,3 +0,0 @@
-FROM nginxinc/nginx-unprivileged
-COPY ./nginx.conf /etc/nginx/conf.d/default.conf
-COPY . /usr/share/nginx/html
\ No newline at end of file
diff --git a/spaces/Xhaheen/tasweer/html2canvas.js b/spaces/Xhaheen/tasweer/html2canvas.js
deleted file mode 100644
index 96e2dc5707b1a584ff7b3b583aea7c6c18d4ea76..0000000000000000000000000000000000000000
--- a/spaces/Xhaheen/tasweer/html2canvas.js
+++ /dev/null
@@ -1,7756 +0,0 @@
-/*!
- * html2canvas 1.4.1
- * Copyright (c) 2022 Niklas von Hertzen
- * Released under MIT License
- */
-(function (global, factory) {
- typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
- typeof define === 'function' && define.amd ? define(factory) :
- (global = typeof globalThis !== 'undefined' ? globalThis : global || self, global.html2canvas = factory());
-}(this, (function () { 'use strict';
-
- /*! *****************************************************************************
- Copyright (c) Microsoft Corporation.
-
- Permission to use, copy, modify, and/or distribute this software for any
- purpose with or without fee is hereby granted.
-
- THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
- REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
- AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
- INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
- LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
- OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
- PERFORMANCE OF THIS SOFTWARE.
- ***************************************************************************** */
- /* global Reflect, Promise */
-
- var extendStatics = function(d, b) {
- extendStatics = Object.setPrototypeOf ||
- ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
- function (d, b) { for (var p in b) if (Object.prototype.hasOwnProperty.call(b, p)) d[p] = b[p]; };
- return extendStatics(d, b);
- };
-
- function __extends(d, b) {
- if (typeof b !== "function" && b !== null)
- throw new TypeError("Class extends value " + String(b) + " is not a constructor or null");
- extendStatics(d, b);
- function __() { this.constructor = d; }
- d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
- }
-
- var __assign = function() {
- __assign = Object.assign || function __assign(t) {
- for (var s, i = 1, n = arguments.length; i < n; i++) {
- s = arguments[i];
- for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];
- }
- return t;
- };
- return __assign.apply(this, arguments);
- };
-
- function __awaiter(thisArg, _arguments, P, generator) {
- function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
- return new (P || (P = Promise))(function (resolve, reject) {
- function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
- function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
- function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }
- step((generator = generator.apply(thisArg, _arguments || [])).next());
- });
- }
-
- function __generator(thisArg, body) {
- var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
- return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
- function verb(n) { return function (v) { return step([n, v]); }; }
- function step(op) {
- if (f) throw new TypeError("Generator is already executing.");
- while (_) try {
- if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
- if (y = 0, t) op = [op[0] & 2, t.value];
- switch (op[0]) {
- case 0: case 1: t = op; break;
- case 4: _.label++; return { value: op[1], done: false };
- case 5: _.label++; y = op[1]; op = [0]; continue;
- case 7: op = _.ops.pop(); _.trys.pop(); continue;
- default:
- if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
- if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
- if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
- if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
- if (t[2]) _.ops.pop();
- _.trys.pop(); continue;
- }
- op = body.call(thisArg, _);
- } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
- if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
- }
- }
-
- function __spreadArray(to, from, pack) {
- if (pack || arguments.length === 2) for (var i = 0, l = from.length, ar; i < l; i++) {
- if (ar || !(i in from)) {
- if (!ar) ar = Array.prototype.slice.call(from, 0, i);
- ar[i] = from[i];
- }
- }
- return to.concat(ar || from);
- }
-
- var Bounds = /** @class */ (function () {
- function Bounds(left, top, width, height) {
- this.left = left;
- this.top = top;
- this.width = width;
- this.height = height;
- }
- Bounds.prototype.add = function (x, y, w, h) {
- return new Bounds(this.left + x, this.top + y, this.width + w, this.height + h);
- };
- Bounds.fromClientRect = function (context, clientRect) {
- return new Bounds(clientRect.left + context.windowBounds.left, clientRect.top + context.windowBounds.top, clientRect.width, clientRect.height);
- };
- Bounds.fromDOMRectList = function (context, domRectList) {
- var domRect = Array.from(domRectList).find(function (rect) { return rect.width !== 0; });
- return domRect
- ? new Bounds(domRect.left + context.windowBounds.left, domRect.top + context.windowBounds.top, domRect.width, domRect.height)
- : Bounds.EMPTY;
- };
- Bounds.EMPTY = new Bounds(0, 0, 0, 0);
- return Bounds;
- }());
- var parseBounds = function (context, node) {
- return Bounds.fromClientRect(context, node.getBoundingClientRect());
- };
- var parseDocumentSize = function (document) {
- var body = document.body;
- var documentElement = document.documentElement;
- if (!body || !documentElement) {
- throw new Error("Unable to get document size");
- }
- var width = Math.max(Math.max(body.scrollWidth, documentElement.scrollWidth), Math.max(body.offsetWidth, documentElement.offsetWidth), Math.max(body.clientWidth, documentElement.clientWidth));
- var height = Math.max(Math.max(body.scrollHeight, documentElement.scrollHeight), Math.max(body.offsetHeight, documentElement.offsetHeight), Math.max(body.clientHeight, documentElement.clientHeight));
- return new Bounds(0, 0, width, height);
- };
-
- /*
- * css-line-break 2.1.0
- * Copyright (c) 2022 Niklas von Hertzen
- * Released under MIT License
- */
- var toCodePoints$1 = function (str) {
- var codePoints = [];
- var i = 0;
- var length = str.length;
- while (i < length) {
- var value = str.charCodeAt(i++);
- if (value >= 0xd800 && value <= 0xdbff && i < length) {
- var extra = str.charCodeAt(i++);
- if ((extra & 0xfc00) === 0xdc00) {
- codePoints.push(((value & 0x3ff) << 10) + (extra & 0x3ff) + 0x10000);
- }
- else {
- codePoints.push(value);
- i--;
- }
- }
- else {
- codePoints.push(value);
- }
- }
- return codePoints;
- };
- var fromCodePoint$1 = function () {
- var codePoints = [];
- for (var _i = 0; _i < arguments.length; _i++) {
- codePoints[_i] = arguments[_i];
- }
- if (String.fromCodePoint) {
- return String.fromCodePoint.apply(String, codePoints);
- }
- var length = codePoints.length;
- if (!length) {
- return '';
- }
- var codeUnits = [];
- var index = -1;
- var result = '';
- while (++index < length) {
- var codePoint = codePoints[index];
- if (codePoint <= 0xffff) {
- codeUnits.push(codePoint);
- }
- else {
- codePoint -= 0x10000;
- codeUnits.push((codePoint >> 10) + 0xd800, (codePoint % 0x400) + 0xdc00);
- }
- if (index + 1 === length || codeUnits.length > 0x4000) {
- result += String.fromCharCode.apply(String, codeUnits);
- codeUnits.length = 0;
- }
- }
- return result;
- };
- var chars$2 = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/';
- // Use a lookup table to find the index.
- var lookup$2 = typeof Uint8Array === 'undefined' ? [] : new Uint8Array(256);
- for (var i$2 = 0; i$2 < chars$2.length; i$2++) {
- lookup$2[chars$2.charCodeAt(i$2)] = i$2;
- }
-
- /*
- * utrie 1.0.2
- * Copyright (c) 2022 Niklas von Hertzen
- * Released under MIT License
- */
- var chars$1$1 = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/';
- // Use a lookup table to find the index.
- var lookup$1$1 = typeof Uint8Array === 'undefined' ? [] : new Uint8Array(256);
- for (var i$1$1 = 0; i$1$1 < chars$1$1.length; i$1$1++) {
- lookup$1$1[chars$1$1.charCodeAt(i$1$1)] = i$1$1;
- }
- var decode$1 = function (base64) {
- var bufferLength = base64.length * 0.75, len = base64.length, i, p = 0, encoded1, encoded2, encoded3, encoded4;
- if (base64[base64.length - 1] === '=') {
- bufferLength--;
- if (base64[base64.length - 2] === '=') {
- bufferLength--;
- }
- }
- var buffer = typeof ArrayBuffer !== 'undefined' &&
- typeof Uint8Array !== 'undefined' &&
- typeof Uint8Array.prototype.slice !== 'undefined'
- ? new ArrayBuffer(bufferLength)
- : new Array(bufferLength);
- var bytes = Array.isArray(buffer) ? buffer : new Uint8Array(buffer);
- for (i = 0; i < len; i += 4) {
- encoded1 = lookup$1$1[base64.charCodeAt(i)];
- encoded2 = lookup$1$1[base64.charCodeAt(i + 1)];
- encoded3 = lookup$1$1[base64.charCodeAt(i + 2)];
- encoded4 = lookup$1$1[base64.charCodeAt(i + 3)];
- bytes[p++] = (encoded1 << 2) | (encoded2 >> 4);
- bytes[p++] = ((encoded2 & 15) << 4) | (encoded3 >> 2);
- bytes[p++] = ((encoded3 & 3) << 6) | (encoded4 & 63);
- }
- return buffer;
- };
- var polyUint16Array$1 = function (buffer) {
- var length = buffer.length;
- var bytes = [];
- for (var i = 0; i < length; i += 2) {
- bytes.push((buffer[i + 1] << 8) | buffer[i]);
- }
- return bytes;
- };
- var polyUint32Array$1 = function (buffer) {
- var length = buffer.length;
- var bytes = [];
- for (var i = 0; i < length; i += 4) {
- bytes.push((buffer[i + 3] << 24) | (buffer[i + 2] << 16) | (buffer[i + 1] << 8) | buffer[i]);
- }
- return bytes;
- };
-
- /** Shift size for getting the index-2 table offset. */
- var UTRIE2_SHIFT_2$1 = 5;
- /** Shift size for getting the index-1 table offset. */
- var UTRIE2_SHIFT_1$1 = 6 + 5;
- /**
- * Shift size for shifting left the index array values.
- * Increases possible data size with 16-bit index values at the cost
- * of compactability.
- * This requires data blocks to be aligned by UTRIE2_DATA_GRANULARITY.
- */
- var UTRIE2_INDEX_SHIFT$1 = 2;
- /**
- * Difference between the two shift sizes,
- * for getting an index-1 offset from an index-2 offset. 6=11-5
- */
- var UTRIE2_SHIFT_1_2$1 = UTRIE2_SHIFT_1$1 - UTRIE2_SHIFT_2$1;
- /**
- * The part of the index-2 table for U+D800..U+DBFF stores values for
- * lead surrogate code _units_ not code _points_.
- * Values for lead surrogate code _points_ are indexed with this portion of the table.
- * Length=32=0x20=0x400>>UTRIE2_SHIFT_2. (There are 1024=0x400 lead surrogates.)
- */
- var UTRIE2_LSCP_INDEX_2_OFFSET$1 = 0x10000 >> UTRIE2_SHIFT_2$1;
- /** Number of entries in a data block. 32=0x20 */
- var UTRIE2_DATA_BLOCK_LENGTH$1 = 1 << UTRIE2_SHIFT_2$1;
- /** Mask for getting the lower bits for the in-data-block offset. */
- var UTRIE2_DATA_MASK$1 = UTRIE2_DATA_BLOCK_LENGTH$1 - 1;
- var UTRIE2_LSCP_INDEX_2_LENGTH$1 = 0x400 >> UTRIE2_SHIFT_2$1;
- /** Count the lengths of both BMP pieces. 2080=0x820 */
- var UTRIE2_INDEX_2_BMP_LENGTH$1 = UTRIE2_LSCP_INDEX_2_OFFSET$1 + UTRIE2_LSCP_INDEX_2_LENGTH$1;
- /**
- * The 2-byte UTF-8 version of the index-2 table follows at offset 2080=0x820.
- * Length 32=0x20 for lead bytes C0..DF, regardless of UTRIE2_SHIFT_2.
- */
- var UTRIE2_UTF8_2B_INDEX_2_OFFSET$1 = UTRIE2_INDEX_2_BMP_LENGTH$1;
- var UTRIE2_UTF8_2B_INDEX_2_LENGTH$1 = 0x800 >> 6; /* U+0800 is the first code point after 2-byte UTF-8 */
- /**
- * The index-1 table, only used for supplementary code points, at offset 2112=0x840.
- * Variable length, for code points up to highStart, where the last single-value range starts.
- * Maximum length 512=0x200=0x100000>>UTRIE2_SHIFT_1.
- * (For 0x100000 supplementary code points U+10000..U+10ffff.)
- *
- * The part of the index-2 table for supplementary code points starts
- * after this index-1 table.
- *
- * Both the index-1 table and the following part of the index-2 table
- * are omitted completely if there is only BMP data.
- */
- var UTRIE2_INDEX_1_OFFSET$1 = UTRIE2_UTF8_2B_INDEX_2_OFFSET$1 + UTRIE2_UTF8_2B_INDEX_2_LENGTH$1;
- /**
- * Number of index-1 entries for the BMP. 32=0x20
- * This part of the index-1 table is omitted from the serialized form.
- */
- var UTRIE2_OMITTED_BMP_INDEX_1_LENGTH$1 = 0x10000 >> UTRIE2_SHIFT_1$1;
- /** Number of entries in an index-2 block. 64=0x40 */
- var UTRIE2_INDEX_2_BLOCK_LENGTH$1 = 1 << UTRIE2_SHIFT_1_2$1;
- /** Mask for getting the lower bits for the in-index-2-block offset. */
- var UTRIE2_INDEX_2_MASK$1 = UTRIE2_INDEX_2_BLOCK_LENGTH$1 - 1;
- var slice16$1 = function (view, start, end) {
- if (view.slice) {
- return view.slice(start, end);
- }
- return new Uint16Array(Array.prototype.slice.call(view, start, end));
- };
- var slice32$1 = function (view, start, end) {
- if (view.slice) {
- return view.slice(start, end);
- }
- return new Uint32Array(Array.prototype.slice.call(view, start, end));
- };
- var createTrieFromBase64$1 = function (base64, _byteLength) {
- var buffer = decode$1(base64);
- var view32 = Array.isArray(buffer) ? polyUint32Array$1(buffer) : new Uint32Array(buffer);
- var view16 = Array.isArray(buffer) ? polyUint16Array$1(buffer) : new Uint16Array(buffer);
- var headerLength = 24;
- var index = slice16$1(view16, headerLength / 2, view32[4] / 2);
- var data = view32[5] === 2
- ? slice16$1(view16, (headerLength + view32[4]) / 2)
- : slice32$1(view32, Math.ceil((headerLength + view32[4]) / 4));
- return new Trie$1(view32[0], view32[1], view32[2], view32[3], index, data);
- };
- var Trie$1 = /** @class */ (function () {
- function Trie(initialValue, errorValue, highStart, highValueIndex, index, data) {
- this.initialValue = initialValue;
- this.errorValue = errorValue;
- this.highStart = highStart;
- this.highValueIndex = highValueIndex;
- this.index = index;
- this.data = data;
- }
- /**
- * Get the value for a code point as stored in the Trie.
- *
- * @param codePoint the code point
- * @return the value
- */
- Trie.prototype.get = function (codePoint) {
- var ix;
- if (codePoint >= 0) {
- if (codePoint < 0x0d800 || (codePoint > 0x0dbff && codePoint <= 0x0ffff)) {
- // Ordinary BMP code point, excluding leading surrogates.
- // BMP uses a single level lookup. BMP index starts at offset 0 in the Trie2 index.
- // 16 bit data is stored in the index array itself.
- ix = this.index[codePoint >> UTRIE2_SHIFT_2$1];
- ix = (ix << UTRIE2_INDEX_SHIFT$1) + (codePoint & UTRIE2_DATA_MASK$1);
- return this.data[ix];
- }
- if (codePoint <= 0xffff) {
- // Lead Surrogate Code Point. A Separate index section is stored for
- // lead surrogate code units and code points.
- // The main index has the code unit data.
- // For this function, we need the code point data.
- // Note: this expression could be refactored for slightly improved efficiency, but
- // surrogate code points will be so rare in practice that it's not worth it.
- ix = this.index[UTRIE2_LSCP_INDEX_2_OFFSET$1 + ((codePoint - 0xd800) >> UTRIE2_SHIFT_2$1)];
- ix = (ix << UTRIE2_INDEX_SHIFT$1) + (codePoint & UTRIE2_DATA_MASK$1);
- return this.data[ix];
- }
- if (codePoint < this.highStart) {
- // Supplemental code point, use two-level lookup.
- ix = UTRIE2_INDEX_1_OFFSET$1 - UTRIE2_OMITTED_BMP_INDEX_1_LENGTH$1 + (codePoint >> UTRIE2_SHIFT_1$1);
- ix = this.index[ix];
- ix += (codePoint >> UTRIE2_SHIFT_2$1) & UTRIE2_INDEX_2_MASK$1;
- ix = this.index[ix];
- ix = (ix << UTRIE2_INDEX_SHIFT$1) + (codePoint & UTRIE2_DATA_MASK$1);
- return this.data[ix];
- }
- if (codePoint <= 0x10ffff) {
- return this.data[this.highValueIndex];
- }
- }
- // Fall through. The code point is outside of the legal range of 0..0x10ffff.
- return this.errorValue;
- };
- return Trie;
- }());
-
- /*
- * base64-arraybuffer 1.0.2
- * Copyright (c) 2022 Niklas von Hertzen
- * Released under MIT License
- */
- var chars$3 = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/';
- // Use a lookup table to find the index.
- var lookup$3 = typeof Uint8Array === 'undefined' ? [] : new Uint8Array(256);
- for (var i$3 = 0; i$3 < chars$3.length; i$3++) {
- lookup$3[chars$3.charCodeAt(i$3)] = i$3;
- }
-
- var base64$1 = 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';
-
- var LETTER_NUMBER_MODIFIER = 50;
- // Non-tailorable Line Breaking Classes
- var BK = 1; // Cause a line break (after)
- var CR$1 = 2; // Cause a line break (after), except between CR and LF
- var LF$1 = 3; // Cause a line break (after)
- var CM = 4; // Prohibit a line break between the character and the preceding character
- var NL = 5; // Cause a line break (after)
- var WJ = 7; // Prohibit line breaks before and after
- var ZW = 8; // Provide a break opportunity
- var GL = 9; // Prohibit line breaks before and after
- var SP = 10; // Enable indirect line breaks
- var ZWJ$1 = 11; // Prohibit line breaks within joiner sequences
- // Break Opportunities
- var B2 = 12; // Provide a line break opportunity before and after the character
- var BA = 13; // Generally provide a line break opportunity after the character
- var BB = 14; // Generally provide a line break opportunity before the character
- var HY = 15; // Provide a line break opportunity after the character, except in numeric context
- var CB = 16; // Provide a line break opportunity contingent on additional information
- // Characters Prohibiting Certain Breaks
- var CL = 17; // Prohibit line breaks before
- var CP = 18; // Prohibit line breaks before
- var EX = 19; // Prohibit line breaks before
- var IN = 20; // Allow only indirect line breaks between pairs
- var NS = 21; // Allow only indirect line breaks before
- var OP = 22; // Prohibit line breaks after
- var QU = 23; // Act like they are both opening and closing
- // Numeric Context
- var IS = 24; // Prevent breaks after any and before numeric
- var NU = 25; // Form numeric expressions for line breaking purposes
- var PO = 26; // Do not break following a numeric expression
- var PR = 27; // Do not break in front of a numeric expression
- var SY = 28; // Prevent a break before; and allow a break after
- // Other Characters
- var AI = 29; // Act like AL when the resolvedEAW is N; otherwise; act as ID
- var AL = 30; // Are alphabetic characters or symbols that are used with alphabetic characters
- var CJ = 31; // Treat as NS or ID for strict or normal breaking.
- var EB = 32; // Do not break from following Emoji Modifier
- var EM = 33; // Do not break from preceding Emoji Base
- var H2 = 34; // Form Korean syllable blocks
- var H3 = 35; // Form Korean syllable blocks
- var HL = 36; // Do not break around a following hyphen; otherwise act as Alphabetic
- var ID = 37; // Break before or after; except in some numeric context
- var JL = 38; // Form Korean syllable blocks
- var JV = 39; // Form Korean syllable blocks
- var JT = 40; // Form Korean syllable blocks
- var RI$1 = 41; // Keep pairs together. For pairs; break before and after other classes
- var SA = 42; // Provide a line break opportunity contingent on additional, language-specific context analysis
- var XX = 43; // Have as yet unknown line breaking behavior or unassigned code positions
- var ea_OP = [0x2329, 0xff08];
- var BREAK_MANDATORY = '!';
- var BREAK_NOT_ALLOWED$1 = '×';
- var BREAK_ALLOWED$1 = '÷';
- var UnicodeTrie$1 = createTrieFromBase64$1(base64$1);
- var ALPHABETICS = [AL, HL];
- var HARD_LINE_BREAKS = [BK, CR$1, LF$1, NL];
- var SPACE$1 = [SP, ZW];
- var PREFIX_POSTFIX = [PR, PO];
- var LINE_BREAKS = HARD_LINE_BREAKS.concat(SPACE$1);
- var KOREAN_SYLLABLE_BLOCK = [JL, JV, JT, H2, H3];
- var HYPHEN = [HY, BA];
- var codePointsToCharacterClasses = function (codePoints, lineBreak) {
- if (lineBreak === void 0) { lineBreak = 'strict'; }
- var types = [];
- var indices = [];
- var categories = [];
- codePoints.forEach(function (codePoint, index) {
- var classType = UnicodeTrie$1.get(codePoint);
- if (classType > LETTER_NUMBER_MODIFIER) {
- categories.push(true);
- classType -= LETTER_NUMBER_MODIFIER;
- }
- else {
- categories.push(false);
- }
- if (['normal', 'auto', 'loose'].indexOf(lineBreak) !== -1) {
- // U+2010, – U+2013, 〜 U+301C, ゠ U+30A0
- if ([0x2010, 0x2013, 0x301c, 0x30a0].indexOf(codePoint) !== -1) {
- indices.push(index);
- return types.push(CB);
- }
- }
- if (classType === CM || classType === ZWJ$1) {
- // LB10 Treat any remaining combining mark or ZWJ as AL.
- if (index === 0) {
- indices.push(index);
- return types.push(AL);
- }
- // LB9 Do not break a combining character sequence; treat it as if it has the line breaking class of
- // the base character in all of the following rules. Treat ZWJ as if it were CM.
- var prev = types[index - 1];
- if (LINE_BREAKS.indexOf(prev) === -1) {
- indices.push(indices[index - 1]);
- return types.push(prev);
- }
- indices.push(index);
- return types.push(AL);
- }
- indices.push(index);
- if (classType === CJ) {
- return types.push(lineBreak === 'strict' ? NS : ID);
- }
- if (classType === SA) {
- return types.push(AL);
- }
- if (classType === AI) {
- return types.push(AL);
- }
- // For supplementary characters, a useful default is to treat characters in the range 10000..1FFFD as AL
- // and characters in the ranges 20000..2FFFD and 30000..3FFFD as ID, until the implementation can be revised
- // to take into account the actual line breaking properties for these characters.
- if (classType === XX) {
- if ((codePoint >= 0x20000 && codePoint <= 0x2fffd) || (codePoint >= 0x30000 && codePoint <= 0x3fffd)) {
- return types.push(ID);
- }
- else {
- return types.push(AL);
- }
- }
- types.push(classType);
- });
- return [indices, types, categories];
- };
- var isAdjacentWithSpaceIgnored = function (a, b, currentIndex, classTypes) {
- var current = classTypes[currentIndex];
- if (Array.isArray(a) ? a.indexOf(current) !== -1 : a === current) {
- var i = currentIndex;
- while (i <= classTypes.length) {
- i++;
- var next = classTypes[i];
- if (next === b) {
- return true;
- }
- if (next !== SP) {
- break;
- }
- }
- }
- if (current === SP) {
- var i = currentIndex;
- while (i > 0) {
- i--;
- var prev = classTypes[i];
- if (Array.isArray(a) ? a.indexOf(prev) !== -1 : a === prev) {
- var n = currentIndex;
- while (n <= classTypes.length) {
- n++;
- var next = classTypes[n];
- if (next === b) {
- return true;
- }
- if (next !== SP) {
- break;
- }
- }
- }
- if (prev !== SP) {
- break;
- }
- }
- }
- return false;
- };
- var previousNonSpaceClassType = function (currentIndex, classTypes) {
- var i = currentIndex;
- while (i >= 0) {
- var type = classTypes[i];
- if (type === SP) {
- i--;
- }
- else {
- return type;
- }
- }
- return 0;
- };
- var _lineBreakAtIndex = function (codePoints, classTypes, indicies, index, forbiddenBreaks) {
- if (indicies[index] === 0) {
- return BREAK_NOT_ALLOWED$1;
- }
- var currentIndex = index - 1;
- if (Array.isArray(forbiddenBreaks) && forbiddenBreaks[currentIndex] === true) {
- return BREAK_NOT_ALLOWED$1;
- }
- var beforeIndex = currentIndex - 1;
- var afterIndex = currentIndex + 1;
- var current = classTypes[currentIndex];
- // LB4 Always break after hard line breaks.
- // LB5 Treat CR followed by LF, as well as CR, LF, and NL as hard line breaks.
- var before = beforeIndex >= 0 ? classTypes[beforeIndex] : 0;
- var next = classTypes[afterIndex];
- if (current === CR$1 && next === LF$1) {
- return BREAK_NOT_ALLOWED$1;
- }
- if (HARD_LINE_BREAKS.indexOf(current) !== -1) {
- return BREAK_MANDATORY;
- }
- // LB6 Do not break before hard line breaks.
- if (HARD_LINE_BREAKS.indexOf(next) !== -1) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB7 Do not break before spaces or zero width space.
- if (SPACE$1.indexOf(next) !== -1) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB8 Break before any character following a zero-width space, even if one or more spaces intervene.
- if (previousNonSpaceClassType(currentIndex, classTypes) === ZW) {
- return BREAK_ALLOWED$1;
- }
- // LB8a Do not break after a zero width joiner.
- if (UnicodeTrie$1.get(codePoints[currentIndex]) === ZWJ$1) {
- return BREAK_NOT_ALLOWED$1;
- }
- // zwj emojis
- if ((current === EB || current === EM) && UnicodeTrie$1.get(codePoints[afterIndex]) === ZWJ$1) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB11 Do not break before or after Word joiner and related characters.
- if (current === WJ || next === WJ) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB12 Do not break after NBSP and related characters.
- if (current === GL) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB12a Do not break before NBSP and related characters, except after spaces and hyphens.
- if ([SP, BA, HY].indexOf(current) === -1 && next === GL) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB13 Do not break before ‘]’ or ‘!’ or ‘;’ or ‘/’, even after spaces.
- if ([CL, CP, EX, IS, SY].indexOf(next) !== -1) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB14 Do not break after ‘[’, even after spaces.
- if (previousNonSpaceClassType(currentIndex, classTypes) === OP) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB15 Do not break within ‘”[’, even with intervening spaces.
- if (isAdjacentWithSpaceIgnored(QU, OP, currentIndex, classTypes)) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB16 Do not break between closing punctuation and a nonstarter (lb=NS), even with intervening spaces.
- if (isAdjacentWithSpaceIgnored([CL, CP], NS, currentIndex, classTypes)) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB17 Do not break within ‘——’, even with intervening spaces.
- if (isAdjacentWithSpaceIgnored(B2, B2, currentIndex, classTypes)) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB18 Break after spaces.
- if (current === SP) {
- return BREAK_ALLOWED$1;
- }
- // LB19 Do not break before or after quotation marks, such as ‘ ” ’.
- if (current === QU || next === QU) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB20 Break before and after unresolved CB.
- if (next === CB || current === CB) {
- return BREAK_ALLOWED$1;
- }
- // LB21 Do not break before hyphen-minus, other hyphens, fixed-width spaces, small kana, and other non-starters, or after acute accents.
- if ([BA, HY, NS].indexOf(next) !== -1 || current === BB) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB21a Don't break after Hebrew + Hyphen.
- if (before === HL && HYPHEN.indexOf(current) !== -1) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB21b Don’t break between Solidus and Hebrew letters.
- if (current === SY && next === HL) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB22 Do not break before ellipsis.
- if (next === IN) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB23 Do not break between digits and letters.
- if ((ALPHABETICS.indexOf(next) !== -1 && current === NU) || (ALPHABETICS.indexOf(current) !== -1 && next === NU)) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB23a Do not break between numeric prefixes and ideographs, or between ideographs and numeric postfixes.
- if ((current === PR && [ID, EB, EM].indexOf(next) !== -1) ||
- ([ID, EB, EM].indexOf(current) !== -1 && next === PO)) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB24 Do not break between numeric prefix/postfix and letters, or between letters and prefix/postfix.
- if ((ALPHABETICS.indexOf(current) !== -1 && PREFIX_POSTFIX.indexOf(next) !== -1) ||
- (PREFIX_POSTFIX.indexOf(current) !== -1 && ALPHABETICS.indexOf(next) !== -1)) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB25 Do not break between the following pairs of classes relevant to numbers:
- if (
- // (PR | PO) × ( OP | HY )? NU
- ([PR, PO].indexOf(current) !== -1 &&
- (next === NU || ([OP, HY].indexOf(next) !== -1 && classTypes[afterIndex + 1] === NU))) ||
- // ( OP | HY ) × NU
- ([OP, HY].indexOf(current) !== -1 && next === NU) ||
- // NU × (NU | SY | IS)
- (current === NU && [NU, SY, IS].indexOf(next) !== -1)) {
- return BREAK_NOT_ALLOWED$1;
- }
- // NU (NU | SY | IS)* × (NU | SY | IS | CL | CP)
- if ([NU, SY, IS, CL, CP].indexOf(next) !== -1) {
- var prevIndex = currentIndex;
- while (prevIndex >= 0) {
- var type = classTypes[prevIndex];
- if (type === NU) {
- return BREAK_NOT_ALLOWED$1;
- }
- else if ([SY, IS].indexOf(type) !== -1) {
- prevIndex--;
- }
- else {
- break;
- }
- }
- }
- // NU (NU | SY | IS)* (CL | CP)? × (PO | PR))
- if ([PR, PO].indexOf(next) !== -1) {
- var prevIndex = [CL, CP].indexOf(current) !== -1 ? beforeIndex : currentIndex;
- while (prevIndex >= 0) {
- var type = classTypes[prevIndex];
- if (type === NU) {
- return BREAK_NOT_ALLOWED$1;
- }
- else if ([SY, IS].indexOf(type) !== -1) {
- prevIndex--;
- }
- else {
- break;
- }
- }
- }
- // LB26 Do not break a Korean syllable.
- if ((JL === current && [JL, JV, H2, H3].indexOf(next) !== -1) ||
- ([JV, H2].indexOf(current) !== -1 && [JV, JT].indexOf(next) !== -1) ||
- ([JT, H3].indexOf(current) !== -1 && next === JT)) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB27 Treat a Korean Syllable Block the same as ID.
- if ((KOREAN_SYLLABLE_BLOCK.indexOf(current) !== -1 && [IN, PO].indexOf(next) !== -1) ||
- (KOREAN_SYLLABLE_BLOCK.indexOf(next) !== -1 && current === PR)) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB28 Do not break between alphabetics (“at”).
- if (ALPHABETICS.indexOf(current) !== -1 && ALPHABETICS.indexOf(next) !== -1) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB29 Do not break between numeric punctuation and alphabetics (“e.g.”).
- if (current === IS && ALPHABETICS.indexOf(next) !== -1) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB30 Do not break between letters, numbers, or ordinary symbols and opening or closing parentheses.
- if ((ALPHABETICS.concat(NU).indexOf(current) !== -1 &&
- next === OP &&
- ea_OP.indexOf(codePoints[afterIndex]) === -1) ||
- (ALPHABETICS.concat(NU).indexOf(next) !== -1 && current === CP)) {
- return BREAK_NOT_ALLOWED$1;
- }
- // LB30a Break between two regional indicator symbols if and only if there are an even number of regional
- // indicators preceding the position of the break.
- if (current === RI$1 && next === RI$1) {
- var i = indicies[currentIndex];
- var count = 1;
- while (i > 0) {
- i--;
- if (classTypes[i] === RI$1) {
- count++;
- }
- else {
- break;
- }
- }
- if (count % 2 !== 0) {
- return BREAK_NOT_ALLOWED$1;
- }
- }
- // LB30b Do not break between an emoji base and an emoji modifier.
- if (current === EB && next === EM) {
- return BREAK_NOT_ALLOWED$1;
- }
- return BREAK_ALLOWED$1;
- };
- var cssFormattedClasses = function (codePoints, options) {
- if (!options) {
- options = { lineBreak: 'normal', wordBreak: 'normal' };
- }
- var _a = codePointsToCharacterClasses(codePoints, options.lineBreak), indicies = _a[0], classTypes = _a[1], isLetterNumber = _a[2];
- if (options.wordBreak === 'break-all' || options.wordBreak === 'break-word') {
- classTypes = classTypes.map(function (type) { return ([NU, AL, SA].indexOf(type) !== -1 ? ID : type); });
- }
- var forbiddenBreakpoints = options.wordBreak === 'keep-all'
- ? isLetterNumber.map(function (letterNumber, i) {
- return letterNumber && codePoints[i] >= 0x4e00 && codePoints[i] <= 0x9fff;
- })
- : undefined;
- return [indicies, classTypes, forbiddenBreakpoints];
- };
- var Break = /** @class */ (function () {
- function Break(codePoints, lineBreak, start, end) {
- this.codePoints = codePoints;
- this.required = lineBreak === BREAK_MANDATORY;
- this.start = start;
- this.end = end;
- }
- Break.prototype.slice = function () {
- return fromCodePoint$1.apply(void 0, this.codePoints.slice(this.start, this.end));
- };
- return Break;
- }());
- var LineBreaker = function (str, options) {
- var codePoints = toCodePoints$1(str);
- var _a = cssFormattedClasses(codePoints, options), indicies = _a[0], classTypes = _a[1], forbiddenBreakpoints = _a[2];
- var length = codePoints.length;
- var lastEnd = 0;
- var nextIndex = 0;
- return {
- next: function () {
- if (nextIndex >= length) {
- return { done: true, value: null };
- }
- var lineBreak = BREAK_NOT_ALLOWED$1;
- while (nextIndex < length &&
- (lineBreak = _lineBreakAtIndex(codePoints, classTypes, indicies, ++nextIndex, forbiddenBreakpoints)) ===
- BREAK_NOT_ALLOWED$1) { }
- if (lineBreak !== BREAK_NOT_ALLOWED$1 || nextIndex === length) {
- var value = new Break(codePoints, lineBreak, lastEnd, nextIndex);
- lastEnd = nextIndex;
- return { value: value, done: false };
- }
- return { done: true, value: null };
- },
- };
- };
-
- // https://www.w3.org/TR/css-syntax-3
- var FLAG_UNRESTRICTED = 1 << 0;
- var FLAG_ID = 1 << 1;
- var FLAG_INTEGER = 1 << 2;
- var FLAG_NUMBER = 1 << 3;
- var LINE_FEED = 0x000a;
- var SOLIDUS = 0x002f;
- var REVERSE_SOLIDUS = 0x005c;
- var CHARACTER_TABULATION = 0x0009;
- var SPACE = 0x0020;
- var QUOTATION_MARK = 0x0022;
- var EQUALS_SIGN = 0x003d;
- var NUMBER_SIGN = 0x0023;
- var DOLLAR_SIGN = 0x0024;
- var PERCENTAGE_SIGN = 0x0025;
- var APOSTROPHE = 0x0027;
- var LEFT_PARENTHESIS = 0x0028;
- var RIGHT_PARENTHESIS = 0x0029;
- var LOW_LINE = 0x005f;
- var HYPHEN_MINUS = 0x002d;
- var EXCLAMATION_MARK = 0x0021;
- var LESS_THAN_SIGN = 0x003c;
- var GREATER_THAN_SIGN = 0x003e;
- var COMMERCIAL_AT = 0x0040;
- var LEFT_SQUARE_BRACKET = 0x005b;
- var RIGHT_SQUARE_BRACKET = 0x005d;
- var CIRCUMFLEX_ACCENT = 0x003d;
- var LEFT_CURLY_BRACKET = 0x007b;
- var QUESTION_MARK = 0x003f;
- var RIGHT_CURLY_BRACKET = 0x007d;
- var VERTICAL_LINE = 0x007c;
- var TILDE = 0x007e;
- var CONTROL = 0x0080;
- var REPLACEMENT_CHARACTER = 0xfffd;
- var ASTERISK = 0x002a;
- var PLUS_SIGN = 0x002b;
- var COMMA = 0x002c;
- var COLON = 0x003a;
- var SEMICOLON = 0x003b;
- var FULL_STOP = 0x002e;
- var NULL = 0x0000;
- var BACKSPACE = 0x0008;
- var LINE_TABULATION = 0x000b;
- var SHIFT_OUT = 0x000e;
- var INFORMATION_SEPARATOR_ONE = 0x001f;
- var DELETE = 0x007f;
- var EOF = -1;
- var ZERO = 0x0030;
- var a = 0x0061;
- var e = 0x0065;
- var f = 0x0066;
- var u = 0x0075;
- var z = 0x007a;
- var A = 0x0041;
- var E = 0x0045;
- var F = 0x0046;
- var U = 0x0055;
- var Z = 0x005a;
- var isDigit = function (codePoint) { return codePoint >= ZERO && codePoint <= 0x0039; };
- var isSurrogateCodePoint = function (codePoint) { return codePoint >= 0xd800 && codePoint <= 0xdfff; };
- var isHex = function (codePoint) {
- return isDigit(codePoint) || (codePoint >= A && codePoint <= F) || (codePoint >= a && codePoint <= f);
- };
- var isLowerCaseLetter = function (codePoint) { return codePoint >= a && codePoint <= z; };
- var isUpperCaseLetter = function (codePoint) { return codePoint >= A && codePoint <= Z; };
- var isLetter = function (codePoint) { return isLowerCaseLetter(codePoint) || isUpperCaseLetter(codePoint); };
- var isNonASCIICodePoint = function (codePoint) { return codePoint >= CONTROL; };
- var isWhiteSpace = function (codePoint) {
- return codePoint === LINE_FEED || codePoint === CHARACTER_TABULATION || codePoint === SPACE;
- };
- var isNameStartCodePoint = function (codePoint) {
- return isLetter(codePoint) || isNonASCIICodePoint(codePoint) || codePoint === LOW_LINE;
- };
- var isNameCodePoint = function (codePoint) {
- return isNameStartCodePoint(codePoint) || isDigit(codePoint) || codePoint === HYPHEN_MINUS;
- };
- var isNonPrintableCodePoint = function (codePoint) {
- return ((codePoint >= NULL && codePoint <= BACKSPACE) ||
- codePoint === LINE_TABULATION ||
- (codePoint >= SHIFT_OUT && codePoint <= INFORMATION_SEPARATOR_ONE) ||
- codePoint === DELETE);
- };
- var isValidEscape = function (c1, c2) {
- if (c1 !== REVERSE_SOLIDUS) {
- return false;
- }
- return c2 !== LINE_FEED;
- };
- var isIdentifierStart = function (c1, c2, c3) {
- if (c1 === HYPHEN_MINUS) {
- return isNameStartCodePoint(c2) || isValidEscape(c2, c3);
- }
- else if (isNameStartCodePoint(c1)) {
- return true;
- }
- else if (c1 === REVERSE_SOLIDUS && isValidEscape(c1, c2)) {
- return true;
- }
- return false;
- };
- var isNumberStart = function (c1, c2, c3) {
- if (c1 === PLUS_SIGN || c1 === HYPHEN_MINUS) {
- if (isDigit(c2)) {
- return true;
- }
- return c2 === FULL_STOP && isDigit(c3);
- }
- if (c1 === FULL_STOP) {
- return isDigit(c2);
- }
- return isDigit(c1);
- };
- var stringToNumber = function (codePoints) {
- var c = 0;
- var sign = 1;
- if (codePoints[c] === PLUS_SIGN || codePoints[c] === HYPHEN_MINUS) {
- if (codePoints[c] === HYPHEN_MINUS) {
- sign = -1;
- }
- c++;
- }
- var integers = [];
- while (isDigit(codePoints[c])) {
- integers.push(codePoints[c++]);
- }
- var int = integers.length ? parseInt(fromCodePoint$1.apply(void 0, integers), 10) : 0;
- if (codePoints[c] === FULL_STOP) {
- c++;
- }
- var fraction = [];
- while (isDigit(codePoints[c])) {
- fraction.push(codePoints[c++]);
- }
- var fracd = fraction.length;
- var frac = fracd ? parseInt(fromCodePoint$1.apply(void 0, fraction), 10) : 0;
- if (codePoints[c] === E || codePoints[c] === e) {
- c++;
- }
- var expsign = 1;
- if (codePoints[c] === PLUS_SIGN || codePoints[c] === HYPHEN_MINUS) {
- if (codePoints[c] === HYPHEN_MINUS) {
- expsign = -1;
- }
- c++;
- }
- var exponent = [];
- while (isDigit(codePoints[c])) {
- exponent.push(codePoints[c++]);
- }
- var exp = exponent.length ? parseInt(fromCodePoint$1.apply(void 0, exponent), 10) : 0;
- return sign * (int + frac * Math.pow(10, -fracd)) * Math.pow(10, expsign * exp);
- };
- var LEFT_PARENTHESIS_TOKEN = {
- type: 2 /* LEFT_PARENTHESIS_TOKEN */
- };
- var RIGHT_PARENTHESIS_TOKEN = {
- type: 3 /* RIGHT_PARENTHESIS_TOKEN */
- };
- var COMMA_TOKEN = { type: 4 /* COMMA_TOKEN */ };
- var SUFFIX_MATCH_TOKEN = { type: 13 /* SUFFIX_MATCH_TOKEN */ };
- var PREFIX_MATCH_TOKEN = { type: 8 /* PREFIX_MATCH_TOKEN */ };
- var COLUMN_TOKEN = { type: 21 /* COLUMN_TOKEN */ };
- var DASH_MATCH_TOKEN = { type: 9 /* DASH_MATCH_TOKEN */ };
- var INCLUDE_MATCH_TOKEN = { type: 10 /* INCLUDE_MATCH_TOKEN */ };
- var LEFT_CURLY_BRACKET_TOKEN = {
- type: 11 /* LEFT_CURLY_BRACKET_TOKEN */
- };
- var RIGHT_CURLY_BRACKET_TOKEN = {
- type: 12 /* RIGHT_CURLY_BRACKET_TOKEN */
- };
- var SUBSTRING_MATCH_TOKEN = { type: 14 /* SUBSTRING_MATCH_TOKEN */ };
- var BAD_URL_TOKEN = { type: 23 /* BAD_URL_TOKEN */ };
- var BAD_STRING_TOKEN = { type: 1 /* BAD_STRING_TOKEN */ };
- var CDO_TOKEN = { type: 25 /* CDO_TOKEN */ };
- var CDC_TOKEN = { type: 24 /* CDC_TOKEN */ };
- var COLON_TOKEN = { type: 26 /* COLON_TOKEN */ };
- var SEMICOLON_TOKEN = { type: 27 /* SEMICOLON_TOKEN */ };
- var LEFT_SQUARE_BRACKET_TOKEN = {
- type: 28 /* LEFT_SQUARE_BRACKET_TOKEN */
- };
- var RIGHT_SQUARE_BRACKET_TOKEN = {
- type: 29 /* RIGHT_SQUARE_BRACKET_TOKEN */
- };
- var WHITESPACE_TOKEN = { type: 31 /* WHITESPACE_TOKEN */ };
- var EOF_TOKEN = { type: 32 /* EOF_TOKEN */ };
- var Tokenizer = /** @class */ (function () {
- function Tokenizer() {
- this._value = [];
- }
- Tokenizer.prototype.write = function (chunk) {
- this._value = this._value.concat(toCodePoints$1(chunk));
- };
- Tokenizer.prototype.read = function () {
- var tokens = [];
- var token = this.consumeToken();
- while (token !== EOF_TOKEN) {
- tokens.push(token);
- token = this.consumeToken();
- }
- return tokens;
- };
- Tokenizer.prototype.consumeToken = function () {
- var codePoint = this.consumeCodePoint();
- switch (codePoint) {
- case QUOTATION_MARK:
- return this.consumeStringToken(QUOTATION_MARK);
- case NUMBER_SIGN:
- var c1 = this.peekCodePoint(0);
- var c2 = this.peekCodePoint(1);
- var c3 = this.peekCodePoint(2);
- if (isNameCodePoint(c1) || isValidEscape(c2, c3)) {
- var flags = isIdentifierStart(c1, c2, c3) ? FLAG_ID : FLAG_UNRESTRICTED;
- var value = this.consumeName();
- return { type: 5 /* HASH_TOKEN */, value: value, flags: flags };
- }
- break;
- case DOLLAR_SIGN:
- if (this.peekCodePoint(0) === EQUALS_SIGN) {
- this.consumeCodePoint();
- return SUFFIX_MATCH_TOKEN;
- }
- break;
- case APOSTROPHE:
- return this.consumeStringToken(APOSTROPHE);
- case LEFT_PARENTHESIS:
- return LEFT_PARENTHESIS_TOKEN;
- case RIGHT_PARENTHESIS:
- return RIGHT_PARENTHESIS_TOKEN;
- case ASTERISK:
- if (this.peekCodePoint(0) === EQUALS_SIGN) {
- this.consumeCodePoint();
- return SUBSTRING_MATCH_TOKEN;
- }
- break;
- case PLUS_SIGN:
- if (isNumberStart(codePoint, this.peekCodePoint(0), this.peekCodePoint(1))) {
- this.reconsumeCodePoint(codePoint);
- return this.consumeNumericToken();
- }
- break;
- case COMMA:
- return COMMA_TOKEN;
- case HYPHEN_MINUS:
- var e1 = codePoint;
- var e2 = this.peekCodePoint(0);
- var e3 = this.peekCodePoint(1);
- if (isNumberStart(e1, e2, e3)) {
- this.reconsumeCodePoint(codePoint);
- return this.consumeNumericToken();
- }
- if (isIdentifierStart(e1, e2, e3)) {
- this.reconsumeCodePoint(codePoint);
- return this.consumeIdentLikeToken();
- }
- if (e2 === HYPHEN_MINUS && e3 === GREATER_THAN_SIGN) {
- this.consumeCodePoint();
- this.consumeCodePoint();
- return CDC_TOKEN;
- }
- break;
- case FULL_STOP:
- if (isNumberStart(codePoint, this.peekCodePoint(0), this.peekCodePoint(1))) {
- this.reconsumeCodePoint(codePoint);
- return this.consumeNumericToken();
- }
- break;
- case SOLIDUS:
- if (this.peekCodePoint(0) === ASTERISK) {
- this.consumeCodePoint();
- while (true) {
- var c = this.consumeCodePoint();
- if (c === ASTERISK) {
- c = this.consumeCodePoint();
- if (c === SOLIDUS) {
- return this.consumeToken();
- }
- }
- if (c === EOF) {
- return this.consumeToken();
- }
- }
- }
- break;
- case COLON:
- return COLON_TOKEN;
- case SEMICOLON:
- return SEMICOLON_TOKEN;
- case LESS_THAN_SIGN:
- if (this.peekCodePoint(0) === EXCLAMATION_MARK &&
- this.peekCodePoint(1) === HYPHEN_MINUS &&
- this.peekCodePoint(2) === HYPHEN_MINUS) {
- this.consumeCodePoint();
- this.consumeCodePoint();
- return CDO_TOKEN;
- }
- break;
- case COMMERCIAL_AT:
- var a1 = this.peekCodePoint(0);
- var a2 = this.peekCodePoint(1);
- var a3 = this.peekCodePoint(2);
- if (isIdentifierStart(a1, a2, a3)) {
- var value = this.consumeName();
- return { type: 7 /* AT_KEYWORD_TOKEN */, value: value };
- }
- break;
- case LEFT_SQUARE_BRACKET:
- return LEFT_SQUARE_BRACKET_TOKEN;
- case REVERSE_SOLIDUS:
- if (isValidEscape(codePoint, this.peekCodePoint(0))) {
- this.reconsumeCodePoint(codePoint);
- return this.consumeIdentLikeToken();
- }
- break;
- case RIGHT_SQUARE_BRACKET:
- return RIGHT_SQUARE_BRACKET_TOKEN;
- case CIRCUMFLEX_ACCENT:
- if (this.peekCodePoint(0) === EQUALS_SIGN) {
- this.consumeCodePoint();
- return PREFIX_MATCH_TOKEN;
- }
- break;
- case LEFT_CURLY_BRACKET:
- return LEFT_CURLY_BRACKET_TOKEN;
- case RIGHT_CURLY_BRACKET:
- return RIGHT_CURLY_BRACKET_TOKEN;
- case u:
- case U:
- var u1 = this.peekCodePoint(0);
- var u2 = this.peekCodePoint(1);
- if (u1 === PLUS_SIGN && (isHex(u2) || u2 === QUESTION_MARK)) {
- this.consumeCodePoint();
- this.consumeUnicodeRangeToken();
- }
- this.reconsumeCodePoint(codePoint);
- return this.consumeIdentLikeToken();
- case VERTICAL_LINE:
- if (this.peekCodePoint(0) === EQUALS_SIGN) {
- this.consumeCodePoint();
- return DASH_MATCH_TOKEN;
- }
- if (this.peekCodePoint(0) === VERTICAL_LINE) {
- this.consumeCodePoint();
- return COLUMN_TOKEN;
- }
- break;
- case TILDE:
- if (this.peekCodePoint(0) === EQUALS_SIGN) {
- this.consumeCodePoint();
- return INCLUDE_MATCH_TOKEN;
- }
- break;
- case EOF:
- return EOF_TOKEN;
- }
- if (isWhiteSpace(codePoint)) {
- this.consumeWhiteSpace();
- return WHITESPACE_TOKEN;
- }
- if (isDigit(codePoint)) {
- this.reconsumeCodePoint(codePoint);
- return this.consumeNumericToken();
- }
- if (isNameStartCodePoint(codePoint)) {
- this.reconsumeCodePoint(codePoint);
- return this.consumeIdentLikeToken();
- }
- return { type: 6 /* DELIM_TOKEN */, value: fromCodePoint$1(codePoint) };
- };
- Tokenizer.prototype.consumeCodePoint = function () {
- var value = this._value.shift();
- return typeof value === 'undefined' ? -1 : value;
- };
- Tokenizer.prototype.reconsumeCodePoint = function (codePoint) {
- this._value.unshift(codePoint);
- };
- Tokenizer.prototype.peekCodePoint = function (delta) {
- if (delta >= this._value.length) {
- return -1;
- }
- return this._value[delta];
- };
- Tokenizer.prototype.consumeUnicodeRangeToken = function () {
- var digits = [];
- var codePoint = this.consumeCodePoint();
- while (isHex(codePoint) && digits.length < 6) {
- digits.push(codePoint);
- codePoint = this.consumeCodePoint();
- }
- var questionMarks = false;
- while (codePoint === QUESTION_MARK && digits.length < 6) {
- digits.push(codePoint);
- codePoint = this.consumeCodePoint();
- questionMarks = true;
- }
- if (questionMarks) {
- var start_1 = parseInt(fromCodePoint$1.apply(void 0, digits.map(function (digit) { return (digit === QUESTION_MARK ? ZERO : digit); })), 16);
- var end = parseInt(fromCodePoint$1.apply(void 0, digits.map(function (digit) { return (digit === QUESTION_MARK ? F : digit); })), 16);
- return { type: 30 /* UNICODE_RANGE_TOKEN */, start: start_1, end: end };
- }
- var start = parseInt(fromCodePoint$1.apply(void 0, digits), 16);
- if (this.peekCodePoint(0) === HYPHEN_MINUS && isHex(this.peekCodePoint(1))) {
- this.consumeCodePoint();
- codePoint = this.consumeCodePoint();
- var endDigits = [];
- while (isHex(codePoint) && endDigits.length < 6) {
- endDigits.push(codePoint);
- codePoint = this.consumeCodePoint();
- }
- var end = parseInt(fromCodePoint$1.apply(void 0, endDigits), 16);
- return { type: 30 /* UNICODE_RANGE_TOKEN */, start: start, end: end };
- }
- else {
- return { type: 30 /* UNICODE_RANGE_TOKEN */, start: start, end: start };
- }
- };
- Tokenizer.prototype.consumeIdentLikeToken = function () {
- var value = this.consumeName();
- if (value.toLowerCase() === 'url' && this.peekCodePoint(0) === LEFT_PARENTHESIS) {
- this.consumeCodePoint();
- return this.consumeUrlToken();
- }
- else if (this.peekCodePoint(0) === LEFT_PARENTHESIS) {
- this.consumeCodePoint();
- return { type: 19 /* FUNCTION_TOKEN */, value: value };
- }
- return { type: 20 /* IDENT_TOKEN */, value: value };
- };
- Tokenizer.prototype.consumeUrlToken = function () {
- var value = [];
- this.consumeWhiteSpace();
- if (this.peekCodePoint(0) === EOF) {
- return { type: 22 /* URL_TOKEN */, value: '' };
- }
- var next = this.peekCodePoint(0);
- if (next === APOSTROPHE || next === QUOTATION_MARK) {
- var stringToken = this.consumeStringToken(this.consumeCodePoint());
- if (stringToken.type === 0 /* STRING_TOKEN */) {
- this.consumeWhiteSpace();
- if (this.peekCodePoint(0) === EOF || this.peekCodePoint(0) === RIGHT_PARENTHESIS) {
- this.consumeCodePoint();
- return { type: 22 /* URL_TOKEN */, value: stringToken.value };
- }
- }
- this.consumeBadUrlRemnants();
- return BAD_URL_TOKEN;
- }
- while (true) {
- var codePoint = this.consumeCodePoint();
- if (codePoint === EOF || codePoint === RIGHT_PARENTHESIS) {
- return { type: 22 /* URL_TOKEN */, value: fromCodePoint$1.apply(void 0, value) };
- }
- else if (isWhiteSpace(codePoint)) {
- this.consumeWhiteSpace();
- if (this.peekCodePoint(0) === EOF || this.peekCodePoint(0) === RIGHT_PARENTHESIS) {
- this.consumeCodePoint();
- return { type: 22 /* URL_TOKEN */, value: fromCodePoint$1.apply(void 0, value) };
- }
- this.consumeBadUrlRemnants();
- return BAD_URL_TOKEN;
- }
- else if (codePoint === QUOTATION_MARK ||
- codePoint === APOSTROPHE ||
- codePoint === LEFT_PARENTHESIS ||
- isNonPrintableCodePoint(codePoint)) {
- this.consumeBadUrlRemnants();
- return BAD_URL_TOKEN;
- }
- else if (codePoint === REVERSE_SOLIDUS) {
- if (isValidEscape(codePoint, this.peekCodePoint(0))) {
- value.push(this.consumeEscapedCodePoint());
- }
- else {
- this.consumeBadUrlRemnants();
- return BAD_URL_TOKEN;
- }
- }
- else {
- value.push(codePoint);
- }
- }
- };
- Tokenizer.prototype.consumeWhiteSpace = function () {
- while (isWhiteSpace(this.peekCodePoint(0))) {
- this.consumeCodePoint();
- }
- };
- Tokenizer.prototype.consumeBadUrlRemnants = function () {
- while (true) {
- var codePoint = this.consumeCodePoint();
- if (codePoint === RIGHT_PARENTHESIS || codePoint === EOF) {
- return;
- }
- if (isValidEscape(codePoint, this.peekCodePoint(0))) {
- this.consumeEscapedCodePoint();
- }
- }
- };
- Tokenizer.prototype.consumeStringSlice = function (count) {
- var SLICE_STACK_SIZE = 50000;
- var value = '';
- while (count > 0) {
- var amount = Math.min(SLICE_STACK_SIZE, count);
- value += fromCodePoint$1.apply(void 0, this._value.splice(0, amount));
- count -= amount;
- }
- this._value.shift();
- return value;
- };
- Tokenizer.prototype.consumeStringToken = function (endingCodePoint) {
- var value = '';
- var i = 0;
- do {
- var codePoint = this._value[i];
- if (codePoint === EOF || codePoint === undefined || codePoint === endingCodePoint) {
- value += this.consumeStringSlice(i);
- return { type: 0 /* STRING_TOKEN */, value: value };
- }
- if (codePoint === LINE_FEED) {
- this._value.splice(0, i);
- return BAD_STRING_TOKEN;
- }
- if (codePoint === REVERSE_SOLIDUS) {
- var next = this._value[i + 1];
- if (next !== EOF && next !== undefined) {
- if (next === LINE_FEED) {
- value += this.consumeStringSlice(i);
- i = -1;
- this._value.shift();
- }
- else if (isValidEscape(codePoint, next)) {
- value += this.consumeStringSlice(i);
- value += fromCodePoint$1(this.consumeEscapedCodePoint());
- i = -1;
- }
- }
- }
- i++;
- } while (true);
- };
- Tokenizer.prototype.consumeNumber = function () {
- var repr = [];
- var type = FLAG_INTEGER;
- var c1 = this.peekCodePoint(0);
- if (c1 === PLUS_SIGN || c1 === HYPHEN_MINUS) {
- repr.push(this.consumeCodePoint());
- }
- while (isDigit(this.peekCodePoint(0))) {
- repr.push(this.consumeCodePoint());
- }
- c1 = this.peekCodePoint(0);
- var c2 = this.peekCodePoint(1);
- if (c1 === FULL_STOP && isDigit(c2)) {
- repr.push(this.consumeCodePoint(), this.consumeCodePoint());
- type = FLAG_NUMBER;
- while (isDigit(this.peekCodePoint(0))) {
- repr.push(this.consumeCodePoint());
- }
- }
- c1 = this.peekCodePoint(0);
- c2 = this.peekCodePoint(1);
- var c3 = this.peekCodePoint(2);
- if ((c1 === E || c1 === e) && (((c2 === PLUS_SIGN || c2 === HYPHEN_MINUS) && isDigit(c3)) || isDigit(c2))) {
- repr.push(this.consumeCodePoint(), this.consumeCodePoint());
- type = FLAG_NUMBER;
- while (isDigit(this.peekCodePoint(0))) {
- repr.push(this.consumeCodePoint());
- }
- }
- return [stringToNumber(repr), type];
- };
- Tokenizer.prototype.consumeNumericToken = function () {
- var _a = this.consumeNumber(), number = _a[0], flags = _a[1];
- var c1 = this.peekCodePoint(0);
- var c2 = this.peekCodePoint(1);
- var c3 = this.peekCodePoint(2);
- if (isIdentifierStart(c1, c2, c3)) {
- var unit = this.consumeName();
- return { type: 15 /* DIMENSION_TOKEN */, number: number, flags: flags, unit: unit };
- }
- if (c1 === PERCENTAGE_SIGN) {
- this.consumeCodePoint();
- return { type: 16 /* PERCENTAGE_TOKEN */, number: number, flags: flags };
- }
- return { type: 17 /* NUMBER_TOKEN */, number: number, flags: flags };
- };
- Tokenizer.prototype.consumeEscapedCodePoint = function () {
- var codePoint = this.consumeCodePoint();
- if (isHex(codePoint)) {
- var hex = fromCodePoint$1(codePoint);
- while (isHex(this.peekCodePoint(0)) && hex.length < 6) {
- hex += fromCodePoint$1(this.consumeCodePoint());
- }
- if (isWhiteSpace(this.peekCodePoint(0))) {
- this.consumeCodePoint();
- }
- var hexCodePoint = parseInt(hex, 16);
- if (hexCodePoint === 0 || isSurrogateCodePoint(hexCodePoint) || hexCodePoint > 0x10ffff) {
- return REPLACEMENT_CHARACTER;
- }
- return hexCodePoint;
- }
- if (codePoint === EOF) {
- return REPLACEMENT_CHARACTER;
- }
- return codePoint;
- };
- Tokenizer.prototype.consumeName = function () {
- var result = '';
- while (true) {
- var codePoint = this.consumeCodePoint();
- if (isNameCodePoint(codePoint)) {
- result += fromCodePoint$1(codePoint);
- }
- else if (isValidEscape(codePoint, this.peekCodePoint(0))) {
- result += fromCodePoint$1(this.consumeEscapedCodePoint());
- }
- else {
- this.reconsumeCodePoint(codePoint);
- return result;
- }
- }
- };
- return Tokenizer;
- }());
-
- var Parser = /** @class */ (function () {
- function Parser(tokens) {
- this._tokens = tokens;
- }
- Parser.create = function (value) {
- var tokenizer = new Tokenizer();
- tokenizer.write(value);
- return new Parser(tokenizer.read());
- };
- Parser.parseValue = function (value) {
- return Parser.create(value).parseComponentValue();
- };
- Parser.parseValues = function (value) {
- return Parser.create(value).parseComponentValues();
- };
- Parser.prototype.parseComponentValue = function () {
- var token = this.consumeToken();
- while (token.type === 31 /* WHITESPACE_TOKEN */) {
- token = this.consumeToken();
- }
- if (token.type === 32 /* EOF_TOKEN */) {
- throw new SyntaxError("Error parsing CSS component value, unexpected EOF");
- }
- this.reconsumeToken(token);
- var value = this.consumeComponentValue();
- do {
- token = this.consumeToken();
- } while (token.type === 31 /* WHITESPACE_TOKEN */);
- if (token.type === 32 /* EOF_TOKEN */) {
- return value;
- }
- throw new SyntaxError("Error parsing CSS component value, multiple values found when expecting only one");
- };
- Parser.prototype.parseComponentValues = function () {
- var values = [];
- while (true) {
- var value = this.consumeComponentValue();
- if (value.type === 32 /* EOF_TOKEN */) {
- return values;
- }
- values.push(value);
- values.push();
- }
- };
- Parser.prototype.consumeComponentValue = function () {
- var token = this.consumeToken();
- switch (token.type) {
- case 11 /* LEFT_CURLY_BRACKET_TOKEN */:
- case 28 /* LEFT_SQUARE_BRACKET_TOKEN */:
- case 2 /* LEFT_PARENTHESIS_TOKEN */:
- return this.consumeSimpleBlock(token.type);
- case 19 /* FUNCTION_TOKEN */:
- return this.consumeFunction(token);
- }
- return token;
- };
- Parser.prototype.consumeSimpleBlock = function (type) {
- var block = { type: type, values: [] };
- var token = this.consumeToken();
- while (true) {
- if (token.type === 32 /* EOF_TOKEN */ || isEndingTokenFor(token, type)) {
- return block;
- }
- this.reconsumeToken(token);
- block.values.push(this.consumeComponentValue());
- token = this.consumeToken();
- }
- };
- Parser.prototype.consumeFunction = function (functionToken) {
- var cssFunction = {
- name: functionToken.value,
- values: [],
- type: 18 /* FUNCTION */
- };
- while (true) {
- var token = this.consumeToken();
- if (token.type === 32 /* EOF_TOKEN */ || token.type === 3 /* RIGHT_PARENTHESIS_TOKEN */) {
- return cssFunction;
- }
- this.reconsumeToken(token);
- cssFunction.values.push(this.consumeComponentValue());
- }
- };
- Parser.prototype.consumeToken = function () {
- var token = this._tokens.shift();
- return typeof token === 'undefined' ? EOF_TOKEN : token;
- };
- Parser.prototype.reconsumeToken = function (token) {
- this._tokens.unshift(token);
- };
- return Parser;
- }());
- var isDimensionToken = function (token) { return token.type === 15 /* DIMENSION_TOKEN */; };
- var isNumberToken = function (token) { return token.type === 17 /* NUMBER_TOKEN */; };
- var isIdentToken = function (token) { return token.type === 20 /* IDENT_TOKEN */; };
- var isStringToken = function (token) { return token.type === 0 /* STRING_TOKEN */; };
- var isIdentWithValue = function (token, value) {
- return isIdentToken(token) && token.value === value;
- };
- var nonWhiteSpace = function (token) { return token.type !== 31 /* WHITESPACE_TOKEN */; };
- var nonFunctionArgSeparator = function (token) {
- return token.type !== 31 /* WHITESPACE_TOKEN */ && token.type !== 4 /* COMMA_TOKEN */;
- };
- var parseFunctionArgs = function (tokens) {
- var args = [];
- var arg = [];
- tokens.forEach(function (token) {
- if (token.type === 4 /* COMMA_TOKEN */) {
- if (arg.length === 0) {
- throw new Error("Error parsing function args, zero tokens for arg");
- }
- args.push(arg);
- arg = [];
- return;
- }
- if (token.type !== 31 /* WHITESPACE_TOKEN */) {
- arg.push(token);
- }
- });
- if (arg.length) {
- args.push(arg);
- }
- return args;
- };
- var isEndingTokenFor = function (token, type) {
- if (type === 11 /* LEFT_CURLY_BRACKET_TOKEN */ && token.type === 12 /* RIGHT_CURLY_BRACKET_TOKEN */) {
- return true;
- }
- if (type === 28 /* LEFT_SQUARE_BRACKET_TOKEN */ && token.type === 29 /* RIGHT_SQUARE_BRACKET_TOKEN */) {
- return true;
- }
- return type === 2 /* LEFT_PARENTHESIS_TOKEN */ && token.type === 3 /* RIGHT_PARENTHESIS_TOKEN */;
- };
-
- var isLength = function (token) {
- return token.type === 17 /* NUMBER_TOKEN */ || token.type === 15 /* DIMENSION_TOKEN */;
- };
-
- var isLengthPercentage = function (token) {
- return token.type === 16 /* PERCENTAGE_TOKEN */ || isLength(token);
- };
- var parseLengthPercentageTuple = function (tokens) {
- return tokens.length > 1 ? [tokens[0], tokens[1]] : [tokens[0]];
- };
- var ZERO_LENGTH = {
- type: 17 /* NUMBER_TOKEN */,
- number: 0,
- flags: FLAG_INTEGER
- };
- var FIFTY_PERCENT = {
- type: 16 /* PERCENTAGE_TOKEN */,
- number: 50,
- flags: FLAG_INTEGER
- };
- var HUNDRED_PERCENT = {
- type: 16 /* PERCENTAGE_TOKEN */,
- number: 100,
- flags: FLAG_INTEGER
- };
- var getAbsoluteValueForTuple = function (tuple, width, height) {
- var x = tuple[0], y = tuple[1];
- return [getAbsoluteValue(x, width), getAbsoluteValue(typeof y !== 'undefined' ? y : x, height)];
- };
- var getAbsoluteValue = function (token, parent) {
- if (token.type === 16 /* PERCENTAGE_TOKEN */) {
- return (token.number / 100) * parent;
- }
- if (isDimensionToken(token)) {
- switch (token.unit) {
- case 'rem':
- case 'em':
- return 16 * token.number; // TODO use correct font-size
- case 'px':
- default:
- return token.number;
- }
- }
- return token.number;
- };
-
- var DEG = 'deg';
- var GRAD = 'grad';
- var RAD = 'rad';
- var TURN = 'turn';
- var angle = {
- name: 'angle',
- parse: function (_context, value) {
- if (value.type === 15 /* DIMENSION_TOKEN */) {
- switch (value.unit) {
- case DEG:
- return (Math.PI * value.number) / 180;
- case GRAD:
- return (Math.PI / 200) * value.number;
- case RAD:
- return value.number;
- case TURN:
- return Math.PI * 2 * value.number;
- }
- }
- throw new Error("Unsupported angle type");
- }
- };
- var isAngle = function (value) {
- if (value.type === 15 /* DIMENSION_TOKEN */) {
- if (value.unit === DEG || value.unit === GRAD || value.unit === RAD || value.unit === TURN) {
- return true;
- }
- }
- return false;
- };
- var parseNamedSide = function (tokens) {
- var sideOrCorner = tokens
- .filter(isIdentToken)
- .map(function (ident) { return ident.value; })
- .join(' ');
- switch (sideOrCorner) {
- case 'to bottom right':
- case 'to right bottom':
- case 'left top':
- case 'top left':
- return [ZERO_LENGTH, ZERO_LENGTH];
- case 'to top':
- case 'bottom':
- return deg(0);
- case 'to bottom left':
- case 'to left bottom':
- case 'right top':
- case 'top right':
- return [ZERO_LENGTH, HUNDRED_PERCENT];
- case 'to right':
- case 'left':
- return deg(90);
- case 'to top left':
- case 'to left top':
- case 'right bottom':
- case 'bottom right':
- return [HUNDRED_PERCENT, HUNDRED_PERCENT];
- case 'to bottom':
- case 'top':
- return deg(180);
- case 'to top right':
- case 'to right top':
- case 'left bottom':
- case 'bottom left':
- return [HUNDRED_PERCENT, ZERO_LENGTH];
- case 'to left':
- case 'right':
- return deg(270);
- }
- return 0;
- };
- var deg = function (deg) { return (Math.PI * deg) / 180; };
-
- var color$1 = {
- name: 'color',
- parse: function (context, value) {
- if (value.type === 18 /* FUNCTION */) {
- var colorFunction = SUPPORTED_COLOR_FUNCTIONS[value.name];
- if (typeof colorFunction === 'undefined') {
- throw new Error("Attempting to parse an unsupported color function \"" + value.name + "\"");
- }
- return colorFunction(context, value.values);
- }
- if (value.type === 5 /* HASH_TOKEN */) {
- if (value.value.length === 3) {
- var r = value.value.substring(0, 1);
- var g = value.value.substring(1, 2);
- var b = value.value.substring(2, 3);
- return pack(parseInt(r + r, 16), parseInt(g + g, 16), parseInt(b + b, 16), 1);
- }
- if (value.value.length === 4) {
- var r = value.value.substring(0, 1);
- var g = value.value.substring(1, 2);
- var b = value.value.substring(2, 3);
- var a = value.value.substring(3, 4);
- return pack(parseInt(r + r, 16), parseInt(g + g, 16), parseInt(b + b, 16), parseInt(a + a, 16) / 255);
- }
- if (value.value.length === 6) {
- var r = value.value.substring(0, 2);
- var g = value.value.substring(2, 4);
- var b = value.value.substring(4, 6);
- return pack(parseInt(r, 16), parseInt(g, 16), parseInt(b, 16), 1);
- }
- if (value.value.length === 8) {
- var r = value.value.substring(0, 2);
- var g = value.value.substring(2, 4);
- var b = value.value.substring(4, 6);
- var a = value.value.substring(6, 8);
- return pack(parseInt(r, 16), parseInt(g, 16), parseInt(b, 16), parseInt(a, 16) / 255);
- }
- }
- if (value.type === 20 /* IDENT_TOKEN */) {
- var namedColor = COLORS[value.value.toUpperCase()];
- if (typeof namedColor !== 'undefined') {
- return namedColor;
- }
- }
- return COLORS.TRANSPARENT;
- }
- };
- var isTransparent = function (color) { return (0xff & color) === 0; };
- var asString = function (color) {
- var alpha = 0xff & color;
- var blue = 0xff & (color >> 8);
- var green = 0xff & (color >> 16);
- var red = 0xff & (color >> 24);
- return alpha < 255 ? "rgba(" + red + "," + green + "," + blue + "," + alpha / 255 + ")" : "rgb(" + red + "," + green + "," + blue + ")";
- };
- var pack = function (r, g, b, a) {
- return ((r << 24) | (g << 16) | (b << 8) | (Math.round(a * 255) << 0)) >>> 0;
- };
- var getTokenColorValue = function (token, i) {
- if (token.type === 17 /* NUMBER_TOKEN */) {
- return token.number;
- }
- if (token.type === 16 /* PERCENTAGE_TOKEN */) {
- var max = i === 3 ? 1 : 255;
- return i === 3 ? (token.number / 100) * max : Math.round((token.number / 100) * max);
- }
- return 0;
- };
- var rgb = function (_context, args) {
- var tokens = args.filter(nonFunctionArgSeparator);
- if (tokens.length === 3) {
- var _a = tokens.map(getTokenColorValue), r = _a[0], g = _a[1], b = _a[2];
- return pack(r, g, b, 1);
- }
- if (tokens.length === 4) {
- var _b = tokens.map(getTokenColorValue), r = _b[0], g = _b[1], b = _b[2], a = _b[3];
- return pack(r, g, b, a);
- }
- return 0;
- };
- function hue2rgb(t1, t2, hue) {
- if (hue < 0) {
- hue += 1;
- }
- if (hue >= 1) {
- hue -= 1;
- }
- if (hue < 1 / 6) {
- return (t2 - t1) * hue * 6 + t1;
- }
- else if (hue < 1 / 2) {
- return t2;
- }
- else if (hue < 2 / 3) {
- return (t2 - t1) * 6 * (2 / 3 - hue) + t1;
- }
- else {
- return t1;
- }
- }
- var hsl = function (context, args) {
- var tokens = args.filter(nonFunctionArgSeparator);
- var hue = tokens[0], saturation = tokens[1], lightness = tokens[2], alpha = tokens[3];
- var h = (hue.type === 17 /* NUMBER_TOKEN */ ? deg(hue.number) : angle.parse(context, hue)) / (Math.PI * 2);
- var s = isLengthPercentage(saturation) ? saturation.number / 100 : 0;
- var l = isLengthPercentage(lightness) ? lightness.number / 100 : 0;
- var a = typeof alpha !== 'undefined' && isLengthPercentage(alpha) ? getAbsoluteValue(alpha, 1) : 1;
- if (s === 0) {
- return pack(l * 255, l * 255, l * 255, 1);
- }
- var t2 = l <= 0.5 ? l * (s + 1) : l + s - l * s;
- var t1 = l * 2 - t2;
- var r = hue2rgb(t1, t2, h + 1 / 3);
- var g = hue2rgb(t1, t2, h);
- var b = hue2rgb(t1, t2, h - 1 / 3);
- return pack(r * 255, g * 255, b * 255, a);
- };
- var SUPPORTED_COLOR_FUNCTIONS = {
- hsl: hsl,
- hsla: hsl,
- rgb: rgb,
- rgba: rgb
- };
- var parseColor = function (context, value) {
- return color$1.parse(context, Parser.create(value).parseComponentValue());
- };
- var COLORS = {
- ALICEBLUE: 0xf0f8ffff,
- ANTIQUEWHITE: 0xfaebd7ff,
- AQUA: 0x00ffffff,
- AQUAMARINE: 0x7fffd4ff,
- AZURE: 0xf0ffffff,
- BEIGE: 0xf5f5dcff,
- BISQUE: 0xffe4c4ff,
- BLACK: 0x000000ff,
- BLANCHEDALMOND: 0xffebcdff,
- BLUE: 0x0000ffff,
- BLUEVIOLET: 0x8a2be2ff,
- BROWN: 0xa52a2aff,
- BURLYWOOD: 0xdeb887ff,
- CADETBLUE: 0x5f9ea0ff,
- CHARTREUSE: 0x7fff00ff,
- CHOCOLATE: 0xd2691eff,
- CORAL: 0xff7f50ff,
- CORNFLOWERBLUE: 0x6495edff,
- CORNSILK: 0xfff8dcff,
- CRIMSON: 0xdc143cff,
- CYAN: 0x00ffffff,
- DARKBLUE: 0x00008bff,
- DARKCYAN: 0x008b8bff,
- DARKGOLDENROD: 0xb886bbff,
- DARKGRAY: 0xa9a9a9ff,
- DARKGREEN: 0x006400ff,
- DARKGREY: 0xa9a9a9ff,
- DARKKHAKI: 0xbdb76bff,
- DARKMAGENTA: 0x8b008bff,
- DARKOLIVEGREEN: 0x556b2fff,
- DARKORANGE: 0xff8c00ff,
- DARKORCHID: 0x9932ccff,
- DARKRED: 0x8b0000ff,
- DARKSALMON: 0xe9967aff,
- DARKSEAGREEN: 0x8fbc8fff,
- DARKSLATEBLUE: 0x483d8bff,
- DARKSLATEGRAY: 0x2f4f4fff,
- DARKSLATEGREY: 0x2f4f4fff,
- DARKTURQUOISE: 0x00ced1ff,
- DARKVIOLET: 0x9400d3ff,
- DEEPPINK: 0xff1493ff,
- DEEPSKYBLUE: 0x00bfffff,
- DIMGRAY: 0x696969ff,
- DIMGREY: 0x696969ff,
- DODGERBLUE: 0x1e90ffff,
- FIREBRICK: 0xb22222ff,
- FLORALWHITE: 0xfffaf0ff,
- FORESTGREEN: 0x228b22ff,
- FUCHSIA: 0xff00ffff,
- GAINSBORO: 0xdcdcdcff,
- GHOSTWHITE: 0xf8f8ffff,
- GOLD: 0xffd700ff,
- GOLDENROD: 0xdaa520ff,
- GRAY: 0x808080ff,
- GREEN: 0x008000ff,
- GREENYELLOW: 0xadff2fff,
- GREY: 0x808080ff,
- HONEYDEW: 0xf0fff0ff,
- HOTPINK: 0xff69b4ff,
- INDIANRED: 0xcd5c5cff,
- INDIGO: 0x4b0082ff,
- IVORY: 0xfffff0ff,
- KHAKI: 0xf0e68cff,
- LAVENDER: 0xe6e6faff,
- LAVENDERBLUSH: 0xfff0f5ff,
- LAWNGREEN: 0x7cfc00ff,
- LEMONCHIFFON: 0xfffacdff,
- LIGHTBLUE: 0xadd8e6ff,
- LIGHTCORAL: 0xf08080ff,
- LIGHTCYAN: 0xe0ffffff,
- LIGHTGOLDENRODYELLOW: 0xfafad2ff,
- LIGHTGRAY: 0xd3d3d3ff,
- LIGHTGREEN: 0x90ee90ff,
- LIGHTGREY: 0xd3d3d3ff,
- LIGHTPINK: 0xffb6c1ff,
- LIGHTSALMON: 0xffa07aff,
- LIGHTSEAGREEN: 0x20b2aaff,
- LIGHTSKYBLUE: 0x87cefaff,
- LIGHTSLATEGRAY: 0x778899ff,
- LIGHTSLATEGREY: 0x778899ff,
- LIGHTSTEELBLUE: 0xb0c4deff,
- LIGHTYELLOW: 0xffffe0ff,
- LIME: 0x00ff00ff,
- LIMEGREEN: 0x32cd32ff,
- LINEN: 0xfaf0e6ff,
- MAGENTA: 0xff00ffff,
- MAROON: 0x800000ff,
- MEDIUMAQUAMARINE: 0x66cdaaff,
- MEDIUMBLUE: 0x0000cdff,
- MEDIUMORCHID: 0xba55d3ff,
- MEDIUMPURPLE: 0x9370dbff,
- MEDIUMSEAGREEN: 0x3cb371ff,
- MEDIUMSLATEBLUE: 0x7b68eeff,
- MEDIUMSPRINGGREEN: 0x00fa9aff,
- MEDIUMTURQUOISE: 0x48d1ccff,
- MEDIUMVIOLETRED: 0xc71585ff,
- MIDNIGHTBLUE: 0x191970ff,
- MINTCREAM: 0xf5fffaff,
- MISTYROSE: 0xffe4e1ff,
- MOCCASIN: 0xffe4b5ff,
- NAVAJOWHITE: 0xffdeadff,
- NAVY: 0x000080ff,
- OLDLACE: 0xfdf5e6ff,
- OLIVE: 0x808000ff,
- OLIVEDRAB: 0x6b8e23ff,
- ORANGE: 0xffa500ff,
- ORANGERED: 0xff4500ff,
- ORCHID: 0xda70d6ff,
- PALEGOLDENROD: 0xeee8aaff,
- PALEGREEN: 0x98fb98ff,
- PALETURQUOISE: 0xafeeeeff,
- PALEVIOLETRED: 0xdb7093ff,
- PAPAYAWHIP: 0xffefd5ff,
- PEACHPUFF: 0xffdab9ff,
- PERU: 0xcd853fff,
- PINK: 0xffc0cbff,
- PLUM: 0xdda0ddff,
- POWDERBLUE: 0xb0e0e6ff,
- PURPLE: 0x800080ff,
- REBECCAPURPLE: 0x663399ff,
- RED: 0xff0000ff,
- ROSYBROWN: 0xbc8f8fff,
- ROYALBLUE: 0x4169e1ff,
- SADDLEBROWN: 0x8b4513ff,
- SALMON: 0xfa8072ff,
- SANDYBROWN: 0xf4a460ff,
- SEAGREEN: 0x2e8b57ff,
- SEASHELL: 0xfff5eeff,
- SIENNA: 0xa0522dff,
- SILVER: 0xc0c0c0ff,
- SKYBLUE: 0x87ceebff,
- SLATEBLUE: 0x6a5acdff,
- SLATEGRAY: 0x708090ff,
- SLATEGREY: 0x708090ff,
- SNOW: 0xfffafaff,
- SPRINGGREEN: 0x00ff7fff,
- STEELBLUE: 0x4682b4ff,
- TAN: 0xd2b48cff,
- TEAL: 0x008080ff,
- THISTLE: 0xd8bfd8ff,
- TOMATO: 0xff6347ff,
- TRANSPARENT: 0x00000000,
- TURQUOISE: 0x40e0d0ff,
- VIOLET: 0xee82eeff,
- WHEAT: 0xf5deb3ff,
- WHITE: 0xffffffff,
- WHITESMOKE: 0xf5f5f5ff,
- YELLOW: 0xffff00ff,
- YELLOWGREEN: 0x9acd32ff
- };
-
- var backgroundClip = {
- name: 'background-clip',
- initialValue: 'border-box',
- prefix: false,
- type: 1 /* LIST */,
- parse: function (_context, tokens) {
- return tokens.map(function (token) {
- if (isIdentToken(token)) {
- switch (token.value) {
- case 'padding-box':
- return 1 /* PADDING_BOX */;
- case 'content-box':
- return 2 /* CONTENT_BOX */;
- }
- }
- return 0 /* BORDER_BOX */;
- });
- }
- };
-
- var backgroundColor = {
- name: "background-color",
- initialValue: 'transparent',
- prefix: false,
- type: 3 /* TYPE_VALUE */,
- format: 'color'
- };
-
- var parseColorStop = function (context, args) {
- var color = color$1.parse(context, args[0]);
- var stop = args[1];
- return stop && isLengthPercentage(stop) ? { color: color, stop: stop } : { color: color, stop: null };
- };
- var processColorStops = function (stops, lineLength) {
- var first = stops[0];
- var last = stops[stops.length - 1];
- if (first.stop === null) {
- first.stop = ZERO_LENGTH;
- }
- if (last.stop === null) {
- last.stop = HUNDRED_PERCENT;
- }
- var processStops = [];
- var previous = 0;
- for (var i = 0; i < stops.length; i++) {
- var stop_1 = stops[i].stop;
- if (stop_1 !== null) {
- var absoluteValue = getAbsoluteValue(stop_1, lineLength);
- if (absoluteValue > previous) {
- processStops.push(absoluteValue);
- }
- else {
- processStops.push(previous);
- }
- previous = absoluteValue;
- }
- else {
- processStops.push(null);
- }
- }
- var gapBegin = null;
- for (var i = 0; i < processStops.length; i++) {
- var stop_2 = processStops[i];
- if (stop_2 === null) {
- if (gapBegin === null) {
- gapBegin = i;
- }
- }
- else if (gapBegin !== null) {
- var gapLength = i - gapBegin;
- var beforeGap = processStops[gapBegin - 1];
- var gapValue = (stop_2 - beforeGap) / (gapLength + 1);
- for (var g = 1; g <= gapLength; g++) {
- processStops[gapBegin + g - 1] = gapValue * g;
- }
- gapBegin = null;
- }
- }
- return stops.map(function (_a, i) {
- var color = _a.color;
- return { color: color, stop: Math.max(Math.min(1, processStops[i] / lineLength), 0) };
- });
- };
- var getAngleFromCorner = function (corner, width, height) {
- var centerX = width / 2;
- var centerY = height / 2;
- var x = getAbsoluteValue(corner[0], width) - centerX;
- var y = centerY - getAbsoluteValue(corner[1], height);
- return (Math.atan2(y, x) + Math.PI * 2) % (Math.PI * 2);
- };
- var calculateGradientDirection = function (angle, width, height) {
- var radian = typeof angle === 'number' ? angle : getAngleFromCorner(angle, width, height);
- var lineLength = Math.abs(width * Math.sin(radian)) + Math.abs(height * Math.cos(radian));
- var halfWidth = width / 2;
- var halfHeight = height / 2;
- var halfLineLength = lineLength / 2;
- var yDiff = Math.sin(radian - Math.PI / 2) * halfLineLength;
- var xDiff = Math.cos(radian - Math.PI / 2) * halfLineLength;
- return [lineLength, halfWidth - xDiff, halfWidth + xDiff, halfHeight - yDiff, halfHeight + yDiff];
- };
- var distance = function (a, b) { return Math.sqrt(a * a + b * b); };
- var findCorner = function (width, height, x, y, closest) {
- var corners = [
- [0, 0],
- [0, height],
- [width, 0],
- [width, height]
- ];
- return corners.reduce(function (stat, corner) {
- var cx = corner[0], cy = corner[1];
- var d = distance(x - cx, y - cy);
- if (closest ? d < stat.optimumDistance : d > stat.optimumDistance) {
- return {
- optimumCorner: corner,
- optimumDistance: d
- };
- }
- return stat;
- }, {
- optimumDistance: closest ? Infinity : -Infinity,
- optimumCorner: null
- }).optimumCorner;
- };
- var calculateRadius = function (gradient, x, y, width, height) {
- var rx = 0;
- var ry = 0;
- switch (gradient.size) {
- case 0 /* CLOSEST_SIDE */:
- // The ending shape is sized so that that it exactly meets the side of the gradient box closest to the gradient’s center.
- // If the shape is an ellipse, it exactly meets the closest side in each dimension.
- if (gradient.shape === 0 /* CIRCLE */) {
- rx = ry = Math.min(Math.abs(x), Math.abs(x - width), Math.abs(y), Math.abs(y - height));
- }
- else if (gradient.shape === 1 /* ELLIPSE */) {
- rx = Math.min(Math.abs(x), Math.abs(x - width));
- ry = Math.min(Math.abs(y), Math.abs(y - height));
- }
- break;
- case 2 /* CLOSEST_CORNER */:
- // The ending shape is sized so that that it passes through the corner of the gradient box closest to the gradient’s center.
- // If the shape is an ellipse, the ending shape is given the same aspect-ratio it would have if closest-side were specified.
- if (gradient.shape === 0 /* CIRCLE */) {
- rx = ry = Math.min(distance(x, y), distance(x, y - height), distance(x - width, y), distance(x - width, y - height));
- }
- else if (gradient.shape === 1 /* ELLIPSE */) {
- // Compute the ratio ry/rx (which is to be the same as for "closest-side")
- var c = Math.min(Math.abs(y), Math.abs(y - height)) / Math.min(Math.abs(x), Math.abs(x - width));
- var _a = findCorner(width, height, x, y, true), cx = _a[0], cy = _a[1];
- rx = distance(cx - x, (cy - y) / c);
- ry = c * rx;
- }
- break;
- case 1 /* FARTHEST_SIDE */:
- // Same as closest-side, except the ending shape is sized based on the farthest side(s)
- if (gradient.shape === 0 /* CIRCLE */) {
- rx = ry = Math.max(Math.abs(x), Math.abs(x - width), Math.abs(y), Math.abs(y - height));
- }
- else if (gradient.shape === 1 /* ELLIPSE */) {
- rx = Math.max(Math.abs(x), Math.abs(x - width));
- ry = Math.max(Math.abs(y), Math.abs(y - height));
- }
- break;
- case 3 /* FARTHEST_CORNER */:
- // Same as closest-corner, except the ending shape is sized based on the farthest corner.
- // If the shape is an ellipse, the ending shape is given the same aspect ratio it would have if farthest-side were specified.
- if (gradient.shape === 0 /* CIRCLE */) {
- rx = ry = Math.max(distance(x, y), distance(x, y - height), distance(x - width, y), distance(x - width, y - height));
- }
- else if (gradient.shape === 1 /* ELLIPSE */) {
- // Compute the ratio ry/rx (which is to be the same as for "farthest-side")
- var c = Math.max(Math.abs(y), Math.abs(y - height)) / Math.max(Math.abs(x), Math.abs(x - width));
- var _b = findCorner(width, height, x, y, false), cx = _b[0], cy = _b[1];
- rx = distance(cx - x, (cy - y) / c);
- ry = c * rx;
- }
- break;
- }
- if (Array.isArray(gradient.size)) {
- rx = getAbsoluteValue(gradient.size[0], width);
- ry = gradient.size.length === 2 ? getAbsoluteValue(gradient.size[1], height) : rx;
- }
- return [rx, ry];
- };
-
- var linearGradient = function (context, tokens) {
- var angle$1 = deg(180);
- var stops = [];
- parseFunctionArgs(tokens).forEach(function (arg, i) {
- if (i === 0) {
- var firstToken = arg[0];
- if (firstToken.type === 20 /* IDENT_TOKEN */ && firstToken.value === 'to') {
- angle$1 = parseNamedSide(arg);
- return;
- }
- else if (isAngle(firstToken)) {
- angle$1 = angle.parse(context, firstToken);
- return;
- }
- }
- var colorStop = parseColorStop(context, arg);
- stops.push(colorStop);
- });
- return { angle: angle$1, stops: stops, type: 1 /* LINEAR_GRADIENT */ };
- };
-
- var prefixLinearGradient = function (context, tokens) {
- var angle$1 = deg(180);
- var stops = [];
- parseFunctionArgs(tokens).forEach(function (arg, i) {
- if (i === 0) {
- var firstToken = arg[0];
- if (firstToken.type === 20 /* IDENT_TOKEN */ &&
- ['top', 'left', 'right', 'bottom'].indexOf(firstToken.value) !== -1) {
- angle$1 = parseNamedSide(arg);
- return;
- }
- else if (isAngle(firstToken)) {
- angle$1 = (angle.parse(context, firstToken) + deg(270)) % deg(360);
- return;
- }
- }
- var colorStop = parseColorStop(context, arg);
- stops.push(colorStop);
- });
- return {
- angle: angle$1,
- stops: stops,
- type: 1 /* LINEAR_GRADIENT */
- };
- };
-
- var webkitGradient = function (context, tokens) {
- var angle = deg(180);
- var stops = [];
- var type = 1 /* LINEAR_GRADIENT */;
- var shape = 0 /* CIRCLE */;
- var size = 3 /* FARTHEST_CORNER */;
- var position = [];
- parseFunctionArgs(tokens).forEach(function (arg, i) {
- var firstToken = arg[0];
- if (i === 0) {
- if (isIdentToken(firstToken) && firstToken.value === 'linear') {
- type = 1 /* LINEAR_GRADIENT */;
- return;
- }
- else if (isIdentToken(firstToken) && firstToken.value === 'radial') {
- type = 2 /* RADIAL_GRADIENT */;
- return;
- }
- }
- if (firstToken.type === 18 /* FUNCTION */) {
- if (firstToken.name === 'from') {
- var color = color$1.parse(context, firstToken.values[0]);
- stops.push({ stop: ZERO_LENGTH, color: color });
- }
- else if (firstToken.name === 'to') {
- var color = color$1.parse(context, firstToken.values[0]);
- stops.push({ stop: HUNDRED_PERCENT, color: color });
- }
- else if (firstToken.name === 'color-stop') {
- var values = firstToken.values.filter(nonFunctionArgSeparator);
- if (values.length === 2) {
- var color = color$1.parse(context, values[1]);
- var stop_1 = values[0];
- if (isNumberToken(stop_1)) {
- stops.push({
- stop: { type: 16 /* PERCENTAGE_TOKEN */, number: stop_1.number * 100, flags: stop_1.flags },
- color: color
- });
- }
- }
- }
- }
- });
- return type === 1 /* LINEAR_GRADIENT */
- ? {
- angle: (angle + deg(180)) % deg(360),
- stops: stops,
- type: type
- }
- : { size: size, shape: shape, stops: stops, position: position, type: type };
- };
-
- var CLOSEST_SIDE = 'closest-side';
- var FARTHEST_SIDE = 'farthest-side';
- var CLOSEST_CORNER = 'closest-corner';
- var FARTHEST_CORNER = 'farthest-corner';
- var CIRCLE = 'circle';
- var ELLIPSE = 'ellipse';
- var COVER = 'cover';
- var CONTAIN = 'contain';
- var radialGradient = function (context, tokens) {
- var shape = 0 /* CIRCLE */;
- var size = 3 /* FARTHEST_CORNER */;
- var stops = [];
- var position = [];
- parseFunctionArgs(tokens).forEach(function (arg, i) {
- var isColorStop = true;
- if (i === 0) {
- var isAtPosition_1 = false;
- isColorStop = arg.reduce(function (acc, token) {
- if (isAtPosition_1) {
- if (isIdentToken(token)) {
- switch (token.value) {
- case 'center':
- position.push(FIFTY_PERCENT);
- return acc;
- case 'top':
- case 'left':
- position.push(ZERO_LENGTH);
- return acc;
- case 'right':
- case 'bottom':
- position.push(HUNDRED_PERCENT);
- return acc;
- }
- }
- else if (isLengthPercentage(token) || isLength(token)) {
- position.push(token);
- }
- }
- else if (isIdentToken(token)) {
- switch (token.value) {
- case CIRCLE:
- shape = 0 /* CIRCLE */;
- return false;
- case ELLIPSE:
- shape = 1 /* ELLIPSE */;
- return false;
- case 'at':
- isAtPosition_1 = true;
- return false;
- case CLOSEST_SIDE:
- size = 0 /* CLOSEST_SIDE */;
- return false;
- case COVER:
- case FARTHEST_SIDE:
- size = 1 /* FARTHEST_SIDE */;
- return false;
- case CONTAIN:
- case CLOSEST_CORNER:
- size = 2 /* CLOSEST_CORNER */;
- return false;
- case FARTHEST_CORNER:
- size = 3 /* FARTHEST_CORNER */;
- return false;
- }
- }
- else if (isLength(token) || isLengthPercentage(token)) {
- if (!Array.isArray(size)) {
- size = [];
- }
- size.push(token);
- return false;
- }
- return acc;
- }, isColorStop);
- }
- if (isColorStop) {
- var colorStop = parseColorStop(context, arg);
- stops.push(colorStop);
- }
- });
- return { size: size, shape: shape, stops: stops, position: position, type: 2 /* RADIAL_GRADIENT */ };
- };
-
- var prefixRadialGradient = function (context, tokens) {
- var shape = 0 /* CIRCLE */;
- var size = 3 /* FARTHEST_CORNER */;
- var stops = [];
- var position = [];
- parseFunctionArgs(tokens).forEach(function (arg, i) {
- var isColorStop = true;
- if (i === 0) {
- isColorStop = arg.reduce(function (acc, token) {
- if (isIdentToken(token)) {
- switch (token.value) {
- case 'center':
- position.push(FIFTY_PERCENT);
- return false;
- case 'top':
- case 'left':
- position.push(ZERO_LENGTH);
- return false;
- case 'right':
- case 'bottom':
- position.push(HUNDRED_PERCENT);
- return false;
- }
- }
- else if (isLengthPercentage(token) || isLength(token)) {
- position.push(token);
- return false;
- }
- return acc;
- }, isColorStop);
- }
- else if (i === 1) {
- isColorStop = arg.reduce(function (acc, token) {
- if (isIdentToken(token)) {
- switch (token.value) {
- case CIRCLE:
- shape = 0 /* CIRCLE */;
- return false;
- case ELLIPSE:
- shape = 1 /* ELLIPSE */;
- return false;
- case CONTAIN:
- case CLOSEST_SIDE:
- size = 0 /* CLOSEST_SIDE */;
- return false;
- case FARTHEST_SIDE:
- size = 1 /* FARTHEST_SIDE */;
- return false;
- case CLOSEST_CORNER:
- size = 2 /* CLOSEST_CORNER */;
- return false;
- case COVER:
- case FARTHEST_CORNER:
- size = 3 /* FARTHEST_CORNER */;
- return false;
- }
- }
- else if (isLength(token) || isLengthPercentage(token)) {
- if (!Array.isArray(size)) {
- size = [];
- }
- size.push(token);
- return false;
- }
- return acc;
- }, isColorStop);
- }
- if (isColorStop) {
- var colorStop = parseColorStop(context, arg);
- stops.push(colorStop);
- }
- });
- return { size: size, shape: shape, stops: stops, position: position, type: 2 /* RADIAL_GRADIENT */ };
- };
-
- var isLinearGradient = function (background) {
- return background.type === 1 /* LINEAR_GRADIENT */;
- };
- var isRadialGradient = function (background) {
- return background.type === 2 /* RADIAL_GRADIENT */;
- };
- var image = {
- name: 'image',
- parse: function (context, value) {
- if (value.type === 22 /* URL_TOKEN */) {
- var image_1 = { url: value.value, type: 0 /* URL */ };
- context.cache.addImage(value.value);
- return image_1;
- }
- if (value.type === 18 /* FUNCTION */) {
- var imageFunction = SUPPORTED_IMAGE_FUNCTIONS[value.name];
- if (typeof imageFunction === 'undefined') {
- throw new Error("Attempting to parse an unsupported image function \"" + value.name + "\"");
- }
- return imageFunction(context, value.values);
- }
- throw new Error("Unsupported image type " + value.type);
- }
- };
- function isSupportedImage(value) {
- return (!(value.type === 20 /* IDENT_TOKEN */ && value.value === 'none') &&
- (value.type !== 18 /* FUNCTION */ || !!SUPPORTED_IMAGE_FUNCTIONS[value.name]));
- }
- var SUPPORTED_IMAGE_FUNCTIONS = {
- 'linear-gradient': linearGradient,
- '-moz-linear-gradient': prefixLinearGradient,
- '-ms-linear-gradient': prefixLinearGradient,
- '-o-linear-gradient': prefixLinearGradient,
- '-webkit-linear-gradient': prefixLinearGradient,
- 'radial-gradient': radialGradient,
- '-moz-radial-gradient': prefixRadialGradient,
- '-ms-radial-gradient': prefixRadialGradient,
- '-o-radial-gradient': prefixRadialGradient,
- '-webkit-radial-gradient': prefixRadialGradient,
- '-webkit-gradient': webkitGradient
- };
-
- var backgroundImage = {
- name: 'background-image',
- initialValue: 'none',
- type: 1 /* LIST */,
- prefix: false,
- parse: function (context, tokens) {
- if (tokens.length === 0) {
- return [];
- }
- var first = tokens[0];
- if (first.type === 20 /* IDENT_TOKEN */ && first.value === 'none') {
- return [];
- }
- return tokens
- .filter(function (value) { return nonFunctionArgSeparator(value) && isSupportedImage(value); })
- .map(function (value) { return image.parse(context, value); });
- }
- };
-
- var backgroundOrigin = {
- name: 'background-origin',
- initialValue: 'border-box',
- prefix: false,
- type: 1 /* LIST */,
- parse: function (_context, tokens) {
- return tokens.map(function (token) {
- if (isIdentToken(token)) {
- switch (token.value) {
- case 'padding-box':
- return 1 /* PADDING_BOX */;
- case 'content-box':
- return 2 /* CONTENT_BOX */;
- }
- }
- return 0 /* BORDER_BOX */;
- });
- }
- };
-
- var backgroundPosition = {
- name: 'background-position',
- initialValue: '0% 0%',
- type: 1 /* LIST */,
- prefix: false,
- parse: function (_context, tokens) {
- return parseFunctionArgs(tokens)
- .map(function (values) { return values.filter(isLengthPercentage); })
- .map(parseLengthPercentageTuple);
- }
- };
-
- var backgroundRepeat = {
- name: 'background-repeat',
- initialValue: 'repeat',
- prefix: false,
- type: 1 /* LIST */,
- parse: function (_context, tokens) {
- return parseFunctionArgs(tokens)
- .map(function (values) {
- return values
- .filter(isIdentToken)
- .map(function (token) { return token.value; })
- .join(' ');
- })
- .map(parseBackgroundRepeat);
- }
- };
- var parseBackgroundRepeat = function (value) {
- switch (value) {
- case 'no-repeat':
- return 1 /* NO_REPEAT */;
- case 'repeat-x':
- case 'repeat no-repeat':
- return 2 /* REPEAT_X */;
- case 'repeat-y':
- case 'no-repeat repeat':
- return 3 /* REPEAT_Y */;
- case 'repeat':
- default:
- return 0 /* REPEAT */;
- }
- };
-
- var BACKGROUND_SIZE;
- (function (BACKGROUND_SIZE) {
- BACKGROUND_SIZE["AUTO"] = "auto";
- BACKGROUND_SIZE["CONTAIN"] = "contain";
- BACKGROUND_SIZE["COVER"] = "cover";
- })(BACKGROUND_SIZE || (BACKGROUND_SIZE = {}));
- var backgroundSize = {
- name: 'background-size',
- initialValue: '0',
- prefix: false,
- type: 1 /* LIST */,
- parse: function (_context, tokens) {
- return parseFunctionArgs(tokens).map(function (values) { return values.filter(isBackgroundSizeInfoToken); });
- }
- };
- var isBackgroundSizeInfoToken = function (value) {
- return isIdentToken(value) || isLengthPercentage(value);
- };
-
- var borderColorForSide = function (side) { return ({
- name: "border-" + side + "-color",
- initialValue: 'transparent',
- prefix: false,
- type: 3 /* TYPE_VALUE */,
- format: 'color'
- }); };
- var borderTopColor = borderColorForSide('top');
- var borderRightColor = borderColorForSide('right');
- var borderBottomColor = borderColorForSide('bottom');
- var borderLeftColor = borderColorForSide('left');
-
- var borderRadiusForSide = function (side) { return ({
- name: "border-radius-" + side,
- initialValue: '0 0',
- prefix: false,
- type: 1 /* LIST */,
- parse: function (_context, tokens) {
- return parseLengthPercentageTuple(tokens.filter(isLengthPercentage));
- }
- }); };
- var borderTopLeftRadius = borderRadiusForSide('top-left');
- var borderTopRightRadius = borderRadiusForSide('top-right');
- var borderBottomRightRadius = borderRadiusForSide('bottom-right');
- var borderBottomLeftRadius = borderRadiusForSide('bottom-left');
-
- var borderStyleForSide = function (side) { return ({
- name: "border-" + side + "-style",
- initialValue: 'solid',
- prefix: false,
- type: 2 /* IDENT_VALUE */,
- parse: function (_context, style) {
- switch (style) {
- case 'none':
- return 0 /* NONE */;
- case 'dashed':
- return 2 /* DASHED */;
- case 'dotted':
- return 3 /* DOTTED */;
- case 'double':
- return 4 /* DOUBLE */;
- }
- return 1 /* SOLID */;
- }
- }); };
- var borderTopStyle = borderStyleForSide('top');
- var borderRightStyle = borderStyleForSide('right');
- var borderBottomStyle = borderStyleForSide('bottom');
- var borderLeftStyle = borderStyleForSide('left');
-
- var borderWidthForSide = function (side) { return ({
- name: "border-" + side + "-width",
- initialValue: '0',
- type: 0 /* VALUE */,
- prefix: false,
- parse: function (_context, token) {
- if (isDimensionToken(token)) {
- return token.number;
- }
- return 0;
- }
- }); };
- var borderTopWidth = borderWidthForSide('top');
- var borderRightWidth = borderWidthForSide('right');
- var borderBottomWidth = borderWidthForSide('bottom');
- var borderLeftWidth = borderWidthForSide('left');
-
- var color = {
- name: "color",
- initialValue: 'transparent',
- prefix: false,
- type: 3 /* TYPE_VALUE */,
- format: 'color'
- };
-
- var direction = {
- name: 'direction',
- initialValue: 'ltr',
- prefix: false,
- type: 2 /* IDENT_VALUE */,
- parse: function (_context, direction) {
- switch (direction) {
- case 'rtl':
- return 1 /* RTL */;
- case 'ltr':
- default:
- return 0 /* LTR */;
- }
- }
- };
-
- var display = {
- name: 'display',
- initialValue: 'inline-block',
- prefix: false,
- type: 1 /* LIST */,
- parse: function (_context, tokens) {
- return tokens.filter(isIdentToken).reduce(function (bit, token) {
- return bit | parseDisplayValue(token.value);
- }, 0 /* NONE */);
- }
- };
- var parseDisplayValue = function (display) {
- switch (display) {
- case 'block':
- case '-webkit-box':
- return 2 /* BLOCK */;
- case 'inline':
- return 4 /* INLINE */;
- case 'run-in':
- return 8 /* RUN_IN */;
- case 'flow':
- return 16 /* FLOW */;
- case 'flow-root':
- return 32 /* FLOW_ROOT */;
- case 'table':
- return 64 /* TABLE */;
- case 'flex':
- case '-webkit-flex':
- return 128 /* FLEX */;
- case 'grid':
- case '-ms-grid':
- return 256 /* GRID */;
- case 'ruby':
- return 512 /* RUBY */;
- case 'subgrid':
- return 1024 /* SUBGRID */;
- case 'list-item':
- return 2048 /* LIST_ITEM */;
- case 'table-row-group':
- return 4096 /* TABLE_ROW_GROUP */;
- case 'table-header-group':
- return 8192 /* TABLE_HEADER_GROUP */;
- case 'table-footer-group':
- return 16384 /* TABLE_FOOTER_GROUP */;
- case 'table-row':
- return 32768 /* TABLE_ROW */;
- case 'table-cell':
- return 65536 /* TABLE_CELL */;
- case 'table-column-group':
- return 131072 /* TABLE_COLUMN_GROUP */;
- case 'table-column':
- return 262144 /* TABLE_COLUMN */;
- case 'table-caption':
- return 524288 /* TABLE_CAPTION */;
- case 'ruby-base':
- return 1048576 /* RUBY_BASE */;
- case 'ruby-text':
- return 2097152 /* RUBY_TEXT */;
- case 'ruby-base-container':
- return 4194304 /* RUBY_BASE_CONTAINER */;
- case 'ruby-text-container':
- return 8388608 /* RUBY_TEXT_CONTAINER */;
- case 'contents':
- return 16777216 /* CONTENTS */;
- case 'inline-block':
- return 33554432 /* INLINE_BLOCK */;
- case 'inline-list-item':
- return 67108864 /* INLINE_LIST_ITEM */;
- case 'inline-table':
- return 134217728 /* INLINE_TABLE */;
- case 'inline-flex':
- return 268435456 /* INLINE_FLEX */;
- case 'inline-grid':
- return 536870912 /* INLINE_GRID */;
- }
- return 0 /* NONE */;
- };
-
- var float = {
- name: 'float',
- initialValue: 'none',
- prefix: false,
- type: 2 /* IDENT_VALUE */,
- parse: function (_context, float) {
- switch (float) {
- case 'left':
- return 1 /* LEFT */;
- case 'right':
- return 2 /* RIGHT */;
- case 'inline-start':
- return 3 /* INLINE_START */;
- case 'inline-end':
- return 4 /* INLINE_END */;
- }
- return 0 /* NONE */;
- }
- };
-
- var letterSpacing = {
- name: 'letter-spacing',
- initialValue: '0',
- prefix: false,
- type: 0 /* VALUE */,
- parse: function (_context, token) {
- if (token.type === 20 /* IDENT_TOKEN */ && token.value === 'normal') {
- return 0;
- }
- if (token.type === 17 /* NUMBER_TOKEN */) {
- return token.number;
- }
- if (token.type === 15 /* DIMENSION_TOKEN */) {
- return token.number;
- }
- return 0;
- }
- };
-
- var LINE_BREAK;
- (function (LINE_BREAK) {
- LINE_BREAK["NORMAL"] = "normal";
- LINE_BREAK["STRICT"] = "strict";
- })(LINE_BREAK || (LINE_BREAK = {}));
- var lineBreak = {
- name: 'line-break',
- initialValue: 'normal',
- prefix: false,
- type: 2 /* IDENT_VALUE */,
- parse: function (_context, lineBreak) {
- switch (lineBreak) {
- case 'strict':
- return LINE_BREAK.STRICT;
- case 'normal':
- default:
- return LINE_BREAK.NORMAL;
- }
- }
- };
-
- var lineHeight = {
- name: 'line-height',
- initialValue: 'normal',
- prefix: false,
- type: 4 /* TOKEN_VALUE */
- };
- var computeLineHeight = function (token, fontSize) {
- if (isIdentToken(token) && token.value === 'normal') {
- return 1.2 * fontSize;
- }
- else if (token.type === 17 /* NUMBER_TOKEN */) {
- return fontSize * token.number;
- }
- else if (isLengthPercentage(token)) {
- return getAbsoluteValue(token, fontSize);
- }
- return fontSize;
- };
-
- var listStyleImage = {
- name: 'list-style-image',
- initialValue: 'none',
- type: 0 /* VALUE */,
- prefix: false,
- parse: function (context, token) {
- if (token.type === 20 /* IDENT_TOKEN */ && token.value === 'none') {
- return null;
- }
- return image.parse(context, token);
- }
- };
-
- var listStylePosition = {
- name: 'list-style-position',
- initialValue: 'outside',
- prefix: false,
- type: 2 /* IDENT_VALUE */,
- parse: function (_context, position) {
- switch (position) {
- case 'inside':
- return 0 /* INSIDE */;
- case 'outside':
- default:
- return 1 /* OUTSIDE */;
- }
- }
- };
-
- var listStyleType = {
- name: 'list-style-type',
- initialValue: 'none',
- prefix: false,
- type: 2 /* IDENT_VALUE */,
- parse: function (_context, type) {
- switch (type) {
- case 'disc':
- return 0 /* DISC */;
- case 'circle':
- return 1 /* CIRCLE */;
- case 'square':
- return 2 /* SQUARE */;
- case 'decimal':
- return 3 /* DECIMAL */;
- case 'cjk-decimal':
- return 4 /* CJK_DECIMAL */;
- case 'decimal-leading-zero':
- return 5 /* DECIMAL_LEADING_ZERO */;
- case 'lower-roman':
- return 6 /* LOWER_ROMAN */;
- case 'upper-roman':
- return 7 /* UPPER_ROMAN */;
- case 'lower-greek':
- return 8 /* LOWER_GREEK */;
- case 'lower-alpha':
- return 9 /* LOWER_ALPHA */;
- case 'upper-alpha':
- return 10 /* UPPER_ALPHA */;
- case 'arabic-indic':
- return 11 /* ARABIC_INDIC */;
- case 'armenian':
- return 12 /* ARMENIAN */;
- case 'bengali':
- return 13 /* BENGALI */;
- case 'cambodian':
- return 14 /* CAMBODIAN */;
- case 'cjk-earthly-branch':
- return 15 /* CJK_EARTHLY_BRANCH */;
- case 'cjk-heavenly-stem':
- return 16 /* CJK_HEAVENLY_STEM */;
- case 'cjk-ideographic':
- return 17 /* CJK_IDEOGRAPHIC */;
- case 'devanagari':
- return 18 /* DEVANAGARI */;
- case 'ethiopic-numeric':
- return 19 /* ETHIOPIC_NUMERIC */;
- case 'georgian':
- return 20 /* GEORGIAN */;
- case 'gujarati':
- return 21 /* GUJARATI */;
- case 'gurmukhi':
- return 22 /* GURMUKHI */;
- case 'hebrew':
- return 22 /* HEBREW */;
- case 'hiragana':
- return 23 /* HIRAGANA */;
- case 'hiragana-iroha':
- return 24 /* HIRAGANA_IROHA */;
- case 'japanese-formal':
- return 25 /* JAPANESE_FORMAL */;
- case 'japanese-informal':
- return 26 /* JAPANESE_INFORMAL */;
- case 'kannada':
- return 27 /* KANNADA */;
- case 'katakana':
- return 28 /* KATAKANA */;
- case 'katakana-iroha':
- return 29 /* KATAKANA_IROHA */;
- case 'khmer':
- return 30 /* KHMER */;
- case 'korean-hangul-formal':
- return 31 /* KOREAN_HANGUL_FORMAL */;
- case 'korean-hanja-formal':
- return 32 /* KOREAN_HANJA_FORMAL */;
- case 'korean-hanja-informal':
- return 33 /* KOREAN_HANJA_INFORMAL */;
- case 'lao':
- return 34 /* LAO */;
- case 'lower-armenian':
- return 35 /* LOWER_ARMENIAN */;
- case 'malayalam':
- return 36 /* MALAYALAM */;
- case 'mongolian':
- return 37 /* MONGOLIAN */;
- case 'myanmar':
- return 38 /* MYANMAR */;
- case 'oriya':
- return 39 /* ORIYA */;
- case 'persian':
- return 40 /* PERSIAN */;
- case 'simp-chinese-formal':
- return 41 /* SIMP_CHINESE_FORMAL */;
- case 'simp-chinese-informal':
- return 42 /* SIMP_CHINESE_INFORMAL */;
- case 'tamil':
- return 43 /* TAMIL */;
- case 'telugu':
- return 44 /* TELUGU */;
- case 'thai':
- return 45 /* THAI */;
- case 'tibetan':
- return 46 /* TIBETAN */;
- case 'trad-chinese-formal':
- return 47 /* TRAD_CHINESE_FORMAL */;
- case 'trad-chinese-informal':
- return 48 /* TRAD_CHINESE_INFORMAL */;
- case 'upper-armenian':
- return 49 /* UPPER_ARMENIAN */;
- case 'disclosure-open':
- return 50 /* DISCLOSURE_OPEN */;
- case 'disclosure-closed':
- return 51 /* DISCLOSURE_CLOSED */;
- case 'none':
- default:
- return -1 /* NONE */;
- }
- }
- };
-
- var marginForSide = function (side) { return ({
- name: "margin-" + side,
- initialValue: '0',
- prefix: false,
- type: 4 /* TOKEN_VALUE */
- }); };
- var marginTop = marginForSide('top');
- var marginRight = marginForSide('right');
- var marginBottom = marginForSide('bottom');
- var marginLeft = marginForSide('left');
-
- var overflow = {
- name: 'overflow',
- initialValue: 'visible',
- prefix: false,
- type: 1 /* LIST */,
- parse: function (_context, tokens) {
- return tokens.filter(isIdentToken).map(function (overflow) {
- switch (overflow.value) {
- case 'hidden':
- return 1 /* HIDDEN */;
- case 'scroll':
- return 2 /* SCROLL */;
- case 'clip':
- return 3 /* CLIP */;
- case 'auto':
- return 4 /* AUTO */;
- case 'visible':
- default:
- return 0 /* VISIBLE */;
- }
- });
- }
- };
-
- var overflowWrap = {
- name: 'overflow-wrap',
- initialValue: 'normal',
- prefix: false,
- type: 2 /* IDENT_VALUE */,
- parse: function (_context, overflow) {
- switch (overflow) {
- case 'break-word':
- return "break-word" /* BREAK_WORD */;
- case 'normal':
- default:
- return "normal" /* NORMAL */;
- }
- }
- };
-
- var paddingForSide = function (side) { return ({
- name: "padding-" + side,
- initialValue: '0',
- prefix: false,
- type: 3 /* TYPE_VALUE */,
- format: 'length-percentage'
- }); };
- var paddingTop = paddingForSide('top');
- var paddingRight = paddingForSide('right');
- var paddingBottom = paddingForSide('bottom');
- var paddingLeft = paddingForSide('left');
-
- var textAlign = {
- name: 'text-align',
- initialValue: 'left',
- prefix: false,
- type: 2 /* IDENT_VALUE */,
- parse: function (_context, textAlign) {
- switch (textAlign) {
- case 'right':
- return 2 /* RIGHT */;
- case 'center':
- case 'justify':
- return 1 /* CENTER */;
- case 'left':
- default:
- return 0 /* LEFT */;
- }
- }
- };
-
- var position = {
- name: 'position',
- initialValue: 'static',
- prefix: false,
- type: 2 /* IDENT_VALUE */,
- parse: function (_context, position) {
- switch (position) {
- case 'relative':
- return 1 /* RELATIVE */;
- case 'absolute':
- return 2 /* ABSOLUTE */;
- case 'fixed':
- return 3 /* FIXED */;
- case 'sticky':
- return 4 /* STICKY */;
- }
- return 0 /* STATIC */;
- }
- };
-
- var textShadow = {
- name: 'text-shadow',
- initialValue: 'none',
- type: 1 /* LIST */,
- prefix: false,
- parse: function (context, tokens) {
- if (tokens.length === 1 && isIdentWithValue(tokens[0], 'none')) {
- return [];
- }
- return parseFunctionArgs(tokens).map(function (values) {
- var shadow = {
- color: COLORS.TRANSPARENT,
- offsetX: ZERO_LENGTH,
- offsetY: ZERO_LENGTH,
- blur: ZERO_LENGTH
- };
- var c = 0;
- for (var i = 0; i < values.length; i++) {
- var token = values[i];
- if (isLength(token)) {
- if (c === 0) {
- shadow.offsetX = token;
- }
- else if (c === 1) {
- shadow.offsetY = token;
- }
- else {
- shadow.blur = token;
- }
- c++;
- }
- else {
- shadow.color = color$1.parse(context, token);
- }
- }
- return shadow;
- });
- }
- };
-
- var textTransform = {
- name: 'text-transform',
- initialValue: 'none',
- prefix: false,
- type: 2 /* IDENT_VALUE */,
- parse: function (_context, textTransform) {
- switch (textTransform) {
- case 'uppercase':
- return 2 /* UPPERCASE */;
- case 'lowercase':
- return 1 /* LOWERCASE */;
- case 'capitalize':
- return 3 /* CAPITALIZE */;
- }
- return 0 /* NONE */;
- }
- };
-
- var transform$1 = {
- name: 'transform',
- initialValue: 'none',
- prefix: true,
- type: 0 /* VALUE */,
- parse: function (_context, token) {
- if (token.type === 20 /* IDENT_TOKEN */ && token.value === 'none') {
- return null;
- }
- if (token.type === 18 /* FUNCTION */) {
- var transformFunction = SUPPORTED_TRANSFORM_FUNCTIONS[token.name];
- if (typeof transformFunction === 'undefined') {
- throw new Error("Attempting to parse an unsupported transform function \"" + token.name + "\"");
- }
- return transformFunction(token.values);
- }
- return null;
- }
- };
- var matrix = function (args) {
- var values = args.filter(function (arg) { return arg.type === 17 /* NUMBER_TOKEN */; }).map(function (arg) { return arg.number; });
- return values.length === 6 ? values : null;
- };
- // doesn't support 3D transforms at the moment
- var matrix3d = function (args) {
- var values = args.filter(function (arg) { return arg.type === 17 /* NUMBER_TOKEN */; }).map(function (arg) { return arg.number; });
- var a1 = values[0], b1 = values[1]; values[2]; values[3]; var a2 = values[4], b2 = values[5]; values[6]; values[7]; values[8]; values[9]; values[10]; values[11]; var a4 = values[12], b4 = values[13]; values[14]; values[15];
- return values.length === 16 ? [a1, b1, a2, b2, a4, b4] : null;
- };
- var SUPPORTED_TRANSFORM_FUNCTIONS = {
- matrix: matrix,
- matrix3d: matrix3d
- };
-
- var DEFAULT_VALUE = {
- type: 16 /* PERCENTAGE_TOKEN */,
- number: 50,
- flags: FLAG_INTEGER
- };
- var DEFAULT = [DEFAULT_VALUE, DEFAULT_VALUE];
- var transformOrigin = {
- name: 'transform-origin',
- initialValue: '50% 50%',
- prefix: true,
- type: 1 /* LIST */,
- parse: function (_context, tokens) {
- var origins = tokens.filter(isLengthPercentage);
- if (origins.length !== 2) {
- return DEFAULT;
- }
- return [origins[0], origins[1]];
- }
- };
-
- var visibility = {
- name: 'visible',
- initialValue: 'none',
- prefix: false,
- type: 2 /* IDENT_VALUE */,
- parse: function (_context, visibility) {
- switch (visibility) {
- case 'hidden':
- return 1 /* HIDDEN */;
- case 'collapse':
- return 2 /* COLLAPSE */;
- case 'visible':
- default:
- return 0 /* VISIBLE */;
- }
- }
- };
-
- var WORD_BREAK;
- (function (WORD_BREAK) {
- WORD_BREAK["NORMAL"] = "normal";
- WORD_BREAK["BREAK_ALL"] = "break-all";
- WORD_BREAK["KEEP_ALL"] = "keep-all";
- })(WORD_BREAK || (WORD_BREAK = {}));
- var wordBreak = {
- name: 'word-break',
- initialValue: 'normal',
- prefix: false,
- type: 2 /* IDENT_VALUE */,
- parse: function (_context, wordBreak) {
- switch (wordBreak) {
- case 'break-all':
- return WORD_BREAK.BREAK_ALL;
- case 'keep-all':
- return WORD_BREAK.KEEP_ALL;
- case 'normal':
- default:
- return WORD_BREAK.NORMAL;
- }
- }
- };
-
- var zIndex = {
- name: 'z-index',
- initialValue: 'auto',
- prefix: false,
- type: 0 /* VALUE */,
- parse: function (_context, token) {
- if (token.type === 20 /* IDENT_TOKEN */) {
- return { auto: true, order: 0 };
- }
- if (isNumberToken(token)) {
- return { auto: false, order: token.number };
- }
- throw new Error("Invalid z-index number parsed");
- }
- };
-
- var time = {
- name: 'time',
- parse: function (_context, value) {
- if (value.type === 15 /* DIMENSION_TOKEN */) {
- switch (value.unit.toLowerCase()) {
- case 's':
- return 1000 * value.number;
- case 'ms':
- return value.number;
- }
- }
- throw new Error("Unsupported time type");
- }
- };
-
- var opacity = {
- name: 'opacity',
- initialValue: '1',
- type: 0 /* VALUE */,
- prefix: false,
- parse: function (_context, token) {
- if (isNumberToken(token)) {
- return token.number;
- }
- return 1;
- }
- };
-
- var textDecorationColor = {
- name: "text-decoration-color",
- initialValue: 'transparent',
- prefix: false,
- type: 3 /* TYPE_VALUE */,
- format: 'color'
- };
-
- var textDecorationLine = {
- name: 'text-decoration-line',
- initialValue: 'none',
- prefix: false,
- type: 1 /* LIST */,
- parse: function (_context, tokens) {
- return tokens
- .filter(isIdentToken)
- .map(function (token) {
- switch (token.value) {
- case 'underline':
- return 1 /* UNDERLINE */;
- case 'overline':
- return 2 /* OVERLINE */;
- case 'line-through':
- return 3 /* LINE_THROUGH */;
- case 'none':
- return 4 /* BLINK */;
- }
- return 0 /* NONE */;
- })
- .filter(function (line) { return line !== 0 /* NONE */; });
- }
- };
-
- var fontFamily = {
- name: "font-family",
- initialValue: '',
- prefix: false,
- type: 1 /* LIST */,
- parse: function (_context, tokens) {
- var accumulator = [];
- var results = [];
- tokens.forEach(function (token) {
- switch (token.type) {
- case 20 /* IDENT_TOKEN */:
- case 0 /* STRING_TOKEN */:
- accumulator.push(token.value);
- break;
- case 17 /* NUMBER_TOKEN */:
- accumulator.push(token.number.toString());
- break;
- case 4 /* COMMA_TOKEN */:
- results.push(accumulator.join(' '));
- accumulator.length = 0;
- break;
- }
- });
- if (accumulator.length) {
- results.push(accumulator.join(' '));
- }
- return results.map(function (result) { return (result.indexOf(' ') === -1 ? result : "'" + result + "'"); });
- }
- };
-
- var fontSize = {
- name: "font-size",
- initialValue: '0',
- prefix: false,
- type: 3 /* TYPE_VALUE */,
- format: 'length'
- };
-
- var fontWeight = {
- name: 'font-weight',
- initialValue: 'normal',
- type: 0 /* VALUE */,
- prefix: false,
- parse: function (_context, token) {
- if (isNumberToken(token)) {
- return token.number;
- }
- if (isIdentToken(token)) {
- switch (token.value) {
- case 'bold':
- return 700;
- case 'normal':
- default:
- return 400;
- }
- }
- return 400;
- }
- };
-
- var fontVariant = {
- name: 'font-variant',
- initialValue: 'none',
- type: 1 /* LIST */,
- prefix: false,
- parse: function (_context, tokens) {
- return tokens.filter(isIdentToken).map(function (token) { return token.value; });
- }
- };
-
- var fontStyle = {
- name: 'font-style',
- initialValue: 'normal',
- prefix: false,
- type: 2 /* IDENT_VALUE */,
- parse: function (_context, overflow) {
- switch (overflow) {
- case 'oblique':
- return "oblique" /* OBLIQUE */;
- case 'italic':
- return "italic" /* ITALIC */;
- case 'normal':
- default:
- return "normal" /* NORMAL */;
- }
- }
- };
-
- var contains = function (bit, value) { return (bit & value) !== 0; };
-
- var content = {
- name: 'content',
- initialValue: 'none',
- type: 1 /* LIST */,
- prefix: false,
- parse: function (_context, tokens) {
- if (tokens.length === 0) {
- return [];
- }
- var first = tokens[0];
- if (first.type === 20 /* IDENT_TOKEN */ && first.value === 'none') {
- return [];
- }
- return tokens;
- }
- };
-
- var counterIncrement = {
- name: 'counter-increment',
- initialValue: 'none',
- prefix: true,
- type: 1 /* LIST */,
- parse: function (_context, tokens) {
- if (tokens.length === 0) {
- return null;
- }
- var first = tokens[0];
- if (first.type === 20 /* IDENT_TOKEN */ && first.value === 'none') {
- return null;
- }
- var increments = [];
- var filtered = tokens.filter(nonWhiteSpace);
- for (var i = 0; i < filtered.length; i++) {
- var counter = filtered[i];
- var next = filtered[i + 1];
- if (counter.type === 20 /* IDENT_TOKEN */) {
- var increment = next && isNumberToken(next) ? next.number : 1;
- increments.push({ counter: counter.value, increment: increment });
- }
- }
- return increments;
- }
- };
-
- var counterReset = {
- name: 'counter-reset',
- initialValue: 'none',
- prefix: true,
- type: 1 /* LIST */,
- parse: function (_context, tokens) {
- if (tokens.length === 0) {
- return [];
- }
- var resets = [];
- var filtered = tokens.filter(nonWhiteSpace);
- for (var i = 0; i < filtered.length; i++) {
- var counter = filtered[i];
- var next = filtered[i + 1];
- if (isIdentToken(counter) && counter.value !== 'none') {
- var reset = next && isNumberToken(next) ? next.number : 0;
- resets.push({ counter: counter.value, reset: reset });
- }
- }
- return resets;
- }
- };
-
- var duration = {
- name: 'duration',
- initialValue: '0s',
- prefix: false,
- type: 1 /* LIST */,
- parse: function (context, tokens) {
- return tokens.filter(isDimensionToken).map(function (token) { return time.parse(context, token); });
- }
- };
-
- var quotes = {
- name: 'quotes',
- initialValue: 'none',
- prefix: true,
- type: 1 /* LIST */,
- parse: function (_context, tokens) {
- if (tokens.length === 0) {
- return null;
- }
- var first = tokens[0];
- if (first.type === 20 /* IDENT_TOKEN */ && first.value === 'none') {
- return null;
- }
- var quotes = [];
- var filtered = tokens.filter(isStringToken);
- if (filtered.length % 2 !== 0) {
- return null;
- }
- for (var i = 0; i < filtered.length; i += 2) {
- var open_1 = filtered[i].value;
- var close_1 = filtered[i + 1].value;
- quotes.push({ open: open_1, close: close_1 });
- }
- return quotes;
- }
- };
- var getQuote = function (quotes, depth, open) {
- if (!quotes) {
- return '';
- }
- var quote = quotes[Math.min(depth, quotes.length - 1)];
- if (!quote) {
- return '';
- }
- return open ? quote.open : quote.close;
- };
-
- var paintOrder = {
- name: 'paint-order',
- initialValue: 'normal',
- prefix: false,
- type: 1 /* LIST */,
- parse: function (_context, tokens) {
- var DEFAULT_VALUE = [0 /* FILL */, 1 /* STROKE */, 2 /* MARKERS */];
- var layers = [];
- tokens.filter(isIdentToken).forEach(function (token) {
- switch (token.value) {
- case 'stroke':
- layers.push(1 /* STROKE */);
- break;
- case 'fill':
- layers.push(0 /* FILL */);
- break;
- case 'markers':
- layers.push(2 /* MARKERS */);
- break;
- }
- });
- DEFAULT_VALUE.forEach(function (value) {
- if (layers.indexOf(value) === -1) {
- layers.push(value);
- }
- });
- return layers;
- }
- };
-
- var webkitTextStrokeColor = {
- name: "-webkit-text-stroke-color",
- initialValue: 'currentcolor',
- prefix: false,
- type: 3 /* TYPE_VALUE */,
- format: 'color'
- };
-
- var webkitTextStrokeWidth = {
- name: "-webkit-text-stroke-width",
- initialValue: '0',
- type: 0 /* VALUE */,
- prefix: false,
- parse: function (_context, token) {
- if (isDimensionToken(token)) {
- return token.number;
- }
- return 0;
- }
- };
-
- var CSSParsedDeclaration = /** @class */ (function () {
- function CSSParsedDeclaration(context, declaration) {
- var _a, _b;
- this.animationDuration = parse(context, duration, declaration.animationDuration);
- this.backgroundClip = parse(context, backgroundClip, declaration.backgroundClip);
- this.backgroundColor = parse(context, backgroundColor, declaration.backgroundColor);
- this.backgroundImage = parse(context, backgroundImage, declaration.backgroundImage);
- this.backgroundOrigin = parse(context, backgroundOrigin, declaration.backgroundOrigin);
- this.backgroundPosition = parse(context, backgroundPosition, declaration.backgroundPosition);
- this.backgroundRepeat = parse(context, backgroundRepeat, declaration.backgroundRepeat);
- this.backgroundSize = parse(context, backgroundSize, declaration.backgroundSize);
- this.borderTopColor = parse(context, borderTopColor, declaration.borderTopColor);
- this.borderRightColor = parse(context, borderRightColor, declaration.borderRightColor);
- this.borderBottomColor = parse(context, borderBottomColor, declaration.borderBottomColor);
- this.borderLeftColor = parse(context, borderLeftColor, declaration.borderLeftColor);
- this.borderTopLeftRadius = parse(context, borderTopLeftRadius, declaration.borderTopLeftRadius);
- this.borderTopRightRadius = parse(context, borderTopRightRadius, declaration.borderTopRightRadius);
- this.borderBottomRightRadius = parse(context, borderBottomRightRadius, declaration.borderBottomRightRadius);
- this.borderBottomLeftRadius = parse(context, borderBottomLeftRadius, declaration.borderBottomLeftRadius);
- this.borderTopStyle = parse(context, borderTopStyle, declaration.borderTopStyle);
- this.borderRightStyle = parse(context, borderRightStyle, declaration.borderRightStyle);
- this.borderBottomStyle = parse(context, borderBottomStyle, declaration.borderBottomStyle);
- this.borderLeftStyle = parse(context, borderLeftStyle, declaration.borderLeftStyle);
- this.borderTopWidth = parse(context, borderTopWidth, declaration.borderTopWidth);
- this.borderRightWidth = parse(context, borderRightWidth, declaration.borderRightWidth);
- this.borderBottomWidth = parse(context, borderBottomWidth, declaration.borderBottomWidth);
- this.borderLeftWidth = parse(context, borderLeftWidth, declaration.borderLeftWidth);
- this.color = parse(context, color, declaration.color);
- this.direction = parse(context, direction, declaration.direction);
- this.display = parse(context, display, declaration.display);
- this.float = parse(context, float, declaration.cssFloat);
- this.fontFamily = parse(context, fontFamily, declaration.fontFamily);
- this.fontSize = parse(context, fontSize, declaration.fontSize);
- this.fontStyle = parse(context, fontStyle, declaration.fontStyle);
- this.fontVariant = parse(context, fontVariant, declaration.fontVariant);
- this.fontWeight = parse(context, fontWeight, declaration.fontWeight);
- this.letterSpacing = parse(context, letterSpacing, declaration.letterSpacing);
- this.lineBreak = parse(context, lineBreak, declaration.lineBreak);
- this.lineHeight = parse(context, lineHeight, declaration.lineHeight);
- this.listStyleImage = parse(context, listStyleImage, declaration.listStyleImage);
- this.listStylePosition = parse(context, listStylePosition, declaration.listStylePosition);
- this.listStyleType = parse(context, listStyleType, declaration.listStyleType);
- this.marginTop = parse(context, marginTop, declaration.marginTop);
- this.marginRight = parse(context, marginRight, declaration.marginRight);
- this.marginBottom = parse(context, marginBottom, declaration.marginBottom);
- this.marginLeft = parse(context, marginLeft, declaration.marginLeft);
- this.opacity = parse(context, opacity, declaration.opacity);
- var overflowTuple = parse(context, overflow, declaration.overflow);
- this.overflowX = overflowTuple[0];
- this.overflowY = overflowTuple[overflowTuple.length > 1 ? 1 : 0];
- this.overflowWrap = parse(context, overflowWrap, declaration.overflowWrap);
- this.paddingTop = parse(context, paddingTop, declaration.paddingTop);
- this.paddingRight = parse(context, paddingRight, declaration.paddingRight);
- this.paddingBottom = parse(context, paddingBottom, declaration.paddingBottom);
- this.paddingLeft = parse(context, paddingLeft, declaration.paddingLeft);
- this.paintOrder = parse(context, paintOrder, declaration.paintOrder);
- this.position = parse(context, position, declaration.position);
- this.textAlign = parse(context, textAlign, declaration.textAlign);
- this.textDecorationColor = parse(context, textDecorationColor, (_a = declaration.textDecorationColor) !== null && _a !== void 0 ? _a : declaration.color);
- this.textDecorationLine = parse(context, textDecorationLine, (_b = declaration.textDecorationLine) !== null && _b !== void 0 ? _b : declaration.textDecoration);
- this.textShadow = parse(context, textShadow, declaration.textShadow);
- this.textTransform = parse(context, textTransform, declaration.textTransform);
- this.transform = parse(context, transform$1, declaration.transform);
- this.transformOrigin = parse(context, transformOrigin, declaration.transformOrigin);
- this.visibility = parse(context, visibility, declaration.visibility);
- this.webkitTextStrokeColor = parse(context, webkitTextStrokeColor, declaration.webkitTextStrokeColor);
- this.webkitTextStrokeWidth = parse(context, webkitTextStrokeWidth, declaration.webkitTextStrokeWidth);
- this.wordBreak = parse(context, wordBreak, declaration.wordBreak);
- this.zIndex = parse(context, zIndex, declaration.zIndex);
- }
- CSSParsedDeclaration.prototype.isVisible = function () {
- return this.display > 0 && this.opacity > 0 && this.visibility === 0 /* VISIBLE */;
- };
- CSSParsedDeclaration.prototype.isTransparent = function () {
- return isTransparent(this.backgroundColor);
- };
- CSSParsedDeclaration.prototype.isTransformed = function () {
- return this.transform !== null;
- };
- CSSParsedDeclaration.prototype.isPositioned = function () {
- return this.position !== 0 /* STATIC */;
- };
- CSSParsedDeclaration.prototype.isPositionedWithZIndex = function () {
- return this.isPositioned() && !this.zIndex.auto;
- };
- CSSParsedDeclaration.prototype.isFloating = function () {
- return this.float !== 0 /* NONE */;
- };
- CSSParsedDeclaration.prototype.isInlineLevel = function () {
- return (contains(this.display, 4 /* INLINE */) ||
- contains(this.display, 33554432 /* INLINE_BLOCK */) ||
- contains(this.display, 268435456 /* INLINE_FLEX */) ||
- contains(this.display, 536870912 /* INLINE_GRID */) ||
- contains(this.display, 67108864 /* INLINE_LIST_ITEM */) ||
- contains(this.display, 134217728 /* INLINE_TABLE */));
- };
- return CSSParsedDeclaration;
- }());
- var CSSParsedPseudoDeclaration = /** @class */ (function () {
- function CSSParsedPseudoDeclaration(context, declaration) {
- this.content = parse(context, content, declaration.content);
- this.quotes = parse(context, quotes, declaration.quotes);
- }
- return CSSParsedPseudoDeclaration;
- }());
- var CSSParsedCounterDeclaration = /** @class */ (function () {
- function CSSParsedCounterDeclaration(context, declaration) {
- this.counterIncrement = parse(context, counterIncrement, declaration.counterIncrement);
- this.counterReset = parse(context, counterReset, declaration.counterReset);
- }
- return CSSParsedCounterDeclaration;
- }());
- // eslint-disable-next-line @typescript-eslint/no-explicit-any
- var parse = function (context, descriptor, style) {
- var tokenizer = new Tokenizer();
- var value = style !== null && typeof style !== 'undefined' ? style.toString() : descriptor.initialValue;
- tokenizer.write(value);
- var parser = new Parser(tokenizer.read());
- switch (descriptor.type) {
- case 2 /* IDENT_VALUE */:
- var token = parser.parseComponentValue();
- return descriptor.parse(context, isIdentToken(token) ? token.value : descriptor.initialValue);
- case 0 /* VALUE */:
- return descriptor.parse(context, parser.parseComponentValue());
- case 1 /* LIST */:
- return descriptor.parse(context, parser.parseComponentValues());
- case 4 /* TOKEN_VALUE */:
- return parser.parseComponentValue();
- case 3 /* TYPE_VALUE */:
- switch (descriptor.format) {
- case 'angle':
- return angle.parse(context, parser.parseComponentValue());
- case 'color':
- return color$1.parse(context, parser.parseComponentValue());
- case 'image':
- return image.parse(context, parser.parseComponentValue());
- case 'length':
- var length_1 = parser.parseComponentValue();
- return isLength(length_1) ? length_1 : ZERO_LENGTH;
- case 'length-percentage':
- var value_1 = parser.parseComponentValue();
- return isLengthPercentage(value_1) ? value_1 : ZERO_LENGTH;
- case 'time':
- return time.parse(context, parser.parseComponentValue());
- }
- break;
- }
- };
-
- var elementDebuggerAttribute = 'data-html2canvas-debug';
- var getElementDebugType = function (element) {
- var attribute = element.getAttribute(elementDebuggerAttribute);
- switch (attribute) {
- case 'all':
- return 1 /* ALL */;
- case 'clone':
- return 2 /* CLONE */;
- case 'parse':
- return 3 /* PARSE */;
- case 'render':
- return 4 /* RENDER */;
- default:
- return 0 /* NONE */;
- }
- };
- var isDebugging = function (element, type) {
- var elementType = getElementDebugType(element);
- return elementType === 1 /* ALL */ || type === elementType;
- };
-
- var ElementContainer = /** @class */ (function () {
- function ElementContainer(context, element) {
- this.context = context;
- this.textNodes = [];
- this.elements = [];
- this.flags = 0;
- if (isDebugging(element, 3 /* PARSE */)) {
- debugger;
- }
- this.styles = new CSSParsedDeclaration(context, window.getComputedStyle(element, null));
- if (isHTMLElementNode(element)) {
- if (this.styles.animationDuration.some(function (duration) { return duration > 0; })) {
- element.style.animationDuration = '0s';
- }
- if (this.styles.transform !== null) {
- // getBoundingClientRect takes transforms into account
- element.style.transform = 'none';
- }
- }
- this.bounds = parseBounds(this.context, element);
- if (isDebugging(element, 4 /* RENDER */)) {
- this.flags |= 16 /* DEBUG_RENDER */;
- }
- }
- return ElementContainer;
- }());
-
- /*
- * text-segmentation 1.0.3
- * Copyright (c) 2022 Niklas von Hertzen
- * Released under MIT License
- */
- var base64 = '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-
- /*
- * utrie 1.0.2
- * Copyright (c) 2022 Niklas von Hertzen
- * Released under MIT License
- */
- var chars$1 = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/';
- // Use a lookup table to find the index.
- var lookup$1 = typeof Uint8Array === 'undefined' ? [] : new Uint8Array(256);
- for (var i$1 = 0; i$1 < chars$1.length; i$1++) {
- lookup$1[chars$1.charCodeAt(i$1)] = i$1;
- }
- var decode = function (base64) {
- var bufferLength = base64.length * 0.75, len = base64.length, i, p = 0, encoded1, encoded2, encoded3, encoded4;
- if (base64[base64.length - 1] === '=') {
- bufferLength--;
- if (base64[base64.length - 2] === '=') {
- bufferLength--;
- }
- }
- var buffer = typeof ArrayBuffer !== 'undefined' &&
- typeof Uint8Array !== 'undefined' &&
- typeof Uint8Array.prototype.slice !== 'undefined'
- ? new ArrayBuffer(bufferLength)
- : new Array(bufferLength);
- var bytes = Array.isArray(buffer) ? buffer : new Uint8Array(buffer);
- for (i = 0; i < len; i += 4) {
- encoded1 = lookup$1[base64.charCodeAt(i)];
- encoded2 = lookup$1[base64.charCodeAt(i + 1)];
- encoded3 = lookup$1[base64.charCodeAt(i + 2)];
- encoded4 = lookup$1[base64.charCodeAt(i + 3)];
- bytes[p++] = (encoded1 << 2) | (encoded2 >> 4);
- bytes[p++] = ((encoded2 & 15) << 4) | (encoded3 >> 2);
- bytes[p++] = ((encoded3 & 3) << 6) | (encoded4 & 63);
- }
- return buffer;
- };
- var polyUint16Array = function (buffer) {
- var length = buffer.length;
- var bytes = [];
- for (var i = 0; i < length; i += 2) {
- bytes.push((buffer[i + 1] << 8) | buffer[i]);
- }
- return bytes;
- };
- var polyUint32Array = function (buffer) {
- var length = buffer.length;
- var bytes = [];
- for (var i = 0; i < length; i += 4) {
- bytes.push((buffer[i + 3] << 24) | (buffer[i + 2] << 16) | (buffer[i + 1] << 8) | buffer[i]);
- }
- return bytes;
- };
-
- /** Shift size for getting the index-2 table offset. */
- var UTRIE2_SHIFT_2 = 5;
- /** Shift size for getting the index-1 table offset. */
- var UTRIE2_SHIFT_1 = 6 + 5;
- /**
- * Shift size for shifting left the index array values.
- * Increases possible data size with 16-bit index values at the cost
- * of compactability.
- * This requires data blocks to be aligned by UTRIE2_DATA_GRANULARITY.
- */
- var UTRIE2_INDEX_SHIFT = 2;
- /**
- * Difference between the two shift sizes,
- * for getting an index-1 offset from an index-2 offset. 6=11-5
- */
- var UTRIE2_SHIFT_1_2 = UTRIE2_SHIFT_1 - UTRIE2_SHIFT_2;
- /**
- * The part of the index-2 table for U+D800..U+DBFF stores values for
- * lead surrogate code _units_ not code _points_.
- * Values for lead surrogate code _points_ are indexed with this portion of the table.
- * Length=32=0x20=0x400>>UTRIE2_SHIFT_2. (There are 1024=0x400 lead surrogates.)
- */
- var UTRIE2_LSCP_INDEX_2_OFFSET = 0x10000 >> UTRIE2_SHIFT_2;
- /** Number of entries in a data block. 32=0x20 */
- var UTRIE2_DATA_BLOCK_LENGTH = 1 << UTRIE2_SHIFT_2;
- /** Mask for getting the lower bits for the in-data-block offset. */
- var UTRIE2_DATA_MASK = UTRIE2_DATA_BLOCK_LENGTH - 1;
- var UTRIE2_LSCP_INDEX_2_LENGTH = 0x400 >> UTRIE2_SHIFT_2;
- /** Count the lengths of both BMP pieces. 2080=0x820 */
- var UTRIE2_INDEX_2_BMP_LENGTH = UTRIE2_LSCP_INDEX_2_OFFSET + UTRIE2_LSCP_INDEX_2_LENGTH;
- /**
- * The 2-byte UTF-8 version of the index-2 table follows at offset 2080=0x820.
- * Length 32=0x20 for lead bytes C0..DF, regardless of UTRIE2_SHIFT_2.
- */
- var UTRIE2_UTF8_2B_INDEX_2_OFFSET = UTRIE2_INDEX_2_BMP_LENGTH;
- var UTRIE2_UTF8_2B_INDEX_2_LENGTH = 0x800 >> 6; /* U+0800 is the first code point after 2-byte UTF-8 */
- /**
- * The index-1 table, only used for supplementary code points, at offset 2112=0x840.
- * Variable length, for code points up to highStart, where the last single-value range starts.
- * Maximum length 512=0x200=0x100000>>UTRIE2_SHIFT_1.
- * (For 0x100000 supplementary code points U+10000..U+10ffff.)
- *
- * The part of the index-2 table for supplementary code points starts
- * after this index-1 table.
- *
- * Both the index-1 table and the following part of the index-2 table
- * are omitted completely if there is only BMP data.
- */
- var UTRIE2_INDEX_1_OFFSET = UTRIE2_UTF8_2B_INDEX_2_OFFSET + UTRIE2_UTF8_2B_INDEX_2_LENGTH;
- /**
- * Number of index-1 entries for the BMP. 32=0x20
- * This part of the index-1 table is omitted from the serialized form.
- */
- var UTRIE2_OMITTED_BMP_INDEX_1_LENGTH = 0x10000 >> UTRIE2_SHIFT_1;
- /** Number of entries in an index-2 block. 64=0x40 */
- var UTRIE2_INDEX_2_BLOCK_LENGTH = 1 << UTRIE2_SHIFT_1_2;
- /** Mask for getting the lower bits for the in-index-2-block offset. */
- var UTRIE2_INDEX_2_MASK = UTRIE2_INDEX_2_BLOCK_LENGTH - 1;
- var slice16 = function (view, start, end) {
- if (view.slice) {
- return view.slice(start, end);
- }
- return new Uint16Array(Array.prototype.slice.call(view, start, end));
- };
- var slice32 = function (view, start, end) {
- if (view.slice) {
- return view.slice(start, end);
- }
- return new Uint32Array(Array.prototype.slice.call(view, start, end));
- };
- var createTrieFromBase64 = function (base64, _byteLength) {
- var buffer = decode(base64);
- var view32 = Array.isArray(buffer) ? polyUint32Array(buffer) : new Uint32Array(buffer);
- var view16 = Array.isArray(buffer) ? polyUint16Array(buffer) : new Uint16Array(buffer);
- var headerLength = 24;
- var index = slice16(view16, headerLength / 2, view32[4] / 2);
- var data = view32[5] === 2
- ? slice16(view16, (headerLength + view32[4]) / 2)
- : slice32(view32, Math.ceil((headerLength + view32[4]) / 4));
- return new Trie(view32[0], view32[1], view32[2], view32[3], index, data);
- };
- var Trie = /** @class */ (function () {
- function Trie(initialValue, errorValue, highStart, highValueIndex, index, data) {
- this.initialValue = initialValue;
- this.errorValue = errorValue;
- this.highStart = highStart;
- this.highValueIndex = highValueIndex;
- this.index = index;
- this.data = data;
- }
- /**
- * Get the value for a code point as stored in the Trie.
- *
- * @param codePoint the code point
- * @return the value
- */
- Trie.prototype.get = function (codePoint) {
- var ix;
- if (codePoint >= 0) {
- if (codePoint < 0x0d800 || (codePoint > 0x0dbff && codePoint <= 0x0ffff)) {
- // Ordinary BMP code point, excluding leading surrogates.
- // BMP uses a single level lookup. BMP index starts at offset 0 in the Trie2 index.
- // 16 bit data is stored in the index array itself.
- ix = this.index[codePoint >> UTRIE2_SHIFT_2];
- ix = (ix << UTRIE2_INDEX_SHIFT) + (codePoint & UTRIE2_DATA_MASK);
- return this.data[ix];
- }
- if (codePoint <= 0xffff) {
- // Lead Surrogate Code Point. A Separate index section is stored for
- // lead surrogate code units and code points.
- // The main index has the code unit data.
- // For this function, we need the code point data.
- // Note: this expression could be refactored for slightly improved efficiency, but
- // surrogate code points will be so rare in practice that it's not worth it.
- ix = this.index[UTRIE2_LSCP_INDEX_2_OFFSET + ((codePoint - 0xd800) >> UTRIE2_SHIFT_2)];
- ix = (ix << UTRIE2_INDEX_SHIFT) + (codePoint & UTRIE2_DATA_MASK);
- return this.data[ix];
- }
- if (codePoint < this.highStart) {
- // Supplemental code point, use two-level lookup.
- ix = UTRIE2_INDEX_1_OFFSET - UTRIE2_OMITTED_BMP_INDEX_1_LENGTH + (codePoint >> UTRIE2_SHIFT_1);
- ix = this.index[ix];
- ix += (codePoint >> UTRIE2_SHIFT_2) & UTRIE2_INDEX_2_MASK;
- ix = this.index[ix];
- ix = (ix << UTRIE2_INDEX_SHIFT) + (codePoint & UTRIE2_DATA_MASK);
- return this.data[ix];
- }
- if (codePoint <= 0x10ffff) {
- return this.data[this.highValueIndex];
- }
- }
- // Fall through. The code point is outside of the legal range of 0..0x10ffff.
- return this.errorValue;
- };
- return Trie;
- }());
-
- /*
- * base64-arraybuffer 1.0.2
- * Copyright (c) 2022 Niklas von Hertzen
- * Released under MIT License
- */
- var chars = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/';
- // Use a lookup table to find the index.
- var lookup = typeof Uint8Array === 'undefined' ? [] : new Uint8Array(256);
- for (var i = 0; i < chars.length; i++) {
- lookup[chars.charCodeAt(i)] = i;
- }
-
- var Prepend = 1;
- var CR = 2;
- var LF = 3;
- var Control = 4;
- var Extend = 5;
- var SpacingMark = 7;
- var L = 8;
- var V = 9;
- var T = 10;
- var LV = 11;
- var LVT = 12;
- var ZWJ = 13;
- var Extended_Pictographic = 14;
- var RI = 15;
- var toCodePoints = function (str) {
- var codePoints = [];
- var i = 0;
- var length = str.length;
- while (i < length) {
- var value = str.charCodeAt(i++);
- if (value >= 0xd800 && value <= 0xdbff && i < length) {
- var extra = str.charCodeAt(i++);
- if ((extra & 0xfc00) === 0xdc00) {
- codePoints.push(((value & 0x3ff) << 10) + (extra & 0x3ff) + 0x10000);
- }
- else {
- codePoints.push(value);
- i--;
- }
- }
- else {
- codePoints.push(value);
- }
- }
- return codePoints;
- };
- var fromCodePoint = function () {
- var codePoints = [];
- for (var _i = 0; _i < arguments.length; _i++) {
- codePoints[_i] = arguments[_i];
- }
- if (String.fromCodePoint) {
- return String.fromCodePoint.apply(String, codePoints);
- }
- var length = codePoints.length;
- if (!length) {
- return '';
- }
- var codeUnits = [];
- var index = -1;
- var result = '';
- while (++index < length) {
- var codePoint = codePoints[index];
- if (codePoint <= 0xffff) {
- codeUnits.push(codePoint);
- }
- else {
- codePoint -= 0x10000;
- codeUnits.push((codePoint >> 10) + 0xd800, (codePoint % 0x400) + 0xdc00);
- }
- if (index + 1 === length || codeUnits.length > 0x4000) {
- result += String.fromCharCode.apply(String, codeUnits);
- codeUnits.length = 0;
- }
- }
- return result;
- };
- var UnicodeTrie = createTrieFromBase64(base64);
- var BREAK_NOT_ALLOWED = '×';
- var BREAK_ALLOWED = '÷';
- var codePointToClass = function (codePoint) { return UnicodeTrie.get(codePoint); };
- var _graphemeBreakAtIndex = function (_codePoints, classTypes, index) {
- var prevIndex = index - 2;
- var prev = classTypes[prevIndex];
- var current = classTypes[index - 1];
- var next = classTypes[index];
- // GB3 Do not break between a CR and LF
- if (current === CR && next === LF) {
- return BREAK_NOT_ALLOWED;
- }
- // GB4 Otherwise, break before and after controls.
- if (current === CR || current === LF || current === Control) {
- return BREAK_ALLOWED;
- }
- // GB5
- if (next === CR || next === LF || next === Control) {
- return BREAK_ALLOWED;
- }
- // Do not break Hangul syllable sequences.
- // GB6
- if (current === L && [L, V, LV, LVT].indexOf(next) !== -1) {
- return BREAK_NOT_ALLOWED;
- }
- // GB7
- if ((current === LV || current === V) && (next === V || next === T)) {
- return BREAK_NOT_ALLOWED;
- }
- // GB8
- if ((current === LVT || current === T) && next === T) {
- return BREAK_NOT_ALLOWED;
- }
- // GB9 Do not break before extending characters or ZWJ.
- if (next === ZWJ || next === Extend) {
- return BREAK_NOT_ALLOWED;
- }
- // Do not break before SpacingMarks, or after Prepend characters.
- // GB9a
- if (next === SpacingMark) {
- return BREAK_NOT_ALLOWED;
- }
- // GB9a
- if (current === Prepend) {
- return BREAK_NOT_ALLOWED;
- }
- // GB11 Do not break within emoji modifier sequences or emoji zwj sequences.
- if (current === ZWJ && next === Extended_Pictographic) {
- while (prev === Extend) {
- prev = classTypes[--prevIndex];
- }
- if (prev === Extended_Pictographic) {
- return BREAK_NOT_ALLOWED;
- }
- }
- // GB12 Do not break within emoji flag sequences.
- // That is, do not break between regional indicator (RI) symbols
- // if there is an odd number of RI characters before the break point.
- if (current === RI && next === RI) {
- var countRI = 0;
- while (prev === RI) {
- countRI++;
- prev = classTypes[--prevIndex];
- }
- if (countRI % 2 === 0) {
- return BREAK_NOT_ALLOWED;
- }
- }
- return BREAK_ALLOWED;
- };
- var GraphemeBreaker = function (str) {
- var codePoints = toCodePoints(str);
- var length = codePoints.length;
- var index = 0;
- var lastEnd = 0;
- var classTypes = codePoints.map(codePointToClass);
- return {
- next: function () {
- if (index >= length) {
- return { done: true, value: null };
- }
- var graphemeBreak = BREAK_NOT_ALLOWED;
- while (index < length &&
- (graphemeBreak = _graphemeBreakAtIndex(codePoints, classTypes, ++index)) === BREAK_NOT_ALLOWED) { }
- if (graphemeBreak !== BREAK_NOT_ALLOWED || index === length) {
- var value = fromCodePoint.apply(null, codePoints.slice(lastEnd, index));
- lastEnd = index;
- return { value: value, done: false };
- }
- return { done: true, value: null };
- },
- };
- };
- var splitGraphemes = function (str) {
- var breaker = GraphemeBreaker(str);
- var graphemes = [];
- var bk;
- while (!(bk = breaker.next()).done) {
- if (bk.value) {
- graphemes.push(bk.value.slice());
- }
- }
- return graphemes;
- };
-
- var testRangeBounds = function (document) {
- var TEST_HEIGHT = 123;
- if (document.createRange) {
- var range = document.createRange();
- if (range.getBoundingClientRect) {
- var testElement = document.createElement('boundtest');
- testElement.style.height = TEST_HEIGHT + "px";
- testElement.style.display = 'block';
- document.body.appendChild(testElement);
- range.selectNode(testElement);
- var rangeBounds = range.getBoundingClientRect();
- var rangeHeight = Math.round(rangeBounds.height);
- document.body.removeChild(testElement);
- if (rangeHeight === TEST_HEIGHT) {
- return true;
- }
- }
- }
- return false;
- };
- var testIOSLineBreak = function (document) {
- var testElement = document.createElement('boundtest');
- testElement.style.width = '50px';
- testElement.style.display = 'block';
- testElement.style.fontSize = '12px';
- testElement.style.letterSpacing = '0px';
- testElement.style.wordSpacing = '0px';
- document.body.appendChild(testElement);
- var range = document.createRange();
- testElement.innerHTML = typeof ''.repeat === 'function' ? '👨'.repeat(10) : '';
- var node = testElement.firstChild;
- var textList = toCodePoints$1(node.data).map(function (i) { return fromCodePoint$1(i); });
- var offset = 0;
- var prev = {};
- // ios 13 does not handle range getBoundingClientRect line changes correctly #2177
- var supports = textList.every(function (text, i) {
- range.setStart(node, offset);
- range.setEnd(node, offset + text.length);
- var rect = range.getBoundingClientRect();
- offset += text.length;
- var boundAhead = rect.x > prev.x || rect.y > prev.y;
- prev = rect;
- if (i === 0) {
- return true;
- }
- return boundAhead;
- });
- document.body.removeChild(testElement);
- return supports;
- };
- var testCORS = function () { return typeof new Image().crossOrigin !== 'undefined'; };
- var testResponseType = function () { return typeof new XMLHttpRequest().responseType === 'string'; };
- var testSVG = function (document) {
- var img = new Image();
- var canvas = document.createElement('canvas');
- var ctx = canvas.getContext('2d');
- if (!ctx) {
- return false;
- }
- img.src = "data:image/svg+xml,";
- try {
- ctx.drawImage(img, 0, 0);
- canvas.toDataURL();
- }
- catch (e) {
- return false;
- }
- return true;
- };
- var isGreenPixel = function (data) {
- return data[0] === 0 && data[1] === 255 && data[2] === 0 && data[3] === 255;
- };
- var testForeignObject = function (document) {
- var canvas = document.createElement('canvas');
- var size = 100;
- canvas.width = size;
- canvas.height = size;
- var ctx = canvas.getContext('2d');
- if (!ctx) {
- return Promise.reject(false);
- }
- ctx.fillStyle = 'rgb(0, 255, 0)';
- ctx.fillRect(0, 0, size, size);
- var img = new Image();
- var greenImageSrc = canvas.toDataURL();
- img.src = greenImageSrc;
- var svg = createForeignObjectSVG(size, size, 0, 0, img);
- ctx.fillStyle = 'red';
- ctx.fillRect(0, 0, size, size);
- return loadSerializedSVG$1(svg)
- .then(function (img) {
- ctx.drawImage(img, 0, 0);
- var data = ctx.getImageData(0, 0, size, size).data;
- ctx.fillStyle = 'red';
- ctx.fillRect(0, 0, size, size);
- var node = document.createElement('div');
- node.style.backgroundImage = "url(" + greenImageSrc + ")";
- node.style.height = size + "px";
- // Firefox 55 does not render inline tags
- return isGreenPixel(data)
- ? loadSerializedSVG$1(createForeignObjectSVG(size, size, 0, 0, node))
- : Promise.reject(false);
- })
- .then(function (img) {
- ctx.drawImage(img, 0, 0);
- // Edge does not render background-images
- return isGreenPixel(ctx.getImageData(0, 0, size, size).data);
- })
- .catch(function () { return false; });
- };
- var createForeignObjectSVG = function (width, height, x, y, node) {
- var xmlns = 'http://www.w3.org/2000/svg';
- var svg = document.createElementNS(xmlns, 'svg');
- var foreignObject = document.createElementNS(xmlns, 'foreignObject');
- svg.setAttributeNS(null, 'width', width.toString());
- svg.setAttributeNS(null, 'height', height.toString());
- foreignObject.setAttributeNS(null, 'width', '100%');
- foreignObject.setAttributeNS(null, 'height', '100%');
- foreignObject.setAttributeNS(null, 'x', x.toString());
- foreignObject.setAttributeNS(null, 'y', y.toString());
- foreignObject.setAttributeNS(null, 'externalResourcesRequired', 'true');
- svg.appendChild(foreignObject);
- foreignObject.appendChild(node);
- return svg;
- };
- var loadSerializedSVG$1 = function (svg) {
- return new Promise(function (resolve, reject) {
- var img = new Image();
- img.onload = function () { return resolve(img); };
- img.onerror = reject;
- img.src = "data:image/svg+xml;charset=utf-8," + encodeURIComponent(new XMLSerializer().serializeToString(svg));
- });
- };
- var FEATURES = {
- get SUPPORT_RANGE_BOUNDS() {
- var value = testRangeBounds(document);
- Object.defineProperty(FEATURES, 'SUPPORT_RANGE_BOUNDS', { value: value });
- return value;
- },
- get SUPPORT_WORD_BREAKING() {
- var value = FEATURES.SUPPORT_RANGE_BOUNDS && testIOSLineBreak(document);
- Object.defineProperty(FEATURES, 'SUPPORT_WORD_BREAKING', { value: value });
- return value;
- },
- get SUPPORT_SVG_DRAWING() {
- var value = testSVG(document);
- Object.defineProperty(FEATURES, 'SUPPORT_SVG_DRAWING', { value: value });
- return value;
- },
- get SUPPORT_FOREIGNOBJECT_DRAWING() {
- var value = typeof Array.from === 'function' && typeof window.fetch === 'function'
- ? testForeignObject(document)
- : Promise.resolve(false);
- Object.defineProperty(FEATURES, 'SUPPORT_FOREIGNOBJECT_DRAWING', { value: value });
- return value;
- },
- get SUPPORT_CORS_IMAGES() {
- var value = testCORS();
- Object.defineProperty(FEATURES, 'SUPPORT_CORS_IMAGES', { value: value });
- return value;
- },
- get SUPPORT_RESPONSE_TYPE() {
- var value = testResponseType();
- Object.defineProperty(FEATURES, 'SUPPORT_RESPONSE_TYPE', { value: value });
- return value;
- },
- get SUPPORT_CORS_XHR() {
- var value = 'withCredentials' in new XMLHttpRequest();
- Object.defineProperty(FEATURES, 'SUPPORT_CORS_XHR', { value: value });
- return value;
- },
- get SUPPORT_NATIVE_TEXT_SEGMENTATION() {
- // eslint-disable-next-line @typescript-eslint/no-explicit-any
- var value = !!(typeof Intl !== 'undefined' && Intl.Segmenter);
- Object.defineProperty(FEATURES, 'SUPPORT_NATIVE_TEXT_SEGMENTATION', { value: value });
- return value;
- }
- };
-
- var TextBounds = /** @class */ (function () {
- function TextBounds(text, bounds) {
- this.text = text;
- this.bounds = bounds;
- }
- return TextBounds;
- }());
- var parseTextBounds = function (context, value, styles, node) {
- var textList = breakText(value, styles);
- var textBounds = [];
- var offset = 0;
- textList.forEach(function (text) {
- if (styles.textDecorationLine.length || text.trim().length > 0) {
- if (FEATURES.SUPPORT_RANGE_BOUNDS) {
- var clientRects = createRange(node, offset, text.length).getClientRects();
- if (clientRects.length > 1) {
- var subSegments = segmentGraphemes(text);
- var subOffset_1 = 0;
- subSegments.forEach(function (subSegment) {
- textBounds.push(new TextBounds(subSegment, Bounds.fromDOMRectList(context, createRange(node, subOffset_1 + offset, subSegment.length).getClientRects())));
- subOffset_1 += subSegment.length;
- });
- }
- else {
- textBounds.push(new TextBounds(text, Bounds.fromDOMRectList(context, clientRects)));
- }
- }
- else {
- var replacementNode = node.splitText(text.length);
- textBounds.push(new TextBounds(text, getWrapperBounds(context, node)));
- node = replacementNode;
- }
- }
- else if (!FEATURES.SUPPORT_RANGE_BOUNDS) {
- node = node.splitText(text.length);
- }
- offset += text.length;
- });
- return textBounds;
- };
- var getWrapperBounds = function (context, node) {
- var ownerDocument = node.ownerDocument;
- if (ownerDocument) {
- var wrapper = ownerDocument.createElement('html2canvaswrapper');
- wrapper.appendChild(node.cloneNode(true));
- var parentNode = node.parentNode;
- if (parentNode) {
- parentNode.replaceChild(wrapper, node);
- var bounds = parseBounds(context, wrapper);
- if (wrapper.firstChild) {
- parentNode.replaceChild(wrapper.firstChild, wrapper);
- }
- return bounds;
- }
- }
- return Bounds.EMPTY;
- };
- var createRange = function (node, offset, length) {
- var ownerDocument = node.ownerDocument;
- if (!ownerDocument) {
- throw new Error('Node has no owner document');
- }
- var range = ownerDocument.createRange();
- range.setStart(node, offset);
- range.setEnd(node, offset + length);
- return range;
- };
- var segmentGraphemes = function (value) {
- if (FEATURES.SUPPORT_NATIVE_TEXT_SEGMENTATION) {
- // eslint-disable-next-line @typescript-eslint/no-explicit-any
- var segmenter = new Intl.Segmenter(void 0, { granularity: 'grapheme' });
- // eslint-disable-next-line @typescript-eslint/no-explicit-any
- return Array.from(segmenter.segment(value)).map(function (segment) { return segment.segment; });
- }
- return splitGraphemes(value);
- };
- var segmentWords = function (value, styles) {
- if (FEATURES.SUPPORT_NATIVE_TEXT_SEGMENTATION) {
- // eslint-disable-next-line @typescript-eslint/no-explicit-any
- var segmenter = new Intl.Segmenter(void 0, {
- granularity: 'word'
- });
- // eslint-disable-next-line @typescript-eslint/no-explicit-any
- return Array.from(segmenter.segment(value)).map(function (segment) { return segment.segment; });
- }
- return breakWords(value, styles);
- };
- var breakText = function (value, styles) {
- return styles.letterSpacing !== 0 ? segmentGraphemes(value) : segmentWords(value, styles);
- };
- // https://drafts.csswg.org/css-text/#word-separator
- var wordSeparators = [0x0020, 0x00a0, 0x1361, 0x10100, 0x10101, 0x1039, 0x1091];
- var breakWords = function (str, styles) {
- var breaker = LineBreaker(str, {
- lineBreak: styles.lineBreak,
- wordBreak: styles.overflowWrap === "break-word" /* BREAK_WORD */ ? 'break-word' : styles.wordBreak
- });
- var words = [];
- var bk;
- var _loop_1 = function () {
- if (bk.value) {
- var value = bk.value.slice();
- var codePoints = toCodePoints$1(value);
- var word_1 = '';
- codePoints.forEach(function (codePoint) {
- if (wordSeparators.indexOf(codePoint) === -1) {
- word_1 += fromCodePoint$1(codePoint);
- }
- else {
- if (word_1.length) {
- words.push(word_1);
- }
- words.push(fromCodePoint$1(codePoint));
- word_1 = '';
- }
- });
- if (word_1.length) {
- words.push(word_1);
- }
- }
- };
- while (!(bk = breaker.next()).done) {
- _loop_1();
- }
- return words;
- };
-
- var TextContainer = /** @class */ (function () {
- function TextContainer(context, node, styles) {
- this.text = transform(node.data, styles.textTransform);
- this.textBounds = parseTextBounds(context, this.text, styles, node);
- }
- return TextContainer;
- }());
- var transform = function (text, transform) {
- switch (transform) {
- case 1 /* LOWERCASE */:
- return text.toLowerCase();
- case 3 /* CAPITALIZE */:
- return text.replace(CAPITALIZE, capitalize);
- case 2 /* UPPERCASE */:
- return text.toUpperCase();
- default:
- return text;
- }
- };
- var CAPITALIZE = /(^|\s|:|-|\(|\))([a-z])/g;
- var capitalize = function (m, p1, p2) {
- if (m.length > 0) {
- return p1 + p2.toUpperCase();
- }
- return m;
- };
-
- var ImageElementContainer = /** @class */ (function (_super) {
- __extends(ImageElementContainer, _super);
- function ImageElementContainer(context, img) {
- var _this = _super.call(this, context, img) || this;
- _this.src = img.currentSrc || img.src;
- _this.intrinsicWidth = img.naturalWidth;
- _this.intrinsicHeight = img.naturalHeight;
- _this.context.cache.addImage(_this.src);
- return _this;
- }
- return ImageElementContainer;
- }(ElementContainer));
-
- var CanvasElementContainer = /** @class */ (function (_super) {
- __extends(CanvasElementContainer, _super);
- function CanvasElementContainer(context, canvas) {
- var _this = _super.call(this, context, canvas) || this;
- _this.canvas = canvas;
- _this.intrinsicWidth = canvas.width;
- _this.intrinsicHeight = canvas.height;
- return _this;
- }
- return CanvasElementContainer;
- }(ElementContainer));
-
- var SVGElementContainer = /** @class */ (function (_super) {
- __extends(SVGElementContainer, _super);
- function SVGElementContainer(context, img) {
- var _this = _super.call(this, context, img) || this;
- var s = new XMLSerializer();
- var bounds = parseBounds(context, img);
- img.setAttribute('width', bounds.width + "px");
- img.setAttribute('height', bounds.height + "px");
- _this.svg = "data:image/svg+xml," + encodeURIComponent(s.serializeToString(img));
- _this.intrinsicWidth = img.width.baseVal.value;
- _this.intrinsicHeight = img.height.baseVal.value;
- _this.context.cache.addImage(_this.svg);
- return _this;
- }
- return SVGElementContainer;
- }(ElementContainer));
-
- var LIElementContainer = /** @class */ (function (_super) {
- __extends(LIElementContainer, _super);
- function LIElementContainer(context, element) {
- var _this = _super.call(this, context, element) || this;
- _this.value = element.value;
- return _this;
- }
- return LIElementContainer;
- }(ElementContainer));
-
- var OLElementContainer = /** @class */ (function (_super) {
- __extends(OLElementContainer, _super);
- function OLElementContainer(context, element) {
- var _this = _super.call(this, context, element) || this;
- _this.start = element.start;
- _this.reversed = typeof element.reversed === 'boolean' && element.reversed === true;
- return _this;
- }
- return OLElementContainer;
- }(ElementContainer));
-
- var CHECKBOX_BORDER_RADIUS = [
- {
- type: 15 /* DIMENSION_TOKEN */,
- flags: 0,
- unit: 'px',
- number: 3
- }
- ];
- var RADIO_BORDER_RADIUS = [
- {
- type: 16 /* PERCENTAGE_TOKEN */,
- flags: 0,
- number: 50
- }
- ];
- var reformatInputBounds = function (bounds) {
- if (bounds.width > bounds.height) {
- return new Bounds(bounds.left + (bounds.width - bounds.height) / 2, bounds.top, bounds.height, bounds.height);
- }
- else if (bounds.width < bounds.height) {
- return new Bounds(bounds.left, bounds.top + (bounds.height - bounds.width) / 2, bounds.width, bounds.width);
- }
- return bounds;
- };
- var getInputValue = function (node) {
- var value = node.type === PASSWORD ? new Array(node.value.length + 1).join('\u2022') : node.value;
- return value.length === 0 ? node.placeholder || '' : value;
- };
- var CHECKBOX = 'checkbox';
- var RADIO = 'radio';
- var PASSWORD = 'password';
- var INPUT_COLOR = 0x2a2a2aff;
- var InputElementContainer = /** @class */ (function (_super) {
- __extends(InputElementContainer, _super);
- function InputElementContainer(context, input) {
- var _this = _super.call(this, context, input) || this;
- _this.type = input.type.toLowerCase();
- _this.checked = input.checked;
- _this.value = getInputValue(input);
- if (_this.type === CHECKBOX || _this.type === RADIO) {
- _this.styles.backgroundColor = 0xdededeff;
- _this.styles.borderTopColor =
- _this.styles.borderRightColor =
- _this.styles.borderBottomColor =
- _this.styles.borderLeftColor =
- 0xa5a5a5ff;
- _this.styles.borderTopWidth =
- _this.styles.borderRightWidth =
- _this.styles.borderBottomWidth =
- _this.styles.borderLeftWidth =
- 1;
- _this.styles.borderTopStyle =
- _this.styles.borderRightStyle =
- _this.styles.borderBottomStyle =
- _this.styles.borderLeftStyle =
- 1 /* SOLID */;
- _this.styles.backgroundClip = [0 /* BORDER_BOX */];
- _this.styles.backgroundOrigin = [0 /* BORDER_BOX */];
- _this.bounds = reformatInputBounds(_this.bounds);
- }
- switch (_this.type) {
- case CHECKBOX:
- _this.styles.borderTopRightRadius =
- _this.styles.borderTopLeftRadius =
- _this.styles.borderBottomRightRadius =
- _this.styles.borderBottomLeftRadius =
- CHECKBOX_BORDER_RADIUS;
- break;
- case RADIO:
- _this.styles.borderTopRightRadius =
- _this.styles.borderTopLeftRadius =
- _this.styles.borderBottomRightRadius =
- _this.styles.borderBottomLeftRadius =
- RADIO_BORDER_RADIUS;
- break;
- }
- return _this;
- }
- return InputElementContainer;
- }(ElementContainer));
-
- var SelectElementContainer = /** @class */ (function (_super) {
- __extends(SelectElementContainer, _super);
- function SelectElementContainer(context, element) {
- var _this = _super.call(this, context, element) || this;
- var option = element.options[element.selectedIndex || 0];
- _this.value = option ? option.text || '' : '';
- return _this;
- }
- return SelectElementContainer;
- }(ElementContainer));
-
- var TextareaElementContainer = /** @class */ (function (_super) {
- __extends(TextareaElementContainer, _super);
- function TextareaElementContainer(context, element) {
- var _this = _super.call(this, context, element) || this;
- _this.value = element.value;
- return _this;
- }
- return TextareaElementContainer;
- }(ElementContainer));
-
- var IFrameElementContainer = /** @class */ (function (_super) {
- __extends(IFrameElementContainer, _super);
- function IFrameElementContainer(context, iframe) {
- var _this = _super.call(this, context, iframe) || this;
- _this.src = iframe.src;
- _this.width = parseInt(iframe.width, 10) || 0;
- _this.height = parseInt(iframe.height, 10) || 0;
- _this.backgroundColor = _this.styles.backgroundColor;
- try {
- if (iframe.contentWindow &&
- iframe.contentWindow.document &&
- iframe.contentWindow.document.documentElement) {
- _this.tree = parseTree(context, iframe.contentWindow.document.documentElement);
- // http://www.w3.org/TR/css3-background/#special-backgrounds
- var documentBackgroundColor = iframe.contentWindow.document.documentElement
- ? parseColor(context, getComputedStyle(iframe.contentWindow.document.documentElement).backgroundColor)
- : COLORS.TRANSPARENT;
- var bodyBackgroundColor = iframe.contentWindow.document.body
- ? parseColor(context, getComputedStyle(iframe.contentWindow.document.body).backgroundColor)
- : COLORS.TRANSPARENT;
- _this.backgroundColor = isTransparent(documentBackgroundColor)
- ? isTransparent(bodyBackgroundColor)
- ? _this.styles.backgroundColor
- : bodyBackgroundColor
- : documentBackgroundColor;
- }
- }
- catch (e) { }
- return _this;
- }
- return IFrameElementContainer;
- }(ElementContainer));
-
- var LIST_OWNERS = ['OL', 'UL', 'MENU'];
- var parseNodeTree = function (context, node, parent, root) {
- for (var childNode = node.firstChild, nextNode = void 0; childNode; childNode = nextNode) {
- nextNode = childNode.nextSibling;
- if (isTextNode(childNode) && childNode.data.trim().length > 0) {
- parent.textNodes.push(new TextContainer(context, childNode, parent.styles));
- }
- else if (isElementNode(childNode)) {
- if (isSlotElement(childNode) && childNode.assignedNodes) {
- childNode.assignedNodes().forEach(function (childNode) { return parseNodeTree(context, childNode, parent, root); });
- }
- else {
- var container = createContainer(context, childNode);
- if (container.styles.isVisible()) {
- if (createsRealStackingContext(childNode, container, root)) {
- container.flags |= 4 /* CREATES_REAL_STACKING_CONTEXT */;
- }
- else if (createsStackingContext(container.styles)) {
- container.flags |= 2 /* CREATES_STACKING_CONTEXT */;
- }
- if (LIST_OWNERS.indexOf(childNode.tagName) !== -1) {
- container.flags |= 8 /* IS_LIST_OWNER */;
- }
- parent.elements.push(container);
- childNode.slot;
- if (childNode.shadowRoot) {
- parseNodeTree(context, childNode.shadowRoot, container, root);
- }
- else if (!isTextareaElement(childNode) &&
- !isSVGElement(childNode) &&
- !isSelectElement(childNode)) {
- parseNodeTree(context, childNode, container, root);
- }
- }
- }
- }
- }
- };
- var createContainer = function (context, element) {
- if (isImageElement(element)) {
- return new ImageElementContainer(context, element);
- }
- if (isCanvasElement(element)) {
- return new CanvasElementContainer(context, element);
- }
- if (isSVGElement(element)) {
- return new SVGElementContainer(context, element);
- }
- if (isLIElement(element)) {
- return new LIElementContainer(context, element);
- }
- if (isOLElement(element)) {
- return new OLElementContainer(context, element);
- }
- if (isInputElement(element)) {
- return new InputElementContainer(context, element);
- }
- if (isSelectElement(element)) {
- return new SelectElementContainer(context, element);
- }
- if (isTextareaElement(element)) {
- return new TextareaElementContainer(context, element);
- }
- if (isIFrameElement(element)) {
- return new IFrameElementContainer(context, element);
- }
- return new ElementContainer(context, element);
- };
- var parseTree = function (context, element) {
- var container = createContainer(context, element);
- container.flags |= 4 /* CREATES_REAL_STACKING_CONTEXT */;
- parseNodeTree(context, element, container, container);
- return container;
- };
- var createsRealStackingContext = function (node, container, root) {
- return (container.styles.isPositionedWithZIndex() ||
- container.styles.opacity < 1 ||
- container.styles.isTransformed() ||
- (isBodyElement(node) && root.styles.isTransparent()));
- };
- var createsStackingContext = function (styles) { return styles.isPositioned() || styles.isFloating(); };
- var isTextNode = function (node) { return node.nodeType === Node.TEXT_NODE; };
- var isElementNode = function (node) { return node.nodeType === Node.ELEMENT_NODE; };
- var isHTMLElementNode = function (node) {
- return isElementNode(node) && typeof node.style !== 'undefined' && !isSVGElementNode(node);
- };
- var isSVGElementNode = function (element) {
- return typeof element.className === 'object';
- };
- var isLIElement = function (node) { return node.tagName === 'LI'; };
- var isOLElement = function (node) { return node.tagName === 'OL'; };
- var isInputElement = function (node) { return node.tagName === 'INPUT'; };
- var isHTMLElement = function (node) { return node.tagName === 'HTML'; };
- var isSVGElement = function (node) { return node.tagName === 'svg'; };
- var isBodyElement = function (node) { return node.tagName === 'BODY'; };
- var isCanvasElement = function (node) { return node.tagName === 'CANVAS'; };
- var isVideoElement = function (node) { return node.tagName === 'VIDEO'; };
- var isImageElement = function (node) { return node.tagName === 'IMG'; };
- var isIFrameElement = function (node) { return node.tagName === 'IFRAME'; };
- var isStyleElement = function (node) { return node.tagName === 'STYLE'; };
- var isScriptElement = function (node) { return node.tagName === 'SCRIPT'; };
- var isTextareaElement = function (node) { return node.tagName === 'TEXTAREA'; };
- var isSelectElement = function (node) { return node.tagName === 'SELECT'; };
- var isSlotElement = function (node) { return node.tagName === 'SLOT'; };
- // https://html.spec.whatwg.org/multipage/custom-elements.html#valid-custom-element-name
- var isCustomElement = function (node) { return node.tagName.indexOf('-') > 0; };
-
- var CounterState = /** @class */ (function () {
- function CounterState() {
- this.counters = {};
- }
- CounterState.prototype.getCounterValue = function (name) {
- var counter = this.counters[name];
- if (counter && counter.length) {
- return counter[counter.length - 1];
- }
- return 1;
- };
- CounterState.prototype.getCounterValues = function (name) {
- var counter = this.counters[name];
- return counter ? counter : [];
- };
- CounterState.prototype.pop = function (counters) {
- var _this = this;
- counters.forEach(function (counter) { return _this.counters[counter].pop(); });
- };
- CounterState.prototype.parse = function (style) {
- var _this = this;
- var counterIncrement = style.counterIncrement;
- var counterReset = style.counterReset;
- var canReset = true;
- if (counterIncrement !== null) {
- counterIncrement.forEach(function (entry) {
- var counter = _this.counters[entry.counter];
- if (counter && entry.increment !== 0) {
- canReset = false;
- if (!counter.length) {
- counter.push(1);
- }
- counter[Math.max(0, counter.length - 1)] += entry.increment;
- }
- });
- }
- var counterNames = [];
- if (canReset) {
- counterReset.forEach(function (entry) {
- var counter = _this.counters[entry.counter];
- counterNames.push(entry.counter);
- if (!counter) {
- counter = _this.counters[entry.counter] = [];
- }
- counter.push(entry.reset);
- });
- }
- return counterNames;
- };
- return CounterState;
- }());
- var ROMAN_UPPER = {
- integers: [1000, 900, 500, 400, 100, 90, 50, 40, 10, 9, 5, 4, 1],
- values: ['M', 'CM', 'D', 'CD', 'C', 'XC', 'L', 'XL', 'X', 'IX', 'V', 'IV', 'I']
- };
- var ARMENIAN = {
- integers: [
- 9000, 8000, 7000, 6000, 5000, 4000, 3000, 2000, 1000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 90, 80, 70,
- 60, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1
- ],
- values: [
- 'Ք',
- 'Փ',
- 'Ւ',
- 'Ց',
- 'Ր',
- 'Տ',
- 'Վ',
- 'Ս',
- 'Ռ',
- 'Ջ',
- 'Պ',
- 'Չ',
- 'Ո',
- 'Շ',
- 'Ն',
- 'Յ',
- 'Մ',
- 'Ճ',
- 'Ղ',
- 'Ձ',
- 'Հ',
- 'Կ',
- 'Ծ',
- 'Խ',
- 'Լ',
- 'Ի',
- 'Ժ',
- 'Թ',
- 'Ը',
- 'Է',
- 'Զ',
- 'Ե',
- 'Դ',
- 'Գ',
- 'Բ',
- 'Ա'
- ]
- };
- var HEBREW = {
- integers: [
- 10000, 9000, 8000, 7000, 6000, 5000, 4000, 3000, 2000, 1000, 400, 300, 200, 100, 90, 80, 70, 60, 50, 40, 30, 20,
- 19, 18, 17, 16, 15, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1
- ],
- values: [
- 'י׳',
- 'ט׳',
- 'ח׳',
- 'ז׳',
- 'ו׳',
- 'ה׳',
- 'ד׳',
- 'ג׳',
- 'ב׳',
- 'א׳',
- 'ת',
- 'ש',
- 'ר',
- 'ק',
- 'צ',
- 'פ',
- 'ע',
- 'ס',
- 'נ',
- 'מ',
- 'ל',
- 'כ',
- 'יט',
- 'יח',
- 'יז',
- 'טז',
- 'טו',
- 'י',
- 'ט',
- 'ח',
- 'ז',
- 'ו',
- 'ה',
- 'ד',
- 'ג',
- 'ב',
- 'א'
- ]
- };
- var GEORGIAN = {
- integers: [
- 10000, 9000, 8000, 7000, 6000, 5000, 4000, 3000, 2000, 1000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 90,
- 80, 70, 60, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1
- ],
- values: [
- 'ჵ',
- 'ჰ',
- 'ჯ',
- 'ჴ',
- 'ხ',
- 'ჭ',
- 'წ',
- 'ძ',
- 'ც',
- 'ჩ',
- 'შ',
- 'ყ',
- 'ღ',
- 'ქ',
- 'ფ',
- 'ჳ',
- 'ტ',
- 'ს',
- 'რ',
- 'ჟ',
- 'პ',
- 'ო',
- 'ჲ',
- 'ნ',
- 'მ',
- 'ლ',
- 'კ',
- 'ი',
- 'თ',
- 'ჱ',
- 'ზ',
- 'ვ',
- 'ე',
- 'დ',
- 'გ',
- 'ბ',
- 'ა'
- ]
- };
- var createAdditiveCounter = function (value, min, max, symbols, fallback, suffix) {
- if (value < min || value > max) {
- return createCounterText(value, fallback, suffix.length > 0);
- }
- return (symbols.integers.reduce(function (string, integer, index) {
- while (value >= integer) {
- value -= integer;
- string += symbols.values[index];
- }
- return string;
- }, '') + suffix);
- };
- var createCounterStyleWithSymbolResolver = function (value, codePointRangeLength, isNumeric, resolver) {
- var string = '';
- do {
- if (!isNumeric) {
- value--;
- }
- string = resolver(value) + string;
- value /= codePointRangeLength;
- } while (value * codePointRangeLength >= codePointRangeLength);
- return string;
- };
- var createCounterStyleFromRange = function (value, codePointRangeStart, codePointRangeEnd, isNumeric, suffix) {
- var codePointRangeLength = codePointRangeEnd - codePointRangeStart + 1;
- return ((value < 0 ? '-' : '') +
- (createCounterStyleWithSymbolResolver(Math.abs(value), codePointRangeLength, isNumeric, function (codePoint) {
- return fromCodePoint$1(Math.floor(codePoint % codePointRangeLength) + codePointRangeStart);
- }) +
- suffix));
- };
- var createCounterStyleFromSymbols = function (value, symbols, suffix) {
- if (suffix === void 0) { suffix = '. '; }
- var codePointRangeLength = symbols.length;
- return (createCounterStyleWithSymbolResolver(Math.abs(value), codePointRangeLength, false, function (codePoint) { return symbols[Math.floor(codePoint % codePointRangeLength)]; }) + suffix);
- };
- var CJK_ZEROS = 1 << 0;
- var CJK_TEN_COEFFICIENTS = 1 << 1;
- var CJK_TEN_HIGH_COEFFICIENTS = 1 << 2;
- var CJK_HUNDRED_COEFFICIENTS = 1 << 3;
- var createCJKCounter = function (value, numbers, multipliers, negativeSign, suffix, flags) {
- if (value < -9999 || value > 9999) {
- return createCounterText(value, 4 /* CJK_DECIMAL */, suffix.length > 0);
- }
- var tmp = Math.abs(value);
- var string = suffix;
- if (tmp === 0) {
- return numbers[0] + string;
- }
- for (var digit = 0; tmp > 0 && digit <= 4; digit++) {
- var coefficient = tmp % 10;
- if (coefficient === 0 && contains(flags, CJK_ZEROS) && string !== '') {
- string = numbers[coefficient] + string;
- }
- else if (coefficient > 1 ||
- (coefficient === 1 && digit === 0) ||
- (coefficient === 1 && digit === 1 && contains(flags, CJK_TEN_COEFFICIENTS)) ||
- (coefficient === 1 && digit === 1 && contains(flags, CJK_TEN_HIGH_COEFFICIENTS) && value > 100) ||
- (coefficient === 1 && digit > 1 && contains(flags, CJK_HUNDRED_COEFFICIENTS))) {
- string = numbers[coefficient] + (digit > 0 ? multipliers[digit - 1] : '') + string;
- }
- else if (coefficient === 1 && digit > 0) {
- string = multipliers[digit - 1] + string;
- }
- tmp = Math.floor(tmp / 10);
- }
- return (value < 0 ? negativeSign : '') + string;
- };
- var CHINESE_INFORMAL_MULTIPLIERS = '十百千萬';
- var CHINESE_FORMAL_MULTIPLIERS = '拾佰仟萬';
- var JAPANESE_NEGATIVE = 'マイナス';
- var KOREAN_NEGATIVE = '마이너스';
- var createCounterText = function (value, type, appendSuffix) {
- var defaultSuffix = appendSuffix ? '. ' : '';
- var cjkSuffix = appendSuffix ? '、' : '';
- var koreanSuffix = appendSuffix ? ', ' : '';
- var spaceSuffix = appendSuffix ? ' ' : '';
- switch (type) {
- case 0 /* DISC */:
- return '•' + spaceSuffix;
- case 1 /* CIRCLE */:
- return '◦' + spaceSuffix;
- case 2 /* SQUARE */:
- return '◾' + spaceSuffix;
- case 5 /* DECIMAL_LEADING_ZERO */:
- var string = createCounterStyleFromRange(value, 48, 57, true, defaultSuffix);
- return string.length < 4 ? "0" + string : string;
- case 4 /* CJK_DECIMAL */:
- return createCounterStyleFromSymbols(value, '〇一二三四五六七八九', cjkSuffix);
- case 6 /* LOWER_ROMAN */:
- return createAdditiveCounter(value, 1, 3999, ROMAN_UPPER, 3 /* DECIMAL */, defaultSuffix).toLowerCase();
- case 7 /* UPPER_ROMAN */:
- return createAdditiveCounter(value, 1, 3999, ROMAN_UPPER, 3 /* DECIMAL */, defaultSuffix);
- case 8 /* LOWER_GREEK */:
- return createCounterStyleFromRange(value, 945, 969, false, defaultSuffix);
- case 9 /* LOWER_ALPHA */:
- return createCounterStyleFromRange(value, 97, 122, false, defaultSuffix);
- case 10 /* UPPER_ALPHA */:
- return createCounterStyleFromRange(value, 65, 90, false, defaultSuffix);
- case 11 /* ARABIC_INDIC */:
- return createCounterStyleFromRange(value, 1632, 1641, true, defaultSuffix);
- case 12 /* ARMENIAN */:
- case 49 /* UPPER_ARMENIAN */:
- return createAdditiveCounter(value, 1, 9999, ARMENIAN, 3 /* DECIMAL */, defaultSuffix);
- case 35 /* LOWER_ARMENIAN */:
- return createAdditiveCounter(value, 1, 9999, ARMENIAN, 3 /* DECIMAL */, defaultSuffix).toLowerCase();
- case 13 /* BENGALI */:
- return createCounterStyleFromRange(value, 2534, 2543, true, defaultSuffix);
- case 14 /* CAMBODIAN */:
- case 30 /* KHMER */:
- return createCounterStyleFromRange(value, 6112, 6121, true, defaultSuffix);
- case 15 /* CJK_EARTHLY_BRANCH */:
- return createCounterStyleFromSymbols(value, '子丑寅卯辰巳午未申酉戌亥', cjkSuffix);
- case 16 /* CJK_HEAVENLY_STEM */:
- return createCounterStyleFromSymbols(value, '甲乙丙丁戊己庚辛壬癸', cjkSuffix);
- case 17 /* CJK_IDEOGRAPHIC */:
- case 48 /* TRAD_CHINESE_INFORMAL */:
- return createCJKCounter(value, '零一二三四五六七八九', CHINESE_INFORMAL_MULTIPLIERS, '負', cjkSuffix, CJK_TEN_COEFFICIENTS | CJK_TEN_HIGH_COEFFICIENTS | CJK_HUNDRED_COEFFICIENTS);
- case 47 /* TRAD_CHINESE_FORMAL */:
- return createCJKCounter(value, '零壹貳參肆伍陸柒捌玖', CHINESE_FORMAL_MULTIPLIERS, '負', cjkSuffix, CJK_ZEROS | CJK_TEN_COEFFICIENTS | CJK_TEN_HIGH_COEFFICIENTS | CJK_HUNDRED_COEFFICIENTS);
- case 42 /* SIMP_CHINESE_INFORMAL */:
- return createCJKCounter(value, '零一二三四五六七八九', CHINESE_INFORMAL_MULTIPLIERS, '负', cjkSuffix, CJK_TEN_COEFFICIENTS | CJK_TEN_HIGH_COEFFICIENTS | CJK_HUNDRED_COEFFICIENTS);
- case 41 /* SIMP_CHINESE_FORMAL */:
- return createCJKCounter(value, '零壹贰叁肆伍陆柒捌玖', CHINESE_FORMAL_MULTIPLIERS, '负', cjkSuffix, CJK_ZEROS | CJK_TEN_COEFFICIENTS | CJK_TEN_HIGH_COEFFICIENTS | CJK_HUNDRED_COEFFICIENTS);
- case 26 /* JAPANESE_INFORMAL */:
- return createCJKCounter(value, '〇一二三四五六七八九', '十百千万', JAPANESE_NEGATIVE, cjkSuffix, 0);
- case 25 /* JAPANESE_FORMAL */:
- return createCJKCounter(value, '零壱弐参四伍六七八九', '拾百千万', JAPANESE_NEGATIVE, cjkSuffix, CJK_ZEROS | CJK_TEN_COEFFICIENTS | CJK_TEN_HIGH_COEFFICIENTS);
- case 31 /* KOREAN_HANGUL_FORMAL */:
- return createCJKCounter(value, '영일이삼사오육칠팔구', '십백천만', KOREAN_NEGATIVE, koreanSuffix, CJK_ZEROS | CJK_TEN_COEFFICIENTS | CJK_TEN_HIGH_COEFFICIENTS);
- case 33 /* KOREAN_HANJA_INFORMAL */:
- return createCJKCounter(value, '零一二三四五六七八九', '十百千萬', KOREAN_NEGATIVE, koreanSuffix, 0);
- case 32 /* KOREAN_HANJA_FORMAL */:
- return createCJKCounter(value, '零壹貳參四五六七八九', '拾百千', KOREAN_NEGATIVE, koreanSuffix, CJK_ZEROS | CJK_TEN_COEFFICIENTS | CJK_TEN_HIGH_COEFFICIENTS);
- case 18 /* DEVANAGARI */:
- return createCounterStyleFromRange(value, 0x966, 0x96f, true, defaultSuffix);
- case 20 /* GEORGIAN */:
- return createAdditiveCounter(value, 1, 19999, GEORGIAN, 3 /* DECIMAL */, defaultSuffix);
- case 21 /* GUJARATI */:
- return createCounterStyleFromRange(value, 0xae6, 0xaef, true, defaultSuffix);
- case 22 /* GURMUKHI */:
- return createCounterStyleFromRange(value, 0xa66, 0xa6f, true, defaultSuffix);
- case 22 /* HEBREW */:
- return createAdditiveCounter(value, 1, 10999, HEBREW, 3 /* DECIMAL */, defaultSuffix);
- case 23 /* HIRAGANA */:
- return createCounterStyleFromSymbols(value, 'あいうえおかきくけこさしすせそたちつてとなにぬねのはひふへほまみむめもやゆよらりるれろわゐゑをん');
- case 24 /* HIRAGANA_IROHA */:
- return createCounterStyleFromSymbols(value, 'いろはにほへとちりぬるをわかよたれそつねならむうゐのおくやまけふこえてあさきゆめみしゑひもせす');
- case 27 /* KANNADA */:
- return createCounterStyleFromRange(value, 0xce6, 0xcef, true, defaultSuffix);
- case 28 /* KATAKANA */:
- return createCounterStyleFromSymbols(value, 'アイウエオカキクケコサシスセソタチツテトナニヌネノハヒフヘホマミムメモヤユヨラリルレロワヰヱヲン', cjkSuffix);
- case 29 /* KATAKANA_IROHA */:
- return createCounterStyleFromSymbols(value, 'イロハニホヘトチリヌルヲワカヨタレソツネナラムウヰノオクヤマケフコエテアサキユメミシヱヒモセス', cjkSuffix);
- case 34 /* LAO */:
- return createCounterStyleFromRange(value, 0xed0, 0xed9, true, defaultSuffix);
- case 37 /* MONGOLIAN */:
- return createCounterStyleFromRange(value, 0x1810, 0x1819, true, defaultSuffix);
- case 38 /* MYANMAR */:
- return createCounterStyleFromRange(value, 0x1040, 0x1049, true, defaultSuffix);
- case 39 /* ORIYA */:
- return createCounterStyleFromRange(value, 0xb66, 0xb6f, true, defaultSuffix);
- case 40 /* PERSIAN */:
- return createCounterStyleFromRange(value, 0x6f0, 0x6f9, true, defaultSuffix);
- case 43 /* TAMIL */:
- return createCounterStyleFromRange(value, 0xbe6, 0xbef, true, defaultSuffix);
- case 44 /* TELUGU */:
- return createCounterStyleFromRange(value, 0xc66, 0xc6f, true, defaultSuffix);
- case 45 /* THAI */:
- return createCounterStyleFromRange(value, 0xe50, 0xe59, true, defaultSuffix);
- case 46 /* TIBETAN */:
- return createCounterStyleFromRange(value, 0xf20, 0xf29, true, defaultSuffix);
- case 3 /* DECIMAL */:
- default:
- return createCounterStyleFromRange(value, 48, 57, true, defaultSuffix);
- }
- };
-
- var IGNORE_ATTRIBUTE = 'data-html2canvas-ignore';
- var DocumentCloner = /** @class */ (function () {
- function DocumentCloner(context, element, options) {
- this.context = context;
- this.options = options;
- this.scrolledElements = [];
- this.referenceElement = element;
- this.counters = new CounterState();
- this.quoteDepth = 0;
- if (!element.ownerDocument) {
- throw new Error('Cloned element does not have an owner document');
- }
- this.documentElement = this.cloneNode(element.ownerDocument.documentElement, false);
- }
- DocumentCloner.prototype.toIFrame = function (ownerDocument, windowSize) {
- var _this = this;
- var iframe = createIFrameContainer(ownerDocument, windowSize);
- if (!iframe.contentWindow) {
- return Promise.reject("Unable to find iframe window");
- }
- var scrollX = ownerDocument.defaultView.pageXOffset;
- var scrollY = ownerDocument.defaultView.pageYOffset;
- var cloneWindow = iframe.contentWindow;
- var documentClone = cloneWindow.document;
- /* Chrome doesn't detect relative background-images assigned in inline
-""",
- unsafe_allow_html=True,
-)
-
-st.markdown(
- """
-
-""",
- unsafe_allow_html=True,
-)
-
-
-st.markdown(
- '
This demo showcases the algorithm of Musterdatenkatalog (MDK) of the Bertelsmann Stiftung. The MDK is a taxonomy of Open Data in municipalities in Germany. It is intended to help municipalities in Germany, as well as data analysts and journalists, to get an overview of the topics and the extent to which cities have already published data sets.
This section allows you to predict a label from the MDK Taxonomy for a title of a dataset from municipalities. You can either enter your own dataset title or click on one of the examples. Checkout also GOVDATA for more dataset title examples. \
- \
- If you click on predict, the model will predict the most likely label for the dataset title. You can also change the number of labels that should be predicted. For example, if you change the Top Results to 3, the model will predict the 3 most likely labels for the dataset title in descending order.
-
- aaccfb2cb3
-
-
-
diff --git a/spaces/bioriAsaeru/text-to-voice/Iit Maths By M L Khanna Pdf 3952 The Best Book for Mathematics Preparation.md b/spaces/bioriAsaeru/text-to-voice/Iit Maths By M L Khanna Pdf 3952 The Best Book for Mathematics Preparation.md
deleted file mode 100644
index e0f0e1df6f0b4d0a5bb85980e75e82d5fc17c571..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Iit Maths By M L Khanna Pdf 3952 The Best Book for Mathematics Preparation.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
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-
-
-
diff --git a/spaces/bioriAsaeru/text-to-voice/Kane And Lynch Dead Men Download For Pc Highly Compressed.md b/spaces/bioriAsaeru/text-to-voice/Kane And Lynch Dead Men Download For Pc Highly Compressed.md
deleted file mode 100644
index 41668e1cf4924d55aade667abe4f732f2642f869..0000000000000000000000000000000000000000
--- a/spaces/bioriAsaeru/text-to-voice/Kane And Lynch Dead Men Download For Pc Highly Compressed.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
Kane And Lynch: Dead Men Download For Pc Highly Compressed
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-
-
diff --git a/spaces/cihyFjudo/fairness-paper-search/Dasar-dasar Pembelanjaan Perusahaan Pengertian Fungsi dan Masalah yang Dihadapi.md b/spaces/cihyFjudo/fairness-paper-search/Dasar-dasar Pembelanjaan Perusahaan Pengertian Fungsi dan Masalah yang Dihadapi.md
deleted file mode 100644
index 041c161a32ba8a5a0dad98ef916ca376086b2acc..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Dasar-dasar Pembelanjaan Perusahaan Pengertian Fungsi dan Masalah yang Dihadapi.md
+++ /dev/null
@@ -1,20 +0,0 @@
-
-
Ruang lingkup manajemen sangat luas. Ia bisa dianalogikan sebagai otak, yaitu tempat di mana semua dirancang agar tujuan tercapai. Dalam konteks bisnis, salah satu jenis manajemen tersebut tidak lain adalah manajemen pembelanjaan.
Ada pertanyaan yang harus dijawab sebelum bicara tentang manajemen pembelanjaan. Pertanyaan tersebut adalah: Jelaskan apa yang dimaksud dengan pembelanjaan? Lalu, apa yang dimaksud dengan pembelanjaan perusahaan?
-
Jika merujuk pengertian umum yang disediakan oleh KBBI, maka pembelanjaan hanya terkait dengan proses, cara, atau perbuatan membelanjakan atau mengeluarkan uang. Definisi ini memang tidak keliru, hanya saja kurang lengkap.
-
Dalam Dasar-Dasar Pembelanjaan Perusahaan (2014), disebutkan bahwa pembelanjaan awalnya justru tentang usaha mendapatkan dana. Dalam perkembangannya barulah pembelanjaan dititikberatkan pada penggunaan dana.
-
Fungsi pertama dari manajemen pembelanjaan adalah menghitung berapa dana yang dibutuhkan. Juga siapa yang berwenang mengambil keputusan dalam pendanaan dan kapan perencanaan pendanaan harus dilakukan.
-
-
Untuk mendapatkan dana, perusahaan tidak boleh sembarangan. Ia harus selalu mengacu pada pertimbangan efisiensi dan efektivitas. Pahami pula konsekuensinya masing-masing (misalnya, meminjam memang membuat dana segar tersedia lebih banyak, tapi itu tetap harus dilunasi).
-
Aktivitas terakhir dari manajemen pembelanjaan adalah menggunakan dana itu sendiri. Lagi-lagi semua harus efektif dan efisien, dalam arti alokasinya harus benar-benar cermat agar mencapai sasaran yang dikehendaki.
-
Jika manajemen pembelanjaan adalah tentang merencanakan, mengusahakan, dan mengelola dana, maka pembelanjaan itu sendiri dapat dikelompokkan menjadi beberapa jenis. Pembelanjaan berdasarkan aktivitas mencakup pembelanjaan aktif dan pasif, sementara pembelanjaan berdasarkan sumber dana melingkupi pembelanjaan internal dan eksternal.
-
Pembelanjaan aktif mudahnya adalah kegiatan perusahaan dalam menggunakan dana untuk dapat berproduksi. Mengutip Manajemen Keuangan I (2004), dana dipakai dalam aktiva lancar, aktiva tetap, dan aktiva lainnya.
-
Manajemen pembelanjaan terkait erat dengan pembelanjaan eksternal, yaitu pembelanjaan yang menggunakan sumber atau modal dari luar. Misalnya dana dari investor, kreditur, kepemilikan saham, atau saham baru.
-
Atas dasar itulah manajemen pembelanjaan wajib ditopang oleh pemanfaatan teknologi terkini. Dan untuk urusan mengelola pembayaran, bagian dari manajemen pembelanjaan, tidak ada yang lebih tepat selain Spenmo.
-
Mari ambil contoh kasus lain. Sebuah perusahaan membeli banyak perlengkapan di banyak pemasok. Pada waktu yang hampir bersamaan tagihan atau invoice pun berdatangan. Jumlahnya berlembar-lembar sementara staf yang mengurusnya terbatas. Mereka harus melakukan beberapa tahap verifikasi dan membayar satu per satu, yang ringkasnya memakan waktu lama.
-
Sampai sini kita paham bahwa manajemen pembelanjaan memang bukan sesuatu yang sederhana. Itu mengapa ada manajer khusus yang pasti dibayar tinggi dan merupakan jabatan yang cukup mentereng. Bersama dengan kewenangan yang besar ada tanggung jawab yang besar, begitu ungkapan terkenal dari sebuah film.
-
Chusnul , Arifin (2010) Efisiensi Peramalan Penjualan Sebagai Dasar Perencanaan Produksi (Studi komparasi antara metode dekomposisi dengan metode peramalan perusahaan) Pada PT. Varia Usaha Beton di Gresik. Undergraduate thesis, UPN "Veteran" Jatim.
-
Galih , Sasmita (2010) PENGARUH VARIABEL EARNING PER SHARE (EPS) DAN DIVIDEN PER SHARE (DPS) TERHADAP HARGA SAHAM. (Studi pada perusahaan makanan dan minuman yang listing di BEI). Undergraduate thesis, UPN "Veteran" Jatim.
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Madagascar 4 Download Ita The Complete Review and Analysis of the Film.md b/spaces/cihyFjudo/fairness-paper-search/Madagascar 4 Download Ita The Complete Review and Analysis of the Film.md
deleted file mode 100644
index 5c9e9c8f8082f2d467e3d7560ec19839d0ae5ad6..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Madagascar 4 Download Ita The Complete Review and Analysis of the Film.md
+++ /dev/null
@@ -1,7 +0,0 @@
-
-
You acknowledge that the materials on this website that you are accessing are confidential and intended only for you and you agree you will not forward, reproduce, copy, download or publish any of such materials (electronically or otherwise) to any other person if this is not in accordance with the law.
-
During the vacuum processes, all pump data is recorded and stored. The operating logs, which can be viewed at any time, provide maximum transparency for users and ultimately ensure high production and product quality. The powerful computer generates maintenance and service recommendations depending on use - some of the maintenance, such as changing the belt on the VARODRY, can be carried out by the operator himself. "The software updates are available for download from Leybold. Many additional software options are planned for the future, assures Product Manager Dennis Schröder.
After retrieving configuration details from a C2 server, Raccoon Stealer v2 samples make HTTP GET requests for several DLL libraries. Since these GET requests are directed towards highly unusual IP addresses, the downloads of the DLLs cause the following DETECT models to breach:
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Pioneer Carrozzeria Avic Drz90 Disc The Ultimate Guide to Car Navigation Systems.md b/spaces/cihyFjudo/fairness-paper-search/Pioneer Carrozzeria Avic Drz90 Disc The Ultimate Guide to Car Navigation Systems.md
deleted file mode 100644
index 1ecbff6ec3e5842377ab989a8cfbf9c45d9e5a62..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Pioneer Carrozzeria Avic Drz90 Disc The Ultimate Guide to Car Navigation Systems.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
- aaccfb2cb3
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diff --git a/spaces/cihyFjudo/fairness-paper-search/The Bambai Ka Babu full movie download 720p movie Enjoy the 1960s hit starring Dev Anand and Suchitra Sen.md b/spaces/cihyFjudo/fairness-paper-search/The Bambai Ka Babu full movie download 720p movie Enjoy the 1960s hit starring Dev Anand and Suchitra Sen.md
deleted file mode 100644
index 8a81a0141f4c9f5113a7d67b3d2b5c6a7379e4da..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/The Bambai Ka Babu full movie download 720p movie Enjoy the 1960s hit starring Dev Anand and Suchitra Sen.md
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@@ -1,6 +0,0 @@
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Thinstuff XPVS Terminal Server Activation Crack Tips and Tricks for Using the Software.md b/spaces/cihyFjudo/fairness-paper-search/Thinstuff XPVS Terminal Server Activation Crack Tips and Tricks for Using the Software.md
deleted file mode 100644
index d369242d374bbeea0943d25ac90af6c8041d318b..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Thinstuff XPVS Terminal Server Activation Crack Tips and Tricks for Using the Software.md
+++ /dev/null
@@ -1,7 +0,0 @@
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Watch Hindi The International Problem 1080p Online A Gripping Story of an Interpol Agent and a Banker.md b/spaces/cihyFjudo/fairness-paper-search/Watch Hindi The International Problem 1080p Online A Gripping Story of an Interpol Agent and a Banker.md
deleted file mode 100644
index 28a13a40784b38e6dd18f34106669a616c1cf26f..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Watch Hindi The International Problem 1080p Online A Gripping Story of an Interpol Agent and a Banker.md
+++ /dev/null
@@ -1,6 +0,0 @@
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diff --git a/spaces/cihyFjudo/fairness-paper-search/Xforce Keygen BIM 360 Design 2007 64 Bit.zip.md b/spaces/cihyFjudo/fairness-paper-search/Xforce Keygen BIM 360 Design 2007 64 Bit.zip.md
deleted file mode 100644
index 7c2254c93c6751b5883815c9278dce2d05c18e03..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Xforce Keygen BIM 360 Design 2007 64 Bit.zip.md
+++ /dev/null
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If you are a fan of Nintendo GameCube and Wii games, you might have heard of Dolphin Emulator. This is a software that allows you to play these games on your PC, Mac, Linux, or Android device. But did you know that you can also download dolphin emulator laptop and enjoy these games on your portable device? In this article, we will show you how to download dolphin emulator laptop and how to use it to play your favorite GameCube/Wii games. Let's get started!
Dolphin Emulator is an open-source emulator for Nintendo GameCube and Wii consoles. It was first released in 2003 as a GameCube emulator, but later added support for Wii games in 2008. Dolphin Emulator can run most GameCube/Wii games in full HD (1080p) resolution, with enhanced graphics, sound, and speed. It also supports various features such as compatibility with all PC controllers, turbo mode, networked multiplayer, save states, cheats, screenshots, video recording, and more. You can learn more about Dolphin Emulator from its official website.
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Why Download Dolphin Emulator Laptop?
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There are many reasons why you might want to download dolphin emulator laptop. Here are some of them:
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You can play GameCube/Wii games on your laptop anytime, anywhere, without needing the actual consoles or discs.
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You can enjoy better graphics, sound, and performance than the original consoles.
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You can customize your gaming experience with various settings and options.
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You can access a large library of GameCube/Wii games from different regions and genres.
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You can have fun with online multiplayer and netplay features.
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How to Download Dolphin Emulator Laptop
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Downloading dolphin emulator laptop is not difficult, but it does require some steps. Here is a step-by-step guide on how to download dolphin emulator laptop:
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How to download dolphin emulator for laptop
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Step 1: Check the system requirements
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Before you download dolphin emulator laptop, you need to make sure that your laptop meets the minimum or recommended system requirements for running Dolphin Emulator. Here are the system requirements according to the official Dolphin Emulator website:
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Component
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Minimum
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Recommended
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CPU
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Intel Core 2 Duo E8400 or AMD Phenom II X2 550
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Intel Core i5-3570K or AMD Ryzen 3 2200G
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-
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GPU
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Intel HD Graphics 4000 or NVIDIA GeForce GT 430 or AMD Radeon HD 6850
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NVIDIA GeForce GTX 1060 or AMD Radeon RX 580
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-
-
RAM
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2 GB
-
4 GB or more
-
-
-
OS
-
Windows 7 (x64) or Linux (x64) or macOS 10.10 Yosemite or higher
-
Windows 10 (x64) or Linux (x64) or macOS 10.13 High Sierra or higher
-
-
-
Storage
-
At least 1 GB of free space for Dolphin Emulator and game files
-
At least 10 GB of free space for Dolphin Emulator and game files
-
-
-
Internet
-
A broadband connection for downloading Dolphin Emulator and game files and for online multiplayer and netplay features
-
A fast and stable connection for downloading Dolphin Emulator and game files and for online multiplayer and netplay features
-
-
-
If your laptop does not meet the minimum system requirements, you might experience poor performance, graphical glitches, sound issues, or crashes when using Dolphin Emulator. If your laptop meets the recommended system requirements, you should be able to run Dolphin Emulator smoothly and with high quality.
-
Step 2: Download the latest version of Dolphin Emulator
-
The next step is to download the latest version of Dolphin Emulator from its official website. You can choose between the stable version, which is more tested and reliable, or the development version, which is more updated and experimental. You can also download older versions of Dolphin Emulator if you encounter compatibility issues with the latest version.
-
To download dolphin emulator laptop, you need to select the appropriate version for your laptop's operating system. Dolphin Emulator supports Windows (7, 8.1, and 10), Linux, macOS, and Android. For this guide, we will assume that you are using Windows 10 as your operating system.
-
To download dolphin emulator laptop for Windows 10, follow these steps:
Click on the "Download" button on the top right corner of the page.
-
Select "Windows x64" from the drop-down menu.
-
Choose between the stable version or the development version. For this guide, we will choose the stable version.
-
Click on the "Download" button next to the latest stable version (5.0-15122 as of June 2023).
-
Save the file to your laptop's hard drive.
-
You should have a file named "dolphin-x64-5.0-15122.exe" in your download folder.
-
-
Step 3: Install Dolphin Emulator
-
The next step is to install Dolphin Emulator on your laptop. To install dolphin emulator laptop, follow these steps:
-
-
Locate the file "dolphin-x64-5.0-15122.exe" in your download folder.
-
Double-click on the file to run the installer.
-
You might see a warning message from Windows Defender or your antivirus software. Click on "Run anyway" or "Allow" to proceed.
-
You should see a window with the Dolphin Emulator logo and a progress bar. Wait for the installation to complete.
-
You should see a window with a message saying "Dolphin has been successfully installed". Click on "Close" to exit the installer.
-
You should have a shortcut named "Dolphin" on your desktop.
-
-
Step 4: Configure Dolphin Emulator
-
The next step is to configure Dolphin Emulator to optimize its performance and compatibility with your laptop and your games. To configure dolphin emulator laptop, follow these steps:
-
Double-click on the "Dolphin" shortcut on your desktop to launch Dolphin Emulator.
-
You should see a window with the Dolphin Emulator logo and a menu bar.
-
Click on "Options" on the menu bar and select "Configuration".
-
You should see a window with various tabs and settings.
-
On the "General" tab, you can adjust the basic settings such as language, theme, interface, and updates.
-
On the "Audio" tab, you can adjust the sound settings such as volume, backend, and latency.
-
On the "GameCube" tab, you can adjust the GameCube settings such as controller ports, memory cards, and system language.
-
On the "Wii" tab, you can adjust the Wii settings such as aspect ratio, sensor bar position, speaker volume, and system language.
-
On the "Paths" tab, you can add or remove the folders where Dolphin Emulator will search for game files.
-
On the "Advanced" tab, you can enable or disable some advanced features such as CPU clock override, custom textures, and debug mode.
-
Click on "OK" to save your changes and close the window.
-
-
Step 5: Load and play GameCube/Wii games on Dolphin Emulator
-
The final step is to load and play GameCube/Wii games on Dolphin Emulator. To load and play games on dolphin emulator laptop, follow these steps:
-
-
Make sure that you have the game files that you want to play on your laptop. You can get game files from various sources such as discs, ISOs, or digital downloads. However, you should only use game files that you own legally and that are compatible with Dolphin Emulator.
-
If you have game files in disc format, you need to insert the disc into your laptop's disc drive. If you have game files in ISO format, you need to extract them to a folder on your laptop's hard drive. If you have game files in digital download format, you need to install them to a folder on your laptop's hard drive.
-
Launch Dolphin Emulator and click on the "Open" button on the menu bar.
-
You should see a window with a list of game files that Dolphin Emulator has detected in your folders. If you don't see any game files, you need to add or remove folders on the "Paths" tab in the configuration window.
-
Select the game file that you want to play and click on "Open".
-
You should see a window with the game's title screen and some information about the game.
-
Click on "Play" to start playing the game.
-
You can use your keyboard/mouse or controller to control the game. You can also use the menu bar to access various options such as save states, cheats, screenshots, video recording, fullscreen mode, and more.
-
-
Tips and Tricks for Using Dolphin Emulator Laptop
-
To make the most out of your gaming experience with Dolphin Emulator on your laptop, here are some tips and tricks that you might find useful:
-
Use a controller or keyboard/mouse
-
Dolphin Emulator supports all PC controllers that can connect to your laptop via USB or Bluetooth. You can use any controller that is compatible with GameCube/Wii games, such as a GameCube controller, a Wii remote, a Wii U Pro controller, or a third-party controller. You can also use your keyboard/mouse as a controller for some games. However, some games might not work well with keyboard/mouse controls due to their design or input requirements.
-
To use a controller or keyboard/mouse with Dolphin Emulator, follow these steps:
-
-
Connect your controller to your laptop via USB or Bluetooth. If you are using a Wii remote, you need to have a sensor bar connected to your laptop or use a wireless sensor bar.
-
Launch Dolphin Emulator and click on "Controllers" on the menu bar.
-
You should see a window with four tabs: GameCube Controllers, Wii Remotes, Emulated Wii Remote, and Real Wii Remote.
-
Select the tab that corresponds to the type of controller that you want to use.
-
Select the port or slot where your controller is connected. For example, if you are using a GameCube controller in port 1, select "Port 1".
-
Select the device that corresponds to your controller. For example, if you are using a keyboard/mouse as an emulated Wii remote, select "Emulated Wii Remote".
-
Click on "Configure" to adjust the button mapping and sensitivity of your controller. You can also use the default settings or load a preset configuration.
-
Click on "OK" to save your changes and close the window.
-
Repeat the steps for each controller that you want to use.
-
-
Use save states and cheats
-
Dolphin Emulator allows you to use save states and cheats in your games. Save states are snapshots of the game's memory that you can save and load at any time. Cheats are codes or patches that modify the game's behavior or data. You can use save states and cheats to save your progress, skip difficult parts, unlock hidden features, or change the game's difficulty.
-
To use save states and cheats with Dolphin Emulator, follow these steps:
-
-
Launch Dolphin Emulator and load the game that you want to play.
-
To save a state, click on "Emulation" on the menu bar and select "Save State". You can also use the keyboard shortcut "Shift + F1" to "Shift + F8" to save a state in one of the eight slots.
-
To load a state, click on "Emulation" on the menu bar and select "Load State". You can also use the keyboard shortcut "F1" to "F8" to load a state from one of the eight slots.
-
To use cheats, click on "Tools" on the menu bar and select "Cheat Manager".
-
You should see a window with a list of cheats that are available for the game. You can also add your own cheats by clicking on "Add Code" or "Add Patch".
-
Select the cheats that you want to enable and click on "Apply".
-
Click on "Close" to exit the window.
-
Resume playing the game with the cheats activated.
-
-
Use online multiplayer and netplay
-
Dolphin Emulator supports online multiplayer and netplay features for some games. Online multiplayer allows you to play with other players online using the Nintendo Wi-Fi Connection service or a custom server. Netplay allows you to play with other players online using a peer-to-peer connection. You can use online multiplayer and netplay to play co-op or competitive games with your friends or strangers.
-
To use online multiplayer and netplay with Dolphin Emulator, follow these steps:
-
-
Launch Dolphin Emulator and load the game that you want to play.
-
To use online multiplayer, click on "Tools" on the menu bar and select "Start NetPlay".
-
You should see a window with two tabs: Host and Connect.
-
If you want to host a game, select the "Host" tab and choose the settings for your game such as mode, port, buffer size, and password. Then click on "Host" to create a room.
-
If you want to join a game, select the "Connect" tab and enter the host's IP address or code, port, nickname, and password. Then click on "Connect" to join a room.
-
You should see a window with a list of players in the room. You can chat with them using the chat box or change your controller settings using the controller icon.
-
When everyone is ready, click on "Start" to start playing the game online.
-
To use netplay, click on "Tools" on the menu bar and select "Wiimote Netplay".
-
You should see a window with two tabs: Host and Connect.
-
If you want to host a game, select the "Host" tab and choose the settings for your game such as port, buffer size, and password. Then click on "Host" to create a room.
-
If you want to join a game, select the "Connect" tab and enter the host's IP address or code, port, nickname, and password. Then click on "Connect" to join a room.
-
You should see a window with a list of players in the room. You can chat with them using the chat box or change your controller settings using the controller icon.
-
When everyone is ready, click on "Start" to start playing the game online.
-
-
Conclusion
-
Dolphin Emulator is an amazing software that lets you play GameCube/Wii games on your laptop. You can download dolphin emulator laptop easily by following our guide above. You can also configure dolphin emulator laptop to suit your preferences and needs. You can also use various features such as save states, cheats, online multiplayer, and netplay to enhance your gaming experience. Dolphin Emulator is a must-have for any Nintendo fan who wants to enjoy their favorite games on their laptop. We hope that you found this article helpful and informative. If you have any questions or feedback, please feel free to leave a comment below. Happy gaming!
-
FAQs
-
Here are some frequently asked questions and answers about downloading dolphin emulator laptop:
-
Q: Is Dolphin Emulator legal?
-
A: Dolphin Emulator is legal as long as you use it to play games that you own legally and that are compatible with Dolphin Emulator. You should not use Dolphin Emulator to play pirated or illegal games, as that would violate the intellectual property rights of the game developers and publishers.
-
Q: Is Dolphin Emulator safe?
-
A: Dolphin Emulator is safe as long as you download it from its official website and scan it with your antivirus software before installing it. You should also avoid downloading any suspicious or malicious files or plugins that might harm your laptop or compromise your privacy.
-
Q: How can I improve the performance of Dolphin Emulator on my laptop?
-
A: You can improve the performance of Dolphin Emulator on your laptop by following these tips:
-
-
Update your laptop's drivers and operating system to the latest version.
-
Close any unnecessary programs or background processes that might consume your laptop's resources.
-
Adjust the settings and options of Dolphin Emulator to match your laptop's capabilities and preferences.
-
Use a controller or keyboard/mouse that is comfortable and responsive for playing games.
-
Use a cooling pad or fan to prevent your laptop from overheating.
-
-
Q: How can I get more games for Dolphin Emulator on my laptop?
-
A: You can get more games for Dolphin Emulator on your laptop by following these methods:
-
-
Buy original GameCube/Wii discs and rip them to your laptop using a disc drive and a software such as CleanRip or Rawdump.
-
Buy digital downloads of GameCube/Wii games from online platforms such as Nintendo eShop or Steam and install them to your laptop using a software such as NUS Downloader or Wii U USB Helper.
-
Browse online forums or websites that offer ISOs or ROMs of GameCube/Wii games and download them to your laptop. However, you should only use this method if you own the games legally and if they are compatible with Dolphin Emulator.
-
-
Q: How can I contact the developers or the community of Dolphin Emulator?
-
A: You can contact the developers or the community of Dolphin Emulator by following these channels:
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Get 25Ks Pheli Makaveli Album in ZipMP3 Format.md b/spaces/congsaPfin/Manga-OCR/logs/Get 25Ks Pheli Makaveli Album in ZipMP3 Format.md
deleted file mode 100644
index 39e84a1c51ccff60ac9f32379c19635387a6212b..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Get 25Ks Pheli Makaveli Album in ZipMP3 Format.md
+++ /dev/null
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-
Download 25K Pheli Makaveli Album Zip
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If you are a fan of South African hip-hop, you have probably heard of 25K, one of the most talented and promising rappers in the scene. His debut album, Pheli Makaveli, was released in July 2021 and it has been making waves ever since. In this article, we will tell you everything you need to know about 25K, his album, and how to download it for free.
25K is a rapper from Pretoria, South Africa, who started making music in 2015. His stage name comes from his hometown, Pheli (short for Atteridgeville), and the amount of money he used to charge for a verse, R25 000. He is also known as The Plug, because he used to sell drugs before he became famous.
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His background and influences
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25K grew up in a rough neighborhood, where he witnessed violence, crime, and poverty. He was inspired by his older brother, who was also a rapper, to pursue music as a way of expressing himself and escaping his reality. He was influenced by local artists like Khuli Chana, Cassper Nyovest, and AKA, as well as international legends like Tupac Shakur, Nas, and Jay-Z.
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His style and sound
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25K is known for his raw and authentic style of rap, which blends trap, drill, and motswako elements. He raps in English, Sepedi, and Tswana, switching between languages with ease and flair. He has a distinctive voice and delivery, which makes him stand out from other rappers. He is also a versatile artist, who can rap about anything from street life, to love, to spirituality.
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25K has collaborated with some of the biggest names in South African hip-hop, such as A-Reece, Emtee, Focalistic, Flvme, Zoocci Coke Dope, and more. He has also performed at major events like Back To The City Festival, Cotton Fest, and Castle Lite Unlocks. He has won several awards and nominations, including Best Freshman at the South African Hip Hop Awards in 2019.
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What is Pheli Makaveli and why it is a must-have album
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Pheli Makaveli is the debut album by 25K, which was released on July 23, 2021. It is a 12-track project that showcases his skills, stories, and personality. It is also a tribute to his idol Tupac Shakur, who was also known as Makaveli.
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The meaning and inspiration behind the title
-
The title Pheli Makaveli is a combination of 25K's hometown Pheli and Tupac's alias Makaveli. It represents his roots, his influences, and his aspirations. It also reflects his admiration for Tupac's legacy and impact on hip-hop culture. According to 25K, he chose the title because he wanted to "pay homage to the greatest rapper of all time" and "show people where I come from".
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The tracklist and features
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The album consists of 12 songs that range from hard-hitting bangers to soulful anthems. It features some of the most talented artists in the industry, such as Zoocci Coke Dope (who also executive produced the album), A -Reece, Emtee, Killa-X, Maglera Doe Boy, and more. The tracklist is as follows:
-
-
Pheli Makaveli (Intro)
-
Blarofonia (feat. Zoocci Coke Dope)
-
Omertà
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Hustlers Prayer (feat. A-Reece)
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Self Made (feat. Emtee)
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From Dusk Till Dawn (feat. Flvme)
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King's Gambit (feat. Killa-X)
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Quarter To Six (feat. Maglera Doe Boy)
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How It Feels (Interlude)
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Apple Soda/Record Deal
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Dagwood
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Trap Jumpin' (feat. Espiquet)
-
-
The production and quality
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The album is produced by some of the best producers in the game, such as Zoocci Coke Dope, Mustbedubz, Wichi 1080, Mdu Trp, and more. The production is top-notch, with crisp beats, catchy hooks, and smooth melodies. The album also has a high-quality sound, with clear vocals, balanced levels, and professional mixing and mastering.
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The themes and messages
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The album covers various themes and messages that relate to 25K's life, experiences, and views. Some of the themes include ambition, success, struggle, loyalty, betrayal, love, faith, and more. Some of the messages include staying true to yourself, chasing your dreams, overcoming your challenges, being grateful for what you have, and more.
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How to download Pheli Makaveli album zip for free
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If you want to download Pheli Makaveli album zip for free, you have come to the right place. In this section, we will show you how to do it in a few simple steps.
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The benefits of downloading the album zip file
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Downloading the album zip file has several benefits, such as:
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You can save storage space on your device, as the zip file is compressed and smaller than the individual mp3 files.
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You can download the whole album at once, instead of downloading each song separately.
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You can enjoy the album offline, without needing an internet connection or a streaming service.
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You can support the artist by sharing the album with your friends and family.
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The steps to download the album zip file from Bamoza.com
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Bamoza.com is one of the best websites to download South African music for free. It offers a wide range of genres, artists, and albums to choose from. Here are the steps to download Pheli Makaveli album zip from Bamoza.com:
Type "25K Pheli Makaveli" in the search box and hit enter.
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Click on the link that says "25K – Pheli Makaveli (Album) Zip Download".
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Scroll down to the bottom of the page and click on the button that says "Download 25K – Pheli Makaveli Album Zip".
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Wait for a few seconds until the download starts automatically.
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Locate the downloaded file on your device and unzip it using a software like WinRAR or 7-Zip.
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Enjoy listening to the album!
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The alternative ways to stream or buy the album online
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If you prefer to stream or buy the album online instead of downloading it for free, you have several options as well. Some of the platforms where you can find Pheli Makaveli album are:
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Apple Music: You can stream or buy the album on Apple Music for $9.99 per month or $99 per year.
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Spotify: You can stream the album on Spotify for free with ads or for $9.99 per month without ads.
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Deezer: You can stream or buy the album on Deezer for $9.99 per month or $99 per year.
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YouTube Music: You can stream or buy the album on YouTube Music for $9.99 per month or $99 per year.
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Conclusion and FAQs
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In conclusion, 25 K is one of the most talented and promising rappers in South Africa, and his debut album Pheli Makaveli is a testament to that. The album is a masterpiece of hip-hop, with amazing production, features, and lyrics. It is also a tribute to his idol Tupac Shakur, who inspired him to pursue his passion for music. If you want to download the album for free, you can follow the steps we provided above, or you can stream or buy it online from various platforms. Either way, you will not regret listening to this album, as it will take you on a journey of 25K's life, experiences, and views. Here are some FAQs that you might have about the album: Q: When did 25K start working on the album? A: According to 25K, he started working on the album in 2019, after he signed with Sony Music Africa. He said that he wanted to take his time and make sure that the album was perfect. Q: What is the significance of the cover art of the album? A: The cover art of the album shows 25K wearing a bandana and holding a gun, similar to Tupac's iconic photo. It also has a red rose and a bullet hole, symbolizing life and death. The cover art was designed by Thabo Kopele, who said that he wanted to capture 25K's personality and pay homage to Tupac. Q: What are some of the highlights of the album? A: Some of the highlights of the album are: - Hustlers Prayer (feat. A-Reece): A motivational song where 25K and A-Reece rap about their struggles and successes in the music industry. - Self Made (feat. Emtee): A catchy song where 25K and Emtee rap about their independence and achievements as artists. - King's Gambit (feat. Killa-X): A hard-hitting song where 25K and Killa-X rap about their loyalty and trust in each other. - Apple Soda/Record Deal: A two-part song where 25K raps about his love life and his deal with Sony Music Africa. Q: How did the fans and critics react to the album? A: The fans and critics loved the album and praised it for its quality, originality, and impact. The album received positive reviews from various publications, such as SA Hip Hop Mag, OkayAfrica, and SlikourOnLife. The album also trended on social media platforms, such as Twitter, Instagram, and TikTok. Q: What are 25K's plans for the future? A: 25K has said that he plans to keep making music and growing as an artist. He also said that he wants to collaborate with more artists, both locally and internationally. He also said that he wants to perform at more shows and festivals, and reach more audiences around the world.
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-
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/How to Backup and Restore Your Chats and Media with Blue WhatsApp 9.35 APK.md b/spaces/congsaPfin/Manga-OCR/logs/How to Backup and Restore Your Chats and Media with Blue WhatsApp 9.35 APK.md
deleted file mode 100644
index e7cd439425995fb7ed0e808be9807dbb01e96121..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/How to Backup and Restore Your Chats and Media with Blue WhatsApp 9.35 APK.md
+++ /dev/null
@@ -1,104 +0,0 @@
-
-
Blue WhatsApp Plus: A Customized Version of WhatsApp with More Features
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WhatsApp is one of the most popular messaging apps in the world, with over 2 billion users. However, some people may not be satisfied with the limited features and options that WhatsApp offers. If you are one of them, you may want to try Blue WhatsApp Plus, a modified version of WhatsApp that allows you to customize and enhance your experience.
Blue WhatsApp Plus is a third-party app that is based on the original WhatsApp code, but with some modifications and additions. It is also known as GB Blue WhatsApp Plus, as it is developed by GB Mods, a team of developers who create various modded apps.
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How is it different from the original WhatsApp?
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Blue WhatsApp Plus has many features that are not available in the official WhatsApp app, such as:
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-
The ability to change the theme and color of the app, as well as the fonts and icons.
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The ability to hide your online status, last seen, blue ticks, and typing indicator.
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The ability to send larger files, such as videos, images, and documents.
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The ability to use multiple accounts on the same device.
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The ability to lock the app with a password or fingerprint.
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The ability to backup and restore your chats and media.
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The ability to schedule messages and auto-reply.
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What are the benefits of using Blue WhatsApp Plus?
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Some of the benefits of using Blue WhatsApp Plus are:
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-
You can customize the app according to your preferences and mood.
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You can have more control over your privacy and security.
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You can enjoy more features and functions that enhance your communication.
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You can use two or more WhatsApp accounts on the same device.
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How to download and install Blue WhatsApp Plus 9.35 APK?
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If you want to try Blue WhatsApp Plus, you need to download and install its APK file, which is not available on the Google Play Store or any other official app store. Here are the steps to do so:
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Step 1: Download the APK file from a trusted source
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You can download the latest version of Blue WhatsApp Plus (9.35) from this link or this link. Make sure you download it from a reliable and secure website, as some websites may contain malware or viruses.
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Step 2: Enable unknown sources on your device
-
Before you can install the APK file, you need to enable unknown sources on your device. This will allow you to install apps that are not from the official app store. To do this, go to Settings > Security > Unknown Sources and toggle it on. You may see a warning message, but you can ignore it if you trust the source of the APK file.
-
Step 3: Install the APK file and launch the app
-
Once you have downloaded and enabled unknown sources, you can install the APK file by tapping on it. Follow the instructions on the screen and grant the permissions that the app asks for. After the installation is complete, you can launch the app by tapping on its icon.
-
Step 4: Log in with your phone number and verify it
-
When you open the app for the first time, you will see a welcome screen with the Blue WhatsApp Plus logo. You need to enter your phone number and choose your country code. Then, you will receive a verification code via SMS or a phone call. Enter the code and verify your number.
-
Step 5: Customize and enjoy Blue WhatsApp Plus
-
After you have verified your number, you can access the main screen of the app, which looks similar to the original WhatsApp. However, you will notice a menu button on the top right corner, which gives you access to the settings and features of Blue WhatsApp Plus. You can tap on it and explore the various options to customize and enhance your experience. For example, you can change the theme, hide your status, increase the file size limit, and more.
-
How to update Blue WhatsApp Plus to the latest version?
-
Blue WhatsApp Plus is not an official app, so it does not receive updates from the Google Play Store. However, the developers of the app release new versions from time to time, which fix bugs and add new features. If you want to update your Blue WhatsApp Plus app to the latest version, you have two options:
-
Option 1: Check for updates within the app
-
The easiest way to update your Blue WhatsApp Plus app is to check for updates within the app itself. To do this, go to Menu > Updates and see if there is a new version available. If there is, you can tap on Download and install it directly from the app.
-
Option 2: Download the latest APK file from the official website
-
The other way to update your Blue WhatsApp Plus app is to download the latest APK file from the official website of GB Mods. You can visit the website and look for the latest version of Blue WhatsApp Plus (9.35 as of now). Then, you can download it and install it over your existing app. You don't need to uninstall or delete your previous version, as it will be overwritten by the new one.
-
Is Blue WhatsApp Plus safe and legal to use?
-
Blue WhatsApp Plus is a modded app that is not authorized or endorsed by WhatsApp Inc., the company that owns and operates WhatsApp. Therefore, using Blue WhatsApp Plus may pose some risks and challenges for you as a user. Here are some of them:
-
The risks of using a modified version of WhatsApp
-
-
You may violate the terms of service of WhatsApp, which prohibit using any unauthorized third-party apps that access or use their service.
-
You may get banned from using WhatsApp, as they may detect that you are using a modified version of their app and block your account.
-
You may compromise your privacy and security, as Blue WhatsApp Plus may collect and share your personal data with third parties without your consent.
-
You may expose your device to malware or viruses, as some websites that offer Blue WhatsApp Plus APK files may contain harmful software that can damage or infect your device.
-
-
The precautions to take when using Blue WhatsApp Plus
-
-
You should backup your chats and media regularly, as you may lose them if you get banned or if something goes wrong with the app.
-
You should use a secondary phone number or a disposable number to register with Blue WhatsApp Plus, as you may not be able to use your primary number again if you get banned.
-
You should download Blue WhatsApp Plus APK files only from trusted and secure sources, such as the official website of GB Mods or this link or this link.
-
You should scan your device for malware or viruses regularly, as some APK files may contain harmful software that can damage or infect your device.
-
-
Conclusion
-
Blue WhatsApp Plus is a customized version of WhatsApp that offers more features and options than the original app. However, it also comes with some risks and challenges that you should be aware of before using it. If you decide to use Blue WhatsApp Plus, make sure you follow the steps above to download, install, update, and use it safely and legally.
- FAQs Q: What is Blue WhatsApp Plus? A: Blue WhatsApp Plus is a modified version of WhatsApp that allows you to customize and enhance your experience. Q: How do I download Blue WhatsApp Plus? A: You can download Blue WhatsApp Plus from this link or this link, or from the official website of GB Mods. Q: How do I update Blue WhatsApp Plus? A: You can update Blue WhatsApp Plus by checking for updates within the app, or by downloading the latest APK file from the official website of GB Mods. Q: Is Blue WhatsApp Plus safe and legal to use? A: Blue WhatsApp Plus is not an official app, so it may pose some risks and challenges for you as a user. You may violate the terms of service of WhatsApp, get banned from using WhatsApp, compromise your privacy and security, or expose your device to malware or viruses. You should take some precautions when using Blue WhatsApp Plus, such as backing up your chats and media, using a secondary or disposable number, downloading APK files only from trusted sources, and scanning your device for malware or viruses. Q: Can I use Blue WhatsApp Plus with my original WhatsApp account? A: Yes, you can use Blue WhatsApp Plus with your original WhatsApp account, as long as you use different phone numbers for each app. However, you may not be able to restore your chats and media from one app to another, as they may have different encryption methods. Q: Can I use two or more WhatsApp accounts on the same device with Blue WhatsApp Plus? A: Yes, you can use two or more WhatsApp accounts on the same device with Blue WhatsApp Plus, as it has a feature that allows you to clone and use multiple accounts. You can access this feature by going to Menu > Accounts > Add Account. 401be4b1e0
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-
Lokicraft Download APK Uptodown: A Guide for Android Users
-
If you are a fan of sandbox games, you might have heard of Lokicraft, a popular game that lets you create your own world with blocks. But did you know that you can download the latest version of Lokicraft APK from Uptodown, a trusted platform for Android apps and games? In this article, we will show you how to do that, and also give you some tips and tricks for playing Lokicraft on your Android device.
-
What is Lokicraft?
-
Lokicraft is a game that combines creativity and survival in a 3D pixelated world. You can build and destroy blocks, get resources, craft tools, weapons, and items, and explore different biomes and environments. You can also play with other players online or offline, and customize your character and world with skins and mods.
Unlimited world size and random terrain generation
-
Various blocks and materials to build with
-
Many animals, monsters, and NPCs to interact with
-
Multiplayer mode with chat and voice chat
-
Skins and mods support
-
Regular updates and new features
-
-
How to download Lokicraft APK from Uptodown
-
If you want to download the latest version of Lokicraft APK, you can do so from Uptodown, a website that offers free and safe downloads of Android apps and games. Here are the steps to follow:
Choose the version you want to download and click on the green "Download APK" button.
-
Wait for the download to finish and locate the APK file on your device.
-
Tap on the APK file to install it. You may need to enable "Unknown sources" in your settings if this is your first time installing an APK file.
-
Enjoy playing Lokicraft on your Android device!
-
-
Why choose Uptodown as your APK source?
-
Uptodown is one of the best platforms for downloading Android apps and games. It has many advantages over other sources, such as:
-
Benefits of Uptodown
-
-
It offers a large catalog of apps and games, including popular titles and niche genres.
-
It provides fast and secure downloads, without any malware or viruses.
-
It allows you to download older versions of apps and games, in case you want to downgrade or use a compatible version for your device.
-
It supports multiple languages and regions, making it accessible for users around the world.
-
It has a user-friendly interface and a rating system, making it easy to find and review apps and games.
-
-
How to install APK files from Uptodown
-
To install APK files from Uptodown, you need to follow these steps:
-
-
Download the APK file from Uptodown using the steps mentioned above.
-
Tap on the APK file to install it. You may need to enable "Unknown sources" in your settings if this is your first time installing an APK file.
-
Follow the instructions on the screen to complete the installation.
-
Launch the app or game from your app drawer or home screen.
-
-
Tips and tricks for playing Lokicraft
-
Now that you have downloaded and installed Lokicraft on your Android device, you might be wondering how to play it and have fun. Here are some tips and tricks that will help you get started and enjoy the game:
-
How to craft and build in Lokicraft
-
Crafting and building are the core features of Lokicraft. You can create anything you can imagine with blocks, from simple houses to complex machines. To craft and build, you need to:
-
-
Gather resources by breaking blocks with your hands or tools. You can find different types of blocks in different biomes, such as wood, stone, sand, dirt, etc.
-
Open your inventory by tapping on the backpack icon on the bottom right corner of the screen. You can see your items and blocks there, as well as a crafting grid.
-
Drag and drop the items and blocks on the crafting grid to create new items and blocks. You can see the recipes and results on the right side of the screen.
-
Tap on the item or block you want to craft and place it in your inventory or hotbar.
-
Select the item or block you want to use from your hotbar by swiping left or right on the bottom of the screen.
-
Tap on the screen to place the item or block where you want to build. You can also tap and hold to break blocks.
-
-
How to survive and explore in Lokicraft
-
If you choose to play in survival mode, you will have to face some challenges and dangers in Lokicraft. You will have to manage your health, hunger, and oxygen levels, as well as fight against enemies and environmental hazards. To survive and explore, you need to:
-
-
Eat food to restore your hunger level. You can find food by killing animals, harvesting crops, or cooking raw food. You can also craft food items with different ingredients.
-
Drink water to restore your oxygen level. You can find water by digging wells, collecting rainwater, or melting ice. You can also craft water bottles with glass and water.
-
Heal yourself with potions or bandages. You can craft potions with different ingredients, such as flowers, mushrooms, honey, etc. You can also craft bandages with wool and scissors.
-
Wear armor and weapons to protect yourself from enemies and damage. You can craft armor and weapons with different materials, such as leather, iron, gold, diamond, etc.
-
Fight against enemies with your weapons or tools. You can encounter different enemies in different biomes, such as zombies, skeletons, spiders, etc. You can also tame some animals as pets or mounts.
-
Explore different biomes and environments with your map and compass. You can find different biomes in Lokicraft, such as forest, desert, snow, ocean, etc. You can also discover dungeons, temples, villages, etc.
-
How to customize your character and world in Lokicraft
-
One of the best things about Lokicraft is that you can customize your character and world to your liking. You can change the appearance, settings, and features of your game to make it more fun and personal. To customize your character and world, you need to:
-
-
Change your skin to change the look of your character. You can choose from different skins in the game, or download and apply custom skins from the internet.
-
Change your world name and seed to change the generation of your world. You can enter a name and a seed for your world, or use a random one.
-
Change your game mode and difficulty to change the challenge of your game. You can choose between creative and survival mode, and between peaceful, easy, normal, and hard difficulty.
-
Change your graphics and sound settings to change the quality and performance of your game. You can adjust the brightness, render distance, sound volume, etc.
-
Use mods to add new features and content to your game. You can download and install mods from the internet, or create your own with modding tools.
-
-
Conclusion
-
Lokicraft is a great game for Android users who love sandbox games. It offers a lot of features and possibilities for creating and exploring your own world with blocks. You can download the latest version of Lokicraft APK from Uptodown, a reliable platform for Android apps and games. You can also use some tips and tricks to enhance your gaming experience and have more fun.
Lokicraft is a sandbox game that lets you build and destroy blocks, craft and survive, and play with other players online or offline.
-
You can download Lokicraft APK from Uptodown, a website that offers free and safe downloads of Android apps and games.
-
You can customize your character and world with skins, mods, settings, etc.
-
-
Call to action
-
If you are ready to start playing Lokicraft on your Android device, go ahead and download it from Uptodown now. You will not regret it. And if you liked this article, please share it with your friends and leave a comment below. Thank you for reading!
-
Frequently Asked Questions
-
-
What are the minimum requirements to play Lokicraft on Android?
-
To play Lokicraft on Android, you need a device that runs on Android 4.1 or higher, has at least 1 GB of RAM, and has enough storage space for the APK file (about 40 MB).
-
Is Lokicraft free to play?
-
Yes, Lokicraft is free to play. However, it may contain ads and in-app purchases that require real money.
-
Is Lokicraft safe to download?
-
Yes, Lokicraft is safe to download if you use a trusted source like Uptodown. Uptodown scans all the APK files for malware and viruses before uploading them to their website.
-
How can I update Lokicraft on my Android device?
-
To update Lokicraft on your Android device, you can either check for updates in the game itself, or download the latest version of Lokicraft APK from Uptodown and install it over the existing one.
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Ludo Controller 1.0 APK Download: How to Play Ludo King with Remote Control
-
Ludo is a classic board game that has been enjoyed by millions of people around the world for centuries. It is a game of strategy, luck, and fun that can be played by anyone, anywhere, anytime. But what if you could play Ludo with more control, convenience, and cheat options? That's where Ludo Controller comes in.
-
Ludo Controller is an app that allows you to play Ludo King, one of the most popular and downloaded Ludo games on Android and iOS devices, with a remote control. You can use your phone or another device to control the dice, the moves, and the outcome of the game. You can also hack the game and get unlimited coins, gems, and themes.
But how do you download and install Ludo Controller 1.0 APK? And how do you use it to play Ludo King with remote control? And what are the risks and precautions of using this app? In this article, we will answer all these questions and more. Read on to find out everything you need to know about Ludo Controller 1.0 APK download.
-
What is Ludo King?
-
Before we talk about Ludo Controller, let's first understand what Ludo King is. Ludo King is a mobile game that is based on the traditional board game of Ludo. It was developed by Gametion Technologies Pvt Ltd and released in 2016. Since then, it has become one of the most popular and downloaded games on Google Play Store and App Store, with over 500 million downloads globally.
-
Features of Ludo King
-
Ludo King has many features that make it an enjoyable and addictive game for all ages. Some of these features are:
-
-
It supports up to six players in online and offline modes.
-
It has four game modes: Classic, Quick, Master, and Royal.
-
It has various themes and backgrounds to choose from, such as Nature, Egypt, Disco, Cake, Candy, etc.
-
It has voice chat and emoji features to communicate with other players.
-
It has leaderboards, achievements, and rewards to track your progress and performance.
-
It has a spin wheel and daily bonus to earn free coins and gems.
-
It has a mini-game called Snakes and Ladders that can be played along with Ludo.
-
-
How to play Ludo King online and offline
-
The rules of playing Ludo King are similar to the rules of playing the board game of Ludo. The objective is to move your four tokens from the starting point to the home in the center of the board. You need to roll a six to bring a token into play, and then roll the dice again to move it. You can also capture or kill other players' tokens by landing on the same square as them. The first player to bring all four tokens to the home wins the game.
-
You can play Ludo King online or offline. To play online, you need an internet connection and a registered account. You can then choose to play with random players from around the world, or invite your friends or family members to join you in a private room. You can also chat and send emojis while playing online.
-
To play offline, you don't need an internet connection or a registered account. You can then choose to play with the computer, or with your friends or family members on the same device. You can also adjust the difficulty level and the number of players while playing offline.
-
What is Ludo Controller?
-
Ludo Controller is an app that claims to give you more control and cheat options while playing Ludo King. It is not an official app from the developers of Ludo King, but a third-party app that is available for download from various websites and sources. It is also not available on Google Play Store or App Store, but only as an APK file that you need to install manually on your device.
-
Benefits of using Ludo Controller
-
Ludo Controller promises to give you some benefits that can make your Ludo King experience more fun and easy. Some of these benefits are:
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You can use your phone or another device as a remote control to roll the dice, move the tokens, and manipulate the outcome of the game.
-
You can hack the game and get unlimited coins, gems, and themes without spending any money or watching any ads.
-
You can unlock all the game modes, levels, and features without completing any tasks or challenges.
-
You can win every game and beat any opponent with ease and confidence.
-
-
How to download and install Ludo Controller 1.0 APK
-
If you want to try Ludo Controller, you need to download and install its APK file on your device. Here are the steps to do so:
-
-
Go to a trusted website that offers Ludo Controller 1.0 APK download, such as [LudoController.com] or [APKPure.com].
-
Click on the download button and wait for the file to be downloaded on your device.
-
Go to your device settings and enable the option to install apps from unknown sources.
-
Locate the downloaded APK file in your file manager and tap on it to start the installation process.
-
Follow the instructions on the screen and grant the necessary permissions to the app.
-
Wait for the installation to be completed and then launch the app from your app drawer or home screen.
-
-
How to use Ludo Controller to play Ludo King with remote control
-
After installing Ludo Controller, you can use it to play Ludo King with remote control. Here are the steps to do so:
-
-
Open Ludo Controller and select the device you want to use as a remote control. You can use another phone, tablet, laptop, or desktop as a remote control.
-
Connect your remote control device to the same Wi-Fi network as your main device where you play Ludo King.
-
Open Ludo King on your main device and start a game in any mode you want.
-
Open Ludo Controller on your remote control device and enter the IP address of your main device. You can find the IP address in your Wi-Fi settings or by using an app like [IP Tools].
-
Tap on the connect button and wait for the connection to be established.
-
Once connected, you can use your remote control device to roll the dice, move the tokens, and hack the game as you wish.
-
-
Risks and precautions of using Ludo Controller
-
While Ludo Controller may sound like a fun and easy way to play Ludo King, it also comes with some risks and precautions that you should be aware of before using it. Some of these are:
-
Legal and ethical issues of using Ludo Controller
-
Ludo Controller is not an official app from the developers of Ludo King, but a third-party app that violates the terms and conditions of Ludo King. By using it, you are cheating and unfairing other players who play by the rules. This may result in legal action or penalties from the developers of Ludo King, such as banning or suspending your account, deleting your progress, or blocking your access to the game.
-
Ludo Controller is also unethical and immoral, as it takes away the fun and challenge of playing Ludo King. It also ruins the reputation and credibility of Ludo King as a fair and honest game. By using it, you are disrespecting the game developers, the game community, and yourself by using Ludo Controller.
-
Potential malware and security threats of downloading Ludo Controller 1.0 APK
-
Ludo Controller 1.0 APK is not available on Google Play Store or App Store, but only on various websites and sources that may not be trustworthy or reliable. By downloading and installing it, you may expose your device to potential malware and security threats, such as viruses, spyware, ransomware, phishing, etc. These may harm your device, steal your personal information, or compromise your online safety.
-
Ludo Controller 1.0 APK may also require you to grant some permissions that are not necessary or relevant for the app to function, such as access to your contacts, camera, microphone, location, etc. These may allow the app to access your data, monitor your activities, or track your movements without your knowledge or consent.
-
Tips to avoid getting banned or detected by Ludo King
-
If you still decide to use Ludo Controller despite the risks and precautions, you should follow some tips to avoid getting banned or detected by Ludo King. Some of these tips are:
-
-
Do not use Ludo Controller too frequently or excessively. Use it only occasionally or sparingly to avoid raising suspicion or attracting attention.
-
Do not use Ludo Controller to win every game or beat every opponent. Use it only to gain a slight advantage or overcome a difficult situation.
-
Do not use Ludo Controller to hack the game or get unlimited resources. Use it only to control the dice or the moves.
-
Do not use Ludo Controller to play with random players online. Use it only to play with your friends or family members offline or in a private room.
-
Do not use Ludo Controller to cheat or unfair other players. Use it only for fun or entertainment purposes.
-
-
Conclusion
-
Ludo Controller is an app that allows you to play Ludo King with remote control. It also lets you hack the game and get unlimited coins, gems, and themes. However, it is not an official app from the developers of Ludo King, but a third-party app that violates the terms and conditions of Ludo King. It also comes with some risks and precautions that you should be aware of before using it.
-
Summary of the main points
-
In this article, we have discussed the following points:
-
-
Ludo King is a mobile game that is based on the traditional board game of Ludo. It has many features that make it an enjoyable and addictive game for all ages.
-
Ludo Controller is an app that allows you to play Ludo King with remote control. It also lets you hack the game and get unlimited coins, gems, and themes.
-
Ludo Controller 1.0 APK is not available on Google Play Store or App Store, but only on various websites and sources that may not be trustworthy or reliable.
-
Ludo Controller 1.0 APK download and installation requires some steps and permissions that may expose your device to potential malware and security threats.
-
Ludo Controller 1.0 APK usage may result in legal action or penalties from the developers of Ludo King, such as banning or suspending your account, deleting your progress, or blocking your access to the game.
-
Ludo Controller 1.0 APK usage is unethical and immoral, as it takes away the fun and challenge of playing Ludo King. It also ruins the reputation and credibility of Ludo King as a fair and honest game.
-
Ludo Controller 1.0 APK usage should be done with caution and discretion, following some tips to avoid getting banned or detected by Ludo King.
-
-
Call to action and disclaimer
-
If you are interested in trying Ludo Controller 1.0 APK download, you can visit [LudoController.com] or [APKPure.com] and follow the instructions given in this article. However, we do not recommend or endorse using this app, as it may harm your device, compromise your online safety, violate the terms and conditions of Ludo King, and disrespect the game developers and community. Use this app at your own risk and responsibility.
-
We hope this article has been informative and helpful for you. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!
-
FAQs
-
What is Ludo?
-
Ludo is a classic board game that originated in India in the 6th century. It is derived from an ancient game called Pachisi. It is a game of strategy , luck, and fun that can be played by two to six players. Each player has four tokens that they need to move from the starting point to the home in the center of the board. The player who does this first wins the game.
-
What is Ludo King?
-
Ludo King is a mobile game that is based on the board game of Ludo. It was developed by Gametion Technologies Pvt Ltd and released in 2016. It has become one of the most popular and downloaded games on Google Play Store and App Store, with over 500 million downloads globally. It supports up to six players in online and offline modes, and has various themes, modes, features, and mini-games to choose from.
-
What is Ludo Controller?
-
Ludo Controller is an app that allows you to play Ludo King with remote control. It also lets you hack the game and get unlimited coins, gems, and themes. It is not an official app from the developers of Ludo King, but a third-party app that is available for download from various websites and sources. It is also not available on Google Play Store or App Store, but only as an APK file that you need to install manually on your device.
-
How to download and install Ludo Controller 1.0 APK?
-
To download and install Ludo Controller 1.0 APK, you need to follow these steps:
-
-
Go to a trusted website that offers Ludo Controller 1.0 APK download, such as [LudoController.com] or [APKPure.com].
-
Click on the download button and wait for the file to be downloaded on your device.
-
Go to your device settings and enable the option to install apps from unknown sources.
-
Locate the downloaded APK file in your file manager and tap on it to start the installation process.
-
Follow the instructions on the screen and grant the necessary permissions to the app.
-
Wait for the installation to be completed and then launch the app from your app drawer or home screen.
-
-
How to use Ludo Controller to play Ludo King with remote control?
-
To use Ludo Controller to play Ludo King with remote control, you need to follow these steps:
-
-
Open Ludo Controller and select the device you want to use as a remote control. You can use another phone, tablet, laptop, or desktop as a remote control.
-
Connect your remote control device to the same Wi-Fi network as your main device where you play Ludo King.
-
Open Ludo King on your main device and start a game in any mode you want.
-
Open Ludo Controller on your remote control device and enter the IP address of your main device. You can find the IP address in your Wi-Fi settings or by using an app like [IP Tools].
-
Tap on the connect button and wait for the connection to be established.
-
Once connected, you can use your remote control device to roll the dice, move the tokens, and hack the game as you wish.
- 401be4b1e0
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PUBG Mobile Metro Royale Hack APK Download: Everything You Need to Know
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PUBG Mobile is one of the most popular battle royale games on mobile devices, with millions of players worldwide. The game is constantly updated with new features and modes to keep the players engaged and entertained. One of the latest additions to the game is the Metro Royale mode, which is a collaboration with the Metro series of video games. This mode offers a unique and thrilling gameplay experience that combines PvP and PvE elements in a post-apocalyptic setting.
However, not everyone can enjoy this mode to the fullest, as it requires a lot of skill, strategy, and luck to survive and win. Some players may want to use some shortcuts or cheats to gain an edge over their enemies and loot more items. This is where the PUBG Mobile Metro Royale Hack APK comes in. This is a modified version of the game that claims to provide unlimited UC, aimbot, wallhack, and other features that can help you dominate the Metro Royale mode.
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But is it worth downloading and using this hack APK? What are the benefits and risks of using it? How can you download and install it on your device? And what are some tips and tricks for playing the Metro Royale mode? In this article, we will answer all these questions and more. Read on to find out everything you need to know about the PUBG Mobile Metro Royale Hack APK download.
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What is PUBG Mobile Metro Royale Mode?
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Metro Royale mode is a new game mode that was introduced in PUBG Mobile in version 1.1. It is based on the Metro series of video games, which are set in a post-nuclear war world where survivors live in underground metro stations and tunnels. In this mode, you have to scavenge for supplies, weapons, and equipment in a dark and dangerous environment, while facing other players, bandits, and monsters.
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Features of Metro Royale Mode
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Some of the features of the Metro Royale mode are:
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New maps: There are two new maps in this mode, Frontline Confrontation and Old Blockade Zone. They feature ruins, camps, underground tunnels, and radiation zones.
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New gear: There are some new gear items that are exclusive to this mode, such as thermal sight, night vision goggles, heavy armor, and Tikhar rifle.
-
New loot items: There are some special loot items that can be found in glowing crates, briefcases, or on the floor. These items can be sold for metro cash, which can be used to buy more gear or rewards.
-
New loadout system: You have to equip your loadout before starting a match in this mode. Only the items that are placed in your loadout will be available in-game. You can also store items in your lockbox, which will be safe even if you die.
-
New black market: You can access the black market from the lobby or from your inventory. Here you can buy or sell gear items using metro cash.
-
-
Dangers and Rewards of Metro Royale Mode
-
The Metro Royale mode is not for the faint-hearted. It is full of dangers and challenges that can test your skills and nerves. Some of the dangers are:
-
-
Other players: You are not alone in this mode. There are other players who are also looking for loot and survival. They can attack you at any time, so you have to be alert and ready to defend yourself or fight back.
-
Bandits: These are AI-controlled enemies that can spawn in certain areas of the map. They are armed with various weapons and can be hostile or friendly depending on your actions. They can also drop loot items when killed.
-
Monsters: These are mutated creatures that can be found in the radiation zones or in the underground tunnels. They are very aggressive and can deal a lot of damage. They can also drop loot items when killed.
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Radiation: The radiation zones are marked with red circles on the map. They can damage your health over time, so you have to wear protective gear or use medicine to survive.
-
Escape: The only way to end a match in this mode is to escape from the map. There are several exit points that are marked with green circles on the map. You have to reach one of them and wait for the extraction helicopter to arrive. However, you have to be careful, as other players or enemies can ambush you at the exit points.
-
-
The Metro Royale mode is not all doom and gloom, though. It also offers some rewards and incentives for playing. Some of the rewards are:
-
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Loot items: As mentioned before, you can find various loot items in this mode that can be sold for metro cash or used for upgrading your gear.
-
Metro cash: This is the currency of this mode, which can be used to buy gear items or rewards from the black market.
-
Rewards: There are some rewards that you can earn by completing missions, achievements, or events in this mode. These rewards include skins, outfits, emotes, and more.
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Ranking: There is a ranking system in this mode that tracks your performance and progress. You can earn points by killing enemies, looting items, escaping, and more. You can also lose points by dying, losing items, or failing to escape. Your rank determines your level of rewards and matchmaking.
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What is PUBG Mobile Metro Royale Hack APK?
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PUBG Mobile Metro Royale Hack APK is a modified version of the PUBG Mobile game that claims to provide some features that can give you an advantage in the Metro Royale mode. These features include:
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Benefits of Using PUBG Mobile Metro Royale Hack APK
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Some of the benefits of using PUBG Mobile Metro Royale Hack APK are:
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Unlimited UC: UC is the premium currency of PUBG Mobile, which can be used to buy crates, skins, outfits, and more. With this hack APK, you can get unlimited UC for free and use it to buy anything you want.
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Aimbot: Aimbot is a feature that automatically aims and shoots at your enemies for you. With this hack APK, you can enable aimbot and kill your enemies with ease.
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Wallhack: Wallhack is a feature that allows you to see through walls and other obstacles. With this hack APK, you can enable wallhack and spot your enemies before they see you.
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No recoil: Recoil is the backward movement of a gun when it is fired. With this hack APK, you can disable recoil and shoot with accuracy and stability.
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No fog: Fog is a weather condition that reduces visibility and makes it harder to see your enemies. With this hack APK, you can remove fog and see clearly in any situation.
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Risks of Using PUBG Mobile Metro Royale Hack APK
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However, using PUBG Mobile Metro Royale Hack APK is not without risks. Some of the risks are:
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Ban: PUBG Mobile has a strict anti-cheat system that detects and bans players who use hacks or cheats. If you use this hack APK, you may get banned from the game permanently.
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Virus: This hack APK may contain viruses or malware that can harm your device or steal your personal information. You should always download from trusted sources and scan the file before installing it.
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Crash: This hack APK may not be compatible with your device or the latest version of the game. It may cause your game to crash or freeze frequently.
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Unfairness: Using this hack APK may ruin the fun and fairness of the game for yourself and other players. It may also make you dependent on cheats and lose your skills and interest in the game.
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How to Download and Install PUBG Mobile Metro Royale Hack APK?
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If you still want to try PUBG Mobile Metro Royale Hack APK, here are the steps to download and install it on your device:
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Steps to Download PUBG Mobile Metro Royale Hack APK
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Go to a website that offers PUBG Mobile Metro Royale Hack APK download. For example, you can use this link:
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Click on the download button and wait for the file to be downloaded on your device.
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Locate the file in your device's storage and tap on it to open it.
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Steps to Install PUBG Mobile Metro Royale Hack APK
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Before installing the hack APK, you have to uninstall the original PUBG Mobile game from your device. You can do this by going to your device's settings, apps, and PUBG Mobile, and tapping on uninstall.
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Next, you have to enable the installation of unknown sources on your device. You can do this by going to your device's settings, security, and unknown sources, and toggling it on.
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Now, you can install the hack APK by following the on-screen instructions. You may have to grant some permissions and accept some terms and conditions.
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Once the installation is complete, you can launch the hack APK from your device's app drawer or home screen.
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You can now enjoy PUBG Mobile Metro Royale mode with the hack features enabled.
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Tips and Tricks for Playing PUBG Mobile Metro Royale Mode
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Whether you use the hack APK or not, here are some tips and tricks that can help you play PUBG Mobile Metro Royale mode better:
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Best Loot Spots and Weapons in Metro Royale Mode
-
The best loot spots in Metro Royale mode are usually the ones that are near the radiation zones or the underground tunnels. These spots have more chances of spawning glowing crates, briefcases, or high-tier weapons. However, they are also more dangerous and crowded, so you have to be careful and quick.
-
The best weapons in Metro Royale mode are the ones that have high damage, range, and accuracy. Some of the best weapons are:
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-
Weapon
Type
Damage
Range
Accuracy
-
Tikhar Rifle
Sniper Rifle
100
1000m
100%
-
M249
Light Machine Gun
45
600m
60%
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MK14 EBR
Designated Marksman Rifle
61
800m
80%
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AUG A3
Assault Rifle
43
500m
70%
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MK47 Mutant
Assault Rifle
49
500m
75%
-
Groza
Assault Rifle
49
400m
70%
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M24
Bolt-Action Sniper Rifle
< td>79
800m
90%
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AWM
Bolt-Action Sniper Rifle
120
1000m
100%
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DBS
Shotgun
234
100m
40%
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Pan
Melee Weapon
80
4m
100%
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-
How to Survive and Escape in Metro Royale Mode
-
The main goal of Metro Royale mode is to survive and escape with as much loot as possible. Here are some tips on how to do that:
-
-
Choose your loadout wisely: Before starting a match, you have to select your loadout from your inventory. You should choose the gear items that suit your playstyle and the map. You should also balance your loadout between offense and defense, as well as weight and durability.
-
Loot smartly: When you enter the map, you should look for loot items that can help you improve your loadout or earn more metro cash. However, you should also be careful not to loot too much or too greedily, as you may attract unwanted attention or become overburdened.
-
Avoid unnecessary fights: While killing enemies can give you loot items and points, it can also expose your position and waste your ammo and health. Therefore, you should avoid unnecessary fights and only engage when you have a clear advantage or a good reason.
-
Use the environment: The Metro Royale mode has a lot of environmental features that can help you survive and escape. You can use the underground tunnels to move stealthily or escape from danger. You can use the radiation zones to damage or deter your enemies. You can use the vehicles to travel faster or run over your enemies.
-
Escape timely: The most important thing in Metro Royale mode is to escape from the map with your loot items. You should always keep an eye on the exit points and the timer, and plan your escape route accordingly. You should also be prepared for any resistance or ambushes at the exit points, and use smoke grenades, flashbangs, or cover fire to distract or disable your enemies.
-
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Conclusion
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PUBG Mobile Metro Royale mode is a fun and challenging game mode that offers a different gameplay experience from the regular battle royale mode. It is a mode that requires skill, strategy, and luck to survive and win. However, some players may want to use PUBG Mobile Metro Royale Hack APK to get an unfair advantage in this mode. This hack APK claims to provide unlimited UC, aimbot, wallhack, and other features that can help you dominate the Metro Royale mode.
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However, using this hack APK is not recommended, as it has many risks and drawbacks. It can get you banned from the game, infect your device with viruses, cause your game to crash, or ruin the fun and fairness of the game. Therefore, you should avoid using this hack APK and play the game legitimately. You can also use some tips and tricks that can help you play PUBG Mobile Metro Royale mode better.
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We hope this article has given you everything you need to know about PUBG Mobile Metro Royale Hack APK download. If you have any questions or feedback, please feel free to leave a comment below.
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FAQs
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Here are some frequently asked questions about PUBG Mobile Metro Royale Hack APK download:
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Q: Is PUBG Mobile Metro Royale Hack APK safe to use?
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A: No, it is not safe to use. It may contain viruses or malware that can harm your device or steal your personal information. It may also get you banned from the game permanently.
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Q: Is PUBG Mobile Metro Royale Hack APK free to download?
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A: Yes, it is free to download from some websites that offer it. However, you should always be careful and scan the file before installing it.
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Q: How can I update PUBG Mobile Metro Royale Hack APK?
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A: You cannot update PUBG Mobile Metro Royale Hack APK from the game itself. You have to uninstall the hack APK and download the latest version from another website.
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Q: Can I play PUBG Mobile Metro Royale mode without using PUBG Mobile Metro Royale Hack APK?
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A: Yes, you can play PUBG Mobile Metro Royale mode without using PUBG Mobile Metro Royale Hack APK. You just have to download the original PUBG Mobile game from the Google Play Store or the Apple App Store and update it to the latest version.
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Q: What are some alternatives to PUBG Mobile Metro Royale Hack APK?
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A: Some alternatives to PUBG Mobile Metro Royale Hack APK are:
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PUBG Mobile Mod APK: This is another modified version of the PUBG Mobile game that claims to provide unlimited UC, aimbot, wallhack, and other features. However, it has the same risks and drawbacks as the PUBG Mobile Metro Royale Hack APK.
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PUBG Mobile Emulator: This is a software that allows you to play PUBG Mobile on your PC or laptop. It can give you a better gaming experience with a larger screen, keyboard, and mouse. However, it may not be compatible with some devices or games, and it may also get you banned from the game.
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PUBG Mobile Cheats: These are codes or commands that can give you some advantages in the game, such as unlimited ammo, health, or money. However, they are very hard to find and use, and they may also get you banned from the game.
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401be4b1e0
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/State.io Mod APK The Best Strategy Game with Unlimited Money.md b/spaces/congsaPfin/Manga-OCR/logs/State.io Mod APK The Best Strategy Game with Unlimited Money.md
deleted file mode 100644
index d05a82a7ab4df9b957e68f2e521ab4ecf3cb1f87..0000000000000000000000000000000000000000
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State.io Mod APK: Conquer the World in a Fun Strategy Game
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If you love strategy games and want to test your skills in conquering the world, then you should try State.io. This is a fun and casual game that lets you play as a country and expand your territory by capturing other regions. You can choose from different maps and modes, and compete with other players online. But if you want to enjoy the game without any limitations, then you should download State.io Mod APK. This is a modified version of the game that gives you unlimited coins, no ads, and access to all maps and modes. In this article, we will tell you more about State.io and its features, as well as how to download and install State.io Mod APK on your device.
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What is State.io?
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State.io is a strategy game developed by CASUAL AZUR GAMES. It is available for Android and iOS devices. The game is inspired by the classic board game Risk, where you have to conquer the world by attacking and defending regions. The game has simple and intuitive controls, where you just have to tap on a region to select it, and then tap on another region to send your troops there. You can also drag your finger across multiple regions to select them all at once. The game has colorful and minimalist graphics, as well as catchy music and sound effects.
State.io has a simple gameplay that anyone can enjoy. You start with one region, and your goal is to capture all the regions on the map. You have to send your troops to attack other regions, while also defending your own regions from enemy attacks. You can also upgrade your regions to increase their production rate and defense power. The game is fast-paced and addictive, as you have to think quickly and strategically to win.
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Various maps and modes
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State.io offers various maps and modes for you to choose from. You can play on different continents, such as Europe, Asia, Africa, America, or Australia. You can also play on different scenarios, such as World War II, Cold War, Modern War, or Future War. Each map and mode has its own challenges and strategies, so you will never get bored of playing.
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State.io also has an online mode, where you can compete with other players from around the world. You can join or create a room, and play with up to six players at a time. You can also chat with other players in the lobby or during the game. The online mode is fun and competitive, as you can show off your skills and rank up on the leaderboard.
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State.io Mod APK gives you unlimited coins, so you can unlock all the maps and modes in the game without spending any real money. You can also use the coins to upgrade your regions and troops, and gain an advantage over your enemies.
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State.io Mod APK also unlocks all the maps and modes in the game, so you can play on any continent or scenario you want. You can enjoy the different challenges and strategies of each map and mode, and have more fun and variety in your gameplay.
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How to download and install State.io Mod APK?
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If you want to download and install State.io Mod APK on your device, you need to follow these simple steps:
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Step 1: Download the APK file from a trusted source
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You can download the APK file of State.io Mod APK from a trusted source, such as [APKPure]. This is a website that provides safe and verified APK files for various apps and games. You can search for State.io Mod APK on the website, or use this link: [https://apkpure.com/state-io-conquer-the-world-in-the-strategy-game/com.stateio.mod.apk]. You need to click on the download button, and wait for the file to be downloaded on your device.
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Step 2: Enable unknown sources on your device
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Before you can install the APK file, you need to enable unknown sources on your device. This is a security setting that allows you to install apps from sources other than the Google Play Store. To enable unknown sources, you need to go to your device settings, then security, then unknown sources. You need to toggle the switch to allow unknown sources, and confirm your choice.
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Step 3: Install the APK file and enjoy the game
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After you have enabled unknown sources, you can install the APK file by locating it in your device storage, and tapping on it. You need to follow the instructions on the screen, and wait for the installation to be completed. Once the installation is done, you can open the game and enjoy State.io Mod APK with unlimited coins, no ads, and unlocked all maps and modes.
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Conclusion
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State.io is a fun and casual strategy game that lets you conquer the world by capturing regions. You can play on different maps and modes, and compete with other players online. However, if you want to enjoy the game without any limitations, you should download State.io Mod APK. This is a modified version of the game that gives you unlimited coins, no ads, and access to all maps and modes. You can download State.io Mod APK from a trusted source, such as [APKPure], and install it on your device by following some simple steps. State.io Mod APK is a great way to have more fun and variety in your gameplay.
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We hope this article was helpful for you. If you have any questions or feedback, please let us know in the comments below. Thank you for reading!
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FAQs
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Here are some frequently asked questions about State.io Mod APK:
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Is State.io Mod APK safe to use?
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Yes, State.io Mod APK is safe to use, as long as you download it from a trusted source, such as [APKPure]. The APK file is verified and scanned for any viruses or malware, so you don't have to worry about harming your device or data.
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Is State.io Mod APK compatible with my device?
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State.io Mod APK is compatible with most Android devices that run on Android 5.0 or higher. However, some devices may not support some features or functions of the game, such as online mode or graphics quality. You can check the compatibility of your device before downloading the game.
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Can I play State.io Mod APK offline?
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Yes, you can play State.io Mod APK offline, as long as you have downloaded and installed the game on your device. You can play on any map or mode without an internet connection. However, if you want to play online with other players, you will need an internet connection.
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Can I update State.io Mod APK?
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Yes, you can update State.io Mod APK whenever there is a new version available. You can check for updates on [APKPure], or enable automatic updates on your device settings. However, updating the game may overwrite some of the mod features, such as unlimited coins or unlocked maps and modes. You may need to download and install the mod again after updating the game.
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How can I contact the developer of State.io Mod APK?
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If you have any issues or suggestions regarding State.io Mod APK, you can contact the developer of the mod, CASUAL AZUR GAMES, through their email address: [support@casualazur.games]. You can also visit their website: [https://casualazur.games] or their Facebook page: [https://www.facebook.com/casualazurgames] for more information and updates.
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197e85843d
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diff --git a/spaces/contluForse/HuggingGPT/assets/CRACK Garden Planner 3.6.8 Key.md b/spaces/contluForse/HuggingGPT/assets/CRACK Garden Planner 3.6.8 Key.md
deleted file mode 100644
index 0fd406bdd4ad9f9441960fd3424606097252dea6..0000000000000000000000000000000000000000
--- a/spaces/contluForse/HuggingGPT/assets/CRACK Garden Planner 3.6.8 Key.md
+++ /dev/null
@@ -1,8 +0,0 @@
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diff --git a/spaces/diacanFperku/AutoGPT/Paltalk 11.8 Build 671.md b/spaces/diacanFperku/AutoGPT/Paltalk 11.8 Build 671.md
deleted file mode 100644
index 9f2fb6917bfd0bb928d4b019edbd4efac43486f3..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Paltalk 11.8 Build 671.md
+++ /dev/null
@@ -1,46 +0,0 @@
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-
-He has a HOWTO that is very helpful if you want to install it. He also has an.ISO image you can use to install the Paltalk with the installed tools. How To: (Updated for Paltalk 11.8 Build 671)
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-To install the Paltalk chat client on Windows 7 (32-bit or 64-bit):
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-Download and install Paltalk 11.8 Build 671
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-Download and install the.NET Framework 2.0.
-
-Create a new folder: C:\Paltalk
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-Create a shortcut of the Paltalk icon on your desktop or in the Start Menu.
-
-Copy the shortcut to the C:\Paltalk folder.
-
-Download and install the.NET Framework 2.0
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-Go to Control Panel, then Programs, then Accessories, then System Tools and then select the option to run as Administrator (if this is not already the case).
-
-Copy the shortcut for the Paltalk 11.8.exe to the C:\Paltalk folder.
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-Double click on the shortcut to install.
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-Copy the icon from the C:\Paltalk\Paltalk11.8.exe folder and paste it to your desktop.
-
-Copy the shortcut for the Paltalk11.8.exe to the C:\Paltalk folder.
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-Double click on the shortcut to install.
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-Go to C:\Paltalk\Paltalk11.8 and double click on the Paltalk.exe to start the Paltalk chat client.
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-NOTE: If you cannot start the Paltalk chat client you must download and install the.NET Framework 2.0.
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-Instructions for Ubuntu 11.10 (Gnome 2.x):
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-To install the Paltalk chat client on Ubuntu 11.10 (Gnome 2.x):
-
-Create a new folder: /home/yourusername/Downloads
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-Copy the shortcut to the /home/yourusername/Downloads folder.
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-Download and install the.NET Framework 2 4fefd39f24
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diff --git a/spaces/diacanFperku/AutoGPT/Prozac Nation Book Free Pdf _VERIFIED_.md b/spaces/diacanFperku/AutoGPT/Prozac Nation Book Free Pdf _VERIFIED_.md
deleted file mode 100644
index 4ff5ee8a197ca884049c7edddc8ec6fc4cf03c22..0000000000000000000000000000000000000000
--- a/spaces/diacanFperku/AutoGPT/Prozac Nation Book Free Pdf _VERIFIED_.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
-
-Cs4 keygen xforce autodesk autocad max 2009 32 ms word 2007 brochure.... So you will get . ... AutoCAD MEP 2009 Update 1 (Service Pack 1) 32-bit (EN/DE. ... Plant.... Listen to ... Control.6.Winall.Cracked-ASSault Keygenl. 1fdad05405
-
-
-
diff --git a/spaces/farukozderim/a/README.md b/spaces/farukozderim/a/README.md
deleted file mode 100644
index e058b1aba9e1be07b69a75717c8ecee5d211d4eb..0000000000000000000000000000000000000000
--- a/spaces/farukozderim/a/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: A
-emoji: ⚡
-colorFrom: green
-colorTo: pink
-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/fatiXbelha/sd/Block Puzzle 2022 A New and Exciting Challenge for Puzzle Lovers.md b/spaces/fatiXbelha/sd/Block Puzzle 2022 A New and Exciting Challenge for Puzzle Lovers.md
deleted file mode 100644
index 2c0d73180dcbb647dff345edbcdb8054bdf3ce75..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Block Puzzle 2022 A New and Exciting Challenge for Puzzle Lovers.md
+++ /dev/null
@@ -1,135 +0,0 @@
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-
Download Block Puzzle 2022: A Fun and Addictive Puzzle Game for All Ages
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Are you looking for a new puzzle game to challenge your brain and have fun at the same time? If so, you should download Block Puzzle 2022, a simple but addictive puzzle game that will keep you entertained for hours. Block Puzzle 2022 is a free jigsaw puzzle game that you can enjoy anywhere, anytime, without needing an internet connection. All you have to do is drag, drop, and fill up the grid with colorful blocks. Sounds easy, right? Well, not so fast. You have to match the blocks by making full horizontal or vertical rows, and try not to leave any blanks. The more lines you clear, the higher your score will be. But be careful, the game will end if there is no space left for any more blocks. Are you ready to test your skills and have some fun? Here's everything you need to know about Block Puzzle 2022.
Block Puzzle 2022 is a puzzle game developed by BigCode Games and A2Z Game Studio. It is inspired by the classic block games like Tetris and Sudoku, but with a modern twist. Block Puzzle 2022 features dazzling jewels and gems that you have to match and clear from the wooden grid. The game has different modes and levels to suit your preferences and abilities. You can choose from simple, hard, or expert modes, and play hundreds of puzzle levels with different themes and backgrounds. You can also use special props and effects to enhance your gameplay and score more points.
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How to play Block Puzzle 2022
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The gameplay of Block Puzzle 2022 is very simple and intuitive. Here are the basic steps:
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-
Drag the given colorful blocks and put them into the wooden grid.
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Match the blocks by making full horizontal or vertical rows.
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Clear the blocks from the grid and get points.
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Use the hammer in the bar button to break the blocks at once, once the bar is filled.
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Try not to leave any blanks on the grid.
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The game will end if there is no space left for any more blocks.
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Why you should download Block Puzzle 2022
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There are many reasons why you should download Block Puzzle 2022 and play it right now. Here are some of them:
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It's free and offline
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Block Puzzle 2022 is a free game that you can download from Google Play Store or Crazy Games Online. You don't need to pay anything or register an account to play it. You also don't need an internet connection to enjoy it. You can play it offline anytime, anywhere, whether you are at home, at work, or on the go.
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It has various themes and levels
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Block Puzzle 2022 has a lot of variety and diversity in its design and content. You can choose from different themes and backgrounds, such as forest, wood, abstract, or adventure. You can also play different levels of difficulty, from simple to hard to expert. Each level has its own challenges and surprises that will keep you hooked and interested.
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It trains your brain and relaxes your mind
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Block Puzzle 2022 is not only a fun game, but also a brain-training game that will improve your logic, concentration, memory, and spatial skills. It will also help you relax your mind and relieve stress. You can play Block Puzzle 2022 whenever you need a break or a mental challenge. It is suitable for all ages and levels of players.
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It has amazing graphics and sound effects
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Block Puzzle 2022 has stunning graphics and sound effects that will make you feel like you are playing with real jewels and gems. The blocks are colorful and shiny, and the grid is realistic and wooden. The game also has soothing music and satisfying sounds that will enhance your mood and experience.
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It has a ranking board and social sharing features
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Block Puzzle 2022 has a ranking board that shows your score and rank among other players around the world. You can see how you compare with others and challenge yourself to beat them. You can also share your achievements and screenshots with your friends and family on social media platforms like Facebook, Twitter, or Instagram. You can also invite them to play the game and compete with them.
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Where to download Block Puzzle 2022
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Block Puzzle 2022 is available for download on two platforms:
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Google Play Store
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If you have an Android device, you can download Block Puzzle 2022 from the Google Play Store. Just follow these steps:
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-
Open the Google Play Store app on your device.
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Search for "Block Puzzle 2022" in the search bar.
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Select the game from the list of results.
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Tap on the "Install" button and wait for the game to download and install.
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Tap on the "Open" button to launch the game and enjoy.
-
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Crazy Games Online
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If you prefer to play Block Puzzle 2022 on your browser, you can visit Crazy Games Online, a website that offers free online games for various genres and categories. Just follow these steps:
Use your mouse or touchpad to drag and drop the blocks into the grid.
-
Have fun playing Block Puzzle 2022 online.
-
-
Tips and tricks for playing Block Puzzle 2022
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If you want to master Block Puzzle 2022 and get a high score, here are some tips and tricks that you should know:
-
Use the hammer to break blocks
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The hammer is a special prop that you can use to break any block on the grid at once. You can activate it by filling up the bar at the bottom of the screen. The bar fills up as you clear lines from the grid. Once it is full, you can tap on the hammer icon and then tap on any block you want to break. This can help you clear some space or get rid of unwanted blocks.
-
Remove multiple lines at once to get a higher score
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The more lines you clear at once, the higher your score will be. You can get bonus points for clearing two, three, or four lines at once. You can also get extra points for clearing lines with special blocks, such as stars, hearts, or diamonds. Try to arrange the blocks in a way that allows you to clear multiple lines at once.
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Plan ahead and avoid leaving blanks
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The key to playing Block Puzzle 2022 is to plan ahead and think strategically. You should always look at the next blocks that are coming up and see where they fit best on the grid. You should also try to avoid leaving blanks or gaps on the grid, as they will limit your options and make it harder to clear lines. Try to fill up every space on the grid as much as possible.
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Conclusion
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Block Puzzle 2022 is a fun and addictive puzzle game that will keep you entertained for hours. It is a free game that you can download from Google Play Store or Crazy Games Online. It has various themes and levels, amazing graphics and sound effects, a ranking board and social sharing features, and a hammer prop that can break any block at once. It also trains your brain and relaxes your mind, making it suitable for all ages and levels of players. If you are looking for a new puzzle game to challenge your brain and have fun at the same time, you should download Block Puzzle 2022 today.
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FAQs
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What are the minimum requirements to play Block Puzzle 2022?
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The minimum requirements to play Block Puzzle 2022 are:
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An Android device with Android 4.4 or higher, or a browser that supports HTML5.
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A screen resolution of 800x480 or higher.
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A stable internet connection (only for downloading the game or playing online).
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How can I get more hammers in Block Puzzle 2022?
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You can get more hammers in Block Puzzle 2022 by:
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Clearing more lines from the grid and filling up the bar.
-
Watching ads or completing offers in the game.
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Purchasing them with real money in the game store.
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How can I reset my progress in Block Puzzle 2022?
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You can reset your progress in Block Puzzle 2022 by:
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Going to the settings menu in the game and tapping on the "Reset" button.
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Uninstalling and reinstalling the game on your device or browser.
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How can I contact the developers of Block Puzzle 2022?
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You can contact the developers of Block Puzzle 2022 by:
Following them on social media platforms like Facebook, Twitter, or Instagram.
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What are some similar games to Block Puzzle 2022?
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Some similar games to Block Puzzle 2022 are:
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Block Puzzle Jewel: A classic block puzzle game with jewel blocks and various modes.
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Wood Block Puzzle: A relaxing and addictive puzzle game with wooden blocks and simple rules.
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Tetris: The original and iconic block puzzle game that started it all.
-
197e85843d
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-
\ No newline at end of file
diff --git a/spaces/fatiXbelha/sd/Classic Spider Solitaire - A Fun and Addictive Way to Train Your Brain.md b/spaces/fatiXbelha/sd/Classic Spider Solitaire - A Fun and Addictive Way to Train Your Brain.md
deleted file mode 100644
index c8bdff56d642caeebae4962fe83fdc8d28c54b3d..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Classic Spider Solitaire - A Fun and Addictive Way to Train Your Brain.md
+++ /dev/null
@@ -1,132 +0,0 @@
-
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Download Classic Spider Solitaire: A Fun and Challenging Card Game
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If you are looking for a card game that is easy to learn, hard to master, and fun to play, then you should download classic spider solitaire. Spider solitaire is one of the most popular versions of solitaire, a game that has been around for centuries. In this article, we will tell you what spider solitaire is, why you should download it, and how to play it.
Spider solitaire is a type of solitaire game that uses two decks of cards. The goal is to arrange all the cards in the same suit from king to ace in eight piles, called foundations. To do this, you have to move cards between ten columns, called tableau, in descending order. You can also deal new cards from the stock when you run out of moves.
-
The history and popularity of Spider Solitaire
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Spider solitaire was first introduced in 1949 by a British author named F.R. Innes in his book "The Complete Patience Book". It was later popularized by Microsoft Windows, which included it as one of the default games since 1998. Since then, spider solitaire has become one of the most played card games in the world, with millions of fans and online players.
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The rules and objectives of Spider Solitaire
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The rules of spider solitaire are simple, but the game can be quite challenging. Here are the basic rules:
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You start with 54 cards face up in ten tableau columns, with six cards in the first four columns and five cards in the rest.
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You can move any card or a sequence of cards in the same suit to another column if the top card is one rank lower than the card you are moving.
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You can also move any card or a sequence of cards to an empty column.
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You can deal a new card to each column by clicking on the stock when there are no more moves available.
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You can move a complete sequence of 13 cards in the same suit from king to ace to one of the foundations.
-
You win the game when you have moved all 104 cards to the foundations.
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The different levels and variations of Spider Solitaire
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Spider solitaire has three levels of difficulty: one suit (easy), two suits (medium), and four suits (hard). The more suits you have, the harder it is to find matching cards and complete sequences. You can also try different variations of spider solitaire, such as spiderette (which uses only one deck), scorpion (which allows you to move any face-up card), or wasp (which has no stock).
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Why should you download classic Spider Solitaire?
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Spider solitaire is not only a fun game, but also a beneficial one. Here are some reasons why you should download classic spider solitaire:
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Playing spider solitaire can help you improve your mental skills, such as concentration, memory, logic, and problem-solving. It can also help you relax, reduce stress, and have fun. Moreover, playing spider solitaire can boost your self-esteem, as you can challenge yourself and achieve your goals.
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The features and advantages of classic Spider Solitaire
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Classic spider solitaire is
Classic spider solitaire is the original and most authentic version of the game. It has the following features and advantages:
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It is free to download and play.
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It has a simple and elegant design, with clear graphics and smooth animations.
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It has a user-friendly interface, with easy controls and settings.
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It has a customizable difficulty level, with one, two, or four suits to choose from.
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It has a statistics and score system, with personal records and achievements to track your progress.
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It has a hint and undo function, to help you when you are stuck or make a mistake.
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It has an auto-save and resume feature, to let you continue your game anytime and anywhere.
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The best sources and platforms to download classic Spider Solitaire
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Classic spider solitaire is available for various devices and platforms, such as Windows, Mac, Android, iOS, and online. You can download it from reputable sources, such as:
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-
Platform
Source
-
Windows
[Microsoft Store]
-
Mac
[App Store]
-
Android
[Google Play]
-
iOS
[App Store]
-
Online
[Solitaire Bliss]
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How to download and play classic Spider Solitaire?
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Downloading and playing classic spider solitaire is easy and fast. Here are the steps to follow:
-
The steps to download classic Spider Solitaire on your device
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-
Go to the source that matches your platform (see the table above).
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Click on the download or install button.
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Wait for the download or installation to complete.
-
Launch the game from your device or browser.
-
Select your preferred difficulty level (one, two, or four suits).
-
Start playing and enjoy!
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The tips and tricks to master classic Spider Solitaire
-
Playing classic spider solitaire can be challenging, especially if you choose the higher difficulty levels. Here are some tips and tricks to help you master the game:
-
-
Try to expose the hidden cards as soon as possible, as they can block your moves.
-
Try to create empty columns as soon as possible, as they can give you more flexibility and space.
-
Try to build sequences in the same suit as much as possible, as they can be moved easily and quickly to the foundations.
-
Try to avoid mixing suits in the same column, as they can make it harder to form sequences.
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Try to deal new cards only when you have no more moves left, as they can change the layout of the game.
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Try to plan ahead and think strategically, as every move can affect the outcome of the game.
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The challenges and rewards of classic Spider Solitaire
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Classic spider solitaire is a game that can challenge your mind and test your skills. It can also reward you with satisfaction and enjoyment. Here are some of the challenges and rewards of playing classic spider solitaire:
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The challenges:
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You have to deal with a large number of cards (104) and columns (10).
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You have to manage multiple suits (one, two, or four) and colors (red and black).
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You have to balance between moving cards and dealing new cards.
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You have to overcome random factors and luck elements.
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-
The rewards:
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You can improve your mental abilities, such as concentration, memory, logic, and problem-solving.
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You can relax your mind, reduce stress, and have fun.
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You can challenge yourself and achieve your goals.
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You can track your progress and compare your scores with others.
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diff --git a/spaces/fatiXbelha/sd/Download Black Emojis for Free The Best Sites and Apps to Find Them.md b/spaces/fatiXbelha/sd/Download Black Emojis for Free The Best Sites and Apps to Find Them.md
deleted file mode 100644
index 13f6320c68b0b9ffc2d7fcfbd51ab6d1db27d0a6..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download Black Emojis for Free The Best Sites and Apps to Find Them.md
+++ /dev/null
@@ -1,130 +0,0 @@
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-
How to Download Black Emojis and Why You Should Use Them
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Emojis are those small icons that convey emotions, feelings, and thoughts in text messages, emails, and social media. They are everywhere these days because they increase the precision and nuance of our often super-brief and open-to-misunderstanding communications. Sometimes a picture is worth a thousand words.
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But did you know that there are also black emojis? These are emojis that have a black color or feature black people or culture. They are not the same as the dark skin tone modifiers that can be applied to some human emojis. Black emojis are independent symbols that have their own meaning and history.
In this article, we will explain what black emojis are, where they came from, how to get them on your device, and why you should use them in your communication. Read on to learn more about these cool and expressive icons.
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What are black emojis and where did they come from?
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The origin and meaning of black emojis
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Black emojis are not a new phenomenon. They have been around since the early days of emoji creation in Japan in the late 1990s. The word emoji comes from the Japanese words "e" (picture) and "moji" (character). One of the first emoji sets, created by Shigetaka Kurita for the Japanese phone company NTT Docomo, included some black emojis, such as a black heart, a black star, a black flag, and a black snowman.
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These black emojis were not meant to represent race or ethnicity, but rather to convey different meanings or moods. For example, the black heart emoji could express sorrow, morbidity, or dark humor, while the black flag emoji could symbolize anarchy or piracy. The black snowman emoji was actually a pre-existing Unicode character that was intended to be a solid (filled) glyph of a snowman, compared to an outlined one.
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However, as emojis became more popular and widespread across different platforms and cultures, some people started to use them to represent black people or culture. For instance, some users adopted the black fist emoji as a symbol of Black Power or Black Lives Matter movements. Others used the black moon emoji to refer to Black Twitter or Black excellence.
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The difference between black emojis and dark skin tone modifiers
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It is important to note that black emojis are not the same as the dark skin tone modifiers that can be applied to some human emojis. The dark skin tone modifier is one of the five skin tone options that were introduced in 2015 as part of the Unicode Standard. The Unicode Standard is the character coding system that supports emoji rendering on different platforms.
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The dark skin tone modifier is based on the Fitzpatrick Scale, a numerical classification of human skin color that ranges from Type 1 (light skin) to Type 6 (dark skin). The dark skin tone modifier corresponds to Type 6 on the Fitzpatrick Scale and is described as "deeply pigmented dark brown to black". It can be added to a range of human emoji characters to change their appearance and make them more diverse and inclusive.
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For example, you can add the dark skin tone modifier to the waving hand emoji to create the waving hand: dark skin tone emoji. You can also add it to the woman emoji to create the woman: dark skin tone emoji. However, you cannot add it to non-human emojis, such as animals, food, or objects.
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How to get black emojis on your device
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For iOS users
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If you have an iPhone or iPad, you can easily access black emojis and dark skin tone modifiers on your device. Here are the steps to follow:
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Open the app that you want to use, such as Messages, Mail, or WhatsApp.
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Tap on the text field where you want to type your message.
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Tap on the emoji icon on the keyboard to open the emoji menu.
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Swipe left or right to browse through the different categories of emojis.
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To find black emojis, look for the ones that have a black color or feature black people or culture, such as the black heart, the black fist, or the black moon.
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To use dark skin tone modifiers, tap and hold on a human emoji that supports skin tones, such as the face, hand, or person emojis. A pop-up menu will appear with five skin tone options. Tap on the darkest one to select it.
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Tap on the emoji that you want to use and it will appear in your text field. You can also tap on multiple emojis to add them to your message.
-
Tap on the send button to send your message with the black emojis or dark skin tone modifiers.
-
-
For Android users
-
If you have an Android phone or tablet, you can also access black emojis and dark skin tone modifiers on your device. However, the availability and appearance of these icons may vary depending on your device model, operating system version, and keyboard app. Here are the general steps to follow:
-
-
Open the app that you want to use, such as Messages, Gmail, or Facebook Messenger.
-
Tap on the text field where you want to type your message.
-
Tap on the emoji icon on the keyboard to open the emoji menu. If you don't see the emoji icon, you may need to switch to another keyboard app that supports emojis, such as Gboard or SwiftKey.
-
Swipe left or right to browse through the different categories of emojis.
-
To find black emojis, look for the ones that have a black color or feature black people or culture, such as the black heart, the black fist, or the black moon.
-
To use dark skin tone modifiers, tap and hold on a human emoji that supports skin tones, such as the face, hand, or person emojis. A pop-up menu will appear with five skin tone options. Tap on the darkest one to select it.
-
Tap on the emoji that you want to use and it will appear in your text field. You can also tap on multiple emojis to add them to your message.
-
Tap on the send button to send your message with the black emojis or dark skin tone modifiers.
-
-
Why you should use black emojis in your communication
-
To express yourself better and more creatively
-
One of the main reasons why you should use black emojis in your communication is that they can help you express yourself better and more creatively. Emojis are a form of visual language that can convey emotions, feelings, and thoughts in a way that words alone cannot. They can also add personality and flair to your messages and make them more engaging and memorable.
-
Black emojis are especially useful for expressing yourself if you identify as black or have a connection with black culture. They can help you show your pride, identity, and heritage in a fun and creative way. They can also help you communicate with other people who share your background or interests and create a sense of community and belonging.
-
To show support and solidarity for diversity and inclusion
-
Another reason why you should use black emojis in your communication is that they can show your support and solidarity for diversity and inclusion. Diversity and inclusion are important values that promote respect, acceptance, and appreciation for different people and cultures. They also foster innovation, creativity, and collaboration in various fields and domains.
-
By using black emojis in your communication, you can demonstrate that you care about diversity and inclusion and that you respect and celebrate different people and cultures. You can also show your awareness and understanding of current issues and events that affect black people and communities around the world. For example, you can use the black fist emoji to show your support for Black Lives Matter or other social justice movements. You can also use the black moon emoji to congratulate someone for their achievements or excellence.
-
To have fun and spice up your messages
-
A final reason why you should use black emojis in your communication is that they can simply make your messages more fun and spicy. Emojis are a great way to add some humor, sarcasm, or irony to your messages and make them more lively and interesting. They can also help you break the ice, flirt, or tease someone in a playful way.
-
Black emojis are particularly fun and spicy because they can add some flavor and attitude to your messages and make them stand out from the crowd. They can also help you express some unique and specific emotions or situations that may not be covered by the standard emojis. For example, you can use the black heart emoji to show your love for something dark or edgy, or the black cat emoji to indicate bad luck or mischief.
-
Conclusion
-
Black emojis are more than just icons with a black color or feature black people or culture. They are a way of expressing yourself better and more creatively, showing your support and solidarity for diversity and inclusion, and having fun and spicing up your messages. They are also a part of the history and evolution of emojis as a form of visual language that transcends words and boundaries.
-
If you want to use black emojis in your communication, you can easily get them on your device by following the steps we outlined above. You can also explore different platforms and apps that offer more black emojis or allow you to create your own. The possibilities are endless!
-
So what are you waiting for? Start using black emojis today and see how they can enrich your communication and make it more fun and engaging!
-
FAQs
-
What are some examples of black emojis?
-
Some examples of black emojis are:
-
-
? Black heart
-
✊? Raised fist: dark skin tone
-
? New moon face
-
?☠️ Pirate flag
-
?? Ninja cat
-
-
How many black emojis are there?
-
There is no definitive answer to this question, as different platforms and apps may have different sets of emojis that include black emojis. However, according to Emojipedia, a website that tracks emoji updates and usage, there are currently 1,367 emojis that support skin tone modifiers, including the dark skin tone modifier. There are also 79 emojis that have a black color or feature black people or culture, such as the black heart, the black fist, or the black moon.
-
Are black emojis racist?
-
No, black emojis are not racist. They are a way of celebrating diversity and inclusion and expressing yourself better and more creatively. However, some people may use black emojis inappropriately or offensively, such as using them to mock or stereotype black people or culture. This is not acceptable and should be avoided. You should always use black emojis respectfully and appropriately, and be mindful of the context and the audience of your communication.
-
Can I create my own black emojis?
-
Yes, you can create your own black emojis if you want to. There are some platforms and apps that allow you to customize or design your own emojis, such as Bitmoji, Emoji Maker, or Emoji Builder. You can also use stickers, GIFs, or memes that feature black people or culture as alternatives to emojis. However, you should be careful not to infringe on any intellectual property rights or violate any terms of service when creating or using your own black emojis.
-
Where can I find more information about black emojis?
-
If you want to learn more about black emojis, you can check out some of these resources:
-
-
[Emojipedia]: A website that tracks emoji updates and usage.
-
[Emoji Foundation]: A website that provides information and education about emojis.
-
[Black Emoji Project]: A project that aims to create more diverse and inclusive emojis for black people.
-
[AfroMoji]: An app that offers a collection of African American stickers and emojis.
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[Black Emojis Matter]: A book that explores the history and meaning of black emojis.
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[The Origin of Black Emojis]: An article that explains the origin and meaning of some of the first black emojis.
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diff --git a/spaces/fatiXbelha/sd/Download Cooking Madness -A Chefs Game for PC and Experience the Cooking Craze.md b/spaces/fatiXbelha/sd/Download Cooking Madness -A Chefs Game for PC and Experience the Cooking Craze.md
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Cooking Madness Download: How to Play the Best Cooking Game of 2023
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Do you love cooking games? Do you want to experience the thrill of running your own restaurant? Do you want to unleash your inner chef and create amazing dishes? If you answered yes to any of these questions, then you should download Cooking Madness, the best cooking game of 2023. In this article, we will tell you everything you need to know about Cooking Madness, how to download it, how to play it, and why you should play it. Let's get started!
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What is Cooking Madness?
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Cooking Madness is a fun and addictive time-management game that will test your cooking skills and speed. You will be serving delicious dishes to hungry customers in various restaurants around the world. You will also be able to upgrade your kitchen and dishes, complete missions and achievements, and participate in events and contests. Here are some of the features of Cooking Madness:
In Cooking Madness, you will have to tap as fast as you can while keeping an eye on the time. You will have to prepare, cook, and serve dishes according to the customers' orders and preferences. You will also have to deal with different types of customers, such as impatient ones, generous ones, or picky ones. You will have to satisfy them all and earn tips and coins. You will also have to manage your resources, such as ingredients, utensils, and appliances. You will have to balance quality and speed, as well as creativity and efficiency.
-
A variety of cuisines and restaurants to explore
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Cooking Madness will take you on a culinary journey around the world. You will be able to cook and serve dishes from different cuisines, such as American, Chinese, Italian, Japanese, Mexican, French, and more. You will also be able to unlock new restaurants as you progress on your journey. Each restaurant has its own theme, menu, customers, and challenges. You will be able to discover and enjoy the diversity and richness of global gastronomy.
-
A challenge for your cooking skills and speed
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Cooking Madness is not a game for the faint-hearted. It will challenge you with its fast-paced gameplay, its increasing difficulty levels, its diverse missions, and its special events. You will have to master different cooking techniques, such as frying, baking, grilling, boiling, steaming, mixing, etc. You will also have to learn how to use different kitchen appliances, such as ovens, stoves, microwaves, blenders, mixers, etc. You will have to cope with the rush hours, the peak seasons, the special orders, and the unexpected situations. You will have to prove that you are a mad chef who can handle any cooking madness.
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How to Download Cooking Madness?
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Cooking Madness is available for free on various platforms. You can download it on your Android device, your iOS device, or your PC or Mac. Here are the steps for each platform:
-
For Android devices
-
If you have an Android device, you can download Cooking Madness from the Google Play Store. Here are the steps:
-
-
Open the Google Play Store app on your device.
-
Search for "Cooking Madness" in the search bar.
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Select "Cooking Madness -A Chef's Game" from or get for free by completing tasks or spinning the wheel.
-
Complete missions and achievements
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Cooking Madness will keep you motivated and rewarded by giving you various missions and achievements to complete. You will see the missions on the left side of the screen, and the achievements on the right side of the screen. The missions are short-term goals that you can complete within a level or a restaurant. The achievements are long-term goals that you can complete across the whole game. Completing missions and achievements will give you coins, gems, boosters, or other rewards. You will also be able to track your progress and compare your performance with other players.
-
Participate in events and contests
-
Cooking Madness will also spice up your gameplay by offering you various events and contests to participate in. You will see the events and contests on the bottom of the screen, or on the map. The events and contests are special challenges that you can join for a limited time. They will test your cooking skills and speed with different themes, rules, and rewards. Some examples of events and contests are: Halloween event, Christmas event, World Chef contest, Crazy Cooking contest, etc. Participating in events and contests will give you a chance to win coins, gems, boosters, or other prizes. You will also be able to have fun and compete with other players.
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Why You Should Play Cooking Madness?
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Cooking Madness is not just a game, it's an experience. It's a game that will make you feel like a real chef, a game that will make you happy, a game that will make you addicted. Here are some reasons why you should play Cooking Madness:
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It's fun and easy to play
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Cooking Madness is a game that anyone can play and enjoy. It has simple controls, colorful graphics, catchy music, and smooth animations. It has a user-friendly interface, a helpful tutorial, and a clear feedback system. It has a balanced difficulty curve, a fair reward system, and a generous free-to-play model. It has a lot of humor, charm, and personality. It's a game that will make you smile, laugh, and have fun.
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It's free and updated regularly
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Cooking Madness is a game that you can download and play for free. You don't need to pay anything to enjoy the game, unless you want to buy some optional items or features. You can also earn free coins, gems, boosters, or other rewards by playing the game, watching ads, completing tasks, or spinning the wheel. Cooking Madness is also a game that is updated regularly with new content and features. You can expect new cuisines, restaurants, dishes, customers, missions, achievements, events, contests, and more. You can also expect bug fixes, performance improvements, and customer support.
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It's suitable for all ages and tastes
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Cooking Madness is a game that everyone can enjoy regardless of their age or taste. It's a game that appeals to both casual and hardcore gamers, both young and old, both male and female. It's a game that caters to different preferences, such as food, culture, style, or challenge. It's a game that stimulates different senses, such as sight, sound, touch, or taste. It's a game that offers different benefits, such as relaxation, entertainment, education, or inspiration. It's a game that you can play anytime, anywhere, with anyone, or by yourself.
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Conclusion
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Cooking Madness is the best cooking game of 2023. It's a fun and addictive time-management game that will test your cooking skills and speed. It's a game that will take you on a culinary journey around the world. It's a game that will challenge you with its fast-paced gameplay, its increasing difficulty levels, its diverse missions, and its special events. It's a game that will reward you with coins, gems, boosters, or other prizes. It's a game that will make you feel like a real chef, a game that will make you happy, a game that will make you addicted. If you love cooking games, you should download Cooking Madness and play it now. You won't regret it!
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FAQs
-
Here are some frequently asked questions about Cooking Madness:
-
-
-
Question
-
Answer
-
-
-
How can I get more coins in Cooking Madness?
-
You can get more coins by serving more dishes, earning more tips, completing more missions and achievements, participating in more events and contests, watching more ads, spinning the wheel more often, or buying them with real money.
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-
-
How can I get more gems in Cooking Madness?
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You can get more gems by completing certain tasks, spinning the wheel occasionally, or buying them with real money.
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-
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How can I get more boosters in Cooking Madness?
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You can get more boosters by completing certain achievements, participating in certain events or contests, or buying them with coins or gems.
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-
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How can I unlock more restaurants in Cooking Madness?
-
You can unlock more restaurants by earning enough stars in the previous restaurants.
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-
-
How can I contact the developers of Cooking Madness?
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diff --git a/spaces/fatiXbelha/sd/Download Final Kombat and Explore the History of Mortal Kombat.md b/spaces/fatiXbelha/sd/Download Final Kombat and Explore the History of Mortal Kombat.md
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-
How to Download Final Kombat: A Complete Guide
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If you are a fan of fighting games, you have probably heard of Final Kombat, one of the most iconic and successful franchises in the genre. Final Kombat is a series of games that pits various characters, each with their own unique abilities and personalities, against each other in brutal and bloody battles. Whether you want to unleash your inner warrior, test your skills, or just have some fun, Final Kombat is a game that you should definitely try.
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But how can you download Final Kombat and enjoy it on your device? In this article, we will show you everything you need to know about downloading Final Kombat, from its history and features to its requirements and sources. We will also give you some tips on how to make the most out of your gaming experience. By the end of this article, you will be ready to enter the world of Final Kombat and face your opponents with confidence.
Before we dive into the details of downloading Final Kombat, let's take a look at the history of this amazing game series. How did it start, how did it evolve, and what makes it so popular?
-
The Origins of Final Kombat
-
Final Kombat was created by Midway Games, a video game company based in Chicago, in the early 1990s. The original idea was to make a fighting game featuring Jean-Claude Van Damme, a famous martial arts actor, but the deal fell through. Instead, the developers decided to create their own characters and story, inspired by movies like Enter the Dragon and Big Trouble in Little China.
-
The first game, titled Final Kombat, was released in arcades in 1992. It featured eight playable characters, each with their own fighting style and special moves. The game also introduced the concept of fatalities, finishing moves that allowed the player to kill their opponent in a gruesome way. The game was a huge hit, thanks to its realistic graphics, fast-paced gameplay, and violent content.
-
The Evolution of Final Kombat
-
Following the success of the first game, Midway Games released several sequels and spin-offs over the years, expanding the roster of characters, improving the graphics and sound, adding new features and modes, and developing the story and lore. Some of the most notable titles include:
-
-
Final Kombat II (1993): Added more characters, stages, fatalities, and secrets.
-
Final Kombat 3 (1995): Introduced the run
-
Final Kombat 4 (1997): The first game to use 3D graphics and weapons.
-
Final Kombat: Deadly Alliance (2002): Revamped the gameplay and introduced the krypt, a mode where the player can unlock various items.
-
Final Kombat: Deception (2004): Added more modes, such as puzzle, chess, and konquest.
-
Final Kombat: Armageddon (2006): Featured the largest roster of characters and the ability to create custom fighters.
-
Final Kombat vs. DC Universe (2008): A crossover game with characters from the DC Comics universe.
-
Final Kombat (2011): A reboot of the series that retold the events of the first three games with updated graphics and gameplay.
-
Final Kombat X (2015): Introduced the variation system, where each character has three different styles to choose from.
-
Final Kombat 11 (2019): The latest game in the series, featuring a time-traveling story and customizable gear.
-
-
The Legacy of Final Kombat
-
Besides the games, Final Kombat has also spawned various media adaptations, such as movies, TV shows, comics, books, and toys. Some of the most well-known examples are:
-
-
Final Kombat (1995): A live-action movie that followed the plot of the first game and starred actors like Christopher Lambert, Robin Shou, and Bridgette Wilson.
-
Mortal Kombat: Annihilation (1997): A sequel to the first movie that was poorly received by critics and fans.
-
Mortal Kombat: Defenders of the Realm (1996): An animated TV series that aired on USA Network and featured voice actors like Clancy Brown, Ron Perlman, and Cree Summer.
-
Mortal Kombat: Conquest (1998-1999): A live-action TV series that focused on the adventures of a young Kung Lao, a descendant of the original champion of Final Kombat.
-
Mortal Kombat: Legacy (2011-2013): A web series that reimagined the origins and stories of various characters in a realistic and gritty way.
-
Mortal Kombat Legends: Scorpion's Revenge (2020): An animated movie that retold the events of the first game from the perspective of Scorpion, one of the most popular characters in the series.
-
Mortal Kombat (2021): A reboot of the live-action movie franchise that featured a new cast and storyline.
-
-
The Features of Final Kombat
-
Now that you know some of the history of Final Kombat, let's take a look at some of the features that make this game series so fun and exciting. What can you expect from playing Final Kombat?
-
The Characters of Final Kombat
-
One of the main attractions of Final Kombat is its diverse and colorful cast of characters. There are over 70 characters in the series, each with their own backstory, personality, appearance, and abilities. Some of them are humans, some are aliens, some are gods, some are cyborgs, some are zombies, and some are even animals. Some of them are heroes, some are villains, some are anti-heroes, and some are neutral. Some of them are related to each other, some are friends, some are enemies, and some are rivals. Some of them have been in every game, some have appeared only once or twice, and some have been killed off or resurrected.
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No matter what kind of character you prefer, you will surely find someone who suits your taste in Final Kombat. Here are some examples of the most iconic characters in the series:
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Name
-
Description
-
Faction
-
Fatality Example
-
-
-
Liu Kang
-
A Shaolin monk who is the main protagonist and champion of Final Kombat. He fights with martial arts and fireballs.
-
Earthrealm
-
The Dragon: Liu Kang transforms into a dragon and bites his opponent in half
-
-
-
Scorpion
-
A specter who seeks vengeance for the murder of his clan and family. He fights with a kunai and chain and hellfire.
-
Netherrealm
-
Toastie: Scorpion removes his mask and breathes fire on his opponent, burning them to a crisp
-
-
-
Sub-Zero
-
A ninja who belongs to the Lin Kuei clan. He fights with ice powers and can freeze his opponents.
-
Earthrealm
-
Spine Rip: Sub-Zero grabs his opponent's head and rips it off along with the spine
-
-
-
Sonya Blade
-
A special forces officer who is the leader of the Outer World Investigation Agency. She fights with military skills and gadgets.
-
Earthrealm
-
Kiss of Death: Sonya blows a kiss that creates a pink energy ring that slices her opponent in half
-
-
-
Raiden
-
The god of thunder and the protector of Earthrealm. He fights with lightning and teleportation.
-
Earthrealm
-
Electric Fly: Raiden flies towards his opponent and electrocutes them until they explode
-
-
-
Shang Tsung
-
A sorcerer who serves the evil emperor Shao Kahn. He fights with magic and can shapeshift into other characters.
-
Outworld
-
Soul Steal: Shang Tsung drains his opponent's soul and takes their appearance
-
-
-
Goro
-
A four-armed half-dragon who is the champion of Outworld. He fights with brute strength and can crush his opponents.
-
Outworld
-
Rip Off: Goro rips off two of his opponent's arms and beats them with them
-
-
-
Kano
-
A mercenary and the leader of the Black Dragon crime syndicate. He fights with a cybernetic eye and a knife.
-
Black Dragon
-
Heart Rip: Kano plunges his hand into his opponent's chest and rips out their heart
-
-
-
These are just some of the many characters you can choose from in Final Kombat. Each character has their own strengths, weaknesses, combos, and fatalities, so you can experiment with different styles and strategies.
-
The Gameplay of Final Kombat
-
The gameplay of Final Kombat is simple to learn but hard to master. The basic controls are:
-
-
Punch: Use this to perform quick and light attacks.
-
Kick: Use this to perform stronger and slower attacks.
-
Block: Use this to defend yourself from incoming attacks.
-
Jump: Use this to avoid low attacks or to perform aerial attacks.
-
Crouch: Use this to avoid high attacks or to perform low attacks.
-
Special Move: Use this to perform unique attacks that vary depending on the character. For example, Scorpion can throw his kunai and chain, Sub-Zero can shoot ice balls, and Raiden can summon lightning bolts.
-
Fatality: Use this to finish off your opponent in a spectacular way when they are low on health. You need to input a specific combination of buttons within a certain distance from your opponent to execute a fatality.
-
-
The gameplay of Final Kombat is fast-paced, intense, and satisfying. You need to use your skills, timing, and strategy to defeat your opponent. You also need to watch out for the environmental hazards, such as spikes, acid pools, or falling objects, that can damage you or your opponent. You can also use the interactables, such as weapons, vehicles, or animals, that can give you an advantage in combat.
-
The Modes of Final Kombat
-
The modes of Final Kombat are varied and fun. You can play alone or with others, online or offline, casually or competitively. Here are some of the modes you can enjoy in Final Kombat:
-
-
Fight: This is the basic mode where you can choose your character, stage, and difficulty level, and fight against the computer or another player.
-
Tower: This is a mode where you have to fight against a series of opponents with different challenges and rewards. For example, you can play the classic tower, where you have to defeat 10 opponents and face the final boss, or the living tower, where the conditions change every day, hour, or minute.
-
Story: This is a mode where you can follow the narrative of Final Kombat and play as different characters in each chapter. You can watch cutscenes, make choices, and fight against enemies that are relevant to the plot.
-
Online: This is a mode where you can connect with other players around the world and compete in various modes, such as ranked, casual, king of the hill, or team battle. You can also join or create rooms, where you can chat, spectate, or challenge other players.
-
Krypt: This is a mode where you can explore a vast and mysterious area and unlock various items, such as costumes, fatalities, concept art, or music. You need to use koins, the currency of Final Kombat, to open chests or interact with objects. You can also encounter secrets, puzzles, or enemies in the krypt.
-
Kollection: This is a mode where you can view your unlocked items, such as characters, stages, fatalities, brutalities, intros, victories, or bios. You can also customize your characters with different gear, skins, abilities, or augments.
-
-
The Requirements of Final Kombat
-
Before you download Final Kombat, you need to make sure that your device meets the requirements of the game. Depending on the platform and version of the game you want to play, the requirements may vary. Here are some general guidelines for the requirements of Final Kombat:
-
The System Requirements of Final Kombat
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The system requirements of Final Kombat are the specifications of your device's hardware and software that affect the performance and compatibility of the game. If your device does not meet the minimum system requirements of Final Kombat, you may not be able to run the game at all. If your device meets or exceeds the recommended system requirements of Final Kombat, you may be able to enjoy the game with better graphics and speed.
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The system requirements of Final Kombat depend on the platform and version of the game you want to play. For example, if you want to play Final Kombat 11 on PC, you need to have at least:
Graphics: NVIDIA® GeForce™ GTX 670 or NVIDIA® GeForce™ GTX 1050 / AMD® Radeon™ HD 7950 or AMD® Radeon™ R9 270
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DirectX: Version 11
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Network: Broadband Internet connection
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If you want to play Final Kombat 11 on PS4, you need to have at least:
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If you want to play Final Kombat 11 on Xbox One, you need to have at least:
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If you want to play Final Kombat 11 on Nintendo Switch, you need to have at least:
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The Storage Requirements of Final Kombat
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The storage requirements of Final Kombat are the amount of space you need to download and install the game on your device. The storage requirements of Final Kombat depend on the platform and version of the game you want to play, as well as the updates and DLCs you want to add. Generally, the more content and features the game has, the more storage space it requires.
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The storage requirements of Final Kombat may vary depending on the source and format of the game. For example, if you download the game from an online store, you may need more space than if you buy a physical disc. If you download the game in a compressed or digital format, you may need less space than if you download it in a full or physical format.
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Here are some examples of the storage requirements of Final Kombat 11 for different platforms and versions:
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Platform
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Version
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Storage Requirement
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PC
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Standard Edition (Digital)
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60 GB
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PC
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Ultimate Edition (Digital)
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80 GB
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PS4
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Standard Edition (Disc)
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50 GB + 20 GB update
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PS4
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Ultimate Edition (Disc)
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50 GB + 40 GB update + DLCs
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Xbox One
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Standard Edition (Disc)
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50 GB + 20 GB update
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You can check the exact storage requirement of Final Kombat 11 for your platform and version on the official website or the online store page. You can also check the available storage space on your device before downloading or installing the game.
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The Internet Requirements of Final Kombat
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The internet requirements of Final Kombat are the quality and speed of your internet connection that affect the performance and functionality of the game. If your internet connection is slow, unstable, or unreliable, you may experience lag, disconnects, or errors while playing Final Kombat.
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The internet requirements of Final Kombat depend on the mode and feature of the game you want to use. For example, if you want to play online multiplayer modes, you need a faster and more stable internet connection than if you want to play offline single-player modes. If you want to download updates or DLCs, you need a more reliable and secure internet connection than if you want to play with a physical disc.
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Here are some general guidelines for the internet requirements of Final Kombat:
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Online multiplayer modes: You need a broadband internet connection with a minimum speed of 5 Mbps for download and upload. You also need a low ping or latency, which is the time it takes for data to travel between your device and the server. Ideally, your ping should be below 100 ms.
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Online features and updates: You need a broadband internet connection with a minimum speed of 3 Mbps for download and upload. You also need a secure and stable connection that does not drop or interrupt frequently.
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Offline modes: You do not need an internet connection to play offline modes, such as fight, tower, or story. However, some features or content may not be available or updated without an internet connection.
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DLCs: You need an internet connection to purchase and download DLCs, such as new characters, stages, costumes, or fatalities. The speed and reliability of your connection may affect the time and quality of your download.
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You can test your internet speed and ping using various online tools or websites. You can also improve your internet connection by using a wired connection instead of a wireless one, closing other applications that use bandwidth, or contacting your internet service provider.
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The Sources of Final Kombat
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Now that you know the requirements of Final Kombat, let's see where you can get the game from. There are different sources of Final Kombat, depending on the platform and version of the game you want to play. Here are some of the most common sources of Final Kombat:
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The Official Website of Final Kombat
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The official website of Final Kombat is the best place to get the latest and most accurate information about the game, such as its features, updates, DLCs, events, and news. You can also watch trailers, videos, screenshots, and artwork of the game. You can also access the forums, where you can interact with other fans and developers of the game.
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The official website of Final Kombat also allows you to purchase and download the game for PC. You can choose from different editions and bundles of the game, such as the standard edition, the ultimate edition, or the kombat pack. You can also get discounts and bonuses if you pre-order or buy the game from the official website.
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The official website of Final Kombat is https://www.mortalkombat.com/, where you can find everything you need to know about the game.
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The Online Stores of Final Kombat
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The online stores of Final Kombat are the places where you can buy and download the game for different platforms, such as PS4, Xbox One, Nintendo Switch, or mobile devices. You can choose from different editions and bundles of the game, such as the standard edition, the ultimate edition, or the kombat pack. You can also get discounts and bonuses if you pre-order or buy the game from certain online stores.
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Some of the most popular online stores of Final Kombat are:
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Steam: This is an online platform where you can buy and download games for PC. You can also access various features, such as cloud saving, achievements, community, and workshop. You can find Final Kombat 11 on Steam at https://store.steampowered.com/app/976310/Mortal_Kombat11/.
Xbox Store: This is an online store where you can buy and download games for Xbox One. You can also access various features, such as achievements, friends, and game pass. You can find Final Kombat 11 on Xbox Store at https://www.microsoft.com/en-us/p/mortal-kombat-11-ultimate/9nq1z9jwzj9f.
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Nintendo eShop: This is an online store where you can buy and download games for Nintendo Switch. You can also access various features, such as news, friends, and wishlist. You can find Final Kombat 11 on Nintendo eShop at https://www.nintendo.com/games/detail/mortal-kombat-11-switch/.
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Google Play: This is an online store where you can buy and download games for Android devices. You can also access various features, such as achievements, leaderboards, and cloud saving. You can find Final Kombat Mobile on Google Play at https://play.google.com/store/apps/details?id=com.wb.goog.mkx&hl=en_US&gl=US.
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App Store: This is an online store where you can buy and download games for iOS devices. You can also access various features, such as achievements, leaderboards, and cloud saving. You can find Final Kombat Mobile on App Store at https://apps.apple.com/us/app/mortal-kombat/id949701151.
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The Torrent Sites of Final Kombat
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The torrent sites of Final Kombat are the places where you can download the game for free using a peer-to-peer file sharing protocol. However, this method is illegal, risky, and unethical. You may face legal consequences if you are caught downloading pirated games. You may also expose your device to viruses, malware, or hackers if you download from untrusted sources. You may also miss out on updates , DLCs, or online features if you download from torrent sites. You may also ruin the experience and reputation of the game and its developers if you download from torrent sites.
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We strongly advise you to avoid downloading Final Kombat from torrent sites and to support the official sources of the game. Not only will you get a better and safer gaming experience, but you will also show your appreciation and respect for the hard work and creativity of the people behind Final Kombat.
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The Steps to Download Final Kombat
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Now that you know the sources of Final Kombat, let's see how you can download the game and start playing. The steps to download Final Kombat may vary depending on the platform and source of the game you choose, but here are some general steps that apply to most cases:
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Step 1: Choose Your Source
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The first step to download Final Kombat is to choose your source of the game. You need to decide which platform and version of the game you want to play, and which source of the game you want to use. For example, if you want to play Final Kombat 11 on PC, you can choose between the official website, Steam, or torrent sites. If you want to play Final Kombat Mobile on Android, you can choose between Google Play or torrent sites.
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As we mentioned before, we recommend you to use the official sources of the game, as they are legal, safe, and reliable. You can find the links to the official sources of Final Kombat in the previous section.
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Step 2: Follow the Instructions
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The second step to download Final Kombat is to follow the instructions of your chosen source of the game. You need to follow the steps and requirements of your source of the game to download and install the game on your device. For example, if you choose to download Final Kombat 11 from Steam, you need to:
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Create or log in to your Steam account.
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Search for Final Kombat 11 on the Steam store.
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Select the edition or bundle of the game you want to buy.
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Add the game to your cart and proceed to checkout.
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Pay for the game using your preferred method.
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Wait for the game to download and install on your PC.
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Launch the game from your Steam library.
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If you choose to download Final Kombat Mobile from Google Play, you need to:
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Create or log in to your Google account.
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Search for Final Kombat Mobile on Google Play.
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Select the game and tap on Install.
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Wait for the game to download and install on your Android device.
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Launch the game from your app drawer.
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The instructions may differ depending on your source of the game, but they are usually easy and straightforward. You can also find more detailed instructions on the official website or online store page of your chosen source of the game. If you have any trouble or questions, you can contact the customer support or the community of your source of the game.
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Step 3: Install and Launch the Game
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The third step to download Final Kombat is to install and launch the game on your device. You need to make sure that the game is properly installed and ready to play on your device. For example, if you download Final Kombat 11 from Steam, you need to:
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Check the integrity of the game files on Steam.
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Update your drivers and software to the latest versions.
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Adjust your settings and preferences according to your device and preferences.
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Launch the game from your Steam library or desktop shortcut.
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If you download Final Kombat Mobile from Google Play, you need to:
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Check the permissions and notifications of the game on your Android device.
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Update the game to the latest version on Google Play.
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Adjust your settings and preferences according to your device and preferences.
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Launch the game from your app drawer or home screen.
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The installation and launching process may differ depending on your source of the game, but they are usually simple and quick. You can also find more detailed instructions on the official website or online store page of your chosen source of the game. If you have any trouble or questions, you can contact the customer support or the community of your source of the game.
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The Tips to Enjoy Final Kombat
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Congratulations! You have successfully downloaded Final Kombat and are ready to play. But before you jump into the action, let us give you some tips on how to enjoy Final Kombat to the fullest. Here are some tips that will help you improve your skills, have more fun, and avoid frustration while playing Final Kombat:
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Tip 1: Learn the Basics
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The first tip to enjoy Final Kombat is to learn the basics of the game. You need to familiarize yourself with the controls, mechanics, and features of the game. You need to know how to move, attack, block, jump, crouch, and perform special moves and fatalities. You need to know how to use environmental hazards, interactables, variations, and gear. You need to know how to access different modes, features, and options of the game.
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You can learn the basics of Final Kombat by playing the tutorial mode, where you can practice various aspects of the game in a guided and interactive way. You can also learn the basics of Final Kombat by reading the manual of the game, where you can find detailed explanations and instructions of the game. You can also learn the basics of Final Kombat by watching videos or reading articles online, where you can find tips, tricks, and guides from experts and fans of the game.
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Tip 2: Practice Your Moves
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The second tip to enjoy Final Kombat is to practice your moves. You need to master the moves of your chosen character and learn how to execute them effectively and efficiently. You need to know how to perform combos, counters, and escapes. You need to know how to adapt to different situations and opponents. You need to know how to use your strengths and exploit your weaknesses.
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You can practice your moves in Final Kombat by playing the practice mode, where you can train with a dummy or a friend in a customizable environment. You can also practice your moves in Final Kombat by playing the tower mode, where you can face different challenges and rewards. You can also practice your moves in Final Kombat by playing the online mode, where you can test your skills against other players around the world.
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Tip 3: Challenge Your Friends
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The third tip to enjoy Final Kombat is to challenge your friends. You need to share your passion and excitement for the game with others. You need to have fun and friendly competition with your friends. You need to show off your skills and learn from your mistakes. You need to make memories and bond with your friends.
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You can challenge your friends in Final Kombat by playing the local mode, where you can play with up to four players on the same device or screen. You can also challenge your friends in Final Kombat by playing the online mode, where you can play with up to eight players on different devices or screens. You can also challenge your friends in Final Kombat by joining or creating rooms, where you can chat, spectate, or fight with other players.
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Conclusion and FAQs
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In conclusion, Final Kombat is a game that you should not miss if you love fighting games. It has a rich history, a diverse cast of characters, a simple but deep gameplay, a variety of modes and features, and a loyal and passionate fanbase. It is a game that will keep you entertained, challenged, and satisfied for hours.
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To download Final Kombat, you need to choose your source of the game, follow the instructions of your source of the game, and install and launch the game on your device. You also need to make sure that your device meets the requirements of the game, such as system, storage, and internet requirements. You can also improve your gaming experience by learning the basics, practicing your moves, and challenging your friends.
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We hope that this article has helped you understand how to download Final Kombat and enjoy it to the fullest. If you have any questions or feedback, please feel free to contact us or leave a comment below. We would love to hear from you.
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Here are some FAQs that may answer some of your queries:
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Q: How much does Final Kombat cost?
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A: The price of Final Kombat may vary depending on the platform, version, edition, bundle, or source of the game you choose. Generally, the standard edition of Final Kombat 11 costs around $60 USD for PC, PS4, Xbox One, and Nintendo Switch. The ultimate edition of Final Kombat 11 costs around $100 USD for PC, PS4 and Xbox One, and $60 USD for Nintendo Switch. The kombat pack of Final Kombat 11 costs around $40 USD for all platforms. The price of Final Kombat Mobile is free, but it has in-app purchases. You can check the exact price of Final Kombat on the official website or the online store page of your chosen source of the game.
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Q: Is Final Kombat suitable for children?
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A: Final Kombat is a game that contains graphic violence, gore, blood, and profanity. It is rated M for Mature by the ESRB, 18+ by PEGI, and R18+ by ACB. It is not suitable for children or anyone who is sensitive to such content. You should exercise caution and discretion when playing or watching Final Kombat.
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Q: How can I get better at Final Kombat?
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A: The best way to get better at Final Kombat is to practice and learn from your experience. You should try different characters, modes, and strategies to find what works best for you. You should also watch or read tutorials, guides, and tips from experts and fans of the game. You should also challenge yourself and play against stronger opponents to improve your skills and confidence.
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Q: What are some of the best characters in Final Kombat?
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A: The best characters in Final Kombat may vary depending on your personal preference, playstyle, and skill level. However, some of the characters that are generally considered to be strong, versatile, and popular are:
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Liu Kang: He is a well-rounded character with fast and powerful attacks, good mobility, and easy combos. He can also use his fireballs to zone or pressure his opponents.
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Scorpion: He is a tricky character with high damage, good mix-ups, and excellent mobility. He can also use his kunai and chain to catch or punish his opponents.
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Sub-Zero: He is a defensive character with strong zoning, counter-attacking, and setups. He can also use his ice powers to freeze or trap his opponents.
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Sonya Blade: She is an aggressive character with high pressure, fast combos, and good projectiles. She can also use her gadgets to surprise or overwhelm her opponents.
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Raiden: He is a versatile character with balanced stats, good range, and useful abilities. He can also use his lightning and teleportation to control or confuse his opponents.
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Q: How can I unlock more content in Final Kombat?
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A: You can unlock more content in Final Kombat by playing the game and earning rewards. You can earn koins, which are the currency of Final Kombat, by completing matches, towers, challenges, or achievements. You can use koins to open chests or interact with objects in the krypt, where you can find various items, such as costumes, fatalities, concept art, or music. You can also earn souls, which are another currency of Final Kombat, by performing fatalities or brutalities. You can use souls to unlock special areas or items in the krypt. You can also earn hearts, which are another currency of Final Kombat, by performing fatalities or brutalities on specific characters. You can use hearts to open special chests in the krypt.
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You can also unlock more content in Final Kombat by purchasing DLCs, such as new characters , stages, costumes, or fatalities. You can purchase DLCs from the official website or the online store of your chosen source of the game. You can also get discounts and bonuses if you buy DLCs in bundles or packs.
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You can also unlock more content in Final Kombat by participating in events, such as tournaments, seasons, or quests. You can participate in events by playing online or offline modes, such as ranked, casual, king of the hill, or team battle. You can also participate in events by joining or creating rooms, where you can chat, spectate, or fight with other players. You can earn rewards, such as koins, souls, hearts, gear, skins, or abilities, by completing objectives or ranking high in events.
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Q: How can I contact the developers of Final Kombat?
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A: You can contact the developers of Final Kombat by visiting their official website or social media pages. You can also contact them by sending them an email or a message. Here are some of the ways you can contact the developers of Final Kombat:
You can also contact the developers of Final Kombat by joining their forums, where you can interact with other fans and developers of the game. You can find the forums on their official website at https://www.mortalkombat.com/community/forums/.
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diff --git a/spaces/fatiXbelha/sd/Download Get Color - Water Sort Puzzle APK for Android - Free and Fun Puzzle Game.md b/spaces/fatiXbelha/sd/Download Get Color - Water Sort Puzzle APK for Android - Free and Fun Puzzle Game.md
deleted file mode 100644
index 624172f5e416e75224f76ac9d4270dafce702b21..0000000000000000000000000000000000000000
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Get Color Water Sort Puzzle APK: A Fun and Addictive Game for Android
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If you are looking for a fun and addictive game to exercise your brain and relax your mind, you should try Color Water Sort Puzzle. This is a puzzle game that challenges you to sort the colored water in the glasses until all colors are in the same glass. Sounds easy, right? Well, not so fast. You have to be careful not to mix different colors, or you will have to start over. In this article, we will tell you what Color Water Sort Puzzle is, how to get it for your Android device, and why you should play it.
Color Water Sort Puzzle is a game developed by vnstart LLC, a Vietnamese company that specializes in casual games. The game was released in October 2022 and has since gained over 100 million downloads on Google Play Store. It has also received positive reviews from users and critics alike, who praise its simple yet challenging gameplay, its colorful graphics, and its relaxing sound effects.
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The gameplay
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The gameplay of Color Water Sort Puzzle is very easy to learn but hard to master. You have a set of glasses filled with different colors of water. Your goal is to sort them by color, so that each glass contains only one color. You can pour water from one glass to another, as long as there is enough space in the destination glass and the colors match. You can also use empty glasses as temporary containers. You have to complete each level with the least number of moves possible, and you can earn stars and coins as rewards. You can use coins to buy hints or skip levels if you get stuck.
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Color Water Sort Puzzle has many features that make it an enjoyable game for all ages. Some of them are:
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Over 1000 levels of varying difficulty, from easy to hard.
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Different themes and backgrounds, such as ocean, forest, desert, and more.
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Different types of glasses, such as round, square, hexagon, and more.
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Different colors of water, such as red, blue, green, yellow, purple, and more.
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Smooth animations and sound effects that create a relaxing atmosphere.
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No time limit or pressure. You can play at your own pace and take breaks whenever you want.
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No internet connection required. You can play offline anytime and anywhere.
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No ads or in-app purchases. The game is completely free to download and play.
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The benefits
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Color Water Sort Puzzle is not only a fun game but also a beneficial one. It can help you improve your cognitive skills, such as:
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Logic and reasoning. You have to think carefully before making a move and plan ahead for the best outcome.
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Memory and concentration. You have to remember the colors and positions of the glasses and focus on the task at hand.
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Creativity and problem-solving. You have to find new ways to sort the colors and overcome the challenges.
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Besides, playing Color Water Sort Puzzle can also help you reduce stress and anxiety, as it can calm your mind and distract you from negative thoughts. It can also boost your mood and confidence, as you can feel satisfied and proud of your achievements.
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How to get Color Water Sort Puzzle APK for Android?
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If you want to play Color Water Sort Puzzle on your Android device, you have two options: download it from Google Play Store or download it from other sources as an APK file. An APK file is an Android application package that contains all the files needed to install an app on your device. Here are the steps to get Color Water Sort Puzzle APK for Android:
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Download from Uptodown
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Download from APKCombo
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Install and enjoy
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After you have downloaded and installed Color Water Sort Puzzle APK on your Android device, you can launch the game and start playing. You will see a tutorial that explains the basic rules and controls of the game. You can also access the settings menu to adjust the sound, language, and theme of the game. You can also view your progress, achievements, and leaderboard in the game menu. Have fun sorting the colors and challenging your brain!
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Conclusion
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Color Water Sort Puzzle is a fun and addictive game that you can play on your Android device. It is a puzzle game that tests your logic, memory, creativity, and problem-solving skills. It also helps you relax your mind and reduce stress. You can download it for free from Google Play Store or from other sources as an APK file. If you are looking for a game that is simple yet challenging, colorful yet soothing, and enjoyable yet beneficial, you should try Color Water Sort Puzzle today!
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FAQs
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Here are some frequently asked questions about Color Water Sort Puzzle:
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Q: How many levels are there in Color Water Sort Puzzle?
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A: There are over 1000 levels in Color Water Sort Puzzle, ranging from easy to hard. You can unlock new levels by completing the previous ones or by using coins.
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Q: How do I get more coins in Color Water Sort Puzzle?
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A: You can get more coins by completing levels, watching ads, or rating the game. You can use coins to buy hints or skip levels if you get stuck.
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Q: How do I reset the game in Color Water Sort Puzzle?
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A: If you want to reset the game and start from scratch, you can go to the settings menu and tap on "Reset Game". This will erase all your progress, achievements, and coins.
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Q: Is Color Water Sort Puzzle safe to download?
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A: Yes, Color Water Sort Puzzle is safe to download from Google Play Store or from other trusted websites such as Uptodown or APKCombo. The game does not contain any viruses, malware, or spyware.
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Q: Can I play Color Water Sort Puzzle on PC?
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A: Yes, you can play Color Water Sort Puzzle on PC by using an Android emulator such as BlueStacks or NoxPlayer. You can download the emulator from their official websites and install it on your PC. Then you can download Color Water Sort Puzzle APK from Uptodown or APKCombo and install it on the emulator. You can then launch the game and play it with your mouse or keyboard.
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diff --git a/spaces/fatiXbelha/sd/Download MilkChoco Mod Menu Apk for Android and Enjoy Unlimited Gems.md b/spaces/fatiXbelha/sd/Download MilkChoco Mod Menu Apk for Android and Enjoy Unlimited Gems.md
deleted file mode 100644
index 7d6216aab06e55fdf46a794437a660f873964ccb..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Download MilkChoco Mod Menu Apk for Android and Enjoy Unlimited Gems.md
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MilkChoco Mod Menu Apk: A Fun and Exciting Multiplayer Shooting Game
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If you are looking for a fun and exciting multiplayer shooting game, you should try MilkChoco. MilkChoco is a 5 vs 5 online game where you can choose from different characters and modes to enjoy the thrill of shooting. You can also use the MilkChoco mod menu apk to get unlimited money and gems, as well as other features that will make your gaming experience more enjoyable. In this article, we will tell you more about MilkChoco and how to download and install the MilkChoco mod menu apk on your Android device.
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What is MilkChoco?
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MilkChoco is a multiplayer shooting game developed by GameParadiso. It has over 10 million downloads on Google Play Store and a 4.2-star rating. The game features cute and colorful graphics, as well as various characters and modes to choose from.
You can choose from over 20 different characters, each with their own skills and abilities. For example, you can play as a sniper, a bomber, a medic, a ghost, and more.
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You can play in different modes, such as deathmatch, team deathmatch, battle royale, escort, and clan war. Each mode has its own rules and objectives.
-
You can customize your character with different costumes, weapons, accessories, and skins. You can also upgrade your weapons and skills with money and gems.
-
You can join or create a clan with other players and compete in clan wars. You can also chat with your clan members and friends in the game.
-
You can enjoy the game with smooth controls and fast-paced action. The game also supports offline mode, so you can play without an internet connection.
-
-
How to play MilkChoco
-
To play MilkChoco, you need to download and install the game from Google Play Store or from the official website. Then, you need to create an account or log in with your Facebook or Google account. After that, you can choose your character and mode and start playing. You can use the virtual joystick to move your character and the buttons to shoot, aim, reload, jump, and use skills. You can also switch between first-person and third-person views by tapping the camera icon. You can earn money and gems by playing matches, completing missions, watching ads, or buying them with real money.
-
What is MilkChoco Mod Menu Apk?
-
MilkChoco mod menu apk is a modified version of the original game that gives you access to some extra features that are not available in the official version. Some of these features are:
-
Benefits of MilkChoco Mod Menu Apk
-
-
You can get unlimited money and gems, which you can use to buy and upgrade anything in the game.
-
You can unlock all the characters, costumes, weapons, accessories, and skins in the game.
-
You can use the mod menu to enable or disable various options, such as god mode, aimbot, wallhack, speed hack, invisibility, and more.
-
You can enjoy the game without any ads or interruptions.
-
You can play the game without any root or jailbreak required.
-
-
How to download and install MilkChoco Mod Menu Apk
-
To download and install MilkChoco mod menu apk on your Android device, you need to follow these steps:
-
-
First, you need to uninstall the original version of the game from your device.
-
Then, you need to download the MilkChoco mod menu apk file from a trusted source. You can use this link to download it
Next, you need to enable the installation of apps from unknown sources on your device. You can do this by going to Settings > Security > Unknown Sources and turning it on.
-
After that, you need to locate the downloaded apk file on your device and tap on it to start the installation process. You may need to grant some permissions to the app.
-
Finally, you need to wait for the installation to finish and then open the app. You will see the mod menu icon on the screen. You can tap on it to access the mod options and enjoy the game.
-
-
Conclusion
-
MilkChoco is a fun and exciting multiplayer shooting game that you can play with your friends or other players online. You can choose from different characters and modes and customize your appearance and weapons. You can also use the MilkChoco mod menu apk to get unlimited money and gems, as well as other features that will make your gaming experience more enjoyable. However, you should be careful when using the mod apk, as it may not be safe or legal. You should also respect the other players and not use the mod apk to cheat or ruin their fun. We hope this article has helped you learn more about MilkChoco and how to download and install the MilkChoco mod menu apk on your Android device.
-
FAQs
-
Here are some frequently asked questions about MilkChoco and MilkChoco mod menu apk:
-
-
Q: Is MilkChoco free to play?
-A: Yes, MilkChoco is free to play, but it contains in-app purchases that you can buy with real money.
-
Q: Is MilkChoco mod menu apk safe to use?
-A: MilkChoco mod menu apk is not an official version of the game, so it may not be safe or secure. It may contain viruses or malware that can harm your device or steal your data. It may also cause your account to be banned or suspended by the game developers. Therefore, you should use it at your own risk and discretion.
-
Q: Is MilkChoco mod menu apk legal to use?
-A: MilkChoco mod menu apk is not legal to use, as it violates the terms and conditions of the game. It also infringes the intellectual property rights of the game developers and publishers. Therefore, you should not use it if you want to respect the law and the game creators.
-
Q: Can I play MilkChoco with my friends?
-A: Yes, you can play MilkChoco with your friends by joining or creating a clan or a room. You can also chat with your friends in the game.
-
Q: Can I play MilkChoco offline?
-A: Yes, you can play MilkChoco offline by using the offline mode option in the game settings. However, you will not be able to access some features or modes that require an internet connection.
- 401be4b1e0
-
-
\ No newline at end of file
diff --git a/spaces/fclong/summary/fengshen/examples/longformer/README.md b/spaces/fclong/summary/fengshen/examples/longformer/README.md
deleted file mode 100644
index ef4706898b87d2f10eff5df2db24ae3a182ce673..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/examples/longformer/README.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# longformer model (Chinese),one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
-We modify the original position code of longformer to rotational position coding,and on the basis of [chinese_roformer_L-12_H-768_A-12.zip](https://github.com/ZhuiyiTechnology/roformer), use 180G of data to continue training
-
-## Usage
-There is no structure of Longformer-base in [Transformers](https://github.com/huggingface/transformers), you can run follow code to get structure of longformer from [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
-
- ```shell
- git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git
- ```
-
-### Load Model
-```python
-from fengshen import LongformerModel
-from fengshen import LongformerConfig
-from transformers import BertTokenizer
-
-tokenizer = BertTokenizer.from_pretrained("IDEA-CCNL/Erlangshen-Longformer-110M")
-config = LongformerConfig.from_pretrained("IDEA-CCNL/Erlangshen-Longformer-110M")
-model = LongformerModel.from_pretrained("IDEA-CCNL/Erlangshen-Longformer-110M")
-```
-
-
-
-## Citation
-If you find the resource is useful, please cite the following website in your paper.
-
-```
-@misc{Fengshenbang-LM,
- title={Fengshenbang-LM},
- author={IDEA-CCNL},
- year={2021},
- howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
-}
-```
diff --git a/spaces/fclong/summary/fengshen/examples/zen2_finetune/fs_zen2_large_tnews.sh b/spaces/fclong/summary/fengshen/examples/zen2_finetune/fs_zen2_large_tnews.sh
deleted file mode 100644
index ec081cd3191f951c3815af423329540a219b0114..0000000000000000000000000000000000000000
--- a/spaces/fclong/summary/fengshen/examples/zen2_finetune/fs_zen2_large_tnews.sh
+++ /dev/null
@@ -1,93 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=zen2_large_tnews # create a short name for your job
-#SBATCH --nodes=1 # node count
-#SBATCH --ntasks=1 # total number of tasks across all nodes
-#SBATCH --cpus-per-task=30 # cpu-cores per task (>1 if multi-threaded tasks)
-#SBATCH --gres=gpu:1 # number of gpus per node
-#SBATCH --mail-type=ALL # send email when job begins, ends or failed etc.
-#SBATCH -o %x-%j.log # output and error file name (%x=job name, %j=job id)
-
-
-# export CUDA_VISIBLE_DEVICES='2'
-export TORCH_EXTENSIONS_DIR=/cognitive_comp/ganruyi/tmp/torch_extendsions
-
-MODEL_NAME=zen2_large
-
-TASK=tnews
-
-ZERO_STAGE=1
-STRATEGY=deepspeed_stage_${ZERO_STAGE}
-
-ROOT_DIR=/cognitive_comp/ganruyi/experiments/classification_finetune/${MODEL_NAME}_${TASK}
-if [ ! -d ${ROOT_DIR} ];then
- mkdir -p ${ROOT_DIR}
- echo ${ROOT_DIR} created!!!!!!!!!!!!!!
-else
- echo ${ROOT_DIR} exist!!!!!!!!!!!!!!!
-fi
-
-DATA_DIR=/cognitive_comp/yangping/data/ChineseCLUE_DATA/${TASK}_public/
-PRETRAINED_MODEL_PATH=IDEA-CCNL/Erlangshen-ZEN2-345M-Chinese
-
-CHECKPOINT_PATH=${ROOT_DIR}/ckpt/
-OUTPUT_PATH=${ROOT_DIR}/predict.json
-
-DATA_ARGS="\
- --data_dir $DATA_DIR \
- --train_data train.json \
- --valid_data dev.json \
- --test_data test1.1.json \
- --train_batchsize 32 \
- --valid_batchsize 16 \
- --max_seq_length 128 \
- --texta_name sentence \
- --label_name label \
- --id_name id \
- --task_name tnews \
- "
-
-MODEL_ARGS="\
- --learning_rate 2e-5 \
- --weight_decay 0.01 \
- --warmup_ratio 0.01 \
- --num_labels 15 \
- "
-
-MODEL_CHECKPOINT_ARGS="\
- --monitor val_acc \
- --save_top_k 3 \
- --mode max \
- --every_n_train_steps 400 \
- --save_weights_only True \
- --dirpath $CHECKPOINT_PATH \
- --filename model-{epoch:02d}-{val_acc:.4f} \
- "
-
-TRAINER_ARGS="\
- --max_epochs 10 \
- --gpus 1 \
- --check_val_every_n_epoch 1 \
- --val_check_interval 400 \
- --default_root_dir $ROOT_DIR \
- "
-
-
-options=" \
- --pretrained_model_path $PRETRAINED_MODEL_PATH \
- --vocab_file $PRETRAINED_MODEL_PATH/vocab.txt \
- --do_lower_case \
- --output_save_path $OUTPUT_PATH \
- $DATA_ARGS \
- $MODEL_ARGS \
- $MODEL_CHECKPOINT_ARGS \
- $TRAINER_ARGS \
-"
-SCRIPT_PATH=/cognitive_comp/ganruyi/Fengshenbang-LM/fengshen/examples/zen2_finetune/fengshen_sequence_level_ft_task.py
-/home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options
-
-# SINGULARITY_PATH=/cognitive_comp/ganruyi/pytorch21_06_py3_docker_image_v2.sif
-# python3 $SCRIPT_PATH $options
-# source activate base
-# singularity exec --nv -B /cognitive_comp/:/cognitive_comp/ $SINGULARITY_PATH /home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options
-# /home/ganruyi/anaconda3/bin/python $SCRIPT_PATH $options
-
diff --git a/spaces/fengmuxi/ChatGpt-Web/app/api/user/restmail/route.ts b/spaces/fengmuxi/ChatGpt-Web/app/api/user/restmail/route.ts
deleted file mode 100644
index 80a86fd5a441fb219a818cdc139acbec709525df..0000000000000000000000000000000000000000
--- a/spaces/fengmuxi/ChatGpt-Web/app/api/user/restmail/route.ts
+++ /dev/null
@@ -1,20 +0,0 @@
-import { NextRequest } from "next/server";
-import { getIP } from "../../auth";
-
-export async function POST(req: NextRequest) {
- try {
- const mail=req.nextUrl.searchParams.get("mail")
- let res=await fetch("https://eladmin.dwzynj.top/api/code/email/resetPass?email="+mail, {
- method: "POST",
- headers:{
- "UserIp": String(getIP(req))
- }
- })
- let msg=await res.json()
- // console.log(res.status)
- return new Response(JSON.stringify(msg))
- } catch (e) {
- console.error("[eladmin] ", e);
- return new Response(JSON.stringify(e));
- }
-}
diff --git a/spaces/fffiloni/audioldm-text-to-audio-generation-copy/audioldm/clap/training/lp_main.py b/spaces/fffiloni/audioldm-text-to-audio-generation-copy/audioldm/clap/training/lp_main.py
deleted file mode 100644
index c2d4e8c85aaa3c8e4221963ef56a815cc14f354f..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/audioldm-text-to-audio-generation-copy/audioldm/clap/training/lp_main.py
+++ /dev/null
@@ -1,670 +0,0 @@
-from cmath import cos
-from inspect import getargs
-import logging
-import os
-import random
-from datetime import datetime
-import bisect
-import copy
-from sched import scheduler
-import numpy as np
-import torch
-import torch.backends.cudnn as cudnn
-from torch import optim
-from torch.cuda.amp import GradScaler
-import faulthandler
-import pathlib
-import argparse
-import time
-
-try:
- import wandb
-except ImportError:
- wandb = None
-
-try:
- import torch.utils.tensorboard as tensorboard
-except ImportError:
- tensorboard = None
-
-try:
- import horovod.torch as hvd
-except ImportError:
- hvd = None
-
-from open_clip import create_model_and_transforms, trace_model, create_model
-from training.data import get_data
-from training.params import parse_args
-from training.distributed import is_master, init_distributed_device, world_info_from_env
-from training.logger import setup_logging
-from training.scheduler import cosine_lr
-from training.lp_train import train_one_epoch, evaluate
-from open_clip.utils import get_tar_path_from_dataset_name, dataset_split, get_optimizer
-from open_clip.utils import load_p, load_class_label
-from open_clip.linear_probe import LinearProbe
-
-
-def maintain_ckpts(args, startidx, all_idx_len):
- for i in reversed(range(startidx, all_idx_len)):
- if os.path.exists(os.path.join(args.checkpoint_path, f"epoch_top_{i}.pt")):
- os.rename(
- os.path.join(args.checkpoint_path, f"epoch_top_{i}.pt"),
- os.path.join(args.checkpoint_path, f"epoch_top_{i+1}.pt"),
- )
- if os.path.exists(
- os.path.join(args.checkpoint_path, f"epoch_top_{all_idx_len}.pt")
- ):
- os.remove(os.path.join(args.checkpoint_path, f"epoch_top_{all_idx_len}.pt"))
- return
-
-
-def update_top_k_performance(
- new_metrics_inputs, current_top_k_ckpt_metrics, args, ckpt, bignumbetter=True
-):
- """
- Record the top-k performance of the current epoch.
- current_top_k_metrics is a dictionary of the form: {1: top_1_ckpt_measure, 2: top_2_ckpt_measure, ...}
- """
- if isinstance(new_metrics_inputs, (list, tuple)):
- new_metrics_inputs = np.mean(new_metrics_inputs)
- return update_top_k_performance(
- new_metrics_inputs,
- current_top_k_ckpt_metrics,
- args=args,
- ckpt=ckpt,
- bignumbetter=bignumbetter,
- )
- elif isinstance(new_metrics_inputs, dict):
- new_metrics_inputs = np.mean(list(new_metrics_inputs.values()))
- return update_top_k_performance(
- new_metrics_inputs,
- current_top_k_ckpt_metrics,
- args=args,
- ckpt=ckpt,
- bignumbetter=bignumbetter,
- )
- elif isinstance(new_metrics_inputs, (float, int)):
- update_flag = {k: False for k in current_top_k_ckpt_metrics.keys()}
- sorted_keys = sorted(current_top_k_ckpt_metrics.keys())
- sorted_values = sorted(
- current_top_k_ckpt_metrics.values(), reverse=bignumbetter
- )
- sorted_values_ = copy.deepcopy(sorted_values)
- sorted_values.append(new_metrics_inputs)
- sorted_values = sorted(sorted_values, reverse=bignumbetter)
- sorted_values = sorted_values[:-1]
-
- if sorted_values == sorted_values_:
- return current_top_k_ckpt_metrics, new_metrics_inputs
- else:
- for i in range(len(sorted_keys)):
- if current_top_k_ckpt_metrics[sorted_keys[i]] != sorted_values[i]:
- current_top_k_ckpt_metrics[sorted_keys[i]] = sorted_values[i]
- update_flag[sorted_keys[i]] = True
- for i in range(len(update_flag)):
- if update_flag[i]:
- maintain_ckpts(args, i, len(sorted_keys))
- torch.save(
- ckpt,
- os.path.join(args.checkpoint_path, f"epoch_top_{i}.pt"),
- )
- break
- return current_top_k_ckpt_metrics, new_metrics_inputs
-
-
-# def updateifNone(a, b):
-# a = b if None else a
-# return a
-
-
-def is_pretrained_params(n):
- return (
- n.startswith("clap_model.transformer")
- or n in ["clap_model.positional_embedding", "clap_model.text_projection"]
- or n.startswith("clap_model.token_embedding")
- or n.startswith("clap_model.ln_final")
- or n.startswith("clap_model.logit_scale_t")
- )
-
-
-def random_seed(seed=42, rank=0):
- torch.manual_seed(seed + rank)
- np.random.seed(seed + rank)
- random.seed(seed + rank)
-
-
-def config_lp_optimizer(model, data, args):
- # set wd-related params to 0 if use adam optimizer
- if args.optimizer == "adam":
- args.wd = 0
- args.wd_pretrained = 0
- args.wd_new = 0
-
- in_clap = lambda n, p: n.startswith("clap_model")
-
- named_parameters = list(model.named_parameters())
-
- optimizer = {}
- scheduler = {}
-
- # freeze text encoder
- text_freeze_parameters = [
- p
- for n, p in named_parameters
- if n.startswith("clap_model.transformer")
- or n in ["clap_model.positional_embedding", "clap_model.text_projection"]
- or n.startswith("clap_model.token_embedding")
- or n.startswith("clap_model.ln_final")
- ]
-
- if args.freeze_text:
- logging.info("Freeze Text!!!!")
- for k in text_freeze_parameters:
- k.requires_grad = False
-
- if not args.lp_freeze:
- exclude = (
- lambda n, p: p.ndim < 2
- or "bn" in n
- or "ln" in n
- or "bias" in n
- or "logit_scale" in n
- )
- include = lambda n, p: not exclude(n, p)
-
- # (yusong): we do not split the learning rate anymore
- # p for n, p in named_parameters if in_clap(n,p) and exclude(n, p) and p.requires_grad
- gain_or_bias_params = [
- p for n, p in named_parameters if exclude(n, p) and p.requires_grad
- ]
- # rest_params = [p for n, p in named_parameters if in_clap(n,p) and include(n, p) and p.requires_grad]
- rest_params = [
- p for n, p in named_parameters if include(n, p) and p.requires_grad
- ]
-
- if args.train_data is None:
- optimizer = None
- scheduler = None
- else:
- total_steps = data["train"].dataloader.num_batches * args.epochs
-
- if args.split_opt:
- for x in ["lr", "beta1", "beta2", "eps", "wd"]:
- for y in ["_new", "_pretrained"]:
- if getattr(args, x + y) is None:
- setattr(args, x + y, getattr(args, x))
-
- gain_or_bias_pretrained_params = [
- p
- for n, p in named_parameters
- if (exclude(n, p) and p.requires_grad) and is_pretrained_params(n)
- ]
- rest_pretrained_params = [
- p
- for n, p in named_parameters
- if (include(n, p) and p.requires_grad) and is_pretrained_params(n)
- ]
- gain_or_bias_new_params = [
- p
- for n, p in named_parameters
- if (exclude(n, p) and p.requires_grad)
- and (not is_pretrained_params(n))
- ]
- rest_new_params = [
- p
- for n, p in named_parameters
- if (include(n, p) and p.requires_grad)
- and (not is_pretrained_params(n))
- ]
-
- pretrained_params_optimizer = get_optimizer(
- [
- {"params": gain_or_bias_pretrained_params, "weight_decay": 0.0},
- {
- "params": rest_pretrained_params,
- "weight_decay": args.wd_pretrained,
- },
- ],
- lr=args.lr_pretrained,
- betas=(args.beta1_pretrained, args.beta2_pretrained),
- eps=args.eps_pretrained,
- momentum=args.momentum_pretrained,
- optimizer_name=args.optimizer,
- )
- pretrained_params_scheduler = cosine_lr(
- pretrained_params_optimizer,
- args.lr_pretrained,
- args.warmup,
- total_steps,
- )
-
- new_params_optimizer = get_optimizer(
- [
- {"params": gain_or_bias_new_params, "weight_decay": 0.0},
- {"params": rest_new_params, "weight_decay": args.wd_new},
- ],
- lr=args.lr_new,
- betas=(args.beta1_new, args.beta2_new),
- eps=args.eps_new,
- momentum=args.momentum_new,
- optimizer_name=args.optimizer,
- )
- new_params_scheduler = cosine_lr(
- new_params_optimizer, args.lr_new, args.warmup, total_steps
- )
-
- optimizer["text"] = pretrained_params_optimizer
- optimizer["audio"] = new_params_optimizer
- scheduler["text"] = pretrained_params_scheduler
- scheduler["audio"] = new_params_scheduler
-
- if args.horovod:
- pretrained_params_optimizer = hvd.DistributedOptimizer(
- pretrained_params_optimizer,
- named_parameters=model.named_parameters(),
- )
- new_params_optimizer = hvd.DistributedOptimizer(
- new_params_optimizer, named_parameters=model.named_parameters()
- )
- hvd.broadcast_parameters(model.state_dict(), root_rank=0)
- hvd.broadcast_optimizer_state(
- pretrained_params_optimizer, root_rank=0
- )
- hvd.broadcast_optimizer_state(new_params_optimizer, root_rank=0)
- else:
-
- optimizer["clap"] = get_optimizer(
- [
- {"params": gain_or_bias_params, "weight_decay": 0.0},
- {"params": rest_params, "weight_decay": args.wd},
- ],
- lr=args.lr,
- betas=(args.beta1, args.beta2),
- eps=args.eps,
- momentum=args.momentum,
- optimizer_name=args.optimizer,
- )
- scheduler["clap"] = cosine_lr(
- optimizer["clap"], args.lr, args.warmup, total_steps
- )
-
- if args.horovod:
- optimizer["clap"] = hvd.DistributedOptimizer(
- optimizer["clap"], named_parameters=model.named_parameters()
- )
- hvd.broadcast_parameters(model.state_dict(), root_rank=0)
- hvd.broadcast_optimizer_state(optimizer["clap"], root_rank=0)
-
- # linear probe optimizer
- else:
- lp_params = [
- p for n, p in named_parameters if (not in_clap(n, p)) and p.requires_grad
- ]
- lp_optim = get_optimizer(
- lp_params,
- lr=args.lp_lr,
- betas=(args.beta1, args.beta2),
- eps=args.eps,
- momentum=0.9,
- optimizer_name=args.optimizer,
- )
- optimizer["lp"] = lp_optim
-
- return optimizer, scheduler, text_freeze_parameters
-
-
-def main():
- args = parse_args()
-
- time.sleep(args.sleep)
-
- # sanitize model name for filesystem / uri use, easier if we don't use / in name as a rule?
- args.amodel = args.amodel.replace("/", "-")
- # download sizes.json file
-
- # (yusong): the below two lines are for debug
- # print("setting up faulthandler")
- # faulthandler.register(10)
-
- random.seed(args.seed)
- torch.manual_seed(args.seed)
- torch.cuda.manual_seed(args.seed)
- torch.cuda.manual_seed_all(args.seed)
- np.random.seed(args.seed)
- args.class_index_dict = load_class_label(args.class_label_path)
-
- # get the name of the experiments
- if args.name is None:
- args.name = "-".join(
- [
- datetime.now().strftime("%Y_%m_%d-%H_%M_%S"),
- f"linear_probe" f"model_{args.amodel}",
- f"lr_{args.lr}",
- f"b_{args.batch_size}",
- f"j_{args.workers}",
- f"p_{args.precision}",
- ]
- )
-
- # discover initial world args early so we can log properly
- args.distributed = False
- args.local_rank, args.rank, args.world_size = world_info_from_env()
-
- if args.remotedata and is_master(args):
- for dataset_name in args.datasetnames:
- for split in dataset_split[dataset_name]:
- if not os.path.exists(f"./json_files/{dataset_name}/{split}"):
- os.makedirs(f"./json_files/{dataset_name}/{split}")
- os.system(
- f"aws s3 cp s3://s-laion-audio/webdataset_tar/{dataset_name}/{split}/sizes.json ./json_files/{dataset_name}/{split}/sizes.json"
- )
-
- args.log_path = None
- if is_master(args, local=args.log_local):
- log_base_path = os.path.join(args.logs, args.name)
- os.makedirs(log_base_path, exist_ok=True)
- log_filename = f"out-{args.rank}" if args.log_local else "out.log"
- args.log_path = os.path.join(log_base_path, log_filename)
-
- # avoid log dir in same name:
- postfix = 0
- while os.path.exists(args.log_path):
- postfix += 1
- log_base_path_new = log_base_path + "-" + str(postfix)
- os.makedirs(log_base_path_new, exist_ok=True)
- log_filename = f"out-{args.rank}" if args.log_local else "out.log"
- args.log_path = os.path.join(log_base_path_new, log_filename)
- # print(
- # "Error. Experiment already exists. Use --name {} to specify a new experiment."
- # )
- # return -1
-
- # Set logger
- args.log_level = logging.DEBUG if args.debug else logging.INFO
- setup_logging(args.log_path, args.log_level)
-
- # fully initialize distributed device environment
- device = init_distributed_device(args)
-
- args.wandb = "wandb" in args.report_to or "all" in args.report_to
- args.tensorboard = "tensorboard" in args.report_to or "all" in args.report_to
- if is_master(args):
- args.tensorboard_path = (
- os.path.join(args.logs, args.name, "tensorboard")
- if args.tensorboard
- else ""
- )
- args.checkpoint_path = os.path.join(args.logs, args.name, "checkpoints")
- for dirname in [args.tensorboard_path, args.checkpoint_path]:
- if dirname:
- os.makedirs(dirname, exist_ok=True)
- else:
- args.tensorboard_path = ""
- args.checkpoint_path = ""
-
- if args.copy_codebase:
- copy_codebase(args)
-
- assert args.precision in ["amp", "fp16", "fp32"]
- if args.precision == "fp16":
- logging.warning(
- "It is recommended to use AMP mixed-precision instead of FP16. "
- "FP16 support needs further verification and tuning, especially for train."
- )
-
- if args.horovod:
- logging.info(
- f"Running in horovod mode with multiple processes / nodes. Device: {args.device}."
- f"Process (global: {args.rank}, local {args.local_rank}), total {args.world_size}."
- )
- elif args.distributed:
- logging.info(
- f"Running in distributed mode with multiple processes. Device: {args.device}."
- f"Process (global: {args.rank}, local {args.local_rank}), total {args.world_size}."
- )
- else:
- logging.info(f"Running with a single process. Device {args.device}.")
-
- logging.info(f"openai cache dir: {os.path.expanduser(args.openai_model_cache_dir)}")
-
- # Create CLAP model
- clap_model, clap_model_cfg = create_model(
- args.amodel,
- args.tmodel,
- args.pretrained,
- precision=args.precision,
- device=device,
- jit=args.torchscript,
- force_quick_gelu=args.force_quick_gelu,
- openai_model_cache_dir=os.path.expanduser(args.openai_model_cache_dir),
- skip_params=False,
- pretrained_audio=args.pretrained_audio,
- pretrained_text=args.pretrained_text,
- enable_fusion=args.enable_fusion,
- fusion_type=args.fusion_type,
- )
-
- args.lp_out_ch = len(list(args.class_index_dict.keys()))
- # Linear Probe
- logging.info(f"linear probe using mlp: {args.lp_mlp}")
- logging.info(f"linear probe using freeze: {args.lp_freeze}")
- logging.info(f"linear probe act layer: {args.lp_act}")
- logging.info(f"linear probe out ch: {args.lp_out_ch}")
- logging.info(f"linear probe learning rate (if applicable): {args.lp_lr}")
- logging.info(f"linear probe loss func: {args.lp_loss}")
- logging.info(f"linear probe lp_metrics: {args.lp_metrics}")
-
- model = LinearProbe(
- clap_model,
- mlp=args.lp_mlp,
- freeze=args.lp_freeze,
- in_ch=512,
- out_ch=args.lp_out_ch,
- act=args.lp_act,
- ) # in_ch is fixed (i.e., 512)
- model = model.to(device)
-
- if args.horovod:
- with torch.no_grad():
- for param in model.parameters():
- param.set_(param.contiguous())
-
- if args.trace:
- model = trace_model(model, batch_size=args.batch_size, device=device)
-
- if is_master(args):
- logging.info("Linear Probe CLAP Model:")
- logging.info(f"{str(clap_model)}")
- logging.info("Params:")
- params_file = os.path.join(args.logs, args.name, "params.txt")
- with open(params_file, "w") as f:
- for name in sorted(vars(args)):
- val = getattr(args, name)
- logging.info(f" {name}: {val}")
- f.write(f"{name}: {val}\n")
-
- if args.distributed and not args.horovod:
- if args.use_bn_sync:
- model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
- ddp_args = {}
- if args.ddp_static_graph:
- # this doesn't exist in older PyTorch, arg only added if enabled
- ddp_args["static_graph"] = True
- model = torch.nn.parallel.DistributedDataParallel(
- model, device_ids=[device], find_unused_parameters=True, **ddp_args
- )
-
- data = get_data(args, clap_model_cfg)
- assert len(data), "At least one train or eval dataset must be specified."
- if args.trace:
- assert "train" not in data, "Cannot train with traced model"
-
- optimizer, scheduler, text_freeze_parameters = config_lp_optimizer(
- model, data, args
- )
-
- scaler = GradScaler() if args.precision == "amp" else None
-
- # optionally resume from a checkpoint
- start_epoch = 0
- if args.resume is not None:
- if os.path.isfile(args.resume):
- checkpoint = torch.load(args.resume, map_location=device)
- if "epoch" in checkpoint:
- # resuming a train checkpoint w/ epoch and optimizer state
- start_epoch = checkpoint["epoch"]
- sd = checkpoint["state_dict"]
- if not args.distributed and next(iter(sd.items()))[0].startswith(
- "module"
- ):
- sd = {k[len("module.") :]: v for k, v in sd.items()}
- model.load_state_dict(sd)
- if args.split_opt:
- if optimizer is not None:
- for k, o_ in optimizer.items():
- o_.load_state_dict(checkpoint[k + "_" + "optimizer"])
- if optimizer is not None:
- optimizer.load_state_dict(checkpoint["optimizer"])
- if scaler is not None and "scaler" in checkpoint:
- scaler.load_state_dict(checkpoint["scaler"])
- logging.info(
- f"=> resuming checkpoint '{args.resume}' (epoch {start_epoch})"
- )
- else:
- # loading a bare (model only) checkpoint for fine-tune or evaluation
- model.load_state_dict(checkpoint)
- logging.info(
- f"=> loaded checkpoint '{args.resume}' (epoch {start_epoch})"
- )
- if args.freeze_text:
- print("Freeze Text!!!!")
- for k in text_freeze_parameters:
- k.requires_grad = False
- else:
- logging.info("=> no checkpoint found at '{}'".format(args.resume))
-
- cudnn.benchmark = True
- cudnn.deterministic = False
-
- # determine if this worker should save logs and checkpoints. only do so if it is rank == 0
- args.save_logs = args.logs and args.logs.lower() != "none" and is_master(args)
- writer = None
- if args.save_logs and args.tensorboard:
- assert tensorboard is not None, "Please install tensorboard."
- writer = tensorboard.SummaryWriter(args.tensorboard_path)
-
- if args.wandb and is_master(args):
- assert wandb is not None, "Please install wandb."
- logging.debug("Starting wandb.")
- args.train_sz = data["train"].dataloader.num_samples
- if args.val_data is not None:
- args.val_sz = data["val"].dataloader.num_samples
- # you will have to configure this for your project!
- wandb.init(
- project="clap",
- notes=args.wandb_notes,
- name=args.wandb_notes,
- tags=[],
- config=vars(args),
- )
- if args.debug:
- wandb.watch(model, log="all")
- wandb.save(params_file)
- logging.debug("Finished loading wandb.")
-
- if "train" not in data:
- evaluate(model, data, start_epoch, args, writer)
- return
- elif start_epoch == 0 and "val" in data and not args.no_eval:
- evaluate(model, data, 0, args, writer)
- if args.save_top_performance:
- current_top_k_ckpt_metrics = {
- i: 0 for i in range(args.save_top_performance)
- } # initialize the top-k metric for ckpts to 0
-
- for epoch in range(start_epoch, args.epochs):
- # freeze the text param after (include) args.freeze_text_after, this is -1 by default
- if epoch == args.freeze_text_after:
- print("Text pretrained parameters are freezed since this epoch.")
- for k in text_freeze_parameters:
- k.requires_grad = False
- if is_master(args):
- logging.info(f"Start epoch {epoch}")
-
- train_one_epoch(model, data, epoch, optimizer, scaler, scheduler, args, writer)
- completed_epoch = epoch + 1
-
- if (
- any(v in data for v in ("val", "imagenet-val", "imagenet-v2"))
- and not args.no_eval
- ):
- metrics = evaluate(model, data, completed_epoch, args, writer)
- if args.save_top_performance:
- top_k_dataset = args.top_k_checkpoint_select_dataset
- top_k_metric = args.top_k_checkpoint_select_metric
- filtered_metrics = [
- v
- for k, v in metrics.items()
- if top_k_metric in k and top_k_dataset in k
- ] # check all R@10 metrics (all dataset) and use it to update the ckpt
- # Saving checkpoints.
- if args.save_logs:
- opt_dict = {
- k + "_" + "optimizer": v.state_dict() for k, v in optimizer.items()
- }
- checkpoint_dict = {
- "epoch": completed_epoch,
- "name": args.name,
- "state_dict": model.state_dict(),
- }
- checkpoint_dict.update(opt_dict)
- if scaler is not None:
- checkpoint_dict["scaler"] = scaler.state_dict()
-
- if completed_epoch == args.epochs or (
- args.save_frequency > 0 and (completed_epoch % args.save_frequency) == 0
- ):
- torch.save(
- checkpoint_dict,
- os.path.join(args.checkpoint_path, f"epoch_{completed_epoch}.pt"),
- )
- if args.save_most_recent:
- torch.save(
- checkpoint_dict,
- os.path.join(args.checkpoint_path, f"epoch_latest.pt"),
- )
- if args.save_top_performance and not args.no_eval:
- update_top_k_performance(
- filtered_metrics,
- current_top_k_ckpt_metrics,
- args,
- checkpoint_dict,
- bignumbetter=True,
- )
-
- if args.wandb and is_master(args):
- wandb.finish()
-
-
-def copy_codebase(args):
- from shutil import copytree, ignore_patterns
-
- new_code_path = os.path.join(args.logs, args.name, "code")
- if os.path.exists(new_code_path):
- print(
- f"Error. Experiment already exists at {new_code_path}. Use --name to specify a new experiment."
- )
- return -1
- print(f"Copying codebase to {new_code_path}")
- current_code_path = os.path.realpath(__file__)
- for _ in range(3):
- current_code_path = os.path.dirname(current_code_path)
- copytree(
- current_code_path, new_code_path, ignore=ignore_patterns("log", "logs", "wandb")
- )
- print("Done copying code.")
- return 1
-
-
-if __name__ == "__main__":
- main()
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/object-inspect/readme.markdown b/spaces/fffiloni/controlnet-animation-doodle/node_modules/object-inspect/readme.markdown
deleted file mode 100644
index 9ff6bec3661be5c09ed95bfc0971de871280fe6c..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/object-inspect/readme.markdown
+++ /dev/null
@@ -1,86 +0,0 @@
-# object-inspect [![Version Badge][2]][1]
-
-string representations of objects in node and the browser
-
-[![github actions][actions-image]][actions-url]
-[![coverage][codecov-image]][codecov-url]
-[![dependency status][5]][6]
-[![dev dependency status][7]][8]
-[![License][license-image]][license-url]
-[![Downloads][downloads-image]][downloads-url]
-
-[![npm badge][11]][1]
-
-# example
-
-## circular
-
-``` js
-var inspect = require('object-inspect');
-var obj = { a: 1, b: [3,4] };
-obj.c = obj;
-console.log(inspect(obj));
-```
-
-## dom element
-
-``` js
-var inspect = require('object-inspect');
-
-var d = document.createElement('div');
-d.setAttribute('id', 'beep');
-d.innerHTML = 'woooiiiii';
-
-console.log(inspect([ d, { a: 3, b : 4, c: [5,6,[7,[8,[9]]]] } ]));
-```
-
-output:
-
-```
-[
...
, { a: 3, b: 4, c: [ 5, 6, [ 7, [ 8, [ ... ] ] ] ] } ]
-```
-
-# methods
-
-``` js
-var inspect = require('object-inspect')
-```
-
-## var s = inspect(obj, opts={})
-
-Return a string `s` with the string representation of `obj` up to a depth of `opts.depth`.
-
-Additional options:
- - `quoteStyle`: must be "single" or "double", if present. Default `'single'` for strings, `'double'` for HTML elements.
- - `maxStringLength`: must be `0`, a positive integer, `Infinity`, or `null`, if present. Default `Infinity`.
- - `customInspect`: When `true`, a custom inspect method function will be invoked (either undere the `util.inspect.custom` symbol, or the `inspect` property). When the string `'symbol'`, only the symbol method will be invoked. Default `true`.
- - `indent`: must be "\t", `null`, or a positive integer. Default `null`.
- - `numericSeparator`: must be a boolean, if present. Default `false`. If `true`, all numbers will be printed with numeric separators (eg, `1234.5678` will be printed as `'1_234.567_8'`)
-
-# install
-
-With [npm](https://npmjs.org) do:
-
-```
-npm install object-inspect
-```
-
-# license
-
-MIT
-
-[1]: https://npmjs.org/package/object-inspect
-[2]: https://versionbadg.es/inspect-js/object-inspect.svg
-[5]: https://david-dm.org/inspect-js/object-inspect.svg
-[6]: https://david-dm.org/inspect-js/object-inspect
-[7]: https://david-dm.org/inspect-js/object-inspect/dev-status.svg
-[8]: https://david-dm.org/inspect-js/object-inspect#info=devDependencies
-[11]: https://nodei.co/npm/object-inspect.png?downloads=true&stars=true
-[license-image]: https://img.shields.io/npm/l/object-inspect.svg
-[license-url]: LICENSE
-[downloads-image]: https://img.shields.io/npm/dm/object-inspect.svg
-[downloads-url]: https://npm-stat.com/charts.html?package=object-inspect
-[codecov-image]: https://codecov.io/gh/inspect-js/object-inspect/branch/main/graphs/badge.svg
-[codecov-url]: https://app.codecov.io/gh/inspect-js/object-inspect/
-[actions-image]: https://img.shields.io/endpoint?url=https://github-actions-badge-u3jn4tfpocch.runkit.sh/inspect-js/object-inspect
-[actions-url]: https://github.com/inspect-js/object-inspect/actions
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io-parser/build/cjs/is-binary.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io-parser/build/cjs/is-binary.js
deleted file mode 100644
index 4b7c23478c6abc616b9342908bcdb3c6b74294c4..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io-parser/build/cjs/is-binary.js
+++ /dev/null
@@ -1,55 +0,0 @@
-"use strict";
-Object.defineProperty(exports, "__esModule", { value: true });
-exports.hasBinary = exports.isBinary = void 0;
-const withNativeArrayBuffer = typeof ArrayBuffer === "function";
-const isView = (obj) => {
- return typeof ArrayBuffer.isView === "function"
- ? ArrayBuffer.isView(obj)
- : obj.buffer instanceof ArrayBuffer;
-};
-const toString = Object.prototype.toString;
-const withNativeBlob = typeof Blob === "function" ||
- (typeof Blob !== "undefined" &&
- toString.call(Blob) === "[object BlobConstructor]");
-const withNativeFile = typeof File === "function" ||
- (typeof File !== "undefined" &&
- toString.call(File) === "[object FileConstructor]");
-/**
- * Returns true if obj is a Buffer, an ArrayBuffer, a Blob or a File.
- *
- * @private
- */
-function isBinary(obj) {
- return ((withNativeArrayBuffer && (obj instanceof ArrayBuffer || isView(obj))) ||
- (withNativeBlob && obj instanceof Blob) ||
- (withNativeFile && obj instanceof File));
-}
-exports.isBinary = isBinary;
-function hasBinary(obj, toJSON) {
- if (!obj || typeof obj !== "object") {
- return false;
- }
- if (Array.isArray(obj)) {
- for (let i = 0, l = obj.length; i < l; i++) {
- if (hasBinary(obj[i])) {
- return true;
- }
- }
- return false;
- }
- if (isBinary(obj)) {
- return true;
- }
- if (obj.toJSON &&
- typeof obj.toJSON === "function" &&
- arguments.length === 1) {
- return hasBinary(obj.toJSON(), true);
- }
- for (const key in obj) {
- if (Object.prototype.hasOwnProperty.call(obj, key) && hasBinary(obj[key])) {
- return true;
- }
- }
- return false;
-}
-exports.hasBinary = hasBinary;
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io-parser/build/esm-debug/is-binary.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io-parser/build/esm-debug/is-binary.d.ts
deleted file mode 100644
index fa18261899c45c63e4389728d49fdd20070e6dcb..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/socket.io-parser/build/esm-debug/is-binary.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-/**
- * Returns true if obj is a Buffer, an ArrayBuffer, a Blob or a File.
- *
- * @private
- */
-export declare function isBinary(obj: any): boolean;
-export declare function hasBinary(obj: any, toJSON?: boolean): any;
diff --git a/spaces/firsk/ai_otto/text/tone_sandhi.py b/spaces/firsk/ai_otto/text/tone_sandhi.py
deleted file mode 100644
index 6a6e4c3e64f1a9e8b9da73fc6fbebf8a33e5602d..0000000000000000000000000000000000000000
--- a/spaces/firsk/ai_otto/text/tone_sandhi.py
+++ /dev/null
@@ -1,769 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from typing import List
-from typing import Tuple
-
-import jieba
-from pypinyin import lazy_pinyin
-from pypinyin import Style
-
-
-class ToneSandhi:
- def __init__(self):
- self.must_neural_tone_words = {
- "麻烦",
- "麻利",
- "鸳鸯",
- "高粱",
- "骨头",
- "骆驼",
- "马虎",
- "首饰",
- "馒头",
- "馄饨",
- "风筝",
- "难为",
- "队伍",
- "阔气",
- "闺女",
- "门道",
- "锄头",
- "铺盖",
- "铃铛",
- "铁匠",
- "钥匙",
- "里脊",
- "里头",
- "部分",
- "那么",
- "道士",
- "造化",
- "迷糊",
- "连累",
- "这么",
- "这个",
- "运气",
- "过去",
- "软和",
- "转悠",
- "踏实",
- "跳蚤",
- "跟头",
- "趔趄",
- "财主",
- "豆腐",
- "讲究",
- "记性",
- "记号",
- "认识",
- "规矩",
- "见识",
- "裁缝",
- "补丁",
- "衣裳",
- "衣服",
- "衙门",
- "街坊",
- "行李",
- "行当",
- "蛤蟆",
- "蘑菇",
- "薄荷",
- "葫芦",
- "葡萄",
- "萝卜",
- "荸荠",
- "苗条",
- "苗头",
- "苍蝇",
- "芝麻",
- "舒服",
- "舒坦",
- "舌头",
- "自在",
- "膏药",
- "脾气",
- "脑袋",
- "脊梁",
- "能耐",
- "胳膊",
- "胭脂",
- "胡萝",
- "胡琴",
- "胡同",
- "聪明",
- "耽误",
- "耽搁",
- "耷拉",
- "耳朵",
- "老爷",
- "老实",
- "老婆",
- "老头",
- "老太",
- "翻腾",
- "罗嗦",
- "罐头",
- "编辑",
- "结实",
- "红火",
- "累赘",
- "糨糊",
- "糊涂",
- "精神",
- "粮食",
- "簸箕",
- "篱笆",
- "算计",
- "算盘",
- "答应",
- "笤帚",
- "笑语",
- "笑话",
- "窟窿",
- "窝囊",
- "窗户",
- "稳当",
- "稀罕",
- "称呼",
- "秧歌",
- "秀气",
- "秀才",
- "福气",
- "祖宗",
- "砚台",
- "码头",
- "石榴",
- "石头",
- "石匠",
- "知识",
- "眼睛",
- "眯缝",
- "眨巴",
- "眉毛",
- "相声",
- "盘算",
- "白净",
- "痢疾",
- "痛快",
- "疟疾",
- "疙瘩",
- "疏忽",
- "畜生",
- "生意",
- "甘蔗",
- "琵琶",
- "琢磨",
- "琉璃",
- "玻璃",
- "玫瑰",
- "玄乎",
- "狐狸",
- "状元",
- "特务",
- "牲口",
- "牙碜",
- "牌楼",
- "爽快",
- "爱人",
- "热闹",
- "烧饼",
- "烟筒",
- "烂糊",
- "点心",
- "炊帚",
- "灯笼",
- "火候",
- "漂亮",
- "滑溜",
- "溜达",
- "温和",
- "清楚",
- "消息",
- "浪头",
- "活泼",
- "比方",
- "正经",
- "欺负",
- "模糊",
- "槟榔",
- "棺材",
- "棒槌",
- "棉花",
- "核桃",
- "栅栏",
- "柴火",
- "架势",
- "枕头",
- "枇杷",
- "机灵",
- "本事",
- "木头",
- "木匠",
- "朋友",
- "月饼",
- "月亮",
- "暖和",
- "明白",
- "时候",
- "新鲜",
- "故事",
- "收拾",
- "收成",
- "提防",
- "挖苦",
- "挑剔",
- "指甲",
- "指头",
- "拾掇",
- "拳头",
- "拨弄",
- "招牌",
- "招呼",
- "抬举",
- "护士",
- "折腾",
- "扫帚",
- "打量",
- "打算",
- "打点",
- "打扮",
- "打听",
- "打发",
- "扎实",
- "扁担",
- "戒指",
- "懒得",
- "意识",
- "意思",
- "情形",
- "悟性",
- "怪物",
- "思量",
- "怎么",
- "念头",
- "念叨",
- "快活",
- "忙活",
- "志气",
- "心思",
- "得罪",
- "张罗",
- "弟兄",
- "开通",
- "应酬",
- "庄稼",
- "干事",
- "帮手",
- "帐篷",
- "希罕",
- "师父",
- "师傅",
- "巴结",
- "巴掌",
- "差事",
- "工夫",
- "岁数",
- "屁股",
- "尾巴",
- "少爷",
- "小气",
- "小伙",
- "将就",
- "对头",
- "对付",
- "寡妇",
- "家伙",
- "客气",
- "实在",
- "官司",
- "学问",
- "学生",
- "字号",
- "嫁妆",
- "媳妇",
- "媒人",
- "婆家",
- "娘家",
- "委屈",
- "姑娘",
- "姐夫",
- "妯娌",
- "妥当",
- "妖精",
- "奴才",
- "女婿",
- "头发",
- "太阳",
- "大爷",
- "大方",
- "大意",
- "大夫",
- "多少",
- "多么",
- "外甥",
- "壮实",
- "地道",
- "地方",
- "在乎",
- "困难",
- "嘴巴",
- "嘱咐",
- "嘟囔",
- "嘀咕",
- "喜欢",
- "喇嘛",
- "喇叭",
- "商量",
- "唾沫",
- "哑巴",
- "哈欠",
- "哆嗦",
- "咳嗽",
- "和尚",
- "告诉",
- "告示",
- "含糊",
- "吓唬",
- "后头",
- "名字",
- "名堂",
- "合同",
- "吆喝",
- "叫唤",
- "口袋",
- "厚道",
- "厉害",
- "千斤",
- "包袱",
- "包涵",
- "匀称",
- "勤快",
- "动静",
- "动弹",
- "功夫",
- "力气",
- "前头",
- "刺猬",
- "刺激",
- "别扭",
- "利落",
- "利索",
- "利害",
- "分析",
- "出息",
- "凑合",
- "凉快",
- "冷战",
- "冤枉",
- "冒失",
- "养活",
- "关系",
- "先生",
- "兄弟",
- "便宜",
- "使唤",
- "佩服",
- "作坊",
- "体面",
- "位置",
- "似的",
- "伙计",
- "休息",
- "什么",
- "人家",
- "亲戚",
- "亲家",
- "交情",
- "云彩",
- "事情",
- "买卖",
- "主意",
- "丫头",
- "丧气",
- "两口",
- "东西",
- "东家",
- "世故",
- "不由",
- "不在",
- "下水",
- "下巴",
- "上头",
- "上司",
- "丈夫",
- "丈人",
- "一辈",
- "那个",
- "菩萨",
- "父亲",
- "母亲",
- "咕噜",
- "邋遢",
- "费用",
- "冤家",
- "甜头",
- "介绍",
- "荒唐",
- "大人",
- "泥鳅",
- "幸福",
- "熟悉",
- "计划",
- "扑腾",
- "蜡烛",
- "姥爷",
- "照顾",
- "喉咙",
- "吉他",
- "弄堂",
- "蚂蚱",
- "凤凰",
- "拖沓",
- "寒碜",
- "糟蹋",
- "倒腾",
- "报复",
- "逻辑",
- "盘缠",
- "喽啰",
- "牢骚",
- "咖喱",
- "扫把",
- "惦记",
- }
- self.must_not_neural_tone_words = {
- "男子",
- "女子",
- "分子",
- "原子",
- "量子",
- "莲子",
- "石子",
- "瓜子",
- "电子",
- "人人",
- "虎虎",
- }
- self.punc = ":,;。?!“”‘’':,;.?!"
-
- # the meaning of jieba pos tag: https://blog.csdn.net/weixin_44174352/article/details/113731041
- # e.g.
- # word: "家里"
- # pos: "s"
- # finals: ['ia1', 'i3']
- def _neural_sandhi(self, word: str, pos: str, finals: List[str]) -> List[str]:
- # reduplication words for n. and v. e.g. 奶奶, 试试, 旺旺
- for j, item in enumerate(word):
- if (
- j - 1 >= 0
- and item == word[j - 1]
- and pos[0] in {"n", "v", "a"}
- and word not in self.must_not_neural_tone_words
- ):
- finals[j] = finals[j][:-1] + "5"
- ge_idx = word.find("个")
- if len(word) >= 1 and word[-1] in "吧呢啊呐噻嘛吖嗨呐哦哒额滴哩哟喽啰耶喔诶":
- finals[-1] = finals[-1][:-1] + "5"
- elif len(word) >= 1 and word[-1] in "的地得":
- finals[-1] = finals[-1][:-1] + "5"
- # e.g. 走了, 看着, 去过
- # elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}:
- # finals[-1] = finals[-1][:-1] + "5"
- elif (
- len(word) > 1
- and word[-1] in "们子"
- and pos in {"r", "n"}
- and word not in self.must_not_neural_tone_words
- ):
- finals[-1] = finals[-1][:-1] + "5"
- # e.g. 桌上, 地下, 家里
- elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}:
- finals[-1] = finals[-1][:-1] + "5"
- # e.g. 上来, 下去
- elif len(word) > 1 and word[-1] in "来去" and word[-2] in "上下进出回过起开":
- finals[-1] = finals[-1][:-1] + "5"
- # 个做量词
- elif (
- ge_idx >= 1
- and (word[ge_idx - 1].isnumeric() or word[ge_idx - 1] in "几有两半多各整每做是")
- ) or word == "个":
- finals[ge_idx] = finals[ge_idx][:-1] + "5"
- else:
- if (
- word in self.must_neural_tone_words
- or word[-2:] in self.must_neural_tone_words
- ):
- finals[-1] = finals[-1][:-1] + "5"
-
- word_list = self._split_word(word)
- finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
- for i, word in enumerate(word_list):
- # conventional neural in Chinese
- if (
- word in self.must_neural_tone_words
- or word[-2:] in self.must_neural_tone_words
- ):
- finals_list[i][-1] = finals_list[i][-1][:-1] + "5"
- finals = sum(finals_list, [])
- return finals
-
- def _bu_sandhi(self, word: str, finals: List[str]) -> List[str]:
- # e.g. 看不懂
- if len(word) == 3 and word[1] == "不":
- finals[1] = finals[1][:-1] + "5"
- else:
- for i, char in enumerate(word):
- # "不" before tone4 should be bu2, e.g. 不怕
- if char == "不" and i + 1 < len(word) and finals[i + 1][-1] == "4":
- finals[i] = finals[i][:-1] + "2"
- return finals
-
- def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]:
- # "一" in number sequences, e.g. 一零零, 二一零
- if word.find("一") != -1 and all(
- [item.isnumeric() for item in word if item != "一"]
- ):
- return finals
- # "一" between reduplication words should be yi5, e.g. 看一看
- elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]:
- finals[1] = finals[1][:-1] + "5"
- # when "一" is ordinal word, it should be yi1
- elif word.startswith("第一"):
- finals[1] = finals[1][:-1] + "1"
- else:
- for i, char in enumerate(word):
- if char == "一" and i + 1 < len(word):
- # "一" before tone4 should be yi2, e.g. 一段
- if finals[i + 1][-1] == "4":
- finals[i] = finals[i][:-1] + "2"
- # "一" before non-tone4 should be yi4, e.g. 一天
- else:
- # "一" 后面如果是标点,还读一声
- if word[i + 1] not in self.punc:
- finals[i] = finals[i][:-1] + "4"
- return finals
-
- def _split_word(self, word: str) -> List[str]:
- word_list = jieba.cut_for_search(word)
- word_list = sorted(word_list, key=lambda i: len(i), reverse=False)
- first_subword = word_list[0]
- first_begin_idx = word.find(first_subword)
- if first_begin_idx == 0:
- second_subword = word[len(first_subword) :]
- new_word_list = [first_subword, second_subword]
- else:
- second_subword = word[: -len(first_subword)]
- new_word_list = [second_subword, first_subword]
- return new_word_list
-
- def _three_sandhi(self, word: str, finals: List[str]) -> List[str]:
- if len(word) == 2 and self._all_tone_three(finals):
- finals[0] = finals[0][:-1] + "2"
- elif len(word) == 3:
- word_list = self._split_word(word)
- if self._all_tone_three(finals):
- # disyllabic + monosyllabic, e.g. 蒙古/包
- if len(word_list[0]) == 2:
- finals[0] = finals[0][:-1] + "2"
- finals[1] = finals[1][:-1] + "2"
- # monosyllabic + disyllabic, e.g. 纸/老虎
- elif len(word_list[0]) == 1:
- finals[1] = finals[1][:-1] + "2"
- else:
- finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
- if len(finals_list) == 2:
- for i, sub in enumerate(finals_list):
- # e.g. 所有/人
- if self._all_tone_three(sub) and len(sub) == 2:
- finals_list[i][0] = finals_list[i][0][:-1] + "2"
- # e.g. 好/喜欢
- elif (
- i == 1
- and not self._all_tone_three(sub)
- and finals_list[i][0][-1] == "3"
- and finals_list[0][-1][-1] == "3"
- ):
- finals_list[0][-1] = finals_list[0][-1][:-1] + "2"
- finals = sum(finals_list, [])
- # split idiom into two words who's length is 2
- elif len(word) == 4:
- finals_list = [finals[:2], finals[2:]]
- finals = []
- for sub in finals_list:
- if self._all_tone_three(sub):
- sub[0] = sub[0][:-1] + "2"
- finals += sub
-
- return finals
-
- def _all_tone_three(self, finals: List[str]) -> bool:
- return all(x[-1] == "3" for x in finals)
-
- # merge "不" and the word behind it
- # if don't merge, "不" sometimes appears alone according to jieba, which may occur sandhi error
- def _merge_bu(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
- new_seg = []
- last_word = ""
- for word, pos in seg:
- if last_word == "不":
- word = last_word + word
- if word != "不":
- new_seg.append((word, pos))
- last_word = word[:]
- if last_word == "不":
- new_seg.append((last_word, "d"))
- last_word = ""
- return new_seg
-
- # function 1: merge "一" and reduplication words in it's left and right, e.g. "听","一","听" ->"听一听"
- # function 2: merge single "一" and the word behind it
- # if don't merge, "一" sometimes appears alone according to jieba, which may occur sandhi error
- # e.g.
- # input seg: [('听', 'v'), ('一', 'm'), ('听', 'v')]
- # output seg: [['听一听', 'v']]
- def _merge_yi(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
- new_seg = []
- # function 1
- for i, (word, pos) in enumerate(seg):
- if (
- i - 1 >= 0
- and word == "一"
- and i + 1 < len(seg)
- and seg[i - 1][0] == seg[i + 1][0]
- and seg[i - 1][1] == "v"
- ):
- new_seg[i - 1][0] = new_seg[i - 1][0] + "一" + new_seg[i - 1][0]
- else:
- if (
- i - 2 >= 0
- and seg[i - 1][0] == "一"
- and seg[i - 2][0] == word
- and pos == "v"
- ):
- continue
- else:
- new_seg.append([word, pos])
- seg = new_seg
- new_seg = []
- # function 2
- for i, (word, pos) in enumerate(seg):
- if new_seg and new_seg[-1][0] == "一":
- new_seg[-1][0] = new_seg[-1][0] + word
- else:
- new_seg.append([word, pos])
- return new_seg
-
- # the first and the second words are all_tone_three
- def _merge_continuous_three_tones(
- self, seg: List[Tuple[str, str]]
- ) -> List[Tuple[str, str]]:
- new_seg = []
- sub_finals_list = [
- lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
- for (word, pos) in seg
- ]
- assert len(sub_finals_list) == len(seg)
- merge_last = [False] * len(seg)
- for i, (word, pos) in enumerate(seg):
- if (
- i - 1 >= 0
- and self._all_tone_three(sub_finals_list[i - 1])
- and self._all_tone_three(sub_finals_list[i])
- and not merge_last[i - 1]
- ):
- # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
- if (
- not self._is_reduplication(seg[i - 1][0])
- and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
- ):
- new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
- merge_last[i] = True
- else:
- new_seg.append([word, pos])
- else:
- new_seg.append([word, pos])
-
- return new_seg
-
- def _is_reduplication(self, word: str) -> bool:
- return len(word) == 2 and word[0] == word[1]
-
- # the last char of first word and the first char of second word is tone_three
- def _merge_continuous_three_tones_2(
- self, seg: List[Tuple[str, str]]
- ) -> List[Tuple[str, str]]:
- new_seg = []
- sub_finals_list = [
- lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
- for (word, pos) in seg
- ]
- assert len(sub_finals_list) == len(seg)
- merge_last = [False] * len(seg)
- for i, (word, pos) in enumerate(seg):
- if (
- i - 1 >= 0
- and sub_finals_list[i - 1][-1][-1] == "3"
- and sub_finals_list[i][0][-1] == "3"
- and not merge_last[i - 1]
- ):
- # if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
- if (
- not self._is_reduplication(seg[i - 1][0])
- and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
- ):
- new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
- merge_last[i] = True
- else:
- new_seg.append([word, pos])
- else:
- new_seg.append([word, pos])
- return new_seg
-
- def _merge_er(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
- new_seg = []
- for i, (word, pos) in enumerate(seg):
- if i - 1 >= 0 and word == "儿" and seg[i - 1][0] != "#":
- new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
- else:
- new_seg.append([word, pos])
- return new_seg
-
- def _merge_reduplication(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
- new_seg = []
- for i, (word, pos) in enumerate(seg):
- if new_seg and word == new_seg[-1][0]:
- new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
- else:
- new_seg.append([word, pos])
- return new_seg
-
- def pre_merge_for_modify(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
- seg = self._merge_bu(seg)
- try:
- seg = self._merge_yi(seg)
- except:
- print("_merge_yi failed")
- seg = self._merge_reduplication(seg)
- seg = self._merge_continuous_three_tones(seg)
- seg = self._merge_continuous_three_tones_2(seg)
- seg = self._merge_er(seg)
- return seg
-
- def modified_tone(self, word: str, pos: str, finals: List[str]) -> List[str]:
- finals = self._bu_sandhi(word, finals)
- finals = self._yi_sandhi(word, finals)
- finals = self._neural_sandhi(word, pos, finals)
- finals = self._three_sandhi(word, finals)
- return finals
diff --git a/spaces/florim/MedGPT/autogpt/memory/local.py b/spaces/florim/MedGPT/autogpt/memory/local.py
deleted file mode 100644
index 803b6dc6ebb430285f423cda592fa3e902e9a4a6..0000000000000000000000000000000000000000
--- a/spaces/florim/MedGPT/autogpt/memory/local.py
+++ /dev/null
@@ -1,136 +0,0 @@
-from __future__ import annotations
-
-import dataclasses
-import os
-from typing import Any, List
-
-import numpy as np
-import orjson
-
-from autogpt.llm_utils import create_embedding_with_ada
-from autogpt.memory.base import MemoryProviderSingleton
-
-EMBED_DIM = 1536
-SAVE_OPTIONS = orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_SERIALIZE_DATACLASS
-
-
-def create_default_embeddings():
- return np.zeros((0, EMBED_DIM)).astype(np.float32)
-
-
-@dataclasses.dataclass
-class CacheContent:
- texts: List[str] = dataclasses.field(default_factory=list)
- embeddings: np.ndarray = dataclasses.field(
- default_factory=create_default_embeddings
- )
-
-
-class LocalCache(MemoryProviderSingleton):
- """A class that stores the memory in a local file"""
-
- def __init__(self, cfg) -> None:
- """Initialize a class instance
-
- Args:
- cfg: Config object
-
- Returns:
- None
- """
- self.filename = f"{cfg.memory_index}.json"
- if os.path.exists(self.filename):
- try:
- with open(self.filename, "w+b") as f:
- file_content = f.read()
- if not file_content.strip():
- file_content = b"{}"
- f.write(file_content)
-
- loaded = orjson.loads(file_content)
- self.data = CacheContent(**loaded)
- except orjson.JSONDecodeError:
- print(f"Error: The file '{self.filename}' is not in JSON format.")
- self.data = CacheContent()
- else:
- print(
- f"Warning: The file '{self.filename}' does not exist. "
- "Local memory would not be saved to a file."
- )
- self.data = CacheContent()
-
- def add(self, text: str):
- """
- Add text to our list of texts, add embedding as row to our
- embeddings-matrix
-
- Args:
- text: str
-
- Returns: None
- """
- if "Command Error:" in text:
- return ""
- self.data.texts.append(text)
-
- embedding = create_embedding_with_ada(text)
-
- vector = np.array(embedding).astype(np.float32)
- vector = vector[np.newaxis, :]
- self.data.embeddings = np.concatenate(
- [
- self.data.embeddings,
- vector,
- ],
- axis=0,
- )
-
- with open(self.filename, "wb") as f:
- out = orjson.dumps(self.data, option=SAVE_OPTIONS)
- f.write(out)
- return text
-
- def clear(self) -> str:
- """
- Clears the redis server.
-
- Returns: A message indicating that the memory has been cleared.
- """
- self.data = CacheContent()
- return "Obliviated"
-
- def get(self, data: str) -> list[Any] | None:
- """
- Gets the data from the memory that is most relevant to the given data.
-
- Args:
- data: The data to compare to.
-
- Returns: The most relevant data.
- """
- return self.get_relevant(data, 1)
-
- def get_relevant(self, text: str, k: int) -> list[Any]:
- """ "
- matrix-vector mult to find score-for-each-row-of-matrix
- get indices for top-k winning scores
- return texts for those indices
- Args:
- text: str
- k: int
-
- Returns: List[str]
- """
- embedding = create_embedding_with_ada(text)
-
- scores = np.dot(self.data.embeddings, embedding)
-
- top_k_indices = np.argsort(scores)[-k:][::-1]
-
- return [self.data.texts[i] for i in top_k_indices]
-
- def get_stats(self) -> tuple[int, tuple[int, ...]]:
- """
- Returns: The stats of the local cache.
- """
- return len(self.data.texts), self.data.embeddings.shape
diff --git a/spaces/fuckyoudeki/AutoGPT/autogpt/commands/times.py b/spaces/fuckyoudeki/AutoGPT/autogpt/commands/times.py
deleted file mode 100644
index 3c9b8a4fc67a251c9e81a8c4a725cd1e25fcbebe..0000000000000000000000000000000000000000
--- a/spaces/fuckyoudeki/AutoGPT/autogpt/commands/times.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from datetime import datetime
-
-
-def get_datetime() -> str:
- """Return the current date and time
-
- Returns:
- str: The current date and time
- """
- return "Current date and time: " + datetime.now().strftime("%Y-%m-%d %H:%M:%S")
diff --git a/spaces/gagan3012/T5-Summarization/src/models/model.py b/spaces/gagan3012/T5-Summarization/src/models/model.py
deleted file mode 100644
index 3ac821434b9730c9fc7def05cb1fc5adf7804a7d..0000000000000000000000000000000000000000
--- a/spaces/gagan3012/T5-Summarization/src/models/model.py
+++ /dev/null
@@ -1,582 +0,0 @@
-import shutil
-from getpass import getpass
-from pathlib import Path
-
-import torch
-import pandas as pd
-from huggingface_hub import HfApi, Repository
-from transformers import (
- AdamW,
- T5ForConditionalGeneration,
- T5TokenizerFast as T5Tokenizer,
- MT5Tokenizer,
- MT5ForConditionalGeneration,
- ByT5Tokenizer,
-)
-from torch.utils.data import Dataset, DataLoader
-import pytorch_lightning as pl
-from pytorch_lightning.loggers import MLFlowLogger, WandbLogger
-from pytorch_lightning import Trainer
-from pytorch_lightning.callbacks.early_stopping import EarlyStopping
-from pytorch_lightning import LightningDataModule
-from pytorch_lightning import LightningModule
-from datasets import load_metric
-from tqdm.auto import tqdm
-
-# from dagshub.pytorch_lightning import DAGsHubLogger
-
-
-torch.cuda.empty_cache()
-pl.seed_everything(42)
-
-
-class DataModule(Dataset):
- """
- Data Module for pytorch
- """
-
- def __init__(
- self,
- data: pd.DataFrame,
- tokenizer: T5Tokenizer,
- source_max_token_len: int = 512,
- target_max_token_len: int = 512,
- ):
- """
- :param data:
- :param tokenizer:
- :param source_max_token_len:
- :param target_max_token_len:
- """
- self.data = data
- self.target_max_token_len = target_max_token_len
- self.source_max_token_len = source_max_token_len
- self.tokenizer = tokenizer
-
- def __len__(self):
- return len(self.data)
-
- def __getitem__(self, index: int):
- data_row = self.data.iloc[index]
-
- input_encoding = self.tokenizer(
- data_row["input_text"],
- max_length=self.source_max_token_len,
- padding="max_length",
- truncation=True,
- return_attention_mask=True,
- add_special_tokens=True,
- return_tensors="pt",
- )
-
- output_encoding = self.tokenizer(
- data_row["output_text"],
- max_length=self.target_max_token_len,
- padding="max_length",
- truncation=True,
- return_attention_mask=True,
- add_special_tokens=True,
- return_tensors="pt",
- )
-
- labels = output_encoding["input_ids"]
- labels[labels == 0] = -100
-
- return dict(
- keywords=data_row["input_text"],
- text=data_row["output_text"],
- keywords_input_ids=input_encoding["input_ids"].flatten(),
- keywords_attention_mask=input_encoding["attention_mask"].flatten(),
- labels=labels.flatten(),
- labels_attention_mask=output_encoding["attention_mask"].flatten(),
- )
-
-
-class PLDataModule(LightningDataModule):
- def __init__(
- self,
- train_df: pd.DataFrame,
- test_df: pd.DataFrame,
- tokenizer: T5Tokenizer,
- source_max_token_len: int = 512,
- target_max_token_len: int = 512,
- batch_size: int = 4,
- split: float = 0.1,
- num_workers: int = 2,
- ):
- """
- :param data_df:
- :param tokenizer:
- :param source_max_token_len:
- :param target_max_token_len:
- :param batch_size:
- :param split:
- """
- super().__init__()
- self.train_df = train_df
- self.test_df = test_df
- self.split = split
- self.batch_size = batch_size
- self.target_max_token_len = target_max_token_len
- self.source_max_token_len = source_max_token_len
- self.tokenizer = tokenizer
- self.num_workers = num_workers
-
- def setup(self, stage=None):
- self.train_dataset = DataModule(
- self.train_df,
- self.tokenizer,
- self.source_max_token_len,
- self.target_max_token_len,
- )
- self.test_dataset = DataModule(
- self.test_df,
- self.tokenizer,
- self.source_max_token_len,
- self.target_max_token_len,
- )
-
- def train_dataloader(self):
- """training dataloader"""
- return DataLoader(
- self.train_dataset,
- batch_size=self.batch_size,
- shuffle=True,
- num_workers=self.num_workers,
- )
-
- def test_dataloader(self):
- """test dataloader"""
- return DataLoader(
- self.test_dataset,
- batch_size=self.batch_size,
- shuffle=False,
- num_workers=self.num_workers,
- )
-
- def val_dataloader(self):
- """validation dataloader"""
- return DataLoader(
- self.test_dataset,
- batch_size=self.batch_size,
- shuffle=False,
- num_workers=self.num_workers,
- )
-
-
-class LightningModel(LightningModule):
- """PyTorch Lightning Model class"""
-
- def __init__(
- self,
- tokenizer,
- model,
- learning_rate,
- adam_epsilon,
- weight_decay,
- output: str = "outputs",
- ):
- """
- initiates a PyTorch Lightning Model
- Args:
- tokenizer : T5 tokenizer
- model : T5 model
- output (str, optional): output directory to save model checkpoints. Defaults to "outputs".
- """
- super().__init__()
- self.model = model
- self.tokenizer = tokenizer
- self.output = output
- self.learning_rate = learning_rate
- self.adam_epsilon = adam_epsilon
- self.weight_decay = weight_decay
-
- def forward(self, input_ids, attention_mask, decoder_attention_mask, labels=None):
- """forward step"""
- output = self.model(
- input_ids,
- attention_mask=attention_mask,
- labels=labels,
- decoder_attention_mask=decoder_attention_mask,
- )
-
- return output.loss, output.logits
-
- def training_step(self, batch, batch_size):
- """training step"""
- input_ids = batch["keywords_input_ids"]
- attention_mask = batch["keywords_attention_mask"]
- labels = batch["labels"]
- labels_attention_mask = batch["labels_attention_mask"]
-
- loss, outputs = self(
- input_ids=input_ids,
- attention_mask=attention_mask,
- decoder_attention_mask=labels_attention_mask,
- labels=labels,
- )
- self.log("train_loss", loss, prog_bar=True, logger=True)
- return loss
-
- def validation_step(self, batch, batch_size):
- """validation step"""
- input_ids = batch["keywords_input_ids"]
- attention_mask = batch["keywords_attention_mask"]
- labels = batch["labels"]
- labels_attention_mask = batch["labels_attention_mask"]
-
- loss, outputs = self(
- input_ids=input_ids,
- attention_mask=attention_mask,
- decoder_attention_mask=labels_attention_mask,
- labels=labels,
- )
- self.log("val_loss", loss, prog_bar=True, logger=True)
- return loss
-
- def test_step(self, batch, batch_size):
- """test step"""
- input_ids = batch["keywords_input_ids"]
- attention_mask = batch["keywords_attention_mask"]
- labels = batch["labels"]
- labels_attention_mask = batch["labels_attention_mask"]
-
- loss, outputs = self(
- input_ids=input_ids,
- attention_mask=attention_mask,
- decoder_attention_mask=labels_attention_mask,
- labels=labels,
- )
-
- self.log("test_loss", loss, prog_bar=True, logger=True)
- return loss
-
- def configure_optimizers(self):
- """configure optimizers"""
- model = self.model
- no_decay = ["bias", "LayerNorm.weight"]
- optimizer_grouped_parameters = [
- {
- "params": [
- p
- for n, p in model.named_parameters()
- if not any(nd in n for nd in no_decay)
- ],
- "weight_decay": self.weight_decay,
- },
- {
- "params": [
- p
- for n, p in model.named_parameters()
- if any(nd in n for nd in no_decay)
- ],
- "weight_decay": 0.0,
- },
- ]
- optimizer = AdamW(
- optimizer_grouped_parameters, lr=self.learning_rate, eps=self.adam_epsilon
- )
- self.opt = optimizer
- return [optimizer]
-
-
-class Summarization:
- """Custom Summarization class"""
-
- def __init__(self) -> None:
- """initiates Summarization class"""
- pass
-
- def from_pretrained(self, model_type="t5", model_name="t5-base") -> None:
- """
- loads T5/MT5 Model model for training/finetuning
- Args:
- model_name (str, optional): exact model architecture name, "t5-base" or "t5-large". Defaults to "t5-base".
- :param model_type:
- """
- if model_type == "t5":
- self.tokenizer = T5Tokenizer.from_pretrained(f"{model_name}")
- self.model = T5ForConditionalGeneration.from_pretrained(
- f"{model_name}", return_dict=True
- )
- elif model_type == "mt5":
- self.tokenizer = MT5Tokenizer.from_pretrained(f"{model_name}")
- self.model = MT5ForConditionalGeneration.from_pretrained(
- f"{model_name}", return_dict=True
- )
- elif model_type == "byt5":
- self.tokenizer = ByT5Tokenizer.from_pretrained(f"{model_name}")
- self.model = T5ForConditionalGeneration.from_pretrained(
- f"{model_name}", return_dict=True
- )
-
- def train(
- self,
- train_df: pd.DataFrame,
- eval_df: pd.DataFrame,
- source_max_token_len: int = 512,
- target_max_token_len: int = 512,
- batch_size: int = 8,
- max_epochs: int = 5,
- use_gpu: bool = True,
- outputdir: str = "models",
- early_stopping_patience_epochs: int = 0, # 0 to disable early stopping feature
- learning_rate: float = 0.0001,
- adam_epsilon: float = 0.01,
- num_workers: int = 2,
- weight_decay: float = 0.0001,
- ):
- """
- trains T5/MT5 model on custom dataset
- Args:
- train_df (pd.DataFrame): training datarame. Dataframe must have 2 column --> "input_text" and "output_text"
- eval_df ([type], optional): validation datarame. Dataframe must have 2 column --> "input_text" and
- "output_text"
- source_max_token_len (int, optional): max token length of source text. Defaults to 512.
- target_max_token_len (int, optional): max token length of target text. Defaults to 512.
- batch_size (int, optional): batch size. Defaults to 8.
- max_epochs (int, optional): max number of epochs. Defaults to 5.
- use_gpu (bool, optional): if True, model uses gpu for training. Defaults to True.
- outputdir (str, optional): output directory to save model checkpoints. Defaults to "outputs".
- early_stopping_patience_epochs (int, optional): monitors val_loss on epoch end and stops training,
- if val_loss does not improve after the specied number of epochs. set 0 to disable early stopping.
- Defaults to 0 (disabled)
- :param learning_rate:
- :param adam_epsilon:
- """
- self.target_max_token_len = target_max_token_len
- self.data_module = PLDataModule(
- train_df,
- eval_df,
- self.tokenizer,
- batch_size=batch_size,
- source_max_token_len=source_max_token_len,
- target_max_token_len=target_max_token_len,
- num_workers=num_workers,
- )
-
- self.T5Model = LightningModel(
- tokenizer=self.tokenizer,
- model=self.model,
- output=outputdir,
- learning_rate=learning_rate,
- adam_epsilon=adam_epsilon,
- weight_decay=weight_decay,
- )
-
- MLlogger = MLFlowLogger(
- experiment_name="Summarization",
- tracking_uri="https://dagshub.com/gagan3012/summarization.mlflow",
- )
-
- WandLogger = WandbLogger(project="summarization-dagshub")
-
- # logger = DAGsHubLogger(metrics_path='reports/training_metrics.txt')
-
- early_stop_callback = (
- [
- EarlyStopping(
- monitor="val_loss",
- min_delta=0.00,
- patience=early_stopping_patience_epochs,
- verbose=True,
- mode="min",
- )
- ]
- if early_stopping_patience_epochs > 0
- else None
- )
-
- gpus = -1 if use_gpu and torch.cuda.is_available() else 0
-
- trainer = Trainer(
- logger=[WandLogger, MLlogger],
- callbacks=early_stop_callback,
- max_epochs=max_epochs,
- gpus=gpus,
- progress_bar_refresh_rate=5,
- )
-
- trainer.fit(self.T5Model, self.data_module)
-
- def load_model(
- self, model_type: str = "t5", model_dir: str = "models", use_gpu: bool = False
- ):
- """
- loads a checkpoint for inferencing/prediction
- Args:
- model_type (str, optional): "t5" or "mt5". Defaults to "t5".
- model_dir (str, optional): path to model directory. Defaults to "outputs".
- use_gpu (bool, optional): if True, model uses gpu for inferencing/prediction. Defaults to True.
- """
- if model_type == "t5":
- self.tokenizer = T5Tokenizer.from_pretrained(f"{model_dir}")
- self.model = T5ForConditionalGeneration.from_pretrained(
- f"{model_dir}", return_dict=True
- )
- elif model_type == "mt5":
- self.tokenizer = MT5Tokenizer.from_pretrained(f"{model_dir}")
- self.model = MT5ForConditionalGeneration.from_pretrained(
- f"{model_dir}", return_dict=True
- )
- elif model_type == "byt5":
- self.tokenizer = ByT5Tokenizer.from_pretrained(f"{model_dir}")
- self.model = T5ForConditionalGeneration.from_pretrained(
- f"{model_dir}", return_dict=True
- )
-
- if use_gpu:
- if torch.cuda.is_available():
- self.device = torch.device("cuda")
- else:
- raise Exception(
- "exception ---> no gpu found. set use_gpu=False, to use CPU"
- )
- else:
- self.device = torch.device("cpu")
-
- self.model = self.model.to(self.device)
-
- def save_model(self, model_dir="models"):
- """
- Save model to dir
- :param model_dir:
- :return: model is saved
- """
- path = f"{model_dir}"
- self.tokenizer.save_pretrained(path)
- self.model.save_pretrained(path)
-
- def predict(
- self,
- source_text: str,
- max_length: int = 512,
- num_return_sequences: int = 1,
- num_beams: int = 2,
- top_k: int = 50,
- top_p: float = 0.95,
- do_sample: bool = True,
- repetition_penalty: float = 2.5,
- length_penalty: float = 1.0,
- early_stopping: bool = True,
- skip_special_tokens: bool = True,
- clean_up_tokenization_spaces: bool = True,
- ):
- """
- generates prediction for T5/MT5 model
- Args:
- source_text (str): any text for generating predictions
- max_length (int, optional): max token length of prediction. Defaults to 512.
- num_return_sequences (int, optional): number of predictions to be returned. Defaults to 1.
- num_beams (int, optional): number of beams. Defaults to 2.
- top_k (int, optional): Defaults to 50.
- top_p (float, optional): Defaults to 0.95.
- do_sample (bool, optional): Defaults to True.
- repetition_penalty (float, optional): Defaults to 2.5.
- length_penalty (float, optional): Defaults to 1.0.
- early_stopping (bool, optional): Defaults to True.
- skip_special_tokens (bool, optional): Defaults to True.
- clean_up_tokenization_spaces (bool, optional): Defaults to True.
- Returns:
- list[str]: returns predictions
- """
- input_ids = self.tokenizer.encode(
- source_text, return_tensors="pt", add_special_tokens=True
- )
-
- input_ids = input_ids.to(self.device)
- generated_ids = self.model.generate(
- input_ids=input_ids,
- num_beams=num_beams,
- max_length=max_length,
- repetition_penalty=repetition_penalty,
- length_penalty=length_penalty,
- early_stopping=early_stopping,
- top_p=top_p,
- top_k=top_k,
- num_return_sequences=num_return_sequences,
- )
- preds = self.tokenizer.decode(
- generated_ids[0],
- skip_special_tokens=skip_special_tokens,
- clean_up_tokenization_spaces=clean_up_tokenization_spaces,
- )
- return preds
-
- def evaluate(self, test_df: pd.DataFrame, metrics: str = "rouge"):
- metric = load_metric(metrics)
- input_text = test_df["input_text"]
- references = test_df["output_text"]
- references = references.to_list()
-
- predictions = [self.predict(x) for x in tqdm(input_text)]
-
- results = metric.compute(predictions=predictions, references=references)
-
- output = {
- "Rouge_1 Low Precision": results["rouge1"].low.precision,
- "Rouge_1 Low recall": results["rouge1"].low.recall,
- "Rouge_1 Low F1": results["rouge1"].low.fmeasure,
- "Rouge_1 Mid Precision": results["rouge1"].mid.precision,
- "Rouge_1 Mid recall": results["rouge1"].mid.recall,
- "Rouge_1 Mid F1": results["rouge1"].mid.fmeasure,
- "Rouge_1 High Precision": results["rouge1"].high.precision,
- "Rouge_1 High recall": results["rouge1"].high.recall,
- "Rouge_1 High F1": results["rouge1"].high.fmeasure,
- "Rouge_2 Low Precision": results["rouge2"].low.precision,
- "Rouge_2 Low recall": results["rouge2"].low.recall,
- "Rouge_2 Low F1": results["rouge2"].low.fmeasure,
- "Rouge_2 Mid Precision": results["rouge2"].mid.precision,
- "Rouge_2 Mid recall": results["rouge2"].mid.recall,
- "Rouge_2 Mid F1": results["rouge2"].mid.fmeasure,
- "Rouge_2 High Precision": results["rouge2"].high.precision,
- "Rouge_2 High recall": results["rouge2"].high.recall,
- "Rouge_2 High F1": results["rouge2"].high.fmeasure,
- "Rouge_L Low Precision": results["rougeL"].low.precision,
- "Rouge_L Low recall": results["rougeL"].low.recall,
- "Rouge_L Low F1": results["rougeL"].low.fmeasure,
- "Rouge_L Mid Precision": results["rougeL"].mid.precision,
- "Rouge_L Mid recall": results["rougeL"].mid.recall,
- "Rouge_L Mid F1": results["rougeL"].mid.fmeasure,
- "Rouge_L High Precision": results["rougeL"].high.precision,
- "Rouge_L High recall": results["rougeL"].high.recall,
- "Rouge_L High F1": results["rougeL"].high.fmeasure,
- "rougeLsum Low Precision": results["rougeLsum"].low.precision,
- "rougeLsum Low recall": results["rougeLsum"].low.recall,
- "rougeLsum Low F1": results["rougeLsum"].low.fmeasure,
- "rougeLsum Mid Precision": results["rougeLsum"].mid.precision,
- "rougeLsum Mid recall": results["rougeLsum"].mid.recall,
- "rougeLsum Mid F1": results["rougeLsum"].mid.fmeasure,
- "rougeLsum High Precision": results["rougeLsum"].high.precision,
- "rougeLsum High recall": results["rougeLsum"].high.recall,
- "rougeLsum High F1": results["rougeLsum"].high.fmeasure,
- }
- return output
-
- def upload(self, hf_username, model_name):
- hf_password = getpass("Enter your HuggingFace password")
- if Path("./models").exists():
- shutil.rmtree("./models")
- token = HfApi().login(username=hf_username, password=hf_password)
- del hf_password
- model_url = HfApi().create_repo(token=token, name=model_name, exist_ok=True)
- model_repo = Repository(
- "./model",
- clone_from=model_url,
- use_auth_token=token,
- git_email=f"{hf_username}@users.noreply.huggingface.co",
- git_user=hf_username,
- )
-
- readme_txt = f"""
- ---
- Summarisation model {model_name}
- """.strip()
-
- (Path(model_repo.local_dir) / "README.md").write_text(readme_txt)
- self.save_model()
- commit_url = model_repo.push_to_hub()
-
- print("Check out your model at:")
- print(commit_url)
- print(f"https://huggingface.co/{hf_username}/{model_name}")
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/engine/test.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/engine/test.py
deleted file mode 100644
index 8dbeef271db634ec2dadfda3bc0b5ef9c7a677ff..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/engine/test.py
+++ /dev/null
@@ -1,202 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import pickle
-import shutil
-import tempfile
-import time
-
-import torch
-import torch.distributed as dist
-
-import annotator.uniformer.mmcv as mmcv
-from annotator.uniformer.mmcv.runner import get_dist_info
-
-
-def single_gpu_test(model, data_loader):
- """Test model with a single gpu.
-
- This method tests model with a single gpu and displays test progress bar.
-
- Args:
- model (nn.Module): Model to be tested.
- data_loader (nn.Dataloader): Pytorch data loader.
-
- Returns:
- list: The prediction results.
- """
- model.eval()
- results = []
- dataset = data_loader.dataset
- prog_bar = mmcv.ProgressBar(len(dataset))
- for data in data_loader:
- with torch.no_grad():
- result = model(return_loss=False, **data)
- results.extend(result)
-
- # Assume result has the same length of batch_size
- # refer to https://github.com/open-mmlab/mmcv/issues/985
- batch_size = len(result)
- for _ in range(batch_size):
- prog_bar.update()
- return results
-
-
-def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False):
- """Test model with multiple gpus.
-
- This method tests model with multiple gpus and collects the results
- under two different modes: gpu and cpu modes. By setting
- ``gpu_collect=True``, it encodes results to gpu tensors and use gpu
- communication for results collection. On cpu mode it saves the results on
- different gpus to ``tmpdir`` and collects them by the rank 0 worker.
-
- Args:
- model (nn.Module): Model to be tested.
- data_loader (nn.Dataloader): Pytorch data loader.
- tmpdir (str): Path of directory to save the temporary results from
- different gpus under cpu mode.
- gpu_collect (bool): Option to use either gpu or cpu to collect results.
-
- Returns:
- list: The prediction results.
- """
- model.eval()
- results = []
- dataset = data_loader.dataset
- rank, world_size = get_dist_info()
- if rank == 0:
- prog_bar = mmcv.ProgressBar(len(dataset))
- time.sleep(2) # This line can prevent deadlock problem in some cases.
- for i, data in enumerate(data_loader):
- with torch.no_grad():
- result = model(return_loss=False, **data)
- results.extend(result)
-
- if rank == 0:
- batch_size = len(result)
- batch_size_all = batch_size * world_size
- if batch_size_all + prog_bar.completed > len(dataset):
- batch_size_all = len(dataset) - prog_bar.completed
- for _ in range(batch_size_all):
- prog_bar.update()
-
- # collect results from all ranks
- if gpu_collect:
- results = collect_results_gpu(results, len(dataset))
- else:
- results = collect_results_cpu(results, len(dataset), tmpdir)
- return results
-
-
-def collect_results_cpu(result_part, size, tmpdir=None):
- """Collect results under cpu mode.
-
- On cpu mode, this function will save the results on different gpus to
- ``tmpdir`` and collect them by the rank 0 worker.
-
- Args:
- result_part (list): Result list containing result parts
- to be collected.
- size (int): Size of the results, commonly equal to length of
- the results.
- tmpdir (str | None): temporal directory for collected results to
- store. If set to None, it will create a random temporal directory
- for it.
-
- Returns:
- list: The collected results.
- """
- rank, world_size = get_dist_info()
- # create a tmp dir if it is not specified
- if tmpdir is None:
- MAX_LEN = 512
- # 32 is whitespace
- dir_tensor = torch.full((MAX_LEN, ),
- 32,
- dtype=torch.uint8,
- device='cuda')
- if rank == 0:
- mmcv.mkdir_or_exist('.dist_test')
- tmpdir = tempfile.mkdtemp(dir='.dist_test')
- tmpdir = torch.tensor(
- bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
- dir_tensor[:len(tmpdir)] = tmpdir
- dist.broadcast(dir_tensor, 0)
- tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
- else:
- mmcv.mkdir_or_exist(tmpdir)
- # dump the part result to the dir
- mmcv.dump(result_part, osp.join(tmpdir, f'part_{rank}.pkl'))
- dist.barrier()
- # collect all parts
- if rank != 0:
- return None
- else:
- # load results of all parts from tmp dir
- part_list = []
- for i in range(world_size):
- part_file = osp.join(tmpdir, f'part_{i}.pkl')
- part_result = mmcv.load(part_file)
- # When data is severely insufficient, an empty part_result
- # on a certain gpu could makes the overall outputs empty.
- if part_result:
- part_list.append(part_result)
- # sort the results
- ordered_results = []
- for res in zip(*part_list):
- ordered_results.extend(list(res))
- # the dataloader may pad some samples
- ordered_results = ordered_results[:size]
- # remove tmp dir
- shutil.rmtree(tmpdir)
- return ordered_results
-
-
-def collect_results_gpu(result_part, size):
- """Collect results under gpu mode.
-
- On gpu mode, this function will encode results to gpu tensors and use gpu
- communication for results collection.
-
- Args:
- result_part (list): Result list containing result parts
- to be collected.
- size (int): Size of the results, commonly equal to length of
- the results.
-
- Returns:
- list: The collected results.
- """
- rank, world_size = get_dist_info()
- # dump result part to tensor with pickle
- part_tensor = torch.tensor(
- bytearray(pickle.dumps(result_part)), dtype=torch.uint8, device='cuda')
- # gather all result part tensor shape
- shape_tensor = torch.tensor(part_tensor.shape, device='cuda')
- shape_list = [shape_tensor.clone() for _ in range(world_size)]
- dist.all_gather(shape_list, shape_tensor)
- # padding result part tensor to max length
- shape_max = torch.tensor(shape_list).max()
- part_send = torch.zeros(shape_max, dtype=torch.uint8, device='cuda')
- part_send[:shape_tensor[0]] = part_tensor
- part_recv_list = [
- part_tensor.new_zeros(shape_max) for _ in range(world_size)
- ]
- # gather all result part
- dist.all_gather(part_recv_list, part_send)
-
- if rank == 0:
- part_list = []
- for recv, shape in zip(part_recv_list, shape_list):
- part_result = pickle.loads(recv[:shape[0]].cpu().numpy().tobytes())
- # When data is severely insufficient, an empty part_result
- # on a certain gpu could makes the overall outputs empty.
- if part_result:
- part_list.append(part_result)
- # sort the results
- ordered_results = []
- for res in zip(*part_list):
- ordered_results.extend(list(res))
- # the dataloader may pad some samples
- ordered_results = ordered_results[:size]
- return ordered_results
diff --git a/spaces/ggwvits/vits-uma-genshin-honkai/transforms.py b/spaces/ggwvits/vits-uma-genshin-honkai/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/ggwvits/vits-uma-genshin-honkai/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails=None,
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
-
- if tails is None:
- spline_fn = rational_quadratic_spline
- spline_kwargs = {}
- else:
- spline_fn = unconstrained_rational_quadratic_spline
- spline_kwargs = {
- 'tails': tails,
- 'tail_bound': tail_bound
- }
-
- outputs, logabsdet = spline_fn(
- inputs=inputs,
- unnormalized_widths=unnormalized_widths,
- unnormalized_heights=unnormalized_heights,
- unnormalized_derivatives=unnormalized_derivatives,
- inverse=inverse,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative,
- **spline_kwargs
- )
- return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
- bin_locations[..., -1] += eps
- return torch.sum(
- inputs[..., None] >= bin_locations,
- dim=-1
- ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- tails='linear',
- tail_bound=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
- outside_interval_mask = ~inside_interval_mask
-
- outputs = torch.zeros_like(inputs)
- logabsdet = torch.zeros_like(inputs)
-
- if tails == 'linear':
- unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
- constant = np.log(np.exp(1 - min_derivative) - 1)
- unnormalized_derivatives[..., 0] = constant
- unnormalized_derivatives[..., -1] = constant
-
- outputs[outside_interval_mask] = inputs[outside_interval_mask]
- logabsdet[outside_interval_mask] = 0
- else:
- raise RuntimeError('{} tails are not implemented.'.format(tails))
-
- outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
- inputs=inputs[inside_interval_mask],
- unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
- unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
- unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
- inverse=inverse,
- left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
- min_bin_width=min_bin_width,
- min_bin_height=min_bin_height,
- min_derivative=min_derivative
- )
-
- return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
- unnormalized_widths,
- unnormalized_heights,
- unnormalized_derivatives,
- inverse=False,
- left=0., right=1., bottom=0., top=1.,
- min_bin_width=DEFAULT_MIN_BIN_WIDTH,
- min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
- min_derivative=DEFAULT_MIN_DERIVATIVE):
- if torch.min(inputs) < left or torch.max(inputs) > right:
- raise ValueError('Input to a transform is not within its domain')
-
- num_bins = unnormalized_widths.shape[-1]
-
- if min_bin_width * num_bins > 1.0:
- raise ValueError('Minimal bin width too large for the number of bins')
- if min_bin_height * num_bins > 1.0:
- raise ValueError('Minimal bin height too large for the number of bins')
-
- widths = F.softmax(unnormalized_widths, dim=-1)
- widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
- cumwidths = torch.cumsum(widths, dim=-1)
- cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
- cumwidths = (right - left) * cumwidths + left
- cumwidths[..., 0] = left
- cumwidths[..., -1] = right
- widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
- derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
- heights = F.softmax(unnormalized_heights, dim=-1)
- heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
- cumheights = torch.cumsum(heights, dim=-1)
- cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
- cumheights = (top - bottom) * cumheights + bottom
- cumheights[..., 0] = bottom
- cumheights[..., -1] = top
- heights = cumheights[..., 1:] - cumheights[..., :-1]
-
- if inverse:
- bin_idx = searchsorted(cumheights, inputs)[..., None]
- else:
- bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
- input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
- input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
- input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
- delta = heights / widths
- input_delta = delta.gather(-1, bin_idx)[..., 0]
-
- input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
- input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
- input_heights = heights.gather(-1, bin_idx)[..., 0]
-
- if inverse:
- a = (((inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta)
- + input_heights * (input_delta - input_derivatives)))
- b = (input_heights * input_derivatives
- - (inputs - input_cumheights) * (input_derivatives
- + input_derivatives_plus_one
- - 2 * input_delta))
- c = - input_delta * (inputs - input_cumheights)
-
- discriminant = b.pow(2) - 4 * a * c
- assert (discriminant >= 0).all()
-
- root = (2 * c) / (-b - torch.sqrt(discriminant))
- outputs = root * input_bin_widths + input_cumwidths
-
- theta_one_minus_theta = root * (1 - root)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - root).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, -logabsdet
- else:
- theta = (inputs - input_cumwidths) / input_bin_widths
- theta_one_minus_theta = theta * (1 - theta)
-
- numerator = input_heights * (input_delta * theta.pow(2)
- + input_derivatives * theta_one_minus_theta)
- denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
- * theta_one_minus_theta)
- outputs = input_cumheights + numerator / denominator
-
- derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
- + 2 * input_delta * theta_one_minus_theta
- + input_derivatives * (1 - theta).pow(2))
- logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
- return outputs, logabsdet
diff --git a/spaces/giacomov/pdffigures2/Dockerfile b/spaces/giacomov/pdffigures2/Dockerfile
deleted file mode 100644
index ec6eb1d11dc7dc0d233d4587f868b175ec34c328..0000000000000000000000000000000000000000
--- a/spaces/giacomov/pdffigures2/Dockerfile
+++ /dev/null
@@ -1,17 +0,0 @@
-# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
-# you will also find guides on how best to write your Dockerfile
-
-FROM condaforge/mambaforge:23.1.0-1
-
-RUN mamba install -y sbt=1.7.1 git gradio
-
-RUN useradd -m -u 1000 user
-USER user
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:$PATH
-
-WORKDIR $HOME/app
-
-COPY --chown=user . $HOME/app
-
-ENTRYPOINT python app.py
diff --git a/spaces/giswqs/Streamlit/apps/vector.py b/spaces/giswqs/Streamlit/apps/vector.py
deleted file mode 100644
index 57c214a8210c26497675df40e75a3e85bdd591a3..0000000000000000000000000000000000000000
--- a/spaces/giswqs/Streamlit/apps/vector.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import os
-import fiona
-import geopandas as gpd
-import streamlit as st
-
-
-def save_uploaded_file(file_content, file_name):
- """
- Save the uploaded file to a temporary directory
- """
- import tempfile
- import os
- import uuid
-
- _, file_extension = os.path.splitext(file_name)
- file_id = str(uuid.uuid4())
- file_path = os.path.join(tempfile.gettempdir(), f"{file_id}{file_extension}")
-
- with open(file_path, "wb") as file:
- file.write(file_content.getbuffer())
-
- return file_path
-
-
-def app():
-
- st.title("Upload Vector Data")
-
- row1_col1, row1_col2 = st.columns([2, 1])
- width = 950
- height = 600
-
- with row1_col2:
-
- backend = st.selectbox(
- "Select a plotting backend", ["folium", "kepler.gl", "pydeck"], index=2
- )
-
- if backend == "folium":
- import leafmap.foliumap as leafmap
- elif backend == "kepler.gl":
- import leafmap.kepler as leafmap
- elif backend == "pydeck":
- import leafmap.deck as leafmap
-
- url = st.text_input(
- "Enter a URL to a vector dataset",
- "https://github.com/giswqs/streamlit-geospatial/raw/master/data/us_states.geojson",
- )
-
- data = st.file_uploader(
- "Upload a vector dataset", type=["geojson", "kml", "zip", "tab"]
- )
-
- container = st.container()
-
- if data or url:
- if data:
- file_path = save_uploaded_file(data, data.name)
- layer_name = os.path.splitext(data.name)[0]
- elif url:
- file_path = url
- layer_name = url.split("/")[-1].split(".")[0]
-
- with row1_col1:
- if file_path.lower().endswith(".kml"):
- fiona.drvsupport.supported_drivers["KML"] = "rw"
- gdf = gpd.read_file(file_path, driver="KML")
- else:
- gdf = gpd.read_file(file_path)
- lon, lat = leafmap.gdf_centroid(gdf)
- if backend == "pydeck":
-
- column_names = gdf.columns.values.tolist()
- random_column = None
- with container:
- random_color = st.checkbox("Apply random colors", True)
- if random_color:
- random_column = st.selectbox(
- "Select a column to apply random colors", column_names
- )
-
- m = leafmap.Map(center=(lat, lon))
- m.add_gdf(gdf, random_color_column=random_column)
- st.pydeck_chart(m)
-
- else:
- m = leafmap.Map(center=(lat, lon), draw_export=True)
- m.add_gdf(gdf, layer_name=layer_name)
- # m.add_vector(file_path, layer_name=layer_name)
- if backend == "folium":
- m.zoom_to_gdf(gdf)
- m.to_streamlit(width=width, height=height)
-
- else:
- with row1_col1:
- m = leafmap.Map()
- st.pydeck_chart(m)
diff --git a/spaces/gligen/demo/dataset/__init__.py b/spaces/gligen/demo/dataset/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/Fsx Flightsim Tools Addon Converter X Mega Fuente Pared Papeluc.md b/spaces/gotiQspiryo/whisper-ui/examples/Fsx Flightsim Tools Addon Converter X Mega Fuente Pared Papeluc.md
deleted file mode 100644
index 05ed0064984ef53b71fd9c3caf7c644cdc4cbdea..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/Fsx Flightsim Tools Addon Converter X Mega Fuente Pared Papeluc.md
+++ /dev/null
@@ -1,6 +0,0 @@
-
Fsx Flightsim Tools Addon Converter X Mega fuente pared papeluc
- )
-}
diff --git a/spaces/hugging-fellows/img-to-music/constants.py b/spaces/hugging-fellows/img-to-music/constants.py
deleted file mode 100644
index 86863d1b778d4c66f0d8e1e0b699f1bb937c1d50..0000000000000000000000000000000000000000
--- a/spaces/hugging-fellows/img-to-music/constants.py
+++ /dev/null
@@ -1,9 +0,0 @@
-import numpy as np
-import os
-
-MUBERT_LICENSE = os.environ.get('MUBERT_LICENSE')
-MUBERT_TOKEN = os.environ.get('MUBERT_TOKEN')
-
-MUBERT_MODE = "loop"
-MUBERT_TAGS_STRING = 'tribal,action,kids,neo-classic,run 130,pumped,jazz / funk,ethnic,dubtechno,reggae,acid jazz,liquidfunk,funk,witch house,tech house,underground,artists,mystical,disco,sensorium,r&b,agender,psychedelic trance / psytrance,peaceful,run 140,piano,run 160,setting,meditation,christmas,ambient,horror,cinematic,electro house,idm,bass,minimal,underscore,drums,glitchy,beautiful,technology,tribal house,country pop,jazz & funk,documentary,space,classical,valentines,chillstep,experimental,trap,new jack swing,drama,post-rock,tense,corporate,neutral,happy,analog,funky,spiritual,sberzvuk special,chill hop,dramatic,catchy,holidays,fitness 90,optimistic,orchestra,acid techno,energizing,romantic,minimal house,breaks,hyper pop,warm up,dreamy,dark,urban,microfunk,dub,nu disco,vogue,keys,hardcore,aggressive,indie,electro funk,beauty,relaxing,trance,pop,hiphop,soft,acoustic,chillrave / ethno-house,deep techno,angry,dance,fun,dubstep,tropical,latin pop,heroic,world music,inspirational,uplifting,atmosphere,art,epic,advertising,chillout,scary,spooky,slow ballad,saxophone,summer,erotic,jazzy,energy 100,kara mar,xmas,atmospheric,indie pop,hip-hop,yoga,reggaeton,lounge,travel,running,folk,chillrave & ethno-house,detective,darkambient,chill,fantasy,minimal techno,special,night,tropical house,downtempo,lullaby,meditative,upbeat,glitch hop,fitness,neurofunk,sexual,indie rock,future pop,jazz,cyberpunk,melancholic,happy hardcore,family / kids,synths,electric guitar,comedy,psychedelic trance & psytrance,edm,psychedelic rock,calm,zen,bells,podcast,melodic house,ethnic percussion,nature,heavy,bassline,indie dance,techno,drumnbass,synth pop,vaporwave,sad,8-bit,chillgressive,deep,orchestral,futuristic,hardtechno,nostalgic,big room,sci-fi,tutorial,joyful,pads,minimal 170,drill,ethnic 108,amusing,sleepy ambient,psychill,italo disco,lofi,house,acoustic guitar,bassline house,rock,k-pop,synthwave,deep house,electronica,gabber,nightlife,sport & fitness,road trip,celebration,electro,disco house,electronic'
-MUBERT_TAGS = np.array(MUBERT_TAGS_STRING.split(','))
\ No newline at end of file
diff --git a/spaces/hushell/pmf_with_gis/models/deploy.py b/spaces/hushell/pmf_with_gis/models/deploy.py
deleted file mode 100644
index 272a3a1e51b4aab86195dc0f39ff6b5d186d06dc..0000000000000000000000000000000000000000
--- a/spaces/hushell/pmf_with_gis/models/deploy.py
+++ /dev/null
@@ -1,389 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.distributed as dist
-from copy import deepcopy
-from tqdm import tqdm
-from timm.utils import accuracy
-from .protonet import ProtoNet
-from .utils import trunc_normal_, DiffAugment
-
-
-def is_dist_avail_and_initialized():
- if not dist.is_available():
- return False
- if not dist.is_initialized():
- return False
- return True
-
-
-def get_rank():
- if not is_dist_avail_and_initialized():
- return 0
- return dist.get_rank()
-
-
-def is_main_process():
- return get_rank() == 0
-
-
-@torch.jit.script
-def entropy_loss(x):
- return torch.sum(-F.softmax(x, 1) * F.log_softmax(x, 1), 1).mean()
-
-
-def unique_indices(x):
- """
- Ref: https://github.com/rusty1s/pytorch_unique
- """
- unique, inverse = torch.unique(x, sorted=True, return_inverse=True)
- perm = torch.arange(inverse.size(0), dtype=inverse.dtype, device=inverse.device)
- inverse, perm = inverse.flip([0]), perm.flip([0])
- perm = inverse.new_empty(unique.size(0)).scatter_(0, inverse, perm)
- return unique, perm
-
-
-class ProtoNet_Auto_Finetune(ProtoNet):
- def __init__(self, backbone, num_iters=50, aug_prob=0.9,
- aug_types=['color', 'translation'], lr_lst=[0.01, 0.001, 0.0001]):
- super().__init__(backbone)
- self.num_iters = num_iters
- self.lr_lst = lr_lst
- self.aug_types = aug_types
- self.aug_prob = aug_prob
-
- state_dict = backbone.state_dict()
- self.backbone_state = deepcopy(state_dict)
-
- def forward(self, supp_x, supp_y, qry_x):
- """
- supp_x.shape = [B, nSupp, C, H, W]
- supp_y.shape = [B, nSupp]
- qry_x.shape = [B, nQry, C, H, W]
- """
- B, nSupp, C, H, W = supp_x.shape
- num_classes = supp_y.max() + 1 # NOTE: assume B==1
- device = qry_x.device
-
- criterion = nn.CrossEntropyLoss()
- supp_x = supp_x.view(-1, C, H, W)
- qry_x = qry_x.view(-1, C, H, W)
- supp_y_1hot = F.one_hot(supp_y, num_classes).transpose(1, 2) # B, nC, nSupp
- supp_y = supp_y.view(-1)
-
- def single_step(z, mode=True, x=None, y=None, y_1hot=None):
- '''
- z = Aug(supp_x) or qry_x
- global vars: supp_x, supp_y, supp_y_1hot
- '''
- with torch.set_grad_enabled(mode):
- # recalculate prototypes from supp_x with updated backbone
- proto_f = self.backbone.forward(x).unsqueeze(0)
-
- if y_1hot is None:
- prototypes = proto_f
- else:
- prototypes = torch.bmm(y_1hot.float(), proto_f) # B, nC, d
- prototypes = prototypes / y_1hot.sum(dim=2, keepdim=True) # NOTE: may div 0
-
- # compute feature for z
- feat = self.backbone.forward(z)
- feat = feat.view(B, z.shape[0], -1) # B, nQry, d
-
- # classification
- logits = self.cos_classifier(prototypes, feat) # B, nQry, nC
- loss = None
-
- if mode: # if enable grad, compute loss
- loss = criterion(logits.view(len(y), -1), y)
-
- return logits, loss
-
- # load trained weights
- self.backbone.load_state_dict(self.backbone_state, strict=True)
-
- #zz = DiffAugment(supp_x, ["color", "offset", "offset_h", "offset_v", "translation", "cutout"], 1., detach=True)
- proto_y, proto_i = unique_indices(supp_y)
- proto_x = supp_x[proto_i]
- zz_i = np.setdiff1d(range(len(supp_x)), proto_i.cpu().numpy())
- zz_x = supp_x[zz_i]
- zz_y = supp_y[zz_i]
-
- best_lr = 0
- max_acc1 = 0
-
- if len(zz_y) > 0:
- # eval non-finetuned weights (lr=0)
- logits, _ = single_step(zz_x, False, x=proto_x)
- max_acc1 = accuracy(logits.view(len(zz_y), -1), zz_y, topk=(1,))[0]
- print(f'## *lr = 0: acc1 = {max_acc1}\n')
-
- for lr in self.lr_lst:
- # create optimizer
- opt = torch.optim.Adam(self.backbone.parameters(),
- lr=lr,
- betas=(0.9, 0.999),
- weight_decay=0.)
-
- # main loop
- _num_iters = 50
- pbar = tqdm(range(_num_iters)) if is_main_process() else range(_num_iters)
- for i in pbar:
- opt.zero_grad()
- z = DiffAugment(proto_x, self.aug_types, self.aug_prob, detach=True)
- _, loss = single_step(z, True, x=proto_x, y=proto_y)
- loss.backward()
- opt.step()
- if is_main_process():
- pbar.set_description(f' << lr = {lr}: loss = {loss.item()}')
-
- logits, _ = single_step(zz_x, False, x=proto_x)
- acc1 = accuracy(logits.view(len(zz_y), -1), zz_y, topk=(1,))[0]
- print(f'## *lr = {lr}: acc1 = {acc1}\n')
-
- if acc1 > max_acc1:
- max_acc1 = acc1
- best_lr = lr
-
- # reset backbone state
- self.backbone.load_state_dict(self.backbone_state, strict=True)
-
- print(f'***Best lr = {best_lr} with acc1 = {max_acc1}.\nStart final loop...\n')
-
- # create optimizer
- opt = torch.optim.Adam(self.backbone.parameters(),
- lr=best_lr,
- betas=(0.9, 0.999),
- weight_decay=0.)
-
- # main loop
- pbar = tqdm(range(self.num_iters)) if is_main_process() else range(self.num_iters)
- for i in pbar:
- opt.zero_grad()
- z = DiffAugment(supp_x, self.aug_types, self.aug_prob, detach=True)
- _, loss = single_step(z, True, x=supp_x, y=supp_y, y_1hot=supp_y_1hot)
- loss.backward()
- opt.step()
- if is_main_process():
- pbar.set_description(f' >> lr = {best_lr}: loss = {loss.item()}')
-
- logits, _ = single_step(qry_x, False, x=supp_x, y_1hot=supp_y_1hot) # supp_x has to pair with y_1hot
-
- return logits
-
-
-class ProtoNet_Finetune(ProtoNet):
- def __init__(self, backbone, num_iters=50, lr=5e-2, aug_prob=0.9,
- aug_types=['color', 'translation']):
- super().__init__(backbone)
- self.num_iters = num_iters
- self.lr = lr
- self.aug_types = aug_types
- self.aug_prob = aug_prob
-
- def load_state_dict(self, state_dict, strict=True):
- super().load_state_dict(state_dict, strict)
-
- state_dict = self.backbone.state_dict()
- self.backbone_state = deepcopy(state_dict)
-
- def forward(self, supp_x, supp_y, x):
- """
- supp_x.shape = [B, nSupp, C, H, W]
- supp_y.shape = [B, nSupp]
- x.shape = [B, nQry, C, H, W]
- """
- # reset backbone state
- self.backbone.load_state_dict(self.backbone_state, strict=True)
-
- if self.lr == 0:
- return super().forward(supp_x, supp_y, x)
-
- B, nSupp, C, H, W = supp_x.shape
- num_classes = supp_y.max() + 1 # NOTE: assume B==1
- device = x.device
-
- criterion = nn.CrossEntropyLoss()
- supp_x = supp_x.view(-1, C, H, W)
- x = x.view(-1, C, H, W)
- supp_y_1hot = F.one_hot(supp_y, num_classes).transpose(1, 2) # B, nC, nSupp
- supp_y = supp_y.view(-1)
-
- # create optimizer
- opt = torch.optim.Adam(self.backbone.parameters(),
- lr=self.lr,
- betas=(0.9, 0.999),
- weight_decay=0.)
-
- def single_step(z, mode=True):
- '''
- z = Aug(supp_x) or x
- '''
- with torch.set_grad_enabled(mode):
- # recalculate prototypes from supp_x with updated backbone
- supp_f = self.backbone.forward(supp_x)
- supp_f = supp_f.view(B, nSupp, -1)
- prototypes = torch.bmm(supp_y_1hot.float(), supp_f) # B, nC, d
- prototypes = prototypes / supp_y_1hot.sum(dim=2, keepdim=True) # NOTE: may div 0
-
- # compute feature for z
- feat = self.backbone.forward(z)
- feat = feat.view(B, z.shape[0], -1) # B, nQry, d
-
- # classification
- logits = self.cos_classifier(prototypes, feat) # B, nQry, nC
- loss = None
-
- if mode: # if enable grad, compute loss
- loss = criterion(logits.view(B*nSupp, -1), supp_y)
-
- return logits, loss
-
- # main loop
- pbar = tqdm(range(self.num_iters)) if is_main_process() else range(self.num_iters)
- for i in pbar:
- opt.zero_grad()
- z = DiffAugment(supp_x, self.aug_types, self.aug_prob, detach=True)
- _, loss = single_step(z, True)
- loss.backward()
- opt.step()
- if is_main_process():
- pbar.set_description(f'lr{self.lr}, nSupp{nSupp}, nQry{x.shape[0]}: loss = {loss.item()}')
-
- logits, _ = single_step(x, False)
- return logits
-
-
-class ProtoNet_AdaTok(ProtoNet):
- def __init__(self, backbone, num_adapters=1, num_iters=50, lr=5e-2, momentum=0.9, weight_decay=0.):
- super().__init__(backbone)
- self.num_adapters = num_adapters
- self.num_iters = num_iters
- self.lr = lr
- self.momentum = momentum
- self.weight_decay = weight_decay
-
- def forward(self, supp_x, supp_y, x):
- """
- supp_x.shape = [B, nSupp, C, H, W]
- supp_y.shape = [B, nSupp]
- x.shape = [B, nQry, C, H, W]
- """
- B, nSupp, C, H, W = supp_x.shape
- nQry = x.shape[1]
- num_classes = supp_y.max() + 1 # NOTE: assume B==1
- device = x.device
-
- criterion = nn.CrossEntropyLoss()
- supp_x = supp_x.view(-1, C, H, W)
- x = x.view(-1, C, H, W)
- supp_y_1hot = F.one_hot(supp_y, num_classes).transpose(1, 2) # B, nC, nSupp
- supp_y = supp_y.view(-1)
-
- # prepare adapter tokens
- ada_tokens = torch.zeros(1, self.num_adapters, self.backbone.embed_dim, device=device)
- trunc_normal_(ada_tokens, std=.02)
- ada_tokens = ada_tokens.detach().requires_grad_()
- #optimizer = torch.optim.SGD([ada_tokens],
- optimizer = torch.optim.Adadelta([ada_tokens],
- lr=self.lr,
- #momentum=self.momentum,
- weight_decay=self.weight_decay)
-
- def single_step(mode=True):
- with torch.set_grad_enabled(mode):
- supp_f = self.backbone.forward(supp_x, ada_tokens)
- supp_f = supp_f.view(B, nSupp, -1)
-
- # B, nC, nSupp x B, nSupp, d = B, nC, d
- prototypes = torch.bmm(supp_y_1hot.float(), supp_f)
- prototypes = prototypes / supp_y_1hot.sum(dim=2, keepdim=True) # NOTE: may div 0
-
- if mode == False: # no grad
- feat = self.backbone.forward(x, ada_tokens)
- feat = feat.view(B, nQry, -1) # B, nQry, d
-
- logits = self.cos_classifier(prototypes, feat) # B, nQry, nC
- loss = None
- else:
- with torch.enable_grad():
- logits = self.cos_classifier(prototypes, supp_f) # B, nQry, nC
- loss = criterion(logits.view(B*nSupp, -1), supp_y)
-
- return logits, loss
-
- pbar = tqdm(range(self.num_iters)) if is_main_process() else range(self.num_iters)
- for i in pbar:
- optimizer.zero_grad()
- _, loss = single_step(True)
- loss.backward()
- optimizer.step()
- if is_main_process():
- pbar.set_description(f'loss = {loss.item()}')
-
- logits, _ = single_step(False)
- return logits
-
-
-class ProtoNet_AdaTok_EntMin(ProtoNet):
- def __init__(self, backbone, num_adapters=1, num_iters=50, lr=5e-3, momentum=0.9, weight_decay=0.):
- super().__init__(backbone)
- self.num_adapters = num_adapters
- self.num_iters = num_iters
- self.lr = lr
- self.momentum = momentum
- self.weight_decay = weight_decay
-
- def forward(self, supp_x, supp_y, x):
- """
- supp_x.shape = [B, nSupp, C, H, W]
- supp_y.shape = [B, nSupp]
- x.shape = [B, nQry, C, H, W]
- """
- B, nSupp, C, H, W = supp_x.shape
- num_classes = supp_y.max() + 1 # NOTE: assume B==1
- device = x.device
-
- criterion = entropy_loss
- supp_x = supp_x.view(-1, C, H, W)
- x = x.view(-1, C, H, W)
- supp_y_1hot = F.one_hot(supp_y, num_classes).transpose(1, 2) # B, nC, nSupp
-
- # adapter tokens
- ada_tokens = torch.zeros(1, self.num_adapters, self.backbone.embed_dim, device=device)
- trunc_normal_(ada_tokens, std=.02)
- ada_tokens = ada_tokens.detach().requires_grad_()
- optimizer = torch.optim.SGD([ada_tokens],
- lr=self.lr,
- momentum=self.momentum,
- weight_decay=self.weight_decay)
-
- def single_step(mode=True):
- with torch.set_grad_enabled(mode):
- supp_f = self.backbone.forward(supp_x, ada_tokens)
- supp_f = supp_f.view(B, nSupp, -1)
-
- # B, nC, nSupp x B, nSupp, d = B, nC, d
- prototypes = torch.bmm(supp_y_1hot.float(), supp_f)
- prototypes = prototypes / supp_y_1hot.sum(dim=2, keepdim=True) # NOTE: may div 0
-
- feat = self.backbone.forward(x, ada_tokens)
- feat = feat.view(B, x.shape[1], -1) # B, nQry, d
-
- logits = self.cos_classifier(prototypes, feat) # B, nQry, nC
- loss = criterion(logits.view(-1, num_classes))
-
- return logits, loss
-
- pbar = tqdm(range(self.num_iters)) if is_main_process() else range(self.num_iters)
- for i in pbar:
- optimizer.zero_grad()
- _, loss = single_step(True)
- loss.backward()
- optimizer.step()
- if is_main_process():
- pbar.set_description(f'loss = {loss.item()}')
-
- logits, _ = single_step(False)
- return logits
diff --git a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/util/skin_mask.py b/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/util/skin_mask.py
deleted file mode 100644
index fe8f2eb584fc94c617f244a18268125d3968c029..0000000000000000000000000000000000000000
--- a/spaces/hyxue/HiFiFace-inference-demo/Deep3DFaceRecon_pytorch/util/skin_mask.py
+++ /dev/null
@@ -1,179 +0,0 @@
-"""This script is to generate skin attention mask for Deep3DFaceRecon_pytorch
-"""
-import math
-import os
-
-import cv2
-import numpy as np
-
-
-class GMM:
- def __init__(self, dim, num, w, mu, cov, cov_det, cov_inv):
- self.dim = dim # feature dimension
- self.num = num # number of Gaussian components
- self.w = w # weights of Gaussian components (a list of scalars)
- self.mu = mu # mean of Gaussian components (a list of 1xdim vectors)
- self.cov = cov # covariance matrix of Gaussian components (a list of dimxdim matrices)
- self.cov_det = cov_det # pre-computed determinet of covariance matrices (a list of scalars)
- self.cov_inv = cov_inv # pre-computed inverse covariance matrices (a list of dimxdim matrices)
-
- self.factor = [0] * num
- for i in range(self.num):
- self.factor[i] = (2 * math.pi) ** (self.dim / 2) * self.cov_det[i] ** 0.5
-
- def likelihood(self, data):
- assert data.shape[1] == self.dim
- N = data.shape[0]
- lh = np.zeros(N)
-
- for i in range(self.num):
- data_ = data - self.mu[i]
-
- tmp = np.matmul(data_, self.cov_inv[i]) * data_
- tmp = np.sum(tmp, axis=1)
- power = -0.5 * tmp
-
- p = np.array([math.exp(power[j]) for j in range(N)])
- p = p / self.factor[i]
- lh += p * self.w[i]
-
- return lh
-
-
-def _rgb2ycbcr(rgb):
- m = np.array([[65.481, 128.553, 24.966], [-37.797, -74.203, 112], [112, -93.786, -18.214]])
- shape = rgb.shape
- rgb = rgb.reshape((shape[0] * shape[1], 3))
- ycbcr = np.dot(rgb, m.transpose() / 255.0)
- ycbcr[:, 0] += 16.0
- ycbcr[:, 1:] += 128.0
- return ycbcr.reshape(shape)
-
-
-def _bgr2ycbcr(bgr):
- rgb = bgr[..., ::-1]
- return _rgb2ycbcr(rgb)
-
-
-gmm_skin_w = [0.24063933, 0.16365987, 0.26034665, 0.33535415]
-gmm_skin_mu = [
- np.array([113.71862, 103.39613, 164.08226]),
- np.array([150.19858, 105.18467, 155.51428]),
- np.array([183.92976, 107.62468, 152.71820]),
- np.array([114.90524, 113.59782, 151.38217]),
-]
-gmm_skin_cov_det = [5692842.5, 5851930.5, 2329131.0, 1585971.0]
-gmm_skin_cov_inv = [
- np.array(
- [
- [0.0019472069, 0.0020450759, -0.00060243998],
- [0.0020450759, 0.017700525, 0.0051420014],
- [-0.00060243998, 0.0051420014, 0.0081308950],
- ]
- ),
- np.array(
- [
- [0.0027110141, 0.0011036990, 0.0023122299],
- [0.0011036990, 0.010707724, 0.010742856],
- [0.0023122299, 0.010742856, 0.017481629],
- ]
- ),
- np.array(
- [
- [0.0048026871, 0.00022935172, 0.0077668377],
- [0.00022935172, 0.011729696, 0.0081661865],
- [0.0077668377, 0.0081661865, 0.025374353],
- ]
- ),
- np.array(
- [
- [0.0011989699, 0.0022453172, -0.0010748957],
- [0.0022453172, 0.047758564, 0.020332102],
- [-0.0010748957, 0.020332102, 0.024502251],
- ]
- ),
-]
-
-gmm_skin = GMM(3, 4, gmm_skin_w, gmm_skin_mu, [], gmm_skin_cov_det, gmm_skin_cov_inv)
-
-gmm_nonskin_w = [0.12791070, 0.31130761, 0.34245777, 0.21832393]
-gmm_nonskin_mu = [
- np.array([99.200851, 112.07533, 140.20602]),
- np.array([110.91392, 125.52969, 130.19237]),
- np.array([129.75864, 129.96107, 126.96808]),
- np.array([112.29587, 128.85121, 129.05431]),
-]
-gmm_nonskin_cov_det = [458703648.0, 6466488.0, 90611376.0, 133097.63]
-gmm_nonskin_cov_inv = [
- np.array(
- [
- [0.00085371657, 0.00071197288, 0.00023958916],
- [0.00071197288, 0.0025935620, 0.00076557708],
- [0.00023958916, 0.00076557708, 0.0015042332],
- ]
- ),
- np.array(
- [
- [0.00024650150, 0.00045542428, 0.00015019422],
- [0.00045542428, 0.026412144, 0.018419769],
- [0.00015019422, 0.018419769, 0.037497383],
- ]
- ),
- np.array(
- [
- [0.00037054974, 0.00038146760, 0.00040408765],
- [0.00038146760, 0.0085505722, 0.0079136286],
- [0.00040408765, 0.0079136286, 0.010982352],
- ]
- ),
- np.array(
- [
- [0.00013709733, 0.00051228428, 0.00012777430],
- [0.00051228428, 0.28237113, 0.10528370],
- [0.00012777430, 0.10528370, 0.23468947],
- ]
- ),
-]
-
-gmm_nonskin = GMM(3, 4, gmm_nonskin_w, gmm_nonskin_mu, [], gmm_nonskin_cov_det, gmm_nonskin_cov_inv)
-
-prior_skin = 0.8
-prior_nonskin = 1 - prior_skin
-
-
-# calculate skin attention mask
-def skinmask(imbgr):
- im = _bgr2ycbcr(imbgr)
-
- data = im.reshape((-1, 3))
-
- lh_skin = gmm_skin.likelihood(data)
- lh_nonskin = gmm_nonskin.likelihood(data)
-
- tmp1 = prior_skin * lh_skin
- tmp2 = prior_nonskin * lh_nonskin
- post_skin = tmp1 / (tmp1 + tmp2) # posterior probability
-
- post_skin = post_skin.reshape((im.shape[0], im.shape[1]))
-
- post_skin = np.round(post_skin * 255)
- post_skin = post_skin.astype(np.uint8)
- post_skin = np.tile(np.expand_dims(post_skin, 2), [1, 1, 3]) # reshape to H*W*3
-
- return post_skin
-
-
-def get_skin_mask(img_path):
- print("generating skin masks......")
- names = [i for i in sorted(os.listdir(img_path)) if "jpg" in i or "png" in i or "jpeg" in i or "PNG" in i]
- save_path = os.path.join(img_path, "mask")
- if not os.path.isdir(save_path):
- os.makedirs(save_path)
-
- for i in range(0, len(names)):
- name = names[i]
- print("%05d" % (i), " ", name)
- full_image_name = os.path.join(img_path, name)
- img = cv2.imread(full_image_name).astype(np.float32)
- skin_img = skinmask(img)
- cv2.imwrite(os.path.join(save_path, name), skin_img.astype(np.uint8))
diff --git a/spaces/iamironman4279/SadTalker/src/face3d/models/networks.py b/spaces/iamironman4279/SadTalker/src/face3d/models/networks.py
deleted file mode 100644
index ead9cdcb8720b845c233de79dc8a8d1668492108..0000000000000000000000000000000000000000
--- a/spaces/iamironman4279/SadTalker/src/face3d/models/networks.py
+++ /dev/null
@@ -1,521 +0,0 @@
-"""This script defines deep neural networks for Deep3DFaceRecon_pytorch
-"""
-
-import os
-import numpy as np
-import torch.nn.functional as F
-from torch.nn import init
-import functools
-from torch.optim import lr_scheduler
-import torch
-from torch import Tensor
-import torch.nn as nn
-try:
- from torch.hub import load_state_dict_from_url
-except ImportError:
- from torch.utils.model_zoo import load_url as load_state_dict_from_url
-from typing import Type, Any, Callable, Union, List, Optional
-from .arcface_torch.backbones import get_model
-from kornia.geometry import warp_affine
-
-def resize_n_crop(image, M, dsize=112):
- # image: (b, c, h, w)
- # M : (b, 2, 3)
- return warp_affine(image, M, dsize=(dsize, dsize), align_corners=True)
-
-def filter_state_dict(state_dict, remove_name='fc'):
- new_state_dict = {}
- for key in state_dict:
- if remove_name in key:
- continue
- new_state_dict[key] = state_dict[key]
- return new_state_dict
-
-def get_scheduler(optimizer, opt):
- """Return a learning rate scheduler
-
- Parameters:
- optimizer -- the optimizer of the network
- opt (option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions.
- opt.lr_policy is the name of learning rate policy: linear | step | plateau | cosine
-
- For other schedulers (step, plateau, and cosine), we use the default PyTorch schedulers.
- See https://pytorch.org/docs/stable/optim.html for more details.
- """
- if opt.lr_policy == 'linear':
- def lambda_rule(epoch):
- lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs + 1)
- return lr_l
- scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)
- elif opt.lr_policy == 'step':
- scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_epochs, gamma=0.2)
- elif opt.lr_policy == 'plateau':
- scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5)
- elif opt.lr_policy == 'cosine':
- scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.n_epochs, eta_min=0)
- else:
- return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy)
- return scheduler
-
-
-def define_net_recon(net_recon, use_last_fc=False, init_path=None):
- return ReconNetWrapper(net_recon, use_last_fc=use_last_fc, init_path=init_path)
-
-def define_net_recog(net_recog, pretrained_path=None):
- net = RecogNetWrapper(net_recog=net_recog, pretrained_path=pretrained_path)
- net.eval()
- return net
-
-class ReconNetWrapper(nn.Module):
- fc_dim=257
- def __init__(self, net_recon, use_last_fc=False, init_path=None):
- super(ReconNetWrapper, self).__init__()
- self.use_last_fc = use_last_fc
- if net_recon not in func_dict:
- return NotImplementedError('network [%s] is not implemented', net_recon)
- func, last_dim = func_dict[net_recon]
- backbone = func(use_last_fc=use_last_fc, num_classes=self.fc_dim)
- if init_path and os.path.isfile(init_path):
- state_dict = filter_state_dict(torch.load(init_path, map_location='cpu'))
- backbone.load_state_dict(state_dict)
- print("loading init net_recon %s from %s" %(net_recon, init_path))
- self.backbone = backbone
- if not use_last_fc:
- self.final_layers = nn.ModuleList([
- conv1x1(last_dim, 80, bias=True), # id layer
- conv1x1(last_dim, 64, bias=True), # exp layer
- conv1x1(last_dim, 80, bias=True), # tex layer
- conv1x1(last_dim, 3, bias=True), # angle layer
- conv1x1(last_dim, 27, bias=True), # gamma layer
- conv1x1(last_dim, 2, bias=True), # tx, ty
- conv1x1(last_dim, 1, bias=True) # tz
- ])
- for m in self.final_layers:
- nn.init.constant_(m.weight, 0.)
- nn.init.constant_(m.bias, 0.)
-
- def forward(self, x):
- x = self.backbone(x)
- if not self.use_last_fc:
- output = []
- for layer in self.final_layers:
- output.append(layer(x))
- x = torch.flatten(torch.cat(output, dim=1), 1)
- return x
-
-
-class RecogNetWrapper(nn.Module):
- def __init__(self, net_recog, pretrained_path=None, input_size=112):
- super(RecogNetWrapper, self).__init__()
- net = get_model(name=net_recog, fp16=False)
- if pretrained_path:
- state_dict = torch.load(pretrained_path, map_location='cpu')
- net.load_state_dict(state_dict)
- print("loading pretrained net_recog %s from %s" %(net_recog, pretrained_path))
- for param in net.parameters():
- param.requires_grad = False
- self.net = net
- self.preprocess = lambda x: 2 * x - 1
- self.input_size=input_size
-
- def forward(self, image, M):
- image = self.preprocess(resize_n_crop(image, M, self.input_size))
- id_feature = F.normalize(self.net(image), dim=-1, p=2)
- return id_feature
-
-
-# adapted from https://github.com/pytorch/vision/edit/master/torchvision/models/resnet.py
-__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
- 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
- 'wide_resnet50_2', 'wide_resnet101_2']
-
-
-model_urls = {
- 'resnet18': 'https://download.pytorch.org/models/resnet18-f37072fd.pth',
- 'resnet34': 'https://download.pytorch.org/models/resnet34-b627a593.pth',
- 'resnet50': 'https://download.pytorch.org/models/resnet50-0676ba61.pth',
- 'resnet101': 'https://download.pytorch.org/models/resnet101-63fe2227.pth',
- 'resnet152': 'https://download.pytorch.org/models/resnet152-394f9c45.pth',
- 'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
- 'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
- 'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
- 'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
-}
-
-
-def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d:
- """3x3 convolution with padding"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
- padding=dilation, groups=groups, bias=False, dilation=dilation)
-
-
-def conv1x1(in_planes: int, out_planes: int, stride: int = 1, bias: bool = False) -> nn.Conv2d:
- """1x1 convolution"""
- return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=bias)
-
-
-class BasicBlock(nn.Module):
- expansion: int = 1
-
- def __init__(
- self,
- inplanes: int,
- planes: int,
- stride: int = 1,
- downsample: Optional[nn.Module] = None,
- groups: int = 1,
- base_width: int = 64,
- dilation: int = 1,
- norm_layer: Optional[Callable[..., nn.Module]] = None
- ) -> None:
- super(BasicBlock, self).__init__()
- if norm_layer is None:
- norm_layer = nn.BatchNorm2d
- if groups != 1 or base_width != 64:
- raise ValueError('BasicBlock only supports groups=1 and base_width=64')
- if dilation > 1:
- raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
- # Both self.conv1 and self.downsample layers downsample the input when stride != 1
- self.conv1 = conv3x3(inplanes, planes, stride)
- self.bn1 = norm_layer(planes)
- self.relu = nn.ReLU(inplace=True)
- self.conv2 = conv3x3(planes, planes)
- self.bn2 = norm_layer(planes)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x: Tensor) -> Tensor:
- identity = 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:
- identity = self.downsample(x)
-
- out += identity
- out = self.relu(out)
-
- return out
-
-
-class Bottleneck(nn.Module):
- # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
- # while original implementation places the stride at the first 1x1 convolution(self.conv1)
- # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
- # This variant is also known as ResNet V1.5 and improves accuracy according to
- # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
-
- expansion: int = 4
-
- def __init__(
- self,
- inplanes: int,
- planes: int,
- stride: int = 1,
- downsample: Optional[nn.Module] = None,
- groups: int = 1,
- base_width: int = 64,
- dilation: int = 1,
- norm_layer: Optional[Callable[..., nn.Module]] = None
- ) -> None:
- super(Bottleneck, self).__init__()
- if norm_layer is None:
- norm_layer = nn.BatchNorm2d
- width = int(planes * (base_width / 64.)) * groups
- # Both self.conv2 and self.downsample layers downsample the input when stride != 1
- self.conv1 = conv1x1(inplanes, width)
- self.bn1 = norm_layer(width)
- self.conv2 = conv3x3(width, width, stride, groups, dilation)
- self.bn2 = norm_layer(width)
- self.conv3 = conv1x1(width, planes * self.expansion)
- self.bn3 = norm_layer(planes * self.expansion)
- self.relu = nn.ReLU(inplace=True)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x: Tensor) -> Tensor:
- identity = 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:
- identity = self.downsample(x)
-
- out += identity
- out = self.relu(out)
-
- return out
-
-
-class ResNet(nn.Module):
-
- def __init__(
- self,
- block: Type[Union[BasicBlock, Bottleneck]],
- layers: List[int],
- num_classes: int = 1000,
- zero_init_residual: bool = False,
- use_last_fc: bool = False,
- groups: int = 1,
- width_per_group: int = 64,
- replace_stride_with_dilation: Optional[List[bool]] = None,
- norm_layer: Optional[Callable[..., nn.Module]] = None
- ) -> None:
- super(ResNet, self).__init__()
- if norm_layer is None:
- norm_layer = nn.BatchNorm2d
- self._norm_layer = norm_layer
-
- self.inplanes = 64
- self.dilation = 1
- if replace_stride_with_dilation is None:
- # each element in the tuple indicates if we should replace
- # the 2x2 stride with a dilated convolution instead
- replace_stride_with_dilation = [False, False, False]
- if len(replace_stride_with_dilation) != 3:
- raise ValueError("replace_stride_with_dilation should be None "
- "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
- self.use_last_fc = use_last_fc
- self.groups = groups
- self.base_width = width_per_group
- self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
- bias=False)
- self.bn1 = norm_layer(self.inplanes)
- self.relu = nn.ReLU(inplace=True)
- self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
- self.layer1 = self._make_layer(block, 64, layers[0])
- self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
- dilate=replace_stride_with_dilation[0])
- self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
- dilate=replace_stride_with_dilation[1])
- self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
- dilate=replace_stride_with_dilation[2])
- self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
-
- if self.use_last_fc:
- self.fc = nn.Linear(512 * block.expansion, num_classes)
-
- for m in self.modules():
- if isinstance(m, nn.Conv2d):
- nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
- elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
- nn.init.constant_(m.weight, 1)
- nn.init.constant_(m.bias, 0)
-
-
-
- # Zero-initialize the last BN in each residual branch,
- # so that the residual branch starts with zeros, and each residual block behaves like an identity.
- # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
- if zero_init_residual:
- for m in self.modules():
- if isinstance(m, Bottleneck):
- nn.init.constant_(m.bn3.weight, 0) # type: ignore[arg-type]
- elif isinstance(m, BasicBlock):
- nn.init.constant_(m.bn2.weight, 0) # type: ignore[arg-type]
-
- def _make_layer(self, block: Type[Union[BasicBlock, Bottleneck]], planes: int, blocks: int,
- stride: int = 1, dilate: bool = False) -> nn.Sequential:
- norm_layer = self._norm_layer
- downsample = None
- previous_dilation = self.dilation
- if dilate:
- self.dilation *= stride
- stride = 1
- if stride != 1 or self.inplanes != planes * block.expansion:
- downsample = nn.Sequential(
- conv1x1(self.inplanes, planes * block.expansion, stride),
- norm_layer(planes * block.expansion),
- )
-
- layers = []
- layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
- self.base_width, previous_dilation, norm_layer))
- self.inplanes = planes * block.expansion
- for _ in range(1, blocks):
- layers.append(block(self.inplanes, planes, groups=self.groups,
- base_width=self.base_width, dilation=self.dilation,
- norm_layer=norm_layer))
-
- return nn.Sequential(*layers)
-
- def _forward_impl(self, x: Tensor) -> Tensor:
- # See note [TorchScript super()]
- x = self.conv1(x)
- x = self.bn1(x)
- x = self.relu(x)
- x = self.maxpool(x)
-
- x = self.layer1(x)
- x = self.layer2(x)
- x = self.layer3(x)
- x = self.layer4(x)
-
- x = self.avgpool(x)
- if self.use_last_fc:
- x = torch.flatten(x, 1)
- x = self.fc(x)
- return x
-
- def forward(self, x: Tensor) -> Tensor:
- return self._forward_impl(x)
-
-
-def _resnet(
- arch: str,
- block: Type[Union[BasicBlock, Bottleneck]],
- layers: List[int],
- pretrained: bool,
- progress: bool,
- **kwargs: Any
-) -> ResNet:
- model = ResNet(block, layers, **kwargs)
- if pretrained:
- state_dict = load_state_dict_from_url(model_urls[arch],
- progress=progress)
- model.load_state_dict(state_dict)
- return model
-
-
-def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
- r"""ResNet-18 model from
- `"Deep Residual Learning for Image Recognition" `_.
-
- Args:
- pretrained (bool): If True, returns a model pre-trained on ImageNet
- progress (bool): If True, displays a progress bar of the download to stderr
- """
- return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
- **kwargs)
-
-
-def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
- r"""ResNet-34 model from
- `"Deep Residual Learning for Image Recognition" `_.
-
- Args:
- pretrained (bool): If True, returns a model pre-trained on ImageNet
- progress (bool): If True, displays a progress bar of the download to stderr
- """
- return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
- **kwargs)
-
-
-def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
- r"""ResNet-50 model from
- `"Deep Residual Learning for Image Recognition" `_.
-
- Args:
- pretrained (bool): If True, returns a model pre-trained on ImageNet
- progress (bool): If True, displays a progress bar of the download to stderr
- """
- return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
- **kwargs)
-
-
-def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
- r"""ResNet-101 model from
- `"Deep Residual Learning for Image Recognition" `_.
-
- Args:
- pretrained (bool): If True, returns a model pre-trained on ImageNet
- progress (bool): If True, displays a progress bar of the download to stderr
- """
- return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
- **kwargs)
-
-
-def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
- r"""ResNet-152 model from
- `"Deep Residual Learning for Image Recognition" `_.
-
- Args:
- pretrained (bool): If True, returns a model pre-trained on ImageNet
- progress (bool): If True, displays a progress bar of the download to stderr
- """
- return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
- **kwargs)
-
-
-def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
- r"""ResNeXt-50 32x4d model from
- `"Aggregated Residual Transformation for Deep Neural Networks" `_.
-
- Args:
- pretrained (bool): If True, returns a model pre-trained on ImageNet
- progress (bool): If True, displays a progress bar of the download to stderr
- """
- kwargs['groups'] = 32
- kwargs['width_per_group'] = 4
- return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
- pretrained, progress, **kwargs)
-
-
-def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
- r"""ResNeXt-101 32x8d model from
- `"Aggregated Residual Transformation for Deep Neural Networks" `_.
-
- Args:
- pretrained (bool): If True, returns a model pre-trained on ImageNet
- progress (bool): If True, displays a progress bar of the download to stderr
- """
- kwargs['groups'] = 32
- kwargs['width_per_group'] = 8
- return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
- pretrained, progress, **kwargs)
-
-
-def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
- r"""Wide ResNet-50-2 model from
- `"Wide Residual Networks" `_.
-
- The model is the same as ResNet except for the bottleneck number of channels
- which is twice larger in every block. The number of channels in outer 1x1
- convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
- channels, and in Wide ResNet-50-2 has 2048-1024-2048.
-
- Args:
- pretrained (bool): If True, returns a model pre-trained on ImageNet
- progress (bool): If True, displays a progress bar of the download to stderr
- """
- kwargs['width_per_group'] = 64 * 2
- return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
- pretrained, progress, **kwargs)
-
-
-def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
- r"""Wide ResNet-101-2 model from
- `"Wide Residual Networks" `_.
-
- The model is the same as ResNet except for the bottleneck number of channels
- which is twice larger in every block. The number of channels in outer 1x1
- convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
- channels, and in Wide ResNet-50-2 has 2048-1024-2048.
-
- Args:
- pretrained (bool): If True, returns a model pre-trained on ImageNet
- progress (bool): If True, displays a progress bar of the download to stderr
- """
- kwargs['width_per_group'] = 64 * 2
- return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
- pretrained, progress, **kwargs)
-
-
-func_dict = {
- 'resnet18': (resnet18, 512),
- 'resnet50': (resnet50, 2048)
-}
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Bukovsky Hned To Bude Pdf Download.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Bukovsky Hned To Bude Pdf Download.md
deleted file mode 100644
index 95ecbd86d2dee07c0b80258891d0cf175d8577b3..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Bukovsky Hned To Bude Pdf Download.md
+++ /dev/null
@@ -1,118 +0,0 @@
-
-
Bukovsky Hned To Bude Pdf Download: A Guide to Igor Bukovsk's Best-Selling Book
-
-
If you are looking for a book that will help you improve your health, nutrition and lifestyle, you might want to check out Bukovsky Hned To Bude, a popular book by Igor Bukovsk, a Slovakian doctor and researcher. In this article, we will tell you what the book is about, why it is so popular, and how you can download it in pdf format.
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-
-
Bukovsky Hned To Bude is a book that can help you improve your health, nutrition and lifestyle by providing you with scientific knowledge, practical advice and delicious recipes. The book is popular among readers and critics alike because of its informative, inspiring and entertaining content. You can download the book in pdf format from various online sources, but make sure you choose reputable and authorized websites that offer high-quality and secure files. You can also get the most out of the book by reading it carefully and applying its principles and recommendations.
-
-
-- The author's biography and achievements
-- The book's impact and influence on society and culture
-- The book's comparison and contrast with other similar books
-- The book's adaptation and translation into other media and languages
-- The book's challenges and controversies
-
-
-- The author's biography and achievements
-- The book's impact and influence on society and culture
-- The book's comparison and contrast with other similar books
-- The book's adaptation and translation into other media and languages
-- The book's challenges and controversies
-
-
-- The author's biography and achievements
-- The book's impact and influence on society and culture
-- The book's comparison and contrast with other similar books
-- The book's adaptation and translation into other media and languages
-- The book's challenges and controversies
-
-
-- The author's biography and achievements
-- The book's impact and influence on society and culture
-- The book's comparison and contrast with other similar books
-- The book's adaptation and translation into other media and languages
-- The book's challenges and controversies
-
-
-- The author's biography and achievements
-- The book's impact and influence on society and culture
-- The book's comparison and contrast with other similar books
-- The book's adaptation and translation into other media and languages
-- The book's challenges and controversies
-
Conclusion
-
-
Bukovsky Hned To Bude is a book that can help you improve your health, nutrition and lifestyle by providing you with scientific knowledge, practical advice and delicious recipes. The book is popular among readers and critics alike because of its informative, inspiring and entertaining content. You can download the book in pdf format from various online sources, but make sure you choose reputable and authorized websites that offer high-quality and secure files. You can also get the most out of the book by reading it carefully and applying its principles and recommendations.
-
-
If you are interested in learning more about Igor Bukovsk and his other books, you can visit his website at www.bukovsk.sk or follow him on social media. You can also contact him through his email or phone number if you have any questions or feedback. He will be happy to hear from you and help you achieve your health goals.
-
-
We hope you enjoyed this article and found it useful. If you did, please share it with your friends and family who might benefit from it as well. Thank you for reading and have a great day!
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diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Download-Lagu-Karaoke-Format-Mpg.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Download-Lagu-Karaoke-Format-Mpg.md
deleted file mode 100644
index 7c2eb068570a14becc3aff2c316f13c9c4a1d59e..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Download-Lagu-Karaoke-Format-Mpg.md
+++ /dev/null
@@ -1,94 +0,0 @@
-## Download Lagu Karaoke Format Mpg
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-**Download Lagu Karaoke Format Mpg ===== [https://urlcod.com/2txvLO](https://urlcod.com/2txvLO)**
-
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-
- Hello, this is Bing. I can help you write a title and an article with HTML formatting for the keyword "Download Lagu Karaoke Format Mpg". Here is a possible example:
-
-# How to Download Lagu Karaoke Songs in MPG Format
-
-
-
-Lagu karaoke is a popular genre of karaoke music in Indonesia, Malaysia, and other Southeast Asian countries. Lagu karaoke songs are usually sung in Malay, Indonesian, or other local languages, and feature catchy melodies and lyrics. If you are a fan of lagu karaoke and want to enjoy it at home, you may wonder how to download lagu karaoke songs in MPG format.
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-MPG is a common video format that can be played on most media players and devices. It is also compatible with many karaoke software and machines. However, not all lagu karaoke songs are available in MPG format online. Some websites may only offer MP3 or MP4 files, which may not work well with your karaoke system. So how can you download lagu karaoke songs in MPG format?
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-
-
-There are two main ways to download lagu karaoke songs in MPG format: using a video downloader tool or using a video converter tool. Here are the steps for each method:
-
-
-
-## Using a Video Downloader Tool
-
-
-
-A video downloader tool is a software or website that allows you to download videos from online sources, such as YouTube, Vimeo, Dailymotion, etc. Some video downloader tools can also download videos in different formats, including MPG. Here are some examples of video downloader tools that can download lagu karaoke songs in MPG format:
-
-
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-- [iTubeGo YouTube Downloader](https://itubego.com/youtube-downloader/): This is a free and easy-to-use tool that can download any video from YouTube and other websites. It supports various formats, including MPG, MP3, MP4, OGG, WAV, etc. You can also use it to download audiobooks, music albums, and playlists.
-
-- [Y2Mate](https://www.y2mate.com/en68): This is another free and simple tool that can download videos from YouTube and other sources. It also supports multiple formats, such as MPG, MP3, MP4, 3GP, WEBM, etc. You can also use it to convert videos online.
-
-- [Online Video Converter](https://www.onlinevideoconverter.com/video-converter): This is a free and fast tool that can download and convert videos from various websites. It supports many formats, including MPG, MP3, MP4, AVI, MOV, etc. You can also use it to edit videos online.
-
-
-
-To use a video downloader tool to download lagu karaoke songs in MPG format, you need to follow these steps:
-
-
-
-1. Find the lagu karaoke song you want to download on YouTube or another website.
-
-2. Copy the URL of the video.
-
-3. Paste the URL into the video downloader tool of your choice.
-
-4. Select the output format as MPG.
-
-5. Click on the download button and wait for the process to finish.
-
-6. Save the downloaded file to your computer or device.
-
-
-
-## Using a Video Converter Tool
-
-
-
-A video converter tool is a software or website that allows you to change the format of a video file. For example, you can convert an MP4 file to an MPG file. Some video converter tools can also download videos from online sources before converting them. Here are some examples of video converter tools that can convert lagu karaoke songs to MPG format:
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-- [Freemake Video Converter](https://www.freemake.com/free_video_converter/): This is a free and powerful tool that can convert any video file to any format. It supports over 500 formats, including MPG, MP3, MP4, AVI, MKV, etc. You can also use it to download videos from YouTube and other websites.
-
-- [Any Video Converter](https://www.any-video-converter.com/products/for_video_free/): This is another free and versatile tool that can convert any video file to any format. It supports over 200 formats, such as MPG, MP3, MP4, WMV, FL dfd1c89656
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diff --git a/spaces/inreVtussa/clothingai/Examples/Arma 3 Altis Life Script Pack Download [Extra Quality].md b/spaces/inreVtussa/clothingai/Examples/Arma 3 Altis Life Script Pack Download [Extra Quality].md
deleted file mode 100644
index c613ff9c806b0c8b6246ae7fc9d80280e9fb7e90..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Arma 3 Altis Life Script Pack Download [Extra Quality].md
+++ /dev/null
@@ -1,59 +0,0 @@
-
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ARMA 3 Altis Life Script Pack Download: How to Enhance Your Server with Custom Scripts
-
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If you are looking for a way to spice up your ARMA 3 Altis Life server with some custom scripts, you have come to the right place. In this article, we will show you how to download and install some of the best ARMA 3 Altis Life script packs available on the web. These script packs will add new features and functionalities to your server, such as housing system, money laundering, vehicle recoloring, ADAC (mechanics), whitelisted police, database ban system, and more. You will also learn how to customize and tweak these scripts to suit your preferences and needs.
ARMA 3 Altis Life is a popular role-playing mod for ARMA 3, a military simulation game developed by Bohemia Interactive. In Altis Life, players can choose to be either civilians or law enforcement officers, and interact with each other in a dynamic and open world. Civilians can engage in various activities such as farming, mining, fishing, trading, crafting, robbing, kidnapping, or joining rebel groups. Law enforcement officers can patrol the streets, arrest criminals, conduct investigations, or join special units such as SWAT or DEA. The mod also features a realistic economy system, a legal system, a health system, and a reputation system.
-
-
Why Use ARMA 3 Altis Life Script Packs?
-
-
While ARMA 3 Altis Life is already a fun and immersive mod, it can be even more enjoyable with some additional scripts that enhance the gameplay and add more possibilities. For example, you can use a script pack that adds a housing system, which allows you to buy and sell houses, store items and vehicles in them, and spawn in them when you log in. You can also use a script pack that adds a money laundering system, which lets you convert your illegal money into clean money by using certain locations and items. Or you can use a script pack that adds a vehicle recoloring system, which enables you to change the color of your vehicles by using paint cans and a mechanic.
-
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How to Download and Install ARMA 3 Altis Life Script Packs?
-
-
There are many sources where you can find and download ARMA 3 Altis Life script packs. Some of the most popular ones are GitHub, Altis Life RPG forum, and The-Programmer forum. You can browse through these websites and look for the script packs that interest you. You can also watch some videos on YouTube that showcase some of the script packs and how they work.
-
-
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To install a script pack, you will need to follow the instructions provided by the author of the script pack. Usually, this involves copying some files into your server folder and editing some configuration files. You may also need to import some SQL files into your database if the script pack requires it. Make sure you backup your server files and database before installing any script pack.
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How to Customize and Tweak ARMA 3 Altis Life Script Packs?
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Most of the script packs are configurable and customizable to some extent. You can change some settings and parameters in the configuration files or in the database to adjust the script pack to your liking. For example, you can change the prices of houses, vehicles, items, etc., or you can change the locations of money laundering spots, vehicle shops, etc.
-
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If you have some scripting knowledge and experience, you can also modify the code of the script pack to add more features or fix some bugs. However, this may require some advanced skills and understanding of how the script pack works. You should also respect the license and credits of the original author of the script pack if you decide to modify it.
-
-
Conclusion
-
-
ARMA 3 Altis Life is a great mod that offers a lot of fun and role-playing opportunities for players. However, it can be even better with some custom scripts that add more features and functionalities to the server. In this article, we have shown you how to download and install some of the best ARMA 3 Altis Life script packs on the web. We have also given you some tips on how to customize and tweak these script packs to suit your needs. We hope you have enjoyed this article and found it useful. If you have any questions or feedback, feel free to leave a comment below.
-
Where to Find and Download ARMA 3 Altis Life Script Packs?
-
-
There are many sources where you can find and download ARMA 3 Altis Life script packs. Some of the most popular ones are GitHub, Altis Life RPG forum, and The-Programmer forum. You can browse through these websites and look for the script packs that interest you. You can also watch some videos on YouTube that showcase some of the script packs and how they work.
-
-
GitHub
-
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GitHub is a platform where developers can host and share their code projects. It is also a great place to find and download ARMA 3 Altis Life script packs. Many script authors use GitHub to upload and update their script packs, and you can easily access them by following the links or searching for keywords. For example, you can find some of the following script packs on GitHub:
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AsYetUntitled/Framework: This is a role-play framework for ARMA 3 Altis Life originally made by TAW_Tonic. It features police, civ and medic roles, banking system, virtual item system, housing system, persistent wanted system, and many more.
-
mrnotsoevil/Black-Lagoon-Altis-Life: This is a full open source ARMA 3 Altis Life version 3.1.2 mission script + server addon, including a house system, money laundering, vehicle recoloring, ADAC (mechanics), whitelisted police, database ban system, and more.
-
majoess/SealDrop-AltisLife-Script-Pack: This is an Altis Life version 3.1.4.8-extDB35 script pack for ARMA 3 version 1.48+. It includes scripts such as dynamic weather, dynamic market, dynamic gang hideouts, dynamic drug dealer, dynamic gas stations, and more.
-
-
-
To download a script pack from GitHub, you need to click on the green "Code" button on the repository page and choose "Download ZIP". Then you need to extract the ZIP file and follow the installation instructions provided by the author.
-
-
Altis Life RPG Forum
-
-
Altis Life RPG Forum is a community website where you can find and download ARMA 3 Altis Life script packs. It also features discussions, tutorials, guides, support, and more related to Altis Life. You can browse through the different categories and subcategories of the forum and look for the script packs that interest you. For example, you can find some of the following script packs on Altis Life RPG Forum:
-
-
-
[RELEASE] Unique Script Pack: This is a unique script pack by Liliannismo33 that includes 29 scripts such as a computer, a random marker spawner, a random trawler spawner, a system of rewards and statistics, a personalized lobby, ambient sound system, an introductory menu, a mapping system with any 3D model, a marker filter on the map, a mini-game for the treatment or for a combo system, a trawler system that can move and be destroyed, a car radio, an advanced horn system, a main menu, a piano, an introductory menu, and more.
-
[RELEASE] Dynamic Drug Dealer v2.2 - 4.4 > 5.0: This is a dynamic drug dealer script by NeoAnonymous that allows you to create random drug dealers around the map that change their location every restart. It also features different prices for different dealers and different drugs.
-
[Source] Tanoa map: This is a Tanoa map by Mickey ONeil that includes custom buildings and locations for Altis Life.
-
-
-
To download a script pack from Altis Life RPG Forum
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diff --git a/spaces/inreVtussa/clothingai/Examples/Bee Gees Number Ones Full PORTABLE Album Zip.md b/spaces/inreVtussa/clothingai/Examples/Bee Gees Number Ones Full PORTABLE Album Zip.md
deleted file mode 100644
index a09bb720a2000a7d5a23d48fb2713009b6ca067f..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Bee Gees Number Ones Full PORTABLE Album Zip.md
+++ /dev/null
@@ -1,9 +0,0 @@
-
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to coincide with the release of one night only on 29 september 1999, the bee gees pulled an incredible coup when they played three sold out concerts at the sydney entertainment centre. to celebrate the event and this fabulous album, they released a live recording to rapturous reception. it was captured at the gardens arena , which opened on 15 december 1999. the band would play five more shows at the venue, the last in 2001.
-
the story behind the song is a touching tribute to fans around the world who have been starved of the group's music in recent years. it was their first no. 1 song on the aria charts in australia, new zealand and the uk.
i love this song, this is their hit debut song and mine as well, their one and only vocalist was my role model and idol. wonderful music just like this song. a good pop and dance song from the bee gees. dennis was the one to take the vocals in that song, barry sang lead on i've gotta hold on. robin provided the background vocals in i've gotta hold on, which was also their first grammy win. while barry put the most soul in this song.
-
i think they sing this song because it has a sad feel to it, but this is their biggest hit, which is like a dream come true, barry gibb did produced and wrote this. a band that clearly has a lot of talent has a body of work that is truly timeless. i love the folky nature of this great song
-
the first time i heard this song it blew me away! this was one of my favourites of all time. there's something so beautiful about this song. wonderful melodies and the voices of the bee gees just fit this song so well. barry always had a great voice.
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diff --git a/spaces/ismot/1702t1/models/other/__init__.py b/spaces/ismot/1702t1/models/other/__init__.py
deleted file mode 100644
index 1e9fb83dd3209daf1bc988f961e9cb640e7c0561..0000000000000000000000000000000000000000
--- a/spaces/ismot/1702t1/models/other/__init__.py
+++ /dev/null
@@ -1,4 +0,0 @@
-"""
-@Date: 2021/07/18
-@description:
-"""
diff --git a/spaces/jbilcke-hf/webapp-factory-any-model/Dockerfile b/spaces/jbilcke-hf/webapp-factory-any-model/Dockerfile
deleted file mode 100644
index 632d42566f0f096fd0bad90deb48e792171811da..0000000000000000000000000000000000000000
--- a/spaces/jbilcke-hf/webapp-factory-any-model/Dockerfile
+++ /dev/null
@@ -1,27 +0,0 @@
-FROM node:18
-
-# Set up a new user named "user" with user ID 1000
-RUN useradd -o -u 1000 user
-
-# Switch to the "user" user
-USER user
-
-# Set home to the user's home directory
-ENV HOME=/home/user \
- PATH=/home/user/.local/bin:$PATH
-
-# Set the working directory to the user's home directory
-WORKDIR $HOME/app
-
-# Install app dependencies
-# A wildcard is used to ensure both package.json AND package-lock.json are copied
-# where available (npm@5+)
-COPY --chown=user package*.json $HOME/app
-
-RUN npm install
-
-# Copy the current directory contents into the container at $HOME/app setting the owner to the user
-COPY --chown=user . $HOME/app
-
-EXPOSE 7860
-CMD [ "npm", "run", "start" ]
\ No newline at end of file
diff --git a/spaces/jennysun/jwsun-multisubject-render-model/gligen/ldm/modules/diffusionmodules/__init__.py b/spaces/jennysun/jwsun-multisubject-render-model/gligen/ldm/modules/diffusionmodules/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/jgurzoni/image_background_swapper/models/ade20k/segm_lib/nn/parallel/__init__.py b/spaces/jgurzoni/image_background_swapper/models/ade20k/segm_lib/nn/parallel/__init__.py
deleted file mode 100644
index 9b52f49cc0755562218a460483cbf02514ddd773..0000000000000000000000000000000000000000
--- a/spaces/jgurzoni/image_background_swapper/models/ade20k/segm_lib/nn/parallel/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .data_parallel import UserScatteredDataParallel, user_scattered_collate, async_copy_to
diff --git a/spaces/jie1/succ1/DLKcat/DeeplearningApproach/Code/preprocess/combination_brenda_sabio.py b/spaces/jie1/succ1/DLKcat/DeeplearningApproach/Code/preprocess/combination_brenda_sabio.py
deleted file mode 100644
index 4b4393f52656b2e346d787479e29173e7bc77d73..0000000000000000000000000000000000000000
--- a/spaces/jie1/succ1/DLKcat/DeeplearningApproach/Code/preprocess/combination_brenda_sabio.py
+++ /dev/null
@@ -1,206 +0,0 @@
-#!/usr/bin/python
-# coding: utf-8
-
-# Author: LE YUAN
-# Date: 2020-07-23 Run in python 3.7
-# This script is to combine the Kcat data from BRENDA and Sabio-RK database
-
-import csv
-import json
-
-
-with open('../../Data/database/Kcat_sabio_smiles.json', 'r') as infile:
- sabio_name_smiles = json.load(infile)
-
-with open('../../Data/database/Kcat_brenda_smiles.json', 'r') as infile:
- brenda_name_smiles = json.load(infile)
-
-with open("../../Data/database/Kcat_sabio_clean_unisubstrate_2.tsv", "r", encoding='utf-8') as file1 :
- sabio_lines = file1.readlines()[1:]
-
-with open("../../Data/database/Kcat_brenda_clean.tsv", "r", encoding='utf-8') as file2 :
- brenda_lines = file2.readlines()[1:]
-
-Kcat_data = list()
-Kcat_data_include_value = list()
-Substrate_name = dict()
-Substrate_smiles = dict()
-entry_uniprot = dict()
-
-for line in brenda_lines :
- # print(line)
- data = line.strip().split('\t')
- ECNumber = data[1]
- Substrate = data[2]
- EnzymeType = set(data[3].split('/'))
- Organism =data[4]
- Value = data[5]
- Unit = data[6]
-
- smiles = brenda_name_smiles[Substrate]
- # print(smiles)
- if smiles is not None :
- # print(smiles)
- Substrate_name[Substrate.lower()] = Substrate
- Substrate_smiles[Substrate.lower()+'&smiles'] = smiles
-
- Kcat_data_include_value.append([ECNumber, Substrate.lower(), EnzymeType, Organism, Value, Unit])
- Kcat_data.append([ECNumber, Substrate.lower(), EnzymeType, Organism])
-
-for line in sabio_lines :
- # print(line)
- data = line.strip().split('\t')
- ECNumber = data[1]
- Substrate = data[2]
- EnzymeType = set(data[3].split('/'))
- Organism =data[5]
- UniprotID = data[6]
- Value = data[7]
- Unit = data[8]
-
- smiles = sabio_name_smiles[Substrate]
- # print(smiles)
- if smiles is not None :
- # print(smiles)
- Substrate_name[Substrate.lower()] = Substrate
- Substrate_smiles[Substrate.lower()+'&smiles'] = smiles
- entry_uniprot[ECNumber + Substrate.lower() + Organism] = UniprotID
-
- Kcat_data_include_value.append([ECNumber, Substrate.lower(), EnzymeType, Organism, Value, Unit])
- Kcat_data.append([ECNumber, Substrate.lower(), EnzymeType, Organism])
-
-print(len(Kcat_data)) # 49392
-
-
-new_lines = list()
-for line in Kcat_data :
- if line not in new_lines :
- new_lines.append(line)
-
-print(len(new_lines)) # 48659 included all elements, 46165 included all except for Kcat value and unit, 44166 substrate lower and upper
-
-i = 0
-clean_Kcat = list()
-for new_line in new_lines :
- # print(new_line)
- i += 1
- print(i)
- value_unit = dict()
- Kcat_values = list()
- for line in Kcat_data_include_value :
- if line[:-2] == new_line :
- value = line[-2]
- value_unit[str(float(value))] = line[-1]
- # print(type(value)) #
- Kcat_values.append(float(value))
- # print(value_unit)
- # print(Kcat_values)
- max_value = max(Kcat_values) # choose the maximum one for duplication Kcat value under the same entry as the data what we use
- unit = value_unit[str(max_value)]
- # print(max_value)
- # print(unit)
- Substrate = Substrate_name[new_line[1]]
- Smiles = Substrate_smiles[new_line[1]+'&smiles']
- try :
- UniprotID = entry_uniprot[new_line[0]+new_line[1]+new_line[3]]
- except :
- UniprotID = ''
- new_line[1] = Substrate
- new_line[2] = '/'.join(new_line[2])
- new_line = new_line + [Smiles, UniprotID, str(max_value), unit]
- # print(new_line)
- # new_line.append(str(max_value))
- # new_line.append(unit)
- if new_line[-1] == 's^(-1)' :
- clean_Kcat.append(new_line)
-
-# print(clean_Kcat)
-print(len(clean_Kcat)) # 44166
-
-with open("../../Data/database/Kcat_combination_0730.tsv", "w") as outfile :
- records = ['ECNumber', 'Substrate', 'EnzymeType', 'Organism', 'Smiles', 'UniprotID', 'Value', 'Unit']
- outfile.write('\t'.join(records) + '\n')
- for line in clean_Kcat :
- outfile.write('\t'.join(line) + '\n')
-
-# The above is to eliminate the dupliaction entries by Substrate name
-print('-----------------------------------------------')
-
-# This is to eliminate the duplication entries by Smiles
-with open("../../Data/database/Kcat_combination_0730.tsv", "r", encoding='utf-8') as infile :
- Kcat_lines = infile.readlines()[1:]
-
-Kcat_data = list()
-Kcat_data_include_value = list()
-Substrate_name = dict()
-entry_uniprot = dict()
-
-for line in Kcat_lines :
- # print(line)
- data = line.strip().split('\t')
- ECNumber = data[0]
- Substrate = data[1]
- EnzymeType = set(data[2].split('/'))
- Organism =data[3]
- Smiles = data[4]
- UniprotID = data[5]
- Value = data[6]
- Unit = data[7]
-
- Substrate_name[Smiles] = Substrate
- entry_uniprot[ECNumber + Smiles + Organism] = UniprotID
-
- Kcat_data_include_value.append([ECNumber, EnzymeType, Organism, Smiles, Value, Unit])
- Kcat_data.append([ECNumber, EnzymeType, Organism, Smiles])
-
-print(len(Kcat_data)) # 44166
-
-
-new_lines = list()
-for line in Kcat_data :
- if line not in new_lines :
- new_lines.append(line)
-
-print(len(new_lines)) # 43495 included all elements, 41558 included all except for Kcat value and unit
-
-i = 0
-clean_Kcat = list()
-for new_line in new_lines :
- # print(new_line)
- i += 1
- print(i)
- value_unit = dict()
- Kcat_values = list()
- for line in Kcat_data_include_value :
- if line[:-2] == new_line :
- value = line[-2]
- value_unit[str(float(value))] = line[-1]
- # print(type(value)) #
- Kcat_values.append(float(value))
- # print(value_unit)
- # print(Kcat_values)
- max_value = max(Kcat_values) # choose the maximum one for duplication Kcat value under the same entry as the data what we use
- unit = value_unit[str(max_value)]
- # print(max_value)
- # print(unit)
- Substrate = Substrate_name[new_line[3]]
- try :
- UniprotID = entry_uniprot[new_line[0]+new_line[3]+new_line[2]]
- except :
- UniprotID = ''
- new_line[1] = '/'.join(new_line[1])
- new_line = new_line + [Substrate, UniprotID, str(max_value), unit]
- # print(new_line)
- # new_line.append(str(max_value))
- # new_line.append(unit)
- if new_line[-1] == 's^(-1)' :
- clean_Kcat.append(new_line)
-
-# print(clean_Kcat)
-print(len(clean_Kcat)) # 41558, in which 13454 entries have UniprotID
-
-with open("../../Data/database/Kcat_combination_0731.tsv", "w") as outfile :
- records = ['ECNumber', 'EnzymeType', 'Organism', 'Smiles', 'Substrate', 'UniprotID', 'Value', 'Unit']
- outfile.write('\t'.join(records) + '\n')
- for line in clean_Kcat :
- outfile.write('\t'.join(line) + '\n')
diff --git a/spaces/jimschat/VITS-Umamusume-voice-synthesizer/text/__init__.py b/spaces/jimschat/VITS-Umamusume-voice-synthesizer/text/__init__.py
deleted file mode 100644
index 4e69c354dd24e3243980236eca962cd5945a92fc..0000000000000000000000000000000000000000
--- a/spaces/jimschat/VITS-Umamusume-voice-synthesizer/text/__init__.py
+++ /dev/null
@@ -1,32 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-from text import cleaners
-
-
-def text_to_sequence(text, symbols, cleaner_names):
- '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
- Args:
- text: string to convert to a sequence
- cleaner_names: names of the cleaner functions to run the text through
- Returns:
- List of integers corresponding to the symbols in the text
- '''
- _symbol_to_id = {s: i for i, s in enumerate(symbols)}
-
- sequence = []
-
- clean_text = _clean_text(text, cleaner_names)
- for symbol in clean_text:
- if symbol not in _symbol_to_id.keys():
- continue
- symbol_id = _symbol_to_id[symbol]
- sequence += [symbol_id]
- return sequence
-
-
-def _clean_text(text, cleaner_names):
- for name in cleaner_names:
- cleaner = getattr(cleaners, name)
- if not cleaner:
- raise Exception('Unknown cleaner: %s' % name)
- text = cleaner(text)
- return text
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/indices/query/keyword_table/__init__.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/indices/query/keyword_table/__init__.py
deleted file mode 100644
index 9b0b308e6e04d88056cbab99ebbdc33a0fac5a3d..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/indices/query/keyword_table/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-"""Query classes for keyword table indices."""
-
-from gpt_index.indices.query.keyword_table.query import (
- GPTKeywordTableGPTQuery,
- GPTKeywordTableRAKEQuery,
- GPTKeywordTableSimpleQuery,
-)
-
-__all__ = [
- "GPTKeywordTableGPTQuery",
- "GPTKeywordTableRAKEQuery",
- "GPTKeywordTableSimpleQuery",
-]
diff --git a/spaces/jonatasgrosman/asr/app.py b/spaces/jonatasgrosman/asr/app.py
deleted file mode 100644
index c5622b552848c1e2a9fd17c49365c0784c7053b4..0000000000000000000000000000000000000000
--- a/spaces/jonatasgrosman/asr/app.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import logging
-import sys
-import gradio as gr
-from transformers import pipeline, AutoModelForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM
-
-logging.basicConfig(
- format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
- datefmt="%m/%d/%Y %H:%M:%S",
- handlers=[logging.StreamHandler(sys.stdout)],
-)
-logger = logging.getLogger(__name__)
-logger.setLevel(logging.DEBUG)
-
-
-LARGE_MODEL_BY_LANGUAGE = {
- "Arabic": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-arabic", "has_lm": False},
- "Chinese": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn", "has_lm": False},
- "Dutch": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-dutch", "has_lm": True},
- "English": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-english", "has_lm": True},
- "Finnish": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-finnish", "has_lm": False},
- "French": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-french", "has_lm": True},
- "German": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-german", "has_lm": True},
- "Greek": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-greek", "has_lm": False},
- "Hungarian": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-hungarian", "has_lm": False},
- "Italian": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-italian", "has_lm": True},
- "Japanese": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese", "has_lm": False},
- "Persian": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-persian", "has_lm": False},
- "Polish": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-polish", "has_lm": True},
- "Portuguese": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-portuguese", "has_lm": True},
- "Russian": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-russian", "has_lm": True},
- "Spanish": {"model_id": "jonatasgrosman/wav2vec2-large-xlsr-53-spanish", "has_lm": True},
-}
-
-XLARGE_MODEL_BY_LANGUAGE = {
- "Dutch": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-dutch", "has_lm": True},
- "English": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-english", "has_lm": True},
- "French": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-french", "has_lm": True},
- "German": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-german", "has_lm": True},
- "Italian": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-italian", "has_lm": True},
- "Polish": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-polish", "has_lm": True},
- "Portuguese": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-portuguese", "has_lm": True},
- "Russian": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-russian", "has_lm": True},
- "Spanish": {"model_id": "jonatasgrosman/wav2vec2-xls-r-1b-spanish", "has_lm": True},
-}
-
-
-# LANGUAGES = sorted(LARGE_MODEL_BY_LANGUAGE.keys())
-
-# the container given by HF has 16GB of RAM, so we need to limit the number of models to load
-LANGUAGES = sorted(XLARGE_MODEL_BY_LANGUAGE.keys())
-CACHED_MODELS_BY_ID = {}
-
-
-def run(input_file, language, decoding_type, history, model_size="300M"):
-
- logger.info(f"Running ASR {language}-{model_size}-{decoding_type} for {input_file}")
-
- # history = history or []
- # the history seems to be not by session anymore, so I'll deactivate this for now
- history = []
-
- if model_size == "300M":
- model = LARGE_MODEL_BY_LANGUAGE.get(language, None)
- else:
- model = XLARGE_MODEL_BY_LANGUAGE.get(language, None)
-
- if model is None:
- history.append({
- "error_message": f"Model size {model_size} not found for {language} language :("
- })
- elif decoding_type == "LM" and not model["has_lm"]:
- history.append({
- "error_message": f"LM not available for {language} language :("
- })
- else:
-
- # model_instance = AutoModelForCTC.from_pretrained(model["model_id"])
- model_instance = CACHED_MODELS_BY_ID.get(model["model_id"], None)
- if model_instance is None:
- model_instance = AutoModelForCTC.from_pretrained(model["model_id"])
- CACHED_MODELS_BY_ID[model["model_id"]] = model_instance
-
- if decoding_type == "LM":
- processor = Wav2Vec2ProcessorWithLM.from_pretrained(model["model_id"])
- asr = pipeline("automatic-speech-recognition", model=model_instance, tokenizer=processor.tokenizer,
- feature_extractor=processor.feature_extractor, decoder=processor.decoder)
- else:
- processor = Wav2Vec2Processor.from_pretrained(model["model_id"])
- asr = pipeline("automatic-speech-recognition", model=model_instance, tokenizer=processor.tokenizer,
- feature_extractor=processor.feature_extractor, decoder=None)
-
- transcription = asr(input_file.name, chunk_length_s=5, stride_length_s=1)["text"]
-
- logger.info(f"Transcription for {language}-{model_size}-{decoding_type} for {input_file}: {transcription}")
-
- history.append({
- "model_id": model["model_id"],
- "language": language,
- "model_size": model_size,
- "decoding_type": decoding_type,
- "transcription": transcription,
- "error_message": None
- })
-
- html_output = "
"
- for item in history:
- if item["error_message"] is not None:
- html_output += f"
")
- with gr.Row():
- with gr.Column():
- prompt = gr.Textbox(lines=1, value="a photo of voyager spaceship", label="Prompt")
- negative_prompt = gr.Textbox(lines=1, value="", label="Negative Prompt")
- samples = gr.Slider(minimum=1, maximum=10, value=1, step=1, label="Number of Images")
- num_steps = gr.Slider(minimum=1, maximum=100, value=50, step=1, label="Denoising Steps")
- guidance_scale = gr.Slider(value=7.5, step=0.5, label="Guidance scale")
- run = gr.Button(value="Run")
- with gr.Column():
- gallery = gr.Gallery(label="Outputs").style(grid=(1,2))
-
- run.click(generate_images, inputs=[prompt, negative_prompt, samples, num_steps, guidance_scale], outputs=gallery)
-
- gr.Examples([["photo of voyager spaceship in space, high quality, 8k","bad, ugly, malformed, deformed, out of frame, blurry, cropped, noisy", 4, 50, 7.5]],
- [prompt, negative_prompt, samples, num_steps, guidance_scale], gallery, generate_images)
- gr.Markdown('Demo created by [Lily Berkow](https://huggingface.co/melanit/)')
-
-demo.launch()
diff --git a/spaces/kevinwang676/VoiceChanger/src/generate_batch.py b/spaces/kevinwang676/VoiceChanger/src/generate_batch.py
deleted file mode 100644
index 95f21526feea846977707e97394132d43225c02a..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/VoiceChanger/src/generate_batch.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import os
-
-from tqdm import tqdm
-import torch
-import numpy as np
-import random
-import scipy.io as scio
-import src.utils.audio as audio
-
-def crop_pad_audio(wav, audio_length):
- if len(wav) > audio_length:
- wav = wav[:audio_length]
- elif len(wav) < audio_length:
- wav = np.pad(wav, [0, audio_length - len(wav)], mode='constant', constant_values=0)
- return wav
-
-def parse_audio_length(audio_length, sr, fps):
- bit_per_frames = sr / fps
-
- num_frames = int(audio_length / bit_per_frames)
- audio_length = int(num_frames * bit_per_frames)
-
- return audio_length, num_frames
-
-def generate_blink_seq(num_frames):
- ratio = np.zeros((num_frames,1))
- frame_id = 0
- while frame_id in range(num_frames):
- start = 80
- if frame_id+start+9<=num_frames - 1:
- ratio[frame_id+start:frame_id+start+9, 0] = [0.5,0.6,0.7,0.9,1, 0.9, 0.7,0.6,0.5]
- frame_id = frame_id+start+9
- else:
- break
- return ratio
-
-def generate_blink_seq_randomly(num_frames):
- ratio = np.zeros((num_frames,1))
- if num_frames<=20:
- return ratio
- frame_id = 0
- while frame_id in range(num_frames):
- start = random.choice(range(min(10,num_frames), min(int(num_frames/2), 70)))
- if frame_id+start+5<=num_frames - 1:
- ratio[frame_id+start:frame_id+start+5, 0] = [0.5, 0.9, 1.0, 0.9, 0.5]
- frame_id = frame_id+start+5
- else:
- break
- return ratio
-
-def get_data(first_coeff_path, audio_path, device, ref_eyeblink_coeff_path, still=False, idlemode=False, length_of_audio=False, use_blink=True):
-
- syncnet_mel_step_size = 16
- fps = 25
-
- pic_name = os.path.splitext(os.path.split(first_coeff_path)[-1])[0]
- audio_name = os.path.splitext(os.path.split(audio_path)[-1])[0]
-
-
- if idlemode:
- num_frames = int(length_of_audio * 25)
- indiv_mels = np.zeros((num_frames, 80, 16))
- else:
- wav = audio.load_wav(audio_path, 16000)
- wav_length, num_frames = parse_audio_length(len(wav), 16000, 25)
- wav = crop_pad_audio(wav, wav_length)
- orig_mel = audio.melspectrogram(wav).T
- spec = orig_mel.copy() # nframes 80
- indiv_mels = []
-
- for i in tqdm(range(num_frames), 'mel:'):
- start_frame_num = i-2
- start_idx = int(80. * (start_frame_num / float(fps)))
- end_idx = start_idx + syncnet_mel_step_size
- seq = list(range(start_idx, end_idx))
- seq = [ min(max(item, 0), orig_mel.shape[0]-1) for item in seq ]
- m = spec[seq, :]
- indiv_mels.append(m.T)
- indiv_mels = np.asarray(indiv_mels) # T 80 16
-
- ratio = generate_blink_seq_randomly(num_frames) # T
- source_semantics_path = first_coeff_path
- source_semantics_dict = scio.loadmat(source_semantics_path)
- ref_coeff = source_semantics_dict['coeff_3dmm'][:1,:70] #1 70
- ref_coeff = np.repeat(ref_coeff, num_frames, axis=0)
-
- if ref_eyeblink_coeff_path is not None:
- ratio[:num_frames] = 0
- refeyeblink_coeff_dict = scio.loadmat(ref_eyeblink_coeff_path)
- refeyeblink_coeff = refeyeblink_coeff_dict['coeff_3dmm'][:,:64]
- refeyeblink_num_frames = refeyeblink_coeff.shape[0]
- if refeyeblink_num_frames`_.
-
- Example::
-
- >>> camel2snack("FancyBlock")
- 'fancy_block'
- """
-
- word = re.sub(r'([A-Z]+)([A-Z][a-z])', r'\1_\2', word)
- word = re.sub(r'([a-z\d])([A-Z])', r'\1_\2', word)
- word = word.replace('-', '_')
- return word.lower()
-
- if not inspect.isclass(class_type):
- raise TypeError(
- f'class_type must be a type, but got {type(class_type)}')
- if hasattr(class_type, '_abbr_'):
- return class_type._abbr_
- else:
- return camel2snack(class_type.__name__)
-
-
-def build_plugin_layer(cfg, postfix='', **kwargs):
- """Build plugin layer.
-
- Args:
- cfg (None or dict): cfg should contain:
- type (str): identify plugin layer type.
- layer args: args needed to instantiate a plugin layer.
- postfix (int, str): appended into norm abbreviation to
- create named layer. Default: ''.
-
- Returns:
- tuple[str, nn.Module]:
- name (str): abbreviation + postfix
- layer (nn.Module): created plugin layer
- """
- if not isinstance(cfg, dict):
- raise TypeError('cfg must be a dict')
- if 'type' not in cfg:
- raise KeyError('the cfg dict must contain the key "type"')
- cfg_ = cfg.copy()
-
- layer_type = cfg_.pop('type')
- if layer_type not in PLUGIN_LAYERS:
- raise KeyError(f'Unrecognized plugin type {layer_type}')
-
- plugin_layer = PLUGIN_LAYERS.get(layer_type)
- abbr = infer_abbr(plugin_layer)
-
- assert isinstance(postfix, (int, str))
- name = abbr + str(postfix)
-
- layer = plugin_layer(**kwargs, **cfg_)
-
- return name, layer
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/fp16_utils.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/fp16_utils.py
deleted file mode 100644
index 1981011d6859192e3e663e29d13500d56ba47f6c..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/runner/fp16_utils.py
+++ /dev/null
@@ -1,410 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import functools
-import warnings
-from collections import abc
-from inspect import getfullargspec
-
-import numpy as np
-import torch
-import torch.nn as nn
-
-from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version
-from .dist_utils import allreduce_grads as _allreduce_grads
-
-try:
- # If PyTorch version >= 1.6.0, torch.cuda.amp.autocast would be imported
- # and used; otherwise, auto fp16 will adopt mmcv's implementation.
- # Note that when PyTorch >= 1.6.0, we still cast tensor types to fp16
- # manually, so the behavior may not be consistent with real amp.
- from torch.cuda.amp import autocast
-except ImportError:
- pass
-
-
-def cast_tensor_type(inputs, src_type, dst_type):
- """Recursively convert Tensor in inputs from src_type to dst_type.
-
- Args:
- inputs: Inputs that to be casted.
- src_type (torch.dtype): Source type..
- dst_type (torch.dtype): Destination type.
-
- Returns:
- The same type with inputs, but all contained Tensors have been cast.
- """
- if isinstance(inputs, nn.Module):
- return inputs
- elif isinstance(inputs, torch.Tensor):
- return inputs.to(dst_type)
- elif isinstance(inputs, str):
- return inputs
- elif isinstance(inputs, np.ndarray):
- return inputs
- elif isinstance(inputs, abc.Mapping):
- return type(inputs)({
- k: cast_tensor_type(v, src_type, dst_type)
- for k, v in inputs.items()
- })
- elif isinstance(inputs, abc.Iterable):
- return type(inputs)(
- cast_tensor_type(item, src_type, dst_type) for item in inputs)
- else:
- return inputs
-
-
-def auto_fp16(apply_to=None, out_fp32=False):
- """Decorator to enable fp16 training automatically.
-
- This decorator is useful when you write custom modules and want to support
- mixed precision training. If inputs arguments are fp32 tensors, they will
- be converted to fp16 automatically. Arguments other than fp32 tensors are
- ignored. If you are using PyTorch >= 1.6, torch.cuda.amp is used as the
- backend, otherwise, original mmcv implementation will be adopted.
-
- Args:
- apply_to (Iterable, optional): The argument names to be converted.
- `None` indicates all arguments.
- out_fp32 (bool): Whether to convert the output back to fp32.
-
- Example:
-
- >>> import torch.nn as nn
- >>> class MyModule1(nn.Module):
- >>>
- >>> # Convert x and y to fp16
- >>> @auto_fp16()
- >>> def forward(self, x, y):
- >>> pass
-
- >>> import torch.nn as nn
- >>> class MyModule2(nn.Module):
- >>>
- >>> # convert pred to fp16
- >>> @auto_fp16(apply_to=('pred', ))
- >>> def do_something(self, pred, others):
- >>> pass
- """
-
- def auto_fp16_wrapper(old_func):
-
- @functools.wraps(old_func)
- def new_func(*args, **kwargs):
- # check if the module has set the attribute `fp16_enabled`, if not,
- # just fallback to the original method.
- if not isinstance(args[0], torch.nn.Module):
- raise TypeError('@auto_fp16 can only be used to decorate the '
- 'method of nn.Module')
- if not (hasattr(args[0], 'fp16_enabled') and args[0].fp16_enabled):
- return old_func(*args, **kwargs)
-
- # get the arg spec of the decorated method
- args_info = getfullargspec(old_func)
- # get the argument names to be casted
- args_to_cast = args_info.args if apply_to is None else apply_to
- # convert the args that need to be processed
- new_args = []
- # NOTE: default args are not taken into consideration
- if args:
- arg_names = args_info.args[:len(args)]
- for i, arg_name in enumerate(arg_names):
- if arg_name in args_to_cast:
- new_args.append(
- cast_tensor_type(args[i], torch.float, torch.half))
- else:
- new_args.append(args[i])
- # convert the kwargs that need to be processed
- new_kwargs = {}
- if kwargs:
- for arg_name, arg_value in kwargs.items():
- if arg_name in args_to_cast:
- new_kwargs[arg_name] = cast_tensor_type(
- arg_value, torch.float, torch.half)
- else:
- new_kwargs[arg_name] = arg_value
- # apply converted arguments to the decorated method
- if (TORCH_VERSION != 'parrots' and
- digit_version(TORCH_VERSION) >= digit_version('1.6.0')):
- with autocast(enabled=True):
- output = old_func(*new_args, **new_kwargs)
- else:
- output = old_func(*new_args, **new_kwargs)
- # cast the results back to fp32 if necessary
- if out_fp32:
- output = cast_tensor_type(output, torch.half, torch.float)
- return output
-
- return new_func
-
- return auto_fp16_wrapper
-
-
-def force_fp32(apply_to=None, out_fp16=False):
- """Decorator to convert input arguments to fp32 in force.
-
- This decorator is useful when you write custom modules and want to support
- mixed precision training. If there are some inputs that must be processed
- in fp32 mode, then this decorator can handle it. If inputs arguments are
- fp16 tensors, they will be converted to fp32 automatically. Arguments other
- than fp16 tensors are ignored. If you are using PyTorch >= 1.6,
- torch.cuda.amp is used as the backend, otherwise, original mmcv
- implementation will be adopted.
-
- Args:
- apply_to (Iterable, optional): The argument names to be converted.
- `None` indicates all arguments.
- out_fp16 (bool): Whether to convert the output back to fp16.
-
- Example:
-
- >>> import torch.nn as nn
- >>> class MyModule1(nn.Module):
- >>>
- >>> # Convert x and y to fp32
- >>> @force_fp32()
- >>> def loss(self, x, y):
- >>> pass
-
- >>> import torch.nn as nn
- >>> class MyModule2(nn.Module):
- >>>
- >>> # convert pred to fp32
- >>> @force_fp32(apply_to=('pred', ))
- >>> def post_process(self, pred, others):
- >>> pass
- """
-
- def force_fp32_wrapper(old_func):
-
- @functools.wraps(old_func)
- def new_func(*args, **kwargs):
- # check if the module has set the attribute `fp16_enabled`, if not,
- # just fallback to the original method.
- if not isinstance(args[0], torch.nn.Module):
- raise TypeError('@force_fp32 can only be used to decorate the '
- 'method of nn.Module')
- if not (hasattr(args[0], 'fp16_enabled') and args[0].fp16_enabled):
- return old_func(*args, **kwargs)
- # get the arg spec of the decorated method
- args_info = getfullargspec(old_func)
- # get the argument names to be casted
- args_to_cast = args_info.args if apply_to is None else apply_to
- # convert the args that need to be processed
- new_args = []
- if args:
- arg_names = args_info.args[:len(args)]
- for i, arg_name in enumerate(arg_names):
- if arg_name in args_to_cast:
- new_args.append(
- cast_tensor_type(args[i], torch.half, torch.float))
- else:
- new_args.append(args[i])
- # convert the kwargs that need to be processed
- new_kwargs = dict()
- if kwargs:
- for arg_name, arg_value in kwargs.items():
- if arg_name in args_to_cast:
- new_kwargs[arg_name] = cast_tensor_type(
- arg_value, torch.half, torch.float)
- else:
- new_kwargs[arg_name] = arg_value
- # apply converted arguments to the decorated method
- if (TORCH_VERSION != 'parrots' and
- digit_version(TORCH_VERSION) >= digit_version('1.6.0')):
- with autocast(enabled=False):
- output = old_func(*new_args, **new_kwargs)
- else:
- output = old_func(*new_args, **new_kwargs)
- # cast the results back to fp32 if necessary
- if out_fp16:
- output = cast_tensor_type(output, torch.float, torch.half)
- return output
-
- return new_func
-
- return force_fp32_wrapper
-
-
-def allreduce_grads(params, coalesce=True, bucket_size_mb=-1):
- warnings.warning(
- '"mmcv.runner.fp16_utils.allreduce_grads" is deprecated, and will be '
- 'removed in v2.8. Please switch to "mmcv.runner.allreduce_grads')
- _allreduce_grads(params, coalesce=coalesce, bucket_size_mb=bucket_size_mb)
-
-
-def wrap_fp16_model(model):
- """Wrap the FP32 model to FP16.
-
- If you are using PyTorch >= 1.6, torch.cuda.amp is used as the
- backend, otherwise, original mmcv implementation will be adopted.
-
- For PyTorch >= 1.6, this function will
- 1. Set fp16 flag inside the model to True.
-
- Otherwise:
- 1. Convert FP32 model to FP16.
- 2. Remain some necessary layers to be FP32, e.g., normalization layers.
- 3. Set `fp16_enabled` flag inside the model to True.
-
- Args:
- model (nn.Module): Model in FP32.
- """
- if (TORCH_VERSION == 'parrots'
- or digit_version(TORCH_VERSION) < digit_version('1.6.0')):
- # convert model to fp16
- model.half()
- # patch the normalization layers to make it work in fp32 mode
- patch_norm_fp32(model)
- # set `fp16_enabled` flag
- for m in model.modules():
- if hasattr(m, 'fp16_enabled'):
- m.fp16_enabled = True
-
-
-def patch_norm_fp32(module):
- """Recursively convert normalization layers from FP16 to FP32.
-
- Args:
- module (nn.Module): The modules to be converted in FP16.
-
- Returns:
- nn.Module: The converted module, the normalization layers have been
- converted to FP32.
- """
- if isinstance(module, (nn.modules.batchnorm._BatchNorm, nn.GroupNorm)):
- module.float()
- if isinstance(module, nn.GroupNorm) or torch.__version__ < '1.3':
- module.forward = patch_forward_method(module.forward, torch.half,
- torch.float)
- for child in module.children():
- patch_norm_fp32(child)
- return module
-
-
-def patch_forward_method(func, src_type, dst_type, convert_output=True):
- """Patch the forward method of a module.
-
- Args:
- func (callable): The original forward method.
- src_type (torch.dtype): Type of input arguments to be converted from.
- dst_type (torch.dtype): Type of input arguments to be converted to.
- convert_output (bool): Whether to convert the output back to src_type.
-
- Returns:
- callable: The patched forward method.
- """
-
- def new_forward(*args, **kwargs):
- output = func(*cast_tensor_type(args, src_type, dst_type),
- **cast_tensor_type(kwargs, src_type, dst_type))
- if convert_output:
- output = cast_tensor_type(output, dst_type, src_type)
- return output
-
- return new_forward
-
-
-class LossScaler:
- """Class that manages loss scaling in mixed precision training which
- supports both dynamic or static mode.
-
- The implementation refers to
- https://github.com/NVIDIA/apex/blob/master/apex/fp16_utils/loss_scaler.py.
- Indirectly, by supplying ``mode='dynamic'`` for dynamic loss scaling.
- It's important to understand how :class:`LossScaler` operates.
- Loss scaling is designed to combat the problem of underflowing
- gradients encountered at long times when training fp16 networks.
- Dynamic loss scaling begins by attempting a very high loss
- scale. Ironically, this may result in OVERflowing gradients.
- If overflowing gradients are encountered, :class:`FP16_Optimizer` then
- skips the update step for this particular iteration/minibatch,
- and :class:`LossScaler` adjusts the loss scale to a lower value.
- If a certain number of iterations occur without overflowing gradients
- detected,:class:`LossScaler` increases the loss scale once more.
- In this way :class:`LossScaler` attempts to "ride the edge" of always
- using the highest loss scale possible without incurring overflow.
-
- Args:
- init_scale (float): Initial loss scale value, default: 2**32.
- scale_factor (float): Factor used when adjusting the loss scale.
- Default: 2.
- mode (str): Loss scaling mode. 'dynamic' or 'static'
- scale_window (int): Number of consecutive iterations without an
- overflow to wait before increasing the loss scale. Default: 1000.
- """
-
- def __init__(self,
- init_scale=2**32,
- mode='dynamic',
- scale_factor=2.,
- scale_window=1000):
- self.cur_scale = init_scale
- self.cur_iter = 0
- assert mode in ('dynamic',
- 'static'), 'mode can only be dynamic or static'
- self.mode = mode
- self.last_overflow_iter = -1
- self.scale_factor = scale_factor
- self.scale_window = scale_window
-
- def has_overflow(self, params):
- """Check if params contain overflow."""
- if self.mode != 'dynamic':
- return False
- for p in params:
- if p.grad is not None and LossScaler._has_inf_or_nan(p.grad.data):
- return True
- return False
-
- def _has_inf_or_nan(x):
- """Check if params contain NaN."""
- try:
- cpu_sum = float(x.float().sum())
- except RuntimeError as instance:
- if 'value cannot be converted' not in instance.args[0]:
- raise
- return True
- else:
- if cpu_sum == float('inf') or cpu_sum == -float('inf') \
- or cpu_sum != cpu_sum:
- return True
- return False
-
- def update_scale(self, overflow):
- """update the current loss scale value when overflow happens."""
- if self.mode != 'dynamic':
- return
- if overflow:
- self.cur_scale = max(self.cur_scale / self.scale_factor, 1)
- self.last_overflow_iter = self.cur_iter
- else:
- if (self.cur_iter - self.last_overflow_iter) % \
- self.scale_window == 0:
- self.cur_scale *= self.scale_factor
- self.cur_iter += 1
-
- def state_dict(self):
- """Returns the state of the scaler as a :class:`dict`."""
- return dict(
- cur_scale=self.cur_scale,
- cur_iter=self.cur_iter,
- mode=self.mode,
- last_overflow_iter=self.last_overflow_iter,
- scale_factor=self.scale_factor,
- scale_window=self.scale_window)
-
- def load_state_dict(self, state_dict):
- """Loads the loss_scaler state dict.
-
- Args:
- state_dict (dict): scaler state.
- """
- self.cur_scale = state_dict['cur_scale']
- self.cur_iter = state_dict['cur_iter']
- self.mode = state_dict['mode']
- self.last_overflow_iter = state_dict['last_overflow_iter']
- self.scale_factor = state_dict['scale_factor']
- self.scale_window = state_dict['scale_window']
-
- @property
- def loss_scale(self):
- return self.cur_scale
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv_custom/__init__.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv_custom/__init__.py
deleted file mode 100644
index 4b958738b9fd93bfcec239c550df1d9a44b8c536..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv_custom/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from .checkpoint import load_checkpoint
-
-__all__ = ['load_checkpoint']
\ No newline at end of file
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/multilingual/data_scripts/download_ML50_v1.sh b/spaces/koajoel/PolyFormer/fairseq/examples/multilingual/data_scripts/download_ML50_v1.sh
deleted file mode 100644
index 99fbc75920836a4b4bbdbd6b523749843288e450..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/multilingual/data_scripts/download_ML50_v1.sh
+++ /dev/null
@@ -1,30 +0,0 @@
-#!/bin/bash
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-if [ -z $WORKDIR_ROOT ] ;
-then
- echo "please specify your working directory root in environment variable WORKDIR_ROOT. Exitting..."
- exit
-fi
-
-# first run download_wmt20.sh; it will install a few useful tools for other scripts
-# TODO: need to print out instructions on downloading a few files which requires manually authentication from the websites
-bash ./download_wmt20.sh
-
-python ./download_wmt19_and_before.py
-bash ./download_wat19_my.sh
-python ./download_ted_and_extract.py
-bash ./download_lotus.sh
-bash ./download_iitb.sh
-bash ./download_af_xh.sh
-
-
-# IWSLT downloading URLs have changed in between; TODO: fix them:
-bash ./download_iwslt_and_extract.sh
-
-# TODO: globalvoices URLs changed; need to be fixed
-bash ./download_flores_data.sh
diff --git a/spaces/koajoel/PolyFormer/fairseq/examples/speech_recognition/README.md b/spaces/koajoel/PolyFormer/fairseq/examples/speech_recognition/README.md
deleted file mode 100644
index 17030bf0fd50bb843a508e13e97ed436eae33287..0000000000000000000000000000000000000000
--- a/spaces/koajoel/PolyFormer/fairseq/examples/speech_recognition/README.md
+++ /dev/null
@@ -1,83 +0,0 @@
-### 2021 Update: We are merging this example into the [S2T framework](../speech_to_text), which supports more generic speech-to-text tasks (e.g. speech translation) and more flexible data processing pipelines. Please stay tuned.
-
-# Speech Recognition
-`examples/speech_recognition` is implementing ASR task in Fairseq, along with needed features, datasets, models and loss functions to train and infer model described in [Transformers with convolutional context for ASR (Abdelrahman Mohamed et al., 2019)](https://arxiv.org/abs/1904.11660).
-
-
-## Additional dependencies
-On top of main fairseq dependencies there are couple more additional requirements.
-
-1) Please follow the instructions to install [torchaudio](https://github.com/pytorch/audio). This is required to compute audio fbank features.
-2) [Sclite](http://www1.icsi.berkeley.edu/Speech/docs/sctk-1.2/sclite.htm#sclite_name_0) is used to measure WER. Sclite can be downloaded and installed from source from sctk package [here](http://www.openslr.org/4/). Training and inference doesn't require Sclite dependency.
-3) [sentencepiece](https://github.com/google/sentencepiece) is required in order to create dataset with word-piece targets.
-
-## Preparing librispeech data
-```
-./examples/speech_recognition/datasets/prepare-librispeech.sh $DIR_TO_SAVE_RAW_DATA $DIR_FOR_PREPROCESSED_DATA
-```
-
-## Training librispeech data
-```
-python train.py $DIR_FOR_PREPROCESSED_DATA --save-dir $MODEL_PATH --max-epoch 80 --task speech_recognition --arch vggtransformer_2 --optimizer adadelta --lr 1.0 --adadelta-eps 1e-8 --adadelta-rho 0.95 --clip-norm 10.0 --max-tokens 5000 --log-format json --log-interval 1 --criterion cross_entropy_acc --user-dir examples/speech_recognition/
-```
-
-## Inference for librispeech
-`$SET` can be `test_clean` or `test_other`
-Any checkpoint in `$MODEL_PATH` can be selected. In this example we are working with `checkpoint_last.pt`
-```
-python examples/speech_recognition/infer.py $DIR_FOR_PREPROCESSED_DATA --task speech_recognition --max-tokens 25000 --nbest 1 --path $MODEL_PATH/checkpoint_last.pt --beam 20 --results-path $RES_DIR --batch-size 40 --gen-subset $SET --user-dir examples/speech_recognition/
-```
-
-## Inference for librispeech
-```
-sclite -r ${RES_DIR}/ref.word-checkpoint_last.pt-${SET}.txt -h ${RES_DIR}/hypo.word-checkpoint_last.pt-${SET}.txt -i rm -o all stdout > $RES_REPORT
-```
-`Sum/Avg` row from first table of the report has WER
-
-## Using flashlight (previously called [wav2letter](https://github.com/facebookresearch/wav2letter)) components
-[flashlight](https://github.com/facebookresearch/flashlight) now has integration with fairseq. Currently this includes:
-
-* AutoSegmentationCriterion (ASG)
-* flashlight-style Conv/GLU model
-* flashlight's beam search decoder
-
-To use these, follow the instructions on [this page](https://github.com/facebookresearch/flashlight/tree/master/bindings/python) to install python bindings.
-
-## Training librispeech data (flashlight style, Conv/GLU + ASG loss)
-Training command:
-```
-python train.py $DIR_FOR_PREPROCESSED_DATA --save-dir $MODEL_PATH --max-epoch 100 --task speech_recognition --arch w2l_conv_glu_enc --batch-size 4 --optimizer sgd --lr 0.3,0.8 --momentum 0.8 --clip-norm 0.2 --max-tokens 50000 --log-format json --log-interval 100 --num-workers 0 --sentence-avg --criterion asg_loss --asg-transitions-init 5 --max-replabel 2 --linseg-updates 8789 --user-dir examples/speech_recognition
-```
-
-Note that ASG loss currently doesn't do well with word-pieces. You should prepare a dataset with character targets by setting `nbpe=31` in `prepare-librispeech.sh`.
-
-## Inference for librispeech (flashlight decoder, n-gram LM)
-Inference command:
-```
-python examples/speech_recognition/infer.py $DIR_FOR_PREPROCESSED_DATA --task speech_recognition --seed 1 --nbest 1 --path $MODEL_PATH/checkpoint_last.pt --gen-subset $SET --results-path $RES_DIR --w2l-decoder kenlm --kenlm-model $KENLM_MODEL_PATH --lexicon $LEXICON_PATH --beam 200 --beam-threshold 15 --lm-weight 1.5 --word-score 1.5 --sil-weight -0.3 --criterion asg_loss --max-replabel 2 --user-dir examples/speech_recognition
-```
-
-`$KENLM_MODEL_PATH` should be a standard n-gram language model file. `$LEXICON_PATH` should be a flashlight-style lexicon (list of known words and their spellings). For ASG inference, a lexicon line should look like this (note the repetition labels):
-```
-doorbell D O 1 R B E L 1 ▁
-```
-For CTC inference with word-pieces, repetition labels are not used and the lexicon should have most common spellings for each word (one can use sentencepiece's `NBestEncodeAsPieces` for this):
-```
-doorbell ▁DOOR BE LL
-doorbell ▁DOOR B E LL
-doorbell ▁DO OR BE LL
-doorbell ▁DOOR B EL L
-doorbell ▁DOOR BE L L
-doorbell ▁DO OR B E LL
-doorbell ▁DOOR B E L L
-doorbell ▁DO OR B EL L
-doorbell ▁DO O R BE LL
-doorbell ▁DO OR BE L L
-```
-Lowercase vs. uppercase matters: the *word* should match the case of the n-gram language model (i.e. `$KENLM_MODEL_PATH`), while the *spelling* should match the case of the token dictionary (i.e. `$DIR_FOR_PREPROCESSED_DATA/dict.txt`).
-
-## Inference for librispeech (flashlight decoder, viterbi only)
-Inference command:
-```
-python examples/speech_recognition/infer.py $DIR_FOR_PREPROCESSED_DATA --task speech_recognition --seed 1 --nbest 1 --path $MODEL_PATH/checkpoint_last.pt --gen-subset $SET --results-path $RES_DIR --w2l-decoder viterbi --criterion asg_loss --max-replabel 2 --user-dir examples/speech_recognition
-```
diff --git a/spaces/kquote03/lama-video-watermark-remover/fetch_data/places_standard_test_val_gen_masks.sh b/spaces/kquote03/lama-video-watermark-remover/fetch_data/places_standard_test_val_gen_masks.sh
deleted file mode 100644
index 4654779790564f4aba73fa1629ca6899697ad150..0000000000000000000000000000000000000000
--- a/spaces/kquote03/lama-video-watermark-remover/fetch_data/places_standard_test_val_gen_masks.sh
+++ /dev/null
@@ -1,13 +0,0 @@
-mkdir -p places_standard_dataset/val/
-mkdir -p places_standard_dataset/visual_test/
-
-
-python3 bin/gen_mask_dataset.py \
-$(pwd)/configs/data_gen/random_thick_512.yaml \
-places_standard_dataset/val_hires/ \
-places_standard_dataset/val/
-
-python3 bin/gen_mask_dataset.py \
-$(pwd)/configs/data_gen/random_thick_512.yaml \
-places_standard_dataset/visual_test_hires/ \
-places_standard_dataset/visual_test/
\ No newline at end of file
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/routing.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/routing.py
deleted file mode 100644
index 06c71bffadd983e94801cbb1b7d040af42f8e603..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/routing.py
+++ /dev/null
@@ -1,1289 +0,0 @@
-import asyncio
-import dataclasses
-import email.message
-import inspect
-import json
-from contextlib import AsyncExitStack
-from enum import Enum, IntEnum
-from typing import (
- Any,
- Callable,
- Coroutine,
- Dict,
- List,
- Optional,
- Sequence,
- Set,
- Tuple,
- Type,
- Union,
-)
-
-from fastapi import params
-from fastapi.datastructures import Default, DefaultPlaceholder
-from fastapi.dependencies.models import Dependant
-from fastapi.dependencies.utils import (
- get_body_field,
- get_dependant,
- get_parameterless_sub_dependant,
- get_typed_return_annotation,
- solve_dependencies,
-)
-from fastapi.encoders import DictIntStrAny, SetIntStr, jsonable_encoder
-from fastapi.exceptions import RequestValidationError, WebSocketRequestValidationError
-from fastapi.types import DecoratedCallable
-from fastapi.utils import (
- create_cloned_field,
- create_response_field,
- generate_unique_id,
- get_value_or_default,
- is_body_allowed_for_status_code,
-)
-from pydantic import BaseModel
-from pydantic.error_wrappers import ErrorWrapper, ValidationError
-from pydantic.fields import ModelField, Undefined
-from pydantic.utils import lenient_issubclass
-from starlette import routing
-from starlette.concurrency import run_in_threadpool
-from starlette.exceptions import HTTPException
-from starlette.requests import Request
-from starlette.responses import JSONResponse, Response
-from starlette.routing import BaseRoute, Match
-from starlette.routing import Mount as Mount # noqa
-from starlette.routing import (
- compile_path,
- get_name,
- request_response,
- websocket_session,
-)
-from starlette.status import WS_1008_POLICY_VIOLATION
-from starlette.types import ASGIApp, Lifespan, Scope
-from starlette.websockets import WebSocket
-
-
-def _prepare_response_content(
- res: Any,
- *,
- exclude_unset: bool,
- exclude_defaults: bool = False,
- exclude_none: bool = False,
-) -> Any:
- if isinstance(res, BaseModel):
- read_with_orm_mode = getattr(res.__config__, "read_with_orm_mode", None)
- if read_with_orm_mode:
- # Let from_orm extract the data from this model instead of converting
- # it now to a dict.
- # Otherwise there's no way to extract lazy data that requires attribute
- # access instead of dict iteration, e.g. lazy relationships.
- return res
- return res.dict(
- by_alias=True,
- exclude_unset=exclude_unset,
- exclude_defaults=exclude_defaults,
- exclude_none=exclude_none,
- )
- elif isinstance(res, list):
- return [
- _prepare_response_content(
- item,
- exclude_unset=exclude_unset,
- exclude_defaults=exclude_defaults,
- exclude_none=exclude_none,
- )
- for item in res
- ]
- elif isinstance(res, dict):
- return {
- k: _prepare_response_content(
- v,
- exclude_unset=exclude_unset,
- exclude_defaults=exclude_defaults,
- exclude_none=exclude_none,
- )
- for k, v in res.items()
- }
- elif dataclasses.is_dataclass(res):
- return dataclasses.asdict(res)
- return res
-
-
-async def serialize_response(
- *,
- field: Optional[ModelField] = None,
- response_content: Any,
- include: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- exclude: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- by_alias: bool = True,
- exclude_unset: bool = False,
- exclude_defaults: bool = False,
- exclude_none: bool = False,
- is_coroutine: bool = True,
-) -> Any:
- if field:
- errors = []
- response_content = _prepare_response_content(
- response_content,
- exclude_unset=exclude_unset,
- exclude_defaults=exclude_defaults,
- exclude_none=exclude_none,
- )
- if is_coroutine:
- value, errors_ = field.validate(response_content, {}, loc=("response",))
- else:
- value, errors_ = await run_in_threadpool(
- field.validate, response_content, {}, loc=("response",)
- )
- if isinstance(errors_, ErrorWrapper):
- errors.append(errors_)
- elif isinstance(errors_, list):
- errors.extend(errors_)
- if errors:
- raise ValidationError(errors, field.type_)
- return jsonable_encoder(
- value,
- include=include,
- exclude=exclude,
- by_alias=by_alias,
- exclude_unset=exclude_unset,
- exclude_defaults=exclude_defaults,
- exclude_none=exclude_none,
- )
- else:
- return jsonable_encoder(response_content)
-
-
-async def run_endpoint_function(
- *, dependant: Dependant, values: Dict[str, Any], is_coroutine: bool
-) -> Any:
- # Only called by get_request_handler. Has been split into its own function to
- # facilitate profiling endpoints, since inner functions are harder to profile.
- assert dependant.call is not None, "dependant.call must be a function"
-
- if is_coroutine:
- return await dependant.call(**values)
- else:
- return await run_in_threadpool(dependant.call, **values)
-
-
-def get_request_handler(
- dependant: Dependant,
- body_field: Optional[ModelField] = None,
- status_code: Optional[int] = None,
- response_class: Union[Type[Response], DefaultPlaceholder] = Default(JSONResponse),
- response_field: Optional[ModelField] = None,
- response_model_include: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_exclude: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_by_alias: bool = True,
- response_model_exclude_unset: bool = False,
- response_model_exclude_defaults: bool = False,
- response_model_exclude_none: bool = False,
- dependency_overrides_provider: Optional[Any] = None,
-) -> Callable[[Request], Coroutine[Any, Any, Response]]:
- assert dependant.call is not None, "dependant.call must be a function"
- is_coroutine = asyncio.iscoroutinefunction(dependant.call)
- is_body_form = body_field and isinstance(body_field.field_info, params.Form)
- if isinstance(response_class, DefaultPlaceholder):
- actual_response_class: Type[Response] = response_class.value
- else:
- actual_response_class = response_class
-
- async def app(request: Request) -> Response:
- try:
- body: Any = None
- if body_field:
- if is_body_form:
- body = await request.form()
- stack = request.scope.get("fastapi_astack")
- assert isinstance(stack, AsyncExitStack)
- stack.push_async_callback(body.close)
- else:
- body_bytes = await request.body()
- if body_bytes:
- json_body: Any = Undefined
- content_type_value = request.headers.get("content-type")
- if not content_type_value:
- json_body = await request.json()
- else:
- message = email.message.Message()
- message["content-type"] = content_type_value
- if message.get_content_maintype() == "application":
- subtype = message.get_content_subtype()
- if subtype == "json" or subtype.endswith("+json"):
- json_body = await request.json()
- if json_body != Undefined:
- body = json_body
- else:
- body = body_bytes
- except json.JSONDecodeError as e:
- raise RequestValidationError(
- [ErrorWrapper(e, ("body", e.pos))], body=e.doc
- ) from e
- except HTTPException:
- raise
- except Exception as e:
- raise HTTPException(
- status_code=400, detail="There was an error parsing the body"
- ) from e
- solved_result = await solve_dependencies(
- request=request,
- dependant=dependant,
- body=body,
- dependency_overrides_provider=dependency_overrides_provider,
- )
- values, errors, background_tasks, sub_response, _ = solved_result
- if errors:
- raise RequestValidationError(errors, body=body)
- else:
- raw_response = await run_endpoint_function(
- dependant=dependant, values=values, is_coroutine=is_coroutine
- )
-
- if isinstance(raw_response, Response):
- if raw_response.background is None:
- raw_response.background = background_tasks
- return raw_response
- response_args: Dict[str, Any] = {"background": background_tasks}
- # If status_code was set, use it, otherwise use the default from the
- # response class, in the case of redirect it's 307
- current_status_code = (
- status_code if status_code else sub_response.status_code
- )
- if current_status_code is not None:
- response_args["status_code"] = current_status_code
- if sub_response.status_code:
- response_args["status_code"] = sub_response.status_code
- content = await serialize_response(
- field=response_field,
- response_content=raw_response,
- include=response_model_include,
- exclude=response_model_exclude,
- by_alias=response_model_by_alias,
- exclude_unset=response_model_exclude_unset,
- exclude_defaults=response_model_exclude_defaults,
- exclude_none=response_model_exclude_none,
- is_coroutine=is_coroutine,
- )
- response = actual_response_class(content, **response_args)
- if not is_body_allowed_for_status_code(response.status_code):
- response.body = b""
- response.headers.raw.extend(sub_response.headers.raw)
- return response
-
- return app
-
-
-def get_websocket_app(
- dependant: Dependant, dependency_overrides_provider: Optional[Any] = None
-) -> Callable[[WebSocket], Coroutine[Any, Any, Any]]:
- async def app(websocket: WebSocket) -> None:
- solved_result = await solve_dependencies(
- request=websocket,
- dependant=dependant,
- dependency_overrides_provider=dependency_overrides_provider,
- )
- values, errors, _, _2, _3 = solved_result
- if errors:
- await websocket.close(code=WS_1008_POLICY_VIOLATION)
- raise WebSocketRequestValidationError(errors)
- assert dependant.call is not None, "dependant.call must be a function"
- await dependant.call(**values)
-
- return app
-
-
-class APIWebSocketRoute(routing.WebSocketRoute):
- def __init__(
- self,
- path: str,
- endpoint: Callable[..., Any],
- *,
- name: Optional[str] = None,
- dependency_overrides_provider: Optional[Any] = None,
- ) -> None:
- self.path = path
- self.endpoint = endpoint
- self.name = get_name(endpoint) if name is None else name
- self.path_regex, self.path_format, self.param_convertors = compile_path(path)
- self.dependant = get_dependant(path=self.path_format, call=self.endpoint)
- self.app = websocket_session(
- get_websocket_app(
- dependant=self.dependant,
- dependency_overrides_provider=dependency_overrides_provider,
- )
- )
-
- def matches(self, scope: Scope) -> Tuple[Match, Scope]:
- match, child_scope = super().matches(scope)
- if match != Match.NONE:
- child_scope["route"] = self
- return match, child_scope
-
-
-class APIRoute(routing.Route):
- def __init__(
- self,
- path: str,
- endpoint: Callable[..., Any],
- *,
- response_model: Any = Default(None),
- status_code: Optional[int] = None,
- tags: Optional[List[Union[str, Enum]]] = None,
- dependencies: Optional[Sequence[params.Depends]] = None,
- summary: Optional[str] = None,
- description: Optional[str] = None,
- response_description: str = "Successful Response",
- responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
- deprecated: Optional[bool] = None,
- name: Optional[str] = None,
- methods: Optional[Union[Set[str], List[str]]] = None,
- operation_id: Optional[str] = None,
- response_model_include: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_exclude: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_by_alias: bool = True,
- response_model_exclude_unset: bool = False,
- response_model_exclude_defaults: bool = False,
- response_model_exclude_none: bool = False,
- include_in_schema: bool = True,
- response_class: Union[Type[Response], DefaultPlaceholder] = Default(
- JSONResponse
- ),
- dependency_overrides_provider: Optional[Any] = None,
- callbacks: Optional[List[BaseRoute]] = None,
- openapi_extra: Optional[Dict[str, Any]] = None,
- generate_unique_id_function: Union[
- Callable[["APIRoute"], str], DefaultPlaceholder
- ] = Default(generate_unique_id),
- ) -> None:
- self.path = path
- self.endpoint = endpoint
- if isinstance(response_model, DefaultPlaceholder):
- return_annotation = get_typed_return_annotation(endpoint)
- if lenient_issubclass(return_annotation, Response):
- response_model = None
- else:
- response_model = return_annotation
- self.response_model = response_model
- self.summary = summary
- self.response_description = response_description
- self.deprecated = deprecated
- self.operation_id = operation_id
- self.response_model_include = response_model_include
- self.response_model_exclude = response_model_exclude
- self.response_model_by_alias = response_model_by_alias
- self.response_model_exclude_unset = response_model_exclude_unset
- self.response_model_exclude_defaults = response_model_exclude_defaults
- self.response_model_exclude_none = response_model_exclude_none
- self.include_in_schema = include_in_schema
- self.response_class = response_class
- self.dependency_overrides_provider = dependency_overrides_provider
- self.callbacks = callbacks
- self.openapi_extra = openapi_extra
- self.generate_unique_id_function = generate_unique_id_function
- self.tags = tags or []
- self.responses = responses or {}
- self.name = get_name(endpoint) if name is None else name
- self.path_regex, self.path_format, self.param_convertors = compile_path(path)
- if methods is None:
- methods = ["GET"]
- self.methods: Set[str] = {method.upper() for method in methods}
- if isinstance(generate_unique_id_function, DefaultPlaceholder):
- current_generate_unique_id: Callable[
- ["APIRoute"], str
- ] = generate_unique_id_function.value
- else:
- current_generate_unique_id = generate_unique_id_function
- self.unique_id = self.operation_id or current_generate_unique_id(self)
- # normalize enums e.g. http.HTTPStatus
- if isinstance(status_code, IntEnum):
- status_code = int(status_code)
- self.status_code = status_code
- if self.response_model:
- assert is_body_allowed_for_status_code(
- status_code
- ), f"Status code {status_code} must not have a response body"
- response_name = "Response_" + self.unique_id
- self.response_field = create_response_field(
- name=response_name, type_=self.response_model
- )
- # Create a clone of the field, so that a Pydantic submodel is not returned
- # as is just because it's an instance of a subclass of a more limited class
- # e.g. UserInDB (containing hashed_password) could be a subclass of User
- # that doesn't have the hashed_password. But because it's a subclass, it
- # would pass the validation and be returned as is.
- # By being a new field, no inheritance will be passed as is. A new model
- # will be always created.
- self.secure_cloned_response_field: Optional[
- ModelField
- ] = create_cloned_field(self.response_field)
- else:
- self.response_field = None # type: ignore
- self.secure_cloned_response_field = None
- if dependencies:
- self.dependencies = list(dependencies)
- else:
- self.dependencies = []
- self.description = description or inspect.cleandoc(self.endpoint.__doc__ or "")
- # if a "form feed" character (page break) is found in the description text,
- # truncate description text to the content preceding the first "form feed"
- self.description = self.description.split("\f")[0].strip()
- response_fields = {}
- for additional_status_code, response in self.responses.items():
- assert isinstance(response, dict), "An additional response must be a dict"
- model = response.get("model")
- if model:
- assert is_body_allowed_for_status_code(
- additional_status_code
- ), f"Status code {additional_status_code} must not have a response body"
- response_name = f"Response_{additional_status_code}_{self.unique_id}"
- response_field = create_response_field(name=response_name, type_=model)
- response_fields[additional_status_code] = response_field
- if response_fields:
- self.response_fields: Dict[Union[int, str], ModelField] = response_fields
- else:
- self.response_fields = {}
-
- assert callable(endpoint), "An endpoint must be a callable"
- self.dependant = get_dependant(path=self.path_format, call=self.endpoint)
- for depends in self.dependencies[::-1]:
- self.dependant.dependencies.insert(
- 0,
- get_parameterless_sub_dependant(depends=depends, path=self.path_format),
- )
- self.body_field = get_body_field(dependant=self.dependant, name=self.unique_id)
- self.app = request_response(self.get_route_handler())
-
- def get_route_handler(self) -> Callable[[Request], Coroutine[Any, Any, Response]]:
- return get_request_handler(
- dependant=self.dependant,
- body_field=self.body_field,
- status_code=self.status_code,
- response_class=self.response_class,
- response_field=self.secure_cloned_response_field,
- response_model_include=self.response_model_include,
- response_model_exclude=self.response_model_exclude,
- response_model_by_alias=self.response_model_by_alias,
- response_model_exclude_unset=self.response_model_exclude_unset,
- response_model_exclude_defaults=self.response_model_exclude_defaults,
- response_model_exclude_none=self.response_model_exclude_none,
- dependency_overrides_provider=self.dependency_overrides_provider,
- )
-
- def matches(self, scope: Scope) -> Tuple[Match, Scope]:
- match, child_scope = super().matches(scope)
- if match != Match.NONE:
- child_scope["route"] = self
- return match, child_scope
-
-
-class APIRouter(routing.Router):
- def __init__(
- self,
- *,
- prefix: str = "",
- tags: Optional[List[Union[str, Enum]]] = None,
- dependencies: Optional[Sequence[params.Depends]] = None,
- default_response_class: Type[Response] = Default(JSONResponse),
- responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
- callbacks: Optional[List[BaseRoute]] = None,
- routes: Optional[List[routing.BaseRoute]] = None,
- redirect_slashes: bool = True,
- default: Optional[ASGIApp] = None,
- dependency_overrides_provider: Optional[Any] = None,
- route_class: Type[APIRoute] = APIRoute,
- on_startup: Optional[Sequence[Callable[[], Any]]] = None,
- on_shutdown: Optional[Sequence[Callable[[], Any]]] = None,
- # the generic to Lifespan[AppType] is the type of the top level application
- # which the router cannot know statically, so we use typing.Any
- lifespan: Optional[Lifespan[Any]] = None,
- deprecated: Optional[bool] = None,
- include_in_schema: bool = True,
- generate_unique_id_function: Callable[[APIRoute], str] = Default(
- generate_unique_id
- ),
- ) -> None:
- super().__init__(
- routes=routes,
- redirect_slashes=redirect_slashes,
- default=default,
- on_startup=on_startup,
- on_shutdown=on_shutdown,
- lifespan=lifespan,
- )
- if prefix:
- assert prefix.startswith("/"), "A path prefix must start with '/'"
- assert not prefix.endswith(
- "/"
- ), "A path prefix must not end with '/', as the routes will start with '/'"
- self.prefix = prefix
- self.tags: List[Union[str, Enum]] = tags or []
- self.dependencies = list(dependencies or []) or []
- self.deprecated = deprecated
- self.include_in_schema = include_in_schema
- self.responses = responses or {}
- self.callbacks = callbacks or []
- self.dependency_overrides_provider = dependency_overrides_provider
- self.route_class = route_class
- self.default_response_class = default_response_class
- self.generate_unique_id_function = generate_unique_id_function
-
- def route(
- self,
- path: str,
- methods: Optional[List[str]] = None,
- name: Optional[str] = None,
- include_in_schema: bool = True,
- ) -> Callable[[DecoratedCallable], DecoratedCallable]:
- def decorator(func: DecoratedCallable) -> DecoratedCallable:
- self.add_route(
- path,
- func,
- methods=methods,
- name=name,
- include_in_schema=include_in_schema,
- )
- return func
-
- return decorator
-
- def add_api_route(
- self,
- path: str,
- endpoint: Callable[..., Any],
- *,
- response_model: Any = Default(None),
- status_code: Optional[int] = None,
- tags: Optional[List[Union[str, Enum]]] = None,
- dependencies: Optional[Sequence[params.Depends]] = None,
- summary: Optional[str] = None,
- description: Optional[str] = None,
- response_description: str = "Successful Response",
- responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
- deprecated: Optional[bool] = None,
- methods: Optional[Union[Set[str], List[str]]] = None,
- operation_id: Optional[str] = None,
- response_model_include: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_exclude: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_by_alias: bool = True,
- response_model_exclude_unset: bool = False,
- response_model_exclude_defaults: bool = False,
- response_model_exclude_none: bool = False,
- include_in_schema: bool = True,
- response_class: Union[Type[Response], DefaultPlaceholder] = Default(
- JSONResponse
- ),
- name: Optional[str] = None,
- route_class_override: Optional[Type[APIRoute]] = None,
- callbacks: Optional[List[BaseRoute]] = None,
- openapi_extra: Optional[Dict[str, Any]] = None,
- generate_unique_id_function: Union[
- Callable[[APIRoute], str], DefaultPlaceholder
- ] = Default(generate_unique_id),
- ) -> None:
- route_class = route_class_override or self.route_class
- responses = responses or {}
- combined_responses = {**self.responses, **responses}
- current_response_class = get_value_or_default(
- response_class, self.default_response_class
- )
- current_tags = self.tags.copy()
- if tags:
- current_tags.extend(tags)
- current_dependencies = self.dependencies.copy()
- if dependencies:
- current_dependencies.extend(dependencies)
- current_callbacks = self.callbacks.copy()
- if callbacks:
- current_callbacks.extend(callbacks)
- current_generate_unique_id = get_value_or_default(
- generate_unique_id_function, self.generate_unique_id_function
- )
- route = route_class(
- self.prefix + path,
- endpoint=endpoint,
- response_model=response_model,
- status_code=status_code,
- tags=current_tags,
- dependencies=current_dependencies,
- summary=summary,
- description=description,
- response_description=response_description,
- responses=combined_responses,
- deprecated=deprecated or self.deprecated,
- methods=methods,
- operation_id=operation_id,
- response_model_include=response_model_include,
- response_model_exclude=response_model_exclude,
- response_model_by_alias=response_model_by_alias,
- response_model_exclude_unset=response_model_exclude_unset,
- response_model_exclude_defaults=response_model_exclude_defaults,
- response_model_exclude_none=response_model_exclude_none,
- include_in_schema=include_in_schema and self.include_in_schema,
- response_class=current_response_class,
- name=name,
- dependency_overrides_provider=self.dependency_overrides_provider,
- callbacks=current_callbacks,
- openapi_extra=openapi_extra,
- generate_unique_id_function=current_generate_unique_id,
- )
- self.routes.append(route)
-
- def api_route(
- self,
- path: str,
- *,
- response_model: Any = Default(None),
- status_code: Optional[int] = None,
- tags: Optional[List[Union[str, Enum]]] = None,
- dependencies: Optional[Sequence[params.Depends]] = None,
- summary: Optional[str] = None,
- description: Optional[str] = None,
- response_description: str = "Successful Response",
- responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
- deprecated: Optional[bool] = None,
- methods: Optional[List[str]] = None,
- operation_id: Optional[str] = None,
- response_model_include: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_exclude: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_by_alias: bool = True,
- response_model_exclude_unset: bool = False,
- response_model_exclude_defaults: bool = False,
- response_model_exclude_none: bool = False,
- include_in_schema: bool = True,
- response_class: Type[Response] = Default(JSONResponse),
- name: Optional[str] = None,
- callbacks: Optional[List[BaseRoute]] = None,
- openapi_extra: Optional[Dict[str, Any]] = None,
- generate_unique_id_function: Callable[[APIRoute], str] = Default(
- generate_unique_id
- ),
- ) -> Callable[[DecoratedCallable], DecoratedCallable]:
- def decorator(func: DecoratedCallable) -> DecoratedCallable:
- self.add_api_route(
- path,
- func,
- response_model=response_model,
- status_code=status_code,
- tags=tags,
- dependencies=dependencies,
- summary=summary,
- description=description,
- response_description=response_description,
- responses=responses,
- deprecated=deprecated,
- methods=methods,
- operation_id=operation_id,
- response_model_include=response_model_include,
- response_model_exclude=response_model_exclude,
- response_model_by_alias=response_model_by_alias,
- response_model_exclude_unset=response_model_exclude_unset,
- response_model_exclude_defaults=response_model_exclude_defaults,
- response_model_exclude_none=response_model_exclude_none,
- include_in_schema=include_in_schema,
- response_class=response_class,
- name=name,
- callbacks=callbacks,
- openapi_extra=openapi_extra,
- generate_unique_id_function=generate_unique_id_function,
- )
- return func
-
- return decorator
-
- def add_api_websocket_route(
- self, path: str, endpoint: Callable[..., Any], name: Optional[str] = None
- ) -> None:
- route = APIWebSocketRoute(
- self.prefix + path,
- endpoint=endpoint,
- name=name,
- dependency_overrides_provider=self.dependency_overrides_provider,
- )
- self.routes.append(route)
-
- def websocket(
- self, path: str, name: Optional[str] = None
- ) -> Callable[[DecoratedCallable], DecoratedCallable]:
- def decorator(func: DecoratedCallable) -> DecoratedCallable:
- self.add_api_websocket_route(path, func, name=name)
- return func
-
- return decorator
-
- def websocket_route(
- self, path: str, name: Union[str, None] = None
- ) -> Callable[[DecoratedCallable], DecoratedCallable]:
- def decorator(func: DecoratedCallable) -> DecoratedCallable:
- self.add_websocket_route(path, func, name=name)
- return func
-
- return decorator
-
- def include_router(
- self,
- router: "APIRouter",
- *,
- prefix: str = "",
- tags: Optional[List[Union[str, Enum]]] = None,
- dependencies: Optional[Sequence[params.Depends]] = None,
- default_response_class: Type[Response] = Default(JSONResponse),
- responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
- callbacks: Optional[List[BaseRoute]] = None,
- deprecated: Optional[bool] = None,
- include_in_schema: bool = True,
- generate_unique_id_function: Callable[[APIRoute], str] = Default(
- generate_unique_id
- ),
- ) -> None:
- if prefix:
- assert prefix.startswith("/"), "A path prefix must start with '/'"
- assert not prefix.endswith(
- "/"
- ), "A path prefix must not end with '/', as the routes will start with '/'"
- else:
- for r in router.routes:
- path = getattr(r, "path") # noqa: B009
- name = getattr(r, "name", "unknown")
- if path is not None and not path:
- raise Exception(
- f"Prefix and path cannot be both empty (path operation: {name})"
- )
- if responses is None:
- responses = {}
- for route in router.routes:
- if isinstance(route, APIRoute):
- combined_responses = {**responses, **route.responses}
- use_response_class = get_value_or_default(
- route.response_class,
- router.default_response_class,
- default_response_class,
- self.default_response_class,
- )
- current_tags = []
- if tags:
- current_tags.extend(tags)
- if route.tags:
- current_tags.extend(route.tags)
- current_dependencies: List[params.Depends] = []
- if dependencies:
- current_dependencies.extend(dependencies)
- if route.dependencies:
- current_dependencies.extend(route.dependencies)
- current_callbacks = []
- if callbacks:
- current_callbacks.extend(callbacks)
- if route.callbacks:
- current_callbacks.extend(route.callbacks)
- current_generate_unique_id = get_value_or_default(
- route.generate_unique_id_function,
- router.generate_unique_id_function,
- generate_unique_id_function,
- self.generate_unique_id_function,
- )
- self.add_api_route(
- prefix + route.path,
- route.endpoint,
- response_model=route.response_model,
- status_code=route.status_code,
- tags=current_tags,
- dependencies=current_dependencies,
- summary=route.summary,
- description=route.description,
- response_description=route.response_description,
- responses=combined_responses,
- deprecated=route.deprecated or deprecated or self.deprecated,
- methods=route.methods,
- operation_id=route.operation_id,
- response_model_include=route.response_model_include,
- response_model_exclude=route.response_model_exclude,
- response_model_by_alias=route.response_model_by_alias,
- response_model_exclude_unset=route.response_model_exclude_unset,
- response_model_exclude_defaults=route.response_model_exclude_defaults,
- response_model_exclude_none=route.response_model_exclude_none,
- include_in_schema=route.include_in_schema
- and self.include_in_schema
- and include_in_schema,
- response_class=use_response_class,
- name=route.name,
- route_class_override=type(route),
- callbacks=current_callbacks,
- openapi_extra=route.openapi_extra,
- generate_unique_id_function=current_generate_unique_id,
- )
- elif isinstance(route, routing.Route):
- methods = list(route.methods or [])
- self.add_route(
- prefix + route.path,
- route.endpoint,
- methods=methods,
- include_in_schema=route.include_in_schema,
- name=route.name,
- )
- elif isinstance(route, APIWebSocketRoute):
- self.add_api_websocket_route(
- prefix + route.path, route.endpoint, name=route.name
- )
- elif isinstance(route, routing.WebSocketRoute):
- self.add_websocket_route(
- prefix + route.path, route.endpoint, name=route.name
- )
- for handler in router.on_startup:
- self.add_event_handler("startup", handler)
- for handler in router.on_shutdown:
- self.add_event_handler("shutdown", handler)
-
- def get(
- self,
- path: str,
- *,
- response_model: Any = Default(None),
- status_code: Optional[int] = None,
- tags: Optional[List[Union[str, Enum]]] = None,
- dependencies: Optional[Sequence[params.Depends]] = None,
- summary: Optional[str] = None,
- description: Optional[str] = None,
- response_description: str = "Successful Response",
- responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
- deprecated: Optional[bool] = None,
- operation_id: Optional[str] = None,
- response_model_include: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_exclude: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_by_alias: bool = True,
- response_model_exclude_unset: bool = False,
- response_model_exclude_defaults: bool = False,
- response_model_exclude_none: bool = False,
- include_in_schema: bool = True,
- response_class: Type[Response] = Default(JSONResponse),
- name: Optional[str] = None,
- callbacks: Optional[List[BaseRoute]] = None,
- openapi_extra: Optional[Dict[str, Any]] = None,
- generate_unique_id_function: Callable[[APIRoute], str] = Default(
- generate_unique_id
- ),
- ) -> Callable[[DecoratedCallable], DecoratedCallable]:
- return self.api_route(
- path=path,
- response_model=response_model,
- status_code=status_code,
- tags=tags,
- dependencies=dependencies,
- summary=summary,
- description=description,
- response_description=response_description,
- responses=responses,
- deprecated=deprecated,
- methods=["GET"],
- operation_id=operation_id,
- response_model_include=response_model_include,
- response_model_exclude=response_model_exclude,
- response_model_by_alias=response_model_by_alias,
- response_model_exclude_unset=response_model_exclude_unset,
- response_model_exclude_defaults=response_model_exclude_defaults,
- response_model_exclude_none=response_model_exclude_none,
- include_in_schema=include_in_schema,
- response_class=response_class,
- name=name,
- callbacks=callbacks,
- openapi_extra=openapi_extra,
- generate_unique_id_function=generate_unique_id_function,
- )
-
- def put(
- self,
- path: str,
- *,
- response_model: Any = Default(None),
- status_code: Optional[int] = None,
- tags: Optional[List[Union[str, Enum]]] = None,
- dependencies: Optional[Sequence[params.Depends]] = None,
- summary: Optional[str] = None,
- description: Optional[str] = None,
- response_description: str = "Successful Response",
- responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
- deprecated: Optional[bool] = None,
- operation_id: Optional[str] = None,
- response_model_include: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_exclude: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_by_alias: bool = True,
- response_model_exclude_unset: bool = False,
- response_model_exclude_defaults: bool = False,
- response_model_exclude_none: bool = False,
- include_in_schema: bool = True,
- response_class: Type[Response] = Default(JSONResponse),
- name: Optional[str] = None,
- callbacks: Optional[List[BaseRoute]] = None,
- openapi_extra: Optional[Dict[str, Any]] = None,
- generate_unique_id_function: Callable[[APIRoute], str] = Default(
- generate_unique_id
- ),
- ) -> Callable[[DecoratedCallable], DecoratedCallable]:
- return self.api_route(
- path=path,
- response_model=response_model,
- status_code=status_code,
- tags=tags,
- dependencies=dependencies,
- summary=summary,
- description=description,
- response_description=response_description,
- responses=responses,
- deprecated=deprecated,
- methods=["PUT"],
- operation_id=operation_id,
- response_model_include=response_model_include,
- response_model_exclude=response_model_exclude,
- response_model_by_alias=response_model_by_alias,
- response_model_exclude_unset=response_model_exclude_unset,
- response_model_exclude_defaults=response_model_exclude_defaults,
- response_model_exclude_none=response_model_exclude_none,
- include_in_schema=include_in_schema,
- response_class=response_class,
- name=name,
- callbacks=callbacks,
- openapi_extra=openapi_extra,
- generate_unique_id_function=generate_unique_id_function,
- )
-
- def post(
- self,
- path: str,
- *,
- response_model: Any = Default(None),
- status_code: Optional[int] = None,
- tags: Optional[List[Union[str, Enum]]] = None,
- dependencies: Optional[Sequence[params.Depends]] = None,
- summary: Optional[str] = None,
- description: Optional[str] = None,
- response_description: str = "Successful Response",
- responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
- deprecated: Optional[bool] = None,
- operation_id: Optional[str] = None,
- response_model_include: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_exclude: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_by_alias: bool = True,
- response_model_exclude_unset: bool = False,
- response_model_exclude_defaults: bool = False,
- response_model_exclude_none: bool = False,
- include_in_schema: bool = True,
- response_class: Type[Response] = Default(JSONResponse),
- name: Optional[str] = None,
- callbacks: Optional[List[BaseRoute]] = None,
- openapi_extra: Optional[Dict[str, Any]] = None,
- generate_unique_id_function: Callable[[APIRoute], str] = Default(
- generate_unique_id
- ),
- ) -> Callable[[DecoratedCallable], DecoratedCallable]:
- return self.api_route(
- path=path,
- response_model=response_model,
- status_code=status_code,
- tags=tags,
- dependencies=dependencies,
- summary=summary,
- description=description,
- response_description=response_description,
- responses=responses,
- deprecated=deprecated,
- methods=["POST"],
- operation_id=operation_id,
- response_model_include=response_model_include,
- response_model_exclude=response_model_exclude,
- response_model_by_alias=response_model_by_alias,
- response_model_exclude_unset=response_model_exclude_unset,
- response_model_exclude_defaults=response_model_exclude_defaults,
- response_model_exclude_none=response_model_exclude_none,
- include_in_schema=include_in_schema,
- response_class=response_class,
- name=name,
- callbacks=callbacks,
- openapi_extra=openapi_extra,
- generate_unique_id_function=generate_unique_id_function,
- )
-
- def delete(
- self,
- path: str,
- *,
- response_model: Any = Default(None),
- status_code: Optional[int] = None,
- tags: Optional[List[Union[str, Enum]]] = None,
- dependencies: Optional[Sequence[params.Depends]] = None,
- summary: Optional[str] = None,
- description: Optional[str] = None,
- response_description: str = "Successful Response",
- responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
- deprecated: Optional[bool] = None,
- operation_id: Optional[str] = None,
- response_model_include: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_exclude: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_by_alias: bool = True,
- response_model_exclude_unset: bool = False,
- response_model_exclude_defaults: bool = False,
- response_model_exclude_none: bool = False,
- include_in_schema: bool = True,
- response_class: Type[Response] = Default(JSONResponse),
- name: Optional[str] = None,
- callbacks: Optional[List[BaseRoute]] = None,
- openapi_extra: Optional[Dict[str, Any]] = None,
- generate_unique_id_function: Callable[[APIRoute], str] = Default(
- generate_unique_id
- ),
- ) -> Callable[[DecoratedCallable], DecoratedCallable]:
- return self.api_route(
- path=path,
- response_model=response_model,
- status_code=status_code,
- tags=tags,
- dependencies=dependencies,
- summary=summary,
- description=description,
- response_description=response_description,
- responses=responses,
- deprecated=deprecated,
- methods=["DELETE"],
- operation_id=operation_id,
- response_model_include=response_model_include,
- response_model_exclude=response_model_exclude,
- response_model_by_alias=response_model_by_alias,
- response_model_exclude_unset=response_model_exclude_unset,
- response_model_exclude_defaults=response_model_exclude_defaults,
- response_model_exclude_none=response_model_exclude_none,
- include_in_schema=include_in_schema,
- response_class=response_class,
- name=name,
- callbacks=callbacks,
- openapi_extra=openapi_extra,
- generate_unique_id_function=generate_unique_id_function,
- )
-
- def options(
- self,
- path: str,
- *,
- response_model: Any = Default(None),
- status_code: Optional[int] = None,
- tags: Optional[List[Union[str, Enum]]] = None,
- dependencies: Optional[Sequence[params.Depends]] = None,
- summary: Optional[str] = None,
- description: Optional[str] = None,
- response_description: str = "Successful Response",
- responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
- deprecated: Optional[bool] = None,
- operation_id: Optional[str] = None,
- response_model_include: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_exclude: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_by_alias: bool = True,
- response_model_exclude_unset: bool = False,
- response_model_exclude_defaults: bool = False,
- response_model_exclude_none: bool = False,
- include_in_schema: bool = True,
- response_class: Type[Response] = Default(JSONResponse),
- name: Optional[str] = None,
- callbacks: Optional[List[BaseRoute]] = None,
- openapi_extra: Optional[Dict[str, Any]] = None,
- generate_unique_id_function: Callable[[APIRoute], str] = Default(
- generate_unique_id
- ),
- ) -> Callable[[DecoratedCallable], DecoratedCallable]:
- return self.api_route(
- path=path,
- response_model=response_model,
- status_code=status_code,
- tags=tags,
- dependencies=dependencies,
- summary=summary,
- description=description,
- response_description=response_description,
- responses=responses,
- deprecated=deprecated,
- methods=["OPTIONS"],
- operation_id=operation_id,
- response_model_include=response_model_include,
- response_model_exclude=response_model_exclude,
- response_model_by_alias=response_model_by_alias,
- response_model_exclude_unset=response_model_exclude_unset,
- response_model_exclude_defaults=response_model_exclude_defaults,
- response_model_exclude_none=response_model_exclude_none,
- include_in_schema=include_in_schema,
- response_class=response_class,
- name=name,
- callbacks=callbacks,
- openapi_extra=openapi_extra,
- generate_unique_id_function=generate_unique_id_function,
- )
-
- def head(
- self,
- path: str,
- *,
- response_model: Any = Default(None),
- status_code: Optional[int] = None,
- tags: Optional[List[Union[str, Enum]]] = None,
- dependencies: Optional[Sequence[params.Depends]] = None,
- summary: Optional[str] = None,
- description: Optional[str] = None,
- response_description: str = "Successful Response",
- responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
- deprecated: Optional[bool] = None,
- operation_id: Optional[str] = None,
- response_model_include: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_exclude: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_by_alias: bool = True,
- response_model_exclude_unset: bool = False,
- response_model_exclude_defaults: bool = False,
- response_model_exclude_none: bool = False,
- include_in_schema: bool = True,
- response_class: Type[Response] = Default(JSONResponse),
- name: Optional[str] = None,
- callbacks: Optional[List[BaseRoute]] = None,
- openapi_extra: Optional[Dict[str, Any]] = None,
- generate_unique_id_function: Callable[[APIRoute], str] = Default(
- generate_unique_id
- ),
- ) -> Callable[[DecoratedCallable], DecoratedCallable]:
- return self.api_route(
- path=path,
- response_model=response_model,
- status_code=status_code,
- tags=tags,
- dependencies=dependencies,
- summary=summary,
- description=description,
- response_description=response_description,
- responses=responses,
- deprecated=deprecated,
- methods=["HEAD"],
- operation_id=operation_id,
- response_model_include=response_model_include,
- response_model_exclude=response_model_exclude,
- response_model_by_alias=response_model_by_alias,
- response_model_exclude_unset=response_model_exclude_unset,
- response_model_exclude_defaults=response_model_exclude_defaults,
- response_model_exclude_none=response_model_exclude_none,
- include_in_schema=include_in_schema,
- response_class=response_class,
- name=name,
- callbacks=callbacks,
- openapi_extra=openapi_extra,
- generate_unique_id_function=generate_unique_id_function,
- )
-
- def patch(
- self,
- path: str,
- *,
- response_model: Any = Default(None),
- status_code: Optional[int] = None,
- tags: Optional[List[Union[str, Enum]]] = None,
- dependencies: Optional[Sequence[params.Depends]] = None,
- summary: Optional[str] = None,
- description: Optional[str] = None,
- response_description: str = "Successful Response",
- responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
- deprecated: Optional[bool] = None,
- operation_id: Optional[str] = None,
- response_model_include: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_exclude: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_by_alias: bool = True,
- response_model_exclude_unset: bool = False,
- response_model_exclude_defaults: bool = False,
- response_model_exclude_none: bool = False,
- include_in_schema: bool = True,
- response_class: Type[Response] = Default(JSONResponse),
- name: Optional[str] = None,
- callbacks: Optional[List[BaseRoute]] = None,
- openapi_extra: Optional[Dict[str, Any]] = None,
- generate_unique_id_function: Callable[[APIRoute], str] = Default(
- generate_unique_id
- ),
- ) -> Callable[[DecoratedCallable], DecoratedCallable]:
- return self.api_route(
- path=path,
- response_model=response_model,
- status_code=status_code,
- tags=tags,
- dependencies=dependencies,
- summary=summary,
- description=description,
- response_description=response_description,
- responses=responses,
- deprecated=deprecated,
- methods=["PATCH"],
- operation_id=operation_id,
- response_model_include=response_model_include,
- response_model_exclude=response_model_exclude,
- response_model_by_alias=response_model_by_alias,
- response_model_exclude_unset=response_model_exclude_unset,
- response_model_exclude_defaults=response_model_exclude_defaults,
- response_model_exclude_none=response_model_exclude_none,
- include_in_schema=include_in_schema,
- response_class=response_class,
- name=name,
- callbacks=callbacks,
- openapi_extra=openapi_extra,
- generate_unique_id_function=generate_unique_id_function,
- )
-
- def trace(
- self,
- path: str,
- *,
- response_model: Any = Default(None),
- status_code: Optional[int] = None,
- tags: Optional[List[Union[str, Enum]]] = None,
- dependencies: Optional[Sequence[params.Depends]] = None,
- summary: Optional[str] = None,
- description: Optional[str] = None,
- response_description: str = "Successful Response",
- responses: Optional[Dict[Union[int, str], Dict[str, Any]]] = None,
- deprecated: Optional[bool] = None,
- operation_id: Optional[str] = None,
- response_model_include: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_exclude: Optional[Union[SetIntStr, DictIntStrAny]] = None,
- response_model_by_alias: bool = True,
- response_model_exclude_unset: bool = False,
- response_model_exclude_defaults: bool = False,
- response_model_exclude_none: bool = False,
- include_in_schema: bool = True,
- response_class: Type[Response] = Default(JSONResponse),
- name: Optional[str] = None,
- callbacks: Optional[List[BaseRoute]] = None,
- openapi_extra: Optional[Dict[str, Any]] = None,
- generate_unique_id_function: Callable[[APIRoute], str] = Default(
- generate_unique_id
- ),
- ) -> Callable[[DecoratedCallable], DecoratedCallable]:
- return self.api_route(
- path=path,
- response_model=response_model,
- status_code=status_code,
- tags=tags,
- dependencies=dependencies,
- summary=summary,
- description=description,
- response_description=response_description,
- responses=responses,
- deprecated=deprecated,
- methods=["TRACE"],
- operation_id=operation_id,
- response_model_include=response_model_include,
- response_model_exclude=response_model_exclude,
- response_model_by_alias=response_model_by_alias,
- response_model_exclude_unset=response_model_exclude_unset,
- response_model_exclude_defaults=response_model_exclude_defaults,
- response_model_exclude_none=response_model_exclude_none,
- include_in_schema=include_in_schema,
- response_class=response_class,
- name=name,
- callbacks=callbacks,
- openapi_extra=openapi_extra,
- generate_unique_id_function=generate_unique_id_function,
- )
-
- def on_event(
- self, event_type: str
- ) -> Callable[[DecoratedCallable], DecoratedCallable]:
- def decorator(func: DecoratedCallable) -> DecoratedCallable:
- self.add_event_handler(event_type, func)
- return func
-
- return decorator
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/feaLib/lookupDebugInfo.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/feaLib/lookupDebugInfo.py
deleted file mode 100644
index d4da7de0aed6b87dae6a1d4b417f1c6e099fe1e0..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/feaLib/lookupDebugInfo.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from typing import NamedTuple
-
-LOOKUP_DEBUG_INFO_KEY = "com.github.fonttools.feaLib"
-LOOKUP_DEBUG_ENV_VAR = "FONTTOOLS_LOOKUP_DEBUGGING"
-
-
-class LookupDebugInfo(NamedTuple):
- """Information about where a lookup came from, to be embedded in a font"""
-
- location: str
- name: str
- feature: list
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/varLib/instancer/__main__.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/varLib/instancer/__main__.py
deleted file mode 100644
index 64ffff2b9fdf58d8a557de7c1ae631b5c6fb4b6f..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/varLib/instancer/__main__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-import sys
-from fontTools.varLib.instancer import main
-
-if __name__ == "__main__":
- sys.exit(main())
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-ed471d18.css b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-ed471d18.css
deleted file mode 100644
index ea1ca6e8707c04b1e1a4517219c87d1bdab91f99..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-ed471d18.css
+++ /dev/null
@@ -1 +0,0 @@
-span.svelte-1vnmhm4{text-shadow:0 0 8px rgba(0,0,0,.5)}progress.svelte-1vnmhm4{margin-right:var(--size-3);border-radius:var(--radius-sm);width:var(--size-full);height:var(--size-2)}progress.svelte-1vnmhm4::-webkit-progress-bar{border-radius:2px;background-color:#fff3;overflow:hidden}progress.svelte-1vnmhm4::-webkit-progress-value{background-color:#ffffffe6}video.svelte-1vnmhm4{background-color:#000;width:var(--size-full);height:var(--size-full);object-fit:contain}.mirror.svelte-1vnmhm4{transform:scaleX(-1)}.controls.svelte-1vnmhm4{position:absolute;bottom:0;transition:.5s;margin:var(--size-2);border-radius:var(--radius-md);background:var(--color-grey-800);padding:var(--size-2) var(--size-1);width:calc(100% - .75rem);width:calc(100% - var(--size-2) * 2)}.inner.svelte-1vnmhm4{display:flex;justify-content:space-between;align-items:center;padding-right:var(--size-2);padding-left:var(--size-2);width:var(--size-full);height:var(--size-full)}.icon.svelte-1vnmhm4{display:flex;justify-content:center;cursor:pointer;width:var(--size-6);color:#fff}.time.svelte-1vnmhm4{flex-shrink:0;margin-right:var(--size-3);margin-left:var(--size-3);color:#fff;font-size:var(--text-sm);font-family:var(--font-mono)}.wrap.svelte-1vnmhm4{background-color:var(--background-fill-secondary)}.file-name.svelte-a6ruol{padding:var(--size-6);font-size:var(--text-xxl);word-break:break-all}.file-size.svelte-a6ruol{padding:var(--size-2);font-size:var(--text-xl)}.download.svelte-90pr3x{position:absolute;top:6px;right:6px}
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_template.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_template.py
deleted file mode 100644
index 915cdeb210bb6c8c59af58352eeccf24ef0fc0b6..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/matplotlib/backends/backend_template.py
+++ /dev/null
@@ -1,213 +0,0 @@
-"""
-A fully functional, do-nothing backend intended as a template for backend
-writers. It is fully functional in that you can select it as a backend e.g.
-with ::
-
- import matplotlib
- matplotlib.use("template")
-
-and your program will (should!) run without error, though no output is
-produced. This provides a starting point for backend writers; you can
-selectively implement drawing methods (`~.RendererTemplate.draw_path`,
-`~.RendererTemplate.draw_image`, etc.) and slowly see your figure come to life
-instead having to have a full-blown implementation before getting any results.
-
-Copy this file to a directory outside the Matplotlib source tree, somewhere
-where Python can import it (by adding the directory to your ``sys.path`` or by
-packaging it as a normal Python package); if the backend is importable as
-``import my.backend`` you can then select it using ::
-
- import matplotlib
- matplotlib.use("module://my.backend")
-
-If your backend implements support for saving figures (i.e. has a `print_xyz`
-method), you can register it as the default handler for a given file type::
-
- from matplotlib.backend_bases import register_backend
- register_backend('xyz', 'my_backend', 'XYZ File Format')
- ...
- plt.savefig("figure.xyz")
-"""
-
-from matplotlib import _api
-from matplotlib._pylab_helpers import Gcf
-from matplotlib.backend_bases import (
- FigureCanvasBase, FigureManagerBase, GraphicsContextBase, RendererBase)
-from matplotlib.figure import Figure
-
-
-class RendererTemplate(RendererBase):
- """
- The renderer handles drawing/rendering operations.
-
- This is a minimal do-nothing class that can be used to get started when
- writing a new backend. Refer to `.backend_bases.RendererBase` for
- documentation of the methods.
- """
-
- def __init__(self, dpi):
- super().__init__()
- self.dpi = dpi
-
- def draw_path(self, gc, path, transform, rgbFace=None):
- pass
-
- # draw_markers is optional, and we get more correct relative
- # timings by leaving it out. backend implementers concerned with
- # performance will probably want to implement it
-# def draw_markers(self, gc, marker_path, marker_trans, path, trans,
-# rgbFace=None):
-# pass
-
- # draw_path_collection is optional, and we get more correct
- # relative timings by leaving it out. backend implementers concerned with
- # performance will probably want to implement it
-# def draw_path_collection(self, gc, master_transform, paths,
-# all_transforms, offsets, offset_trans,
-# facecolors, edgecolors, linewidths, linestyles,
-# antialiaseds):
-# pass
-
- # draw_quad_mesh is optional, and we get more correct
- # relative timings by leaving it out. backend implementers concerned with
- # performance will probably want to implement it
-# def draw_quad_mesh(self, gc, master_transform, meshWidth, meshHeight,
-# coordinates, offsets, offsetTrans, facecolors,
-# antialiased, edgecolors):
-# pass
-
- def draw_image(self, gc, x, y, im):
- pass
-
- def draw_text(self, gc, x, y, s, prop, angle, ismath=False, mtext=None):
- pass
-
- def flipy(self):
- # docstring inherited
- return True
-
- def get_canvas_width_height(self):
- # docstring inherited
- return 100, 100
-
- def get_text_width_height_descent(self, s, prop, ismath):
- return 1, 1, 1
-
- def new_gc(self):
- # docstring inherited
- return GraphicsContextTemplate()
-
- def points_to_pixels(self, points):
- # if backend doesn't have dpi, e.g., postscript or svg
- return points
- # elif backend assumes a value for pixels_per_inch
- # return points/72.0 * self.dpi.get() * pixels_per_inch/72.0
- # else
- # return points/72.0 * self.dpi.get()
-
-
-class GraphicsContextTemplate(GraphicsContextBase):
- """
- The graphics context provides the color, line styles, etc. See the cairo
- and postscript backends for examples of mapping the graphics context
- attributes (cap styles, join styles, line widths, colors) to a particular
- backend. In cairo this is done by wrapping a cairo.Context object and
- forwarding the appropriate calls to it using a dictionary mapping styles
- to gdk constants. In Postscript, all the work is done by the renderer,
- mapping line styles to postscript calls.
-
- If it's more appropriate to do the mapping at the renderer level (as in
- the postscript backend), you don't need to override any of the GC methods.
- If it's more appropriate to wrap an instance (as in the cairo backend) and
- do the mapping here, you'll need to override several of the setter
- methods.
-
- The base GraphicsContext stores colors as an RGB tuple on the unit
- interval, e.g., (0.5, 0.0, 1.0). You may need to map this to colors
- appropriate for your backend.
- """
-
-
-########################################################################
-#
-# The following functions and classes are for pyplot and implement
-# window/figure managers, etc.
-#
-########################################################################
-
-
-class FigureManagerTemplate(FigureManagerBase):
- """
- Helper class for pyplot mode, wraps everything up into a neat bundle.
-
- For non-interactive backends, the base class is sufficient. For
- interactive backends, see the documentation of the `.FigureManagerBase`
- class for the list of methods that can/should be overridden.
- """
-
-
-class FigureCanvasTemplate(FigureCanvasBase):
- """
- The canvas the figure renders into. Calls the draw and print fig
- methods, creates the renderers, etc.
-
- Note: GUI templates will want to connect events for button presses,
- mouse movements and key presses to functions that call the base
- class methods button_press_event, button_release_event,
- motion_notify_event, key_press_event, and key_release_event. See the
- implementations of the interactive backends for examples.
-
- Attributes
- ----------
- figure : `matplotlib.figure.Figure`
- A high-level Figure instance
- """
-
- # The instantiated manager class. For further customization,
- # ``FigureManager.create_with_canvas`` can also be overridden; see the
- # wx-based backends for an example.
- manager_class = FigureManagerTemplate
-
- def draw(self):
- """
- Draw the figure using the renderer.
-
- It is important that this method actually walk the artist tree
- even if not output is produced because this will trigger
- deferred work (like computing limits auto-limits and tick
- values) that users may want access to before saving to disk.
- """
- renderer = RendererTemplate(self.figure.dpi)
- self.figure.draw(renderer)
-
- # You should provide a print_xxx function for every file format
- # you can write.
-
- # If the file type is not in the base set of filetypes,
- # you should add it to the class-scope filetypes dictionary as follows:
- filetypes = {**FigureCanvasBase.filetypes, 'foo': 'My magic Foo format'}
-
- def print_foo(self, filename, **kwargs):
- """
- Write out format foo.
-
- This method is normally called via `.Figure.savefig` and
- `.FigureCanvasBase.print_figure`, which take care of setting the figure
- facecolor, edgecolor, and dpi to the desired output values, and will
- restore them to the original values. Therefore, `print_foo` does not
- need to handle these settings.
- """
- self.draw()
-
- def get_default_filetype(self):
- return 'foo'
-
-
-########################################################################
-#
-# Now just provide the standard names that backend.__init__ is expecting
-#
-########################################################################
-
-FigureCanvas = FigureCanvasTemplate
-FigureManager = FigureManagerTemplate
diff --git a/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_image.py b/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_image.py
deleted file mode 100644
index 0e513a8bc1594c9ce2ba47ce3fe3b497269b7f16..0000000000000000000000000000000000000000
--- a/spaces/lambdalabs/LambdaSuperRes/KAIR/utils/utils_image.py
+++ /dev/null
@@ -1,1016 +0,0 @@
-import os
-import math
-import random
-import numpy as np
-import torch
-import cv2
-from torchvision.utils import make_grid
-from datetime import datetime
-# import torchvision.transforms as transforms
-import matplotlib.pyplot as plt
-from mpl_toolkits.mplot3d import Axes3D
-os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
-
-
-'''
-# --------------------------------------------
-# Kai Zhang (github: https://github.com/cszn)
-# 03/Mar/2019
-# --------------------------------------------
-# https://github.com/twhui/SRGAN-pyTorch
-# https://github.com/xinntao/BasicSR
-# --------------------------------------------
-'''
-
-
-IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
-
-
-def is_image_file(filename):
- return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
-
-
-def get_timestamp():
- return datetime.now().strftime('%y%m%d-%H%M%S')
-
-
-def imshow(x, title=None, cbar=False, figsize=None):
- plt.figure(figsize=figsize)
- plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
- if title:
- plt.title(title)
- if cbar:
- plt.colorbar()
- plt.show()
-
-
-def surf(Z, cmap='rainbow', figsize=None):
- plt.figure(figsize=figsize)
- ax3 = plt.axes(projection='3d')
-
- w, h = Z.shape[:2]
- xx = np.arange(0,w,1)
- yy = np.arange(0,h,1)
- X, Y = np.meshgrid(xx, yy)
- ax3.plot_surface(X,Y,Z,cmap=cmap)
- #ax3.contour(X,Y,Z, zdim='z',offset=-2,cmap=cmap)
- plt.show()
-
-
-'''
-# --------------------------------------------
-# get image pathes
-# --------------------------------------------
-'''
-
-
-def get_image_paths(dataroot):
- paths = None # return None if dataroot is None
- if isinstance(dataroot, str):
- paths = sorted(_get_paths_from_images(dataroot))
- elif isinstance(dataroot, list):
- paths = []
- for i in dataroot:
- paths += sorted(_get_paths_from_images(i))
- return paths
-
-
-def _get_paths_from_images(path):
- assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
- images = []
- for dirpath, _, fnames in sorted(os.walk(path)):
- for fname in sorted(fnames):
- if is_image_file(fname):
- img_path = os.path.join(dirpath, fname)
- images.append(img_path)
- assert images, '{:s} has no valid image file'.format(path)
- return images
-
-
-'''
-# --------------------------------------------
-# split large images into small images
-# --------------------------------------------
-'''
-
-
-def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
- w, h = img.shape[:2]
- patches = []
- if w > p_max and h > p_max:
- w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
- h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
- w1.append(w-p_size)
- h1.append(h-p_size)
- # print(w1)
- # print(h1)
- for i in w1:
- for j in h1:
- patches.append(img[i:i+p_size, j:j+p_size,:])
- else:
- patches.append(img)
-
- return patches
-
-
-def imssave(imgs, img_path):
- """
- imgs: list, N images of size WxHxC
- """
- img_name, ext = os.path.splitext(os.path.basename(img_path))
- for i, img in enumerate(imgs):
- if img.ndim == 3:
- img = img[:, :, [2, 1, 0]]
- new_path = os.path.join(os.path.dirname(img_path), img_name+str('_{:04d}'.format(i))+'.png')
- cv2.imwrite(new_path, img)
-
-
-def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=512, p_overlap=96, p_max=800):
- """
- split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
- and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
- will be splitted.
-
- Args:
- original_dataroot:
- taget_dataroot:
- p_size: size of small images
- p_overlap: patch size in training is a good choice
- p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
- """
- paths = get_image_paths(original_dataroot)
- for img_path in paths:
- # img_name, ext = os.path.splitext(os.path.basename(img_path))
- img = imread_uint(img_path, n_channels=n_channels)
- patches = patches_from_image(img, p_size, p_overlap, p_max)
- imssave(patches, os.path.join(taget_dataroot, os.path.basename(img_path)))
- #if original_dataroot == taget_dataroot:
- #del img_path
-
-'''
-# --------------------------------------------
-# makedir
-# --------------------------------------------
-'''
-
-
-def mkdir(path):
- if not os.path.exists(path):
- os.makedirs(path)
-
-
-def mkdirs(paths):
- if isinstance(paths, str):
- mkdir(paths)
- else:
- for path in paths:
- mkdir(path)
-
-
-def mkdir_and_rename(path):
- if os.path.exists(path):
- new_name = path + '_archived_' + get_timestamp()
- print('Path already exists. Rename it to [{:s}]'.format(new_name))
- os.rename(path, new_name)
- os.makedirs(path)
-
-
-'''
-# --------------------------------------------
-# read image from path
-# opencv is fast, but read BGR numpy image
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# get uint8 image of size HxWxn_channles (RGB)
-# --------------------------------------------
-def imread_uint(path, n_channels=3):
- # input: path
- # output: HxWx3(RGB or GGG), or HxWx1 (G)
- if n_channels == 1:
- img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE
- img = np.expand_dims(img, axis=2) # HxWx1
- elif n_channels == 3:
- img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G
- if img.ndim == 2:
- img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG
- else:
- img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB
- return img
-
-
-# --------------------------------------------
-# matlab's imwrite
-# --------------------------------------------
-def imsave(img, img_path):
- img = np.squeeze(img)
- if img.ndim == 3:
- img = img[:, :, [2, 1, 0]]
- cv2.imwrite(img_path, img)
-
-def imwrite(img, img_path):
- img = np.squeeze(img)
- if img.ndim == 3:
- img = img[:, :, [2, 1, 0]]
- cv2.imwrite(img_path, img)
-
-
-
-# --------------------------------------------
-# get single image of size HxWxn_channles (BGR)
-# --------------------------------------------
-def read_img(path):
- # read image by cv2
- # return: Numpy float32, HWC, BGR, [0,1]
- img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE
- img = img.astype(np.float32) / 255.
- if img.ndim == 2:
- img = np.expand_dims(img, axis=2)
- # some images have 4 channels
- if img.shape[2] > 3:
- img = img[:, :, :3]
- return img
-
-
-'''
-# --------------------------------------------
-# image format conversion
-# --------------------------------------------
-# numpy(single) <---> numpy(uint)
-# numpy(single) <---> tensor
-# numpy(uint) <---> tensor
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# numpy(single) [0, 1] <---> numpy(uint)
-# --------------------------------------------
-
-
-def uint2single(img):
-
- return np.float32(img/255.)
-
-
-def single2uint(img):
-
- return np.uint8((img.clip(0, 1)*255.).round())
-
-
-def uint162single(img):
-
- return np.float32(img/65535.)
-
-
-def single2uint16(img):
-
- return np.uint16((img.clip(0, 1)*65535.).round())
-
-
-# --------------------------------------------
-# numpy(uint) (HxWxC or HxW) <---> tensor
-# --------------------------------------------
-
-
-# convert uint to 4-dimensional torch tensor
-def uint2tensor4(img):
- if img.ndim == 2:
- img = np.expand_dims(img, axis=2)
- return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
-
-
-# convert uint to 3-dimensional torch tensor
-def uint2tensor3(img):
- if img.ndim == 2:
- img = np.expand_dims(img, axis=2)
- return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
-
-
-# convert 2/3/4-dimensional torch tensor to uint
-def tensor2uint(img):
- img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
- if img.ndim == 3:
- img = np.transpose(img, (1, 2, 0))
- return np.uint8((img*255.0).round())
-
-
-# --------------------------------------------
-# numpy(single) (HxWxC) <---> tensor
-# --------------------------------------------
-
-
-# convert single (HxWxC) to 3-dimensional torch tensor
-def single2tensor3(img):
- return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
-
-
-# convert single (HxWxC) to 4-dimensional torch tensor
-def single2tensor4(img):
- return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
-
-
-# convert torch tensor to single
-def tensor2single(img):
- img = img.data.squeeze().float().cpu().numpy()
- if img.ndim == 3:
- img = np.transpose(img, (1, 2, 0))
-
- return img
-
-# convert torch tensor to single
-def tensor2single3(img):
- img = img.data.squeeze().float().cpu().numpy()
- if img.ndim == 3:
- img = np.transpose(img, (1, 2, 0))
- elif img.ndim == 2:
- img = np.expand_dims(img, axis=2)
- return img
-
-
-def single2tensor5(img):
- return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
-
-
-def single32tensor5(img):
- return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
-
-
-def single42tensor4(img):
- return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
-
-
-# from skimage.io import imread, imsave
-def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
- '''
- Converts a torch Tensor into an image Numpy array of BGR channel order
- Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
- Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
- '''
- tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp
- tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1]
- n_dim = tensor.dim()
- if n_dim == 4:
- n_img = len(tensor)
- img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
- img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
- elif n_dim == 3:
- img_np = tensor.numpy()
- img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
- elif n_dim == 2:
- img_np = tensor.numpy()
- else:
- raise TypeError(
- 'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
- if out_type == np.uint8:
- img_np = (img_np * 255.0).round()
- # Important. Unlike matlab, numpy.uint8() WILL NOT round by default.
- return img_np.astype(out_type)
-
-
-'''
-# --------------------------------------------
-# Augmentation, flipe and/or rotate
-# --------------------------------------------
-# The following two are enough.
-# (1) augmet_img: numpy image of WxHxC or WxH
-# (2) augment_img_tensor4: tensor image 1xCxWxH
-# --------------------------------------------
-'''
-
-
-def augment_img(img, mode=0):
- '''Kai Zhang (github: https://github.com/cszn)
- '''
- if mode == 0:
- return img
- elif mode == 1:
- return np.flipud(np.rot90(img))
- elif mode == 2:
- return np.flipud(img)
- elif mode == 3:
- return np.rot90(img, k=3)
- elif mode == 4:
- return np.flipud(np.rot90(img, k=2))
- elif mode == 5:
- return np.rot90(img)
- elif mode == 6:
- return np.rot90(img, k=2)
- elif mode == 7:
- return np.flipud(np.rot90(img, k=3))
-
-
-def augment_img_tensor4(img, mode=0):
- '''Kai Zhang (github: https://github.com/cszn)
- '''
- if mode == 0:
- return img
- elif mode == 1:
- return img.rot90(1, [2, 3]).flip([2])
- elif mode == 2:
- return img.flip([2])
- elif mode == 3:
- return img.rot90(3, [2, 3])
- elif mode == 4:
- return img.rot90(2, [2, 3]).flip([2])
- elif mode == 5:
- return img.rot90(1, [2, 3])
- elif mode == 6:
- return img.rot90(2, [2, 3])
- elif mode == 7:
- return img.rot90(3, [2, 3]).flip([2])
-
-
-def augment_img_tensor(img, mode=0):
- '''Kai Zhang (github: https://github.com/cszn)
- '''
- img_size = img.size()
- img_np = img.data.cpu().numpy()
- if len(img_size) == 3:
- img_np = np.transpose(img_np, (1, 2, 0))
- elif len(img_size) == 4:
- img_np = np.transpose(img_np, (2, 3, 1, 0))
- img_np = augment_img(img_np, mode=mode)
- img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
- if len(img_size) == 3:
- img_tensor = img_tensor.permute(2, 0, 1)
- elif len(img_size) == 4:
- img_tensor = img_tensor.permute(3, 2, 0, 1)
-
- return img_tensor.type_as(img)
-
-
-def augment_img_np3(img, mode=0):
- if mode == 0:
- return img
- elif mode == 1:
- return img.transpose(1, 0, 2)
- elif mode == 2:
- return img[::-1, :, :]
- elif mode == 3:
- img = img[::-1, :, :]
- img = img.transpose(1, 0, 2)
- return img
- elif mode == 4:
- return img[:, ::-1, :]
- elif mode == 5:
- img = img[:, ::-1, :]
- img = img.transpose(1, 0, 2)
- return img
- elif mode == 6:
- img = img[:, ::-1, :]
- img = img[::-1, :, :]
- return img
- elif mode == 7:
- img = img[:, ::-1, :]
- img = img[::-1, :, :]
- img = img.transpose(1, 0, 2)
- return img
-
-
-def augment_imgs(img_list, hflip=True, rot=True):
- # horizontal flip OR rotate
- hflip = hflip and random.random() < 0.5
- vflip = rot and random.random() < 0.5
- rot90 = rot and random.random() < 0.5
-
- def _augment(img):
- if hflip:
- img = img[:, ::-1, :]
- if vflip:
- img = img[::-1, :, :]
- if rot90:
- img = img.transpose(1, 0, 2)
- return img
-
- return [_augment(img) for img in img_list]
-
-
-'''
-# --------------------------------------------
-# modcrop and shave
-# --------------------------------------------
-'''
-
-
-def modcrop(img_in, scale):
- # img_in: Numpy, HWC or HW
- img = np.copy(img_in)
- if img.ndim == 2:
- H, W = img.shape
- H_r, W_r = H % scale, W % scale
- img = img[:H - H_r, :W - W_r]
- elif img.ndim == 3:
- H, W, C = img.shape
- H_r, W_r = H % scale, W % scale
- img = img[:H - H_r, :W - W_r, :]
- else:
- raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
- return img
-
-
-def shave(img_in, border=0):
- # img_in: Numpy, HWC or HW
- img = np.copy(img_in)
- h, w = img.shape[:2]
- img = img[border:h-border, border:w-border]
- return img
-
-
-'''
-# --------------------------------------------
-# image processing process on numpy image
-# channel_convert(in_c, tar_type, img_list):
-# rgb2ycbcr(img, only_y=True):
-# bgr2ycbcr(img, only_y=True):
-# ycbcr2rgb(img):
-# --------------------------------------------
-'''
-
-
-def rgb2ycbcr(img, only_y=True):
- '''same as matlab rgb2ycbcr
- only_y: only return Y channel
- Input:
- uint8, [0, 255]
- float, [0, 1]
- '''
- in_img_type = img.dtype
- img.astype(np.float32)
- if in_img_type != np.uint8:
- img *= 255.
- # convert
- if only_y:
- rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
- else:
- rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
- [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
- if in_img_type == np.uint8:
- rlt = rlt.round()
- else:
- rlt /= 255.
- return rlt.astype(in_img_type)
-
-
-def ycbcr2rgb(img):
- '''same as matlab ycbcr2rgb
- Input:
- uint8, [0, 255]
- float, [0, 1]
- '''
- in_img_type = img.dtype
- img.astype(np.float32)
- if in_img_type != np.uint8:
- img *= 255.
- # convert
- rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
- [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
- rlt = np.clip(rlt, 0, 255)
- if in_img_type == np.uint8:
- rlt = rlt.round()
- else:
- rlt /= 255.
- return rlt.astype(in_img_type)
-
-
-def bgr2ycbcr(img, only_y=True):
- '''bgr version of rgb2ycbcr
- only_y: only return Y channel
- Input:
- uint8, [0, 255]
- float, [0, 1]
- '''
- in_img_type = img.dtype
- img.astype(np.float32)
- if in_img_type != np.uint8:
- img *= 255.
- # convert
- if only_y:
- rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
- else:
- rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
- [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
- if in_img_type == np.uint8:
- rlt = rlt.round()
- else:
- rlt /= 255.
- return rlt.astype(in_img_type)
-
-
-def channel_convert(in_c, tar_type, img_list):
- # conversion among BGR, gray and y
- if in_c == 3 and tar_type == 'gray': # BGR to gray
- gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
- return [np.expand_dims(img, axis=2) for img in gray_list]
- elif in_c == 3 and tar_type == 'y': # BGR to y
- y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
- return [np.expand_dims(img, axis=2) for img in y_list]
- elif in_c == 1 and tar_type == 'RGB': # gray/y to BGR
- return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
- else:
- return img_list
-
-
-'''
-# --------------------------------------------
-# metric, PSNR, SSIM and PSNRB
-# --------------------------------------------
-'''
-
-
-# --------------------------------------------
-# PSNR
-# --------------------------------------------
-def calculate_psnr(img1, img2, border=0):
- # img1 and img2 have range [0, 255]
- #img1 = img1.squeeze()
- #img2 = img2.squeeze()
- if not img1.shape == img2.shape:
- raise ValueError('Input images must have the same dimensions.')
- h, w = img1.shape[:2]
- img1 = img1[border:h-border, border:w-border]
- img2 = img2[border:h-border, border:w-border]
-
- img1 = img1.astype(np.float64)
- img2 = img2.astype(np.float64)
- mse = np.mean((img1 - img2)**2)
- if mse == 0:
- return float('inf')
- return 20 * math.log10(255.0 / math.sqrt(mse))
-
-
-# --------------------------------------------
-# SSIM
-# --------------------------------------------
-def calculate_ssim(img1, img2, border=0):
- '''calculate SSIM
- the same outputs as MATLAB's
- img1, img2: [0, 255]
- '''
- #img1 = img1.squeeze()
- #img2 = img2.squeeze()
- if not img1.shape == img2.shape:
- raise ValueError('Input images must have the same dimensions.')
- h, w = img1.shape[:2]
- img1 = img1[border:h-border, border:w-border]
- img2 = img2[border:h-border, border:w-border]
-
- if img1.ndim == 2:
- return ssim(img1, img2)
- elif img1.ndim == 3:
- if img1.shape[2] == 3:
- ssims = []
- for i in range(3):
- ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
- return np.array(ssims).mean()
- elif img1.shape[2] == 1:
- return ssim(np.squeeze(img1), np.squeeze(img2))
- else:
- raise ValueError('Wrong input image dimensions.')
-
-
-def ssim(img1, img2):
- C1 = (0.01 * 255)**2
- C2 = (0.03 * 255)**2
-
- img1 = img1.astype(np.float64)
- img2 = img2.astype(np.float64)
- kernel = cv2.getGaussianKernel(11, 1.5)
- window = np.outer(kernel, kernel.transpose())
-
- mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid
- mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
- mu1_sq = mu1**2
- mu2_sq = mu2**2
- mu1_mu2 = mu1 * mu2
- sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
- sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
- sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
-
- ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
- (sigma1_sq + sigma2_sq + C2))
- return ssim_map.mean()
-
-
-def _blocking_effect_factor(im):
- block_size = 8
-
- block_horizontal_positions = torch.arange(7, im.shape[3] - 1, 8)
- block_vertical_positions = torch.arange(7, im.shape[2] - 1, 8)
-
- horizontal_block_difference = (
- (im[:, :, :, block_horizontal_positions] - im[:, :, :, block_horizontal_positions + 1]) ** 2).sum(
- 3).sum(2).sum(1)
- vertical_block_difference = (
- (im[:, :, block_vertical_positions, :] - im[:, :, block_vertical_positions + 1, :]) ** 2).sum(3).sum(
- 2).sum(1)
-
- nonblock_horizontal_positions = np.setdiff1d(torch.arange(0, im.shape[3] - 1), block_horizontal_positions)
- nonblock_vertical_positions = np.setdiff1d(torch.arange(0, im.shape[2] - 1), block_vertical_positions)
-
- horizontal_nonblock_difference = (
- (im[:, :, :, nonblock_horizontal_positions] - im[:, :, :, nonblock_horizontal_positions + 1]) ** 2).sum(
- 3).sum(2).sum(1)
- vertical_nonblock_difference = (
- (im[:, :, nonblock_vertical_positions, :] - im[:, :, nonblock_vertical_positions + 1, :]) ** 2).sum(
- 3).sum(2).sum(1)
-
- n_boundary_horiz = im.shape[2] * (im.shape[3] // block_size - 1)
- n_boundary_vert = im.shape[3] * (im.shape[2] // block_size - 1)
- boundary_difference = (horizontal_block_difference + vertical_block_difference) / (
- n_boundary_horiz + n_boundary_vert)
-
- n_nonboundary_horiz = im.shape[2] * (im.shape[3] - 1) - n_boundary_horiz
- n_nonboundary_vert = im.shape[3] * (im.shape[2] - 1) - n_boundary_vert
- nonboundary_difference = (horizontal_nonblock_difference + vertical_nonblock_difference) / (
- n_nonboundary_horiz + n_nonboundary_vert)
-
- scaler = np.log2(block_size) / np.log2(min([im.shape[2], im.shape[3]]))
- bef = scaler * (boundary_difference - nonboundary_difference)
-
- bef[boundary_difference <= nonboundary_difference] = 0
- return bef
-
-
-def calculate_psnrb(img1, img2, border=0):
- """Calculate PSNR-B (Peak Signal-to-Noise Ratio).
- Ref: Quality assessment of deblocked images, for JPEG image deblocking evaluation
- # https://gitlab.com/Queuecumber/quantization-guided-ac/-/blob/master/metrics/psnrb.py
- Args:
- img1 (ndarray): Images with range [0, 255].
- img2 (ndarray): Images with range [0, 255].
- border (int): Cropped pixels in each edge of an image. These
- pixels are not involved in the PSNR calculation.
- test_y_channel (bool): Test on Y channel of YCbCr. Default: False.
- Returns:
- float: psnr result.
- """
-
- if not img1.shape == img2.shape:
- raise ValueError('Input images must have the same dimensions.')
-
- if img1.ndim == 2:
- img1, img2 = np.expand_dims(img1, 2), np.expand_dims(img2, 2)
-
- h, w = img1.shape[:2]
- img1 = img1[border:h-border, border:w-border]
- img2 = img2[border:h-border, border:w-border]
-
- img1 = img1.astype(np.float64)
- img2 = img2.astype(np.float64)
-
- # follow https://gitlab.com/Queuecumber/quantization-guided-ac/-/blob/master/metrics/psnrb.py
- img1 = torch.from_numpy(img1).permute(2, 0, 1).unsqueeze(0) / 255.
- img2 = torch.from_numpy(img2).permute(2, 0, 1).unsqueeze(0) / 255.
-
- total = 0
- for c in range(img1.shape[1]):
- mse = torch.nn.functional.mse_loss(img1[:, c:c + 1, :, :], img2[:, c:c + 1, :, :], reduction='none')
- bef = _blocking_effect_factor(img1[:, c:c + 1, :, :])
-
- mse = mse.view(mse.shape[0], -1).mean(1)
- total += 10 * torch.log10(1 / (mse + bef))
-
- return float(total) / img1.shape[1]
-
-'''
-# --------------------------------------------
-# matlab's bicubic imresize (numpy and torch) [0, 1]
-# --------------------------------------------
-'''
-
-
-# matlab 'imresize' function, now only support 'bicubic'
-def cubic(x):
- absx = torch.abs(x)
- absx2 = absx**2
- absx3 = absx**3
- return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
- (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
-
-
-def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
- if (scale < 1) and (antialiasing):
- # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
- kernel_width = kernel_width / scale
-
- # Output-space coordinates
- x = torch.linspace(1, out_length, out_length)
-
- # Input-space coordinates. Calculate the inverse mapping such that 0.5
- # in output space maps to 0.5 in input space, and 0.5+scale in output
- # space maps to 1.5 in input space.
- u = x / scale + 0.5 * (1 - 1 / scale)
-
- # What is the left-most pixel that can be involved in the computation?
- left = torch.floor(u - kernel_width / 2)
-
- # What is the maximum number of pixels that can be involved in the
- # computation? Note: it's OK to use an extra pixel here; if the
- # corresponding weights are all zero, it will be eliminated at the end
- # of this function.
- P = math.ceil(kernel_width) + 2
-
- # The indices of the input pixels involved in computing the k-th output
- # pixel are in row k of the indices matrix.
- indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
- 1, P).expand(out_length, P)
-
- # The weights used to compute the k-th output pixel are in row k of the
- # weights matrix.
- distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
- # apply cubic kernel
- if (scale < 1) and (antialiasing):
- weights = scale * cubic(distance_to_center * scale)
- else:
- weights = cubic(distance_to_center)
- # Normalize the weights matrix so that each row sums to 1.
- weights_sum = torch.sum(weights, 1).view(out_length, 1)
- weights = weights / weights_sum.expand(out_length, P)
-
- # If a column in weights is all zero, get rid of it. only consider the first and last column.
- weights_zero_tmp = torch.sum((weights == 0), 0)
- if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
- indices = indices.narrow(1, 1, P - 2)
- weights = weights.narrow(1, 1, P - 2)
- if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
- indices = indices.narrow(1, 0, P - 2)
- weights = weights.narrow(1, 0, P - 2)
- weights = weights.contiguous()
- indices = indices.contiguous()
- sym_len_s = -indices.min() + 1
- sym_len_e = indices.max() - in_length
- indices = indices + sym_len_s - 1
- return weights, indices, int(sym_len_s), int(sym_len_e)
-
-
-# --------------------------------------------
-# imresize for tensor image [0, 1]
-# --------------------------------------------
-def imresize(img, scale, antialiasing=True):
- # Now the scale should be the same for H and W
- # input: img: pytorch tensor, CHW or HW [0,1]
- # output: CHW or HW [0,1] w/o round
- need_squeeze = True if img.dim() == 2 else False
- if need_squeeze:
- img.unsqueeze_(0)
- in_C, in_H, in_W = img.size()
- out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
- kernel_width = 4
- kernel = 'cubic'
-
- # Return the desired dimension order for performing the resize. The
- # strategy is to perform the resize first along the dimension with the
- # smallest scale factor.
- # Now we do not support this.
-
- # get weights and indices
- weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
- in_H, out_H, scale, kernel, kernel_width, antialiasing)
- weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
- in_W, out_W, scale, kernel, kernel_width, antialiasing)
- # process H dimension
- # symmetric copying
- img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
- img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
-
- sym_patch = img[:, :sym_len_Hs, :]
- inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
- sym_patch_inv = sym_patch.index_select(1, inv_idx)
- img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
-
- sym_patch = img[:, -sym_len_He:, :]
- inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
- sym_patch_inv = sym_patch.index_select(1, inv_idx)
- img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
- out_1 = torch.FloatTensor(in_C, out_H, in_W)
- kernel_width = weights_H.size(1)
- for i in range(out_H):
- idx = int(indices_H[i][0])
- for j in range(out_C):
- out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
-
- # process W dimension
- # symmetric copying
- out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
- out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
-
- sym_patch = out_1[:, :, :sym_len_Ws]
- inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
- sym_patch_inv = sym_patch.index_select(2, inv_idx)
- out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
-
- sym_patch = out_1[:, :, -sym_len_We:]
- inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
- sym_patch_inv = sym_patch.index_select(2, inv_idx)
- out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
- out_2 = torch.FloatTensor(in_C, out_H, out_W)
- kernel_width = weights_W.size(1)
- for i in range(out_W):
- idx = int(indices_W[i][0])
- for j in range(out_C):
- out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
- if need_squeeze:
- out_2.squeeze_()
- return out_2
-
-
-# --------------------------------------------
-# imresize for numpy image [0, 1]
-# --------------------------------------------
-def imresize_np(img, scale, antialiasing=True):
- # Now the scale should be the same for H and W
- # input: img: Numpy, HWC or HW [0,1]
- # output: HWC or HW [0,1] w/o round
- img = torch.from_numpy(img)
- need_squeeze = True if img.dim() == 2 else False
- if need_squeeze:
- img.unsqueeze_(2)
-
- in_H, in_W, in_C = img.size()
- out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
- kernel_width = 4
- kernel = 'cubic'
-
- # Return the desired dimension order for performing the resize. The
- # strategy is to perform the resize first along the dimension with the
- # smallest scale factor.
- # Now we do not support this.
-
- # get weights and indices
- weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
- in_H, out_H, scale, kernel, kernel_width, antialiasing)
- weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
- in_W, out_W, scale, kernel, kernel_width, antialiasing)
- # process H dimension
- # symmetric copying
- img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
- img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)
-
- sym_patch = img[:sym_len_Hs, :, :]
- inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
- sym_patch_inv = sym_patch.index_select(0, inv_idx)
- img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)
-
- sym_patch = img[-sym_len_He:, :, :]
- inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
- sym_patch_inv = sym_patch.index_select(0, inv_idx)
- img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
-
- out_1 = torch.FloatTensor(out_H, in_W, in_C)
- kernel_width = weights_H.size(1)
- for i in range(out_H):
- idx = int(indices_H[i][0])
- for j in range(out_C):
- out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
-
- # process W dimension
- # symmetric copying
- out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
- out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)
-
- sym_patch = out_1[:, :sym_len_Ws, :]
- inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
- sym_patch_inv = sym_patch.index_select(1, inv_idx)
- out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)
-
- sym_patch = out_1[:, -sym_len_We:, :]
- inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
- sym_patch_inv = sym_patch.index_select(1, inv_idx)
- out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
-
- out_2 = torch.FloatTensor(out_H, out_W, in_C)
- kernel_width = weights_W.size(1)
- for i in range(out_W):
- idx = int(indices_W[i][0])
- for j in range(out_C):
- out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
- if need_squeeze:
- out_2.squeeze_()
-
- return out_2.numpy()
-
-
-if __name__ == '__main__':
- img = imread_uint('test.bmp', 3)
-# img = uint2single(img)
-# img_bicubic = imresize_np(img, 1/4)
-# imshow(single2uint(img_bicubic))
-#
-# img_tensor = single2tensor4(img)
-# for i in range(8):
-# imshow(np.concatenate((augment_img(img, i), tensor2single(augment_img_tensor4(img_tensor, i))), 1))
-
-# patches = patches_from_image(img, p_size=128, p_overlap=0, p_max=200)
-# imssave(patches,'a.png')
-
-
-
-
-
-
-
diff --git a/spaces/latent-consistency/Real-Time-LCM-ControlNet-Lora-SD1.5/app-controlnetlora.py b/spaces/latent-consistency/Real-Time-LCM-ControlNet-Lora-SD1.5/app-controlnetlora.py
deleted file mode 100644
index d5e119864e7892e823fbd278e81d2a8f90e0cbf1..0000000000000000000000000000000000000000
--- a/spaces/latent-consistency/Real-Time-LCM-ControlNet-Lora-SD1.5/app-controlnetlora.py
+++ /dev/null
@@ -1,321 +0,0 @@
-import asyncio
-import json
-import logging
-import traceback
-from pydantic import BaseModel
-
-from fastapi import FastAPI, WebSocket, HTTPException, WebSocketDisconnect
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import (
- StreamingResponse,
- JSONResponse,
- HTMLResponse,
- FileResponse,
-)
-
-from diffusers import (
- StableDiffusionControlNetImg2ImgPipeline,
- ControlNetModel,
- LCMScheduler,
-)
-from compel import Compel
-import torch
-
-from canny_gpu import SobelOperator
-
-# from controlnet_aux import OpenposeDetector
-# import cv2
-
-try:
- import intel_extension_for_pytorch as ipex
-except:
- pass
-from PIL import Image
-import numpy as np
-import gradio as gr
-import io
-import uuid
-import os
-import time
-import psutil
-
-
-MAX_QUEUE_SIZE = int(os.environ.get("MAX_QUEUE_SIZE", 0))
-TIMEOUT = float(os.environ.get("TIMEOUT", 0))
-SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None)
-TORCH_COMPILE = os.environ.get("TORCH_COMPILE", None)
-HF_TOKEN = os.environ.get("HF_TOKEN", None)
-
-WIDTH = 512
-HEIGHT = 512
-
-
-# check if MPS is available OSX only M1/M2/M3 chips
-mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
-xpu_available = hasattr(torch, "xpu") and torch.xpu.is_available()
-device = torch.device(
- "cuda" if torch.cuda.is_available() else "xpu" if xpu_available else "cpu"
-)
-
-# change to torch.float16 to save GPU memory
-torch_dtype = torch.float16
-
-print(f"TIMEOUT: {TIMEOUT}")
-print(f"SAFETY_CHECKER: {SAFETY_CHECKER}")
-print(f"MAX_QUEUE_SIZE: {MAX_QUEUE_SIZE}")
-print(f"device: {device}")
-
-if mps_available:
- device = torch.device("mps")
- device = "cpu"
- torch_dtype = torch.float32
-
-controlnet_canny = ControlNetModel.from_pretrained(
- "lllyasviel/control_v11p_sd15_canny", torch_dtype=torch_dtype
-).to(device)
-
-canny_torch = SobelOperator(device=device)
-
-model_id = "nitrosocke/mo-di-diffusion"
-lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
-
-if SAFETY_CHECKER == "True":
- pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
- model_id,
- controlnet=controlnet_canny,
- )
-else:
- pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
- model_id,
- safety_checker=None,
- controlnet=controlnet_canny,
- )
-
-pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
-pipe.set_progress_bar_config(disable=True)
-pipe.to(device=device, dtype=torch_dtype).to(device)
-pipe.unet.to(memory_format=torch.channels_last)
-
-
-if psutil.virtual_memory().total < 64 * 1024**3:
- pipe.enable_attention_slicing()
-
-# Load LCM LoRA
-pipe.load_lora_weights(
- lcm_lora_id,
- use_auth_token=HF_TOKEN,
-)
-
-compel_proc = Compel(
- tokenizer=pipe.tokenizer,
- text_encoder=pipe.text_encoder,
- truncate_long_prompts=False,
-)
-if TORCH_COMPILE:
- pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
- pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True)
-
- pipe(
- prompt="warmup",
- image=[Image.new("RGB", (768, 768))],
- control_image=[Image.new("RGB", (768, 768))],
- )
-
-
-user_queue_map = {}
-
-
-class InputParams(BaseModel):
- seed: int = 2159232
- prompt: str
- guidance_scale: float = 8.0
- strength: float = 0.5
- steps: int = 4
- lcm_steps: int = 50
- width: int = WIDTH
- height: int = HEIGHT
- controlnet_scale: float = 0.8
- controlnet_start: float = 0.0
- controlnet_end: float = 1.0
- canny_low_threshold: float = 0.31
- canny_high_threshold: float = 0.78
- debug_canny: bool = False
-
-
-def predict(
- input_image: Image.Image, params: InputParams, prompt_embeds: torch.Tensor = None
-):
- generator = torch.manual_seed(params.seed)
-
- control_image = canny_torch(
- input_image, params.canny_low_threshold, params.canny_high_threshold
- )
- results = pipe(
- control_image=control_image,
- prompt_embeds=prompt_embeds,
- generator=generator,
- image=input_image,
- strength=params.strength,
- num_inference_steps=params.steps,
- guidance_scale=params.guidance_scale,
- width=params.width,
- height=params.height,
- output_type="pil",
- controlnet_conditioning_scale=params.controlnet_scale,
- control_guidance_start=params.controlnet_start,
- control_guidance_end=params.controlnet_end,
- )
- nsfw_content_detected = (
- results.nsfw_content_detected[0]
- if "nsfw_content_detected" in results
- else False
- )
- if nsfw_content_detected:
- return None
- result_image = results.images[0]
- if params.debug_canny:
- # paste control_image on top of result_image
- w0, h0 = (200, 200)
- control_image = control_image.resize((w0, h0))
- w1, h1 = result_image.size
- result_image.paste(control_image, (w1 - w0, h1 - h0))
-
- return result_image
-
-
-app = FastAPI()
-app.add_middleware(
- CORSMiddleware,
- allow_origins=["*"],
- allow_credentials=True,
- allow_methods=["*"],
- allow_headers=["*"],
-)
-
-
-@app.websocket("/ws")
-async def websocket_endpoint(websocket: WebSocket):
- await websocket.accept()
- if MAX_QUEUE_SIZE > 0 and len(user_queue_map) >= MAX_QUEUE_SIZE:
- print("Server is full")
- await websocket.send_json({"status": "error", "message": "Server is full"})
- await websocket.close()
- return
-
- try:
- uid = str(uuid.uuid4())
- print(f"New user connected: {uid}")
- await websocket.send_json(
- {"status": "success", "message": "Connected", "userId": uid}
- )
- user_queue_map[uid] = {"queue": asyncio.Queue()}
- await websocket.send_json(
- {"status": "start", "message": "Start Streaming", "userId": uid}
- )
- await handle_websocket_data(websocket, uid)
- except WebSocketDisconnect as e:
- logging.error(f"WebSocket Error: {e}, {uid}")
- traceback.print_exc()
- finally:
- print(f"User disconnected: {uid}")
- queue_value = user_queue_map.pop(uid, None)
- queue = queue_value.get("queue", None)
- if queue:
- while not queue.empty():
- try:
- queue.get_nowait()
- except asyncio.QueueEmpty:
- continue
-
-
-@app.get("/queue_size")
-async def get_queue_size():
- queue_size = len(user_queue_map)
- return JSONResponse({"queue_size": queue_size})
-
-
-@app.get("/stream/{user_id}")
-async def stream(user_id: uuid.UUID):
- uid = str(user_id)
- try:
- user_queue = user_queue_map[uid]
- queue = user_queue["queue"]
-
- async def generate():
- last_prompt: str = None
- prompt_embeds: torch.Tensor = None
- while True:
- data = await queue.get()
- input_image = data["image"]
- params = data["params"]
- if input_image is None:
- continue
- # avoid recalculate prompt embeds
- if last_prompt != params.prompt:
- print("new prompt")
- prompt_embeds = compel_proc(params.prompt)
- last_prompt = params.prompt
-
- image = predict(
- input_image,
- params,
- prompt_embeds,
- )
- if image is None:
- continue
- frame_data = io.BytesIO()
- image.save(frame_data, format="JPEG")
- frame_data = frame_data.getvalue()
- if frame_data is not None and len(frame_data) > 0:
- yield b"--frame\r\nContent-Type: image/jpeg\r\n\r\n" + frame_data + b"\r\n"
-
- await asyncio.sleep(1.0 / 120.0)
-
- return StreamingResponse(
- generate(), media_type="multipart/x-mixed-replace;boundary=frame"
- )
- except Exception as e:
- logging.error(f"Streaming Error: {e}, {user_queue_map}")
- traceback.print_exc()
- return HTTPException(status_code=404, detail="User not found")
-
-
-async def handle_websocket_data(websocket: WebSocket, user_id: uuid.UUID):
- uid = str(user_id)
- user_queue = user_queue_map[uid]
- queue = user_queue["queue"]
- if not queue:
- return HTTPException(status_code=404, detail="User not found")
- last_time = time.time()
- try:
- while True:
- data = await websocket.receive_bytes()
- params = await websocket.receive_json()
- params = InputParams(**params)
- pil_image = Image.open(io.BytesIO(data))
-
- while not queue.empty():
- try:
- queue.get_nowait()
- except asyncio.QueueEmpty:
- continue
- await queue.put({"image": pil_image, "params": params})
- if TIMEOUT > 0 and time.time() - last_time > TIMEOUT:
- await websocket.send_json(
- {
- "status": "timeout",
- "message": "Your session has ended",
- "userId": uid,
- }
- )
- await websocket.close()
- return
-
- except Exception as e:
- logging.error(f"Error: {e}")
- traceback.print_exc()
-
-
-@app.get("/", response_class=HTMLResponse)
-async def root():
- return FileResponse("./static/controlnetlora.html")
diff --git a/spaces/latent-consistency/Real-Time-LCM-Text-to-Image-Lora-SD1.5/canny_gpu.py b/spaces/latent-consistency/Real-Time-LCM-Text-to-Image-Lora-SD1.5/canny_gpu.py
deleted file mode 100644
index be6c2f75ef6554a0122f4ebd96301080a8e24303..0000000000000000000000000000000000000000
--- a/spaces/latent-consistency/Real-Time-LCM-Text-to-Image-Lora-SD1.5/canny_gpu.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import torch
-import torch.nn as nn
-from torchvision.transforms import ToTensor, ToPILImage
-from PIL import Image
-
-class SobelOperator(nn.Module):
- def __init__(self, device="cuda"):
- super(SobelOperator, self).__init__()
- self.device = device
- self.edge_conv_x = nn.Conv2d(1, 1, kernel_size=3, padding=1, bias=False).to(
- self.device
- )
- self.edge_conv_y = nn.Conv2d(1, 1, kernel_size=3, padding=1, bias=False).to(
- self.device
- )
-
- sobel_kernel_x = torch.tensor(
- [[-1.0, 0.0, 1.0], [-2.0, 0.0, 2.0], [-1.0, 0.0, 1.0]], device=self.device
- )
- sobel_kernel_y = torch.tensor(
- [[-1.0, -2.0, -1.0], [0.0, 0.0, 0.0], [1.0, 2.0, 1.0]], device=self.device
- )
-
- self.edge_conv_x.weight = nn.Parameter(sobel_kernel_x.view((1, 1, 3, 3)))
- self.edge_conv_y.weight = nn.Parameter(sobel_kernel_y.view((1, 1, 3, 3)))
-
- @torch.no_grad()
- def forward(self, image: Image.Image, low_threshold: float, high_threshold: float):
- # Convert PIL image to PyTorch tensor
- image_gray = image.convert("L")
- image_tensor = ToTensor()(image_gray).unsqueeze(0).to(self.device)
-
- # Compute gradients
- edge_x = self.edge_conv_x(image_tensor)
- edge_y = self.edge_conv_y(image_tensor)
- edge = torch.sqrt(edge_x**2 + edge_y**2)
-
- # Apply thresholding
- edge = edge / edge.max() # Normalize to 0-1
- edge[edge >= high_threshold] = 1.0
- edge[edge <= low_threshold] = 0.0
-
- # Convert the result back to a PIL image
- return ToPILImage()(edge.squeeze(0).cpu())
diff --git a/spaces/lcipolina/Print_Gallery/setup.py b/spaces/lcipolina/Print_Gallery/setup.py
deleted file mode 100644
index 36a3d5a9fa92c3aebdf5ad903ddfbc7dcd025b0d..0000000000000000000000000000000000000000
--- a/spaces/lcipolina/Print_Gallery/setup.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from setuptools import setup
-
-setup(
- name="glide-text2im",
- packages=[
- "glide_text2im",
- "glide_text2im.clip",
- "glide_text2im.tokenizer",
- ],
- package_data={
- "glide_text2im.tokenizer": [
- "bpe_simple_vocab_16e6.txt.gz",
- "encoder.json.gz",
- "vocab.bpe.gz",
- ],
- "glide_text2im.clip": ["config.yaml"],
- },
- install_requires=[
- "Pillow",
- "attrs",
- "torch",
- "filelock",
- "requests",
- "tqdm",
- "ftfy",
- "regex",
- ],
- author="OpenAI",
-)
diff --git a/spaces/lightli/bingo-newbing/src/components/ui/select.tsx b/spaces/lightli/bingo-newbing/src/components/ui/select.tsx
deleted file mode 100644
index 77f12c2996f541b97663de4c9e20ab34d4ec2fac..0000000000000000000000000000000000000000
--- a/spaces/lightli/bingo-newbing/src/components/ui/select.tsx
+++ /dev/null
@@ -1,123 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as SelectPrimitive from '@radix-ui/react-select'
-
-import { cn } from '@/lib/utils'
-import {
- IconArrowDown,
- IconCheck,
- IconChevronUpDown
-} from '@/components/ui/icons'
-
-const Select = SelectPrimitive.Root
-
-const SelectGroup = SelectPrimitive.Group
-
-const SelectValue = SelectPrimitive.Value
-
-const SelectTrigger = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, children, ...props }, ref) => (
-
- {children}
-
-
-
-
-))
-SelectTrigger.displayName = SelectPrimitive.Trigger.displayName
-
-const SelectContent = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, children, position = 'popper', ...props }, ref) => (
-
-
-
- {children}
-
-
-
-))
-SelectContent.displayName = SelectPrimitive.Content.displayName
-
-const SelectLabel = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
-
-))
-SelectLabel.displayName = SelectPrimitive.Label.displayName
-
-const SelectItem = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, children, ...props }, ref) => (
-
-
-
-
-
-
- {children}
-
-))
-SelectItem.displayName = SelectPrimitive.Item.displayName
-
-const SelectSeparator = React.forwardRef<
- React.ElementRef,
- React.ComponentPropsWithoutRef
->(({ className, ...props }, ref) => (
-
-))
-SelectSeparator.displayName = SelectPrimitive.Separator.displayName
-
-export {
- Select,
- SelectGroup,
- SelectValue,
- SelectTrigger,
- SelectContent,
- SelectLabel,
- SelectItem,
- SelectSeparator
-}
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/Crack NEW! Cakewalk.Sonar.Platinum Instruments Plugins-R2R.md b/spaces/lincquiQcaudo/Top-20-Diffusion/Crack NEW! Cakewalk.Sonar.Platinum Instruments Plugins-R2R.md
deleted file mode 100644
index e5b3b3c5d8a41ae92f59a13cab9000cb520375f4..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/Crack NEW! Cakewalk.Sonar.Platinum Instruments Plugins-R2R.md
+++ /dev/null
@@ -1,33 +0,0 @@
-
-
How to Crack Cakewalk Sonar Platinum Instruments Plugins-R2R
-
If you are looking for a way to crack Cakewalk Sonar Platinum Instruments Plugins-R2R, you have come to the right place. In this article, I will show you how to download, install, and activate the plugins for free.
-
Cakewalk Sonar Platinum is a powerful digital audio workstation that offers advanced technology, effortless workflow, and an inviting interface. It comes with a collection of instruments and plugins that can enhance your music production and creativity. However, these plugins are not cheap and require a license to use them.
Fortunately, there is a way to crack them and use them without paying anything. All you need is a torrent client, a crack file, and some patience. Here are the steps to follow:
-
-
Download the torrent file for Cakewalk Sonar Platinum Instrument Collection v1.0.0.15-R2R from here [^1^] or here [^2^]. You will need a torrent client like uTorrent or BitTorrent to download it.
-
Open the torrent file and select the destination folder for the download. Wait until the download is complete.
-
Extract the downloaded file using WinRAR or 7-Zip. You will get a folder named Cakewalk.SONAR.Platinum.Instrument.Collection.v1.0.0.15-R2R.
-
Run the setup.exe file inside the folder and follow the installation instructions. Choose the plugins you want to install and select your VST folder.
-
Do not run Sonar Platinum or any of the plugins after installation.
-
Download the crack file for Cakewalk Sonar Platinum Instruments Plugins-R2R from here [^3^]. Extract it using WinRAR or 7-Zip.
-
Copy and paste the crack files into your VST folder, replacing the original files.
-
Run Sonar Platinum and enjoy your cracked plugins.
-
-
Congratulations! You have successfully cracked Cakewalk Sonar Platinum Instruments Plugins-R2R. Now you can use them to create amazing music without any limitations.
-
Note: This article is for educational purposes only. We do not condone piracy or illegal downloading of software. If you like Cakewalk Sonar Platinum Instruments Plugins-R2R, please support the developers and buy them from their official website.
-
-
In this section, I will explain some of the features and benefits of Cakewalk Sonar Platinum Instruments Plugins-R2R. These plugins are designed to enhance your music production and creativity with high-quality sounds, effects, and tools. Here are some of the plugins you can use:
-
-
Addictive Drums 2: This is a powerful drum production software that lets you create realistic and expressive drum tracks. You can choose from hundreds of drum kits, presets, and patterns, or create your own custom sounds. You can also mix and match drums from different kits, add effects, and tweak the settings to suit your style.
-
Cakewalk Sound Center: This is a versatile instrument plugin that gives you access to a wide range of sounds, from acoustic and electric guitars, to pianos, synths, strings, and more. You can easily browse and load sounds, adjust the parameters, and layer up to four sounds for rich and complex tones.
-
Session Drummer 3: This is a virtual drummer that can play along with your songs. You can choose from dozens of drum kits and styles, or import your own MIDI files. You can also edit the drum patterns, change the tempo and groove, and add effects.
-
Square I: This is a classic analog synth plugin that emulates the sound of the Roland SH-101. You can create fat and warm basses, leads, pads, and effects with its simple and intuitive interface. You can also use the arpeggiator, sequencer, and modulation features to add movement and variation to your sounds.
-
Roland Groove Synth: This is a groovebox plugin that combines a drum machine, a synth, and a sequencer. You can create groovy beats and melodies with its built-in sounds and patterns, or import your own samples. You can also tweak the sounds with filters, envelopes, LFOs, and effects.
-
Cakewalk TTS-1: This is a general MIDI synth plugin that lets you play any MIDI file with realistic sounds. You can choose from 256 instruments and 9 drum sets, covering various genres and styles. You can also adjust the volume, pan, reverb, and chorus for each channel.
-
DropZone SFZ Player: This is a sampler plugin that lets you load and play SFZ files. SFZ files are a format for creating complex and realistic sampled instruments. You can find thousands of free SFZ files online, or create your own with tools like Cakewalk's Dimension Pro.
-
Studio Instruments Drums: This is a simple and easy-to-use drum plugin that lets you play realistic acoustic drums. You can choose from four different kits: Rock, Jazz, Electronic, and Urban. You can also adjust the volume and pan for each drum, and add reverb and compression effects.
-
Studio Instruments Bass: This is a simple and easy-to-use bass plugin that lets you play realistic electric bass. You can choose from four different models: Fingered Bass, Picked Bass, Fretless Bass, and Slapped Bass. You can also adjust the tone and volume for each string, and add chorus and distortion effects.
-
Studio Instruments Strings: This is a simple and easy-to-use strings plugin that lets you play realistic orchestral strings. You can choose from four different sections: Violin, Viola, d5da3c52bf
-
-
\ No newline at end of file
diff --git a/spaces/lnyan/stablediffusion-infinity/PyPatchMatch/examples/cpp_example_run.sh b/spaces/lnyan/stablediffusion-infinity/PyPatchMatch/examples/cpp_example_run.sh
deleted file mode 100644
index cfb62557fe4a5bb0ee1aa48d7aa8582c23f4e6fc..0000000000000000000000000000000000000000
--- a/spaces/lnyan/stablediffusion-infinity/PyPatchMatch/examples/cpp_example_run.sh
+++ /dev/null
@@ -1,18 +0,0 @@
-#! /bin/bash
-#
-# cpp_example_run.sh
-# Copyright (C) 2020 Jiayuan Mao
-#
-# Distributed under terms of the MIT license.
-#
-
-set -x
-
-CFLAGS="-std=c++14 -O2 $(pkg-config --cflags opencv)"
-LDFLAGS="$(pkg-config --libs opencv)"
-g++ $CFLAGS cpp_example.cpp -I../csrc/ -L../ -lpatchmatch $LDFLAGS -o cpp_example.exe
-
-export DYLD_LIBRARY_PATH=../:$DYLD_LIBRARY_PATH # For macOS
-export LD_LIBRARY_PATH=../:$LD_LIBRARY_PATH # For Linux
-time ./cpp_example.exe
-
diff --git a/spaces/louisedrumm/TutorBot/README.md b/spaces/louisedrumm/TutorBot/README.md
deleted file mode 100644
index 9eb11f2989f6be8ef8bdbfae4e153ca2b10652bd..0000000000000000000000000000000000000000
--- a/spaces/louisedrumm/TutorBot/README.md
+++ /dev/null
@@ -1,16 +0,0 @@
----
-title: Tutorbot
-emoji: 🐨
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: mit
----
-LD note 5th Aug 23: not working as need to connect my own Openai token
-Update: now connected to my openai api token
-Next steps: figure out how the finetuning works (or should work) in app.py
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/ma-xu/LIVE/pybind11/include/pybind11/eigen.h b/spaces/ma-xu/LIVE/pybind11/include/pybind11/eigen.h
deleted file mode 100644
index 22139def6013b47005df22be778bd6984e05ea1d..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/pybind11/include/pybind11/eigen.h
+++ /dev/null
@@ -1,607 +0,0 @@
-/*
- pybind11/eigen.h: Transparent conversion for dense and sparse Eigen matrices
-
- Copyright (c) 2016 Wenzel Jakob
-
- All rights reserved. Use of this source code is governed by a
- BSD-style license that can be found in the LICENSE file.
-*/
-
-#pragma once
-
-#include "numpy.h"
-
-#if defined(__INTEL_COMPILER)
-# pragma warning(disable: 1682) // implicit conversion of a 64-bit integral type to a smaller integral type (potential portability problem)
-#elif defined(__GNUG__) || defined(__clang__)
-# pragma GCC diagnostic push
-# pragma GCC diagnostic ignored "-Wconversion"
-# pragma GCC diagnostic ignored "-Wdeprecated-declarations"
-# ifdef __clang__
-// Eigen generates a bunch of implicit-copy-constructor-is-deprecated warnings with -Wdeprecated
-// under Clang, so disable that warning here:
-# pragma GCC diagnostic ignored "-Wdeprecated"
-# endif
-# if __GNUC__ >= 7
-# pragma GCC diagnostic ignored "-Wint-in-bool-context"
-# endif
-#endif
-
-#if defined(_MSC_VER)
-# pragma warning(push)
-# pragma warning(disable: 4127) // warning C4127: Conditional expression is constant
-# pragma warning(disable: 4996) // warning C4996: std::unary_negate is deprecated in C++17
-#endif
-
-#include
-#include
-
-// Eigen prior to 3.2.7 doesn't have proper move constructors--but worse, some classes get implicit
-// move constructors that break things. We could detect this an explicitly copy, but an extra copy
-// of matrices seems highly undesirable.
-static_assert(EIGEN_VERSION_AT_LEAST(3,2,7), "Eigen support in pybind11 requires Eigen >= 3.2.7");
-
-PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
-
-// Provide a convenience alias for easier pass-by-ref usage with fully dynamic strides:
-using EigenDStride = Eigen::Stride;
-template using EigenDRef = Eigen::Ref;
-template using EigenDMap = Eigen::Map;
-
-PYBIND11_NAMESPACE_BEGIN(detail)
-
-#if EIGEN_VERSION_AT_LEAST(3,3,0)
-using EigenIndex = Eigen::Index;
-#else
-using EigenIndex = EIGEN_DEFAULT_DENSE_INDEX_TYPE;
-#endif
-
-// Matches Eigen::Map, Eigen::Ref, blocks, etc:
-template using is_eigen_dense_map = all_of, std::is_base_of, T>>;
-template using is_eigen_mutable_map = std::is_base_of, T>;
-template using is_eigen_dense_plain = all_of>, is_template_base_of>;
-template using is_eigen_sparse = is_template_base_of;
-// Test for objects inheriting from EigenBase that aren't captured by the above. This
-// basically covers anything that can be assigned to a dense matrix but that don't have a typical
-// matrix data layout that can be copied from their .data(). For example, DiagonalMatrix and
-// SelfAdjointView fall into this category.
-template using is_eigen_other = all_of<
- is_template_base_of,
- negation, is_eigen_dense_plain, is_eigen_sparse>>
->;
-
-// Captures numpy/eigen conformability status (returned by EigenProps::conformable()):
-template struct EigenConformable {
- bool conformable = false;
- EigenIndex rows = 0, cols = 0;
- EigenDStride stride{0, 0}; // Only valid if negativestrides is false!
- bool negativestrides = false; // If true, do not use stride!
-
- EigenConformable(bool fits = false) : conformable{fits} {}
- // Matrix type:
- EigenConformable(EigenIndex r, EigenIndex c,
- EigenIndex rstride, EigenIndex cstride) :
- conformable{true}, rows{r}, cols{c} {
- // TODO: when Eigen bug #747 is fixed, remove the tests for non-negativity. http://eigen.tuxfamily.org/bz/show_bug.cgi?id=747
- if (rstride < 0 || cstride < 0) {
- negativestrides = true;
- } else {
- stride = {EigenRowMajor ? rstride : cstride /* outer stride */,
- EigenRowMajor ? cstride : rstride /* inner stride */ };
- }
- }
- // Vector type:
- EigenConformable(EigenIndex r, EigenIndex c, EigenIndex stride)
- : EigenConformable(r, c, r == 1 ? c*stride : stride, c == 1 ? r : r*stride) {}
-
- template bool stride_compatible() const {
- // To have compatible strides, we need (on both dimensions) one of fully dynamic strides,
- // matching strides, or a dimension size of 1 (in which case the stride value is irrelevant)
- return
- !negativestrides &&
- (props::inner_stride == Eigen::Dynamic || props::inner_stride == stride.inner() ||
- (EigenRowMajor ? cols : rows) == 1) &&
- (props::outer_stride == Eigen::Dynamic || props::outer_stride == stride.outer() ||
- (EigenRowMajor ? rows : cols) == 1);
- }
- operator bool() const { return conformable; }
-};
-
-template struct eigen_extract_stride { using type = Type; };
-template
-struct eigen_extract_stride> { using type = StrideType; };
-template
-struct eigen_extract_stride> { using type = StrideType; };
-
-// Helper struct for extracting information from an Eigen type
-template struct EigenProps {
- using Type = Type_;
- using Scalar = typename Type::Scalar;
- using StrideType = typename eigen_extract_stride::type;
- static constexpr EigenIndex
- rows = Type::RowsAtCompileTime,
- cols = Type::ColsAtCompileTime,
- size = Type::SizeAtCompileTime;
- static constexpr bool
- row_major = Type::IsRowMajor,
- vector = Type::IsVectorAtCompileTime, // At least one dimension has fixed size 1
- fixed_rows = rows != Eigen::Dynamic,
- fixed_cols = cols != Eigen::Dynamic,
- fixed = size != Eigen::Dynamic, // Fully-fixed size
- dynamic = !fixed_rows && !fixed_cols; // Fully-dynamic size
-
- template using if_zero = std::integral_constant;
- static constexpr EigenIndex inner_stride = if_zero::value,
- outer_stride = if_zero::value;
- static constexpr bool dynamic_stride = inner_stride == Eigen::Dynamic && outer_stride == Eigen::Dynamic;
- static constexpr bool requires_row_major = !dynamic_stride && !vector && (row_major ? inner_stride : outer_stride) == 1;
- static constexpr bool requires_col_major = !dynamic_stride && !vector && (row_major ? outer_stride : inner_stride) == 1;
-
- // Takes an input array and determines whether we can make it fit into the Eigen type. If
- // the array is a vector, we attempt to fit it into either an Eigen 1xN or Nx1 vector
- // (preferring the latter if it will fit in either, i.e. for a fully dynamic matrix type).
- static EigenConformable conformable(const array &a) {
- const auto dims = a.ndim();
- if (dims < 1 || dims > 2)
- return false;
-
- if (dims == 2) { // Matrix type: require exact match (or dynamic)
-
- EigenIndex
- np_rows = a.shape(0),
- np_cols = a.shape(1),
- np_rstride = a.strides(0) / static_cast(sizeof(Scalar)),
- np_cstride = a.strides(1) / static_cast(sizeof(Scalar));
- if ((fixed_rows && np_rows != rows) || (fixed_cols && np_cols != cols))
- return false;
-
- return {np_rows, np_cols, np_rstride, np_cstride};
- }
-
- // Otherwise we're storing an n-vector. Only one of the strides will be used, but whichever
- // is used, we want the (single) numpy stride value.
- const EigenIndex n = a.shape(0),
- stride = a.strides(0) / static_cast(sizeof(Scalar));
-
- if (vector) { // Eigen type is a compile-time vector
- if (fixed && size != n)
- return false; // Vector size mismatch
- return {rows == 1 ? 1 : n, cols == 1 ? 1 : n, stride};
- }
- else if (fixed) {
- // The type has a fixed size, but is not a vector: abort
- return false;
- }
- else if (fixed_cols) {
- // Since this isn't a vector, cols must be != 1. We allow this only if it exactly
- // equals the number of elements (rows is Dynamic, and so 1 row is allowed).
- if (cols != n) return false;
- return {1, n, stride};
- }
- else {
- // Otherwise it's either fully dynamic, or column dynamic; both become a column vector
- if (fixed_rows && rows != n) return false;
- return {n, 1, stride};
- }
- }
-
- static constexpr bool show_writeable = is_eigen_dense_map::value && is_eigen_mutable_map::value;
- static constexpr bool show_order = is_eigen_dense_map::value;
- static constexpr bool show_c_contiguous = show_order && requires_row_major;
- static constexpr bool show_f_contiguous = !show_c_contiguous && show_order && requires_col_major;
-
- static constexpr auto descriptor =
- _("numpy.ndarray[") + npy_format_descriptor::name +
- _("[") + _(_<(size_t) rows>(), _("m")) +
- _(", ") + _(_<(size_t) cols>(), _("n")) +
- _("]") +
- // For a reference type (e.g. Ref) we have other constraints that might need to be
- // satisfied: writeable=True (for a mutable reference), and, depending on the map's stride
- // options, possibly f_contiguous or c_contiguous. We include them in the descriptor output
- // to provide some hint as to why a TypeError is occurring (otherwise it can be confusing to
- // see that a function accepts a 'numpy.ndarray[float64[3,2]]' and an error message that you
- // *gave* a numpy.ndarray of the right type and dimensions.
- _(", flags.writeable", "") +
- _(", flags.c_contiguous", "") +
- _(", flags.f_contiguous", "") +
- _("]");
-};
-
-// Casts an Eigen type to numpy array. If given a base, the numpy array references the src data,
-// otherwise it'll make a copy. writeable lets you turn off the writeable flag for the array.
-template handle eigen_array_cast(typename props::Type const &src, handle base = handle(), bool writeable = true) {
- constexpr ssize_t elem_size = sizeof(typename props::Scalar);
- array a;
- if (props::vector)
- a = array({ src.size() }, { elem_size * src.innerStride() }, src.data(), base);
- else
- a = array({ src.rows(), src.cols() }, { elem_size * src.rowStride(), elem_size * src.colStride() },
- src.data(), base);
-
- if (!writeable)
- array_proxy(a.ptr())->flags &= ~detail::npy_api::NPY_ARRAY_WRITEABLE_;
-
- return a.release();
-}
-
-// Takes an lvalue ref to some Eigen type and a (python) base object, creating a numpy array that
-// reference the Eigen object's data with `base` as the python-registered base class (if omitted,
-// the base will be set to None, and lifetime management is up to the caller). The numpy array is
-// non-writeable if the given type is const.
-template
-handle eigen_ref_array(Type &src, handle parent = none()) {
- // none here is to get past array's should-we-copy detection, which currently always
- // copies when there is no base. Setting the base to None should be harmless.
- return eigen_array_cast(src, parent, !std::is_const::value);
-}
-
-// Takes a pointer to some dense, plain Eigen type, builds a capsule around it, then returns a numpy
-// array that references the encapsulated data with a python-side reference to the capsule to tie
-// its destruction to that of any dependent python objects. Const-ness is determined by whether or
-// not the Type of the pointer given is const.
-template ::value>>
-handle eigen_encapsulate(Type *src) {
- capsule base(src, [](void *o) { delete static_cast(o); });
- return eigen_ref_array(*src, base);
-}
-
-// Type caster for regular, dense matrix types (e.g. MatrixXd), but not maps/refs/etc. of dense
-// types.
-template
-struct type_caster::value>> {
- using Scalar = typename Type::Scalar;
- using props = EigenProps;
-
- bool load(handle src, bool convert) {
- // If we're in no-convert mode, only load if given an array of the correct type
- if (!convert && !isinstance>(src))
- return false;
-
- // Coerce into an array, but don't do type conversion yet; the copy below handles it.
- auto buf = array::ensure(src);
-
- if (!buf)
- return false;
-
- auto dims = buf.ndim();
- if (dims < 1 || dims > 2)
- return false;
-
- auto fits = props::conformable(buf);
- if (!fits)
- return false;
-
- // Allocate the new type, then build a numpy reference into it
- value = Type(fits.rows, fits.cols);
- auto ref = reinterpret_steal(eigen_ref_array(value));
- if (dims == 1) ref = ref.squeeze();
- else if (ref.ndim() == 1) buf = buf.squeeze();
-
- int result = detail::npy_api::get().PyArray_CopyInto_(ref.ptr(), buf.ptr());
-
- if (result < 0) { // Copy failed!
- PyErr_Clear();
- return false;
- }
-
- return true;
- }
-
-private:
-
- // Cast implementation
- template
- static handle cast_impl(CType *src, return_value_policy policy, handle parent) {
- switch (policy) {
- case return_value_policy::take_ownership:
- case return_value_policy::automatic:
- return eigen_encapsulate(src);
- case return_value_policy::move:
- return eigen_encapsulate(new CType(std::move(*src)));
- case return_value_policy::copy:
- return eigen_array_cast(*src);
- case return_value_policy::reference:
- case return_value_policy::automatic_reference:
- return eigen_ref_array(*src);
- case return_value_policy::reference_internal:
- return eigen_ref_array(*src, parent);
- default:
- throw cast_error("unhandled return_value_policy: should not happen!");
- };
- }
-
-public:
-
- // Normal returned non-reference, non-const value:
- static handle cast(Type &&src, return_value_policy /* policy */, handle parent) {
- return cast_impl(&src, return_value_policy::move, parent);
- }
- // If you return a non-reference const, we mark the numpy array readonly:
- static handle cast(const Type &&src, return_value_policy /* policy */, handle parent) {
- return cast_impl(&src, return_value_policy::move, parent);
- }
- // lvalue reference return; default (automatic) becomes copy
- static handle cast(Type &src, return_value_policy policy, handle parent) {
- if (policy == return_value_policy::automatic || policy == return_value_policy::automatic_reference)
- policy = return_value_policy::copy;
- return cast_impl(&src, policy, parent);
- }
- // const lvalue reference return; default (automatic) becomes copy
- static handle cast(const Type &src, return_value_policy policy, handle parent) {
- if (policy == return_value_policy::automatic || policy == return_value_policy::automatic_reference)
- policy = return_value_policy::copy;
- return cast(&src, policy, parent);
- }
- // non-const pointer return
- static handle cast(Type *src, return_value_policy policy, handle parent) {
- return cast_impl(src, policy, parent);
- }
- // const pointer return
- static handle cast(const Type *src, return_value_policy policy, handle parent) {
- return cast_impl(src, policy, parent);
- }
-
- static constexpr auto name = props::descriptor;
-
- operator Type*() { return &value; }
- operator Type&() { return value; }
- operator Type&&() && { return std::move(value); }
- template using cast_op_type = movable_cast_op_type;
-
-private:
- Type value;
-};
-
-// Base class for casting reference/map/block/etc. objects back to python.
-template struct eigen_map_caster {
-private:
- using props = EigenProps;
-
-public:
-
- // Directly referencing a ref/map's data is a bit dangerous (whatever the map/ref points to has
- // to stay around), but we'll allow it under the assumption that you know what you're doing (and
- // have an appropriate keep_alive in place). We return a numpy array pointing directly at the
- // ref's data (The numpy array ends up read-only if the ref was to a const matrix type.) Note
- // that this means you need to ensure you don't destroy the object in some other way (e.g. with
- // an appropriate keep_alive, or with a reference to a statically allocated matrix).
- static handle cast(const MapType &src, return_value_policy policy, handle parent) {
- switch (policy) {
- case return_value_policy::copy:
- return eigen_array_cast(src);
- case return_value_policy::reference_internal:
- return eigen_array_cast(src, parent, is_eigen_mutable_map::value);
- case return_value_policy::reference:
- case return_value_policy::automatic:
- case return_value_policy::automatic_reference:
- return eigen_array_cast(src, none(), is_eigen_mutable_map::value);
- default:
- // move, take_ownership don't make any sense for a ref/map:
- pybind11_fail("Invalid return_value_policy for Eigen Map/Ref/Block type");
- }
- }
-
- static constexpr auto name = props::descriptor;
-
- // Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return
- // types but not bound arguments). We still provide them (with an explicitly delete) so that
- // you end up here if you try anyway.
- bool load(handle, bool) = delete;
- operator MapType() = delete;
- template using cast_op_type = MapType;
-};
-
-// We can return any map-like object (but can only load Refs, specialized next):
-template struct type_caster